Plevin Richard J, Jones Jason, Kyle Page, Levy Aaron W, Shell Michael J, Tanner Daniel J
Consultant, Portland, OR, USA.
ICF International Inc., Fairfax, VA, USA.
J Clean Prod. 2022 May;349:1-10. doi: 10.1016/j.jclepro.2022.131477.
Estimates of biofuel carbon intensity are uncertain and depend on modeled land use change (LUC) emissions. While analysts have focused on economic and agronomic assumptions affecting the quantity of land converted, researchers have paid less attention to how models classify land into broad categories and designate some categories as ineligible for LUC. To explore the effect of these land representation attributes, we use three versions of a global human and Earth systems model, GCAM, and compute the "carbon intensity of land-use change" (CI-LUC) from increased U.S. corn ethanol production. We consider uncertainty in model parameters along with the choice of land representation and find the latter is one of the most influential parameters on estimated CI-LUC. A version of the model that protects 90% of non-commercial land reduced estimated CI-LUC by an average of 32% across Monte Carlo trials compared to our baseline model. Another version that mimics the GTAP-BIO-ADV land representation, which protects all non-commercial land, reduced CI-LUC by an average of 19%. The results of this experiment demonstrate that land representation in biofuel LUC models is an important determinant of CI-LUC.
生物燃料碳强度的估计并不确定，且取决于模拟的土地利用变化（LUC）排放。虽然分析人士关注影响土地转换数量的经济和农艺假设，但研究人员较少关注模型如何将土地划分为大致类别并将某些类别指定为不符合土地利用变化的条件。为了探究这些土地表征属性的影响，我们使用了全球人类与地球系统模型GCAM的三个版本，并根据美国玉米乙醇产量的增加计算“土地利用变化的碳强度”（CI-LUC）。我们考虑了模型参数的不确定性以及土地表征的选择，发现后者是影响估计的CI-LUC的最具影响力的参数之一。与我们的基线模型相比，一个保护90%非商业用地的模型版本在蒙特卡罗试验中使估计的CI-LUC平均降低了32%。另一个模仿GTAP-BIO-ADV土地表征（保护所有非商业用地）的版本使CI-LUC平均降低了19%。该实验结果表明，生物燃料土地利用变化模型中的土地表征是CI-LUC的一个重要决定因素。
