Beef

Cocoa

Coffee

Palm oil

Wood pulp

Brazil

Colombia

Côte d'Ivoire

Ecuador

Ghana

Indonesia

Peru

Cattle deforestation is the amount of deforestation associated with the production of beef for a given year. Cattle deforestation builds on pasture deforestation and assumes a 5 year life cycle from birth to slaughter, accounting for the average pasture deforestation during the 5 year lifespan.

Cattle deforestation estimates the amount of deforestation associated with the production of beef for a given year. Cattle deforestation combines the area deforested for pastures and cattle production, assuming a 5 year life cycle from birth to slaughter, accounting for the average pasture deforestation during the 5 year lifespan.

Brazil - Cattle deforestation

Cattle deforestation per ton is the pasture deforestation associated with the full production life-cycle of beef for a given year, expressed in hectares deforested per ton of carcass. We assume a 5 year life cycle from birth to slaughter accounting for average production and pasture deforestation during the life cycle.

Cattle deforestation per ton estimates the amount of deforestation associated with the production of beef for a given year, expressed in hectares deforested per ton of carcass. Cattle assumes a 5 year life cycle from birth to slaughter accounting for the average production and pasture deforestation during the life cycle.

Brazil - Cattle deforestation per ton

Estimated cattle production in tonnes of carcass.

Cattle production (ton carcass) per municipality, per year, is calculated as: herd size (per muni/yr) * slaughter rate (per state/yr) * carcass weight (per state/yr). Herd size is derived from IBGE cattle heads (IBGE, 2025), slaughter rate: extrapolates Annualpec slaughter data (IEG & FNP, 2019), based on IBGE slaughter data and corrected for SIGSIF animal movements (MAPA, 2019). Carcass weight is derived from IBGE carcass weights (IBGE, 2025)

Brazil - Cattle production

Gross CO₂-eq emissions from cattle deforestation (using a 5-year allocation period), estimate emission from the loss of the native vegetation biomass (above- and below-ground, litter, and deadwood; excluding soil carbon).

Gross greenhouse gas (GHG) emissions from cattle deforestation, expressed in tonnes of CO₂-equivalent (t CO₂-eq), are determined by calculating the emissions resulting from the removal of native vegetation carbon stocks. Carbon stocks include the above and below ground biomass including necromass. The carbon stocks are based on the values provided by SEEG v12 (SEEG, 2024). Carbon is converted into CO₂-eq by multiplying the values by 3.67, based on the ratio between the molecular weight of C and CO₂.

Brazil - Gross emissions from cattle deforestation

Gross CO₂-eq emissions from cattle deforestation per ton of beef, assuming a 5-year cattle lifecycle from birth to slaughter. Estimates are based on the loss of native vegetation biomass (above- and below-ground, litter, and deadwood; excluding soil carbon), with carbon stocks converted to CO₂-eq by multiplying by 3.67.

Brazil - Gross emissions from cattle deforestation per ton

Gross CO₂-eq emissions from pasture deforestation (using a 5-year allocation period), estimate emissions from the loss of the native vegetation biomass (above- and below-ground, litter, and deadwood; excluding soil carbon).

Gross greenhouse gas (GHG) emissions from pasture deforestation, expressed in tonnes of CO₂-equivalent (t CO₂-eq), are determined by calculating the emissions resulting from the removal (land conversion) of native vegetation carbon stocks. Carbon stocks include the above and below ground biomass including necromass. The carbon stocks are based on the values provided by SEEG v12 (SEEG, 2024). Carbon is converted into CO₂-eq by multiplying the values by 3.67, based on the ratio between the molecular weight of C and CO₂.

Brazil - Gross emissions from pasture deforestation

Net CO₂-eq emissions attributable to cattle deforestation (using a 5-year allocation period). Estimates are based on the difference between the biomass stored in the native vegetation biomass (above- and below-ground, litter, and deadwood; excluding soil carbon).

Net greenhouse gas (GHG) emissions from cattle deforestation, expressed in tonnes of CO₂-equivalent (t CO₂-eq), are estimated based on the difference between the biomass stored in the native vegetation biomass before land conversion and the carbon stored in pastures. Carbon stocks include above and below ground biomass including necromass. Both the native vegetation and the pasture carbon stocks are based on estimates provided by SEEG v12 (SEEG, 2024). Carbon is converted into CO₂-eq by multiplying the values by 3.67, based on the ratio between the molecular weight of C and CO₂.

Brazil - Net emissions from cattle deforestation

Net CO₂-eq emissions from cattle deforestation per ton of beef, based on the biomass difference between native vegetation and pasture, assuming a 5-year cattle lifecycle from birth to slaughter.

Brazil - Net emissions from cattle deforestation per ton

Net CO₂-eq emissions attributable to pasture deforestation (using a 5-year allocation period). Estimates are based on the difference between the biomass stored in the native vegetation biomass (above- and below-ground, litter, and deadwood; excluding soil carbon).

Net greenhouse gas (GHG) emissions from pasture deforestation, expressed in tonnes of CO₂-equivalent (t CO₂-eq), are determined by calculating the emissions resulting from the removal of native vegetation carbon stocks, due to deforestation for pasture, and the carbon stored in pasture. Carbon stocks include above and below ground biomass including necromass. Both the native vegetation and the pasture carbon stocks are based on estimates provided by SEEG v12 (SEEG, 2024). Carbon is converted into CO₂-eq by multiplying the values by 3.67, based on the ratio between the molecular weight of C and CO₂.

Brazil - Net emissions from pasture deforestation

Pasture deforestation is the amount of deforestation across a five-year allocation period that is converted to pasture. To calculate it, we overlapped the annual pasture maps from MapBiomas with the annual territorial deforestation five years prior to the target year for pasture, including the year the pasture was detected.

Pasture deforestation is quantified as the total area of deforestation converted to pasture within a five-year allocation period. We calculate this by spatially overlapping annual pasture maps from MapBiomas with annual territorial deforestation data. This analysis identifies all deforestation that occurred within the five years prior to, and including, the year pasture was detected, yielding a cumulative estimate of deforestation linked to pasture land use. The territorial deforestation data is a composite dataset, integrating information from Prodes Amazon (INPE, 2025), Prodes Cerrado (INPE, 2025), and MapBiomas deforestation (MapBiomas, 2025; Souza et al., 2020) data for all other biomes.

Brazil - Pasture deforestation

Area of pasture derived from Mapbiomas collection 10 (MapBiomas, 2025)

Brazil - Pasture area

Percent export traded under a Company Zero Deforestation Commitment (ZDC) or the G4 (Compromisso Público da Pecuária – CPP) commitment.

Percent beef export volume traded under a Company Zero Deforestation Commitment (ZDC) (Global Canopy, 2025; Thomsen, 2025), G4 (Compromisso Público da Pecuária – CPP) (Greenpeace, 2009). NA for areas where no beef is exported from. ZDCs are assessed by Global Canopy following the Forest 500 guidance for those companies handling together 90% of total beef exports.

Brazil - Beef exported under a ZDC

Percent production under a Company Zero Deforestation Commitment (ZDC) or the G4 (Compromisso Público da Pecuária – CPP) commitment. Note, trader commitments within the domestic supply-chain are not assessed here.

Percent production volume (incl. exports and domestic) traded under a Company Zero Deforestation Commitment (ZDC) (Global Canopy, 2025; Thomsen, 2025), G4 (Compromisso Público da Pecuária – CPP) (Greenpeace, 2009). ZDCs are assessed by Global Canopy following the Forest 500 guidance for those companies handling together 90% of total beef exports.

Brazil - Beef produced under a ZDC

Gross emissions from territorial deforestation. Estimates are based on the total removal of the biomass stored in the native vegetation, including above and below ground biomass, litter and deadwood. 

Gross greenhouse gas (GHG) emissions from territorial  deforestation, expressed in tonnes of CO₂-equivalent (t CO₂-eq) are determined by calculating the emissions resulting from the removal (land conversion) of native vegetation carbon stocks. Carbon stocks include the above and below ground biomass including necromass. The carbon stocks are based on the values provided by SEEG v12 (SEEG, 2024). Carbon is converted into CO₂-eq by multiplying the values by 3.67, based on the ratio between the molecular weight of C and CO₂.

Brazil - Gross emissions from deforestation

Gross CO₂-eq emissions from soy deforestation (using a 5-year allocation period), estimate emissions from the loss of the native vegetation biomass, including above- and below-ground, litter, and deadwood, while excluding soil carbon.

Gross greenhouse gas (GHG) emissions from soy deforestation, expressed in tonnes of CO₂-equivalent (t CO₂-eq) are determined by calculating the emissions resulting from the removal (land conversion) of native vegetation carbon stocks. Carbon stocks include the above and below ground biomass including necromass. The carbon stocks are based on the values provided by SEEG v12 (SEEG, 2024). Carbon is converted into CO₂-eq by multiplying the values by 3.67, based on the ratio between the molecular weight of C and CO₂.

Brazil - Gross emissions from soy deforestation

Volume of soy per jurisdiction that was certified according to the Round Table on Responsible Soy (RTRS) Standard for Responsible Soy Production. No conversion of natural vegetation is permitted after June 2016 under the RTRS Principles and Criteria.

Volume of soy produced per jurisdiction that was certified according to the Round Table on Responsible Soy (RTRS) Standard for Responsible Soy Production. No conversion of natural vegetation is permitted after June 2016 under the RTRS Principles and Criteria (RTRS, 2024).

Brazil - Round Table on Responsible Soy (RTRS)

Soy harvested area derived from Instituto Brasileiro de Geografia e Estatística (IBGE, 2022).

Brazil - Soy area

Soy deforestation is the amount of deforestation across a five-year allocation period that is converted to soy. To calculate it, we overlapped the annual soy maps from GLAD, in the southern hemisphere, and Mapbiomas soy, for the northern hemisphere with the annual territorial deforestation up to six years prior to the soy harvest, excluding the time from planting to harvest. 

Soy deforestation is quantified as the total area of deforestation converted to soy within a five-year allocation period accounting for a one year lag between planting and harvest. We calculate this by spatially overlapping territorial deforestation and annual soy maps from GLAD in the southern hemisphere (Song et al., 2021) and Mapbiomas soy in the northern hemisphere (MapBiomas, 2025; Souza et al., 2020). This analysis identifies all deforestation that occurred within the five years prior to planting, yielding a cumulative estimate of deforestation linked to soy production. The territorial deforestation data is a composite dataset, integrating information from Prodes Amazon (INPE, 2025), Prodes Cerrado (INPE, 2025), and MapBiomas deforestation (MapBiomas, 2025; Souza et al., 2020) data for all other biomes.

Brazil - Soy deforestation

Soy production tons derived from Instituto Brasileiro de Geografia e Estatística (IBGE, 2022).

Brazil - Soy production

Soy yield was derived from Instituto Brasileiro de Geografia e Estatística (IBGE, 2022).

Brazil - Soy yield

Percentage of the soy that is exported by a trader with a zero deforestation commitment.

Percentage of soy that is exported by a trader with a zero deforestation commitment (ZDC) (Global Canopy, 2025; Thomsen, 2025), including the Soy Moratorium in the Brazilian Amazon (Abiove, 2024). ZDCs are assessed by Global Canopy following the Forest 500 guidance for those companies handling together 90% of total soy exports.

Brazil - Soy exported under a ZDC

Total deforestation is defined as the conversion of primary native vegetation.

The territorial deforestation data is a composite dataset, integrating information from Prodes Amazon (INPE, 2025), Prodes Cerrado (INPE, 2025), and MapBiomas deforestation (MapBiomas, 2025; Souza et al., 2020) data for all other biomes. Deforestation is defined as the conversion of primary native vegetation. 

Brazil - Deforestation

Gross CO₂-eq. emissions from deforestation, estimated from the loss of above- and below-ground biomass (excluding soil carbon) from the EC JRC Tropical Moist Forest dataset (Vancutsem et al., 2021). Carbon stocks were converted to CO₂-eq. by multiplying by 3.67. Note that values for the most recent 2 years may change in the future when new versions of the Tropical Moist Forest dataset are released, updated to reflect whether recent forest change shows temporary degradation or permanent deforestation.

Gross (greenhouse gas) emissions from territorial deforestation (tonnes of CO2-eq) were calculated by estimating the maximum CO2-eq potentially emitted due to the full removal of the biomass as a result of territorial deforestation, estimated in a given year. This metric is calculated by multiplying the area of territorial deforestation by an estimate of the carbon stock contained in each hectare of converted land. Carbon stocks represent the total carbon stored in above- and below-ground biomass, but does not include carbon stocks in the soil. We create local estimates of biomass density (carbon stocks per hectare) using data from (Soto-Navarro et al., 2020). Carbon is converted into CO₂-eq emissions by multiplying by a factor of 44/12 ( or 3.67), based on the ratio between the molecular weight of C and CO₂ following (Pachauri et al., 2015).

Côte d’Ivoire - Gross greenhouse gas emissions from deforestation

Cocoa deforestation is the amount of deforestation within a 15-year allocation period that is converted to cocoa plantations. To calculate it, we overlapped the cocoa area map from (Kalischek et al., 2023) (representing cocoa plantations in 2019 to 2021) with the annual loss of undisturbed Tropical Moist Forests from the EC JRC Tropical Moist Forest dataset (Vancutsem et al., 2021), applying a four-year lag between deforestation and first potential harvest to reflect the time from planting to harvest. Note that this does not account for cocoa expansion after 2021, and values for the most recent 2 years may change in the future when new versions of the Tropical Moist Forest dataset are released, updated to reflect whether recent forest change shows temporary degradation or permanent deforestation.

This metric estimates how much deforestation is associated with the cocoa exports in the selected year and department. Here, deforestation was defined as the conversion from undisturbed forest to non-forest (Renier et al., 2023; Vancutsem et al., 2021). To estimate the deforestation associated with the cocoa exports in a specific year,  we used a) a four-year lag between cocoa planting and the first harvest (the first year of potential cocoa exports), and b) aggregated deforestation at the location of cocoa production across 15 years. By setting these parameters we are assuming that the plantations will be ready for harvesting, and consequently starting to contribute to the volume traded, in the fourth year after the deforestation event. For example, cocoa exported in 2021 was planted in 2017 or earlier. We calculate cocoa deforestation in 2021 (Vancutsem et al., 2021) as the sum of deforestation defined by the area converted from undisturbed forest in 2002 to non-forest in 2017 at the location of the cocoa plantation (Kalischek et al., 2023).

Côte d’Ivoire - Cocoa deforestation

Gross CO₂-equivalent emissions from cocoa deforestation (15-year allocation period, 4-year lag), estimated from the loss of above- and below-ground biomass (excluding soil carbon). Carbon stocks were converted to CO₂-equivalent by multiplying by 3.67. To calculate above and belowground biomass loss, we overlapped the cocoa area map from (Kalischek et al., 2023) (representing cocoa plantations in 2019 to 2021) with the loss of undisturbed Tropical Moist Forests from the EC JRC Tropical Moist Forest dataset (Vancutsem et al., 2021), applying a four-year lag between deforestation and first potential harvest to reflect the time from planting to harvest. Note that values for the most recent 2 years may change in the future when new versions of the Tropical Moist Forest dataset are released, updated to reflect whether recent forest change shows temporary degradation or permanent deforestation.

GHG emissions from cocoa deforestation (tonnes of CO₂-eq) were calculated within a given year and department by estimating all of the CO₂-eq. potentially emitted due to cocoa deforestation. Here, deforestation was defined as the conversion from undisturbed forest to deforested land (Vancutsem et al., 2021) within the period starting 19 years and ending 4 years before the detection of cocoa plantation by (Kalischek et al., 2023). This metric is calculated by multiplying the deforested area by an estimate of the carbon stock contained in each hectare of deforested land. Carbon stocks represent the total carbon stored in above- and below-ground biomass, but does not include carbon stocks in the soil. We create local estimates of biomass density (carbon stocks per hectare) using data from (Soto-Navarro et al., 2020). Carbon is converted into CO₂-eq emissions by multiplying by a factor of 44/12 ( or 3.67), based on the ratio between the molecular weight of C and CO₂ following (Pachauri et al., 2015).

Côte d’Ivoire - Gross emissions from cocoa deforestation

Average cocoa yield combining cocoa production (annual) and the cocoa area map from (Kalischek et al., 2023) (representing cocoa plantation in 2019 to 2021).

Cocoa yield per department was calculated based on cocoa area and cocoa production as tonnes produced per hectare (t/ha).

Côte d’Ivoire - Cocoa yield

Net greenhouse gas emissions from cocoa deforestation (15-year allocation period, 4-year lag), were calculated as the difference between gross emissions from cocoa deforestation and sequestration in new cocoa plantations. To calculate above and belowground biomass loss, we overlapped the cocoa area map from (Kalischek et al., 2023) (representing cocoa plantations in 2019 to 2021) with the loss of undisturbed Tropical Moist Forests from the EC JRC Tropical Moist Forest dataset (Vancutsem et al., 2021), applying a four-year lag between deforestation and first potential harvest to reflect the time from planting to harvest. Note that values for the most recent 2 years may change in the future when new versions of the Tropical Moist Forest dataset are released, updated to reflect whether recent forest change shows temporary degradation or permanent deforestation.

We calculate net greenhouse gas emissions from annual deforestation as the difference between gross greenhouse gas emissions from cocoa deforestation and carbon sequestration in cocoa plantations. To account for carbon sequestration, we adopted estimates from (Vervuurt et al., 2022) for Cote d'Ivoire of 19 t C ha-1 in cocoa trees and 4 t C ha-1 in shade trees.

Côte d’Ivoire - Net emissions from cocoa deforestation

Estimated production of cocoa based on national production data, cocoa areas, and cocoa suitability. Cocoa areas are from (Kalischek et al., 2023), based on satellite observations from 2019 to 2021.

This metric represents the estimated volume of cocoa production per department in Côte d'Ivoire in tonnes (t) per year. Department level cocoa production was calculated based on national production statistics, derived from the International Cocoa Organization (ICCO, 2023) and allocated to the individual departments weighted based on the cocoa area (see above) and relative cocoa suitability (Schroth et al., 2016) of plantations (Kalischek et al., 2023). Note, only the total cocoa production from ICCO varied between 2019 to 2022, while cocoa area and suitability were assumed stable for the whole period.

Côte d’Ivoire - Production of cocoa

Percentage of cocoa that is traded by traders with a zero deforestation commitment.

Zero-deforestation commitments (ZDCs) were obtained from Global Canopy's research determining whether an exporting company group has a commitment or goal for zero deforestation in its cocoa supply chain in the year of cocoa export. A commitment is accepted as zero deforestation if an exporter group explicitly uses the phrases 'zero deforestation', 'zero conversion' in its sustainability goals, current policies, or if they have a policy (or target) for 100% certification where the certification is equivalent to a zero-deforestation standard. \nCommitments are reported as 'company commitment' (if the specific company has a ZDC), 'none' (if the company does not have one), or 'unknown' (if the company's ZDC position was not checked).

Côte d’Ivoire - Percentage of (total) cocoa that is exported under a zero deforestation commitment

Deforestation of undisturbed Tropical Moist Forest derived from EC JRC Tropical Moist Forest dataset (Vancutsem et al., 2021). Note that values for the most recent 2 years may change in the future when new versions of the Tropical Moist Forest dataset are released, updated to reflect whether recent forest change shows temporary degradation or permanent deforestation.

Deforestation maps were derived from the European Commission's Joint Research Centre 's TMF dataset Version 1 from 2023 (Vancutsem et al., 2021) using 41 years of Landsat time series at 30 metre spatial resolution and aggregated at department level. Deforestation refers to the year of change in land cover, from undisturbed or degraded forest (see above) to non-forest (Vancutsem et al., 2021).

Côte d’Ivoire - Deforestation

Degradation of Tropical Moist Forest derived from EC JRC Tropical Moist Forest dataset (Vancutsem et al., 2021). Note that values for the most recent 2 years may change in the future when new versions of the Tropical Moist Forest dataset are released, updated to reflect whether recent forest change shows temporary degradation or permanent deforestation.

Maps of degraded forest were derived from the European Commission's Joint Research Centre's TMF dataset Version 1 from 2023  (Vancutsem et al., 2021) using 41 years of Landsat time series at 30 metre spatial resolution and aggregated at department level. Forest degradation was defined as a disturbance in the tree cover canopy  visible from space over a short time period (< 2.5 years). A 'forest degradation' pixel remains forested land; i.e.,  covered by existing or regrowing trees, whereas a 'deforestation' pixel leads to the long-term (visible > 2.5 years) conversion into non-forested land  (Vancutsem et al., 2021).

Côte d’Ivoire - Degradation

Area of undisturbed Tropical Moist Forest derived from EC JRC Tropical Moist Forest dataset (Vancutsem et al., 2021). Note that values for the most recent 2 years may change in the future when new versions of the Tropical Moist Forest dataset are released, updated to reflect whether recent forest change shows temporary degradation or permanent deforestation.

Undisturbed forest maps were derived from the European Commission's Joint Research Centre's forest cover change in tropical moist forests (TMF) dataset Version 1 from 2023 (Vancutsem et al., 2021) using 41 years of Landsat time series at 30 metre spatial resolution and aggregated at department level. Undisturbed forest was defined as a closed evergreen or semi-evergreen forest without any disturbance observed over the full Landsat historical dataset (Vancutsem et al., 2021).

Côte d’Ivoire - Undisturbed forest

Ghana - Gross greenhouse gas emissions from deforestation

Ghana - Cocoa deforestation embedded in trade

Ghana - Gross emissions from cocoa deforestation

We calculate net greenhouse gas emissions from annual deforestation as the difference between gross greenhouse gas emissions from cocoa deforestation and carbon sequestration in cocoa plantations. To account for carbon sequestration, we adopted estimates from (Vervuurt et al., 2022) of 19 t C ha-1 in cocoa trees and 4 t C ha-1 in shade trees.

Ghana - Net emissions from cocoa deforestation

Ghana - Deforestation

Ghana - Degradation

Ghana - Undisturbed forest

The total burned area per year, according to data from the Ministry of Forestry, Government of Indonesia (2025).

To estimate the area burned, we relied upon the official burned area dataset from Indonesia’s Ministry of Forestry (MoF, 2025). The dataset has been published yearly since 2015 by MoF and is mapped at 1:250,000 scale on a monthly basis by visual interpretation of the extent of forest and land fires from Landsat-8, Landsat-9 and Sentinel-2 satellite imagery. The visually identified burned areas are then validated by experts using hotspot data from the Terra-Aqua MODIS Satellite, NOAA 20, S-NPP and Landsat-8 as well as field reports. It is worth noting that recent research indicates that this data might underestimate the total burned area. For example, Gaveau et al. (2021) present evidence that the 2019 burned area was 90% greater than the area reported by MoF.

Indonesia - Burned areas

Burned peatlands refer to the peat areas that have been affected by fire, as identified from burned area data (Ministry of Forestry, 2025)

Indonesia - Burned peatlands

Gross greenhouse gas emissions from territorial deforestation. Estimates are based on the total biomass removal stored in above- and below-ground biomass in forests (excl. soil carbon). CO₂-eq were derived by multiplying carbon stocks by 3.67.

Gross greenhouse gas emissions from territorial deforestation (tonnes of CO2-eq) were calculated by estimating the maximum CO2-eq potentially emitted due to the full removal of the biomass as a result of territorial deforestation. This metric is calculated by multiplying territorial deforestation by the estimated carbon stock per hectare of converted land. Carbon stocks include above and below-ground biomass, excluding soil biomass. We derived local above-ground woody biomass density (carbon stocks per hectare) from Harris et al. (2021). We estimated below-ground biomass density from the above-ground biomass based on the ratio of below-ground to above-ground biomass derived from Spawn et al. (2010). We converted biomass to carbon using conversion factors of 0.47 t C t-1 for above-ground and 0.39 t C t-1 for below-ground, respectively (Diop et al., 2016). Finally, we converted carbon to CO2-eq emissions by multiplying the carbon estimates by a factor of 44/12 (or 3.67), based on the ratio between the molecular weight of C and CO₂ following IPCC (2014).

Indonesia - Gross emissions from deforestation

Greenhouse gas emissions from peat soil burning linked to palm oil production in a given year. The emissions are calculated based on the area of overlap between the burned areas (Ministry of Forestry, 2025) and peat areas (Ministry of Agriculture, 2019) and attributed to oil palm based on the different land uses. An average emission factor of 824 tonnes of CO₂-eq./ha was applied, following Rodríguez Vásquez et al. (2021).

GHG emissions from fires on peat soils in a given year were estimated based on the overlap between burned areas (Ministry of Forestry, 2025) and peat areas (Ministry of Agriculture, 2019), multiplied by an emission factor of 824 t CO₂ eq ha⁻¹, following the average emission estimates provided by Rodríguez Vásquez et al. (2021). This value represents the average CO₂ released per hectare during a single peat fire event, derived from measurements of burn depth, bulk density, and emission factors from peatland sites in Kalimantan, Sumatra, and Malaysia. Because peat fires may result from multiple land uses, emissions were allocated to the oil palm sector according to the proportion of modified peatlands within each FFB production unit that are covered by oil palm. Modified peatlands are defined as peat soils used for agriculture, including rice paddies, oil palm, pulpwood plantations, aquaculture, and other agricultural uses, as identified by MapBiomas Indonesia Collection 4 (2025). This choice reflects the fact that fire is often used as land management to clear the land before it is used agriculturally and might then spread to neighbouring areas (Purnomo et al., 2017), whereas fires in intact peatland forests are uncommon (Marlier et al., 2015).

Indonesia - Emissions from peat burning

Greenhouse gas emissions from peat subsidence linked to palm oil production in a given year. The emissions are calculated based on the area of industrial oil palm plantations located on peatland multiplied by an emission factor of 90 t CO₂-eq./ha, following Cooper et al. (2020).

GHG emissions from peat subsidence in a given year were estimated based on the area of industrial oil palm plantations located on peat soils within each FFB production unit for the target year. This area was multiplied by an emission factor of 90 t CO₂ eq ha⁻¹ yr⁻¹, averaged over a 30-year plantation cycle, to account for methane, nitrous oxide, and carbon dioxide emissions resulting from peat oxidation and subsidence after drainage (Cooper et al., 2020). 

Indonesia - Emissions from peat subsidence

Gross greenhouse gas emissions from land converted to industrial oil palm plantations in a given year, including both forest and non-forest areas. Emissions were estimated by multiplying the converted area by the estimated carbon stocks per hectare, using above-ground biomass data from Harris et al. (2021) and applying root-to-shoot ratios from Spawn et al. (2020) to estimate below-ground biomass. Estimates are based on total biomass removal (above- and below-ground) and exclude soil carbon. CO₂-equivalents were calculated by multiplying total carbon stocks by 3.67.

Gross greenhouse gas emissions from industrial palm oil conversion (tonnes of CO2-eq) were calculated within a given year by estimating all of the CO2-eq potentially emitted due to annual oil palm land use change, including both from natural forest and non-forest land covers (as defined in Gaveau et al. (2022). This metric is calculated by multiplying the area converted each year to palm oil by an estimate of the carbon stock (above and below ground, excluding soil carbon stocks) contained in each hectare of converted land. Above-ground woody biomass density (carbon stocks per hectare) was derived from Harris et al. (2021). Below-ground biomass density was estimated based on Harris et al.’s above-ground biomass density and the ratio of below-ground to above-ground biomass derived from Spawn et al. (2020). To convert biomass to carbon density, we used conversion factors of 0.47 t C t-1 for aboveground and 0.39 t C-1 for belowground, respectively (Diop et al., 2016). Carbon is converted into CO₂-eq emissions by multiplying by a factor of 44/12 (or 3.67), based on the ratio between the molecular weight of C and CO₂ following IPCC (2014).

Indonesia - Gross emissions from land-use change

Net greenhouse gas emissions from land-use change were calculated as the difference between gross emissions from oil palm plantation expansion and carbon sequestration in new industrial oil palm plantations in the target year. Values may be negative if oil palm plantations are established on non-forest land.

Net greenhouse gas emissions from industrial oil palm were calculated as the difference between gross emissions from oil palm plantation expansion and carbon sequestration in new industrial oil palm plantations. Carbon sequestration was estimated by multiplying the area of new industrial oil palm plantations in the target year by the biomass carbon stock of industrial oil palm plantations per hectare. We used values from Khasanah et al. (2015) for above-ground biomass and applied a 1:4 root-to-shoot ratio from Guillaume et al. (2018) to account for below-ground biomass. Carbon stocks were converted to CO₂-equivalent emissions using a factor of 3.67 (IPCC, 2014).

Indonesia - Net emissions from land-use change

The extent of productive industrial oil palm plantations identified by remote sensing and expert photo interpretation, following the definition of the TreeMap’s Nusantara Atlas (Gaveau et al., 2022).

Oil palm planted area is the extent of productive industrial oil palm. It was calculated using data provided by Gaveau et al. (2022), who mapped the year in which oil palm is first planted in a given location. This dataset can be viewed at Nusantara Atlas (The TreeMap, 2025). Oil palms were assumed to start producing fresh fruit bunches (FFB) in the third year after planting and continue to produce for up to 30 years. Based on these assumptions,the productive oil palm planted area assigned is the total area planted  three years before the target year. For example, the productive oil palm planted area in 2018 is represented by the total area mapped as oil palm plantation in 2015 by Gaveau et. al. (Gaveau et al., 2022).

Indonesia - Oil palm planted area

The total area of natural forest loss associated with industrial oil palm plantations in hectares over a ten year period in the year of harvest/export

This metric estimates how much deforestation is associated with industrial palm oil production in a given year.  This includes the full plantation cycle, from establishment, planting and harvest in a particular year. To estimate the deforestation associated with industrial oil palm production in a specific year, we used a three-year lag between deforestation for oil palm and the first harvest, and aggregated deforestation at the location of oil palm production across 10 years. Deforestation for industrial oil palm is derived from Gaveau et al. (Gaveau et al., 2022; The TreeMap, 2025). By setting these parameters we are assuming that oil palms will start to produce, and consequently start to contribute to the volume traded in a given year, in the third year after the deforestation event for new plantations. These trees will continue to produce for around 30 years; however, here we are accounting only for the first ten years of production. For example, the total area of land that was classified as forest in 2007, but had been converted to oil palm plantations by 2017, would be considered as areas of deforestation contributing to industrial oil palm deforestation in 2020.

Indonesia - Historical deforestation for palm oil (industrial)

The total area of forest areas converted to industrial oil palm plantations in the selected year

Annual deforestation for industrial oil palm measures the forest area cleared for oil palm plantations in the target year, following the definition of rapid forest conversion to industrial oil palm in Gaveau et al. (2022). The data is derived from Gaveau et al. (Gaveau et al., 2022; The TreeMap, 2025) 

Indonesia - Annual palm deforestation (industrial)

Area of peatland of all depths planted with oil palm.

Oil palm on peatlands refers to the total area of oil palm (Gaveau et al., 2022; The TreeMap, 2025) planted on peatlands. Peatland areas were derived from Indonesia's official peatland map, which was published by the Indonesian Center for Agricultural Land Resources Research and Development within the Ministry of Agriculture in 2019 (Ministry of Agriculture, 2019). 

Indonesia - Oil palm planted on peat

Total annual greenhouse gas emissions from peatland subsidence, peat burning, and land use change associated with palm oil production, accounting for the carbon sequestration of the new oil palm plantations.

This metric estimates the major sources of GHG emissions from palm oil plantations in Indonesia: subsidence on drained peatlands, fire on peatlands, and land use change, i.e., the clearing of vegetation (forest and non-forest) to establish oil palm plantations. The GHG sequestration from new plantations was also estimated and incorporated into the net GHG emissions reported by Trase.

Indonesia - Total emissions from oil palm plantations

Peatland areas were derived from Indonesia's official peat map, produced by the Indonesian Center for Agricultural Land Resources Research and Development within the Ministry of Agriculture (2019).

Indonesia - Peatland area

Area of remaining natural forest, following the definition of The TreeMap’s Nusantara Atlas (Gaveau et al., 2021).

Natural forest area is the total area of remaining forest derived from Gaveau et al.(2022). Gaveau et al. (2022) combine primary forest cover in 2000 (Margono et al., 2014), and the global forest change data developed at the University of Maryland (Hansen et al., 2013). This definition of ‘forest’ excludes young regenerating forests, agroforests, scrublands, tree plantations, agricultural lands, grasslands, and non-vegetated areas (Gaveau et al., 2022). 

Indonesia - Natural forest

This metric represents the total deforestation of forests occurring in a single year (Gaveau et al., 2022; The TreeMap, 2025). To calculate territorial deforestation, Gaveau et al. (2022) combine primary forest in  2000 as identified by Margono et al. (2014) and the global forest change  data developed at the University of Maryland (Hansen et al., 2013). Excluding forest losses outside primary forest in 2000 (Margono et al. 2014), ensure that the deforestation reported measures forest area change rather than changes in planted trees 

Indonesia - Deforestation

Total area of natural forest converted to pulpwood plantations within the boundaries of a wood supplier’s concession in the selected year, following the definition of the TreeMap’s Nusantara Atlas (Gaveau et al., 2022). 

This metric represents total deforestation for conversion to pulpwood plantations in a single year, per concession. A subset of the annual deforestation metric, it is restricted to only those areas identified as natural forest one year before the target year and consistent with patterns of expansion of pulpwood plantations in the target year, according to the classification of Gaveau et al. (2022). 

Indonesia - Deforestation for planted pulpwood

Total burned area within the boundaries of a wood supplier’s concession in the selected year, according to data from Indonesia’s Ministry of Forestry. Burned areas may include natural forest, eucalyptus or acacia plantations for wood fiber, and other land covers.

This metric estimates the area burned within each concession in the target year. To measure burned area, we rely upon the burned area dataset from MoF. The dataset  has been published yearly since 2015 by MoF and is mapped at 1:250,000 scale on a monthly basis by visual interpretation of the extent of forest and land fires from Landsat-8, Landsat-9 and Sentinel-2 satellite imagery. The visually identified burned areas are then validated by experts using hotspot data from the Terra-Aqua MODIS Satellite, NOAA 20, S-NPP and Landsat-8 as well as field reports (Endrawanti et al., 2018). It is worth noting that recent research indicates that this data might underestimate the total burned area. For example, Gaveau et al. (2021), present evidence that the 2019 burned area was 90% greater than the area reported by MoF.

Total area burned on peatlands within the boundaries of a wood supplier’s concession in the selected year.

Burned peat refers to peat areas within pulpwood concession boundaries that have been affected by fire, as identified from burned area data (Ministry of Forestry, 2025)

The concession area of the industrial timber plantations (HTI - Hutan Tanaman Industri) based on maps from Indonesia’s Ministry of Forestry.

Indonesia - Concession area

Total deforestation within a pulpwood concession in a given year.

This metric represents the total deforestation of natural forests, occurring in a single year, within a specific concession. To calculate this metric, Gaveau et al. (2022) combines natural forests (as defined above) with the annual tree cover loss dataset developed by the University of Maryland (Hansen et al., 2013).  The tree cover loss dataset measures the removal of trees (tree height >5 m) if the canopy cover falls below 30%. It does not distinguish between removal of natural forests and planted trees. Collaborators at TreeMap then conduct additional photo interpretation and analysis to filter out errors. The same data sources are used on The TreeMap’s mapping platform The TreeMap’s Nusantara Atlas (2025). The metric is available on Trase Supply Chain’s Sankey diagram for active concessions trading in the target year and for all concessions on the platform’s map tool.

Indonesia - Deforestation inside concession

Total area of natural forest converted to pulpwood plantations within the boundaries of a wood supplier’s concession, measured cumulatively starting from the year after the concession’s license was first issued (or from 2001 for concessions established before 2000) through the selected year. The concession license year is available on the download page.

This metric measures annual deforestation for conversion to pulpwood plantation (Gaveau et al., 2022; The TreeMap, 2025), beginning in the year after a concession was first established. Thus, this metric follows the same definition as Deforestation for pulp per year but uses the concession’s establishment year as the baseline, regardless of later ownership changes. 

Indonesia - Cumulative deforestation for planted pulpwood inside concession since start 

Estimated area of deforestation for wood pulp that occurred on peatlands within the boundaries of a wood supplier’s concession in the selected year.

This metric represents total deforestation for conversion to pulpwood plantations in a single year, per concession occurring on peatlands (Ministry of Agriculture, 2019). 

Indonesia - Peatland deforestation for planted pulpwood

GHG emissions from peat soil burning, calculated as the overlap between burned areas and peatlands within pulpwood concessions in each year, multiplied by an emission factor of 824 tonnes of CO₂-eq per hectare. This factor follows average emission estimates for fire events on peat reported by Rodríguez Vásquez et al., (2021).

This metric represents the GHG emissions from peat burning (tonnes of CO₂-eq) in a single year per concession. The emissions are calculated based on the area of overlap between the burned areas and peat areas (more details above) multiplied by 824 tonnes of CO₂-eq following the average emission factor estimates provided by Rodríguez Vásquez et al., (2021). The value represents an average amount of CO₂-eq per hectare that is released into the atmosphere in a unique event of fire on peat. Rodríguez Vásquez et al., (2021) based their estimate on other studies  from peatlands sites in Kalimantan, Sumatra, and Malaysia where depth of burn, bulk density, and average emissions factors were measured.

Indonesia - Emissions from peatland burning

GHG emissions from peat subsidence are calculated based on the planted area of pulpwood on peat multiplied by an emission factor of 73.4 tCO₂-eq per hectare per year. This factor reflects a constant loss of 20 tonnes of Carbon for every hectare and year due to subsidence after peat drainage, as described by Carlson, Goodman and May-Tobin, (2015) and as recommended by the IPCC's Wetlands Supplement (2013).

The metric represents the (greenhouse gas) emissions from peat subsidence (tonnes of CO₂-eq) in a single year per concession, assigned to the target year. The emissions are calculated based on pulpwood planted area on peat (more details above) inside the concession for the target year. This area is multiplied by an emission factor of 73.4 tCO₂-eq, indicating a constant 20 tonnes of C loss for every hectare and year due to subsidence after peat drainage, as described by Carlson, Goodman and May-Tobin, (2015) and as recommended by the IPCC's Wetlands Supplement (2013).

GHG emissions from land converted to pulpwood plantations in a given year and concession, including both natural forest and non-forest areas. Calculated by multiplying the converted area by estimated carbon stocks per hectare, using above-ground biomass data (Harris et al., 2021) and applying root-to-shoot ratios from Spawn et al. (2020) to estimate below-ground biomass. Kabupaten and province-level values include areas both inside and outside concessions.

GHG emissions from industrial wood pulp land conversion (tonnes of CO₂-eq) were calculated by estimating all of the CO₂-eq potentially emitted due to annual land use change to pulpwood plantation, including both from natural forest and non-forest land cover. (as defined in Gaveau et al. (2022)), estimated within a given year and concession. This metric is calculated by multiplying the area converted each year to pulpwood plantation by an estimate of the carbon stock contained in each hectare of converted land independent of the fact whether than converted land was before forest or non-forest as we assumed that the carbon stock density map will account for the difference in carbon stocks between forest and non-forest.. Carbon stocks represent the total carbon stored in above- and below-ground biomass, but does not include carbon stocks in the soil. We create local estimates of above-ground woody biomass density (carbon stocks per hectare) using data from Harris  et al. (2021). Below-ground biomass density was estimated based on Harris et al. (2021)’s above-ground biomass density and the ratio of below-ground to above-ground biomass derived from Spawn et al. (2010). To convert biomass to carbon density, we used conversion factors of 0.47 t C t-1 for aboveground and 0.39 t C -1 for belowground, respectively (Diop et al., 2016). Carbon is converted into CO₂-eq emissions by multiplying by a factor of 44/12 ( or 3.67), based on the ratio between the molecular weight of C and CO₂ following IPCC (2014).

Indonesia - Gross land-use change emissions

Net GHG emissions from land-use change were calculated as the difference between gross emissions from wood pulp deforestation and carbon sequestration from new pulpwood plantations. Values may be negative if pulpwood plantations are established on non-forest land.

Net greenhouse gas emissions from industrial pulpwood plantation were calculated as the difference between gross emissions from pulpwood plantation expansion and carbon sequestration in new industrial pulpwood plantations. Carbon sequestration was estimated by multiplying the area of new industrial oil palm plantations in the target year by the biomass carbon stock of industrial pulpwood plantations across a plantation's full rotation. Based on research by Deshmukh et al., (2023), we assume that fully mature pulp plantations will, on average, store 42.4 tC/ha. We then adjust this maximum to estimate the average biomass within the plantation over its full rotation, as described by CFA & Quantis, (2022). This adjustment reflects the fact that perennial plantation systems are cyclical - while the plantation will contain lesser or greater stocks of carbon in individual years, the average across the full rotation can serve as a useful estimate of the carbon impact of land use change into long-term plantation forestry. Carbon stocks were converted to CO₂-equivalent emissions using a factor of 3.67 (IPCC, 2014).

Indonesia - Net land-use change emissions

Total area of peatlands within the boundaries of the wood supplier’s concession area

Total area of industrial acacia or eucalyptus plantations located on peat within a wood supplier’s concession, as detected by remote sensing analysis. A six-year lag is applied to reflect when plantations become productive and actively contribute to trade flows.

Pulpwood planted area on peat is the area of peatland within  concessions that has been planted with pulpwood species, as defined by Gaveau et al.(2022), i.e. a subset of the total peat area. Trase identifies peatlands using Indonesia's official peatland map, which was produced by the Indonesian Centre for Agricultural Land Resources Research and Development (Balai Besar Penelitian dan Pengembangan Sumberdaya Lahan Pertanian in Indonesian) within the Ministry of Agriculture (Indonesian Center for Agricultural Land Resources Research and Development, Ministry of Agriculture (MoA), Government of Indonesia, 2019).

Indonesia - Planted area on peatlands

Area of remaining natural forest in the concession, following the definition of The TreeMap’s Nusantara Atlas (Gaveau et al., 2021).

Net total GHG emissions from peatland subsidence, peat burning, land conversion and deforestation for wood pulp, accounting for the carbon sequestration of the new pulpwood plantations.

This metric estimates the major sources of GHG emissions from pulpwood plantations in Indonesia: subsidence on drained peatlands, fire on peatlands, and land use change, i.e., the clearing of vegetation (forest and non-forest) to establish pulpwood plantations. The GHG sequestration from new plantations was also estimated and incorporated into the net GHG emissions reported by Trase.

Indonesia - Total GhG emissions

Total area of industrial acacia or eucalyptus plantations within a wood supplier’s concession, as detected by remote sensing analysis. A six-year lag is applied to reflect when plantations become productive and actively contribute to trade flows. Kabupaten and province-level values include areas both inside and outside concessions.

Pulpwood plantation area represents the extent of productive industrial pulpwood, most commonly Acacia mangium and Acacia crassicarpa. It was calculated using data provided by Gaveau et al. (2022), who mapped the annual expansion of industrial pulpwood plantations by visually inspecting a time series of cloud-free Landsat composites from 2000 to 2024. They also referred to indicative maps of pulpwood concessions and reviewed various online and press reports to identify the locations of these plantations. Because the mapped expansion year reflects the establishment rather than the mature stage contributing to trade flows, we applied a six-year lag between the detected expansion and the calculation of active pulpwood plantation area. For example, deforestation detected in 2016 for pulpwood production is interpreted as a productive plantation in 2022. The boundaries of the plantations were delineated at a 1:50,000 scale based on expert photo interpretation. Beyond spectral information, the visual interpretation also incorporated the structure of the landscape. Pulpwood plantations typically appear as long linear or rectangular blocks located near harvesting trails, with lower density than that of other forestry crops such as oil palm. The final mapped area includes recently harvested, immature, and mature stands, as well as failed plantations. Scattered community plantations were not reported, as they are more difficult to detect through remote sensing. 

Indonesia - Planted area for pulpwood

Estimated deforestation for the pulpwood harvest, assuming a six-year delay between forest clearing, planting and harvest. Historical deforestation defines all deforestation occurring within the 10 years prior to planting (from 16 to 6 years before harvest) as driven by the commodity.

This metric estimates how much historical deforestation is associated with the wood pulp supply chain. The metric specifically focuses on areas where the establishment of pulpwood plantations are estimated to be mature enough to be harvested in the target year, considering the year of deforestation detection for pulpwood expansion. We use a retrospective approach, aggregating the deforestation across ten years, with a six-year lag between the first detection of deforestation and the year of harvest (also the year of export). By setting these parameters we are assuming that the plantations will be ready for harvesting, and consequently start to contribute to the volume traded in the sixth year after the deforestation event for new plantations. For example, the total area of land that was classified as forest in 2003, but had been converted to pulpwood plantations by 2014, would be considered as areas of deforestation directly connected to the wood pulp trade in 2020.

Indonesia - Deforestation embedded in pulp trade

Production of pulpwood in cubic metres based on official pulp mill reports of woodfiber sources.

Pulpwood supply was mapped using annual industrial wood supply plans (Rencana Pemenuhan Bahan Baku Industri, RPBBI) submitted by Indonesia’s six active pulp mills and one wood chip mill to the Ministry of Forestry (MoF). These reports list suppliers, supplier types, and volumes in cubic metres. Two suppliers holding Land Conversion Permits (Izin Pemanfaatan Kayu, IPK) were classified as sourcing from natural forests. About 96% of pulpwood between 2015–2024 originated from industrial wood fibre plantation concessions (Hutan Tanaman Industri, HTI). Concession boundaries were identified from MoF’s Forest Utilization License dataset (2022–2025). Additional concessions linked to the Royal Golden Eagle (RGE) Group were included due to potential pulpwood expansion. Imports, traders, and community suppliers were aggregated as reported in the RPBBI. 

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