Harmonizing the assessment of biodiversity effects ...

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Harmonizing the assessment of biodiversity effects from land and water use within LCA

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Francesca Verones*£, Mark A.J. Huijbregts$, Abhishek Chaudhary#, Laura de Baan#, Thomas Koellner§, Stefanie Hellweg#

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Norwegian University of Science and Technology (NTNU), Industrial Ecology Programme, 7491 Trondheim, Norway Radboud University Nijmegen, Department of Environmental Science, Institute for Water and Wetland Research, 6500 GL Nijmegen, The Netherlands #

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ETH Zurich, Institute of Environmental Engineering, 8093 Zurich, Switzerland

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Professorship of Ecological Services, Faculty of Biology, Chemistry and Earth Sciences, BayCEER, University of Bayreuth, 95440 Bayreuth, Germany *

Corresponding author e-mail: [email protected]; phone: +47 73 59 89 46

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Supporting Information

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16 pages

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7 figures

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10 tables

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26 February 2015

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Table of Contents S1. Abbreviations and parameters ...................................................................................................... 3

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S2. General LCA introduction .............................................................................................................. 3

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S3. Z-values for use in Species-area relationships............................................................................... 4

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S4. Crop distribution and inventories for land and water use ............................................................ 4

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S5. Definition of taxa ........................................................................................................................... 5

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S6. Global species numbers and vulnerability ..................................................................................... 5

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S7. Ecoregions and major watersheds in Kenya .................................................................................. 6

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S8. Global correlations ........................................................................................................................ 7

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S9. Further results – land transformation and alternative aggregation options ................................ 9

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S10. Description of method’s extensions .......................................................................................... 15

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S11. Literature cited .......................................................................................................................... 15

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S1. Abbreviations and parameters A a t p i j FF EF CF IS inventory S TL GR VS W trans occ LU SW

area of a spatial unit subscript, index for spatial aggregation subscript, taxon subscript, Pixel sunscript, species subscript, stressor fate factor effect factor characterization factor impact score inventory flow species number threat level geographical range area vulnerability score aggregation weight land transformation land occupation land use surface water

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S2. General LCA introduction

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In general, life cycle assessments (LCAs) consist of four phases, namely (1) goal and scope phase, (2) life cycle inventory collection, (3) life cycle impact assessment and (4) interpretation.1, 2 For this work mostly the life cycle impact assessment is relevant, since we developed a way of harmonizing different impact assessment approaches.

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In life cycle inventories, so called inventory flows are collected, for example the amount of land used to produce a certain product or the amount of water used for irrigation of the same product. The aim of the following life cycle impact assessment is to translate these physical flows into environmental impacts, thereby often using, for ecosystems, the unit PDF (potentially disappeared fraction of species). In order to translate a physical flow to an environmental impact, we need a so called characterization factor (CF), which is developed and provided for different stressors (such as water consumption and different land use types: annual crops, permanent crops, intensive and extensive forestry, urban land and pasture). For the case of water and land use, the CF tells the user how much damage is to be expected (in different parts of the world) per m2 of land occupied per year or per m3 of water consumed. (The second element of land use is land transformation. It assesses the impact of transforming a landscape, including the time lag between a degraded and potentially recovered, natural state. Since transformation impacts are not available for water, we neglect this part in the main manuscript). The CF itself consists of two parts, a fate factor (FF) and an effect factor (EF).

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The FF informs in general about the way an emission, for example, is spreading in the environment. However, for water and land use we do not have emissions, but physical changes in the availability of a certain habitat type (e.g, wetland or primary forest). The fate factor for water use indicates the change in wetland area because of an additional m3 of water that is consumed. For land occupation the fate factor indicates the loss of habitat area for a m2 of land used. Since 1 m2 of land occupied results in the loss of 1 m2 of habitat, the fate factor for land occupation is 1.

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The effect factor (EF) links the loss of a habitat area to a potential species loss. This is in our case for both water and land occupation done with the help of species-area relationships.

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Multiplying fate and effect factors results in the characterization factors. Multiplying the latter with the inventory parameters (e.g. how much irrigation water was used for producing 1 kg of tea), results in the impact score (IS), indicating the environmental impact that we are interested in from an LCA perspective.

S3. Z-values for use in Species-area relationships

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The z-values are identical with the slope parameter of a species-area relationship and basically indicate the rate of change in species numbers per unit of area.3

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The values used for the wetland areas were 0.37 for birds, 0.34 for mammals, 0.2 for amphibians and 0.33 for reptiles, derived from Drakare et al.4, as explained in the Supporting Information of Verones et al.5

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For land use, the z-values were also derived from Drakare et al.4. For island ecoregions, z = 0.258, for forest ecoregions, z = 0.344 and for non-forest ecoregions, z = 0.211. Upper and lower confidence intervals for these values were incorporated using 1000 Monte Carlo simulations and assuming a triangular distribution (see also the supplementary information from de Baan et al.6 for details).

S4. Crop distribution and inventories for land and water use

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The values used for land and water use in the case study are given along with the relevant references in Table S1. The spatial distribution of coffee, tea and sugarcane production are shown in Figure S1 and are given per ecoregion and watershed in Table S2.

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Table S1: Inventory parameters for land and water use.

Coffee Tea Sugarcane

Land occupation [m2yr/kg]

Land transformation [m2/kg]

34.59 4.50 0.12

0.00 0.23 0.003

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Water consumption [m3/kg] 1.08 (deficit irrig.) 0.08 (deficit irrig.) 0.07

Refs land use

Refs water use

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9, 10

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Figure S1: Distribution of the production areas of tea (light green12), irrigated sugarcane (black12) and coffee (dark green12) in Kenya and as a closer snapshot. The background map is taken from ref 13.

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Table S2: Amount of crop area from the spatial data (Figure S1)12 and the shares of each crop in the relevant ecoregions and major watersheds (see also Figure S2, Figure S3 and Table S4).

Total area [ha] from shapefile Share in ecoregions/watersheds [%] AT0108 AT0711 AT0716 AT0721 AT1005 Africa, East Central Coast Nile Rift Valley Shebelli - Juba

Sugarcane 51868

Tea 395001

Coffee 422212

14 0 65 21

91 5 3 0.3 0.01 39 59 2 0.1

34 66 0 0 0 98 0 1 2

0 100 0 0

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S5. Definition of taxa The term “taxa” is defined by the International Commission on Zoological Nomenclature as follows: “A taxonomic unit, whether named or not: i.e. a population, or group of populations of organisms which are usually inferred to be phylogenetically related and which have characters in common which differentiate (q.v.) the unit (e.g. a geographic population, a genus, a family, an order) from other such units. A taxon encompasses all included taxa of lower rank (q.v.) and individual organisms.14” It is thus a very broad term, and we use it on the level of classes (mammals, birds, reptiles and amphibians).

S6. Global species numbers and vulnerability The global numbers of species that were considered in each taxonomic group are shown in Table S3. These taxa include all known animal species for which spatial data was available from the International Union for Conservation of Nature (IUCN)15 or, for birds, from BirdLife.16 Species that are already extinct were not included in the analysis.

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Table S3: Overview of the species number per taxa that are considered on a global scale, as well as the global vulnerability score (VS) per taxon and the average geographical range (GR) per taxon. Taxa (t) All Birds All Mammals All Amphibians All Reptiles SUM

Global species number (St,world) 10104 5386 6251 3384 25125

Global VS 2.89E-01 4.47E-01 5.90E-01 4.59E-01 1.79

Average GR [km2] 3.5E+06 2.7E+06 2.9E+05 4.4E+05 7.0E+06

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S7. Ecoregions and major watersheds in Kenya In Figure S2 the ecoregions of Kenya are shown. The description of the ecoregions is given in Table S4.

Ecoregion code

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Figure S2: Ecoregions in Kenya with ecoregion codes. The data on terrestrial ecosystems is from WWF17. The ecoregion codes are explained in Table S4.

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Table S4: List of ecoregions codes and names that are present in Kenya.17 Ecoregion Code AT0108 AT0109 AT0125 AT0705 AT0711 AT0715 AT0716 AT0721 AT0901 AT1005 AT1313 AT1402 Lake

Ecoregion name East African montane forests Eastern Arc forests Northern Zanzibar-Inhambane coastal forest mosaic East Sudanian savanna Northern Acacia-Commiphora bushlands and thickets Somali Acacia-Commiphora bushlands and thickets Southern Acacia-Commiphora bushlands and thickets Victoria Basin forest-savanna mosaic East African halophytics East African montane moorlands Masai xeric grasslands and shrublands East African mangroves Lake

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In Figure S3 the four major watersheds in Kenya are shown.

Watershed Names

Africa, East Central Coast Nile Rift Valley Shebelli - Juba

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Figure S3: Major watersheds in Kenya. Watershed data is adapted from the FAO Geonetwork. 18

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The ecologically relevant spatial unit for land occupation is an ecoregion, for water use it is a watershed (upstream of the wetlands). Globally, there are 827 ecoregions17 and 233 major watersheds (adapted from ref18). The average size of an ecoregion is 26 decimal degrees squared (standard deviation: 197) and the average shape area of a watershed is 66 decimal degrees squared (standard deviation: 117). The areas of ecoregions vary in a wider range (5E-04 – 5513 decimal degrees squared) than the watersheds (9E-03 – 707 decimal degrees squared). The median for ecoregions is 6 decimal degrees squared, the one for watersheds is 23 decimal degrees squared.

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S8. Global correlations The correlations between CFs calculated with and without VS are shown in Table S5. In general, reptiles and amphibians are particularly sensitive towards an inclusion of the VS. This is because among these species there are many that have both a small geographic range and a high threat level, in total leading to a large VS. The correlations differ between 0.16 and 0.99. The average correlation between the CF maps with and without VS is for SW consumption 0.64, for land occupation 0.58 and for land transformation 0.63. The correlation between CFs with and without VS for land transformation varied between the same values and shows the same tendencies as land occupation. This highlights the importance of the additional information gained by including VS.

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Table S5: Correlations between global maps of CFs calculated with and without VS t,p for surface water (SW) consumption and six different land use types for land occupation and transformation.

water consumption

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SW

annual crops

land occupation

permanent crops

pasture

urban areas

extensive forestry

intensive forestry

annual crops

land transfromation

permanent crops

pasture

urban areas

extensive forestry

intensive forestry

Reptiles Birds Mammals Amphibians Reptiles Birds Mammals Amphibians Reptiles Birds Mammals Amphibians Reptiles Birds Mammals Amphibians Reptiles Birds Mammals Amphibians Reptiles Birds Mammals Amphibians Reptiles Birds Mammals Amphibians Reptiles Birds Mammals Amphibians Reptiles Birds Mammals Amphibians Reptiles Birds Mammals Amphibians Reptiles Birds Mammals Amphibians Reptiles Birds Mammals Amphibians Reptiles Birds Mammals Amphibians

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Correlation between maps 0.92 0.87 0.46 0.27 0.72 0.12 0.79 0.85 0.72 0.12 0.82 0.85 0.70 0.11 0.69 0.78 0.74 0.11 0.74 0.85 0.70 0.11 0.74 0.87 0.87 0.27 0.90 0.87 0.69 0.10 0.68 0.81 0.69 0.10 0.66 0.84 0.65 0.10 0.61 0.76 0.69 0.10 0.65 0.82 0.69 0.08 0.68 0.85 0.82 0.21 0.83 0.84

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S9. Further results – land transformation and alternative aggregation options The transformation impact considers the time required for an ecosystem to recover after a hypothetical future land abandonment (i.e. transforming natural habitat in slowly recovering ecosystems is considered more detrimental than in fast recovering ones). Land transformation indicates the impacts of land use change. The transformation impact highlights the reduced biodiversity in the future, in hypothetical state that is similar to the pre-transformation state, taking into account the future land abandonment and time lag for recovery. The CFs for transformation (CFtrans,LU) are calculated by multiplying half of the required recovery time treg with the calculated CF for occupation (CFocc,LU) (Equation S1). The recovery times are taken from Curran et al.19

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Equation S1

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After CFtrans,LU is calculated for all taxa, the same aggregation options and procedures are applied as for the land occupation.

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In the following tables and figures, additional results and analyses are presented for clarification of results and discussion from the main manuscript.

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Figure S4: Spatially differentiated effect factors for A) land occupation from permanent crops, B) land occupation from annual crops and C) surface water consumption. Shown here are aggregated EFs over all taxa, aggregated with option 1. Outlined in dark green is the coffee area (permanent crops), in light green the tea area (permanent crop) and in black the irrigated sugarcane area (annual crop).

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B)

C)

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Figure S7: Relative contributions to the overall impact of sugarcane, tea and coffee for the four aggregation options (A, B, C, D) of the different taxa and stressors.

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Table S6: Contribution (percentage) of land occupation, transformation and water use to the total impacts with and without consideration of the VS. Coffee has no transformation impact, since in the recent past years the area of coffee production has been decreasing.

Option 1

Option 2

Option 3

Option 4

Stressor Total water use Total occupation Total transformation Total water use Total occupation Total transformation Total water use Total occupation Total transformation Total water use Total occupation Total transformation

with VS [%] Sugarcane Tea 97.53 34.51 1.21 13.49 1.26 51.99 96.39 27.39 1.77 15.42 1.84 57.19 97.53 34.51 1.21 13.49 1.26 51.99 96.60 29.46 1.70 14.56 1.71 55.98

Coffee 6.57 93.43 0.00 3.78 96.22 0.00 6.57 93.43 0.00 5.42 94.58 0.00

without VS [%] Sugarcane Tea Coffee 99.95 90.46 84.38 0.02 1.86 15.62 0.03 7.68 0.00 99.94 90.18 83.87 0.03 1.91 16.13 0.03 7.91 0.00 99.95 90.46 84.38 0.02 1.86 15.62 0.03 7.68 0.00 99.95 90.65 84.75 0.02 1.82 15.25 0.03 7.53 0.00

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Table S7: Contribution (percentage) of land occupation and water use to the total impacts (neglecting transformation) with and without consideration of the VS.

Option 1 Option 2 Option 3 Option 4

Total water use Total occupation Total water use Total occupation Total water use Total occupation Total water use Total occupation

Sugarcane 98.77 1.23 98.19 1.81 98.77 1.23 98.27 1.73

with VS [%] Tea 71.89 28.11 63.99 36.01 71.89 28.11 66.92 33.08

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Coffee 6.57 93.43 3.78 96.22 6.57 93.43 5.42 94.58

without VS [%] Sugarcane Tea Coffee 99.98 97.98 84.38 0.02 2.02 15.62 99.97 97.92 83.87 0.03 2.08 16.13 99.98 97.98 84.38 0.02 2.02 15.62 99.98 98.03 84.75 0.02 1.97 15.25

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Table S8: Comparison of the magnitude and ranks of impacts [PDF/kg] for water consumption and land occupation impacts for the different taxonomic groups. The lower the rank, the larger the impact. Crop

Sugarcane

Tea

Coffee

Taxon (stressor) Amphibians (land occ.) Amphibians (water) Birds (land occ.) Birds (water) Mammals (land occ.) Mammals (water) Reptiles (land occ.) Reptiles (water) Amphibians (land occ.) Amphibians (water) Birds (land occ.) Birds (water) Mammals (land occ.) Mammals (water) Reptiles (land occ.) Reptiles (water) Amphibians (land occ.) Amphibians (water) Birds (land occ.) Birds (water) Mammals (land occ.) Mammals (water) Reptiles (land occ.) Reptiles (water)

option 1 [PDF/kg] 3.11E-18 7.27E-17 2.13E-19 2.91E-16 1.39E-18 9.10E-17 9.57E-19 7.70E-19 4.78E-17 5.18E-17 2.51E-17 2.08E-16 2.92E-17 8.29E-17 3.19E-17 6.48E-19 2.35E-16 1.29E-17 1.52E-16 6.13E-17 1.14E-16 3.12E-17 1.06E-15 4.42E-18




Rank option 1 4 3 8 1 5 2 6 7 4 3 7 1 6 2 5 8 2 7 3 5 4 6 1 8

Rank option 2 4 3 8 1 6 2 5 7 5 4 7 1 6 2 3 8 2 7 4 5 3 6 1 8

Rank option 3 4 3 8 1 6 2 5 7 4 3 7 1 6 2 5 8 2 7 3 5 4 6 1 8

Rank option 4 4 2 8 1 5 3 6 7 4 3 7 1 6 2 5 8 2 7 4 5 3 6 1 8

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Table S9: Overall impact score and ranking for the different aggregation options. The lower the rank, the larger the impact.

total coffee total tea total sugarcane

option 1 [PDF/kg] 1.67E-15 4.77E-16 4.61E-16

option 2 [PDF/kg] 2.53E-15 4.36E-16 3.68E-16

option 3 [PDF/kg] 6.76E-15 1.93E-15 1.87E-15

option 4 [PDF/kg] 4.27E-16 1.07E-16 9.58E-17

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Rank option 1

Rank option 2

Rank option 3

Rank option 4

1 2 3

1 2 3

1 2 3

1 2 3

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Table S10: Impact scores for water use and land occupation for the different crops and their ranking order for the different aggregation options. The lower the rank, the larger the impact. Sugarcane water sugarcane land occ. Sugarcane Tea water tea land occ. tea Coffee land occ. coffee water coffee

option 1 [PDF/kg] 4.56E-16 5.66E-18 option 1 [PDF/kg] 3.43E-16 1.34E-16 option 1 [PDF/kg] 1.56E-15 1.10E-16



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Rank option 1 1 2 Rank option 1 1 2 Rank option 1 1 2




S10. Description of method’s extensions

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Water consumption

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The approach taken in Verones et al.20 is based on the previous work (Verones et al.5, 21). The largest difference is the geographical coverage of wetlands. The focus in the original publications was on wetlands of international importance according to the Ramsar Convention.22 In the extension of this work the number of wetlands considered is increased to more than 20’000 wetlands, thus taking a large number of wetlands into account that are not listed as internationally relevant. This improves the spatial coverage, especially for surface water-fed wetlands. In Kenya, the number of wetlands has been increased from 5 to 12, thus refining the characterization factors. In the extension the threat level for data deficient species was changed. Originally, it was assumed as 0.2, the same like for least concern species. However, since a lot of data deficient species are endemic or small-ranged, we changed the threat level for the data deficient species to 1, the same like for critically endangered species. This slightly changes the results of the vulnerability score maps. This updated vulnerability score is applied here for both land and water use assessments.

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Additionally, also impacts on vascular plant species are investigated. Since this is not done on a wetland specific basis, we decided to leave the plants out of this study here.

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Land occupation and transformation

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In Chaudhary et al.23 the land use types considered are ‘intensive forestry’, ‘extensive forestry’, ‘annual crops’, ‘permanent crops’, ‘pasture’ and ‘urban. In de Baan et al.6 the considered land use types are ‘agriculture’, ‘pasture’, ‘managed forests’, ‘urban area’, and ‘natural habitat’. This is a first difference between the approaches, however both use a matrix-calibrated approach for quantifying characterization factors. Further, Chaudhary et al.23 introduce a vulnerability score on an ecoregion level, that is based on the same data as in Verones et al.20

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