Land Use Change Metrics

You can now access and report statistical Land Use Change (sLUC) Emission Factors for your specific crops and sourcing regions from the Sustainability Insights platform. sLUC EFs will help you better understand and manage your company's greenhouse gas (GHG) emissions resulting from land use change in your supply chain. sLUC emission factors estimate greenhouse gas (GHG) emissions associated with recent land-use change linked to agricultural production, reported as an emissions intensity per unit of crop output. 

The sLUC factors used in Sustainability Insights are based on a geospatial, standardized approach that focuses on emissions from conversion of forested land at multiple “traceability levels” (administrative scales).

NOTE ON sLUC

A statistical land-use change (sLUC) emission factor is an emissions intensity that estimates greenhouse gas (GHG) emissions associated with a statistical probability of deforestation-driven land-use change linked to crop production.  This statistical method, as recommended by the GHG Protocol, provides a standardized and scalable way for companies to account for land use change emissions when direct, farm-level traceability isn't available.

Land use change & emissions data

WRI’s Statistical Land Use Change Emissions data

Sustainability Insights sLUC emission factors are derived from World Resources Institute (WRI) Land & Carbon Lab’s Statistical Land Use Change Emissions dataset and accompanying methodology guidebook (Fitts et al., 2025). WRI has developed a robust and transparent methodology to calculate these EFs using globally consistent, open-source geospatial data.

WRI’s methodology is aligned with the Greenhouse Gas (GHG) Protocol's Land Sector and Removals Standard (LSRS) and aims to create a standardized and reliable way for companies to account for and report on their land use change emissions.

The EFs are comprehensive, including emissions from deforestation, associated soil carbon losses, and yield factors. They are available for the reporting years 2020-2024.

WRI methodology overview

WRI’s full methodology can be found in the Geospatial methods for corporate GHG accounting of deforestation and land occupation publication. In summary:

WRI’s method is designed to estimate emissions from deforestation associated with agricultural expansion. The guidebook describes the use of globally consistent geospatial layers and screening to focus on deforestation (loss of tall, woody vegetation above the canopy/height thresholds) that is relevant for crop commodity accounting. Once deforestation is identified, WRI estimates the resulting GHG emissions, including biomass-related emissions and associated soil carbon emissions.

WRI then connects deforestation-related emissions to specific crop categories to produce crop-specific results. This is the “statistical” aspect: the factors are designed for situations where you can attribute sourcing to a region but may not have farm-level land-use change attribution.

WRI’s method incorporates time-weighted discounting in compliance with LSRS’s requirements for emissions reporting. This concept accounts for the idea that more recent deforestation carries greater weight in current-year reporting than older deforestation within the assessment window.

Finally, WRI produces an EF by combining the discounted, crop-attributed deforestation emissions, and the crop yield for the corresponding reporting geography and year. 

Calculating a supply-shed specific LUC EF

To ensure comprehensive coverage and provide you with the best available data, we have implemented a gap-filling methodology. While we primarily rely on the detailed county / local (admin2) level dataset from WRI, there may be instances where data is not available for a specific admin2 region.

In such cases, we gap-fill using data from the next available administrative level, starting with the state or province level, and if necessary country level. This approach ensures that you always have an EF for your sourcing regions, based on the most accurate and granular data available.

SI further processes the WRI sLUC EF to produce an EF that is specific to your supply sheds and sub-regions. To calculate a specific EF for your custom region, we use an area-weighted averaging method.

For each sourcing region in the platform, the percentage of the supply shed total area that overlaps with each underlying administrative reporting scale region from the WRI dataset is determined. These percentages are then used as weights to calculate the average EF for your custom sourcing region.

Example:

If your sourcing region overlaps with three local regions:

• 40% of your sourcing region is in Admin2 Region A (EF = 10 tCO2e/t)
• 35% of your sourcing region is in Admin2 Region B (EF = 15 tCO2e/t)
• 25% of your sourcing region is in Admin2 Region C (EF = 12 tCO2e/t)

The sourcing-region specific EF would be calculated as follows:

(0.40 * 10) + (0.35 * 15) + (0.25 * 12) = 4 + 5.25 + 3 = 12.25 tCO2e/t