Planning for the evolution of the electric grid with a long-run marginal emission rate

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 !Defter capturing the projected role of renewable generators in the evolution of the power sector.

Automated summary

Emissions factors are widely used to estimate the impact of interventions on emissions in the electric sector, but commonly used metrics neglect the influence of electricity demand changes on the structural evolution of the grid. This study evaluates a lesser known metric, the long-run marginal emission rate (LRMER), and finds that it outperforms other metrics in predicting emissions from interventions, suggesting that adopting LRMER could improve decision-making.