The Potential Impacts from the Mathematical Logic of IPCC Tier 1 and Tier 2 Soil Organic Carbon Estimation Methods in Product-Level Life Cycle Assessment

IPCC Tier 1 and Tier 2 soil organic carbon (SOC) estimation methods were originally designed for national inventories and involve considerations for accounting convention. Their calculations may affect the results of life cycle assessment (LCA) when SOC balance is included. This study aims to evaluate how the scaling and time-based equilibrium assumptions used in IPCC Tier 1 and Tier 2 methods influence SOC stock estimations from their mathematical logic, and to quantify the impacts in a product-level LCA for Finnish wheat production. We compared the mathematical logic of IPCC Tier 1 and Tier 2 methods against a basic equation that accounts for both initial and newly formed SOC stocks. The comparisons were further parameterized using local data for Finland and integrated into a cradle-to-gate attributional LCA to determine the carbon footprint of wheat production, using 1 kg of wheat grain dry matter yield as the functional unit. The results showed that soils with higher initial SOC storage tended to lose more C when receiving equal C input. Compared with the basic equation, the scaling method with local coefficient constrained the range of SOC loss. For different initial SOC stocks, the net greenhouse gas balance (NGHGB) for SOC balance ranged 0.19–0.75 kg CO2-eq kg− 1 of yield using the basic equation, but the range was narrowed to 0.34–0.56 kg CO2-eq kg− 1 of yield with scaling equation. Both calculations revealed significant C loss from soil compared to the NGHGB for wheat, which was 0.56 kg CO2-eq kg− 1 of yield when SOC was not considered. However, the 20-year time-based steady SOC storage did not account for the NGHGB resulting from the SOC storage changes. The carbon footprint from soil carbon balance is considerable compared with other processes for crop production. The SOC balance is influenced by initial SOC storage, a factor that may be overlooked in time-based steady SOC models, while the scaling method underestimates the variations in SOC loss across different initial SOC stocks.