摘要：SummaryEmissions 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.Graphical abstractDisplay OmittedHighlights•A long-run marginal emission rate captures both operational and structural impacts•A LRMER was compared against short-run marginal and average emission rates•The LRMER outperformed both the SRMER and AER at estimating emission impacts•Integrating SRMER across an intervention’s lifetime may not describe its total impactEnergy resources; Energy policy; Energy sustainability
