About
Across tropical production regions in Southeast Asia, South America and West Africa, land-use change linked to commodity expansion remains a major source of greenhouse gas (GHG) emissions. Commodities such as palm oil and cocoa are often produced in landscapes characterised by a complex mix of natural forests, agricultural systems, peatlands, and developing production areas. Historical forest conversion continues to generate long-term carbon impacts, creating ongoing risk for climate reporting and sustainability commitments.
To address these challenges, a spatially explicit carbon accounting framework was developed to quantify land-use change (LUC) emissions and link them directly to modern commodity production systems. The approach establishes a robust, science-based foundation for emissions reporting, risk assessment, and supply chain decision-making across multiple regions and commodities.
The Challenge
Accurately quantifying land-use change emissions across diverse commodity landscapes presents several key challenges:
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LAND-USE CHANGE COMPLEXITY
Forest conversion often occurs over extended timeframes and may involve multiple transitions between land uses before stabilising as commodity production. This complexity makes it difficult to attribute emissions to specific commodities or production areas, particularly where indirect or delayed conversion pathways are involved.
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TEMPORAL CARBON DYNAMICS
Carbon emissions from deforestation occur rapidly, while carbon recovery through regrowth can take decades. Conventional accounting approaches often fail to capture this imbalance, limiting the ability to represent the ongoing climate impact of historical land-use change.
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SPATIAL DATA AND ATTRIBUTION
Linking emissions to specific supply chain land units requires high-resolution spatial data and consistent attribution methods. Variability in land cover, ecological conditions, and soil characteristics across regions can introduce uncertainty into emissions estimates.
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DATA GAPS AND INCONSISTENCY
Publicly available long-term land cover time series are often incomplete or inconsistent, particularly in tropical regions. This limits the ability to capture historical deforestation events and may lead to underestimation of current emissions associated with commodity production.
Methodology
Our approach establishes a spatially explicit modelling framework to quantify discounted land-use change emissions and attribute them to commodity production areas across multiple regions.
A high-resolution time series of land cover change is developed by our partner organisation (Starling) using annual satellite data to detect natural forest conversion over time and subsequent commodity type. This is combined with spatial datasets describing forest type, soil class, climate zone, and peat presence to refine emissions estimates.
Land-use change emissions are calculated using a 20-year retrospective accounting period. Emissions occurring within this window are distributed across time using a discounting approach, producing a weighted annual emissions profile that reflects the ongoing climate impact of historical deforestation and aligns with GHG Protocol guidance.
The assessment focuses on direct and landscape-level land-use change and includes the following carbon pools:
- Aboveground biomass
- Belowground biomass
- Dead organic matter
- Soil carbon
Multiple emissions pathways are incorporated into the analysis, including:
- Biomass clearing and decomposition
- Fire and burning processes
- Mineral soil emissions
- Organic (peat) soil emissions
Total emissions are linked to commodity production through integration with production data. This enables results to be expressed both as total emissions per unit area and as commodity-specific emission factors (e.g. emissions per tonne of product).
Outputs are generated at fine spatial resolution and aggregated to relevant reporting units such as production areas, sourcing regions, and administrative boundaries. The supply chain tracking is completed by our partner Earthworm. The framework maintains transparency by preserving gas-specific emissions and underlying drivers.
Outcome
The implementation of this discounted land-use change framework provides a comprehensive and scalable approach for understanding emissions across commodity supply chains. It enables organisations to:
- Quantify the ongoing climate impact of historical deforestation linked to production systems
- Identify high-risk sourcing regions associated with elevated land-use change emissions
- Compare production areas based on emissions intensity to support procurement and investment decisions
- Improve alignment with international standards, including GHG Protocol Land Sector and Removals guidance and SBTi FLAG frameworks
- Support targeted interventions, such as:
- Avoiding sourcing from deforestation-linked areas
- Prioritising lower-emissions production regions
- Improving land management practices
By linking historical land-use change to present-day production through a discounted accounting approach, the framework provides a more accurate and decision-relevant representation of climate impact. It supports data-driven, spatially informed strategies for reducing emissions and advancing sustainable commodity production at scale.