Understanding Uncertainty in Market-Mediated Responses to US Oilseed Biodiesel Demand: Sensitivity of ILUC Emission Estimates to GLOBIOM Parametric Uncertainty

The life cycle greenhouse gas (GHG) emissions of biofuels depend on uncertain estimates of induced land use change (ILUC) and subsequent emissions from carbon stock changes. Demand for oilseed-based biofuels is associated with particularly complex market and supply chain dynamics, which must be considered. Using the global partial equilibrium model GLOBIOM, this study explores the uncertainty in market-mediated impacts and ILUC-related emissions from increasing demand for soybean biodiesel in the United States in the period 2020-2050. A one-at-a-time (OAT) analysis and a Monte Carlo (MC) analysis are performed to assess the sensitivity of modeled ILUC-GHG emissions intensities (gCO₂e/MJ) to varying key economic and biophysical model parameters. Additionally, the influence of the approach on the simulation of future ILUC effects is explored using two alternative ILUC-GHG metrics: a comparative-static approach for 2030 and a recursive-dynamic approach using model outputs through 2050. We find that projected ILUC-GHG values largely vary based on which vegetable oils replace diverted soybean oil, market responses to coproducts, and the carbon content of land converted for agricultural use. These are all, in turn, subject to decision uncertainty through the choice of the modeling approach and the time horizon considered for each ILUC-GHG metric. Given the longer simulation period, ILUC-GHG emission uncertainty ranges increase under the recursive-dynamic approach (42.4 ± 25.9 gCO₂e/MJ) compared to the comparative-static approach (40.8 ± 20.5 gCO₂e/MJ). The combination of MC analysis with other techniques such as Bayesian Additive Regression Trees (BART) is powerful for understanding model behavior and clarifying the sensitivity of market responses, ILUC, and associated GHG emissions to specific model parameters when simulated with global economic models. The BART reveals that biophysical parameters generate more linear ILUC-GHG responses to changes in assumed parameter values while changes in economic parameters lead to more nonlinear ILUC-GHG results as multiple effects at the interplay of food, feed, and fuel uses overlap. The choice of the recursive-dynamic metric allows capturing the longer-term evolution of ILUC while generating additional uncertainties derived from the baseline definition.