Spatial and temporal dimensions of Life Cycle ...

Doctoraatsproefschrift nr. 1258 aan de faculteit Bio-ingenieurswetenschappen van de KU Leuven

Spatial and temporal dimensions of Life Cycle Assessment: application to greenhouse gas emissions of bioenergy

Joana Almeida

Dissertation presented in partial fulfilment of the requirements for the degree of Doctor in Bioscience Engineering

Members of the Examination Committee: Dirk SPRINGAEL Wim DEWULF Ana Luísa FERNANDO Jos VAN ORSHOVEN Anton VAN ROMPAEY Bart MUYS Wouter ACHTEN

Professor – KU Leuven Professor – KU Leuven Professor – Universidade Nova de Lisboa Professor – KU Leuven Professor – KU Leuven Professor – KU Leuven Professor – Université libre de Bruxelles

May 2015

Chairman Examiner Examiner Examiner Examiner Supervisor Supervisor

© 2015 KU Leuven, Science, Engineering & Technology Uitgegeven in eigen beheer, ALMEIDA Joana, CASCAIS

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ACKNOWLEDGEMENTS This thesis is funded by a doctoral grant of Science and Technology Foundation Portugal (FCT), financed by Plano Operational Potencial Humano do Quadro Estratégico de Referência Nacional (POPH/QREN). Several other institutions pitched in at one point or the other, namely the Research Foundation – Flanders (FWO) allowing me to travel, the Flemish Interuniversity Cooperation (VLIRUOS) with the travel grants of Jeroen and Leen and Annelies’ IWT doctoral fellowship. Also the support from the KLIMOS+ Acropolis Platform (DGD-VLIR-ARES) is greatly acknowledged. The ERA-ARD Jatrophability project funded by the Belgian Development Cooperation (DGD), and coordinated by the Belgian Royal Museum for Central Africa. The KU Leuven team cooperated in Mali with MaliBiocarburant Foundation (MSBA), Mali Folkecenter (MFC) and Institut d’Economie Rurale du Mali (IER). Although our partnership was cut short I appreciate the willingness of Galp Energia’s Biofuels team to collaborate and dialogue and, most of all, for their hospitality in Mozambique. I thank the jury and the assessor committee of this doctorate for their efforts in making this thesis a better work. My promoters were relentless in making me pick up my slack with the subtleness that only great managers of people can accomplish. (This is a compliment.) Bart (are you reading this part as well?), thank you for taking me in and for not losing sight of me albeit all my meandering. Your patience was my doctorate’s greatest virtue. Wouter, I’ve been a proud recipient of your friendship. You are the master of pep talks and how you always manage to be available to answer the most stupid questions and never lose your cool is a mystery to me. I am so, so grateful to the colleagues who did such a good job with the field work in Mali and followed up with the publications, as well as to those in Leuven always approachable and helpful (It’s a long list, I was very “asky”). A special acknowledgement to those who worked so hard in materializing two of these chapters: Jereb-I mean Jeroen and my friend Annelies.

i

Naming all the people I’ve daily interacted with at the BNL would make this even more embarrassingly long. Besides, I’d forget names and make it awkward. You made me laugh, taught me Kubb, got me to start running and even got me on a horse once (!), hit me in the face with a paddle, helped me with statistics, lent me your dogs, let me in your lives… I don’t know what will become of my ties to Belgium, but I know they can’t be undone. Most of the (other) friends I made in Leuven already left, but one or two might get to read this: thanks for being the closest thing to a family, drama and Sunday lunches and everything. À minha família e aos meus amigos: obrigada por terem tido saudades minhas enquanto eu estava ocupada a produzir este livrinho, por atenderem sempre o telefone e desculpem não ter ligado mais vezes. E obrigada pelas remessas de comida. Um “props” muito especial para os meus sobrinhos por acharem fixe ter uma tia cientista. Luísa, deste-me (dás-me) as duas coisas que eu mais preciso: muito apoio e, principalmente, um cantinho para onde regresso todos os dias. É lá, contigo, que estão os pontos de referência a usar quando o stress me troca as prioridades. Sim, porque isto é tudo muito bonito, mas há coisas mais importantes na vida! No começo desta empreitada, sentei-me a explicar à minha chorosa avó que me vinha embora e que se calhar já não voltava. Eram os últimos momentos de luz antes da escuridão a ter envolvido por completo, e ela fez-me uma recomendação do topo dos seus 90 anos: “Não te detenhas. Faz o que quiseres, tens de ser é feliz”. Tem-me desempatado as decisões todas.

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TABLE OF CONTENTS List of tables

vii

List of figures

viii

List of abbreviations

x

Abstract

xiii

Samenvatting

xv

1.

Introduction 1.1.Climate change and mitigation 1.2.Biofuels and land use change 1.3.Life cycle assessment 1.3.1. General methodology 1.3.2. Potential challenges and problems 1.3.2.1. Land use and land use change 1.3.2.2. Biogenic carbon and delayed emissions 1.3.3. Time and space issues in LCA 1.4.About this thesis 1.4.1. Context and study area 1.4.2. Aim and objectives 1.4.3. Outline

1 1 2 4 4 6 6 8 8 11 11 15 16

2.

Generic life cycle assessment of the Jatropha biodiesel system Abstract 2.1.Introduction 2.2.Materials and Methods 2.2.1. Goal, Scope, and System Boundaries 2.2.1.1. Scenarios 2.2.2. Life Cycle Inventory Analysis 2.2.2.1. Data Collection 2.2.2.2. Production System 2.2.2.3. Assumptions 2.2.3. Impact Assessment 2.2.4. Sensitivity Analysis 2.3.Results and discussion 2.3.1. Non-renewable Energy Requirement 2.3.2. Global Warming Potential 2.3.3. Ozone Layer Depletion 2.3.4. Terrestrial Acidification and Eutrophication 2.3.5. Sensitivity Analysis 2.3.6. Life Cycle Interpretation 2.4.Conclusion

19 19 19 21 21 23 23 23 24 26 26 27 27 28 28 29 29 29 31 32

iii

3.

Life cycle assessment of Jatropha-based bioenergy in Mali Abstract 3.1.Introduction 3.2.Materials and methods 3.2.1. Goal and scope definition and data collection 3.2.2. Functional unit 3.2.3. Reference system 3.2.4. System description and allocation 3.2.4.1. Cultivation 3.2.4.2. Biodiesel production and use 3.2.4.3. Allocation 3.2.5. Impact assessment 3.2.5.1. Energy resources 3.2.5.2. Global warming potential 3.3.Results 3.3.1. Energy analysis 3.3.2. Global warming potential 3.4.Discussion 3.5.Conclusions

4.

Land use change impact on carbon stocks of Jatropha plantations 57 Abstract 57 4.1.Introduction 58 4.2.Materials and methods 59 4.2.1. General setup 59 4.2.2. Data collection 60 4.2.3. Statistical data analysis 61 4.3.Results 62 4.3.1. Description of fields 62 4.3.2. Soil conditions 63 4.3.3. Biomass carbon 63 4.3.3.1. Jatropha biomass, allometric relation and root-to-shoot ratio 63 4.3.3.2. Biomass carbon stocks in the different land uses 66 4.3.4. Soil carbon 68 4.3.4.1. Soil organic carbon concentrations and its controlling factors 68 4.3.4.2. Soil carbon stocks in the different land uses 69 4.3.4.3. Spatial variability 71 4.3.4.4. Total C stock and estimation of C debt 72 4.4.Discussion 73 4.4.1. Biomass carbon in Jatropha plantations 73 4.4.2. Soil carbon in Jatropha plantations 74 4.4.3. Land use change impact and carbon sequestration by Jatropha plantations 75 4.5.Conclusions 77

5.

Greenhouse gas emission timing in the global warming potential of Jatropha 79 Abstract 79 iv

35 35 35 37 37 39 39 40 40 43 43 44 44 44 45 45 48 50 54

5.1.Introduction 5.2.Methods 5.2.1. Scope and system definition 5.2.2. Life cycle Inventory 5.2.3. Global warming potential calculation 5.3.Results and discussion 5.3.1. Greenhouse gas emission inventory 5.3.2. Global Warming Potential assessment 5.4.Conclusions

80 85 85 86 88 89 89 92 94

6.

Spatial optimization of Jatropha-based electricity supply chains Abstract 6.1.Introduction 6.2.Methods 6.2.1. Study area 6.2.2. Electricity demand scenarios 6.2.3. OPTIMASS 6.2.4. Non-spatially dependent optimization parameters 6.2.5. Spatially dependent optimization parameters 6.2.5.1. Cultivation areas and land use change emission 6.2.6. Calculation of global warming potential of supply chain inputs 6.2.7. Sensitivity analysis 6.3.Results 6.3.1. Global warming potential 6.3.2. Land use change emissions 6.3.3. Optimal supply chain 6.3.4. Sensitivity analysis 6.3.5. Replaced land cover types 6.4.Discussion 6.4.1. The optimal supply chains 6.4.2. Land use change emissions 6.4.3. By-product handling 6.5.Conclusions

97 97 98 99 99 101 102 104 106 107 109 110 111 111 112 113 115 117 117 117 119 121 122

7.

Conclusions and perspectives 7.1.Research questions and challenges addressed 7.2.Jatropha sustainability and land use change 7.3.Time-mindful LCA 7.4.Spatially explicit LCA of Jatropha supply chains 7.5.Applying LCA in developing countries 7.5.1. Limitations of inventory 7.5.2. Consequential LCA 7.5.3. Recommendations 7.6.A missing piece: indirect land use change 7.7.Towards integrated sustainability assessment of land-based production systems

123 123 125 127 129 132 132 132 133 134 136

References

139

v

Annex 1

154

Annex 2

189

vi

LIST OF TABLES Table 2.1 – Key inputs and outputs of the Jatropha system presented per 1 MJ of produced biodiesel.

25

Table 3.1 – Plantation set up of the selected outgrowers and the two factory fields.

41

Table 3.2 – Fossil Depletion, Cumulative Energy Demand and Global Warming Potential of the two farming systems and a normal average at two yield levels, in comparison to the reference system.

45

Table 4.1 – Mean values and standard deviations (within brackets) of edaphic variables for the soil types in both ecoregions.

64

Table 4.2 – Averages of measurements on individual Jatropha trees, grouped per ecoregion and soil type.

65

Table 4.3 – Average and standard deviation of Jatropha carbon stock, total biomass carbon stock, soil organic carbon stock and total carbon stock grouped per ecoregion and land use.

68

Table 4.4 – Coefficient of variation (of soil organic carbon stocks within and between fields.

71

Table 6.1 – Description of the three electrification scenarios and their electricity demand fed into the model.

102

Table 6.2 – Total GWP resulting from fulfilling the demands of scenario 1, 2 and 3 and the corresponding GWP efficiency.

111

Table 6.3 – Absolute GWP of the supply chain if substitution of fertilizers by Jatropha by-products is taken into account and if LUC emissions are excluded.

117

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LIST OF FIGURES Figure 1.1 – Structure of the life cycle assessment framework.

5

Figure 1.2 – Measuring and expressing LUC emissions in function of time and area with the aid of GIS and temporally dynamic LCA methods to use LCA to evaluate whether or not biofuels lead to GHG emission reductions.

9

Figure 1.3 - Range of nation-wide areas planted with Jatropha.

12

Figure 1.4 – Field scenes of the Jatrophability Mali project.

14

Figure 1.5 – Logical relationships between thesis chapters and their correspondence to the research hypotheses.

17

Figure 2.1 – System boundaries of the Jatropha system, the reference system and the system boundary expansion.

22

Figure 2.2 – Distribution of data sources used in this LCA.

24

Figure 2.3 – Life cycle impacts of the Jatropha biodiesel compared to the reference system.

27

Figure 2.4 – Sensitivity analysis of NRER and GWP regarding yield variation.

30

Figure 3.1 – The production system in Koulikoro and its relation to the reference system.

38

Figure 3.2 – Monthly precipitation in Koulikoro in 2012 and 2012 compared with the climatological average.

42

Figure 3.3 – Average yields measured in 2011 and 2012 among outgrowers and factory fields.

42

Figure 3.4 – CED of the factory- and outgrower -based biodiesel under low and high yields.

46

Figure 3.5 – FD of the factory- and outgrowers -based biodiesel under low and high yields.

47

Figure 3.6 – GWP of the factory- and outgrowers -based biodiesel under low and high yields.

49

Figure 4.1 – Location of study sites in relation to Köppen-Geiger climate classification.

60

Figure 4.2 – Example pictures of the three land uses in Koulikoro and Garalo.

62

Figure 4.3 – Allometric relation for woody dry aboveground biomass of individual Jatropha trees based on their crown area.

66

Figure 4.4 – Partitioning of total biomass carbon stock between aboveground and belowground biomass and between the different vegetation elements for each land use type.

67

Figure 4.5 – Relation of soil organic carbon density with soil depth in cropland, Jatropha and fallow for the Garalo and Koulikoro ecoregions.

69

Figure 4.6 – Average differences in soil organic carbon stocks between the

70

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three land uses for each soil layer. Figure 4.7 – Linear regression of soil organic carbon stock (0-30 cm) as a function of the best predicting soil variable for Koulikoro and Garalo.

70

Figure 4.8 – Carbon stocks per land use type and differences between these land use types.

72

Figure 5.1 – Difference between static and dynamic GWP approaches relative to emission timing and cut-offs at time horizons of 20, 100 and 500 years and their link to emission pulses and their radiative forcing.

84

Figure 5.2 – Distribution over time of the Jatropha production system in Koulikoro.

88

Figure 5.3 – Total GHG mass emitted by the stages of electricity production from Jatropha.

90

Figure 5.4 – Annual CO2 emission from soil of the Jatropha plantation starting from establishment on cropland or fallow land.

90

Figure 5.5 – IPCC Global Warming Potentials and Dynamic GWP of Jatropha biodiesel-based electrification upon conversion of cropland and fallow land.

93

Figure 6.1 – Location of the study area, Malian infrastructure of relevance to this study and location of water bodies and nature reserves.

100

Figure 6.2 – Overview of the inter-relations between the different components of the supply chain optimization modelling process.

103

Figure 6.3 – Schematic supply chain of off-grid and on-grid Jatropha-based electricity production, corresponding to the system boundaries of the LCIA.

105

Figure 6.4 – Potential operation sites.

106

Figure 6.5 – Contribution of the stages in the supply chain to the GWP efficiency of each scenario.

112

Figure 6.6 – Emissions from land use change amortized over a period of 20 years in function of area and of the yields of Jatropha in each potential cultivation area in Southern Mali.

113

Figure 6.7 – Spatial layout of Jatropha-based electricity supply chains for three different scenarios in the base case and the sensitivity analysis to plantation area constraint and the effect of LUC emissions.

114

Figure 6.8 – Land cover types found in the selected cells for Jatropha cultivation.

118

ix

LIST OF ABBREVIATIONS ACRONYMS AND SYMBOLS a (w)AGB B4B BD BGB BMC C C/N CA CDM CED CH4 CO2 CV DBH dLUC DynGWP eq FAR FP FU g GHG(s) GIS GWP iLUC IPCC K LCA LCI LCIA LHV LU LUC LUC E

Radiative efficiency (woody) Above ground biomass Biomass For Bioenergy Bulk density Below ground biomass Biomass carbon Carbon Carbon to Nitrogen ratio Crown area Clean Development Mechanism Cumulative energy demand Methane Carbon dioxide Coefficient of variation Diameter at breast height Direct land use change Dynamic Global Warming Potential Equivalent Fourth Assessment Report Fossil depletion Functional Unit Emitted mass of greenhouse gas Greenhouse gas(es) Geographic information systems Global warming potential Indirect land use change International Panel on Climate Change Potassium Life cycle assessment Life cycle inventory Life cycle impact assessment Lower heating value Land use Land use change Land use change emission x

MAP MAT MBSA MeOH MILP N N2O NaOH NGO NRER OLD P SOC SOM TAE TH

Mean annual precipitation Mean annual temperature MaliBiocarburant Methanol Multi Integer Linear Programming Nitrogen Nitrogen oxide Sodium hydroxide Non-governmental organization Non-renewable energy requirement Ozone layer depletion Phosphorus Soil organic carbon Soil organic matter Terrestrial acidification and eutrophication Time horizon

UNITS ha kg km kWh l MJ MWh p t tkm

Hectare Kilogram Kilometre Kilowatt hour litre Mega joule Megawatt hour part Tonne Tonne kilometre

xi

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ABSTRACT Bioenergy was put forward as a way to reduce greenhouse gas (GHG) emissions of energy provision. In order to ascertain that this goal is met it is necessary to compare the cumulative emissions of bioenergy and fossil-based energy supply chains. One well-accepted method to do so is life cycle assessment (LCA), which records all the material and energy flows in and out of a supply chain and estimates their impact on climate through the global warming potential (GWP) indicator. Land use change (LUC) emissions are claimed to potentially invert the emission reduction potential of bioenergy feedstocks, but are often left out of LCAs due to methodological difficulties and a lack of data. In fact, GHG emissions from LUC are distributed in time and space, in contrast to the static and spatially-abstract nature of LCA. Bioenergy has motivated a great deal of efforts in making life cycle inventories (LCIs) more complete in terms of LUC emissions and also in making LCA more receptive to their spatial and temporal variations. The ultimate goal is to accurately estimate LUC emissions and their impact on climate, and to better value emissions occurring at different moments in a life cycle. This thesis aims at estimating and integrating LUC emissions in LCA of bioenergy, gauging the importance of time considerations in GWP and the potential of spatially explicit LCIs. Here, we focus on the case of Jatropha initiatives in Mali. Jatropha is an oil-yielding bioenergy crop that was promoted as an accessible, decentralized energy source for rural populations in developing countries. Previously published Jatropha LCAs reported it to have lower emissions than fossil fuels. Such LCAs, however, did not take into account the full scope of GHG emissions as LUC emissions were left out and many lacked reliable yield information. Their magnitude for Jatropha is highly site specific and therefore largely unknown. We started by making a generic LCA, with literature data collected during early Jatropha investments, and a site specific one with field data, including on yields. Next, we measured LUC emissions based on field data and estimated Jatropha’s carbon debt. The field data consisted of measurements of carbon content in soil and biomass in Jatropha plantations and proxies of previous land uses: cropland and fallow land. We conclude that yield and previous land use are key factors in the environmental performance of this production system. Improving productivity in degraded lands seems to be the priority, starting with an engagement of growers in tending and harvesting Jatropha plants.

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Based on the data collected in the first part of the thesis, we built an LCI in annual time steps for a whole rotation span of Jatropha. We used RothC model to determine how the soil organic carbon (SOC) content evolves under Jatropha throughout the years, revealing that Jatropha plantations lose SOC rapidly after LUC and a new equilibrium is found after 9-10 rotations (180-200 years). We analyzed this LCI with the GWP metrics currently adopted by the IPCC and also with a novel dynamic LCA approach that addresses the effects of emission timing and time horizon choice in the GWP of the life cycle. Our results were, however, inconclusive regarding its advantages relative to the classic GWP of IPCC. The time span of carbon sequestration and release in the Jatropha system is too short to have different signals in the two approaches, which was expected. In addition, dynamic LCA yields a wide range of results depending on the time of analysis, maintaining the subjectivity of this choice. Further on, we explored new spatial applications of LCA in a spatially explicit supply chain optimization exercise. We used a pre-existing optimization model and parameterized it with life cycle impact assessment data for the production of electricity from Jatropha oil in Mali. The goal was to obtain the spatial outline and input requirements of the optimal supply chains to fulfil a certain electricity demand in Mali. We included a spatially-explicit inventory of LUC emissions in function of harvestable Jatropha seed for the Southern part of Mali, based on estimated yields of Trabucco et al. (2010). This approach successfully modeled supply chains optimized for minimal GWP. This mainly linked with finding the parts of the country where the best LUC emission to seed yield ratio is obtainable, as these are the factors most decisive on the final GHG emission balance. These optimal cultivation areas are located not on degraded lands, but on more productive areas and conflict with cropland. This thesis showed how life cycle thinking can serve the betterment and sustainable design of land-based production systems and how their spatial and temporal dimensions challenge the LCA methodology. While a small contribution is done here to the time issues in LCA, we demonstrated the usefulness of extensive, time-specific LCIs to appreciate the sustainability of a bioenergy initiative. On the one hand, we showed that including LUC in the LCI can compromise Jatropha’s low GHG emission premise. On the other hand, such comprehensive LCIs add value to life cycle thinking as a framework to conjecture prospective, more efficient Jatropha and other crop-based bioenergy supply chains. Future research should include improving LCIs through better, more specific data on the emissions from land conversion and land occupation with bioenergy, namely extending them with approachable indirect land use change estimates.

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SAMENVATTING Bio-energie is naar voor geschoven als een middel om de uitstoot van broeikasgassen gelinkt aan energievoorziening te reduceren. Om zeker te zijn dat deze doelstelling behaald wordt, is het noodzakelijk om de cumulatieve uitstoot van bio-energie en fossiele energie te vergelijken over hun hele productie- en bevoorradingsketen. Een breed aanvaarde methode om dit te doen is levenscyclusanalyse (LCA). Deze methode neemt alle materiaal- en energiestromen van de productie- en bevoorradingsketen in rekening, en berekend hun impact op het klimaat via de opwarmingspotentieel indicator (Global Warming Potential - GWP). Emissies gerelateerd aan landgebruiksveranderingen kunnen mogelijks het emissiereductiepotentieel van bioenergiebronnen teniet doen, maar worden vaak niet opgenomen in LCAs omwille van methodologische moeilijkheden en gebrek aan gegevens. Broeikasgasemissies van landgebruiksverandering zijn gespreid in tijd en ruimte, wat een tegenstelling oplevert met de statische en ruimtelijk abstracte aard van LCA. Bio-energie heeft echter aangezet tot grote inspanningen

om

levenscyclusinventarisaties

te

vervolledigen

met

emissies

van

landgebruiksveranderingen, en om LCA meer in staat te stellen met ruimtelijke en temporele variaties om te gaan. Het ultieme doel is om de emissies van landgebruiksveranderingen, en hun impact op het klimaat, accuraat te berekenen, en om emissies die op verschillende momenten voorkomen beter naar impactwaarde te kunnen schatten. Dit proefschrift heeft tot doel emissies van landgebruiksveranderingen te berekenen en te integreren in LCAs van bioenergie, en om daarbij het belang van temporele variaties in GWP en het potentieel van ruimtelijke expliciete LCAs te evalueren. We richten ons hier op de gevalstudie van Jatropha-initiatieven in Mali. Jatropha is een olieproducerendebio-energiegewas

dat

gepromoot

werd

als

een

toegankelijke,

gedecentraliseerde energiebron voor landelijke populaties in ontwikkelingslanden. Eerder gepubliceerde Jatropha LCAs geven aan dat het een lagere uitstoot veroorzaakt dan fossiele brandstoffen. Deze LCAs brachten echter niet het volledige spectrum aan broeikasgasemissies in rekening omdat de emissies van landgebruiksveranderingen niet werden opgenomen. De grootte van deze emissies is sterk locatieafhankelijk en daardoor grotendeels onbekend. We zijn gestart met het uitvoeren van een generische LCA, met literatuurdata uit de Jatropha hype, en met een locatie specifieke LCA met velddata, inclusief oogstdata. Vervolgens hebben we de emissies van landgebruiksveranderingen berekend op basis van veldgegevens, en hebben we de bijhorende koolstofschuld bepaald. De veldgegevens bestonden uit metingen van de koolstofinhoud van bodem en biomassa in Jatrophaplantages en uit aanwijzingen van

xv

het voorgaande landgebruik op die plaats: akkerland of braakland. We concluderen dat oogst en het voormalige landgebruik sleutelfactoren zijn in de milieubalans van dit productiesysteem. Het verbeteren van de productiviteit in gedegradeerde landen lijkt dan ook prioritair, waarbij aandacht nodig is voor het engagement van de producenten om hun planten goed te verzorgen en volledig te oogsten. Gebaseerd op de gegevens verzameld in het eerste deel van dit proefschrift, hebben we een levenscyclusinventaris opgebouwd in jaarlijkse tijdsstappen voor een volledige rotatieperiode van een Jatropha plantage. We gebruikten het RothC model om evoluties van de bodem organische koolstof (BOK) onder jatropha doorheen de jaren te bepalen. De resultaten van de BOK modellering toonden aan dat Jatropha plantages snel koolstof verliezen na de landgebruiksverandering en dat een nieuw evenwicht na 9-10 rotaties (180-200 jaar) bereikt wordt. We analyseerden deze levenscyclusinventaris met de GWP indicatoren die momenteel door de IPCC worden gebruikt, maar ook met een nieuwe, dynamische LCA aanpak. Deze laatste biedt antwoordt op de effecten van wanneer broeikasgassen worden uitgestoten en met de methodologische keuze van analytische tijdshorizon. Onze resultaten laten echter geen concrete conclusie toe aangaande het voordeel van deze dynamische aanpak ten opzichte van de statische, klassieke IPCC GWP. De periodes in het Jatropha systeem waarin er koolstof wordt opgeslagen en uitgestoten zijn immers te kort om een duidelijk verschillend signaal te geven in beide aanpakken. Dit was niet onverwacht. Bijkomend kan gezegd dat, hoewel de dynamische LCA een breed spectrum aan resultaten aanbiedt afhankelijk van de geselecteerde tijdshorizon, deze selectie een subjectieve keuze blijft. Verder zijn we nieuwe mogelijke ruimtelijke applicaties van LCA nagegaan in een oefening van ruimtelijk expliciete optimalisatie van een bevoorraadingsketen. We gebruikten een bestaand optimalisatiemodel en parametriseerden dit met levenscyclus impact gegevens van de productie van elektriciteit uit Jatropha in Mali. Het doel was een ruimtelijke schets en input noden te bekomen voor de optimale keten welke aan een bepaalde elektriciteitsvraag in Mali zou kunnen beantwoorden. We sloten ook een ruimtelijk expliciete inventaris van emissies veroorzaakt door landgebruiksveranderingen in, in relatie met de oogstbare hoeveelheid Jatropha zaden voor het zuidelijke deel van Mali. Deze oogstschattingen zijn gebaseerd op werk van Trabucco et al. (2010). Deze aanpak leidde tot het succesvol modeleren van ketens die geoptimaliseerd zijn voor minimale GWP. Deze modelering houdt voornamelijk een zoektocht in naar gronden waar de meest gunstige verhouding kon gevonden worden tussen emissies door landgebruiksverandering en zaadoogst. Dit waren immers de meest beslissende factoren voor de uiteindelijke broeikasgasbalans. Deze optimale teeltlocaties bevinden zich xvi

niet in gedegradeerde gronden, maar in vochtige, hoog productieve streken waar het mogelijk is geen huidige akkerlandbouw om te vormen. Dit proefschrift toont hoe levenscyclusdenken kan gebruikt worden bij het verbeteren en het duurzaam ontwerpen van landgebaseerde productiesystemen, en hoe de ruimtelijke en temporele dimensies van zulke systemen de LCA methode uitdagen. Hoewel hier slechts een kleine bijdrage wordt geleverd tot de temporele aspecten in LCA, tonen we de bruikbaarheid van extensieve, tijd specifieke levenscyclusinventarisatie bij het doorgronden van de duurzaamheid van bio-energie initiatieven. Enerzijds tonen we dat het opnemen van landgebruiksveranderingen in de levenscyclusinventarisatie het uitgangspunt rond Jatropha’s uitstoot kan compromitteren. Anderzijds, leveren zulke omvattende inventarisaties ook een toegevoegde waarde aan het levenscyclusdenken als raamwerk, om zo prospectief meer efficiënte systemen, zij het Jatropha of andere bio-energiesystemen, te ontwerpen. Toekomstig onderzoek zou moeten focussen op het verbeteren van levenscyclusinventerisatie met

betere,

meer

specifieke

gegevens

over

emissies

van

landgebruik

en

landgebruiksverandering veroorzaakt door bio-energie, meer specifiek met een uitbreiding naar schattingen van indirecte landgebruiksveranderingen.

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Chapter 1

1. INTRODUCTION 1.1. CLIMATE CHANGE AND MITIGATION Since the first evidences divulged in the 1800’s, mankind has been accumulating confirmation of a shift in climatic patterns at the global level linked to an acceleration of anthropogenic greenhouse gas (GHG) emissions to the atmosphere (Weart, 2008). We can consider ourselves certain that the global average temperature is increasing at a rate previously unseen, and that it correlates well with a sharp, human-driven increase of GHG concentrations in the atmosphere. The causality between the two facts is explained by the greenhouse gas effect, that describes how certain gases, such as carbon dioxide (CO2) and methane (CH4), affect the radiative efficiency of the atmosphere, meaning that they alter its capacity to deflect solar energy from the Earth’s proximity (Feldman et al., 2015; IPCC, 2007). There are also many uncertainties surrounding this paradigm, namely how, through which pathways and in what magnitude climate change will happen (Schiermeier, 2010). There is room to improve the models that predict global warming, the scale of its environmental, social and economic impacts and when and how tipping points occur (Moss et al., 2010). The need of a society that grows in population and wealth underlies this increase of atmospheric GHG concentrations. Mankind’s economic activities tend to deplete carbon from its sinks in the ecosystems (such as vegetation, soil and fossil fuel reserves) and displace it to the atmosphere where it accumulates. In fact, the main origin of GHG is burning fossil fuels for energy supply and for transportation (49%), followed by deforestation and land clearing (17%), agricultural activities (14%) and industry (19%) (IPCC, 2007). The modern humanindustrial system is largely fuelled by reserves of coal, oil and natural gas, which are being consumed much faster than they were generated. Their finiteness and potential scarcity have been determinant in the geopolitical sphere, deeming many countries energetically and, hence, economically dependent of others. In 1990, the United Nations began negotiating a new regime that would initiate the transition of the world into a sustainable, low-GHG society. By 1994 enough countries had ratified an agreement to limit their GHG emissions and the Kyoto Protocol was born. The reduction targets were based mainly on each country’s willingness to act, instead of on climate change models. They aimed to cap emissions of industrialized countries, the so-called annex I 1

Introduction

countries, rather than of the whole world (Betsill, 2005). Several instruments were created under the Kyoto protocol, such as the Clean Development Mechanisms (CDM), Joint Implementation (JI) and International Emission Trading (IET). These mechanisms allow emission trading and emission out-sourcing among industrialized countries or between industrialized and non-complying, annex II countries (UNFCCC, 2014). Regardless of its success in mitigating climate change, the Kyoto protocol swayed the strategic context of policy making and economic growth towards climate change mitigation. As scientific awareness became political compliance, the signs of the so-desired transition into a low-carbon society began to show in the shape of emission reduction strategies (Betsill, 2005). Today, we know of policies for energy efficiency, taxes on pollution, subvention of low-carbon transportation and a shift in energy mixes that incorporate increasingly more renewable energy in detriment of fossil fuels (IPCC, 2014).

1.2. BIOFUELS AND LAND USE CHANGE Against this backdrop of climate change and fuel energy dependence, the urgency grew to develop renewable energy carriers (Bessou et al., 2011). Future energy mix will have to include renewable energies, such as photovoltaic and hydroelectric. Some of the main emitter regions of the world have established targets to develop renewable energy sources and replace fossil fuels with biomass use. By 2020, the European Union aims to have reached 20% penetration of renewable in its energy mix, with a specific goal of a minimum of 10% for the transportation sector (EC, 2009). China has a target of 15% by 2020 for renewable contribution to energy use, supported notably by hydropower. Binding plans for the development of biofuels were never approved at a national level (Bessou et al., 2011). The goal in the United States of America is for biomass to supply 5% of the nation’s power, 25% of its chemicals and 20% of transportation fuels by 2030 (Perlack et al., 2005). Biofuels were predicted to cover needs for liquid fuels and needs of rural areas in developing countries while they transition from charcoal and firewood (Asch & Huelsebusch, 2009). Biofuels were put forward so as to decrease energy generation-related emissions under the premise of carbon neutrality: carbon sequestered through photosynthesis during plant growth balance out the tailpipe GHG emissions of these biofuels. Therefore, the promise of GHG emission reduction lied solely on the efficiency of the production operations, from the cropping to the use of fuel, without taking biogenic fluxes into account. 2

Chapter 1

Any human process relying on energy from biomass (e.g. food) by definition needs large land areas due to low efficiency of solar energy storage through photosynthesis (1-3.5%). The requirement of land is further amplified by product demand. Thus, bioenergy is expected to claim increasing areas of land throughout the continents, resulting in land use change (LUC) (Berndes et al., 2012). Land conversion to biofuel cultivation disturbs standing biomass and soil carbon stocks, which can be directly measured (Brandão et al., 2011; Fargione et al., 2008). Direct land use change (dLUC) is mostly treated as a balance of carbon stocks before and after the implementation of the bioenergy crop. The difference was considered a CO 2 emission. This balance is usually estimated from literature references, software tools or default values. Njakou Djomo and Ceulemans (2012) reviewed the dLUC intensity of biofuel energy and reached a range of -98 g CO2 MJ-1 to 481 g CO2 MJ-1. Another approach is to calculate a carbon (C) debt: the carbon which is released upon LUC is a liability that the biofuel incurs in and that can be paid back by emission savings of fossil fuel substitution (Fargione et al., 2008). The time that it takes to payback the C debt is often used as an indicator of a biofuel’s environmental sustainability. In addition to the immediate shift in the carbon stocks, there is also a prospective shift in the amount of carbon that would be captured by that portion of land under the previous LU. This perspective implies the definition of a reference scenario and as well as of the forecast of C stock behaviour. DLUC can trigger subsequent LUC, occurring elsewhere owing to displacement of the activity on the previous land use (LU), an effect called indirect land use change (iLUC) (Lapola et al., 2010; Melillo et al., 2009; Plevin et al., 2010; Searchinger et al., 2008). The causality between dlUC and iLUC is much lower than dLUC and empirical proofs of iLUC are scarce. For this reason, iLUC emissions can only be calculated through models. Allocation models assign iLUC to feedstock conversion routes based on historical data on LUC and yield. Market equilibrium models operate in response to a change in demand and are rooted in market links. Whilst the former is characterized by transparency and oversimplification, the latter holds more detail but also more uncertainties and difficult implementation (Delzeit et al., 2012; Fritsche et al., 2010a; Fritsche et al., 2010b; Wicke et al., 2012). ILUC has hardly been empirically verified and all existing data is based on projections, presenting a large range of GHG emissions (Njakou Djomo & Ceulemans, 2012; Wicke et al., 2012). Differences in results cannot be explained by the type of model alone, but rather by factors such as underlying assumptions and datasets (Berndes et al., 2012; Njakou Djomo & Ceulemans, 2012; Wicke et al., 2012).

3

Introduction

It cannot be ignored that bioenergy may be merely shifting the burden of energy supply onto the burden of land clearing and agricultural operations. Depending on its magnitude, LUC can negate bioenergy’s GHG advantage over fossil fuels (Berndes et al., 2012; Kim et al., 2009; Melillo et al., 2009; Searchinger et al., 2008).

1.3. LIFE CYCLE ASSESSMENT

1.3.1. GENERAL METHODOLOGY It is generally agreed upon that Life Cycle Assessment (LCA) is an appropriate tool for greenhouse gas emission calculation of bioenergy product systems. LCA is fit to gather a comprehensive picture of the emissions involved in every stage of a bioenergy system and translate it into a climate impact by measuring its global warming potential (GWP) expressed in mass of carbon dioxide equivalents (kg CO2 eq) for a given functional unit. The equivalency of each GHG with CO2 is done by comparing their forcing on the atmosphere’s radiative capacity. The life cycle assessment of biofuels generally points to favourable GHG balances compared to fossil fuels, with the cultivation phase as a significant contributor to the GWP. Most published studies on the impact assessment of bioenergy estimate the GWP and all of them indicate a positive effect relatively to fossil diesel (Cherubini & Strømman, 2011). The potential reduction of GHG emissions by biofuels relative to fossil fuels ranges between 14% and 89% (Bessou et al., 2011). LCA provides an internationally standardized framework (ISO 14040:2006) for environmental impact assessment of production and consumption. It considers that the environmental implications of a good or service can only be fully understood if the assessment covers their whole lifespan, from production to use to end of life. In terms of execution, an LCA is a phased yet iterative process (figure 1.1). During the goal and scope definition, the LCA practitioner establishes the aim, rationale and the object of the study, and explains the study’s limitations determined by assumptions and methodological choices. At this stage, also a functional unit (FU) must be defined, which is the unit that best

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Chapter 1

communicates the function or productive output of the system, and to which the results of the impact study will be expressed (JRC et al., 2010).

Figure 1.1 – Structure of the life cycle assessment framework.

The system boundaries are set in this first step, delimiting the life cycle stages to be considered (figure 1.1). The boundaries can be seen as an empty flow-chart which is filled in with inventory data in the second step. Whilst making the life cycle inventory (LCI) the practitioner collects information on the processes and elementary flows that compose the life cycle and quantifies them. The result is a life cycle model containing transfers of energy and materials between the ecosphere and the technosphere. These flows can then be analysed with environmental impact assessment methods in the third life cycle impact assessment (LCIA) step. The latter read the life cycle model and translate it into an impact to the environment in one or multiple aspects, depending on the impact categories previously selected (JRC et al., 2010). The inventory and the impact assessment steps can be performed in dedicated LCA software that are normally integrated with LCA databases where background data on life cycle flows can be found. The most well known commercial LCA software and the one used on this thesis is SimaPro (PRé, the Netherlands) (see Box 1.1 for an insight). Other relevant software are Umberto (IFU Hamburg, Germany) and GaBi (thinkstep, Germany), while Open LCA (GreenDelta, Germany) offers an open source solution.

5

Introduction

After the necessary iterations among the above-described steps, when the results have sufficient quality, they can be interpreted in a fourth and last step. Interpreting results may include sensitivity and uncertainty analysis, which reflect, for instance, on the limitations drawn in the scope definition stage. The finality of an LCA is then reached when, in light of a critical appraisal of the whole LCA process and its results, conclusions are drawn and potential recommendations are made so as to guide the performance of the studied good or service (JRC et al., 2010).

BOX 1.1 What is SimaPro® and how does it work? SimaPro® is the leading commercial software for LCA. It works as a console containing a library of LCA databases (e.g. ecoinvent, ELCD, Agri-Food) and also of impact assessment methodologies (e.g. ReCiPe, Ecoindicator99, Water footprint). The LCA database library compiles inventory information on a comprehensive set of processes from a wide range of sectors, from chemical substances and building materials to energy and food. For each process, the database entry specifies all raw materials required by it as well as emissions to air, soil and water. SimaPro® allows for creating new processes from those available in the database and link them in their life cycle stages, hence creating a process tree describing a production system. Depending on the system boundaries set by the practitioner, it is also possible to different include allocation options and end of life processes. Finally, the methods available in the respective database read a processes or a set of unit processes linked in a life cycle and calculate its environmental impact.

1.3.2. POTENTIAL CHALLENGES AND PROBLEMS

1.3.2.1.

LAND USE AND LAND USE CHANGE

The widespread environmental sustainability profiling of bioenergy products with LCA highlighted its difficulty in handling impacts of LU and LUC (reviewed e.g. by Bessou et al.

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(2011) and Cherubini and Strømman (2011)). There are different ways in which LU and LUC and its implications can be understood in the LCA methodology: i)

LU as an impact category in LCA, implies that “LU is a damage to ecosystems due to the effects of land occupation” (Finkbeiner et al., 2014), mainly in terms of biodiversity and ecological functions;

ii)

or LUC as shifts in land occupation, as described before in this text, resulting in a new LU with effects spread throughout time, among which, GHG emissions from dLUC and iLUC.

An impact of LU and LUC in either understanding occurs when there is a shift in the purpose for which a land area is used. This leads to a disturbance, requiring a certain period until a new steady state is reached. The impact is therefore measured in the difference between the initial state and the new steady state (van der Voet, 2001), with implications on the development of characterization factors. Other gaps to be solved are defining baseline scenarios and the data resolution (Hellweg & Milá i Canals, 2014). Concerning point (i), LU impact indicators are being developed reflecting effects of LU on several ecosystem quality criteria (Antón et al., 2007; de Baan et al., 2013; Koellner & Scholz, 2008; Lindeijer, 2000; Scholz, 2007). Although impact assessment methods such as Ecoindicator99 and ReCiPe consider LU impact in terms of land occupation, ongoing efforts aim to link this information with effects on the ecosystem in terms of quality or function (de Baan et al., 2013). The main challenges ahead focus on the establishment of guidelines with characterization factors for LU impacts, as well as testing the feasibility of existing indicators (Finkbeiner et al., 2014). The focus of this thesis will be on LUC emissions (point (ii)) and what are the challenges in including them in emission profiling with LCA. When included in LCA studies, LUC emissions are assumed to occur in the moment of LUC, whereas LUC can have prolonged effects. The most obvious challenge is, therefore, the estimation of LUC emissions throughout its whole time span. It is possible to resort to default values and national emission inventories to estimate dLUC (IPCC, 2006). However, default values are limited in the number of conversions and the spatial resolution. Alternatively, dLUC can be gauged with repeated field measurements over time or by modelling plant and soil C dynamics. Either alternative represents an increase in effort and methodological intricacy of the LCI. iLUC emissions have an additional tier of complexity: available models are of difficult access to the LCA

7

Introduction

practitioner due to paywalls, low transparency or data-intensity. Moreover, indirect market effects are only foreseen in consequential LCA and are inconsistent with the attributional approach (Finkbeiner et al., 2014).

1.3.2.2.

BIOGENIC CARBON AND DELAYED EMISSIONS

As explained above, the basic premise of bioenergy is that substituting fossil carbon with biogenic carbon can mitigate climate change, because “biogenic carbon is carbon dioxide stored in or released from biomass” (Finkbeiner et al., 2014), and all that is released has been and will be eventually sequestered, and vice versa. This deems the biomass production and use cycle carbon neutral. Although there is no common practice in how to include biogenic carbon in LCA, several authors suggest that it should be separated into individual flows throughout the life cycle or into time steps (DeCicco, 2012; Finkbeiner et al., 2014). This would increase transparency and avoid misestimating emissions due to carbon fluxes lost into by-products, waste streams or non-CO2 GHGs or due to temporal cut-offs. Temporal cut-offs are implied by impact-assessment indicators that confine the measurable impact of an emission to a given time range. The GWP, for instance, confines the impact of GHGs to cut-offs in the radiative forcing of GHGs of 20, 100 or 500 years, called time horizons. Time cut-offs are of particular importance on what concerns emissions that occur very late in the life cycle of a product, such biogenic carbon sequestered in forest biomass. As reviewed by Finkbeiner and colleagues (2014), well-accepted guidelines have been selfrevising in this matter. While PAS2050:2008 initially opted for discounting, which gives a lower weight to late emissions with a cut-off at 100 years, it later revised that option and ISO14040:2006 foresees no cut-offs and the guidelines of the World Resource Institute do not allow discounting.

1.3.3. TIME AND SPACE ISSUES IN LCA The issues with the LUC emissions and C fluxes described in the previous section have two things in common: temporal and spatial specificities that LCA does not comport. Because LU and LUC emissions occur and differ in function of location and time, the abstraction LCA inherently makes of time and space is in direct conflict with the reality. In fact, LCA operates

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in steady-state conditions and makes no spatial considerations. How these issues connect with the integration of LU and LUC emissions in LCA is summarized in figure 1.2. If Geographic Information Systems (GIS) are used to aid the estimation of LUC emissions, LCA will provide new insight and new potential on the relative importance and spatial variability of emission sources. Addressing temporal issues implies recognizing the impact of C storage and release cycles between different C sinks. We will elaborate on this in detail in the following paragraphs.

Figure 1.2 – Measuring and expressing LUC emissions in function of time and area with the aid of GIS and temporally dynamic LCA methods to use LCA to evaluate whether or not biofuels lead to GHG emission reductions. The arrows illustrate the pathways to integrate LUC emissions of biofuels in LCA alongside the opening up of LCA to temporal and spatial attributes of LU and LUC related impacts. The grey are corresponds to the issues dealt with in this thesis.

There is a difference, for a long time unseen by LCA, between emitting region and affected region, which can be discriminated with spatial data sets containing local receptor parameters in the impact assessment step. This would recognize that “Emissions generated by a product’s life cycle occur at many locations, enter multiple media (air, water, land), and cause impacts in relation to local environmental sensitivities” (Reap et al., 2008). GIS can map emitting and receiving environments and aid LCA regionalization, thus approximating estimated to actual

9

Introduction

impacts (Hellweg & Milá i Canals, 2014). This concurrently enables location-specific characterization factors for existing indicators and geographically resolves LU impacts in LCA (Geyer et al., 2010; Mutel et al., 2011; Núñez et al., 2010). Most of these new indicators and issues go over spatially distributed and site-related impacts, such as biodiversity, water and productivity. However, climate change, which is the impact of LUC emissions, is felt at the global scale. This unified receiving environment implies that spatial rendering of LCIA is not important for impact resolution. Nonetheless, GHG emissions can have multiple sources with emitting patterns with their own spatial variability within one production system alone. Acknowledging this spatial dimension enables the spatial allocation of the different stages of a production system, including LU and LUC-related processes. This can be particularly useful on what concerns emissions from LUC and emissions of biofuel production systems, since both are related with the agricultural phase and its diverse ecosystem setting and cropping systems. The fact that agricultural stage accounts for 80% of GHG emissions of, for instance, vegetable oils (Bessou et al., 2011) only serves to illustrate the pertinence of bridging this knowledge and practice gap. Linking geoinformation with LCIs also allows for the identification of impact hotspots in the supply chain that may be relevant to other, non land-based systems (Sedlbauer et al., 2007; Steinberger et al., 2009). In addition, it opens up new applications for LCA beyond product profiling, such as LCA-based spatially explicit supply chain optimization (De Meyer et al., 2013a; You & Wang, 2011). Besides being distributed in space, impact and sources of impact are also distributed in time. The moment and the rate of release of a pollutant may have implications on its impact due to different sensitivities of the receiving environment in function of time. These sensitivities can owe to the characteristics of the environment itself, or to rebound effects and tipping points which interfere with environmental responses. Without time dynamics “changes in pollution profiles as well as ecosystem responses are averaged, and impacts with sufficiently long delays may even be ignored” (Reap et al., 2008). Indeed, another temporal limitation of LCA is the use of time horizons, which limit the analytical span during which an impact is considered to occur and beyond which the impact is not captured. The alternative would be infinite analytical times, encompassing the entire time span of impact, but diminishing the importance of short-term impacts (Reap et al., 2008). The temporal dynamics of disturbances in cycles of release and capture of carbon by the soil and biomass and its influence on global warming are of particular concern to bioenergy. Current GWP computing methods actualize emissions of different gases throughout the

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Chapter 1

production chain to one single pulse in reference to the atmospheric residence time of CO 2 within a time horizon. This simultaneously implies the two main limitations imposed by lack of time consideration in LCA explained before. The GWP distorts emission profiles of products which involve temporary carbon storage, such as biofuels from perennial cultivation (Brandão et al., 2013). Several approaches have been proposed to solve this issue, ranging from remodelling the GWP indicator (Kendall, 2012; Levasseur et al., 2010) to economic discounting of emissions (O'Hare et al., 2009) and the use of alternative metrics (Edwards & Trancik, 2014; Peters et al., 2011).

1.4. ABOUT THIS THESIS

1.4.1. CONTEXT AND STUDY AREA In this thesis we operate on Jatropha-based bioenergy production and use. We start by a having a global look on Jatropha-bioenergy systems, and then focus on case study of Jatropha initiatives and supply chains in Mali. The latter was characterized through a field campaign under the auspices of the Jatrophability Mali project (ERA ARD EU FP6 (Achten et al., 2012)). This campaign gathered data on the production system and the LUC impact of Jatropha bioenergy projects (figure 1.3) in Jatropha initiatives in the towns of Koulikoro and Garalo. Jatropha curcas L. (henceforth called Jatropha) is an oil-yielding tree that has existed in pantropical regions and was exploited by soap makers and traditional medicine practitioners for centuries (Heller, 1996). From its seed, oil can extracted to be used directly in engines or electricity generators or transesterified into biodiesel, when it can also be used in automobile engines. In the first half of the 2000’s, Jatropha enjoyed a hype supported by wrongful claims of high productivity, hardiness and no competition with food production. Jatropha was presented as an affordable and accessible technology to substitute fossil fuels, mitigating climate change with the aid of C sequestration in plantations that would thrive in marginal lands (Fairless, 2007; Heller, 1996).

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Introduction

Figure 1.3 – Field scenes of the Jatrophability Mali project. A - interviews and questionnaires for data collection; B - extraction of soil rings for laboratory analysis; C - destructive measurements of Jatropha plant parts; D - Jatropha field in Koulikoro, Mali. Image credits to Jeroen Degerickx, Leen Vervoort, Iria Soto and Pieter Moonen.

Although many investments on Jatropha started during this period, there was very little scientific knowledge on Jatropha production and use to support them. A quick consult of the Web of KnowledgeTM (Thomson Reuters, USA) reveals that 83% of all indexed articles on Jatropha were published after the year 2000, of which 97% were published after 2008. The first review paper on Jatropha for bioenergy dates from 2000 and was based on 19 references (Openshaw, 2000) while the following one dates from 2008 and cites 132 sources (Achten et al., 2008). In 2008, there was 9×105 ha of land cultivated with Jatropha in the world (GEXSI, 2008). At that time, it predicted that by 2015 there would be 15×106 ha of Jatropha plantations (GEXSI, 2008). These figures were probably never met in the face of the frustration of investors and farmers when large commercial plantations started to fail for lack of agronomical knowledge, yields falling short from expectations and the instability of oil prices and of the energy policies of industrialized countries, which were the target markets of Jatropha’s largest ventures. Field surveys in 2011 of 111 Jatropha projects found out that the majority (61%) of

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Chapter 1

plantations started between 2007-2009 had been abandoned when the trees had barely reached their mature age (Wahl et al., 2012). Nowadays, the Jatropha scene is in the midst of mixed signals, from reports of bust to news of new significant investments and continued efforts, reflecting a diversity of experiences and perceptions on this crop (Lane, 2014; Maltitz et al., 2014; Romijn & Caniëls, 2011; Sanderson, 2009; Singh et al., 2014; Skutch et al., 2012). The fossil fuel substitution and climate change mitigation motive has been in the meantime confronted with emissions from land use change (Achten et al., 2013; Achten & Verchot, 2011; Lapola et al., 2010). Currently, there are no reliable statistics on Jatropha’s global seed production or cultivated area (figure 1.4), nor a complete overview of existing Jatropha initiatives. The most comprehensive existing data sets, to our knowledge, are (i) the Land Matrix Database, reporting on large trans-national land deals, (ii) the project sample collected in the survey of Wahl et al. (2012) and (iii) the participatory databank of Jatropha growers patent in Jatropha Book (Jatrophabook, 2015; LandMatrix.org, 2015; Wahl et al., 2012). The chart in figure 1.4 was compiled from these sources and shows, per country with available data, representing Jatropha cultivated areas (a range is shown when different areas are reported for the same country). Regardless of the real situation, the latest market predictions of the OECD and FAO (2011) foresaw an increase of 71% in the global Jatropha biofuel production in the next 5 years. It is likely that this increase will be supported by Asian countries, such as Indonesia and the Philippines which tend to concentrate the largest area of Jatropha (GEXSI, 2008; GreenOdin, 2014; Jatrophabook, 2015; Wahl et al., 2012). Available data, however suggests that Africa currently holds the highest number of Jatropha projects in all continents, albeit these occupying smaller areas (Jatrophabook, 2015; Wahl et al., 2012). In West Africa, Jatropha is mostly present in projects meant for improving livelihoods by providing an energy carrier that will allow mechanisation and increase incomes (Diop et al., 2013; Wahl et al., 2012). Despite the hardships that arrested many commercial Jatropha plantations, such rural development initiatives endure on a pro-underserved standpoint: rather than profit, Jatropha is expected to bring a decentralized fuel source, jobs and empowerment to the reached population. In addition, commercial ventures are sensitive to the prices of fossil fuels and to renewable energy policies in Europe. With fluctuating fossil fuel prices and Europe’s revision of its blending directives, the window for exporting Jatropha shrunk. On the other hand, small rural development initiatives prevail on the fact that fossil-based energy was

13

Introduction

mostly inaccessible to population, technologically and/or financially. Even so, these small supply chains are often fettered by low productivity and unreliable downstream processing (Diop et al., 2013).

Figure 1.4 – Range of total areas planted with Jatropha (Jatrophabook, 2015; LandMatrix.org, 2015; Wahl et al., 2012). Mali is signed in a black lined box. 14

Chapter 1

Jatropha is a highlight of Mali’s National Biofuel Development Strategy dating from 2009, which aimed to promote rural development and decreasing dependence from fossil fuels (Coulibaly & Bonfigloli, 2012). Mali holds one of the lowest primary energy demands in the World: in 2011, 1.2 GJ capita-1 versus an African average of 16.8 GJ capita-1 (EIA, 2015). Moreover, it is overwhelming dependent on imported fossil fuels (EIA, 2015; UNSD, 2014). There, the majority of the population, who lives in rural settings, depends largely on firewood and charcoal for energy and has no access to the electricity grid. In 2011, there were between 4500 and 8000 ha of Jatropha plantations in Mali (Favretto et al., 2012; Wahl et al., 2012). The land deals currently registered as active in the Land Matrix Database point towards 11000 ha, which is 2% of the global land area dealt for and actively growing Jatropha (LandMatrix.org, 2015). Due to the lack of reliable statistics on Jatropha cultivation, it is unclear if the difference between 2011 and now consists of a growth or a misestimate in either source.

1.4.2. AIM AND OBJECTIVES This thesis delves into the issues of time and space in the emission patterns of the LCA of biofuels. It aims at estimating and integrating LUC emissions in LCA of bioenergy, gauging the importance of time considerations in GWP and the potential of spatially explicit life cycle assessments. Hypothesis 1: The life cycle and land use change circumstances of Jatropha plantations are determinant to whether or not Jatropha-based energy has lower emissions than fossil fuel-based energy. We start from the hypothesis that the life cycle and the LUC-related GHG emissions of Jatropha-based bioenergy can determine whether or not providing energy from Jatropha-based biofuels has lower GWP relative to providing energy from fossil fuels. To test this hypothesis we must first quantify LUC emissions, which are largely unknown, and the life cycle GWP of answering the following questions: A.

What is the magnitude of LCA and dLUC emissions of Jatropha-based bioenergy in the case study area?

15

Introduction

B.

How do these emissions compare with the emissions of a same service provided by fossil fuels?

Once the full emission profile of the Jatropha project is known, its time and space issues can be investigated through the second hypothesis. Hypothesis 2: Temporal and spatially explicit LCIs have the potential to determine the emission saving feasibility of Jatropha projects. Our second hypothesis is that treating this comprehensive emission inventory in a temporal and spatially specific manner will provide new insights on the GWP of the system. The objectives linked to this hypothesis are i) the development a time-step GHG emission inventory and its treatment with alternative, time-mindful GWP indicators; and ii) the development of a geo-referenced GHG emission inventory to explore novel, LCA-based applications. We then answer the following questions: C.

Are existing time-resolved GWPs relevant to perennial bioenergy systems?

D.

What is the effect of minding both time and LUC emissions in GWP calculations?

E.

What is the added value of spatially explicit life cycle assessments in the case of land based systems?

1.4.3. OUTLINE In the second chapter we set the scene with a generic LCA of Jatropha-based biodiesel production. This LCA outlines the production system practiced globally and estimates average expected environmental impact. In Chapters 3 and 4 we zoom into Mali with sitespecific LCA of Jatropha-based electrification, followed by the quantification of LUC emissions. Next, in Chapter 5, we turn these data in an LCI in time steps and test timeresolved GWPs versus the classical static approach. Once we have tested the sensitivity of GWP to emission timing in this case study, we can step outside the LCA box and mix it with other disciplines. We see that in Chapter 6, where we deal with space issues with an exercise

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Chapter 1

of LCA-based spatially explicit supply chain optimization (figure 1.5). This chapter has a broader geographical scope, encompassing the whole Southern region of Mali.

Figure 1.5 – Logical relationships between thesis chapters and their correspondence to the research hypotheses.

17

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Chapter 2

2. GENERIC LIFE CYCLE ASSESSMENT OF THE JATROPHA BIODIESEL SYSTEM Adapted from Almeida J*, Achten WMJ, Duarte MP, Mendes B, Muys B 2011. Benchmarking the environmental performance of the Jatropha biodiesel system through a generic life cycle assessment. Environmental Science and Technology, 45 (12), 5447-5453.*Co-designed study, executed the study and co-wrote the article.

ABSTRACT In addition to available country or site-specific life cycle studies on Jatropha biodiesel we present a generic, location-independent life cycle assessment and provide a general but indepth analysis of the environmental performance of Jatropha biodiesel for transportation. Additionally, we assess the influence of changes in by-product use and production chain. In our assessments, we went beyond the impact on energy requirement and global warming by including impacts on ozone layer and terrestrial acidification and eutrophication. The basic Jatropha biodiesel system consumes eight times less non-renewable energy than conventional diesel and reduces greenhouse gas emissions by 51%. This result coincides with the lower limit of the range of reduction percentages available in literature for this system and for other liquid biofuels. The impact on the ozone layer is also lower than that provoked by fossil diesel, although eutrophication and acidification increase eight times. This study investigates the general impact trends of the Jatropha system, although not considering land-use change. The results are useful as a benchmark against which other biodiesel systems can be evaluated, to calculate repayment times for land-use change induced carbon loss or as guideline with default values for assessing the environmental performance of specific variants of the system.

2.1. INTRODUCTION Biofuels have been hyped as renewable liquid energy sources that reduce nations’ dependency on fossil fuels and as a climate change mitigation option (Verrastro & Ladislaw, 2007). Investigating if biofuels are a good option to reduce greenhouse gas (GHG) emissions

19

Generic LCA of the Jatropha biodiesel system

compared to the fossil fuel system relies on the quantification of such reductions. This needs in-depth analysis and depends on many aspects of the biofuel production and use process (“well-to-wheel”). Life cycle assessment (LCA) is considered as one of the best available tools for such analysis (Cherubini et al., 2009). Among the available biofuel crops, Jatropha curcas L. (further called Jatropha) received much attention as a non-food feedstock (Gübitz et al., 1999). Jatropha produces toxic oil and is a perennial with a clear drought avoidance strategy and relatively higher water use efficiency (Maes et al., 2009b). As such, Jatropha would not compete with food production or with the maintenance of forest ecosystem services (e.g., biomass carbon stocks) (Makkar & Becker, 2009), therefore minimizing biofuels’ pressure on land (Inderwildi & King, 2009). However, due to the lack of scientific knowledge, industrial Jatropha production and expansion has been risky (Achten et al., 2010b; Achten et al., 2007). Currently, science is catching up with the made investments through acquired insights in aspects such as optimal agro-practices and biophysical limits (Achten et al., 2010c; Behera et al., 2010), crop behaviour (e.g. Achten et al. (2010c), Ghezehei et al., (2009), Lapola et al. (2009), Li et al. (2010), Maes et al. (2009c)) and by-product use (e.g. El Diwani et al. (2009), Sharma & Pandey (2009)). Several location-specific LCA studies on Jatropha biodiesel have been published recently, pointing to a favourable GHG and energy balance (Achten et al., 2010a; Gmünder et al., 2010; Lam et al., 2009; Ndong et al., 2009; Ou et al., 2009; Prueksakorn & Gheewala, 2008; Prueksakorn et al., 2010; Reinhardt et al., 2007; Yan et al., 2010). Most of these studies only consider energy and/or GHG balance, while other impact categories are also important to get insight into the overall environmental performance of a biofuel system (Cherubini et al., 2009). Further aspects such as water footprint and land-use impacts are still under discussion (Jongschaap et al., 2009; Li et al., 2010; Maes et al., 2009a; Makkar & Becker, 2009). In this paper we present a generic, location-independent LCA providing a general and profound insight into the environmental performance of the production and use of Jatropha biodiesel. With this study we aim to: (i)

broaden the scope of available information on the system’s performance by assessing environmental impacts beyond the energy and GHG balance;

(ii)

provide benchmark values of these impacts useful in the evaluation of specific Jatropha projects;

20

Chapter 2

(iii)

assess the influence of changes in yield, by-product use and production chain setup on the environmental performance;

(iv)

identify performance improvement options.

2.2. MATERIALS AND METHODS The LCA exercise was executed in accordance to the International Organization for Standardization guidelines ISO 14040:2006.

2.2.1. GOAL, SCOPE, AND S YSTEM B OUNDARIES The Jatropha system was analyzed from “well-to-wheel”, from Jatropha cultivation to biodiesel consumption (figure 2.1). Operations included field preparation and nursery, cultivation, harvesting, oil extraction, transesterification, end use and transportation of inputs and outputs. Construction and maintenance of infrastructure and equipment were accounted at all stages. Following the typical setup of most current Jatropha activities, a decentralized production chain with local consumption of biodiesel was modeled as base scenario (figure 2.1). The environmental performance of the Jatropha biodiesel system was evaluated by quantifying its non-renewable energy requirement (NRER) [MJ], global warming potential (GWP) [kg CO2-eq], ozone layer depletion (OLD) [kg CFC11-eq] and terrestrial eutrophication and acidification (TEA) [kg SO2-eq]. The results were reported per mega joule (MJ) of biodiesel delivered by the system. This measure is the functional unit (FU). The performance of the Jatropha system was compared to the performance of fossil diesel, which is the reference system. The reference system includes crude oil extraction, refining, regional distribution and storage, end use, and intermediate transportation (figure 2.1). In all scenarios the allocation of environmental burdens to by-product was avoided by expanding the system boundaries (Box 2.1) (Jensen et al., 1997; Suh et al., 2010). Byproducts were included in the Jatropha system and their functional equivalents in the reference system. Hence, the system was debited with the environmental burdens of the production of the functionally equivalent products in the reference system (figure 2.1). 21

Generic LCA of the Jatropha biodiesel system

Figure 2.1 – System boundaries of the Jatropha system, the reference system and the system boundary expansion. *: by-products refer to Scenario A.

BOX 2.1 What is allocation in LCA? In case a production system produces by-products next to its main product, the materials and energy flows as well as associated

environmental

releases

should be partitioned over the different products according to clearly stated allocation procedures (Jensen et al., 1997). The ISO standards allow pro-ratio allocation by mass ratio, energy content ratio and economic value ratio. However, ISO recommends avoiding allocation by expanding the system boundaries or doing substitution. In case the system’s by-products are used for a certain function and, as such, avoid the conventional provision of this function, the environmental load of this avoided production process abates the environmental burden of the system under research (Suh et al., 2010). Hence, the functional equivalent of each by product was identified, its impact measured and subtracted to the impact of the system.

22

Chapter 2

2.2.1.1.

SCENARIOS

The base scenario represents the current situation of Jatropha cultivation, as based on the questionnaires and expert interviews (see section 2.2.2.1). Other scenarios look at (A) alternative by-product usage (proposed as a potential life cycle system improvement (Gmünder et al., 2010)) and (B) product chain centralization (a probable way forward (GEXSI, 2008)): 

Scenario A. In this scenario anaerobic digestion of the seed cake is added to the system. The biogas and effluent produced during this step avoid natural gas and inorganic fertilizer production in the reference system.



Scenario B. In this scenario the locally extracted oil is transported to a centralized transesterification unit, which requires an extra transportation step between extraction and transesterification.

2.2.2. LIFE CYCLE INVENTORY ANALYSIS

2.2.2.1.

DATA COLLECTION

The primary data provided insight both into the actual practices and hand input (materials, water, and energy) and output (product and by-product quantities). The data were gathered from (i) questionnaires submitted to Jatropha entrepreneurs with replies from Mexico, Brazil, and Tanzania with a response rate of 43%; (ii) direct observations and consultation with experts in 25 Jatropha sites spread throughout eight Indian states (100% response rate); (iii) literature studies reporting from India and Thailand (Prueksakorn & Gheewala, 2008; Prueksakorn et al., 2010; Reinhardt et al., 2008; Reinhardt et al., 2007). The geographic distribution of these data sources is depicted in figure 2.2. Transport occurs at nearly all stages of the product system and is an influential contribution in a LCA (Spielmann & Scholz, 2005). Jatropha plantations were listed from visited sites and information divulged in online literature or conveyed by the questionnaires and personal communications with entrepreneurs (table A1 and figure 2.2.). Distances were calculated

23

Generic LCA of the Jatropha biodiesel system

between these and input production locations with a protocol that is described in the Annex to this chapter. Background data were acquired from life cycle inventory databases ecoinvent v.2.1 (Swiss Centre for Life Cycle Inventories, Switzerland), BUWAL 250 (Swiss Department of the Environment, Transport, Energy and Communications, Switzerland) and ETH-ESU 96 (Swiss Federal Institute of Technology Zurich, Switzerland). Descriptive statistics (mean and standard deviation) were calculated for the inventory data. The resulting means used in the LCA are given in table 2.1.

Figure 2.2 - Distribution of data sources used in this LCA. The dots indicate plantations used to calculate transportation distances (grey shading emphasizes the countries where they are located). Questionnaire respondents and expert consultations occur in the countries shaded in orange. Countries in brown refer to literature sources (India is both brown and orange). References to these locations are in Annex 1.

2.2.2.2.

PRODUCTION SYSTEM

The production system is based on a rotation period of 20 years with a plant density of 2500 plants ha-1 (Achten et al., 2010c; Tewari, 2007) yielding an average 4.3 t ha -1 of dry seeds (direct observations and literature (Prueksakorn & Gheewala, 2008; Reinhardt et al., 2007;

24

Chapter 2

Shukla, 2006; Tobin, 2005)). A sensitivity analysis was executed to acquire insight into the effect of the yield on the LCA results in each impact category (see 2.4.4). Table 2.1 - Key inputs and outputs of the Jatropha system presented per 1 MJ of produced biodiesel.

Input

Amount

Unit

N

2.8

g

P

0.89

g

K

1.86

g

N

6.63

g

P

2.15

g

K

4.79

g

Irrigated water

0.56

m3

Methanol

1.36

g

Sodium hydroxide

0.22

g

Electricity

26.08

Wh

Road freight

1.03

km

Rail freight

0.6

km

Sea freight

8.17

km

Output

Amount

Unit

Biodiesel

25.6

g

N2O

0.094

g

NO3

2.82

g

NH3

0.94

g

NOx

54.69

mg

PM

3.42

mg

Seed cake

133.31

g

Glycerine

4.55

g

Fertilization in establishment

Fertilization in cultivation

Transport

Fertilizer field emissions

Biodiesel end use emissions

Seedlings are grown in polybags in nurseries. (Tewari, 2007) Prior to transplantation, the field is prepared with a tractor working 6 h ha-1 (questionnaires). The use of fertilizer during plantation establishment and rotation period is based on direct observations and literature (Prueksakorn & Gheewala, 2008; Reinhardt et al., 2007) (table 2.1). NO3, NH3 and N2O field 25

Generic LCA of the Jatropha biodiesel system

emissions to air and water from N application were included according to IPCC guidelines (IPCC, 2006). Pests are prevented by using pyrethroid insecticides (questionnaires). Oil is extracted by cold pressing with an electric screw press (Reinhardt et al., 2007) with an extraction rate of 16.32 kg oil per 100 kg dry seed (Achten et al., 2008). Transesterification converts 97% of the oil mass into biodiesel (Achten et al., 2008). Reagents and catalyst inputs follow a 0.2 methanol: oil and a 0.01 NaOH:oil mass ratio (Achten et al., 2008), with methanol having a recovery ratio of 0.739 (own data) (table 2.1). The exhaust emissions of biodiesel combustion correspond to emission profile of a Toyota Hilux pick-up truck (Toyota, 2009), adjusted for biodiesel characteristics (Demirbas & Balat, 2006).

2.2.2.3.

ASSUMPTIONS

Based on on-field experience, questionnaires and literature review we assumed manual weeding, pruning and harvesting. Information about Jatropha’s irrigation practice is scarce and imprecise. It is known that optimal growth levels occur in regions with 1500 mm rainfall (Trabucco et al., 2010). We assumed that the seed cake is applied as soil amendment, but not in the Jatropha fields of its origin, as indicated by questionnaire responses. It was further assumed that the supplied energy follows the energy mix prevalent in the regions where Jatropha is produced: coal-generated electricity and fossil diesel for transportation (IEA, 2008).

2.2.3. IMPACT ASSESSMENT Environmental impact assessment was executed with SimaPro® LCA software (PRé, The Netherlands) (Box 1.1) using IMPACT2002+ method. For each impact the share of the different contributing production phases (Jatropha cultivation, including nursery and field preparation, oil extraction, biogas production (in scenario A only), oil transesterification and end use) are indicated. The avoided production burdens of products analogue to Jatropha byproduct are credited to the production phase of the by-product. Transportation is part of the burden of the hauled commodity.

26

Chapter 2

2.2.4. SENSITIVITY ANALYSIS In general, yield has an important role in the overall environmental impact of a land-based system. Yield showed great variability among literature and direct observations. Keeping all yield-independent variables constant, the LCA was executed for the base scenario for seven yield values (0.5, 1; 1.5; 2, 2.5, 3.5, and 5 t ha -1) based on the global yield classification used by (Trabucco et al., 2010).

2.3. RESULTS AND DISCUSSION

Figure 2.3 - Life cycle impacts of Jatropha biodiesel (circle: total impact) compared to the reference system (dash).The bar stacks represent the contribution of different production phases. Impact categories: non-renewable energy requirement (NRER), global warming potential (GWP), ozone layer depletion (OLD) and terrestrial acidification and eutrophication (TAE).

27

Generic LCA of the Jatropha biodiesel system

2.3.1. NON -RENEWABLE ENERGY REQUIREMENT The results show a favourable performance of Jatropha-based biodiesel system regarding the fossil alternative. The base system (0.48 MJ FU-1) consumes nearly 8 times less per FU than the reference system (3.88 MJ FU-1) (reduction of 88%) (figure 2.3). Cultivation consumes much more energy than the remaining phases (68% of total). This contradiction with literature (Ndong et al., 2009; Ou et al., 2009; Prueksakorn & Gheewala, 2008; Prueksakorn et al., 2010) is a combined effect of different input quantities (namely fertilizers and methanol) and different allocation options. Scenario B consumes ca. 5 times less than the reference system (reduction of 78%). This is a consequence of the additional expenditure in hauling oil to centralized transesterification units. Biogas production from seed cake (scenario A) reduces NRER by 105%. As the extraction phase is not credited for the seed cake as fertilizer anymore, its contribution to the NRER increases compared to the base scenario. However, the production of biogas and its effluent (credited to ‘biogas production’ in figure 2.3) offsets this increase.

2.3.2. GLOBAL W ARMING P OTENTIAL The base system reduces GHG emissions by 51% compared to the reference system (0.05 kg CO2 eq FU-1 versus 0.1 kg CO2 eq FU-1). This result represents the lower part of the range of reduction percentages available in literature (49-72%) (Lam et al., 2009). In contrast to what was found with NRER, the base and A scenarios show about the same reduction in terms of GWP (54%). If seed cake is used to replace an energy source more pollutant than natural gas (e.g. coal), the GHG savings are higher. Scenario B shows a lower GWP reduction (30.5%) than the base scenario, owing to the extra transportation step. The cultivation phase is the main contributor in all scenarios (76% in the base system, 55% and 74% in scenarios A and B). The fertilizer chain (production, transport, and field emissions from application) represents the largest share in this contribution: 91%. This observation coincides with previous Jatropha biodiesel LCA studies (Achten et al., 2010a; Lam et al., 2009; Ndong et al., 2009; Reinhardt et al., 2007). End use contributes with 4%. The credit attributed to the transesterification step for the avoidance of glycerine production reduces the overall GWP impact of the system in this step.

28

Chapter 2

2.3.3. OZONE LAYER DEPLETION Opting for Jatropha biodiesel instead of fossil diesel has a clear advantage in terms of ozone layer depletion (figure 2.3). The base system emits 8.8×10-9 kg CFC11 eq FU-1, in contrast to an emission of 2.1×10-8 kg CFC11 eq FU-1 in the reference system. The main contributor to this result is the avoidance of glycerine production, which overcompensates the impact of the remaining phases. While scenario A has a similar result, the transport burden in scenario B slightly increases the emissions of ozone-depleting gases compared to the base scenario.

2.3.4. TERRESTRIAL ACIDIFICATION AND EUTROPHICATION Different from what we had found for the previous impact categories, TAE potential is greater in the biodiesel system than in the reference system. The base scenario scores 1.1×10-2 kg SO2 eq FU-1 versus 1.3×10-3 kg SO2 eq FU-1 in the reference system, which is an 8 times increase (figure 2.3). Reinhardt et al. (2007) estimated an increase of 4.5 times in acidification and 3.5 times increase in eutrophication impact FU-1. The discrepancy is owed to lower fertilization levels. The cultivation phase is the biggest contributor to this impact (92%, 86%, and 87% in the base, A and B scenarios respectively) owing mainly to NH3 and NO3 field emissions from fertilizer application. Since this phase is common to the base system and the alternative scenarios, this impact category shows the same impact trend in all scenarios. Scenario B, which bears additional SO2 eq emissions due to the transport of oil to centralized transesterification plants, shows only a slightly increased impact. The few credits arise from seed cake use, but are negligible.

2.3.5. SENSITIVITY ANALYSIS NRER and GWP show little variation above yields of 1.5 t ha-1 but are high below that yield level (figure 2.4). Figure 2.4 shows that with a yield of 0.5 t ha -1 the Jatropha biodiesel system outperforms the reference system in terms of NRER. In fact, the reference system shows lower GWP if Jatropha yields are lower than 1 t ha-1.

29

Generic LCA of the Jatropha biodiesel system

Figure 2.4 – Sensitivity analysis of NRER (A) and GWP (B) regarding yield variation by 0.5, 1; 1.5; 2, 2.5, 3.5, and 5 t ha-1. “Base” is the reference yield of 4.3 t ha-1 considered in the base system.

30

Chapter 2

2.3.1. LIFE CYCLE INTERPRETATION Interpreting LCA results and gaining insight in the overall environmental performance of a production system is not straightforward. It depends on the evaluation of the relative importance of each environmental impact category and their trade-offs. Although several impact assessment methodologies are available to perform such evaluations, we opted into work with a methodology which enables the presentation and discussion of impacts and tradeoffs in their basic units. The LCA outcome presented in this paper suggests that Jatropha biodiesel system is a promising alternative transportation fuel system to fossil diesel. The NRER, GWP, and OLD are considerably lower than the reference system. One important contribution to this positive balance arises from seed cake use. The extent to which it does so depends on its finality: energy generation is more favourable than artificial fertilizer displacement (confirming Reinhardt et al. (2007)). The trade-offs for these benefits are higher eutrophication and acidification. This increase is a general trend in a shift from a fossil to biodiesel system, and is mainly triggered by nitrogen related burdens during the agricultural phase (Kim & Dale, 2005). The results indicate that the Jatropha biodiesel system has roughly half of the GWP of the equivalent fossil system. It should be noted that these figures do not include land-use change impact, hence omitting a potential large impact through carbon stock loss (Brandão et al., 2011; Lal, 2004a), as exemplified by Bailis & Baka (2010) and Lapola et al. (2010). Compared to other biofuels, Jatropha’s GWP reduction rate falls among the lower values available in literature. Studies on palm oil biodiesel point to GWP reductions from 38% to 79.5% (Achten et al., 2010d; Pleanjai et al., 2009; Wicke et al., 2008; Yee et al., 2009; Zah et al., 2007) and other fuels such as sunflower, soy, and rapeseed biodiesel (40-65% reductions) (Cherubini et al., 2009; Zah et al., 2007), switchgrass, and corn stover ethanol (57% and 65%) (Spatari et al., 2005) and soybean fuels (57-74%) (Huo et al., 2008) but these crops often grow in more humid climates and might provoke a bigger carbon debt due to land-use change. The reliability of the results of a LCA depends largely on scope and inventory attributes. Our scope and inventory involved several types of data sources which increased data completeness. However, great variability leads to high uncertainty. This is not unexpected owing to the system’s immatureness and the nature of this LCA. Still, it does not compromise 31

Generic LCA of the Jatropha biodiesel system

result fitness as corroborated by the sensitivity analysis, which probed the impacts of variability around the yield. The average seed yield is a crucial factor in the distribution of impact per FU and is quite high in this study (4.3 t ha-1 yr-1). The yields that were accounted for in inventory were very wide ranged, which echoes a scientific knowledge gap (Trabucco et al., 2010). The wide range can be caused by different climatic and abiotic conditions, or different chemical, physical or management inputs, factors which are inherently variable as well. In addition, some of the yield data were estimates that may be an exaggeration of real potentials. The sensitivity analysis shows us that a yield of 0.5 t ha-1 yr-1 is minimal necessary to attain improvement in NRER. Improvement of GWP compared to the fossil fuel system only comes above 1 t ha -1 yr-1 . Hence, despite Jatropha’s variable yields (Achten et al., 2008), the performance of its biodiesel relatively to conventional fossil diesel can be expected to be positive in terms of NRER and GWP. The influence of the yield on the other impact categories is negligible and is unlikely to alter the system’s position relative to the reference system. Fertilizers are the main stressors in the environmental performance of the entire system, followed by the remaining inputs of the cultivation phase. Hence, optimized cultivation practices (e.g., soil amendment, pruning, spacing, irrigation) could considerably reduce environmental impacts. System enhancement should also involve seizing by-product maximum potential. However, by-product use is only realistic if it has a place on the market. Further improvement options lie in investing in superior seed lines and in plant breeding programs or choosing to deploy Jatropha activities in areas best suited, which can attain the double goal of increasing yield and minimizing cultivation inputs (Divakara et al., 2010). Still, our results suggest that aiming at yield improvement does not offer much room for impact mitigation. In general, educated management options based on scientifically solid information tend to reduce environmental impacts, besides leading to sound investments.

2.4. CONCLUSION This generic LCA draws a comprehensive environmental profile of the Jatropha biodiesel system according to early production conditions reported by different sources from around the world. Jatropha shows promise of being an energy carrier for transport with lower impact than fossil fuels, with the exception of eutrophication and acidification impacts. It is however clear

32

Chapter 2

that several knowledge gaps are patent at this stage (namely site-specific yield and land use change data) and that there is room for the optimization of the production system. By using averages of both inputs and outputs we came to the generic trend of the environmental (site- and country-independent) performance of the Jatropha system for transportation biodiesel. Impact results show the inherent potential of the Jatropha system. They are useful as benchmark values against which improvements can be measured. Likewise, they could be used as guideline if it is impossible to assess the environmental performance of a specific situation (e.g., in carbon credit application through the UNFCCC Clean Development Mechanism Energy Projects).

33

34

Chapter 3

3. LIFE CYCLE ASSESSMENT OF JATROPHABASED BIOENERGY IN MALI Adapted from Almeida J*, Moonen PC, Soto I, Achten WMJ, Muys B 2014. Effect of farming system and yield in the life cycle assessment of Jatropha-based bioenergy in Mali. Energy for Sustainable Development 23, 258-265. * Designed the study, executed assessment and result analysis and wrote the article.

ABSTRACT Jatropha has been promoted by governments in sub-Saharan Africa as a smallholder energy crop with the promise of additional revenue and energetic self-sufficiency. In this case-study located in Southern Mali we performed a comparative life cycle assessment shedding light on the influence of smallholder participation and yield fluctuations on the global warming potential, fossil resources depletion and energy demand of Jatropha-based rural electrification in comparison to a fossil fuel-based reference. We found that the global warming potential of Jatropha-based electrification can be 13% higher to 20% lower than fossil diesel, depending on the yield. In terms of energy use and fossil fuel depletion, Jatropha is more favourable than fossil-based electricity. In either perspective, the activities related to cultivating and processing seeds from small farmers accentuate the environmental impacts, owing to their low yields. We conclude that outgrower engagement in tending and harvesting Jatropha plants is a key factor for improving the environmental performance of the system.

3.1. INTRODUCTION Despite its low energy demand of only 3% of the global energy production, Africa has a fast growth rate of per capita energy use of 4.1% annually (IEA, 2011), which is more than anywhere else in the world. This growth rate is driven by improved infrastructure and the pursuit of better living standards. In 2010, Mali’s national rate of access to electricity was 27.1%; this figure is as low as 14% in rural areas (versus a global average of 93.7 and 68% respectively) (Coulibaly & Bonfigloli, 2012; IEA, 2011). As the benefits of electrification

35

LCA of Jatropha-based bioenergy in Mali

range from improving health and education to reducing food insecurity and inequality, access to electricity has been considered an important strategy for development (Dasappa, 2011; Lahimer et al., 2013; Mohammed et al., 2013). Biomass is by large the main energy carrier in Sub-Saharan Africa, where rural populations and low income earners rely on the combustion of traditional fuels such as wood and charcoal for cooking and heating (Dasappa, 2011; Mohammed et al., 2013). Given the high bioenergy potential of most sub-Saharan countries, it is claimed that harnessing and structuring the collection, conversion and use of biomass must be part of a greater developmental plan for energy security in the region (Mohammed et al., 2013). It is in the context of improving access to electricity that governments and NGO’s throughout the region have been supporting projects for Jatropha-based off-grid energy solutions (Achten et al., 2007; Eckart & Henshaw, 2012; Favretto, 2013; Garg et al., 2011; Gilbert, 2011). Jatropha curcas L. is a small tree that yields oil-bearing seeds. Once extracted the high quality oil can be used directly or converted into biodiesel, either being suitable for use in engines of automobiles or electrical power generation. It was thought initially that Jatropha would fulfil the promise of empowering communities with self-provision of energy and additional income with very little impact on the environment, owing to its low requirements of fertilizers, pesticides and water. Moreover, its proclivity for marginal soils would ensure little competition with food resources (Fairless, 2007). These early hype claims have been in the meantime rebated by experience (reviewed by Contran et al. (2013)). The lack of improved high yielding varieties (Achten et al., 2009; Contran et al., 2013) has often turned it into a drawback for investors and small farmers (Skutch et al., 2012). Jatropha’s contribution to biodiesel production in developing countries is predicted to be 10% by 2020, a modest contribution hindered by a low-competitive yield capacity in face of crops such as soybean and oil palm (OECD/FAO, 2011). Life cycle assessment is widely used as a sustainability evaluation framework for bioenergy studies. This standardized and well accepted tool accounts for all the inputs and outputs of a product’s life cycle, from raw material acquisition, over processing, distribution and use, till final disposal, to comprehensively profile its environmental impact. Earlier LCAs indicate that Jatropha-based biofuels have a favourable environmental performance relatively to fossil fuels in terms of greenhouse gas (GHG) emissions and energy use (e.g. Achten et al. (2010), Almeida et al. (2011), Gmünder et al. (2010), Ndong et al. (2009)).

36

Chapter 3

Because energy use and efficiency and climate change mitigation are intended by biofuel models, these are also the environmental issues that Jatropha-based bioenergy LCAs mostly tend to. In this study we assess the global warming potential (GWP), cumulative energy demand (CED) and fossil depletion (FD) of generating electricity from Jatropha biodiesel in the region of Koulikoro, in Southern Mali. The centralized conversion facility is sourcing seeds from own fields as well as from a network of outgrowers present in the region. The involvement of outgrowers is an integrated approach to energy provision that results in more resilient systems from the socio-economic perspective (Muys et al., 2014). In this study, we aim to investigate the effect of this mixed supply on the environmental impact. A comparative LCA approach further includes evaluating the systems’ performance for two measured yield levels conditioned by climatic conditions in two consecutive years.

3.2. MATERIALS AND METHODS

3.2.1. GOAL AND SCOPE DEFINITION AND DATA COLLECTION The aim of this LCA exercise is to calculate the cumulative GHG emissions, fossil fuel use and energy use and efficiency of the production of Jatropha biodiesel-based electricity production in the region of Koulikoro (13°N, 7°W), in Southern Mali. The system comprises all phases of the production chain, from nursery to combustion in the electrical generator, and assumes a plantation rotation time of 20 years (figure 3.1). Seed processing and fuel production occur centrally at the facilities of MaliBiocarburant (MBSA) in the town of Koulikoro. The seeds are coming from an outgrower scheme and from fields managed by MBSA. MBSA produces biodiesel to fuel both automotive and stationary engines, according to their needs. The end use phase was modeled with technology available in the factory: combustion of the biodiesel in the factory-owned electrical generator. It is acknowledged, however, that plant oil would be the most reasonable choice to feed a stationary engine (Muys et al., 2014). For this reason, the environmental impact of bio-oil-based electrification is also highlighted in the results. Similar in-engine efficiency and tailpipe emissions for biodiesel and bio-oil were assumed (Soltic et al., 2009).

37

LCA of Jatropha-based bioenergy in Mali

Figure 3.1 – The production system in Koulikoro and its relation to the reference system. The dashed lines represent the system boundaries, the dotted arrows indicate substitution by system boundary expansion, and the grey boxes show the outgrowers’ farming system.

The inputs to the system are fertilizers, pesticides, transesterification reagents, transport of seeds between fields and the press and the transport of equipment for extraction, transesterification and electricity generation before installing in the MBSA facilities. Land use is excluded from the system and, as such, potential GHG emissions resulting of land use and land use change were not taken into account. Likewise, carbon sequestration in the Jatropha biomass and carbon tailpipe emissions is excluded. Data were collected on all production stages through direct observation and questionnaires (see Annex 1) presented to MBSA. Information on the cultivation practices of outgrowers

38

Chapter 3

were retrieved from household interviews (Soto et al., 2013). Inventory gaps left by the questionnaires and interviews were filled with information from literature. Background data to all processes was retrieved from the LCA database ecoinvent ® v.2.2 (ETH and EMPA, Switzerland). Although most data in this database does not have an African context, the background processes used in this LCA (such as chemicals and machinery) is imported to Mali, according to the questionnaires. Processes parameterized with global data were used whenever possible, making the use of ecoinvent® defendable. Only a small set of consultations with the outgrowers resulted in a data set complete enough to enter the life cycle inventory. The sample of outgrowers was determined in two steps. First, among Jatropha growers in the region, 5 were selected for their well maintained Jatropha plantations and their willingness to collaborate in this study. Out of these, 4 supplied data on management practices and yield and were used in this LCA.

3.2.2. FUNCTIONAL UNIT Output related units are commonly used in studies aimed at comparing different ends to a feedstock. This study reports GWP, FD and CED on the basis of the final product. The functional unit is, hence, 1 MJ electrical.

3.2.3. REFERENCE SYSTEM This is an attributional LCA (Box 3.1), as its the objective is not to emulate a scenario of fossil fuel substitution, nor to estimate the GHG emissions, fossil depletion and energy use reduction potential from a baseline of using fossil fuels for the same productive goal (Plevin et al., 2014). Rather, the impacts of the Jatropha electrification system are compared to the functional equivalent of its final product, which it aims to be an alternative to: fossil dieselbased electricity. 1 MJ

electrical

produced from fossil diesel is the benchmark for biodiesel-

based electricity. All operations required to the fulfilment of this functional unit are included in the impact, from extraction of crude fossil oil to its use in generators and to the transport in between operations.

39

LCA of Jatropha-based bioenergy in Mali BOX 3.1 What is the distinction between attributional and consequential LCA? The key differences between attributional and consequential LCA focus in scope and inventory modelling phases. The following table (adapted from Brander et al. (2009)) summarizes them. Attributional

Consequential

Application

Understanding the total emissions directly associated to a life cycle.

Understanding the change in emissions resulting of a purchasing or policy decision that leads to a change in output of a product.

System boundary

Processes and flows directly involved in the life cycle.

Processes and flows directly and indirectly affected by the marginal output of the life cycle.

Allocation

Partition or system boundary expansion.

Substitution.

Data

Average data and no market effects.

Marginal data and market effects.

Time-scale

Single moment, single production level.

According to the time-scale of the change.

Uncertainty

Low (balance-based relationships between flows).

High (economic modelling of flows).

3.2.4. SYSTEM DESCRIPTION AND ALLOCATION

3.2.4.1.

CULTIVATION

The farming practices and the yields vary among outgrowers and the experimental fields of MBSA (table 3.1). Pesticides are applied in the nursery of MBSA, but not during the plantation. The production of the pesticides was accounted for. Fertilizers consist of organic residues, manure and seed cake slurry, applied separately or in composted mixtures during plantation in both farming systems, and in lower amounts in the nursery of MSBA. Since these products are waste streams of various sources, it was considered that energy invested in their production is null. Likewise, their production was considered not to be burdened by emissions. One farmer applied 0.06 t ha-1 urea in his fields, and in this case the production of

40

Chapter 3

that fertilizer was accounted for (table 3.1). Composting occurs in open air and the resulting GHG emissions were included in the GWP (IPCC, 2006). Direct and indirect GHG emissions from N-fertilizer application were included according to IPCC (2006), based on a nitrogen content of 0.74% in manure and 1.22% in rural waste (Kumar & Shivay, 2008; Prueksakorn & Gheewala, 2008; Zublena et al., 1996). Table 3.1 – Plantation set up of the selected outgrowers and the two factory fields.

Fertilizer Producer

Harvested dry seeds Spacing

Application (t ha-1)

(m×m)

Type

2011

2012

(kg ha-1)

(kg ha-1)

Outgrower #1

0

5×2

170

494

Outgrower #2

0

5×2

139

1686

Outgrower #3

0.06

Urea

4×3

422

3155

Outgrower #4

0.9

Manure

4×4

208

2024

MBSA

1.5

Organic waste

2×2

1225

3364

MBSA

1.5

Organic waste

5×2

511

3155

Due to its low precipitation, predicted Jatropha yields in Koulikoro fall in the bottom half range of predicted yields for Mali (Trabucco et al., 2010). Moreover, biomass productivity in this region is highly variable due to erratic rainfall patterns typical of the Sahelian zone (Nygaard et al., 2010). A correct monitoring of Jatropha seed yields is not evident, and trustful yield data are very scarce in the literature (Muys et al., 2014). Our yield assessments are underestimations. Due to the a-synchronic flowering, fruiting and maturation, the harvesting period can last up to 9 months. Such a long period is not compatible with the labour availability of farmers, which tends to increase harvesting efficiency with fruiting density. Hence, the difference in rainfall levels between 2011 and 2012 (figure 3.2) augmented by differences in harvest efforts carved a steep difference in harvest between both years (figure 3.3) (Soto et al., 2013). The (air-dried) seed yield in the factory fields varied between 808 (±504) kg ha-1 in 2011 and 3259 (±148) kg ha-1 in 2012, and among outgrowers between 234 (±128) kg ha-1 and 1840 (±1095) kg ha-1 (figure 3.3). We took these harvests as yield proxies of two yield levels: a high level corresponding to the 2012 observations, and a low level corresponding to the 2011 ones. We used these high and low yields as constant for years 3 to 20 of the 20-year rotation cycle, while the first two years were considered without yield. The life cycle impact assessment (LCIA) was performed for 41

LCA of Jatropha-based bioenergy in Mali

both yield levels. The results were then averaged for each farming system and for one year, keeping the low and high yield levels separated. A normal average of all fields under study is also presented. 300 250

Climatological average - Total 989 2011 - Total 781 2012 - Total 958

mm

200 150 100 50 0 Jan Feb Mar Apr May Jun

Jul

Aug Sep Oct Nov Dec

Figure 3.2 – Monthly precipitation in Koulikoro in 2012 and 2012 (courtesy of IPR-IFRA) compared with the climatological average (source: New_LocClim (FAO, Rome, Italy)). The total annual precipitation is indicated in the legend.

Figure 3.3 - Average yields measured in 2011 (L) and 2012 (H) among outgrowers (O) and factory fields (F). The white bars represent the average yield per farming system and year and the error bars their standard deviations. 42

Chapter 3

3.2.4.2.

BIODIESEL PRODUCTION AND USE

The seeds are harvested manually by the outgrowers and the MSBA workers, and is all used in the factory’s conversion facilities. Seeds are transported from the outgrower’s fields by the farmer and motorized transport for an average distance of 22.5 km was included in the inventory. From 1 kg of seeds, 0.29 kg of oil is extracted. The press operates with a power of 75 kW and a load of 104 kg seeds h-1. Transesterification requires a mix of oil with 3% catalyst, 17% methanol (m/m), of which 25 to 30% is recuperated. 10% of generated fuel loops back into the system to generate electricity for the conversion facilities. The transesterification efficiency was assumed to be 96% (Achten et al., 2008) (table A4). The biodiesel is used at a rate of 0.086 l MJ-1 of generated electricity. Tailpipe emissions were included at a rate of 4.86 g MJ-1 NOx and 4 mg MJ-1 N2O (Almeida et al., 2011; Oberweis & Al-Shemmeri, 2010). The seed cake is meant for biogas production. For the anaerobic digestion, each kg seed cake is mixed with 2 kg of cow manure. We considered that digesting 1 kg of cow manure and 1 kg of seed cake yields 0.05 m3 and 0.39 m3 biogas, respectively (Achten et al., 2008; CBE, 2012; Chandra et al., 2006), physically leaking at a rate of 10% (Flesch et al., 2011). The left-over slurry joins the compost meant for fertilization.

3.2.4.3.

ALLOCATION

Allocation of impacts to by-product allocation is avoided by expanding system boundaries (figure 3.1). Therefore, we assessed the functional equivalents to by-products and determined their amounts. The system is then credited with the avoided production of those functional equivalents. System boundary expansion avoids allocation of impacts to all by-products, namely glycerine from transesterification and biogas and slurry from anaerobic digestion, which replace synthetic crude glycerine, natural gas and an equivalent N fertilizer, respectively. The proxies for equivalence were mass for glycerine, lower heating value for gas and the N contents for fertilizers.

43

LCA of Jatropha-based bioenergy in Mali

3.2.5. IMPACT ASSESSMENT This study reports on the impact of the production chain on climate and energy resources, encompassing the Global Warming Potential (GWP), Fossil Depletion (FD) and Cumulative Energy Demand (CED). The LCIA was performed with the software SimaPro® (PRé, the Netherlands).

3.2.5.1.

ENERGY RESOURCES

The impacts on energy resources are indicated in terms of energy use efficiency and use of fossil fuels. The impact on fossil fuel resources was estimated with the midpoint FD indicator of the ReCiPe method (Goedkoop et al., 2012). This communicates the amount of fossil fuels invested in the life cycle (in kg oil eq). In addition, CED (MJ eq) was calculated with its dedicated indicator. This is the sum of the renewable and non-renewable energy invested in the full life cycle of all inputs to the system.

3.2.5.2.

GLOBAL WARMING POTENTIAL

The GWP (kg CO2 eq) was assessed with ReCiPe’s Climate Change category and hierarchic perspective. This considers IPCC’s Fourth assessment report’s GWP for 100 year time horizons (IPCC, 2007).

44

Chapter 3

3.3. RESULTS Table 3.2 – Fossil Depletion (FD), Cumulative Energy Demand (CED) and Global Warming Potential (GWP) of the two farming systems and a normal average at two yield levels, in comparison to the reference system.

FD

CED

GWP

(g oil eq MJ-1)

(MJ eq MJ-1)

(g CO2 eq MJ-1)

Low

-30.80

-1.28

112.47

Outgrowers

High

-32.33

-1.44

72.59

Factory

Low

-40.34

-0.73

44.72

Factory

High

-40.97

-1.89

41.79

Average

Low

-33.98

-1.10

89.89

Average

High

-34.21

-1.54

65.78

29

0.39

79.4

Farming system

Yield level

Outgrowers

Reference

3.3.1. ENERGY ANALYSIS Due to the combined credits of glycerine and biogas production, the CED is throughout negative, indicating that the system offsets all of its energy requirements though substitution pathways in the reference system. To generate 1 MJ eq

electrical

there is a requirement of -1.10 MJ

or -1.54 MJ eq, under low and high yield conditions, respectively (table 3.2).

The CED of outgrower-based electrification is -1.28 to -1.44 MJ eq MJ-1, while the CED of the factory is -0.73 and -1.89 MJ

eq

MJ-1 (low to high yields). This disparaged energy efficiency

between the two seed origins is caused by the fact that the outgrowers-based oil requires between 83 and 100% more energy to be extracted and processed (figure 3.4). Coincidently, the extraction and transesterification phases are the main demanders of energy in either farming system; the reagents for methyl-ester production comprise 54 to 93% of the energy input, while electricity follows with 7 to 25%. Furthermore, the farmers transport the seeds to the factory by motorized vehicles, which results in 16 to 19% of the energy demand. The energy requirements of cultivation are negligible, owing to the predominant use of fertilizers from waste-streams.

45

LCA of Jatropha-based bioenergy in Mali

Figure 3.4 – CED of the factory- and outgrowers -based biodiesel under low (A) and high (B) yields. Each bar represents the emissions of one life cycle phase corresponding to the outgrowers- (black) and the factory-based (white) farming systems. The grey columns indicate the average of all fields and error bars indicate its standard deviation.

Because of the weight of the avoided allocation of biogas in these results (figure 3.4), we show also the sensitivity to the opposite assumption: that biogas is considered a second energy carrier contributing to the FU. In this case, average CED for all farming systems under low yield is 0.21 MJ

eq

MJ-1, whilst for high yield it is 0.2 MJ

biodiesel requires on average 0.23 to 0.21 MJ 0.11 to 0.1 MJ

eq

MJ-1. Outgrower-based

-1

eq

MJ and the factory’s production demands

-1

eq

MJ (low to high yields). These figures are based in the fact that biogas-

based electricity corresponds to circa 79% of the total energy produced, and consumes 0.0275 m3 MJ-1electrical (ecoinvent® v2.2). The contribution to the depletion of fossil fuels follows the same trends of relative contributions of the different production steps, showing that the energy inputs to the system are mainly non-renewable (figure 3.5). The output of biogas and thus avoided natural gas production, results in a negative FD, meaning that while the production chain depletes 17.51 46

Chapter 3

to 22.23 g oil

eq

MJ-1, it avoids the depletion of 57.40 to 61.86 g oil

therefore -33.98 and -34.42 g oil

eq

eq

MJ-1. The final FD is

MJ-1, under low and high productivity, respectively (table

3.2).

Figure 3.5 – FD of the factory- and outgrowers -based biodiesel under low (A) and high (B) yields. Each bar represents the emissions of one life cycle phase corresponding to the outgrowers- (black) and the factory-based (white) farming systems. The grey columns indicate the average of all fields and error bars indicate its standard deviation.

Because of the important contribution of transesterification to energy use, if electricity is generated from oil the energy efficiency of the system would look different. The CED lowers around 35% on average to -1.51 and -1.98 MJ

eq

MJ-1 for low and high yield conditions

respectively. The system inputs with highest energy demands are electricity (51%-53% for outgrowers, 90-95% for the factory), followed by the transport of seeds by the outgrowers (40-44%).

47

LCA of Jatropha-based bioenergy in Mali

The depletion of fossil resources is also greatly affected by biofuel conversion choices. If transesterification is avoided, FD is up to 63% lower (on average 43.56 g oil

eq

MJ-1)

Electricity accounts for 25-46% of FD, while transport uses 54-48% of fossil fuels. Electricity generation based on fossil diesel uses 0.39 MJ eq MJ-1. Because it is fossil based, the reference system leads to the depletion of fossil fuels, at a rate of 29 g oil

eq

MJ-1 (table

3.2).

3.3.2. GLOBAL WARMING POTENTIAL Electricity production from outgrown seeds emits 112 g CO2

eq

MJ-1 in a low yield level, a

-1

figure that drops 36% to 72.59 g CO2 eq MJ as the yield increases (table 3.2). Factory-based production emits 44.72 g CO2

eq

MJ-1 in a low yield level and 41.79 g CO2

eq

MJ-1 in high

yields. On average (non-weighted), the system’s GWP with lower productivity is 89.89 g CO2 eq

MJ-1 and is 65.78 g CO2 eq MJ-1 with higher.

The GWP of the production of 1 MJ fossil-based electricity is 79.4 g CO2 eq MJ-1 (table 3.2). This is 12% lower than the GWP of Jatropha-based biodiesel electricity in low yield conditions, and 21% higher than the GWP verified in high yield conditions. A linear regression of the average GWP for both yield levels indicates that above 2027 kg seeds ha-1 Jatropha’s system’s GWP is lower than that of fossil diesel. Depending on the farming system and the considered yield level, the relative contribution of the production stages varies. As suggested by its lower overall GWP, the factory-based system does not require seed transport and harvests more seeds. The emissions from cultivation, transport and electricity combined comprise less than 12% of the total (figure 3.6). The main contributors are biogas leakage (54% to 57%), and methanol and NaOH production combined (32% to 37%). In the case of outgrower-based biodiesel, biogas production accounts for 26% to 38% of GHG emissions, owing to the methane leakage during digestion. The production of the necessary transesterification reagents emits between 17 and 24% of the total, while the transport of seeds emits around 13.5% and electricity consumption circa 15.5%. The relative importance of the cultivation stage is solely related to fertilization, which occurs at the same rate regardless of the yield. Hence, the emissions drop from being 28% of the total to 6% when the yield is higher.

48

Chapter 3

This disparity influences the average distribution of emission sources between yield levels. With lower yields, biogas accounts for 31% of the GWP, while the cultivation and the transesterification reagents account for 19% and 24%, respectively. Electricity and seed transport follow with 13% and 11%. With higher yield, the GWP of the cultivation stage drops to 4%. The main contributors to the GWP are then biogas (41%), transesterification (26%), electricity (15%) and seed transport (13%).

Figure 3.6 – GWP of the factory- and outgrowers -based biodiesel under low (A) and high (B) yields. Each bar represents the emissions of one life cycle phase corresponding to the outgrowers- (black) and the factory-based (white) farming systems. The grey columns indicate the average of all fields and error bars indicate its standard deviation.

The GWP of the production system without transesterification is 6% to 9% lower than the system at hand. In this case, the benefit of bypassing transesterification is limited because the avoided emissions of glycerine avoidance partly offset the burden of the transesterification reagents. This decrease is not enough to overcome the disadvantage regarding fossil diesel attained with lower yields.

49

LCA of Jatropha-based bioenergy in Mali

3.4. DISCUSSION The sustainability of the system’s setup was analysed with three indicators reflecting the GHG, fossil fuel use and energy demand of the generation of electrical energy from Jatropha plants. The results indicate the contributions of the different steps of biodiesel-based electrification to the GWP, FP and energy use, making a distinction biofuel attained from outgrown seeds and seeds from the factories’ experimental fields. This way, it is possible to gauge the impacts of the production chain with multiple sources or starting in one farming system. The latter shows that the resources invested in a supply chain based on outgrowers is not reciprocated in productivity, leading to higher cumulative emissions relatively to the energy which is supplied by the system in the end. This is particularly evident when comparing the energy gain from both farming systems (table 3.2), where, for the same energy output, the input is highest for outgrowers. Moreover, the GWP analysis shows that the emissions of the cultivation stage are higher in small farmers’ plantations (figure 3.6), despite the lower overall inputs of fertilizers (table 3.1). This apparent contradiction owes mainly to the use of “waste-stream” fertilizers in the factory, where materials applied in soil enrichment would otherwise be disposed (organic farm residues or manure), being therefore devoid of environmental burden. These points imply that in the conditions verified in Koulikoro outgrowers farming systems are less GHG and energy efficient. Because the FU is productivity-dependent, the results are affected a great deal by variability in yields among the different sampled farming systems (figure 3.3), leading to a wide range of results. Still, the difference in averaged impacts between yield levels for one farming system is not as striking as between farming systems. Furthermore, most inputs to the supply chain are associated proportionally with the amount of harvested seeds. The only exception is the inputs to cultivation, which are the same regardless of the yield level. The consequence of this can be seen in figure 3.6, for instance: relatively to the other stages of the production system, the emissions from cultivation are more important in the lower yield scenario than in the higher yield. In fact, the fulfilment of the harvest and climate seem to dictate yields (Soto et al., 2013), and not fertilization intensity. Our data suggests that, in this case, improved fertilization management does not necessarily result in impact reduction, contrarily to what is mentioned in literature (Almeida et al., 2011). Because the fertilization level is kept constant regardless of yield level, fertilization’s largest influence on the GWP is not through its effect on yield and the FU, but through its GHG emission load. Given that rainfall levels are unreliable in this region, this is likely to repeat in 50

Chapter 3

reality, since fertilization is effective in enhancing yields only when sufficient water is available. Moreover, relying on reduced fertilization to cut GHG emissions might proof futile also because some farmers do not fertilize, risking hampering productivity on the long run. Yield expectations are also sensitive to fluctuations in the natural environment, as observed on the yield records explained in the methodological section, particularly when confronted with the average 1 t ha-1 productivity predicted by Trabucco et al. (2010) for this region. The quality of the plot where the plantation is established might affect productivity as well: the factory might choose a good land while farmers tend to cultivate in the ones of poorest quality due to their lower financial ability or the will to reserve the best soil for cash crops or staple food. The factory also implements irrigation at the nursery stage, contrarily to the farmers. This practice has been advertised to boost plant development. More care can be taken, especially in the outgrowers’ model, with weeding, pruning, spacing and pest and disease management, with the perspective of improving yields with little consequence to absolute GHG emissions. Finally, a more thorough harvesting campaign would convey an increased productivity (thus a higher yield). This would require an increase in the already limited availability and opportunity costs of human labour to collect Jatropha fruits for bioenergy use (Grimsby et al., 2012). In addition, the effect on the LCA impacts of improved harvesting campaigns may be limited. If the neglected portion of harvest is higher in more pooreryielding years, a large percentage of a low yield is missed, which corresponds to a low amount of yield nonetheless. A linear regression (y -0.0236x 12 .26 between average yield and average GWP for both yield levels coarsely suggests that when the harvest fulfils at least 2027 kg seeds ha-1, the GHG emissions of 1 MJ of Jatropha-based electricity are lower than those of 1 MJ of fossildiesel-based electricity. This threshold is twice the yield predicted by Trabucco et al. (2010) for the region. This suggests a contradiction with the possibility of attaining better harvests. The limit is also more than twice higher than what had been previously modelled: 1000 kg ha 1

(Almeida et al., 2011; Chapter 2) and 783 kg ha-1 (Achten & Verchot, 2011). This

discrepancy, along with natural yield limitations, seems to suggest that the other stages of the production system, besides harvest, may have room for improvement and should be focused on for effective GHG emission reduction. Our analysis supports that electrification from oil rather than biodiesel has a slightly lower GWP and significantly improves the energy efficiency of the system. Transesterification is a relatively sophisticated procedure when set in the downstream of such low-input biofuel

51

LCA of Jatropha-based bioenergy in Mali

production chain. Hence, it significantly stresses the environmental performance of the system, mainly due to the production of its reagents. Furthermore, it is a biomass conversion step, which implies an additional loss of matter. Although the LHV of biodiesel is higher, the final energy efficiency does not seem to vary significantly between vegetable oil and its methyl ester (Soltic et al., 2009). Hence, the conversion seems to result in a loss of energy as well. Data from rapeseed oil and methyl ester further suggest that there is no significant variation among those fuels at the level of exhaust emissions (Soltic et al., 2009). Indeed straight oil is a commonly advised and de facto implemented option in setups of Jatrophabased rural electrification with combustion stationary engines (Bouffaron et al., 2012; Muys et al., 2014). Such option is less tangible when transportation is also envisioned, as automotive engines are not customarily prepared to accommodate, for instance, the higher viscosity of oil relatively to that of diesel (Clark et al., 1984). Another step which deserves attention in emission reduction is the valorisation of seed cake through biogas production. Although the GWP is credited with avoided natural gas production, anaerobic digestion commonly leaks methane at a rate of 10%, putting biogas in the list of top contributors to the GWP of this system (figure 3.6). If seed cake were exclusively applied in fertilization as is common (Almeida et al., 2011), these emissions would be avoided. There is however a trade-off with obtaining an additional and valuable energy carrier. Improvements on this point of the production system require the balancing of these two factors. Other issues, such as economic and social benefits from using biogas, are also to be considered but are out of the scope of this discussion. In another interpretation, these results may suggest that smallholder participation is not environmentally beneficial to the system and could be bypassed with all cultivation being handled by the factory. From a socioeconomic perspective, the exclusion of smallholders seems detrimental to an integrated rural development. Outgrower schemes might contribute in generating ownership of the biofuel venture by benefit and cost sharing. Additionally, under a competitive market, the participation of smallholders (either as shareholders or as seed providers) might give them a certain degree of power, negotiation capacity and ability to appeal to third parties (i.e. government), especially if organized under a cooperative (Muys et al., 2014; van Eijck et al., 2014a; van Eijck et al., 2014b). A study by van Eijck and colleagues (2014) concludes that the involvement of smallholders in such Jatropha-bioenergy systems buffers the negative impacts on the livelihoods in case of backlash. On the other hand, relying on seed supply from outgrowers seems to be an added risk for factories, due to

52

Chapter 3

lack of legal frameworks and difficult engagement of the smallholders, often motivated by low remuneration (Achten et al., 2014). There is a variety of LCAs of Jatropha-based energy and fuels published in peer-reviewed literature. Having different geographical settings and yields, these studies report on a wide range of environmental impact scores. Although it is not the scope of this study to review past Jatropha LCAs, it stands to attention that each set of methodological decisions, such as choice of allocation strategy and system boundaries setting, are known to influence the outcomes of an LCA (Luo et al., 2009) and are not consistently applied throughout those studies. Therefore, the following paragraphs aim to contextualize the present results against other case studies and to find common threads of improvement opportunities. Data from plantations in Ivory Coast with an average yield of 4 t ha -1 resulted in very similar energy analysis. The energy input of 0.21 MJ MJ-1 is lead by transesterification (61%), followed by cultivation (12%) (Ndong et al., 2009). Other LCAs comprising the end use phase, such as the present one, have indicated higher CED of 0.5 MJ MJ -1 in China (Ou et al., 2009), 0.74 in India (Achten et al., 2010a) and 0.43 in Tanzania (Eshton et al., 2013). On the first study, 34% of the energy input corresponds to cultivation and 66% do fuel production. The latter two articles saw a larger energy demand for the agricultural stage. Cradle-to-gate (from resource extraction to the factory gate) energy analysis in Thailand reports energy use of 0.7 MJ MJ-1 (Prueksakorn & Gheewala, 2008). Largest energy consumer is agriculture, followed by biodiesel production. The same authors, in another study, report higher energy demand (1.57 MJ MJ-1). In this analysis, the two main responsible for energy demand are also agriculture and biodiesel production, mainly owing to fertilization and the reagents for transesterification (Prueksakorn et al., 2010). LCAs performed in Africa report a lower GWP than that verified in this study: 20.4 and 24 g CO2

eq

MJ-1, with cultivation being the largest emitter and biodiesel production having little

contribution to the GWP (Eshton et al., 2013; Ndong et al., 2009). Ou et al. (2009) report a GWP of 51.97 g CO2 eq MJ-1, to which cultivation contributes with 47% and the production of biofuel with 52%. These records are lower than those of the present study, but other LCA’s show higher figures. For instance, Lam et al. (2009) estimate a GWP of 295.14 g CO2 eq MJ-1 on a cradle-to-gate LCA based on a 5 t ha-1 seed yield, with cultivation accounting for half of emissions (Lam et al., 2009). Other published GWPs are 235.03 g CO 2 (Wang et al., 2011) and 123.7 g CO2

eq

eq

MJ-1 of biodiesel

MJ-1 produced in an automobile engine fuelled by

Jatropha biodiesel (Achten et al., 2010a), pushed by emissions from fertilizer application. 53

LCA of Jatropha-based bioenergy in Mali

Gmünder and colleagues (2010) reported on a thorough inventory for the rural electrification based on Jatropha straight oil in India. This system emits 74.57 g CO 2 eq MJ-1. In this LCA, oil extraction accounts for 47.8% of emissions, the end use for 23.4% and cultivation for 20.7%. The CED in this study varies between 0.75 and 1 MJ MJ-1. That study considers a yield of 2.5 t ha-1 and that 1 MJ requires 0.44 kg seeds, while in the study at hand it requires 0.3 kg. The overall emissions are in line with the results of this study, despite the facts that the India case requires more inputs for a lower energetic yield and that the target fuel is oil, rather than diesel.

3.5. CONCLUSIONS Among the different possible business models for the exploitation of Jatropha’s bioenergy potential, the integration of the efforts of smallholders with centralized plantations and conversion facilities has been promoted as a rural development strategy. This approach has been proved to improve access to land, involvement of the community and social stability. From the point of view of the private investor, an outgrower scheme adds the risk of uncontrolled supply to the inherent risks of investment in Jatropha, such as yield instability. On the other hand, outsourcing seeds reduces the investment of time and resources in own plantations and the need for dedicated land occupation. In this comparative LCA, we investigate the sensitivity to fluctuations in yield and to the participation of outgrowers of the environmental impacts of a Jatropha-based rural electrification system and of its advantage relatively to fossil fuels. The energy efficiency and fossil depletion of Jatropha-based electricity is favourable relatively to the equivalent fossil-based electricity generation. On the other hand, whether or not the Jatropha system attains lower global warming potential (GWP) than fossil energy depends on the yield level. On average, under the conditions that lead to the smallest harvest, the GWP of fossil electricity is lower than the GWP of Jatropha bioenergy. With higher yields, the GWP decreases and from circa 2 t ha-1 it becomes low enough for Jatropha to become a viable and sustainable alternative to fossil fuels. It can be concluded that the environmental sustainability of a centrally operated system inclusive of smallholder cultivation depends on the success of the harvest and the productivity of the crop. While the sensitivity to productivity has been previously documented, the influence of an outgrower regime has not been investigated thoroughly. Due to its 54

Chapter 3

ramifications in the socio-economic dimensions of sustainability, a consistent outlook on bioenergy projects of such nature ought to consider possible trade-offs. From this analysis, it can be inferred that financial incentives and communication with outgrowers so as to engage them in the harvesting campaigns and in optimal management practices can considerable improve life cycle energy efficiency and GHG emission reduction.

55

56

Chapter 4

4. LAND USE CHANGE IMPACT OF JATROPHA PLANTATIONS ON CARBON STOCKS Adapted from Degerickx J, Almeida J*, Moonen P, Vervoort L, Muys B, Achten WMJ. Impact of land use change to Jatropha bioenergy plantations on biomass and soil carbon stocks: a field study in Mali. Submitted to GCB Bioenergy. * Contributed to study design, result analysis and article writing.

ABSTRACT Small-scale Jatropha cultivation and biodiesel production has the potential of contributing to local development, energy security and greenhouse gas (GHG) mitigation. In recent years however, the GHG mitigation potential of biofuel crops is heavily disputed due to the occurrence of a carbon debt, caused by CO2 emissions from biomass and soil after land use change (LUC). Most published carbon footprint studies of Jatropha report modelled results based on a very limited database. In particular, little empirical data exist on the effects of Jatropha on biomass and soil C stocks. In this study we used field data to quantify these C pools in three land uses in Mali, i.e. Jatropha plantations, annual cropland and fallow land, to estimate both the Jatropha C debt and its C sequestration potential. Four years old Jatropha plantations hold on average 5 t C ha-1 in their above- and belowground woody biomass, which is considerably lower compared to results from other regions. This can be explained by the adverse growing conditions and poor management. No significant soil organic carbon (SOC) sequestration could be demonstrated after four years of cultivation. While the conversion of cropland to Jatropha does not entail significant C losses, the replacement of fallow land results in an average C debt of 32.2 t C ha-1, mainly caused by biomass removal. Retaining native savannah woodland trees on the field during LUC and improved crop management focusing on SOC conservation can play an important role in reducing Jatropha’s C debt. Although planting Jatropha on degraded, carbon-poor cropland results in a limited C debt, the low biomass production and seed yield attained on these lands reduce Jatropha’s potential to sequester C and replace fossil fuels. Therefore, future research should mainly focus on increasing Jatropha’s crop productivity in these degraded lands.

57

LUC impact of Jatropha plantations on carbon stocks

4.1. INTRODUCTION The current demand for reducing greenhouse gas (GHG) emissions, in combination with the depletion of fossil fuel reserves and the growing concern on energy security and independence (Verrastro & Ladislaw, 2007) led to a growing interest in the production of liquid biofuels. In this context, Jatropha curcas L., a tropical deciduous shrub, was claimed to provide high oil yields on degraded lands with minimal nutrient and management inputs, thereby avoiding competition with food production (Achten et al., 2010a; Francis et al., 2005). However, more recent research has come to disprove these early claims (van Eijck et al., 2014a) and a large fraction of Jatropha initiatives failed because of low yields due to insufficient agronomic knowledge (Nielsen et al., 2013; Singh et al., 2014). Despite this negative experience, small-scale Jatropha cultivation can still play an important role as a local energy source in low income areas (e.g. Sahel region), thereby contributing to local development, energy security and GHG mitigation (Achten et al., 2010b; Nielsen et al., 2013). The latter can be attained through (i) C sequestration in Jatropha biomass and soil during cultivation and (ii) the production of biodiesel to replace fossil fuels (Becker et al., 2013; van Rooijen, 2014). Besides the well-known environmental benefits, GHG mitigation can boost the economic viability of Jatropha projects through C trading mechanisms (Nielsen et al., 2013; van Rooijen, 2014). In recent years however, the GHG mitigation potential of crop-based liquid biofuels has been heavily debated. In particular, land use change (LUC) due to biofuel crop establishment may create initial losses in soil and biomass C stocks as a result of increased microbial decomposition and burning. This C debt can have a significant negative impact on the biofuel’s GHG balance (Fargione et al., 2008). In the case of Jatropha, multiple studies have been made addressing this particular issue (Achten & Verchot, 2011; Bailis & Baka, 2010; Bailis & McCarthy, 2011; Romijn, 2011; Vang Rasmussen et al., 2012). A wide variety of C debts and associated repayment times have been reported, the latter ranging from a few years up to multiple centuries. The repayment time depends (i) on the C debt created (i.e. the land cover which is replaced by Jatropha) and (ii) on the life cycle CO2 reduction potential of the biofuel substituting fossil fuel (kg CO2 ha-1 year-1), indicating a high dependency on local conditions. However, for both aspects data quality (measurements versus modeled estimation) and assumptions (e.g. assumed yields, fertilizer use and field emissions) also play an important role. Most studies conclude that GHG mitigation through Jatropha production can only be achieved when it is planted on degraded lands poor in C stocks (Achten & Verchot, 58

Chapter 4

2011; Romijn, 2011). However, the accuracy of these earlier analyses can be questioned, since frequent use is made of default values and non-validated estimates of seed yield and C stocks, which are in turn based on very little empirical data. This practice can give rise to significant errors in the analysis of Jatropha C debts, as the magnitude and dynamics of C stocks depend strongly on local biophysical conditions (Powers et al., 2011). In addition, assumptions are frequently made which have not been verified in the field (e.g. soil organic carbon (SOC) remaining constant upon LUC), adding more uncertainty to currently available estimates. Therefore, there is an urgent need for more empirical data on Jatropha C stocks compared to other LUs in order to verify the results reported by Romijn (2011) and Vang Rasmussen et al. (2012). To answer this call for more empirical data, a field study was set up in Mali with the aim of quantifying soil and biomass C stocks in small-scale Jatropha plantations and comparing these with other LUs. Mali is one of the few sub-Saharan countries explicitly encouraging Jatropha cultivation in its policy, aiming for a 20% replacement of diesel by Jatropha oil by 2023 (Favretto et al., 2012). Whereas traditionally Jatropha was mainly grown as a living fence for local soap production, its cultivation was recently redirected towards small-scale plantations for local energy production. By 2011, this resulted in a total area of almost 5000 ha of Jatropha, mainly situated in the provinces of Koulikoro, Sikasso and Kayes (Favretto et al., 2012).

4.2. MATERIALS AND METHODS

4.2.1. GENERAL SETUP The impact of LUC on biomass and soil C stocks was studied using the C stock change method (UNFCCC, 2009). Since a monitoring study was practically unfeasible, we applied the ergodic principle, i.e. presenting assumed changes over time by comparing different LU classes in space at one point in time. C stocks were measured in 18 triplets of neighbouring fields, each comparing Jatropha, cropland and fallow LU, thereby assuming that all factors other than the effect of LU are constant within each triplet (spatially paired site design) (Conteh, 1999). Half of the sites were situated near the city of Koulikoro in the central part of the country, whereas the other half was located in and around Garalo, a smaller village in the

59

LUC impact of Jatropha plantations on carbon stocks

Southern province of Sikasso (figure 4.1). A detailed description of the study sites and the criteria applied for field selection can be found in the Annex to this chapter and in Box 4.1.

Figure 4.1– Location of study sites in relation to Köppen-Geiger climate classification; Bsh = Hot steppe climate and Aw = Tropical savannah climate.

4.2.2. DATA COLLECTION General information on each field was gathered using a brief, semi-structured interview with the field's owner. Destructive measurements of woody aboveground biomass (wAGB) were conducted on a representative sample of 46 Jatropha trees from the selected fields to develop multiple allometric relations for Jatropha. The best performing equation was then further used to assess the wAGB of all Jatropha plantations. wAGB of other species present in the fields was estimated using respective allometric relations derived from literature. Only long term C pools, i.e. perennial shrubs and trees, were included and were assessed in random plots. Belowground biomass (BGB) was estimated using root-to-shoot ratios, obtained from local destructive measurements of Jatropha in the selected fields (n = 17) and literature for other species. The resulting biomass values were converted to C stocks in t ha -1 using C content data from literature. In addition to biomass, soil cover (defined as the percentage of soil covered by vegetation) was estimated based on measured crown dimensions (Jatropha, mature 60

Chapter 4

trees) or estimates on sight. Four soil layers were sampled in each field, i.e. 0-5, 5-10, 10-20 and 20-30 cm, for which both SOC concentration (CN analyzer) and bulk density (gravimetric method) were determined to calculate SOC stocks in t ha-1. Jatropha fields were sampled most intensively to study the spatial variability of SOC (3 plots × 2 sampling locations per field; figure A2). Additionally, soil texture (by laser diffraction analysis) and pH-H2O were measured on four mixed samples per field, one for each soil layer. A detailed description of the used methods for biomass and soil C measurements can be found in the Annex to this chapter.

BOX 4.1 Characteristics of the study sites Data for this study was collected in two distinct ecoregions in Mali, West Africa: Koulikoro (12°51’N, 7°33’W), in the central part of the country and Garalo (10°59’N, 7°26’W) in the Southern province of Sikasso. Koulikoro is situated in the Sudanian agro-ecological zone, which is characterized by a semi-arid climate (mean annual temperature (MAT) of 27.6°C and mean annual precipitation (MAP) of 815 mm; New_LocClim (FAO, Rome, Italy), dry woodlands (Magin, 2011) and farming systems integrating sedentary livestock-rearing with crop production (Coulibaly, 2003). Garalo, belonging to the North-Guinean zone, has a subhumid climate (MAT = 27.0°C, MAP = 1142 mm; New_LocClim (FAO, Rome, Italy)) giving rise to a more lush savannah vegetation and a larger diversity of crops (Coulibaly, 2003). The land mainly consists of ferruginous, highly degraded soils with a high fraction of lowactivity clays (Keita, 2002). Due to the deposition of Saharan dust, Lixisols dominate the landscape in Koulikoro, whereas Ferric and Plinthic Acrisols are the main soil types in Garalo (FAO, 2007).

4.2.3. STATISTICAL DATA ANALYSIS Whenever appropriate, the data were lognormal transformed to meet the criteria of parametric statistical tests. Relations between the different variables (i.e. Jatropha tree dimensions, Jatropha biomass, root-to-shoot ratio, total biomass C stock and SOC stock on the one hand and ecoregion, LU, biomass, soil cover, field age, planting distance, use of tillage and intercropping, soil texture, pH, soil bulk density and soil depth on the other hand) were identified using stepwise multiple linear regression, correlation analysis and ANOVA, the latter in combination with Tukey post hoc tests. The effects of known confounding factors were eliminated as much as possible by calculating partial correlations or applying ANCOVA 61

LUC impact of Jatropha plantations on carbon stocks

analysis. Throughout this study, statistical analyses were conducted in SPSS 17.0 (IBM, Chicago, USA) and a significance level (α) of 0.05 was used, unless stated otherwise. To determine the impact of LUC on SOC using all gathered data, mixed ANOVA was used in which LU was included as a fixed factor and a unique field ID as a random factor, nested in LU to account for subsampling. This analysis was conducted in SAS 9.3 (SAS Institute Inc., Cary, USA) using the MIXED procedure.

4.3. RESULTS

4.3.1. DESCRIPTION OF FIELDS

Figure 4.2 – Example pictures of the three land uses in (a) Koulikoro and (b) Garalo.

With the exception of one missing fallow land in Koulikoro, nine fields of each LU type were visited in each ecoregion. The Jatropha plantations under study are 3-5 years old and most frequently established on former cropland. In Koulikoro, Jatropha is always mixed with other crops and wide planting distances of 5×2 m are frequently used, whereas in Garalo intercropping is rare and smaller planting distances of 3×3 and 4×3 m are applied. Furthermore, Jatropha fields are generally ploughed once a year and receive no irrigation or pruning. Cropland most frequently consists of monocultures and is ploughed once a year. In 62

Chapter 4

both ecoregions, crops are mainly cultivated in agroforestry parkland systems, where some mature, widely spaced trees (e.g. Vitellaria paradoxa C.F. Gaertner, Parkia biglobosa (Jacq.) R. Br. ex G. Don and Mangifera indica L.) are kept on the field. These provide nutrients to the crops and an extra income to the farmer through the selling of non-wood tree products like mango fruit and Shea nuts. Major crops are corn, cotton and sesame in Garalo and corn, sorghum and millet in Koulikoro. Fallow vegetation consists in both ecoregions of bushes combined with mature trees up to 15 m high. Examples of the three LUs are presented in figure 4.2.

4.3.2. SOIL CONDITIONS In both ecoregions two soil types can be distinguished based on hierarchical cluster analysis: sandy versus loamy soils in Koulikoro and gravel versus non-gravel soils in Garalo. Mean characteristics of these soil types are presented in table 4.1. The loamy soils in Koulikoro closely resemble the non-gravel soils in Garalo. Since no significant differences in soil conditions between the different LU types could be found in both ecoregions, field selection meets the criteria of a paired site sampling design.

4.3.3. BIOMASS CARBON

4.3.3.1.

J ATROPHA BIOMASS , ALLOMETRIC RELATION AND ROOT - TO- SHOOT RATIO

A summary of plant dimensions and biomass measurements of individual Jatropha trees is given in table 4.2. Nonlinear regression analysis resulted in the crown area (in m²) to be selected as the best predicting variable for wAGB (R² = 0.803; see equation 4.1 and figure 4.3). The average root-to-shoot ratio for Jatropha amounts to 0.48 (table 4.2). (eq. 4.1) with wAGB = woody aboveground biomass in kg and CA = crown area in m².

63

LUC impact of Jatropha plantations on carbon stocks Table 4.1 – Mean values and standard deviations (within brackets) of edaphic variables for the soil types in both ecoregions. BD = bulk density; C/N = Carbon/Nitrogen.

Ecoregion

Soil class

# Jatropha fields

Sandy

2

Loamy

7

Gravel

6

Non-gravel

3

BD

% Sand

% Silt

% Clay

% Gravel

pH

C/N ratio

76.7

17.3

6.0

0.0

4.9

13.7

1.39

(7.4)

(5.8)

(2.1)

(0.0)

(0.4)

(1.0)

(0.05)

42.1

45.4

12.5

0.0

5.2

13.1

1.35

(7.00)

(5.7)

(3.5)

(0.0)

(0.5)

(0.8)

(0.04)

45.3

43.1

11.6

60.8

5.1

14.7

1.47

(11.4)

(7.2)

(4.9)

(14.3)

(0.4)

(1.2)

(0.08)

43.7

42.8

13.5

1.1

5.1

14.5

1.41

(12.5)

(6.4)

(7.7)

(2.2)

(0.2)

(1.9)

(0.07)

(g cm-3)

Koulikoro

Garalo

64

Chapter 4 Table 4.2 – Averages of measurements on individual Jatropha trees, grouped per ecoregion and soil type. Numbers in brackets represent standard deviation and number of samples respectively. “All” stands for the total mean and standard deviation calculated according to stratified random sampling design. wAGB = dry woody aboveground biomass per tree; BGB = dry belowground biomass per tree; R/S = root-to-shoot ratio.

Ecoregion

Soil type

Loamy

Age (years) 3.45 (0.64/203)

Basal area (cm²)

Height (m)

# primary branches

Crown area (m²)

wAGB

BGB

(kg)

(kg)

91.98 (55.45/203)

1.70 (0.47/203)

4.16 (1.86/203)

2.97

2.55

2.14

0.46

(2.33/203)

(2.39/11)

(0.69/2)

(0.25/2)

127.57 (44.63/54)

1.80 (0.30/54)

5.69 (1.79/54)

3.43

2.67

2.35

0.59

(1.65/54)

(1.59/6)

(-/1)

(-/1)

120.25 (47.70/164)

1.89 (0.31/164)

4.14 (1.55/164)

3.09

3.58

1.57

0.44

(1.80/164)

(2.46/20)

(1.13/11)

(0.11/11)

99.43 (42.73/81)

1.74 (0.30/81)

4.26 (1.28/81)

2.57

3.28

2.69

0.61

(1.47/81)

(2.15/9)

(1.18/3)

(0.13/3)

106.25 (2.23/502)

1.78

4.33 (0.07/502)

3.00

3.16

1.88

0.48

(0.09/502)

(0.34/46)

(0.27/17)

(0.04/17)

R/S

Koulikoro Sandy

Gravel

3.50 (0.50/54) 4.55 (0.50/164)

Garalo Non-gravel

All

-

4.00 (0.00/81) 3.90 (0.02/502)

(0.02/502)

65

LUC impact of Jatropha plantations on carbon stocks

Figure 4.3 – Allometric relation for woody dry aboveground biomass (wAGB) of individual Jatropha trees based on their crown area (CA).

Based on these results, the average Jatropha plant dimensions and biomass were calculated for each field and in turn related to the following field characteristics: ecoregion, soil class, soil texture (clay and silt percentages), intercropping, planting distance, tillage, plantation age and biomass of mature trees present in the field. None of these variables was found to have a significant effect on Jatropha dimensions or biomass. According to table 4.2 however, Jatropha aboveground biomass growth is largest in well-drained soils (sandy soils in Koulikoro and gravel soils in Garalo) and root development is stimulated most in stone free, coarse textured soils.

4.3.3.2.

BIOMASS CARBON STOCKS IN THE DIFFERENT LAND

USES

Mature trees, although low in abundance, represent the largest share of biomass C in all LU types (figure 4.4). On average, only 18.6 % of the total biomass stock in a Jatropha plantation is in Jatropha trees. The partitioning of the biomass C stock among the different vegetation

66

Chapter 4

elements is similar in both ecoregions, with the exception of the fraction of shrub biomass in fallow land being higher in Garalo (31.4 %) compared to Koulikoro (11.4 %).

Figure 4.4 – Partitioning of total biomass carbon stock in Koulikoro (a) and Garalo (b) between aboveground and belowground biomass (AGB and BGB, respectively) and between the different vegetation elements for each land use type. BGB is read below the horizontal axis and AGB above it. The stacked bars represent the vegetation elements: trees, shrubs and Jatropha.

LU has a pronounced effect on the biomass C stock in both ecoregions (table 4.3). A significant difference was found between fallow and Jatropha on the one hand (P=0.026 for Garalo and P=0.020 for Koulikoro) and fallow and cropland on the other hand (P=0.004 for Garalo and P=0.010 for Koulikoro). Biomass C stocks in Jatropha plantations are not significantly different from those under annual cropland. This is explained by a similar presence of mature trees in both LUs. Depending on the density and dimensions of these scattered trees in the landscape, the variability in biomass C stocks within each LU is high, implying that the impact of LUC is highly variable as well. The effect of other environmental variables (ecoregion, intercropping, planting distance, age, soil type, previous LU and tillage) on the biomass C was verified, but no significant effects were found.

67

LUC impact of Jatropha plantations on carbon stocks Table 4.3 – Average and standard deviation (within brackets) of Jatropha carbon stock, total biomass carbon stock, soil organic carbon stock (SOC) and total carbon stock grouped per ecoregion and land use (significant differences between land uses are indicated using differing letters).

Ecoregion

Land use

Cropland Koulikoro

Jatropha Fallow

(t ha-1) -

Jatropha

Fallow

Total biomass C (t ha-1)

SOC

Total C

(t ha-1)

(t ha-1)

7.97 a

17.12 a

25.09 a

(5.08)

(10.92)

(7.73) a

28.30 a

11.26

(1.64)

(7.65)

(5.90)

(12.67)

44.75 b

28.08 a

72.83 b

(41.37)

(16.06)

(44.15)

9.74 a

14.66 a

24.40 a

(6.49)

(10.93)

-

(6.28)

2.01

13.70

(1.25)

(9.41) 27.00

-

a

17.04

a

2.63

-

Cropland

Garalo

Jatropha C

13.77

a

(6.91) b

(12.92)

27.47 a (15.02)

a

47.61 b

(10.23)

(14.39)

20.61

4.3.4. SOIL CARBON

4.3.4.1.

SOIL ORGANIC CARBON CONCENTRATIONS AND ITS

CONTROLLING FACTORS

SOC concentrations measured in Garalo generally show a logarithmic decrease with depth, being most pronounced in fallow land, followed by Jatropha and cropland (figure 4.5). In Koulikoro, cultivated soils are found to be more homogeneous and are more depleted in organic matter at the surface as compared to Garalo. The latter difference is only found significant for Jatropha (P=0.004). SOC concentrations in fallow land are similar between the two ecoregions. Although SOC concentrations are higher under fallow compared to cropland and Jatropha in all soil layers (figure 4.5), the difference is found to be only significant in the upper 5 cm for Garalo and 10 cm for Koulikoro (table A5).

68

Chapter 4

Figure 4.5 – Relation of soil organic carbon (SOC) density with soil depth in cropland, Jatropha and fallow for the Garalo (a) and Koulikoro (b) ecoregions. The error bars represent standard error of the mean.

The stepwise multiple linear regressions performed on all data points (R² = 0.479) reveal that the percentage of gravel explains most of the observed variance in SOC concentrations (R² = 0.174). Further significant contributions are made by silt percentage, depth, pH, sand percentage and BD. The last three variables each improved R² by less than 0.05, meaning that their contribution is limited. A list of all significant correlations of SOC density (kg m-3) with other soil variables per ecoregion, LU and depth is included table A6. Specifically for Koulikoro, SOC is mainly determined by clay and silt contents in all LUs. In fallow land, a strong positive relation was also found between SOC and pH. In Garalo, SOC is strongly negatively correlated with gravel content in all LUs. In addition, a negative correlation with pH was found for cropland and a positive correlation with clay percentage was identified for fallow. In general, correlations of SOC with other soil variables are most pronounced under fallow, followed by Jatropha and then cropland.

4.3.4.2.

SOIL CARBON STOCKS IN THE DIFFERENT LAND USES

SOC stocks are found to follow the same trend as biomass C, i.e. being largest under fallow and without significant differences between cropland and Jatropha (table 4.3). The effect of LUC is primarily visible in the upper soil layers (figure 4.6).

69

LUC impact of Jatropha plantations on carbon stocks

Figure 4.6 – Average differences in soil organic carbon stocks between the three land uses for each soil layer. Significant differences between land uses are indicated for each soil layer using letters: a soil layer marked with ‘a’ differs significantly from the same layer in another land use marked with ‘b’, but not from ‘a’ or ‘ab’; bold numbers represent the total soil carbon stock.

Figure 4.7 – Linear regression of soil organic carbon (SOC) stock (0-30 cm) as a function of the best predicting soil variable for Koulikoro (a) and Garalo (b). The black vertical lines represent the demarcation of the soil classes.

Multiple linear regression analysis identified the percentage of silt to be the best predicting variable for SOC stocks in Koulikoro, while gravel content fulfils this role for Garalo soils 70

Chapter 4

(figure 4.7). In addition to these soil variables, biomass and soil cover can be determining factors for SOC stocks in all LUs, while the effect of field age seems to be less important (table A7). Specifically for Jatropha, SOC stocks in the surface layer are positively affected by a well-developed vegetation layer. No significant effects of previous LU, tillage or intercropping were found on SOC stocks in Jatropha fields using ANCOVA.

4.3.4.3.

SPATIAL VARIABILITY

A paired t-test was conducted to look for significant differences in SOC between the two sampling locations within Jatropha plantations, i.e. directly underneath the shrubs versus in between the shrubs (figure A2). A significant difference is only found for the third soil layer (10-20 cm), where values are larger underneath the shrubs (4.72 t ha-1) compared to between the shrubs (4.35 t ha-1). Finally, the within field spatial variability of SOC, expressed by means of the coefficient of variation (CV), is compared to the between field variability (table 4.4). Spatial variability is largest in fallow and lowest in Jatropha fields, but none of these differences are statistically significant. In all LUs, the within field CV varies widely between the different fields, making it difficult to estimate the number of samples needed for an accurate estimation of SOC stock in a particular LU. The variability between different fields is the largest source of variation, exceeding the local within field variability by a factor 2 to 3. Table 4.4 – Coefficient of variation (CV) of soil organic carbon stocks within and between fields.

Within field CV (%) Mean

Standard deviation

Minimum

Maximum

Between field CV (%)

Cropland

12.13

13.51

0.87

44.06

29.67

Jatropha

10.27

6.36

0.82

19.41

34.60

Fallow

21.40

12.97

8.75

43.20

53.46

Cropland

22.59

17.17

3.54

62.74

44.27

Jatropha

16.63

13.77

1.54

38.72

50.18

Fallow

25.76

20.79

4.54

62.09

50.08

Land use

Koulikoro

Garalo

71

LUC impact of Jatropha plantations on carbon stocks

4.3.4.4.

TOTAL C STOCK AND ESTIMATION OF C DEBT

Total C stock differs significantly between fallow land and cultivated land, i.e. cropland and Jatropha (figure 4.8). The same trends were found for the two ecoregions (table 4.3), which are therefore displayed together. In cropland and Jatropha fields, most C is stored in the soil, while in fallow land biomass is the dominant C pool. On average four years after the conversion of fallow land to Jatropha, a significant C debt of 28.6 t C ha-1 remains to be paid off. Based on the non-significant differences in SOC between cropland and Jatropha, it can be assumed that SOC sequestration in a timeframe of four years is negligible, and consequently, the initial C debt can be estimated by adding the remaining C debt and the C stock in Jatropha biomass after four years. This results in a total C debt of 32.2 t C ha-1, which can be mainly attributed to biomass removal and decay upon LU conversion (73%, figure 4.8). The C gain upon conversion of cropland to Jatropha is not significantly different from zero. It should be noted that standard deviations of total C stocks, and hence of the differences between the LUs, are large. This means that the C debt can vary widely in function of the local situation.

Figure 4.8 – Carbon stocks per land use type and differences between these land use types; error bars represent the standard deviation of the total carbon stock.

72

Chapter 4

4.4. DISCUSSION

4.4.1. BIOMASS CARBON IN JATROPHA PLANTATIONS Allometric relations based on stem diameter are most frequently used in literature to estimate the aboveground biomass of Jatropha (Achten et al., 2010c; Firdaus & Husni, 2012; Ghezehei et al., 2009; Hellings et al., 2012). However, due to the specific tree architecture of Jatropha, i.e. branching close to the soil surface, stem diameter is often difficult to measure. In this study, crown area was found to be the best alternative to predict wAGB. The use of this predictor variable should however be restricted to cases where there is no pruning and canopy closure is not yet reached, since these factors highly influence crown dimensions. This shows that the allometric relation to be used for Jatropha biomass estimation should be both location- and management-specific. Regarding the estimation of belowground biomass, the average root-to-shoot ratio observed in this study (0.48) is higher compared to the value of 0.32 reported by Hellings et al. (2012) in similar climatic conditions (Northern Tanzania), but agrees well with the value of 0.51 found by (Torres et al., 2011) for a humid climate in Brazil, both for a similar plant age. Hence, caution should be exercised when using any of these values as a default root-to-shoot ratio for Jatropha in future studies, as this plant characteristic is not only affected by climate, but also by other factors, like soil conditions (cf. table 4.2). Jatropha biomass stocks for 4 year old plantations reported in this study (on average 5.04 t ha 1

) are at the lower end of the range between 9 and 28 t ha-1 reported in literature for various

locations and planting distances (Bailis & McCarthy, 2011; Firdaus et al., 2010; Firdaus & Husni, 2012; Reinhardt et al., 2008; Torres et al., 2011; Wani et al., 2012) mainly owing to the relatively low amount of rainfall, poor soil conditions and lack of management in the sites at hand. In addition, it should be noted that plant mortality, although frequently observed (on average 30% in Garalo, mainly due to termite activity), was not taken into account in the calculation of Jatropha biomass. Due to this simplification, the biomass results reported here represent the achievable biomass under current management practices and likely overestimate reality. Previous studies reported that Jatropha biomass production is primarily affected by the environment, followed by seed dimensions and plant accession (Achten et al., 2010c; Kaushik et al., 2007). The relation with environmental conditions could not be confirmed in the study at hand, since no significant differences in biomass were found between different soil types or 73

LUC impact of Jatropha plantations on carbon stocks

ecoregions. This can be due to multiple factors, including the small amount of observations, the large standard deviations observed for Jatropha biomass (table 4.2), the presence of unmeasured, confounding factors (e.g. fertilization), the unbalanced design (e.g. for soil conditions, see table 4.1) and the small variability in the predictor variable (e.g. age).

4.4.2. SOIL CARBON IN JATROPHA PLANTATIONS In general, SOC densities found in this study for cropland and fallow (respectively 16 and 22 t ha-1) agree well with the range of 10-20 t ha-1 reported in similar environmental conditions (Saiz et al., 2012; Takimoto et al., 2009; Tschakert et al., 2004; Woomer et al., 2004), but are slightly lower than the IPCC default values for a tropical dry climate and low activity clay soils (20 and 35 t ha-1 respectively; IPCC, 2006). The logarithmic relation between SOC and soil depth found in this study is confirmed by Jobbagy & Jackson (2000) and Walker & Desanker (2004) for various ecosystems around the globe. Clay particles are known to stabilize SOC and explain the clear relation with soil texture found in both ecoregions (Jobbagy & Jackson, 2000; Takimoto et al., 2009; Walker & Desanker, 2004). Cover by trees and crops increases the litter input to the soil and provides shade, thereby lowering soil temperature and hence the SOM decomposition rate, which explains the relation with soil cover and biomass (Dawson & Smith, 2007; Schlesinger & Andrews, 2000). Specifically for Jatropha, the revealed correlations indicate the importance of good crop management (i.e. good crop health, continuous soil cover) in order to protect SOC levels, which is a recognized prerequisite for sustainable agriculture (Lal, 2004b). In this study, the well-known dependency of SOC on mean annual rainfall (Jobbagy & Jackson, 2000) is not confirmed, as SOC stocks in Koulikoro are generally larger compared to the more humid Garalo (table 4.3). This can be partly explained by the large differences in soil conditions between the two ecoregions (table 4.1): in Garalo, the majority of soils were gravelly, a soil type which holds less potential for storing SOC compared to the clay-loam soils of Koulikoro.

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4.4.3. LAND USE CHANGE IMPACT AND CARBON SEQUESTRATION BY JATROPHA PLANTATIONS SOC stocks in Jatropha plantations were measured three to five years after LUC, making it impossible to distinguish the negative effect of LUC from the positive effect of C sequestration (Conteh, 1999) and thus preventing an accurate estimation of both C debts and sequestration rates. Despite this drawback, some qualitative conclusions can still be made. The C debt created by converting cropland to Jatropha is generally low and is compensated within three to five years of Jatropha cultivation through C sequestration in Jatropha biomass. There is no significant SOC sequestration taking place within the first five years after Jatropha establishment, as there are no differences found in SOC content between Jatropha versus cropland nor between inter-row and within row locations in Jatropha plantations. Multiple monitoring studies have demonstrated the positive effect of Jatropha cultivation on several soil properties, including SOC (Ogunwole et al., 2008; Srivastava et al., 2014; Wani et al., 2012). Converting cropland to Jatropha thus may have a positive effect on SOC in the long term, but further monitoring is required to confirm this trend for our case. Despite the negligible SOC sequestration estimated in our case study, SOC should not be disregarded from future C sequestration assessments of Jatropha. The high share of SOC in the total ecosystem C stock (38-64%, which agrees well with the range of reported values for West African savannah systems, i.e. 30-90% (Tschakert et al., 2004; Bationo et al., 2007; Takimoto et al., 2008)) highlights the importance of this C pool and stresses the need for good crop management practices (Lal, 2004) to avoid the loss of SOC during cultivation. Converting fallow land to Jatropha has a clear negative impact on C stocks (figure 4.8). The biomass C stock is affected the most, with an average decrease of 24 t C ha -1 (71%). Due to the protection of some tree species, such as shea trees (Vitellaria paradoxa C.F. Gaertner), not all biomass is removed upon LUC. These few mature trees still make up the largest fraction of biomass after five years of Jatropha cultivation (figure 4.8), which clearly shows their benefits from a GHG mitigation perspective. In addition to biomass C, on average 8 t SOC ha-1 (34%) is lost, which is at the lower end of the 20-60% range that is reported in literature for the conversion of natural land to cropland in similar conditions (Elberling et al., 2003; Walker & Desanker, 2004). Although the total initial C debt of 32 t C ha-1 is at the lower end of the wide range found for various biofuel crops and LUs (0-940 t ha-1, Fargione et al., 2008), it still represents a considerable environmental impact. These results are in line with the estimations of Achten et al. (2013) for the conversion of scrubland in semi-arid regions (24-28 t C ha-1). 75

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On average 11.3% of this debt is paid off after four years of Jatropha cultivation through biomass sequestration. The remaining C debt of 28.4 t C ha-1 can be further compensated through substitution of fossil fuels by the produced biodiesel and additional C sequestration in the plantation. For this case study, an average biofuel C repayment rate of 0.09 t C ha-1 year-1 was estimated based on a Jatropha life cycle analysis in Koulikoro, assuming a seed yield of 0.6 t ha-1 year-1 (Almeida et al., 2014). Hence, it would take on average 306 years of Jatropha cultivation to repay the C debt. One can conclude that Jatropha plantations should only be established in degraded ecosystems with low initial biomass and soil C stocks, as is also recommended by e.g. Achten & Verchot (2011) and Romijn (2011). However, the initial C stocks in soil and biomass are not the only factors that should be considered. Oil yields on degraded lands are often low, giving rise to low repayment rates and hence long repayment times. Low yields incentivize farmers to shift Jatropha to more productive lands, containing more C and thus giving rise to higher C debts. This trend may cause additional indirect LUCs, which again increase the C debt (Achten & Verchot, 2011; Lapola et al., 2010). Hence, there is a need for more agronomical research aiming at stabilizing and optimizing Jatropha yields on degraded lands. Still, in regions such as the Sahel, where rainfall is erratic, significant annual yield variations are expected, causing C repayment rates to be highly variable from one year to another. The C debt often neglects the fate of the C stocks in the biomass and soil of Jatropha plantations. While the C sequestered in biomass will be in principle released after the rotation ends, the variation of SOC is unknown. Due to the lack of chronosequences and the absence of correlation with age (Baumert, 2014), it is not possible to infer from the data here presented or any other published data whether there is sequestration or loss of SOC throughout the lifetime of a Jatropha plantation. While a trend of SOC sequestration may speed up the repayment time, a trend of loss will postpone it. The repayment of the C debt is further based on the assumption that there is 100% substitution of the fossil fuel in question. However, it is not always the case. It can be argued that in Mali the availability of a liquid fuel in a rural setting may instead add to the energy which is already consumed, given that the energy demand is increasing rapidly in this part of the world (CIA, 2014). Alternatively, Jatropha oil or biodiesel can replace fuel wood or charcoal, which are the most common fuels in the region, particularly in rural areas (Dasappa, 2011). These fuels are obtained with negligible energy input. In case they are taken from sustainably managed woodland they are fully renewable and truly C neutral. In such case, the

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repayment would not exist. Jatropha oil can be diverted to the cosmetic industry or small scale soap production instead of energy (Contran et al., 2013), a very attractive practice to smallholders for its simplicity and profitability. Based on the LCA model of Chapter 3, the ratio of materials stated in Contran et al. (2013) and assuming that the reaction is heated with fuel wood, the global warming potential (GWP) of Jatropha-based soap production in Koulikoro would amount to 1.2 kg CO2 eq kg-1 soap. The GWP of soaps present in ecoinvent® v3 database (The Swiss Centre for Life Cycle Inventories, Switzerland) is on average 5.6 kg CO2 eq kg-1 soap. With soap production, the C debt here reported would be repaid within 61.3 years. Hence, Jatropha C debts seem to be thoroughly high and its repayment questionable and sensitive to non-objective substitution pathways, inciting the revision of mitigation objectives surrounding Jatropha projects.

4.5. CONCLUSIONS In this study, biomass and soil C stocks were measured in Jatropha plantations, cropland and fallow land in Mali to assess the impact of LUC on the crop’s C balance. These empirical, local C data can serve as valuable input for local Jatropha biofuel policy (Witcover et al., 2013), Jatropha sustainability and C sequestration assessments (van Eijck et al., 2014a) and for estimating benefits from selling Jatropha based C credits. Moreover, these results can be further applied for the calibration and validation of local LUC and SOC models (e.g. RothC, DayCent), as data in this region is particularly scarce, despite its large potential for SOC sequestration (Saiz et al., 2012). These results cannot be generalized without caution, since C dynamics are known to be highly dependent on environmental characteristics and local management factors (Powers et al., 2011). The spatially paired site design applied in this study only results in an approximation of the C dynamics under Jatropha. Monitoring studies using a stock change approach with a time span of more than five years should be conducted on Jatropha plantations to further assess its biomass and soil C sequestration potential, as data on plantations older than five years is particularly scarce for this biofuel tree (Vang Rasmussen et al., 2012). In addition, there is a need for more detailed studies that quantify the amount of C lost during LUC, e.g. using the eddy covariance technique (Zenone et al., 2013). Finally, future studies aiming at assessing the effect of LU on SOC are advised to not only determine total SOC stocks, but also to look at the different fractions of SOC (particulate organic matter (OM) versus stable OM; fractions

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of humic acid, fulvic acid and humin), as this can provide valuable information regarding the quantity of newly sequestered SOC (Guimarães et al., 2013; Zimmermann et al., 2013). Land conversion to Jatropha plantations can have a considerable impact on C mitigation, depending largely on the previous LU. It is however important to realize that Jatropha cultivation and the associated LUC can have various other environmental, economic and social effects, either positive or negative (Achten & Verchot, 2011). Research has pointed out positive effects on the level of increased erosion control (Reubens et al., 2011) and, on the societal side, empowerment of rural communities involved in smallholder projects (van Eijck et al., 2014a). Negative issues pertain mostly to failure in secure access to food and land as well as economic unviability (Skutch et al., 2012; van Eijck et al., 2014b). Hence, this study should be seen as part of a larger complex story and should be complemented with a more holistic study in which all these other impacts are included.

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5. GREENHOUSE GAS EMISSION TIMING IN THE GLOBAL WARMING POTENTIAL OF JATROPHA Adapted from Almeida J*, Degerickx J, Achten WMJ, Muys B. Greenhouse gas emission timing in life cycle assessment and the global warming potential of perennial energy crops. In preparation for International Journal of Life Cycle Assessment. *Designed the study, executed assessment and result analysis and wrote the article.

ABSTRACT Presently, climate metrics in life cycle assessment (LCA) aggregate the emission of greenhouse gases in one impact-triggering instant, rather than accommodating its distribution along a timeline. This static approach overlooks changing sensitivities of physical, social or economical receiving environments to emissions. There have been efforts to make the global warming potential metric (GWP) dynamic, motivated by the case of temporary carbon storage, particularly in wood products, which are claimed to mitigate climate change. In this article, we test the use of dynamic LCA on bioenergy production from Jatropha, an oilyielding tree offering temporary carbon sequestration in its biomass. The final product of the system is the electricity generated on site with Jatropha oil, thus being short-lived. We built a life cycle inventory including carbon stock changes in soil and biomass upon two land conversion scenarios (Jatropha after cropland and fallow land) in Mali. The assessment was made for one and for 10 consecutive rotations and yielded dynamic emission profiles over the years. We found that dynamic LCA is sensitive to variations in emission over time, and that it resulted in lower GWP compared to the conventional static approach following IPCC rules, except for early times of analysis. But the largest variation occurs between readings at different times of analysis within dynamic LCA. Hence, although a difference can be seen between approaches, the strongest influence on the results is the choice of time of analysis, which remains subjective since the grounds for choosing it are unclear in dynamic LCA methodology. In the particular case of this short-lived product, we did not find an added value to the provoked increase in complexity to LCA.

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5.1. INTRODUCTION The Global Warming Potential (GWP) is the widely accepted indicator recommended by IPCC since 1990 to convey the impact of greenhouse gas (GHG) emissions on climate change (Shine et al., 1990). The GWP of a GHG reflects the radiative forcing integrated over a time horizon (TH) of a given GHG relatively to the integrated radiative forcing of CO 2 (equation 5.1) (IPCC, 2007).

(eq. 5.1) With GWPTH i = the global warming potential of an emission at time 0 of substance i caused by its radiative forcing in the period between time 0 and time horizon TH; ai = radiative efficiency of substance i, Ci(t) = time dependent decay of substance i. In life cycle assessment (LCA), the impact category global warming potential is then calculated using GWPTH i as characterization factor of the respective greenhouse gases for a chosen TH (equation 5.2). The LCA impact category GWP reflects the cumulative effect of all GHGs emitted in a life cycle, by summing the GWPs of emitted GHG by their emitted mass. (eq. 5.2) With GWPTH = the global warming potential of all emissions during the life cycle caused by their radiative forcing between time 0 and time horizon TH, assuming that their emissions took place in year 0; gi = cumulative emitted mass of substance i in all the life cycle. The GWP is a relatively simple metric merited with its transparency and its feature of combining the impact of multiple emission pulses of multiple GHGs in one indicator. However, its design has been the target of criticism. The static approach of time implies that all emissions taking place throughout the life cycle are considered to take place at time 0 of the integration interval of equation 5.1. The practitioner can close the integration interval at 20, 100 or 500 years. Opting for a shorter TH emphasizes short-term climate change processes, while a longer TH focuses on longer ones. This is because the TH influences the weight of timing of emissions as it serves as a cut-off point beyond which the radiative forcing is ignored.

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Despite the lack of scientific consensus, a subjective policy-driven choice led to 100 years as the most commonly used TH in GHG accounting and impact studies, including LCA (Brandão et al., 2013). Moreover, regardless of which TH is chosen, the pre-determination of 20, 100 and 500 years of time of analysis is perceived as an arbitrary choice (Brandão et al., 2013; Kendall, 2014; Levasseur et al., 2010). In addition, although emission pulses occur at different moments in the life cycle, the GWP does not acknowledge emission timing. Instead, as explained before, it actualizes all emissions to moment 0 of the time of analysis and cuts off their radiative efficiency at a moment independent of the duration of the life cycle. Hence, the time of analysis is abstract from the temporal boundaries of the life cycle, creating a temporal inconsistency (Levasseur et al., 2010). This lack of regard for life cycle temporal boundaries, allied with the choice of TH, is understood to skew the importance of different emission moments because, while a short TH burdens early emissions, a longer one belittles them. This can be a limitation in the LCAs of products with long life cycles because it prevents postponed emission (e.g. through C sequestration) to be valuated (Levasseur et al., 2012). The significance of emission valuation is explained by the notion that emissions occurring today are more harmful than those occurring in the future, seeing as climate change action ought to be taken as early as possible in order to prevent greater damage. In addition, the rate at which emissions occur and the background concentrations they encounter in the atmosphere will affect their impact on the atmosphere (Cherubini et al., 2014; Reisinger et al., 2011). The latter impacts are inherently ignored by the GWP, which has motivated arguments in favour of its replacement with so-called absolute metrics, such as temperature change (Aamaas et al., 2013; Cherubini et al., 2013; Kendall, 2014; Olivié & Peters, 2013; Peters et al., 2011). There is not yet a consensual methodology to cover those temporal fluctuations, nor to address the issue of TH choice, in LCA (Brandão et al., 2013). Several authors have come forward with improvements or alternatives to the conventional methodology, either through the use of absolute metrics or by resolving the GWP in time. Time-mindful GWP assessments have been proposed by O’Hare and colleagues (2009), who oppose the cumulative radiative forcing of a fossil fuel to that of its biofuel substitute. In that study, a comprehensive inventory including GHG emissions from land use change (LUC) was temporally discriminated through a cost-benefit analysis, thus discounting the value of emissions (O'Hare et al., 2009). Discounting is, evidently, highly sensitive to the discount 81

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rate, whose definition is non consensual. Both ISO and PAS guidelines do not accept discounting in LCA (Finkbeiner et al., 2014). Yearly GHG accounting through the entire production chain, including LUC-related emissions, requires measurements in every production-related location and yields a timespecific life cycle inventory (LCI). An example of such a time-step approach is Annual Basis Carbon accounting, which aims to cover all GHG emissions from source and end-use by making the exhaustive accounting of biogenic and non-biogenic emissions at every year of the life cycle (DeCicco, 2012). Although formally presented as an alternative approach to LCA, these annual GHG inventories can rather be seen as the basis of time specific LCA. In fact, two other approaches dealing with the static nature of GWP and its use in LCA start from the creation of times-step LCIs. These methods address the temporal disparity between the GWP’s time of analysis TH and the actual duration of evaluated life cycle. Kendall et al. (2012) proposed Time Adjusted Warming Potentials, where emissions are integrated in time between their time of occurrence in the life cycle and a TH. This method maintains the concept of TH as a cut-off but adds THs at 30 and 50 years. Levasseur et al. (2010) developed the temporally dynamic GWP (henceforth designated DynGWP), which, like the static approach, expresses a radiative forcing per emitted mass of GHG integrated in time of analysis. The crucial difference relative to the IPCC’s GWP is that a DynGWP can be calculated for any time t for an emission occurring at a time j, instead of a set of fixed THs for emissions occurring at one single pulse at time 0. This can be seen in the following manner: while the time frame of the time of analysis of the IPCC’s GWP consists of three snapshots at 20, 100 and 500 years, the value of DynGWP can be read at any year t of a radiative effect curve up to 2000 years (figure 5.1). In addition, while the IPCC’s approach counts all emission pulses as occurring in moment 0 of the time of analysis, DynGWP adds emission pulses to its calculation as they happen (figure 5.1). DynGWP operates through dynamic characterization factors (DCFs) specific for each GHG and each moment of their decay curve, which are applied on a temporally explicit LCI. The DCFs are analogous to the numerator in equation 5.1, as they too consist of the integral over time of the radiative forcing of a GHG. Only the integrated time interval of DCFs runs from the real moment of emission (j) and any year (t) elapsed after it, instead of running between year 0 and a TH like in GWPTH (equation 5.3). The DCF of a GHG is then treated in i reference to the radiative forcing of CO2 over the same interval, similarly to the equation describing the GWPTH i . To calculate a temporally differentiated GWP of a life cycle DCFS 82

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are multiplied by the mass of the emission pulses (g) that have been inventoried in annual time steps (equation 5.3). Adding up the results yields an annual cumulative GWP at any year up to 2000 years (equation 5.3).

-

-

-

With DynGWP t

rel =

(eq. 5.3) -

the dynamic global warming potential of the life cycle at time t, ai =

radiative efficiency of substance i, gi,j = the emitted mass of substance i at time j, Ci(t) = time dependent decay of substance i. Dynamic LCA aims at bringing consistency between timing of emissions and timing of impact, enabling the differentiation of moments of sequestration and subsequent release of biomass C. The goal is to increase analytical accuracy of LCA and of valuation of carbon storage. It opened up new debates on decision making in LCA and introduced new perspectives in the analysis of long life cycles, such as those of forestry or cement products (Dyckhoff & Kasah, 2014; Levasseur et al., 2013; Yang & Chen, 2014).

Figure 5.1 (next page) – Difference between static and dynamic GWP approaches relative to emission timing and cut-offs at time horizons of 20, 100 and 500 years and their link to emission pulses and their radiative forcing. The example is a fictitious life cycle with emission pulses consisting of 50 and 25 kg CO2 (blue) at years 0 and 40 respectively and 2 kg CH4 (red) at year 124 (A). B depicts the radiative forcing of these emission pulses. The charts in C and D show the DynGWP and the classic GWP of the life cycle, respectively, where blue indicates a climate impact of CO2 emissions, red of CH4 and purple of both.

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In this article, we evaluate if dynamic LCA is useful for the case of medium-rotation perennial land-based bioenergy systems generating short-lived products and how this is affected by length of the period of land use occupation. Our case study is a Jatropha plantation in central Mali yielding oil for energy generation. We investigate if the choices of TH on the one hand and between conventional and dynamic GWP calculation on the other hand affect the LCA results of a perennial crop. While previous research on this case and methodological duality, assuming a simple one rotation length life cycle and no emissions from LUC, suggested that TH is the single most important factor to be considered (Almeida et al., 2013), we test these conclusions with addition of emissions from LUC and for various durations of the life cycle, and therefore short (20 years) and long (more than 200 years) land occupation periods. The hypothetical scenarios represent two arbitrary periods of one and 10 rotations, respectively, occurring on the same land area in succession. Since there is no clear consensus on the extent of temporal boundaries in the LCA of bioenergy projects, it was here considered that the boundary runs from land clearing at the beginning of one rotation to land clearing in the end of that or the nine following rotations, respectively. Revegetation at the end of cultivation was therefore excluded. The scenarios also compare the conversion of cropland and of fallow land to Jatropha.

5.2. METHODS

5.2.1. SCOPE AND SYSTEM DEFINITION The system under investigation simulates a hypothetical cradle-to-grave configuration for electricity generation from Jatropha-based biodiesel in the town of Koulikoro, Mali. The production chain described here simulates a production system based on operational conditions previously reported for Koulikoro (Almeida et al., 2014b – Chapter 3). The cradle-to-cradle system consists of three main stages: cultivation, biofuel production and biofuel use. Cultivation includes the operation of mechanically removing previous vegetation and ploughing, and the production and use of fertilizers and pesticides. Weeding and harvesting are done manually. Cultivation takes place in the vicinity of the facilities where extraction of oil takes place. These latter operations include required infrastructure and electricity from local, fossil diesel-based generation. System boundaries include the

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production of inputs to the system and direct emissions from fertilization, burning fossil diesel for electricity in generators and also tailpipe emissions from the combustion of Jatropha oil in generators (table A8). The transport of system inputs imported to Koulikoro and the distribution of the oil to end users are excluded. The LCA model was parameterized with direct observations and data from literature, ecoinvent® v2.2 (Ecoinvent Center, Switzerland) and GREET 2013 (Argonne National Laboratory, USA) (table A9). Allocation to by-products was avoided by system boundary expansion. It was considered that Jatropha seed cake, the by-product from oil extraction, avoids the production of N fertilizer, on the basis of equivalent N content (table A9).

5.2.2. LIFE CYCLE INVENTORY The GHG emissions related to land use were part of the analysis, including CO 2 fluxes related to sequestration/release cycles in biomass and soil. It was considered that a decrease in the stock of the soil and biomass pools between two consecutive years consists of an emission, whereas an increase implies sequestration. Two direct LUC scenarios were evaluated, i.e. Jatropha establishment on (i) former cropland and on (ii) fallow land. Carbon sequestration in Jatropha roots and stems was assumed to follow the growth curve published by (Achten et al., 2013) to a maximum C stock of 10.23 t ha -1 when the plantation is 20 years old. The biomass C stocks of adjacent cropland and fallow land measured by (Vervoort, 2012) were used as stand-in for previous land use. This study observed that in both land uses, there are trees (e.g. shea trees– Vitellaria paradoxa) that are not felled due to cultural and economic value. Since fallow land in this location remains undisturbed for long time periods, it accumulates more tree biomass. We assumed that in Jatropha plantations the trees present in cropland remain, and that the trees present in fallow land are felled to the same tree density found in cropland. Hence, it was assumed that the conversion from cropland does not entail a C emission, and that the conversion from fallow entails a loss of 6.47 t C ha -1 in the shrub layer and 18.22 t C ha-1 in the tree layer (table A10) (Vervoort, 2012). Soil C stock changes were modeled with RothC v.2.1. (Coleman & Jenkinson, 2008), which has been successfully tested in the Sahel (e.g. Nakamura et al. (2011) and Takimoto et al. (2009)). The model was used to simulate the effect of land conversion from fallow land and from cropland to Jatropha and the land occupation with Jatropha. This was done by doing two 86

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separate runs, one for the conversion of fallow land and another for the conversion of cropland, starting the model with one of the initial land uses, changing the land cover to Jatropha and maintaining Jatropha for one or 10 rotations. RothC requires the input of soil parameters, litter fall and manure inputs for all land uses and climate data. The initial soil C stocks and the clay contents of cropland and fallow land conversion scenarios were approximated by the SOC levels and clay fraction reported for respectively cropland and fallow in Koulikoro at 30 cm depth (table A10) (Degerickx, 2012). The remaining soil parameters (microbial biomass, decomposable and resistant plant material, and humified and inert organic matter) were estimated with pedotransfer functions designed for use with RothC (Falloon et al., 1998; Weihermüller et al., 2013). Total annual C input from fresh leaf litter and (manure on cropland) in the initial land use was retrieved from literature (table A10). Cropland was assumed to consist of pearl millet and maize intercropping obeying the cropping calendar in this region of the Sahel, with soil preparation starting in April and harvesting finishing in December (ACAPS, 2012; Shetty et al., 1991). C inputs from leaf litter in Jatropha plantations was assumed to increase proportionally with tree age until canopy closure at 7 years (table A11). This timing was gauged according to literature sources and expert observations, taking into account that there is little biomass production (Almeida et al., 2014b; Baumert, 2014; Vervoort, 2012). No pruning is done, so litter input pertains only to leaf shedding. Litter fall intensity was estimated with literature data for 2 and 4 year old trees plantations in a semi-arid environment in India and extrapolated linearly for years 1 to 7 (Nallathambi Gunaseelan, 2009). Its monthly distribution was assumed to consist of one main shedding period after the rainy season (September – November), whilst the leaves start sprouting with the onset of the rains in May. The soil was considered uncovered from December to April until the canopy cover closes. Soil was considered to be covered year round from then onwards. Climatic data were extracted from the Local Climate Estimator tool (FAO, 2013). The modelling was set to start in August, when the soil profile is assumed to be at field capacity (Kaonga & Coleman, 2008). The impact assessment was first performed for one rotation and later also for 10 successive ones. For the consecutive rotations the same emissions from the production chain and biomass were used, whereas C fluxes from soil were modeled with RothC in a continuum over 10 rotations. The inventory was divided in annual time steps, discriminating emission sources, meaning that for each year in the 21-year rotation, there is a set of emissions from land use and from 87

GHG emission timing in LCA and the GWP of Jatropha

the operations in the production chain (figure 5.2). In the first year of each rotation the land is prepared to establish Jatropha in the next year, which means land clearing in the first rotation after fallow, land preparation in the first year after cropland, and destruction of the old plantation in all consecutive rotations. From year 2 until year 21 of each rotation Jatropha is managed during 20 years. In the third year of management the yield reaches acceptable levels to start the production of biodiesel. A constant harvest of 1 t ha-1 dry seeds was considered for the rest of the rotation length (17 years). This value was extracted from the predicted yields map of Trabucco et al. (2010) and is specific for the region of Koulikoro. This yield proxy was used because not sufficient yield measurements are available and because there is no known empirical relationship between yield and biomass growth for Jatropha (Almeida et al., 2014b – Chapter 3). When successive rotations are analyzed, the above-described calendar of operations was repeated. The exception is the production of equipment and infrastructure, which only occurs once at the beginning of the life cycle.

Figure 5.2 – Distribution over time of the Jatropha production system in Koulikoro. The grey arrows indicate the production processes, while the white arrows show their distribution along one rotation lasting 20 years.

5.2.3. GLOBAL WARMING POTENTIAL CALCULATION The result of each annual LCI was converted into an inventory of GHG. For some processes, direct emission factors (table A8) were used. The majority of processes were extracted from ecoinvent® (table A9). Their emission factors were calculated from the inventory of airborne emissions function SimaPro® (PRé, the Netherlands). Not all known GHGs were included in this LCI, because not all GHGs found in the SimaPro® inventory were present in the GWP 88

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calculation methods and vice-versa. Therefore, the GHGs included in this analysis were limited to a subset (table A12) present in both (i) SimaPro ® inventory and (ii) the two impact assessment methods used (i.e. the IPCC’s Fourth Assessment Report (FAR) and the Dynamic LCA calculation tool) (IPCC, 2007; Levasseur, 2013). The static GWP was calculated by multiplying the IPCC’s FAR GWP characterization factors (IPCC, 2007) of each GHG, and respective to the IPCC GWP time horizon assess, with its total emitted mass and is henceforth referred to as IPCC GWP. The DynGWP was calculated with the DynCO2 tool of (Levasseur, 2013). The functional unit (FU) of this study is 1 MJ of electricity produced in an average year in a generator consuming 0.09 l oil MJ-1 (Almeida et al., 2014b).

5.3. RESULTS AND DISCUSSION

5.3.1. GREENHOUSE GAS EMISSION INVENTORY The main contributor to the GHG emissions and the GWP is land use change, regardless of previous land cover. Accordingly, the single largest mass of GHG emitted is CO2, followed by N2O and then CH4. Table A12 in Annex 1 shows the inventory obtained for the system processes excluding LUC. Figure 5.3 shows the contribution of the different intervening processes in the production system in terms of total GHG emissions. When fallow land is converted, 86% of emissions arise from biomass clearing and 12% from SOC losses. SOC losses correspond to circa 88% of total GHG emissions of a full rotation when cropland is converted. The following largest source of emissions is oil extraction: 12% in the case of conversion of cropland and 2% in conversion of fallow land (figure 5.3). The fluxes of CO 2 to and from Jatropha biomass are not shown in figure 5.3 as their balance is 0. Although these shares are not the main result of this study, they reveal that the emissions from LUC are higher than those of land occupation with Jatropha. In terms of the two previous land covers, the overall burden of converting fallow land is higher than the burden of converting cropland, due to the higher initial biomass C stock, which is lost (figure 5.3). This illustrates how sensitive the GWP of a project of this nature is to the land cover type it replaces.

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Figure 5.4 depicts the evolution of CO2 emissions from soil throughout 10 rotations. These emissions are highest during the first cycle after LUC. This is due to a stage of rapid loss of SOC until the fourth cycle, after which the SOC stabilizes at around 10.5 t C ha -1, regardless of the initial SOC content. This pattern of rapid loss after LUC followed by stabilization has

Figure 5.3 – Total GHG mass emitted by the stages of electricity production from Jatropha.

Figure 5.4 – Annual CO2 emission (in t ha-1) from soil of the Jatropha plantation starting from establishment on cropland (full line) or fallow land (dashed line). 90

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been observed in other studies, as reviewed by Bessou et al. (2011). The overall decline in SOC can be explained by phenomena described in literature included in the modelling inputs (Bationo et al., 2000). The input of C to the soil from leaf shedding is very low, when comparing with wetter climates, for instance. Moreover, there are periods of soil bareness in the beginning of each rotation until the crop reaches maturity, leading to high C losses. CO2 emissions are higher upon the conversion of fallow land due to the higher initial SOC content. These model outputs have not been validated because a monitoring experiment could not be conducted, but the aim of this simulation was rather to provide indicative values that contribute to the performance of the GWP indicators under study, than to gain in depth understanding of SOC dynamics under Jatropha over time. In addition, because all plantations in the area were young and SOC did not correlate to age, a chronosequence from field measurements could not be derived for a 20 year cycle (Degerickx, 2012). However, SOC values measured during a first rotation in fields of several ages fall within the simulated range for the first cycle of 14 to 21 t C ha-1 (Degerickx, 2012). The RothC model was chosen due to its robustness, simplicity, and low data requirements, all of which match the small body of knowledge available on Jatropha cultivation/agronomy. However, it does not consider certain cultivation aspects that possibly influence these results. For instance, soil cover is only represented by a binary value without the possibility to specify its value through time and hence does not reflect spacing, canopy development, variable cover with shed leaves nor the presence of understory vegetation. The results are also heavily reliant on assumptions on litter fall data, since it influences how much C supplied to the soil in Jatropha plantations. The lack of field data on the SOC of older plantations and the high spatial variability of such measurements thwart the validation of these results. As such, they should be considered carefully as they are not a thorough assessment of the impact of Jatropha on land occupation. Proposed guidelines for the assessment of ecological impacts of LU and LUC (Koellner & Scholtz, 2008) include the period of re-establishment of the previous land use, after the occupation, in this case, with Jatropha, which was excluded from this LUC emission calculation. Since it would lengthen the life cycle length in 1 year (in the case of conversion of cropland) to over a decade (in the conversion of fallow land), the effect in these results would be smearing the initial emission from land conversion over a longer period. In addition, the burden of GHG emissions from LUC attributed to Jatropha could be reduced due to the sequestration of C in the growing biomass.

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GHG emission timing in LCA and the GWP of Jatropha

5.3.2. GLOBAL W ARMING P OTENTIAL ASSESSMENT The charts in figure 5.5 show the results of the impact assessment with the IPCC GWP and DynGWP for one and 10 rotation cycles. The IPCC GWP results are calculated and reported for discrete time horizons (20, 100 and 500 years), while the DynGWP is calculated and reported showing continuous curves with a time of analysis until 350 years. The values in figure 5.5 correspond to the impact of 1 MJ of electricity generated over an averaged year during the rotation(s) length. For this reason, albeit accumulating more emissions, the GWP of multiple cycles seems to be lower, particularly when comparing the dynamic modelling. This is even more evident in the conversion of fallow land. This is because the largest GHG emission pulse, from the lost biomass upon land clearing, smears over the longer life cycle and also decreases considerably along multiple rotation cycles. Within any time of analysis (table A13), the investment in multiple rotations is more advantageous than in a single one. The IPCC GWP varies very little with the different time horizons, being slightly lower at 500 years. On the other hand, the DynGWP decreases with longer times of analysis, plummeting sharply in the first decades of t and stabilizing afterwards. The values of DynGWP of the single cycle simulations, after those first decades, are lower than those of the IPCC GWP, while the multicycle simulations result in very similar figures throughout (table A13). The stabilization occurs later in the conversion of cropland than of fallow land. The single largest emission pulse common to all scenarios is the clearing of biomass at the beginning of the plantation. In the single-cycle simulations, it drives the emission peak in the first year followed by a decrease in the DynGWP. This decrease is due to the fact that the sequestration in Jatropha biomass offsets the remaining annual emissions, reason for which there is no build-up of GHG concentrations. The decrease is more pronounced in the case of conversion of cropland due to the lower initial emission from biomass clearing. In the multi-cycle simulations, the successive Jatropha clearings are immediately compensated by another round of carbon sequestration over time. This irregularity of the emission pulses throughout the years is barely visible in the DynGWP curves because of the small emission pulse from clearing the Jatropha plantations. Such curves had not been seen in a previous analysis which excluded the emissions from LUC (Almeida et al., 2013).

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Figure 5.5 – IPCC Global Warming Potentials (A) and Dynamic GWP (B) of Jatropha biodieselbased electrification upon conversion of cropland and fallow land. JC stands for Jatropha.

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GHG emission timing in LCA and the GWP of Jatropha

This suggests that in the LCA of short-rotation perennials dynamic LCA can yield lower GWPs in comparison with the classic IPCC estimations. In fact, this discrepancy is inherent to the methods. In the IPCC approach, the net emissions of the entire life cycle reached the atmosphere in the first year and their effect is traceable after 100 and 500 years. Dynamic LCA instantaneously captures the gradual release of GHG alternated with its sequestration. This means that besides the initial peak of GHG release, there is little variation in GHG concentrations originating in this life cycle, in which each emission pulse is partially offset by a moment of sequestration. Because the GHG decay rates and radiative forcings are the same in either methodology, the progression of the DynGWP lags behind.

5.4. CONCLUSIONS From the practitioners’ point of view, if standard product profiling is the goal of the LCA, we cannot conclude whether or not this type of life cycle elicits the need to use a temporally specific GWP. Because it deals with a short-lived product (oil which is consumed immediately for electricity) and because SOC does not accumulate in the plantations, the period of temporary carbon sequestration in the biomass is short. In this particular case, the practitioner may opt for more immediate metrics, such as the IPCC GWP, which also eases the choice of time of analysis. There is a much higher variability between readings taken at different, wide-apart t (such as 20, 100 and 500 years, too match the THs of static GWPs) in dynamic LCA. Contrarily to what was intended by the method, this does not ease the decision of cut-off moment and, to our view, increases reporting complexity (Dyckhoff & Kasah, 2014). While this reasoning holds for amortized GHG accounting, which spreads their impact over a time of analysis, it does not address the aim of systematically and readily account for the implications of the length of the life cycle and its related land occupation period. In fact, the effect of choosing a cut-off moment in a time of analysis depends directly on these two aspects. In the specific case of climate mitigating technologies, such as biofuels, it can be ventured that the carbon debt and repayment time are clearer in communicating the impact of land occupation on the GWP. Nonetheless, dynamic LCA may play a role in the environmental profiling of any type of land-based system in a wider global warming assessment view. Its temporal explicitness matches that of other properties of the land cover, which operate directly on radiative forcing. For instance, there are efforts for recognizing the importance of albedo in climate change as

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well as for its inclusion in LCA metrics (Bright et al., 2012; Muñoz et al., 2010; Ollinger et al., 2008; Port et al., 2012). In addition other temporally dynamic mechanisms are currently excluded from GWP modelling, such as interactions between land cover and land-atmosphere C fluxes, GHG emission rates and the effect of background GHG concentrations (Cherubini et al., 2014; Lammertsma et al., 2011; Ollinger et al., 2008; Port et al., 2012; Reisinger et al., 2011). A temporally dynamic perspective on GHG accounting is central to realize such inclusive pathways for climate change impact assessment.

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6. SPATIAL OPTIMIZATION OF JATROPHA-BASED ELECTRICITY SUPPLY CHAINS Adapted from Almeida J*, De Meyer A, Achten WMJ, Cattrysse D, Van Orshoven J, Muys B. Spatial optimization of Jatropha-based electricity supply chains including the effect of emissions from land use change. Submitted to BioEnergy Research. *Contributed to study design and to modelling, collected the data, analyzed the results and wrote the article.

ABSTRACT As a contribution to realistic integration of Jatropha in rural development after the boom and bust trajectory of this tropical biofuel crop, this article proposes a modelling approach to probe the feasibility of Jatropha-based electrification in rural Africa and the layout of such a supply chain. We applied the optimization model OPTIMASS to define the design of Jatropha-based on- grid and off-grid electrification in Southern Mali with minimal global warming potential (GWP). The model is parameterized with GWP data for all stages of the supply chain, including emissions triggered by land use change (LUC) in function of harvested seeds. The spatial analysis module connected to OPTIMASS enables locating each operation of the supply chain including the cultivation sites. The goal is to assess the size, location and requirements of the necessary infrastructure to reach 10% substitution of fossil fuels for Jatropha in electricity production. This paper explores this possibility for a current and two future electricity demand scenarios: powering only off-grid population and powering on and off-grid population in 2020, both accounting for the 41% increase in rural electrification aimed at by the Malian government. The resulting optimal supply chain layout demonstrates that Jatropha-based electrification risks conflicts with cropland being feasible with an area 17448, 75674 and 106697 ha of land for the respective scenarios. The GWP analysis suggests that even in optimal conditions each kWh generated by the optimal supply chains is always higher than 1 kWh generated with fossil diesel.

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6.1. INTRODUCTION Mali’s current low level of electrification and its population’s increasing demand for electricity has motivated efforts to diversify electrical energy sources and to decentralize electricity generation as mentioned by the national policy for the development of renewable energy (Coulibaly & Bonfigloli, 2012). This policy has set the goals for renewable energies to reach 10% of the energy mix by 2015 (Coulibaly & Bonfigloli, 2012). Furthermore, it aims at stimulating the development of the biofuel sector, particularly to boost local energy generation and to promote rural electrification. Jatropha was identified and encouraged as a potential feedstock to satisfy off-grid and on-grid energy solutions able to ensure energy self-sufficiency and provide additional revenues to small farmers (Achten et al., 2010b; Brittaine & Lutaladio, 2010; Fairless, 2007). This small tree yields seeds, of which the oil can be extracted for direct use or conversion to biodiesel. Several studies indicate that Jatropha’s promise of being a sustainable fuel can be fulfilled in small scale systems meeting the energy needs of local communities in rural areas, rather than in massive production for large scale overseas consumption (Muys et al., 2014; van Eijck et al., 2014a). Motivation has, however, dwindled due to widespread frustration among small and large Jatropha farmers, investors and targeted communities, who faced both agronomical challenges and the lack of a value chain befitting their needs downstream of cultivation (Achten et al., 2014; Favretto et al., 2013; Schut et al., 2011; Skutch et al., 2012). In the midst of the boom and bust trajectory of Jatropha, biofuels became the target of heavy criticism due to estimates that greenhouse gas (GHG) emissions caused by converting land into bioenergy crops may surpass the benefit of replacing fossil fuels (Fargione et al., 2008; Kleiner, 2008). The magnitude of emissions from direct land use change (LUC) depends on local site conditions, on the previous land use and on the fate of C stocks beyond the LUC event. For this reason, the choice of land onto which to implement bioenergy plantations becomes paramount to ensure that they meet their purported goal of mitigating climate change. At a strategic decision level, optimization of supply chains can help to define the long-term geographical layout of biomass production and conversion plants, and to select the optimal technologies for biomass conversion to bioenergy complying with certain goals (Natarajan et al., 2012; Panichelli & Gnansounou, 2008; Tittmann et al., 2010). In order to optimize for minimal environmental impact, it is crucial to identify and quantify the impact at all stages in 98

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the chain, so that the optimization models are fully parameterized with environmental impact information for each operation. This is, however, often not the case, as most existing optimization models focus on individual parts of the supply chain (De Meyer et al., 2014). This can be solved by integrating life cycle thinking in optimization, whereby the supply chain is seen as a system with its own life cycle inventory (LCI) and subject to a life cycle impact assessment (LCIA). Moreover, as supply chains are often spread over a geographical extent, the integration of spatial analysis can support the definition of the optimization problem, the parameterization and visualization of supply chain layouts. The combination of mathematical optimization models, LCIA and spatial analysis has been previously shown suitable to support strategic decision questions on bioenergy supply chains (De Meyer et al., 2013a; De Meyer et al., 2013b; Mele et al., 2011). In this case study, life cycle assessment (LCA) metrics for climate change, the Global Warming Potential (GWP), is combined with decision optimization. The decision environment is implemented in a multi-component modelling setup, featuring a life cycle inventory, spatial modelling and the mixed integer linear programming (MILP) model, OPTIMASS (De Meyer et al., in press). The overall goal is to define the optimal supply chain setup in terms of lowest GWP for on-grid and off-grid electrification with Jatropha-based biofuels in Southern Mali. The main variables to be optimized are the location and dimension of production, processing and use sites. OPTIMASS provides both the spatial configuration of the supply chain and its cumulative GWP. For this purpose, all relevant inputs and emissions of the Jatropha-to-electricity supply chain must be defined, including LUC emissions. To our knowledge, this spatial optimization exercise for improving energy security in developing countries is novel. In addition, the incorporation of a dedicated LUC emissions assessment makes it the most complete GWP parameterization of a bioenergy supply chain optimization model yet.

6.2. METHODS

6.2.1. STUDY AREA The study area is the south of Mali, comprising the provinces of Kayes, Koulikoro, Sikasso, Ségou and Mopti, and the Bamako capital district (total extent 427266.7 km2). Mali is a

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landlocked country in West Africa, with nearly 16 million inhabitants, the majority of which live in this Southern region (figure 6.1) (UNSD, 2014).

Figure 6.1 – Location of the study area, Malian infrastructure of relevance to this study (roads, thermal power plants and electricity transmission network) and location of water bodies and nature reserves.

According to the latest statistics, only 2 .1% of the country’s population has access to electricity (14% in rural areas), and the annual pro capita electricity consumption is 108.5 kWh, one of the lowest in the world (CIA, 2014; Coulibaly & Bonfigloli, 2012; UNSD, 2014). Mali’s electricity mix is partially dependent on imports, with 40% coming from fossil fuels and the remaining 60% from national hydropower (Venugopal, 2014). The low electrification rate, particularly in rural areas, and the dependence of external fuels has motivated a national energy policy targeting a further penetration of 10% renewable sources in the country’s fossil energy based electricity production. Renewables, more particularly Jatropha-based biofuels, are also seen as one of the solutions for off-grid electricity generation in rural areas, meant to increase the rural electrification rate to 55% by 2015 (Coulibaly & Bonfigloli, 2012).

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Predicted Jatropha plantation yields in the study area can range from a few kilogram dry seeds per hectare in the North to nearly 3 t ha-1 in the Southernmost tip of the country (Trabucco et al., 2010) (figure A3).

6.2.2. ELECTRICITY DEMAND SCENARIOS The OPTIMASS tool can be used as a pull model, which means that the optimization is triggered by a pre-set energy demand, rather than by the biomass supply. As such, this exercise tests three electricity demand scenarios that satisfy goals set by Malian bioenergy policy (Coulibaly & Bonfigloli, 2012). Each demand corresponds to an annual amount of electrical energy to be satisfied by Jatropha-based biofuels. The three scenarios differ in the size of the population, the supply setting (on-grid versus rural off-grid) and per user demand to be served by Jatropha-based electrification (table 6.1). All three scenarios correspond to 10% of the fossil-based electricity consumed by a certain population group to be supplied by Jatropha biofuels. In the reference scenario the population already connected to the electrification grid is served. In this scenario the current demand was defined according to the most recent national statistics of 2012 (UNSD, 2014). For the two subsequent demand levels we project onto the year 2020, taking into account an increase in the rural electrification rate to 55% (Coulibaly & Bonfigloli, 2012). The second scenario satisfies 10% substitution of fossil-based electricity required by rural off-grid population in 2020, while the third scenario satisfies on-grid and rural off-grid population in 2020 (table 6.1). The projected population increase (DESA, 2014) and the expected fast annual electricity demand rise of 10% are foreseen in scenarios 2 and 3 (Coulibaly & Bonfigloli, 2012) (table 6.1). It is assumed that hydroelectric stations remain the source of 60% of generated electricity. The total annual demand of Jatropha-based electricity in each scenario is thus calculated according to equation 6.1. (eq. 6.1) This also implies different configurations for the final stage of the supply chain. While in Scenario 1 all biofuel is fired in existing thermal power plants, in Scenario 2 it is used in diesel engines to be installed in areas not connected to the electricity distribution network, and in Scenario 3 it can be used in existing thermal power plants and additional off-grid generators. How much energy is generated in each location and with what technology is 101

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optimized in function of data such as population size, transportation distances, proximity to electricity network and capacity of the thermal power plants. Table 6.1 – Description of the three electrification scenarios and their electricity demand fed into the model.

Scenario 1

Scenario 2

Scenario 3

(current)

(future off-grid)

(future off- and on-grid)

2012

2020

2020

Substitution of fossil energy based 10% electricity

10%

10%

Rural electrification

14.21%

55%

55%

Served population (Million)

4.3

6.8

10.2

Target population setting

On-grid

Rural off-grid

On-grid and rural offgrid

Annual demand per user (kWh)

108.52

232.63

232.63

Year

Total Annual Demand (MWh)

4

4

1.86×10

6.31×10

9.53×104

6.2.3. OPTIMASS The OPTIMASS tool has two operational units: the information system and the optimization model (figure 6.2). The information system consists of a generic and flexible database and the connection to a geographical information system (GIS). The database contains the parameters required in the optimization (e.g. operation characteristics, allowed combinations between operations) (De Meyer et al., in press). The database contains the results of a generic cradleto-gate analysis of the upstream Biomass-for-Bioenergy (B4B) supply chain highlighting six key operations from cultivating biomass until the delivery of the generated electricity: i.e. biomass production and harvest, transport, pre-treatment, storage and conversion to bioenergy (De Meyer et al., 2013a). The connection to a GIS allows for characterization and visualization of the problem and the optimization result and/or computation of spatial parameters involved in the problem. The optimization model in OPTIMASS is a deterministic, static, multi-echelon, multi-product MILP model. This means that it does not consider uncertainties or time, that it handles multiple locations and that it is equipped to handle several products. OPTIMASS is meant to optimize strategic and tactical decisions for all kinds of upstream B4B supply chains based on 102

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the maximal net energy output, maximal revenue, minimal GWP and/or maximal job input. These decisions encompass the selection of the optimal technology and capacity for each operation, for locations, and for allocation of biomass and intermediate products from production sites to operation facilities and between facilities. Therefore, the MILP addresses the problem as an extended, capacitated facility location planning problem in which at each facility the characteristics of the biomass product can change due to handling operations and by-products from the conversion process can re-enter the supply chain (De Meyer et al., in press). De Meyer et al. (in press) covers the description of MILP model as well as an illustration of its functionalities and possibilities based on the supply of biomass derived from low input high diversity systems to anaerobic digesters or composting facilities in a Belgian province.

Figure 6.2 – Overview of the inter-relations between the different components of the supply chain optimization modelling process. The grey boxes represent the three main modules: the land use change emission modelling, the information system, and the mixed integer linear programming (MILP) module. Boxes with thin dashed lines indicate the components holding GHG emissions data. The white boxes represent stand-alone modelling steps. Within the land use change emission assessment, these are the clustering of cells and the RothC modelling for

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Spatial optimization of Jatropha-based electricity supply chains cell clusters. In addition, there is the LCA software SimaPro®, which provides the emission factors for the background data feeding into the information system. Within OPTIMASS there are sub-modules of the information system: the database containing GHG emission information and the query module, which is the spatial analysis component. Several geodatasets were included in the GIS module, which, although not represented in this flowchart, are listed in Annex 1.

In this study, OPTIMASS encompasses integer variables to determine the optimal biomass production sites and the optimal location, technology and capacity of storage, pre-treatment and conversion facilities. Product flows are defined by continuous variables determining the allocated quantities of raw biomass materials and intermediate products from biomass production sites to operation facilities and of raw biomass materials and intermediate products between operation facilities. Transformation coefficients are introduced to consider changes in product characteristics due to pre-treatment operations. The constraints impose physical and regulatory limitations on the combinations between biomass products and operations and operations mutually. Other constraints regulate the product flow by ensuring mass balances within operations and between facilities. A final group of constraints ensures the energy demand or the demand for a certain by-product. For this study, we selected the minimization of the GWP over the whole supply chain as the single objective of the optimization.

6.2.4. NON -SPATIALLY DEPENDENT OPTIMIZATION PARAMETERS

Parameters independent of spatial data are those intrinsic to the inputs and operations of the supply chain that do not change as a function of their location. They are sequence of operations, their inter-relations and the efficiencies of oil extraction and electricity generation. Emission factors of producing a given unit of system inputs, such as the GHG emissions of producing 1 kg of fertilizers and pesticides or one unit of stationary equipment and of transporting 1 ton of oil for 1 km), also fit in this category: although input amounts ultimately depend on spatial parameters, these emission factors do not. The supply chain of Jatropha-based electricity generation can be divided in three main phases: cultivation, biofuel production and electricity generation (figure 6.3). The layout and inputs of

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these operations are based on field information collected from Jatropha projects in Mali, as well as literature (Achten et al., 2008; Chapter 2 and Chapter 3). Input quantification to all operations is given in table A15 of Annex 1.

Figure 6.3 – Schematic supply chain of off-grid and on-grid Jatropha-based electricity production, corresponding to the system boundaries of the LCIA. The grey zones define the three main operations: (a) Cultivation, (b) Biofuel production, (c) Electricity generation. The boxes indicate the operation processes in the supply chain. The remaining elements are the inputs to the supply chain whose emissions of provision and use are included in the LCIA. The arrows represent fluxes of materials and energy connecting the inputs with the operation processes and between operation processes.

The quantified operations and their inputs are depicted in figure 6.3. The operations included in cultivation (figure 6.3 - a) are the preparation of the field, irrigation, fertilization and application of pesticides. Periodical weeding and harvesting also occur, but no related emissions are included as they are done manually. The fruits are then processed by electrical dehulling and the seeds are dehusked with an electrical dehusker and air dried (figure 6.3 - b). Mechanical presses extract 16 g of oil per 100 g of seeds (Achten et al., 2008). Biodiesel is not produced because it has the same energy yield and tailpipe emissions than oil (Soltic et 105

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al., 2009), not compensating for the added environmental burden of transesterification (Almeida et al., 2014b). So both vapour turbine connected to the grid and stationary diesel engines are assumed to run on untreated plant oil. It was considered that with 1 kg of oil large power plants generate 4.5 kWh electricity, while smaller, less-efficient stand alone generators used off-grid generate 3.5 kWh (Achten et al., 2008; Almeida et al., 2014b; Honorio et al., 2003). Modifications to the thermal power plants so as to accommodate biofuels and distribution losses are excluded from this analysis, as it is assumed that all existing thermal power plants in Mali are able to use liquid biofuels to a maximum blend of 10%. The location, capacity and related GHG emissions of power plants are extracted from literature (Davis et al., 2014a). It is assumed that no new power plants are built, whereas the impact of producing new diesel generators in rural “c c

” is taken into account (figure 6.3 - c). By cercles it is

understood the second-level administrative divisions of Malian territory. Storage tanks for oil are also foreseen in the database.

6.2.5. SPATIALLY DEPENDENT OPTIMIZATION PARAMETERS The location-dependent inputs (operation sites and transport links – figure 6.4) are calculated in a pre-processing stage using ArcGIS® software (ESRI, USA), where the intervening processes and their parameters are geo-referenced. These parameters are all possible operation sites and transportation distances, seed yield and land use change emissions. Necessary geodatasets are listed in table A14 of Annex 1.

Figure 6.4 – Potential operation sites.

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It was assumed that potential sites of biofuel production and seed and biofuel storage are located in the vicinity of main towns or main road nodes. The potential sites to implement stand-alone generators are the cercles that are not located in the vicinity (more than 10 km distance) of the transmission network. OPTIMASS determines if dehusking and dehulling occurs at the cultivation site, the biofuel production site, the storage site or the electricity generation site, and assumes that oil is produced in independent biofuel production sites or at the electricity generation site. The transportation distances of all possible paths between two potential operation sites are determined on a layer of the existing Malian road network and stored in the database. Three possible transportation modes are considered: lorry, motorcycle and tractor.

6.2.5.1.

CULTIVATION AREAS AND LAND USE CHANGE

EMISSION

Particular attention is given to the attributes of potential cultivation sites (figure 6.4), in order to calculate LUC emissions (figure 6.2). The study area is divided in a grid of 45×45 km (202500 ha) cells, the spatial resolution being limited by computational power. Each cell was then characterized by an average yield estimated from Trabucco et al. (2010) (figure A3) and an average LUC emission. The average yield and average LUC emission are attributed to the centre of the cell for the purpose of calculating transport distances. Land has been a central topic in the discussion on Jatropha’s sustainability (e.g. preferential occupation of degraded lands to limit LUC emissions and avoid conflicts with food production) (Quinn et al., 2015; Romijn, 2011). OPTIMASS does not determine where Jatropha cultivation occurs within the cell. This implies that any land cover within a cell can potentially be replaced with Jatropha plantations, with the exception of urban areas, water bodies and protected areas (such as natural parks and nature reserves). By considering any land cover as replaceable, OPTIMASS can freely deliberate on the LUC emissions and allocate cultivation to the lowest possible emission regardless of the land cover replaced. The total LUC emission of a cell (LUC Ej) is the CO2 released upon the disturbance caused by removing part of its land cover to establish Jatropha, and is calculated as the sum of the biomass carbon loss and the soil carbon loss.

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As to the biomass C loss (BMCj), only the initial clearing of biomass is included. The C sequestered in Jatropha biomass is excluded because it is assumed to be released again by the end of the rotation, when the standing biomass is removed and decomposes. Because the exact position within the cell of the land conversion to Jatropha is unknown, the emission was calculated as the average per hectare biomass C stock of the cell multiplied by the Jatropha cultivation area allocated to the cell (figure 6.2). The average biomass carbon content per hectare of each cell (figure A3) was calculated from the available Global Biomass Carbon Map of Ruesch & Gibbs (2008). This geodataset provides biomass carbon content (t ha-1) information with a resolution of 1×1 km for the year 2000 (Ruesch & Gibbs, 2008). Concerning soil organic carbon (SOC), the emission from lost SOC is expressed as the fraction of initial SOC lost (or accumulated) after one Jatropha rotation of 20 years. This fraction of SOC emitted (or sequestered in case of SOC gain) (SOCc) is modeled with the process-based agroecosystem carbon flux model RothC 26.3 (Coleman & Jenkinson, 2008) (figure 6.2). In order to simplify the SOC modelling step, the cells were clustered in 10 classes based on clay content, mean annual temperature and total annual precipitation as variables. It has been observed that the variation of SOC upon conversion to Jatropha is determined by initial soil properties, climate and Jatropha’s litter fall intensity (Almeida et al., 2014a). For each cell class, RothC was parameterized with class specific soil and climate data. The SOC at 30 cm depth and clay contents of each cell were averaged from available geodatasets (table A13) with a resolution of 1×1 km for SOC and 10×10 km for the clay fraction. The remaining soil data (decomposable and resistant matter, microbial biomass, humified organic matter and inert organic matter) were estimated from those SOC and clay fraction data using pedotransfer functions developed for use with Roth C (Falloon et al., 1998; Weihermüller et al., 2013). Litter fall data was retrieved from literature on Jatropha grown in different regions of the world and fitted to Mali according to its rainfall range (Nallathambi Gunaseelan, 2009; Negussie et al., (submitted); Wani et al., 2012). As mentioned in the beginning of this section, the total emission of a cell due to land conversion to Jatropha (LUC Ej) is estimated on a hectare basis and then multiplied by OPTIMASS with the area required by the electricity demand, see equation 6.2. This area allocation to a cell is based on the tonnage of seed optimally harvestable in that cell and was derived from the yield map of Trabucco et al. (2010). The geodatasets (table A14) and the detailed description of the steps taken to calculate LUC E is given in Annex 1. (eq. 6.2)

c

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where LUC Ej is the LUC emission (in CO2) of a cell j, BMCj is its current biomass carbon stock, SOCj is its SOC stock before LUC (figure A3), SOCc is the percent change in SOC stock due to one rotation of Jatropha cultivation for a cluster of cells c including cell j. 3.664 is the mass ratio between CO2 and emitted C. Because the optimization responds to an annual demand the obtained total LUC E j needs to be allocated over years. Since Jatropha plantations are considered to have a lifespan of 20 years, SOC emissions after year 20 (which are anyway negligible) are excluded and emissions are amortized the emissions equally over the 20-year period. This period of amortization coincides with the directives in the life cycle GHG assessment guidelines PAS2050:2011 (BSI, 2011). In this manner, LUC emissions were uniformly distributed in time and, thanks to the spatially-based estimation of SOC and biomass C stocks, also spatially distributed as recommended by Davis et al. (2014b). Finally, in order to verify which land covers are displaced by Jatropha plantations, the selected cultivation cells in the optimal supply chains were intersected with a land cover map (EC & JRC, 2003).

6.2.6. CALCULATION OF GLOBAL WARMING POTENTIAL OF SUPPLY CHAIN INPUTS

Background data on the production and use of the materials and fuels used by the supply chain (fertilizers, pesticides, diesel, electricity, transport, machinery and stationary equipment) are extracted from the ecoinvent® v3 database (Centre for Life Cycle Inventories, Switzerland). Because this study models a prospective system with potential impact on policy and on the demand for input products, ecoinvent® consequential LCA datasets are used (box 6.1) (Plevin et al., 2014; Weidema, 2011). These datasets are the result of economic modelling of the impact of increased demand of supply chain inputs on the market (Weidema, 2011). The emissions caused by these background processes were estimated with ReCiPe’s GWP midpoint hierarchical (Goedkoop et al., 2012) in SimaPro® (PRé, the Netherlands). Direct N2O emissions to air from fertilizer application (IPCC, 2006) were also included. Tailpipe emissions are assumed to be the same in generators and thermoelectric turbines and were extracted from the GREET 2013 model (Argonne National Laboratory, USA). These emissions exclude biogenic carbon-based GHGs.

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6.2.7. SENSITIVITY ANALYSIS The sensitivity of the GWP and the geographical layout of the optimal supply chain to LUC emissions and to land availability are tested. For this purpose, OPTIMASS is run with two extra setups: (i) with a limit of 5000 ha to be occupied in each selected cultivation area; and (ii) without taking into account LUC emissions. In these simulations, the remaining parameters remained the same. Despite being parameterized with LCIA data, this study is not an LCA. For this reason, it deviates from the LCA framework, as in not considering the fate of by-products. In LCA byproducts warrant a share of the environmental burden of the whole system (allocation). Alternatively to allocation, by-products can be considered to displace the production of functionally equivalent products, either by substitution or avoided allocation. As the underlying framework of LCIA in this study is consequential LCA, the by-products of the supply chain lead to substitution. Through substitution, the system is expanded so as to include the effects of the addition of by-products to their market, thus substituting the products that satisfy the same costumer needs. This implies that the additional output of byproducts to the market they fit in displaces the marginal supplier of a functionally equivalent product in that market (Weidema et al., 2009). This substitution pathway is identified through market models, in accordance with consequential life cycle modelling. As a result of substitution, the system is credited with the environmental burdens of the displaced product. In life cycle-based optimization there is no guideline on how to handle by-products. A sensitivity analysis is thus conducted of the impact of the optimal supply chain on the GWP, whereby the available by-products (discarded fruit parts and seed cake) are substituting functional equivalents in the market. The first step is to identify such products and a second is to re-calculate the GWP of the supply chain including the substitution.

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6.3. RESULTS

6.3.1. GLOBAL WARMING POTENTIAL The total GWP of the supply chain predictably increases with larger demands, thus being lowest in Scenario 1 (2.27×107 kg CO2 eq) and highest in Scenario 3 (1.38×108 kg CO2 eq) (table 6.2). When evaluated in function of generated electricity, Scenario 2 (rural electrification) has the highest GWP impact: 1.56 CO2 eq kWh-1, while scenario 1 is the most efficient: 1.22 kg CO2 eq kWh-1. Table 6.2 – Total GWP resulting from fulfilling the demands of scenario 1, 2 and 3 and the corresponding GWP efficiency. Scenario 3

Scenario 1

Scenario 2

(current)

(future off-grid)

(future on- and offgrid)

Total supply chain GWP (kg CO2 eq)

2.27×107

9.81×107

1.38×108

GWP efficiency (kg CO2 eq kWh-1)

1.22

1.56

1.45

In all scenarios, the largest sources of GHG emissions are LUC (78%) and cultivation (19%) (figure 6.5). Operations downstream from cultivation are only responsible for 3% of emissions. This includes transport, emissions from energy use for dehusking, dehulling and oil extraction, the production and use of generators (in scenarios 2 and 3) and tailpipe emissions from electricity production.

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Figure 6.5 – Contribution of the stages in the supply chain to the GWP efficiency of each scenario. The stacked bars represent broad supply chain stages.

6.3.2. LAND USE CHANGE EMISSIONS The lowest possible LUC E resulting from Jatropha establishment in a potential cultivation area is 14.7 t CO2 ha-1 while the maximum is 382.5 t CO2 ha-1. When amortized over a period of 20 years, Jatropha plantations emit 0.7 t CO2 ha-1 yr-1, while it can reach up to 19.1 t CO2 ha-1 yr-1 in less favourable parts of the country (figure 6.6 - A). On average, cells have a LUC emission of 210.2 (±292.2) t CO2 ha-1 or 3.7 (±2.9) t CO2 ha-1 yr-1. The CO2 intensity per seed yield ranges from 0.7 to 96.2 t CO2 t-1 seed yr-1 (figure 6.6 - B). The fraction of SOC lost after one rotation of Jatropha ranges from 6 to 37%, resulting in an emission of 4.7 to 61 t CO2 ha-1 among cultivation areas (0.2 to 3.1 t CO2 ha-1 yr-1). The loss of SOC under Jatropha is for the whole Southern Mali on average 24.5 (±10.8) t CO2 ha-1 (1.2 (±0.5) t CO2 ha-1 yr-1). Emitted CO2 upon biomass clearing can range from 2.4 to 102.8 t CO2 ha-1, which is 0.1 to 5.1 t CO2 ha-1 yr-1 of a 20-year period.

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Figure 6.6 – Emissions from land use change amortized over a period of 20 years in function of area (a) and of the yields of Jatropha (b) in each potential cultivation area in Southern Mali. The size of the rings represents the relative amount of CO2 emitted per ha (a) or ton of seeds harvestable in each cell (b).

6.3.3. OPTIMAL SUPPLY CHAIN The same cultivation cell and storage site, located in the province of Sikasso, were selected by the model as optimal for all scenarios (figure 6.7, left column). The amount of dry seeds needed to meet the demand of the optimal supply chains in scenario 1, 2 and 3 is 25823 t, 111998 t and 157911 t respectively. The dry seed yield in this cell is 1480 kg ha -1, which is above the median of the Jatropha yield range in all cells (31 to 2500 kg ha -1). Given the productivity within this particular cell, this requires the occupation of 17448, 75674 and 106697 ha with Jatropha plantations. This corresponds to 9, 37 and 53% of the total cell area. 113

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The emission from LUC in this area is 20.3 t CO2 ha-1 or 0.7 t CO2 t-1 seed yr-1. The lost CO2 due to the removed biomass is 0.7 t ha-1 yr-1 and 6% of the initial SOC content is released, which amounts to 0.3 t CO2 ha-1 yr-1.

Figure 6.7 – Spatial layout of the optimal Jatropha-based electricity supply chains for three different scenarios (a) current scenario; (b) future scenario with off-grid contribution; and c) future scenario with on-grid contribution). The left column contains the results of the base case while the centre and right columns show the results of the sensitivity analysis to a cap of 5000 ha of Jatropha plantations in each selected cultivation area and the exclusion of LUC emissions from the model parameters. Selected cultivation sites are marked with a star, storage sites with a circle, and the placement of generators with a light pentagon. The thermal power plants fed by the supply chain are indicated with dark pentagons of different sizes proportional to the power plant’s capacity. Transport links between sites are represented by grey lines.

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The fruit is dehulled and the seeds are dehusked on site and transported to the transformation site, where the seeds dry and the oil is extracted. In the baseline and rural electrification scenarios (1 and 2, respectively) the GWP of transport is minimized by producing electricity primarily in the vicinity of storage. In Scenario 1, Jatropha oil is used in the four power plants closer to the cultivation site, coinciding with the power plants with higher capacity (figure 6.7, left column - A). In Scenario 2, the nearest off-grid cercle has generators installed (figure 6.7, left column - B), with a respective capacity of 63077.19 MWh yr -1. The electricity demand in Scenario 3 requires more electricity generation sites: 16 in total (figure 6.7, left column - C). In this scenario, thermal power plants outside the study area also receive Jatropha oil, and in addition, the same cercle as in Scenario 2 is electrified. It can be observed that the higher the demand, the farther the oil travels due to the limitations in the capacity of the thermal power plants. The fact that Jatropha is not necessarily cultivated near the use points indicates that the emissions from transporting the oil are lower than the LUC emissions per harvestable seeds of other sites.

6.3.4. SENSITIVITY ANALYSIS With a 5000 ha limit on the plantation size in each selected cultivation area, the complexity of the supply chain increases, with more cultivation sites being selected: 3, 18 and 22 sites in scenarios 1, 2 and 3, respectively (figure 6.7, central column). In relation to the base case, the total area required by plantations decreases in Scenario 1 to 13822 ha and increases in scenarios 2 and 3 to 80110 and 109216 ha. Because there are more cultivation areas, the number of cercles receiving generators also increases to 5 in Scenario 2 and to 4 in Scenario 3. With the constraint on the plantation area The GWP efficiency worsens by 11.6%, 60.4% and 83.9% scenarios 1, 2 and 3 respectively, underlined by LUC emissions rising 19-105%. In case LUC emissions are excluded, the total GWP of the optimal supply chain decreases 86% (table 6.3). These percentages are slightly higher than the contribution of LUC emissions to the GWP because the geographical layout of the supply chain in this sensitivity analysis is readjusted (figure 6.7, right column), slightly lowering transportation emissions. In all scenarios there are two selected cultivation areas, storage sites and electrified cercles, where rural electrification is foreseen (scenarios 2 and 3). These remain in the far south of the study area. The yield is higher than in the baseline scenarios (above 2 t ha-1). The required land area ranges from 10328 ha to 64824 ha, which is 2-16% of the area of the selected cells.

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Unlike other oil-bearing seed plants, Jatropha’s toxicity prevents for now the use of its protein-rich seed cake as animal feed (Contran et al., 2013). A well-accepted valorisation route is to recuperate the hulls, shells and seed cake for soil amendment (Achten et al., 2008; Contran et al., 2013). For that it is necessary to ascertain what type of fertilizer it would displace. It can be argued that the additional available seed cake displaces artificial fertilizers, as was assumed in the sensitivity analysis. This substitution is justifiable in the light that manufactured fertilizer consumption has seen a sharp increase in Mali in recent years (AfricaFertilizer, 2013). However, West African fertilizer markets are small and highly fragmented, increasing costs and hampering access to fertilizers (Morris et al., 2007). The application of mineral fertilizers in Mali is scarce and mostly subsidized, although only for certain crops (Druilhe & Barreiro-Hurlé, 2012; RECA, 2011). In 2004, 44% of all cultivated land was not fertilized and 29% saw organic fertilizers only (CountrySTAT, 2013). Hence, most fertilizer consumption is possibly translated into mixed agricultural-livestock systems. The potential manure production for existing livestock suggests that this is an amply available and more affordable source of soil nutrition (Kamuanga et al., 2008). In small scale production, Jatropha seed cake is known to be mixed with cattle manure and other agricultural residues (Almeida et al., 2014b), ensuring a low-cost soil amendment resource. Although organic waste from Jatropha cultivation is likely to displace some demand for the manure market, a decrease in demand for manure may have effect on its supply chain. This is because manure is a valorised waste product of the beef market, which is the driver of market demand, with a domestic production 40% below internal demand in the region (Kamuanga et al., 2008). Besides, price and tonnage data are virtually non-existent. The substitution ratio can be quantified by physicochemical parameters of the substituted and the substitute products, such as NPK content (table A15). However, physicochemical equivalency is insufficient when dealing with fertilizers, because there are other factors determining to which extent Jatropha waste is adopted by the market, such as adequacy to the targeted crop and spreading mechanisms. Moreover, the cost of transporting it back to cultivated areas might reduce its competitiveness. In this case it was opted to investigate the impact of the substitution of artificial fertilizers by husks and shells from fruit and seed pre-treatment and also of the seed cake left from the extraction of oil. This procedure was parameterized with nutrient equivalencies described in table A15, based on data compiled by Contran and colleagues (2013). The sensitivity analysis to by-product substitution revealed that the GWP could be lowered if, as can be done in LCA, the credit from the value of by-products is granted to the supply chain (table 6.3). By-product 116

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substitution offsets 93% of the supply chain’s emission lowering the GWP to 1.67×106, 1.67×106 and 9.64×106 kg CO2 eq in scenarios 1, 2 and 3 respectively. Table 6.3 – Absolute GWP of the supply chain if substitution of fertilizers by Jatropha byproducts is taken into account, if there is a limit of 5000 ha of Jatropha plantations per cultivation and if LUC emissions are excluded.

Scenario 3

Scenario 1

Scenario 2

(current)

(future off-grid)

(future off-grid and on-grid)

Substitution of fertilizers

1.67×106

1.67×106

9.64×106

5000 ha limit

2.54×107

1.57×108

2.55×108

6

7

1.95×107

Exclusion of LUC E

3.23×10

1.36×10

6.3.5. REPLACED LAND COVER TYPES On the following page, figure 6.8 shows the intersect of the selected cultivation areas with the land cover classification of the area. Cropland is the predominant land cover in the areas selected in the base case as in the sensitivity analysis cases. The lowest overlap with cropland occurs when a 5000 ha extension limit is considered (82-84% overlap) and the highest is in the base case (94%) overlap.

6.1. DISCUSSION

6.1.1. THE OPTIMAL SUPPLY CHAINS The optimal set-ups (figure 6.7) and the GWP efficiency (table 6.2) both suggest that using Jatropha oil for rural electrification is less advantageous in terms of GWP than replacing fossil fuels with Jatropha oil in large thermal power plants. However, this result must be seen in the light that the efficiency of on-grid electrification does not consider distribution losses and any necessary technological adaptations to Jatropha oil use. If data were available, these factors could be included and might sway the GWP efficiency towards rural electrification.

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Figure 6.8 – Land cover types found in the selected cells for Jatropha cultivation in the base case (thin line, top map), without LUC emissions (thick line, top map) and with a 5000 ha limit (bottom map).

The GWP of the optimal supply chains is in line with a Jatropha LCA performed in Mali: 1.73 kg CO2 eq kWh-1 upon the conversion of cropland and 5.14 kg CO2 eq kWh-1 of fallow, with 58-86% of emissions originating from LUC (Almeida et al., 2014a). These discrepancies can be due to the fact that the aforementioned LCAs gauged the GWP of sub-optimal production systems in terms of cultivation management, harvest success and downstream efficiency,

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which are commonplace in Jatropha bioenergy projects (Almeida et al., 2014b; Favretto et al., 2013). In contrast, a supply chain laid out and dimensioned using OPTIMASS inherently results in the lowest possible GWP and the best GWP-to-yield relation taking into account the user-defined constraints and definitions. This emphasizes the idea that there is room for improvement of existing Jatropha initiatives on what concerns their mitigation potential and this can be explored by the approach of combining spatially explicit supply chain optimization and LCA data. The ex-ante modelling can dimension and locate the technologies required to achieve a certain objective, whilst showing promise of better land allocation for energy crop cultivation. Improving the reliability of the selection of cultivation sites can also be foreseen by running OPTIMASS with a more comprehensive set of parameters and objective functions that optimize for socio-economic and other environmental issues. Still, additional risks to productivity inherent to investments in Jatropha remain valid, as will be discussed in this text later on. The GWP of electricity generation from fossil fuel (0.27 kg CO2 eq kWh-1, GREET 2013) is 4.5-6 times less than the GWP efficiency of the optimal Jatropha supply chains. If the substitution of fertilizers with Jatropha by-products is taken into account, then the GWP optimal supply chains is 60-66% lower than if the same amount of energy was provided by fossil fuels. Hence, harnessing the entirety of the Jatropha value chain is crucial do make it GWP competitive relative to fossil fuels.

6.1.2. LAND USE CHANGE EMISSIONS Since direct LUC has been indicated as a crucial factor in the GHG emission profile of biofuels (Njakou Djomo & Ceulemans, 2012), this optimization exercise aimed at allocating land for cultivation, so as to minimize CO2 emissions from land clearing and soil disturbance. The here presented method to estimate spatially allocated LUC emissions can in fact be implemented to any other optimization model capable of spatial analysis. Alternatively, it can be used as a standalone input for GWP quantification methods, such as Annual Based Carbon (ABC) accounting (DeCicco, 2012) or LCA. Given that OPTIMASS deliberates the best compromise between yield and GHG emissions to select the cultivation areas (figure 6.6) in order to design a supply chain with the lowest possible GWP, the impact of LUC in the GWP can differ in non-optimal supply chains with non-optimal LUCE/yield ratios. When seen from the perspective of yield, rather in function of 119

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area, zones with lower yields – corresponding to drier parts of the country – are less desirable even in terms of CO2 emissions. Wetter and more productive areas can present a lower emission/yield ratio, in contrast with the idea that converting non-marginal land to Jatropha has dire consequences to its biofuels’ GWP (figure 6.6). Still, aside from protected areas, the value of previous land use is not considered in this study, and in some areas lower GWP may be a trade-off with other ecosystem services and with increased land pressure (figure 6.8). The estimation of carbon stocks in the current land use is coarse and the impact of Jatropha on SOC stocks is quite heavily reliant on assumptions and extrapolations. As such, rather than a rigorous reference for the carbon balance of Jatropha plantations, the maps in figure 6.6 provide a screening of the predictable impact on soil and biomass carbon stocks if Jatrophaprojects are implemented. This suggests preferential areas but ought to be validated with field measurements prior to the selection of particular sites. Upon this closer look, the conflict with other valuable ecosystem services can also be avoided. The facts that the SOC under Jatropha is empirically modeled and that the modelling is based on data extrapolations from other sites and plantations of several ages is due to limited data availability. This problem of lack of chronosequences of SOC under Jatropha plantations, which prompted the use of a soil carbon dynamics model to predict the evolution of SOC throughout time, has been reported before (Baumert, 2014; Degerickx, 2012). Most plantations are relatively young and, when measured, SOC shows little or no correlation with tree age. Baumert (2014) suggests that there is accumulation of carbon in hedges and young plantations, but her long-term estimations point towards carbon losses, as we also conclude in this study. In an attempt to fill this knowledge gap with a comprehensive empirical model, RothC was chosen due to its relatively limited data requirements, to match low data availability, and because it has been shown to accurately predict SOC dynamics in the Sahel (Nakamura et al., 2011; Takimoto et al., 2009). However, the model shows serious limitations like ignoring mechanical soil disturbance, which emphasizes the need for field validation of the LUC emissions. Further uncertainty arises from the fact that Jatropha is still a semi-domesticated crop from the agronomical point of view, with much work still ongoing in terms of selecting plant varieties and fine-tuning cultivation practices. Termites, pests and the unstable climate of the Sahelian region are also threats to attaining the yields purported by the productivity map of Trabucco et al. (2010). This does not necessarily means that lower yields should be expected

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(Achten et al., 2012), but it serves to remind that the choice of a low LUCE/yield location is not the sole factor playing in the GWP of Jatropha cultivation. Although an indirect LUC (iLUC) emission modelling was not in the scope of this study, we acknowledge that it is another aspect of land occupation that ought to be evaluated. Achten and Verchot (2011) report on the probable magnitude of iLUC impact from Jatropha plantations in Ghana, but only in the case that agricultural crops are displaced, which would be the case for the optimal supply chain. If we assume similar conditions between the two countries, we could expect an additional emission of 11 t CO2 ha-1 upon the establishment of Jatropha in cropland (Achten & Verchot, 2011). The conflict with food production can be foreseen in the optimization procedure by adding a constraint in OPTIMASS denying the occupation of cropland. Since our approach is forced to simplifications due to lack of both computing power as of reliable data for the study region, it has ample room for improvement. The conflict with computing power comes from the trait inherent to integer programming: the time required to solve the problem is exponential to its number of variables (Scharge, 2012). The more potential operation sites there is the more variables the problem includes, as each sites has a number of possible relations with all the other sites. A first step is to increase hardware capacity so as to match the requirements of OPTIMASS when dealing with more potential cultivation sites and, therefore, decrease the size of land units from 45×45 km, gaining precision in the location of cultivation areas. Secondly, because the independent modelling of SOC is very time consuming, it is unpractical to use the current approach to other crops or countries or in a multi-crop and multi-product optimization as OPTIMASS has previously performed (De Meyer et al., 2013b). This can be solved by integrating RothC, or another existing SOC model, into the GIS module in order to automatize the SOC modelling per area. The protocol described in the Annexes is enabled for this possibility since all allocation steps are related to readily available variables, such as climate and soil properties.

6.1.3. BY-PRODUCT HANDLING Because it can have a deep influence in the GWP of the supply chain (table 6.3), it becomes important to model by-product handling in a realistic manner. Jatropha organic waste does not have a price tag or a verified market placement in sub-Saharan Africa. Hence, determining the most likely receiving market is not straightforward. For this reason, the sensitivity analysis 121

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must be seen in the light that the lack of market data thwarts the modelling of a substitution pathway and relies on the assumption that there are no such barriers to market penetration by this product. Hence, substitution can in this case be considered to be arbitrary as displacement routes in avoided allocation.

6.2. CONCLUSIONS Although governments and NGO’s in developing countries promote Jatropha as feedstock to curb oil dependency and endorse rural development, past experience has proven that the launch of such initiatives without sound planning has hampered their success and also the desirable goal to cut back greenhouse gas emissions of energy provision. Optimization modelling parameterized with LCIA and LUC information is here demonstrated to be suitable to plan bioenergy endeavours. Being adaptable to multiple crops, final products, potential sites and operations and optimization objectives (whether or not combined), OPTIMASS can be used to screen the national and regional potential to implement bioenergy policies or to plan for specific goals. This study also shows that in a country with very low electricity consumption rates and low electrification rates, soundly planned bioenergy supply chains can make a difference in reducing the dependence from fossil fuels and electrifying off-grid, rural households. Even in the face of one of the fastest growing electricity demand rates in the world, our results suggest that a Jatropha approach could realize those goals in a sustainable manner. The location of plantations is shown to be crucial to attain low LUC-related emissions and viable yields. This clarification is made possible by expressing the impact of LUC in function of yield rather than land area. Simultaneously, OPTIMASS gauges the required equipment and its capacity and logistic requirements for the supply chain to work. This valuable information covers aspects which have been seen to compromise projects for Jatropha-based electrification in Mali, such as the lack of oil presses (Favretto et al., 2013). Overall, this perspective on resource application shifts the focus from assessing the consequences and potentials of the projected use of a resource to if and how the resource can be used for optimal effect and meeting an anticipated potential.

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7. CONCLUSIONS AND PERSPECTIVES 7.1. RESEARCH QUESTIONS AND CHALLENGES ADDRESSED The overall aim of this thesis was to gauge the greenhouse gas (GHG) emission profile of Jatropha-based bioenergy in Mali, using this case to reflect on the time and space issues of life cycle assessment (LCA). Not only we proposed to assess the emissions of the life cycle and land use change of Jatropha, we also aimed to explore the implications of their temporal and spatial distribution on the behaviour and performance of assessment methods. In the first scene-setting question we asked how much greenhouse gases a Jatropha bioenergy initiative emits as a consequence of its product’s life cycle and the direct land use change (dLUC) it triggers (research question A)? And, if the goal of that initiative is to have a greenhouse gas mitigation effect, are the emissions lower or higher than those of fossil fuels (research question B)? These questions were answered in a generic (Chapter 2) and a site-specific LCA (Chapter 3) and a dLUC impact assessment (Chapter 4). In these chapters we also investigated the factors conditioning the emissions of the Jatropha life cycle and the carbon debt caused by its dLUC emissions. Next we used the same data to parameterize a recently published adaptation of the global warming potential (GWP) calculation method – dynamic GWP -, which considers emission timing, rather than being static like the classical GWP (Chapter 5). Our goal was to know what would be the effect of using dynamic GWPs on perennial bioenergy systems (research question C). We also asked: what is the effect of minding both time and LUC emissions GWP calculations (research question D)? For these latter two questions we needed a time explicit inventory with the annual emissions of the Jatropha initiative, including emissions from land use change (LUC) and land use (LU) (Chapter 5). For this stage we focused only in the Koulikoro region because data quality in Garalo was unsatisfactory. As the Jatropha plantations in our study area are young, creating an inventory for the rest of its lifetime required some assumptions and modelling as well. While emissions from operations such as cultivation and oil extraction can be assumed to be the same every year, emissions of land occupation vary in time, because biomass grows at a non-linear pace and soil processes are dynamic. Although the biomass organic carbon (BOC) accumulation curve of Jatropha had been estimated before, there are no known 123

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chronosequences of soil organic carbon (SOC) stocks of Jatropha in semi-arid regions for the whole lifetime of a plantation. This called for the modelling of SOC in Jatropha plantations in Koulikoro. We did so for two arbitrary periods: one and 10 rotations. This allowed us to probe the effect of using dynamic GWP on two different life cycle lengths (20 years and 200 years), both with different emission profiles due to the land-use related emissions. The last chapter (Chapter 6) is dedicated to the spatial aspects in LCA. Our goal was to answer the question: What is the added value in spatially explicit LCAs, in the case of land based systems such as the Jatropha bioenergy system (research question E)? Can spatially explicit LCAs, through supply chain optimization, assist in land use management towards optimal GHG reduction? We looked ahead to a scenario where Jatropha is a bioenergy feedstock in Mali, fulfilling fossil fuel substitution and rural electrification targets, and optimized such a supply chain at regional scale for maximal GHG emission reduction. Although the optimization procedure is not an LCA, it was parameterized with life cycle impact assessment (LCIA) information of each of its operations. With this purpose, we created a spatially explicit life cycle inventory (LCI) for the whole supply chain and, beyond that initial objective, a spatially explicit LCIA method to gauge LUC emissions. Because supply chain optimization has the potential for strategic and tactical decision support in policy making and because this case study gauges the effect of a prospective production system and its shifts in production of all intervening products, we used the consequential LCA approach in the LCIA of intervening products with consequential unit processes from ecoinvent® v3. Although this alone does not justify the classification of this study as a consequential LCA, by attempting to consider an additional characteristic of consequential LCA, which was the substitution effect of Jatropha by-products, we were left with a clear impression on what consequential LCA can and cannot accomplish when the LCA takes place in a developing country. In Chapter 6 we expanded our study area beyond Koulikoro to the whole southern provinces of Mali and the Bamako district. To calculate LUC emissions, we once again aimed at predicting also the emissions from land use with Jatropha. We did so by repeating the methodology applied previously in Koulikoro and applying it all over the study region. For this we used spatially explicit data on edaphic factors to predict SOC stocks variation under Jatropha and to link LUC emissions with productivity. As for the yield, another important factor determining the GHG balance of the Jatropha bioenergy system, we used yield estimation modeled for these regions of Mali.

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By the recount of this investigative course it can be seen that several hurdles had to be taken and additional research questions appeared. In this closing chapter we look to answer to all issues by synthesizing the main findings of the previous chapters and integrating their main conclusions.

7.2. JATROPHA SUSTAINABILITY AND LAND USE CHANGE Jatropha was identified and encouraged as a potential feedstock to satisfy local off-grid and on-grid energy solutions in developing countries whilst also being an energy carrier with lower GHG emissions than fossil-based ones. In Mali, Jatropha was highlighted in the governmental policy for renewable energy sources for rural development and energy independence. Existing environmental sustainability studies point towards a variable but comparatively lower GWP of Jatropha biofuel relative to fossil fuels. However, as with other bioenergy sources, there is a concern that when GWP assessments would encompass emissions resulting from LUC, this picture could be reversed. Nonetheless, the magnitude of these LUC emissions for Jatropha bioenergy projects is seldom assessed. In addition, most existing Jatropha LCA’s and dLUC assessments rely on estimations of crucial data such as seed yields and C stocks in soil and biomass. In Chapters 2 to 4 of this thesis, we confronted the premise of a consistent and sustainable energy carrier by quantify the GWP of a Jatropha LCA and its dLUC effect. The generic LCA reported in Chapter 2 was run with data collected from young plantations and literature available by the first signs of distress after the Jatropha hype. Consequently, most data was still biased by lack of field experience. For instance, an average yield of 4.3 t ha -1 was proved to be optimistic as field reports on Jatropha’s performance mounted. It was also seen in this LCA to be a crucial factor in the GWP of Jatropha-based biofuels relative to fossil fuels. In contrast with the outcome of the review-based approach, the inventory and the conclusions of the Malian-set LCA of Chapter 3 are less positive. In Chapter 3, variable and relatively high GWPs reflect a rough production system from the efficiency point of view, with incomplete harvests and unstable yields which aggravate emissions per unit of energy harvested. We see that Jatropha can have higher emissions than fossil fuels. Moreover, as reported in Chapter 4, dLUC emissions generate a carbon debt of up to 32 t C ha -1 after the conversion of fallow land. Due to poor performance in reducing GHG emissions from energy

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provision, this debt has a repayment time of several centuries, and it would even be longer, if the assumption of 100% fossil fuel substitution is not met. The repayment time, and whether there is repayment at all, also depends on the evolution of SOC stocks after LUC, throughout the life cycle. The carbon debt typically looks at emissions on the moment of LUC, and impacts during land occupation with Jatropha are scarce. The modelling of SOC content in function of time presented in Chapter 5 and amplified in Chapter 6 replicated a known effect of LUC on SOC stocks: rapid initial loss and stabilization in the long term. Under Malian edaphic conditions Jatropha plantations can lead to, in a 20-year rotation, the loss of up to 37% of initial SOC (LUC from fallow to Jatropha, Chapter 5). Moreover, only towards the end of our simulation period of 10 rotations, the net emission of CO2 from soil stopped. We thus testify that growing Jatropha to reduce GHG can be a frustrating goal, but that the crop shows potential for energy self-sufficiency and for other uses, such as its more traditional purpose of soap production. How accurate and how large the reduction can be depends largely on a case-to-case assessment of dLUC emissions and on yield levels and reliability. It may also depend on the methodology used to estimate this reduction. In fact, by using attributional LCA (as we have done) we can merely compare two technologies and their GWP, not assess an emission reduction potential. This was the case of the LCAs reported in chapters 2 and 3. In order to estimate a GHG reduction in transitioning from a technology to another, the indirect effects of that transition must be assessed. The appropriate tool for that is consequential LCA, as it informs on the direct and indirect effects of the increased output of a product in the demand for itself and for other products in and outside its life cycle. In the case of Jatropha, or another bioenergy technology, consequential LCA would model effect of the additional availability of Jatropha biofuel and its by-products on the market and how that would reflect, for instance, on the consumption of fossil fuels. On the other hand, the Jatropha model studied in Chapter 3 and 5 are meant to reach populations and locations where fossils fuels are unavailable to start with. That, in fact, seems to be the predominant niche of small Jatropha initiatives: a stand-in for fossil fuels where the fossil fuel regime did not reach. One of the main commodities to be affected by the increased demand for Jatropha is land and other crops, through either expansion of croplands or displacement of other crops. For this reason, the emissions of land use change due to expansion and displacement (hence, direct and indirect land use change) can be calculated generically with statistical data or market models through consequential LCA.

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The optimization procedure in Chapter 6 directly aims at substitution of fossil fuels and stages a large scale decision-making scenario. This chapter cannot be categorized as an LCA, nor does it ask a consequential LCA question, as stated before. Conversely, it started with a fixed demand, which is by definition met by the product, and answers the question of how much product output is necessary and how it can be obtained. As such, despite the presence of consequential LCA elements, the supply chain’s GWP was not consequentially modeled. If it had been, instead of selecting cultivation sites based on their LUC emission factor, OPTIMASS would have had to operate with likelihood of a given land being cultivated on and of a crop’s production of being displaced. In consequential LCA, this likelihood is estimated with market data: marginal lands are expanded on first and marginal crops are displaced first. So, instead of optimizing the supply chain for lowest possible GWP it would have optimized it according to market mechanisms and then calculated GWP of the resulting supply chain. But the GWP and the conclusions we draw from it is only one sustainability criterion. How and to which extent Jatropha is to be grown in Mali, and anywhere else, depends also on a socio-economic analysis and the degree to which communities can be involved and get shared benefits. The latter two factors can improve cultivation management and thus influence the GWP.

7.3. TIME-MINDFUL LCA Despite being widely adopted, the IPCC GWP indicator calculation has been debated, both due to its calculation as its use in LCA to communicate emission reduction potential of bioenergy products. Its simplicity and transparency have a trade-off in its static nature, as this indicator does not recognize the different moments at which the GHG emissions occur along the life cycle of a product. In addition, the GWP considers the effect on the radiative forcing that an emission of a GHG with a determined atmospheric decay rate has had after a certain time horizon (TH), most commonly 100 years. This approach has been contested because the TH is an arbitrary cut-off point and because all emissions are actualized to one instant t=0, when in fact they may occur at different points in time. The choice of time horizon is seen by several authors as both a subjective choice and an inconsistency with the temporal boundaries of the life cycle. As a consequence, emissions that are postponed due to, for instance, C storage in long-lived products, have the same value of emissions occurring immediately

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heedless of delaying impacts on climate. To avoid misevaluation and misestimation, it has been argued that it is relevant to differentiate between emissions occurring at different moments along the cycle and also to consider more versatile cut-off points that are sensitive to the length of the life cycle. Dynamic LCA is an approach set forth to modify IPCC’s GWP by lending it a temporal dimension, yielding the indicator DynGWP. In contrast with the static approach (i.e. the IPCC GWP indicator) reported in Chapter 3, DynGWPs integrate emissions between their year of occurrence an any chosen time of analysis up to 2000 years. Chapter 5 aimed at demonstrating the use of this alternative metric and the effect of non-standard analytical time. Hence, we subjected a temporally discriminated LCI for 1 and 10 rotation time of 20 years to the analysis with DynGWP. While DynGWPs had been applied to long lived products (such as wooden construction materials) and land-based systems with long rotation times (such as forestry), we asked how applying the DynGWPs to short lived products and short rotation lengths, would affect the results compared to the static, classic IPCC GWP. The results reveal that longer analytical times (e.g. 500 years, instead of 20 and 100 years) lead to consistently more modest emission profiles, as early emissions have a lower importance in longer analytical times. In addition, at 20 years the disparity between the results of the different approaches is larger than later on. Thus, Chapter 5 tells us that the choice of a time of analysis is a larger influence the GWP than the choice of approach. Still, it is not possible to conclude from the work here done which indicator is best suited. Despite matching its aims, from the perspective of the practitioner dynamic LCA holds several disadvantages. For one, it increases the methodological complexity of the LCA because it requires an additional, stand-alone tool to calculate DynGWP (the DynCO2). DynCO2 in turn, requires the previous use of another LCA tool to assess the GHG inventory of the LCI, such as commercial software packages. As such, it is likely that to employ DynGWPs the practitioner will use a standard LCA tool plus DynCO2, instead of only its standard LCA modelling. Moreover, because emissions add to the radiative forcing curve as they happen, it is necessary to choose an analytical time longer than the life cycle in order to not leave out any emissions. This does not happen with the static GWP because all emissions are actualized to time 0 and therefore always included regardless of time horizon. This can be solved by choosing an analytical time longer than the life cycle, with the drawback with long analytical time’s early emissions loose importance. Hence, there may be situations in which the practitioner must opt between neglecting delayed emissions and belittling early ones.

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Finally, the choice of time of analysis, which is equivalent to choosing a TH, still has to happen. In fact, DynGWP have 2000 possible times of analysis, rather than 3, to choose from (IPCC 20yr, 100yr and 500yr). This versatility is presented as a strong suit of the method but does not eliminate the problem of the subjectivity of the choice. It creates further hindrances to the product comparability functionality of LCA, as it opens up the possibility of each product having a different time of analysis. On the other hand, dynamic LCA is more telling than the static approach of the emission profile of the product and of when the most impactful emission pulses occur. This not only is required for adequate emission valuation but also useful in identifying hotspots in a comprehensive inventory of the product system. In fact, as previously suggested in literature, making a full yearly inventory of emissions stimulates a level of data completeness often lacking in LCAs of land-based systems, particularly on what concerns biogenic carbon fluxes. To our view, the great utility of this exercise lies in the expression of the LCI and of GHG emissions minding their moment of occurrence. This time-step GHG profile, including temporal variations in the emissions from LUC and LU, revealed the progression of biogenic carbon stocks in plantations and its impact to the overall emission outline. Without this effort, we would not have the information necessary to critically evaluate the repayment of the carbon debt and the utility of Jatropha as an emission mitigator in less than optimal production circumstances. There is a growing body of knowledge on how GHG concentrations and their sinks interact dynamically, influencing each other and the climate. This, however, is not taken into account by LCA. The GWP itself considers that the radiative effect of the atmosphere is constant in time, regardless of GHG concentration. Although it is not the goal of dynamic LCA as it has been proposed, dynamic LCA thinking may be the framework for dynamic LCIA, in which the dynamics of receiving environments are considered.

7.4. SPATIALLY EXPLICIT LCA OF JATROPHA SUPPLY CHAINS The little information that exists on LUC emissions of Jatropha was not taken into consideration in the design and decision phase, when Jatropha initiatives were being planned, but rather after their implementation. Given that LUC emissions are estimated to be a determinant factor in the advantage of biofuels over fossil fuels, it is desirable to include them in ongoing efforts to structure and optimize bioenergy supply chains. In Chapter 6, we report 129

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on a simple yet novel approach to include LUC emissions for a large geographical region in a spatially explicit supply chain optimization exercise. While spatial features in bioenergy supply chains optimization already exist, a spatially dynamic LUC estimation module is a novelty and a likely valuable addition. We further rehearse the optimization of Jatropha-based electrification supply chains in Mali for low-GWP objectives. This approach of LCI geo-referencing demonstrates the potential of spatial applications of LCA in the study of the sustainability of land-based systems. The procedure described in Chapter 6 can be applied to other crops, regions and spatially explicit optimization models heedless of the discussed improvement options discussed. Furthermore, this spatial optimization exercise simulated optimal supply chains that would satisfy the Malian government’s goals of fossil energy substitution as well as of increasing rural electrification rates. Overall, this chapter exemplifies a methodology that can be used to plan and structure rural electrification initiatives – one of Jatropha’s main promises. The results of the optimization exercise highlights that the choice of cultivation sites determines the GWP. This is because LUC emissions depend on local yields and local edaphic conditions, such as biomass and soil carbon stocks and climate. Because LUC emission estimation relied only on quantitative variables (climate, soil properties, etc.), the qualitative classification of land cover was not considered in this chapter. As such, we cannot conclude which land cover shifts (e.g. cropland to Jatropha or shrubland to Jatropha) would lead to higher GWP. Chapters 4 and 5 confirm the natural supposition that replacing land covers which are richer in biomass will lead to higher emissions, the fact that the optimal cultivation are not located in the northern, less productive parts of the study area areas suggests that net loss of C stocks is not the only factor at hand. The second factor is, in fact, yield, which determines the efficiency or “worthiness” of converting an area to Jatropha. The supply chain layout and dimension demonstrates that Jatropha-based electrification is feasible with up to 107 thousand ha of land requirement. The optimal supply chains, although optimized for minimal GHG emissions, lead to higher GHG emissions than if the same amount of energy was produced with fossil fuels, in line with the conclusions of the LCA and dLUC estimations in Chapters 3 and 4. In addition, the optimal supply chains would be likely to trigger the displacement of cropland. This implies both a competition with food production and the aggravation of the land pressure issues that characterize this region. Also indirect land use change (iLUC) would be likely to happen, aggravating the GWP of the supply chain.

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These observations, based on an extensive and site-specific inventory, shed a light on the possible trade-offs of Jatropha. Albeit being a locally sourced energy carrier that may promote rural development, Jatropha-based bioenergy cannot mitigate GHG emissions from energy provision and has the potential for conflict with food production, even in optimal conditions. It is therefore a positive aspect that Mali’s renewable energy policy envisions a diverse set of energy technologies, including photovoltaic with a GWP of 0.1 kg CO 2 eq kWh-1. However, the applied methodologies do not come without fault. The extensiveness of the LUC emission and the yield inventory has a trade-off in its accuracy as the scale of the exercise required simplifications. This is mainly due to the choice of models used to simulate SOC stocks under Jatropha land use and also the optimization model. The former, RothC, because (i) it was too labour intensive since it was not automated in the optimization procedure, (ii) and too parsimonious with data as not enough data was available to parameterize a more complex model. The limitations of the optimization model OPTIMASS pertain to its programming nature, as the time required to solve a multi-integer linear problem increases exponentially with the number of variables. In this case, the number of variables the problem has to consider equals all the possible combinations between sites in terms of interrelation, relative distances and inputs and outputs flowing between them. Since the number of conversion and end use sites were limited by infrastructural criteria (e.g. electrical grid, population, road network), the amount of variables depended mainly on the number cultivation sites, and so did the problem solving time. This was the reason for which the spatial resolution of the cultivation site assignment was so coarse, in contrast with the high resolution of the original geodatasets on C stocks and yields. In addition, OPTIMASS was not enabled to meet the temporal specificities of the Jatropha supply chain, such as the seasonality of harvests and of electricity demand peaks, thus underestimating the need for storage facilities. In the meantime, the model has been advances in this aspect. Still, the choice of OPTIMASS for this exercise was based on its advantages over other models: it considers all stages of the supply chain, and offers versatility to different feedstocks, full integration with GIS and the possibility to further study this case with multi-objective optimization.

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7.5. APPLYING LCA IN DEVELOPING COUNTRIES

7.5.1. LIMITATIONS OF INVENTORY The production of bioenergy carriers based on Jatropha cropping is not present in mainstream LCA databases such as ecoinvent® (Swiss Centre for Life Cycle Inventories, Switzerland) and ELCD (JRC, Italy). Hence, processes leading to Jatropha-based bioenergy must be modeled from cradle to gate. It is possible, however, to use unit processes present in ecoinvent ® as inputs to the system, being chemical products or transport. In this thesis, the LCI was consistently done in this manner. Foreground data on system layout and quantification was site-specific, while background data on intervening unit processes was mostly retrieved from ecoinvent® (and tabled values in GREET 2013 and IPCC reports for direct emission factors). Despite its increasing coverage of country-specific processes, ecoinvent® still does not cover many regions of the world such as most of Africa and South America. For this reason, when using this tool, the practitioner is opting out of a more specific geographical coverage. While this may not be important for products that are imported to the country at hand, it can be so for other inputs such as electricity and transport. When building the LCI and selecting emission factors of Jatropha-based bioenergy production in Mali, we were mindful of these limitations and sought to include local data when possible. However, products such as chemicals (e.g. fertilizers and transesterification), machinery and fossil fuels were retrieved from ecoinvent®. When available, global averaged data sets were selected for processes and emission factors so as to distance the LCI from European conditions, predominant in ecoinvent®. Direct emission factors were, however, generalized from sources that are not specific for West African conditions.

7.5.2. CONSEQUENTIAL LCA Another limitation related to geographical scope and data unavailability in databases lies in adopting the consequential perspective on LCA. The consequential approach to life cycle thinking states that everything that comes in and goes out of a production system is retrieved or added to the market, in a pathway that must be traceable for all products of a life cycle. For

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this reason, consequential LCAs purport a systematic, economic model-mediated protocol for dealing with the market effects of changes in product availability in the market, final product and by-products included. The allocation of impacts to by-products is avoided by detecting and quantifying an effective substitution of a market-equivalent to the by-product. While production processes in ecoinvent® are presented with consequential and attributional modelling, when the practitioner must create its own process she/he must model the consequential substitution pathways. As mentioned before, although the optimization exercise (Chapter 6) did not aim to do a consequential system modelling, consequential unit processes were used due to the nature of the supply chain. However, when attempting to also include by-product handling by substitution, difficulties arose. Some crops, for instance, yield a significant amount of residues that can be valorised. If such by-products have multiple uses and all of them intervene in small fragmented markets, common in developing countries, there is no clear thread to follow in a substitution pathway. This renders substitution mostly arbitrary, like system boundary expansion is criticized to be. These difficulties also apply to other product flows intervening in the life cycle, namely the final product. Although not covered in this thesis, indirect effects such as rebound effects might also be hard to quantify for the same reasons. Tracing product flows in a consequential inventory can be challenging particularly in regions and with products where informal or fragmented markets are prominent. Although large scale production may result in stronger signals on the market (and even in that case, given the various actors involved), consequential system modelling remains a challenge for those analyzing non-industrial, small-scale systems in developing countries.

7.5.3. RECOMMENDATIONS In future work, data quality can be improved in different ways. -

The source of system inputs should be identified, in order to ascertain whether or not non-global ecoinvent® processes are defendable. When imported, the transport from production site should be included in the input’s burden.

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-

A more intensive collection of local data should be implemented namely with the measuring of tailpipe emissions from all stationary and automotive engines involved. This is a point where Jatropha LCAs severely lack accurate information. The great variability present in literature suggests that it is relevant to perform such site-specific measurements, faithful to the used engines and the attained fuel quality.

-

Seek out alternatives to emission factors calculated based on ecoinvent ® processes. For instance, the GWP intensity of Malian electricity is reported in literature sources and can be used instead of generic or alternative grid electricity from databases.

-

Although not relevant for GWP assessments, characterization factors may also be biased towards certain geographical settings and ought to be chosen accordingly. Although regionalized LCA has not yet reached mainstream LCA tools, the other end of the spectrum – choosing exclusively European impact indicators – can lead to misestimations.

-

Inform the target audience of the LCA about geographical mismatches.

-

A standard practice in LCAs should be to report sensitivity analysis to allocation options. This is particularly important in the case of performing a consequential LCA in such settings where substitution pathways may be doubtful.

7.6. A MISSING PIECE : INDIRECT LAND USE CHANGE The premise behind iLUC is that an increased demand of a land area triggers a demand from the land tenure market with repercussions that go beyond dLUC. Indirect effects can be measured with allocation and market-equilibrium models. Allocation models assign LUC to feedstock conversion routes based on historical data on LUC and yield, as well as assumptions on, for instance, by-product use and the resulting displacement of other products. Market equilibrium models calculate an amount and approximate location of LUC in response to a change in demand. Another, novel approach would be to model iLUC-related GHG emissions through the probabilistic modelling of land flows on a regional scale. The basic concept is that if Jatropha plantations remove a land use under a demand level surpassing land availability defined by 134

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land elasticity, that particular land use needs to occur elsewhere, potentially displacing other land uses, and so on. This causes a chain of consequential land flows which the model predicts and locates within a certain geographical extent. The likelihood of one land area being occupied within that chain is modelled in function of biophysical and socio-economic factors of the region. Such approach should: 1.

identify situations of land use displacements triggered by Jatropha cultivation in Mali;

2.

identify a land use displacement chain within Malian borders;

3.

geographically locate displacements within Malian borders;

4.

estimate GHG emissions resulting from land use change.

Such iLUC modelling approach would be compatible with our vision on spatially explicit LCAs. This is because by modelling each event of displaced land separately, it would in fact be modelling consecutive, related dLUC events and their LUC emissions. Hence, the model would yield separate, geo-referenced, land flows and their GWP to be incorporated in the LCI. This regionally delimitated approach to iLUC is not compatible with consequential LCA, which dictates that iLUC flows are incorporated in a global land use tenure market. Nonetheless, the approach localized iLUC modelling has the power to capture potential land tenure shifts with a higher resolution and to locate them in space. Despite possible misestimation of the magnitude of iLUC-related emissions, a regional and GIS-supported iLUC model provides refined emission inferences. Furthermore, it is rich in information that can be used by national level decision making on any land-based system policies, from forestry to rural development, as well as on supply chain layout. This ultimate goal would complement the decision-making support potential of the LCA-based optimization presented in Chapter 6. As a direct consequence of dLUC, a full LUC emission accounting of a production system ought to include iLUC as well. The relevance of iLUC data is, however, debatable in the case of Jatropha in Mali. Albeit the abundance of cropland in Koulikoro and Garalo and the records of conflicts over arable land, we see in Chapter 4 that part of the land in the aforementioned regions is in fact unused fallow land. This fallow has been abandoned for long enough for it to have evolved towards the characteristics of a secondary forest. Even if 135

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Jatropha is, in fact, implemented on cropland (due to, for instance, lower efforts in biomass clearing), the presence of fallow land may buffer the need for subsequent cropland displacement. The trade-off is the higher carbon debt and potentially higher SOC losses throughout Jatropha’s life cycle.

7.7. TOWARDS INTEGRATED SUSTAINABILITY ASSESSMENT OF LAND-BASED PRODUCTION SYSTEMS LCA still presents certain limitations, on one hand, and the potential to adapt to other techniques and expand its possibilities, on the other. Overall, an ideal sustainability assessment tool for land-based systems such as Jatropha would consider LU, dLUC and iLUC emissions, and therefore time and space. This conflicts with LCA’s inherent static and nonspatial nature. The most remarkable concern in the LCA of land-based systems is perhaps the accounting of biogenic carbon, an issue that faces both data completeness and impact assessment issues. This led to call for completeness and for accounting emissions in function of time, which has been heard also outside of the LCA framework. By expressing the LCIs in function of time and completing them with LUC we gained a new perspective of what is relatively more important to the sustainability of Jatropha. Considering time in the inventory lead us to calculate the impacts of land occupation and brought up the whole magnitude of LUC emissions. As predicted, excluding the latter can skew the vision of how sustainable are Jatropha initiatives. On the other hand, our results suggest that no matter how unsatisfactory IPCC approach to GWP may be due to its static nature, dynamic LCA does not necessarily present an alternative in the study of all land-based systems. In this thesis we showed that the impact of LUC on climate is an issue typified by high spatial complexity, which does not only involve the impact assessment, but also the inventory stage of LCA. We demonstrate how to equip LCA with features from other disciplines that can open up this framework to providing answers typically not asked from LCA. Indeed, by merging LCA with spatial analysis in the inventory stage, we created highly specific LCIs with the potential to be used in, for example, supply chain optimization. This approach also improved data quality because it allowed for the spatial-explicit assessment of LUC emissions. This resulted in local emission factors, similarly to local characterization factors in development in the framework of regional LCA. Although the demonstrated approach lacks in 136

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precision it can play into decision support systems and be a guide for probing of field conditions. The main limitation to the assessment of land-based systems in tropical settings remains data quality. Out of the range of mainstream LCA tools, LCAs of production systems set in or involving developing countries require a great deal of assumptions and data approximations in all stages. It is not yet possible to discern the magnitude of the difference of using geographically adequate datasets. Still, both from the specifications of industrial and agricultural process and the impact, the drawback can be presumed important. Concerning the assessment of emissions from LUC, the main shortcoming lies in adequacy of modelling iLUC. Whereas there are grounds to assess dLUC emissions, LCA has been slow to catch up with iLUC modelling and iLUC modelling is still unfeasible for many practitioners. But can we continue to accept GWP assessments with an incomplete LUC inventories when land is a key resource? In fact, greenhouse gases are merely one of the emissions of a production system, much like land is only one of its required resources. Site conditions not only influence emission flows but also the relevance and magnitude of impacts. As such, the sustainability of a bioenergy project is sensitive to the vulnerabilities of the environments on which its supply chain is based. The vulnerability of an environment can be due to the scarcity of a certain type of resource, such as water. This can be covered by regionalized characterization factors, through which regionalized LCA can be executed. But not only needs the physical environment to accommodate the implications of bioenergy supply chains. This leads us to the final point of an integrated sustainability assessment: the multidimensional character of sustainability. Through its ability to convey environmental, social and economic impacts, the LCA framework can provide a comprehensive vision of the 3 dimensions of sustainability of a product or practice. Beyond environmental impacts, life cycle techniques can be used to assess also social and economical impacts through social LCA and life cycle costing. The environmental, economical and social sides of LCA can be brought together to do ad-hoc evaluations of supply chains. Moreover, as we showed on this thesis, they can also be merged with techniques such as supply chain optimization to make strategic decisions so as to structure resilient and sustainable value chains of products, including bioenergy.

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153

ANNEX 1 SUPPLEMENTARY MATERIAL QUICK INDEX OF CONTENTS Supplements to Chapter 2 Questionnaires used in Chapters 2 and 3 Supplements to Chapter 3 Supplements to Chapter 4 Supplements to Chapter 5 Supplements to Chapter 6 References

154 159 167 168 175 181 186

SUPPLEMENTS TO CHAPTER 2

CALCULATION OF TRANSPORTATION DISTANCES Being a generic life cycle assessment, referring not to any sole specific cultivation and biodiesel production location but to a general model, the transportation distances taken into account in this study deserved special attention. In this sense, a protocol was elaborated in order to establish generic transport data to include in the LCI (figure A1). This study focused transport issues mainly on input transportation from production/distribution points to plantation sites. The considered inputs were: fertilizers, pesticides, cultivation machinery, methanol and sodium hydroxide. The protocol ran as follows: 1.

Several known significant Jatropha plantation (table A1) were located and visualized through geographic information systems software and grouped by countries.

154

2.

For each country, the local availability of each input was verified (FAO, 2006) (Table A3). a.

If local production of the input is assured, the main city/commercial centre of the country (or state, in the case of large nations such as India or Brazil) was assumed as the provenance of the input;

b.

If not produced locally, the nearest main international seaport was considered the first order provenance of the input and localized (www.worldportsource.com) (table A2).

3.

For inputs not produced locally, a world main producer/supplier and where its main production facility is located were identified. Its nearest seaport (often coincident with the production facility location) served as the second order provenance of the input (table A3).

4.

Distances were calculated between plantation sites and the provenance sites for each output or the nearest main city/sea port. a.

Road distances were calculated through web mapping services. The Google Earth© (Google Inc., California, USA) and the Microsoft Visual Earth© (Microsoft Corporation, Washington, USA) frameworks were both used, although Microsoft’s tool was preferred due to worldwide availability of information, which Google Earth© did not provide.

b.

Sea distances were calculated by World Shipping RegisterTM online distance calculator tool (e-ships.net/dist.htm).

c.

Jatropha is the main feedstock for biodiesel production in India and many of the known plantations are located there. In that nation, rail transport is the most representative of the modal split (www.indianrail.gov.in). Hence, the distances measured within its borders were attributed to rail freight transport and each input was considered to travel both by rail and road in the same percentage as the sum of Indian distances take in the sum of the total measured distances (equation A1).

155

ratio Rail freight 

 distances within India  total of distances

(eq. A1)

Figure A1 - Input transport distance estimation protocol. Table A1 – Main producers of each input and main origin international port.

Input MeOH

Producer Methanex

Fertilizers

Potashcorp

Pesticides

Zeneca (Syngenta) Caterpillar DowChem

Machinery NaOH

Port Point Lisas, Trinidad y Tobago Point Lisas, Trinidad y Tobago Felixtowe, United Kingdom Chicago, Illinois, USA Freeport, Texas, USA

156

References (Kable, 2009) (Plunkett, 2008) (Plunkett, 2008) (Plunkett, 2007) (Plunkett, 2008)

Table A2 - Jatropha plantation locations by country used in the transportation distances calculations. Sources for plantations in Swaziland and Philippines as well as some in India and Zambia must be kept confidential.

Country

Region/Town

Reference

Brazil

Minas Gerais and Mato Grosso do Sul

(Jatropha.de, 2009), personal communication Maurício Moller

China

Yunnan

(Weyerhaeuser et al., 2007)

Ethiopia

Arba Minch

(Jatropha.de, 2009)

Honduras

Yoro

(FACT, 2009)

India

Assam, Chhattisgarh, , Madhya Pradesh, Uttar Pradesh, Personal visit WMJ Achten, personal communication Prof. Tamil Nadu, Jharkand, Himachal Pradesh , Haryana, Paramathma, personal communication Mr. Lobo, (Euler & Gorriz, 2004) Rajasthan, Raipur

Indonesia

Java, Timor and Flores

(Jatropha.de, 2009)

Kenya

Mwingi

personal communication Jan Vandenabeele

Mali

Bamako and Commune de Garalo

(FACT, 2009; Jatropha.de, 2009)

Mozambique

Bilibiza, Chimoio and Xai-Xai

(CNW, 2007; FACT, 2009)

Tanzania

Rulenge, Matemanga and Arusha

(FACT, 2009), Diligent plantation at Arusha airport

Zambia

Eastern

(Jatropha.de, 2009)

157

Table A3 – Nearest main seaport of each country and local availability () and nearest supply centres of inputs per country and per input.

Main nearest seaport Country

Brazil China Ethiopia Honduras India Indonesia Kenya Mali Mexico Mozambique Philippines Swaziland Tanzania Zambia

Rio de Janeiro (Minas Gerais) / Santos (Mato Grosso do Sul) Hong Kong Djibouti La Tela Mumbai Jakarta (Java) / Belawan (Sumatra) Mombasa Dakar Veracruz Maputo Davao Maputo Dar es Salaam Dar es Salaam

MeOH Nearest supply centre Belo  Horizonte / Curitiba  Kunming

Fertilizers Nearest supply centre Belo  Horizonte / Curitiba  Kunming

Pesticides Nearest supply centre Belo  Horizonte / Curitiba  Kunming

Machinery Nearest supply centre Belo  Horizonte / Curitiba  Kunming  Addis Ababa

NaOH Nearest supply centre



Kunming



Mumbai



Vijaipur





State capital



State capital



Jakarta



Jakarta



Jakarta



Ciudad Mexico



Ciudad Mexico



Davao

158

de

State capital

de

QUESTIONNAIRES USED IN CHAPTERS 2 AND 3 An exact copy of the questionnaires submitted to Jatropha entrepreneurs follows. For better convenience, only the first part (“Part I: on Jatropha fields owned and currently managed by your institution”) is copied because Part II and III contain the same questions. Introduction Dear, This questionnaire starts with some identification questions. This information will not be used in further analyses or reporting. The rest of the question in this ‘identification section’ is to get some insight in the conditions in which your institution works with Jatropha. After the identification, the questionnaire consists of three parts. The three parts all contain the same questions, but these questions handle about three different ‘situations’. 

Part I: on Jatropha fields owned and currently managed by your institution



Part II: on Jatropha field owned and currently managed by out-growers (only in case your institution organizes an out-growers scheme)



Part III: on future production practices your institution aims at (if different from current practices)



if your institution currently owns and manages Jatropha fields, fill in Part I;



if your institution organizes an out-growers scheme, fill in Part II;



if your institution has future practices which it aims for, fill in Part III for the relevant production processes (e.g. your institution will extract oil in the future, but at

So,

this moment the press is not installed yet) The questionnaire will ask types and amounts of cultivation inputs, specifications of machinery used during cultivation, oil extraction and transesterification, and where these inputs and machines were made (in order to estimate transport distances) (indicated as ‘Produced in [country]’). Each part contains three pages with questions ((i) nursery and 159

field preparation, (ii) plantation establishment and management and (iii) oil extraction

transesterification

transport

products’ use). Each page with questions is followed by

an empty page in which you can give comments on the respective topic. It is important to note that the questionnaire only handles about Jatropha block plantations (i.e. fields of any size on which Jatropha is cultivated as monoculture aiming at harvesting the Jatropha seeds/seeds). Identification Name: Name of Institution representing: Position in Institution: Phone number: E-mail address: Does your institution owns and manages own Jatropha block plantations? Yes; No If yes: - Amount of hectares currently under Jatropha: [ha] - Location of these plantations: (as specific as possible; preferably GPS coordinates) Country: State/Province/District: Closest major city: Village name: GPS coordinates: - Amount of hectares per age of Jatropha: 1 year old 2 year old 3 year old Jatropha Jatropha Jatropha [ha] [ha] [ha] - Major land use type before Jatropha plantation establishment:

4 year old Jatropha [ha]

Closed tree cover 160

5 year old Jatropha [ha]

6 year old Jatropha [ha]

Open tree cover Mosaic: tree cover + natural vegetation Closed shrub cover Closed Herbaceous cover Sparse Herbaceous or shrub cover Bare area Cultivated and managed areas Mosaic: Cropland + shrub or grass cover Mosaic: Cropland + tree cover - Major soil type: - Projected amount of hectares under Jatropha:

[ha] by

[year]

- Fill in Part I of the questionnaire for these owned block plantations. Does your institution organize an out-growers system? Yes; No If yes: - Amount of farmers involved in current out-growers system: - Total amount of hectares currently under Jatropha owned by out-growers:

[ha]

- Fill in Part II of the questionnaire for these out-grower plantings Does your institution concrete future management and processing plans (e.g. at this moment your institution has no oil extraction yet, but plans to extract oil in the near future) - Fill in Part III of the questionnaire for these future plans Nursery and field preparation Nursery method (

with seeds;

cuttings) Produced in [country]

Size Polybags Seedbeds

[diameter – depth] [m2] Amount of seedlings per bed (estimate):

161

Use of fertilizer, pesticides and herbicides in Nursery ( Specify Type

Others (specify) HERBICIDES

Irrigation in Nursery (

No) Amount [kg/bag] OR [kg/bed]

Produced in [country]

Urea Rock phosphate KCl Manure -

FERTILIZERS

PESTICIDES

Yes;

-

Yes;

No)

Irrigation method

manual mechanical (e.g. pump + hose) gravitation Other (specify):

Number of irrigations during nursing period: Water source

tube well (pump) surface water (River, surfacing spring, …) rain water

In case of using pumps Pump type: -

Power [KWh or hp] (indicate)

Capacity [m³ water/hour]

Running time [hours/irrigation]

162

Pump produced in [country]

Field preparation Activity (select)

Type of machine used

Work time [hours/hectare]

Machine [country]

produced

in

Clearing field Plowing field Leveling field Digging planting pits Other (specify): Further comments on nursery and field preparation practices:

Plantation establishment and management Plantation establishment  Spacing [m×m]: × (mainly used spacing)  Fertilizer during plantation establishment ( Yes; (= Fertilizer applied during planting/filling planting pits)

No)

Specify Type

Produced in [country]

Urea Rock phosphate KCl Manure -

Others (specify )

FERTILIZERS

Amount [kg/ha]

-

Plantation management (YAP: Years after planting) Specify Type

Amount

Amount 163

Amount

Produced in

1st YAP [kg/ha]

Others (specify) HERBICIDES

3rd YAP [kg/ha]

[country]

Urea Rock phosphate KCl Manure -

FERTILIZERS

PESTICIDES

2nd YAP [kg/ha]

-

-

IRRIGATION Irrigation method

manual mechanical (e.g. pump + hose) gravitation Other (specify):

Number of irrigations during nursing period: tube well (pump) surface water (River, surfacing spring, …) rain water

Water source In case of using pumps Pump type: -

Power [KWh or hp] (indicate)

Capacity [m³ water/hour]

Running time [hours/irrigation]

Further comments on Plantation establishment and management practices:

164

Pump produced in [country]

Oil extraction Machine use

Machine (specify)

boiler

type

Power source

Power [eKWh OR (specify)

Capacity hp]

wood diesel Other (specify) diesel electricity Other (specify) diesel electricity Other (specify)

press

filter press

Machine produced in [country]

[kg seed/hr]

[kg oil/hr]

Transesterification Reagents: Type

Consumption [kg/kg oil]

Produced in [country]

Catalyst Alcohol Reaction time [min]: Reaction temperature [°C]: Machine used for heat production Boiler (steam) Termopac Other (specify)

Power source

Machine [country]

produced

wood diesel electricity Other (specify)

Transport Means of transport

Capacity [kg/vehicle]

165

Distance [Km]

in

(e.g. 18000 kg/lorry) Field to Press Press to Transesterification Transesterification to End-Use Production and uses Cultivation 1st YAP

2nd YAP

3rd YAP

Seed/seed yield [kg/ha] By-product uses By product Pruned wood Husks (= fruit minus seeds) Seed cake Glycerine

Use left on field left on field back to field burned sold

burned burned burned

Other (specify): Other (specify): Other (specify): Other (specify):

Further comments on oil extraction, transesterification, transport and product use practices:

166

4th YAP

5th YAP

SUPPLEMENTS TO CHAPTER 3 Table A4 - Life cycle inputs (other than fertilizers) in function of seeds processed from each field in 2011 and 2012, amortized over 20 years of rotation.

2011 #

Motorbike (km)

Lorry (kgkm)

Outgrower #1

22.50

Outgrower #2

22.50

Outgrower #3

0.63 cm) or 0.1314 (DBH < 0.63 cm). (Nouvellet et al., 2006)

2) Trees of dry tropical forest (generic):

AGB  exp  1.996  2.32  ln DBH  103

[EQ. A3]

with AGB = aboveground biomass [t]; DBH = diameter at breast height [cm]. (UNFCCC, 2006)

3) Shrubs (generic):

  AGB     BAi  H  wD  3 

[EQ.A4]

with AGB = aboveground biomass [t]; BAi = basal area of branch i [m²]; H = height of shrub [m]; wD = wood density (= 0.62 T m -3; UNFCCC, 2006). (UNFCCC, 2006)

169

BELOWGROUND BIOMASS

Belowground biomass (BGB) was estimated using root-to-shoot ratios. For Jatropha, a region specific value was obtained based on the destructive analysis of a sample of trees selected from the visited fields (n = 17). After measurement of plant dimensions (see higher), these trees were uprooted and their dry BGB was determined in a similar way as described above for AGB. For other species, literature values were used, i.e. 0.28 and 0.56 for trees in subtropical dry forest with more and less than 20 t AGB ha-1 respectively and 0.32 for scrubland in subtropical steppe (IPCC, 2006).

CARBON CONTENT Total biomass estimates (AGB + BGB in t ha-1) were converted to C stocks using literature values of C content: 0.46 for Jatropha (average value based on Firdaus et al., 2010; Torres et al., 2011; Firdaus & Husni, 2012; Hellings et al., 2012) and 0.50 for other tree species (IPCC, 2006).

BIOMASS SAMPLING DESIGN

Different sampling strategies were used in the three LUs. Jatropha biomass was estimated in three square plots per field, each containing nine healthy and representative Jatropha trees. The results were averaged per plot and converted to a per hectare basis using the plots’ surface area. In Jatropha fields and annual cropland, all mature trees occurring on the field were measured individually (i.e. species, diameter at breast height (DBH), canopy width and height). The resulting total biomass was divided by the field’s surface area to yield a biomass estimate in t ha-1. In fallow land, a nested plot design was used which consisted of one 10×10 m plot situated in a 20×20 m plot. All trees with DBH > 6 cm were measured in the large plot, while other trees and shrubs were only appraised in the small plot.

170

SOIL COVER

Soil cover, here defined as the percentage of soil covered by vegetation, was estimated in the three LUs based on a nested sampling design. The herb layer was assessed in four 1×1 m plots placed diagonally in each plot (or field in case of cropland) at regular intervals. The shrub layer (incl. Jatropha) was studied at plot level, while trees were assessed at field level, except in fallow lands where the large 20×20 m plots were used. Soil cover of Jatropha and mature trees was calculated based on the measured plant dimensions and the plot/field surface area, whereas cover of herbs and shrubs was estimated on sight.

SOIL ORGANIC CARBON (SOC) The upper 30 cm of the soil profile, divided into four soil layers (0-5 cm, 5-10 cm, 10-20 cm and 20-30 cm), was considered for the quantification of SOC, which is in agreement with several C inventory guidelines (e.g. Macdicken, 1997; Kucharik et al., 2003; Ravindranath & Ostwald, 2007). The SOC stock in t ha-1 of each soil layer was calculated separately according to eq. A5. These values were then summed to obtain the total SOC stock of each soil profile. SOCstock 

SOC G    BD  1    d  10 100  100 

(eq. A5)

with SOC = SOC concentration [g C (100 g soil)-1]; BD = bulk density [kg m-3]; G = mass fraction of coarse fragments (> 2 mm) [g (100 g soil)-1] and d = depth of soil layer [m]. Sampling locations per LU type are given in figure A2. In each Jatropha plot sample A1 was mixed with B1 and A2 with B2, yielding two BD samples and two SOC samples for each soil layer per plot. Under cropland, three samples were taken per field for both SOC and BD per depth. In each fallow plot (10×10 m), three transects were defined, each on which two sampling locations were positioned, respectively 1 and 3 m away from the plot border. SOC samples were bulked per transect and depth, yielding three SOC samples and one BD sample per depth.

171

Figure A2 – Soil sampling locations per land use type (letters represent sampling transects, while numbers refer to sampling locations).

SOC samples were air-dried, passed through a 2 mm sieve, ground and homogenized with a mortar, oven-dried at 60°C and analyzed using the automated dry combustion method (Carlo Erba 1110 Elemental Analyzer). As nitrogen levels are determined in the same analysis, these results were also used to calculate the C/N ratio. BD was determined by drying samples with a fixed volume of 100 cm³ overnight (105°C) and weighing them on a precision balance. These samples were then passed through a 2 mm sieve to calculate the mass fraction of gravel in the soil.

ADDITIONAL SOIL MEASUREMENTS Soil texture and pH were measured on one mixed sample per field. These samples resulted from bulking all BD samples originating from the same field, irrespective of depth. Soil texture was measured using laser diffraction analysis (Beckman Coulter – LS 13 320 Laser Diffraction Particle Size Analyzer) and the pH-H2O was measured using an electrode according to the method of van Reeuwijk (2002).

172

Table A5 – Means and standard deviations (between brackets) of soil organic carbon (SOC) concentrations, SOC densities and soil bulk densities (BD) per depth for different land uses in the two ecoregions (significant differences between land uses are indicated with differing letters).

Ecoregion

Land use

SOC % 0.378 a (0.154) 0.397 c (0.156) 0.448 ef (0.169) 0.426 (0.149) 0.469 a (0.223) 0.437 cd (0.158) 0.410 e (0.149) 0.380 (0.121) 0.912 b (0.695) 0.709 d (0.610) 0.589 f (0.370) 0.501 (0.254) 0.783 g (0.374) 0.664 (0.254) 0.664 (0.456) 0.541 (0.202) 0.794 gh (0.292) 0.621 (0.199) 0.494 (0.171) 0.515 (0.182)

Depth (cm) 0-5 5-10

Cropland 10-20 20-30 0-5

Koulikoro

5-10 Jatropha

10-20 20-30 0-5 5-10

Fallow 10-20 20-30 0-5 5-10 Cropland 10-20 20-30 Garalo 0-5 5-10 Jatropha 10-20 20-30

173

SOC (kg m-3) 5.121 a (2.166) 5.448 c (2.151) 5.831 (2.165) 6.007 (2.206) 6.001 a (2.925) 5.893 c (0.444) 5.387 (2.043) 5.387 (1.731) 12.030 b (8.923) 9.853 d (8.480) 7.499 (4.504) 7.037 (3.568) 6.465 g (2.843) 5.187 (2.516) 5.008 (3.449) 3.827 (2.276) 7.192 gh (3.522) 5.208 (2.610) 3.717 (2.304) 3.850 (2.621)

BD (kg m-3) 1345 (46.1) 1379 (54.6) 1312 (74.6) 1408 (72.9) 1276 (90.2) 1346 (64.6) 1319 (100.1) 1424 (85.7) 1347 (107.4) 1377 (79.2) 1281 (62.3) 1410 (74.9) 1369 (50.8) 1377 (77.7) 1441 (95.4) 1499 (96.4) 1403 (95.5) 1453 (84.3) 1393 (104.4) 1488 (164.8)

0.794 h (0.343) 0.789 (0.419) 0.704 (0.367) 0.621 (0.296)

0-5 5-10 Fallow 10-20 20-30

11.590 h (8.901) 6.736 (3.903) 5.695 (3.969) 4.905 (2.848)

1392 (208.6) 1354 (94.6) 1471 (105.4) 1573 (242.9)

Table A6 - Summary of highly significant (α < 0.01) correlations between soil organic carbon density (kg m-³) and soil texture, gravel content, pH and bulk density. * Depth at which the correlation is highly significant: 1 = 0-5 cm; 2 = 5-10 cm; 3 = 10-20 cm; 4 = 20-30 cm.

Ecoregion

Soil variable Sand %

Koulikoro

Silt %

Clay % pH Gravel % Garalo

pH Clay % (corrected for gravel %)

Land use

Depth *

Cropland Jatropha Fallow Cropland Jatropha Fallow Cropland Jatropha Fallow Fallow Cropland Jatropha Fallow Cropland

1, 2, 3 1, 2, 3, 4 1, 2, 3, 4 1, 2, 3 1, 2, 3, 4 1, 2, 3, 4 1, 2 1, 4 1, 2, 3, 4 1, 2, 3, 4 2, 3, 4 2, 3, 4 1, 2, 3, 4 2, 4

Average Spearman correlation coefficient -0.50 -0.76 -0.80 0.54 0.83 0.81 0.47 0.51 0.73 0.81 -0.65 -0.70 -0.76 -0.49

Fallow

2

0.49

174

Table A7 - Summary of significant partial correlations between soil organic carbon stock (t ha-1) and land use age, biomass and soil cover. * Depth at which the correlation is highly significant: 1 = 0-5 cm; 2 = 5-10 cm; 3 = 10-20 cm; 4 = 20-30 cm.

Ecoregion

Variable

Koulikoro

Age Soil cover by herbs Amount of mature trees

Depth *

Jatropha Jatropha

3, 4 1 4

Average correlation coefficient 0.76 0.46 0.98

1 1, 3 1, 4 4 1, 2 1, 2 3

0.45 -0.96 0.99 -0.98 0.68 0.61 -0.96

Fallow Jatropha Fallow Cropland Fallow Jatropha Jatropha

Total biomass Soil cover by herbs Garalo

Land use

Jatropha soil cover Total soil cover Amount of mature trees

Fallow

partial

SUPPLEMENTS TO CHAPTER 5 Table A8 - Direct emission factors.

Input Fertilizers

GHG N2O

Reference 0.002

kg kg

-1

N

(IPCC, 2006)

-1

Diesel in generator

CH4

0.03

g kg

diesel

GREET 2013

Diesel in generator

N2O

0.04

g kg-1 diesel

GREET 2013

-1

GREET 2013

Diesel in generator

CO2

3148.4

g kg

Biodiesel in generator

CH4

4×10-3

g MJ-1 electrical

N2O

-3

Biodiesel in generator Biodiesel in generator

CO2

4×10 8.3

diesel

g MJ

-1

g MJ

-1

175

(Demirbas & Balat, 2006)

electrical

(Demirbas & Balat, 2006)

electrical

(Oberweis & Al-Shemmeri, 2010)

Table A9 - LCA model parameters and their respective ecoinvent® process (when applicable).

Input/output factors

ecoinvent® process name

unit -1

Yield

1

t ha

Fertilizer (N) application rate

31.45

kg ha-1

(Trabucco et al., 2010) -1

Pesticide application rate

0.63

kg ha

Tractor use

60.00

km ha-1

Tractor

4.29

Tractor fuel use

0.04

kg.h ha

(Contran et al., 2013)

Pyrethroid-compound (GLO)

(Almeida et al., 2011)

Transport, tractor and trailer, agricultural (GLO)

kg km-1

Generator capacity

1.6×10

Generator fuel use

0.09

l MJ-1

Extraction efficiency

16.32

%

Press energy use

Nitrogen fertiliser, as N (GLO)

(Almeida et al., 2011) -1

7

Generator, 200kW electrical (GLO)

ecoinvent® v3 (Almeida et al., 2014b) (Achten et al., 2008)

-1

kWh kg 9

ecoinvent® v3 ecoinvent® v2.2

kWh

0.60

Reference

kg oil

(Reinhardt et al., 2008)

oil

1.08×10

Seed cake water content

5.90

%

(Nallathambi Gunaseelan, 2009)

Seed cake N content

5.11

%

(Contran et al., 2013)

LHV Jatropha biodiesel

39.10

MJ kg-1

Density Jatropha biodiesel

0.88

Kg l

Transesterification efficiency

96.00

%

Glycerine purification efficiency

90

%

Methanol input rate

0.20

Oil mill (CH) construction

ecoinvent® v3

Press capacity

(Achten et al., 2008)

-1

(Achten et al., 2008) (Achten et al., 2008) -1

kg kg

oil

Glycerine (RoW) production, from epichlorohydrin

(Enguídanos et al., 2002)

Methanol (GLO)

(Achten et al., 2008)

176

Sodium hydroxide input rate Transesterification energy use

0.01 0.42

kg kg-1 oil kWh

Sodium hydroxide, without water, in 50% solution state (GLO) (Achten et al., 2008)

kg-1

(Reinhardt et al., 2008)

biodiesel

177

Table A10 – Parameters used to estimate carbon stocks in soil and biomass in previous land uses cropland and fallow land.

C input from biomass (t ha-1) (Kaonga & Coleman, 2008; Loum et al., 2014)

Land use

Fallow land Cropland

Millet Maize

0.29 1.8 2.7

C input from manure (t ha-1) (Lekasi et al., 2001; Rockström & de Rouw, 1997; Shetty et al., 1991) 0 2.34

SOC (t ha-1) (Degerickx, 2012)

C stock in cleared biomass (t ha-1) (Vervoort, 2012)

27.85 17.12

24.69 0

Table A11 – Annual carbon inputs to soil in Jatropha plantations from leaf shedding and manure spreading and annual carbon sequestration in root and stem biomass of Jatropha plants.

Year

C input from litter fall (t ha-1 yr-1) (Nallathambi Gunaseelan, 2009)

1 2 3 4 5 6 7 8 9 10 to 20

0.04 0.07 0.11 0.15 0.19 0.22 0.26 0.26 0.26 0.26

C input from manure (t ha-1 yr-1) (Contran et al., 2013; Lekasi et al., 2001) 1.49

178

C sequestration in root and stem biomass (t ha-1 yr-1) (Achten et al., 2013) 0.89 2.32 4.3 6 7.16 7.77 8.39 9 9.61 10.23

Table A12 – LCI of annual GHG emissions (kg FU-1), excluding emissions related to land use and land use change.

LCI Year 1

2

3

4 to 21

1

1

22

GHG emission Rotation 1

1 -3

-3

CO2

1.80×10

4.82×10

N2O

5.94×10-4

5.94×10-4

-10

2 -2

1.38×10-3

5.83×10-4

5.94×10-4

4.31×10 2.02×10-4

-10

-11

-4.08×10

7.09×10-10

HFC-152a

7.09×10

7.0610

HFC-134a

3.76×10-10

2.53×10-10

-4.16×10-10

3.76×10-10

-12

-13

-12

CFC-113

2.91×10

9.03×10

-3.39×10

2.91×10-12

CFC-114

4.81×10-11

4.41×10-11

-7.71×10-11

4.81×10-11

HCFC-124

2.91×10-12

9×10-13

-3.38×10-12

2.91×10-12

-5

-5

-5

CH4

2.43×10

1.91×10

7.92×10

2.43×10-5

Halon 1211

1.29×10-10

1.18×10-10

1.23×10-9

1.29×10-10

Halon 1301

-10

1.17×10

-11

HCFC-22

3.97×10

-10

9.42×10

1.17×10-10

6.13×10-10

4.13×10-10

3.80×10-10

6.13×10-10

CFC-12

1.53×10-11

1.33×10-11

-3.16×10-11

1.53×10-11

CFC-11

-14

3.77×10

-14

3.66×10

-14

-4.39×10

3.77×10-14

SF6

3.46×10-10

2.89×10-10

-3.84×10-10

3.46×10-10

179

Table A13 – GWP (kg CO2 eq MJ-1 yr-1) calculated for both previous land cover scenarios and one or 10 rotation cycles. IPCC’s GWP are shown for time horizons of 20, 100 and 500 years. Dynamic GWP are shown with times of analysis of 20, 100, 500, 1000 and 2000 years.

IPCC GWP Time of analysis

DynGWP

20

100

500

20

100

500

1000

2000

Cropland

-0.29

-0.29

-0.37

-0.27

-0.03

-0.04

-0.04

-0.04

Fallow land

2.26

2.27

2.19

2.05

0.23

0.23

0.23

0.22

Cropland (10 rotations)

-0.55

-0.55

-0.63

-0.03

-0.14

-0.52

-0.60

-0.65

Fallow land (10 rotations)

-0.25

-0.25

-0.33

0.21

0.14

-0.22

-0.31

-0.35

Previous land cover

180

SUPPLEMENTS TO CHAPTER 6

SUPPLY CHAIN PARAMETERIZATION Table A14 – Geodatasets used in the estimation of spatial parameters and the final spatial layout the supply chain.

Parameter Transport distances Storage, conversion and use sites

Cultivation sites LUC emissions

Land cover intersect

Layer Cercles, cities and towns Roads Electricity transmission network Thermal power plants Climatic data Jatropha yields Protected areas Water bodies Biomass carbon stocks Soil carbon content Clay fraction Global land cover 2000

181

Source (ESRI, 1992) (ADB, 2011) (ADB, 2011) (ADB, 2011; Davis et al., 2014b) (Hijmans et al., 2005) (Trabucco et al., 2010) (UNEP & IUCN-WCMC, 2013) (ESRI, 1992) (Ruesch & Gibbs, 2008) (Hiederer & Köchy, 2012) (Batjes, 2012) (EC & JRC, 2003)

Figure A3 – Jatropha yield (a), biomass carbon stock (b) and soil organic carbon stocks (c) in the study region.

182

Table A15 – Data for estimation of inputs to the operations of the supply chain, from cultivation to delivery of fuel at the conversion sites.

Operation Cultivation

Input Polybags

12500

N fertilizer

24.30

P fertilizer

3.85

K fertilizer

22.95

Reference -1

(Almeida et al., 2011)

kg ha -1

(Maes et al., 2009b)

l ha

-1

(Contran et al., 2013)

-1

(Contran et al., 2013)

-1

(Contran et al., 2013)

-1

(Almeida et al., 2011)

kg ha kg ha kg ha

0.63

kg ha

ecoinvent®

-1

Tractor diesel use

0.04

kg km

Oil storage tanks

0.91

p kg oil-1

ecoinvent®, (Achten et al., 2008)

Biodiesel storage tanks

0.88

p kg diesel-1

ecoinvent®, (Achten et al., 2008)

kWh kg seeds-1

(Contran et al., 2013)

Pre-treatments and Diesel for dehusking/dehulling conversion Energy requirements extraction Transport

Unit

82.13

Nursery irrigation

Pesticides Storage

Amount

-3

3.25×10

-1

0.6

(Reinhardt et al., 2008)

kWh oil -10

Oil press

2.07×10

p kg seeds

Lorry diesel use

0.2

kg km-1

183

-1

ecoinvent® ecoinvent®

LAND USE CHANGE EMISSIONS The following steps describe the protocol of the estimation of LUC emission factor in function of land area. 1 2

3 4

5

The study area is divided equally into cells of 45×45 km. Estimation of BMCj: a. The C stocks in biomass (Ruesch & Gibbs, 2008) are averaged per cell. b. The average carbon stock is considered to be a potential emission in case of land occupation, in function of the required cultivation area. Estimation of SOCj: a. The average SOC (Hiederer & Köchy, 2012) of each cell is calculated. Estimation of SOCc: a. The average monthly rainfall, temperature, evaporation and clay fraction or every land unit are calculated (Batjes, 2012; Hijmans et al., 2005). b. 10 classes of cells are grouped with cluster analysis with the software PCORD® (MJM Software Design, USA) taking the clay fraction, average annual temperature and annual precipitation as variables. c. For each class of cell, the accumulation or loss of SOC is estimated with RothC 23.6. RothC is parameterized with soil properties, climate data and management practices (litter fall and manure input) (Coleman & Jenkinson, 2008). The parameterization of RothC goes according to the following procedure: i. Cell climate data: average monthly rainfall, temperature, evaporation clay fraction estimated in point 4a). ii. Management of starting conditions: cropland was taken as proxy of previous land use for every cell and litter fall intensity and manure inputs were retrieved from literature (Lekasi et al., 2001; Loum et al., 2014; Rockström & de Rouw, 1997; Shetty et al., 1991). iii. Management conditions of Jatropha: litter fall intensities per climate zone of FAO were identified in literature data (Nallathambi Gunaseelan, 2009; Negussie et al., (submitted); Wani et al., 2012). Each cell was attributed with a litter fall intensity according to its precipitation level. iv. A chronosequence of litter fall and soil cover index was taken into account so as to follow the growth curve of Jatropha, with a maximum canopy reached at year 7. v. Soil properties are derived with appropriate pedotransfer functions (Falloon et al., 1998; Weihermüller et al., 2013) from average SOC content estimated in point 3 and the average clay content estimated in point 4 a). d. A percentage of SOC variation after 20 years was calculated from the monthly SOC content for 20 years of a Jatropha plantation yielded by RothC. The land use change emission from SOC was calculated as SOCc of each SOCj contained in each cell class. 184

6

The sum of emissions from SOC and biomass clearing were summed and divided by the average yield of (Trabucco et al., 2010) each cell so as to obtain a land use change emission per productive unit, i.e. the harvested dry seed. This is considered a potential emission that occurs in case of land occupation, in function of the required cultivation area.

Figure A4 – Excerpt of figure 5.6, depicting the protocol for modelling of land use change emissions. The numbers above the boxes indicate the corresponding step in the protocol.

SENSITIVITY ANALYSIS Table A16 – Nutrient output as N, P and K (in tonnes) of seed cake, husks and shells as a result of fulfilling the electricity demand on each scenario (Contran et al., 2013).

Seed cake

Husks and shells

N (t)

P (t)

K (t)

N (t)

P (t)

K (t)

Scenario 1

28.0

10.4

7.4

2.8

0.2

10.9

Scenario 2

121.4

45.1

32.1

12.2

0.9

47.1

Scenario 3

171.2

63.6

45.2

17.2

1.3

66.4

185

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188

ANNEX 2 CURRICULUM VITAE ABOUT THE CANDIDATE I was born (in 1984) and raised in the Portuguese coastal town of Cascais. After a Bachelor in Cellular and Molecular Biology, I mastered in Energy and Renewable Energy, both at NOVA University, in Lisbon. I met Wouter and Bart at the Division of Forest and Landscape of the KU Leuven while there as an Erasmus dissertation student. That master thesis project introduced me to the world of life cycle assessment. After a research fellowship back at NOVA on the field of bioenergy, I successfully secured a doctoral grant, powered by a serious case of joint scientific curiosity and successful collaboration between Bart, Wouter and me. It is as a doctoral aspirant of the Portuguese Foundation for Science and Technology that I hereby pursue a Ph.D. degree from the KU Leuven. I like dogs, the sea, poetry and travelling.

LIST OF PUBLICATIONS

ARTICLES IN INTERNATIONALLY REVIEWED ACADEMIC JOURNALS

PUBLISHED Almeida, J., Achten, W.M.J., Verbist, B., Heuts, R., Schrevens, E., Muys, B. 2014. Carbon and water footprints and energy use of greenhouse tomato production in Northern Italy. Journal of Industrial Ecology, 18 (6), 898-908. Almeida, J., Moonen, P., Soto, I., Achten. W.M.J., Muys, B. 2014. Effect of farming system and yield in the life cycle assessment of Jatropha-based bioenergy in Mali. Energy for Sustainable Development, 23, 258-265.

189

Almeida J., Verbist, B., W.M.J. Achten, W., Maertens, M., Muys, B. 2014. Sustainability in Development Cooperation: Preliminary Findings on the Carbon Footprint of Development Aid Organizations. Sustainable Development, 22 (5), 349-359. Achten, W.M.J.,* Almeida, J.,* Muys, B. 2013. Carbon footprint of science: more than flying. Ecological Indicators, 34, 352-355. *Joint first author. Almeida, J., Achten, W. M.J, Duarte, M., Mendes, B., Muys, B. 2011. Benchmarking the Environmental Performance of the Jatropha Biodiesel System through a Generic Life Cycle Assessment. Environmental Science & Technology, 45 (12), 5447-5453. *Joint first author. Achten, W. M.J, Almeida, J., Fobelets, V., Bolle, E., Mathijs, E., Singh, V., Tewari, D., Verchot, L., Muys, B. 2010. Life cycle assessment of Jatropha biodiesel as transportation fuel in rural India. Applied Energy, 87 (12), 3652-3660. Achten, W. M.J, Vandenbempt, P., Almeida, J., Mathijs, E., Muys, B. 2010. Life cycle assessment of a palm oil system with simultaneous production of biodiesel and cooking oil in Cameroon. Environmental Science & Technology, 44 (12), 4809-4815. Fernando, A., Duarte, M., Almeida, J., Boleo, S., Mendes, B. 2010. Environmental impact assessment of energy crops cultivation in Europe. Biofuels, Bioproducts and Biorefining, 4 (6), 594-604.

UNDER REVIEW OR IN PREPARATION Degerickx, J., Almeida, J., Moonen, P., Vervoort, L., Muys, B., Achten, W.M.J. Impact of land use change to Jatropha bioenergy plantations on biomass and soil carbon stocks: a field study in Mali. Under review in GCB Bioenergy. Roffeis, M., Muys, B., Almeida, J., Achten, W.M.J., Pastor, B., Velásquez, Y., MartinezSanchez, A.I., Rojo, S. Pig manure treatment with House Fly (Musca domestica) rearing - An environmental Life Cycle Assessment. Under review in Journal of Insects as Food and Feed. Almeida, J., Degerickx, J., Achten, W.M.J., Muys, B. Greenhouse gas emission timing in life cycle assessment and the global warming potential of perennial energy crops. In preparation for International Journal of Life Cycle Assessment. Almeida, J.,* De Meyer, A.,* Achten, W.M.J., Cattrysse, D., Van Orshoven, J., Muys, B. Spatial optimization of Jatropha based electricity supply chains including the effect of emissions from land use change. In preparation for BioEnergy Research. *Joint first author.

PAPERS AT INTERNATIONAL SCIENTIFIC CONFERENCES AND SYMPOSIA , PUBLISHED IN FULL IN PROCEEDINGS Almeida, J., De Meyer, A., Achten, W.M.J., Cattrysse, D., Van Orshoven, J., Muys, B. 2015 Land use change emissions in bioenergy supply chain optimization. Proceedings of the Young 190

Researchers Conference in Biomass. World Sustainable Energy Days, 25-27 February 2015, Wels, Austria. Almeida, J., Moonen, P., Soto, I., Achten, W.M.J., Muys, B. 2013. Time Explicit Global Warming Potential of Jatropha Biofuel Production in Mali. Fulfilling LCA's Promise Proceedings from the LCA XIII International Conference. LCA XIII International Conference. Orlando, Florida, 1-3 October 2013 (pp. 68-77). De Meyer, A., Almeida, J., Achten, W.M.J., Muys, B., Cattrysse, D., Van Orshoven, J. 2013. Incorporating life cycle impact assessment in a mathematical model to optimize strategic decisions in biomass-for-bioenergy supply chains. F ’ – Proceedings from the LCA XIII International Conference. LCA XIII International Conference. Orlando, Florida, United States, 1-3 October 2013 (pp. 24-33). Fernando, A., Duarte, M., Almeida, J., Boléo, S., Mendes, B. 2011. Environmental pros and cons of energy crops cultivation in Europe. Proceedings of the 19th European Biomass Conference and Exhibition. 19th European Biomass Conference and Exhibition. Berlin, 6-10 June 2011 (pp. 38-40). Fernando, A., Duarte, M., Almeida, J., Boleo, S., di Virgilio, N., Mendes, B. 2010. The influence of crop management in the environmental impact of energy crops production. In Spitzer, J. (Ed.), Dallemand, J. (Ed.), Baxter, D. (Ed.), Ossenbrink, H. (Ed.), Grassi, A. (Ed.), Helm, P. (Ed.), Proceedings of the 18th European Biomass Conference and Exhibition, From Research to Industry and Markets. 18th European Biomass Conference and Exhibition, From Research to Industry and Markets. Lyon, France, 3-7 May 2010 (pp. 2275-2279). Achten, W.M.J., Almeida, J., Vandenbempt, P., Bolle, E., Fobelets, V., Singh, V., Verchot, L., Mathijs, E., Muys, B. 2010. Life cycle assessments of biodiesels: Jatropha versus palm oil. In Notarnicola, B. (Ed.), Settanni, E. (Ed.), Tassielli, G. (Ed.), Giungato, P. (Ed.), Proceedings of the 7th International Conference on LCA in the Agri-Food Sector. International Conference on LCA in the Agri-Food Sector. Bari, Italy, 22-24 September 2010 (pp. 113-118).

MEETING ABSTRACTS , PRESENTED AT SCIENTIFIC CONFERENCES AND SYMPOSIA , PUBLISHED OR NOT PUBLISHED IN PROCEEDINGS OR JOURNALS

Almeida, J., Degerickx, J., Achten, W.M.J., Muys, B. 2014. Land use change-related CO2 emissions in the LCA of Jatropha-based electrification in Mali. Creating benefit through life cycle thinking: Book of Abstracts of Ecobalance 2014. 11th International Conference on Ecobalance. Tsukuba, Japan, 27-30 October 2014, Abstract No. 29E3-3. Muys, B., Cardellini, G., Almeida, J., Achten,W.M.J. 2014. Approaches to evaluate the carbon neutrality of the forest value chain. Sustaining Forests, Sustaining People: The Role of Research: vol. 16 (5). XXIV IUFRO World Congress. Salt Lake City, USA, 5–11 October 2014, 223-224.

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Almeida, J., Achten, W.M.J., Muys, B. 2013. How the inclusion of Land Use Change can eliminate different shortcomings of LCA. Fulfilling LCA's Promise - Abstracts from the LCA XIII International Conference. LCA International Conference. Orlando, FL, October 1-3 2013, 262. Mederos, M., Almeida, J., Achten, W.M.J., Lapa, N., Muys, B. 2013. Feed production: The environmental consequences of protein production and use for animal breeding. Studiedag Starters in het Natuur- en Bosonderzoek. Brussels, Belgium, 15 March 2013. Almeida, J., Achten, W.M.J., Verbist, B., Muys, B. 2012. Carbon footprint and energy use of different options of greenhouse tomato production. Book of Abstracts of the LCA Food 2012 Conference. International Conference on Life Cycle Assessment in the Agri-Food sector. Saint-Malo, France, October 1-4, 2012, 719. Almeida, J., Achten, W.M.J., Duarte, M., Mendes, B., Muys, B. 2012. Benchmarking the life cycle environmental impacts of Jatropha biodiesel. Studiedag Starters in het Natuur- en Bosonderzoek. Brussels, Belgium, 16 March 2012.

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