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.

中文翻译：

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了解市场介导的对美国油籽生物柴油需求反应的不确定性：ILUC 排放估计对 GLOBIOM 参数不确定性的敏感性


生物燃料的生命周期温室气体 （GHG） 排放取决于对诱导土地利用变化 （ILUC） 和碳储量变化的后续排放的不确定估计。对油籽基生物燃料的需求与特别复杂的市场和供应链动态有关，必须考虑到这一点。本研究使用全球部分均衡模型 GLOBIOM，探讨了 2020-2050 年期间美国对大豆生物柴油需求增加的市场介导影响和 ILUC 相关排放的不确定性。进行一次一次 （OAT） 分析和蒙特卡洛 （MC） 分析，以评估建模的 ILUC-GHG 排放强度 （gCO₂e/MJ） 对不同关键经济和生物物理模型参数的敏感性。此外，使用两种替代的 ILUC-GHG 指标探讨了该方法对未来 ILUC 影响模拟的影响：2030 年的比较静态方法和使用到 2050 年模型输出的递归动态方法。我们发现，预计的 ILUC-GHG 值在很大程度上取决于哪些植物油替代了转移的大豆油、市场对副产品的反应以及转为农业用途的土地的碳含量。反过来，这些都受到决策不确定性的影响，因为每个 ILUC-GHG 指标都考虑了建模方法和时间范围。鉴于模拟时间较长，与比较静态方法（40.8 ± 20.5 gCO₂e/MJ）相比，递归动力学方法（42.4 ± 25.9 gCO₂e/MJ）下的 ILUC-GHG 排放不确定性范围增加。 MC 分析与贝叶斯加性回归树 （BART） 等其他技术相结合，对于理解模型行为和阐明市场反应、ILUC 和相关温室气体排放对特定模型参数的敏感性非常有用。BART 揭示了生物物理参数对假设参数值的变化产生更线性的 ILUC-GHG 响应，而经济参数的变化会导致更多的非线性 ILUC-GHG 结果，因为食品、饲料和燃料用途相互作用的多重效应重叠。递归动态指标的选择可以捕捉 ILUC 的长期演变，同时产生从基线定义得出的额外不确定性。