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 "id","name","shortName","nameCode","description","unitId","metadata"
-"936d0a9f-fe48-42b4-9433-63282d4dada5","Deforestation footprint (sLUC)","Deforestation footprint (sLUC)","DF_LUC_T","The deforestation footprint (sLUC) indicator quantifies the annual average area of deforestation within a 50km radius attributable to the raw material sourced.","6970f9b8-eba0-4fee-b6ee-2723ce6604d4","{ ""name"": ""Deforestation footprint (sLUC)"", ""short name"": ""Deforestation footprint (sLUC)"", ""name code"": ""DF_LUC_T"", ""indicator type"": ""landscape-level"", ""impact type"": ""Natural ecosystem conversion"", ""units"": ""ha/yr"", ""description"": ""The deforestation footprint (sLUC) indicator quantifies the annual average area of deforestation within a 50km radius attributable to the raw material sourced."", ""interpretation"":""Deforestation footprint estimates the area of deforestation occurring within a 50km radius that is attributable to the quantity of raw material sourced using a statistical land use change (sLUC) approach. The indicator assumes that deforestation is driven by demand for land area."", ""license"": ""CC BY 4.0"", ""geographic coverage"": ""Global coverage (excluding Antarctica and other Arctic islands)."", ""citation"": [ ""Chuvieco, Emilio, Joshua Lizundia-Loiola, Maria Lucrecia Pettinari, Ruben Ramo, Marc Padilla, Kevin Tansey, Florent Mouillot, et al. 2018. ‘Generation and Analysis of a New Global Burned Area Product Based on MODIS 250 m Reflectance Bands and Thermal Anomalies’. Earth System Science Data 10 (4): 2015–31. https://doi.org/10.5194/essd-10-2015-2018."", ""Hansen, M. C., P. V. Potapov, R. Moore, M. Hancher, S. A. Turubanova, A. Tyukavina, D. Thau, et al. 2013. ‘High-Resolution Global Maps of 21st-Century Forest Cover Change’. Science 342 (6160): 850–53. https://doi.org/10.1126/science.1244693"", ""Mazur, Elise, Michelle Sims, Elizabeth Goldman, Martina Schneider, Fred Stolle, Marco Daldoss Pirri, and Craig Beatty. 2023. ‘SBTN Natural Lands Map: Technical Documentation’. SBTN. https://sciencebasedtargetsnetwork.org/wp-content/uploads/2023/05/Technical-Guidance-2023-Step3-Land-v0.3-Natural-Lands-Map.pdf."", ""Potapov, Peter, Matthew C. Hansen, Lars Laestadius, Svetlana Turubanova, Alexey Yaroshenko, Christoph Thies, Wynet Smith, et al. 2017. ‘The Last Frontiers of Wilderness: Tracking Loss of Intact Forest Landscapes from 2000 to 2013’. Science Advances 3 (1): e1600821. https://doi.org/10.1126/sciadv.1600821."", ""Potapov, Peter, Matthew C. Hansen, Amy Pickens, Andres Hernandez-Serna, Alexandra Tyukavina, Svetlana Turubanova, Viviana Zalles, et al. 2022. ‘The Global 2000-2020 Land Cover and Land Use Change Dataset Derived From the Landsat Archive: First Results’. Frontiers in Remote Sensing 3 (April): 856903. https://doi.org/10.3389/frsen.2022.856903. "", ""Turubanova, Svetlana, Peter V Potapov, Alexandra Tyukavina, and Matthew C Hansen. 2018. ‘Ongoing Primary Forest Loss in Brazil, Democratic Republic of the Congo, and Indonesia’. Environmental Research Letters 13 (7): 074028. https://doi.org/10.1088/1748-9326/aacd1c."" ], ""source"": [ ""Hansen/UMD/Google/USGS/NASA"", ""Mazur/Potapov/Turubanova/Chuvieco"" ], ""frequency of updates"": ""Annual"", ""date of content"": ""2001-2022"", ""resolution"": ""100x100 meters"" }"
-"157b5f22-916b-4981-84c7-f6607ec65445","GHG emissions from deforestation (sLUC)","GHGs (deforestation, sLUC)","GHG_LUC_T","The GHG emissions from deforestation (sLUC) indicator quantifies the annual average emissions of greenhouse gas (GHG) associated with deforestation within a 50km radius attributable to the raw material sourced.","a0e8110c-fbde-4c8c-ac19-f0f69078b96b","{ ""name"": ""GHG emissions from deforestation (sLUC)"", ""short name"": ""GHGs (deforestation, sLUC)"", ""name code"": ""GHG_LUC_T"", ""indicator type"": ""landscape-level"", ""impact type"": ""Climate"", ""units"": ""tCO2eq/yr"", ""description"": ""The GHG emissions from deforestation (sLUC) indicator quantifies the annual average emissions of greenhouse gas (GHG) associated with deforestation within a 50km radius attributable to the raw material sourced."", ""interpretation"": ""Provides an estimate of the annual average greenhouse gas emissions arising from deforestation events occurring since 2002, within a 50km proximity of where a raw material was sourced and attributable to that raw material. Emissions are calculated from the deforestation rates and the vulnerable carbon, the amount of biomass and soil carbon that would be lost in a land use change event typical for the location."", ""license"": ""CC BY 4.0"", ""geographic coverage"": ""Global coverage."", ""citation"": [ ""Noon, Monica L., Allie Goldstein, Juan Carlos Ledezma, Patrick R. Roehrdanz, Susan C. Cook-Patton, Seth A. Spawn-Lee, Timothy Maxwell Wright, et al. 2021. ‘Mapping the Irrecoverable Carbon in Earth’s Ecosystems’. Nature Sustainability 5 (1): 37–46. https://doi.org/10.1038/s41893-021-00803-6."", ""ESA. 2017. ‘Land Cover CCI Product User Guide Version 2. Technical Report’. maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdf."", ""Hansen, M. C., P. V. Potapov, R. Moore, M. Hancher, S. A. Turubanova, A. Tyukavina, D. Thau, et al. 2013. ‘High-Resolution Global Maps of 21st-Century Forest Cover Change’. Science 342 (6160): 850–53. https://doi.org/10.1126/science.1244693."" ], ""source"": [ ""Noon/Mazur/Potapov/Turubanova/Chuvieco"", ""Hansen/UMD/Google/USGS/NASA"" ], ""frequency of updates"": ""Annual"", ""date of content"": ""2001-2021"", ""resolution"": ""100x100 meters"" }"
-"3a718205-10c1-4e11-81b3-cb8965e378c7","Biodiversity importance of natural ecosystems converted to cropland (FLII)","Forest landscape integrity loss","BL_LUC_S","The biodiversity importance of natural ecosystems converted to cropland (FLII) indicators quantifies the average forest landscape integrity score of natural ecosystems that have been converted to cropland within a 50km radius attributable to the raw material sourced.","1129394b-6baa-41dd-9409-80d808dbc32e","{ ""name"": ""Biodiversity importance of natural ecosystems converted to cropland (FLII)"", ""short name"": ""Forest landscape integrity loss"", ""name code"": ""BL_LUC_S"", ""indicator type"": ""landscape-level"", ""impact type"": ""Biodiversity"", ""units"": ""score"", ""description"": ""The biodiversity importance of natural ecosystems converted to cropland (FLII) indicators quantifies the average forest landscape integrity score of natural ecosystems that have been converted to cropland within a 50km radius attributable to the raw material sourced."", ""interpretation"": ""Quantifies the biodiversity importance of land use change events, specifically here the net expansion of cropland into natural ecosystems. Biodiversity importance is measured here as the Forest Landscape Integrity Index, which represents how ecological intact forest ecosystems are. So higher values indicate that more intact forest landscapes, those with greater ecological importance, have been lost. "", ""license"": """", ""geographic coverage"": ""Global coverage."", ""citation"": [ ""Grantham, H. S., A. Duncan, T. D. Evans, K. R. Jones, H. L. Beyer, R. Schuster, J. Walston, et al. 2020. ‘Anthropogenic Modification of Forests Means Only 40% of Remaining Forests Have High Ecosystem Integrity’. Nature Communications 11 (1): 5978. https://doi.org/10.1038/s41467-020-19493-3."" ], ""source"": [ ""Grantham et al. 2020"" ], ""frequency of updates"": """", ""date of content"": ""2020"", ""resolution"": ""100x100 meters"" }"
-"9c2124c7-5df0-40d5-962e-d35480d48cd3","Surface or groundwater use","Water use","UWU_T","The surface or groundwater use indicator estimates the volume of surface or groundwater that is consumed in the production of the raw material sourced.","9c0da38a-6371-4c79-879b-218fc39c4700","{ ""name"": ""Surface or groundwater use"", ""short name"": ""Water use"", ""name code"": ""UWU_T"", ""indicator type"": ""farm-level"", ""impact type"": ""Water quantity"", ""units"": ""Mm3/yr"", ""description"": ""The surface or groundwater use indicator estimates the volume of surface or groundwater that is consumed in the production of the raw material sourced."", ""interpretation"": ""The water use indicator describes the average volume of water consumed in the production of raw materials in a given country context. It is intended to align with the Science Based Targets Network (SBTN) water quantity target indicator (Science Based Targets Network 2023b)."", ""license"": ""CC BY 4.0"", ""geographic coverage"": ""Global coverage."", ""citation"": [ ""Mekonnen, M.M. & Hoekstra, A.Y. (2011) The green, blue and grey water footprint of crops and derived crop products, Hydrology and Earth System Sciences, 15(5): 1577-1600."", ""Mekonnen, M.M. & Hoekstra, A.Y. (2012) A global assessment of the water footprint of farm animal products, Ecosystems, 15(3): 401–415."" ], ""source"": [ ""Mekonnen 2011"", ""Mekonnen 2012"" ], ""frequency of updates"": """", ""date of content"": ""1996-2005"", ""resolution"": """" }"
-"ffdd6f19-6737-4a10-9d36-5243d3f14b45","Excess surface or groundwater use","Unsustainable water use","UWUSR_T","The excess surface or groundwater use indicator calculates the volume by which the water consumption associated with the production of the raw material sourced must be decreased to reduce pressure on nature.","9c0da38a-6371-4c79-879b-218fc39c4700","{ ""name"": ""Excess surface or groundwater use"", ""short name"": ""Unsustainable water use"", ""name code"": ""UWUSR_T"", ""indicator type"": ""farm-level"", ""impact type"": ""Water quantity"", ""units"": ""Mm3/yr"", ""description"": ""The excess surface or groundwater use indicator calculates the volume by which the water consumption associated with the production of the raw material sourced must be decreased to reduce pressure on nature."", ""interpretation"": ""The unsustainable water use indicator shows the amount by which water use would need to be reduced in order to reduce pressure on local watersheds and return them to a maximum allowable level of basin-wide withdrawals, according to the Science Based Targets Network (SBTN) water quantity target approach (Science Based Targets Network 2023b). Unsustainable water use is measured as a proportion of total water use."", ""license"": ""CC BY 4.0"", ""geographic coverage"": ""Global coverage."", ""citation"": [ ""Kuzma, S., M.F.P. Bierkens, S.Lakshman, T. Luo, L. Saccoccia, E. H. Sutanudjaja, and R. Van Beek. 2023. “Aqueduct 4.0: Updated decision-relevant global water risk indicators.” Technical Note. Washington, DC: World Resources Institute. Available online at: doi.org/10.46830/ writn.23.00061."", ""Mekonnen, M.M. & Hoekstra, A.Y. (2011) The green, blue and grey water footprint of crops and derived crop products, Hydrology and Earth System Sciences, 15(5): 1577-1600."", ""Mekonnen, M.M. & Hoekstra, A.Y. (2012) A global assessment of the water footprint of farm animal products, Ecosystems, 15(3): 401–415."" ], ""source"": [ ""Aqueduct 4.0 2019"", ""Mekonnen 2011"", ""Mekonnen 2012"" ], ""frequency of updates"": """", ""date of content"": ""2019"", ""resolution"": """" }"
-"5c595ac7-f144-485f-9f32-601f6faae9fe","Land use footprint for production","Land footprint","LI","The land use footprint for production indicator quantifies the total land area required to produce the raw material sourced.","6970f9b8-eba0-4fee-b6ee-2723ce6604d4","{ ""name"": ""Land use footprint for production"", ""short name"": ""Land footprint"", ""name code"": ""LI"", ""indicator type"": ""farm-level"", ""impact type"": ""Land use"", ""units"": ""ha/yr"", ""description"": ""The Land use footprint for production indicator quantifies the total land area required to produce the raw material sourced."", ""interpretation"":""The land footprint indicator describes the total area of land required to produce the quantity of a raw material sourced. It is designed to align with the Science Based Targets Network’s (SBTN) land footprint reduction target (Science Based Targets Network 2023a)."", ""license"": ""CC BY 4.0"", ""geographic coverage"": ""Global coverage."", ""citation"": [ ""International Food Policy Research Institute. 2019. 'Global Spatially-Disaggregated Crop Production Statistics Data for 2010 Version 2.0'. Harvard Dataverse. https://doi.org/10.7910/DVN/PRFF8V."" ], ""source"": [ ""Mapspam 2010"", ""LGW3 2010"" ], ""frequency of updates"": """", ""date of content"": ""2010"", ""resolution"": ""10x10 kilometers"" }"
-"1994e7bf-5442-4061-ba48-6320574263ad","GHG emissions from farm management","GHGs (farm management)","GHG","The GHG emissions from farm management indicator quantifies the amount of greenhouse gas (GHG) emissions, including CO2, N2O and CH4, arising from farm-management of the raw material sourced.","a0e8110c-fbde-4c8c-ac19-f0f69078b96b","{ ""name"": ""GHG emissions from farm management"", ""short name"": ""GHGs (farm management)"", ""name code"": ""GHG"", ""indicator type"": ""farm-level"", ""impact type"": ""Climate"", ""units"": ""tCO2eq/yr"", ""description"": ""The GHG emissions from farm management indicator quantifies the amount of greenhouse gas (GHG) emissions, including CO2, N2O and CH4, arising from farm-management of the raw material sourced."", ""interpretation"": ""Estimates the emissions of greenhouse gasses (CO2, N2O and CH4 expressed in terms of CO2 equivalent global warming potential) arising from farm management practices in the production of agricultural commodities. It is intended to align with the guidance for calculating within farm gate emissions from the land sector (Greenhouse Gas Protocol 2022)."", ""license"": ""CC BY 4.0"", ""geographic coverage"": ""Global coverage."", ""citation"": [ ""Halpern, Benjamin S., Melanie Frazier, Juliette Verstaen, Paul-Eric Rayner, Gage Clawson, Julia L. Blanchard, Richard S. Cottrell, et al. 2022. ‘The Environmental Footprint of Global Food Production’. Nature Sustainability 5 (12): 1027–39. https://doi.org/10.1038/s41893-022-00965-x."" ], ""source"": [ ""Halpern et al 2021"" ], ""frequency of updates"": """", ""date of content"": ""2017"", ""resolution"": ""10x10 Kilimeters"" }"
-"d5f945c9-8636-45a2-a7c9-67a1dc8e687a","Freshwater nutrient load assimilation volume","Nutrient load","WQ","The freshwater nutrient load assimilation volume indicator estimates the annual average water volume required to assimilate the nutrient load added by the raw material sourced.","9c0da38a-6371-4c79-879b-218fc39c4700","{ ""name"": ""Freshwater nutrient load assimilation volume"", ""short name"": ""Nutrient load"", ""name code"": ""WQ"", ""indicator type"": ""farm-level"", ""impact type"": ""Water quality"", ""units"": ""Mm3/yr"", ""description"": ""The freshwater nutrient load assimilation volume indicator estimates the annual average water volume required to assimilate the nutrient load added by the raw material sourced."", ""interpretation"":""The nutrient load indicator describes the average volume of freshwater required to absorb the nutrient load created by production of the raw material. It is intended to align with the Science Based Targets Network (SBTN) water quality target indicator (Science Based Targets Network 2023b)."", ""license"": ""CC BY 4.0"", ""geographic coverage"": ""Global coverage."", ""citation"": [ ""Mekonnen, M.M. & Hoekstra, A.Y. (2011) The green, blue and grey water footprint of crops and derived crop products, Hydrology and Earth System Sciences, 15(5): 1577-1600."", ""Mekonnen, M.M. & Hoekstra, A.Y. (2012) A global assessment of the water footprint of farm animal products, Ecosystems, 15(3): 401–415."" ], ""source"": [ ""Mekonnen 2011"", ""Mekonnen 2012"" ], ""frequency of updates"": """", ""date of content"": ""1996-2005"", ""resolution"": """" }"
-"a39394be-ad57-41bc-9c2c-be0949ec6193","Excess freshwater nutrient load assimilation volume","Excess nutrient load","NAC","The excess freshwater nutrient load assimilation volume indicator aims to quantify the volume by which nutrient load associated with the raw material sourced must be decreased to achieve the desired instream nutrient concentration.","9c0da38a-6371-4c79-879b-218fc39c4700","{ ""name"": ""Excess freshwater nutrient load assimilation volume"", ""short name"": ""Excess nutrient load"", ""name code"": ""NAC"", ""indicator type"": ""farm-level"", ""impact type"": ""Water quality"", ""units"": ""Mm3/yr"", ""description"": ""The excess freshwater nutrient load assimilation volume indicator aims to quantify the volume by which nutrient load associated with the raw material sourced must be decreased to achieve the desired instream nutrient concentration."", ""interpretation"": ""The excess nutrient load indicator describes the extent to which nutrient loads must be reduced to meet the desired nutrient concentration following the Science Based Targets Network (SBTN) water quality target indicator approach (Science Based Targets Network 2023b). The reduction is measured as a proportion of the total nutrient load indicator and expressed in terms of the volume of freshwater required to absorb the excess pollutants."", ""license"": ""CC BY 4.0"", ""geographic coverage"": ""Global coverage."", ""citation"": [ ""McDowell, R. W., A. Noble, P. Pletnyakov, B. E. Haggard and L. M. Mosley, 2020. Global Mapping of Freshwater Nutrient Enrichment and Periphyton Growth Potential. Scientific Reports.https://doi.org/10.1038/s41598-020-60279-w."", ""Benjamin S. Halpern, Melanie Frazier, Juliette Verstaen, Paul-Eric Rayner, Gage Clawson, Julia L. Blanchard, Richard S. Cottrell, Halley E. Froehlich, Jessica A. Gephart, Nis S. Jacobsen, Caitlin D. Kuempel, Peter B. McIntyre, Marc Metian, Daniel Moran, Kirsty L. Nash, Johannes Többen, David R. Williams. (2021) The environmental footprint of global food production.Scientific Reports.https://doi.org/10.1038/s41893-022-00965-x"",""Mekonnen, M.M. & Hoekstra, A.Y. (2011) The green, blue and grey water footprint of crops and derived crop products, Hydrology and Earth System Sciences, 15(5): 1577-1600."", ""Mekonnen, M.M. & Hoekstra, A.Y. (2012) A global assessment of the water footprint of farm animal products, Ecosystems, 15(3): 401–415."" ], ""source"": [ ""McDowell et al 2020"", ""Mekonnen 2011"", ""Mekonnen 2012"" ], ""frequency of updates"": """", ""date of content"": """", ""resolution"": """" }"
-"5c133ba4-da24-46db-9c6c-ece7520f01b0","Cropland expansion in natural ecosystems","Net cropland expansion","NECR","The annual average area of cropland expansion into natural ecosystems occuring within a 50km radius attributable to the raw material sourced.","6970f9b8-eba0-4fee-b6ee-2723ce6604d4","{ ""name"": ""Cropland expansion in natural ecosystems"", ""short name"": ""Net cropland expansion"", ""name code"": ""NECR"", ""indicator type"": ""landscape-level"", ""impact type"": ""Natural ecosystem conversion"", ""units"": ""ha/yr"", ""description"": ""The cropland expansion in natural ecosystems indicator quantifies the annual average area of cropland expansion into natural ecosystems occuring within a 50km radius attributable to the raw material sourced."", ""interpretation"": ""An estimate of the annual net area of cropland expansion into natural ecosystems since 2020 within a 50km radius that is attributable to the quantity of raw material sourced using a statistical land use change (sLUC) approach. This indicator assumes that land conversion is driven by demand for land in the local area and is a conservative estimate of the amount of natural ecosystems that are lost to cropland expansion in the local area. It is intended to assist companies in prioritizing sourcing areas in alignment with Zero Deforestation and Zero Land Conversion commitments, such as the Accountability Framework Initiative (AFI) and the Science Based Targets Network (SBTN) Zero Natural Land Conversion target."", ""license"": ""CC BY 4.0"", ""geographic coverage"": ""Global coverage."", ""citation"": [ ""Mazur, Elise, Michelle Sims, Elizabeth Goldman, Martina Schneider, Fred Stolle, Marco Daldoss Pirri, and Craig Beatty. 2023. ‘SBTN Natural Lands Map: Technical Documentation’. SBTN. https://sciencebasedtargetsnetwork.org/wp-content/uploads/2023/05/Technical-Guidance-2023-Step3-Land-v0.3-Natural-Lands-Map.pdf."", ""Karra, Krishna, Caitlin Kontgis, Zoe Statman-Weil, Joseph C. Mazzariello, Mark Mathis, and Steven P. Brumby. 2021. ‘Global Land Use / Land Cover with Sentinel 2 and Deep Learning’. In 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 4704–7. Brussels, Belgium: IEEE. https://doi.org/10.1109/IGARSS47720.2021.9553499."" ], ""source"": [ ""Esri/Sentinel-2 10m land use/land cover"", ""SBTN/WRI/Systemiq/WWF"" ], ""frequency of updates"": ""Annual"", ""date of content"": ""2023"", ""resolution"": ""100x100 meters"" }"
+"936d0a9f-fe48-42b4-9433-63282d4dada5","Deforestation footprint (sLUC)","Deforestation footprint (sLUC)","DF_SLUC","The deforestation footprint (sLUC) indicator quantifies the annual average area of deforestation within a 50km radius attributable to the raw material sourced.","6970f9b8-eba0-4fee-b6ee-2723ce6604d4","{ ""name"": ""Deforestation footprint (sLUC)"", ""short name"": ""Deforestation footprint (sLUC)"", ""name code"": ""DF_SLUC"", ""indicator type"": ""landscape-level"", ""impact type"": ""Natural ecosystem conversion"", ""units"": ""ha/yr"", ""description"": ""The deforestation footprint (sLUC) indicator quantifies the annual average area of deforestation within a 50km radius attributable to the raw material sourced."", ""interpretation"":""Deforestation footprint estimates the area of deforestation occurring within a 50km radius that is attributable to the quantity of raw material sourced using a statistical land use change (sLUC) approach. The indicator assumes that deforestation is driven by demand for land area."", ""license"": ""CC BY 4.0"", ""geographic coverage"": ""Global coverage (excluding Antarctica and other Arctic islands)."", ""citation"": [ ""Chuvieco, Emilio, Joshua Lizundia-Loiola, Maria Lucrecia Pettinari, Ruben Ramo, Marc Padilla, Kevin Tansey, Florent Mouillot, et al. 2018. ‘Generation and Analysis of a New Global Burned Area Product Based on MODIS 250 m Reflectance Bands and Thermal Anomalies’. Earth System Science Data 10 (4): 2015–31. https://doi.org/10.5194/essd-10-2015-2018."", ""Hansen, M. C., P. V. Potapov, R. Moore, M. Hancher, S. A. Turubanova, A. Tyukavina, D. Thau, et al. 2013. ‘High-Resolution Global Maps of 21st-Century Forest Cover Change’. Science 342 (6160): 850–53. https://doi.org/10.1126/science.1244693"", ""Mazur, Elise, Michelle Sims, Elizabeth Goldman, Martina Schneider, Fred Stolle, Marco Daldoss Pirri, and Craig Beatty. 2023. ‘SBTN Natural Lands Map: Technical Documentation’. SBTN. https://sciencebasedtargetsnetwork.org/wp-content/uploads/2023/05/Technical-Guidance-2023-Step3-Land-v0.3-Natural-Lands-Map.pdf."", ""Potapov, Peter, Matthew C. Hansen, Lars Laestadius, Svetlana Turubanova, Alexey Yaroshenko, Christoph Thies, Wynet Smith, et al. 2017. ‘The Last Frontiers of Wilderness: Tracking Loss of Intact Forest Landscapes from 2000 to 2013’. Science Advances 3 (1): e1600821. https://doi.org/10.1126/sciadv.1600821."", ""Potapov, Peter, Matthew C. Hansen, Amy Pickens, Andres Hernandez-Serna, Alexandra Tyukavina, Svetlana Turubanova, Viviana Zalles, et al. 2022. ‘The Global 2000-2020 Land Cover and Land Use Change Dataset Derived From the Landsat Archive: First Results’. Frontiers in Remote Sensing 3 (April): 856903. https://doi.org/10.3389/frsen.2022.856903. "", ""Turubanova, Svetlana, Peter V Potapov, Alexandra Tyukavina, and Matthew C Hansen. 2018. ‘Ongoing Primary Forest Loss in Brazil, Democratic Republic of the Congo, and Indonesia’. Environmental Research Letters 13 (7): 074028. https://doi.org/10.1088/1748-9326/aacd1c."" ], ""source"": [ ""Hansen/UMD/Google/USGS/NASA"", ""Mazur/Potapov/Turubanova/Chuvieco"" ], ""frequency of updates"": ""Annual"", ""date of content"": ""2001-2022"", ""resolution"": ""100x100 meters"" }"
+"157b5f22-916b-4981-84c7-f6607ec65445","GHG emissions from deforestation (sLUC)","GHGs (deforestation, sLUC)","GHG_DEF_SLUC","The GHG emissions from deforestation (sLUC) indicator quantifies the annual average emissions of greenhouse gas (GHG) associated with deforestation within a 50km radius attributable to the raw material sourced.","a0e8110c-fbde-4c8c-ac19-f0f69078b96b","{ ""name"": ""GHG emissions from deforestation (sLUC)"", ""short name"": ""GHGs (deforestation, sLUC)"", ""name code"": ""GHG_DEF_SLUC"", ""indicator type"": ""landscape-level"", ""impact type"": ""Climate"", ""units"": ""tCO2eq/yr"", ""description"": ""The GHG emissions from deforestation (sLUC) indicator quantifies the annual average emissions of greenhouse gas (GHG) associated with deforestation within a 50km radius attributable to the raw material sourced."", ""interpretation"": ""Provides an estimate of the annual average greenhouse gas emissions arising from deforestation events occurring since 2002, within a 50km proximity of where a raw material was sourced and attributable to that raw material. Emissions are calculated from the deforestation rates and the vulnerable carbon, the amount of biomass and soil carbon that would be lost in a land use change event typical for the location."", ""license"": ""CC BY 4.0"", ""geographic coverage"": ""Global coverage."", ""citation"": [ ""Noon, Monica L., Allie Goldstein, Juan Carlos Ledezma, Patrick R. Roehrdanz, Susan C. Cook-Patton, Seth A. Spawn-Lee, Timothy Maxwell Wright, et al. 2021. ‘Mapping the Irrecoverable Carbon in Earth’s Ecosystems’. Nature Sustainability 5 (1): 37–46. https://doi.org/10.1038/s41893-021-00803-6."", ""ESA. 2017. ‘Land Cover CCI Product User Guide Version 2. Technical Report’. maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdf."", ""Hansen, M. C., P. V. Potapov, R. Moore, M. Hancher, S. A. Turubanova, A. Tyukavina, D. Thau, et al. 2013. ‘High-Resolution Global Maps of 21st-Century Forest Cover Change’. Science 342 (6160): 850–53. https://doi.org/10.1126/science.1244693."" ], ""source"": [ ""Noon/Mazur/Potapov/Turubanova/Chuvieco"", ""Hansen/UMD/Google/USGS/NASA"" ], ""frequency of updates"": ""Annual"", ""date of content"": ""2001-2021"", ""resolution"": ""100x100 meters"" }"
+"3a718205-10c1-4e11-81b3-cb8965e378c7","Biodiversity importance of natural ecosystems converted to cropland (FLII)","Forest landscape integrity loss","FLIL","The biodiversity importance of natural ecosystems converted to cropland (FLII) indicators quantifies the average forest landscape integrity score of natural ecosystems that have been converted to cropland within a 50km radius attributable to the raw material sourced.","1129394b-6baa-41dd-9409-80d808dbc32e","{ ""name"": ""Biodiversity importance of natural ecosystems converted to cropland (FLII)"", ""short name"": ""Forest landscape integrity loss"", ""name code"": ""FLIL"", ""indicator type"": ""landscape-level"", ""impact type"": ""Biodiversity"", ""units"": ""score"", ""description"": ""The biodiversity importance of natural ecosystems converted to cropland (FLII) indicators quantifies the average forest landscape integrity score of natural ecosystems that have been converted to cropland within a 50km radius attributable to the raw material sourced."", ""interpretation"": ""Quantifies the biodiversity importance of land use change events, specifically here the net expansion of cropland into natural ecosystems. Biodiversity importance is measured here as the Forest Landscape Integrity Index, which represents how ecological intact forest ecosystems are. So higher values indicate that more intact forest landscapes, those with greater ecological importance, have been lost. "", ""license"": """", ""geographic coverage"": ""Global coverage."", ""citation"": [ ""Grantham, H. S., A. Duncan, T. D. Evans, K. R. Jones, H. L. Beyer, R. Schuster, J. Walston, et al. 2020. ‘Anthropogenic Modification of Forests Means Only 40% of Remaining Forests Have High Ecosystem Integrity’. Nature Communications 11 (1): 5978. https://doi.org/10.1038/s41467-020-19493-3."" ], ""source"": [ ""Grantham et al. 2020"" ], ""frequency of updates"": """", ""date of content"": ""2020"", ""resolution"": ""100x100 meters"" }"
+"9c2124c7-5df0-40d5-962e-d35480d48cd3","Surface or groundwater use","Water use","WU","The surface or groundwater use indicator estimates the volume of surface or groundwater that is consumed in the production of the raw material sourced.","9c0da38a-6371-4c79-879b-218fc39c4700","{ ""name"": ""Surface or groundwater use"", ""short name"": ""Water use"", ""name code"": ""WU"", ""indicator type"": ""farm-level"", ""impact type"": ""Water quantity"", ""units"": ""Mm3/yr"", ""description"": ""The surface or groundwater use indicator estimates the volume of surface or groundwater that is consumed in the production of the raw material sourced."", ""interpretation"": ""The water use indicator describes the average volume of water consumed in the production of raw materials in a given country context. It is intended to align with the Science Based Targets Network (SBTN) water quantity target indicator (Science Based Targets Network 2023b)."", ""license"": ""CC BY 4.0"", ""geographic coverage"": ""Global coverage."", ""citation"": [ ""Mekonnen, M.M. & Hoekstra, A.Y. (2011) The green, blue and grey water footprint of crops and derived crop products, Hydrology and Earth System Sciences, 15(5): 1577-1600."", ""Mekonnen, M.M. & Hoekstra, A.Y. (2012) A global assessment of the water footprint of farm animal products, Ecosystems, 15(3): 401–415."" ], ""source"": [ ""Mekonnen 2011"", ""Mekonnen 2012"" ], ""frequency of updates"": """", ""date of content"": ""1996-2005"", ""resolution"": """" }"
+"ffdd6f19-6737-4a10-9d36-5243d3f14b45","Excess surface or groundwater use","Unsustainable water use","UWU","The excess surface or groundwater use indicator calculates the volume by which the water consumption associated with the production of the raw material sourced must be decreased to reduce pressure on nature.","9c0da38a-6371-4c79-879b-218fc39c4700","{ ""name"": ""Excess surface or groundwater use"", ""short name"": ""Unsustainable water use"", ""name code"": ""UWU"", ""indicator type"": ""farm-level"", ""impact type"": ""Water quantity"", ""units"": ""Mm3/yr"", ""description"": ""The excess surface or groundwater use indicator calculates the volume by which the water consumption associated with the production of the raw material sourced must be decreased to reduce pressure on nature."", ""interpretation"": ""The unsustainable water use indicator shows the amount by which water use would need to be reduced in order to reduce pressure on local watersheds and return them to a maximum allowable level of basin-wide withdrawals, according to the Science Based Targets Network (SBTN) water quantity target approach (Science Based Targets Network 2023b). Unsustainable water use is measured as a proportion of total water use."", ""license"": ""CC BY 4.0"", ""geographic coverage"": ""Global coverage."", ""citation"": [ ""Kuzma, S., M.F.P. Bierkens, S.Lakshman, T. Luo, L. Saccoccia, E. H. Sutanudjaja, and R. Van Beek. 2023. “Aqueduct 4.0: Updated decision-relevant global water risk indicators.” Technical Note. Washington, DC: World Resources Institute. Available online at: doi.org/10.46830/ writn.23.00061."", ""Mekonnen, M.M. & Hoekstra, A.Y. (2011) The green, blue and grey water footprint of crops and derived crop products, Hydrology and Earth System Sciences, 15(5): 1577-1600."", ""Mekonnen, M.M. & Hoekstra, A.Y. (2012) A global assessment of the water footprint of farm animal products, Ecosystems, 15(3): 401–415."" ], ""source"": [ ""Aqueduct 4.0 2019"", ""Mekonnen 2011"", ""Mekonnen 2012"" ], ""frequency of updates"": """", ""date of content"": ""2019"", ""resolution"": """" }"
+"5c595ac7-f144-485f-9f32-601f6faae9fe","Land use footprint for production","Land footprint","LF","The land use footprint for production indicator quantifies the total land area required to produce the raw material sourced.","6970f9b8-eba0-4fee-b6ee-2723ce6604d4","{ ""name"": ""Land use footprint for production"", ""short name"": ""Land footprint"", ""name code"": ""LF"", ""indicator type"": ""farm-level"", ""impact type"": ""Land use"", ""units"": ""ha/yr"", ""description"": ""The Land use footprint for production indicator quantifies the total land area required to produce the raw material sourced."", ""interpretation"":""The land footprint indicator describes the total area of land required to produce the quantity of a raw material sourced. It is designed to align with the Science Based Targets Network’s (SBTN) land footprint reduction target (Science Based Targets Network 2023a)."", ""license"": ""CC BY 4.0"", ""geographic coverage"": ""Global coverage."", ""citation"": [ ""International Food Policy Research Institute. 2019. 'Global Spatially-Disaggregated Crop Production Statistics Data for 2010 Version 2.0'. Harvard Dataverse. https://doi.org/10.7910/DVN/PRFF8V."" ], ""source"": [ ""Mapspam 2010"", ""LGW3 2010"" ], ""frequency of updates"": """", ""date of content"": ""2010"", ""resolution"": ""10x10 kilometers"" }"
+"1994e7bf-5442-4061-ba48-6320574263ad","GHG emissions from farm management","GHGs (farm management)","GHG_FARM","The GHG emissions from farm management indicator quantifies the amount of greenhouse gas (GHG) emissions, including CO2, N2O and CH4, arising from farm-management of the raw material sourced.","a0e8110c-fbde-4c8c-ac19-f0f69078b96b","{ ""name"": ""GHG emissions from farm management"", ""short name"": ""GHGs (farm management)"", ""name code"": ""GHG_FARM"", ""indicator type"": ""farm-level"", ""impact type"": ""Climate"", ""units"": ""tCO2eq/yr"", ""description"": ""The GHG emissions from farm management indicator quantifies the amount of greenhouse gas (GHG) emissions, including CO2, N2O and CH4, arising from farm-management of the raw material sourced."", ""interpretation"": ""Estimates the emissions of greenhouse gasses (CO2, N2O and CH4 expressed in terms of CO2 equivalent global warming potential) arising from farm management practices in the production of agricultural commodities. It is intended to align with the guidance for calculating within farm gate emissions from the land sector (Greenhouse Gas Protocol 2022)."", ""license"": ""CC BY 4.0"", ""geographic coverage"": ""Global coverage."", ""citation"": [ ""Halpern, Benjamin S., Melanie Frazier, Juliette Verstaen, Paul-Eric Rayner, Gage Clawson, Julia L. Blanchard, Richard S. Cottrell, et al. 2022. ‘The Environmental Footprint of Global Food Production’. Nature Sustainability 5 (12): 1027–39. https://doi.org/10.1038/s41893-022-00965-x."" ], ""source"": [ ""Halpern et al 2021"" ], ""frequency of updates"": """", ""date of content"": ""2017"", ""resolution"": ""10x10 Kilimeters"" }"
+"d5f945c9-8636-45a2-a7c9-67a1dc8e687a","Freshwater nutrient load assimilation volume","Nutrient load","NL","The freshwater nutrient load assimilation volume indicator estimates the annual average water volume required to assimilate the nutrient load added by the raw material sourced.","9c0da38a-6371-4c79-879b-218fc39c4700","{ ""name"": ""Freshwater nutrient load assimilation volume"", ""short name"": ""Nutrient load"", ""name code"": ""NL"", ""indicator type"": ""farm-level"", ""impact type"": ""Water quality"", ""units"": ""Mm3/yr"", ""description"": ""The freshwater nutrient load assimilation volume indicator estimates the annual average water volume required to assimilate the nutrient load added by the raw material sourced."", ""interpretation"":""The nutrient load indicator describes the average volume of freshwater required to absorb the nutrient load created by production of the raw material. It is intended to align with the Science Based Targets Network (SBTN) water quality target indicator (Science Based Targets Network 2023b)."", ""license"": ""CC BY 4.0"", ""geographic coverage"": ""Global coverage."", ""citation"": [ ""Mekonnen, M.M. & Hoekstra, A.Y. (2011) The green, blue and grey water footprint of crops and derived crop products, Hydrology and Earth System Sciences, 15(5): 1577-1600."", ""Mekonnen, M.M. & Hoekstra, A.Y. (2012) A global assessment of the water footprint of farm animal products, Ecosystems, 15(3): 401–415."" ], ""source"": [ ""Mekonnen 2011"", ""Mekonnen 2012"" ], ""frequency of updates"": """", ""date of content"": ""1996-2005"", ""resolution"": """" }"
+"a39394be-ad57-41bc-9c2c-be0949ec6193","Excess freshwater nutrient load assimilation volume","Excess nutrient load","ENL","The excess freshwater nutrient load assimilation volume indicator aims to quantify the volume by which nutrient load associated with the raw material sourced must be decreased to achieve the desired instream nutrient concentration.","9c0da38a-6371-4c79-879b-218fc39c4700","{ ""name"": ""Excess freshwater nutrient load assimilation volume"", ""short name"": ""Excess nutrient load"", ""name code"": ""ENL"", ""indicator type"": ""farm-level"", ""impact type"": ""Water quality"", ""units"": ""Mm3/yr"", ""description"": ""The excess freshwater nutrient load assimilation volume indicator aims to quantify the volume by which nutrient load associated with the raw material sourced must be decreased to achieve the desired instream nutrient concentration."", ""interpretation"": ""The excess nutrient load indicator describes the extent to which nutrient loads must be reduced to meet the desired nutrient concentration following the Science Based Targets Network (SBTN) water quality target indicator approach (Science Based Targets Network 2023b). The reduction is measured as a proportion of the total nutrient load indicator and expressed in terms of the volume of freshwater required to absorb the excess pollutants."", ""license"": ""CC BY 4.0"", ""geographic coverage"": ""Global coverage."", ""citation"": [ ""McDowell, R. W., A. Noble, P. Pletnyakov, B. E. Haggard and L. M. Mosley, 2020. Global Mapping of Freshwater Nutrient Enrichment and Periphyton Growth Potential. Scientific Reports.https://doi.org/10.1038/s41598-020-60279-w."", ""Benjamin S. Halpern, Melanie Frazier, Juliette Verstaen, Paul-Eric Rayner, Gage Clawson, Julia L. Blanchard, Richard S. Cottrell, Halley E. Froehlich, Jessica A. Gephart, Nis S. Jacobsen, Caitlin D. Kuempel, Peter B. McIntyre, Marc Metian, Daniel Moran, Kirsty L. Nash, Johannes Többen, David R. Williams. (2021) The environmental footprint of global food production.Scientific Reports.https://doi.org/10.1038/s41893-022-00965-x"",""Mekonnen, M.M. & Hoekstra, A.Y. (2011) The green, blue and grey water footprint of crops and derived crop products, Hydrology and Earth System Sciences, 15(5): 1577-1600."", ""Mekonnen, M.M. & Hoekstra, A.Y. (2012) A global assessment of the water footprint of farm animal products, Ecosystems, 15(3): 401–415."" ], ""source"": [ ""McDowell et al 2020"", ""Mekonnen 2011"", ""Mekonnen 2012"" ], ""frequency of updates"": """", ""date of content"": """", ""resolution"": """" }"
+"5c133ba4-da24-46db-9c6c-ece7520f01b0","Cropland expansion in natural ecosystems","Net cropland expansion","NCE","The annual average area of cropland expansion into natural ecosystems occuring within a 50km radius attributable to the raw material sourced.","6970f9b8-eba0-4fee-b6ee-2723ce6604d4","{ ""name"": ""Cropland expansion in natural ecosystems"", ""short name"": ""Net cropland expansion"", ""name code"": ""NCE"", ""indicator type"": ""landscape-level"", ""impact type"": ""Natural ecosystem conversion"", ""units"": ""ha/yr"", ""description"": ""The cropland expansion in natural ecosystems indicator quantifies the annual average area of cropland expansion into natural ecosystems occuring within a 50km radius attributable to the raw material sourced."", ""interpretation"": ""An estimate of the annual net area of cropland expansion into natural ecosystems since 2020 within a 50km radius that is attributable to the quantity of raw material sourced using a statistical land use change (sLUC) approach. This indicator assumes that land conversion is driven by demand for land in the local area and is a conservative estimate of the amount of natural ecosystems that are lost to cropland expansion in the local area. It is intended to assist companies in prioritizing sourcing areas in alignment with Zero Deforestation and Zero Land Conversion commitments, such as the Accountability Framework Initiative (AFI) and the Science Based Targets Network (SBTN) Zero Natural Land Conversion target."", ""license"": ""CC BY 4.0"", ""geographic coverage"": ""Global coverage."", ""citation"": [ ""Mazur, Elise, Michelle Sims, Elizabeth Goldman, Martina Schneider, Fred Stolle, Marco Daldoss Pirri, and Craig Beatty. 2023. ‘SBTN Natural Lands Map: Technical Documentation’. SBTN. https://sciencebasedtargetsnetwork.org/wp-content/uploads/2023/05/Technical-Guidance-2023-Step3-Land-v0.3-Natural-Lands-Map.pdf."", ""Karra, Krishna, Caitlin Kontgis, Zoe Statman-Weil, Joseph C. Mazzariello, Mark Mathis, and Steven P. Brumby. 2021. ‘Global Land Use / Land Cover with Sentinel 2 and Deep Learning’. In 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 4704–7. Brussels, Belgium: IEEE. https://doi.org/10.1109/IGARSS47720.2021.9553499."" ], ""source"": [ ""Esri/Sentinel-2 10m land use/land cover"", ""SBTN/WRI/Systemiq/WWF"" ], ""frequency of updates"": ""Annual"", ""date of content"": ""2023"", ""resolution"": ""100x100 meters"" }"