Gagnon Pieter, Cole Wesley
National Renewable Energy Laboratory, Golden, CO 80401, USA.
iScience. 2022 Feb 11;25(3):103915. doi: 10.1016/j.isci.2022.103915. eCollection 2022 Mar 18.
Emissions factors are widely used to estimate how various interventions would influence emissions from the electric sector. Both of the most commonly used metrics, however, neglect how changes in electricity demand can influence the structural evolution of the grid (the building and retiring of capital assets, such as generators). This omission can be significant when the factors are intended to comprehensively reflect the consequences of an intervention. In this work we evaluate a lesser known metric-the long-run marginal emission rate (LRMER)-which incorporates both the operational and structural implication of changes in electricity demand. We apply a modeling framework to compare the LRMER to the two near-ubiquitous metrics, and show that the LRMER can outperform the other two metrics at anticipating the emissions induced by a range of interventions. This suggests that adopting the LRMER could improve decision-making, particularly by better capturing the projected role of renewable generators in the evolution of the power sector.
排放因子被广泛用于估计各种干预措施如何影响电力部门的排放。然而，这两种最常用的指标都忽略了电力需求的变化如何影响电网的结构演变（资本资产的建设和退役，如发电机）。当这些因子旨在全面反映一项干预措施的后果时，这种遗漏可能会很显著。在这项工作中，我们评估了一个鲜为人知的指标——长期边际排放率（LRMER），它纳入了电力需求变化的运行和结构影响。我们应用一个建模框架将长期边际排放率与另外两个几乎无处不在的指标进行比较，并表明在预测一系列干预措施引起的排放方面，长期边际排放率可以优于其他两个指标。这表明采用长期边际排放率可以改善决策，特别是通过更好地把握可再生发电机在电力部门演变中的预期作用。
