This report aims at describing the JRC assumptions made to build-up the ...... Data were derived from reports and databases of emission ...... Nitrogen Fertilizer Industry, presentation on Fertilizers Europe website, acccessed May ...... severe competition from barges and water movement, but railroads serving the U.S. East.
Definition of input data to assess GHG default emissions from biofuels in EU legislation V e r si o n 1 a - D e c e m b e r 2 0 1 5
Robert Edwards Adrian O’Connell Monica Padella Declan Mulligan Jacopo Giuntoli Alessandro Agostini Renate Koeble Alberto Moro Luisa Marelli
2016
Report EUR 26853 EN
This publication is a Science for Policy report by the Joint Research Centre, the European Commission’s in-house science service. It aims to provide evidence-based scientific support to the European policy-making process. The scientific output expressed does not imply a policy position of the European Commission. Neither the European Commission nor any person acting on behalf of the Commission is responsible for the use which might be made of this publication. JRC Science Hub https://ec.europa.eu/jrc JRC91621 EUR 26853 EN PDF
ISBN 978-92-79-41253-0
ISSN 1831-9424
doi: 10.2790/38802
LD-NA-26853-EN-N
Print
ISBN 978-92-79-41254-7
ISSN 1018-5593
doi: 10.2790/38877
LD-NA-26853-EN-C
© European Union 2016 Reproduction is authorised provided the source is acknowledged. How to cite: Edwards et al., Definition of input data to assess GHG default emissions from biofuels in EU legislation – Version 1a; EUR 26853 EN; doi: 10.2790/38802 All images © European Union 2016 Abstract The EU legislation contains a set of mandatory targets specific for the EU transport sector which aims at achieving the overall objective of a European sustainably fuelled transport system. In particular, Directives 2009/28/EC (Renewable Energy Directive) and 2009/30/EC (Fuel Quality Directive) fix a threshold of 35% savings of GHG emissions for biofuels and bioliquids and set out the rules for calculating the greenhouse impact of biofuels, bioliquids and their fossil fuels comparators. To help economic operators in calculating GHG emission savings, default and typical values are also listed in the Annexes of the two directives, which were calculated on the basis of the JEC (JRC-EUCAR-CONCAWE consortium) database of input data in its version of 31 July 2008, following a consultation process with stakeholders launched by the Commission. According to article 19.7 in the Renewable Energy Directive and 7d.7 in the Fuel Quality Directive, the Commission can also adapt default values to technical and scientific progress by updating the existing values and by adding additional pathways. On request of the Commission’s DG ENER, the JRC worked on the update of the existing input database, and the list of pathways in the Directives will therefore likely be modified accordingly. This report aims at describing the JRC assumptions made to build-up the dataset used for the calculation of default GHG emissions for the different pathways in annexes of the Directives. Data are updated until december 2015.
Contents Contents ........................................................................................................................................................................................ i List of Tables ............................................................................................................................................................................iii List of Figures ........................................................................................................................................................................... x Executive Summary ........................................................................................................................................................... 13 1. Introduction.................................................................................................................................................................. 15 1.1 Background ............................................................................................................................................................... 15 1.2 Structure of the report ........................................................................................................................................ 16 Part One — General input data and common processes............................................................................... 17 2. General input data for pathways ..................................................................................................................... 18 2.1 Fossil fuels provision ........................................................................................................................................... 18 2.2 Supply of process chemicals and pesticides ........................................................................................... 23 2.2.1 Chemical fertilizers and pesticides...................................................................................................... 23 2.2.2 Chemicals ......................................................................................................................................................... 26 2.2.3 Seeding material........................................................................................................................................... 35 2.2.4 Summary of emission factors for the supply of main products.......................................... 38 2.4 N fertilizer manufacturing emissions calculation ................................................................................. 40 2.5 Diesel, drying and plant protection use in cultivation......................................................................... 46 2.5.1 Diesel use in cultivation ............................................................................................................................ 46 2.5.2 Crop drying....................................................................................................................................................... 47 2.5.3 Pesticides .......................................................................................................................................................... 48 3. Soil emissions from biofuel crop cultivation .............................................................................................. 49 3.1 Background ............................................................................................................................................................... 49 3.2 Pathways of N2O emission from managed soils................................................................................... 49 3.3 General approach to estimate soil N2O emissions from cultivation of potential biofuel crops ..................................................................................................................................................................................... 50 3.4 Determining crop- and site-specific fertilizer-induced emissions (EF1ij)................................. 55 3.5 The Global crop- and site-specific Nitrous Oxide emission Calculator (GNOC) ..................... 57 3.6 The GNOC online tool .......................................................................................................................................... 60 3.7 GNOC results and the JEC-WTW .................................................................................................................... 61 3.8 Manure calculation................................................................................................................................................ 67 3.9 Correction of IPCC method for estimating N2O emissions from leguminous crops............ 68 3.10 Emissions from acidification and liming methodology ................................................................... 75 3.12 Lime application in the United Kingdom and Germany: survey data v disaggregated country total lime consumption ............................................................................................................................. 88 4. Utilities and auxiliary processes ....................................................................................................................... 94 5. Transport processes ................................................................................................................................................ 98 5.1 Road transportation.............................................................................................................................................. 98 5.2 Maritime transportation .................................................................................................................................. 102 5.3 Inland water transportation .......................................................................................................................... 106 5.4 Rail transportation ............................................................................................................................................. 107 i
5.5 Pipeline transportation..................................................................................................................................... 108 References for common input data ........................................................................................................................ 109 Part Two — Liquid biofuels processes and input data................................................................................. 117 6. Biofuels processes and input data ............................................................................................................... 118 6.1 Wheat grain to ethanol.................................................................................................................................... 123 6.2 Maize to ethanol ................................................................................................................................................. 130 6.3 Sugar beet to ethanol ...................................................................................................................................... 137 6.4 Barley to ethanol ................................................................................................................................................ 143 6.5 Sugar cane to ethanol ...................................................................................................................................... 148 6.6 Rye to ethanol ...................................................................................................................................................... 154 6.7 Triticale to ethanol ............................................................................................................................................. 160 6.8 Rapeseed to biodiesel ...................................................................................................................................... 166 6.9 Sunflower to biodiesel ..................................................................................................................................... 177 6.10 Soya oil to biodiesel ....................................................................................................................................... 184 6.10.1 National soy data ................................................................................................................................... 192 6.11 Palm oil to biodiesel ....................................................................................................................................... 201 6.12 Jatropha to biodiesel ..................................................................................................................................... 209 6.13 Waste cooking oil............................................................................................................................................. 216 6.14 Animal fat ............................................................................................................................................................ 218 6.15 HVO ......................................................................................................................................................................... 222 6.16 Black liquor ......................................................................................................................................................... 226 6.17 Wood to Liquid Hydrocarbons ................................................................................................................... 232 6.18 Wood to methanol........................................................................................................................................... 233 6.19 Wood to DME ..................................................................................................................................................... 234 6.20 Straw to ethanol............................................................................................................................................... 235 References for ethanol and biodiesel pathways.............................................................................................. 237 Part Three — Review process.................................................................................................................................... 246 7. Consultation with experts and stakeholders ........................................................................................... 247 7.1 Expert Consultation (November 2011).................................................................................................... 247 7.1.1 Main outcomes of the discussion ..................................................................................................... 248 7.2 Stakeholder meeting (May 2013) .............................................................................................................. 252 7.2.1 Main updates ............................................................................................................................................... 252 Appendix 1. Fuel/feedstock properties .................................................................................................................. 254 Appendix 2. Crop residue management................................................................................................................ 263 Appendix 3. List of questions and answers ........................................................................................................ 269 References for Appendices.......................................................................................................................................... 328
ii
List of Tables Table 1 Emissions associated to the production, supply and combustion of diesel, gasoline and heavy fuel oil ............................................................................................................................................................................................................... 18 Table 2 Mix of sources and conversion pathways chosen to represent a marginal electricity mix and emission factor at power plant outlet to the high-voltage grid (380 kV, 220 kV, 110 kV) (as proposed by EC in SWD(2014) 259) ..................................................................................................................................................................... 20 Table 3 Electricity transmission losses in the high-voltage grid (380 kV, 220 kV, 110 kV)............................ 20 Table 4 Electricity distribution in the medium-voltage grid (10 – 20 kV).................................................................. 20 Table 5 Electricity distribution losses to low voltage (380 V) .......................................................................................... 21 Table 6 Emission factor: hard coal provision.............................................................................................................................. 21 Table 7 Emission factor: natural gas provision (at MP grid) .............................................................................................. 22 Table 8 Supply of P2O5 fertilizer ........................................................................................................................................................ 23 Table 9 Supply of K2O fertilizer .......................................................................................................................................................... 24 Table 10 Limestone mining .................................................................................................................................................................. 24 Table 11 Limestone grinding and drying for the production of CaCO3 ....................................................................... 25 Table 12 Supply of pesticides ............................................................................................................................................................. 25 Table 13 CaO as a process chemical .............................................................................................................................................. 26 Table 14 Supply of hydrogen chloride ........................................................................................................................................... 26 Table 15 Supply of hydrogen via steam reforming of natural gas for HCl .............................................................. 27 Table 16 Supply of chlorine via membrane technology, incl. NaCl supply chain ................................................... 27 Table 17 Supply of Na2CO3 ................................................................................................................................................................... 28 Table 18 Coke production from hard coal.................................................................................................................................... 28 Table 19 Coke-oven gas combustion.............................................................................................................................................. 29 Table 20 Supply of NaOH ...................................................................................................................................................................... 29 Table 21 Supply of NH3 – as process chemical in EU............................................................................................................ 30 Table 22 Supply of H2SO4 ...................................................................................................................................................................... 30 Table 23 Supply of H3PO4 ...................................................................................................................................................................... 31 Table 24 Supply of cyclohexane ........................................................................................................................................................ 31 Table 25 Supply of lubricants ............................................................................................................................................................. 32 Table 26 Supply of alpha-amylase enzymes ............................................................................................................................. 32 Table 27 Supply of gluco-amylase enzymes ............................................................................................................................. 33 Table 28 Supply of sodium methoxide (CH3ONa) .................................................................................................................... 33 Table 29 Supply of sodium via molten-salt electrolysis...................................................................................................... 34 Table 30 Supply of methanol .............................................................................................................................................................. 34 Table 31 Emission factor: wheat provision.................................................................................................................................. 35 Table 32 Emission factor: maize seeds provision.................................................................................................................... 35 Table 33 Emission factor: sugar beet seeds provision ......................................................................................................... 35 Table 34 Emission factor: barley seeds provision ................................................................................................................... 36 Table 35 Emission factor: sugar cane seeds provision......................................................................................................... 36 Table 36 Emission factor: rye seeds provision .......................................................................................................................... 36 Table 37 Emission factor: triticale seeds provision ................................................................................................................ 37 Table 38 Emission factor: rapeseed seeds provision............................................................................................................. 37 Table 39 Emission factor: sunflower seeds provision ........................................................................................................... 37 Table 40 Emission factors for fossil fuels, fertilizers and chemicals........................................................................... 38 Table 41 Nitrogen fertilizer mix used in the EU........................................................................................................................ 40 Table 42 Input data for fertilizer manufacturing emissions calculation .................................................................... 42
iii
Table 43 Diesel use in cultivation derived from CAPRI data ............................................................................................. 46 Table 44 CAPRI drying data .................................................................................................................................................................. 47 Table 45 CAPRI data on primary energy for inputs, used to convert CAPRI output to our input data....... 48 Table 46 Calculation of pesticide use............................................................................................................................................. 48 Table 47 Crop specific parameters to calculate N input from crop residues........................................................... 54 Table 48 Constant and effect values for calculating N2O emissions from agricultural fields after S&B55 Table 49 Potential biofuel crops assignment to S&B vegetation classes ................................................................. 56 Table 50 Changes in crop yield and mineral fertilizer input between 2000 and 2010/11 (i.e. average of 2010 and 2011).......................................................................................................................................................................................... 63 Table 51 Soil nitrous oxide emissions from biofuel feedstock cultivation in 2010/11. The values are weighted averages from suppliers of each crop to the EU market (including EU domestic production). 65 Table 52 Emissions from liming and from neutralization of acid from fertilizer N input. Results are global weighted average emissions from suppliers of each crop to the EU market (including EU domestic production) ................................................................................................................................................................................ 81 Table 53 Limestone and dolomite consumption for the years 2000, as reported in EDGAR v4.1 database (EC-JRC/PBL), and share of limestone and dolomite applied to land use/cover other than cropland ........................................................................................................................................................................................................... 85 Table 54 Lime application recommendations (Agricultural Lime Association, 2012). Values are the amount of ground limestone (with a neutralising value of 54 and 40 % passing through a 150 micron mesh) required to achieve the target soil pH. The Agricultural Lime Association considers a optimum pH between 6.8 and 7.0 for general cropping. For permanent grassland the optimum pH is slightly lower. .............................................................................................................................................................................................................................. 87 Table 55 Lime application at field level (Defra, 2001) and estimation of mean annual application rates on tillage crops in the year 2000 ...................................................................................................................................................... 91 Table 56 Lime application in the United Kingdom in the year 2000, based on this study .............................. 92 Table 57 Process for a NG boiler ...................................................................................................................................................... 94 Table 58 Process for a NG CHP to supply power and heat ................................................................................................ 95 Table 59 Process for a lignite/coal CHP......................................................................................................................................... 95 Table 60 Process for a woodchip-fuelled CHP .......................................................................................................................... 96 Table 61 Process for the energy consumption in an ethanol or FAME depot.......................................................... 96 Table 62 Process for energy consumption in an ethanol/FAME filling station........................................................ 97 Table 63 Fuel consumption for a 40 t truck ............................................................................................................................... 99 Table 64 Fuel consumption for a 40 t truck, weighted average for sugar cane transport ........................... 100 Table 65 Fuel consumption for a MB2213 dumpster truck used for filter mud cake ..................................... 100 Table 66 Fuel consumption for a MB2318 truck used for seed cane transport ................................................. 100 Table 67 Fuel consumption for a MB2318 tanker truck used for vinasse transport ....................................... 101 Table 68 Fuel consumption for a 12 t truck ............................................................................................................................ 101 Table 69 Fuel consumption for a 20 t truck used for jatropha seeds transport ................................................ 102 Table 70 Fuel consumption for a Handymax for goods with bulk density > 0.6 t/m3 (weight-limited load) ................................................................................................................................................................................................................ 103 Table 71 Fuel consumption for a product tanker for ethanol transport .................................................................. 103 Table 72 Fuel consumption for a product tanker for FAME and ethanol transport .......................................... 104 Table 73 Fuel consumption for a product tanker for pure vegetable oil transport ........................................... 104 Table 74 Fuel consumption for a product tanker for ethanol transport from the United States.............. 106 Table 75 Fuel consumption for a bulk carrier for inland navigation.......................................................................... 106 Table 76 Fuel consumption for an oil carrier barge for inland navigation............................................................. 107 Table 77 Fuel consumption for a freight train run on diesel fuel (in the United Sates)................................. 107 Table 78 Fuel consumption for a freight train run on grid electricity ....................................................................... 108 Table 79 Fuel consumption for the pipeline distribution of FAME (5 km) .............................................................. 108
iv
Table 80 Cereal share of ethanol feedstock in the EU (Ecofys, 2011) .................................................................... 122 Table 81 Cultivation of wheat.......................................................................................................................................................... 124 Table 82 Drying of wheat grain ...................................................................................................................................................... 125 Table 83 Handling and storage of wheat grain ..................................................................................................................... 126 Table 84 Transport of wheat grain via 40 t truck (payload 27 t) over a distance of 100 km (one way) ........................................................................................................................................................................................................................... 126 Table 85 Conversion of wheat grain to ethanol .................................................................................................................... 127 Table 86 Transportation of ethanol summary table to the blending depot .......................................................... 128 Table 87 Transport of ethanol to depot via 40 t truck over a distance of 305 km (one way) ................... 128 Table 88 Maritime transport of ethanol over a distance of 1 118 km (one way) ............................................. 128 Table 89 Transport of ethanol over a distance of 153 km via inland ship (one way)..................................... 128 Table 90 Transport of ethanol over a distance of 381 km via train (one way) .................................................. 129 Table 91 Ethanol depot........................................................................................................................................................................ 129 Table 92 Transport of ethanol to depot via 40 t truck over a distance of 150 km (one way) ................... 129 Table 93 Ethanol filling station ....................................................................................................................................................... 129 Table 94 Cultivation of maize in the EU .................................................................................................................................... 131 Table 95 Drying of maize ................................................................................................................................................................... 132 Table 96 Handling and storage of maize .................................................................................................................................. 133 Table 97 Transport of maize via a 40 t truck over a distance of 100 km (one way) ...................................... 133 Table 98 Conversion of maize to ethanol in EU .................................................................................................................... 134 Table 99 Transportation of ethanol summary table ........................................................................................................... 135 Table 100 Transport of ethanol via train over a distance of 1 000 km (Indiana to Baltimore) ................ 135 Table 101 Transport of ethanol via ship (payload 50 000 t) over a distance of 6 800 km (Baltimore to Rotterdam) .................................................................................................................................................................................................. 135 Table 102 Sugar beet cultivation ................................................................................................................................................... 138 Table 103 Transport of sugar beet via 40 t truck over a distance of 30 km (one way) ................................ 140 Table 104 Conversion to ethanol with no biogas from slops ........................................................................................ 141 Table 105 Conversion to ethanol with biogas from slops ............................................................................................... 141 Table 106 Barley cultivation ............................................................................................................................................................. 144 Table 107 Drying of barley................................................................................................................................................................ 145 Table 108 Handling and storage of barley............................................................................................................................... 146 Table 109 Transport of barley grain via 40 t truck over a distance of 100 km (one way) .......................... 146 Table 110 Conversion of barley to ethanol .............................................................................................................................. 147 Table 111 Sugar cane cultivation .................................................................................................................................................. 149 Table 112 Transportation of sugar cane (summary table) ............................................................................................. 150 Table 113 Transport of mud cake via dumpster truck MB2213 over a distance of 8 km (one way)..... 150 Table 114 Transport of seeding material via MB2318 truck over a distance of 20 km (one way)......... 150 Table 115 Transport of sugar cane via 40 t truck over a distance of 20 km (one way) ............................... 150 Table 116 Transport of vinasse summary table ................................................................................................................... 150 Table 117 Transport of vinasse via a tanker truck MB2318 over a distance of 7 km (one way) ............ 151 Table 118 Transport of vinasse via a tanker truck with water cannons over a distance of 14 km (one way)................................................................................................................................................................................................................. 151 Table 119 Transport of vinasse via water channels ........................................................................................................... 151 Table 120 Conversion of sugar cane to ethanol ................................................................................................................... 152 Table 121 Summary transport table of sugar cane ethanol.......................................................................................... 152 Table 122 Transport of ethanol via a 40 t truck a distance of 700 km (one way)........................................... 152 Table 123 Maritime transport of ethanol via ship over a distance of 10 186 km (one way) ..................... 153 Table 124 Rye cultivation ................................................................................................................................................................... 155 Table 125 Drying of rye grain .......................................................................................................................................................... 156
v
Table 126 Handling and storage of rye grain ......................................................................................................................... 157 Table 127 Transport of rye grain via 40 t (payload 27 t) truck over a distance of 100 km (one way) 157 Table 128 Conversion of rye grain to ethanol ........................................................................................................................ 158 Table 129 Triticale cultivation ......................................................................................................................................................... 161 Table 130 Drying of triticale grain ................................................................................................................................................ 162 Table 131 Handling and storage of triticale ............................................................................................................................ 163 Table 132 Transport of triticale via 40 t (payload 27 t) truck over a distance of 100 km (one way) ... 163 Table 133 Conversion of triticale to ethanol ........................................................................................................................... 164 Table 134 Rapeseed cultivation...................................................................................................................................................... 167 Table 135 Rapeseed drying and storage ................................................................................................................................... 168 Table 136 Transportation of rapeseed summary table .................................................................................................... 168 Table 137 Transport of rapeseed over a distance of 163 km via 40 tonne truck (one way) ..................... 168 Table 138 Maritime transport of rapeseed over a distance of 5 000 km (one way)....................................... 169 Table 139 Transport of rapeseed over a distance of 376 km via inland ship (one way) .............................. 169 Table 140 Transport of rapeseed over a distance of 309 km via train (one way)............................................ 169 Table 141 Oil mill: extraction of vegetable oil from rapeseed...................................................................................... 170 Table 142 LHV of rapeseed cultivated in the United States .......................................................................................... 171 Table 143 LHV of rapeseed cultivated in Europe ................................................................................................................. 171 Table 144 LHV of dry rapeseed cake........................................................................................................................................... 172 Table 145 Refining of vegetable oil ............................................................................................................................................. 172 Table 146 Esterification ...................................................................................................................................................................... 173 Table 147 Transportation of FAME summary table to the blending depot............................................................ 174 Table 148 Transport of FAME via 40 t truck over a distance of 305 km (one way) ........................................ 174 Table 149 Maritime transport of FAME over a distance of 1 118 km (one way) .............................................. 174 Table 150 Transport of FAME over a distance of 153 km via inland ship (one way) ...................................... 175 Table 151 Transport of FAME over a distance of 381 km via train (one way).................................................... 175 Table 152 FAME depot ......................................................................................................................................................................... 175 Table 153 Transport of FAME via 40 t truck over a distance of 305 km ............................................................... 175 Table 154 FAME filling station ........................................................................................................................................................ 175 Table 155 Sunflower cultivation ................................................................................................................................................... 178 Table 156 Sunflower drying and storage .................................................................................................................................. 179 Table 157 Transportation of sunflower seed summary table ....................................................................................... 179 Table 158 Transport of sunflower seed over a distance of 292 km via truck (one way) ............................. 179 Table 159 Transport of sunflower seed over a distance of 450 km via train (one way) .............................. 180 Table 160 Oil mill: extraction of vegetable oil from sunflower seed ........................................................................ 180 Table 161 LHV of Sunflower cultivated in the United States ........................................................................................ 181 Table 162 LHV of Sunflower cultivated in Europe. .............................................................................................................. 181 Table 163 LHV of dry sunflower cake ......................................................................................................................................... 182 Table 164 Refining of vegetable oil ............................................................................................................................................. 182 Table 165 Winterisation of sunflower......................................................................................................................................... 183 Table 166 Soy biodiesel made in the EU ................................................................................................................................... 185 Table 167 Total soy biodiesel used in the EU ......................................................................................................................... 185 Table 168 Soybean cultivation (weighted average of exporters to EU, by oil+oil-equivalent seeds)..... 186 Table 169 Drying at 13 % water content.................................................................................................................................. 187 Table 170 Transport of soybeans via 40 t truck over a distance of 373 km (one way)................................ 187 Table 171 Regional truck transport distances ........................................................................................................................ 187 Table 172 Transport of soybeans via diesel train over a distance of 61 km (one way) ................................ 188 Table 173 Regional train transport distances ......................................................................................................................... 188 Table 174 Pre-drying at oil mill ...................................................................................................................................................... 188
vi
Table 175 Oil mill .................................................................................................................................................................................... 189 Table 176 Transport of soy oil via inland ship over a distance of 562 km (one way) .................................... 190 Table 177 Maritime transport of soy oil via ship over a distance of 11 107 km (one way) ....................... 190 Table 178 Regional shipping and barge distances for soy oil to Rotterdam ........................................................ 191 Table 179 Refining of vegetable oil ............................................................................................................................................. 191 Table 180 Soybean cultivation in Brazil ..................................................................................................................................... 192 Table 181 Soybean drying ................................................................................................................................................................. 193 Table 182 Weighted average of transport of soybeans from central-west and south to Brazilian seaport........................................................................................................................................................................................................... 194 Table 183 Transportation by truck ................................................................................................................................................ 194 Table 184 Transportation by train ............................................................................................................................................... 194 Table 185 Transportation by inland waterway ...................................................................................................................... 194 Table 186 Shipping distances to Rotterdam ........................................................................................................................... 195 Table 187 Soybean cultivation in Argentina ............................................................................................................................ 196 Table 188 Soybean drying ................................................................................................................................................................. 197 Table 189 Truck transport of soybeans ..................................................................................................................................... 197 Table 190 Shipping and barge distances to Rotterdam.................................................................................................... 197 Table 191 Soybean cultivation in the United States........................................................................................................... 198 Table 192 Soybean drying ................................................................................................................................................................. 199 Table 193 Transport of soybeans via 40 t truck over a distance of 80 km (one way)................................... 199 Table 194 Transport of soybeans seed via inland ship over a distance of 2 161 km (one way) ............. 200 Table 195 Shipping and barge distances to Rotterdam.................................................................................................... 200 Table 196 Cultivation of oil palm tree (16 % peat) ............................................................................................................ 202 Table 197 Transport of fresh fruit bunches via 12 t truck (payload 7t) over a distance of 50 km (one way)................................................................................................................................................................................................................. 203 Table 198 Storage of fresh fruit bunches ................................................................................................................................ 203 Table 199 Plant oil extraction from fresh fruit bunches (FFB) ..................................................................................... 204 Table 200 LHV of palm oil ................................................................................................................................................................. 205 Table 201 Transport of palm oil summary table .................................................................................................................. 205 Table 202 Transport of palm oil via a 40 t truck over a distance of 120 km (one way) .............................. 206 Table 203 Depot for palm oil ........................................................................................................................................................... 206 Table 204 Maritime transport of palm oil via ship over a distance of 16 287 km (one way).................... 206 Table 205 Refining of vegetable oil from oil palm .............................................................................................................. 207 Table 206 Transportation of FAME summary table to the blending depot............................................................ 207 Table 207 Transport of FAME via 40 t truck over a distance of 305 km (one way) ........................................ 208 Table 208 Maritime transport of FAME over a distance of 1 118 km (one way) .............................................. 208 Table 209 Transport of FAME over a distance of 153 km via inland ship (one way) ...................................... 208 Table 210 Transport of FAME over a distance of 381 km via train (one way).................................................... 208 Table 211 Cultivation of Jatropha seed/plantation - (mechanised cultivation) .................................................. 210 Table 212 N2O from jatropha cultivation calculation ......................................................................................................... 211 Table 213 Transport system of jatropha capsules via 20 t truck (payload 10 t) over a distance of 190 km (one way) ............................................................................................................................................................................................. 212 Table 214 Extraction of vegetable oil from jatropha ......................................................................................................... 212 Table 215 Conversion of Jatropha seed to plant oil via expeller (allocation by energy) ............................... 213 Table 216 Transport of jatropha oil summary table .......................................................................................................... 213 Table 217 Transport of jatropha oil via a 40 t truck over a distance of 150 km (one way) ....................... 214 Table 218 Maritime transport of jatropha oil via ship over a distance of 11 727 km (one way) ............ 214 Table 219 Refining of vegetable oil from jatropha ............................................................................................................. 214 Table 220 Transport of waste oil via 40 t truck over a distance of 100 km........................................................ 216
vii
Table 221 Maritime transport of waste cooking oil via ship over a distance of 7 000 km (ref 1) .......... 216 Table 222 Transesterification of animal fat & used cooking oil to FAME .............................................................. 217 Table 223 By-products used for allocation .............................................................................................................................. 217 Table 224 Fraction of rendering process attributed to products ................................................................................. 218 Table 225 Allocation of emissions of rendering between fat and meat-and bone meal for the case that meat-and bone meal is not considered a waste ................................................................................................................... 219 Table 226 NG per tonne of fat ........................................................................................................................................................ 219 Table 227 Animal fat processing from carcass (biodiesel) (per kg produced fat) ............................................. 220 Table 228 Rendering (per MJ produced fat) ............................................................................................................................ 220 Table 148 Transport of tallow via 40 t truck over a distance of 305 km (one way) ....................................... 221 Table 229 Hydrotreating of vegetable oil (except palm oil and tallow oil) via NExBTL process including H2 generation (generation of a BTL-like fuel) .......................................................................................................................... 222 Table 230 Hydrotreating of palm oil via NExBTL process including H2 generation (generation of a BTL like fuel) ........................................................................................................................................................................................................ 222 Table 231 Hydrotreating of tallow via NExBTL process including H2 generation (generation of a BTL like fuel) ................................................................................................................................................................................................................. 223 Table 232 Transportation of BTL summary table to the blending depot................................................................ 223 Table 233 Transport of BTL via 40 t truck over a distance of 305 km (one way) ............................................ 223 Table 234 Maritime transport of FAME over a distance of 1 118 km (one way) .............................................. 224 Table 235 Transport of BTL over a distance of 153 km via inland ship (one way) .......................................... 224 Table 236 Transport of BTL over a distance of 381 km via train (one way)........................................................ 224 Table 237 Transport of BTL via pipeline .................................................................................................................................... 224 Table 238 BTL depot ............................................................................................................................................................................. 225 Table 239 Transport of BTL via 40 t truck over a distance of 150 km (one way) ............................................ 225 Table 240 BTL filling station............................................................................................................................................................. 225 Table 241 Liquid fuels via gasification of black liquor (methanol, DME, FT liquids) ........................................ 226 Table 242 Black liquor gasification to methanol (Inputs) ................................................................................................ 227 Table 243 Black liquor gasification to methanol (Outputs) ............................................................................................ 227 Table 244 Methanol results .............................................................................................................................................................. 228 Table 245 Black liquor gasification to DME (Inputs) ........................................................................................................... 228 Table 246 Black liquor gasification to DME (outputs)........................................................................................................ 229 Table 247 DME results ......................................................................................................................................................................... 229 Table 248 Black liquor gasification to FT liquids (inputs) ................................................................................................ 230 Table 249 Black liquor gasification to FT liquids (outputs) ............................................................................................. 230 Table 250 BTL plant .............................................................................................................................................................................. 232 Table 251 Transport of FT diesel via a 40 t truck over a distance of 150 km (one way) ............................. 232 Table 252 FT diesel depot .................................................................................................................................................................. 232 Table 253 FT diesel filling station ................................................................................................................................................. 232 Table 254 Methanol production (gasification, synthesis) ................................................................................................ 233 Table 255 Transport of methanol via a 40 t truck over a distance of 150 km (one way) ........................... 233 Table 256 Methanol filling station ................................................................................................................................................ 233 Table 257 DME production (gasification, synthesis) ........................................................................................................... 234 Table 258 Transport of DME via a 40 t truck over a distance of 150 km (one way) ...................................... 234 Table 259 DME filling station ........................................................................................................................................................... 234 Table 260 Conversion of wheat straw to ethanol via hydrolysis and fermentation (before allocation) ........................................................................................................................................................................................................................... 235 Table 261 Conversion of wheat straw to ethanol via hydrolysis and fermentation (after allocation between ethanol and electicity) ...................................................................................................................................................... 235 Table 262 Transport of ethanol to depot via 40 t truck over a distance of 150 km ....................................... 235
viii
Table 263 Ethanol depot..................................................................................................................................................................... 236 Table 264 Ethanol filling station .................................................................................................................................................... 236 Table 264 Fraction of crop residues removed from the field based on JRC/PBL (2010). The residue removal for cereals (excluding maize) in the EU is an expert estimate based on recent literature. ....... 263 Table 265 Fraction of crop residues burnt in the field based on JRC/PBL (2010) and Seabra et al. (2011) for Brazilian sugarcane. ...................................................................................................................................................... 265
ix
List of Figures Figure 1 EU Nitrogen fertilizer production sources ................................................................................................................. 41 Figure 2 Method applied to estimate N2O emissions from fertilized managed soils.......................................... 50 Figure 3 Variation of fertilizer-induced emissions from agricultural soils under different environmental conditions and fertilizer input rates applying the S&B model .......................................................................................... 57 Figure 4 Fertilizer application (mineral fertilizer + 50% of manure) and soil N2O emissions (expressed as gCO2eq MJ-1 of fresh crop) from rapeseed cultivation at different spatial levels based on GNOC (reference year for fertilizer input and yield: 2000) .............................................................................................................. 59 Figure 5 The GNOC online tool............................................................................................................................................................ 60 Figure 6 Weighted global average N2O soil emissions from biofuel feedstock cultivation. Results are weighted by feedstock quantities supplied to the EU market (including EU domestic production). The graph shows emissions based on GNOC calculations for the year 2000, emissions obtained following the IPCC (2006) TIER 1 approach and using the same input data as for the GNOC calculations and the GNOC results corrected for average yield and fertilizer input of 2010 and11. ..................................................... 64 Figure 7: Share of N2O emission sources and pathways of the weighted global average N2O soil emissions in 2010/11. ............................................................................................................................................................................. 65 Figure 8 Distribution of biologically fixed nitrogen in leguminous plants ................................................................. 69 Figure 9 Measurements of soil N2O emissions from soybean cultivation (S&B, 2006 and Alvarez et al. 2012) and country level results based on GNOC ..................................................................................................................... 74 Figure 10 Global distribution of soil pH (FAO/IIASA/ISRIC/ISS-CAS/JRC) and harvested area (Monfreda et al., 2008) ......................................................................................................................................................................................................... 88
x
Glossary AERU ALA AOAC BGN CA CAPRI CEC CENBIO CFB CHP CRF DDGS DG Climate Action DG Energy DNDC EBB EcoLab EDGAR ENSAT ENTSO-E EPA ETS EU FAO FFB FIEs FQD GEMIS GHG GNOC GREET GWP HV HVO IEA IES IET IFA IFEU IMO IPCC
Agriculture & Environment Research Unit Agricultural Lime Association Association of Official Analytical Chemists below-ground nitrogen conventional agriculture Common Agricultural Policy Regional Impact cation exchange capacity Centro Nacional de Referência em Biomassa circulating fluidised bed combined heat and power common reporting format dried distillers' grains with solubles Directorate-General for Climate Action Directorate-General for Energy DeNitrification DeComposition European Biodiesel Board Laboratoire écologie fonctionnelle et environnement Emission Database for Global Atmospheric Research Ecole Nationale Supérieure Agronomique de Toulouse European Network of Transmission System Operators for Electricity Environmental Protection Agency Emissions Trading Scheme European Union Food and Agriculture Organization of the United Nations fresh fruit bunch fertilizer-induced emissions Fuel Quality Directive (2009/30/EC) Globales Emissions-Modell Integrierter Systeme (Global Emission Model of Integrated Systems) greenhouse gas Global crop- and site-specific Nitrous Oxide emission Calculator Gases, Regulated Emissions, and Energy use in Transportation global warming potential high voltage hydrotreated vegetable oil International Energy Agency Institute for the Environment and Sustainability Institute of Energy and Transport International Fertilizer Association Institute for Energy and Environmental Research International Maritime Organization Intergovernmental Panel on Climate Change xi
JEC JRC JRC IES JRC IET LBST LCA LHV LPG LV MPOB MV NG CHP NG NREL NT NUTS POME RED RFA UNICA WTT WTW
JRC-EUCAR-CONCAWE consortium European Commission, Joint Research Centre JRC Institute for the Environment and Sustainability JRC Institute of Energy and Transport Ludwig-Bölkow-Systemtechnik GmbH life cycle assessment lower heating value liquefied petroleum gas low voltage Malaysian Palm Oil Board medium voltage natural gas combined heat and power natural gas National Renewable Energy Laboratory no-tillage Nomenclature of Territorial Units for Statistics palm oil milling effluent Renewable Energy Directive (2009/28/EC) Renewable Fuels Agency Brazilian Sugarcane Industry Association (A União da Indústria de Canade-Açúcar) Well-To-Tank Well-to-Wheels
xii
Executive Summary The Renewable Energy Directive (RED) (2009/28/EC) and the Fuel Quality Directive (FQD) (2009/30/EC) fix a threshold of savings of greenhouse gas (GHG) emissions for biofuels and bioliquids, and set the rules for calculating the greenhouse impact of biofuels, bioliquids and their fossil fuels comparators. To help economic operators to declare the GHG emission savings of their products, default and typical values are also listed in the annexes of the RED and FQD directives. This report describes the assumptions made by the JRC when compiling the updated data set used to calculate default and typical GHG emissions for the different liquid pathways. The input values reported in this report can be directly used by stakeholders to better understand the assumptions and data sources used to calculate typical and default emissions. The database consists of tables detailing the inputs and outputs of the processes used to build the liquid pathways. Data were derived from reports and databases of emission inventories produced by international organizations, such as the Intergovernmental Panel for Climate Change (IPCC), peer-reviewed journal publications as well as original data provided by stakeholders and industrial associations. The geographical scope is European; therefore the data are aimed at being representative of the European average. The report contains general input data used in various pathways (such as fossil fuel provision, supply of chemical fertilizers, pesticides and process chemicals; soil nitrous oxide (N2O) emissions from biofuel crop cultivation, etc.) and specific data for liquid biofuels (20 pathways), e.g. ethanol, biodiesel, and Hydrotrated Vegetable Oil (HVO) production from various feedstocks and some second generation pathways (e.g. wheat straw to ethanol, forest residues to synthetic diesel, etc.). For each pathway, the input data used in all processes (from cultivation of feedstock to coversion, transport and distribution of the final product) are shown and described including their sources. Furthermore, the report describes the review process undertaken by the JRC for the definition of input data and related methodological choices. In particular, it contains the main outcomes of the two meetings organized by the JRC for technical experts and stakeholders (expert workshop in 2011 and stakeholder workshop in 2013). Detailed comments were collected after both meetings and taken into account by the JRC to finalise the dataset and the calculations. There are several possible sources of uncertainty and data variation. The main factor is linked to the geographical variability of some processes (e.g. cultivation techniques and land productivity). The data are aimed at being valid throughout the whole EU, therefore the
13
dataset may not represent exactly each specific condition. In these cases, it is possible and recommended to calculate actual values. Secondly, technological differences may have significant impact; in this case, the values and pathways were disaggregated in order to represent the most common technological options. Thirdly, for some processes there is a lack or scarcity of data; in this regard the largest possible set of modelling and empirical data has been analysed (e.g. publications, handbooks, emissions inventory guidebooks, LCA databases and, whenever available, data from stakeholders).
14
1.
Introduction
1.1 Background European Union (EU) legislation contains a set of mandatory targets specific to the EU transport sector; these have the overall objective of achieving a sustainably fuelled European transport system. In particular, the new Directive (EU) 2015/1513 (so called ‘ILUC Directive’), which amends the Renewable Energy Directive (RED) (2009/28/EC) and the Fuel Quality Directive (FQD) (2009/30/EC), fixes a threshold of 60% savings of greenhouse gas (GHG) emissions for biofuels produced in installations starting operation after October 2015. The rules for calculating the greenhouse impact of biofuels, bioliquids and their fossil fuels comparators are set in the same Directives. To help economic operators calculate GHG emission savings, default and typical values are listed in the annexes of the RED and FQD directives. These were calculated based on the JEC consortium's ( 1) database of input data ( 2), following a stakeholder consultation process launched by the Commission. According to Article 19.7 of the RED and Article 7d.7 of the FQD (as amended in the new Directive 2015/1513), the Commission shall keep the default values under review and be empowered to adopt delegated acts to add new relevant pathways. As requested by the European Commission’s Directorate-General for Energy (DG Energy), the JRC has updated the existing input database, and the current list of pathways in the directives is expected to be modified accordingly. This report describes the assumptions made by the JRC when compiling the updated data set used to calculate default GHG emissions for the different pathways in the directives' annexes.
(1) Meaning the Joint Research Centre (JRC), the European Council for Automotive R&D (EUCAR) and the Conservation of Clean Air and Water in Europe (CONCAWE). (2) Version of 31.7.2008.
15
1.2 Structure of the report The report is basically divided in three parts. The first part (Chapters 2, 3, 4 and 5) describes the data that are used in numerous pathways and includes: -
fossil fuel provision;
-
supply of chemical fertilizers, pesticides and process chemicals;
-
the EU electricity mix;
-
diesel, drying, and plant protection use in cultivation;
-
soil nitrous oxide (N2O) emissions from biofuel crop cultivation;
-
auxiliary plant processes (such as a natural gas boiler);
-
fuel consumption for different means of transportation.
The second part (Chapter 6) describes the specific input data used in the processes that make up the liquid biofuel pathways. The pathways also identify which common data are used. Specific input data used in the processes that refer to the biogas/biomass are described in another JRC report (JRC - IET, 2014). The third part of the report (Chapter 7) describes the review process undertaken by the JRC for the definition of input data and related methodological choices. In particular, it contains the main outcomes of the two meetings organized by the JRC for technical experts and stakeholders: - Expert workshop held in November 2011 in Ispra (IT); - Stakeholder workshop held in May 2013 in Brussels (BE). Detailed comments were collected after both meetings and taken into consideration by the JRC to finalise the dataset and the calculations. Values that were updated following stakeholders/experts comments are underlined along the report. Detailed questions/comments from stakeholders and related JRC answers may be found in Appendix 3.
16
Part One — General input data and common processes
17
2. General input data for pathways This section covers the processes with the input data used for the production and supply of fossil fuels, fertilizers, chemicals and for the European electricity mix. The total emission factors for the whole supply chain are indicated in the table comments and are summarised in Table 40.
2.1 Fossil fuels provision Diesel oil, gasoline and heavy fuel oil provision • •
Figures for crude oil production and transport emissions estimated for EU-mix are based on the OPGEE report (ICCT, 2014). Emissions from refining are those calculated in JEC-WTW v4.a on the basis of marginal emissions saved by producing marginally less of the different products. This makes the refining emissions for gasoline and especially diesel higher than the average for all refinery products, whereas those for heavy fuel oil are lower.
Table 1 Emissions associated to the production, supply and combustion of diesel, gasoline and heavy fuel oil WTW marginal refining emissions + OPGEE production emissions gCO2 eq./MJ final fuel
DIESEL
GASOLINE
HFO
Source
1) Production emissions from OPGEE including transport of crude
11.0
10.8
10.5
Calculated from [1]
3) Refining emissions
8.6
7.0
2.2
2
4) Transport of product
1.1
1.2
0
2
5) Combustion emissions
73.2
73.4
80.6
2
Total emissions
93.9
92.4
93.3
Comments - CO2 emissions from combustion of crude oil = 75.5 gCO2/MJ crude [2] - Crude production emissions (incl. transport of crude) = 10.0 gCO2 eq./MJ crude [1] - Production emissions of diesel and gasoline are calculated based on the factors calculated in JEC-WTW 4a: 1.1 MJ crude / MJ diesel, 1.08 MJ crude / MJ gasoline and 1.05 MJ crude / MJ HFO. Sources 1 ICCT, 2014. 2 JEC-WTT v4a. 18
Electricity grid supply (and Fossil Fuel Comparator) -
The Fossil Fuel Comparator (FFC) used in SWD(2014) 259, for power supplied to the electricity grid assumes a marginal mix of present and perspective fossil power production technologies and feedstocks;
-
For consistency reasons, it is appropriate that the GHG emissions considered for the supply and consumption of electricity in the calculated pathways are considered to be the same 3;
-
This consistency must be maintained also for other fossil sources supply emissions (e.g. natural gas);
-
The emission factors for the supply of chemicals are also calculated using the approach defined in SWD(2014) 259, applying the marginal values for electricity and natural gas supply indicated here.
The marginal mix assumed in in SWD(2014) 259 is reported in Table 2. To be noted that the emissions reported in Table 2 refer to the power plant outlet (high voltage) and do not include transmission and distribution losses. They are thus different from the values reported in JEC WTT 4a report, which refer to low voltage electricity delivered to consumers. It is important to highlight that the values and approach used in this report are appropriate for the specific goal and purposes of these calculations, i.e. to determine the typical and default GHG emissions and GHG savings for specific liquid biofuel pathways in accordance with a methodology designed for regulatory purposes. However, the absolute GHG emission values reported in this report will likely be higher when compared with other literature studies using similar system boundaries and methodology. One reason for this is that the average value of GHG emissions associated to the EU electricity mix supply includes also low or zeroCO2 emission sources such as other renewables and nuclear. For illustration, the value indicated in the JRC WTT 4a for EU mix emissions at power plant outlet is equal to 134 gCO2 eq./MJel. (see JEC WTT 4a, section 3.5.1.8 for more details) resulting in a value of 150 gCO2 eq./MJel. for consumers. This difference will be more significant for the pathways with larger electricity consumption (e.g. the ones including pellet manufacturing).
3
A difference in emissions between consumption and FFC would create fictitious emissions savings.
19
Table 2 Mix of sources and conversion pathways chosen to represent a marginal electricity mix and emission factor at power plant outlet to the high-voltage grid (380 kV, 220 kV, 110 kV) (as proposed by EC in SWD(2014) 259) Pathwa y (JEC)
Electricity production
Unit
Amount
Comment
KOEL1
Conventional hard coal
gCO2 eq./MJel.
261.5
43.5% el efficiency
KOEL2
Coal (IGCC)
gCO2 eq./MJel.
234.6
48% el efficiency
GBEL1b
Natural gas (CCGT)
gCO2 eq./MJel.
118.2
58.1% el efficiency, 4000 km pipe transport of natural gas
GBEL1a
Natural gas (CCGT)
gCO2 eq./MJel.
129.4
58.1% el efficiency,7000 km pipe transport of natural gas
GREL1
Natural gas (CCGT)
gCO2 eq./MJel.
126.5
58.1% el efficiency, LNG
Average
(25/25/16.7/16.7/16.7 %)
gCO2 eq./MJel.
186.4
CO2
Output
g/MJ
169.4
CH4
Output
g/MJ
0.61
N2O
Output
g/MJ
0.006
Emissions
Comments: - The average mix considered consists of: 25% KOEL 1, 25% KOEL 2, 16.7% GBEL1a, 16.7% GBEL1b, 16.7% GREL1. The transmission and distribution losses considered are reported in Table 3, Table 4 and Table 5.
Table 3 Electricity transmission losses in the high-voltage grid (380 kV, 220 kV, 110 kV) Electricity Electricity (HV)
I/O
Unit
Amount
Source
Input
MJ/MJe
1.015
1
Output
MJ
1.0000
Table 4 Electricity distribution in the medium-voltage grid (10 – 20 kV) I/O
Unit
Amount
Source
Electricity (HV)
Input
MJe/MJe
1.038
2
Electricity (MV)
Output
MJ
1.0000
20
Table 5 Electricity distribution losses to low voltage (380 V) I/O
Unit
Amount
Source
Electricity (MV)
Input
MJ/MJe
1.064
2
Electricity (LV)
Output
MJ
1.0000
Comment - The final GHG emission factor for electricity supplied to consumers at 380 V is equal to 209 gCO2 eq./MJ el.. Sources 1 ENTSO-E, 2011. 2 AEEG, 2012.
Hard coal provision Table 6 Emission factor: hard coal provision
Hard coal
I/O
Unit
Amount
Output
MJ
1
Emissions CO2
Output
g/MJ
6.50
CH4
Output
g/MJ
0.385
N2O
Output
g/MJ
2.50E-04
Comments - The total emission factor for the supply of 1 MJ of hard coal is 16.2 gCO2 eq/MJ. - The emission factor for combustion of 1 MJ of hard coal is 96.1 gCO2 eq/MJ. Source 1 JEC-WTT v4; EU coal mix (updated with diesel and HFO factors in Table 5).
21
Natural gas provision Table 7 Emission factor: natural gas provision (at MP grid)
Natural gas
I/O
Unit
Amount
Output
MJ
1
Emissions CO2
Output
g/MJ
11.38
CH4
Output
g/MJ
0.207
N2O
Output
g/MJ
3.61E-04
Comments - The total emission factor for the supply of 1 MJ of natural gas is 16.7 gCO2 eq/MJ. - The emission factor for combustion of 1 MJ of natural gas is 55.08 gCO2 eq/MJ. - The value is obtained as a mix of the following pathways: 33% EU mix (4000 km – GPCG1b); 33% 7000 km (GPCG1a) and 33% LNG (GRCG1). Note that the values reported in WTT v4 refer to compressed natural gas as a final product and thus contain an additional emission due to the final compression of the gas. This is not included in this number since the NG is considered at the level of medium pressure grid. Source 1 JEC-WTT v4.
22
2.2 Supply of process chemicals and pesticides This section includes the processes with the input data used for the production and supply of various chemicals, fertilizers and pesticides used in the pathways. The emissions indicated in the following tables refer only to the emissions associated to the specific process. However, many processes are linked in a 'supply chain', in order to provide the final product. Therefore, for ease of reference, total emission factors for the whole supply chain are indicated in table comments and summarized in Table 40.
2.2.1 Chemical fertilizers and pesticides Phosphorus pentoxide (P2O5) fertilizer supply Table 8 Supply of P2O5 fertilizer I/O
Unit
Amount
Hard coal
Input
MJ/kg
0.57
Diesel oil
Input
MJ/kg
1.12
Electricity
Input
MJ/kg
1.602
Heavy fuel oil (1.8 % S)
Input
MJ/kg
5.00
NG
Input
MJ/kg
3.15
Output
kg
1.0
P2O5 fertilizer
Emissions CO2
-
g/kg
700
CH4
-
g/kg
0.023
N2O
-
g/kg
0.042
Comment - The total emission factor, including upstream emissions, to produce 1 kg of P2O5 fertilizer is 1 176.1 gCO2 eq/kgP2O5. Source 1 Kaltschmitt and Reinhardt, 1997.
23
Potassium oxide (K2O) fertilizer supply Table 9 Supply of K2O fertilizer I/O
Unit
Amount
Diesel oil
Input
MJ/kg
0.54
Electricity
Input
MJ/kg
0.22
NG
Input
MJ/kg
7.5
Output
kg
1.0
K2O fertilizer
Emissions CO2
-
g/kg
453
CH4
-
g/kg
0.021
N2O
-
g/kg
0.0094
Comments - The total emission factor, including upstream emissions, to produce 1 kg of K2O fertilizer is 635.7 gCO2 eq/kgK2O. - K2O fertilizer production and transport. Source 1 Kaltschmitt and Reinhardt, 1997. Limestone (aglime–CaCO3) supply chain The supply chain for the provision of aglime fertilizer includes the processes for the mining, grinding and drying of limestone. The results are quoted per kilogram of CaO in the CaCO3, even though the product is ground limestone. Limestone was once converted to CaO by strong heating (calcining), using fuel. But now, ~90 % of aglime is ground limestone (or dolomite), and even the small amount of CaO which is used on soil is a by-product of industrial processes. Table 10 Limestone mining I/O
Unit
Amount
Diesel
Input
MJ/kg
0.0297
Electricity (MV)
Input
MJ/kg
0.013
Output
kg
1
Limestone
Source 1 GEMIS v. 4.9, 2014, 'Xtra-quarrying\limestone-DE-2010'.
24
Table 11 Limestone grinding and drying for the production of CaCO3 I/O
Unit
Amount
Limestone
Input
kg/kg
1
Electricity (LV)
Input
MJ/kg
0.179
Output
kg
1
CaCO3
Comment - The total emission factor, including upstream emissions, to produce 1 kg of CaO fertilizer is 89.6 gCO2 eq/kgCaO. Source 1 GEMIS v. 4.9, 2014, Nonmetallic minerals\CaCO3 -powder-DE-2000. Since the aglime (CaCO3) inputs to cultivation processes are quoted in terms of the CaO content ('calcium fertilizer as CaO') of the limestone, the inputs per kilogram of CaO are decreased by the molecular weight ratio CaCO3/CaO = 1.785. The total emission factor becomes 50.2 gCO2 eq/kgCaCO3. Pesticides supply chain ‘Pesticides’ is the name given to all ‘plant health products’ including pesticides, herbicides, fungicides and plant hormones. Table 12 Supply of pesticides I/O
Unit
Amount
Hard coal
Input
MJ/kg
7.62
Diesel oil
Input
MJ/kg
58.1
Electricity
Input
MJ/kg
28.48
Heavy fuel oil (1.8 % S)
Input
MJ/kg
32.5
NG
Input
MJ/kg
71.4
Output
kg
1.0
Pesticides
Emissions CO2
-
g/kg
4 921
CH4
-
g/kg
0.18
N2O
-
g/kg
1.51
Comment - The total emission factor, including upstream emissions, to produce 1 kg of pesticides is is 13 896.3 gCO2 eq/kg. Source 1 Kaltschmitt, 1997. 25
2.2.2 Chemicals Calcium oxide (CaO) as a process chemical (not aglime) Table 13 CaO as a process chemical I/O
Unit
Amount
Electricity
Input
MJ/kg
0.08
Heat (from NG burner 95% efficient) Limestone
Input
MJ/kg
3.65
Input
MJ/kg
1.8
Output
kg
1.0
CaO
Emissions CO2
-
g/kg
755
CH4
-
g/kg
0
N2O
-
g/kg
0
Comments - This is pure CaO as a process chemical, not agricultural lime. - The total emission factor to produce 1 kg of CaO as process chemical is 1 100.0 gCO2 eq/kgCaO. Source 1 GEMIS, v. 4.9, 2014; ‘nonmetallic minerals\CaO-GGR-kiln-DE-2010’
Hydrogen chloride (HCI) supply chain Table 14 Supply of hydrogen chloride I/O
Unit
Amount
Chlorine
Input
kg/kg
0.9724
Electricity
Input
MJ/kg
1.2
H2
Input
kg/kg
0.0276
HCl
Output
kg
1.0
Comment - The total emission factor for the supply of 1 kg of HCl is 1 375.5 gCO2 eq/kg. Sources 1 Althaus et al., 2007, Ecoivent report no. 8.
26
Table 15 Supply of hydrogen via steam reforming of natural gas for HCl I/O
Unit
Amount
Input
kg/kg
3.3994
Electricity
Output
MJ/kg
6
H2
Output
kg
1
NG
Emissions CO2
-
g/kg
8 691
CH4
-
g/kg
1.9
Comment - The total emission factor for the supply of 1 kWh of H2 is 341.08gCO2 eq/kWh. Sources 1 Scholz, 1992. 2 Pehnt, 2002. Table 16 Supply of chlorine via membrane technology, incl. NaCl supply chain I/O
Unit
Amount
Heat (from NG boiler)
Input
MJ/kg
0.27
Electricity
Input
MJ/kg
4.87
Na2CO3
Input
kg/kg
0.02
NaCl
Input
kg/kg
0.86
H2
Output
kg/kg
0.01
Chlorine
Output
kg
1
Comment - The total emission factor for the supply of 1 kg of Chlorine (including NaCl supply) is 860.8 gCO2 eq/kg. Source 1 GEMIS v. 4.9, 2014, chem.-inorg\chlorine(membrane)-DE-2010.
27
Sodium carbonate (Na2CO3) supply chain Table 17 Supply of Na2CO3 I/O
Unit
Amount
NaCl
Input
kg/kg
1.55
NG
Input
MJ/kg
1.094
Coal
Input
MJ/kg
7.94
Coke
Input
MJ/kg
2.225
CaCO3
Input
kg/kg
1.13
Na2CO3
Output
kg
1
g/kg
976
Emissions CO2
-
Comment - The total emission factor for the supply of 1 kg of sodium carbonate is 1 267.6 gCO2 eq/kg. Source 1 GEMIS v. 4.9, 2014, chem.-inorganic\sodium carbonate-DE-2000. Table 18 Coke production from hard coal I/O
Unit
Amount
Hard coal
Input
MJ/MJ
1.43
Electricity
Input
MJ/MJ
0.005
Heat (from coke-oven gas) Coke
Input
MJ/MJ
0.266
Output
MJ
1
Heat
Output
MJ/MJ
0.114
g/MJ
0.00177
Emissions CH4
-
Comment - The total emission factor for the supply of 1 kg of Coke is 102.3 gCO2 eq/kg. Source 1 GEMIS v. 4.9, 2014, conversion\coke-DE-2000.
28
Table 19 Coke-oven gas combustion
Coke-oven gas Heat (from coke-oven gas)
I/O
Unit
Amount
Input
MJ/MJ
1.1765
Output
MJ
1
Emissions CO2
Output
g/MJ
51.31
CH4
Output
g/MJ
0.0025
N2O
Output
g/MJ
0.0015
Source GEMIS v. 4.7, 2011, conversion\coke-DE-2000.
Sodium hydroxide (NaOH) supply chain Table 20 Supply of NaOH I/O
Unit
Amount
Electricity
Input
MJ/kg
4.32
Heat (from NG boiler)
Input
MJ/kg
0.240
Na2CO3
Input
kg/kg
0.02
NaCl
Input
kg/kg
0.758
H2
Output
kg/kg
0.0248
NaOH
Output
kg
1.0
Comments - The total emission factor for the supply of 1 kg of NaOH is 764.5 gCO2 eq/kg. - For the upstream processes for provision of Na2CO3, see Table 17. Source 1 GEMIS v. 4.9, 2014, 'chem.-inorg\NaOH (membrane)-DE-2000'.
29
Ammonia (NH3) supply chain Table 21 Supply of NH3 – as process chemical in EU I/O
Unit
Amount
Natural gas
Input
MJ/kg
34.56
Electricity
Input
MJ/kg
0.50
Output
kg
1.0
g/kg
1 917
NH3
Emissions CO2
-
Comment - For the supply of natural gas, see Table 7. - The total emission factor for the supply of 1 kg of Ammonia is 2 554.7 gCO2 eq/kg. Source - Hoxha, A. (Fertilizers Europe (former EFMA)), personal communication, May 2014 and February 2011. Data apply to Fertilizers Europe members only.
Sulphuric acid (H2SO4) supply chain Table 22 Supply of H2SO4 I/O
Unit
Amount
Electricity
Input
MJ/kg
0.76
NG (for S mining)
Input
MJ/kg
1.638
S
Input
kg/kg
0.327
Output
kg
1.0
g/kg
92.4
H2SO4
Emissions CO2
-
Comment - The total emission factor for the supply of 1 kg of H2SO4 is 268.9 gCO2 eq/kg. Source 1 Frischknecht et al., 1996.
30
Phosphoric acid (H3PO4) supply chain Table 23 Supply of H3PO4 I/O
Unit
Amount
Electricity
Input
MJ/kg
11.30
H2SO4
Input
kg/kg
1.70
Heat
Input
MJ/kg
3.60
Phosphate minerals
Input
kg/kg
1.80
Output
kg
1.0
H3PO4
Comment - The total emission factor for the supply of 1 kg of H3PO4 is 3 144.7 gCO2 eq/kg. Source 1 GEMIS version 4.9, 2014, chem.-inorg\phosphoric acid-DE-2000.
Cyclohexane (C6H12) supply chain Table 24 Supply of cyclohexane I/O
Unit
Amount
Crude oil
Input
MJ/kg
9.90
Crude oil (mass)
Input
kg/kg
1.00
Output
kg
1.0
g/kg
723
Cyclohexane CO2
Emissions -
Comment - The total emission factor for the supply of 1 kg of C6H12 is 723 gCO2 eq/kg. Source 1 Macedo et al., 2004.
31
Lubricants supply chain Table 25 Supply of lubricants Crude oil Lubricants CO2
I/O
Unit
Amount
Input
MJ/kg
53.28
Output
kg
1.0
g/kg
947
Emissions -
Comment - The total emission factor for the supply of 1 kg of lubricants is 947 gCO2 eq/kg. Source 1 Köhler et al., 1996.
Alpha-amylase supply chain Table 26 Supply of alpha-amylase enzymes Natural gas Alpha-amylase CO2
I/O
Unit
Amount
Input
MJ/kg
15.00
Output
kg
1.0
Emissions g/kg
1 000
Comment - The assumption is that primary energy mainly consists of NG. No upstream emissions are necessary. - The total emission factor for the supply of 1 kg of alpha-amylase is 1 000 gCO2 eq/kg. Source 1 MacLean and Spatari, 2009.
32
Gluco-amylase supply chain Table 27 Supply of gluco-amylase enzymes Crude oil Gluco-amylase
I/O
Unit
Amount
Input
MJ/kg
97.00
Output
kg
1.0
Emissions g/kg
CO2
7 500
Comment - The assumption is that primary energy mainly consists of crude oil. - The total emission factor for the supply of 1 kg of gluco-amylase is 7 500gCO2 eq/kg. Source: 1 MacLean and Spatari, 2009.
Sodium methoxide (CH3ONa) supply chain Table 28 Supply of sodium methoxide (CH3ONa) I/O
Unit
Amount
Methanol
Input
kg/kg
0.5931
Na
Input
kg/kg
0.4256
H2
Output
kg/kg
0.0187
Sodium methoxide
Output
kg
1.0
Comments - For the supply of Na and methanol, see Table 29 and Table 30. - The total emission factor for the supply of 1 kg of sodium methoxide is 3 276.8 gCO2 eq/kg. Source 1
Du Pont, 2008.
33
Table 29 Supply of sodium via molten-salt electrolysis I/O
Unit
Amount
Electricity
Input
MJ/kg
43.2
NaCl
Input
kg/kg
2.5421
Chlorine
Output
kg/kg
1.5421
Na
Output
kg
1.0
Comments - Emissions included in the sodium methoxide table. Table 30 Supply of methanol I/O
Unit
Amount
NG
Input
kg/kg
0.5838
Air-O2
Input
kg/kg
0.8309
Output
kg
1.0
Methanol
Comment - The total emission factor for the supply of 1 kWh of methanol is 131.8 gCO2 eq/kWh. Including combustion the GHG emissions amount to 379.8 g/kWh of methanol. Source 1 Larsen, 1998.
34
2.2.3 Seeding material Wheat seeds Table 31 Emission factor: wheat provision
Wheat seeds
I/O
Unit
Amount
Output
kg
1
Comment - The total emission factor for the supply of 1 kg of wheat seeds is 289.9 gCO2 eq/kg. Source Kaltschmitt, 1997 Maize seeds Table 32 Emission factor: maize seeds provision
Corn seeds
I/O
Unit
Amount
Output
kg
1
Comment - The total emission factor for the supply of 1 kg of maize seeds is 317.5 gCO2 eq/kg. Source Kaltschmitt, 1997
Sugar beet seeds Table 33 Emission factor: sugar beet seeds provision
Sugar beet seeds
I/O
Unit
Amount
Output
kg
1
Comment - The total emission factor for the supply of 1 kg of sugar beet seeds is 3 820.7 gCO2 eq/kg. Source Kaltschmitt, 1997
35
Barley seeds Table 34 Emission factor: barley seeds provision
Barley seeds
I/O
Unit
Amount
Output
kg
1
Comment - The total emission factor for the supply of 1 kg of barley seeds is 317.5 gCO2 eq/kg. Source Kaltschmitt, 1997 Sugar cane seeds Table 35 Emission factor: sugar cane seeds provision
Sugar cane seeds
I/O
Unit
Amount
Output
kg
1
Comment - The total emission factor for the supply of 1 kg of sugar cane seeds is 4.9 gCO2 eq/kg. Source Macedo, 2004 Rye seeds Table 36 Emission factor: rye seeds provision
Rye seeds
I/O
Unit
Amount
Output
kg
1
Comment - The total emission factor for the supply of 1 kg of rye seeds is 319.7 gCO2 eq/kg. Source Kaltschmitt, 1997
36
Triticale seeds Table 37 Emission factor: triticale seeds provision
Triticale seeds
I/O
Unit
Amount
Output
kg
1
Comment - The total emission factor for the supply of 1 kg of triticale seeds is 306.5 gCO2 eq/kg. Source Kaltschmitt, 1997 Rapeseed seeds Table 38 Emission factor: rapeseed seeds provision
Rapeseed seeds
I/O
Unit
Amount
Output
kg
1
Comment - The total emission factor for the supply of 1 kg of rapeseed seeds is 794.0 gCO2 eq/kg. Source Kaltschmitt, 1997 Sunflower seeds Table 39 Emission factor: sunflower seeds provision
Sunflower seeds
I/O
Unit
Amount
Output
kg
1
Comment - The total emission factor for the supply of 1 kg of sunflower seeds is 794.0 gCO2 eq/kg. Source Kaltschmitt, 1997
37
2.2.4 Summary of emission factors for the supply of main products For ease of reference, Table 40 summarises the emission factors for provision of various fossil fuels and supply of fertilizers. Table 40 Emission factors for fossil fuels, fertilizers and chemicals Net GHG emitted [g CO2 eq./MJ]
CO2 [g/MJ]
CH4 [g/MJ]
N 2O [g/MJ]
Supply
16.67
11.38
0.21
3.61E-04
Combustion
55.08
55.08
Total
71.7
66.45
0.21
3.61E-04
Supply
208.84
189.80
0.68
6.86E-03
Emission factors
Natural Gas
EU el. mix (LV)
EU el. mix (MV)
Hard coal
Lignite
Heavy fuel oil
Diesel
4
Use
0.0
0.0
0.00
0.000
Total
208.8
189.80
0.68
6.86E-03
Supply
196.35
178.45
0.64
6.44E-03
Use
0.0
0.0
0.00
0.000
Total
196.3
178.45
0.64
6.44E-03
Supply
16.21
6.50
0.39
2.50E-04
Combustion
96.11
96.11
Total
112.3
102.62
0.39
2.50E-04
1.44E-03
5.56E-05
1.44E-03
5.56E-05
Supply
1.73
1.68
Combustion
115.0
115.0
Total
116.7
116.68
Supply
12.70
-4
-
-
Combustion
80.60
80.60
0
0
Total
93.3
-
0.00
0.000
Supply
20.70
-
-
-
Combustion
73.25
73.25
0.00
0.00
Total
93.9
-
0.00
0.000
Disaggregated values are not available for Diesel and HFO since the main source used only reports values aggregated as [gCO2 eq.]. However, from the data reported in JEC WTT v.4a, it is clear that the large majority of emissions in diesel and HFO supply are due to CO2 (>90%) and the rest to methane.
38
Gasoline
Supply
19.00
-
-
-
Combustion
73.42
73.42
0.00
0.00
Total
92.4
-
0.00
0.000
CHEMICAL FERTILIZERS AND PESTICIDES N fertilizer
Supply [g/kg]
4 567.4
3 679.00
7.49
2.35
P2O5 fertilizer
Supply [g/kg]
1 176.1
1 112.11
1.92
0.054
K2O fertilizer
Supply [g/kg]
635.7
588.71
1.72
0.014
Aglime (as CaO)
Supply [g/kg]
89.6
82.94
0.23
2.90E-03
Pesticides
Supply [g/kg]
13 896.3
12 480.15
36.13
1.72
CHEMICALS CaO as process chemical
Supply [g/kg]
1 100.0
1 073.72
0.964
0.0073
HCl
Supply [g/kg]
1 375.5
1 252.9
4.43
0.04
H2 (supply for HVO)
Supply [g/MJ]
94.74
87.39
0.28863
0.0005
Chlorine (incl NaCl supply)
Supply [g/kg]
860.8
Na2CO3
Supply [g/kg]
1 267.6
1 148.96
4.67
0.0064
NaOH
Supply [g/kg]
764.5
692.96
2.52
0.028
Ammonia
Supply [g/kg]
2 554.7
2 366.58
7.35
0.015
H2SO4
Supply [g/kg]
268.9
246.59
0.83
0.0055
H3PO4
Supply [g/kg]
3 144.7
2 826.16
11.47
0.1068
Cyclohexane
Supply [g/kg]
723
723
0
0
Lubricants
Supply [g/kg]
947
947
0
0
Alpha-amylase
Supply [g/kg]
1 000
1000
0
0
Gluco-amylase
Supply [g/kg]
7 500
7500
0
0
Na(CH3O)
Supply [g/kg]
3 276.8
2 924.61
12.85
0.1038
Methanol
Supply [g/kWh]
131.8
39
2.4 N fertilizer manufacturing emissions calculation Nitrogen fertilizer production emissions • • • • • • • •
Average for all N fertilizer consumed in the EU, including imports. The data are principally from the emissions reporting by Fertilizers Europe (FE 5) in the frame of ETS. Data for inputs also come via FE, who report data from a world survey of fertilizer plant emissions. There is only one N fertilizer value: mix for urea and AN; mix of EU production and imports. These are sparse data on which N fertilizers are used, where, and for which crop. JRC 2005 value (Kaltschmitt, 2001) was about right in the 2005-to-2007 period. Other figures for EU fertilizer emissions in the literature are often extrapolated from individual factories. There is much scope for producers to reduce emissions by choosing a good fertilizer. Imported urea is assumed to come from the Middle East (expert judgment from Fertilizer Europe); The same default N fertilizer emissions are used for fertilizer applied to foreign crops (even though emissions from making fertilizers are generally higher outside EU, and especially in China).
Table 41 Nitrogen fertilizer mix used in the EU N-fertilizer (mix used in the EU) gCO2/kgN CO2
3 089
CH4
7.49
N2O
2.35
CO2 equiv. Emissions from acidification by fertilizer, wheter or not aglime is used TOTAL EMISSIONS PER KG N
3 977 590 4 567
Comments - For comparison: previous N fert emissions for RED annex calculations: about 6000 gCO2/kgN. - Emissions from acidification: N fertilizers cause acidification in the soil. The acid reacts with carbonate in the soil (or downstreams in river-beds or the sea), releasing CO2. The carbonate can come from rock naturally present in the soil, or from applied agricultural lime. In either case, we attribute these emissions to fertilizer use. Refer to
(5) Fertilizers Europe: see http://www.fertilizerseurope.com online.
40
Section 3.10 for details of this calculation and of emissions from aglime use not attributable to fertilizer. Figure 1 explains the processes in the calculation of emissions from production of N fertilizer used in EU. The calculation uses the input data described in Table 42. Figure 1 EU Nitrogen fertilizer production sources
41
Table 42 Input data for fertilizer manufacturing emissions calculation Ammonia production in the EU 2011 average Fertilizers Europe total-energy use in EU ammonia plants* [7] 35.3 GJ/t NH3 2011 (last available information) energy use for EU ammonia other than NG [8] 0.5 GJ/t NH3 2011 EU NG use for ammonia (latest available information) 34.8 GJ/t NH3 * Includes NG, electricity and other energy inputs. Does not include upstream energy losses. Assumption: fraction of imports (ammonia and solid fertilizers) remains constant at last-reported values: 2008-9 N2O emissions from nitric acid plants in EU 2020 EU average (ETS benchmark) [2] 1.0134 kg N2O/t HNO3 Note: For current emissions, we use the N2O emissions in the ETS 2020 target. Although EU nitric acid plants already surpassed the taget savings, the excess savings will be sold under ETS, so other emissions become attached to nitric acid. Therefore we consider the 2020 ETS target emissions, not the actual emissions from nitric acid. Although the savings in ammonia production emissions fall short of the 2020 targets (according to the latest available data), it is not necessary for producers to buy emissions savings from elsewhere before 2020. Therefore we consider the actual emissions for nitric acid. Minor inputs for EU fertilizer plants (EU data, but assumed the same for outside the EU) Electricity for ammonium nitrate plant 'is less than..'[3]
1
GJ/t AN
Electricity for urea plant [3]
5
GJ/t Urea
Calcium ammonium nitrate is assumed to have same emissions per tonne of N as ammonium nitrate (emissions from CaO are relatively small) Note: urea (= ammonium carbonate) manufacture reacts to ammonia with otherwise-emitted CO2. However, the CO2 is lost when urea decomposes on the field. We count neither the sequestration nor the emission. IMPORTED UREA Assumption: urea is imported from North Africa, especially Egypt [6] (China exports > 50% world urea with much higher (coal) emissions, but it is further away). 75% Fraction of EU-consumed Urea-type fertilizers imported (see table Trade data below). IMPORTED AMMONIUM NITRATE Imports are mostly from Russia, Ukraine and Belarus [6]: we represent them with weighted average of data for Russian and Ukrainian production. 8% Fraction of EU-consumed AN -type fertilizer imported [5]
42
N2O emissions from imported AN production are calculated from the total emissions in quoted in [9] (which we understand come from a complete LCA by Integer Consultants), assuming emissions for AN from other sources are the same as in EU 2007. LCA emissions for AN supply 2013 [9] Russia 3130 g per kg AN 0.35 N/AN 8943 g per kg N in AN Emissions from other-than-N2O* Emissions from N2O Emissions from N2O
3127 5816 19.52
CO2e/kg N in AN CO2e/kg N in AN gN2O/kg N in AN
*calculated by E3database using EU 2007 data on other emissions sources. IMPORTED AMMONIA Fraction of ammonia used in EU which is imported 16% Assumption: all ammonia imports are from Russia, Ukraine and Belarus [6]: we use weighted average data. UPSTREAM ELECTRICITY AND TRANSPORT ASSUMPTIONS Electricity for fertilizer production generated via a natural gas fuelled combined cycle (CCGT) power plant with an efficiency of 55% Transport from Russia to EU via train over a distance of 6000 km Maritime transport of urea from Damietta in Egypt to Rotterdam in the EU over a distance of 6500 km Electricity for the train derived from the Russian electricity mix
43
Natural Gas consumption for ammonia and urea production outside EU [Fertilizer Euorpe 2012] (on-site NG consumption only). NG use NG use NG use NG use NG use NG use kWh/kg N MMbtu/tonne MMbtu/tonne GJ/tonne NH3 GJ/tonne urea kWh/kg in urea 2014 NH3 2014 [1] urea 2014 2014 2014 urea 2014 Russia, Ukraine, 36.9 26.9 34.938 25.5 7.0721 15.161 Belarus N.Africa 37 not reported 35.1 25.6 7.0990 15.219
Trade data EU trade (2009) in kilo tonnes of nitrogen Imports Exports EU consumption % imported per type % of AN and urea in EU-consumed N fertilizer (in terms of N content)
Ammonia
Ammonium nitrate
Calcium ammonium nitrate
NH3 [4]
AN [5]
CAN [4]
3 173 914 13 975 16 %
Urea AN+CAN
165 2 097 8%
2 811
4 907.5
64 %
U [5] 1 524 2 024 75 %
Ammonium sulphate AS [4]
Total U+AS
745
2 769
7 676
36 %
44
Sources 1 2 3 4 5 6 7 8 9
Hoxha, A., Fertilizers Europe, personal communication February 2012 quoting forward projections by Fertecon, a fertilizer consultancy company. Commission proposal for ETS benchmarking of EU fertilizer industry, via Heiko Kunst, Climate Action, December 2010. Werner, A., BASF SE, Chairman of TESC in EFMA, 'Agriculture, fertilizers and Climate change': Presentation at EFMA conference, 12 February 2009, download from EFMA website. Numbers are based on IFA world benchmarking report on fertilizer emissions. IFA statistics for 2009, (http://www.fertilizer.org/ifa/HomePage/STATISTICS/Production-and-trade-statistics) accessed February 2011. Hoxha, A., Fertilizers Europe (former EFMA), personal communication, 20 February 2010. For agricultural use only (important for urea and AN), average of 2008/9 and 2009/10 data. Palliere, C., Fertilizers Europe (former EFMA), personal communication to JRC, December 2010. Hoxha, A., Fertilizers Europe, personal communication, May 2014. Hoxha, A., Fertilizers Europe, personal communication, February 2011. S. Mackle, Fertilizers Europe, 2013: Trade & economic policy outlook of the EU Nitrogen Fertilizer Industry, presentation on Fertilizers Europe website, acccessed May 2014.
45
2.5 Diesel, drying and plant protection use in cultivation Bonn University supplied new input data on diesel use, crop drying and pesticide application from the CAPRI database ( 6). Several pathways have been updated with the new data.
2.5.1 Diesel use in cultivation The CAPRI data used to calculate diesel use in cultivation are shown in Table 43. CAPRI data on diesel use in soya cultivation in the EU has not been used because the soya pathways are derived from a mix of non-EU sources. The diesel and “pesticide” (= sum of pesticides, herbicides, fugicides, plant hormones etc.) from CAPRI are per-ha for EU27 in 2004. They are converted to per-MJ crop (and per-kg crop) using the average FAO yields in 2010-2011, and our usual LHV figures. Table 43 Diesel use in cultivation derived from CAPRI data Crop
Total diesel input (a) MJ/ha
Average of 2010 and 2011 moist yield Kg/ha
MJ diesel/kg of moist crop MJ/kg
Barley
3 240
4 306
0.7525
EU maize
3 311
7 328
0.4519
Rapeseed
2 987
2 877
1.0383
Rye and meslin
3 014
3 143
Sugar beet
3 457
80 760
Sunflower
3 288
1 897
1.7332
Soft wheat
3 276
5 550
0.5902
0.9590 b
0.0428
(a) Total diesel input using diesel LHV of 35.9 MJ/litre. (b) The average equivalent yield at nominal 16% sugar for countries making sugar beet ethanol provided by the Confederation Internationale des Betteravies Europeans (CIBE 2013) has been used. Sources 1 CAPRI data converted to JEC format using information in Ref. 2 and Ref. 3 (e-mail from M. Kempen received in March 2012). 2 Kraenzlein, 2011. 3 Kempen and Kraenzlein, 2008.
(6) See http://www.ilr.uni-bonn.de/agpo/rsrch/capri/capri_e.htm online.
46
4
CGB and CIBE, 2013. French Confederation of Sugar Beet producers and Confederation Internationale des Betteravies Europeans, response to Commission stakeholder meeting in Brussel, May 2013, received by JRC in June 2013.
2.5.2 Crop drying These data were calculated from CAPRI average primary-energy-MJ/ha-results per crop. Table 44 CAPRI drying data Crop
Barley
Primary energy used for drying crop in CAPRI MJ/ha 695.75
Average % of water removed from each crop 0.68
Maize
16 122.73
10.58
Rapeseed
Unchanged
Rye and meslin
310.95
Sugar beet
Not dried
Sunflower
Unchanged
Soft wheat
1 351.03
0.42
1.02
Comments - CAPRI reports the average primary energy per hectare for drying grain. However, JRC use different drying energy and upstream emissions for fuel which are consistent with other processes in RED default calculations. Therefore, JRC consider the sum of the primary energy sources which CAPRI assumes are needed for drying 1 kg grain by 0.1% moisture (0.0231 MJ primary energy). By dividing the total primary energy for drying per hectare by this sum, the average % of water removed from each crop according to CAPRI has been calculated. This is linked to our drying pathways, as explained in each patways affected (wheat, maize, rye, barley, triticale) in Section 6. - Drying of rapeseed and sunflower (not reported by CAPRI) has been corrected by Ludwig-Bölkow-Systemtechnik GmbH (LBST) (Weindorf, W., personal communication, 22 March 2012). There had been a misunderstanding of the text in the original literature. The diesel input for the drying process derived from Umweltbundesamt (the German Federal Environment Agency) (UBA, 1999) is indicated per kilogram of removed water, and not per tonne of rapeseed. The text in UBA (1999) states: 'Storage and drying (per t of corn): 12.6 kWh electricity; 0.12 l of heating oil and 0.1 kWh of electricity per kg of water removed'. Initially, it had been assumed that the amount of heating oil is related to 1 t of rapeseed grain. According to LBST, the light heating oil is often used as heat source for drying (not for diesel engines, for mechanical drives for handling), and as a result, the consumption of light heating oil (= diesel) depends on the water content. In contrast to the 0.1 kWh of electricity plus 0.12 l of heating oil (which are per tonne of removed water) the 12.6 kWh are 47
probably the electricity requirement for handling and therefore per tonne of rapeseed grain.) Sources 1 CAPRI data converted to JEC format using information in Ref. 2 and Ref. 3 (e-mail from M. Kempen received in March 2012). 2 Kraenzlein, 2011. 3 Kempen and Kraenzlein, 2008. 4 UBA, 1999.
2.5.3 Pesticides This is back-calculated from CAPRI’s reported data (MJ primary energy for pesticides)/ha per crop data. Table 45 CAPRI data on primary energy for inputs, used to convert CAPRI output to our input data Direct energy component
Cumulative energy demand
Unit
Diesel
45.7
MJ/l
Electricity (at grid)
11.7
MJ/kWh
Heating gas (in industrial furnace)
47.9
MJ/m3
Heating oil (in industrial furnace)
49.7
MJ/l
Source Ecoinvent, 2003 (shown in Kranzlein, 2011, CAPRI manual, Chapter 7.5, 'Energy use in Agriculture'). Table 46 Calculation of pesticide use Crop Barley
3.915
Kg pesticide/kg of moist crop 0.909
Maize
7.026
0.959
Rapeseed
6.610
2.297
Rye and meslin
1.696
0.540
Sugar beet
18.030
0.223
Sunflower
2.603
1.372
Soft wheat
5.853
1.055
(kg pesticides/ha)
Sources 1 CAPRI data converted to JEC format using information in Ref. 2 and Ref. 3 (e-mail from M. Kempen received in March 2012). 2 Kraenzlein, 2011. 3 Kempen and Kraenzlein, 2008. 4 LBST, 2012. 48
3. Soil emissions from biofuel crop cultivation 3.1 Background Typical soil N2O emission values for wheat, rapeseed, sugar beet and sunflower cultivation in the RED are based on results from the DeNitrification DeComposition (DNDC) biogeochemistry model runs for Europe. For oil palm, maize, soybean and sugar cane, typical soil N2O emissions were calculated following the IPCC (2006) Tier 1 approach (with modifications for soybean and oil palm). The RED (Article 19.2) asks EU Member States to provide typical soil N2O emissions from potential biofuel crops on a NUTS 2 level, 'taking into account different environmental conditions'. However, no guidance on the calculation method is offered. Soil N2O field measurements are costly and are usually not available for all crops and environmental conditions in a country. Complex biogeochemistry models (like the DNDC, for instance) fulfil the RED specification in terms of considering environmental aspects, but would require extensive data input and specific expertise. The IPCC (2006) Tier 1 method to calculate N2O emissions from managed soils is easy to apply, but it does not take into account varying environmental aspects. Therefore, we present an easily replicable approach, applicable for the major crops in most regions of the world that takes into account the influence of soil conditions and climate on the emission of N2O from soils due to potential biofuel crop cultivation.
3.2 Pathways of N2O emission from managed soils According to the IPCC (2006), the emissions of N2O that result from fertilizer N inputs to agricultural soils occur through the following: - the direct pathway (i.e. directly from the soils to which the N is added/released); - two indirect pathways: o following volatilisation of NH3 and NOx from managed soils and the subsequent re-deposition of these gases and their products NH4+ and NO3- to soils and waters; o after leaching and run-off of N, mainly as NO3- (IPCC, 2006).
49
3.3 General approach to estimate soil N2O emissions from cultivation of potential biofuel crops In the IPCC Tier 1 method (IPCC, 2006) to calculate N2O emissions from managed soils, the single global emission factor (EF1) for direct emissions from mineral fertilizer and manure input is based on fertilizer-induced emissions (FIEs). FIEs are defined as the direct emissions from a fertilised plot, minus the emissions from an unfertilised control plot (all other conditions being equal to those of the fertilised plot), expressed as a percentage of the N input from fertilisation (Stehfest and Bouwman, 2006). In our approach, for mineral soils the IPCC Tier 1 emission factor EF1 is substituted with Tier 2 disaggregated crop-specific emission factors for different environmental conditions (EF1ij), by applying the statistical model developed by Stehfest and Bouwman (2006) to calculate crop- and site-specific FIEs (i.e. EF1ij) as outlined in Figure 2.
Mineral Soils
Organic Soils ~
^
Mineral Fertilizer, Manure
# FIE S&B (2006) , TIER2
IPCC (2006), TIER1
+
+
f(N input*, Crop Type, Soil Parameters, Climate)
Direct Emissions
f(N input, Climate Zone)
IPCC (2006), TIER1
Crop Residues
f(N input from Crop Residues, Management Parameters -Residue Removal, On-Field Burning-)
+ Mineral Fertilizer, Manure, Crop Residues
Indirect Emissions (leaching / volatilization)
+ IPCC (2006), TIER1
f(N input, Environmental and Management Parameter -Leaching yes/no, Irrigation yes/no-)
=
=
Σ Soil N2O Emissions
Σ Soil N2O Emissions
#Fertilizer Induced Emissions (FIE) based on the model of Stehfest and Bouwman (2006). ~TIER 1 = global emission factor,^TIER 2 =
crop and site specific emission factor,* from mineral fertilizer and manure
Figure 2 Method applied to estimate N2O emissions from fertilized managed soils
The model of Stehfest and Bouwman (2006) has not been validated for organic soils/peatlands. Hence, the IPCC (2006) the Tier 1 emission factor is maintained for direct emissions from fertilizer input to organic soils. For all other N sources (crop residues, organic soils) and pathways (indirect emissions from mineral soils and organic soils), the IPCC (2006) Tier 1 method is applied. IPCC (2006) does not provide default values for crop residues from some of the potential biofuel crops. In such cases (e.g. oil palm and coconut), the missing parameters were taken from the literature. For
50
soybean, the nitrogen content in below-ground biomass was updated based on recent findings (Singh, 2010; Chudziak & Bauen, 2013) ( 7). Compost, sewage sludge, rendering waste and N input from grazing animals are not considered likely N sources in biofuel crop cultivation. Following the naming conventions in the IPCC (2006) guidelines ( 8), the calculation for a potential biofuel crop at a specific location and under a specific management system (e.g. fertilizer input), can be expressed as: N 2Ototal − N = N 2Odirect − N + N 2Oindirect − N
With N 2 Odirect − N = [( FSN + FON ) • EF1ij ] + [ FCR • EF1 ]
for mineral soils and N 2 Odirect − N = [( FSN + FON ) • EF1 ] + [ FCR • EF1 ] + [ FOS ,CG ,Temp • EF2CG ,Temp ] + [ FOS ,CG ,Trop • EF2CG ,Trop ]
for organic soils and N 2Oindirect − N = [(( FSN • FracGASF ) + ( FON • FracGASM )) • EF4 ] + [( FSN + FON + FCR ) • FracLeach−( H ) • EF5 ]
for both mineral and organic soils. Crop residue N input is calculated for: a.) sugarbeet, sugarcane according IPCC (2006) Vol. 4 Chapter 11 Eq. 11.6, not considering below-ground residues and with the addition of N input from vignasse and filtercake in the case of sugarcane, as FCR = Yield • DRY • (1 − FracBurnt • C f ) • [ RAG • N AG • (1 − FracRe move )] + FVF
b.) coconut and oil palm plantations applying a fixed N input based on literature as IPCC (2006) provides no default calculation method (see Table 47) c.) for all other crops according IPCC (2006) Vol. 4 Chapter 11 Eq. 11.7a 9, 10, as (7) As described in Section 3.9, ‘Correction of IPCC method for estimating N2O emissions from leguminous crops’. (8) Volume 4, Chapter 11. (9) there was an error in Equation 11.7a which has been corrected in the latest version of the IPCC (2006) guidelines. This correction results in a significant increase of the nitrogen input from below-ground crop residues compared to previous calculations reported here. (10) Equation 11.7A in IPCC (2006) Vol.4, Ch. 11 has been modified. The equation as it is given in IPCC (2006) considers that agricultural burning affects below-ground biomass in the same way as above-ground biomass, which seems unlikely and we do not consider this in GNOC. We reported this issue to IPCC and we are waiting for a reply. This change causes a small increase of N input from below-ground crop residues (only in regions
51
FCR = (1 − FracBurnt • C f ) • AGDM • N AG • (1 − FracRe move ) + ( AGDM + Yield • DRY ) • RBG − BIO • N BG
AGDM = (Yield / 1000 • DRY • slope + intercept ) • 1000
Where N 2Ototal − N
= direct and indirect annual N2O–N emissions produced from managed soils; kg
N 2Odirect − N
= annual direct N2O–N emissions produced from managed soils; kg N2O–N ha-1 a-1
N 2Oindirect − N
= annual indirect N2O–N emissions (i.e. annual amount of N2O–N produced from
FSN
= annual synthetic N fertilizers input; kg N ha-1 a-1
FON
= annual animal manure N applied as fertilizer; kg N ha-1 a-1
FCR
= annual amount of N in crop residues (above-ground and below-ground); kg N
FOS ,CG ,Temp
= annual area of managed/drained organic soils under cropland in temperate
FOS ,CG ,Trop
= annual area of managed/drained organic soils under cropland in tropical
FracGASF
= 0.10 (kg N NH3–N + NOx–N) (kg N applied)-1. Volatilisation from synthetic
FracGASM
= 0.20 (kg N NH3–N + NOx–N) (kg N applied)-1. Volatilisation from all organic N
FracLeach − ( H )
= 0.30 kg N (kg N additions) -1. N losses by leaching/run-off for regions where
EF1ij
= Crop and site-specific emission factors for N2O emissions from synthetic
EF1
= 0.01 [kg N2O–N (kg N input) -1]
EF2,CG ,Temp
= 8 kg N ha-1 a-1 for temperate organic crop and grassland soils
EF2CG ,Trop
= 16 kg N ha-1 a-1 for tropical organic crop and grassland soils
EF4
= 0.01 [kg N2O–N (kg N NH3–N + NOx–N volatilised) -1]
EF5
= 0.0075 [kg N2O–N (kg N leaching/run-off) -1]
Yield
= annual fresh yield of the crop (kg ha-1)
N2O–N ha-1 a-1
atmospheric deposition of N volatilised from managed soils and annual amount of N2O–N produced from leaching and run-off of N additions to managed soils in regions where leaching/run-off occurs); kg N2O–N ha-1 a-1
ha-1 a-1
climate; ha a-1 climate; ha a-1 fertilizer
fertilizers applied
leaching/run-off occurs
fertilizer and organic N application to mineral soils (kg N2O–N (kg N input) -1); The calculation of EF1ij is described in Section 3.4
where our data set assumes crop residue in-field burning - s. Table 267) compared to previous calculations reported here.
52
DRY
= dry matter fraction of harvested product [kg d.m. (kg fresh weight)-1] (see Table
FracBurnt
= Fraction of crop area burnt annually [ha (ha)-1] (see Table 267)
Cf
= Combustion factor [dimensionless] (see Table 47)
RAG
= Ratio of above-ground residues dry matter to harvested dry matter yield for the
N AG
= N content of above-ground residues [kg N (kg d.m.)-1] (see Table 47)
FracRe move
= Fraction of above-ground residues removed from field [kg d.m. (kg AGDM)-1]
FVF
= Annual amount of N in sugarcane vignasse and filtercake returned to the field
AGDM
= Above-ground residue dry matter [kg d.m. ha-1]
slope
=
Slope values to calculate AGDM for the different crops from Yield are given in Table 47
intercept
=
Intercept values to calculate AGDM for the different crops from Yield are given in Table 47
RBG − BIO
=
Ratio of belowground residues to above-ground biomass [kg d.m. (kg d.m.)-1] (see Table 47)
47)
crop [kg d.m. (kg d.m.)-1] (see Table 47)
(see Table 266)
[kg N ha-1], calculated as Yield * 0.000508. The amount of N in sugarcane vignasse and filtercake returned to the field per kg of sugar cane harvested is based on the data given in UNICA (2005)
53
Barley Cassava Coconuts Cotton Maize Oil palm fruit Rapeseed Rye Safflower seed Sorghum (grain) Soybeans Sugar beets Sugar cane Sunflower seed Triticale Wheat 1 2 3 4 5 6 7 8 9
10
11
Data sources*
Fixed amount of N in crop residues (kg N ha-1)
RAG
Cf
NBG
RBG_BIO
intercept
slope
NAG
LHV
DRY
Crop
Calculation method
Table 47 Crop specific parameters to calculate N input from crop residues
IPCC (2006) Vol. 4 Ch. 11 Eq. 11.7a 0.865 17 0.007 0.98 0.59 0.22 0.014 0.8 1, 2 IPCC (2006) Vol. 4 Ch. 11 Eq. 11.7a 0.302 16.15 0.019 0.1 1.06 0.2 0.014 0.8 1, 2 Fixed N from crop residues 0.94 32.07 44 1, 3 No inform. on crop residues 0.91 22.64 IPCC (2006) Vol. 4 Ch. 11 Eq. 11.7a 0.86 17.3 0.006 1.03 0.61 0.22 0.007 0.8 1, 2 Fixed N from crop residues 0.66 24 159 1, 4 IPCC (2006) Vol. 4 Ch. 11 Eq. 11.7a 0.91 26.976 0.011 1.5 0 0.19 0.017 0.8 1, 5 IPCC (2006) Vol. 4 Ch. 11 Eq. 11.7a 0.86 17.1 0.005 1.09 0.88 0.22 0.011 0.8 1, 6 No inform.on crop residues 0.91 25.9 IPCC (2006) Vol. 4 Ch. 11 Eq. 11.7a 0.89 17.3 0.007 0.88 1.33 0.22 0.006 0.8 1, 7 IPCC (2006) Vol. 4 Ch. 11 Eq. 11.7a 0.87 23 0.008 0.93 1.35 0.19 0.087 0.8 1, 8 IPCC (2006) Vol. 4 Ch. 11 Eq. 11.6 0.25 16.3 0.004 0.8 0.5 1, 9 IPCC (2006) Vol. 4 Ch. 11 Eq. 11.6 0.275 19.6 0.004 0.8 0.43 1, 10 IPCC (2006) Vol. 4 Ch. 11 Eq. 11.7a 0.9 26.4 0.007 2.1 0 0.22 0.007 0.8 1, 11 IPCC (2006) Vol. 4 Ch. 11 Eq. 11.7a 0.86 16.9 0.006 1.09 0.88 0.22 0.009 0.8 1, 2 IPCC (2006) Vol. 4 Ch. 11 Eq. 11.7a 0.84 17 0.006 1.51 0.52 0.24 0.009 0.9 1, 2 References for parameters DRY and LHV see Appendix 1. Fuel/feedstock properties of this report IPCC (2006) Vol. 4 Chapter 11 Table 11.2 (Factor a=Slope, b=Intercept, NAG, RBG-BIO and NBG) and Chapter 2 Table 2.6 (Factor Cf). For Cassava and Triticale the general values for "Tubers" and "Cereals" respectively, are considered. Magat (2002), Mantiquilla et al. (1994), Koopmans and Koppejan (1998), Bethke (2008) (data compilation by W. Weindorf. Ludwig Boelkow Systemtechnik GmbH, Ottobrunn, Germany) Schmidt (2007) (data compilation by R. Edwards, EC JRC IET, Ispra, Italy) NAG and NBG from Trinsoutrot et al. (1999) Table 1. Residue to seed ratio and factor a is based on Scarlat et al. (2010) Table 1. Ratio of belowground residues to above-ground biomass (RBG-BIO) assumed to be the same as for beans and pulses in IPCC (2006) Vol. 4 Chapter 11 Table 11.2 . IPCC (2006) Vol. 4 Chapter 11 Table 11.2 , value for RBG_BIO assumed to be similar to Grains IPCC (2006) Vol. 4 Chapter 11 Table 11.2 , value for RBG_BIO assumed to be similar to Maize IPCC (2006) Vol. 4 Chapter 11 Table 11.2 , except NBG which is underestimated in IPCC (2006) according Chudziak and Bauen (2013). Due to lack of information on below-ground residues for sugar beet, a modified method was used which does not take into account the below-ground biomass. The value for RAG and N content of above-ground residues was adopted from the EDGAR database (European Commission Joint Research Centre (JRC) / Netherlands Environmental Assessment Agency (PBL), 2010). However there is large disagreement between the RAG and NAG values for Sugar beets applied in different countries (see Adolfsson, 2005). Sugarcane is a semi-perennial crop. Sugarcane is typically replanted every six or seven years. For this period the root system remains alive. As IPCC (2006) does not provide default values, a modified method was used which does not take into account the below-ground biomass. The value for RAG and N content of above-ground residues was adopted from the EDGAR database (European Commission Joint Research Centre (JRC) / Netherlands Environmental Assessment Agency (PBL), 2010). Del Pino Machado, A.S. (2005) gives 0.0072 kg N per kg per dry matter of sunflower shoots. Corbeels et al. (2000) report a 0.0067 kg N per kg per dry matter in stalks. For GNOC a value of 0.007 kg N per kg above-ground residues dry matter was applied. Value - a - for the calculations of N input from crop residues according IPCC (2006) is based on the average of the “residue to crop production” values given for sunflower in Table 1 of Scarlat et al. (2010) Ratio of belowground residues to above-ground biomass and NBG assumed to be the same as IPCC (2006) gives for maize.
54
3.4 Determining crop- and site-specific fertilizer-induced emissions (EF1ij) The Stehfest and Bouwman (2006) statistical model (hereafter referred to as the S&B model) describes on-field N2O emissions from soils under agricultural use, based on the analysis of 1 008 N2O emission measurements in agricultural fields under different environmental conditions and for 6 agricultural land use classes, as:
E = exp(c + ∑ ev) where E = c = ev =
N2O emission (in kg N2O-N ha-1 a-1) constant (see Table 48) effect value for different drivers (see Table 48)
Table 48 Constant and effect values for calculating N2O emissions from agricultural fields after S&B Constant value Parameter Fertilizer input Soil organic C content
-1.516 Parameter class or unit
Effect value (ev) 0.0038 * N application rate in kg N ha-1 a-1 3 % 0.6334 pH 7.3 -0.4836 Soil texture Coarse 0 Medium -0.1528 Fine 0.4312 Climate Subtropical climate 0.6117 Temperate continental climate 0 Temperate oceanic climate 0.0226 Tropical climate -0.3022 Vegetation Cereals 0 Grass -0.3502 Legume 0.3783 None 0.5870 Other 0.4420 Wetland rice -0.8850 Length of experiment 1 yr 1.9910
For the calculations, the potential biofuel crops are assigned to the different vegetation classes as shown in Table 49.
55
Table 49 Potential biofuel crops assignment to S&B vegetation classes Potential biofuel crop S&B vegetation class Barley Cereals Cassava Other Coconut Other Maize Other (a) Oil palm Other Rapeseed Cereals (b) Rye Cereals Safflower Other Sorghum Cereals Soybean Legumes Sugar beet Other Sugar cane Other Sunflower Other Triticale Cereals Wheat Cereals a ) Following the classification of crop types in Stehfest and Bouwman (2006), row crops
are summarised in the vegetation class 'other'. ) Re-evaluating the S&B collection of measurement sites “Rapeseed” showed emissions more similar to the “Cereals” S&B vegetation class than to the row crops vegetation class “Other”. b
Applying the S&B model, the EF1ij for the biofuel crop i at location j is calculated as: EF1ij = (Efert,ij – Eunfert,ij)/Nappl,ij where Efert,ij
=
Eunfert,ij
=
Nappl,ij
=
N2O emission (in kg N2O-N ha-1 a-1) based on S&B, where the fertilizer input is actual N application rate (mineral fertilizer and manure) to the biofuel crop i at location j N2O emission of the biofuel crop i at location j (in kg N2O-N ha-1 a-1) based on S&B. The N application rate is set to 0, all the other parameters are kept the same N input from mineral fertilizer and manure (in kg N ha-1 a-1) to the biofuel crop i at location j
Figure 3 shows the potential variation of the of EF1ij based on the S&B model as described above, for cereals cultivated in temperate oceanic climate on different soils (low-mediumhigh soil organic carbon content, low-medium-high pH, fine-coarse soil texture), and for different levels of fertilizer N input. The red line represents the IPCC (2006) factor (EF1) for direct N2O emissions from fertilizer input based on a global mean of the EF1ij. EF1 is replaced in our approach by the crop- and site- specific EF1ij for direct emissions from mineral fertilizer and manure N input, based on the crop- and site-specific EF1ij, applying the S&B model.
56
Fertilizer induced emissions (kg N2O-N Emissions / kg Fertilizer N input)
0.06
0.05
0.04
0.03
0.02
0.01
0.00 1
51
101
151
201
251
301
351
401
451
501
N input kg ha-1 Agricultural Fields: Minimum case for Cereals in Temperate Oceanic Climate (SOC 7.3; medium soil texture) Agricultural Fields: Mean case for Cereals in Temperate Oceanic Climate (SOC 13% ; pH 5.5-7.3; coarse soil texture) Agricultural Fields: Maximum case for Cereals in Temperate Oceanic Climate (SOC >3% ; pH soil water holding capacity, or where irrigation (except drip irrigation) is employed. The rainy season(s) can be taken as the period(s) when rainfall > 0.5 * pan evaporation. A global map delineating areas where leaching/run-off occurs was compiled based on climate and soil information, as described in Hiederer et al. (2010).
(11) Volume 4, Chapter 10. (12) Volume 4, Chapter 11. (13) Volume 4, Chapter 3 and Chapter 4. (14) Volume 4, Chapetr 11.
58
GNOC results for rapeseed cultivation in Europe Based on GNOC, the country level N2O emissions, e.g. from rapeseed (see Figure 4) vary considerably in Europe, reflecting to a certain extent the fertilizer input. However looking at the emissions at higher resolutions (NUTS II, 5 minutes grid), the variation on sub-country level can be as pronounced as the variation between the countries depending on management and environmental conditions.
Figure 4 Fertilizer application (mineral fertilizer + 50% of manure) and soil N2O emissions (expressed as gCO2eq MJ-1 of fresh crop) from rapeseed cultivation at different spatial levels based on GNOC (reference year for fertilizer input and yield: 2000)
59
3.6 The GNOC online tool The GNOC method allows the calculation of N2O emissions from a wide range of potential biofuel crops, taking into account the influence of varying environmental conditions, as requested by the RED. An online tool (Figure 5) is available at http://gnoc.jrc.ec.europa.eu/ allowing the user to calculate soil N2O emissions for a selected place based on - GNOC default environmental and management data for this place as well as on - site specific information provided by the user (e.g. from field survey or high resolution maps). Figure 5 The GNOC online tool
60
3.7 GNOC results and the JEC-WTW The update of the JEC-WTW data with GNOC results on soil N2O emissions from biofuel crop cultivation required some adjustments and corrections. - Mean emissions of the potential biofuel crop will not equal global mean emissions, but rather the weighted average emissions from suppliers of each crop to the EU market (including EU domestic production). - A correction for changes in yields and fertilizer input since the year 2000 (see Table 50). The final soil N2O emissions are presented in Figure 6 as: 1. The weighted average N2O emissions from biofuel crop cultivation in the year 2000 based on the GNOC (green bars). 2. The weighted global results from running the GNOC with the default IPCC Tier 1 emission factors for direct and indirect N2O emissions from crop cultivation in the year 2000 (blue bars). 3. The weighted global average GNOC results corrected for more recent yield and fertilizer input (year 2010/11) – red bars. These form the basis for the update of the RED default values. For the calculations it is assumed that potential biofuel crops, except oil palm, are not grown on organic soils if there are enough mineral soil area resources available and that there is no disproportionate expansion of crop area onto organic soils in the countries supplying these crops to the EU market between 2000 and 2010In Figure 6 (x axes labels) the shares of crop area on organic soils considered for the emission calculations are noted. Based on original GNOC input data, ~8.5 % of oil palm is grown on organic soils in the main countries supplying palm oil to the EU market, i.e. Indonesia and Malaysia. Contrary to the approach for all other potential biofuel crops, the share of oilpalm cultivated on organic soils is not set to a minimum. It is estimated for the year 2000 by considering equal distribution of oilpalm on all soil types present. According a recent analysis of high resolution satellite data by Miettinen et al., (2012), the area of oil palm plantations in Indonesia and Malaysia more than doubled between 2000 and 2010. Several authors (Wahid et al., 2010; Sheil et al., 2009 cited by Miettinen et al., 2012) also noted that the share of Malaysian oil palm plantations on peatland had increased from 8% in 2003 to 13% in 2009, suggesting that a rapid increase in the area of oil palm cultivation in the region had fallen disproportionately on peatland areas. By 2009, nearly 30% of all oil palm plantations in Malaysia were located on peat soil (Wahid et al., 2010). Considering the shares of oil palm plantations on peatland from the Miettinnen et al. (2012) we calculated a weighted average of 16% palm oil produced on organic soils in the countries importing to the EU market (see Section 6.11).
61
At global average level, the crop type is the main parameter that makes a difference between N2O emissions based on the IPCC (2006) TIER1 approach compared to the method applied in GNOC (see Figure 6). Emissions from cereal feedstock (e.g. wheat, barley, rye) and rapeseed 15 based on GNOC are lower than those calculated by applying the IPCC (2006) Tier 1 approach because the S&B 'effect value' for this vegetation class is lowest, leading to EF1ij below the IPCC (2006) default of 0.01 kg N2O-N per kg of N input. Oilseed and row crops (S&B vegetation type 'other', e.g. sugar beet, maize, sunflower) tend to have higher average emissions based on the GNOC, compared to those generated by applying the IPCC (2006) TIER 1. Emissions from oil palm cultivation are similar for both calculation methods applied. This picture changes at a higher spatial level. Here, soil parameters like pH, texture and soil carbon content may generate a higher variation in N2O emissions (based on the GNOC) from one specific crop grown on different soils, than between crops at average global level. Looking at the partitioning of the N2O sources and pathways we observe large differences between the crops (Figure 7). For the non-leguminous annual crops and sugarcane, the fertilizer application (mineral fertilizer and manure) is the major source of direct N2O emissions from the soil (50%– 70%). Nitrogen from crop residues left in the field contributes between 17 and 35% to the total emissions. The N2O emissions caused by N supply from returning sugarcane vignasse to the field are considered as part of the fertilizer application emissions in the calculations. The situation is different in the perennial oilpalm plantation. There, the fertilizer supply is mainly resulting from incorporation of residues from the previous palms when replanting and/or from residues left in the field during the plantations lifetime. Crop residue N contributes with 40% to total N2O emissions, while the share of N2O from fertilizer input is less than 20%. Fertilizer input to leguminous crops (i.e. soybean) is usually low as nitrogen from the atmosphere is fixed biologically. According to our data almost ~95% of the N2O emissions in soybean cultivations are related to N from crop residues remaining in the field. Based on our analysis only a small share of potential biofuel crops – with the exception of oilpalm –is produced on organic soils (< 1.5%, see Figure 7). However the related emissions contribute with up to 10% (in the case of rye) to total emissions. According IPCC (2006) TIER 1 each ha of crop cultivated organic soil releases an extra 8 kg N2O-N (16kg N2O-N in tropical regions) which would emissionwise correspond to the application of 800 kg (or 1600 kg in the tropics) of fertilizer N. 16% of oilpalm is grown on organic soils, causing ~42% of the soil N2O emissions related to palm oil fruit production. Indirect emissions from leaching and volatilization/re-deposition of N input by mineral fertilizer and manure range from 10% to 15% of the the total N2O emissions except for the 15
In GNOC we apply the cereals effect value to rapeseed as explained in Section 3.4.
62
crops dominantly grown in warm/dry areas where leaching is reduced and/or where crop residues are the dominant source of N supply. Table 50 Changes in crop yield and mineral fertilizer input between 2000 and 2010/11 (i.e. average of 2010 and 2011)
barley maize oilpalm rapeseed rye soybean sugarbeet sugarcane sunflower triticale wheat
GNOC Yield (2000) in kg ha-1
FAO Yield (2010/11) in kg ha-1
GNOC Mineral Fertilizer Input (2000) in kg ha-1
3997 5616 17333 2260 2942 2438 52395 69988 1367 3986 4543
4306 7016 19002 2877 3143 2822 80760 77746 1897 3925 5678
100 106 78 139 76 11 143 66 43 108 101
Mineral Fertilizer Input (2010/11) in kg ha-1
91 138 95 137 62 5 111 69 46 82 112
Mineral Fertilizer Input (2010/11) in kg/tonne crop 21.1 19.7 5.0 47.6 19.7 1.9 1.4 0.9 24.1 20.9 19.8
Comments (Explanatio n below table)
1,2 1,2 1,2 1,2 1,2 1,2 1,2 1,2 1,2 1,2 3,4
Yields and fertilizer input are weighted averages from suppliers of each crop to the EU market (including EU domestic production). Yields are from FAO (2010/2011) and fertilizer input is from IFA or Fertilizers Europe. 2 The total fertilizer used on a crop (whether the data comes from IFA or Fertilizers Europe) refers to EITHER the crop harvested in 2010 or that harvested in 2011, depending on the region. First we divide this by the total production of the crop in that year. Then we apply a slight correction, so that all data are normalized to the average yield in 2010 and 2011 (assuming fertilizer per Ha did not change). 3 Calculation of Average Yield of Ethanol Wheat in EU: 28% of common EU wheat is grown as feed wheat. 72% of common EU wheat is bread wheat variety. Average common wheat yield Y = 0.28F + 0.72B. Also feed-wheat varieties have 5% higher yield than bread wheat varieties. F = 1.05B. B = F/1.05 Y = 0.28F + (0.72/1.05)F = 0.966F Or F = 1.0355Y and B = F/1.05 = 0.9862Y Wheat for ethanol is 3/4 feed wheat variety + 1/4 bread-wheat variety. YE = yield of wheat for ethanol = 3/4(1.0355Y) + 1/4(0.9862Y) YE = 1.023Y Ethanol wheat yield is 2.3% higher than average EU yield. 4 Mineral fertilizer input on wheat is explained in the text box at the end of this section. 1
63
Figure 6 Weighted global average N2O soil emissions from biofuel feedstock cultivation. Results are weighted by feedstock quantities supplied to the EU market (including EU domestic production). The graph shows emissions based on GNOC calculations for the year 2000, emissions obtained following the IPCC (2006) TIER 1 approach and using the same input data as for the GNOC calculations and the GNOC results corrected for average yield and fertilizer input of 2010 and11.
64
Figure 7: Share of N2O emission sources and pathways of the weighted global average N2O soil emissions in 2010/11. Table 51 Soil nitrous oxide emissions from biofuel feedstock cultivation in 2010/11. The values are weighted averages from suppliers of each crop to the EU market (including EU domestic production). Biofuel feedstock
Fresh Yield (2010/11) kg ha-1
Mineral fertilizer input (2010/11) kg N ha-1
Manure input - 50% - (2010/11) kg N ha-1
Soil N2O emissions (2010/11) in gCO2eq MJ-1 of crop
barley
4306
91.0
15.6
12.9
rye
3143
62.0
12.3
11.9
triticale
3925
82
15.0
12.0
wheat
5678
112.3
19.3
13.1
maize
7016
137.9
23.2
16.3
sugarbeet
80760
111.0
22.4
3.6
sugarcane
77746
69.2
17.5
2.0
rapeseed
2877
137.0
22.5
17.7
sunflower
1897
45.7
6.8
11.3
soybean
2822
5.4
0.9
12.3
oilpalm
19002
94.5
8.9
9.1
65
WHY DO WE SUBTRACT 7% OF MINERAL FERTILIZER N INPUT TO FEED-WHEAT? We use data from [Fertilizers Europe 2013] on the N fertilizer per ha used on different EU crops. That gives data on N use per ha for all EU wheat: that includes common wheat (feedquality and bread-quality) and durum wheat. First, we calculate the N per tonne of soft wheat, by dividing the average N/ha by the average yield for common-wheat (reported by EUROSTAT). This removes the lower yield of durum wheat as a source of error. However, bioethanol is made from feed-quality wheat, which has lower protein content than other (bread-quality) soft wheat, which is used for food wheat, needs less fertilizer, and has slightly higher yield. In UK, farmers growing for feed market apply about 30/190 = 16% less N per ha than if they are aiming at bread-wheat [ADAS 2013]. The UK average N per ha is ¬190 for all wheat types, but 2/3 of UK area is sown with feed varieties and 1/3 with bread varieties [ADAS 2013]. Therefore UK feed wheat gets 180 kgN/ha and bread wheat 210 kgN/ha on average. So purpose-grown feed wheat in UK gets 86% of the N per ha on bread wheat. We assume the same ratio applies in rest of EU. Furthermore, purpose-grown feed wheat is mostly grown in NW Europe. It uses varieties which yield about 5% more than bread wheat [HGCA 2013]. Therefore purpose-grown feed wheat uses 86/105 = 82% of the N per tonne of wheat needed for bread wheat. In UK, 1/3 of wheat sown as bread-wheat ends up as surplus-to-demand or below-standard for bread use, and is sold as feed wheat [ADAS 2013]. We assume the same fraction applies in rest of EU. In EU27, 54% of wheat is used for food (bread-quality), and 46% for feed or ethanol ('industrial'). [USDA 2013][USDA 2012]. So we estimate that 18% of EU wheat is grown as bread wheat but used as feed wheat, whilst 28% of EU wheat is purpose-grown feed quality. By algebra, the average N/tonne of purpose-grown feed wheat in EU is 1/(0.28+0.72/.86) = 89.5% of the average N per tonne of wheat in EU27 (whilst bread-wheat requires 1/(0.28*.86+0.72)=104% of average N per tonne). However, not all feed-wheat used for ethanol is purpose-grown: we assume 1/4 of it is surplus or below-standard bread wheat (less than EU average of 1/2 for all feed wheat, because some ethanol producers contract farmers in advance)). Therefore on average the N on ethanol wheat in EU is 89.5*3/4 + 104/4 = 93% of the average N/tonne of wheat in EU. Ref: [ADAS 2013] Personnal Communication, R. Syvester-Bradley (ADAS) to Robert Edwards, May 2013 [USDA 2013] EU27 grain and feed annual GAIN report 1301 [USDA 2012] EU 27 biofuels annual GAIN report NL2020 [HGCA 2013] HGCA recommended winter wheat varieties 2013 http://www.hgca.com/document.aspx?fn=load&media_id=8326&publicationId=6392] 66
3.8 Manure calculation Why do we consider 50 % of manure when calculating N2O emissions? Summary • • • • • • •
The contribution of manure to N2O emissions is minor in most parts of the world; exceptions are parts of EU and US. Manure use tends to be concentrated around livestock farms In the US manure N constitutes approximately 10% of the total N applied to crops. In the US it is estimated that 5% of crop area receives manure. Therefore the fields where manure is applied, receive twice as much N per hectare (on average) as other fields. Therefore we consider that half the manure is not needed by the crop, and is applied for the purpose of getting rid of excess manure. Therefore we only attribute half the manure-N to growing the crop (and by implication, half to manure disposal). This approximately halves the N2O emissions caused by the N in manure.
Details In most of the world, the contribution of manure to the total nitrogen supply of crops is very limited: it becomes important only in areas with large indoor production of livestock, such as EU and parts of US. There is very little data available on actual manure use on crops; the best we have found is from (USDA, 2009). They state that "in principle manure could be spread on much more cropland, mitigating the risks that arise from excessive concentrations of manure and replacing high-priced artificial fertilizers.". However, in practice too much manure is often applied on fields close to manure sources (due the high cost of manure transport), so the fraction of hectares receiving manure is much less than the fraction of N applied as manure. We know that a similar situation applies in Europe, but we do not have EU-wide figures. GNOC's data from FAO and IPCC (via the Edgar database) on manure production from indoor livestock indicate that in US about 9.8% of N applied to crops by farmers is in the form of manure (the rest is synthetic N). USDA, 2009 estimates that 4.7% of US arable crop hectares receive manure (or 5% of all crops including hay and grasses). So we see that the fraction of total N supplied by manure is about double the area-fraction which receives manure. That means the fields where manure is applied receive (on average) twice as much N per hectare as other fields. Therefore we consider that about half the manure is not needed by the crop, and is applied for the purpose of getting rid of excess manure. 67
Accordingly, we only attribute half the manure-N to growing the crop. The other half is attributed to disposing of excess manure by the livestock industry. In the GNOC methodology, this approximately halves the N2O emissions, caused by the N in manure, that are attributed to crops. In processing GNOC data, we need to make an additional assumption about how the manure is distributed to different crops, for all countries. The only data on N applications we have per-crop per-country is for synthetic N. so we need to find a relation between manure application and synthetic nitrogen application... For a given country, we assume that the ratio of (manure N)/(synthetic N) is the same for all crops. This assumption gives estimates of manure use per crop which are closer to those reported in USDA, 2009 than our previous assumption that assumed the same kg manure per hectare for all crops in a region. (This assumption was adopted in JEC-WTWv4 and the draft input data for update of RED annex V, presented to stakeholders in April 2013: howewer we realized that it systematically over-estimated manure on low-intensity crops). Nevertheless USDA, 2009 shows variations in the fraction of manure used for different crops, which we cannot capture with a general rule. Note that our data on synthetic nitrogen use is independent of the amount of manure used on a crop, so assuming a higher fraction of N from manure for a crop would not decrease the amount of synthetic fertilizer in the calculations.
3.9 Correction of IPCC method for estimating N2O emissions from leguminous crops For the calculation of N supply from crop residues remaining in the field to the soil and the subsequent N2O emissions we rely on the TIER 1 approach as described in the IPCC (2006) guidelines. Between 1996 and 2006, the IPCC changed their default emission guidelines for soybeans: this had the effect of drastically reducing the N2O emissions calculated for soybean. We think the true emissions actually lie between the two, as described below. This discussion originated from the staff of E4tech in the United Kingdom in 2008, working on behalf of the United Kingdom's Renewable Fuels Agency (RFA). The resulting correction to the N2O emissions for leguminous plants was incorporated in RFA default values for soybeans. In 2013 E4tech staff (Chudziak & Bauen, 2013) drafted a paper on “A revised default factor for the below ground nitrogen associated with soybeans” describing their findings in detail and concluding with the suggesting to revise the below-ground residue N content of soybean in the IPCC 2006 guidelines from currently 0.008 to 0.087. The JRC agrees with Chudziak & Bauen (2013) that the 2006 IPCC (Tier 1) approach 68
significantly underestimates the N2O emissions from soybeans and probably also other leguminous plants. The old 1996 IPCC methodology for calculating N2O emissions from soil (used in v. 2 of JECWTW) did not consider below-ground nitrogen (BGN) in plants at all, but did assume that the nitrogen naturally fixed by leguminous plants (such as soybean) contributed to the release of N2O. This would mean that the nitrogen-fixing bacteria in the roots were emitting N2O at the same time as they were fixing nitrogen from the air. The distribution of biologically fixed nitrogen in leguminous plants is shown in Figure 8. However, a paper in 2005 by Rochette and Janzen argued that there was little evidence for significant N2O emissions from legumes during the nitrogen fixation process. Therefore, in the revised 2006 methodology (published in 2007), the IPCC no longer include emissions directly from the natural nitrogen-fixing process. On the other hand, the 2006 guidelines do take into account the contribution of below-ground N content of the plants themselves to the nitrogen pool in the soil which contributes to N2O emissions. The IPCC attribute these extra emissions to the current crop. However, Rochette (2004) shows that most of these will actually take place during the following season. He found that although the soil mineral N content under legumes were up to 10 times greater than they were under grass, this was not closely related to the N2O emissions measured during the growth phase of the plant. However, he found greater emissions of N2O after the plant had been harvested, and these were strongly dependent on the soil type. So for the current season, what should be taken into account is the contribution of BGN from the residues of the previous crop. From the point of view of a national average, it does not matter much to which crop a certain amount of soil nitrogen is attributed. But it does make a difference if you are calculating N2O emissions per crop in a rotation. Of course the distinction is not important if the same crop is grown in successive years, which is generally the case in Brazil and Argentina, which supply most of the soybean to EU. Above-ground nitrogen Nitrogen in roots Nitrogen from rhizodeposition
Belowground nitrogen
Figure 8 Distribution of biologically fixed nitrogen in leguminous plants
69
Part of the nitrogen biologically fixed by soy plants ends up in the above- and below-ground crop residues, and in principle, the IPCC (2006) takes emissions from this into account. However, we think the IPCC has seriously underestimated the amount of BGN, by underestimating the below-ground biomass and by disregarding nitrogen from rhizodeposition. Rhizodeposition (Jensen, 1995) is the process whereby N enters the soil from the plant roots in the form of NH4, NO3, amino acids and cell lysates, as well as through decay of sloughedoff and senescent roots. It can now be quantified through techniques such as 15N shoot labelling (Khan et al., 2002). The literature shows that leguminous plants such as soy exude significant volumes of N from their roots (Martens, 2006). Table 11.2 of the 2006 IPCC guidelines gives default factors for estimation of N added to soil from crop residues. According to this, only 16 % (= .19/1.19) of the soybean plant residues are in the underground biomass, and they all have the same nitrogen concentration. These default factors are based on an extensive literature review, with references provided in Annex 11A.1. The default value for BGN content of soybean is taken from a 1925 paper. Whilst E4tech could not obtain a copy of this reference, their review of more recent literature suggests that such a dated work will have missed not only the N released by rhizodeposition, but also that in fine root hairs that are very difficult to collect using the old techniques of physical root recovery. Aruja et al. (2006) confirm that the roots recoverable by traditional methods only contain between 5 % and 10 % of the total N accumulated by the plant. For comparison, Alves (2003) reports results using modern techniques of between 30 % and 35 % of total plant N ( 16). This implies the IPCC has underestimated nitrogen in the roots by at least a factor of 3. If we include also nitrogen from rhizodeposition, the IPCC defaults might seem even further off. Khan (2002b) concluded that the traditional methods only recovered 20 % to 30 % of the total BGN (including that from rhizodeposition) obtained using N-labelling methods. Mayer (2003) found that N rhizodeposition represented about 80 % of the below-ground plant N. These studies suggest that the N from rhizodeposition is roughly four times the BGN in the roots, so at least an order of magnitude greater than the BGN calculated from IPCC defaults. We think that in reality only the part of the biologically fixed nitrogen released by rhizodeposition counts towards N2O emissions from the soil during a particular growing season. The rhizodeposition gradually builds up during the season, but after the harvest all plant residues gradually decay and release their nitrogen into the soil. There are not enough data to estimate the amount of rhizodeposited nitrogen from soy by direct measurements of (16) Similarly, Rochester (1998) found that ~40 % of the N in legumes either resided in, or was released from nodulated roots, and this is confirmed by Russel and Fillery (1996). Rochester (2001) states: 'In the past, belowground N has either been ignored or grossly underestimated when N balances have been calculated for legume crops.'
70
soil nitrogen. A more pragmatic and accurate approach is to back-calculate the total effective BGN, from the combination of below-ground biomass itself plus rhizodeposition, from the measured nitrous oxide emissions from soybeans grown without synthetic nitrogen. That figure would reflect the actual nitrogen content in the soil that is giving rise to N2O emissions. In reference to IPCC Table 11.2 (IPCC, 2006), for the N2O emissions calculation, it is irrelevant whether this is done by changing the default value 'RBG-BIO', (ratio of below-ground to above-ground biomass) or 'NBG', the effective nitrogen concentration in the below-ground biomass (kgN/kg dry matter). We have chosen, in accordance with Chudziak & Bauen (2013) to change only the second value; this is equivalent to assuming that the contribution of dead roots to the mass of below-ground biomass is small. Chudziak & Bauen (2013) started off by averaging measurements of N2O emissions from unfertilised soybean cultivations at 7 sites quoted by S&B. The result is 1.261 kg N-N2O/ha-1. Using the IPCC default direct emissions factor of 0.01 kg N-N2O/kg N(CR) , the the total amount of nitrogen which gave rise to those emissions is 126.1 kg N/ha. By subtracting the nitrogen in the above-ground residues from this, the total amount of N from below-ground biomass can be calculated. Following the IPCC (2006) TIER 1 approach Chudziak & Bauen (2013) calculated 28.4 kg N ha-1 in above-ground residue biomass at a soybean fresh yield of 2600 kg/ha (soybean average yield in Argentina, Brazil and the US given by FAO for the year 2006 )17. Subtracting this from the total N in plant-residue leaves us with an effective 97.7 kgN ha-1 in below-ground biomass. Still following IPCC (2006) TIER 1 approach 18 the below-ground biomass at the given soybean yield is 1124 kg dry matter ha-1. The new co-efficient for N in below-ground biomass is obtained by dividing N in belowground biomass by below-ground residue dry matter: NBG = 97.7/1124 = 0.087 kg N/kgDM below-ground biomass. The JRC recommends using this in place of the default value of 0.008 in Table 11.2 of IPCC (2006), in order to calculate N2O emissions from soy which are comparable with measurements. Checking the results against below-ground-N measurements We can check whether this value is reasonable by looking at which value it implies for the fraction of the total nitrogen associated with the plant. This can be checked against measurements in the literature, which mostly range from 30 % to 35 %, according to Alvez (2003) and a wider literature survey by E4tech.
N in above-ground residues (kg ha-1)= (Fresh yield (t ha-1)* dry matter fraction *slope + intercept) *1000 * N content of above-ground residue dry matter. For soybean IPCC (2006) gives: Dry matter fraction = 0.91, slope = 0.93, intercept = 1.35), N content of above-ground residue dry matter = 0.008 18 Below-ground residue dry matter (kg ha-1): Above-ground biomass dry matter (kg ha-1) * Ratio of belowground residues to above-ground biomass. For soybean IPCC (2006) gives: Ratio of below-ground residues to above-ground biomass = 0.19. 17
71
The nitrogen concentration in the dry matter of soybeans is 6.5 % according to the NREL (2005), corresponding to 154 kg N ha-1 in beans at a fresh yield of 2600 kg ha-1. The total plant N is this plus BGN and above-ground nitrogen in residues. Adding this all up using the figures above gives a total of 280 kgN/ha associated with the plant. Then the fraction of BGN implied by our method is 35 %. This is indeed within the range of measured values, giving us confidence that we are at least approximately correct. Checking the calculated N2O emissions against mesurements In GNOC we implemented the revised factor for N in below-ground soybean residue biomass (NBG = 0.087) and we checked GNOC based country average emissions against N2O field measurements from the Stehfest & Bouwman, 2006 (S&B) data collection and additional measurements in Argentina presented in Alvarez et al. (2012). The filled circles in Figure 9 show the measurement data from the beforementioned sources. The measurement data is orderd by country and in ascending order of the N2O emissions. The S&B data set includes 17 measurements in 3 different countries (US, Canada and China). In addition, data from 4 plots under different management (tillage/no tillage, soybean monoculture, soybean-maize rotation) are available from Argentina. At 11 sites the experiment covered 365 days (dark green circles), at 6 sites the measurements refer only to the soybean vegation period of about 120 days (light green circles). We did not exclude those, even though the measurements do not include e.g. potential emissions resulting from crop residue de-composition during the fallow period. We assume that those measurements represent minimum N2O emissions from soybean cultivation at this location throughout one year. Except 2 of the 4 Chinese sites (dark blue circles) all measurements were carried out in soybean cultivation without additional fertilizer input. From the S&B data collection it is not possible to estimate the amount of N supplied by crop residues. It is mentioned that there were no (above-ground) residues left on the field at the US, Canadian and Chinese sites. However, it can be assumed that the below-ground part of the residues remained in the soil. Yield data, as well as information whether the measurements refer to monocultural sites or soybean is grown in rotation with other crops is not available. Two of the Argentinian sites are soybean monocultures and two are maizesoybean rotations. In both cases the residues from the previous crop remained in the field. GNOC based country average N2O emissions from soybean cultivation (orange dashed line in Figure 9) refer to an average fertilizer application per ha of 2.5 kg N in Argentina, ~25 kg N in the US and Canada and 84 kg N in China (see violet dashed line in Figure 9). The country average per ha yields for the year 2000, which are the basis to calculate the N supply from crop residues, were 2.4 – 2.5 t in Argentina, Canada and the US and 1.7 t in China. The management data considered in GNOC gives 35 and 50% of above-ground soybean residues burnt or removed in Argentina and China respectively. This equals a reduction of N supplied by total (above- and belowground) crop residues of ~11% for China and of ~8% for Argentina. We also calculated a “no fertilizer” case for soybean in the above-mentioned countries. The 72
results are drawn as brown dashed line in Figure 9. GNOC results include direct emissions as well as indirect emissions from leaching, in case of fertilizer application also volatilization/redeposition. These indirect pathways are not covered by the measurement data presented. The red dashed line in Figure 9 gives country average emissions under unfertilized conditions if the default IPCC (2006) factor for N in below-ground biomass (NBG) of 0.008 is applied. At 15 out of the 17 measurement locations the emissions measured in the field exceed the country average emissions calculated using the IPCC (2006) default NBG. The average emissions over all unfertilized measurement sites are 1.29 kg N2O-N ha-1 this compares to an average emission in the 4 countries of 0.28 kg N2O-N ha-1 if a NBG of 0.008 and of 1.15 kg N2O-N ha-1 if the suggested NBG of 0.087 is applied in GNOC (no fertilizer input assumed). For Argentina the country level GNOC results are at the lower end of what has been observed from the measurements. As country level fertilizer inputs in GNOC (violet dashed line) are close to 0 they don’t have a major impact on the final emissions. Measurement data in the US was available from 2 measurement projects in 2 states. Average emissions from these unfertilized measurements are 1.3 kg N2O-N ha-1, this matches the GNOC result for 0 fertilizer application (1.32 kg N2O-N ha-1). Emissions estimated using the GNOC default country-average N input to soybean are 1.63 kg N2O-N ha1 . Canadian sites show a mean emission of 1.01 kg N2O-N ha-1 while GNOC gives 1.69 N2O-N ha-1. However, only one measurement covered an entire year. There, emissions above the GNOC country average value were observed. Looking at the Chinese sites the GNOC results match quite well with the observations under 0 fertilizer input and are in the same range when comparing the emissions under fertilized conditions. IPCC (2006) TIER 1 describes the amount of crop residues (and the N input from this source) as a function of the yield. Average soybean yield for the year 2000 (GNOC default) was fairly lower in China than in the other countries presented in the graph. This, together with the ~50% removal of above-ground residues we assume in GNOC, results in lower average emissions under zero fertilizer input in China compared to the other countries. Although the measurements don’t cover all possible environemtal and management conditions, the presented results underpin the findings of Chudziak & Bauen (2013) that N supply from below-ground soybean residues is around 10 times higher than currently suggested by the IPCC(2006) TIER 1 approach.
73
NBG: Nitrogen in belowground residues Management at the Argentinian measurement sites; Cordoba1 NT CR, Argentina: No tillage, residues from previous crop: corn Cordoba2 RT CR, Argentina: Reduced tillage, residues from previous crop: corn Cordoba3 NT SR, Argentina: No tillage, residues from previous crop: soybean Cordoba4 RT SR, Argentina: Reduced tillage, residues from previous crop: soybean
Figure 9 Measurements of soil N2O emissions from soybean cultivation (S&B, 2006 and Alvarez et al. 2012) and country level results based on GNOC
74
3.10 Emissions from acidification and liming methodology Emissions from neutralisation of fertilizer acidification and application of aglime This section has been kindly reviewed and corrected by Anne and Jean-Luc Probst of the Laboratoire écologie fonctionnelle et environnement (EcoLab) of Ecole Nationale Supérieure Agronomique de Toulouse (ENSAT), France.
(A) Adding neutralisation contribution to fertilizer emissions Acidification from N fertilizer causes emissions whether or not aglime is applied Most N fertilizers generate acid as they are oxidised by bacteria in the soil. Some farmers apply aglime to neutralise the acid. However, we shall attribute CO2 emissions for the neutralisation of this acidity to the fertilizer rather than to the aglime, because most of the neutralisation emissions occur regardless of whether the farmer applies aglime, through reactions with carbonates naturally present in the soil or lower down in the watershed (Semhi, 2000; West, 2005; Perrin, 2010; Brunet, 2011). Calculating the neutralisation emissions if all N acidifies The main fertilizers used globally and in the EU are urea and ammonium nitrate (AN) ( 19). These generate the same amount of acid per kg of N when they oxidise to nitrate in the soil: CH4N2O + 4 O2 = 2 HNO3 + CO2 + H2O ( 20)...................................................(1) (NH4)NO3 + 2O2 = 2HNO3 + H2O.............................................................................................(2) The neutralization reaction of this acid, by carbonates already in the soil, or added as lime: 2HNO3 + CaCO3 = Ca++ + 2(NO3-) + CO2 + H2O.....................................................................(3) emits 1.57 kg CO2/kg N according to stoichiometry. But not all the applied N causes acidification However, not all the N ends up causing acidification. Some of it is absorbed by plants as ammonium ions, before the acid-generating oxidation can occur. Some of the acidification is
(19) Sodium nitrate causes no acidification, but CO2 emissions arise when it is manufactured from alkali. On the other hand, ammonium sulphate and aqueous ammonia give double the acid per kg N. Calcium ammonium nitrate is AN premixed with a variable quantity of lime. (20) The CO2 emitted in this reaction balances the CO2 sequestration the JRC did not count in urea manufacture.
75
reversed when nitrogen is released to the air by denitrifying bacteria ( 21), and some of the acidity is neutralised when plants take up nitrate. Three approaches to estimating the fraction of the stoichiometric CO2 emissions from N fertilizer 1. The Association of Official Analytical Chemists (AOAC) Method 936.01 recommended that farmers apply 1.8 kg CaCO3 per kg N (as urea or ammonium nitrate), to neutralise acidification caused by the fertilizer ( 22) ( 23). Sinistore (2010) uses the AOAC recommendation to estimate CO2 emissions from neutralisation of fertilizer acidity, and also the use of aglime in LCA of Wisconsin corn farms. However, Barak (2000) reported that American farmers actually apply substantially less aglime than is required to neutralise the acidity, according to this guideline. The recommended lime dose is 50 % of the stoichiometric quantity which would be required if all the nitrogen ended up acidifying the soil. Thus the recommendation must take into account the neutralisation coming from plant uptake of nitrogen and the action of denitrifying bacteria. However, Barak (2000) reported that using less lime than recommended by the AOAC was causing a degradation of the buffer capacity of American farm soils (as indicated by its cation exchange capacity, a component of soil fertility). Thus, we assume that the AOAC estimated the amount of lime required to neutralise the acidity from N fertilizer without considering the natural neutralising capacity of the soil. Since the AOAC recommendation was made in 1935, improved techniques may have increased the fraction of N taken up by crops, so that now perhaps less than 50 % of the nitrogen causes acidification. On the other hand, total use of nitrogen fertilizer has greatly increased in the same time, and this tends to reduce the nitrogen uptake efficiency. 2. Another indication of the fraction of applied N causing acidification is the amount of aglime mixed with CAN fertilizer: a mixture of ammonium nitrate and ground limestone, which is supposed to neutralise acidification. The lime content varies, but in Australia, for example, it is said to be typically 8 % Ca and 27 % N, which implies that 21 % of the stoichiometric acidification is neutralised. However, the Australian fertilizer guidelines warn this quantity of lime is insufficient where there is leaching.
(21) The N2O from denitrification is accounted for separately in RED calculations. (22) Barak (2000) explains that Method 936.01 is listed as 'obsolete' by the AOAC not because it was inaccurate, but because few farmers now use it: modern aglime recommendations tend to be based on measured soil pH and soil type, which has the advantage of combating all forms of acidification and of saving lime in cases where the soil already contains sufficient carbonates. (23) The recommendation is reflected in various handbooks and recommendations on fertilizer and soil health around the world. Some of these reduce the recommendation for urea to compensate for urea loss by volatilisation, but that just generates acidification somewhere else, so globally, volatilisation should not be accounted.
76
3. A third way to estimate the fraction of applied N causing acidification is to look at the fate of N in agricultural watersheds. According to Oh (2005), starting from urea or ammonium nitrate, the only part of nitrogen which contributes to acidification is the part which after oxidation is leached into groundwater or run-off (carrying away alkali ions like Ca2+). However, this premise may underestimate acidification, because Barak (1997) says that only some of the N uptake by plants results in neutralisation. Van Bremen (2002) reports the fate of N for watersheds coving most of the north-east of the United States. His results table shows that for agricultural land, a weighted average of 1921 kg N were leached per km2 of watershed, compared to 243+1 954 kgN/km2 taken off in biomass and 5 562 kgN/km2 denitrified. Then a minimum of 24.8 % of the total N input would need neutralisation ( 24). But N fertilizer contributed less than a quarter of the total N supply in this region: less than that from manure (3 979 kgN/km2) or agricultural fixation by plants (3 727 kgN/km2). One would expect N fixed by plants to be less prone to leaching. If we exclude that, the loss by leaching could be up to 51 % of the 'mobile' N. We recall that not all the N taken up by the plant should be excluded from causing acidification, according to Barak (1997), so these are estimates of the minimum value. Conclusion: emissions from neutralisation of fertilizer acidity The emissions from neutralisation of fertilizer acidity lie at between about 25 % and 50 % of the stoichiometric value of 1.57 tonnes CO2/tonne N. We shall use a mean value of 37.5 %, which gives 0.59 tonnes CO2 per tonne N. These emissions should be added to the emissions from N fertilizer provision, and not to liming, as they occur whether or not lime needs to be added to the soil. One may argue this value is too high, because it does not account for some neutralisation reactions (of minor importance in cultivated watersheds) which do not generate CO2: notably weathering of clays ( 25) and feldspar. On the other hand, one may claim it is too low because by considering urea and ammonium nitrate fertilizers only, we miss the fact that most of the other N fertilizers used, such as ammonium sulphate and aqueous ammonia, give at least double the acidification per tonne of N.
(B) Adding other CO2 releases to aglime application Why farmers use aglime Aglime is used to stop acidification due to: (1) CO2 dissolved in rain (24) We do not count changes in carbon stored in the soil, as the final fate of this N has yet to be determined: we suppose some is leached and some taken off as biomass or denitrified, in the same proportions as the other N. For the same reason, we disregard N lost by volatilisation to outside the region. (25) Clay in soil mostly has a pH-buffering effect, which only delays the need to neutralise acidification. However, if liming is insufficient, there is also a long-term weathering reaction, which degrades the soil by reducing its cation exchange capacity (CEC) (Barak, 2000).
77
(2) decay of organic matter in the soil (3) oxidation of N from fertilizer, bio-fixation, and rain (4) to replace Ca taken off in the crop. Mechanism (2) is important in organic/peaty soils, releasing CO2 immediately (Biasi, 2008), but not in mineral soils with near-neutral acidity. (4) only consumes a small fraction of the applied lime (Oh, 2005), and can be omitted in the following discussion. This is a correction to existing calculations for the RED In the calculations of GHG emissions from biofuel cultivation for Annex V of the RED, (and also in JEC-WTW v3), we did not account for CO2 release from aglime reactions in the soil. We shall do so now, but we must avoid double-counting of emissions from the part of aglime which is used in neutralising acidity for N fertilizer, as we have just recommended attributing these to the fertilizer. We now consider only crushed limestone Nowadays, the great majority of aglime is ground limestone (CaCO3) or sometimes dolomite (CaCO3.MgCO3). We now consider ground limestone exclusively in calculating emissions from aglime supply and application. In our previous calculations, for RED Annex V and JEC-WTW v1–v3, we did not account for any emissions from applying aglime to soils, and included 15 % calcined limestone in aglime production emissions. Calcined limestone, CaO or Ca(OH)2, is more costly and is only used when a quick effect is needed. Calcined limestone does not emit CO2 during neutralisation of acid in the soil, but the CO2 and fossil fuel emissions released during production probably make it more GHGintensive than ground limestone. IPCC guidelines on emissions from aglime application are too pessimistic According to IPCC guidelines on national GHG inventories (IPCC, 1997), all the CO2 in aglime is emitted in the end. But according to more recent work (West, 2005; Perrin, 2008), some of it is sequestered. This depends on the pH of the soil. On acid soils Where pH is less than ~6.4) ( 26), aglime is dissolved by soil acids to form predominantly CO2 rather than bicarbonate. Then most of the CO2 in the aglime is released (Biasi, 2008; West, 2005). By stoichiometry, that is 0.44 kgCO2/kg aglime.
(26) JRC calculation based on equilibrium constants of bicarbonate reactions. At this pH, dissolved CO2 and bicarbonate are in equal concentrations (Schulte, 2011) modified from Drever (1982). These are for 25C, so there may be a small error margin, depending on soil temperature.
78
On more neutral soil Above ~pH 6.4 aglime is dissolved mainly as bicarbonate, and part of its CO2 content is sequestered in the end. The bicarbonate is either decomposed by acidity deeper in the soil (releasing the aglime’s CO2) or is exported to the ocean, where some is sequestered (West, 2005) ( 27). The flows in Figure 2 of West (2005) indicate that from 15.17 Mtonnes of bicarbonate ions produced by dissolution of lime (consuming 12.44 Mtonnes of CaCO3 by stoichiometry), a net 0.98 Mtonnes of CO2 are emitted if the whole system, from soil to ocean, is considered ( 28). So if soil pH > 6.4, we will assume that 0.98/12.44 = 0.079 kgCO2 are emitted per kg of aglime applied, apart from the emissions due to neutralisation of the acidification from fertilizer. Avoiding double-counting The carbonate which is dissolved by acidity resulting from N fertilizer is not sequestered at sea or anywhere else (West 2005; Gandois, 2011). But we are already adding this to our emissions from fertilizer. To avoid double-counting, we should first subtract this CO2 emission (estimated to be 0.59 kgCO2/kgN) from our estimate of emissions from aglime dissolved by (1) and (2).
Summary: aglime rules If soil pH > 6.4 (that applies to most crops on temperate mineral soils) Emissions attributed to aglime = (kg aglime applied)*0.079 - (kg of N applied)*0.59 (in kg of CO2/kg lime). If the result is negative, the CO2 emissions attributed to lime are zero (it means they are already covered by the neutralisation emissions attributed to the N fertilizer: insufficient lime in this range of soil pH usually means the N-acidity is also being neutralised by carbonates in the soil). If soil pH < 6.4 Emissions attributed to aglime = (kg aglime applied)*0.44 - (kg of N applied)*0.59 (in kg of (27) All the papers reviewed assume that, as a soluble species, the bicarbonate content of soils or river basins must be roughly steady in the long term, so in the end effectively all bicarbonate produced from aglime dissolution is either decomposed by acidity in the soil (releasing all the carbon content as CO2) or is exported to the ocean. In the ocean, a part of the bicarbonate is converted back to carbonate, releasing some of the CO2 (see discussion and references in West (2005)), whilst some CO2 in the bicarbonate is sequestered as dissolved bicarbonate in the ocean, as well as in deposited carbonate. (28) Oh (2006) shows that in the frame of a river basin, aglime may actually lead to slight sequestration of CO2, but that does not consider what happens to the bicarbonate after it is exported to the ocean.
79
CO2/kg lime). If the amount of aglime applied is less than the amount needed to neutralise acidity from the fertilizer, then no emissions are allocated to soil reactions of lime: they are already covered by the emissions attributed to N fertilizer application (the residual emissions from fertilizer acidification are taking place downstream from the soil). Liming results table Crop groups: The Malaysian Palm Oil Board (MBOP) assures us that no lime is used on oilpalm plantation. Calculated total emissions from lime: emissions from application of lime to counter soil acidity. Calculated in model according to soil pH of crop areas: (kg lime applied * 0.079 if pH > 6.4 and kg lime applied * 0.44 if pH < 6.4). Emissions from neutralising acid from synthetic: the part of the total lime emissions attributed to mineral fertilizer N input. But some of these emissions come from natural carbonates in soil, not only from applied lime (kg mineral fertilizer N applied * 0.59). Emissions from neutralising acid N in manure: Emissions attributed to 50 % of the manure N input (kg manure N applied * 0.5 * 0.59). How to read the table The red text in Table 52 provides input for the further GHG calculations. Figures in column 3 are the updated weighted average figures for calculating aglime supply emissions. Transport distance for Brazil aglime is ~500km. Column 4 shows the extra CO2 emissions which should be added to fertilizer provision to account for emissions from neutralising the acidity generated by the synthetic nitrogen (kg lime applied * 0.079 if pH > 6.4 and kg lime applied * 0.44 if pH < 6.4). In some fields, this is more than the emissions from aglime, because the acidity is neutralised by natural carbonates on or off the field. Column 6 shows the remaining emissions from application of aglime (after subtracting, field by field, the emissions already attributed to synthetic fertilizer reduction). This is caused by neutralisation of pre-existing soil acidity and a little from neutralising acid from N in manure. The figures in Columns 4 and 6 add up to more than the total emissions from aglime in Column 3 because part of the CO2 from N acidification comes from neutralisation by natural carbonates, and not applied aglime.
80
Table 52 Emissions from liming and from neutralization of acid from fertilizer N input. Results are global weighted average emissions from suppliers of each crop to the EU market (including EU domestic production) 6
216.93
3.63
1.37
1
0.2
1.15
maize
107.68
1.25
0.47
0.76
0.1
0.34
rapeseed
245.15
4.05
1.49
1.53
0.2
1.04
rye
273.26
6.27
2.42
1.03
0.26
2.09
soybean av.
139.52
2.82
1.18
0.13
0.09
1.16
156.7
3.13
1.37
0.11
0.11
1.37
229.35
4.53
1.81
0.25
0.05
1.65
19.49
0.4
0.17
0.01
0.02
0.19
sugarbeet
217.99
1.08
0.38
0.42
0.08
0.31
sugarcane
127.21
0.34
0.14
0.1
0.01
0.08
73
2.2
0.71
0.77
0.19
0.67
triticale
243.27
4.12
1.53
1.08
0.21
1.23
wheat
177.55
2.57
0.91
0.9
0.16
0.72
soybean Brazil soybean US soybean Argentina
sunflower
Oil palm
-1
barley
CROP GROUP
REMAING CO2 EMISSIONS FROM LIME AND -1 MANURE (gCO2 MJ crop)
5
EMISSIONS FROM NEUTRLAZING ACID -1 FROM N IN Manure (gCO2 MJ crop)
4 EMISSIONS FROM NEUTRALIZING ACID -1 FROM SYNTHETIC FERTILIZER N (gCO2 MJ crop)
3 TOTAL EMISSIONS FROM LIME (gCO2 MJ crop)
2 AVERAGE AGLIME INPUT per MJ CROP (g -1 CaCO3 MJ crop)
1 AVERAGE AGLIME INPUT BASED ON GNOC -1 (kg CaCO3 ha )
Column
see Section 6.11
81
3.11 Global crop-specific calculation of CO2 emissions from agricultural lime application and fertilizer acidification Global CO2 emissions caused by agricultural lime application are calculated for a 5 min. by 5 min. (~10 km by 10 km) grid, similar to the approach used for soil N2O emissions. For soil parameters, crop species distribution and fertilizer N input, we resorted to the same data set used to calculate soil N2O emissions. As a prerequisite for the emission calculations, the site-specific lime application to a certain crop within a 5 min. by 5 min. grid cell had to be estimated. Country level limestone (CaCO3) and dolomite (CaMg(CO3)2) consumption to counteract acidification of soil (and water bodies) is available from the EDGAR v4.1 database (ECJRC/PBL). In the following, we refer to lime but mean the sum of limestone and dolomite. The data of the EDGAR v4.1 database originate from the reporting of Annex I ( 29) countries to the UNFCCC. Data on lime consumption for the year 2000 are taken from the (mainly year 2008) submissions of the common reporting format (CRF) tables. In EDGAR v4.1, all the lime use reported in CRF Table 5 (IV) is taken into account, regardless of whether lime is applied to agricultural soils, forests or lakes. In the case of Non-Annex I countries (29) which are not obliged to report emissions to UNFCCC, the estimated amount of lime applied is based on the calculated need to balance the use of ammonium fertilizers. It is assumed that all calcium is applied as lime. It should be noted that in reality, several factors affect soil acidity and, subsequently, liming need, and therefore the estimates are highly uncertain (ECJRC/PBL). As the EDGAR v4.1 data set does not distinguish lime input to different land uses/covers, the UNFCCC CRF submissions (2008) for the year 2000 (UNFCCC, 2012) were re-screened and the shares of input into land uses other than that of cropland were subtracted in our calculations for those countries providing the information. Country-level data as extracted from the EDGAR v4.1 database and the shares of lime input to other land uses/cover are listed in Table 53. There are various shortcomings to using the EDGAR v4.1 data set for the calculations of CO2 emissions from liming of potential biofuel crops. However, to our knowledge, this is the only global data set on lime and dolomite application based on values reported officially by the individual Annex I countries. The first of two main shortcomings is that only a few countries report the share of lime applied to land use/cover other than cropland. Especially in developed countries with high shares of managed grasslands/pastoral systems (e.g. New Zealand), this may lead to an (29) Annex 1 in the United Nations Framework Convention on Climate Change lists those countries which are signatories to the Convention and committed to emission reductions. Non-Annex 1 countries are developing countries, and they have no emission reduction targets.
82
overestimation of liming in croplands: the share of input to grassland soils, for instance, is unknown. For non-Annex I countries, the estimated amount of lime is estimated at country level to balance acidity from use of ammonium fertilizer input. The soil status in these countries is not taken into account. Brazil's high share of soils susceptible to acidification led its government to approve a programme in 1998 to improve Brazilian agriculture productivity by intensification of liming. According to national statistics (Bernoux et al., 2003), 19,812 ktonnes of lime were consumed in the year 2000, an amount three times higher than that given in the EDGAR v4.1 data set (6,980 ktonnes). However, it is unknown if the whole amount produced by the statistics was applied to arable land or if liming of permanent pastures, for instance, is occurring as well. Contrary to the Brazilian case, in poorer countries, the values of the EDGAR v4.1 data set might well be overestimated. To break down country-level lime consumption to site-specific application rates, we followed the Agricultural Lime Association (ALA) recommendations (2012) developed in partnership with the University of Hertfordshire's Agriculture & Environment Research Unit (AERU). The application rates recommended are aimed at a target soil pH of 7.0 for arable and 6.5 for permanent grassland. This holds for mineral soils. For organic soils, the target pH is 0.3 and 0.7 pH units lower for arable land and grassland respectively. The ALA recommendations depend on soil pH and texture as well as organic matter content, and on whether the soil is cultivated as arable land or grassland (see Table 54). Based on globally available information on soil pH, organic matter and texture from the Harmonized World Soil Database (FAO/IIASA/ISRIC/ISS-CAS/JRC, 2009) ( 30) and the harvested area of the single crops in the year 2000 (Monfreda et al., 2008), the theoretically required lime input according the recommendations of the ALA (2012) can be calculated for the harvested area of each crop in each 5 min. by 5 min. grid cell. Figure 10 illustrates the underlying data sets for global pH and harvested crop area. The lime was distributed to harvested area (accounts for multiple cropping) rather than to cropland area (physical land area), assuming that areas with double or triple cropping receive higher fertilizer input and need higher rates of lime application to counteract acidity caused by fertilizer N. The final lime input to the grid cell was calibrated in order to fit with the country level lime input from the EDGAR 4.1 database. We compared the results of the disaggregation with field-level data provided in the literature for Germany and the United Kingdom (see Section 3.12): in both cases, the estimated lime input per ha of this work is in the range of what is mentioned in the literature. The total emissions from lime application to the crop on a grid cell bases were calculated as: CO2 Emissionslimetot in kg = kg lime applied * 0.079 if pH ≥ 6.4 and (30) The calculations are based on the dominant soil type in a soil mapping unit of the Harmonized World Soil Database.
83
CO2 Emissionslimetot in kg = kg lime applied * 0.044 if pH < 6.4 However, emissions from lime input required to neutralise fertilizer acidity are attributed to the emissions caused by the fertilizer. These emissions need to be subtracted from the total emissions caused by lime application, to avoid double counting. According the analysis described before, the neutralisation of 1 t of synthetic fertilizer applied releases 0.59 t CO2. From the global data set on crop-specific synthetic fertilizer input data ( 31), the emissions caused by neutralisation of fertilizer input were calculated on the grid cell basis for each crop as: CO2 Emissionsynthfert in kg =kg synthetic N applied * 0.59 If, for a specific crop in a grid cell, the CO2 emissions from lime input exceed the CO2 emissions needed to neutralise synthetic fertilizer N input, we attribute the difference in emissions to lime application. Due to the method of accounting for N input from mineral fertilizer and manure to a specific crop, we also take into account 50 % of the manure input given in the global fertilizer data set, assuming that in the case of biofuel crops we underestimate the total amount of mineral fertilizer, to which (in most cases) no manure is applied ( 32). The emissions resulting from neutralising 50 % of N applied as manure (CO2 Emissions50%man) to the biofuel crops in our database are calculated the same way as for synthetic fertilizer; we consider that in reality, this manure will be applied as synthetic fertilizer to biofuel crops. However, the emissions are not added to the synthetic fertilizer, but to the final lime emissions (CO2 Emissionslime_net), so as to ensure that globally, emissions attributed to synthetic fertilizer are not overestimated. Hence, if CO2 Emissionslimetot ≥ CO2 Emissionsynthfert we calculate CO2 Emissionslime_net= CO2 Emissionslimetot + CO2 Emissions50%man - CO2 Emissionsynthfer otherwise if CO2 Emissionslimetot < CO2 Emissionsynthfert we set CO2 Emissionslime_net = CO2 Emissions50%man Country-and crop-specific emissions (in kg CO2 MJ-1 of fresh crop) attributed to lime application are calculated then as sum of the CO2 Emissionslime_net from each grid cell for each crop in the country divided by the country’s total yield of the crop (in MJ fresh crop).
(31) For a description of the crop-specific mineral fertilizer input, see Section 3.5. (32) For a discussion of the 50 % manure input, see Section 3.6.
84
Country-and crop-specific emissions (in kg CO2 MJ-1 of fresh crop) attributed to synthetic fertilizer input are calculated then as the sum of the CO2 Emissionssynthfer from each grid cell for each crop in the country, divided by the country’s total yield of the crop (in MJ fresh crop). Final emissions (see Table 52) attributed to a specific biofuel crop equal the global weighted average emissions from suppliers of each crop to the EU market (including EU domestic production). Table 53 Limestone and dolomite consumption for the years 2000, as reported in EDGAR v4.1 database (EC-JRC/PBL), and share of limestone and dolomite applied to land use/cover other than cropland Country
Limestone and dolomite consumption in the year 2 000 (1 000 t)
Albania Algeria Argentina Armenia Australia Austria
16 92 424 25 2 404 205
Azerbaijan
8
Bangladesh Belarus Belgium Brazil Bulgaria Cameroon Canada Chile China Colombia Costa Rica Côte d'Ivoire Croatia Cuba Cyprus Czech Republic Denmark
77 3 375 91 6 983 2 17 558 54 5 033 311 107 29 13 240 16 1 087 737
Percentage of limestone and dolomite input to land use other than cropland
3.9
Country
South Korea Kyrgyzstan Latvia Lebanon Libya Lithuania former Yugoslav Republic of Macedonia Malaysia Mexico Moldova Morocco Nepal Netherlands New Zealand Nicaragua Nigeria Norway Pakistan Peru Philippines Poland Portugal Romania Russia
Limestone and dolomite consumption in the year 2 000 (1 000 t)
64 99 5 103 9 29 24 1 695 4 768 6 561 18 255 1 279 25 14 340 259 185 1 122 2 542 67 382 9 267
Dominican Republic
176
Saudi Arabia
122
Ecuador
37
Senegal Serbia and Montenegro Slovakia
50
Egypt El Salvador
2 223 325
Percentage of limestone and dolomite input to other land use than cropland
19.6 (Lakes)
50 2
85
Country
Limestone and dolomite consumption in the year 2 000 (1 000 t)
Estonia
46
Ethiopia Finland France Georgia Germany Greece Guatemala Hungary Iceland India Indonesia Iran Iraq Ireland Israel Italy Japan Jordan Kazakhstan Kenya North Korea
76 918 2 273 47 4 402 598 281 147 5 4 336 1 553 656 65 665 71 1 009 1 989 26 100 83 186
Percentage of limestone and dolomite input to land use other than cropland
6.9 (Forest)
89.8
Country
Limestone and dolomite consumption in the year 2 000 (1 000 t)
Slovenia
2
South Africa Spain Sri Lanka Sudan Sweden Switzerland Syria Taiwan Tajikistan Tanzania Thailand Tunisia Turkey Turkmenistan Ukraine United Kingdom United States Uruguay Uzbekistan Venezuela Vietnam
230 1 680 259 122 272 45 286 1 044 16 35 1 717 208 1 698 265 2 776 2242 20 556 41 719 188 1 365
Percentage of limestone and dolomite input to other land use than cropland
37.9
86
Table 54 Lime application recommendations (Agricultural Lime Association, 2012). Values are the amount of ground limestone (with a neutralising value of 54 and 40 % passing through a 150 micron mesh) required to achieve the target soil pH. The Agricultural Lime Association considers a optimum pH between 6.8 and 7.0 for general cropping. For permanent grassland the optimum pH is slightly lower.
0 2 2 2 2 3 4 4 5 5 6 7 7 8 8 9 10 10 11 11 12 13 13 14 14
0 2 2 2 3 4 4 5 6 6 7 8 8 9 10 11 11 12 13 13 14 15 15 16 17
Recommended lime application (t/ha) 0 0 0 0 0 0 2 0 0 0 0 0 2 0 0 0 0 0 2 0 0 0 0 0 3 2 0 0 0 0 4 2 0 0 0 0 5 3 0 2 2 2 6 4 0 2 2 2 6 5 2 2 2 2 7 6 3 2 2 2 8 7 5 2 3 3 9 8 6 3 3 4 10 9 9 3 4 4 10 10 10 4 4 5 11 11 11 4 5 5 12 12 13 5 5 6 12 13 14 5 6 7 13 14 16 5 6 7 14 15 18 6 7 7 15 16 19 6 7 7 16 17 21 7 7 7 16 18 22 7 7 7 17 19 24 7 7 7 18 20 26 7 7 7 19 21 27 7 7 7
15
17
20
22
29
7
7
7
Peaty soils above 25 % organic matter
Clay loams and clays
Sandy loams and silt loams
Sand and loamy sands
Peaty soils above 25 % organic matter
Organic soils (10 %–25 % organic matter)
Clay loams and clays
Sandy loams and silt loams
Grassland Organic soils (10 %–25 % organic matter)
7 6.9 6.8 6.7 6.6 6.5 6.4 6.3 6.2 6.1 6 5.9 5.8 5.7 5.6 5.5 5.4 5.3 5.2 5.1 5 4.9 4.8 4.7 4.6 4.5 ( 33)
Sand and loamy sands
Measured pH
Arable land
0 0 0 0 0 0 0 0 0 2 2 2 3 4 5 5 6 7 7 7 7 7 7 7 7
0 0 0 0 0 0 0 0 0 0 0 0 0 2 2 4 5 6 7 7 7 7 7 7 7
7
7
(33) For this work, we assume the lime application to soils with pH < 4.5 to be the same as for pH 4.5 soils.
87
Figure 10 Global distribution of soil pH (FAO/IIASA/ISRIC/ISS-CAS/JRC) and harvested area (Monfreda et al., 2008)
3.12 Lime application in the United Kingdom and Germany: survey data v disaggregated country total lime consumption Farm- and field-level information on lime application is scarce. For most countries, lime application in agriculture is given as a country total derived from lime consumption/production in the country. In many cases, even the shares of lime applied to either arable land or grassland are unknown. To check the results of the disaggregation of country level lime consumption to crop level application as described in the previous chapter, we compared the results with field data described in the literature.
88
United Kingdom
In the United Kingdom, the Department for Environment, Food and Rural Affairs (Defra) is sponsoring an annual survey about fertilizer use on farm crops in Great Britain (34) since 1983. In 2000, approximately 1 400 farms were surveyed (Defra, 2001). Lime application is assessed at farm and field level for four different liming products (ground limestone, ground chalk, magnesian limestone and sugar beet lime). Input of all other types of liming products are summarised under the group 'others' (Table 55). Defra lime application data (35) are compared with the results of the disaggregation of country-level lime consumption to the crop level for the year 2000. In the United Kingdom 2.242 million tonnes of CaCO3 (limestone and dolomite) were applied to agricultural fields (EC-JRC/PBL); 37.9 % was applied to grassland, leaving 1.39 million tonnes for arable land (36). From the disaggregation described in the previous section we can calculate an average input of 0.28 tonnes of CaCO3 ha-1 yr-1 to arable land for which lime application is recommended. This is the case for around 4.9 million ha or 91.2 % of the arable land. Lime application recommendations usually give application rates to reach the optimum pH level for the crop cultivation. Thus, the application has to be repeated only if the desired pH level decreases again below a critical threshold. A repetition rate frequently mentioned in lime application recommendations is 5 to 10 years, but it may vary strongly depending on the soil properties, climatic conditions and farming practices. The Defra (2001) survey (Table 55) gives the percentage of crop area receiving dressing and the amount of liming product applied in 2000. Depending on the crop, lime is applied in quantities of 1.1 tonnes CaO per ha to 2.7 tonnes CaO per ha on ~5 % to 35 % of the crops area. On average, for all tilled crops, 2.5 tonnes CaO from all liming products are applied to 8.4 % of the tilled crops area. Ground limestone, ground chalk and magnesian limestone contribute with 2.2 tonnes of CaO ha-1 on 7.7 % of the tilled crops area. From our analysis of the spatial data on soil properties in the United Kingdom (37) we calculated that around 91.2 % of the arable crops area needs lime application to reach optimum pH for cultivation. If we assume that over time all the area will be limed, we can calculate the number of years until all the whole area is limed once by dividing 91.2 % by 7.7 % (the annual share of area limed with ground limestone, ground chalk and magnesian limestone given by Defra). Hence, in 11.8 years, all arable crops growing on soils with non-optimal pH will have been limed once. Assuming liming practices constant over time, a single field gets one application every 11.8 years, on average. The annual application rate of lime can be calculated by dividing the (34) Wales, England and Scotland. (35) Excluding 'sugar beet lime' and 'other'. (36) This excludes permanent grassland. (37) This includes Northern Ireland. It is assumed that conditions in Northern Ireland (share of crop area requiring liming and liming frequency) are not significantly different from the mean conditions in Great Britain.
89
application of 4 tonnes of CaCO3 ha-1 (or 2.2 tonnes of CaO ha-1) by 11.8 years. This results in an average application rate of 0.34 tonnes of CaCO3 ha-1 yr-1 on arable land based on Defra survey data. This compares to 0.28 tonnes of CaCO3 ha-1 yr-1 on arable land with nonoptimal pH conditions, from the spatial disaggregation of country-level lime application (see Table 56).
90
Table 55 Lime application at field level (Defra, 2001) and estimation of mean annual application rates on tillage crops in the year 2000
Spring wheat Winter wheat Spring barley Winter barley Oats Rye/triticale/durum
4.0 0.8 0.9 0.2 4.9 0.8 5.0 0.3 6.5 0.2 1.5 0.4 2.5 2.3
5.9 2.3 2.0 11. 2.0 1.8 8.6 2.4 2.1 4.8 2.2
2.4 2.8 2.4 3.3 1.8 3.4 1.5
2.3 2.2 2.4 2.0
Seed potatoes Early potatoes 2nd early/main crop 35. 2.5 2.1 1.9 2.9 Sugar beet 10.6 9.3 5.3 10. 2.7 12. Spring oilseed rape 1.2 4.7 2.0 4.4 1.2 1.5 2.0 0.4 1.1 11. 2.3 2.0 2.2 1.6 Winter oilseed rape 7.5 1.0 1.8 0.9 2.3 Linseed 20. 2.7 2.5 1.6 Forage maize 15.2 1.8 3.5 2.5 15. 1.9 Root crops for stockfeed 11.9 2.9 0.7 2.2 1.1 1.9 18. 1.7 1.5 2.6 Leafy forage crops 5.0 0.4 13. 2.0 Arable silage/other 12. 12. 0.4 2.5 2.7 2.5 Peas — human Peas — animal 1.9 3.8 1.9 7.6 2.5 0.8 2.6 1.6 Beans — animal 0.3 1.7 0.3 2.3 1.2 0.6 1.2 1.3 Vegetables (brassicae) Vegetables (other) 5.2 0.5 0.7 2.5 8.9 2.5 4.0 2.1 1.3 2.7 Soft fruit Top fruit 4.9 1.9 6.8 0.3 0.2 0.1 Other tillage 0.4 0.3 0.1 0.1 All tillage 4.8 1.1 1.8 0.7 8.4 2.3 2.0 2.2 2.7 2.5 Grass under 5 years 2.7 0.2 1.9 4.8 2.4 1.3 2.4 2.2 Grass 5 years and over 1.6 0.1 1.3 0.3 3.3 2.2 2.8 2.4 2.1 2.1 All grass 1.8 0.1 1.4 0.3 3.6 2.2 2.3 2.4 2.1 2.1 All crops and grass 3.3 0.6 1.6 0.5 6.0 2.3 2.0 2.3 2.5 2.3 #
3 149 119 78 12 3 1 0 0 89 7 47 0 27 11 14 5 2 7 5 4 14 1 8 5 611 102 130 232 843
62 2796 881 841 199 51 21 14 227 273 73 525 60 149 78 56 40 96 112 170 56 126 47 78 117 7148 1280 2744 4024 1117
Weighted average application rate of ground limestone, ground chalk and magnesian limestone (tonnes of CaCO3 ha-1) Annual application rate (tonnes of CaCO3 ha-1 yr-1)
Liming frequency (years)
Total no of fields
Calculation of CaCO3 input per ha#
Fields limed
All
Other
Sugar beet lime
Magnesian limestone
Ground chalk
Ground limestone
Average field rate of CaO equivalent (tonnes/ha)
All
Other
Sugar beet lime
Magnesian limestone
Ground chalk
Ground limestone
Crop type
Crop area receiving dressing (%)
16.0 8.5 11.1 19.0
4.1 3.9 4.1 3.3
0.25 0.45 0.37 0.18
3.6 11.5 8.9
4.0 2.8 4.0
1.10 0.24 0.45
4.4 6.2 4.9 7.1
4.5 3.5 4.2 4.8
1.00 0.57 0.85 0.67
12.0 45.6
3.0 1.2
0.25 0.03
14.3
4.6
0.32
13.4 130.
0.5 0.2 4.0 4.2 4.1 4.1 4.0
0.04 0.00 0.34 0.22 0.14 0.15 0.24
11.8 19.0 30.4 27.6 16.6
1kg of CaCO3 corresponds to 1.7857 kg of CaO.
91
Annual lime (limestone and dolomite) application rate (tonnes of CaCO3 ha-1 yr-1)
Crop area where lime applications is recommended (ha)
Crop
Table 56 Lime application in the United Kingdom in the year 2000, based on this study
Wheat
1 648 298
0.29
Barley
1 061 485
0.34
95 219
0.32
Rye
4 787
0.27
Triticale
9 592
0.30
Potato
146 029
0.32
Sugar beet
159 844
0.26
Rapeseed
412 317
0.32
Oilseeds
68 062
0.32
Forage
968 124
0.17
Fibres
15 077
0.30
Pulses
199 277
0.32
Vegetables
105 234
0.30
8 785
0.29
4 902 130
0.28
Other cereals
Fruits All crops
Germany Study 1 For wheat and rye, De Vries (2006) suggests 0.3 t to 0.4 t of CaO (0.54 - 0.71 tonnes of CaCO3) per ha and year on soils in Mecklenburg-Vorpommern (north-eastern Germany). Study 2 Ahlgrimm et al. (2000) cited in Hirschfeld et al. (2008) assume 0.35 t CaO (~0.63 t CaCO3 per ha) as a kind of default value for all crops under conventional farming. Study 3 On the basis of statistical data in Germany, Knappe et al. (2008) classified different farm types and assessed the fertilizer/lime application requirement based on nutrient balances.
92
For conventional farming based on manure and mineral fertilizer input, Knappe et al. (2008) ( 38) calculated an annual deficit of ~0.16 - 0.25 tonnes CaO (0.29 - 0.45 tonnes CaCO3) to neutralise acidification from fertilizer N input on arable land and 0.07 tonnes CaO (~ 0.12 tonnes of CaCO3) on permanent grassland. In conventional farming systems, when applying mineral fertilizer in combination with sewage sludge or compost (Knappe et al., 2008) ( 39), the additional liming requirements decrease depending on the amount of sewage sludge or compost applied. There might be even a CaO surplus, especially in case of compost application. In their approach, they do not consider lime application for optimising soil pH. Results of our work: From our study we get 0.48 t CaCO3 per ha in Germany as average input for all arable crop species on soils where lime is applied.
(38) See Tables C14 and C17. (39) See Tables C15 and C16.
93
4. Utilities and auxiliary processes This section contains the processes for utilities such as boilers and power plants that are used throughout the various pathways in Chapter 6. NG boiler Table 57 Process for a NG boiler Steam from NG boiler (10 MW) I/O Unit
Amount
Source
NG
Input
MJ/MJheat
1.111
1,2
Electricity
Input
MJ/MJheat
0.020
2
Output
MJ
1.0
Steam CH4
Output
Emissions g/MJheat
N2O
Output
g/MJheat
0.0028
1
0.00112
1
Comments - Electricity taken from the grid at 0.4kV. - Thermal efficiency = 90 % (based on LHV). - CO2 emissions from natural gas combustion are considered to be 198.27 gCO2/kWh. Sources 1 GEMIS v. 4.9, 2014, gas-boiler-DE 2010. 2 GEMIS v. 4.9, 2014, gas-heat plant-medium-DE 2010.
94
NG CHP Table 58 Process for a NG CHP to supply power and heat Power and heat from a NG CHP I/O
Unit
Amount
Input
MJ/MJsteam
2.3867
Steam
Output
MJ
1.00
Electricity
Output
MJ/MJsteam
0.7900
CH4
Output
g/MJheat
0.01
N2O
Output
g/MJheat
0.002387
NG
Emissions
Comments - Steam: 160–180°C. - CO2 emissions from natural gas combustion are considered to be 198.27 gCO2/kWh. Source 1 Nitsch et al., 1999. Lignite/coal CHP Table 59 Process for a lignite/coal CHP Power and heat from a lignite/coal CHP I/O
Unit
Amount
Sources
Lignite
Input
MJ/MJsteam
1.405
1
Steam
Output
MJ
1.0
Electricity
Output
MJ/MJsteam
0.222
1
Emissions CH4
Output
g/MJsteam
0.0023
N2O
Output
g/MJsteam
0.0126
Comments - Represents a plant with a capacity of 34.2 MWth. - Replaces electricity from a lignite-fuelled ST. - CO2 emissions from lignite combustion are considered to be 414 gCO2/kWh. Sources 1 Larivé, J-F., CONCAWE, personal communication, February 2008. 2 Punter et al., 2004. 95
Woodchip–fuelled CHP (NEW PROCESS) Table 60 Process for a woodchip-fuelled CHP Power and heat from a woodchip-fuelled CHP I/O
Unit
Woodchips
Input
MJ/MJsteam
Steam
Output
MJ
Electricity
Output
MJ/MJsteam
Amount 2.132 1.0 0.361
Emissions CH4
Output
g/MJ
0.0057
N2O
Output
g/MJ
0.00114
Comments - This replaces electricity from a straw-fuelled ST process (as requested at the workshop). - Represents a plant with a capacity of 34.2 MWth. - Thermal efficiency should be considered as obtained at optimum load. The CHP can be dimensioned on a different electricity load and thus reach a lower thermal efficiency. Source 1 Punter et al., 2004. Ethanol/FAME depot Table 61 Process for the energy consumption in an ethanol or FAME depot Ethanol/FAME depot I/O
Unit
Amount
FAME/ethanol
Input
MJ/MJ
1.0
Electricity
Input
MJ/MJ
0.00084
FAME/ethanol
Input
MJ
1.0
Source 1 Dautrebande, 2002.
96
Ethanol/FAME filling station Table 62 Process for energy consumption in an ethanol/FAME filling station Ethanol/FAME filling station I/O
Unit
Amount
FAME/ethanol
Input
MJ/MJ
1.0
Electricity
Input
MJ/MJ
0.0034
FAME/ethanol
Input
MJ
1.0
Source 1 Dautrebande, 2002.
97
5.
Transport processes
This section contains all the processes that pertain to fuel consumption for all the vehicles and means of transportation used in all the pathways. The section is structured by road, maritime, inland and rail transportation. The processes are recalled in each pathway in Chapter 6.
5.1 Road transportation 40 t truck (27 t payload) The common means of transport considered for road transport is a 40 t truck with a payload of 27 t. For the transport of solid materials, a flatbed truck transporting a container is considered. The weight of such a tank is considered, for the sake of simplicity, to be equal to 1 t. For the transport of liquids and pellets, special tank trucks are used. It is assumed that such trucks have the same general fuel efficiency and general payload of the truck for solids but with a higher, 2 t, weight for the tank, to account for the pneumatic system. The payload of a typical trailer truck with a gross weight of 40 t for the transport of wood chips with push floor trailer amounts to 90 m³ (e.g. “Schubboden”). The mass of the semitrailer tractor amounts to about 7.6 t (see e.g.: MERCEDES-BENZ 1844 LS 4x2, 400 kW) and the mass of the trailer for the transport of wood chips (92 m³) ranges between 7.5 and 7.9 t. Then the net payload amounts to (40-7.6-7.5…7.9) t = 24.5…24.9 t. For the DAF CF 75.360 the empty mass is indicated with 6.5 t which would lead to a net payload of up to 26 t. The truck considered in this work is a 40 t truck with a payload of 27 t, a part of the 27 t consists of payload specific structure. Assuming a net payload of 26 t leads to a “tank” mass of 1 t. The truck fuel consumption is linearised on the weight transported and on the distance. The amount of tonnes per kilometre is calculated from the formula (in this case, for solid fuels transport):
t ⋅ km Distance = MJ goods
(27 )[t ]⋅ x [km] MJ goods kg dry ⋅ Solids kg tot kg dry
(27 − tank )[t ]⋅ LHVdry
98
This value is calculated and reported for each pathway in the following chapters of this report, and the specific LHV and moisture content of the analysed materials will also be highlighted. In order to obtain the final fuel consumption of the transportation process, the 'distance' process needs to be multiplied by the fuel consumption of the vehicle considered. For the case of a 40 t truck, this value and the associated emissions are reported in Table 63. Table 63 Fuel consumption for a 40 t truck I/O
Unit
Amount
Source
Input
MJ/tkm
0.811
1
Distance
Output
tkm
1.00
CH4
Output
N2O
Output
Diesel
Emissions g/tkm g/tkm
0.0034
1
0.0015
1
Comments - The return voyage (empty) is taken into account in this value. - This process is commonly used for the transportation of solids and liquids. - The fuel consumption corresponds to 30.53 l/100 km. - The fuel consumption and emissions are a weighted average of Tier 2 values among different Euro classes based on the fleet composition indicated in the COPERT model. Sources 1 EMEP/EEA air pollutant emission inventory guidebook, Technical report N12/2013. Part B 1.A.3.b.i-iv.
99
40 t truck (27 t payload) for sugar cane Table 64 Fuel consumption for a 40 t truck, weighted average for sugar cane transport I/O
Unit
Amount
Input
MJ/tkm
1.37
Distance
Output
tkm
1.0000
CH4
Emissions Output
g/tkm
0.001
N2O
Output
g/tkm
0.0039
Diesel
Source 1 Macedo et al., 2004. MB2213 Dumpster truck Table 65 Fuel consumption for a MB2213 dumpster truck used for filter mud cake I/O
Unit
Amount
Input
MJ/tkm
3.60
Distance
Output
tkm
1.00
CH4
Emissions Output
g/tkm
0.000
N2O
Output
g/tkm
0.000
Diesel
Source 1 Macedo et al., 2004.
MB2318 Tanker truck for seed cane Table 66 Fuel consumption for a MB2318 truck used for seed cane transport I/O
Unit
Amount
Input
MJ/tkm
2.61
Distance
Output
tkm
1.00
CH4
Emissions Output
g/tkm
0.000
N2O
Output
g/tkm
0.000
Diesel
100
MB2318 Tanker truck for vinasse Table 67 Fuel consumption for a MB2318 tanker truck used for vinasse transport Diesel Distance CH4 N2O
I/O
Unit
Amount
Input
MJ/tkm
2.16
tkm
1.00
g/tkm g/tkm
0.000 0.000
Output Emissions Output Output
Source 1 Macedo et al., 2004.
12 t truck (6.35 t payload) This process represents a smaller truck used for the transportation of specific materials such as fresh fruit bunches (FFBs). Table 68 Fuel consumption for a 12 t truck I/O
Unit
Amount
Source
Input
MJ/tkm
2.238
1,2
Distance
Output
tkm
1.00
CH4
Output
Emissions g/tkm
0.002
3
N2O
Output
g/tkm
0.0015
3
Diesel
Comment - Process used for transport of FFBs in the palm oil pathway. Sources 1 Lastauto Omnibus Katalog, 2010. 2 Choo et al., 2011. 3 GEMIS v.4.7, 2011, 'truck-Diesel-EU-2010'.
101
20 t truck (10 t payload) This process represents a truck used for the transportation of specific materials such as jatropha seeds. Table 69 Fuel consumption for a 20 t truck used for jatropha seeds transport 20 t truck (payload: 5 t) Diesel Distance
I/O
Unit
Amount
Source
Input
MJ/tkm
1.80
1, 2
Output
tkm
1.00
CH4
Output
Emissions g/tkm
N2O
Output
g/tkm
0.0016
2
0.0012
2
Sources 1 Lastauto Omnibus Katalog 2010. 2 GEMIS v.4.7, 2011, 'truck-Diesel-EU-2010'.
Note on transportation distance Distance is multiplied by 2 because of the return voyage (empty). Vehicle assumption: 'Mercedes Axor 1833 L Pritsche': 24.5 to 26.0 l of diesel per 100 km (Lastauto Omnibus Katalog, 2010). The 'Mercedes Sprinter Axor 1833 L Pritsche' has a gross weight of 18.0 t (slightly lighter than a 20 t truck) and a payload of 10.75 t (using a higher platform gate may lead to a lower net payload of about 10 t). A fuel consumption of 25 l per 100 km would lead to a 0.90 MJ/tkm, without taking into account the return voyage and 1.80 MJ/tkm including the return voyage (Werner Weindorf, LBST, personal communication, March 2012).
5.2 Maritime transportation Handymax (37 000 t payload) For soybeans only pathways involving transport of the oil were considered, and the shipping emissions for the maize-ethanol pathway ignores the fraction of maize which is imported. The only remaining use for shipping by bulk carrier is a share of 4.4% of the transport of rapeseed. The average size of vessels carrying rapeseed is considered larger than that for wood-chips, characterized by deadweight 40 000 tonnes, which falls into the ‘handymax’ size class.
102
Table 70 Fuel consumption for a Handymax for goods with bulk density > 0.6 t/m3 (weight-limited load)
Heavy fuel oil Distance
I/O
Unit
Amount
Input
MJ/tkm
0.1009
Output
tkm
1.00
Comments - Valid for payloads with bulk density >0.6 t/m3. - The return voyage is considered empty and it is included in the value. - LHV heavy fuel oil = 40.5 MJ/kg. - Oil consumption = 2.492 gHFO/tkm. Sources 1 IMO, 2009. 2 Edwards et al., JRC, own calculations, 2011.
Product tanker (12 617 t payload) This process is used to account for the direct import of ethanol produced from sugar cane. Table 71 Fuel consumption for a product tanker for ethanol transport
Heavy fuel oil Distance
I/O
Unit
Amount
Input
MJ/tkm
0.115
Output
tkm
1.000
Comments - New data considering av. 90 % loading on outward trip [3] and 85 % of that on the return trip [2]. - The process is used in 'sugar cane to ethanol' pathway to account for ethanol import. - Heavy fuel oil consumption = 2.8432 gHFO/tkm. - LHV heavy fuel oil = 40.5 MJ/kg. Sources 1 IMO, 2009. 2 Personal communication between Odfell Tankers AS, Bergen, Norway and the JRC. 26 Jan. 2012, 3 JRC estimate based on sea distances between intermediate ports, following discussion in [1]. 4 JRC interpolation of emissions data from [1].
103
Product tanker (15 000 t payload) This process is used to account for the direct import of FAME and ethanol. Table 72 Fuel consumption for a product tanker for FAME and ethanol transport Product tanker (payload: 15 000 t) Heavy fuel oil Distance
I/O
Unit
Amount
Input
MJ/tkm
0.1712
Output
tkm
1.00
Comments - The return voyage is considered empty and it is included in the data. - Heavy fuel oil consumption = 4.2278 gHFO/tkm. - LHV heavy fuel oil = 40.5 MJ/kg. - This process is used for the transportation of FAME. Source 1 IMO, 2009. Product tanker (22 560 t payload) This process is used to account for the direct import of vegetable oil from jatropha, palm, soya and waste cooking oil. Table 73 Fuel consumption for a product tanker for pure vegetable oil transport Product tanker (payload: 22 560 t) Heavy fuel oil Distance
I/O
Unit
Amount
Input
MJ/tkm
0.09547
Output
tkm
1.000
Comments - Heavy fuel oil consumption = 2.3573 gHFO/tkm. - New data considering av. 90% loading on outward trip (Ref 2) and 85% of that on the return trip (Ref 1). - LHV heavy fuel oil = 40.5 MJ/kg. Sources 1 Odfell Tankers AS, Bergen, Norway, 26 January 2012, personal communication to JRC. 2 JRC estimate based on sea distances between intermediate ports. 3 JRC interpolation of emissions data from Ref. 5. 4 IMO, 2009.
104
Additional notes: Personal communication (18 May 2012) with Arild Viste (AV) of Odfjell Tankers provided the following clarifications: - Average size of ship for ethanol transport from Brazil: 14–16 ktonnes dwt. - Average size of ship for soy-oil transport from S America: 20–30 ktonnes dwt. - Stowage ratio (design density of cargo) for chemical tankers 0.8 to 0.85, so ethanol loading is (just) volume-limited. - Because of fast growth in Brazil, at present there are actually more liquid chemicals going to South America from Europe/Africa than vice versa, but this varies with time. - The largest component of liquid chemicals returning to South America is phosphoric acid from Morocco to Brazil, used to make fertilizers. - AV agrees that on a world scale, the IMO '68 %' is a good guess for average of full load carried, but it is higher on the South America route for chemicals. - Palm oil from Asia represents a more complicated issue, but the situation is similar. - Larger ships have lower average percentage filling of cargo-carrying capacity. - In both directions, the ships typically make several calls at several ports to fill up for the Atlantic crossing. JRC comments on soy oil and biodiesel We know from Odfjell that the return voyage across the Atlantic contained 15 % less cargo than the outward voyage. But we cannot assume that even the ships on the outward voyage were fully loaded during the entire voyage. The ships typically call at several ports to fill up for the Atlantic crossing and to discharge at the other end, during which time they are running at part-load, and in particular the part of the return journey from the EU to Morocco is probably under ballast. We shall assume that the ship is 90 % full on average during the outward trip, and 90 %*(1-0.15) = 77 % loaded on the return trip. Then on average, the ship is 83 % loaded for the round trip, which is considerably higher than the IMO estimate of 64 % average for chemical tankers. We have not added any distance to account for the intermediate stops.
105
Product tanker (50 000 t payload) This process is used to account for the direct import of ethanol from the United States. Table 74 Fuel consumption for a product tanker for ethanol transport from the United States I/O
Unit
Amount
Heavy fuel oil
Input
MJ/tkm
0.124
Distance
Output
tkm
1.00
Comments - Loading factor considered to be 55 %. - Heavy fuel oil consumption = 1.7 gHFO/tkm. - LHV heavy fuel oil = 40.5 MJ/kg. Sources 1 Frischknecht et al., 1996. 2 Specht, ZSW, personal communication, 28 May 1999.
5.3 Inland water transportation Bulk carrier barge (8 800 t payload) This process represents a barge used to carry bulk materials on inland waters. It is used for the transport of rapeseed and soy beans feedstocks. Table 75 Fuel consumption for a bulk carrier for inland navigation I/O
Unit
Amount
Source
Input
MJ/tkm
0.324
1,2
Distance
Output
tkm
1.00
CH4
Emissions Output
g/tkm
0.093
3
N2O
Output
g/tkm
0.0004
3
Diesel
Comments - Empty return trip included. - Used for rapeseed supply. - Used for soy beans supply. Sources 1 Frischknecht et al., 1996. 2 Ilgmann, 1998. 3 GEMIS v. 4.7, 2011, ship-freight-DE-domestic-2010. 106
Oil carrier barge (1 200 t payload) Used for transportation of FAME on inland waters. This process is used in the pathways of FAME from rapeseed, sunflower seeds and soy beans. Table 76 Fuel consumption for an oil carrier barge for inland navigation I/O
Unit
Amount
Source
Input
MJ/tkm
0.504
1
Distance
Output
tkm
1.00
CH4
Output
Diesel
Emissions g/tkm
0.03
1
Comments - Empty return trip included. - Used in the 'rapeseed to FAME' pathway for FAME distribution. - Used in the 'sunflower to FAME' pathway for FAME distribution. - Used in the 'soy beans to FAME' pathway for FAME distribution. Source 1 Frischknecht et al., 1996.
5.4 Rail transportation Freight train (diesel) The distance parameter is calculated as described above for the road and maritime transport, and the specific values are reported for each pathway in the following sections. The fuel consumption is reported below. Table 77 Fuel consumption for a freight train run on diesel fuel (in the United Sates) I/O
Unit
Amount
Input
MJ/tkm
0.252
Distance
Output
tkm
1.00
CH4
Output
g/tkm
0.005
N2O
Output
g/tkm
0.001
Diesel
Emissions
Comment - This process is used for the transportation of soybean. Source 1 GEMIS v. 4.9, 2014, Train-diesel-freight-CA-2010. 107
Freight train (electric) This process represents the fuel consumption for rail transportation with electric carriages. Table 78 Fuel consumption for a freight train run on grid electricity I/O
Unit
Amount
Electricity
Input
MJ/tkm
0.21
Distance
Output
tkm
1.00
Source 1 GEMIS v. 4.9, 2014, Train-el-freight-DE-2010.
5.5 Pipeline transportation Table 79 Fuel consumption for the pipeline distribution of FAME (5 km) I/O
Unit
Amount
FAME
Input
MJ/MJFAME
1.00
Electricity
Input
MJ/MJFAME
0.0002
Output
MJ
1.00
FAME
Source 1 Dautrebande, 2002.
108
References for common input data Adolfsson, R., 2005. A review of Swedish crop residue statistics used in the greenhouse gas inventory. Swedish Environmental Emission Data (SMED) Report No 65 2005. Swedish Meteorological and Hydrological Institute. Retrieved from www.smed.se. Agenzia per l´Energia Elettrica e il Gas (AEEG), 2012,'Deliberazione 29 dicembre 2011 ARG/elt 196/11'. Agricultural Lime Association (ALA), 2012, 'ALA LIME APPLICATION RECOMMENDATIONS' (http://www.aglime.org.uk/tech/ph_value_and_lime_requirements.php) accessed 15 February 2012. Ahlgrimm, H.-J., Böhme, H., Bramm, A., Dämmgen, U., Flachowsky, G., Höppner, F., Rogasik, J. und Sohler, S., 2000, Bewertung von Verfahren der ökologischen und konventionellen landwirtschaftlichen Produktion im Hinblick auf den Energieeinsatz und bestimmte Schadgasemissionen : Studie als Sondergutachten im Auftrag des Bundesministeriums für Ernährung, Landwirtschaft und Forsten, Bonn. Braunschweig: FAL, III, 206 Seiten, Landbauforschung Völkenrode - Sonderheft 211. Althaus H.-J., Chudacoff M., Hischier R., Jungbluth N., Osses M. and Primas A., 2007. Life Cycle Inventories of Chemicals. Final report ecoinvent data v2.0 No. 8. Swiss Centre for Life Cycle Inventories, Dübendorf, CH. Alvarez, C., Costantini, A., Alvarez, C. R., Alves, B. J. R., Jantalia, C. P., Martellotto, E. E., & Urquiaga, S. , 2012. Soil nitrous oxide emissions under different management practices in the semiarid region of the Argentinian Pampas. Nutrient Cycling in Agroecosystems, 94(2-3), 209–220. doi:10.1007/s10705-012-9534-9 Barak, P., 2000, 'Long-term effects of nitrogen fertilizers on soil acidity', Wisconsin Aglime & Management Conference, Univ. Wisconsin, January 2000 (http://www.soils.wisc.edu/extension/wcmc/proceedings/2A.barak.pdf) accessed 20 January 2012. Bernoux, M., Volkoff, B., Carvalho, M. da C. S and Cerri C. C., 2003, 'CO2 emissions from liming of agricultural soils in Brazil', Global Biogeochemical Cycles, 17(2) 1049. doi:10.1029/2001GB001848. Bethke, C. L. (2008). Nutritional Properties of AGROCOIR; prepared for AgroCoco, El Majo, LLC. Horticultural Soils and Nutrition Consulting, Michigan, USA. Retrieved from http://www.agrococo.com/Bethke/NUTRIENT_ANALYSIS_OF_AGROCOIR.pdf
109
Biasi, C., Lind, S. E., Pekkarinen, N. M., Huttunen, J. T., Shurpali, N. J., Hyvönen, N. P., Repo, M. E. and Martikainen, P. J., 2008, 'Direct experimental evidence for the contribution of lime to CO2 release from managed peat soil', Soil Biology and Biochemistry, 40(10) 2660–2669. Brunet, F., Potot, C., Probst, A. and Probst, J-L, 2011, 'Stable carbon isotope evidence for nitrogenous fertilizer impact on carbonate weathering in a small agricultural watershed', Rapid Communications in Mass Spectrometry, 25(19) 2682–2690. Chudziak, C., & Bauen, A., 2013. A revised default factor for the below ground nitrogen associated with soybeans. Agriculture, Ecosystems & Environment (submitted). Choo, Y. M., Muhamad, H., Hashim, Z., Subramaniam, V., Puah, C. W. and Tan, Y., 2011, 'Determination of GHG contributions by subsystem in the oil palm supply chain using the LCA Approach', International Journal of Life Cycle Assessment, (16) 669–681. Corbeels, M., Hofman, G., & Cleemput, O. Van. (2000). Nitrogen cycling associated with the decomposition of sunflower stalks and wheat straw in a Vertisol, 71–82. Dautrebande, O., 2002, 'TotalFinaElf', January 2002. De Vries, G., 2006, Roggen Anbau und Vermarktung. Hrsg: Roggenforum e.V. Department for Environment, Food and Rural Affairs (Defra), 2001, 'The British Survey of Fertiliser Practice. Fertiliser Use on Farm Crops for Crop Year 2000'. Del Pino Machado, A. S. (2005). Estimating nitrogen mineralization potential of sois and the effect of water and temperature and crop residues on nitrogen net mineralization, (PhD Thesis) (p. 186). Goettingen. Drever, J. I., 1982, The Geochemistry of Natural Waters, Prentice-Hall, Englewood Cliffs, New Jersey. 388 pp. Du Pont, 'DuPont™ Sodium Methylate', 2008 (http://www2.dupont.com/Reactive_Metals/en_US/products/sodium_methylate.html) accessed 20 December 2012. EMEP/EEA air pollutant emission inventory guidebook — 2013 - Technical report N12/2013, http://www.eea.europa.eu/publications/emep-eea-guidebook-2013 European Commission Joint Research Centre (JRC) / Netherlands Environmental Assessment Agency (PBL). (2010). Emission Database for Global Atmospheric Research (EDGAR), release version 4.1. Retrieved from http://edgar.jrc.ec.europa.eu.European Network of Transmission System Operators for Electricity (ENTSO-E), 2011, Statistical Yearbook 2010. European Network of Transmission System Operators for Electricity (ENTSO-E), 2011, Statistical Yearbook 2010, 2011.
110
Eurostat, 2012, Online data 'Supply of electricity' table code: nrg_105a. FAO (1998): World Reference Base for Soil Resources. World Soil Resources Reports 84. FAO, Rome. 88pp. (ISBN 92-5-104141-5). http://www.fao.org/docrep/w8594e/w8594e00.htm FAO/IIASA/ISRIC/ISSCAS/JRC. (2009). Harmonized World Soil Database (Version 1.1). FAO, Rome, Italy and IIASA, Laxenburg, Austria. Retrieved from http://webarchive.iiasa.ac.at/Research/LUC/External-World-soil-database/HTML/Food and Agriculture Organization of the United Nations (FAO), 2010, 'FAO N fertilizer rates (kg/ha) per crop and country' (http://www.fao.org/ag/agl/fertistat/index_en.htm) accessed 16 August 2010. Frischknecht, R. et al., 1996, Ökoinventare von Energiesystemen, 3. Auflage, Teil I, Teil 3, Teil IV, Anhang B. Eidgenössische Technische Hochschule, Gruppe Energie - Stoffe, Umwelt (ESU), Zürich, Schweiz. Projekt gefördert durch das Bundesamt für Energiewirtschaft (BEW) und den Projekt- und Studienfonds der Elektrizitätswirtschaft (PSEL), Juli 1996. Gandois, L., Perrin, A-S. and Probst, A., 2011, 'Impact of nitrogenous fertilizer-induced proton release on cultivated soils with contrasting carbonate contents: a column experiment', Geochimica et Cosmochimica Acta, 75(5) 1185–1198. Globales Emissions-Modell Integrierter Systeme (GEMIS), 2014, version 4.9, 'IINAS — About GEMIS' (http://www.iinas.org/gemis-en.html). Gregorich, E. G., Rochette, P., St-Georges, P., McKim, U. F. and Chan, C., 2008, 'Tillage effects on N2O emission from soils under corn and soybeans in eastern Canada', Canadian Journal of Soil Science, (88) 153–161. Hedden, K . Jess, A. Engler-Bunte-Institut, Universität Karlsruhe (TH), Bereich Gas, Erdöl, Kohle, 1994, Bereich Raffinerien und Ölveredelung; Studie im Rahmen des IKARUS-Projektes, Teilprojekt 4 'Umwandlungssektor'; Forschungszentrum Jülich GmbH (FZJ), Dezember 1994. Hiederer, R., Ramos, F., Capitani, C., Koeble, R., Blujdea, V., Gomez, O., Mulligan D. and Marelli, L., 2010, Biofuels: a New Methodology to Estimate GHG Emissions from Global Land Use Change, EUR 24483 EN. Luxembourg: Office for Official Publications of the European Communities, 150 pp. (Report and data sets available online at http://eusoils.jrc.ec.europa.eu/projects/RenewableEnergy/MoreData.html accessed 21 February 2012.) Hilbert, J. A., Donato, L. V., Muzio, J., and Huega, I., 2010, Comparative analysis of energy consumption and GHG emissions from the production of biodiesel from soybean under conventional and no-till farming systems. Communication to JRC and DG-TREN 09.09.2010. INTA document IIR-BC-INF-06-09 by Instituto Nacional de Technologia Agropecuaria (INTA). Hirschfeld, J., Weiß, J., Preidl, M. and Korbun, T., 2008, Klimawirkungen der Landwirtschaft in Deutschland. Schriftenreihe des Instituts fuer oekologische Wirtschaftsforschung IÖW 186/08. 111
Horowitz, J., Ebel, R. and Ueda, K., 2010, '"No-till" farming is a growing practice', USDA Economic Information Bulletin, no. 70, November 2010. ICCT, 2014. Upstream emissions of Fossil Fuel feedstocks for transport fuels consumed in the European Union. Authors: Chris Malins, Sebastian Galarza, Adam Brandt, Hassan ElHoujeiri, Gary Howorth, Tim Gabriel, Drew Kodjak. Washington D.C.: The International Council on Clean Transportation (ICCT). Report to the European Commission, DG CLIMA. Ilgmann, G., 1998, Gewinner und Verlierer einer CO2-Steuer im Güter- und Personenverk. IMO, 2009. Buhaug, Ø., Corbett, J. J., Eyring, V., Endresen, Ø., Faber, J. et al., 2009, 'Second IMO GHG Study 2009', prepared for International Maritime Organization (IMO), London, UK, April 2009. Instituto Nacional de Tecnología Agropecuaria (INTA), 2011, Actualización Técnica Nº 58 Febrero 2011 (http://inta.gob.ar/documentos/siembradirecta/at_multi_download/file?name=Siembra+Directa+2011.pdf) accessed 5 January 2013. IPCC. (2006). 2006 IPCC Guidelines for National Greenhouse Gas Inventories, Prepared by the National Greenhouse Gas Inventories Programme. (S. Eggleston, L. Buendia, K. Miwa, T. Ngara, & K. Tanabe, Eds.). IGES, Japan. Retrieved from http://www.ipccnggip.iges.or.jp/public/2006gl/index.html IPCC. (2007). Climate Change 2007, The Physical Science Basis. (S. Solomon, D. Qin, M. Manning, Z. Chen, M. Marquis, K. B. Averyt, M. Tignor, et al., Eds.) (p. 996). Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. Retrieved from http://www.ipcc.ch/publications_and_data/publications_ipcc_fourth_assessment_report_wg1_ report_the_physical_science_basis.htmIntergovernmental Panel on Climate Change (IPCC), 1997, Guidelines for National GHG Inventories. J. T. Houghton, L. G. Meira Filho, B. Lim, K. Treanton, I. Mamaty, Y. Bonduki, D. J. Griggs and B. A. Callender (eds) IPCC, Meteorological Office, Bracknell, Great Britain. International Fertilizer Industry Association (IFA), 2010, IFA fertilizer N consumption (kg) per country for the year 2000 (http://www.fertilizer.org/ifa/ifadata/search) accessed 30 September 2010. JEC (Joint Research Centre-EUCAR-CONCAWE collaboration), Well-To-Tank Report Version 4.a. JEC Well-To-Wheels Analysis. Well-To-Wheels Analysis of Future Automotive Fuels and Powertrains in the European Context, EUR 26237 EN, 2014. JRC-IET, 2014. Giuntoli J., Agostini A., Edwards R., Marelli L., 2014. Solid and gaseous bioenergy pathways: input values and GHG emissions. Calculated according to the methodology set in COM(2010) 11 and SWD(2014) 259. JRC Science and Policy Report. EUR 26696 EN. Kaltschmitt, M. and Reinhardt, G., 1997, Nachwachsende Energieträger: Grundlagen, Verfahren, ökologische Bilanzierung; Vieweg 1997; ISBN 3-528-06778-0. 112
Kempen, M. and Kraenzlein, T., 2008, 'Energy Use in Agriculture: A modeling approach to evaluate Energy Reduction Policies'. Paper prepared for presentation at the 107th EAAE Seminar 'Modelling of Agricultural and Rural Development Policies'. Sevilla, Spain, January 29th, February 1st, 2008. Knappe, F., Moehler, S., Ostermayer, A., Lazar, S. and Kaufmann, C., 2008, Vergleichende Auswertung von Stoffeintraegen in Boeden ueber verschiedene Eintragspfade. Umweltbundesamt, Text 36/08. ISSN 1862-4804. Köhler, D., Rosenbauer, G., Schwaiger, K., Wabro, R., 1996, Ganzheitliche energetische Bilanzierung der Energiebereitstellung (GaBiE) - Teil VI Energetische Untersuchung von Blockheizkraftwerken; im Auftrag Bayerische Forschungsstiftung, IAW; Forschungstelle für Energiewirtschaft (FfE); München Oktober 1996. Koopmans, A., & Koppejan, J. (1998). Regional Wood Energy Development in Asia: Agricultural and Forest Residues - Generation Utilization and Availability; presented at the Regional Consultation on Modern Applications of Biomass Energy, 6-10 January 1997, Kuala Retrieved from Lumpur, Malaysia. http://wgbis.ces.iisc.ernet.in/energy/HC270799/RWEDP/acrobat/p_residues.pdf Kraenzlein, T., 2011, 'Energy Use in Agriculture', Chapter 7.5 in CAPRI model documentation 2011. (eds: W. Britz, P. Witzke) (http://www.caprimodel.org/docs/capri_documentation_2011.pdf) accessed 3 January 2013. Larsen, H., H., 1998, Haldor Topsoe A/S, Lyngby, 'Denmark: The 2,400 MTPD Methanol Plant at Tjeldbergodden', presented to 1998 World Methanol Conference, Frankfurt, Germany, December 8-10, prepared by Anders Gedde-Dahl and Karl Jorgen Kristiansen, Statoil a/s, Tjeldbergodden, Norway. Lastauto Omnibus Katalog, (2010). Macedo, I. C., Lima Verde Leal M. R. and da Silva J. E. A .R., 2004, Assessment of greenhouse gas emissions in the production and use of fuel ethanol in Brazil, Government of the State of Sao Paulo; Geraldo Alckmin - Governor; Secretariat of the Environment José Goldemberg Secretary; April 2004. Macedo, I. C., Seabra, J. E. a., & Silva, J. E. a. R., 2008. Green house gases emissions in the production and use of ethanol from sugarcane in Brazil: The 2005/2006 averages and a prediction for 2020. Biomass and Bioenergy, 32(7), 582–595. doi:10.1016/j.biombioe.2007.12.006 Mackle S., Fertilizers Europe, 2013. Trade & economic policy outlook of the EU Nitrogen Fertilizer Industry, presentation on Fertilizers Europe website, acccessed May 2014. MacLean, H. and Spatari, S., 2009, 'The contribution of enzymes and process chemicals to the life cycle of ethanol', Environmental Research Letters, (4)014001, 10 pp. 113
Magat, S. S. (2002). Coconut (Cocos nucifera L.). Agricultural Research and Development Branch, Philippine Coconut Authority. Retrieved from http://www.fertilizer.org/ifa/content/download/8951/133688/version/1/file/coconut.pdf Mantiquilla, J. A., Canja, L. H., Margate, R. Z., & Magat, S. S. (1994). The Use of Organic Fertilizer in Coconut (A Research Note). Philippine Journal of Coconut Studies. Retrieved from www.pcrdf.org/artimages%5Carticle - fertilizer.doc Miettinen, J., Hooijer, A., Tollenaa, D., Page, S., Malins, Ch., Vernimmen, R., Shi, Ch. and Liew, S. C., Historical Analysis and Projection of Oil Palm Plantation Expansion on Peatland in Southeast Asia, White Paper Number 17, February 2012. Indirect Effects of Biofuel Production. Copyright: International Council on Clean Transportation 2012. Monfreda, Ch.; Ramankutty, N. and Jonathan A. Foley, J. A., 2008, 'Farming the planet: 2. Geographic distribution of crop areas, yields, physiological types, and net primary production in the year 2000', Global Biogeochemical Cycles, (22) 1–19. Oh, N-H. and Raymond P., 2006, 'Contribution of agricultural liming to riverine bicarbonate export and CO2 sequestration in the Ohio River basin', Global Biogeochemical Cycles, 20 p. GB3102. Pehnt, M., 2002, Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR), Institut für Thermodynamik, Stuttgart: Ganzheitliche Bilanzierung von Brennstoffzellen in der Energieund Verkehrstechnik, VDI Verlag, Düsseldorf 2002; ISSN 0178-9414; ISBN 3-18-347606-1. Perrin, A.-S., Probst, A., and Probst, J.-L., 2008, Impact of nitrogenous fertilizers on carbonate dissolution in small agricultural catchments: Implications for weathering CO2 uptake at regional and global scales, Geochimica et Cosmochimica Acta, 72 (13) 3105–3123. Powlson, D. S., Whitmore, A. P, and Goulding, K. W. T, 2011, 'Soil carbon sequestration to mitigate climate change: a critical re-examination to identify the true and the false' European Journal of Soil Science, (62) 42-55. Punter, G., Rickeard, D., Larivé, J-F., Edwards, R., Mortimer, N., Horne, R., Bauen, A., Woods, J., 2004, Well-to-Wheel Evaluation for Production of Ethanol from Wheat, A Report by the LowCVP Fuels Working Group, WTW Sub-Group; FWG-P-04-024; October 2004. Rochette, P., 2008, 'No-till only increases N2O emissions in poorly-areated soils', Soil and Tillage Research, (101) 97-100. Scarlat, N., Martinov, M., & Dallemand, J.-F. (2010). Assessment of the availability of agricultural crop residues in the European Union: potential and limitations for bioenergy use. Waste management (New York, N.Y.), 30(10), 1889–97. doi:10.1016/j.wasman.2010.04.016 Schmidt, J. H., 2007. Life cycle assessment of rapeseed oil and palm oil. Part 3: Life cycle inventory of rapeseed oil and palm oil. Aalborg University. Retrieved from http://vbn.aau.dk/files/10388016/inventory_report 114
Scholz, W., 1992, Verfahren zur großtechnischen Erzeugung von Wasserstoff und ihre Umweltproblematik, Berichte aus Technik und Wissenschaft 67/1992, Linde; S. 13–21. Schulte P., Van Geldern R., Freitag H., Karim A., Negrel P., Petelet−Giraud E., Probst A., Probst J. L., Telmer K., Veizer J. and Barth J. A. C., 2011, 'Applications of stable water and carbon isotopes in watershed research: weathering, carbon cycling and water balances', Earth−Science Review, (109) 20−31. Seeger Engineering AG; 2009; http://www.seeger.ag/images/stories/downloads/projektbeschreibungen_en.pdf Semhi, K., Amiotte Suchet, P., Clauer, N., Probst, J. L., 2000, 'Impact of nitrogen fertilizers on the natural weathering-erosion processes and fluvial transport in the Garonne basin', Applied Geochemistry, (15) 865–878 Sheil, D., Casson, A., Meijaard, E., van Nordwijk, M. Gaskell, J., Sunderland-Groves, J., Wertz, K., & Kanninen, M., 2009, 'The impacts and opportunities of oil palm in Southeast Asia: What do we know and what do we need to know?' Occasional paper no. 51, (Bogor: CIFOR). Singh, G. (ed.), 2010, The soybean: botany, production and uses, Copyright CAB International 2010, Oxfordshire, UK. ISBN 978-1-84593-644-0. Sinistore, J. C. and Bland, W. L., 2010, 'Life-Cycle Analysis of Corn Ethanol Production in the Wisconsin Context', Biological Engineering 2(3) 147–163. Stehfest, E. and Bouwman, A. F., 2006, 'N2O and NO emissions from agricultural fields and soils under natural vegetation: summarizing available measurement data and modeling of global annual emissions', Nutrient Cycling in Agroecosystems, V74 (3): 207-228 (http://dx.doi.org/10.1007/s10705-006-9000-7) N2O and NO emission data set available under (http://www.pbl.nl/en/publications/2006/N2OAndNOEmissionFromAgriculturalFieldsAndSoilsU nderNaturalVegetation.html) accessed 5 January 2013. Sustainability of Bioenergy [BioS], 2012, 'Input data relevant to calculating default GHG emissions from biofuels according to RE Directive Methodology' (http://re.jrc.ec.europa.eu/biof/html/input_data_ghg.htm) accessed 20 December 2012. Umweltbundesamt (UBA), 1999, Kraus, K., Niklas, G., Tappe, M., 'Umweltbundesamt, Deutschland: Aktuelle Bewertung des Einsatzes von Rapsöl/RME im Vergleich zu DK; Texte 79/99', ISSN 0722-186X. UNICA – União da Agroindústria Canavieira do Estado de São Paulo, 2005. Sugar Cane’s Energy – Twelve studies on Brasilian sugar cane agribusiness and its sustainability (2nd editio.). Berlendis Editores Ltda., Brazil. Retrieved from http://sugarcane.org/resourcelibrary/books/Sugar Canes Energy - Full book.pdf
115
United Nations Framework Convention on Climate Change (UNFCCC), 'CRF country submissions 2008' (http://unfccc.int/national_reports/annex_i_ghg_inventories/national_inventories_submissions /items/4303.php) accessed 17 April 2012. United States Department of Agriculture (USDA), 2009, ‘Manure use for fertilizer and for energy: Report to Congress’ (http://www.ers.usda.gov/publications/ap-administrativepublication/ap-037.aspx). Wahid, O., Nordiana, A. A., Tarmizi, A. M., Haniff, M. H., & Kushairi, A. D. 2010, 'Mapping of oil palm cultivation in peatland in Malaysia' MPOB information series, MPOB TT no. 473. West, T. O. and McBride, A. C., 2005, 'The contribution of agricultural lime to carbon dioxide emissions in the United States: dissolution, transport, and net emissions', Agriculture, Ecosystems and Environment, 108(2) 145–154.
116
Part Two — Liquid biofuels processes and input data
117
6. Biofuels processes and input data List of liquid biofuels pathways
Ethanol pathways – 1st generation 1 2 3 4 5 6 7
Wheat to ethanol Maize to ethanol Sugar beet to ethanol Barley to ethanol Sugar cane to ethanol Rye to ethanol Triticale to ethanol
Biodiesel pathways – 1st generation 8 9 10 11
Rapeseed to biodiesel Sunflower to biodiesel Soya oil to biodiesel Palm oil to biodiesel
12 13 14
Jatropha oil to biodiesel Waste cooking oil to biodiesel Animal fat
15
Hydrotreated Vegetable Oil processing (HVO)
Second generation pathways 16 17 18 19 20
Black liquor gasification process Forest Residues to Synthetic diesel Forest Residues to Methanol Forest Residues to DME Wheat straw to ethanol
118
Note on Yields There is no one yield fixed for a crop. We are usually obliged to mix data from various sources, all of which have different yields. The data are all combined on the basis of input per tonne (or MJ) of crop. For the most important inputs: N fertilizer and data (diesel, pesticides...) taken from the CAPRI database of EU agriculture, we convert inputs-per-ha to inputs per tonne (or MJ) using the average of 2010 and 2011 yields from FAO.
Why Camelina is not included in this report Camelina is a flowering plant of the family of the Brassicaceae (like broccoli, cauliflower and rapeseed). It is traditionally grown on marginal land and can be planted as a rotation crop for wheat, in the 'fallow' period, so it is a promising sustainable alternative energy crop. Historically, Camelina has been used as a crop for animal feed and vegetable oil in northern Europe and in the Russian–Ukrainian area from the Neolithic period to the years from 1930 to 1940. Unfortunately, Camelina is no longer cultivated in relevant quantities, and there is no established market for either Camelina seeds or Camelina oil. Consequently, reliable technical and market data are missing and we do not have a database large enough to propose a default pathway for Camelina. We have studied an experimental Camelina pathway based on the (scarce) bibliography available, involving test cultivations performed in the northern states of the United States, seed crushing in the central United States, transport of Camelina oil from the United States to the EU, and (by HVO process) production of jet fuel in Europe. However, these values refer only to pilot-stage projects, not to large-scale productions, and the hypotheses of delivering Camelina produced in Montana, United States, to Europe is quite unrealistic, from a market perspective. In fact, American stakeholders are strongly interested in Camelina jet fuel; it is unlikely that the rising American Camelina market will supply European needs, because of the very high internal demand and very low production. From a European market perspective, it should be much more interesting to build a Camelina pathway on cultivation data referring to the following (suitable) production areas: Romania, Spain, northern Europe, Russia and the former Soviet Union areas. Unfortunately, there are no data on Camelina cultivation from these countries.
119
Update of lower heating values in biodiesel pathways LHV of crude and refined vegetable oils Refined vegetable oils have a measured LHV of about 37.0 MJ/kg (Mehta and Anand, 2009). According to the ECN Phyllis ( 40) database of biomaterials, the LHV of refined vegetable oil is 37.2, and of crude vegetable oils, 36.0. The JRC considers that crude vegetable oils used for biodiesel do not differ greatly from refined vegetable oil LHV (as discussed below), so for simplicity, we assumed that the LHV of crude vegetable oils is also 37 MJ/kg. DIESTER ( 41)/EBB state that refining vegetable oil removes about 2.5 % of the mass (so the raw oil is on average above the minimum specification below), but all the compounds removed (except moisture) have an LHV fairly similar to (maybe up to ~20 % lower than) oil. Moisture content is < 0.5 % in the FEDIOL raw rapeseed specification, so the raw oil LHV cannot possibly be more than (0.5 %+2 %*0.2)*37 = 0.3 MJ/kg lower than that of the refined oil: it must be > 36.7 MJ/kg. FEDIOL specifications for crude rapeseed oil - Free fatty acids (as oleic): Max 2.00 %. - Moisture content, Volatile Matter and Impurities: Max 0.50 %. - Lecithin gum (expressed as Phosphorus): Max 750ppm = ~2 % by weight of C43H88NO9P. - Erucic acid (a fatty acid): Max 2.00 %. Chemicals removed in refining DIESTER informed the JRC that the refining for biodiesel consists of the following. - Neutralising (and removing) fatty-acids and lecithin (similar or slightly lower LHV than oil). - Removing any water associated with these. - Removing gums (slightly lower LHV than oil). - For sunflower: removing wax (winterisation = cooling and centrifuging). Wax has similar LHV to oil (the CH2 chains are merely longer) Density of vegetable oils The density of refined vegetable oils (Noureddini et al., 1992; Dorfman, 2000) at 20C is around 0.92 kg/litre. Discussion: according to Noureddini et al. (1992), the density of rapeseed is particularly low, at ~.910; palm's is highest at ~0.924, whilst soybean, corn and sunflower are ~0.922). The density of crude vegetable oils at 20C (CODEX STANDARD 2101999) is not significantly different from this: - crude rapeseed 0.9145 +/- 0.0045; - crude soy 0.920 +/- 0.005; (40) Energy research Centre of the Netherlands (ECN): see http://www.ecn.nl/phyllis/ online. (41) Diester Industrie: see http://www.sofiproteol.com online.
120
-
crude palm 0.925 +/- 0.003, (40C data corrected to 20C using expansion coefficient in Noureddini et al. (1992); crude sunflower 0.9205 +/- 0.0025.
Sources 1 Mehta and Anand, 2009. 2 Noureddini, et al., 1992. 3 Dorfman, 2000. 4 CODEX standard for named vegetable oils. CODEX (http://www.codexalimentarius.org) accessed January 2013.
STAN
210-1999
Calculation of consistent LHVs for by-products: DDGS from ethanol production and Oilseed meals from oil pressing We have relatively reliable data on the lower-heating-values LHV of crops, because we can compare measured data with LHVs calculated from the composition of the crop, which is avaiable from several sources. However, measurements of the LHV of meals and DDGS are much more rare, and furthermore, they have a large range of composition, depending on the efficiency of oil extraction or the composition of the cereal. It is important that we make the LHV of the byproducts consistent with the process yield and the LHV of the crop (and product). The oil crushing or fermentation hardly changes the heat content of the components, because even the fermentation reaction has almost zero enthalpy change. Therefore, we calculate the LHV of the dry-matter part of the by-products (DDGS or oilseed cake) by balancing the LHV of the crop going in and the products coming out. Average cereal pathway An average cereal pathwayhas been calculated for the mix of cereals in EU ethanol production. However, there are different pathways for different sources of heat used in the conversion plant, and different pathways for different ethanol transportation distances (i.e. importing ethanol from beyond 500km). Differences in transportation only account for approximately 3gCO2/MJ of biofuel. Thus, country-specific data for cultivation and processing is not considered as it is the source of process heat and not the geographical location that is the most important variable. Data on the contribution of different cereals to the EU ethanol feedstock extracted from the "biofuels baseline 2008" produced by Ecofys (2011) are shown in Table 80. These data were used in the JRC calculation of the average cereals pathway.
121
Table 80 Cereal share of ethanol feedstock in the EU (Ecofys, 2011) % ethanol feedstock \ cereal crop EU 2007 EU 2008 EU 2009 Average average % of cereals ethanol
Wheat 30% 23% 30% 27%
Maize 7% 18% 23% 21%
Barley 7% 3% 4% 4%
Rye 6% 5% 5% 5%
Triticale 1% 1% 1% 1%
47%
36%
6%
9%
2%
Source Ecofys, 2011. (Ecofys, Agra CEAS, Chalmers University, IIASA and Winrock). Biofuels Baseline 2008: www.ecofys.com/en/publication/biofuels-baseline-2008/
122
6.1 Wheat grain to ethanol Description of pathway The following processes are included in the 'wheat grain to ethanol' pathway.
The data for each process are shown below; significant updates are described in more detail with relevant references. Step 1: Wheat cultivation The new data for wheat cultivation are shown in Table 81. The updated data include the following: • • • • • •
diesel and pesticide use in wheat cultivation updated using data from CAPRI (see Section 2.5); CaCO3 fertilizer use calculated by the JRC (see Section 3.10); N2O emissions calculated by JRC data using the JRC GNOC model (see Section 3.7); CO2 emissions from neutralisation of other soil acidity calculated by the JRC (see Section 3.10); K2O and P2O5 updated using the most recent data available; straw allocation has been removed.
In the following table, source numbers in bold represent the main data source; additional references are used to convert data to MJ per MJ of crop. 123
Table 81 Cultivation of wheat I/O
Unit
Amount
Source
Comment
Diesel
Input
MJ/MJwheat
0.04014
3, 5, 6, 7
See CAPRI data
N fertilizer
Input
kg/MJwheat
0.00134
2, 3
See GNOC data
CaCO3 fertilizer
Input
kg/MJwheat
0.00257
8
See liming data
K2O fertilizer
Input
kg/MJwheat
0.00021
3, 4
P2O5 fertilizer
Input
kg/MJwheat
0.00025
3, 4
3.1 kg K2O/tonne moist crop 3.7 kg P2O5/tonne moist crop
Pesticides
Input
kg/MJwheat
0.00007
3, 5, 6, 7
See CAPRI data
Seeding material
Input
kg/MJwheat
0.00157
1, 3, 4
120 kg/(ha*yr)
Wheat
Output
MJ
1.0000
Field N2O emissions
g/MJwheat
0.044
2
See GNOC data
CO2 from neutralisation of other soil acidity
g/MJwheat
0.719
8
See liming data
Comments - LHV (dry crop) = 17.0 MJ/kg dry wheat grain (Ref. 3). - 13.5 % water content (Ref. 5). Sources 1 Gover et al., 1996. 2 Edwards and Koeble, 2012 (see Chapter 3). 3 Kaltschmitt and Hartmann, 2001. 4 Fertilizers Europe, received by JRC in 2013. 5 CAPRI database, 2012 converted to JEC format using information in Refs 6 and 7. 6 Kraenzlein, 2011. 7 Kempen and Kraenzlein, 2008. 8 JRC: Acidification and liming data (see Section 3.10).
124
Step 2: Drying of wheat grain A new process for drying wheat grain, derived from CAPRI data (see Section 2.5), has been added. The data are shown in Table 82. Table 82 Drying of wheat grain I/O
Unit
Amount
Source
Light heating oil
Input
MJ/MJwheat
0.00150
1, 4
Natural gas
Input
MJ/MJwheat
0.00150
1, 4
Electricity
Input
MJ/MJwheat
0.000251
1, 4
Wheat
Input
MJ/MJwheat
1.0000
Wheat
Output
MJ
1.0000
Comments - 1.02%: average % of water removed to reach traded water content, according to CAPRI data (see Section 2.5). - 2.16 MJ heating oil/tonne of crop at traded water content for 0.1% drying*. - 2.16 MJ NG/tonne of crop at raded water content for 0.1% drying*. - 0.36 MJ electricity/tonne of crop at raded water content for 0.1% drying**. * [UBA 1999] reports that 0.1% drying of grains needs 1.2 kWh= 4.32 MJ of heating oil per tonne of grain. Ecoinvent (ref 5) propose 5 MJ heating oil is needed per kg water evaporated (~0.1% in 1 tonne grain), on the basis of a survey of European literature. UBA data on total MJ heating fuel will be considered, assuming that half comes from NG and half from light heating oil, on the basis of discussions with national experts. Also LPG is used, but this is an intermediate case. ** For electricity, [UBA 1999] reports 0.1% drying of grains needs 0.1 kWh= 0.36MJ per tonne of grain. Ecoinvent (ref 5) reports a higher value (about 1kWh = 3.6 MJ electricity) perhaps including electricity for handling and storage. UBA data has been considered. Sources 1 CAPRI database, 2012, converted to JEC format using information in Refs 2 and 3. 2 Kraenzlein, 2011. 3 Kempen and Kraenzlein, 2008. 4 UBA, 1999. 5 Nemecek and Kägi, 2007.
125
Step 3: Handling and storage of wheat grain Table 83 Handling and storage of wheat grain I/O
Unit
Amount
Source
Wheat
Input
MJ/MJwheat
1.0081
2
Electricity
Input
MJ/MJwheat
0.00039
1
Wheat
Output
MJ
1.0000
Comment - UBA proposes 12.6 kWh electricity per tonne of grain for ventilation during storage of rapeseed. For wheat, Kenkel 2009 reports average of 19 kWh/tonne for Oklahoma, and Kaltschmitt, 1997 only 1.6kWh/tonne. Data from Kaltscmitt 1997 has been used. Sources 1 Kaltschmitt and Reinhardt, 1997. 2 Kenkel, 2009. Step 4: Transportation of wheat grain Table 84 Transport of wheat grain via 40 t truck (payload 27 t) over a distance of 100 km (one way) I/O
Unit
Amount
Distance
Input
tkm/MJwheat
0.0068
Wheat
Input
MJ/MJwheat
1.0100
Wheat
Output
MJ
1.0000
Comment - Fuel consumption for a 40 t truck is reported in Table 63. Source 1 Kaltschmitt and Hartmann, 2001.
126
Step 5: Conversion of wheat grain to ethanol Table 85 Conversion of wheat grain to ethanol I/O
Unit
Amount
Source
Comment
Wheat
Input
MJ/MJethanol
1.8283
3, 4, 5
3.333 twheat grain @ 13.5 % H2O/tethanol
Electricity
Input
MJ/MJethanol
0.0541
2, 4, 5
1.45 GJ/tethanol
Steam
Input
MJ/MJethanol
0.3637
2, 4, 5
9.75 GJ/tethanol
NH3
Input
kg/MJethanol
0.000226
4, 5, 6
2.1 kg/dry t of wheat grain
NaOH
Input
kg/MJethanol
0.000559
4, 5, 6
5.2 kg/dry t of wheat grain
CaO
Input
kg/MJethanol
0.000129
4, 5, 6
1.2 kg/dry t of wheat grain
Alpha-amylase
Input
kg/MJethanol
0.000086
4, 5, 6
0.8 kg/dry t of wheat grain
Glyco-amylase
Input
kg/MJethanol
0.000118
4, 5, 6
1.1 kg/dry t of wheat grain
Ethanol
Output
MJ
1.0000
Comments - 0.374 kg DDGS (at 10% water) / kg wheat (at 13.5% water) (Ref. 4). - 0.300 kg ethanol / kg wheat (at 13.5 % water) (Ref. 4). - LHV DDGS (dry) = 18.75 MJ/kg. Sources 1 Kaltschmitt and Hartmann, 2001. 2 Punter et al., 2004. 3 Kaltschmitt and Reinhardt, 1997. 4 Lywood, W., ENSUS plc, personal communication with Edwards R., JRC, Ispra, 3 December 2010. 5 Hartmann, 1995. 6 MacLean and Spatari, 2009.
Step 5.1: Ethanol plant generation processes Woodchip-fuelled plant generation has been added. The data for the individual plant generation processes are shown in Chapter 4. The processes linked to wheat ethanol are: • • • •
steam generation (Table 57) NG CHP (Table 58) lignite CHP (Table 59) woodchip–fuelled CHP (Table 60)
127
Step 6: Transportation of ethanol to the blending depot The same transport mix used in ‘rapeseed to biodiesel’ has been added but excluding pipeline transport as it is unlikely that ethanol would be transported in this manner. Table 86 Transportation of ethanol summary table to the blending depot Share
Transporter
notes
Distance (km one way)
13.2 %
Truck
Payload 40 t
305
31.6 %
Product tanker
Payload: 15 000 t
1 118
50.8 %
Inland ship/barge
Payload 1 200t
153
4.4 %
Train
381
Table 87 Transport of ethanol to depot via 40 t truck over a distance of 305 km (one way) I/O
Unit
Amount
Distance
Input
tkm/MJethanol
0.0123
Ethanol
Input
MJ/MJethanol
1.0000
Ethanol
Output
MJ
1.0000
Comments - For the fuel consumption of a 40 t truck, see Table 63. - LHV (ethanol) = 26.8 MJ/kg. Table 88 Maritime transport of ethanol over a distance of 1 118 km (one way) I/O
Unit
Amount
Distance
Input
tkm/MJethanol
0.0417
Ethanol
Input
MJ/MJethanol
1.0000
Ethanol
Output
MJ
1.0000
Comment - For the fuel consumption of the product tanker (payload: 15,000 t), see Table 72. Table 89 Transport of ethanol over a distance of 153 km via inland ship (one way) I/O
Unit
Amount
Distance
Input
tkm/MJethanol
0.0057
Ethanol
Input
MJ/MJethanol
1.0000
Ethanol
Output
MJ
1.0000
128
Comment - For the fuel consumption for an inland oil carrier, see Table 76. Table 90 Transport of ethanol over a distance of 381 km via train (one way) I/O
Unit
Amount
Distance
Input
tkm/MJethanol
0.0102
Ethanol
Input
MJ/MJethanol
1.0000
Ethanol
Output
MJ
1.0000
Comments - For the fuel consumption of the freight train, see Table 78.
Step 7: Ethanol depot distribution inputs Table 91 Ethanol depot I/O
Unit
Amount
Ethanol
Input
MJ/MJethanol
1.00000
Electricity
Input
MJ/MJethanol
0.00084
Ethanol
Output
MJ
1.00000
Table 92 Transport of ethanol to depot via 40 t truck over a distance of 150 km (one way) I/O
Unit
Amount
Distance
Input
tkm/MJethanol
0.0060
Ethanol
Input
MJ/MJethanol
1.0000
Ethanol
Output
MJ
1.0000
Table 93 Ethanol filling station I/O
Unit
Amount
Ethanol
Input
MJ/MJethanol
1.0000
Electricity
Input
MJ/MJethanol
0.0034
Output
MJ
1.0000
Ethanol
Comment - Distribution is assumed to be same as for fossil diesel and gasoline. Source 1 Dautrebande, 2002. 129
6.2 Maize to ethanol Description of pathway The following processes are included in the 'maize to ethanol' pathway.
The data for each process of the'maize to ethanol' pathway are shown below, and significant updates are described in more detail with relevant references. Step 1: Maize cultivation in the EU The new data for maize cultivation are shown in Table 94. The updated data include: • • • • • •
updated diesel and pesticide use in EU maize cultivation, using data from CAPRI (see Section 2.5); CaCO3 fertilizer use calculated by the JRC (see Section 3.10); N2O emissions calculated with JRC data using the JRC GNOC model (see Section 3.7); CO2 emissions from neutralisation of other soil acidity calculated by the JRC (see Section 3.10); K2O and P2O5 updated using the most recent data available; the removal of straw.
In the following table, source numbers in bold represent the main data source; additional references are used to convert data to MJ/MJ crop. 130
Table 94 Cultivation of maize in the EU I/O
Unit
Amount
Source
Comment
Diesel
Input
MJ/MJmaize
0.03037
6, 7, 8, 9
See CAPRI data
N fertilizer
Input
kg/MJmaize
0.001321
2, 4, 5, 6
See GNOC data
CaCO3 fertilizer
Input
kg/MJmaize
0.001254
1
See liming data
K2O fertilizer
Input
kg/MJmaize
0.000373
2, 3, 4, 5
P2O5 fertilizer
Input
kg/MJmaize
0.000381
2, 3, 4, 5
Pesticides
Input
kg/MJmaize
0.000064
5, 7, 8, 9
Seeding material
Input
kg/MJmaize
0.000078
3
Maize
Output
MJ
1.0000
Field N2O emissions
g/MJmaize
0.055
6
See GNOC data
CO2 from neutralisation of other soil acidity
g/MJmaize
0.343
1
See liming data
5.5 kg K2O/tonne moist crop 5.6 kg P2O5/tonne moist crop 7.0 kg/(ha*yr) @ 14 % crop moisture 0.0012 tonnes seed/tonne maize production
Comments - LHV (dry crop) = 17.3 MJ/kg dry maize (Ref. 5). - 14 % crop moisture content (Ref. 5). Sources 1 JRC: Acidification and liming data (see Section 3.10). 2 International Fertilizer Association (IFA), 2013. 3 FAOstat data. 4 JRC calculation of the LHV of corn, 22 March 2011. 5 KTBL, 2006. 6 Edwards and Koeble, 2012 (see Chapter 3). 7 CAPRI database, 2012 converted to JEC format using information in Refs 8 and 9. 8 Kranzlein, 2011. 9 Kempen and Kraenzlein, 2008.
Step 2: Drying of maize A new process for drying the maize derived from CAPRI data (see Section 2.5) has been added. The data are shown in Table 95.
131
Table 95 Drying of maize I/O
Unit
Amount
Source
Light heating oil
Input
MJ/MJmaize
0.01536
1, 4
Natural gas
Input
MJ/MJmaize
0.01536
1, 4
Electricity
Input
MJ/MJmaize
0.002560
1, 4
Maize
Input
MJ/MJmaize
1.0000
Maize
Output
MJ
1.0000
Comments: - 10.58%: average % of water removed to reach traded water content, according to CAPRI data (see Section 2.5). - 2.16 MJ heating oil/tonne of crop at traded water content for 0.1% drying*. - 2.16 MJ NG/tonne of crop at raded water content for 0.1% drying*. - 0.36 MJ electricity/tonne of crop at raded water content for 0.1% drying**. * [UBA 1999] reports that 0.1% drying of grains needs 1.2 kWh= 4.32 MJ of heating oil per tonne of grain. Ecoinvent (ref 5) propose 5 MJ heating oil is needed per kg water evaporated (~0.1% in 1 tonne grain), on the basis of a survey of European literature. UBA data on total MJ heating fuel will be considered, assuming that half comes from NG and half from light heating oil, on the basis of discussions with national experts. Also LPG is used, but this is an intermediate case. ** For electricity, [UBA 1999] reports 0.1% drying of grains needs 0.1 kWh= 0.36MJ per tonne of grain. Ecoinvent (ref 5) reports a higher value (about 1kWh = 3.6 MJ electricity) perhaps including electricity for handling and storage. UBA data has been considered. Sources 1 CAPRI database, 2012, converted to JEC format using information in Refs 2 and 3. 2 Kraenzlein, 2011. 3 Kempen and Kraenzlein, 2008. 4 UBA, 1999. 5 Nemecek and Kägi, 2007.
132
Step 3: Handling and storage of maize A new process for handling and storage of maize has been added. The data are shown in Table 96. Table 96 Handling and storage of maize I/O
Unit
Amount
Source
Maize
Input
MJ/MJmaize
1.0081
2
Electricity
Input
MJ/MJmaize
0.00039
1
Maize
Output
MJ
1.0000
Comment - UBA proposes 12.6 kWh electricity per tonne of grain for ventilation during storage of rapeseed. For wheat, Kenkel 2009 reports average of 19 kWh/tonne for Oklahoma, and Kaltschmitt, 1997 only 1.6kWh/tonne. Data from Kaltscmitt 1997 has been used. Sources 1 Kaltschmitt and Reinhardt, 1997. 2 Kenkel, 2009.
Step 4: Transportation of maize Table 97 Transport of maize via a 40 t truck over a distance of 100 km (one way) I/O
Unit
Amount
Distance
Input
tkm/MJcorn
0.0067
Maize
Input
MJ/MJcorn
1.0100
Maize
Output
MJ
1.0000
Comment - For the fuel consumption of a 40 t truck, see Table 63.
133
Step 5: Conversion of maize to ethanol Table 98 Conversion of maize to ethanol in EU I/O
Unit
Amount
Source
Comment
Maize
Input
MJ/MJethanol
1.6487
1, 2, 5
2.8 gal ethanol/bu
Electricity
Input
MJ/MJethanol
0.0485
1, 2, 5
1.08 kWhe/(gal ethanol)
Steam
Input
MJ/MJethanol
0.4021
1, 2, 5
30714 Btu/(gal ethanol)
NH3
Input
MJ/MJethanol
0.000200
1, 2, 3, 5
2.1 kg/dry t of maize [4]
NaOH
Input
kg/MJethanol
0.000496
1, 2, 3, 5
5.2 kg/dry t of maize [4]
CaO
Input
kg/MJethanol
0.000114
1, 2, 3, 5
1.2 kg/dry t of maize [4]
Alpha-amylase
Input
kg/MJethanol
0.000076
1, 2, 3, 5
0.8 kg/dry t of maize [4]
Glyco-amylase
Input
kg/MJethanol
0.000105
1, 2, 3, 5
1.1 kg/dry t of maize [4]
Ethanol
Output
MJ
1.000
Comments - 0.711 kg moist DDGS/(l ethanol) (Ref 1). - LHV DDGS (dry) = 20.15 MJ/kg (calculated from energy balance). Sources 1 California GREET, CARB, 2009. 2 JRC calculation from composition in NRC, 2001 and LHV values in ECN Phyllis database, 22 March 2011. 3 Hartmann, 1995. 4 MacLean and Spatari, 2009. 5 KTBL, 2006. Step 5.1: Ethanol plant generation processes The data for the individual plant generation processes are shown in Chapter 4. The processes linked to maize ethanol are: • steam generation (Table 57) • NG CHP (Table 58) • Coal CHP (Table 59) • woodchip–fuelled CHP (Table 60).
134
Step 6: Transportation of ethanol to the blending depot Transportation of ethanol produced in the EU is the same as for wheat ethanol. However, transportation of cereals-ethanol from beyond 1500km is calculated using the following data: For corn ethanol transport in the United States, a November 2011 article in the Ethanol Producer Magazine titled 'By train, by truck or by boat: how ethanol moves and where it is going', reports: '70 per cent of all U.S. ethanol is hauled by rail, 20 per cent by truck and 10 per cent by barge. But, while rail is the undisputed ruler of U.S. ethanol transport, bottlenecks and tank car shortages have led to many headaches over the past year that will likely continue for some time.' A presentation by Eco-Energy (an ethanol marketing firm) shows the percentages of U.S. ethanol by port in 2011 (42 %, Houston-Galvestone, TX and 27 % New York, NY). Table 99 Transportation of ethanol summary table Transporter
Distance (km)
Description
Train
1 000
Indiana to Baltimore
Ocean bulk carrier (50 000t)
6 800
Baltimore to Rotterdam
Detailed transportation data: Table 100 Transport of ethanol via train over a distance of 1 000 km (Indiana to Baltimore) I/O
Unit
Amount
Distance
Input
tkm/MJethanol
0.0373
Ethanol
Input
MJ/MJethanol
1.0000
Ethanol
Output
MJ
1.0000
Comment - For the fuel consumption for a freight train run on diesel fuel (in the United States), see Table 77. Table 101 Transport of ethanol via ship (payload 50 000 t) over a distance of 6 800 km (Baltimore to Rotterdam) I/O
Unit
Amount
Distance
Input
tkm/MJethanol
0.2536
Ethanol
Input
MJ/MJethanol
1.0000
Ethanol
Output
MJ
1.0000
Comment - For the fuel consumption for a product tanker (50 000 t payload), see Table 74. 135
Step 7: Ethanol depot distribution inputs The same data are used as for wheat ethanol.
136
6.3 Sugar beet to ethanol Description of pathway The following processes are included in the 'sugar beet to ethanol' pathway:
The data for each process are shown below; significant updates are described in more detail with relevant references. Step 1: Sugar beet cultivation The new data for sugar beet cultivation are shown in Table 102. The updated data include: -
updated diesel and pesticide use in sugar beet cultivation, using data from CAPRI (see Section 2.5); CaCO3 fertilizer use calculated by the JRC (see Section 3.10); N2O emissions calculated by JRC data using the JRC GNOC model (see Section 3.7); CO2 from neutralisation of other soil acidity, calculated by the JRC (see Section 3.10). K2O and P2O5 updated using the most recent data available. Sugar beet seed figure and average equivalent yield at nominal 16% sugar updated using new available data.
In the following table, source numbers in bold represent the main data source, additional references are used to convert data to MJ/MJ crop.
137
Table 102 Sugar beet cultivation I/O
Unit
Amount
Source
Source data
Diesel
Input
MJ/MJsugar beet
0.01050
1, 4, 5, 6
See CAPRI DATA
N fertilizer
Input
kg/MJsugar beet
0.000337
2
See GNOC data
CaCO3 fertilizer
Input
kg/MJsugar beet
0.001085
7
See liming data
K2O fertilizer
Input
kg/MJsugar beet
0.00030
3
P2O5 fertilizer
Input
kg/MJsugar beet
0.00017
3
Pesticides
Input
kg/MJsugar beet
0.0000548
1, 4, 5, 6
See CAPRI data
Seeding material
Input
kg/MJsugar beet
0.0000109
9, 10, 11
3.6 kg/(ha*yr)
Sugar beet
Output
MJ
1.0000
Field N2O emissions
g/MJsugar beet
0.0121
2
See GNOC data
CO2 from neutralisation of other soil acidity
g/MJsugar beet
0.3084
7
See liming data
1.2 kg K2O/tonne wet sugar beet 0.7 kg P2O5/tonne wet sugar beet
Comments - LHV = 16.3 MJ/kg dry sugar beet. - Water content = 75 % and a sugar content of 16 %. - 80.76 t/ha average yield in sugar beet ethanol EU countries, at nominal 16% sugar, excluding tops and soil (Refs 8, 9). Sources 1 Dreier et al., 1998. 2 Edwards and Koeble, 2012 (see Chapter 3). 3 Fertilizers Europe, received by JRC in 2013. 4 CAPRI database, energy use data extracted by Markus Kempen of Bonn University, March 2012, converted to JEC format using information in Refs 5 and 6. 5 Kraenzlein, 2011. 6 Kempen and Kraenzlein, 2008. 7 JRC: Acidification and liming data (Section 3.10). 8 European Sugar Industry Association, 2013. 9 CGB and CIBE, 2013. French Confederation of Sugar Beet producers and Confederation Internationale des Betteravies Europeans, response to Commission stakeholder meeting in Brussel, May 2013, received by JRC in June 2013. 10 Rudelsheim et al., 2012. 11 British Beet Research Organisation, 2011.
138
ADDITIONAL COMMENTS: Sugar beet seed figure changed to 3.6 kg/hectare. Figures describe coated seeds, and are based on information from Rudelsheim and Smets (2012), and the British Beet Research Organisation (Spring 2011 bulletin). Average EU sugar beet yield data from FAO was 69.21 tonnes per hectare. However, there is no trade in sugar beet, so it must practically always be grown in the same country as the ethanol factory. Therefore it is appropriate to consider only the yields where it is used for ethanol production. Furthermore, as our processing data is for sugar beet with nominal 16% sugar, we need the average equivalent yield at nominal 16% sugar for countries making sugar beet ethanol. The data used was sourced from Confederation Internationale des Betteravies Europeans (CIBE, 2013). Yield includes sugar beet tops, not normally used in the sugar production process, which are typically used in the ethanol production process. Yields are an average of the 5 years from 2007 to 2012. Comparison to Ademe (2010) JRC figures: 0.0777t ethanol is produced per tonne sugar beet at 16% sugar content. JRC data says 0.486 tonnes of ethanol are produced from one tonne of sugar. Therefore JRC’s figure of 80.76t sugar beet/ha (at nominal 16% sugar and excluding tops) produces 6.28 t ethanol/ha. This agrees roughly with the figure from CIBE (2013), who say the ethanol yield is 6.27 t/ha. Of course, we are using their yield data, but this confirms our data on the sugar-to-ethanol process. In comparison, ADEME (2010) found a higher ethanol production figure per ha, 6.5 t ethanol/ha (median between 6.2 and 6.8 t/ha). It is likely the difference between ADEME, and the JRC/CIBE results are due to ADEME starting off from a relatively high fermentable sugar figure per ha of 15.6 t/ha. According to the CIBE (2013) ratio for conversion of sugar to ethanol, the correct figure for Europe for fermentable sugar yield from sugar beet is 12.9 t/ha. CONCLUSION JRC uses yields from CIBE, and JRC sugar-ethanol plant (from Kaltschmitt 1997) has almost the same ethanol/sugar yield as given by CIBE. At the ethanol plant, ADEME has lower ethanol/sugar yield than JRC or CIBE. The ADEME ethanol plant produces less ethanol from a given amount of sugar than JRC or CIBE figures. ADEME has higher yield of sugar beet and higher sugar content than CIBE. That is because (1) France has better yields than CIBE average, confirmed by FAO, and (2) probably, averages may have been for different years, as ADEME yield (if corrected from actual 18% sugar to effective yield at nominal 16% sugar content) corresponds to average FAO yield for 2008-2009, but yields in previous years were considerably lower. DETAILS ADEME-DIREM 2010 Sugar beet ethanol data.p.111 "~80 tonnes/ha wet beet yield without tops "Beet is 18% sugar". Therefore at 16% nominal sugar this corresponds to 90t/ha. Corresponds to average of 2008 and 2009 yields for France in FAO, but is more than the average until then. suger in beet is thus 14.4 tonnes/ha "+ 13.5 t/ha tops containing 1.2 tonnes/ha sugar" (farmers are not paid for tops). So total fermentable sugar (with bonus from tops) is 15.6 tonnes/ha tops add 1.2/14.4 = 8.33% extra sugar "Ethanol between 6200 and 6800 kg/ha" Average ethanol per ha = 6.5 tonnes/ha So 1 tonne ethanol needs 2.4 tonnes fermentable sugar in beet+tops
139
...Of which sugar, 2.23 tonnes comes from paid-for beet (minus 7.7% for tops) If we don't count any tops, JRC's existing plant would need 2.06 tonnes sugar per tonne ethanol, as calculated from the sugar in the declared beet input. If we would add 7.7% sugar from tops, the JRC sugar-in-beet requirement per tonne ethanol would go up to 2.23 tonnes sugar/tonne ethanol, which is closer to ADEME, but still more efficient. So it looks like the JRC plant definitely includes "bonus sugar' from tops which are not counted as part of the beet going in The ADEME figure of 0.18 kg (humid) pulp per kg beet is much higher than JRC for pulp@10%moisture. Other sources agree with JRC. The ADEME figure is either before drying (water content not specified) or wrong. CGB say 1kg sucrose makes 0.4498 kg ethanol, or 2.22 tonnes sucrose/tonne ethanol. But if tops are considered as a free bonus, it works out about 1/.496 = 2.016 tonnes of sucrose-in-beet per tonne of ethanol. JRC process has 2.059, which is close, and shows that the tops are already included as "free sucrose" in the JRC plant. (including the sugar-from-tops, that makes the total sugar in up to 2.23 tonnes sugar per tonne ethanol in JRC.. Not including tops, CGB say 1 tonne sugar beet @16% sugar makes 100 liters (79.4kg) dry ethanol.
Step 2: Transportation of sugar beet Table 103 Transport of sugar beet via 40 t truck over a distance of 30 km (one way) I/O
Unit
Amount
Distance
Input
tkm/MJsugar beet
0.0074
Sugar beet
Input
MJ/MJsugar beet
1.0000
Sugar beet
Output
MJ
1.0000
Comment - For the fuel consumption of the 40 t truck, see Table 63. Sources 1 Kaltschmitt and Hartmann, 2001. 2 Fahrzeugbau Langendorf GmbH & Co. KG; Waltop, personal communication, 2001. 3 Dreier et al., 1998. Step 3: Conversion to ethanol The data for the conversion of sugar beet to ethanol with no biogas from slops are shown in Table 104.
140
Table 104 Conversion to ethanol with no biogas from slops I/O
Unit
Amount
Source
Comment
Sugar beet
Input
MJ/MJethanol
1.8388
1, 2, 3
0.0777 t ethanol/(t sugar beet @ 76.5 % H2O)
Electricity
Input
MJ/MJethanol
0.0345
1, 3
Steam
Input
MJ/MJethanol
0.2806
1, 3
Ethanol
Output
MJ
1.000
Comment - The ethanol yield includes ethanol from tops, which are not part of the ‘official’ beet yield paid to farmers. - 0.058 t beet pulp/(t sugar beet at 76.5 % water). The data for the conversion of sugar beet to ethanol with biogas from slops are shown in Table 105. Table 105 Conversion to ethanol with biogas from slops I/O
Unit
Amount
Source
Sugar beet
Input
MJ/MJethanol
1.8388
1
Electricity
Input
MJ/MJethanol
0.0398
1
Steam
Input
MJ/MJethanol
0.1043
1
Ethanol
Output
MJ
1.000
Comment 0.0777 t ethanol/(t sugar beet @ 76.5 % H2O)
Comment - 0.058 t beet pulp/(t sugar beet at 76.5 % water). - LHV (sugar beet pulp) wet = 14.4 MJ/kg of pulp (Refs 1, 3). Sources 1 Kaltschmitt and Reinhardt, 1997. 2 Dreier et al., 1998. 3 Hartmann, 1995. Step 3.1: Ethanol plant generation processes The data for the individual plant generation processes are shown in Chapter 4. The processes linked to sugar beet ethanol are: • steam generation (Table 57). • NG CHP (Table 58). • lignite CHP (Table 59).
141
Step 4: Transportation of ethanol to the blending depot The same data are used as for wheat ethanol. Step 5: Ethanol depot distribution inputs The same data are used as for wheat ethanol.
142
6.4 Barley to ethanol Description of pathway The following processes are included in the 'barley to ethanol' pathway.
The data for each process are shown below; significant updates are described in more detail with relevant references.
Step 1: Barley cultivation The new data for barley cultivation are shown in Table 106. The updated data include the following: • • • • • •
updated diesel and pesticide use in barley cultivation, using data from CAPRI (see Section 2.5); CaCO3 fertilizer use calculated by the JRC (see Section 3.10); N2O emissions calculated by JRC data using the JRC GNOC model (see Section 3.7); CO2 emissions from neutralisation of other soil acidity have been calculated by the JRC (see Section 3.10); K2O and P2O5 updated using the most recent data available; straw has been removed. 143
In the following table, source numbers in bold represent the main data source; additional references are used to convert data to MJ/MJ crop. Table 106 Barley cultivation I/O
Unit
Amount
Source
Comment
Diesel
Input
MJ/MJbarley
0.05117
3, 5, 6, 7
See CAPRI data
N fertilizer
Input
kg/MJbarley
0.001437
2, 3, 5
See GNOC data
CaCO3 fertilizer
Input
kg/MJbarley
0.003627
8
See liming data
K2O fertilizer
Input
kg/MJbarley
0.00035
3, 4, 5
P2O5 fertilizer
Input
kg/MJbarley
0.000332
3, 4, 5
5.1 kg K2O/tonne moist crop 4.9 kg P2O5/tonne moist crop
Pesticides
Input
kg/MJbarley
0.0000618
3, 5, 6, 7
See CAPRI data
Seeding material
Input
kg/MJbarley
0.00277
1, 3, 4
170 kg/(ha*yr)
Barley
Output
MJ
1.0000
Field N2O emissions
g/MJbarley
0.0433
2
See GNOC data
CO2 from neutralisation of other soil acidity
g/MJbarley
1.146
8
See liming data
Comments - Assumption: LHV (barley grain) = LHV (wheat grain). - LHV (wheat grain) = 17 MJ/kg of dry substance (Ref. 3). Sources 1 Lechon et al., 2005. 2 Edwards and Koeble, 2012 (see Chapter 3). 3 Kaltschmitt and Hartmann, 2001. 4 Fertilizer Europe, received by JRC in 2013. 5 CAPRI database, Energy use data extracted by Markus Kempen of Bonn University, March 2012, converted to JEC format using information in Refs 6 and 7. 6 Kraenzlein, 2011. 7 Kempen and Kraenzlein, 2008. 8 JRC: Acidification and liming data (Section 3.10). Step 2: Drying of barley A new process for drying the barley derived from CAPRI data (see Section 2.5) has been added. The data are shown in Table 107.
144
Table 107 Drying of barley I/O
Unit
Amount
Source
Light heating oil
Input
MJ/MJbarley
0.001005
1, 4
Natural gas
Input
MJ/MJbarley
0.001005
1, 4
Electricity Barley
Input Input
MJ/MJbarley MJ/MJbarley
0.000167 1.0000
1, 4
Barley
Output
MJ
1.0000
Comments - 0.68%: average % of water removed to reach traded water content, according to CAPRI data (see Section 2.5). - 2.16 MJ heating oil/tonne of crop at traded water content for 0.1% drying*. - 2.16 MJ NG/tonne of crop at raded water content for 0.1% drying*. - 0.36 MJ electricity/tonne of crop at raded water content for 0.1% drying**. * [UBA 1999] reports that 0.1% drying of grains needs 1.2 kWh= 4.32 MJ of heating oil per tonne of grain. Ecoinvent (ref 5) propose 5 MJ heating oil is needed per kg water evaporated (~0.1% in 1 tonne grain), on the basis of a survey of European literature. UBA data on total MJ heating fuel will be considered, assuming that half comes from NG and half from light heating oil, on the basis of discussions with national experts. Also LPG is used, but this is an intermediate case. ** For electricity, [UBA 1999] reports 0.1% drying of grains needs 0.1 kWh= 0.36MJ per tonne of grain. Ecoinvent (ref 5) reports a higher value (about 1kWh = 3.6 MJ electricity) perhaps including electricity for handling and storage. UBA data has been considered. Sources 1 CAPRI database, 2012 converted to JEC format using information in Refs 2 and 3. 2 Kraenzlein, 2011. 3 Kempen and Kraenzlein, 2008. 4 UBA, 1999. 5 Nemecek and Kägi, 2007.
145
Step 3: Handling and storage of barley A new process for handling and storage of barley has been added. The data are shown in Table 108. Table 108 Handling and storage of barley I/O
Unit
Amount
Source
Barley
Input
MJ/MJcorn
1.0081
2
Electricity
Input
MJ/MJcorn
0.00039
1
Barley
Output
MJ
1.0000
Comment - UBA proposes 12.6 kWh electricity per tonne of grain for ventilation during storage of rapeseed. For wheat, Kenkel 2009 reports average of 19 kWh/tonne for Oklahoma, and Kaltschmitt, 1997 only 1.6kWh/tonne. Data from Kaltscmitt 1997 has been used. Sources 1 Kaltschmitt and Reinhardt, 1997. 2 Kenkel, 2009. Step 4: Transportation of barley grain Table 109 Transport of barley grain via 40 t truck over a distance of 100 km (one way) I/O
Unit
Amount
Distance
Input
tkm/MJbarley
0.0068
Barley
Input
MJ/MJbarley
1.0100
Barley
Output
MJ
1.0000
Comment - For the fuel consumption of the 40 t truck, see Table 63.
146
Step 5: Conversion of barley to ethanol Table 110 Conversion of barley to ethanol I/O
Unit
Amount
Source
Barley
Input
MJ/MJethanol
2.0895
1, 4, 5
Comment 3.8095 t barley grain @ 13.5 % H2O/(t ethanol)
Electricity
Input
MJ/MJethanol
0.0541
2, 4, 5
1.45 GJ/(t ethanol)
Steam
Input
MJ/MJethanol
0.3637
2, 4, 5
9.75 GJ/(t ethanol)
NH3
Input
kg/MJethanol
0.000258
4, 5, 6
2.1 kg/dry t of barley grain
NaOH
Input
kg/MJethanol
0.000639
4, 5, 6
5.2 kg/dry t of barley grain
CaO
Input
kg/MJethanol
0.000147
4, 5, 6
1.2 kg/dry t of barley grain
alpha-amylase
Input
kg/MJethanol
0.000098
4, 5, 6
0.8 kg/dry t of barley grain
gluco-amylase
Input
kg/MJethanol
0.000135
4, 5, 6
1.1 kg/dry t of barley grain
Ethanol
Output
MJ
1.000
Comments - 0.448 kg DDGS (at 10% water) / kg barley (at 13.5% water) (Refs 1 and 4). - 0.263 kg ethanol / kg barley (at 13.5 % water) (Ref. 4). - LHV DDGS(dry) = 18.29 MJ/kg. Sources 1 Kaltschmitt and Hartmann, 2001. 2 Punter et al., 2004. 3 Kaltschmitt and Reinhardt, 1997. 4 Lywood, W., ENSUS plc, personal communication to Edwards, R., JRC Ispra, 3 December 2010. 5 Hartmann, 1995. 6 MacLean and Spatari, 2009. Step 5.1: Ethanol plant generation processes The data for the individual plant generation processes are shown in Chapter 4. The processes linked to barley ethanol are: • steam generation (Table 57) • NG CHP (Table 58) • lignite CHP (Table 59) • woodchip-fuelled CHP (Table 60). Step 6: Transportation of ethanol to the blending depot The same data are used as for wheat ethanol. Step 7: Ethanol depot distribution inputs The same data are used as for wheat ethanol. 147
6.5 Sugar cane to ethanol Description of pathway The following processes are included in the sugar cane-to-ethanol pathway:
The data for each process are shown below; significant updates are described in more detail with relevant references. Step 1: Sugar cane cultivation The new data for sugar cane cultivation are shown in Table 111. The updated data include: • • • • •
CaCO3 fertilizer use calculated by the JRC (see Section 3.10); N2O emissions calculated by JRC data using the JRC GNOC model (see Section 3.7); CO2 emissions from neutralisation of other soil acidity, calculated by the JRC (see Section 3.10). K2O and P2O5 updated using the most recent data available. Sugar cane yield updated using new available data.
In the following table, source numbers in bold represent the main data source; additional references are used to convert data to MJ/MJ crop.
148
Table 111 Sugar cane cultivation I/O
Unit
Amount
Source
Comments
Diesel
Input
MJ/MJsugar cane
0.00861
1, 2, 3, 4
1.289 l diesel/(t sugar cane @ 72.5 % H2O )
N fertilizer
Input
kg/MJsugar cane
0.000165
1, 2, 7
See GNOC data
CaCO3 fertilizer
Input
kg/MJsugar cane
0.0003375
8
See liming data
Filter mud cake
Input
kg/MJsugar cane
0.001696
1, 2, 3, 4
600 kg/(ha*yr)
K2O fertilizer
Input
kg/MJsugar cane
0.000184
1, 2, 5, 6
0.99 kg/tonne cane
P2O5 fertilizer
Input
kg/MJsugar cane
0.000058
1, 2, 5, 6
0.31 kg/tonne cane
Pesticides
Input
kg/MJsugar cane
0.000006
1, 2, 3
2.36 kg/(ha*yr)
Seeding material
Input
kg/MJsugar cane
0.005948
1, 2, 3, 9, 10
2104 kg/(ha*yr)
Vinasse
Input
kg/MJsugar cane
0.203195
1, 2, 3, 9, 10
71 867 kg/(ha*yr)
Sugar cane
Output
MJ
1.0000
-
g/MJsugar cane
0.0079
3, 7
Including trash burning
g/MJsugar cane
0.0818
8
See liming data
Field N2O emissions CO2 from neutralisation of other soil acidity
Comments - LHV sugar cane 19.6 MJ/kg of dry substance (Ref. 1). - Water content = 72.5% (Ref. 2). - 78.74 t sugar cane / (ha*yr), average of 5 years of actual harvesting (Ref 9, 10). This yield represents the average yield for the ethanol-producing areas of Brazil over the last 5 years. The previous yield (from Macedo, 2008) was only for Sao Paulo province. The processing data we use, from Macedo 2008, assumes a sugar content of 142.2 kg-sugar/tonne cane. The actual sugar content for all ethanol-producing areas of Brazil is 138.27 kg-sugar/tonne. To use the same processing data, we need to normalize the all-ethanol-brazil sugar cane yield (80.98 t/ha) to the equivalent yield at 142.2 kg-sugar/tonne cane. Sources 1 Dreier, 2000. 2 Kaltschmitt and Hartmann, 2001. 3 Macedo et al., 2008. 4 Macedo et al., 2004. 5 International Fertilizer Association (IFA), 2013 6 FAOstat data. 7 Edwards and Koeble, 2012 (see Chapter 3). 8 JRC: Acidification and liming data (Section 3.10). 9 Brazilian Ministry of Agriculture, Fisheries and Food Supply, Department of Cane Sugar and Agro Energy, 2012. 10 UNICA (Brazilian Sugar Cane Industry Association), 2013. 149
Step 2: Transportation Table 112 Transportation of sugar cane (summary table) Commodity
Transporter
Transport of mud cake
Truck MB2213
Transport of seeding material
Truck MB2318
Transport of sugar cane
Truck (40 t) average
Table 113 Transport of mud cake via dumpster truck MB2213 over a distance of 8 km (one way) I/O
Unit
Amount
Distance
Input
tkm/kg
0.008
Filter mud cake
Input
kg/kg
1.00
Filter mud cake
Output
kg
1.00
Comment - For the fuel consumption of the MB2213 truck, see Table 65. Table 114 Transport of seeding material via MB2318 truck over a distance of 20 km (one way) I/O
Unit
Amount
Distance
Input
tkm/kg
0.020
Seeding material
Input
kg/kg
1.00
Seeding material
Output
kg
1.00
Comment - For the fuel consumption of the MB2318 truck, see Table 66. Table 115 Transport of sugar cane via 40 t truck over a distance of 20 km (one way) I/O
Unit
Amount
Distance
Input
tkm/MJsugar cane
0.0037
Sugar cane
Input
MJ/MJsugar cane
1.0000
Sugar cane
Output
MJ
1.0000
Comment - For the fuel consumption of 40 t truck weighted average for sugar cane, see Table 64. Table 116 Transport of vinasse summary table Share of the vinasse 4.6 %
Transporter Truck MB2318
23.6 %
Tanker truck with water cannons
71.8 %
Water channels
150
Table 117 Transport of vinasse via a tanker truck MB2318 over a distance of 7 km (one way) I/O
Unit
Amount
Distance
Input
tkm/kg
0.007
Vinasse
Input
kg/kg
1.00
Vinasse
Output
kg
1.00
Comment - For the fuel consumption of the MB2318 tanker truck, see MB2318 Tanker truck for vinasse see Table 67. Source 1 Macedo et al., 2004. Table 118 Transport of vinasse via a tanker truck with water cannons over a distance of 14 km (one way) I/O
Unit
Amount
Distance
Input
tkm/kg
0.014
Vinasse
Input
kg/kg
1.00
Vinasse
Output
kg
1.00
Comment - For the fuel consumption, see Table 63. Table 119 Transport of vinasse via water channels I/O
Unit
Amount
Diesel
Input
MJ/kg
0.005
Vinasse
Input
kg/kg
1.00
Vinasse
Output
kg
1.00
151
Step 3: Conversion of sugar cane to ethanol Table 120 Conversion of sugar cane to ethanol I/O
Unit
Amount
Source
Comment
Sugar cane
Input
MJ/MJethanol
2.934
1, 2, 3
86.3 l ethanol/(t sugar cane, 72.5 % H2O)
CaO
Input
kg/MJethanol
0.000506
3, 4
0.93 kg/(t sugar cane, 72.5 % H2O)
Cyclohexane
Input
kg/MJethanol
0.000028
4
0.6 kg/(m ethanol) [3]
H2SO4
Input
kg/MJethanol
0.000425
4
0.00905 kg/(l ethanol) [3]
Lubricants
Input
kg/MJethanol
0.000007
3, 4
0.01337 kg/(t sugar cane, 72.5 % H2O) [3]
Ethanol
Output
MJ
1.000
Electricity
Output
MJ/MJethanol
0.0180
3
9.2 kWh/t of cane, replaces electricity from bagasse
3
Sources 1 Dreier, 2000. 2 Kaltschmitt and Hartmann, 2001. 3 Macedo et al., 2008. 4 Macedo et al., 2004. Step 4: Transport of ethanol to blending depot Transportation of ethanol produced in the EU is the same as for wheat ethanol. However, transportation of ethanol from beyond 1500km is calculated using the following data. Shipping distances have been updated. Table 121 Summary transport table of sugar cane ethanol Transporter
Distance (km one-way)
Truck (40 t, payload 27 t)
700
Ocean bulk carrier
10 186
Truck
150 (Same as all ethanol pathways)
Table 122 Transport of ethanol via a 40 t truck a distance of 700 km (one way) I/O
Unit
Amount
Distance
Input
tkm/MJethanol
0.028
Ethanol
Input
MJ/MJethanol
1.000
Ethanol
Output
MJ
1.000
152
Comment - For the fuel consumption of the 40 t truck, see Table 63. Table 123 Maritime transport of ethanol via ship over a distance of 10 186 km (one way) I/O
Unit
Amount
Distance
Input
tkm/MJethanol
0.380
Ethanol
Input
MJ/MJethanol
1.000
Ethanol
Output
MJ
1.000
Comment - For the fuel consumption of the product tanker, see Table 71.
Step 5: Ethanol depot distribution inputs The same data are used as for wheat ethanol.
153
6.6 Rye to ethanol Description of pathway The following processes are included in the 'rye to ethanol' pathway:
The data for each process are shown below; significant updates are described in more detail with relevant references. Step 1: Rye cultivation The new data for rye cultivation are shown in Table 124. The updated data include: • • • • • •
updated diesel and pesticide use in rye cultivation using data from CAPRI (see Section 2.5); CaCO3 fertilizer use calculated by the JRC (see Section 3.10); N2O emissions calculated by JRC data using the JRC GNOC model (see Section 3.7); CO2 emissions from neutralisation of other soil acidity calculated by the JRC (see Section 3.10); K2O and P2O5 updated using the most recent data available; The removal of straw.
154
In the following table, source numbers in bold represent the main data source; additional references are used to convert data to MJ/MJ crop. Table 124 Rye cultivation I/O
Unit
Amount
Source
Comment
Diesel
Input
MJ/MJrye
0.06521
2, 5, 6, 7
See CAPRI data
N fertilizer
Input
kg/MJrye
0.001342
1, 2, 4
See GNOC data
CaCO3 fertilizer
Input
kg/MJrye
0.006269
8
See liming data
K2O fertilizer
Input
kg/MJrye
0.000325
1, 2, 3, 9
P2O5 fertilizer
Input
kg/MJrye
0.000346
1, 2, 3, 9
Pesticides
Input
kg/MJrye
0.0000367
2, 5, 6, 7
Seeding material
Input
kg/MJrye
0.00120
Rye grain
Output
MJ
1.0000
Field N2O emissions
g/MJrye
0.040
4
See GNOC data
CO2 from neutralisation of other soil acidity
g/MJrye
2.085
8
See liming data
1, 2, 3
4.77 kg K2O/tonne moist crop 5.09 kg P2O5/tonne moist crop See CAPRI data 90 kg/(ha*yr)
Comments - LHV of rye: 17.1 MJ / (dry kg) (Ref. 2). - Water content: 14 % CAPRI assumption (Ref. 5) on traded water content, agreeing with Ref 1. Sources 1 KTBL, 2006. 2 Kaltschmitt and Hartmann, 2001. 3 Kaltschmitt and Reinhardt, 1997. 4 Edwards and Koeble, 2012 (see Chapter 3). 5 CAPRI database, Energy use data extracted by Markus Kempen of Bonn University, March 2012, converted to JEC format using information in Refs 6 and 7. 6 Kraenzlein, 2011. 7 Kempen and Kraenzlein, 2008. 8 JRC: Acidification and liming data (Section 3.10). 9 Fertilizer Europe, received by JRC in 2013.
155
Step 2: Drying of rye grain A new process for drying the rye derived from CAPRI data (see Section 2.5) has been added. The data are shown in Table 125. Table 125 Drying of rye grain I/O
Unit
Amount
Source
Light heating oil
Input
MJ/MJrye
0.000614
1, 4
Natural Gas
Input
MJ/MJrye
0.000614
1, 4
Electricity
Input
MJ/MJrye
0.0001023
1, 4
Rye
Input
MJ/MJrye
1.0000
Rye
Output
MJ
1.0000
Comments - 0.418%: average % of water removed to reach traded water content, according to CAPRI data (see Section 2.5). - 2.16 MJ heating oil/tonne of crop at traded water content for 0.1% drying*. - 2.16 MJ NG/tonne of crop at raded water content for 0.1% drying*. - 0.36 MJ electricity/tonne of crop at raded water content for 0.1% drying**. * [UBA 1999] reports that 0.1% drying of grains needs 1.2 kWh= 4.32 MJ of heating oil per tonne of grain. Ecoinvent (ref 5) propose 5 MJ heating oil is needed per kg water evaporated (~0.1% in 1 tonne grain), on the basis of a survey of European literature. UBA data on total MJ heating fuel will be considered, assuming that half comes from NG and half from light heating oil, on the basis of discussions with national experts. Also LPG is used, but this is an intermediate case. ** For electricity, [UBA 1999] reports 0.1% drying of grains needs 0.1 kWh= 0.36MJ per tonne of grain. Ecoinvent (ref 5) reports a higher value (about 1kWh = 3.6 MJ electricity) perhaps including electricity for handling and storage. UBA data has been considered. Sources 1 CAPRI database, 2012 converted to JEC format using information in Refs 2 and 3. 2 Kraenzlein, 2011. 3 Kempen and Kraenzlein, 2008. 4 UBA, 1999. 5 Nemecek and Kägi, 2007.
Step 3: Handling and storage of rye grain A new process for handling and storage of rye has been added. The data are shown in Table 126. 156
Table 126 Handling and storage of rye grain I/O
Unit
Amount
Source
Rye
Input
MJ/MJrye
1.0081
2
Electricity
Input
MJ/MJrye
0.00039
1
Output
MJ
1.0000
Rye
Comment - UBA proposes 12.6 kWh electricity per tonne of grain for ventilation during storage of rapeseed. For wheat, Kenkel 2009 reports average of 19 kWh/tonne for Oklahoma, and Kaltschmitt, 1997 only 1.6kWh/tonne. Data from Kaltscmitt 1997 has been used. Sources 1 Kaltschmitt and Reinhardt, 1997. 2 Kenkel, 2009. Step 4: Transportation of rye grain Table 127 Transport of rye grain via 40 t (payload 27 t) truck over a distance of 100 km (one way) I/O
Unit
Amount
Distance
Input
tkm/MJrye
0.0068
Rye grain
Input
MJ/MJrye
1.0100
Rye grain
Output
MJ
1.0000
Comment - For the fuel consumption of the 40 t truck, see Table 63.
157
Step 5: Conversion of rye grain to ethanol Table 128 Conversion of rye grain to ethanol I/O
Unit
Amount
Source
Comment
Rye grain
Input
MJ/MJethanol
1.8283
3, 4, 5
3.333 t rye grain @ 13.5 % H2O/(t ethanol)
Electricity
Input
MJ/MJethanol
0.0541
2, 4, 5
1.45 GJ/(t ethanol)
Steam
Input
MJ/MJethanol
0.3637
2, 4, 5
9.75 GJ/(t ethanol)
NH3
Input
kg/MJethanol
0.000226
4, 5, 6
2.1 kg/dry t of rye grain
NaOH
Input
kg/MJethanol
0.000559
4, 5, 6
5.2 kg/dry t of rye grain
CaO
Input
kg/MJethanol
0.000129
4, 5, 6
1.2 kg/dry t of rye grain
alpha-amylase
Input
kg/MJethanol
0.000086
4, 5, 6
0.8 kg/dry t of rye grain
gluco-amylase
Input
kg/MJethanol
0.000118
4, 5, 6
1.1 kg/dry t of rye grain
Ethanol
Output
MJ
1.0000
Comments - 0.374 kg DDGS (at 10% water) / kg wheat (at 13.5% water) (Ref. 4). - 0.300 kg ethanol / kg wheat (at 13.5 % water) (Ref. 4). - As we are applying data on wheat-ethanol process to rye, we use the wheat moisture content. - LHV DDGS (dry) = 18.75 MJ/kg. Sources 1 Kaltschmitt and Hartmann, 2001. 2 Punter et al., 2004. 3 Kaltschmitt and Reinhardt, 1997. 4 Wheat ethanol data from Lywood, W., ENSUS, personal communication to JRC Ispra, 3 December 2010. 5 Hartmann, 1995. 6 MacLean and Spatari, 2009. Step 5.1: Ethanol plant generation processes The data for the individual plant generation processes are shown in Chapter 4. The processes linked to rye ethanol are: • steam generation (Table 57) • NG CHP (Table 58) • lignite CHP (Table 59) • woodchip-fuelled CHP (Table 60).
158
Step 6: Transportation of ethanol to the blending depot The same data are used as for wheat ethanol. Step 7: Ethanol depot distribution inputs The same data are used as for wheat ethanol.
159
6.7 Triticale to ethanol Description of pathway The following processes are included in the 'triticale to ethanol' pathway:
The data for each process are shown below; significant updates are described in more detail with relevant references. Step 1: Triticale cultivation The new data for triticale cultivation are shown in Table 129. The updated data include: • CaCO3 fertilizer use calculated by the JRC (see Section 3.10); • N2O emissions calculated by JRC data using the JRC GNOC model (see Section 3.7); • K2O and P2O5 updated using the most recent data available; • CO2 emissions from neutralisation of other soil acidity calculated by the JRC (see Section 3.10). In the following table, source numbers in bold represent the main data source; additional references are used to convert data to MJ/MJ crop.
160
Table 129 Triticale cultivation I/O
Unit
Amount
Diesel
Input
MJ/MJtriticale
0.0527
N fertilizer
Input
kg/MJtriticale
CaCO3 fertilizer
Input
K2O fertilizer
Source
Comment
7
See CAPRI data
0.001438
1, 2, 4
See GNOC data
kg/MJtriticale
0.004124
5
See liming data
Input
kg/MJtriticale
0.00027
8
P2O5 fertilizer
Input
kg/MJtriticale
0.00030
8
Pesticides
Input
kg/MJtriticale
0.00005
7
Seeding material
Input
kg/MJtriticale
0.00172
1, 2, 3
140 kg/(ha*yr)
Triticale grain
Output
MJ
1.0000
Field N2O emissions
g/MJtriticale
0.040
4
See GNOC data
CO2 from neutralisation of other soil acidity
g/MJtriticale
1.231
5
See liming data
Average of Fertilizers europe data for feedwheat and rye Average of Fertilizers europe data for feedwheat and rye Average of the yieldadjusted CAPRI data for feed-wheat and rye
Comment - LHV of triticale: 16.9 MJ / (dry kg) (Ref. 2). - Water content: 14 %. It is assumed to be equal to rye traded moisture content, which is given by CAPRI (Ref. 7), agreeing with Ref. 1. Sources 1 KTBL, 2006. 2 Kaltschmitt and Hartmann, 2001. 3 Kaltschmitt and Reinhardt, 1997. 4 Edwards and Koeble, 2012 (see Chapter 3). 5 JRC: Acidification and liming data (Section 3.10). 6 Ferilizer Europe, 2013. 7 CAPRI database, Energy use data extracted by Markus Kempen of Bonn University, March 2012. 8 See wheat to ethanol (Section 6.1) and rye to ethanol (Section 6.6) pathways. Step 2: Drying of triticale A new process for drying the triticale has been added (assumed average of wheat and rye drying and storage). The data are shown in Table 130.
161
Table 130 Drying of triticale grain I/O
Unit
Amount
Source
Light heating oil
Input
MJ/MJtriticale
0.001071
1, 4
Natural Gas
Input
MJ/MJtriticale
0.001071
1, 4
Electricity
Input
MJ/MJtriticale
0.0001785
1, 4
Triticale
Input
MJ/MJtriticale
1.0000
Triticale
Output
MJ
1.0000
Comments: - 0.72%: average % of water removed to reach traded water content, according to CAPRI data (see Section 2.5). - 2.16 MJ heating oil/tonne of crop at traded water content for 0.1% drying*. - 2.16 MJ NG/tonne of crop at raded water content for 0.1% drying*. - 0.36 MJ electricity/tonne of crop at raded water content for 0.1% drying**. * [UBA 1999] reports that 0.1% drying of grains needs 1.2 kWh= 4.32 MJ of heating oil per tonne of grain. Ecoinvent (ref 5) propose 5 MJ heating oil is needed per kg water evaporated (~0.1% in 1 tonne grain), on the basis of a survey of European literature. UBA data on total MJ heating fuel will be considered, assuming that half comes from NG and half from light heating oil, on the basis of discussions with national experts. Also LPG is used, but this is an intermediate case. ** For electricity, [UBA 1999] reports 0.1% drying of grains needs 0.1 kWh= 0.36MJ per tonne of grain. Ecoinvent (ref 5) reports a higher value (about 1kWh = 3.6 MJ electricity) perhaps including electricity for handling and storage. UBA data has been considered. Sources 1 CAPRI database, 2012 converted to JEC format using information in Refs 2 and 3. 2 Kraenzlein, 2011. 3 Kempen and Kraenzlein, 2008. 4 UBA, 1999. 5 Nemecek and Kägi, 2007.
Step 3: Handling and storage of triticale A new process for handling and storage of triticale has been added. The data are shown in Table 131.
162
Table 131 Handling and storage of triticale I/O
Unit
Amount
Source
Triticale
Input
MJ/MJtriticale
1.008
2
Electricity
Input
MJ/MJtriticale
0.0003408
1
Output
MJ
1.0000
Triticale
Comment - UBA proposes 12.6 kWh electricity per tonne of grain for ventilation during storage of rapeseed. For wheat, Kenkel 2009 reports average of 19 kWh/tonne for Oklahoma, and Kaltschmitt, 1997 only 1.6kWh/tonne. Data from Kaltscmitt 1997 has been used. Sources 1 Kaltschmitt and Reinhardt, 1997. 2 Kenkel, 2009. Step 4: Transport of triticale Table 132 Transport of triticale via 40 t (payload 27 t) truck over a distance of 100 km (one way) I/O
Unit
Amount
Distance
Input
tkm/MJtriticale
0.0069
Triticale
Input
MJ/MJtriticale
1.0100
Triticale
Output
MJ
1.0000
Comment - For the fuel consumption of the 40 t truck, see Table 63.
163
Step 5: Conversion of triticale to ethanol Table 133 Conversion of triticale to ethanol I/O
Unit
Amount
Source
Comment
Triticale
Input
MJ/MJethanol
1.8283
3, 4, 5
3.333 t triticale @ 13.5 % H2O/(t ethanol)
Electricity
Input
MJ/MJethanol
0.0541
2, 4, 5
1.45 GJ/(t ethanol)
Steam
Input
MJ/MJethanol
0.3637
2, 4, 5
9.75 GJ/(t ethanol)
NH3
Input
kg/MJethanol
0.000226
4, 5, 6
NaOH
Input
kg/MJethanol
0.000559
4, 5, 6
CaO
Input
kg/MJethanol
0.000129
4, 5, 6
alpha-amylase
Input
kg/MJethanol
0.000086
4, 5, 6
gluco-amylase
Input
kg/MJethanol
0.000118
4, 5, 6
Ethanol
Output
MJ
1.0000
2.1 kg/dry t of triticale grain 5.2 kg/dry t of triticale grain 1.2 kg/dry t of triticale grain 0.8 kg/dry t of triticale grain 1.1 kg/dry t of triticale grain
Comment - 0.374 kg DDGS (at 10% water) / kg wheat (at 13.5% water) (Ref. 4). - 0.300 kg ethanol / kg wheat (at 13.5 % water) (Ref. 4). - As we are applying data on wheat-ethanol process to triticale, we use the wheat moisture content. - LHV DDGS (dry) = 18.75 MJ/kg. Sources 1 Kaltschmitt and Hartmann, 2001. 2 Punter et al., 2004. 3 Kaltschmitt and Reinhardt, 1997. 4 Wheat-ethanol data from Lywood, W., ENSUS, personal communication to JRC Ispra, 3 December 2010. 5 Hartmann, 1995. 6 MacLean and Spatari, 2009. Step 5.1: Ethanol plant generation processes The data for the individual plant generation processes are shown in Chapter 4. The processes linked to triticale ethanol are: • steam generation (Table 57) • NG CHP (Table 58) • lignite CHP (Table 59) • woodchip-fuelled CHP (Table 60). 164
Step 6: Transportation of ethanol to the blending depot The same data are used as for wheat ethanol. Step 7: Ethanol depot distribution inputs The same data are used as for wheat ethanol.
165
6.8 Rapeseed to biodiesel Description of pathway The following processes are included in the 'rapeseed to biodiesel' pathway.
The data for each process are shown below; significant updates are described in more detail with relevant references. Step 1: Rapeseed cultivation The new data for rapeseed cultivation are shown in Table 134. The updated data include: • • • • • •
diesel and pesticide use in rapeseed cultivation calculated using data from CAPRI (see Section 2.5); CaCO3 fertilizer use calculated by the JRC (see Section 3.10); N2O emissions calculated by JRC data using the JRC GNOC model (see Section 3.7); CO2 emissions from neutralisation of other soil acidity calculated by the JRC (see Section 3.10); K2O and P2O5 updated using the most recent data available; Lower heating values of rapeseed, rapeseed cake and vegetable oil are applicable to all processes that use these LHV values).
166
Table 134 Rapeseed cultivation I/O
Unit
Amount
Source
Comment
Diesel
Input
MJ/MJrapeseed
0.042297
6, 3, 10
See CAPRI DATA
N fertilizer
Input
kg/MJrapeseed
0.001940
3, 4, 10
See GNOC data
CaCO3 fertilizer
Input
kg/MJrapeseed
0.004048
9
See liming data
kg/MJrapeseed
0.00059
kg/MJrapeseed
0.00045
Pesticides
Input
kg/MJrapeseed
0.0000936
Seeding material
Input
kg/MJrapeseed
0.00008
Rapeseed
Output
MJ
1.0000
Field N2O emissions
g/MJrapeseed
0.0593
4
See GNOC data
CO2 from neutralisation ofother soil acidity
g/MJrapeseed
1.045
9
See liming data
K2O fertilizer P2O5 fertilizer
Input Input
3, 5, 10
14.6 kg K2O/tonne moist crop 11.1 kg P2O5/tonne moist crop
3, 6, 10
See CAPRI data
3, 5, 10
1, 2, 3, 10
6 kg/(ha*yr)
Comments - 9% is approximately the traded water content. The input data refer to a tonne of rapeseed at this water content, even if the fresh harvest has higher water content. - LHV = 27.0 MJ/kg dry rapeseed (JRC calculation using the oil content reported by Diester 2008 (Ref. 10)). Sources 1 EFMA, 2008. 2 Dreier et al., 1998. 3 Rous, J-F, PROLEA, personal communication to Edwards, R., JRC, 27 July 2009. 4 Edwards and Koeble, 2012 (see Chapter 3). 5 Fertilizer Europe, received by JRC in 2013. 6 CAPRI database, Energy use data extracted by Markus Kempen of Bonn University, March 2012, converted to JEC format using information in Refs 6 and 7. 7 Kraenzlein, 2011. 8 Kempen and Kraenzlein, 2008. 9 JRC: Acidification and liming data (see Section 3.10). 10 JRC calculation derived from composition supplied by J-F. Rous, Diester/PROLEA 'bilan vapeur', personal communication, 2008.
167
Step 2: Rapeseed drying and storage Table 135 Rapeseed drying and storage I/O
Unit
Amount
Source
Light heating oil
Input
MJ/MJrapeseed
0.0062
1
NG
Input
MJ/MJrapeseed
0.0062
1
Electricity
Input
MJ/MJrapeseed
0.00301
1
Rapeseed
Input
MJ/MJrapeseed
1.000
Rapeseed
Output
MJ
1.000
Comments - The initial water content is 15 %; the final water content is 9 %. Ref. 1 says 0.1% drying needs 4.32 MJ fuel per tonne grain (see discussion in wheat drying). The assumption is that fuel for drying is half heating oil and half NG. LPG is in-between. - 1kg (~0.1% in 1tonne) water removal needs 0.1kWh (=6kW/tonne) + 12.6 kWh/tonne fixed (ventilation) (Ref. 1). - CAPRI does not report drying emissions for oil seeds; therefore, we kept the original values. Sources 1 UBA, 1999. 2 Dreier et al., 1998. Step 3: Transportation of rapeseed Table 136 Transportation of rapeseed summary table Share
Transporter
type
Distance km
73.70 %
40 tonne truck
Payload 27 t
163
4.40 %
Handymax
Payload 37 000 t
5 000
6.10 %
Inland barge
Payload 8 800 t
376
15.80 %
Train
309
Table 137 Transport of rapeseed over a distance of 163 km via 40 tonne truck (one way) I/O
Unit
Amount
Distance
Input
tkm/MJrapeseed
0.0066
Biomass
Input
MJ/MJrapeseed
1.0100
Biomass
Output
MJ
1.0000
Comment - For the fuel consumption for a 40 t truck, see Table 63. 168
Table 138 Maritime transport of rapeseed over a distance of 5 000 km (one way) I/O
Unit
Amount
Distance
Input
tkm/MJrapeseed
0.2037
Biomass
Input
MJ/MJrapeseed
1.0100
Biomass
Output
MJ
1.0000
Comment - Fuel consumption for Handymax for maritime transport of oilseed (see Table 70).
Table 139 Transport of rapeseed over a distance of 376 km via inland ship (one way) I/O
Unit
Amount
Distance
Input
tkm/MJrapeseed
0.0153
Biomass
Input
MJ/MJrapeseed
1.0100
Biomass
Output
MJ
1.0000
Comment For the fuel consumption of a bulk carrier for inland navigation barge, see - Table 75.
Table 140 Transport of rapeseed over a distance of 309 km via train (one way) I/O
Unit
Amount
Distance
Input
tkm/MJrapeseed
0.0126
Biomass
Input
MJ/MJrapeseed
1.0100
Biomass
Output
MJ
1.0000
Comment - For the fuel consumption of the freight train run on grid electricity, see Table 78. Sources 1 Kaltschmitt and Hartmann, 2001. 2 Fahrzeugbau Langendorf GmbH & Co. KG; Waltop, personal communication, 2001. 3 Dreier et al., 1998. 4 EBB, 2009.
169
Step 4: Oil mill: extraction of vegetable oil from rapeseed Table 141 Oil mill: extraction of vegetable oil from rapeseed I/O
Unit
Amount
Source
Comments
Electricity
Input
MJ/MJoil
0.00972
1, 2
359.60 MJ/(t plant oil)
n-hexane
Input
MJ/MJoil
0.002280
1, 2
1.87 kg/(t plant oil)
Rapeseed
Input
MJ/MJoil
1.57965
1, 2
Steam
Input
MJ/MJoil
0.04326
1, 2
Crude vegetable oil
Output
MJ
1.00000
0.420 kg oil/(kg rapeseed @ 9 % H2O) [5] 1600.6 MJ/(t plant oil)
Comments - LHV = 37 MJ/(kg of oil) (Ref. 2). - 1.338 kg cake/(kg plant oil) (Ref. 1). - 18.38 MJ/ (kg dry cake). - 10.5 %: ref. 6 says 11% in USA; ref. 8 says less than 11% in EU and in the case of rapeseed cake the typical water content is about half a % less than the limit. - 2.3 % excess water in cake which is evaporated to reach 10.5 %. Mass difference of input and output indicates meal released as water vapor. LHV of rapeseed This varies according to the composition of the rapeseed. The oil content was provided by the EBB, and the water content of rapeseed used by them, by PROLEA. We filled out the remaining composition in proportion that found in Nutrient Requirements of Dairy Cattle: Seventh Revised Edition, 2001 (ed. National Academy of Sciences) and then calculated the LHV from the LHV of the components. As expected, this gives a slightly higher LHV than the one JEC-WTT used previously, which was measured on rapeseed with lower oil content.
170
Table 142 LHV of rapeseed cultivated in the United States American Rapeseed [6]
Wet basis
Rapeseed (Canola seed)
36 %
Dry matter basis 0.899 40.5 % 20.5 % 21.2 % 13.2 % 4.6 % 100 %
LHV components MJ/kg [3] 37* 24.5 15.88** 18.27*** 0
Contributions MJ/kg dry matter
Component
Dry matter [5] Oil 14.99 Protein 5.02 Carbohydrate 3.37 Fibre 2.41 Ash 0 SUM 25.8 23.18 MJ of dry matter/kg moist material
* Update JEC value. ** ECN Phyllis database. *** Same as wood.
Table 143 LHV of rapeseed cultivated in Europe EBB/DIESTER rapeseed
Wet basis
Rapeseed Diester spec.
42.6 %
Dry matter basis 0.91 46.8 % 18.3 % 19.0 % 11.8 % 4.1 % 100 %
LHV components MJ/kg [3] 37* 24.5 15.88** 18.27*** 0
Contributions MJ/kg dry matter
Component
Dry matter [5] 17.32 Oil 4.49 Protein 3.01 Carbohydrate 2.16 Fibre 0.00 Ash 26.976 SUM 24.548 MJ of dry matter/kg moist material
* Update JEC value. ** ECN Phyllis database. *** Same as wood.
Comments - Dry-matter composition from ref. 6, except that oil content is raised to that reported in ref. 7. - Other dry-mass components are reduced in proportion. - Water content (9%) from ref. 8.
171
Calculation of consistent LHV of dry rapeseed cake Table 144 LHV of dry rapeseed cake 0.420 0.462 0.538 17.077 9.90 18.38
kg extracted/kgrapeseed, moist kg/kgrapeseed, dry kgcake, dry/kgrapeseed, dry MJ bound in the extracted oil MJ bound in the cake MJ/kgcake, dry
Sources 1 EBB, 2009. 2 Mehta and Anand, 2009. 3 ECN Phyllis database of biomaterials properties. 4 Hartmann, 1995. 5 Rous, J-F, PROLEA, personal communication to Edwards, R., JRC, 27 July 2009. 6 NRC, 2001. 7 M. Rous (Diester), personal communication to JRC, 18 September 2008. 8 Bunge 2012: specifications of oilseed cakes: http://www.bunge.hu/english/ind2_31.htm acessed Sept 2012.
Step 5: Refining of vegetable oil Table 145 Refining of vegetable oil Unit
Amount
Source
Comment
Electricity
MJ/MJoil
0.0009
1, 2
34.38 MJ/(t oil) [1]
H3PO4
kg/MJoil
0.000032
1, 2
1.19 kg/(t oil) [1]
NaOH
kg/MJoil
0.000088
1, 2
3.26 kg/(t oil) [1]
Crude vegetable oil
MJ/MJoil
1.0246
1
Steam
MJ/MJoil
0.0040
1, 2
Plant oil
MJ
1.0000
149.19 MJ/(t oil) [1] 37 MJ/kg of oil [2]
Sources 1 EBB, 2009. 2 Mehta and Anand, 2009.
172
Step 6: Esterification Table 146 Esterification I/O
Unit
Amount
Electricity
Input
MJ/MJFAME
0.00405
Sodium methylate (Na(CH3O)) HCl
Input
kg/MJFAME
Input
Methanol
Source
Comment
1, 2, 4
150.5 MJ/(t FAME) [1]
0.0001145
2, 4, 5, 6
kg/MJFAME
0.000097
1, 2, 4
14.2 kg of 30% solution/(t FAME) [5, 6] 3.61 kg/(t FAME) [1]
Input
MJ/MJFAME
0.05110
1, 2, 4
95.29 kg/(t FAME) [1]
Plant oil
Input
MJ/MJFAME
1.00063
1, 2, 4
Steam
Input
MJ/MJFAME
0.0330
1, 2, 3, 4
FAME
Output
MJ
1.0000
1 229 MJ/(t FAME)
Comments - LHV (FAME) = 37.2 MJ/(kg FAME) (Ref. 2). - 16 MJ / (kg glycerol) (Ref. 4). - 101.87 kg glycerol/(t FAME). Sources 1 EBB, 2009. 2 ECN Phyllis database of biomaterials properties. 3 Rous, personal communication, 23 September 2008. 4 Edwards, JRC, 22 July 2003: calculation with HSC for windows. 5 European Biodiesel Board, - J. Coignac, Comments to Commission's May 2013 stakeholder consultation, received 13 June 2013. 6 European Biodiesel Board, - D. Buttle, personal communication, 2013.
Step 6.1: Plant generation processes The data for the individual plant generation processes are shown in Chapter 4. The process linked to refining of rapeseed is steam generation (Table 57).
173
Step 7: Transportation of FAME to the blending depot Table 147 Transportation of FAME summary table to the blending depot Share
Transporter
notes
Distance (km one way)
11.4 %
Truck
Payload 40 t
305
27.2 %
Product tanker
Payload: 15 000 t
1 118
43.8 %
Inland ship/barge
Payload 1 200t
153
3.8 %
Train
13.8 %
Pipeline
381
Comment - Transport of FAME via pipeline is assumed to be the same as for gasoline. (The number has been supplied by TotalFinaElf without indicating the distance). See Table 79. Source European Biodiesel Board (EBB), personal communication. Table 148 Transport of FAME via 40 t truck over a distance of 305 km (one way) I/O
Unit
Amount
Distance
Input
tkm/MJFAME
0.0088
FAME
Input
MJ/MJFAME
1.0000
FAME
Output
MJ
1.0000
Comment - For the fuel consumption of the 40 t truck, see Table 63. Table 149 Maritime transport of FAME over a distance of 1 118 km (one way) I/O
Unit
Amount
Distance
Input
tkm/MJFAME
0.0301
FAME
Input
MJ/MJFAME
1.0000
FAME
Output
MJ
1.0000
Comment - For the fuel consumption of the product tanker (payload: 15,000 t), see Table 72.
174
Table 150 Transport of FAME over a distance of 153 km via inland ship (one way) I/O
Unit
Amount
Distance
Input
tkm/MJFAME
0.0041
FAME
Input
MJ/MJFAME
1.0000
FAME
Output
MJ
1.0000
Comment - For the fuel consumption for an inland oil carrier, see Table 76. Table 151 Transport of FAME over a distance of 381 km via train (one way) I/O
Unit
Amount
Distance
Input
tkm/MJFAME
0.0102
FAME
Input
MJ/MJFAME
1.0000
FAME
Output
MJ
1.0000
Comments - For the fuel consumption of the freight train, see Table 78.
Step 8: FAME depot distribution inputs Table 152 FAME depot I/O
Unit
Amount
FAME
Input
MJ/MJFAME
1.00000
Electricity
Input
MJ/MJFAME
0.00084
Output
MJ
1.00000
FAME
Table 153 Transport of FAME via 40 t truck over a distance of 305 km I/O
Unit
Amount
Distance
Input
tkm/MJFAME
0.0043
FAME
Input
MJ/MJFAME
1.0000
FAME
Output
MJ
1.0000
Table 154 FAME filling station I/O
Unit
Amount
FAME
Input
MJ/MJFAME
1.0000
Electricity
Input
MJ/MJFAME
0.0034
Output
MJ
1.0000
FAME
175
Comment - Distribution is assumed to be same as for fossil diesel and gasoline. Source 1 Dautrebande, 2002.
176
6.9 Sunflower to biodiesel Description of pathway The following processes are included in the 'sunflower to biodiesel' pathway.
The data for each process are shown below; significant updates are described in more detail with relevant references. Step 1: Sunflower cultivation The new data for sunflower cultivation are shown in Table 155. The updated data include: • • • •
diesel and pesticide use in sunflower cultivation, calculated using data from CAPRI (see Section 2.5); CaCO3 fertilizer use calculated by the JRC (see Section 3.10); N2O emissions calculated by JRC data using the JRC GNOC model (see Section 3.7); CO2 emissions from neutralisation of other soil acidity, calculated by the JRC (see Section 3.10).
177
Table 155 Sunflower cultivation I/O
Unit
Amount
Source
Comment
Diesel
Input
MJ/MJsunfllower seed
0.069909
1, 4, 8, 9
See CAPRI data
N fertilizer
Input
kg/MJsunflower seed
0.000972
1, 2, 8, 9
See GNOC data
CaCO3
Input
kg/MJsunflower seed
0.002199
6
See liming data
K2O fertilizer
Input
kg/MJsunflower seed
0.00036
1, 8, 9
22 kg K2O/(ha*yr) [1]*
P2O5 fertilizer
Input
kg/MJsunflower seed
0.00050
1, 8, 9
30 kg P2O5/(ha*yr) [1]*
Pesticides
Input
kg/MJsunflower seed
0.0000553
1, 3, 8, 9
See CAPRI data
Seeding material
Input
kg/MJsunflower seed
0.000064
1, 7, 8, 9
3.90 kg/(ha*yr)
Sunflower seed
Output
MJ
1.0000
g/MJsunflower seed
0.0379
2
See GNOC data
Field N2O emissions
CO2 from neutralisation g/MJsunflower seed 0.673 6 See liming data of other soil acidity * Data from Fertilizer Europe are not used because in the Fertilizer Europe per-crop data, sunflower is mixed with other oilseeds.
Comments - LHV = 27.2 MJ/kg dry sunflower seed (Ref. 8). - 9.0 % traded water content of sunflower seed (Refs 1 and 9). - The farm inputs relate to tonnes of crop at the traded (post-drying) water content. Sources 1 ADEME, 2002. 2 Edwards and Koeble, 2012 (see Chapter 3). 3 CAPRI database, Energy use data extracted by Markus Kempen of Bonn University, March 2012, converted to JEC format using information in Refs 6 and 7. 4 Kraenzlein, 2011. 5 Kempen and Kraenzlein, 2008. 6 JRC: Acidification and liming data (see Section 3.10). 7 Jósef, 2000. 8 JRC calculation from the oil content supplied by Diester 2008. 9 Rous, J-F, PROLEA, personal communication to Edwards. R., JRC, 27 July 2009.
178
Step 2: Sunflower drying and storage Table 156 Sunflower drying and storage I/O
Unit
Amount
Source
Light heating oil
Input
MJ/MJsunflower seed
0.0061
1
NG
Input
MJ/MJsunflower seed
0.0061
1
Electricity
Input
MJ/MJsunflower seed
0.00298
1
Sunflower seed
Input
MJ/MJsunflower seed
1.000
Sunflower seed
Output
MJ
1.000
Comments - The initial water content is 15 %; the final water content is 9 %. Ref. 1 says 0.1% drying needs 4.32 MJ fuel per tonne grain (see discussion in wheat drying). The assumption is that fuel for drying is half heating oil and half NG. LPG is in-between. - 1kg (~0.1% in 1tonne) water removal needs 0.1kWh (=6kW/tonne) + 12.6 kWh/tonne fixed (ventilation) (Ref. 1). - CAPRI does not report drying emissions for oil seeds; therefore, we kept the original values. Sources 1 UBA, 1999. 2 Dreier et al., 1998. Step 3: Transportation of sunflower seed Table 157 Transportation of sunflower seed summary table Share
Transporter
Notes
68.60 %
40 t truck
Payload 27 t
31.40 %
Electric train
Distance (km one way) 292 450
Table 158 Transport of sunflower seed over a distance of 292 km via truck (one way) I/O
Unit
Amount
Distance
Input
tkm/MJsunflower seed
0.0118
Biomass
Input
MJ/MJsunflower seed
1.0100
Biomass
Output
MJ
1.0000
Comment - For the fuel consumption of the 40 t truck, see Table 63.
179
Table 159 Transport of sunflower seed over a distance of 450 km via train (one way) I/O
Unit
Amount
Distance
Input
tkm/MJsunflower seed
0.0182
Biomass
Input
MJ/MJsunflower seed
1.0000
Biomass
Output
MJ
1.0000
Comment - For the fuel consumption of the electric train, see Table 78. Sources 1 EBB, 2009. 2 Dreieret al., 1998.
Step 4: Oil mill: extraction of vegetable oil from sunflower seed Table 160 Oil mill: extraction of vegetable oil from sunflower seed I/O
Unit
Amount
Source
Original data from
Electricity
Input
MJ/MJoil
0.01123
1, 2
415.50 MJ/(t plant oil) [1]
n-hexane
Input
MJ/MJoil
0.002889
1, 2
2.37 kg/(t plant oil) [1]
Sunflower seed
Input
MJ/MJoil
1.5264
1, 2
0.439 kg oil/(kg seed)
Steam
Input
MJ/MJoil
0.03388
1, 2
1 253.4 MJ/(t plant oil) [1]
Crude vegetable oil
Output
MJ
1.0000
Comments - LHV= 37 MJ/(kg of oil) (Ref. 2). - 1.237 kg cake/kg plant oil (Ref. 1). - 18.15 MJ/(kg dry cake). - Water content (cake): 11.5%; ref. 9 says less than 12%; ref. 5 says 11.5+/-0.5% for safe handling and storage. - 2% excess water which would be in the cake if it were not evaporated in the precooking stage of the crush process. The steam input to the crushing includes the energy to do this drying (Ref. 5).
180
LHV of sunflower seed Table 161 LHV of Sunflower cultivated in the United States U.S. sunflower seeds without hulls [1]
Wet basis
38 %
Dry matter basis
LHV components MJ/kg [3]
0.918 41.9 % 19.2 % 14.9 % 18.9 % 5.1 % 100.00 %
37* 24.5 15.88** 18.27*** 0
Contributions MJ/kg dry matter
Component
Dry matter [5] 15.50 Oil 4.70 Protein 2.37 Carbohydrate 3.45 Fibre 0.00 Ash SUM 26.0 23.89 MJ of dry matter/kg moist material
* Update JEC value. ** ECN Phyllis database. *** Same as wood.
Table 162 LHV of Sunflower cultivated in Europe. EBB/DIESTER sunflower seed
Wet basis
Sunflower seed Diester
44.0 %
Dry matter basis 0.91 48.4 % 17.1 % 13.2 % 16.8 % 4.5 % 100 %
LHV components MJ/kg [3] 37* 24.5 15.88** 18.27*** 0
Contributions MJ/kg dry matter
Component
Dry matter [5] 17.89 Oil 4.18 Protein 2.10 Carbohydrate 3.07 Fibre 0.00 Ash 27.24 SUM 24.8 MJ of dry matter/kg moist material
* Update JEC value. ** ECN Phyllis database. *** Same as wood.
Comments - Dry-matter composition from Ref. 6, except that oil content is raised to that reported in Ref. 7. - Other dry-mass components are reduced in proportion.
181
Calculation of consistent LHV of dry sunflower cake Table 163 LHV of dry sunflower cake 0.439 0.482 0.518 17.85 9.40 18.15
kg extracted/kgseed, moist kg/kgseed, dry kgcake, dry/kgseed, dry MJ bound in the extracted oil MJ bound in the cake MJ/kgcake, dry
Sources 1 EBB, 2009. 2 Mehta and Anand, 2009. 3 ECN Phyllis database of biomaterials properties. 4 Hartmann, 1995. 5 Rous, J-F, PROLEA, personal communication to Edwards, R., JRC, 27 July 2009. 6 NRC, 2001. 7 Rous, M., (Diester), personal communication to JRC, 18 September 2008. 8 EBB, September 2009. 9 Bunge 2012: specifications of oilseed cakes: http://www.bunge.hu/english/ind2_31.htm acessed Sept 2012.
Step 5: Refining of vegetable oil Table 164 Refining of vegetable oil Electricity
I/O Input
Unit MJ/MJoil
Amount 0.00231
Source 1, 2
Comment 85.3 MJ/(t oil) [1]
H3PO4
Input
kg/MJoil
0.000012
1, 2
0.45 kg/(t oil) [1]
NaOH
Input
kg/MJoil
0.000069
1, 2
2.55 kg/(t oil) [1]
Crude vegetable oil Steam
Input
MJ/MJoil
1.0256
1
Input
MJ/MJoil
0.0058
1, 2
Plant oil
Output
MJ
1.0000
215.4 MJ/(t oil) [1] 37 MJ/kg of oil [2]
Sources 1 EBB, 2009. 2 Mehta and Anand, 2009.
182
Step 6: Winterisation of sunflower Table 165 Winterisation of sunflower
Crude vegetable oil Plant oil
I/O
Unit
Amount
Input Output
MJ/MJoil MJ
1.0101 1.0000
Source 1 EBB, 2009. Step 7: Esterification Same input data used as for rapeseed. Step 8: Transport of FAME to the blending depot Same input data used as for rapeseed. Step 9: FAME depot distribution inputs Same input data used as for rapeseed.
183
6.10 Soya oil to biodiesel The 'soya oil import mix to biodiesel' pathway shown in this section includes data on the weighted mix of soy oil/biodiesel imported from Argentina, Brazil and the United States to the EU. The pathway is derived from national data for: - Brazil - Argentina - United States. which are shown in the national soy data (Section 6.10.1). The following processes are included in the ‘soya oil import mix’ pathway.
Transportation of soy to Europe We assume that feedstock is transported to EU in liquid form (as soy-oil or as finished biodiesel), we assume the oil mill is located at the first departure port, so that all inland- and marine shipping is of liquid. As biodiesel and soy-oil have almost the same LHV, and use the same type of ship, the transport emissions are the same for soy-oil or biodiesel. Then the only difference between the two is the site of the refining and esterification. As we do not have consistent countryspecific data for this, we apply the numbers supplied by European Biodiesel Board and FEDOIL to all refining and esterification processes. So the site of the refining and 184
esterification makes no difference to this either. Therefore we can combine the pathways which import biodiesel to EU with those which import soy-oil. Data on EU soy imports Table 166 Soy biodiesel made in the EU Data on EU soy imports 2010 (Mtonnes)
EU soy beans imports
... as soyoil equivalent
0.23 6.05 2.84 9.12
Argentina Brazil United States Total
0.043 1.14 0.53 1.71
Soy biodiesel made in the EU soy-oil imports Total EU soy imports as biodiesel soy-oil production equivalent 0.32 0.36 0.06 1.20 0.00 0.53 0.38 2.09 0.623
Allocated to soy source
0.11 0.36 0.16 0.623
Table 167 Total soy biodiesel used in the EU Data on EU soy imports 2010 (Mtonnes)
Soy biodiesel imports
Argentina Brazil United States Total
1.20 0.00 0.38 1.58
Total soy biodiesel used in the EU Total soy biodiesel % sourced from ... use in EU 1.31 0.36 0.54 2.203
59.38 16.17 24.46 100
Source 1 MVO, 2011.
185
Step 1: Soybean Cultivation Table 168 Soybean cultivation (weighted average of exporters to EU, by oil+oil-equivalent seeds) I/O
Unit
Amount
Source
Comment
Diesel
Input
MJ/MJsoybeans
0.02692
1
N fertilizer
Input
kg/MJsoybeans
0.000095
2
See GNOC data
Ca fertilizer as CaCO3
Input
kg/MJsoybeans
0.002819
3
See liming data
K2O fertilizer
Input
kgMJsoybeans
0.000276
1
P2O5 fertilizer
Input
kg/MJsoybeans
0.000406
1
Pesticides
Input
kg/MJsoybeans
0.0000692
1
Seeding material
Input
kg/MJsoybeans
0.0008482
1
Soybeans
Output
MJ
1.0000
Field N2O emissions
g/MJsoybeans
0.0411
2
See GNOC data
CO2 from neutralisation of other soil acidity
g/MJsoybeans
1.161
3
See liming data
5.5 kg K2O/(tonne moist soya) 8.1 kg P2O5/(tonne moist soya)
Comments - LHV = 23 MJ/kg of dry soybeans (Ref. 5). - 13 % traded water content (Ref. 6), ideal for transport and storage (Ref. 4). - N fertilizer = 0.0019024 kg N/(kg moist soya). This is the average use from Argentina, Brazil and the United States. Sources 1 Derived from national data from Argentina, Brazil and the United States (Section 6.10.1). 2 Edwards and Koeble, 2012 (see Chapter 3). 3 JRC: Acidification and liming data in this report (Section 3.10). 4 EMBRAPA, 2004. 5 Jungbluth et al., 2007. 6 Beuerlein, 2012.
186
Step 2: Drying The values are derived from national average data (Section 6.10.1). Table 169 Drying at 13 % water content LPG MJ/MJ soybean weighted
NG MJ/MJ soybean weighted
Heating oil and diesel MJ/MJ soybean weighted
Electricity MJ/MJ soybean weighted
Argentina
0.000441
0.000655
0.000095
0.000000
Brazil
0.000000
0.000000
0.000338
0.000026
United States
0.000205
0.000627
0.000000
0.000264
Total
0.000646
0.001282
0.000433
0.000290
Step 3: Transportation of soybeans Transport of soybeans via truck (see Table 170) is derived from national average data (Table 171). Table 170 Transport of soybeans via 40 t truck over a distance of 373 km (one way) I/O
Unit
Distance
Input
tkm/MJsoybeans
Amount 0.0186
Soybeans
Input
MJ/MJsoybeans
1.0100
Soybeans
Output
MJ
1.0000
Comment - For the fuel consumption of the 40 t truck, see Table 63. Table 171 Regional truck transport distances Km
%
Contribution to weighted average km
Argentina
350
59.38
207.81
Brazil
900
16.17
145.47
United States
80
24.46
19.57
Total
372.85
187
Transport of soybeans via train (Table 172) is derived from national average data (see Table 173). Table 172 Transport of soybeans via diesel train over a distance of 61 km (one way) I/O
Unit
Amount
Distance
Input
tkm/MJsoybeans
0.0030
Soybeans
Input
MJ/MJsoybeans
1.0100
Soybeans
Output
MJ
1.0000
Comment - For the fuel consumption for a freight train run on diesel fuel, see Table 77. Table 173 Regional train transport distances Km
%
Argentina
0
59.38
Contribution to weighted average km 0.00
Brazil
377
16.17
61.01
United States
0
24.46
0.00
Total
61.01
Step 4: Pre-drying soybeans at oil mill This is Argentine data, but is used for all oil mills because they are all fed with soybeans at 11 % moisture instead of the traded moisture content of 13 %. Although drying to 13 % is optimal for transport and storage of beans, an extra drying step is often needed to reach 11 % moisture before crushing (as reported in the mill data), because otherwise the meal ends up with too much moisture for storage and transport. Table 174 Pre-drying at oil mill Unit
Amount
Source
NG
MJ/MJsoybeans
0.00293
1
Soybeans
MJ/MJsoybeans
1.0000
Soybeans
MJ
1.0000
Comments - Ref 1 says 758 Gkcal to dry 75 % of 40.5 Mtonnes beans at mills. Source 1 de Tower and Bartosik, 2012.
188
Step 5: Extraction of vegetable oil from soybeans The following data have been updated using new information from FEDIOL, 2013 and replacing the data from Jungbluth et al., 2007 (Ecoinvent report). Table 175 Oil mill I/O
Unit
Amount
Source
Comment
Electricity
Input
MJ/MJoil
0.014595
6, 2
150 kWh/(t soybeans) [6]
n-hexane
Input
MJ/MJoil
0.003657
6, 2
3 kg/(t soybeans) [6]
Soybeans
Input
MJ/MJoil
2.87698
6, 2
192.3 kg oil/(t soybeans) [6]
Steam
Input
MJ/MJoil
0.081865
6, 2
3 029 MJ/(t soy oil) [6]
Vegetable oil
Output
MJ
1.0000
37 MJ/(kg vegetable oil) [2]
Comments - 20.5: LHV of the dry matter at 11 %-moist soybeans (Ref 1). - 794 kg moist cake/(t soybeans) (Ref. 5). - 11 %: water content of beans input (Ref 5). Calculation of consistent LHV of dry soybean cake - 0.192 kg oil extracted/kg seed, moist. - 0.216 kg oil/kg seed, dry. - 0.784 kg cake, dry/kg seed, dry (by dry mass balance). - 7.994 MJ bound in the extracted oil. - 15.01 MJ bound in the cake. - 19.14 MJ/kg dry cake. Consistent water content of cake by mass-balance - 110 kg water entering per tonne beans. - 986 total kg out for 1 tonne beans. - 14 lost mass = kg evaporated water. - 96 kg water in cake. - 12.13 % water content of cake. - 16.52 MJ/kg wet LHV used for allocation under RED, using formula in [4]. - 35 % allocation to vegoil. Comments - This data comes from FEDIOL 2013 and replaces data from Jungbluth et al., 2007 (Ecoinvent report). - We also have INTA data from Hilbert, 2010 for Argentina, but it is unclear which data is per tonne of oil and which per tonne of beans. Pradhan, 2011 gives data for one modern US soy-oil-mill but clearly states this does NOT represent the national average. 189
Comments on water content of cake - The moisture content of the oilseed cake was back-calculated by mass-balance from: o the traded water content of beans o the reported process yields of oil and cake process yields. Sources 1 UBA, 1999. 2 Mehta and Anand, 2009. 3 Bunge, 2012. 4 Hartmann, 1995. 5 Jungbluth et al., 2007 (Ecoinvent report). 6 FEDIOL, 2013. Step 5: Transport of soy oil Transport of soy oil via ship and barge (see Table 176 and Table 177) are derived from national average data (
Table 178). Table 176 Transport of soy oil via inland ship over a distance of 562 km (one way) I/O
Unit
Amount
Distance
Input
tkm/MJsoyoil
0.0152
Soy oil
Input
MJ/MJsoyoil
1.0000
Soy oil
Output
MJ
1.0000
Comment - For the fuel consumption for an oil carrier for inland navigation, see Table 76.
Table 177 Maritime transport of soy oil via ship over a distance of 11 107 km (one way) I/O
Unit
Amount
Distance
Input
tkm/MJsoyoil
0.3002
Soy oil
Input
MJ/MJsoyoil
1.0000
Soy oil
Output
MJ
1.0000
Comment - For the fuel consumption for a product tanker for pure vegetable oil transport, see Table 73.
190
Table 178 Regional shipping and barge distances for soy oil to Rotterdam Nautical sea miles
km sea
km barge
%
Contribution to weighted average km (sea)
Contribution to weighted average km (barge)
Argentina (Rosario)
6 584
12 194
0
59.38
7 240
0
Brazil (Mix)
5 565
10 306
209
16.17
1 666
34
United States (New Orleans) Total
4 860
9 000
2 161
24.46
2 201
529
11 107
562
Source 1 Searates.com.
Step 6: Refining of vegetable oil from soybean Table 179 Refining of vegetable oil I/O
Unit
Amount
Source
Comment
Electricity
Input
MJ/MJoil
0.0023
1, 2
85.3 MJ/(t oil) [1]
H3PO4
Input
kg/MJoil
0.000012
1, 2
0.45 kg/(t oil) [1]
NaOH
Input
kg/MJoil
0.000069
1, 2
2.55 kg/(t oil) [1]
Crude vegetable oil
Input
MJ/MJoil
1.0256
1
Steam
Input
MJ/MJoil
0.0058
1, 2
Vegetable oil
Output
MJ
1.0000
215.4 MJ/(t oil) [1] 37 MJ/kg of oil [2]
Sources 1 EBB, 2009. 2 Mehta and Anand, 2009. No winterisation is required for soy oil. Step 7: Esterification Same input data used as for rapeseed. Step 8: Transportation of FAME to the blending depot Same input data used as for rapeseed. 191
Step 9: FAME depot distribution inputs Same input data used as for rapeseed.
6.10.1 National soy data The following pages contain the country-specific input data used to derive the soy pathway described above.
Brazil soya Soybean cultivation Table 180 Soybean cultivation in Brazil I/O
Unit
Amount
Source
Diesel
Input
MJ/MJsoybeans
0.02867
1, 2, 5, 6
N fertilizer
Input
kg/MJsoybeans
0.0000804
5, 11
Ca fertilizer as CaCO3
Input
kg/MJsoybeans
0.003133
12
K2O fertilizer
Input
kgMJsoybeans
0.0010993
4, 5, 8, 9, 10
P2O5 fertilizer
Input
kg/MJsoybeans
0.0011458
4, 5, 8, 9, 10
Pesticides
Input
kg/MJsoybeans
0.0000286
4, 5, 6
Seeding material
Input
kg/MJsoybeans
0.00125
4, 6, 7
Soybeans
Output
MJ
1.0000
Field N2O emissions
g/MJsoybeans
CO2 from neutralisation of other soil acidity
g/MJsoybeans
1.374
Comment
See liming data 22 kg K2O/(tonne moist soya) 22.93 kg P2O5/(tonne moist soya)
11
GNOC calculates EU import weighted av.
12
See liming data
Comments - 23.0 MJ/(kg dry soybean) (Ref. 4): - Diesel use: 1 606 MJ/(ha*yr). No data specific to Brazil was found. This is the value for the United States, derived from Ref. 6. - N fertilizer: 0.00161 kg N/(kg moist soya) calculated in Ref. 11 from data in Ref. 9. - Pesticides (etc.) use: 1.6 kg/(ha*yr): o Pradhan et al., 2011: United States 2006 = 1.6 kg/ha o Jungbluth et al., 2007 (Ecoinvent report): Brazil 2001 = 0.0579 kg/t (=0,2 kg/ha) 192
-
-
o Cederberg, 2001: United States 2004= 0.0476 kg/t (=0.14 kg/ha) (USDA, 2004). Seeding material: 70 kg/(ha*yr): o USA data: 68.9 kg/ha (Ref. 6) o AG data: 70-80 kg/ha (Ref. 7) o United States and BR: 70 kg/ha (Ref. 4). 13 % ideal water content for transport and storage (Ref. 5) EMBRAPA, 2004: 'soybeans are harvested at 18 % humidity', but yields are usually reported at traded water content, which is 13 %.
Sources 1 CENBIO, 2009. 2 Ministério da Agricultura, Pecuária e Abastecimento, 2007. 3 Da Silva et al., 2010. 4 Jungbluth et al., 2007 (Ecoinvent report). 5 EMBRAPA, 2004. 6 Pradhan et al., 2011. 7 Panichelli et al., 2009. 8 FAOstat data. 9 International Fertilizer Association (IFA), 2013. 10 FAPRI, 2012. 11 Edwards and Koeble, 2012 (see Chapter 3). 12 JRC: Acidification and liming (see Section 3.10). Drying of soybeans Table 181 Soybean drying Unit
Amount
Sources
Diesel
MJ/MJsoybean
0.002091
1, 2
Electricity
MJ/MJsoybean
0.000161
1, 2
Soybeans
MJ/MJsoybean
1.000
Soybeans
MJ
1.000
Comment - 13 % final humidity rate after drying (Ref. 3). Sources 1 Da Silva et al., 2010. 2 Marques, 2006. 3 EMBRAPA, 2004.
193
Transport of soybeans Table 182 Weighted average of transport of soybeans from central-west and south to Brazilian seaport 34 865.8 899.8 377.4 208.9
1 2 2 3 4
km km km km
Truck to drying places Truck from drying place to Brazilian port Total truck distance Railway
km
Inland waterway
Comments - 1) 20 km in south Brazil states (weight 0.3) and 40 km in central-west states (weight 0.7). - 2) Da Silva, 2010: central-west (weighting 0.7): 1101 km; south (weighting 0.3): 317 km. - 3) Da Silva, 2010: central-west (weight 0.7): 393 km; south (weight 0.3): 341 km. - 4) Da Silva, 2010: central-west (weight 0.7): 289 km; south (weight 0.3): 22 km. Table 183 Transportation by truck Unit
Amount
Distance
tkm/MJsoybean
0.0450
Soybean
MJ/MJsoybean
1.0100
Soybean
MJ
1.0000
Comment - For the fuel consumption of the 40 t truck, see Table 63. Table 184 Transportation by train Unit
Amount
Distance
tkm/MJsoybean
0.0189
Soybean
MJ/MJsoybean
1.0100
Soybean
MJ
1.0000
Comment - For the fuel consumption for a freight train run on diesel fuel, see Table 77. Table 185 Transportation by inland waterway
194
Unit
Amount
Distance
tkm/MJsoybean
0.0104
Soybean
MJ/MJsoybean
1.0100
Soybean
MJ
1.0000
Comment - For the fuel consumption of a bulk carrier for inland navigation, see - Table 75. Source 1 Da Silva et al., 2010.
Table 186 Shipping distances to Rotterdam Nautical miles sea
km sea
Brazil (Santos)
5 501
10 188
Brazil (Paranagua)
5 629
10 425
Average
5 565
10 306
Sources 1 Reuters, 2012. 2 Salin, 2009. 3 Flaskerud, 2003.
195
Argentina soya Soybean cultivation Table 187 Soybean cultivation in Argentina I/O Diesel
Input
Unit
Amount
MJ/MJsoybeans
0.02594 0.000116
Source 1, 2, 5
N fertilizer
Input
kg/MJsoybeans
Ca fertilizer as CaCO3
Input
kg/MJsoybeans
K2O fertilizer
Input
kgMJsoybeans
P2O5 fertilizer
Input
kg/MJsoybeans
Pesticides
Input
kg/MJsoybeans
0.000097
1
Seeding material
Input
kg/MJsoybeans
0.001396
8
Soybeans
Output
MJ
Field N2O emissions
g/MJsoybeans
CO2 from neutralisation of other soil acidity
g/MJsoybeans
6
0.000399 7 0.000003 0.000286
Comment
3, 4 3, 4
See liming data 0.06 kg K2O/(tonne moist soya) 5.72 kg P2O5/(tonne moist soya) 5.77 kg/ha soybeans
1.0000
0.189
6
GNOC calculates EU import weighted av.
7
See liming data
Comments - 23 MJ/kg of dry soybeans (Ref 5). - 13% water content. - Diesel use: 1541 MJ/(ha*yr) (Refs 1, 2): o 1660 MJ/ha includes 30km transport to store, and drying, according to Ref. 2 quoted in Ref. 1. In the context, we suppose that assumes all drying inputs are diesel. Therefore we subtract the drying energy per ha derived from our drying data. - N fertilizer: 0.00232 kg N/(kg moist soya) . Sources 1 Muzio et al., 2009. 2 SAGPyA, 2008. 3 International Fertilizer Association (IFA), 2013. 4 FAPRI, 2012. 5 Jungbluth et al., 2007 (Ecoinvent report). 6 Edwards and Koeble, 2012 (see Chapter 3). 7 JRC: Acidification and liming (see Section 3.10). 8 Hilbert et al., 2010.
196
Drying Table 188 Soybean drying Unit
Amount
LPG
MJ/MJsoybeans
0.00074
NG
MJ/MJsoybeans
0.00110
Diesel
MJ/MJsoybeans
0.000160
Soybeans
MJ/MJsoybeans
1.000
Soybeans
MJ
1.000
Sources 1 1
Sources 1 De Tower and Bartosik, 2012. Transport of soybeans Truck transport Argentina
Distance km 350
Table 189 Truck transport of soybeans Unit
Amount
Distance
tkm/MJsoybean
0.0175
Soybean
MJ/MJsoybean
1.0100
Soybean
MJ
1.0000
Comment - For the fuel consumption of the 40 t truck, see Table 63. Table 190 Shipping and barge distances to Rotterdam
Argentina (Rosario)
Nautical miles sea
km sea
6 584
12 194
Source Searates.com
197
United States soya Soybean cultivation Table 191 Soybean cultivation in the United States I/O Diesel
Input
Unit
Amount
MJ/MJsoybeans
0.02816 0.000054
N fertilizer
Input
kg/MJsoybeans
Ca fertilizer as CaCO3
Input
kg/MJsoybeans
K2O fertilizer
Input
kgMJsoybeans
P2O5 fertilizer
Input
kg/MJsoybeans
5 8
0.000394 0.000209
Pesticides
Input
kg/MJsoybeans
Seeding material
Input
kg/MJsoybeans
0.00121
Soybeans Field N2O emissions CO2 from neutralisation of other soil acidity
Output
MJ
1.0000
g/MJsoybeans 1.647
Comment
6
0.004529
0.00003
g/MJsoybeans
Source
2, 3 2, 3
See liming data 7.88 kg K2O/(tonne moist soya) 4.18 kg P2O5/(tonne moist soya)
6
1.60 kg/(ha *yr)
6
68.9 kg/(ha *yr)
5
GNOC calculates EU import weighted av.
8
See liming data
Comments - LHV = 23 MJ/kg of dry substance (Ref. 4). - 13.0 % traded moisture content of SB in the United States (Ref. 7). - Diesel use: 1 606 MJ/(ha*yr): o 33.3 li diesel+12.8 gasoline per ha. Possibly some of this is used for drying rather than cultivation. - N fertilizer: 0.00108 kg N/(kg moist soya). Sources 1 Omni Tech International, 2010. 2 International Fertilizer Association (IFA), 2013. 3 FAOstat data. 4 Jungbluth et al., 2007 (Ecoinvent report). 5 Edwards and Koeble, 2012 (see Chapter 3). 6 Pradhan, et al., 2011. 7 Beuerlein, 2012. 8 JRC: Acidification and liming (see Section 3.10).
198
Drying Table 192 Soybean drying Unit
Amount
LPG
MJ/MJsoybeans
0.00084
NG
MJ/MJsoybeans
0.00256
Electricity
MJ/MJsoybeans
0.00108
Soybeans
MJ/MJsoybeans
1.000
Soybeans
MJ
1.000
Sources 1, 2 1 1
Comments - Hypothesis: drying consumes the part of the reported American-soy fuel-forcultivation which is not diesel or gasoline. - 2 litres/ha of LPG. Possibly some of the diesel or gasoline from cultivation is also used for drying, but if so, it would only come off the cultivation emissions. - NG: 4.1 m3/ha. - Electricity: 17.1 kWh/ha. Sources 2 Beuerlein, 2012. 3 Metrology Centre, 2012.
Transport of soybeans Table 193 Transport of soybeans via 40 t truck over a distance of 80 km (one way) Unit
Amount
Distance
tkm/MJsoybean
0.0040
Soybean
MJ/MJsoybean
1.0100
Soybean
MJ
1.0000
Comments - American soybeans board says 300 miles (480 km) for transport of fertilizers to farm. - For the fuel consumption of the 40 t truck, see Table 63. Shipping and barge transport distances Omni Tech International (2010) assumes transport of soybeans from Arkansas via rail to eastern seaboard ports. However, the U.S. Soybean Export Council (USSEC) (2011) says: 'The U.S. Atlantic Coast was once quite important to U.S. soybean exports. But the role of the Atlantic diminished when rail freight rates were deregulated. Under deregulation, railroads serving the Gulf faced 199
severe competition from barges and water movement, but railroads serving the U.S. East Coast had no competition. That has kept rail rates high going east, so that the geographic freight advantage of a shorter voyage to European destinations is generally eaten up by the higher internal transportation costs. Except for local soybean production, all supplies must be railed in from the central United States, and eastern processors usually absorb the local soybean production to supply the region‘s huge poultry industry with soy meal and the populous East Coast with soybean oil. Like PNW ports, the Atlantic Coast export volume tends to grow when ocean freight rates are relatively high, and the freight advantage to Europe from the Atlantic compared to the Gulf grows large enough to compensate for the cost of railing in Midwestern soybeans.' The USDA Agricultural Marketing Service, in its 'Brazil Soybean Transportation 2008/9' and several other reports of soybean export transport costs, use Davenport, Iowa as its typical source for American soybean exports, exporting via the Mississippi. Some 60 % of American soybean exports are said to tranship in the New Orleans region. Therefore, we chose the Mississippi export route via the New Orleans area, which carries 60 % of American soybean exports, according to the USSEC report. Table 194 Transport of soybeans seed via inland ship over a distance of 2 161 km (one way) I/O
Unit
Amount
Distance
Input
tkm/MJsoybeans
0.1080
Soybeans
Input
MJ/MJsoybeans
1.0100
Soybeans
Output
MJ
1.0000
Comments - Davenport, Iowa to New Orleans (Ref. 1). - For the fuel consumption of a bulk carrier for inland navigation, see - Table 75. Source 1 USDA Agricultural Marketing Service, Brazil Soybean Transportation, October 28, 2010, example for United States soybeans export. Table 195 Shipping and barge distances to Rotterdam
New Orleans
Nautical miles sea
km sea
4 860
8 979
200
6.11 Palm oil to biodiesel Description of pathway The following processes are included in the 'palm oil to biodiesel' pathway.
The data for each process are shown below; significant updates are described in more detail with relevant references. NOTE: 16% of the RED-eligible oil-palm area in Indonesia and Malaysia is on peat The sustainability criteria in the RED text do not say that no palm oil may come from peat land. It says that no palm oil for EU biofuel may come from land converted from wetland after 2008. In 2007 there was 957 kha of oilpalm on peat in Indonesia nd 624 in Malaysia; a total of 1581 kha. In 2011 the figures were 1311 and 844kha respectively; a total of 2155 kha [Miettinen 2012]. Interpolating linearly, we estimate a total of 1810 ha on peat in 2008. This peat aerea is eligible to supply palm oil under RED sustainability criteria. In 2013, by extrapolation of the data in [Miettinen et al. 2012] for 2007 and 2011, we estimate that 9575kha of oil-palm are NOT on peat, and therefore also eligible to supply palm oil for biofuel under the sustainability criteria. So the fraction of eligible palm oil area which is on peat is 1810/(1810+9575) = 16%
201
Step 1: Cultivation of oil palm tree (16 % peat) The new data for palm oil tree cultivation are shown in Table 196. The updated data include: • • • • •
diesel and pesticide use in palm oil tree cultivation; CaCO3 fertilizer use updated according to the Malaysian Palm Oil Board comments received in 2013; N2O emissions calculated by JRC data using the JRC GNOC model (see Section 3.7); K2O and P2O5 updated using the most recent data available; CO2 emissions from neutralisation of other soil acidity, calculated by the JRC (see Section 3.10).
Table 196 Cultivation of oil palm tree (16 % peat) I/O
Unit
Amount
Diesel
Input
MJ/MJFFB
0.00537
6
K2O
Input
kg/MJFFB
0.00061
2, 3
N fertilizer
Input
kg/MJFFB
0.00031
4
CaCO3 fertilizer
Input
kg/MJFFB
0.00000
7
P2O5 fertilizer
Input
kg/MJFFB
0.00011
2, 3
EFB compost
Input
kg/MJFFB
0.01420
1
225 kg/(t moist FFB)
Pesticides
Input
kg/MJFFB
0.000047
6
0.744 kg/(t moist FFB)
Fresh fruit bunches (FFBs)
Output
MJ
1.0000
g/MJFFB
0.0305
4
See GNOC data
g/MJFFB
0.00000
5
Field N2O emissions CO2 from neutralisation of other soil acidity
-
Source
Comment 2.37 litres/(t moist FFB) 9.59 kg/(t moist FFB) See GNOC data
1.7352 kg/(t moist FFB)
Comments - LHV = 24 MJ/kg dry substance. - 34 % moisture in FFB (Ref. 8). Sources 1 Schmidt, 2007. 2 International Fertilizer Association (IFA), 2013. 3 FAPRI, 2012. 4 Edwards and Koeble, 2012 (see Chapter 3). 5 As no aglime is used on oil-palm according to Ref. 8, there are no aglime emissions and no excess-over-fertilizer-acidification aglime emissions (explained in [4]). 6 Choo et al., 2011. 7 Comments received from the Malaysian Palm Oil Board (MBOP), 14th June to Commission stakeholder meeting in May 2013.
202
Step 2: Transportation of fresh fruit bunches (FFBs) Table 197 Transport of fresh fruit bunches via 12 t truck (payload 7t) over a distance of 50 km (one way) I/O
Unit
Amount
Distance
Input
tkm/MJFFB
0.0032
FFBs
Input
MJ/MJFFB
1.0000
FFBs
Output
MJ
1.0000
Comment - For the fuel consumption of the 12 t truck, see Table 68. Sources 1 Lastauto Omnibus Katalog, 2010; ETM EuroTransportMedia Verlags- und Veranstaltungs-GmbH, Stand August 2009. 2 Choo et al., 2011.
Step 3: Storage of fresh fruit bunches Table 198 Storage of fresh fruit bunches I/O
Unit
Amount
FFBs
Input
MJ/MJFFB
1.0000
FFBs
Output
MJ
1.0000
Source 1 MPOB personal communication at data review meeting, Ispra, November 2011.
203
Step 4: Oil mill: plant oil extraction from fresh fruit bunches Table 199 Plant oil extraction from fresh fruit bunches (FFB) I/O
Unit
Amount
Source
FFB
Input
MJ/MJoil
2.1427
Grid electricity
Input
MJ/MJoil
0.000078
5
Diesel
Input
MJ/MJoil
0.00445
5
Emission/open POME pond Emission/closed POME pond N2O from shells and fibre combustion Crude palm oil (CPO)
Emission
gCH4/MJoil
0.9844
5
Emission
gCH4/MJoil
0.1477
5
Emission
gN2O/MJoil
0.000996
Output
MJ
1.0000
Comments - Grid electricity: o 1.76 MJe/tonne CPO, after Ref. 5 allocated 1/1.64 of the inputs to oil by mass - Diesel: o 100.33 MJ/tonne CPO, after Ref. 5 allocated 1/1.64 of the inputs to oil by mass - Emission/open POME (Palm Oil Mill Effluent) pond: 22.21 kg/tonne CPO, after Ref. 5 allocated 1/1.64 of the inputs to oil by mass. - Emission/closed POME pond: 85 % of methane emissions assumed captured by methane capture technology (Ref. 5). - 0.36 tonne of solid fuel/tonne CPO, after Ref. 5 allocated 1/1.64 of the inputs to oil by mass; 0.004 g N2O/MJ of solid biofuel (Ref 7). Emissions from palm oil mill We took the emissions from the palm oil mill from the recent publication by MPOB staff [5]. The inputs and emissions reported in that paper are those allocated to 1 t of crude palm oil by mass allocation. According to this paper, making 1 t palm oil produces 0.64 t of useful by-products, so to find the unallocated emissions per tonne palm oil, we have to multiply the figures by 1.64. Most of the heat and power for the mill (a composite of 12 representative mills) comes from burning all the pressed mesocarp fibre and some nutshells in a CHP generator. However, a little grid electricity and diesel are also in the mix. There is a surplus of nutshells, which is exported, according to Ref. 5, as a low-cost fuel. In this update, we calculate emissions for palm oil specifically instead of a combination of palm oil and palm kernel oil. Palm kernel oil is as yet rarely used for biofuel as it has highervalue uses for soap making, etc., competing with tallow.
204
That means allocating part of the mill emissions to palm kernels. As we do not have an LHV for palm kernels, we calculated our energy-based allocation by calculating separately for the two components of the kernels: palm kernel meal and palm kernel oil. The main emission from the mill is methane released from the anaerobic effluent pond. Following the MPOB (ref. 5), without methane capture, 11.94 kg methane per tonne of effluent is emitted, whereas with methane capture, this is reduced by 85 %. Calculation of LHV of palm oil Table 200 LHV of palm oil Component Palm oil Palm kernel meal Palm kernel oil Excess nutshells Allocation to crude palm oil
Wt fraction
Source
0.200 0.029 0.024 0.074
1 2,3 1 5
LHV wet ENER def (MJ/kg) 37 16.4 37 01
Source
84 %
6 2 6 4
Moisture 0 10 0 10
Output in ENER LHV
% % % %
7.393 0.481 0.888 0.000
Total
8.762
LHV of dry part per wet kg 37.0 16.7 37 17.3
Comment 1 The RED already defines nutshells as a residue accorded zero emissions. Therefore they should not be allocated any emissions as a co-product. Sources 1 Schmidt, 2007. 2 Calculated from composition by JRC 'LHV calculator' using composition in Ref. 4. 3 Chin, 1991. 4 Panapanaan and Helin, 2009. 5 Choo et al., 2011. 6 Mehta and Anand, 2009. 7 IPCC, 2006. Step 5: Transport of palm oil Table 201 Transport of palm oil summary table Transporter
Notes
Distance (km one-way)
Truck
Payload 27
120
Product tanker
Payload 22,560
16,287
Comment - Shipping distance: palm oil Kuching (Borneo, between peninsula Malaysia and Indonesia) to Rotterdam. 205
Source Searates.com
Table 202 Transport of palm oil via a 40 t truck over a distance of 120 km (one way) I/O
Unit
Amount
Distance
Input
tkm/MJoil
0.0035
Vegetable oil
Input
MJ/MJoil
1.0000
Vegetable oil
Output
MJ
1.0000
Comment - For the fuel consumption of a 40 t truck, see Table 63. Table 203 Depot for palm oil I/O
Unit
Amount
Vegetable oil
Input
MJ/MJoil
1.0000
Electricity
Input
MJ/MJoil
0.00084
Vegetable oil
Output
MJ
1.0000
Comment - One depot at export and one depot at input terminal Source 1 Dautrebande, 2002.
Table 204 Maritime transport of palm oil via ship over a distance of 16 287 km (one way) I/O
Unit
Amount
Distance
Input
tkm/MJoil
0.4402
Vegetable oil
Input
MJ/MJoil
1.0000
Vegetable oil
Output
MJ
1.0000
Comment - For the fuel consumption of the product tanker (payload 22,560 t), see Table 73.
206
Step 6: Refining of vegetable oil from oil palm Table 205 Refining of vegetable oil from oil palm I/O
Unit
Amount
Source
Comment
Electricity
Input
MJ/MJoil
0.0009
1, 2
34.38 MJ/(t oil) [1]
H3PO4
Input
kg/MJoil
0.000032
1, 2
1.19 kg/(t oil) [1]
NaOH
Input
kg/MJoil
0.000088
1, 2
3.26 kg/(t oil) [1]
Crude vegetable oil
Input
MJ/MJoil
1.0246
1
Steam
Input
MJ/MJoil
0.0040
1, 2
Plant oil
Output
MJ
1.0000
149.2 MJ/(t oil) [1] 37 MJ/kg of oil [2]
Sources 1 EBB, 2009. 2 Edwards, R., 5 September 2012, based on ECN Phyllis database of biomaterials properties. Step 7: Esterification Same input data used as for rapeseed. Step 7: Transportation of FAME to the blending depot The same transport mix used in ‘rapeseed to biodiesel’ has been added, but excluding pipeline transport as it is unlikely that this product would be transported in this manner. Table 206 Transportation of FAME summary table to the blending depot Share
Transporter
notes
Distance (km one way)
13.2 %
Truck
Payload 40 t
305
31.6 %
Product tanker
Payload: 15 000 t
1 118
50.8 %
Inland ship/barge
Payload 1 200t
153
4.4 %
Train
381
207
Table 207 Transport of FAME via 40 t truck over a distance of 305 km (one way) I/O
Unit
Amount
Distance
Input
tkm/MJFAME
0.0088
FAME
Input
MJ/MJFAME
1.0000
FAME
Output
MJ
1.0000
Comment - For the fuel consumption of the 40 t truck, see Table 63. Table 208 Maritime transport of FAME over a distance of 1 118 km (one way) I/O
Unit
Amount
Distance
Input
tkm/MJFAME
0.0301
FAME
Input
MJ/MJFAME
1.0000
FAME
Output
MJ
1.0000
Comment - For the fuel consumption of the product tanker (payload: 15,000 t), see Table 72. Table 209 Transport of FAME over a distance of 153 km via inland ship (one way) I/O
Unit
Amount
Distance
Input
tkm/MJFAME
0.0041
FAME
Input
MJ/MJFAME
1.0000
FAME
Output
MJ
1.0000
Comment - For the fuel consumption for an inland oil carrier, see Table 76. Table 210 Transport of FAME over a distance of 381 km via train (one way) I/O
Unit
Amount
Distance
Input
tkm/MJFAME
0.0102
FAME
Input
MJ/MJFAME
1.0000
FAME
Output
MJ
1.0000
Comments - For the fuel consumption of the freight train, see Table 78.
Step 9: FAME depot distribution inputs Same input data are used as for rapeseed.
208
6.12 Jatropha to biodiesel Description of pathway The following processes are included in the 'Jatropha to biodiesel' pathway.
The data for each process are shown below; significant updates are described in more detail with relevant references. Step 1: Jatropha seed/plantation — (mechanised cultivation) The new data for Jatropha seed plantation are shown in Table 211. All data have been updated.
209
Table 211 Cultivation of Jatropha seed/plantation - (mechanised cultivation) I/O
Unit
Amount
Source
Comment
Diesel
Input
MJ/MJseed
0.05431
1
3 621 MJ/(ha*yr)
K2O fertilizer
Input
kg/MJseed
0.00116
1
77.312 kg/(ha*yr)
N fertilizer
Input
kg/MJseed
0.00031
1
20.8 kg/(ha*yr)
P2O5 fertilizer
Input
kg/MJseed
0.00009
1
5.80 kg/(ha*yr)
Jatropha seed
Output
MJ
1.000
2
2600 kg/(ha*yr)
g/MJseed
0.0241
2, 3
Field N2O emissions
Comments - Diesel use: 101 l/ha per year (Ref. 1). - LHV: 27.28 MJ/(kg dry jatropha seed) (Ref. 2). - Jatropha seed: 6 % water (Ref. 1). - Jatropha capsules: 4160 kg/ha*yr (capsules = seeds + husks) (Ref. 1). Sources 1 IFEU, 2011, based on Reinhardt et al., 2008. 2 IFEU/UU 2011, based on van Eijck, 2011 and Wahl et al., 2009. 3 Paustian et al., 2006.
210
Calculation of N2O from jatropha cultivation Table 212 N2O from jatropha cultivation calculation Best Estimate FSN (input of synthetic fertilizer)
21
kg/(ha*yr) [1]
FCR (N-input from crop residues)
61
kg N/(ha*yr) [1]
Jatropha seed yield
2,600
kg/(ha*yr) [1]
H2O-content jatropha seed
6 %
[1]
Crop0 (production of non-N-fixing crops)
2,444
kg dry biomass/(ha*a)
FracGASF (NH3- and NOx-emissions)
0.1000
kg NH3-N+NOx-N/(kg synthetic fertilizer-N)
FracLEACH (N leaching off)
0.3000
kg N/(kg fertilizer or manure)
EF1
0.01
kg N2O-N/(kg Fertilizer-N)
EF4
0.0100
kg N2O-N/(kg NH3-N+ kg NOx-N-emitted)
EF5
0.0075
kg N2O-N/(kg N leaching off)
Direct N2O-emissions
1.29
kg N2O/ha/a
Indirect N2O-emissions from NH3 and NOxemissions
0.03
kg N2O/ha/a
Indirect emissions form N leaching off
0.29
kg N2O/ha/a
Total
1.61
kg N2O/ha/a
LHV
27.3
MJ/kg of dry substance
Energy jatropha seed yield
66,681
MJ/(ha*a)
18,522
kWh/(ha*a)
0.087
g/kWhjatropha seed
Specific N2O-emissions
Sources 1 IFEU, 2011, based on Reinhardt et al., 2008. 2 Paustian et al., 2006. 3 Jongschaap et al., 2007.
211
Step 2: Transportation of jatropha capsules Table 213 Transport system of jatropha capsules via 20 t truck (payload 10 t) over a distance of 190 km (one way) I/O
Unit
Amount
Distance
Input
tkm/MJ
0.01185
Jatropha seed
Input
MJ/MJ
1.00000
Jatropha seed
Output
MJ
1.00000
Comment - For the fuel consumption of the 20 t truck, see Table 69. Sources 1 van Eijik et al., 2011. 2 IFEU, 2011, based on van Eijck, 2011. 3 Reinhardt et al., 2008. 4 Lastauto Omnibus Katalog, 2010. Step 3: Extraction of vegetable oil from jatropha Table 214 Extraction of vegetable oil from jatropha I/O
Unit
Amount
Source
Jatropha seed
Input
MJ/MJ
3.2239
1, 2
Electricity
Input
MJ/MJ
0.01539
1, 2
Steam
Input
MJ/MJ
0.07291
1, 2
n-hexane
Input
MJ/MJ
0.002268
1, 2
Vegetable oil
Output
MJ
1.0000
Comments - Steam from shells and husks fuelled boiler - Seed cake as fertilizer, husks as fuel (no energy allocation) (Refs 1, 2). Sources 1 IFEU, 2011, based on Reinhardt et al., 2008. 2 IFEU/UU, 2011, based on van Eijck, 2011 and Wahl et al., 2009.
212
Supplementary data on conversion of Jatropha seed to plant oil via expeller. (These data are used to calculate the values in the table above). Table 215 Conversion of Jatropha seed to plant oil via expeller (allocation by energy) Jatropha seed Electricity Steam Jatropha oil Residues (returned to field, mostly) Husks (capsules = seeds + husks) Jatropha cake (cake = meal + shells)
I/O Input Input Input Output
Unit kg MJ MJ kg per t seeds
Amount 1000 122.4 580 215
Residue Residue
kg kg wet per t wet seeds
600 656
Source 7 7 7 7 7
Comments - LHV (crude jatropha oil): 37 MJ/kg (Ref. 2). - LHV (jatropha seed): 27.3 MJ/kg dry substance (Ref. 6). Sources 1 Prueksakorn and Gheewala, 2006. 2 Kerkhof, 2008. 3 Openshaw, 2000. 4 Jongschaap et al., 2007. 5 Pelletbase, 2010. 6 Hartmann, 1995. 7 Reinhardt et al., 2008. 8 IFEU/UU, 2011, based on van Eijck, 2011 and Wahl et al., 2009. Step 4: Transport of jatropha oil Table 216 Transport of jatropha oil summary table Transporter
Type
Distance (km one-way)
Truck (40 t)
Payload 27 t
150
Product tanker
Payload 22,560 t
11 727
213
Table 217 Transport of jatropha oil via a 40 t truck over a distance of 150 km (one way) I/O
Unit
Amount
Distance
Input
tkm/MJoil
0.0045
Vegetable oil
Input
MJ/MJoil
1.0000
Vegetable oil
Output
MJ
1.0000
Comment - For the fuel consumption for the 40 t truck, see Table 63. Table 218 Maritime transport of jatropha oil via ship over a distance of 11 727 km (one way) I/O
Unit
Amount
Distance
Input
tkm/MJoil
0.3169
Vegetable oil
Input
MJ/MJoil
1.0000
Vegetable oil
Output
MJ
1.0000
Comment - For the fuel consumption for the product tanker (payload 22 560 t), see Table 73. Sources 1 IMO, 2009.
Step 5: Refining of vegetable oil from jatropha Table 219 Refining of vegetable oil from jatropha I/O
Unit
Amount
Source
Comment
Electricity
Input
MJ/MJoil
0.000584
1, 2
21.6 MJ/(t oil) [1]
Fuller's earth
Input
kg/MJoil
0.000162
1, 2
6 kg/(t oil) [1]
Crude vegetable oil
Input
MJ/MJoil
1.0417
1
Steam
Input
MJ/MJoil
0.00800
1, 2
Plant oil
Output
MJ
1.0000
296 MJ/(t oil) [1] 37 MJ/kg of oil [2]
Sources 1 IFEU, 2011a (own calculations). 2 Edwards, R., 5 September 2012 based on ECN Phyllis database of biomaterials properties. Step 6: Esterification Same input data are used as for rapeseed. 214
Step 7: Transportation of FAME to the blending depot Same input data are used as for rapeseed. Step 8: FAME depot distribution inputs Same input data are used as for rapeseed.
215
6.13 Waste cooking oil Waste cooking oil (used cooking oil) is defined as a waste in the RED and, therefore, is attributed zero GHG emissions at its point of collection. However, used cooking oil is being brought into the EU from considerable distances. The major world exporter is China, and if its use in the EU continues to increase (as we expect it will), it could supply a large part of the EU used cooking oil market. The following local truck transport is considered for both 500km pathways. Transport of waste cooking oil Table 220 Transport of waste oil via 40 t truck over a distance of 100 km I/O
Unit
Amount
Distance
Input
tkm/MJoil
0.0029
Vegetable oil
Input
MJ/MJoil
1.0000
Vegetable oil
Output
MJ
1.0000
Comment - For the fuel consumption for the 40 t truck, see Table 63. Additional sea transport for pure plant oil from waste cooking oil (pathway: WOPP)>500 km and waste cooking oil to FAME (pathway: WOFA)>500km. Table 221 Maritime transport of waste cooking oil via ship over a distance of 7 000 km (ref 1) I/O
Unit
Amount
Distance
Input
tkm/MJoil
0.1892
Vegetable oil
Input
MJ/MJoil
1.0000
Vegetable oil
Output
MJ
1.0000
Comments - LHV waste cooking oil = 37 MJ/kg. - For the fuel consumption of the product tanker (payload 22 560 t), see Table 73. Sources 1 European Waste-to-Advanced Biofuels Association & Mittelstandverband abfallbasierter Kraftstoffe, 2014. We assume refining emissions are like those of rapeseed oil.
216
Transesterification of used cooking oil to FAME Table 222 Transesterification of animal fat & used cooking oil to FAME I/O
Unit
Amount
Source
Comment
Electricity
Input
MJ/MJFAME
0.00385
1
41 kWh/(t Fat)
H3PO4
Input
kg/MJFAME
0.00037
1
14.2 kg/(t Fat)
KOH
Input
kg/MJFAME
0.00034
1
13.2 kg/(t Fat)
Methanol
Input
MJ/MJFAME
0.0572
1
110 kg/(t Fat)
Fat
Input
MJ/MJFAME
0.9634
2
1025 kgFat/tFAME
NG
Input
MJ/MJFAME
0.0927
1
801 kgSteam/tFat
FAME
Output
MJ
1.0000
1
975 kgFAME/tFat
K2SO4
Output
kg/MJFAME
0.00068
1
26 kg/(t Fat)
Comments - 37.0 MJ/(kg fat). - 4.44 MJ NG/kg steam. - 37.2 MJ/(kg FAME) (Refs 2 and 3). - BDI use more heat in making FAME from cooking oil than companies making FAME from vegetable oils, because BDI distill the biodiesel coming out, to reach quality standards without having to blend with better quality biodiesel. Table 223 By-products used for allocation I/O
Unit
Amount
Source
Comment
Glycerol
Output
MJ/MJFAME
0.0432
1
98 kg/(t Fat)
Bio-oil
Output
MJ/MJFAME
0.0150
1
25 kg/(t Fat)
Comments - 16 MJ/(kg glycerol). - 21.8 MJ/(kg bio-oil). Sources 1 BDI, Input-Output Factsheet, Plant Capacity 50 000 t Biodiesel, Department Research and Development BDI – BioDiesel International AG. 2 Wörgetter et al., 2006. 3 Mittelbach and Remschmidt, 2004. For transport of FAME and distribution, same input data are used as for palm oil
217
6.14 Animal fat LCA guidelines suggest that when waste is upgraded to a product, the emissions for bringing the waste up to zero monetary value are not counted. Thus, for a start, transport to the rendering plant is not included. For the rendering process itself, we estimate that 63% of the emissions do not count, because they serve to bring the product up to zero value, so we attribute only 37% of rendering emissions to the products of rendering (Table 224). Rendering of animal carcass produces different grades of fat (loosely called tallow) and a by-product: meat-and-bone meal. According to the Fat Processors and Renderers Association (EFPRA) (ref. 1), meat-and-bone meal has a positive value, even though its use is still restricted by regulations in the wake of the BSE crisis. In some previous years it has been a waste which required a gate fee for incineration. If meat-and-bone meal is considered a product, then it should be allocated part of the emissions from the rendering process. On the other hand, if national regulations categorize it as a 'waste', all the emissions attributed to products should be allocated to the fat. Of the 37% of rendering emissions which are attributed to adding value to products, less than half (47%) are allocated to fat (Table 225), on the basis of lower heat content (wetdefinition) and the rest to the meat-and-bone meal by-product. There is a danger that diverting animal fat from other uses (such as oleochemicals) to biofuel or bioenergy use could increase GHG emissions. However, this is highly dependent on local conditions (for example, is there an oleochemicals plant within economic traveling distance), so needs to be decided by national governments. Table 224 Fraction of rendering process attributed to products Gate fee for wet animal carcass (negative value) Approx. price Cat 1 meat and bone meal
NWE Price of cat. 1 tallow
Ratio net/gross fat Fraction of the rendering process which is considered to be adding positive value (rather than bring waste up to zero value)
Amount
Unit
-100
Euro/tonne wet carcass
-716
Euro/tonne net fat produced
10
Euro/tonne
25.6
Euro/tonne gross fat produced
27
Euro/tonne net fat produced
375
Euro/tonne
375
Euro/tonne gross fat produced
390
Euro/tonne net fat produced
Source
2
1
0.96 36.7%
Sources 1 European Fat Processors and Renderers Association (EFPRA), 2015. Personal communication: approximate material prices for the EU.
218
Table 225 Allocation of emissions of rendering between fat and meat-and bone meal for the case that meat-and bone meal is not considered a waste kg/kgfat
Dry carcass
Fraction moisture
LHV dry
Ref
LHV 'wet definition'
Heat content of products (per kg fat out)
7,8
17.2
44.05
7
38.3
3.45
Wet meat and 2.56 3.8% 18.0 bone meal Net fat 1.00 1.2% 38.8 production Fraction of animal fat in total LHV of products
38.31 46.5%
Fraction of rendering emissions attributable to animal fat, IF meat and bone meal is NOT considered a waste
17.1%
Ref. 6 gives CO2 emitted (per million pounds of animal waste) from burning NG, and some of the animal fat. These CO2 emissions are converted back into tonnes of NG (and GJ of NG) per tonne of fat ( Table 226). Thus most of their fuel used in rendering is NG; we have not accounted for other (more CO2-intensive) fossil fuels burnt in EU plants which are not on the NG grid, which are also more likely to burn cat 1 fat. We note that cat. 1 animal fat is now generally more expensive than fuel oil or NG. Therefore, the few renderers who formerly burnt Cat. 1 animal fat are now likely to burn fossil fuel. That fat is most likely to be replaced by fuel oil, because the burner for fat does not need to be modified to burn fuel oil, and because the factories may not be on the NG grid. Accordingly, we have added ~10% of fuel oil to the fuel mix in rendering, to replace the equivalent amount of animal fat. In a marginal calculation, small amounts of animal fat used for biodiesel it would all be replaced by fuel oil. However, we have calculated the average emissions for all animal fats. Table 226 NG per tonne of fat 454 tonnes aniwaste
PER MILLION POUNDS ANIMAL WASTE Reported emissions:
t CO2
t Carbon
tonnes fuel
Fat burnt in rendering
6.77
1.85
2.46
NG
49.9
13.61
18.1
LHV NG GJ/tonne (WTW)
50
PER TONNE OF MOIST FAT (including fat which is burnt) Fat burnt in rendering
0.103
0.028
0.037
NG
0.758
0.207
0.276
GJ 0.36 GJ NG/tonne fat 0.0055
219
No emissions are attributed to transport from slaughterhouse to rendering plant, as the material is a waste at this stage. Step 1: Animal fat processing from carcass (biodiesel) Table 227 Animal fat processing from carcass (biodiesel) (per kg produced fat) Rendering
I/O
Unit
Amount
Source
Carcass
Input
dry kg carcass/kg fat
3.45
4
Electricity
Input
MJ/kg tallow
0.65
4
Natural gas
Input
MJ/kg tallow
11.68
4,6
Fuel oil
Input
MJ/kg tallow
1.43
6
Comments - LHV = 38.3 MJ/dry kg. - Water content of carcass: 50%.
Table 228 Rendering (per MJ produced fat) I/O
Unit
Amount
Source
Fat in carcass
Input
MJ/MJtallow (part of carcass)
1.0000
4
Electricity
Input
MJ/MJtallow
0.01704
4, 6
Natural gas
Input
MJ/MJtallow
0.3047
6
Fuel oil
Input
MJ/MJtallow
0.0374
Tallow
Output
MJ
1.0000
Sources 1 Ecoinvent, LCI of tallow production. 2 Notarnicola et al., 2007. 3 Raggi et al., 2007. 4 De Camillis et al., 2010. 5 LCA report from BIODIEPRO project. See http://www.argentenergy.com/articles/article_8.shtml online. 6 US National Renderers Association website: see http://nationalrenderers.org online. 7 ECN database Phyllis 2 accessed 2014. 8 Laraia et al., 2001.
220
Step 2: Tansport of tallow to the plant Table 229 Transport of tallow via 40 t truck over a distance of 150 km (one way) I/O
Unit
Amount
Distance
Input
tkm/MJFAME
0.00435
FAME
Input
MJ/MJFAME
1.0000
FAME
Output
MJ
1.0000
Comment - For the fuel consumption of the 40 t truck, see Table 63.
We assume the rest of the processing is the same as for waste cooking oil.
221
6.15 HVO This process applies to hydrotreating of Rapeseed oil (ROHY), Sunflower oil (SOHY), Soy oil (SYHY), Palm oil (POHY): NExBTL deep hydrogenation process and distribution. Input: crude vegetable oil (ex plant oil extraction, refining is included in the NExBTL process). For input data on supply of the vegetable oils, please refer to the equivalent FAME pathway (e.g. rapeseed to biodiesel, sunflower to biodiesel, palm oil to biodiesel, etc.). Table 230 Hydrotreating of vegetable oil (except palm oil and tallow oil) via NExBTL process including H2 generation (generation of a BTL-like fuel) I/O
Unit
Amount
NG
Input
MJ/MJBTL
0.10981
Vegetable oil
Input
MJ/MJBTL
1.02385
H3PO4
Input
kg/MJBTL
0.0000169
NaOH
Input
kg/MJBTL
0.0000270
N2
Input
kg/MJBTL
0.0000063
Electricity
Input
MJ/MJBTL
0.00155
BTL-like fuel
Output
MJ
1.0000
Source 1 Reinhardt et al., 2006. Table 231 Hydrotreating of palm oil via NExBTL process including H2 generation (generation of a BTL like fuel) I/O
Unit
Amount
Source
NG
Input
MJ/MJBTL
0.08576
1, 2
Vegetable oil
Input
MJ/MJBTL
1.02385
1
H3PO4
Input
kg/MJBTL
0.0000169
1
NaOH
Input
kg/MJBTL
0.0000270
1
N2
Input
kg/MJBTL
0.0000049
1, 2
Electricity
Input
MJ/MJBTL
0.000864
1, 2
Output
MJ
1.0000
BTL-like fuel
Sources 1 Reinhardt et al., 2006. 2 ConocoPhillips, 25 October 2007. 222
Table 232 Hydrotreating of tallow via NExBTL process including H2 generation (generation of a BTL like fuel) I/O
Unit
Amount
Source
NG
Input
MJ/MJBTL
0.08499
1, 2
Vegetable oil
Input
MJ/MJBTL
1.02385
1
H3PO4
Input
kg/MJBTL
0.0000169
1
NaOH
Input
kg/MJBTL
0.0000270
1
N2
Input
kg/MJBTL
0.0000049
1, 2
Electricity
Input
MJ/MJBTL
0.000842
1, 2
Output
MJ
1.0000
BTL-like fuel
Sources 1 Reinhardt et al., 2006. 2 ConocoPhillips, 25 October 2007. Transportation of BTL to the blending depot Table 233 Transportation of BTL summary table to the blending depot Share
Transporter
notes
Distance (km one way)
11.4 %
Truck
Payload 40 t
305
27.2 %
Product tanker
Payload: 15 000 t
1 118
43.8 %
Inland ship/barge
Payload 1 200t
153
3.8 %
Train
13.8 %
Pipeline
381
Table 234 Transport of BTL via 40 t truck over a distance of 305 km (one way) I/O
Unit
Amount
Distance
Input
tkm/MJBTL
0.0075
BTL
Input
MJ/MJBTL
1.0000
BTL
Output
MJ
1.0000
Comment - For the fuel consumption of the 40 t truck, see Table 63.
223
Table 235 Maritime transport of FAME over a distance of 1 118 km (one way) I/O
Unit
Amount
Distance
Input
tkm/MJBTL
0.0254
BTL
Input
MJ/MJBTL
1.0000
BTL
Output
MJ
1.0000
Comment - For the fuel consumption of the product tanker (payload: 15,000 t), see Table 72. Table 236 Transport of BTL over a distance of 153 km via inland ship (one way) I/O
Unit
Amount
Distance
Input
tkm/MJBTL
0.0035
BTL
Input
MJ/MJBTL
1.0000
BTL
Output
MJ
1.0000
Comment - For the fuel consumption for an inland oil carrier, see Table 76. Table 237 Transport of BTL over a distance of 381 km via train (one way) I/O
Unit
Amount
Distance
Input
tkm/MJBTL
0.0087
BTL
Input
MJ/MJBTL
1.0000
BTL
Output
MJ
1.0000
Comments - For the fuel consumption of the freight train, see Table 78. Table 238 Transport of BTL via pipeline I/O
Unit
Amount
Distance
Input
tkm/MJBTL
0.0002
BTL
Input
MJ/MJBTL
1.0000
BTL
Output
MJ
1.0000
Comments - Assumed to be the same as for gasoline
224
Table 239 BTL depot I/O
Unit
Amount
BTL
Input
MJ/MJBTL
1.00000
Electricity
Input
MJ/MJBTL
0.00084
Output
MJ
1.00000
BTL
Source 1 Dautrebande, 2002. Table 240 Transport of BTL via 40 t truck over a distance of 150 km (one way) I/O
Unit
Amount
Distance
Input
tkm/MJBTL
0.0043
BTL
Input
MJ/MJBTL
1.0000
BTL
Output
MJ
1.0000
Table 241 BTL filling station I/O
Unit
Amount
BTL
Input
MJ/MJBTL
1.0000
Electricity
Input
MJ/MJBTL
0.0034
Output
MJ
1.0000
BTL
Source 2 Dautrebande, 2002.
225
6.16 Black liquor This covers 3 processes for making different transport fuels made in pulp mills by gasifying black liquor, including DME, methanol, and FT liquids. These calculations use the allocation methodology prescribed by the Renewable Energy Directive and subsequent communication. When calculated using RED method, the results do not differ signifantly between the fuels; therefore they are combined to a single process. For data on cultivation of roundwood and forestry residues, refers to the input data for solid and gaseous bioenergy pathways (JRC – IET, 2014). Table 242 Liquid fuels via gasification of black liquor (methanol, DME, FT liquids) I/O
Unit
Amount
Dry roundwood
Input
MJ/MJbiofuel
1.500
Dry forest residues
Input
MJ/MJbiofuel
0.4400
Liquid fuels
Output
MJ
1.00
Detailed calculations per fuel 1. Black liquor gasification to methanol Alternatively, some or all of the black liquor can be gasified instead of burnt in the recovery boiler. Various fuels (methanol, DME or Fischer–Tropsch products mix (naphtha, gasoline, diesel) can be made from the gas. Here we used data on the CHEMREC oxygen-blown BL gasification process. Gasification produces less heat and electricity than burning the black liquor in the reference pulp plant. Therefore, extra biomass (in the form of forest residuals) is required to make the plant self-sufficient for heat and electricity. In the modelled plant, the tall oil is gasified along with the black liquor.
226
Table 243 Black liquor gasification to methanol (Inputs)
Input Roundwood (dry tonnes per ADt pulp) Shortfall in electricity (GJ/AD/t) Efficiency of electricity generator Forest residues for more electricity (GJ/ADt) Forest residues for mill (GJ/ADt pulp) Total forest residues input (GJ/ADt pulp) Total forest residues input (tonnes dry wood/ADtonne pulp) Total forest residues input (dry GJ/dry GJ pulp)
GJ/air dried tonnes pulp
GJ (dry definition)
REF
LHV (GJ/dry tonne)
2.05
36.9
2,6
18
2.42
% moisture
Wet LHV (RED definition)
50 %
7.78
7 40 %
7
6.05
18
5.56
7
11.61 0.645 0.73
Table 244 Black liquor gasification to methanol (Outputs)
Outputs Pulp (air-dried tonnes/day) Methanol Total outputs GJ by RED definition/ADt pulp
Dry tonnes/ADt pulp
GJ per ADt pulp, by RED definition
0.9 0.59
14.05 11.78
REF
LHV (GJ/dry tonne)
LHV ref
% moisture
REF
Wet LHV (RED definition)
7
15.88 19.8
3 7
10 % 0 %
3 7
14.05 19.80
25.83
227
Methanol results by RED allocation methodology; inputs allocated equally to GJ of methanol and wood-pulp Table 245 Methanol results Dry tonnes roundwood per GJ (RED definition) outputs PLUS dry tonnes forest residues per GJ (RED definition) output GJ dry roundwood per GJ methanol PLUS GJ dry forest residues allocated per GJ methanol Dry tonnes roundwood allocated per tonne methanol Plus GJ dry forest residues per GJ(RED definition) output
0.079 0.025 1.43 0.45 1.57 0.49
Comments - Effective efficiency of 53.24 %. - These figures need to be combined with the emissions from roundwood provision and forest residue provision to give emissions for methanol. - Note: JEC-WTW, ALTENER reports, and papers from Chemrec all use a marginal calculation for the effective efficiency and emissions. This gives much better results for this process (effective efficiency ~66 %). 2. Black liquor gasification to DME Table 246 Black liquor gasification to DME (Inputs)
Input Roundwood (dry tonnes per ADt pulp) Shortfall in electricity (GJ/AD/t) Efficiency of electricity generator Forest residues for more electricity (GJ/ADt) Extra forest residues for mill (GJ/ADt pulp) Total forest residues input (GJ/ADt pulp) Total forest residues input (tonnes dry wood/ADtonne pulp) Total forest residues input (dry GJ/dry GJ pulp)
2.05
GJ (dry definition)
REF
LHV (GJ/dry tonne)
% moisture
36.9
2,6
18
50 %
2.39
7 40 %
7
5.975
18
5.38
7
11.355
0.631 0.72
228
Comments - Most chemicals used in pulp production are recycled, but we could find no data on the amounts which are not recycled, which would be inputs. - Source for LHV: JRC estimate. Table 247 Black liquor gasification to DME (outputs)
Outputs Pulp (air-dried tonnes/day) DME Total outputs GJ by RED definition/ADt pulp
Dry tonnes/ADt pulp
GJ per ADt pulp, by RED definition
0.9 0.42
14.05 11.87
REF
LHV (GJ/dry tonne)
LHV ref
% moisture
REF
WET LHV (RED definition)
7,WTW
15.88 28.4
3 7
10 % 0 %
3 7
14.05 28.40
25.92
DME results by RED allocation methodology; inputs allocated equally to GJ of methanol and wood pulp Table 248 DME results Dry tonnes roundwood per GJ (RED definition) outputs PLUS dry tonnes forest residues per GJ (RED definition) output GJ dry roundwood per GJ DME PLUS GJ dry forest residues allocated per GJ DME Dry tonnes roundwood allocated per tonne DME PLUS GJ dry forest residues per GJ(RED definition) output
0.079 0.024 1.42 0.44 1.57 0.48
Comments - Effective efficiency of 53.71 %. - These figures need to be combined with the emissions from roundwood provision and forest residue provision to give emissions for DME. - Note: JEC-WTW, ALTENER reports, and papers from Chemrec all use a marginal calculation for the effective efficiency and emissions. This gives much better results for this process (effective efficiency ~66 %).
229
3. Black liquor gasification to FT liquids (BTL) Table 249 Black liquor gasification to FT liquids (inputs) GJ (dry definition)
Input Roundwood (dry tonnes per ADt pulp) Shortfall in electricity (GJ/AD/t)
2.05 3.59
Efficiency of electricity generator Forest residues for more electricity (GJ/ADt) Extra forest residues for mill (GJ/ADt pulp) Total forest residues input (GJ/ADt pulp) Total forest residues input (tonnes dry wood/ADtonne pulp) Total forest residues input (dry GJ/dry GJ pulp)
REF
36.9
2,6 7
40 %
7
LHV (GJ/dry tonne)
% moisture 18
50 %
8.98 7
1.11
18
10.09 0.560 0.64
Comments - Most chemicals used in pulp production are recycled, but we could find no data on the amounts which are not recycled, which would be inputs. - Source for LHV: JRC estimate. Table 250 Black liquor gasification to FT liquids (outputs)
Outputs Pulp (air-dried tonnes/day) DME Total outputs GJ by RED definition/ADt pulp
Dry tonnes/ADt pulp
GJ per ADt pulp, by RED definition
0.9 0.24
14.05 10.55
REF
7,WTW
LHV (GJ/dry tonne) 15.88 43.2
LHV ref 3 WTW
% moisture 10 % 0 %
REF 3 7
Wet LHV (RED definition) 14.05 43.20
24.60
230
FT liquids results by RED allocation methodology; inputs allocated equally to GJ of methanol and wood pulp. Dry tonnes roundwood per GJ (RED definition) outputs PLUS dry tonnes forest residues per GJ (RED definition) output GJ dry roundwood per GJ FT liquids PLUS GJ dry forest residues allocated per GJ FT liquids Dry tonnes roundwood allocated per tonne FT liquids PLUS GJ dry forest residues per GJ (RED definition) output
0.083 0.023 1.50 0.41 1.65 0.45
Comments - Effective efficiency of 53.24 %. - We have chosen the plant in ref.7 in which waxes are cracked to FT liquids. - These figures need to be combined with the emissions from roundwood provision and forest residue provision to give emissions for FT liquids. - Note: JEC-WTW, ALTENER reports, and papers from Chemrec all use a marginal calculation for the effective efficiency and emissions. This gives much better results for this process (effective efficiency ~65 %). Sources 1 Berglin et al., 2003. 2 Berglin et al., 1999. 3 ECN Phyllis database (value for cellulose). 4 “Pulp properties” website: http://www.paperonweb.com/pulppro.htm 5 Ekbom et al., 2003. 6 Landälv, 2007. 7 Ekbom et al., 2005.
231
6.17 Wood to Liquid Hydrocarbons For the feedstock supply, data from “woodchips from SRF poplar 500-2500 km” or “woodchips from forest residues 500-2000 km” pathways (as appropriate) should be considered in the solid and gaseous bioenergy pathways (JRC – IET, 2014). BTL plant Table 251 BTL plant I/O
Unit
Amount
Biomass
Input
MJ/MJFT diesel
2.1288
Dolomite
Input
MJ/MJFT diesel
0.00518
NaOH
Input
MJ/MJFT diesel
0.00000902
Output
MJ
1.000
FT diesel
Source 1, 2 1, 2 1, 2
Sources 1 Hamelinck, 2004. 2 Tijemsen et al., 2002. Table 252 Transport of FT diesel via a 40 t truck over a distance of 150 km (one way) I/O
Unit
Amount
Distance
Input
tkm/MJFT diesel
0.0037
FT diesel
Input
MJ/MJFT diesel
1.0000
FT diesel
Output
MJ
1.0000
Comments - For the fuel consumption of the 40 t truck, see Table 63. Table 253 FT diesel depot I/O
Unit
Amount
BTL
Input
MJ/MJFT diesel
1.00000
Electricity
Input
MJ/MJFT diesel
0.00084
Output
MJ
1.00000
I/O
Unit
Amount
BTL
Input
MJ/MJFT diesel
1.0000
Electricity
Input
MJ/MJFT diesel
0.0034
Output
MJ
1.0000
BTL
Table 254 FT diesel filling station
BTL
Source 1 Dautrebande, 2002. 232
6.18 Wood to methanol For feedstock emissions, please refer to data in “woodchips from SRF poplar 500-2500 km” or “woodchips from forest residues 500-2000 km”, as appropriate, in solid and gaseous bioenergy pathways (JRC – IET, 2014). Methanol plant Table 255 Methanol production (gasification, synthesis) I/O
Unit
Amount
Biomass
Input
MJ/MJMethanol
1.960
Methanol
Output
MJ
1.000
Sources 1 Katofsky, 1993. 2 Dreier et al., 1998. 3 Paisley, 2001. 4 Edwards, R., JRC, 13 October 2003.
Table 256 Transport of methanol via a 40 t truck over a distance of 150 km (one way) I/O
Unit
Amount
Distance
Input
tkm/MJMethanol
0.0081
Methanol
Input
MJ/MJMethanol
1.0000
Methanol
Output
MJ
1.0000
Comment - For the fuel consumption of the 40 t truck, see Table 63. Source 1 Dautrebande, 2002. Table 257 Methanol filling station I/O
Unit
Amount
Methanol
Input
MJ/MJMethanol
1.0000
Electricity
Input
MJ/MJMethanol
0.0034
Methanol
Output
MJ
1.0000
Source 1 Dautrebande, 2002. 233
6.19 Wood to DME For feedstock emissions, please refer to data in “woodchips from SRF poplar 500-2500 km” or “woodchips from forest residues 500-2000 km”, as appropriate, in solid and gaseous bioenergy pathways (JRC – IET, 2014). DME plant Table 258 DME production (gasification, synthesis) Biomass DME
I/O
Unit
Amount
Input
MJ/MJDME
1.960
Output
MJ
1.000
Sources 1 Katofsky, 1993. 2 Dreier et al., 1998. 3 Paisley, 2001. 4 Edwards, R., JRC, 13 October 2003.
Table 259 Transport of DME via a 40 t truck over a distance of 150 km (one way) I/O
Unit
Amount
Distance
Input
tkm/MJDME
0.0074
DME
Input
MJ/MJDME
1.0000
DME
Output
MJ
1.0000
Comment - For the fuel consumption of the 40 t truck, see Table 63. Source 1 Dautrebande, 2002. Table 260 DME filling station I/O
Unit
Amount
DME
Input
MJ/MJDME
1.0000
Electricity
Input
MJ/MJDME
0.0034
Output
MJ
1.0000
DME
Source 1 Dautrebande, 2002.
234
6.20 Straw to ethanol For the supply of straw, the straw supply processes upstream of the pelleting plant in the "straw pellets" pathway should be considered from the solid and gaseous bioenergy pathways (JRC – IET, 2014). Table 261 Conversion of wheat straw to ethanol via hydrolysis and fermentation (before allocation) I/O
Unit
Amount
Source
Straw
Input
MJ/MJethanol
2.2595
1, 2
CaO
Input
MJ/MJethanol
0.0023
1, 2 1, 2
H2SO4
Input
MJ/MJethanol
0.0039
Ethanol
Output
MJ
1.0000
Electricity
Output
MJ/MJethanol
0.052
1
Sources 1 Groves, A., Shell: WtW evaluation of ethanol from lignocellulose; July 2003 (based on Iogen plant). 2 Kaltschmitt and Hartmann, 2001.
Table 262 Conversion of wheat straw to ethanol via hydrolysis and fermentation (after allocation between ethanol and electicity) I/O
Unit
Amount
Source
Straw
Input
MJ/MJethanol
2.2595
1, 2
CaO
Input
MJ/MJethanol
0.0023
1, 2
Input
MJ/MJethanol
0.0039
1, 2
Output
MJ
1.0000
H2SO4 Ethanol
Table 263 Transport of ethanol to depot via 40 t truck over a distance of 150 km I/O
Unit
Amount
Distance
Input
tkm/MJethanol
0.0060
Ethanol
Input
MJ/MJethanol
1.0000
Ethanol
Output
MJ
1.0000
Comments - For the fuel consumption of a 40 t truck, see Table 63. - This is for transport to depot and from the depot to the refuelling station.
235
Table 264 Ethanol depot I/O
Unit
Amount
Ethanol
Input
MJ/MJethanol
1.00000
Electricity
Input
MJ/MJethanol
0.00084
Ethanol
Output
MJ
1.00000
Table 265 Ethanol filling station I/O
Unit
Amount
Ethanol
Input
MJ/MJethanol
1.0000
Electricity
Input
MJ/MJethanol
0.0034
Output
MJ
1.0000
Ethanol
Comment - Distribution is assumed to be same as for fossil diesel and gasoline. Source 1 Dautrebande, 2002.
236
References for ethanol and biodiesel pathways Direction de l’Agriculture et des Bioénergies dei l'Agence de l'Environnement et de la Maîtrise de l’Energie France (ADEME), 2002, Direction des Ressources Energétiques et Minéralis (DIREM), France: bilans énergétiques et gaz à effet de serre des filières de production de biocarburants; rapport - version provisoire, 31 August 2002. BioDiesel International AG (BDI), Input-Output Factsheet, Plant Capacity 50.000 t Biodiesel, Department Research and Development BDI. Berglin, N., Eriksson H. and Berntsson. T., 1999, 'Performance evaluation of competing designs for efficient cogeneration from black liquor', 2nd Biannual J. Gullichsen Colloquiium, Helsinki, September 9-10, 1999. Berglin, N., Lindblom M and Ekbom, T., 2003, 'Preliminary economics of black liquor gasification with motor fuels production', Colloquium on black liquor combustion and gasification, Park City, Utah, May 13-16, 2003. Beuerlein, J., 2012, Bushels, Test Weights and Calculations, (AGF-503-00), Ohio State University FactSheet, Department of Horticulture and Crop Science, Columbus, Ohio (http://ohioline.osu.edu/agf-fact/0503.html) accessed 5 January 2013. Brazilian Ministry of Agriculture, Fisheries and Food Supply, Department of Cane Sugar and Agro Energy. Sugar cane productivity evolution by cut, dated September 19th, 2012. British Beet Research Organisation. Crop establishment and drilling bulletin, Spring 2011. www.uksugarbeet.co.uk. Bunge Növényolajipari Zrt., 2012, 'Specifications of oilseed cakes' (http://www.bunge.hu/english/ind2_31.htm) accessed September 2012. California Air Resources Board (CARB), 2009, Detailed California-Modified GREET Pathway for Corn Ethanol; v.2.0 Jan 2009, from California Air Resources Board website. Centro Nacional de Referência em Biomassa (CENBIO), 2009, COMPETE project report. Cheng Hai T., 2004, 'Selling the green palm oil advantage', Oil Palm Industry Economic Journal, Vol. 4 (1).
237
Chin, F. Y., 1991, Palm Kernel Cake (PKC) as a Supplement for Fattening and Dairy Cattle in Malaysia (http://www.fao.org/ag/AGP/agpc/doc/Proceedings/manado/chap25.htm) accessed 5 January 2013. Choo, Y. M., Muhamad, H., Hashim, Z., Subramaniam, V., Puah, C. W., Tan, Y., 2011, 'Determination of GHG contributions by subsystem in the oil palm supply chain using the LCA Approach', Int J. Life Cycle Assess, (16) 669-681. Da Silva, V. P., van der Werf, H. M. G., Spies, A., Soares, S. R., 2010, 'Variability in environmental impacts of Brazilian soybean according to crop production and transport scenarios', Journal of Environmental Management, (91)9, 1831-1839. Dautrebande, O., 2002, 'TotalFinaElf', January 2002. De Camillis, C., Raggi, A., Petti, L., 2010, 'Developing a Life Cycle Inventory data set for cattle slaughtering', DASTA working paper, Universita' degli Studi 'G. d'Annunzio', Pescara, Italy. De Tower, D. A. and Bartosik, R., 2012, ¿Cuánto combustible se consume en Argentina para secar granos?' (http://www.planetasoja.com.ar/index.php?sec=1&tra=31717&tit=31718) accessed September 2012. Donato, L. B., Hurrga, I. R. and Hilbert, J. A., 2008, 'Energy balance of soybean based biodiesel production in Argentina', INTA document IIR-BC-INF-10-08 by Instituto Nacional de Tecnologia Agropecuaria. Dreier, T., Geiger, B., Saller, A., 1998, Ganzheitliche Prozeßkettenanalyse für die Erzeugung und Anwendung von biogenen Kraftstoffen; Studie im Auftrag der Daimler Benz AG, Stuttgart und des Bayerischen Zentrums für Angewandte Energieforschung e.V. (ZAE); Mai 1998. Dreier, T., 2000, Ganzheitliche Systemanalyse und Potenziale biogener Kraftstoffe; IfE Schriftenreihe, Heft 42; herausgegeben von: Lehrstuhl für Energie-wirtschaft und Anwendungstechnik (IfE), Technische Universität München, Ordinarius: Prof. Dr-Ing. Ulrich Wagner; 2000; ISBN 3-933283-18–3. European Biofuels Board (EBB), 2009, Data supplied to the JRC in September 2009. European Waste-to-Advanced Biofuels Association & Mittelstandverband abfallbasierter Kraftstoffe, 2014. Survey report of overseas imports of UCO. ECN Phyllis database, (http://www.ecn.nl/phyllis/). Ecofys, 2011. (Ecofys, Agra CEAS, Chalmers University, IIASA and Winrock). Biofuels Baseline 2008: www.ecofys.com/en/publication/biofuels-baseline-2008/
238
Ecofys, 2013. Status of the tallow (animal fat) market 2013 Update, https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/266088/ecofy s-status-of-the-tallow-market-v1.2.pdf). Jungbluth N., Chudacoff M., Dauriat A., Dinkel F., Doka G., Faist Emmenegger M., Gnansounou E., Kljun N., Spielmann M., Stettler C. and Sutter J., 2007, Life Cycle Inventories of Bioenergy. Final report ecoinvent data v2.0 No. 17. Swiss Centre for Life Cycle Inventories, Dübendorf, CH. Ekbom, T. et al., 2003, 'Tech and commercial feasibility study of black liquor gasification with methanol/DME production as motor fuels for automotive use', Altener II Report contract no. XVII/4.1030/Z/01-087/2001, December 2003. Ekbom. T., Berglin, N. and Loegdberg, S., 2005, 'Black Liquor Gasification with Motor Fuel Production - BGMF II', Report for contract P21384-1 for Swedish Energy Agency FALT program. Table 4.4 p. 68. EMBRAPA, 2004, Sistemasdeprodução 5: tecnologia de produção de soja, Paraná 2005. EmbrapaSoja, first ed. EMBRAPA, Londrina, Brazil. ETM EuroTransport Media Verlags- und Veranstaltungs-GmbH, Stand, August 2009. European Biodiesel Board, - J. Coignac, Comments to Commission's May 2013 stakeholder consultation, received 13 June 2013. European Waste-to-Advanced Biofuels Association & Mittelstandverband abfallbasierter Kraftstoffe, 2014. Survey report of overseas imports of UCO. FEDIOL, 2013. Lifecycle assessment of EU oilseed crushing and vegetable oil refining’, May 2013. Fertilizer Europe, 2013. Data on fertilizer-per-crop in EU for 2011 from Fertilizers Europe, received by JRC in March 2013. Food and Agriculture Organization (FAO), Prodstat Database, Production and seeds per country per crop (http://faostat.fao.org/) accessed 5 January 2013. Food and Agricultural Policy Research Institute (FAPRI), 2012, Data on production and net exports (for weighting) from FAPRI world agricultural outlook database (http://www.fapri.iastate.edu/tools/outlook.aspx) accessed 5 January 2013. Flaskerud, G., 2003, Brazil’s Production and Impact, North Dakota State University, July 2003. Gover, M. P., Collings, S. A., Hitchcock, G. S., Moon, D. P., Wilkins, G. T., 1996, Alternative Road Transport Fuels - A Preliminary Life-cycle Study for the UK, Volume 2, A study co-funded by the Department of Trade and Industry and the Department of Transport; ETSU, Harwell, March 1996. 239
The Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation Model, (GREET), 2010, Argonne National laboratory. US dept of Energy. Detailed California-Modified GREET pathway for sorghum ethanol, draft 14 Dec 2010. GREET1, 2011, GREET_2011 Worksheets. UChicago Argonne, LLC. Groves, A., Shell: WtW evaluation of ethanol from lignocellulose; July 2003 (based on Iogen plant). Hamelinck, C. N., 2004, Outlook for advanced biofuels, 7 June 2004. Hartmann, H., 1995, Energie aus Biomasse, Teil IX der Reihe Regenerative Energien; VDI GET. Heffer, P., 2009, Assessment of Fertilizer Use by Crop at the Global Level 2006/07 – 2007/08, April 2009, IFA, Paris, France (http://www.fertilizer.org/ifa/HomePage/LIBRARY/Publication-database.html/Assessment-of-Fertilizer-Use-by-Crop-at-theGlobal-Level-2006-07-2007-08.html2) accessed 5 January 2013. Hilbert, J. A., Donato, L. B., Muzio, J. and Huega, I., 2010, 'Comparative analysis of energy consumption and GHG emissions from the production of biodiesel from soybean under conventional and no-tillage farming systems', Communication to JRC and DG-TREN 09.09.2010. INTA document IIR-BC-INF-06-09 by Instituto Nacional de Technologia Agropecuaria (INTA). International fertilizer Association (IFA), 2013. Fertilizer http://www.fertilizer.org/ifa/Home-Page/STATISTICS, accessed in 2013.
use
by
crop
IMO, 2009. Buhaug, Ø., Corbett, J. J., Eyring, V., Endresen, Ø., Faber, J. et al., 2009, Second IMO GHG Study 2009, prepared for International Maritime Organization (IMO), London, UK, April 2009. IFEU, 2011, Institut für Energie und Umweltforschung. INTA, 2011, Actualización Técnica Nº 58 - Febrero 2011 (http://inta.gob.ar/documentos/siembradirecta/at_multi_download/file?name=Siembra+Directa+2011.pdf) accessed 5 January 2013. Intergovernmental Panel on Climate Change (IPPC), 2006. 2006 IPCC Guidelines for National Greenhouse Gas Inventories Volume 2 Energy. Chapter 2. http://www.ipccnggip.iges.or.jp/public/2006gl/vol2.html. Jongschaap, R. E. E., Corré, W. J., Bindraban, P. S., Brandenburg, W. A., 2007, 'Claims and Facts on Jatropha curcas L.', Report 158. Plant Research International B.V., Wageningen, Netherlands; October 2007. Jósef, A., 2000, Növénytermesztök zsebkönyve; Mezögazda; 2000; Harmadik, átdolgozott kiadás; ISBN 9632860357. 240
JRC-IET, 2014. Giuntoli J., Agostini A., Edwards R., Marelli L., 2014. Solid and gaseous bioenergy pathways: input values and GHG emissions. Calculated according to the methodology set in COM(2010) 11 and SWD(2014) 259. JRC Science and Policy Report. EUR 26696 EN. Kaltschmitt, M. and Reinhardt, G., 1997, Nachwachsende Energieträger: Grundlagen, Verfahren, ökologische Bilanzierung; Vieweg 1997; ISBN 3-528-06778-0. Kaltschmitt, M. and Hartmann, H., 2001, (Hrsg.) Energie aus Biomasse - Grundlagen, Techniken und Verfahren; Springer-Verlag Berlin Heidelberg, New York; ISBN 3-540-64853-4. Katofsky, R. E., 1993, 'The Production of Fluid Fuels from Biomass', PU/CEES Report No. 279; The Center for Energy and Environmental Studies; Princeton University; PU/CEES Report No. 279, June 1993 Kempen, M. and Kraenzlein, T., 2008, 'Energy Use in Agriculture: A modeling approach to evaluate Energy Reduction Policies'. Paper prepared for presentation at the 107th EAAE Seminar 'Modelling of Agricultural and Rural Development Policies'. Sevilla, Spain, January 29th, February 1st, 2008. Kenkel, P., 2009. "Grain Handling and Storage Costs in Country Elevators", Univ. Oklahoma 2009. Kerkhof, E., 2008, 'Application of jatropha oil and biogas in a dual fuel engine for rural electrification', Report number: WVT 2008.12; Msc. Thesis, Eindhoven University of Technology, June 2008. Kraenzlein, T., 2011, 'Energy Use in Agriculture', Chapter 7.5 in CAPRI model documentation 2011. (eds: W. Britz, P. Witzke) (http://www.caprimodel.org/docs/capri_documentation_2011.pdf) accessed 3 January 2013. Kuratorium für Technik und Bauwesen in der Landwirtschaft e.V. (KTBL), 2006, LeibnizInstitut für Agrartechnik Potsdam-Bornim e.V. (ATB): Energiepflanzen; KTBL, Darmstadt, 2006; ISBN 13: 978-3-939371-21-2. Landälv, I., 2007, 'The status of the Chemrec black liquor gasification concept', 2nd European Summer School on Renewable Motor Fuels, Warsaw, Poland, 29–31 August 2007, slide 25. Laraia R., Finco A., Riva G., 2001. ‘Farine animali: quantitativi in gioco e metodi di smaltimento’, Convegno ATI 2001. Lastauto Omnibus Katalog, (2010). Lechon, J., Cabal H., Lago C., de la Rua C., Saez R.M., Fernandez M., 2005, 'Análisis de Ciclo de Vida comparativo del etanol de cereales y de la gasolina'. Report CIEMAT/ESYME/0445201/12 ISBN 84-8320-312-X.
241
Macedo, I. C., Lima Verde Leal M. R., da Silva J. E. A. R., 2004, 'Assessment of greenhouse gas emissions in the production and use of fuel ethanol in Brazil', Government of the State of Sao Paulo; Geraldo Alckmin - Governor; Secretariat of the Environment José Goldemberg Secretary; April 2004. Macedo, I. C., Seabra J. E. A., and da Silva J. E. A. R., 2008, 'Greenhouse gases emissions in the production and use of ethanol from sugar cane in Brazil: The 2005/2006 averages and a prediction for 2020', Biomass and Bioenergy. doi:10.1016/j.biombioe.2007.12.006. MacLean, H. and Spatari, S., 2009, 'The contribution of enzymes and process chemicals to the life cycle of ethanol', Environ. Res. Lett. (4)014001, 10 pp. Marques, B. D. A, 2006, Consideraçoes ambientais e exergéticas na fase depós-colheita de gr aos: estudo de caso do Estado do Paraná. Dissertation. Universidade Federaldo Paraná, Curitiba, Brazil (http://hdl.handle.net/1884/3930) accessed 3 January 2013. Mehta P.S. and Anand K., 2009, 'Estimation of a Lower Heating Value of Vegetable Oil and Biodiesel Fuel', Energy Fuels, 23 (8) 3893–3898. Metrology Centre, 2012, 'Verification of auto LPG Dispensers', Part IV of Eighth Schedule, The Legal Metrology (General) Rules, 2010. Specific provision: Part 2 Rule 5(7) (http://www.metrologycentre.com/codes/lpg.html) accessed 3 January 2013. Miettinen, J., Hooijer AL., Tollrenaar D., Page S., Malins C., Vernimmen R., Shi C., Chin Liew S. 2012 ‘Historical analysis and projection of oil palm plantation expansion on peatland in SE Asia’ ICCT White Paper Number 17, February 2012. Ministério da Agricultura, Pecuária e Abastecimento, 2007, National Balance of Sugarcane and Agroenergy. Mittelbach, M. and Remschmidt, C., 2004, Biodiesel, The comprehensive handbook, Institut for Chemistry University Graz. Product Board for Margarine Fats and Oils, 2011, 'MVO Factsheet soy, 2011' (http://www.mvo.nl/Kernactiviteiten/MarktonderzoekenStatistiek/Factsheets/FactsheetSoja20 11/tabid/756/language/en-US/Default.aspx) accessed 3 January 2013. Muzio, J., Hilbert, J.A., Donato, L. B., Arena, P., Allende, D., 2009, 'Argentina's Technical Comments based on information provided by Directorate-General for Energy and Transport on biodiesel from soy bean'. INTA document IIR-BC-INF-14-08, by Instituto Nacional de Technologia Agropecuaria (02/04/09). Nemecek T. and Kägi T., 2007, ‘Lyfe Cycle Inventories of Agricultural Production Systems’. Data v2.0. Ecoinvent Report No. 15, Swiss Centre for Life Cycle Inventories, Zürich and Dübendorf CH, December 2007.
242
Notarnicola, B., Puig, R., Raggi, A., Fullana, P., Tassielli, G., Tarabella, A., Petti, L., De Camillis, C. and Mongelli, I., 2007, 'LCA of Italian and Spanish production systems in an Industrial Ecology perspective', in : Puig, R., Notarnicola, B. and Raggi, A. (eds), Industrial Ecology in the cattle-to-leather supply chain. Milan, Italy, Franco Angeli. National Research Council (NRC), 2001, Nutrient Requirements of Dairy Cattle, Seventh Revised Edition. National Academy Press, Washington. Omni Tech International, 2010, 'Lifecycle impact of soybean production and soy industrial products', report prepared for United Soybean Board. Openshaw, K., 2000, 'A review of Jatropha curcas: an oil plant of unfulfilled promise', Biomass and Bioenergy, 19(1) 1-15. Paisley, M., A., Irving, J., M. and Overend, R., P., 2001, 'A promising power option — the FERCO silvagas biomass gasification process — operating experience at the Burlington gasifier', Proceedings of ASME Turbo Expo 2001, ASME Turbo Expo Land, Sea, & Air 2001, June 4-7, 2001 New Orleans, Louisiana, USA. Panapanaan, V. and Helin, T., 2009, Sustainability of Palm Oil Production and Opportunities for Finnish Technology and Know-How Transfer. Panichelli, L., Dauriat, A., and Gnansounou, E., 2009, 'Life cycle assessment of soybeanbased biodiesel in Argentina for export', International Journal of Life Cycle Assessment, (14) 144–159. Paustian, K., et al., 2006, 'IPCC Guidelines for National Greenhouse Gas Inventories', IPCC National Greenhouse Inventories Programme, published by the Institute for Global Environmental Strategies (IGES), Hayama, Japan on behalf of the Intergovernmental Panel on Climate Change (IPCC), 2006 (http://www.ipccnggip.iges.or.jp/public/2006gl/pdf/4_Volume4/V4_11_Ch11_N2O&CO2.pdf) accessed 3 January 2013. Pelletbase, 2010, 'Pelletbase' (http://www.pelletbase.com/show_info.php?lid=1643) accessed September 2012. Pradhan, A., Shrestha, D.S., McAloon, A., Yee W., Haas, M., Duffield, J.A., 2011, 'Energy Lifecycle assessment of soybean biodiesel revisited', Transactions of the ASABE, 54(3) 10311039. Praj Industries Limited: Sweet Sorghum Ethanol Technology, 14 February 2008. Prueksakorn, K. and Gheewala, S.H., 2006, 'Energy and GHG Implications of Biodiesel Production from Jatropha curcas L', The 2nd Joint International Conference on 'Sustainable Energy and Environment' (SEE 2006), 21-23 November 2006, Bangkok, Thailand.
243
Pulp & Paper Resources & Information, 'Properties of Pulp' (http://www.paperonweb.com/pulppro.htm) accessed November 2012. Punter, G., Rickeard, D., Larivé, J-F., Edwards, R., Mortimer, N., Horne, R., Bauen, A., Woods, J., 2004, Well-to-Wheel Evaluation for Production of Ethanol from Wheat, A Report by the LowCVP Fuels Working Group, WTW Sub-Group; FWG-P-04-024; October 2004. Raggi, A., Petti, L., De Camillis, C., Mercuri, L. and Pagliuca, G., 2007, 'Cattle slaughtering residues: current scenario and potential options for slaughterhouses in Abruzzo' , in: Puig, R., Notarnicola, B. and Raggi, A. (eds), Industrial Ecology in the cattle-to-leather supply chain. Milan, Italy, Franco Angeli. Reinhardt, G., Gärtner, S., O., Helms, H., Rettenmaier, N., 2006, An Assessment of Energy and Greenhouse Gases of NExBTL, Institute for Energy and Environmental Research Heidelberg GmbH (IFEU) by Order of the Neste Oil Corporation, Porvoo, Finland; Final Report; Heidelberg. Reinhardt, G., Becker K., Chaudhary, D. R., Chikara J., Falkenstein E. v., Francis, G., Gartner S., Gandhi, M. R., Ghosh, A., Gosh P. K., Makkar H. P. S., Munch, J., Patolia, J. S., Reddy M. P., Rettenmaier N. and Upadhyay S. C., 2008, 'Basic data for Jatropha production and use Updated version', Heidelberg, Bhavnagar, Hohenheim, Institute for Energy and Environmental Research Heidelberg (IFEU), Central Salt & Marine Chemicals Research Institute (CSMCRI), University of Hohenheim. Reuters, 2012, 'Brazil planning giant Amazon soybean port' (http://brazilportal.wordpress.com/tag/soybean-exports/) accessed 18 February 2012. Renewable Fuels Agency (RFA), 2009, 'Carbon and Sustainability Reporting Within the Renewable Transport Fuel Obligation', Technical Guidance Part Two Carbon Reporting — Default Values and Fuel Chains. Rudelsheim, P. L. J and Smets, G. Baseline information on agricultural practices in the EU Sugar beet (Beta vulgaris L.). Perseus BVBA. May, 2012. Secretaria de Agricultura, Ganaderia, Pesca y Alimentos (SAGPyA), 2008 (http://www.sigagropecuario.gov.ar/ accessed 3 January 2013. Salin, D.L., 2009,' Soybean transportation guide: Brazil 2008, United States Department of Agriculture, (USDA)', rev. 2009. Sauvant, D., Perez, J. M. and Tran, G. (ed.), 2004, 'Tables of Composition and Nutritional Value of Feed Materials: Pigs, Poultry, Cattle, Sheep, Goats, Rabbits, Horses and Fish', Institut national de la recherche agronomique (France), Institut national agronomique Paris-Grignon, Wageningen Academic Publishers, 2004, 304 pp. Schmidt, J., 2007, 'Life cycle assessment of rapeseed oil and palm oil', Ph.D. thesis, Part 3: Life cycle inventory of rapeseed oil and palm oil. 244
Syngenta Agro AG, Dielsdorf: Gardo Gold; 3/2009. Tijemsen, M. J. A., Faaij, A. P. C. and Hamelinck, C. N., 2002, 'Exploration of the possibilities for production of Fischer Tropschliquids and power via biomass gasification', Biomass and Energy, (23) 129-152. Umweltbundesamt (UBA), 1999, Kraus, K., Niklas, G., Tappe, M., 'Umweltbundesamt, Deutschland: Aktuelle Bewertung des Einsatzes von Rapsöl/RME im Vergleich zu DK; Texte 79/99', ISSN 0722-186X. UNICA (Brazilian Sugar Cane Industry Association), Final Report of the 2012/2013 Harvest, South-Central Region, 2013. US Soybean Export Council, 2011, International buyers' guide dated '2008-2011'. Chapter 4: Transporting U.S. Soybeans to Export Markets (http://www.ussec.org.php53-23.dfw11.websitetestlink.com/wp-content/uploads/2012/08/Chap4.pdf accessed 3 January 2013. U.S. Department of Agriculture (USDA), 2010, Brazil Soybean Transportation — Agricultural Marketing Service, October 28, 2010. van Eijck, J., Smeets, E. and Faaij, A., 2012, 'The economic performance of Jatropha, Cassava and Eucalyptus production systems for energy in an East African smallholder setting', GBC Bioenergy, 4(6) 828-845. Wahl, N., Jamnadass, R., Baur, H., Munster, C. and Iiyama, M., 2009, Economic viability of Jatropha curcas L. plantations in Northern Tanzania. Assessing farmers prospects via costbenefit analysis, ICRAF. Working Paper no. 97. Nairobi, World Agroforestry Centre. Woods, J.; Bauen, A., 2003, 'ICCEPT: Technical status review and carbon abatement potential of renewable transport fuels (RTF) in the UK', Research funded by the UK Department for Trade and Industry (DTI), managed by AEA Technologies; work carried out by the Imperial College London, Centre for Energy Policy and Technology (ICCEPT); July 2003 Wörgetter, M., Prankl, H., Rathbauer, J., Bacovsky, D., 2006, 'Local and Innovative Biodiesel', Final report of the ALTENER project No. 4.1030/C/02-022. HBLFA Francisco Josephinum/BLT Biomass – Logistics – Technology.
245
Part Three — Review process
246
7. Consultation with experts and stakeholders 7.1 Expert Consultation (November 2011) In order to guarantee transparency and ensure use of the most up-to-date scientific information and data, the JRC consulted with recognised experts. They discussed and resolved methodological issues and determined the best way to assess both the input data used for calculating default GHG emissions and the processes for future updates. This expert consultation, organised by the JRC’s Institute of Energy and Transport (IET), at JRC Ispra on 22 and 23 November 2011, had the following objectives. •
•
To discuss input data used in the latest JRC calculations of default values for biofuel, bioliquid, biomass and biogas (to be updated in annexes of the relevant directives). The aim of these discussions was to collect the experts' input and comments on the data presented, verify the data quality and ensure that data sources were current. To discuss the need for standardisation activities and for harmonisation of the used conversion factors and input values for GHG calculations.
To facilitate discussion and help experts prepare, input data for all solid, gaseous and liquid biofuels prepared by the JRC and used for GHG calculations were distributed one week prior to the meeting. The presentation of the data was structured as follows. 1. General overview of input data common for all pathways: fossil fuel comparator and crude mixes, transport processes, chemicals and fertilizers. Fuel properties (e.g. lower heating value (LHV), yield and moisture content) were distributed in advance, and were not discussed again during the meeting. 2. Presentation of biogas pathway input data, resulting from the combination of two feedstocks (manure and maize), two outputs (biomethane and electricity) and two groups of upgrading technologies. 3. Presentation of biomass pathway input data: 13 pathways from several feedstocks (e.g. forest or industrial residues, short rotation forestry, roundwood, and agricultural residues) through different process chains (used for power and heat production) were discussed. 4. Presentation of new JRC methodology for calculation of global N2O emissions from cultivation, developed in collaboration with the Climate Change Unit of the JRC’s Institute for the Environment and Sustainability (IES). 5. Presentation of liquid biofuel input data. These included the update of existing input data (e.g. rapeseed, soybean, palm oil, sugar and cereal crops), and the development of new pathways. 247
7.1.1 Main outcomes of the discussion General issues The main issues raised at the workshop are described below (JRC responses are shown in italic font). -
JRC values for flaring emissions are increasing in this new set of data, compared to the previous version (from the Well-to-Wheels (WTW) report, version 2 – 2008 data set), while flaring emissions are observed to have decreased in recent years. The reason for this 'apparent' increase in JRC data is the use of a more detailed and precise methodology for calculating flaring emissions.
-
It was suggested that differentiated emission factors for fossil fuels be used, based on different crude oil mixes for different world regions (instead of using the common EU value), making use of, for example, the US Environmental Protection Agency (EPA) or International Energy Agency (IEA) inventory databases. The JRC will investigate if this is feasible.
-
Shipping emissions: the JRC considered that the return journey of the means of transport was empty. It was argued that the return trip is often used to transport other goods. While this may apply to container ships, it is not the case for chemical tankers or grain carriers: these are specialist ships, which will not easily find a suitable export commodity from the EU for the return journey. Updated ship data based on International Maritime Organization (IMO) data have been used for crop, vegetable oil and ethanol shipping. Sugar cane ethanol, palm oil and soya figures have also been adjusted.
-
The JRC is using the Öko Institute’s (42) Globales Emissions-Modell Integrierter Systeme (GEMIS) database v. 4.5 and v. 4.6 as a source for many input data. More updated versions are now available (4.7 was released in September 2011 and 4.8 in December 2011). New GEMIS data have been taken into account, and been updated in the relevant pathways.
-
Bonn University’s Common Agricultural Policy Regional Impact Analysis (CAPRI) database provides a number of relevant input data for EU cultivation processes, and particularly on diesel use, that may be useful for supplementing the JRC data set. CAPRI data on diesel use in cultivation, drying and pesticide use have been included in many of the pathways.
(42) See http://www.oeko.de/home/dok/546.php online.
248
-
It was proposed that the JRC create and make available a specific database for emissions deriving from the production of fertilizers in use (not only ammonium nitrate and urea), using International Fertilizer Association (IFA) data.
-
Fertilizers: if producers can claim emissions from a specific fertilizer factory, these may have already been 'traded away' under the Emissions Trading Scheme (ETS). Nevertheless, it is permitted, according to DG Energy.
-
More information on the sources of EU fertilizer imports is desirable.
-
The JRC was asked to clarify how the LHV data for feedstocks (e.g. wood, and dried distillers' grains with solubles (DDGS)) are calculated. Tables of LHV values are included in the appendix of this report.
-
Hydrotreated vegetable oil (HVO) fuel properties were not included in the database distributed at the workshop. HVO data were subsequently distributed and are included in this report.
Comments on calculation of global N2O emissions -
The approach for calculating soil N2O emissions received positive feedback, especially the transparency of the methodology and the obtained results. However, it should be stressed that the Stehfest and Bouwman (2006) statistical approach allows the calculation of soil N2O emission for crop groups only; the individual biofuel crops have to be assigned to the corresponding group. A new chapter on N2O methodology is included in this report. N2O calculation and liming emissions have been corrected.
Comments on liquid biofuel pathways Biodiesel pathways -
It was argued that emissions attributed to methanol input should consider the 40 % 'conservatism factor' in biodiesel processing emissions, since the amount of methanol is fixed stoichiometry, and will not vary from plant to plant. However, emissions associated with different processes for methanol production can vary greatly.
-
A Greenpeace report analyses sources of soy biodiesel in the EU; this is useful for calculating a weighted average of EU suppliers. The Greenpeace report comments on the mix of oils used in different EU countries, but not on sources of oils.
249
-
More up-to-date data on Brazilian soy-biodiesel cultivation and processing can be obtained from Centro Nacional de Referência em Biomassa (CENBIO) or Campinas University. More up-to-date data was found and used (these data are described with the soybean pathways).
-
Misprint in soy winterisation yield. This has been corrected.
-
Operational data now available for the NESTE ( 43) HVO process, and other Swedish HVO processes. The experts have agreed to provide the JRC with these data. These data have been included in the update.
-
The Institute for Energy and Environmental Research (IFEU) should be able to provide new data on biodiesel from jatropha seeds. IFEU data on the mechanised pathway in Tanzania was taken into account, as this provides the most realistic yields.
-
The representative of NESTE OIL offered to provide data concerning a possible new pathway for biodiesel from Camelina. Neste provided a mix of future sources. The JRC has not prepared a Camelina pathway, but there is a note on Camelina in Chapter 6.
-
The EBB offered to provide data concerning soybean crushing. No data were received from the EBB. Argentinian and American crushing data were used by the JRC. See the soybean pathway for more details. Palm oil
-
The MPOB noted that there was no decomposition of palm fruit before processing, as this is carried out within 24 hours in Malaysia.
-
Another point for consideration is whether empty fruit bunches might form methane when used as mulch. There are no data to indicate these emissions.
-
There was a suggestion that palm kernel oil processing be separated from the palm oil process (this may be easily done by allocation to the kernels).
-
Various palm oil data were received in paper published by MPOB staff; diesel use in particular needs to be checked.
-
Methane capture from palm oil crushing effluent is only ~85 % effective; moreover, few oil mills in Malaysia are actually currently equipped with such technology.
(43) See http://www.nesteoil.com/ online.
250
Cottonseed oil More data are now available, and representatives from the EBB offered to provide them to JRC. Animal fat It needs to be specified whether the new default pathway applies only to Category 3 animal fats. Categories 1 and 2 should be classified as residues, according to Annex V of the RED. Ethanol pathways -
The natural gas combined heat and power (NG CHP) process for 'steam' should refer to the 'heat' output. This needs to be checked.
-
Electricity and steam use data in the ethanol process need to be checked: the latest processes may be better by 10 % to 15 %.
-
A summary of comparison could be included in the JEC’s Well-To-Tank (WTT) report.
-
There are no straw-fired ethanol plants in the EU, but in Sweden these plants are fired by woodchips. Straw-fired ethanol plants have been replaced by woodchip-fired plants.
-
Argonne National Laboratory in the United States produced an updated survey of fuel used in American corn-ethanol plants.
-
We should not confuse Argonne National Laboratory’s Californian 'Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation' (GREET) data with United States–average GREET data, for which there was an update in September 2011.
-
There was a more detailed data in review of American dry mill ethanol production by Steffen Müller (2008)
-
It was suggested that transportation of corn-ethanol to the EU is occurring by barge via the Mississippi river, rather than by train to Baltimore, as believed at the time. This needs to be checked in the JRC pathways. Update: The general trend is still for transport by train to the east coast of the United States (see Section 6.2 in this report).
-
It was noted that in Brazil, limestone (CaCO3) is used, not calcium oxide (CaO). There may be some confusion here: JRC’s 'CaO for fertilizer' is ~85 % limestone, and only has about 10 % of the emissions of CaO as a process chemical. CaCO3 has been updated in all liquid biofuel pathways.
-
Sweet sorghum–ethanol data from Thailand can be provided by the IFEU. There are also data on cultivation trials in Spain. However, at the moment there seems to be no use of ethanol from sweet sorghum in the EU. There were not sufficient new data to update the pathway. Moreover, this is not included in the update of default values. 251
7.2 Stakeholder meeting (May 2013) A second workshop was organized by the JRC’s Institute of Energy and Transport (IET), in Brussel on 28 May 2013. Representatives from industries, Member States and stakeholders were invited to the meeting. The objective was to present assumptions, input data and methodology used in the latest calculations for input data. After the meeting, stakeholders had the opportunity to send comments and ask for clarifications on the draft report (2013) circulated before the meeting and the data presented during the workshop. The comments and questions were received in June 2013. JRC took them into account for the final updates of the input data which are included in this version of the report. The main changes are shown below and the lists of comments received along with the replies are included in Appendix 3.
7.2.1 Main updates The main issues raised at the stakeholders meeting and the main changes compared to the draft report (2013) include: Fossil fuel comparators (FFC), diesel, heating oil and heavy fuel oil FFCs are calculated using figures for crude oil productions (including flaring and venting) and transport emissions estimated for EU-mix in the OPGEE report (ICCT, 2014). The emissions from refining are those calculated in JEC-WTWv4.1 on the basis of marginal emissions for producing marginally less of the different products. This makes the refining emissions for gasoline and especially diesel higher than the average for all refinery products, whereas those for heavy fuel oil are lower. Natural gas The emission factor for natural gas has been updated to coincide with the new fossil fuel comparator (for when biogas replaces NG). That is a marginal NG mix consisting of an equal mixture of gas from SW Asia, W Siberia and LNG inputs. The input data for each of the three sources is documented in JEC-WTT report v4 and JEC-WTT Appendix 4 (v4). Improved N2O calculation from GNOC New N2O figures have been calculated in GNOC. GNOC now calculates emissions using the latest N and 2010/2011 yield data (only the land use data is still for year 2000 because no more recent data is available). Also the allocation of manure to crops has been improved. We still only count half the manure, but less allocated to crops with lower N requirements. 252
New drying data from CAPRI For Maize, Barley, Rye and Wheat (where the data comes from CAPRI), the data has been recalculated using more information from CAPRI. Now the % of water removed from each crop using CAPRI data has been calculated. Two processes are considered: one process for drying, where inputs are proportional to the kg water dried, and another one for storage, with fixed electricity per tonne of grain. We split the fuel use half-and-half between heating oil and natural gas. P and K fertilizers P and K fertilizers have been updated with new data from Fertilizer Europe (received by JRC in 2013) or from the International Fertilizer Association (IFA) (IFA, September 2013). N fertilizer emissions N fertilizer emissions have been updated using the latest confirmed data on emissions from N fertilizer manufacture from Fertilizer Europe. The EU emissions from AN manufacture indeed already reached the 2020 target, whilst progress in (Russian etc.) plants exporting to EU was slower than anticipated in the draft. New calculation method for electricity credits for biofuel pathways (the ones using CHP in conventional ethanol and ethanol-from-straw). Where processes export electricity, the calculation method has been changed, although this hardly affects the results. Now, instead of a credit, the emissions from making process heat and electricity are allocated to the process or to electricity exports according to exergy. The exergy of used heat at a temperature below 150 C was however treated as if it was at 150 C. Correction of sodium methylate input data Correction made in trans-esterification processes for rapeseed, sunflower, palm oil, Jatropha, and soybean pathways based on information provided by the European Biodiesel Board (EBB). Updates in single pathways Updates and changes in some pathways (sugarbeet, palm oil, animal fats, etc.) are included in this report based on additional information provided by stakeholders. Transport of biodiesel and ethanol The transport mix in the ethanol and biodiesel pathways has been changed to be consistent with the ‘rapeseed to biodiesel’ pathway.
253
Appendix 1. Fuel/feedstock properties Reference Feedstocks By-products
Liquid biofuels (Et-OH) Fuel
Property
Value
Unit
Reference
Gasoline
LHV (mass)
43.2
MJ/kg
WTT App. 1, V4a
LHV (volume)
32.2
MJ/l
WTT App. 1, V4a
Density LHV (mass) LHV (volume)
0.745 43.1 35.9
kg/l MJ/kg MJ/l
WTT App. 1, V4a WTT App. 1, V4a WTT App.1, V4a
Density
0.832
kg/l
WTT App.1, V4a
LHV (mass)
42.0
MJ/kg
WTT App.1, V4a
LHV (volume) Density
34.4 0.820
MJ/l kg/l
WTT App.1, V4a WTT App.1, V4a
LHV (mass) LHV (volume)
44 34.3
MJ/kg MJ/l
WTT App. 1, V4a WTT App.1, V4a
Density
0.780
kg/l
WTT App.1, V4a
LHV (mass)
26.8
MJ/kg
WTT App.1, V4a
LHV (volume)
21.3
MJ/l
WTT App.1, V4a
Diesel
Crude
FT - diesel
Ethanol
Comment
254
Methanol
DME
Sugarbeet
Sugar beet pulp
Wheat (grain)
Wheat (straw)
DDGS
Density LHV (mass)
0.794 19.9
kg/l MJ/kg
WTT App.1, V4a WTT App.1, V4a
LHV (volume)
15.8
MJ/l
WTT App.1, V4a
Density LHV (mass)
0.793 28.4
kg/l MJ/kg
WTT App.1, V4a WTT App.1, V4a
LHV (volume)
19.0
MJ/l
WTT App.1, V4a
Density
0.670
kg/l
WTT App.1, V4a
LHV dry
16.3
MJ/kg dry
Dreier et al., 1998
Moisture
75 %
kg water/kg total
CAPRI data
LHV wet (RED)
2.2
MJ/kg wet
LHV dry
16.1
MJ/kg dry
Moisture
9 %
kg water/kg total
LHV wet (RED)
14.4
MJ/kg wet
LHV dry
17
MJ/kg dry
Kaltschmitt and Hartmann, 2001
Moisture
13.5 %
kg water/kg total
CAPRI data
LHV wet (RED)
14.4
MJ/kg wet
LHV dry
17.2
MJ/kg dry
WTT App.1, V4a
Moisture
13.5 %
kg water/kg total
WTT App.1, V4a
LHV wet (RED)
14.5
MJ/kg wet
LHV dry
18.75
MJ/kg dry
Calculated Kaltschmitt and Reinhardt, 1997
Calculated
Calculated
Calculated Lywood, W., ENSUS plc., e-mail to Robert Edwards, Joint Research Centre
255
(JRC), Ispra, 3 December 2010
(wheat)
Barley (grain)
DDGS (barley)
Sugar cane
Maize (grain)
DDGS
Moisture
10 %
kg water/kg total
LHV wet (RED)
16.6
MJ/kg wet
DDGS/grain
0.375
LHV dry
17
kg wet DDGS/kg wet wheat grains MJ/kg dry
Moisture
13.5 %
kg water/kg total
LHV wet (RED)
14.4
MJ/kg wet
LHV dry
18.3
MJ/kg dry
Moisture
10 %
kg water/kg total
LHV wet (RED)
16.2
MJ/kg wet
Calculated
DDGS/barley
0.448
kg wet DDGS/kg wet barley grains
DDGS @ 10 % moisture and barley grains @ 13.5 % moisture
LHV dry
19.6
MJ/kg dry
Dreier, T., 2000
Moisture
72.5 %
Kaltschmitt, 2001
LHV wet (RED) LHV dry
3.6 17.3
kg water/kg total MJ/kg wet MJ/kg dry
Moisture
14 %
KTBL, 2006
LHV wet (RED)
14.5
kg water/kg total MJ/kg wet
LHV dry
18.4
MJ/kg dry
Calculated DDGS @ 10 % moisture and wheat grains @ 13.5 % moisture Kaltschmitt and Hartmann, 2001
Calculated
Calculated KTBL, 2006
Calculated
256
(maize)
Triticale (grain)
DDGS (triticale)
Rye (grain)
DDGS (rye)
Moisture
10 %
kg water/kg total MJ/kg wet
LHV wet (RED)
16.3
DDGS/maize
0.3015
kg wet DDGS/kg wet corn
LHV dry
16.9
MJ/kg dry
Moisture
14 %
kg water/kg total
Assumed to be the same a rye
LHV wet (RED)
14.2
MJ/kg wet
Calculated
LHV dry
18.7
MJ/kg dry
Same value as for Wheat DDGS
Moisture
10 %
kg water/kg total
LHV wet (RED)
16.6
MJ/kg wet
DDGS/triticale
0.375
LHV dry
17.1
kg wet DDGS/kg wet triticale MJ/kg dry
Moisture
14 %
LHV wet (RED)
14.4
% in whole plant
1.1
LHV dry
18.7
Moisture
10 %
LHV wet (RED)
16.6
Calculated Detailed California-Modified GREET Pathway for Corn Ethanol v.2.0, January 2009, from California Air Resources Board website Kaltschmitt and Hartmann, 2001
DDGS @ 10 % moisture and corn grains @ 14 % moisture
DDGS @ 10 % moisture and triticale grain @ 13.5 % moisture (Same value as for Wheat DDGS) Kaltschmitt and Hartmann, 2001
kg water/kg total MJ/kg wet
CAPRI data
kg straw/kg grain MJ/kg dry
Kaltschmitt and Reinhardt, 1997 Same value as for Wheat DDGS
kg water/kg total MJ/kg wet
257
Black liquor
DDGS/wet rye
0.374
kg wet DDGS/kg wet rye MJ/kg dry
LHV dry
12.1
Moisture
25 %
kg water/kg total
LHV wet (RED)
8.5
MJ/kg wet
DDGS @ 10 % moisture and rye grain @ 13.5 % moisture (Same value as for Wheat DDGS) Berglin et al., 2003
258
Feedstocks By-products Oil Liquid biofuels (biodiesel) Fuel
Property
Value
Unit
Reference
Crude and refined vegetable oil
LHV (mass)
37.0
MJ/kg
WTT App.1, V4a
LHV (volume)
34.0
MJ/l
Density LHV (mass)
0.920 37.2
kg/l MJ/kg
LHV (volume)
33.1
MJ/l
Glycerol
Density LHV (mass)
0.890 16
kg/l MJ/kg
Rapeseed
LHV dry
26.976
MJ/kg dry
JRC calculation (See Chapter 6)
Moisture
9 %
kg water/kg total
Rous, J-F, Prolea, personal communication to JRC, 2012
LHV wet (RED)
24.3
MJ/kg wet
Biodiesel (methyl ester)
Comment
WTT App.1, V4a
Edwards, R, JRC, 22 July 2003: calculation with HSC for windows EU rapeseed only
LHV of rapeseed This varies according to the composition of rapeseed. We have received the oil and water content of rapeseed as used by Diester. The rest of the composition is filled out in proportion to the rest of the composition given in the Nutrient Requirements of Dairy Cattle: Seventh Revised Edition, 2001 (ed. National Academy of Sciences) and then calculate the LHV from the LHV of the
259
components. As expected, this gives a slightly higher LHV than JEC-WTT previously used, measured on rapeseed with lower oil content Rapeseed cake
Sunflower seed
Sunflower cake
Soybeans
Soybeans cake
Palm (fresh
LHV (mass)
18.38
MJ/kg dry
Moisture
11.5 %
kg water/kg total
LHV wet (RED)
16.2
MJ/kg wet
LHV dry
27.245
MJ/kg dry
Moisture
9 %
kg water/kg total
LHV wet (RED) LHV dry
24.5 18.15
MJ/kg wet MJ/kg dry
Moisture
11.5 %
kg water/kg total
LHV wet (RED)
15.78
MJ/kg wet
LHV dry Moisture
23 13 %
LHV wet (RED)
19.7
MJ/kg dry kg water/kg total MJ/kg wet
LHV dry
19.03
MJ/kg dry
Moisture
12.1 %
kg water/kg total
LHV wet (RED)
16.4
MJ/kg wet
LHV dry
24.0
MJ/kg dry
Back-calculated from rapeseed and EBB data on oil mill
Calculated Dreier et al., 1998
Calculated Back-calculated from sunflower and EBB data on oil mill
Calculated Jungbluth et al., 2007
Calculated
Back-calculated from mass balance soybean cake 794 kg cake/1 000 kg moist soybean; 192 kg oil/1 000 kg moist soybean
However, Bunge report has maximum 12.5 %
Calculated
260
fruit bunch)
Palm kernel meal
Jatropha seed
Animal fat (also tallow oil)
Moisture
34 %
LHV wet (RED)
15.0
kg water/kg total MJ/kg wet
LHV dry
18.5
MJ/kg dry
Moisture
10 %
kg water/kg total
LHV wet (RED)
16.4
MJ/kg wet
LHV dry
27.28
MJ/kg dry
IFEU/UU2011 based on van Eijck 2011 and Wahl et al. 2009
Moisture
6 %
kg water/kg total
IFEU 2011 based on Reinhardt et al. 2008
LHV wet (RED)
25.5
MJ/kg wet
LHV dry
38.8
MJ/kg dry
Moisture
1.2 %
kg water/kg total
LHV wet (RED)
38.3
MJ/kg wet
Calculated Kaltschmitt and Reinhardt, 1997
Calculated
Calculated ECN database Phyllis 2
Calculated
261
262
Appendix 2. Crop residue management
0.66 0.2 0.2 0 0 0 0.3 0.3 0 0.2 0.72 0.99 0.2 0.2 0.2 0.66 0.2 0.2 0.16 0.72 0.71 0.16 0.72 0 0.72 0.72 0.2 0.73 0.47 0.2 0.2
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.54 0 0.99 0 0.66 0.2 0 0 0 0.3 1 0.2 0.72 0.16 0.2 0 0.66 0.2 0.72 0.71 0.16 0.72 0 0.72 0.72 0.2 0.47 0.2
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.54
0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2
0.99 0 0.2
0.2
0
0 0
0.3 0
0
0.16 0 0.2 0.2
0 0 0
0 0 0 0
0 0 0
0
0.2 0.2 0.47 0.47
0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0
0.54 0.54 0 0.99 0 0.66 0.66 0.2 0.2 0 0 0 0 0.3 0.3 0 0.2 0.2 0.78 0.72 0.16 0.16 0.2 0.2 0 0.66 0.66 0.2 0.2 0.78 0.71 0.16 0.78
0
0.72 0.71 0.16 0.72 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.54 0.54 0.54 0 0 0.99 0.99 0 0.66 0.66 0.2 0 0.2 0 0 0 0 0 0 0 0.3 0.3 0 0 0 0.72 0 0 0.2 0 0 0.66 0.66 0 0.2
wheat
triticale
sunflower
sugarcane
sugarbeet
soybean
sorghum
safflowe
rye
rapeseed
oilpalm
maize
cotton
0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.54 0.2 0.99 0.2
coconut
EU27 EU27 EU27 EU27 EU27 EU27 EU27 EU27 EU27 EU27 EU27 EU27 EU27 EU27 EU27 EU27 EU27 EU27 EU27 EU27 EU27 EU27 EU27 EU27 EU27
cassava
barley
AUSTRIA BELGIUM BULGARIA CZECH REPUBLIC DENMARK ESTONIA FINLAND FRANCE GERMANY GREECE HUNGARY IRELAND ITALY LATVIA LITHUANIA LUXEMBOURG NETHERLANDS POLAND PORTUGAL ROMANIA SLOVAKIA SLOVENIA SPAIN SWEDEN UNITED KINGDOM AFGHANISTAN ALBANIA ALGERIA ANDORRA ANGOLA ARGENTINA ARMENIA AUSTRALIA AZERBAIJAN BANGLADESH BELARUS BELIZE BENIN BHUTAN BOLIVIA BOSNIA AND HERZEGOWINA BOTSWANA BRAZIL BRUNEI DARUSSALAM BURKINA FASO BURUNDI CAMBODIA CAMEROON CANADA CENTRAL AFRICAN REPUBLIC CHAD CHILE CHINA COLOMBIA
EU27
13 16 20 50 54 61 63 66 51 80 91 95 100 123 121 122 153 166 169 175 193 194 60 195 70 1 4 56 5 2 8 9 12 14 19 24 25 17 31 27 23 32 28 30 18 15 108 39 34 33 200 36 37 43
COUNTRY NAME
COUNTRY ID
Table 266 Fraction of crop residues removed from the field based on JRC/PBL (2010). The residue removal for cereals (excluding maize) in the EU is an expert estimate based on recent literature.
0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2
0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.54 0.2 0.99 0.99 0.2 0.2 0 0 0 0 0.3 0 0 0.2
0.72 0.71 0 0.16 0.72 0 0 0 0.78 0.72 0.78 0.72 0.2 0 0.2 0.47 0.47 0.47 0.47 0.47 0.47 0.2 0.2 0
0.99 0.2 0.2 0.66 0.2
0.71 0.72 0
0.2 0.73 0.2
263
0.2 0.2 0.2 0.2 0.99 0.2 0.2 0.72 0.79 0.71 0.71 0.2 0.72 0.72 0 0.72 0.2 0.2 0.72 0.72 0.2 0.2 0.2 0.95 0.25 0.14 0.98 0.21 0.21 0.2 0.99 0.21 0 0.71 0.71 0.99 0.16 0.99 0.21 0 0.16 0.21 0.66 0.72 0.99 0.2 0.79 0.71 0.16 0.72 0.2 0.2 0 0.99 0.99 0.71 0.13 0.13 0.66 0.99 0 0.2 0.72 0.72 0.2 0.81 0.21 0.2
0.72 0.72 0 0.72 0.2 0.72 0.72 0.2 0.2 0.2 0.16 0.21 0.14 0.98 0.21 0.21 0.2 0.21 0 0.9 0.16 0.16 0.21 0 0.16 0.21 0.66 0.72 0.99 0 0.79 0.71 0.16 0.72 0.72 0.2 0 0.16 0.99 0.71 0.13 0.66 0.16 0 0.2 0.72 0.72
0.99
0.2 0.2 0.99 0.2
0
0.25 0.98
0 0.71
0
0.16 0.16 0.16
0.16 0.66
0 0.2
0 0.16 0.99
0
0.13 0.13 0.16 0
0.81
0.2 0.2 0 0.72
wheat
triticale
sunflower 0.2
0.2
0 0.2 0.2 0 0 0.2 0 0.2 0.99 0.99 0.99 0.99 0.99 0.2 0 0.2
0.78 0.72 0.78 0 0.78 0.72 0.2 0.2 0.78 0.72 0.78 0.2 0.2
sugarcane
0.2 0.2 0.2 0.72 0
sugarbeet
0.2 0.2 0.2 0.78 0
0
0.79 0.71 0.71
0.71
0 0.2 0.81 0.21 0.2
safflowe
rye
rapeseed
oilpalm
cotton
maize 0.79 0.71
0 0.2
soybean
0.2
0.2 0.2 0.2 0.72 0 0.2 0.2 0.2 0.99 0.2
sorghum
0.2 0.2 0.2 0.72
coconut
cassava
barley
COUNTRY NAME CONGO CONGO, THE DEMOCRATIC REPUBLIC O COSTA RICA COTE D'IVOIRE CROATIA CUBA DOMINICAN REPUBLIC ECUADOR EGYPT EL SALVADOR EQUATORIAL GUINEA ERITREA ETHIOPIA FRENCH GUIANA GABON GAMBIA GEORGIA GHANA GUATEMALA GUINEA GUINEA-BISSAU GUYANA HAITI HONDURAS HONG KONG INDIA INDONESIA IRAN, ISLAMIC REPUBLIC OF IRAQ ISRAEL JAMAICA JAPAN JORDAN KAZAKSTAN KENYA KOREA, DEMOCRATIC PEOPLE'S REPUB KOREA, REPUBLIC OF KUWAIT KYRGYZSTAN LAO PEOPLE'S DEMOCRATIC REPUBLIC LEBANON LESOTHO LIBERIA LIBYAN ARAB JAMAHIRIYA MACEDONIA, THE FORMER YUGOSLAV MADAGASCAR MALAWI MALAYSIA MALI MAURITANIA MEXICO MOLDOVA, REPUBLIC OF MONGOLIA MOROCCO MOZAMBIQUE MYANMAR NAMIBIA NEPAL NEW ZEALAND NICARAGUA NIGER NIGERIA NORWAY OMAN PAKISTAN PALESTINIAN TERRITORY, OCCUPIED PANAMA
EU27
COUNTRY ID 41 40 46 38 89 47 55 57 58 187 79 59 62 84 69 77 71 73 83 75 78 86 90 88 87 94 92 96 97 99 101 104 103 105 106 168 111 112 107 113 114 120 115 116 132 128 143 144 133 139 130 127 136 125 138 135 146 155 157 151 148 150 154 158 159 171 160
0.79 0.71 0 0.72 0.72 0
0.79 0.71
0 0.72 0 0.72 0.72 0 0 0
0.2
0.2
0.2
0.25 0.25 0.8 0.25 0.14 0.14 0.98 0.98 0.98 0.98 0.98 0.21 0.21 0.21 0.21 0.21 0.21 0.21 0.21 0 0.16 0.16 0 0.21 0.21 0 0 0 0.5 0.71 0.71 0.71 0.16 0.16 0.16 0.16 0.16 0 0 0 0.16 0.16 0 0.16 0.21 0.21 0.66 0.66 0.66 0.72 0.72 0
0
0
0 0.2
0.95 0.98 0.21 0.21 0.99 0.21 0 0.71 0.99 0.99 0.21 0 0.99 0.21 0.66 0.99 0.2 0.79 0.71
0.79 0.71 0.71 0.71 0.71 0.16 0 0.16 0.78 0.72 0.72 0.78 0.72 0.2 0.2 0.2 0 0.2 0.2 0.2 0 0 0 0 0.16 0.16 0.99 0.99 0.99 0.99 0.99 0.99 0.71 0.71 0.71 0.71 0.71 0.13 0.13 0.13 0.13 0.13 0.13 0.66 0.66 0.16 0 0.99 0 0.2 0.2 0 0.2 0.78 0.72 0.72 0.72 0.78 0.72 0.72 0.72 0.2 0.2 0.21 0.21 0.81 0.81 0.81 0.81 0.21 0.21 0.21 0.2 0
264
0 0.71 0.71 0.14 0.16 0.72 0.2 0.99 0.28 0 0.71 0 0 0.2 0 0.2 0.43 0.43 0.21 0.66 0.66 0.66
0
0
0 0 0 0.14 0
0.66
0 0.2
0.99 0.28 0.28
0 0
0 0 0
0.43 0.43
wheat
triticale
sunflower 0.2
0 0 0 0.71 0.71 0.71 0.71 0.21 0.78 0.72 0 0 0 0.78 0.72 0.79 0.79 0.66 0.66 0.66 0.66 0.16 0 0.79 0.79 0.79 0.2 0 0.66 0.66 0.66 0.66 0 0 0 0.21 0.21 0.21 0.21 0.16 0 0 0 0 0 0.15 0.71 0.71 0.71 0.14 0.14 0.14 0.14 0.16 0.78 0 0.99 0.99 0.99 0.28 0.28 0.28 0.28 0 0 0 0.71 0.71 0.71 0.71 0 0 0 0 0 0 0 0 0 0.2 0.2 0 0.2 0 0 0 0.2 0.2 0 0.2 0.43 0 0.21 0.66 0.66 0.66 0.66 0.66 0.66 0.66 0.66
0 0.2
0.28 0 0.71 0 0 0.2 0 0.2 0.43 0.21 0.66 0.66
sugarcane
0.2
sugarbeet
safflowe
rye
0.2
0.14
0.66 0 0.21 0.16 0 0.15 0.14 0.16 0.72 0.2
0.2 0.21
soybean
0.2 0.2 0.14 0.2 0 0.71 0.21 0.72 0 0.72 0.79 0.66 0.16 0.79
rapeseed
oilpalm
maize
cotton
coconut
cassava
0.16 0.2 0.2 0.2 0.2 0.14 0.2 0 0 0.71 0.21 0.72 0.2 0.72 0.79 0.79 0.66 0.66 0.16 0.79 0.2
sorghum
PAPUA NEW GUINEA PARAGUAY PERU PHILIPPINES PUERTO RICO RUSSIAN FEDERATION RWANDA SAUDI ARABIA SENEGAL SERBIA AND MONTENEGRO SIERRA LEONE SOMALIA SOUTH AFRICA SRI LANKA SUDAN SURINAME SWAZILAND SWITZERLAND SYRIAN ARAB REPUBLIC TAIWAN, PROVINCE OF CHINA TAJIKISTAN TANZANIA, UNITED REPUBLIC OF THAILAND TIMOR LESTE TOGO TRINIDAD AND TOBAGO TUNISIA TURKEY TURKMENISTAN UGANDA UKRAINE UNITED STATES URUGUAY UZBEKISTAN VENEZUELA VIET NAM YEMEN ZAMBIA ZIMBABWE
barley
EU27
COUNTRY NAME
COUNTRY ID 165 170 162 163 167 176 177 178 181 179 186 189 228 119 180 192 196 35 198 212 203 213 202 206 201 208 209 210 205 214 215 217 216 218 220 223 227 229 230
0.2 0.2
0
0 0.71 0.21
0.2
0.2 0.79 0.66 0.79
0.2
0.66 0.2 0.21 0 0.71 0.14
0.99 0.99 0.28 0.28 0 0.71 0 0 0 0.2 0 0.43 0.43 0.21 0.66 0.66
0.03 0.03 0.1 0.1 0.1 0.03 0.03 0.03 0.1 0.1 0.01 0.1 0.1 0.03 0.03
0.03 0.03 0.1 0.1 0.03 0.1 0.03 0.03 0.03 0.1 0.1 0.03 0.01 0.1 0.1 0.03 0.03
0.03 0.03 0.1 0.1 0.03 0.1 0.03 0.03 0.03 0.1 0.1 0.01 0.1 0.1 0.03 0.03
0.03 0.1
0.03 0.03 0.1 0.01
0.03 0.03 0.03 0.1 0.1 0.1 0.1 0.03 0.1 0.1 0.03 0.03 0.03 0.03 0.03 0.1 0.1 0.1 0.1 0.03 0.01 0.01 0.1 0.1 0.1 0.1 0.03 0.03
0.03 0.03 0.1 0.1
0.03 0.03 0.1 0.1 0.01 0.1
wheat
triticale
sunflower
sugarcane
sugarbeet
soybean
sorghum
safflowe
rye
rapeseed
oilpalm
maize
cotton
0.03 0.03 0.1 0.1 0.03 0.1 0.03 0.03 0.03 0.1 0.1 0.03 0.01 0.1 0.1 0.03 0.03
coconut
EU27 EU27 EU27 EU27 EU27 EU27 EU27 EU27 EU27 EU27 EU27 EU27 EU27 EU27 EU27 EU27 EU27
cassava
barley
AUSTRIA BELGIUM BULGARIA CZECH REPUBLIC DENMARK ESTONIA FINLAND FRANCE GERMANY GREECE HUNGARY IRELAND ITALY LATVIA LITHUANIA LUXEMBOURG NETHERLANDS
EU27
13 16 20 50 54 61 63 66 51 80 91 95 100 123 121 122 153
COUNTRY NAME
COUNTRY ID
Table 267 Fraction of crop residues burnt in the field based on JRC/PBL (2010) and Seabra et al. (2011) for Brazilian sugarcane.
0.03 0.03 0.03 0.03 0.1 0.1 0.1 0.1 0.03 0.03 0.1 0.1 0.03 0.03 0.03 0.03 0.03 0.1 0.1 0.1 0.03 0.01 0.01 0.1 0.1 0.1 0.1 0.03 0.03 0.03 0.03
265
0.06 0.23 0.23 0.1 0.23 0.1 0.11 0.11 0.1 0.23 0.13 0 0.23 0.23 0.1 0.06 0.28 0.28 0.43 0.13 0.17 0.43 0.13 0.05 0.13 0.13 0.23 0.01 0.01 0.23 0.23 0.28 0.28 0.23 0.13 0.1 0.23 0.23 0.23 0.23 0.01 0.23 0.23 0.13 0.07 0.17 0.17 0.23 0.13 0.13 0.1 0.13 0.23 0.23 0.13 0.13 0.23 0.23 0.23 0.05 0.23 0.73 0 0.18 0.18 0.23
0.1 0.03 0.1 0.1 0.1 0.03
0.16 0.1 0.01 0.03 0.06 0.23 0.1 0.3 0.1 0.11 0 0.23 0.13 0.43 0.23 0.1 0.06 0.28 0.13 0.17 0.43 0.13 0.05 0.13 0.13 0.23 0.01 0.23 0.18 0.18 0.23 0.13 0.1 0.23 0.23 0.23 0.01 0.23 0.07 0.17 0.13 0.13 0.1 0.13 0.23 0.13 0.13 0.23 0.23 0.23 0.43 0.35 0.73 0 0.18 0.18 0.23
0.1 0.03 0.1 0.1 0.1 0.03 0.03 0.03 0.16
0.1 0.03 0.1 0.1 0.1 0.03 0.03 0.03 0.1
0.01 0.03 0.23 0.23 0.23 0.23 0.1 0.11 0.1 0.1
0.43 0.1
0.1 0.28
0.05 0.05 0.1 0.1
0.1
0.1
0.01
0.01 0.23 0.28 0.28 0.23 0.1 0.1 0.23 0.23 0.01 0.23
0.1
0.1 0.13 0.1 0.1 0.1 0.13 0.23 0.23 0.1 0.13 0.1 0.23 0.23 0.23
0
0.1 0.03 0.1 0.1 0.1 0.03 0.03 0.03 0.16 0.16 0.1 0.1 0.01 0.01 0.01 0.03 0.03 0.06 0.23 0.23 0.1 0.1 0.23 0.23 0.23 0.1 0.1 0.11 0.11 0.1 0.1 0.1 0.23
0.1 0.03 0.1 0.1 0.1 0.03 0.03 0.03
0.23 0.1 0.06 0.28
0.07 0.17 0.99 0.13 0.13 0.1
wheat
triticale
sugarcane
sunflower 0.1 0.03 0.1 0.1 0.1 0.03
0.13 0.13 0.17 0.17 0.43 1 0.43 0.13 0.13 0.05 0.05 0.05 0.05 0.13 0.13 0.23 0.99 0.23 0.01 0.01 0.1 0.01 0.01 0.23 0.99 0.28 0.28 0.28 0.28 0.28 0.23 0.99 0.13 0.13 0.1 0.1 0.1 0.1 0.99 0.99 0.23 0.99 0.01 0.01 0.01 0.01 0.23 0.99
0.07 0.17 0.17
0.17
0.23
sugarbeet
0.1 0.1 0.03 0.03 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.03 0.03 0.03 0.03 0.03 0.16 0.16 0.16 0.16 0.1 0.1 0.01 0.01 0.03 0.06 0.06 0.06 0.23 0.23 0.23 0.99 0.1 0.1 0.23 0.23 0.39 0.1 0.1 0.1 0.11 0.11 0.11 0.1 0.1 0.23 0.23 0.99 0.1 0.13 0.13 0.43 0.43 1 0.23 0.23 0.99 0.1 0.1 0.06 0.06 0.06 0.28 0.28 0.647 0.1 0.17 0.43 0.1
0.23 0.23 0.01 0.01
soybean
sorghum
safflowe
rye
rapeseed
oilpalm
maize
cotton
0.1 0.03 0.1 0.1 0.1 0.03 0.03 0.03 0.16 0.1 0.01 0.03
coconut
EU27 EU27 EU27 EU27 EU27 EU27 EU27 EU27
cassava
barley
COUNTRY NAME POLAND PORTUGAL ROMANIA SLOVAKIA SLOVENIA SPAIN SWEDEN UNITED KINGDOM AFGHANISTAN ALBANIA ALGERIA ANDORRA ANGOLA ARGENTINA ARMENIA AUSTRALIA AZERBAIJAN BANGLADESH BELARUS BELIZE BENIN BHUTAN BOLIVIA BOSNIA AND HERZEGOWINA BOTSWANA BRAZIL BRUNEI DARUSSALAM BURKINA FASO BURUNDI CAMBODIA CAMEROON CANADA CENTRAL AFRICAN REPUBLIC CHAD CHILE CHINA COLOMBIA CONGO CONGO, THE DEMOCRATIC REPUBLIC O COSTA RICA COTE D'IVOIRE CROATIA CUBA DOMINICAN REPUBLIC ECUADOR EGYPT EL SALVADOR EQUATORIAL GUINEA ERITREA ETHIOPIA FRENCH GUIANA GABON GAMBIA GEORGIA GHANA GUATEMALA GUINEA GUINEA-BISSAU GUYANA HAITI HONDURAS HONG KONG INDIA INDONESIA IRAN, ISLAMIC REPUBLIC OF IRAQ ISRAEL JAMAICA
EU27
COUNTRY ID 166 169 175 193 194 60 195 70 1 4 56 5 2 8 9 12 14 19 24 25 17 31 27 23 32 28 30 18 15 108 39 34 33 200 36 37 43 41 40 46 38 89 47 55 57 58 187 79 59 62 84 69 77 71 73 83 75 78 86 90 88 87 94 92 96 97 99 101
0 0.23 0.1 0.06 0.28
0.17 0.13 0.05
0.23 0.01 0.23 0.28
0.1
0.23 0.01 0.23 0.07 0.17
0.1 0.13 0.99 0.13 0.13 0.99 0.99 0.99
0.23 0.23 0.2 0.23 0.73 0.73 0 0 0 0 0 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.99
0.1 0.23
0.23 0.05 0 0.18 0.18
266
0.01 0.18 0.1 0.17 0.17 0 0.43 0 0.21 0.1 0.43 0.18 0.06 0.13 0.01 0.1 0.07 0.17 0.43 0.13 0.23 0.23 0.1 0 0.01 0.17 0.49 0.49 0.06 0 0.5 0.23 0.13 0.13 0.03 0.19 0.21 0.23 0.43 0.23 0.23 0.23 0.23 0.65 0.23 0.1 0.1 0.17 0.21 0.13 0.1 0.13 0.07 0.07 0.06 0.06 0.43 0.07 0.23 0.03 0.18 0.1 0.17 0.17 0.53 0.43 0.13 0.23 0.01 0.3 0.1 0.17 0.1 0.03
0.18 0.1 0 0.43 0.43 0.21 0.1 0.43 0.18 0.06 0.13 0.01 0.1 0.07 0.17 0.43 0.13 0.13 0.23 0.1 0.43 0.01 0.17 0.49 0.06 0.43 0.5 0.23 0.13 0.13
0.1 0.17
0.1
0.43 0.43 0.43
0.43 0.06
0.1
0.1 0.43 0.01
0.49 0.49 0.43 0.5
0.1
0.1
0.1 0.1
0.17 0.17 0.43 0.1 0.1 0.23 0.23 0.23 0.1 0.1 0.43 0.01 0.01 0.17 0.17 0.49 0.49 0.06 0.43
0.07 0.17 0.17 1 0.43 0.13 0.13 0.99 0.23 0.23 0.1 0.43 0.01 0.01 0.17 0.17 0.49 0.49 0.49 0.06 1
0.23 0.23 0.1 0.13 0.1 0.13
0.99 0.13 0.13
0.23 0.23 0.65 0.23 0.1 0.17 0.21 0.13 0.1 0.13 0.07 0.06 0.43 0.07 0.06 0.03 0.18 0.43 0.1 0.22 0.53 0.43 0.13 0.23 0.3 0.1 0.17 0.1 0.03
0.21 0.19 0.21 0.23
0.19
0.19
0.23 0.23 0.65 0.1
0.1
0.1
0.1
0.06
0.03 0.03
0.01 0.3
0.3
0.1 0.1 0.03 0.03
0.1 0.17 0.17 0.21 0.1 0.1 0.1 0.07 0.06 0.06 0.43 0.07 0.23 0.06 0.06 0.03 0.18 0.18 0.43 0.1 0.1 0.22 0.17 0.53 0.53 0.43 0.1
wheat
triticale
0.1 0.1 0.1 0.43 0.43 1 0.43 0.18 0.18 0.06 0.06 0.06 0.13 0.13
0.03 0.03 0.19 0.21 0.23
sunflower
sugarcane
sugarbeet
0.01 0.01 0.01 0.18 0.18 0.1 0.1 0.1 0 0.17 0.17 0.17 0.43 0.43 0.43 0.43 0.43
0.1
0.1
soybean
sorghum
safflowe
rye
rapeseed
oilpalm
maize
cotton
coconut
cassava
barley
COUNTRY NAME JAPAN JORDAN KAZAKSTAN KENYA KOREA, DEMOCRATIC PEOPLE'S REPUB KOREA, REPUBLIC OF KUWAIT KYRGYZSTAN LAO PEOPLE'S DEMOCRATIC REPUBLIC LEBANON LESOTHO LIBERIA LIBYAN ARAB JAMAHIRIYA MACEDONIA, THE FORMER YUGOSLAV MADAGASCAR MALAWI MALAYSIA MALI MAURITANIA MEXICO MOLDOVA, REPUBLIC OF MONGOLIA MOROCCO MOZAMBIQUE MYANMAR NAMIBIA NEPAL NEW ZEALAND NICARAGUA NIGER NIGERIA NORWAY OMAN PAKISTAN PALESTINIAN TERRITORY, OCCUPIED PANAMA PAPUA NEW GUINEA PARAGUAY PERU PHILIPPINES PUERTO RICO RUSSIAN FEDERATION RWANDA SAUDI ARABIA SENEGAL SERBIA AND MONTENEGRO SIERRA LEONE SOMALIA SOUTH AFRICA SRI LANKA SUDAN SURINAME SWAZILAND SWITZERLAND SYRIAN ARAB REPUBLIC TAIWAN, PROVINCE OF CHINA TAJIKISTAN TANZANIA, UNITED REPUBLIC OF THAILAND TIMOR LESTE TOGO TRINIDAD AND TOBAGO TUNISIA TURKEY TURKMENISTAN UGANDA UKRAINE UNITED STATES
EU27
COUNTRY ID 104 103 105 106 168 111 112 107 113 114 120 115 116 132 128 143 144 133 139 130 127 136 125 138 135 146 155 157 151 148 150 154 158 159 171 160 165 170 162 163 167 176 177 178 181 179 186 189 228 119 180 192 196 35 198 212 203 213 202 206 201 208 209 210 205 214 215 217
0.01 0.18 0.1 0.17 0 0 0.21 0.1 0 0.18 0.06 0.01 0.1 0.07 0.17 0.13 0.23 0.1 0 0.01 0.17 0.49
0 0.5 0.23 0.13 0.13 0.03 0.03 0.21 0.19 0.19 0.21 0.21
0.99 1 0.99 0.23 0.23 0.99 0.23 0.65 0.99 0.1 0.1 0.1 0.1 0.17 0.17 0.17 0.21 0.13 0.1 0.1 0.1 0.1 0.13 0.07 0.07 0.06 0.06 0.06 1 0.07 0.07 0.07 0.99 0.06 0.06 0.06 0.03 0.03 0.03 0.03 0.18 0.18 0.18 1 0.1 0.1 0.1 0.17 0.17 0.17 0.53 0.53 0.53
0.99 0.01 0.01 0.01 0.01 0.01 0.3 0.3 0.3 0.3 0.3 0.3 0.1 0.1 0.1 0.1 0.17 0.17 0.17 0.17 0.17 0.1 0.1 0.1 0.1 0.1 0.1 0.03 0.03 0.03 0.03 0.03 0.03
267
URUGUAY UZBEKISTAN VENEZUELA VIET NAM YEMEN ZAMBIA ZIMBABWE
0.23 0.1 0.23 0 0.43 0.21 0.06 0.06 0.06
0.23 0.1 0.23 0.43 0.21 0.06 0.06
0.1 0.43 0.43
0.23 0.23 0.1 0.23 0.23 0.43 0.21 0.06 0.06 0.06 0.06
0.1
wheat
triticale
sunflower
sugarcane
sugarbeet
soybean
sorghum
safflowe
rye
rapeseed
oilpalm
maize
cotton
coconut
cassava
barley
EU27
COUNTRY NAME
COUNTRY ID 216 218 220 223 227 229 230
0.99 0.23 0.23 0.1 0.1 0.99 0.23 1 0.43 0 0.21 0.06 0.06 0.06 0.06 0.06 0.06
268
Appendix 3. List of questions and answers BDBE - German Bioethanol Industry Association Q1)
A reproduction of the cultivation emissions of feed wheat as presented at the workshop in May could not be replicated using the input data in the report and the latest version of the Biograce tool.
JRC: The purpose of the workshop was to present input data and to discuss their correctness. Using the existing BIOGRACE version will not give correct answers, because it needs to be updated with all the new data (for example, updated emissions from fertilizer production, shipping, diesel production, etc.), not just the input data which are specific to feed wheat. The results reported at the meeting were labelled “approximate draft calculation” for example only, and will change again as a result of updates in the consultation process. N2O calculation: As the methods in BIOGRACE and GNOC to calculate N2O emissions are different, the results may differ.
Q2)
The JRC proposes to change the methodology for modeling field N2O emissions without explaining the existing method or the new method.
JRC: The new method to estimate soil N2O emissions from cultivation of potential biofuel crops managed agricultural soils is fully described in Chapter xx of the report. It was also already included in Chapter 4 of the draft report that was sent via email to the participants of the stakeholder meeting in Brussels (28.05.2013): “Edwards, Robert, Declan Mulligan, Jacopo Giuntoli, Alessandro Agostini, Aikaterini Boulamanti, Renate Koeble, Luisa Marelli, Alberto Moro, and Monica Padella, 2013. Assessing GHG Default Emissions from Biofuels in EU Legislation. Review of Input Database to Calculate “Default GHG Emissions”, Following Expert Consultation 22-23 November 2011, Ispra (Italy). European Commission Joint Research Centre Institute for Energy and Transport, doi:10.2788/66442.” In this final version, this Chapter 4 (Chapter 3 in the new report) has been extended, adding all background data and formulas in order to allow the reader to reproduce the methodology step by step. The Global Nitrous Oxide Calculator (GNOC) tool has been also on-line since July 2013: http://gnoc.jrc.ec.europa.eu/, and it is accompanied by a detailed user manual, offering the possibility to calculate the emissions for a selected location based on the method presented. The previous method was described in JEC-WTT report version 2c, which has been on-line since 2007.
269
Q3)
It is not clear if the JRC is following the same energy allocation procedure for co-products as determined by the Renewable Energy Directive and the Fuel Quality Directive. At the May meeting, it was indicated to us that the JRC is indeed considering an alternative methodology for allocation of co-products, but no description of it has been provided to us for consideration and comment.
JRC: Indeed the JRC is following the rules of the RED and FQD, e.g. ENERGY ALLOCATION to coproducts! The only point which was not resolved at the time of the workshop was a proposed (by DG-ENER) change in the way that exports of electricity (or heat) would be accounted, because of difficulties applying the present rules to some solid biomass processing plant. The directive explains that this should be done by calculating a credit of the fuel used in the process, whereas under the new proposal an allocation would be made to the electricity or heat export loosely based on exergy (see also SWD(2014)259 recently published). In the 1st generation biofuels, this only affects plants heated with CHP, and the difference in the final result would be minimal.
Q4)
PROPOSAL B: The JRC should share the complete methodology for EU ethanol pathways with the data inventory, calculations and accompanying references, so the industry is able to compare with real world data and to comment. JRC: Background data (e.g. crop residue parameters) to calculate N2O emissions based on the GNOC method is added now in the new version of the report. All input data and references are included in this very long and detailed report. The methodology applied for the calculations is set by the Directives (and not by the JRC in the report), and everything is transparently explained in the text
Q5)
PROPOSAL C: Amendments to default factors should be constructed subject to a rigorous scientific method, audited and verified by independent experts, and under a standardized procedure, subject to Article 19 paragraph 7 of the RED. JRC: JRC organised in November 2011 a consultation with recognised experts and industry representatives in order to guarantee transparency and ensure the use of the most up-to-date scientific information and data. Methodological issues and the input data used for calculating default GHG emissions were discussed. The main outcomes of the discussions with the experts were summarised in the draft report sent to stakeholders in May 2013. Stakeholders had the opportunity to comment on the input data in the workshop organized by JRC in Brussels in May 2013. Comments and data received from stakeholders have been considered and included in the new version of input data and report.
Q6)
The JRC proposes to substitute an existing model and methodology to define the default factor for field N2O emissions. It offers no justification for why the alternative N2O methodology constitutes scientific progress over the existing model, or that it is the best available N2O model today. JRC: N2O methodology developed by the JRC has been largely explained during the workshop, including scientific progresse vs existing methods (e.g. IPCC).
270
The revision of the existing methodology is proposed by the JRC in close discussion with DG ENER / DG CLIMA for the following reasons: The current default values for cultivation of biofuel feedstock in the RED Annex V are based on 2 different methods. a. default soil N2O emissions from potential biofuel crops grown in Europe (wheat, rapeseed, sugar beet, sunflower) result from the application of the soil chemistry model DNDC for EU15. b. default soil N2O emissions from imported biofuel crops (maize, soybean, sugar cane, oil palm) were calculated according the IPCC (2006) TIER1 approach as at a current stage the global application of DNDC is still a challenge because not enough data is available. Thus, the current default values for feedstock mentioned under a. do not account for possible differences in cultivation emissions in locations outside EU15 (e.g. Eastern Europe, US) and default values for feedstock listed under b. are based on a different methodological approach. A further aspect for looking for an alternative methodology has been the fact that the DNDC model requires specific expertise of the user and it needs to be fed with detailed data (parameterization, daily meteorological parameters e.g.). This led to the situation that EU countries to fulfil their reporting obligations about average biofuel cultivation emissions on NUTS2 level (Article 19.2 of the RED) mainly based their calculation on the IPCC (2006) TIER1 approach. This resulted in methodological inconsistencies between the default values and the average soil emissions calculated on NUTS2 level. Biofuel producers wishing to provide their own emission data will face the same problem. In fact, tools (as e.g. version 4b BIOGRACE) providing assistance in calculating cultivation emissions also had to rely on IPCC(2006) TIER1 for the estimation of N2O emissions from cultivated soils. We defined the minimum requirements of a methodological approach suitable for an update of the default values in the RED as: applicability at least to all major 1st generation biofuel crops covered by the RED applicability in all regions where biofuel feedstock can possibly be cultivated the impact of different environmental conditions on N2O fluxes has to be taken into account (requested in RED Article 19.2) consistency with other greenhouse gas emission reporting obligations (e.g. Kyoto, UNFCCC) published and peer reviewed applicable by non-experts and/or possibility to provide assistance via spreadsheet or web-tools. The new methodology described in the report complies with all the requirements we identified. -
Q7)
PROPOSAL: If sufficient data for individual crop types is not available, then the application of the S&B/GNOC model is unsuitable. To be included in a legislative proposal, more data from field measurements are required.
JRC: Uncertainties in modelling N2O emissions from agricultural soils are considerable, this however holds also for other methodological approaches, as e.g the DNDC model applied to calculate the current default values.
271
See also answer to Q11) and e.g. the publication: Leip, Adrian, Mirko Busto, and Wilfried Winiwarter. 2011. “Developing Spatially Stratified N(2)O Emission Factors for Europe.” Environmental Pollution (Barking, Essex: 1987) 159 (11) (November): 3223–32. doi:10.1016/j.envpol.2010.11.024. http://www.ncbi.nlm.nih.gov/pubmed/21186068.
Q8)
In the May stakeholder meeting, the JRC announced that, contrary to the existing default value methodology, they intend to count GHG emissions from manure and exclude the subtraction of background emissions of unfertilized grassland. Proposal: The JRC should not change its approach to manure and background emissions. JRC: a) Concerning Manure: in the draft report circulated to stakeholders in 2013 there was already a detailed discussion about the “manure” issue and why we suggest to account for 50% of the manure input. Additional explanation has been added in this final version of the report (in Section 3.8). See also answer to Q25). b) Concerning Background Emissions: In GNOC the direct emissions that would occur from a field without fertilizer application are subtracted from the emissions calculated for the same field when fertilizer is applied for biofuel crop cultivation (see Section 3.4). The unfertilized field is a rather “theoretical land use” but basically it can be interpreted as “temporarily uncultivated cropland” as reference land use.
Q9)
The alternative GNOC model proposed by the JRC has been publicly rejected for regulatory purposes by the Commission. The Commission report COM(2010) 427 states: “The accuracy of the input data [in the S&B model], together with the fact that most biofuels and bioliquids fall under the grouping "other crops", when crop type is of major importance for determining the emissions, strongly suggests that this work does not now provide the basis for binding legislative proposals.” Since the input data to S&B and the classification of crops have not been changed, it is not comprehensible why the Commission is now supporting this approach.
JRC: The Commission report COM(2010) 427 is discussing the “ feasibility of drawing up lists of areas in third countries with low greenhouse gas emissions from cultivation”. It is not aimed at discussing in a comprehensive way pros and cons of different methods to calculate N2O emissions from managed agricultural soils. Furthermore, the comments in the report refer to an application of the Stehfest and Bouwman model in the strict sense which was under discussion to be applied for the update of the default values at that time. The current methodology proposed for the update of the default values is based on IPCC (2006) with a regionalized emission factor for direct emissions from fertilizer N input. This regionalized (so-called TIER2) emission factor is based on Stehfest and Bouwman “Fertilizer Induced Emissions” (see report Section 3 for details). In addition, in its reply to a letter to COPA-COGECA sent in June 2013, DG ENER supported the GNOC calculation tool proposed by the JRC (“We are confident that the Stehfest&Bouwman approach used by the JRC for the calculation of cultivation emissions in the default values is a significant methodological improvement compared to the IPCC tier 1 approach, greatly reducing the error margin. We see the Global NOx Calculator (GNOC, based on the S&B approach) as a useful tool which the Commission may provide to stakeholders and which could be used by those
272
concerned with calculations of cultivation emissions and of actual values. Several Member States have shown interest in making this tool available”).
Q10) It is disputable if the proposed methodology on indirect N2O emissions is applicable to a GHG life cycle assessment of a specific crop. The IPCC methodology was developed to report emissions at national level. We doubt that this methodology is appropriate to attribute averaged indirect emissions to a regional, specific crop, especially in a legally binding context. PROPOSAL: The JRC should continue to use the current model for the N2O methodology until either the S&B/GNOC model is independently scientifically validated by and with additional field data inputs (with consideration for the level of uncertainty and data application adequacy), or a new scientifically validated model becomes available. JRC: a) We already face the situation that the IPCC(2006) TIER1 methodology is applied on a local / regional level under the RED Article (19.2). Most EU countries based their calculations of average NUTS2 soil N2O emissions from biofuel feedstock cultivation on IPCC (2006). Also the current version of the BIOGRACE tool (version 4.1) bases soil N2O emission calculations on IPCC (2006), including indirect emissions. As outlined in answer to question Q6), DNDC is a complex soil chemistry model and requires extensive data input as well as specific expertise to be applied. It is not a feasible option for emission reporting for non-EU countries / biofuel producers. The approach we are suggesting for an update of default values is published in peer reviewed papers (see literature references in the report e.g. Stehfest and Bouwman, 2006; Smeets et al., 2011). The IPCC (2006) TIER 1 emission factor for direct emissions from managed agricultural soils due to fertilizer application to the field is based on the same approach, globally averaged however. In order to take into account the influence of regional environmental and management conditions we (re-)disaggregated the global average emissions factor in IPCC(2006).
Q11) Field N2O emissions are reported at a national level for the purpose of greenhouse gas inventories. The JRC makes no statement as to whether the typical value of its proposed alternative model conforms with the typical EU emissions reported by national governments. Proposal: National Governments should decide first whether the proposed alternative model should be adopted for measuring and reporting national field N2O emissions before considering it for biofuel default value purposes. JRC: In the publication Leip, A. et al. (2011) N2O emission results from 10 different models for 6 European countries/country groups are compared. The figure below shows the estimates of direct N2O fluxes from agricultural soils by the 4 models entering into the discussion in the context of the question by BDBE. The new approach proposed for calculating N2O emissions from biofuel cultivation corresponds to “SuB-FIE-JRC” results in the graphic below (and in Leip et al 2011). “SuB-JRC” is the application of the Stehfest and Bouwman model in a strict sense to calculate N2O emissions, N2O emissions from the reference land use “unfertilized managed grassland” are subtracted. Also, results from emissions modelled with DNDC are given. However, it is a DNDC run different from the one used for the actual RED default values and no reference land use is subtracted. Results named “UNFCCC” are taken from the “Annual European community greenhouse gas inventory 1990– 2008 and inventory report 2010 (Submission to the UNFCCC secretariat, European Environment
273
Agency; 2010). These are the emissions reported by the countries based on the methodology described in the revised IPCC(1996) guidelines. Except for Germany, the results based on the proposed new approach (dark blue line in the graphic below) fits best with the emissions reported by the countries to UNFCCC (red line). DNDC (green line) gives remarkably higher emissions for Poland and remarkably lower emissions for France and UK/Ireland compared to the UNFCCC country submissions. The Stehfest and Bouwman model applied in a strict sense with reference land use managed grassland (light blue line) is in line with UNFCCC for UK/Ireland, BENELUX and Germany, but results in higher emissions for the rest of the countries or country groups.
CSH = Czech Republik, Slovakia and Hungary, UK_IRE = UK and Ireland Graphic based on data given in Leip, A, M Busto, M Corazza, P Bergamaschi, R Koeble, R Dechow, Suvi Monni, and W De Vries. 2011. “Estimation of N2O Fluxes at the Regional Scale: Data, Models, Challenges.” Current Opinion in Environmental Sustainability 3 (5): 328–338. doi:10.1016/j.cosust.2011.07.002. http://linkinghub.elsevier.com/retrieve/pii/S1877343511000595
Q12) There is inconsistency in the selection of the vintage of data, with some very recent data and other dating back from the last century. The JRC proposes data sources for the wheat ethanol pathway that range from 1995 to 2012 (with also at least one undated source). Proposal: Data sources should be as recent as possible. JRC: It would be desirable to have all input data for EU average processes available from a single up-to-date unbiased source. Unfortunately, they are not, and JRC had dedicated a long time searching a wide variety of sources for the relevant information. In the end, all the most important data come from fairly recent sources. The only data from the “last century” (1990s) for the wheat pathway are: - amount of seed needed to grow wheat (negligible impact);
274
- energy for drying and storage, where older data were used as they are lower than the more modern sources; - a formula for calculating the wet LHV specified by DG ENER for by-products allocation. The formula has not changed with time.
Q13) The JRC is inconsistently applying yield data. This will result in inconsistency in comparison between pathways, and excessive variation in the performance of individual pathways over time. Yields vary over time due to external factors. The sugar cane yield is quoted as an average over 6 years. However, 2010-2011 data is used for the other crops. Proposal: A 5 year average for yield data should be applied for all crop types. JRC: 2010-2011 average yields are used to be consistent with fertilizers data. 2010 and 2011 (year of Fertilizers Europe data for EU27 crops) straddle the yield trend line (as the figure below shows for wheat for example). EU wheat yield (tonnes/ha) from FAO
6 5 4 3 2 1 0 1971
1981
1991
2001
2011
However, sugarcane is an exception because according to the new data provided by the French Confederation of Sugar Beet producers (CGB) and the Confederation Internationale des Betteravies Europeans (CIBE) following the stakeholder workshop in May 2013 (based on actual data from the Brazilian Ministry of Agriculture), 2011 was the worst year over the last 6 years for Brazil. This is why the average yield of the last 5 years has been considered.
Q14) Unweighted average data is being applied that appears sometimes to incorporate irrelevant and biased data. The JRC is e.g. applying unweighted average data inputs to the sugar beet ethanol pathway. However, sugar beet cannot be stored and is cultivated in close proximity to the ethanol processing plant. Proposal: If weighted average data inputs can be consistently applied, they are preferable to unweighted averages. JRC: JRC was using the weighted average for all EU sugar beet cultivation. Now, with new data from the French Confederation of Sugar Beet producers (CGB) (ePURE member) following the stakeholder workshop in May 2013, they have been updated to be the weighted average of only the EU countries which produce sugar beet ethanol.
275
Q15) The JRC now covers most standard pathway steps for EU ethanol but still misses to provide a default option for the logistics of shipping grain by small barge. Proposal: A default option for the logistics of shipping grain by small barge should be included. JRC: This option is similar to the fuel consumption and emissions of a bulk carrier for inland navigation which is used in the rapeseed pathway. This option can be used in the EU ethanol pathways as well.
BEE - German Renewable Energy Federation Q16) We think that the GHG emission factors for natural gas, diesel and gasoline, published in the JRC’s scientific and policy report “Assessing GHG default emissions from biofuels in EU legislation” (2013) are too low. We have the following questions regarding the calculation of the emission factors: Assumptions for the calculation of the emission factor of natural gas (12.76 gCO2 eq/MJ). Which assumptions are made for the following parameters? • Share of the domestic production and imports • Average transport distance • Share of different regions for imports • Share of pipeline and LNG imports • Methane leakage in the different production steps (production, transport, liquefaction, regasification) • Energy intensity MJ/MJ of the whole production chain in the different regions (mature gas fields in the North sea, arctic fields etc.) JRC: The emission factor for natural gas has been updated in the new version of input data to coincide with the new fossil fuel comparator (for when biogas replaces NG). That is a marginal NG mix consisting of an equal mixture of gas from SW Asia, W Siberia and LNG inputs. The input data for each of the three sources is documented in JEC-WTT report v4 and JEC-WTT Appendix 4 (v 4).
Q17) How does the calculation of the emission factor of natural gas consider the following developments? - Growing imports: the EU is now importing 70% of its gas consumption. Ten years ago the import share was 50%. - Growing liquefied natural gas (LNG) imports: Already 30% of the gas imports are LNG. - Large LNG capacity: The EU is currently using only about half of its LNG capacity and could double its LNG imports. The capacity will further grow by 20% in the next years. Up to 2020 a doubling of the current capacity is expected. - The EU will get access to US shale gas via LNG in the future.
276
- 70 % of EU imports have a high GHG-value (e.g. Russia up to 25 g/MJ, Qatar (LNG) 27 g/MJ, Nigeria up to 38 g/MJ). - Energy and carbon intensity of natural gas from Norway will rise due to the growing depth of fields and because of artic production (incl. LNG production in the arctic re-gion). - The methane leakage of gas from imported gas can be considerably higher than the assumed average value of 0,2 g/MJ (=1% leakage), e.g. 2% leakage value of gas from Russia. - The development of the entire natural gas sector is very dynamic, therefore a sensitivity analysis of the influence of the main parameters on the GHG balance is necessary. JRC: Import shares are the latest available data, from 2011, and can be updated when new data becomes available. In that data, 27% of imports are LNG. We do not attempt to predict future evolution of any parameter, as being too difficult to justify. We cannot simply adopt GHG values suggested by stakeholders without any reference or justification. Please review the input data presented in JEC-WTT report v4 and JEC-WTT Appendix 4 (v 4) comment on that. Emissions from NG supply to plants is differentiated by the pressure it is supplied. Leaks are accounted for, but most leaks occur in the low-pressure municipal distribution; a maintenance problem. These are not affected by the demand for gas or substitution of NG by biogas, and so are not counted. Large conversion plants are usually connected directly to medium or highpressure pipelines. In legislation, we cannot have sensitivities! However, the default values can be updated as new data becomes available.
Q18) Assumptions for the calculation of the emission factor of diesel and gasoline (15.4 gCO2 eq/MJ). Which assumptions are made for the following parameters? • Share of the domestic production and imports • Average transport distance • Share of different regions for imports • Energy intensity MJ/MJ of the whole production chain in the different regions (mature gas fields in the North sea, arctic fields etc.) • Oil field depth • Water to oil ratio (WOR): Proportion of water in extracted oil • Use of improved production technologies (Enhanced oil recovery) • Flaring and venting of accompanying gas and other Methane leakage in the different production steps (average value and range) • Viscosity of petroleum • Sulphur content of petroleum JRC: Figures for crude oil productions (including flaring and venting) and transport emissions estimated for EU-mix in the OPGEE report (ICCT, 2014) are used in the new version of input data. The emissions from refining are those calculated in JEC-WTWv4a on the basis of marginal
277
emissions for producing marginally less of the different products. This refining calculation assumed EU-mix crude slate and other assumptions explained in JEC-WTW v4 and WTT report (Version 4) and WTT Appendix 4 (Version 4). This makes the refining emissions for gasoline and especially diesel higher than the average for all refinery products, whereas those for heavy fuel oil are lower. ICCT, 2014. Upstream emissions of Fossil Fuel feedstocks for transport fuels consumed in the European Union. Authors: Chris Malins, Sebastian Galarza, Adam Brandt, Hassan El-Houjeiri, Gary Howorth, Tim Gabriel, Drew Kodjak. Washington D.C.: The International Council on Clean Transportation (ICCT). Report to the European Commission, DG CLIMA, May 2014.
Q19) How does the calculation of the emission factor of natural gas consider the following developments? • Nearly half of EU’s consumption is imported from Russia, with very high flaring and venting emissions. Another 5 % are coming from Nigeria, which flares the second-largest quantity of accompanying gas after Russia7. • The energy intensity of oil and gas production is increasing: Several studies show a clear decline in the EROI (Energy Return of Investment) of the oil production in differ-ent regions and worldwide8. According to Gagnon et al (2009), the EROI of the global oil and gas production decreased between 1992 and 2006 at about 70% from 26:1 to 18:1. The reasons for the declining EROI are9: o Growing oil field depth o Growing water to oil ratio (increasing age of oil fields) o Improved and new production technologies o Increasing production of unconventional fossil fuels and New Frontier oil (e.g. arctic) • The EROI is an important indicator for the development of the carbon intensity of the oil and gas production especially in regard to mature production and new oil resources: One example: The greenhouse gas intensity of the BP North Sea oil fields increased by aprox. 400% from 2004 to 2011. BP is currently producing less than ¼ of what it was producing in 2004, while the GHG emissions from oil production (excl. emissions from natural gas production) remain stable10. • The GHG value for natural gas used for oil production in the UK has to consider the increasing LNG imports (mainly from Quatar) with a GHG value of about 27 g/MJ (2011 nearly 1/3 of total consumption in the UK11). As an indirect effect of these in-creased imports, the natural gas is consumed on the platforms and can no longer be fed into the British natural gas grid. It must therefore be replaced with imported LNG. A sensitivity analysis is thus necessary to calculate the influence of LNG emission on the GHG balance of UK oil production. JRC: These data are described in JEC-WTTv4 and shown in detail in JEC-WTT Appendix 4 (v4).
278
US United Soybean Board Q20) I’m unclear on how the disaggregation of no-till and conventional tillage in the report accounts for conservation tillage practices such as strip till or ridge till. So I do not know if USDA’s release of new tillage data on May 15 provides new information. The USDA data show that 67% of U.S. soybean acres are “no till or minimum till” compared to 22% where crop residue is plowed down using conventional tillage. (see attached spreadsheet) JRC: There is no distinction between “no tillage” and “conventional agriculture” in the soya pathway in the new version of input data.
Q21) We tried to have USDA-ARS provide some comments on the JRC’s decision about N2O emissions from leguminous crops and the Tier I 2006 IPCC guidelines. Unfortunately we were not able to get those comments in time. We had consulted with James Duffield when we updated our field N2O emission numbers in December 2011; these were the same numbers we used in the Biograce model run we did last year. Our field emissions, with ARS’ input, were 0.000238 – 0.000293 kg/kg soybean. I would still encourage you to reach out to USDA-ARS for future input on this element of the GNOC. James Duffield and Rod Venterea are two scientists in particular who would have beneficial perspectives on this. JRC: Please find a detailed chapter on the revision of the soybean residues N content and the comparison of the emissions from soybean based on the revised nitrogen content in belowground residues with field measurements in Chapter 3 of the new report.
Swedish Gas Association – ENERGIGAS SVERIGE Q22) N2O emissions: One update in the draft report which results in a large impact of the overall default value for bioethanol, biodiesel and biogas from crops is the new calculation methodology for biogenic nitrous oxide (Chapter 4). With the new method, the emissions of nitrous oxide from the cultivation of wheat and canola are approximately 40% and 70% higher respectively compared to the emission levels we expect for wheat and canola grown in Sweden today. We assume the reason for changing the calculation method of the IPCC's more "general" model is to achieve better regional resolution, and adaptation to more specific crops since the new model is based on approximately 1 000 measures of field data from cultivation of different crops in different soil types and in different regions. This new method is likely, however, no increasing the quality of the input data, as many other studies show that biogenic emissions of nitrous oxide can vary greatly within a field, between different time periods, etc. (examples are Swedish studies by Klemedtsson et al.). Hence, there are no scientifically substantiated reasons for this change of methodology. When there are major uncertainties in the input data the common approach is to apply the practice of the precautionary principle. Therefore, until more scientifically reliable data and verified national methods for biogenic emissions of nitrous oxide have been developed, it is justified that the current IPCC method continues to be used. JRC: IPCC (2006) TIER 1 is not the current methodology applied to calculate the default emissions from cultivation of biofuels in Europe (see answer to question Q6)). Please refer also to answers to Q6) and Q11) for the discussion why we propose an update of the methodology to
279
calculate soil N2O emissions under the RED and how the results compare with the IPCC Guidelines for National Greenhouse Gas Inventories (2006) TIER1 method on country level. In fact at EUlevel, the average emissions per crop from the GNOC model now used are very close to the result of IPCC TIER 1 method.
EFOA – The European Fuel Oxygenates Association Q23) EFOA believes that the European Commission is failing to fulfil its remit of assessing the GHG savings for biofuels across their entire lifecycle. The debate on the front-end GHG emissions from indirect land use change is currently being addressed by the European Parliament. However, the final stage of the biofuels lifecycle is not being addressed. This is the stage where the biofuel is blended in the refinery to create the final fuel that will be used by the consumer. That final fuel is a mixture of anything from 8 to 12 components the precise level of which is always optimized by the producer based on their individual properties. These components all require different levels of energy to produce. The current approach is to assume that an average of these components is replaced by every biofuels. This is not correct. All biofuels have different properties and thus will result in a different final fuel “cocktail”. This means that the energy required to produce the final fuel will vary depending on the biofuel used. EFOA believes that in order to make the correct biofuels choices this effect needs to be included when developing the default values. Thus a separate default value is required for every biofuel and the connection of fuel ethers to the alcohol used in their production needs to be changed. Work is currently underway to develop fuel ethers which use not only bio-alcohols but also bio-olefins. Annex III does not recognise this option and we therefore ask that this option be included in order to ensure that they can be brought to market in a timely fashion. We would suggest that this becomes the main entry for MTBE, ETBE, TAME and TAEE with footnotes explaining what the default value should be if only a bio-alcohol is used. Based on our first point the footnotes should not simply be a repetition of the existing default values. We are developing more detailed argumentation on this point and will be happy to share it with you once it is finalised. JRC: EFOA make two points: 1. The use in RED of a fixed fossil fuel comparator for gasoline implies that biofuels (such as ETBE and ethanol) replace gasoline as a whole and not a mixture of particular components which is different from average gasoline in composition and emissions footprint. Answer: EFOA correctly points out that taking this effect into account would mean calculating a separate fossil fuel comparator for each biofuel; depending on what component the biofuels is supposed to replace. One claim is that blending ethanol in gasoline increases gasoline vapour pressure (this does not happen if ethanol is blended in the form of ETBE). To stay inside the volatility limits, the refinery may need to be adjusted to produce a gasoline with lower vapour pressure, and this costs more
280
emissions. In principle this argument is valid; it would lead to a small penalty for ethanol but not ETBE. However, in runs of their European refinery model, CONCAWE found that the effect on EU average refinery emissions is very small, even if one could come up with examples of particular refinery configurations where the effect might be significant. We see no reason to discount the CONCAWE conclusions, as the oil industry has no particular reason to prefer ethanol over ETBE, and ETBE producers are part of CONCAWE. Other claims are made in two studies commissioned by EFOA from consultants. One of these studies (from CE Delft) was published in a peer reviewed journal. However, the journal published a discussion of the paper two editions later (Energy Policy 39 (2011) 7470-7471). The discussion claimed that the paper made a number of dubious assumptions and scenarios, and changing any of them would have led to a different conclusion about the relative emissions reductions achieved by ETBE and ethanol. The authors of the paper did not reply to this discussion, although journal editors would normally invite a reply. Therefore we may conclude that the claimed benefits of ETBE are not substantiated. According to a third source, the JEC-WTWv4 (WTT report), if ETBE replaces MTBE made with fossil methanol, it slightly improves on the emissions savings from using ethanol directly (5g/MJ ethanol, or 1.6g/MJ per MJ ETBE). However, if more ETBE is used than is necessary to achieve the octane specification, one needs to consider the emissions from making the olefin part of ETBE (isobutylene). In this case ETBE actually produces 16g/MJ more GHG emissions than gasoline, and far more than ethanol by itself. At present, EU refiners have no trouble to reach gasoline octane specification, because there is an abundance of high-octane by-product from the catalytic reformers which are run to provide hydrogen. Hydrogen demand in EU refineries has greatly increased in recent years because it is needed to reach stricter standards for sulphur content, and to increase the output of diesel fuel. Therefore ETBE is hardly needed to reach octane standards and so is likely to have higher emissions than blending ethanol directly. Therefore we consider the emissions savings currently accorded to ETBE more than generous. 2. “Work is underway to develop fuel ethers …which use bio-olefins. …We would suggest that this becomes the main entry for MTBE, ETBE TAME and TAEE.” Answer: Until such time as these pathways become significant, it is not appropriate to supply default values, especially as these would depend critically on the exact processes employed. In the meantime producers can always declare their own actual emissions.
EBA - European Biogas Association Q24) Also the regional differences within the EU and other variables should be better taken into account: for example the level of methane emissions depends largely on the climate and temperatures: in cold climates the emissions of manure are significantly lower than the given estimations. Also the new calculation methodology for biogenic nitrous oxide (Chapter 4)
281
should regard the great variation in biogenic emissions of nitrous oxide depending on time periods, regions and even fields. JRC: See answer to question Q6).
DG AGRI – Directorate-General for Agriculture and Rural Development Q25) Accounting of emissions from manure and the corresponding emission savings from the production of artificial nitrogen fertiliser. In the 2009 approach, to our understanding, N2O emissions from manure were not added to the total GHG balance in crop production, and the emission savings from replacing artificial nitrogen fertiliser were fully accounted. This methodology appears reasonable on the rationale that manure is a waste material resulting from animal production, and the associated emissions in using it as a fertiliser should not be accounted to the crop production. In the EU, manure is never produced in order fertilise crops. In many places, it is rather a problem getting rid of it in compliance with environmental requirements. The JRC report presents arguments why the nitrogen emissions from manure are now accounted to 50%. Nevertheless, the arguments in favour of a change in methodology don't appear to result from new scientific insight, and we are not convinced that a change in methodology as compared to the one agreed in 2009 would be justified. The report is not explicit how the emission savings from the replacement of artificial fertiliser are accounted (also half?). The corresponding text (section 4.8 on P. 78) could be made clearer (in particular point 4). JRC: As the data on synthetic N fertilizer use are based ultimately on sales (and not theoretical estimates of crop requirements), the replacement of synthetic N by manure is automatically and fully accounted for. JRC agrees that in some regions manure is a disposal problem. Now we have added more data which support the estimate that this applies to roughly 50% of the manure used. The other 50% actually replaces synthetic N. See also answer to Q8).
Q26) Background emission level for crop production. In 2009, we understand from the discussions (please confirm!) that background emissions were considered those from unfertilised grassland based on the rationale that this is the type of vegetation that would prevail in case that no crop production would take place. The new approach considers background emissions to result from unfertilised cropland, which is not a realistic scenario. Also here, we think that in 2009 a solid consideration about the most appropriate way of calculating the background level has taken place and we have difficulties seeing which new scientific insight could justify a change in the methodology. The choice of methodology for the two points above is to a certain degree arbitrary, which makes it subject to a political decision. For this reason, it is important to have a sensitivity analysis, which can illustrate the impact that the choice of method actually has on the default values.
282
JRC: The IPCC Guidelines for National Greenhouse Gas Inventories (2006) Tier 1 approach for direct emissions is based on fertilizer-induced emission (FIE). The FIE is defined as the direct emission from a fertilized plot, minus the emission from an unfertilized control plot (all other conditions being equal to those of the fertilized plot), expressed as a percentage of the N input. The FIE has been calculated by applying the Stehfest and Bouwman model globally. The global mean FIE resulted in ~1% of the fertilizer N input. This corresponds to the EF1 for direct emissions from mineral fertilizer and manure N input in IPCC(2006) TIER1. We propose the same method to calculate FIE, however disaggregated for different crops and different environmental conditions. This would ensure compliance with IPCC(2006) on a global scale (see also answer to Q6)). If we choose another “reference land use”, we do not follow the logic of a FIE anymore. We actually did also ‘sensitivity’ calculations applying Stehfest and Bouwman in a strict sense and considering unfertilized managed grassland as a reference. The results of the different approaches are presented in the answer to question Q11).
Q27) Emissions from rendering animal fats. As raised by EFPRA at the stakeholder meeting, the separation of waste animal fats is to be attributed to the production of edible products, and not to the waste itself, in line with the 2009 methodology. JRC: See answer to question Q101).
VDB - Association of the German Biofuels Industry Q28) In general we support the comments and input data provided by FEDIOL and EBB. The new data of comprehensive oil mill LCA should be considered for the calculation of the default values. These new calculations show clear deviations between the input data on oil seed crushing and refining given in the JRC draft report and recent oil mill LCA data. JRC: Data provided by FEDIOL on crushing (for soybean) have been included in the present final version of input data and report.
Q29) For a proper calculation of GHG emissions from rapeseed also crop rotation and the preceding crop effect should be taken into consideration. JRC: It is unfortunately impossible to consider crop rotations in the default N2O calculations because very little data on crop rotations is available at any scale. The consideration of management factors as crop rotation requires a dynamic model approach. Regionalized or even EU data about management factor like rotation on EU / global scale for the crops of interest is scarse.
Q30) Regarding GHG emissions from panamax transport we assume that there is a mistake in table 140 where payload of panamax is given with 3000 t only. Typical payload of a panama ship is around 50000 t, so we suppose that GHG emission from panamax transport should be less.
283
JRC: This was a typing mistake (in Table 140). The payload should have been 37,000 t (source: International Maritime Organization, IMO). On checking, we found that although 37,000 tonnes is a reasonable estimate of the size of the cargo, this class of ship is closer to “Handymax”, so we changed the name.
Q31) Fossil fuel comparator: We welcome the revision of the fossil fuel comparator as actual values of Diesel and Gasoline are reported to be higher than the given comparator from the RED and will further increase as the share of marginal oil and gas is rising continously. Furthermore it makes sense to differentiate between Diesel and Gasoline as well as Natural Gas to reflect actual emissions. With regard to the input data for the calculation of the emission factor for Diesel/Gasoline and Natural Gas we support the statement from the German Renewable Energy Federation (BEE). Special attention should be raised on growing shares of Natural Gas imports with high GHG emissions, growing shares of LNG, increasing energy intensity of oil and gas production as well as higher methane leakage as assumed. In addition, you find attached an up-to-date article “The substitution of marginal oil with biofuels“, recently published in the scientific magazine Biofpr – Biofuels, Bioproducts and Biorefining. JRC: Fossil Fuel Comparators have been changed in the new version of input data. See answer to question Q18) for diesel and gasoline and questions Q16) and Q17) for natural gas on the new method.
Q32) Animal Oil Biodiesel: So far the typical and default values for biofuels within the DIRECTIVE 2009/28/EC for “animal oil biodiesel” have been calculated starting at the entrance of the biodiesel plant. Without any doubt, there are good reasons for this. Animal by-products are subject of the regulation (EC) No 1069/2009 since animal byproducts are a potential source of risks to public and animal health and therefore have to be treated in a proper way. Within the European Union in most cases the holder of animal byproducts (food industry of agriculture) has to pay for the service of disposal. For Category 1 and 2 materials this is true in nearly 100 % of the cases. And even for Category 3 material the market value of the byproducts has just a share of 1-3 % of the main products. Within the ANNEX V Rules for calculating the greenhouse gas impact of biofuels, bioliquids and their fossil fuel comparators of the RED it is pointed out that “wastes, agricultural crop residues [..] and residues from processing [..] shall be considered to have zero life-cycle greenhouse gas emissions. The ILCD handbook of the Europeans Commissions - Joint Research Centre is applying the same methodology since it is argued that all treatment processes that are necessary until the treated waste / end of-life product is achieving a market value of zero are within the responsibility of the first system. This is because the waste or end-of-life product is generated by the first system, while a waste can per se not carry any burden of treatment. Animal byproducts certainly have a waste / residue status and have, as described, a negative market value, therefore it is not understandable why the new report of JRC on “Assessing GHG default emissions from biofuels in EU legislation” starts with the “transport of carcass” from food industry of agriculture within their calculations. Also the rendering, as a recycling process in order to achieve a market value of zero, cannot be a burden for the residue/waste. The
284
starting point for animal by-products has to be the outlet of the rendering plant – the point where the treated waste / end of-life product is achieving a market value of zero. JRC: See answer to same question Q101).
ePURE - European Renewable Europe Q33) We question whether there is enough technical and scientific progress on ethanol patwhays that would justify an update according to Art. 19(7) of the RED. ePURE is concerned about the way this process has been organized and managed. We believe that the process to update the default values has to be transparent, non‐arbitrary, scientifically robust and truly open to input from the concerned stakeholders. We fear that the 2 stakeholder meetings that have been so far taken place in this framework do not sufficiently reflect the spirit of transparency and consistency the Commission adheres to. JRC: See answer to Q5).
Q34) The JRC has not provided a complete data inventory for the cereals ethanol pathways. Nor has it provided a complete description of the GHG calculation methodology. This makes it impossible to make a full assessment of what is being proposed and to compare it with existing data and methodology. JRC: The methodology is set and explained in the Directive and it is a simplified LCA methodology. All input data and references are include in the very long and detailed JRC report. See also answer to Q4).
Q35) On p.139 of its report, the JRC addresses the wheat ethanol pathway by stating the following: “The data for each process are shown below; significant updates are described in more detail with relevant references.” However, not all data is given and not all significant updates are described in detail. And the methodology is not fully described. JRC: The input data used in each step of all pathways are reported (that’s why the draft report is so long). And in each step, the main updates and references are described before or after the tables.
Q36) For instance: seed emissions data are not provided. JRC: Seed emissions are now provided in the final version of the report.
Q37) For instance, in the case of the nitrogen input from crop residues, no information/explanation/justification is given for the total amount (kg/ha) of crop residues
285
considered in every pathway, which is the most relevant value for this calculation. In Table 50 of the JRC draft proposal, final results of this calculation are showed as following: - Wheat: 9.3 kg N per moist tonne of crop (42% of mineral N applied) - Maize: 6.9 kg N per moist tonne of crop (37% of mineral N applied) What is the percentage of above ground biomass that is considered to be removed from the plot at the end of cultivation cycle? It is the same value for all crops? It is the same value for all regions? What is the reference of this data? JRC: The calculation of N input from crop residues is mainly based on IPCC Guidelines for National Greenhouse Gas Inventories (2006) Vol. 4, Chapter 11 Eq.11.7a and 11.6. Please refer to the formulas given there or to Chapter 3 of the final report on RED default values. Detailed tables for all input parameters to calculate the crop residues are now added to the report (Chapter 3 and Annex 2). For all European countries we consider a fraction of 20% of aboveground residues of cereals (excluding maize), this is about 30-50% of the straw, being removed from the field. For all other crops 0 residue removal from the field is assumed. A small fraction of the crop residues 1-3% (all crops) is considered to be burned in the field. Different residue removal and infield burning fractions are considered in different countries/regions outside Europe.
Q38) For instance, the JRC proposes to change the methodology for modelling field N2O emissions without explaining the existing method or the new method (see point 4 below). JRC: See answer to Q2).
Q39) For instance, in none of the ethanol cases is it explained in the JRC’s draft proposal if the JRC is following the same energy allocation procedure for co‐products, and there is a concern that it may be being ignored, contrary to the RED and FQD. At the May 28 meeting, it was indicated to us that the JRC is indeed considering an alternative methodology for energy allocation of co‐products, but no description of it has been provided to us for consideration and comment. JRC: Indeed the JRC is strictly following the methodology set by the RED and FQD, and hence using allocation by LHV. See answer to Q3).
Q40) There is an aim to group crops and pathways together where possible and to avoid any WTO action by estimating a weighted average of all feedstock used in the EU based on its origins, at a specific moment in time. Thus wheat and maize would be considered under ‘cereals’. Such an approach will never reflect what will be a continually changing feedstock mix. (OSR is also considered a ‘cereal’ for N2O emission purposes…) JRC: The desire to avoid WTO problems is only one motivation for a common cereals pathway. The other is that the emissions for ethanol made from different cereals are extremely close (even closer than in the previous calculations) and JRC doesn’t think the differences are significant compared with the uncertainties. Therefore a changing cereals mix will not have any significant
286
effect. The extra administrative burden of reporting the feedstock is not justified by any significant difference in emissions.
Q41) The JRC is erroneously mixing marginal data with average data. The RED states: “For biofuels, for the purposes of the calculation referred to in point 4, the fossil fuel comparator EF shall be the latest available actual average emissions from the fossil part of petrol and diesel consumed in the Community as reported under Directive 98/70/EC.” However, the JEC report provided to industry 2 days before the May stakeholder meeting quantifies fossil fuel data thus: “Diesel oil provision • … Includes marginal refinery emissions for making a bit more diesel. These are higher than the average emissions for all petroleum product slates.” Similarly the RED defines a default values thus: “‘default value’ means a value derived from a typical value” Yet, in Table 43, ammonia emissions are estimated from marginal natural gas. Similarly, on page 78 it states that the JRC has calculated the marginal emission of manure. JRC: Methodological issues are dealt by DG ENER. The use of marginal data for fossil fuels comparators is decided by DG ENER and CLIMA, and it gives more favourable emissions savings for biofuels.
Q42) At least one factor is proposed to be included that was previously deliberately excluded on reasoned, scientific grounds. No explanation or scientific justification is offered for this. In the May stakeholder meeting, the JRC announced that, contrary to the existing default value methodology, this time they intend to count GHG emissions from manure. One JRC expert also announced that, also contrary to the existing methodology, there would be no baseline field N2O emissions (for the field without crops). As he put it, ‘if the field didn’t grow biofuel it would be as if it had been transported to another universe.’ This latter comment was later refuted by another JRC expert. The JRC has consistently argued that manure should be excluded and that the baseline reference value should be included. It has made this argument in its prior default evaluation and its three well-to‐wheel reports on biofuels over the last decade. Page 41 of its 2011 wheel‐to-tank report puts it thus: “Background N2O emissions and “reference land use” Soils emit some N2O even if they are not farmed (so‐called “background emissions”). These can be quite significant, especially for organic soils. If we are to model the emissions from EU land in annual set-‐aside used for biofuels, we should subtract the background emissions which would otherwise have occurred. Where annual set‐aside land is not already used for non-food crops, it is either left fallow or, increasingly, planted with a cover crop. We could estimate the background emissions by changing the crop specified in the DNDC soil chemistry model. However, DNDC restricts the possibilities to either another arable crop, fallow or grass. Selecting “fallow” suppresses all vegetative growth; whereas in practice even a fallow field gets a partial covering of weeds, which also act as a cover crop, reducing the loss of nitrogen
287
from the soil by absorbing and storing it until the next ploughing. So we considered that “unfertilized grass” was the best choice offered by DNDC for estimating background emissions. We ignored the small farming inputs for maintaining the field in set-aside. The amount of manure used in EU depends on how much is available rather than on which crop is grown. So the manure used in the “biofuels crop” scenario does not disappear if the field is in set-‐aside instead: even if used on another field, it would cause some N2O emissions. Therefore, it is better to assume that the same amount of manure is used on the set-‐aside field, than to assume none is used. It is quite conceivable that manure would be applied on a field of unfertilized grass (for example, directly by grazing animals), but no-‐one would put manure on a fallow field: another reason for preferring grass to represent emissions from the reference scenario. For biofuels crops grown on voluntary set-‐aside land, [Kaltschmitt 1997] considered as reference crop a field under set-‐aside planted with unfertilized rye grass. This was effectively the same as no reference crop because the N2O emissions were assumed proportional to the extra N applied. [LBST 2002] considered both this scenario and one in which clover (a nitrogen-‐fixing plant) was sown on the reference field. In this case, there was a reduction of between 1 and 2.5% in farming energy inputs (due to a small saving on N fertilizer for the next crop). This is well within the range of overall uncertainties in the farming emissions, and can be neglected, along with the small emissions from establishing the cover crop, which work the other way. [LBST 2002] calculated a negligible effect of the choice reference crop on soil emissions because the saving on nitrous oxide emissions caused by the fertilizer was compensated by soils emissions from the clover. Our study does not assume N2O emissions to be proportional to the N fertilizer rate, and we find significant emissions also from unfertilized land. Therefore we need to subtract the emissions in the reference scenario.” JRC: Please see answers to questions Q6), Q11) and answer b) to Q8). A detailed discussion in this context – applying the Stehfest and Bouwman model and considering different types of reference land use - can be found in: Smeets, Edward M. W., Lex F. Bouwman, Elke Stehfest, Detlef P. van VUUREN, and Adam Posthuma. 2009. “Contribution of N 2 O to the Greenhouse Gas Balance of First-generation Biofuels.” Global Change Biology 15 (3) (March): 780–780. doi:10.1111/j.1365-2486.2009.01872.x. http://doi.wiley.com/10.1111/j.1365-2486.2009.01872.x.
Q43) Modelling with high uncertainty over the resulting estimate, makes up more than half of the cultivation default value of EU biofuels. This overall proportion of modelled estimation is excessively high and unnecessarily high. Modelling is sometimes being proposed as an unnecessary substitute to the use of fact-based official statistics and real world data. One aspect of this problem is that EU‐1 or sub‐regional data is be extrapolated to the whole EU without justification. One example is the model of the amount of wheat originally targeted as milling wheat that does not make the grade, and then is assumed to go into biofuels. The basis for the model is data from the UK which is then extrapolated without justification to the whole of the EU. Other obvious examples of the same problem include the proposed new data factor of the use of lime and the drying of grain, when drying conditions across Europe vary enormously. This extrapolation of a single geographical source raises the inevitable concern that the data may be atypical and biases the result.
288
JRC: Official statistics and real-world data are used whenever these are available on the appropriate geographical level. Unfortunately, they are not available for many input data. Then there is a choice: - Either we take whatever data is available for a particular location or region and extrapolate this to all EU. But this choice is being criticised in the comment above. - Or we go to a lot of trouble to find EU-average data using the best available models (ones which are as much as possible based directly on real measurements). But the use of models is also criticised above. But as long as no other better approaches are proposed, we necessarily have to adopt one of the two above. Three examples are given in the commentaire above: 1. Fraction of feed wheat which was grown as milling wheat: To guarantee transparency, we think we were correct in reporting the source used. We asked DG AGRI experts for EU-wide figures but were told there were none. As the dedicated high yield foodwheat varieties are used in NW Europe, the fraction in UK of non-dedicated feed wheat is likely to be lower than EU as a whole. This leads to a slight under-estimation of emissions from EU feedwheat. The effect is slight because the value assumed only slightly changed the calculation in the 7% reduction in N-per tonne for feed wheat. 2. Drying: JRC is aware that grain drying emissions vary enormously across Europe, which is why drying data have NOT been extrapolated from a single source for all EU. Instead, on the advice of DG AGRI, the CAPRI model has been used to find the average amount of water which must be evaporated per tonne of grain in EU. However, JRC noted that the energy used for drying assumed in CAPRI was rather high, and we found a lower number in the literature. 3. Agricultural lime use: JRC did NOT extrapolate lime use from a single source to the whole of EU. Instead, it started from total lime use, and then allocated this to different crops on a GIS basis in proportion to the recommendations for lime use as a function of soil type and acidity (this is known for different crops in the GNOC database).
Q44) Another aspect of this same problem is that existing official factual data may already exist and is being ignored. For instance in the case of pesticide data, the principal source used by JRC is CAPRI modelling data which quotes pesticide use at 7.1 kg/ha, whereas Eurostat claims that it is 1.3 kg/ha. There are similar huge discrepancies between Eurostat and CAPRI for other crops. JRC: Eurostat say that data on ‘pesticide’ application were reported by Member States without guidelines in how they should be gathered and reported. The figures do not agree with ‘pesticide’ sales data (gathered by Eurostat for economic statistics). The situation was already investigated in depth by CAPRI scientists. Therefore, following advice by DG AGRI, CAPRI data have been considered instead of Eurostat.
289
Q45) When modelling is reasonably adopted to estimate data, the actual modelling approach sometimes seems over-simplistic and consequently biased. In consideration of N fertilizer, JRC proposes to estimate the emissions from N fertilizer, inter alia, by interpolating between an emissions estimate made in 2007 and a regulatory target for 2020. Because of the significance of N fertilizer emissions to biofuel pathways, this adds a huge uncertainty to default values. However, data from major fertiliser producers for the MIN-NO project showed that EU plants are all fitted with N2O abatement technology. And fertilizer production technologies are subject to the Industrial Emissions Directive. This means that a carbon footprint for “best available techniques” (BAT) for emissions control has been developed by the JRC in accordance with industry, and some producer claim to have exceeded BAT. So a more accurate estimate of the environmental performance of the industry ought to be achievable. JRC: No data was published by Fertilizer Europe, at the time of preparation of the first draft circulated to stakeholders. However, following up this comment, we obtained directly from Fertilizer Europe the latest confirmed data on emissions from N fertilizer manufacture and incorporated them. The EU emissions from AN manufacture indeed already reached 2020 target, whilst progress in (Russian etc.) plants exporting to EU was slower than anticipated in the draft.
Q46) The JRC proposes to substitute an existing model with an alternative model to define the default factor for field N2O emissions. The proposed change of model amounts to a change of methodology. The alternative model proposed by the JRC has recently been publicly rejected for regulatory purposes by the Commission. And the scientific literature contends that the JRC’s alternative model has already been superseded by a third model, developed under EU funding. The JRC offers no justification for why the alternative N2O methodology constitutes scientific progress over the existing model, or that today it is the best available N2O model. Nor does it explain why, 3 years after the Commission rejected it, it is now fit for purpose. The JRC proposes to substitute the existing model to estimate typical field N2O emissions with an alternative, based on the model developed by Stehfest and Bouwman. The two models are based on different constructs. The existing model is a process‐based model. The proposed new model is based on statistical relationships. So the proposed change of model amounts to a change of methodology. In 2010 the European Commission reported to the Parliament and Council that: “The Joint Research Centre (JRC) of the Commission is working on analysis of N2O emissions disaggregated down to regional level. The current level of sophistication is a global implementation of the Stehfest and Bouwman model for a range of crops used for biofuels and bioliquids. However; the accuracy of the input data, together with the fact that most biofuels and bioliquids fall under the grouping "other crops", when crop type is of major importance for determining the emissions, strongly suggests that this work does not now provide the basis for binding legislative proposals.” The Commission’s criticism is in part based on the published comments of the authors (Stehfest and Bouwman). It was established at the May stakeholder meeting that the model input data has not subsequently been improved, let alone validated. Industry understands that new data from the UK and France will shortly become available that may resolve the problem of inaccuracy, at least for EU crops.
290
Another similar model, funded through the EU 6th framework programme, and with the participation of Dutch Government, claims to be superior to the model proposed by the JRC. “The comparison with observed EFs from the Stehfest and Bouwman (2006) data set indicates that the EF inference scheme performs on average better than the default IPCC EF and the empirical model developed by Stehfest and Bouwman, as is shown by a higher score on all four statistical measures (Table 4)”. Lesschen JP, Velthof GL, de Vries W, Kros J (2011) Differentiation of nitrous oxide emission factors for Agricultural soils. Environmental Pollution 159: 3215–3222. doi: 10.1016/j.envpol.2011.04.001. JRC: a.) Regarding the question about the “rejection” of the methodology by the Commission, see answer to Q9). b.) The statistical model of Lesschen et al (2011) is based on the same input data as the Stehfest and Bouwman (2006) model. The models differ in the choice of parameters (and their weighting) driving the N2O emissions. Criticisms by ePURE regarding the spatial coverage and crop types in the measurement data set of Stehfest and Bouwman thus hold for both of the models.
Q47) Too little data is being validated, especially for major factors. The unvalidated data sources that the JRC Selects are often not independent, publicly recognized sources. Too many emission factors are generated a single data source. Some of these sources seem extremely suspect. For instance, with respect to agricultural lime, calculating the application rate across all ‘arable crops’ could overestimate the application to biofuel crops. For sugar cane the source of data is Macedo. This is a private source and not as reliable and independent as FAO, and can lead to overoptimistic assessments of the cane default values (yields and sugar content in particular are overestimated). For pesticide data, the principal source used by JRC is CAPRI modelling data which quotes pesticide use at 7,1 kg/ha, whereas Eurostat claims that it is 1,3 kg/ha. There are similar huge discrepancies between Eurostat and CAPRI for other crops. JRC: Agricultural lime is NOT assumed to be used equally on all crops. Neither is lime use in sugarcane taken from Macedo (which is by the way a peer-reviewed paper, and not a “private source”). Sugarcane yield has been adjusted as detailed in Q136).There is no systematic information on lime use on crops in FAO. Even though emissions from lime use are not very significant in the GHG balance, JRC has gone to considerable trouble to make the most accurate possible estimate of lime use on different crops supplied to EU market, using the GIS database on crop distribution in GNOC (see the detailed explanation in the input data report). Lime use is attributed to different crops depending on the soil characteristics where the crops grow, according to recommendations on lime use. Then the lime use is scaled so that total lime use in each country matches the value given in the EDGAR database. We remind ePURE that JRC associates far less emissions to the use of 1 kg agricultural lime than recommended by IPCC. For pesticide data: see answer to Q44).
Q48) JRC proposes to substitute data inputs sometimes with older sources and/or with individual sources and often without explanation or justification. There is inconsistency in the selection of the vintage of data, with some very recent data and other dating back from the last century. The JRC proposes data sources for the wheat ethanol pathway that range from
291
1995 to 2012 (with also at Least one undated source). This mixing of vintage of data inputs is problematic because many data inputs are inter‐related. For example, crop yields and field N2O emissions are related to fertilizer inputs. JRC: See answer to same question Q12).
Q49)
The JRC is also inconsistently applying yield data. This will result in inconsistency in comparison between pathways, and excessive variation in the performance of individual pathways over time. Yields vary over time due to external factors. Presumably to compensate for this the cane yield is quoted as an average over 6 years. However, 2010‐2011 data is used for the other crops.
JRC: See answer to same question Q13).
Q50) Un‐weighted average data is being applied that appears sometimes to incorporate irrelevant and biased data. The JRC is applying un‐weighted average data inputs to the sugar beet ethanol pathway. However, sugar beet cannot be stored and is cultivated in close proximity to the ethanol processing plant. JRC: See answer to same question Q14).
Q51) The JRC now covers most standard pathway steps for EU ethanol but we still miss a default option for the logistics of shipping grain by small barge. JRC: See answer to same question Q15).
COPA-COGECA - European farmers and European agri-cooperatives Q52) The JRC draft report is incomplete in explaining the data and assumptions, the analytical methods, and the results of the respective pathways. This makes it impossible to carry out a complete assessment of the JRC’s proposal. JRC: The report contains only the input data, not the results in terms of emissions. And therefore we had consulted stakeholders to receive comments on the input data proposed by the JRC to make default GHG emissions calculations. The results of the calculation do not, and should not, have any impact on the correctness of the input data: they are chosen independently on the basis of the available evidence, not to achieve a certain result.
Q53) The JRC is trying to establish a new methodology for N2O emissions that is not only valid for the update of default values but would also apply to the calculation of actual values. However, the EC stated itself in 2010 (report COM(2010) 427) that: “The accuracy of the input data [in the S&B model], together with the fact that most biofuels and bioliquids fall under the
292
grouping "other crops", when crop type is of major importance for determining the emissions, strongly suggests that this work does not now provide the basis for binding legislative proposals.” Therefore, COPA-COGECA does not understand why the EC is now supporting this approach. JRC: See answer to Q9). And also reply from DG ENER to the letter from COPA-COGECA in June 2013.
Q54) COPA-COGECA does not agree with the manure calculation procedure. As explained in Section 4.8 (pg78), the JRC acknowledges the difficulty of calculating emissions related to manure application since “the truth is somewhere in the middle”. JRC researchers highlight the “Irrational world of attributional LCA” in which N from manure should account for 100% for N2O emissions but for 0% in terms of N fertiliser production emissions. The calculation procedure is arbitrary and does not satisfy the high quality standards of robust scientific findings, as is the case for material and energy flow data, typically used in process-based attributional LCA. Therefore using arbitrary decisions to calculate GHG emissions will only damage the credibility and reliability of biofuel LCA work. This example demonstrates that the JRC’s approach is unsuitable. JRC: Additional explanation on manure has been added in Section 3.8 of the new report. See also answers to Q8) and Q25).
FEDIOL - EU Vegetable Oil and Proteinmeal Industry Q55) With much regret, FEDIOL was never contacted by the JRC or the European Commission for verification of the oilseeds crushing data that was used, and for participation at previous JRC stakeholder meetings and consultations JRC: FEDIOL IS A MEMBER OF EBB. And the JRC has been always in contact with EBB secretariat, who also participated to the first expert consultation in 2011 etc. Therefore, it is more an issue of internal communication between EEB Members rather than a lack from the JRC.
Q56) In view of the upcoming revision of the Annex V and review of the JRC draft study on emissions from production of biofuels, we are pleased to share with your services the Life Cycle Assessment (LCA) of our sector, covering crushing of oilseeds and refining of vegetable oils for food. We are confident that the data and information provided in the study will be useful in correcting the data provided in 2009, and in particular on the crushing of oilseeds. Some of the differences in crushing figures between the two studies are provided in the Annex to this letter. We also trust that the assumptions and data relating to shipping and transportation will be corrected in the review, as acknowledged during the meeting. Please note, however, that the data on refining in FEDIOL LCA refers to full refining of crude
293
vegetable oils for the food market, thus incompatible for the replacing the data on semirefining in the JRC study.
JRC: JRC uses data provided by EBB for crushing of rapeseed specific for the biofuel sector. However, the new crushing data for soybean provided by FEDIOL have been used to update the soya pathway in the new version of input data and report. For transport, the name of the ship has been changed (from Panamax to Handymax).
UNICA – Brazilian Sugar Cane Industry Association Q57) UNICA congratulates the JRC for the extensive work that has been done to up-‐date the GHG emissions from biofuels and we would like to take this opportunity to submit the following comments to your consideration regarding sugarcane ethanol: 1) Diesel consumption in sugarcane cultivation We would like to know which data has been used: 0.0086 MJ/MJ or 0.797 l/ t of sugarcane? The value 0.797 l/t of sugarcane is consistent with the information provided in Macedo (2004), but it seems to us that the data provided in MJ/MJ does not match the value in liter/t of sugarcane. In relation with this question, we also would like to know what is the percentage of pre‐harvest burned sugarcane (as opposed to mechanically harvested sugarcane) you have assumed in your calculation. JRC: The 0.0086MJ/MJcane figure is the correct updated figure, but indeed it does not correspond to the quoted 0.797 litres diesel per tonne of cane: we accidentally left the old litres/tonne figure in the first version of the draft report (this has been now corrected in the final version).
294
The old data came from Machedo, 2004 (as you noticed), but this was updated with data from Machedo, 2008 44 which are somewhat higher. Thank you for pointing the error in reporting. The share of trash which is burnt is taken as 64.7%, as stated in Machedo, 2004.
Q58) Conversion of sugarcane to ethanol We would like to take this opportunity to reiterate our concerns about the way electricity from bagasse is considered in the methodology to calculate GHG emissions included in annex V of the RED. Here is a simplified representation of a sugarcane mill (see figure attached to the letter). Under the current RED methodology, ethanol can receive credit only for electricity (2), independently of the existence of electricity (4). However, the RED considers that the electricity produced from bagasse at the mill (electricity 2) is substituting electricity from bagasse. For this reason, the emissions are considered to be zero. We disagree with this assumption that, in our opinion, does not capture the real picture of what happens in Brazil. The credit attributed to electricity from bagasse should be calculated taking into consideration the substitution of electricity at the margin that is mainly generated in natural gas thermoelectric power plants as evidenced in Seabra et al. (2011)1 and in Seabra and Macedo (2011). This is the approach used not only Brazil but also by the U.S. Environmental Protection Agency (EPA, 20103) and by the Californian Air Resource Board (CARB, 20094) in their calculation of sugarcane ethanol GHG emissions. We do not understand why the European Union is taking a different approach that is not used by other countries. We will appreciate receiving an explanation on this point. If the Commission does not want to consider the substitution of energy at the margin, then the allocation of emissions should be done between ethanol, electricity (2) and electricity (4) on the basis of energy content. JRC: EU has a bioelectricity policy as well as a biofuel policy. Therefore the rules for how to account for electricity export from biofuel plant must separate GHG savings from biofuels from GHG savings from bioelectricity, which have separate incentives. Giving a credit for saved fossil electricity in the biofuel legislation would count the bioelectricity produced twice.
Q59) Finally, we would like to suggest that in the next revision of the methodology to calculate biofuels GHG emissions, the European Commission considers the possibility to account for credits from the electricity (4) generated from the surplus of bagasse. Both EPA and CARB have developed pathways that integrate this practice. Almost 40% of the ethanol mills in Brazil produce and sell surplus electricity. None of the recent mills are equipped with 21-‐bar class boilers (surplus of 10kwh/t cane) as indicated in p.162 of the JRC report “Assessing GHG default emissions from biofuels in EU legislation”. The standard is 60‐bar class boilers that generate a surplus of 70kwh/t of cane on average. When using the tops and leaves of the 44
Macedo IC, Seabra JEA, Silva JEAR. “Greenhouse gases emissions in the production and use of ethanol from sugar cane in Brazil: the 2005/2006 averages and a prediction for 2020”. Biomass and Bioenergy (2008), doi:10.1016/j.biombioe.2007.12.006
295
cane (available with mechanized harvesting process), this surplus can reach 140 kwh/t of cane. JRC: Electricity export in ethanol production: As we explained on previous occasions, electricity export in RED is not treated by substitution or by allocation. The method is laid down and explained in RED, is uniformly applied, and is not the subject of this consultation. Allocation of bagasse would anyway give practically the same result in this case. The RED method ensures that emissions savings due to bio-electricity production are not falsely credited to biofuel production. This is important, because the biofuels provisions in RED are designed to save emissions specifically in the transport biofuels sector, where emissions savings are generally more costly than in other sectors. Incentives for bio-electricity production are specified separately in EU, and (we suppose) Brazil.
Q60) We would also like to inform the Commission that 28 Brazilian sugarcane mills producing ethanol are certified with the EU approved certification scheme Bonsucro EU. This means that these production units are the only ones in Brazil accredited to sell ethanol to the European Union. All of them are equipped with high‐pressure boilers and sell surplus electricity as indicated by the Brazilian Agency for Electric Energy ANEEL5. All together, these 28 mills have produced in 2011 and 2012, 2.15 billion liters of Bonsucro EU certified ethanol and this figure is increasing as more and more Brazilian mills get certified. This is equivalent to all the ethanol imported by the European Union in 2008 (all supplying countries and all final use for ethanol considered), the record year for imports in the EU. Therefore, we encourage the Commission to consider that the production and commercialization of excess electricity is a standard practice for Brazilian ethanol mills exporting to the EU and to develop a pathway that reflects this fact as EPA and CARB already did. UNICA and the Brazilian scientists who are experts in this field, such as Dr. Macedo and Dr. Seabra, are at your disposal to provide any information you night need to work on this pathway. JRC: Accreditation and steam conditions: The default values must represent any ethanol from sugar cane of any origin which conforms to sustainability criteria, not just ethanol plants which are in the Bonsucro scheme. The steam conditions (21 bar and 300C) are given in [Machedo 2008], page 586. As your letter states, only 40% of Brazilian plants export any electricity. So we suppose the figure of 60 bars steam pressure applies only to plants in the Bonsucro scheme. We would like to thank UNICA for their continuing constructive dialog, and ask you to convey to your experts Prof. Machedo and Dr. Seabra our appreciation of their careful and detailed work which form the basis of our calculations.
Swedish Energy Agency Q61) Regarding N2O emissions: until more scientifically reliable data and verified national methods for biogenic N2O emissions has been developed, it is justified that the current IPCC
296
method continues to be used instead of the suggested GNOC model. No increased "security" in the input data is achieved with this new model, as many other studies show that biogenic emissions of nitrous oxide can vary greatly within a field, between different time periods, etc. (See for example: “Modelling uncertainty for nitrate leaching and nitrous oxide emissions based on a Swedish field experiment with organic crop rotation” Original Research ArticleAgriculture, Ecosystems & Environment, Volume 141, Issues 1–2, April 2011, Pages 167-183 Josefine Nylinder, Maria Stenberg, Per-Erik Jansson, Åsa Kasimir Klemedtsson, Per Weslien, Leif Klemeddtsson. “Methane and nitrous oxide fluxes from a farmed Swedish Histosol”. European Journal of Soil Science 60, 2009: 321-331. Kasimir Klemedtsson, Å., Weslien, P. and Klemedtsson, L) JRC: There is some confusion here. Currently the default values for crops grown in Europe are based on the soil chemistry model DNDC and not IPCC(2006). We are not proposing using the Stehfest and Bouwman model in the strict sense but we use the model to derive emission factors for direct emissions from fertilizer application which take into account environmental conditions. These (TIER 2) emission factors replace the TIER 1 emission factor for direct emissions in the IPCC(2006) TIER 1 approach. This is explained in Chapter 4 of the draft report that was sent via email to the participants of the stakeholder meeting in Brussels (28.05.2013). Applying IPCC (2006) TIER 1 to calculate the default values results in crop average emissions close (in most cases even slightly higher) to the proposed TIER 2 approach (see Section 3.7 of the report).
Q62) Regarding input data for diesel and chemicals: The CAPRI model is used in the report. To decrease insecurity in the data, it would be preferable if current real data from IEA statistics would be used instead of an economic model with assumed values. JRC: IEA does not provide statistics on per-crop diesel consumption and chemicals in the EU. JRC uses data from the CAPRI model as suggested by DG AGRI.
Q63) Regarding Table 31 Fuel consumption for electricity generation in 2009: The numbers in the table for Sweden refers to fuel consumption for both electricity and CHP. For example, three times the actual amount of coal consumption is listed in the table. A file with numbers from Eurostat statistic corrected for this is attached to this e-mail. JRC: There was no mistake as correction for CHP was made in a subsequent step (not reported). However, this question is now irrelevant because a policy decision was made to align emissions for electricity with the FFC for bioelectricity, which is a marginal mix.
MPOB - Malaysian Palm Oil Board Q64) Fresh Fruit Bunches (FFB) are transported to palm oil mills and processed within 24 h after harvesting. As such, there is no storage of FFB required JRC: No storage losses of FFB are now considered In this final version of the report.
297
Q65) The conversion of palm oil to biodiesel is a transesterification process and not esterification JRC: The name has been changed in the new version of the report.
Q66) STEP 1 Cultivation of oil palm tree (16% peat): Percentage of oil palm cultivated on peat is overestimated. Based on the data by MPOB, the % of oil palm planted on peat for year 2002/2003 and 2009/2010 are 8.2% an 13.3% respectively (313,191 ha and 666,038 ha). Based on linear interpolation, for year 2007/2008 it is estimated that oil palm planted on peat is 11.8%, total oil palm planted area by December 2007 is 4.3 million ha. Thus, it is estimated that about 500,000 ha of oil palm is planted on peat. As such, the estimate of `16% oil palm used in the calculation of GHG emissions is too high. A percentage of 12% for the year 2007 is more representative. JRC: JRC calculation is based on an independent source: Miettinen, J., Hooijer AL., Tollrenaar D., Page S., Malins C., Vernimmen R., Shi C., Chin Liew S. 2012 ‘Historical analysis and projection of oil palm plantation expansion on peatland in SE Asia’ ICCT White Paper Number 17, February 2012.
Q67) For default values for palm biodiesel, although growing on peat is allowed prior to the cut-off date of 1 January 2008, there should be available two pathways: one where a proportion of peat is cultivated and one where cultivation is not on peat. Operators must be able to rely on default values where 100% of their cultivation is not on peat. If part of their cultivation is on peat, they should also be able to rely on default values based on the actual weighted average of the proportion of their land on peat. JRC: The purpose of a default value is to apply to material whose origin is unknown except that it obeys the sustainability criteria. For declaring emissions, the GNOC tool allows calculation of N2O emissions with and without peat.
Q68) The 16% peat (organic) soil should not be included in the palm biodiesel pathways because producers who obtain their feedstock from 100% mineral soil will be unfairly penalised. This is especially so for plantations established after 1 January 2008 where palm oil from peatlands are automatically ineligible under the Directive. JRC: If a plantation was set-up on (drained) peatland before 2008 it continues to be a plantation on peatland. As long as there is a loss of soil carbon due to the management, IPCC (2006) accounts for the annual emissions from N mineralised as a result of this loss of carbon.
Q69) The correct reference [8] from the Malaysian Palm Oil Board is Choo et al. (2011). JRC: Thank you for pointing the error in reporting. It has been corrected in the new version of the report.
Q70) N fertiliser for mineral soils should use the figure from Choo et al. (2011) which is lower.
298
JRC: For the estimation of fertilizer N input to a crop in a country we start from country total fertilizer consumption from IFA (reference see report). The country total N consumption is disaggregated to the N input for a specific crop according country and crop specific N input data collected by FAO (reference see report). In a further step, depending on the amount of soil carbon content (see also explanations further down), the fertilizer N input is disaggregated to the crop in a specific 5 minutes grid cell. As it is impossible to obtain specific fertilizer N input data for all countries for all crops for various yield levels, we developed this approach. However we counterchecked the results if further country specific data was available. Based on our calculations according the procedure described above we obtain a mineral fertilizer N input of 87kg/ha to oilpalm for the year 2000 (yield: 18.6 tons) after adjustment to the situation in the year 2010/11 we get an input of 106 kg N/ha at a yield of 20.5 tons. Schmidt (2007) 45 collected fertilizer input to oilpalm from different literature sources. As average from 5 studies Schmidt calculates an N input of 106 kg/ha to mature stands and based on one study he assumes a fertilizer N input of 90 kg/ha to immature plants. Over the whole lifetime of the plantation (2 years immature, 23 years mature) he considers an average fertilizer application of 105 kg N / ha at a yield of ~20 tons/ha. Mineral fertilizer input calculations in GNOC take into account the organic carbon content (SOC) of the soil. The following graph shows the N input to oil palm cultivations in Malaysia as applied in the GNOC data set. As mentioned before, the country average application is 87 kg N / ha (year 2000). However we distributed the mineral fertilizer N input to crops as a function of yield and SOC on the sub-country level. For Malaysia average mineral fertilizer N input in GNOC is 115 kg N/ha for a yield of 23 tons of FFB on mineral soil with low soil carbon and 27 kg N/ha for a yield of 22 tons of FFB on organic soils (>25% soil organic carbon).
45 Schmidt, J. H. (2007). Life cycle assessment of rapeseed oil and palm oil. Part 3: Life cycle inventory of rapeseed oil and palm oil. Aalborg University. Retrieved from http://vbn.aau.dk/files/10388016/inventory_report
299
Q71) N fertiliser used for oil palm cultivated on peat soil is 30 - 50% lower as compared to oil palm cultivated on mineral soil. This is taking into consideration that peat soil contains higher organic matter and thus reduces the need of applying inorganic fertilisers. JRC: See answer to Q70).We actually assume >75% lower.
Q72) Based on actual field practices, calcium carbonate (CaC0 3) is not used for oil palm cultivation on both mineral and peat soils. For acid sulphate and peat soils, the oil palms can grow when the water table is maintained at 50 cm below the soil surface. JRC: All aglime use has been considered zero in the new version of input data. Depth of drainage is not relevant to direct emissions, but surveys show much deeper average depths.
Q73) The use of EFB as mulch in the plantation is not universal and does not contribute significantly to GHG emission. Thus, EFB compost should be excluded from the input data and GHG emissions calculation. JRC: Average annual crop residue N input currently used in GNOC is based on the nutrient balance during the life cycle of an oil palm generation by Schmidt, J. H. (2007), Life cycle assessment of rapeseed oil and palm oil. Part 3: Life cycle inventory of rapeseed oil and palm oil. Aalborg University. Retrieved from http://vbn.aau.dk/files/10388016/inventory_report). Schmidt gives 90 – 106 kg ha-1 yr-1 of mineral N input in addition to ~ 159 kg N (per ha and year as an average of the plantations lifetime) from crop residues. All EFB used as mulch gives N2O emissions, even if these are small. MPOB has in the past assured us that all EFB is recycled as mulch and is not thrown away.
Q74) The use of EFB reduces the use of mineral/inorganic fertilisers, especially nitrogen fertiliser. Therefore, the nitrogen fertiliser used in oil palm plantation should be lower if EFB is considered in the calculation. JRC: The statements in Q73) and Q74) are contradictory. Either EFB mulch is contributing to the N supply, then subsequent N2O emissions should be accounted for, or EFB does not play a role and consequently it can’t have an impact on the mineral fertilizer use. See answers to Q70) and Q73). However, the N input via mineral fertiliser and crop residues in GNOC corresponds to the nutrient balance during the life cycle of on oil palm generation by Schmidt (2007).
Q75) The amount of pesticides used in the input data is extremely high. The amount of pesticides (active ingredients, a.i.) is 0.7436 kg a.i./t FFB (Choo et a/. 2011) as shown in the following table. In Malaysia, the oil palm producers practise Integrated Pest Management (IPM) that manages pests, diseases and weeds using biological control including natural predators and beneficial plants. For example, Bacillus thuringiensis (Bt) and Metarhizium anisopliae are used as natural biopesticides to reduce the use of chemical pesticides for controlling bagworms and rhinoceros beetle (Oryctes rhinoceros). JRC: This correction has been included in the new version of input data and report.
300
Q76) Filed N2O Emission: Figure 6 (page 73) showed the weighted global average N20 soil emissions from biofuel feedstock cultivation. There are three scenarios for oil palm cultivation. Based on our best understanding, the N20 in the figure is the emission at the field after applying the N fertiliser. JRC: What is the evidence for this statement?
Q77) As commented, N fertiliser required for oil palm on peat soil is 30 - 50% lower than the requirement in mineral soil. Therefore, the emission from peat soil (brown colour bar in Figure 6) must be lower than that in mineral soil (yellow colour bar in Figure 6). Thus, the average N20 emission of oil palm (orange colour bar in Figure 6) should be much lower. JRC: Please read carefully the description of the method. According IPCC (2006) TIER 1 cultivated organic soils in tropical regions emit 16kg N2O-N per ha and year independently from mineral fertilizer or crop residue N input. See also answer to Q68) and Q70).
Q78) As discussed, no lime is used in the oil palm plantation. Thus, the emission attributed by the use of lime should be removed. JRC: See answer to Q72).
Q79) Step 4: Oil mill – plant extraction from fresh fruit bunches: Biogas capture Infrastructures: The number of palm oil mills in Malaysia with biogas capture facilities (as of 11 June 2013) is as follows: Status Plant in Operation Under Construction Under Planning
Number of Palm Oil Mill 59 15 148
Under the National Key Economic Area (NKEA), all 430 palm oil mills in Malaysia are expected to install biogas capture / methane avoidance facilities by 2020. JRC: Thank you for the provided information. A pathway FAME from palm oil, methane capture was already included in the input data.
Q80) Calculation of LHV of palm oil: At the palm oil mill, for every tonne of crude palm oil produced, 0.41 tonne of palm kernel is produced. The palm kernel will be further processed at a palm kernel crusher to produce palm kernel oil and palm kernel cake (or palm kernel meal).Thus, at the boundary of palm oil mill, only crude palm oil and palm kernel should be considered for calculation of GHG emission savings. In addition, allocation should be carried out to palm kernel and not as shown in Table 219 (page 221) as additional processes are required to produce palm kernel oil and palm kernel cake.
301
The following table showed the net calorific value of dry forms of selected oil palm biomass. Oil Palm Biomass Palm kernel Palm kernel cake Palm kernel oil
Net Calorific Value Average 38.03 18.89 37.98
Range 37.94 - 38.08 18.88 - 18.8 37.80 - 38.20
JRC: In fact we do allocate to palm kernels as you suggest. The principle is to allocate by LHV to useful products. The part of the palm kernel which is nutshells is defined as a waste so it is not counted in the allocation to the kernel. Therefore we allocate to the rest of the kernels, which is represented by their meal and oil contents. We do not include any emissions from processing of kernels.
Q81) Step 6: Refining of vegetable oil from palm oil: Sodium hydroxide is used in chemical refining to neutralise free fatty acids in crude vegetable oil. In Malaysia, all refineries use physical refining and not chemical refining to produce refined, bleached and deodorised palm oil. Thus, the sodium hydroxide is not used for refining step. JRC: Please provide physical refining data if available.
PROLEA - SOFIPROTEOL Q82) Manure calculation procedure. As explained in the section 4.8 (page 78), the difficulty to calculate emissions related to manure application is assumed by the JRC since "The truth is somewhere in the middle". JRC researchers highlight the "Irrational world of attributional LCA" in which nitrogen from manure should account for 100% for N2O emissions but for 0% in terms of nitrogen fertiliser production emissions. We understand that the JRC researchers would like to apply a consequential LCA, but most of marginal effects are not considered, as well as those of other agricultural commodities or conventional fuels. By this way, we think that this calculation procedure is an arbitrary choice and is not on the quality level of robust scientific findings as it is the case for material and energy flow data, typically used in process based attributional LCA. Therefore, we feel that using arbitrary decisions in the way of GHG emission calculation could only damage credibility and reliability of biofuel LCA work. JRC: In order to calculate average soil-N2O emissions from a crop, one must make a decision on how much of the N2O from the manure actually applied to that crop should be attributed to the crop, and how much of it should be attributed to waste-disposal of the waste from livestock production. As one stakeholder at the Brussels meeting pointed out, it is unfair to attribute all the manure to the crop, because in some regions more manure is applied than is needed for crop growth, as a means of disposing of excess manure. We agreed with this point, explaining this is why we only considered half the actual manure used in calculating soil-N2O emissions. This choice is clearly explained in the report.
302
Please confirm that PROLEA is suggesting all manure to be attributed to the crop: that would raise soil-N2O emissions for rapeseed (for example) by about 12%. It would not impact on the amount of synthetic N fertilizer used, as this data is independent of the data on manure: it derives ultimately from actual sales of N fertilizer, allocated to different crops independently by Fertilizers Europe, the source recommended to us by COPA-COGECA. See also answer to Q8).
Q83) Concerning rapeseed, sunflower, soybean and palm biodiesel we noted: • Yields of industrial steps (extraction, refinery, esterification) are systematically 1 to 4 points below average industrial yields. • Electricity consumption of industrial steps are up to 5 times over-estimated (in comparison to the average electricity consumption of the extraction steps of our industries for example) • Wrong data concerning sodium methylate at the esterification step are proposed. It is indicated that industries use 17,55 kg/Ton of FAME. First, all our industries use a 30% sodium methylate solution. In addition, the value of 17,55 kg/Ton of FAME is much more than our maximum sodium methylate use. Our average sodium methylate use is about 14 kg/Ton of FAME. Given the emission factor of this chemical, these wrong data lead to relevant overestimation of GHG emissions of the esterification step. JRC: There appears to be a failure of internal communication within SOFIPROTEOL-PROLEA. Indeed far from failing to consult SOFIPROTEOL-PROLEA, you are JRC's main source of information on biodiesel processing. We started with a face-face meeting with PROLEA/EBB experts on these issues (a 2nd planned meeting was replaced by a telephone conference due to a transport strike). Since 2008, we have had exchanged 30 e-mails, as well as various telephone conversations. Mostly these were JRC eliciting more details (e.g. on water contents) or asking about other aspects of the process to ensure our calculations were correct. All the data you are challenging was provided by Dr. M. Rous of PROLEA and SOFIPROTEOL, working in collaboration with EBB (see attached spreadsheet of process data we received from Dr. M. Rous of PROLEA-SOFIPROTEOL on 20 July 2009). Possibly the discrepancies you have detected arise because PROLEA has larger and newer plant than the EU average. Dr. Rous worked with EBB to estimate the EBB-average plant data, based on a weighted average of the two most-used technologies. We adopted these figures even though we were aware that this average was optimistic, as it does not include older, smaller and less efficient plant still in use typically in Germany. The discrepancy in sodium methylate use appears to be a misunderstanding: PROLEA probably forgot to specify that the mass data referred to 30% solution and not pure sodium methylate. Thank you for bringing this to our attention.
Agentschap NL Q84) Page 31: the number of “12.75” for HFO cannot be correct, it should be the same 6,64 as in the table!
303
Q85) Page 34: “increased by the molecular weight ratio” should become “decreased by the molecular weight ratio”. It is valuable to add that the number should become 38,67 g CO2,eq/kg CaCO3
Q86) Page 35 - 42: Either you should add the resulting number for CaO as a process chemical (below table 9). Idem for H2 (below table 11), Chlorine (table 12), NaCL (table 13) etc. (until table 16), and for table 18 (NH3), table 21 (cyclohexane), 22 (lubricants), 23 (alpha-amylase enzymes), table 24, table 27 (methanol). Or.. you should complete the overview table on pages 43 and 44. Currently you miss the two enzymes alpha- and gluco-amylase, n-hexane, fuller’s earth (if still used in any of the pathways), limestone (if still used), sodium carbonate, potassium hydroxide, hydrogen, pure CaO for processes, ammonia, cyclo-hexane, lubricants. and methanol (incl. combustion emissions). Q87) Also, you miss all the emissions for seeds (barley seed, corn seed, rye, rapeseed, sorghum (sweet & grain), sugarbeet, sugarcane, sunflower, triticale, wheat)
Q88) Page 41: From the methanol numbers it does not become clear whether the combustion emissions of methanol should be added to the “standard value”. Can you please provide clarity – how should this be done? Can you please add an additional comment under the table?
Q89) Page 43: make sure to give the same number of digits. The numbers for lignite are too low resolution if taking over the CH4 (total) and N2O numbers!!
Q90) Page 53 just above figure 2: error in reference (in pdf version of report) Q91) Page 56: the number “5 315 g CO2,eq/kg N” below table 41 Supply of N fertiliser is not in line with the number given earlier in the document: 6172,12 . One of the two must be wrong as the numbers for g CO2, g CH4 and gN2O, all per kg N, are the same on page 41 and 56.
Q92) Page 57, Table 42: Shouldn’t you make an extrapolation to the year 2013 instead of 2011? JRC: N fertilizer emissions have been updated in the new version of input data and report.
Q93) Page 107: The CH4 and N2O emissions of the NG CHP should be listed in table 56. Q94) Page 115: Why don’t you also give the CH4 and N2O emissions for the sugar cane truck? Idem for the other trucks (MB2318, …)
Q95) Page 125: wouldn’t it be possible to calculate an electricity use per km of pipeline? And wouldn’t this be independent of the fuel, so be the same for oil, biodiesel and ethanol? JRC: Yes, it will be the same. It will be expressed in MJ/MJ of fuel quality liquids.
Q96) Page 138 / table 92: you calculate an average cereal pathway. Does this mean that there will be one average default value for cereals? If so, is this logic given that (a) the variation in GHG emission reduction can be something like 10% or higher and (b) when meeting FQD targets becomes more important, every % emission reduction leads to a higher price of the biofuel. Germany will already shift to %GHG reduction targets in 2015!
304
JRC: The emissions for ethanol made from different cereals are extremely close (even closer than in the previous calculations) and JRC doesn’t think the differences are significant compared with the uncertainties. See also answer to Q40).
Q97) Page 151 / 152/ 153: There is an inconsistency in the water content of sugar beet. On page 151 a water content of 75% is given, in table 113 on page 152 and in table 114 on page 153 a water content of 76,5% is given. This cannot be true and will give unrealistic results. JRC: We understand this is confusing, but it is not a mistake. The water content of sugar beet varies, and different data sources assume different water contents. In order to show how our input data are derived from the raw data, necessarily from different sources, we show also the water content which is assumed for each input data. Several cultivation data derive from CAPRI model, and they assume 75% water, so we standardize on 75% water for cultivation. However, in the sugar beet mill data, the original data assume 76.5% water. This is reported in order to correctly calculate the input and yield per MJ of sugar beet. As the accounting unit inside the calculations is MJ of LHV in the dry part of the material, there is no inconsistency in bringing sugar beet from the cultivation to the processing step.
Q98) Page 244: Tallow “the rest of the process is the same as for rapeseed oil”. On page 236 you write “transesterification of animal fat and UCO… “. This is inconsistent.
Q99) Page 262: I miss a pathway for ethanol from straw. It was presented in Brussels but is not yet included in the report. JRC: Ethanol from straw has been added in the new version of the report.
Q100) Appendix 1: Fuel/feedstock properties - General: Moisture content and yield are not “fuel and feedstock properties” so the table should be split into two tables, one with LHV & density data (which are fuel/feedstock properties) and another with yield and moisture content, and LHV wet (RED) which is also an input value. Another solution might be to have one table but give it another name. - General: I miss many fuel properties, like the LHV values for HVO (hydrogenated vegetable oil), PVO (pure vegetable oil), jatropha seed oil, palm oil, soybean oil, waste cooking oil, bio-oil (the byproduct from producting UCOME from UCO), ….. - On page 352: 17 MJ/kg for maize kernels, whereas on page 363 17,3 MJ/kg is given for maize (grain). This should be made consistent! - Page 367: “pulp” should be further specified - Page 369: “oil cake” should be removed as the LHV of oil cake depends on the type of oil cake, and the individual numbers are given on pages 371 – 375 - Page 370: Density of 0 of RME is incorrect - Page 357 and page 374: palm kernel meal is listed twice - Page 375: Shouldn’t “Jatropha oil + cake + shells” be renamed into “Jatropha cake + shells”?
305
JRC: All the comments and suggestions referring to the draft report (from Q84) to Q91), from Q93) to Q95); Q98); Q100)) have been taken into account in the new version.
EFPRA Q101) Our review of the report indicates that JRC proposes to change the calculation route for animal fats and we believe this is flawed. Within the Annex V rules for calculating the greenhouse gas impact of biofuels, bioliquids and their fossil fuel comparators of the RED, it is pointed out that “wastes, agricultural crop residues [..] and residues from processing [..] shall be considered to have zero life-cycle greenhouse gas emissions.” The ILCD handbook of the Europeans Commissions - Joint Research Centre is applying the same methodology - stated that all treatment processes that are necessary until the treated waste / end of-life product is achieving a market value of zero are within the responsibility of the first system (i.e. meat, eggs, wool etc.). This is because the waste or end-of-life product is generated by the first system, while a waste can per se not carry any burden of treatment. [….] The starting point for the derived animal fat has to be the exit of the rendering plant. This is the point where the product of treated waste / residue achieves a market value bigger than zero. Therefore it is not understandable why in the new report of JRC on “Assessing GHG default emissions from biofuels in EU legislation” the calculation starts with the “transport of carcass” from the meat / food industry. […. Motivations follow] JRC: We would like to thank you for the useful comments you provided that have been taken into account in this new final version of input data and report. LCA guidelines suggest that when waste is upgraded to a product, the emissions for bringing the waste up to zero monetary value are not counted. Thus, for a start, transport to the rendering plant is not included. For the rendering process itself, we estimate that 63% of the emissions do not count, because they serve to bring the product up to zero value, so we attribute only 37% of rendering emissions to the products of rendering. Rendering of animal carcass produces different grades of fat (loosely called tallow) and a byproduct: meat-and-bone meal. According to EFPRA, meat-and-bone meal has a positive value, even though its use is still restricted by regulations in the wake of the BSE crisis (in some previous years it has been a waste which required a gate fee for incineration). If meat-and-bone meal is considered a product, then it should be allocated part of the emissions from the rendering process. On the other hand, if national regulations categorize it as a 'waste', all the emissions attributed to products should be allocated to the fat. Of the 37% of rendering emissions which are attributed to adding value to products, less than half (47%) are allocated to fat, on the basis of lower heat content (wet-definition) ...and the rest to the meat-and-bone meal by-product.
306
Q102) EFPRA also notes the following: In the given literature of JRC only one personal note with UKRA appears, no contact to the European association. We are disappointed by this and would point out that no single member state should be used to extrapolate the case for Europe as a whole. This is particularly the case for UK which has a rendering market that is based on exceptionally intense price competition today, underpinned by government enquiries into competition and monopoly situations. Additionally UK has a different, historically grown system of raw material collection. While the rendering plant collect the material from big slaughterhouses, independent knackers collect dead animals from farms and smaller enterprises like retailers, butchers etc. This English uniqueness cannot be found in the rest of the EU. Collection, transport and processing are in the hands of the rendering plants themselves and must therefore, as explained before, be seen as one important step to remain Europe safe hygienic status quo. JRC: It is not clear where ‘UK specificity’ comes into this discussion. Emissions from transport of carcass are now not considered.
Q103) Apart from this new raw material allocation JRC foresees also a refining step for animal fats before the conversion into biodiesel (esterification). The animal by-product regulation 142/2011 clearly describes in annex IV how animal fats have to be converted. This process is based on the BDI biodiesel process and includes a transesterification and two esterification steps. This is the only allowed process to be used in the EU. The necessary fat quality of 0, 15% insoluble impurities can be achieved by simple cleaning techniques in the rendering plant. An additional refining is not necessary. JRC: We use the BDI process for biodiesel from animal fat. We accept the point that in this case refining is not required.
Q104) EFPRA also recommends not to take the slightest flaring up of a small positive economic value somewhere in the EU as a general overall approach. JRC: Cat. I tallow is now worth >500 euro/tonne in NW Europe. We notice that since Cat. I biodiesel became more valuable than palm oil biodiesel, lard production has decreased by a factor 7. So the argument that animal fat used for biodiesel is a waste material might not be valid.
INTA – Instituto Nacional de Tecnologia Agropecuaria Q105) Page 20: This section, Argentina believes, should include the comments submitted by our country. In order to estimate emissions from maritime transport, it would be necessary to assess the frequency with which ships carrying biodiesel or oil from Argentina return with any other load. Since the updates and changes are usually significant, it is important to use the most updated versions of the models (GEMIS).
307
JRC: The section refers to the outcome of the main issues raised during the workshop in 2011. The new version of the report includes comments received by JRC in June 2013, including INTA. For maritime transport, what you suggest is already implemented using expert judgments elicited from the shipping industry. Maritime emissions are derived from data issued by International Maritime Organization (IMO). Where GEMIS data is used (elsewhere) we checked that it was the latest version.
Q106) Page 21: Consumption data from CAPRI-based models should be checked for consistency with actual Argentine production systems. JRC: CAPRI data are not used in the soya pathway which is derived from a mix of the non-EU sources, including Argentina (as described in the report).
Q107) Page 24: A report by Greenpeace is mentioned regarding the origin of biodiesel, which claims that the product is divided by origin, not by raw material. However, it clearly identifies that in the case of Argentina, biodiesel is produced from soybean [and in the case of Indonesia, from palm]. Also, it is worth stating that INTA is the source of technical information for Argentina, [as CENBIO is for Brazil,] so it would be mentioned whenever data from our country are stated. JRC: The Greenpeace report is just mentioned in the draft report in reporting the main outcomes of the discussion of the workshop held in November 2011. It is not used as a source for the input data. Data provided by INTA are used in the “national soy data” section (Argentina) of the report.
Q108) 25 Page see if for the purposes of the interests of Argentina, it's worth leaving this paragraph. The incorporation of criteria for biodiesel from Jatropha could affect global values. Products from Jatropha are not significant for the volume traded with the EU, and in fact most of the ventures have failed. JRC: The inclusion of a default value for Jatropha is not affecting other pathways. If the ventures fail, the national interest of Argentina is not affected.
Q109) Pages 32-44: Emission factors of agrochemical and fertilizers need to be considered and analyzed in depth according to the bibliographical references to study and analyze the sources of information used. JRC: Emission factors of agrochemical and fertilizers have been updated in the new version of input data using the new figures for fossil fuel comparators (FFC), diesel, heavy fuel oil and natural gas. See answer to next questions Q110) and Q111).
308
Q110) Page 33: Emission factor for diesel supply is 15.4 gCO2 eq/MJ, against a default extraction and refining value of the CDM ACM0017 methodology of 7.12 gCO2Eq/MJhttp://cdm.unfccc.int/methodologies/DB/Z6UFHXTRQJ2PSZ1EOD21IT8FEF4AE7 The source quoted is a 2005 IEA report. JRC: The emission factor for diesel has been updated in this new final version of input data according to figures for crude oil productions and transport emissions estimated for EU-mix in the OPGEE report and the emissions from refining from JEC-WTWv4a.Thank you for the comparison with IEA which would imply the fossil fuel comparator for diesel is too high. See answer to Q18).
Q111) Page 34: Emission factor for natural gas supply is 12.76 gCO2 eq/MJ, and according to Argentina's Second National Communication, fugitive natural gas emissions by the year 2000 were 11.585.340 TnCO2eq for a gas production of 45.135 billion M3 Gas, totalling 7.39 gCO2 eq/MJ. Source: Argentina's National Second Communication Argentina and SIPG - Argentine Oil and Gas Institute. JRC: Emission factor for natural gas has been updated in the new version of input data. See answer to Q16). As we cannot be expected to model emissions from inputs in every country of the world, we assume EU emissions for fertilizer emissions, etc. apply also to inputs in other countries. We are aware that in most cases emissions from EU-produced inputs are lower.
Q112) Page 35: Idem considerations made on page 33 for diesel factor. JRC: See answers above Q110).
Q113) Pages 56-61: In principle, there is no reference to calculations of emissions from urea produced in other countries like Argentina. JRC: JRC cannot calculate individual fertilizer emissions for each world country. However, the soya pathway as a whole benefits from the use of emissions for EU. For example, emissions for N fertilizer production in US are considerably higher.
Q114) Page 56: In 2011, the average electricity emissions factors is 476 gCO2 eq/KWh for the EU and 0,345 gCO2 eq/KWh for Argentina, applying the same methodology. 2011 Thermal generation (MWH) 73.439.392 Hydroelectric generation 39.251.194 (MWH) Nuclear generation (MWH) 5.892.364 Total 118.582.95 0 Emissions (tCO2) 40.954.199
2010 66.231.292 40.226.935
2009 61.339.111 40.318.306
6.691.638 113.149.86 6 36.572.777
7.588.703 109.246.12 0 34.432.572
309
Emissions factor 0.345 0.323 0.315 Imports (MWh) 2.411.995 2.351.910 2.040.098 Source: National Secretariat of http://energia3.mecon.gov.ar/contenidos/verpagina.php?idpagina=2311 extracted estimated de hoja OM SIMPLE:
Energy and
There are no available data for transmission losses in Argentina, but following the same % of losses, the final electric power has a value of 540 gCO2 eq/KWh, against an estimated of 391 gCO2 eq/KWh. JRC: Figures for the EU marginal mix have been used for all countries. However, the soya pathway as a whole benefits from the use of EU data for emissions for input. For example, emissions for electricity in US are considerably higher.
Q115) Page 62 (estimates and calculations provided by engineer Lidia Donato). Table 44 does not include soy as a crop and the data are based on the CAPRI reference. In Argentina there is abundant background on checks and calculations of fuel consumption for the different crops grown extensively under different tillage systems, which should be considered. Direct energy in MJ /ha input used during the agricultural stage CROP
Rapeseed Conventional sunflower No-till sunflower No-till high-tech sunflower First conventional soybean First no-till soybean First no-till high-tech soybean Second no-till soybean Conventional corn No-till corn No-till high-tech corn Conventional sorghum
TOTAL AGRICULTUR AL STAGE (L/ha) 57.31 71.48
ENERGY (kCal/ha)
ENERGY (kJ/ha)
ENERGÍA (MJ/ha)
458 480 571 868
1 916 446 2 390 408
1 916 2 390
36.95
295 592
1 235 575
1 236
41.83
334 628
1 398 745
1 399
59.80
478 400
1 999 712
2 000
43.80
350 400
1 465 672
1 465
53.58
428 600
1 791 548
1 792
36.75
294 000
1 228 920
1 229
126.84
1 014 720
4 241 530
4 242
103.38 134.46
827 040 1 075 680
3 457 027 4 496 342
3 457 4 496
88 85
710 800
2 971 144
2 971
310
No-till sorghum
76.25
610 000
2 549 800
2 550
Comparative analysis based on data in table 44 (page 62): - if we take, for conventional sunflower, 71.48 L/ha x 35.9 Mj/L = 2 566 Mj/ha, the result in the draft is 3 288 Mj/ha; - if we take, for high-tech corn, 134.46 L/ha x 35.9 Mj/L = 4 827 Mj/ha, the result in the draft is 3 311 Mj/ha; - if we take, for no-till corn, 103.38 L/ha x 35.9 Mj/L = 3 711 Mj/ha, the result in the draft is 3 311 Mj/ha; - if we take, for rapeseed, 57.31 L/ha x 35.9 Mj/L = 2 057 Mj/ha, the result in the draft is 2987 Mj/ha. Although soybean is not included as a crop, the calculations below are presented: - if we take for first no-till soybean 43.8 L/ha x 35.9 Mj/L = 1 572 Mj/ha; - if we take, for first high-tech soybean, 53.58 L/ha x 35.9 Mj/L = 1 923 Mj/ha. Background based on CAPRI model documentation 2011: Editors1: W. Britz, P. Witzke (pages 167 and 169), establishing the methodology of calculation which takes into account the European machinery type and distribution, not considering other types of farming systems such as Argentina's. JRC: Diesel consumption data from CAPRI are not used in the soya pathway which is derived from a mix of the non-EU sources, including Argentina. In particular for soybean cultivation in Argentina the figure used (1 541 MJ/ha which is in the report) has been provided by INTA in 2009 (Muzio et al.) and it is very close to the data you provided above.
Q116) Page 62: Argentina produces 75% of the urea consumed in the country (year 2011) in integrated manufacturing plants (natural gas-ammonia-urea-UAN), so the life cycle emissions scheme applied would be invalid, since there is no transport of semi processed products. In the case of monoammonium and diammonium phosphate, the total amount of the fertilizer used is imported. Source: “Información Estadística de la Industria Petroquímica y Química de la Argentina” (Statistical information of the chemical and petrochemical industry in Argentina), 31st Edition, July 2011. Argentine Petrochemical Institute. JRC: See answer to Q113).
Q117) Soil Emissions - Chapter 4, pages 65 to 115. This is a critical chapter of the document given the implications on the total calculation of field primary production emissions, considering that the amount of hectares is significant because biodiesel is a minor component of soybean cultivation. On the other hand, this is the aspect which causes greater controversy and lacks more scientific evidence. The disaggregated crop-specific emission factor needs to be analyzed and validated for the Argentine conditions. In this sense, it is necessary to examine the proposal by Edwards 2012 for below-ground soybean emissions, as the quotation is incomplete and it could not be analyzed.
311
Further, we believe that the update of the biomass content for soybean is not complete and should be verified given the strong impact of primary sector production emissions related to nitrous oxide. (67) According to Miguel Taboada, researcher and director of the Soil Institute and active participant of the IPCC, there is strong scientific evidence that soils cultivated with legumes become rich in labile nitrogen, considered as a source of future mineralization processes (NO3 production). See Jensen, 1996; Khan, 2002a,b, 2003; Martens et al, 2006; Mayer et al, 2003; etc.) However, the evidence relating to the emission of N2O from cultivated soils due to a larger deposit by rhizodeposition is not that strong. In this regard, the papers by Rochette and Janzen (2005) and Rochette et al (2004) support the idea of the overestimation of N2O emissions by the 1996 IPCC methodology. Therefore, the 2006 IPCC methodology suppresses the BNF as a source of N2O emissions. In October 2010, the IPCC convened an expert committee in Geneva, Switzerland, in which Taboada and the world's major experts participated, to analyze whether to change the 2006 methodology on N2O emissions or not. It was concluded that no evidence justified the adoption of another method for estimating N2O, even when member countries are recommended to use the Tier 3 approach in their calculations. The entire scientific background of the recommendations in the JRC document under analysis is based on that greater contribution of rhizosphere N, but, in the opinion of the major national experts, there is no sufficient evidence implying that such mineral N produced generates more N2O emissions. It is a speculation by the JRC without the proper comparison with field data in agricultural systems. For N2O emissions to be significant, several conditions, such as the lack of O2 (anaerobiosis) promoting the denitrification of NO3 and proper soil moisture and temperature, need to be met. In Argentina, favourable emition climatic and edaphic conditions on winter crop residues are practically non-existent in the production area, since soil temperatures are below 14ºC, which is the limit temperature value found. Scientific data currently published in a journal of international renown suggest that N2O emission conditions should not be generalized to all soils. 68 and 69 factors show large variations. The determination of the climatic type in the main production area is between two classes. Very different results may be obtained if one or the other is used. JRC: a) In the new version of the report a comparison between measurement data (including a recent Argentinian field study) in soybean fields and the GNOC results is given (Figure 12). There is a strong indication that the N2O emission results obtained with the updated N content in belowground biomass are much closer to what is observed by field measurements. With the new approach, considering higher N content in belowground biomass, we calculate for the year 2000 an average of ~1. 1 kg N2O-N / ha for soybean cultivated in Argentina (based on yield: 2440kg/ha and total fertilizer N input: ~0.5kg N/ha). Calibrating the emissions to fertilizer input (~0.25kg N/ha) and yield (2825 kg/ha) in 2010/11 the related emissions are ~1.2 kg N2O-N / ha.
312
In the study of Alvarez et al. 2012 46 the N2O emission measurements from soybean cultivation in the Argentinian Pampas region they compare their results with the calculations according IPCC (2006). In the calculations they consider nitrogen content in residues (below-ground and aboveground) to be 3% which is however the value given in IPCC (1996) while the default in IPCC (2006) would be 0.8%. They confirmed via email that a value of 2.3% of N content in above- and belowground residues would be the local value for soybean in this region. Looking at N input from total soybean residues the results are almost similar to what we obtain from our approach with the higher N content in belowground biomass and keeping the IPCC (2006) default N content for above-ground biomass. b) actually we suggested the new approach using a disaggregated emission factor for direct soil emissions (see report) to take into account different environmental conditions (e.g. soil). It is described in Chapter 3 of the report. However, the disaggregated emission factor is applied solely to N input from fertilizer (mineral and manure). From our data the fertilizer input to soybean in Argentina is close to 0, the major source of N supply is resulting from crop residues left in the field. In this case emissions are calculated with the default IPCC (2006) TIER1 emission factor(s).
Q118) It is not known what type of validation the Global Nitrous Oxide Calculator (GNOC) has in the Argentine production systems since it is stated that it can deliver local numbers in a 10 x 10 km grid (70). It is stated that the extrapolation of fertilization data is considered; no mention is made of how the Argentine case is considered. JRC: We are aware that there is more recent data or data at higher resolution available for many countries. However, we needed to rely on harmonized data sets available globally to treat each region in the same way. For the parameters required (crop distribution, fertilizer input etc.) the most recent global data sets were available for the year 2000. GNOC is applied on global level to calculate average soil N2O emissions from a potential biofuel feedstock. The average emissions are the weighted mean emissions of all countries supplying the given feedstock to the EU market and/or feedstock being produced within the EU. To account for management developments since 2000, the weighted mean emissions per MJ were correct based on more recent data (2010/2011) on mineral fertilizer N input and yield. There is no distinction between “no tillage” and “conventional agriculture” in soybean in the new version of the input data. According a literature study of Tanveer (2013) 47, increased N2O emission from no-tilled soils as compared to the tilled soils have been reported by many researchers. Although, a few studies report also equal or lower N2O emissions from no-tilled compared to tilled soils. To facilitate emission calculations for a certain location by a producer we developed the GNOC online tool (http://gnoc.jrc.ec.europa.eu/). It allows the user to calculate the emissions for a 46
Alvarez, C., Costantini, A., Alvarez, C. R., Alves, B. J. R., Jantalia, C. P., Martellotto, E. E., & Urquiaga, S. (2012). Soil nitrous oxide emissions under different management practices in the semiarid region of the Argentinian Pampas. Nutrient Cycling in Agroecosystems, 94(2-3), 209–220. doi:10.1007/s10705-012-9534-9. 47 Tanveer, S. K., Wen, X., Asif, M., & Liao, Y. (2013). Effects of Different Tillage Methods , Nitrogen Fertilizer and Stubble Mulching on Soil Carbon , Emission of CO2 , N2O and Future Strategies. In A. Goyal & M. Asif (Eds.), Crop Production. CC BY 3.0 license. doi:10.5772/56294.
313
specific location based on yield and N input information provided by the user. There is the option to change the values for (almost) all environmental and management variables in the calculation based on specific site information if appropriate.
Q119) The year 2000 is taken as a reference for planted areas, volumes of production, yield, etc., which is out-of-date information. The argument that certain countries do not have highresolution information available is highly detrimental to major suppliers such as Argentina, which has very detailed and updated statistics and geographical information systems. (70) Argentine Soybeans: Planted Area, Harvested Area and Yield Harvested Area Crop Year Planted Area (ha) Production (tn) (ha) 2001/02 11 639 240 11 405 247 30 000 000 2002/03 12 606 845 12 419 995 34 818 550 14 526 606 14 304 539 31 576 751 2003/04 14 037 246 38 300 000 2004/05 15 393 474 15 130 038 40 537 363 2005/06 16 141 337 15 981 264 47 482 786 2006/07 16 608 935 16 389 509 46 238 893 2007/08 2008/09 18 042 895 16 771 003 30 989 469 18 343 940 18 130 799 52 675 466 2009/10 18 902 259 18 764 850 48 888 538 2010/11 18 670 937 17 577 320 40 100 196 2011/12 Source: MAGyP
Yield (tn/ha) 2.63 2.80 2.21 2.73 2.68 2.97 2.82 1.85 2.91 2.61 2.28
No-Till Area by Crop - 2010/2011 Crop Year Crop Percentage Soybeans 89 Corn 82 Sunflower 71 Wheat 88 Sorghum 85 Source: AAPRESID JRC: See answer to Q118).
Q120) Page 70. FAO 2000 data are not only irrelevant, but also completely outdated and in low resolution. The same applies to the application and use of fertilizers. (70) Neither reference to the Global Nitrous Oxide Calculator (GNOC) nor JRC articles on this matter were found. Therefore, its scientific basis, fields of application and correlation with actual field data in different agroecosystems remains unknown. There is a need to check the validation of the work conducted by Monfreda et al (2008) for Argentina with respect to the volume of crop residue above-ground. (71). The mentioned geographic information systems are of low
314
resolution and involve potential errors of great magnitude when calculations are made by crop. JRC: See answer to Q117) and Q118).
Q121) The weighting of emissions referred to in point 1 (72) needs to be checked in relation to the year under consideration. No comments are included in the text in relation to possible differences between IPCC and GNOC methodology for temperate and tropical soils under notillage. (72) JRC: See answer to Q118).
Q122) There is a need for clarification of the values set out in page 73 in relation to the energy value assumed for each selected crop. JRC: The energy content for each feedstock is given in Appendix 1. Fuel/feedstock properties of the report.
Q123) Page 74 contains a safeguard in relation to the application of manure which cannot be applied to Argentina. There is no rationale or validation to justify this decision on real cases of countries which count with abundant information, such as Argentina. JRC: In the draft version of the report presented at the stakeholder meeting, the manure input considered for Argentina was ~4 kg of N input per ha on the average for all crops. We accounted for 2 kg of N input from manure application (50% of 4kg). In the updated version of the calculations the manure application is calibrated according to the mineral fertilizer input to the crop (described in Chapter 4 of the updated report). Now the manure input to soybean in Argentina is assumed to be close to 0 (~ 0.1 kg per ha in the year 2000). This very low amount of manure input has a negligible effect on the final emission results for Argentina (as well in the draft report as in new version).
Q124) Page 79 It is stated that no data are available to comparatively establish N2O emissions under no-tillage systems. However, this should be investigated. Besides, and in relation to changes in carbon stock, no-tillage is given no consideration on the basis of different work that disregard the current situation in Argentina. As a consequence, the argument in relation to the increase in N2O emissions is denied in this specific paragraph. The quotation about possible high lime application in no-tillage does not correspond to the reality of Argentina. JRC: There is no distinction between “no tillage” and “conventional agriculture” in soybean in the updated version of the input data. See also answer to Q118). The average amount of lime application to soils cultivated with soybean in Argentina considered in our calculations is very low (20 kg of lime ha-1 yr-1). The contribution of lime application to total greenhouse gas emissions from soybean cultivation in Argentina is negligible (see Section 3.12 of the report).
315
Q125) Page 80 The JRC explains the large uncertainty in calculations when using the Tier 1 IPCC guidelines. There is not enough scientific field evidence to justify the conclusions arrived at by E4tech, as well as the allegations that the emissions would be found in the estimates made by the IPCC between 1996 and 2006. In Argentina, the preliminary evidence of scientific field work indicates that the level of emissions may be under the 2006 IPCC estimations. JRC: Please provide data or publications evidencing your statements. Actually, a recent publication about N2O measurements in soybean cultivation of the Argentinian pampa (Alvarez et al., 2012)46 supports the conclusion drawn by E4tech, that the IPCC(2006) default N concentration in soybean residues is underestimated. The JRC however challenges the general conclusion in the paper that the IPCC(2006) methodology overestimates N2O emissions in Argentinian soybean cultivations. Alvarez et al. compare the results of the field measurements with the emissions calculated according IPCC(2006) for the same conditions. In their IPCC (2006) TIER1 calculations for soybean monoculture, crop residues are the only source of N accounted for as no fertilizer is applied. Indirect emissions are not considered. For their calculations however they do not apply the default value of 0.8% of nitrogen in above- ground residues given in IPCC(2006 Vol.4 Chapter11 Table 11.2) but a value of 3% (this is the value given in IPCC 1996 Guidelines Table 4-19, page 4.94). This change of the N content increases also the subsequent emissions by a factor of ~3.75 when following the IPCC(2006) TIER1 approach. Contacting Alvarez et al. on this issue they replied that 2.3% is a typical N content in soybean residues (above- and belowground) in the region of their field survey. This leads JRC to the following comments and conclusions: Alvarez et al. claim “the use of the IPCC methodology (2006) generates a significant overestimation of N2O emissions” and they conclude “measured annual N2O–N emissions were generally lower than those calculated using the methodology proposed by the Intergovernmental Panel on Climate Change. Notably lower emissions were measured in the soybean NT crop than those calculated by the IPCC.” However it is not mentioned explicitly in the conclusions, that in their study they do not apply the default IPCC (2006) values for soybean residues. As outlined in the previous paragraph this causes emissions to be 3.75% higher than applying the defaults given in IPCC(2006). Obviously also Alvarez et al. found the residue N content given in IPCC(2006) to be much lower than what is observed in the field. However we wonder why they applied the default value (3% N residue content) of the older IPCC(1996) version instead of the locally observed N content of 2.3%. As the change of N content in residues has an 1:1 impact on the emissions in this case, this would have resulted in emissions ~25% lower than the IPCC TIER1 values given in Alvarez et al. (2012) Figure 6 (the bars referring to soybean monoculture). Then, the general conclusion that IPCC methodology leads to significant overestimation of N2O emissions (in the case of soybean cultivation in Argentina) is untenable. On the contrary one should argue that applying IPCC (2006) default residue contents of 0.8% would lead to a significant underestimation compared to the field measurements in soybean mononculture presented in Alvarez et al. The following table lists the results of the N2O field measurements in soybean monoculture and the calculations according IPCC as given in Alvarez et al.. JRC tried to reproduce the IPCC based values given in the publication, but we could not obtain the yield input data -which is a crucial parameter for the calculations- on which Alvarez et al. based their calculations. Thus, we
316
assumed a fresh soybean yield of 2600 kg ha-1 and calculated N2O emissions according the IPCC (2006) method applying different residue N contents. The other input parameters for the JRCs IPCC TIER 1 calculations of N2O-N emissions from soybean crop residues are given below the table. By applying the IPCC (2006) TIER 1 default for the soybean residue N-content of 0.8%, the observed field N2O emissions in soybean monoculture are clearly underestimated (see column JRC calc. 1). Using the 2.3% N content value (provided by Alvarez et al. as local typical value) still slightly underestimates the emissions observed in the field while using the higher N content in root residues and keeping the IPCC default values for above-ground residues (as JRC assumes for the GNOC calculations) reproduces the field measurements quite well (column JRC calc. 3 and 4) and gives JRC confidence that our correction which is described in detail in Section 3.9 of the report goes in the right direction.
JRC calc. 2
JRC calc. 3
JRC calc. 4
IPCC (2006) with 2.3% N content in aboveand belowground biomass
IPCC (2006) with 0.8% N content in aboveand 8.7% in belowground biomass (as applied in GNOC)
IPCC (2006) with 0.8% N content in aboveand belowground biomass
Alvarez et al. JRC (2012) values calc. 1 given in Figure 6 IPCC (2006) with 3% N content in aboveand belowground biomass
Measurements in soybean monoculture
Alvarez et al. (2012) values given in able 4
IPCC (2006) with 3% N content in aboveand belowground biomass
Comparison of measurement data with emission calculations according IPCC(2006) applying different residue N contents
N2O-N in kg ha-1 1.29 1.8
Reduced tillage No tillage 1.16 1.5 Average 1.23 1.65 0.36 1.36 1.044 1.22 Parameters applied in the JRC calculations No leaching assumed Fresh Yield [kg ha-1]: 2600 Dry matter fraction of harvested product DRY [kg d.m. (kg fresh weight)-1]: 0.87 Slope factor - a - to estimate above-ground residue dry matter AGDM [dimensionless]: 0.93 Intercept - b - to estimate above-ground residue dry matter AGDM [dimensionless]: 1.35 Fraction of above-ground residues removed from field FracRemove [kg d.m. (kg AGDM)-1]: 0 Fraction of crop area burnt annually FracBurnt [ha (ha)-1]: 0 Ratio of belowground residues to above-ground biomass RBG-BIO [kg d.m. (kg d.m.)-1]: 0.19
317
Q126) Page 81 The arguments regarding post-harvest emissions of leguminous plants is relative, insofar as it depends on the succeeding crop in the established rotation, as well as the overall nitrogen balance in soil throughout the rotation cycle. In general, the works cited reflect the reality of the northern hemisphere in experimental conditions of micro plots or laboratory, and have little connection with the reality of Argentina. They make it explicit that what is taken as a basis is soybean production in Brazil. JRC: See answers to Q29) and Q125).
Q127) In page 83, the calculation regarding biomass and crop emissions is highly questionable and arbitrary for the Argentine production system. JRC: See answer to Q125).
Q128) Page 84 U.S., China and Canada’s values which are taken into consideration do not represent the weather conditions, soil and agronomic technologies used in extensive soybean production in Argentina. JRC: In the updated report (Section 3.9) Argentinian measurement data is included in the comparison of GNOC results and field data.
Q129) Page 85 Argentina would like to know its position in the graph (countries). JRC: Figure 10 of the draft report is deleted in the new report, while additional measurement data from Argentina is considered in the comparison of GNOC results and measurement data in Section 3.9 of the new report.
Q130) Page 87 It is worth stating that large scale lime application is virtually nonexistent in Argentina. JRC: See answer to Q124).
Q131) Table 51, page 92 (Es conveniente señalar únicamente las prácticas en la Argentina, pero no criticar el documento). The application of manure on soybean or calcic amendments do not apply. The level of lime application on soybean in Argentina is negligible. Therefore, this calculation should not be applied. As regards pages 95, 97 and 101, this kind of study by country may be conducted to corroborate the absence of this factor in Argentina. JRC: See answer to questions Q123) and Q124).
Q132) Page 122 There is a need to conduct a specific study on cargoes for Argentina JRC: Soy from Argentina is assumed to be shipped as liquid (vegetable oil or biodiesel) which is favourable for the GHG emissions per MJ of biodiesel. The size and return loading of the tankers was obtained from neutral shipping industry experts, and the return loading is considerably higher than IMO averages, so that is also favourable to the emissions. Shipping distances do not take account of intermediate stops, so agrees so again the minimum emissions assumption is taken.
318
Q133) Page 185 (conclusion) In the case of Argentina, almost all soybeans is cultivated under no-tillage. Therefore, taking the least favourable data from conventional cultivation is not applicable. JRC: See answer to question Q118).
CGB – French Confederation of Sugar Beet producers and CIBE – International Confederation of European Beet Growers Q134) N2O methodology The GNOC methodology used to calculate N2O field emissions leads to values higher than what those actually measured in the frame of the NOGAS project. JRC is one of the stakeholders of this project and should have direct access to the main results. Regarding the weight and importance of N2O emissions at field levels on total GHG emissions for the cultivation step, the possibility to provide only actual values for N2O emissions (and to use default values for the rest of the cultivation step) should be considered by the European commission. In such a case, the cultivation step would be divided in two values : “N2O emissions” and “other emission”, allowing economic operators to use default values for both or to provide actual values for N2O emissions and defaults values for other inputs. Otherwise, having only a single block for cultivation step, including high default values for N2O and accurate data the rest would enforce operators to calculate actual values for all items (inputs) on each field resulting in an unreasonable and unbearable administrative burden. JRC: Could you please mention which group (contact partner) at the JRC is involved in the NOGAS project and having access to the data? Dr. Adrian Leip (JRC Institute of Environment and Sustainability), who is mentioned as external expert in the NOGAS dossier, confirmed that he is not involved in the project at the current stage. We would be interested to receive a report or publications with the outcome of the NOGAS project. On the INRA NOGAS website no references to data and/or publications are provided.
Q135) Sugar beet and sugar cane cultivation. In the page 151 of the JRC draft report, table 110 reports data for beet cultivation. The quantity of seeding material seems largely overestimated, JRC mentions 6 kg of seeds per ha (source dated from 1998) while ITB (French technical institute for Sugar Beet) surveys shows an average of 1,28 kg/ha. This data should be actualised, seed material providers (KWS, SES Vanderhave, Florimond Després) could easily confirm the ITB data. JRC: The quantity of seeding material has been updated in the new version of the report using recent data. The new sugar beet seed figure (3.6 kg/ha) refers to pelleted seed. The new references are:
319
Rudelsheim, P. L. J and Smets, G. Baseline information on agricultural practices in the EU Sugar beet (Beta vulgaris L.). Perseus BVBA. May, 2012; and British Beet Research Organisation. Crop establishment and drilling bulletin, Spring 2011. www.uksugarbeet.co.uk. CGB clarified their 1.28 kg/ha of seed refers to un-pelleted seed. However the vast majority of sugar beet seed sown in the EU is pelleted. JRC were in contact with seed providers (Germains, UK).
Q136) Yield problem. The way yields are expressed in the dataset used by JRC for both sugar beet and sugarcane looks quite inaccurate. For these crops, actual yields are based on 2 main components: the weight of matter per ha and its sugar content. Both have to be taken into account in order to use accurate and reliable data. The gross weight refers to the quantity of matter harvested for each hectare while the sugar content of this matter refers to its quality and the expected yield of sugar or ethanol from one ton of processed sugar beet /cane. We talk about sugar content (or richness) for sugar beet (expressed in %, or in kg of sugar per ton of beet) while we talk about TRS (Total recoverable sugar or ATR in Portuguese language) for sugarcane (expressed in kg of sugar per ton of cane). Sugarcane yield In the case of sugarcane, farmers deliver and sell to the plant a net quantity of matter at a given sugar content. Hereunder are the yields and TRS rates for sugarcane in the Centre South of Brazil over the last campaigns Table 1: yields and TRS rates in Centre South Brazil Year
2007/2 008
2008/200 9
Yield (t sugar 84,30 cane/harvested 84,90 (1) (1) ha) TRS (kg 143,63 sugar/ton of 140,11 (2) (2) cane) (1) Brazilian Ministry of Agriculture (2) UNICA harvest report 2012/2013, p.14
2009/201 0
2010/201 1
2011/201 2
5 years average
85,73 (1)
80,45 (1)
69,52 (1)
80,98
129,56 (2)
140,5 (2)
137,54 (2)
138,27
JRC refers to Macedo’s data which is only based on 2005/06 crop with a yield at 87,1 t sugarcane/ha and TRS rate of 14,22% i.e. 142,2 kg sucrose/t of cane. Compared to the table 1, it clearly appears that Macedo’s data is not representative of the average actual data characterizing the Centre South of Brazil over recent years. As there is a structural yield and TRS variability due to climate conditions, only averaged yields should be considered (a 5 years period allowing to absorb climate troubles as it was the case in Brazil in 2011).
320
It also has to be taken into consideration that only around 85% of the whole cultivated areas are harvested each year as around 15% of the total area has to be replanted after 5 or 6 cuts. Regarding the table 1, the reference average yield for sugarcane should therefore be: Apparent yield (t of sugarcane/ harvested ha) x correction rate (harvested area/cultivated area) 80,98 x 0,85 = 68,83 t sugarcane (with a TRS rate of 138,27 kg/ton of cane) / cultivated ha JRC: Sugar cane yield is decreased to 78.74 over 5 years. This takes into account the recent decrease in the average yield for the ethanol-producing areas of Brazil over the last 5 years and the lower yields outside Macedo's (2008) Sao Paolo province. In addition, there is a conversion to the equivalent yield at Macedo's sugar content. The processing data used by the JRC (from Macedo, 2008), assumes a sugar content of 142.2 kg-sugar/tonne cane. However the actual sugar content for all ethanol-producing areas of Brazil is 138.27 kg-sugar/tonne. Therefore in order to use the same processing data, it was necessary to normalize the all-ethanol-brazil sugar cane yield to the equivalent yield at 142.2 kg-sugar/tonne cane.
Q137) Sugar beet yields No sugar beet yield is explicitly mentioned in the draft report (table 110 and following comments). Nevertheless, we know from the past JEC well to wheel analysis that a yield of 68,8 t/ha used to be used. In our view, such a yield is underestimating the reality for two reasons: -
this is old data neglecting the continuous yield increasing trend in the EU
- It is an average yield for the EU, while yields vary significantly from one country to another and it does not reflect actual yields in EU countries actually producing significant amount of ethanol from sugar beet. Sugar beet yields are only expressed in tons of beets paid to the growers i.e. processable tons. Indeed, the grower delivers ex-field a bulk of beets but only a part of it will be finally paid: firstly the waste and soil content (known as “soil tare”), is deducted from the total amount delivered. Secondly, the top of the beet (poorer in sugar) is also deducted and therefore not paid to the farmer (“top tare”). One has to note that tops are also processed in the plants even if they are not paid. Thus, yields are finally expressed in two different ways: Net tonnage of beets (i.e. paid tonnage with “soil tare” and “top tare” deducted) at actual sugar content, or Net tonnage of beets at conventional 16% sugar content. Both are linked by the actual sugar content measured for each load of beets. As the actual sugar content varies from a variety to another, from a field to another, the use of a conventional 16% sugar content yield allows to pay a unique price for the delivered ton of processable sugar beet. It is also a way to compare yields in an easy way between farmers, regions or countries.
321
Attached CIBE data (Confédération Internationale des Betteraviers Européens or European Beet Growers International Confederation) show weighted average for sugar content between 17,6% and 18,81% between 2007 and 2011 for the countries involved in the ethanol production from sugar beet. Producing ethanol from sugar beet needs specific and dedicated infrastructures. This is the reason why, year after year, without significant variations, such a production is structurally located in a limited number of EU countries. Actually, 98% of the alcohol produced from sugar beet in the EU comes from 8 countries (see the excel file “sugar beet ethanol data”), these countries having the best competitiveness in the EU. As the EU is the main ethanol form SB production area in the world, it is essential to stick to its geographical and industrial reality by considering only areas where it is a reality. Our approach is therefore to consider a weighted average of yields and sugar content for the main 8 countries producing ethanol from sugar beet. Such an approach (detailed in the above mentioned excel file) leads to the following data: Table 2 : Yields and sugar content for sugar beet (ethanol purpose) in the EU (CIBE data) Year
2007/2008
Yield (t sugar beet at actual sugar 66,73 content/ ha) Yield (t sugar beet at 16% sugar 75,02 content / ha) Sugar content (kg 179,3 sugar/ton of beet)
2008/2009
2009/2010
2010/2011 2011/2012 5 years average
68,44
72,24
69,49
76,63
70, 71
78,91
85,17
76,60
88,11
80,76
184,2
188,1
176,0
183,7
182,2
In our view, the actual yield of sugar beet to be taken into consideration regarding EU ethanol production is therefore: 70,71 tons of sugar beet (with 182,2 kg of sugar per ton of sugar beet)/ha OR 80,76 tons of sugar beet (with 160 kg of sugar per ton of sugar beet)/ha. In Graph 1 it is possible to appreciate the loss in sugar beet yields that the use of the simple EU 27 average determines if compared to the average of EU major Ethanol Producers. This loss is always huge and is 12% on the 5 year average.
322
Graph 1: EU Beet Yield Average vs Major Ethanol Producers Beet Yield Average - Period 20072012 (t sugar beet at actual sugar content/ ha)
JRC mentions (comments following the table 110) beets with a 75% water content and 16% sugar content. Actually, only beets at actual sugar content have effectively a 75% water content. JRC: The figure of 80.76 t/ha at 16% sugar content as suggested by CGB/CIBE has been considered in the new version of input data. This is the equivalent yield at 16% sugar content for the EU countries making sugar-beet ethanol, and takes into account beet tops, as sugar beet for ethanol takes the whole beet.
Q138) Conversion into ethanol: The conversion yield of sugar crops ton ethanol is directly linked to their sugar content. For this reason, it is not sufficient to consider the rate of dry matter for these crops and to neglect the actual sugar content. The Pasteur formula – calculating the conversion yield of sugar into ethanol - indicates that 1 ton of sucrose gives 0,508 t of ethanol or 6,39 hl. Gay-Lussac considered that one ton of sucrose can be converted into 0,537 t of ethanol (6,76 hl). Nevertheless, it is important to bear in mind that sugarcane yields with a TRS rate include all the processable sugar while sugar beet yields express only the amount of beet paid to the growers. The factory will also process the top (6 to 10% of the yield depending on the country and/or the climatic conditions influencing the shape of the beet). For this reason, we observe that the conversion yield of sugar into ethanol is a bit higher for sugar beet than it is for sugarcane. Sugarcane To produce 1l of dehydrated ethanol, 1,7651 kg of TRS is needed (ORPLANA, Consecana Handbook, p100). This figure means that 1 kg of sucrose is –at an industrial scale- converted into 449,8 g of ethanol which is close to the above mentioned Pasteur yield. Therefore, for the above mentioned period (2007-2011), the average quantity of ethanol produced per harvested hectare was :
323
(80,98 x138,27)/176,51 = 63,43 hl This quantity decreases when calculated for the cultivated hectare: (68,83 x 138,27)/ 176.51 = 53,92 hl Based on 2005/06 Macedo’s data, JRC calculates the following yield: 86,3 hl/t of cane x 72,58 t cane/ha = 62,53 hl /cultivated ha. For the year 05/06, Macedo reports a yield of 86,3 hl ethanol/t sugar cane. Using the actual TRS rate for this crop of 142,2 kg /t sugar cane on one hand and the ORPLANA conversion factor to ethanol on the other hand leads to produce 80,56 litres of dehydrated ethanol, which is rather lower than Macedo’s figure. This can be explained by the fact that Macedo does not indicate if he is talking about a yield of dehydrated ethanol or hydrated ethanol. Orplana considers that to produce 1l of hydrated ethanol, 1,6913 kg of TRS is required. Coming back to Macedo’s work, 1 ton of 2005/06 sugar cane (TRS 142,2 kg sugar/t sugar cane) provide 84 litres of ethanol. It is therefore very likely that Macedo expressed the ethanol conversion yield for hydrated ethanol. This kind of ethanol is not suitable for the European market and is only produced for the internal market. JRC: JRC checked with the authors that the figure for ethanol yield provided by Macedo et al., 2008 refers to dehydrated ethanol. Therefore, this value is still used.
Q139) Conversion into ethanol: Sugar beet It is largely agreed that 1 ton of sugar beet at 16% sugar content allows to produce 1 hl of ethanol, meaning that 160 kg of sugar can be converted into 79,4 kg of ethanol. At an industrial scale, the conversion yield of fermentation of sugar beet is 79,4 /160 = 0,496, a bit higher than for the cane, due to the use of beet tops in the process. JRC mentions a conversion yield of 0,0777 t ethanol /t sugar beet @ 76,5% H2O). This yield is to be linked to the one obtained from 1 ton of sugar beet @ 16% sugar, even if it is slightly lower. This clearly shows that such a conversion factor has to be applied to the conventional sugar beet yield at 16% and not to the gross yield at actual sugar content. The accurate yield to be used here would be 80,76 t @16% sugar. While JRC calculations lead to a yield of ethanol per ha of sugar beet of around 67 hl/ha (68,8 t /ha x 0,0777 t ethanol per t beet@16% sugar), we consider that the actual ethanol production from sugar beet in the EU is more or less 80,76 x 0.0777 = 6,27 t/ha = 79 hl/ha. That makes a huge difference regarding the impact on the GHG balance as all inputs are calculated on a hectare basis. Graph 2: Centre South Brazil Sugarcane vs EU Sugar Beet – Compared ethanol yield/ha cultivated
324
100,00 80,00 60,00 40,00 20,00 0,00
CS Brazil ethanol yield (hl/cultivated ha) EU sugar beet ethanol yield (hl/cultivated ha )
The graph 2 underlines the greater level of ethanol production from one hectare cultivated with sugar beet in the EU than from one hectare cultivated with sugarcane in the Brazilian Centre South. Over a 5 years period, the average difference is more than 46%. JRC: The figure of 80.76 t/ha at 16% sugar content as suggested by CGB/CIBE has been considered in this final version of input data.
Q140) Handling and storage of sugar beet Beets are stored outside in the fields after the harvest and brought to the plants in order to maintain a buffer stock. Therefore, we do not see where and how there could be a consumption of electricity at this point. No explanation is given and a clarification is needed on this topic. JRC: The process has not been considered in this final version of input data.
CEPM – Confederation Europeenne de la production de Mais Q141) N20 and GNOC model: is this new model reduces uncertainties in N20 emission evaluation? Currently, N20 emissions in the RED and the FQD are modeled through the DNDC model instead of the IPCC model, that is also commonly used. Only 3 years after the implementation of the sustainability criteria, the JRC is proposing to support a new model, GNOC, that is inspired from the the Stehfest & Bouwman model and the IPCC approach. However, the European Commission stated itself in 2010 (report COM(2010l 427 ) that: "The accuracy of the input data [in the Stehfest & Bouwman model for direct emissions], together with the fact that most biofuels and bioliquids fall under the grouping "other crops", when crop type is of major importance for determining the emissions, strongly suggests that this work does not now provide the basis for binding legislative proposals." At this stage, even after the 28th May meeting, we have no more answer about the issue wether the GNOC model is really more accurate than the others. But, what is certain, is that
325
the change from DNDC to GNOC will increase overall agricultural GHG default values, sometimes until 5 g C02/MJ of biofuel. That represents 6% of the current fossil fuel GHG default value and an increase of 20% for the agricultural emissions attributed to cereals. Considering that this changes are very important, will impair the competitiveness of the European industry whereas the science is not ready, AGPM and CEPM think that the N20 methodology should not be changed. JRC: See answer to questions Q6) and Q9).
Q142) Manure procedure calculation for liquid biofuels A few years ago, the JRC had advocate that the biofuel policy had no interaction with the development of animal husbandry. Therefore, it did not considered emissions from manure. The new methodology on the contrary assumes that half of the N from manure on crops should be accounted for N20 emissions. AGPM and CEPM disagree with this change of methodology that is based on something like "The truth is somewhere in the middle" between 0% and 100%. Using arbitrary decisions to calculate GHG emission will only damage the credibility and reliability of biofuel Life Cycle Analysis work. JRC: See answer to question Q8).
Q143) Grain maize drying: electricity is not used for drying JRC proposes to use a new drying process based on electricity in the default value methodology. This is not what the CEPM members have reported. Maize is dried with heat made mainly from natural gas, LPG. The usual humidity content is 25% (France, Spain, Italy, Portugal). Eastern maize usually needs no drying. The 2010 French LCA study on biofuels has taken the energy consumption of 0,35 MJ LHV/kg of corn to dry corn from 25% to 15% of humidity. It represents around 830 MJ LHV / ton of water evaporated. Considering the cost of energy, efficiency is an everyday target and this value per ton of water evaporated may not surpass 900 MJ LHV. JRC: We did not assume that the drying process was based on electricity, but we misunderstood that the CAPRI model did. We use output of the CAPRI model to calculate back the amount of water which must be removed from different crops (DG AGRI recommendation). To do this, we need to know what drying process CAPRI assumed, and we misunderstood the CAPRI documentation to say that this was mostly electricity. This has been now corrected with the correct CAPRI assumptions. We chose a lower energy for drying than CAPRI, who took the figures from a literature survey by ECOINVENT. The drying energy depends strongly on whether the drying is done on-farm or at the grain elevators (silo) and our figure reflects a mix of these. The number suggested here (830 MJ/ton of water) is incredibly low, considering that the heat of evaporation of water is 2.44 GJ/tonne.
Q144) DDGS allocation methodology for liquid biofuels It seems that the JRC data, page 147 of its draft document, for conversion of maize to ethanol doesn't take into account the energy allocation methodology that the RED and FQD
326
stipulate. CEPM and AGPM think that the energy allocation must not be changed and that the data have to be displayed accordingly. JRC: See answer to Q3).
Q145) Pesticides data use for grain maize The figure of 7 kg/ha of pesticides use seems very high. For example the 2010 French LCA study on biofueis has taken 2,47 kg/ha. AGPM and CEPM think that JRC should review its reference. JRC: See answer to Q44).
327
References for Appendices European Commission Joint Research Centre (JRC) / Netherlands Environmental Assessment Agency (PBL). (2010). Emission Database for Global Atmospheric Research (EDGAR), release version 4.1. Retrieved from http://edgar.jrc.ec.europa.eu. Seabra, J. E. A., Macedo, I. C., Chum, H. L., Faroni, C. E., & Sarto, C. A. (2011). Life cycle assessment of Brazilian sugarcane products: GHG emissions and energy use. Biofuels, Bioproducts and Biorefining, 5(5), 519–532. doi:10.1002/bbb.289 JEC (Joint Research Centre-EUCAR-CONCAWE collaboration), Well-To-Tank Report Appendix 1- Version 4.a. Conversion factors and fuel properties. Well-To-Wheels Analysis of Future Automotive Fuels and Powertrains in the European Context, EUR 26237 EN, 2014.
328
Europe Direct is a service to help you find answers to your questions about the European Union Free phone number (*): 00 800 6 7 8 9 10 11 (*) Certain mobile telephone operators do not allow access to 00 800 numbers or these calls may be billed. A great deal of additional information on the European Union is available on the Internet. It can be accessed through the Europa server http://europa.eu
How to obtain EU publications Our publications are available from EU Bookshop (http://bookshop.europa.eu), where you can place an order with the sales agent of your choice. The Publications Office has a worldwide network of sales agents. You can obtain their contact details by sending a fax to (352) 29 29-42758.
LD-NA-26853-EN-N
JRC Mission As the Commission’s in-house science service, the Joint Research Centre’s mission is to provide EU policies with independent, evidence-based scientific and technical support throughout the whole policy cycle. Working in close cooperation with policy Directorates-General, the JRC addresses key societal challenges while stimulating innovation through developing new methods, tools and standards, and sharing its know-how with the Member States, the scientific community and international partners.
Serving society Stimulating innovation Supporting legislation
doi:10.2790/38802 ISBN 978-92-79-41253-0
