How Much Are We Saving after All? Characterizing the Effects of Commonly Varying Assumptions on Emissions and Damage Estimates in PJM

Abstract

In recent years, several methods have emerged to estimate the emissions and health, environmental, and climate change damages avoided by interventions such as energy efficiency, demand response, and the integration of renewables. However, differing assumptions employed in these analyses could yield contradicting recommendations regarding intervention implementation. We test the magnitude of the effect of using different key assumptions-average vs marginal emissions, year of calculation, temporal and regional scope, and inclusion of nonemitting generation-to estimate Mid-Atlantic region power pool (PJM) emissions and damage factors. We further highlight the importance of factor selection by evaluating three illustrative 2017 power system examples in PJM. We find that for a simple building lighting intervention, using average emissions factors incorporating nonemitting generation <i>underestimates</i> avoided damages by 45% compared to marginal factors. For PJM demand response, outdated marginal emissions factors from 2016 <i>overestimate</i> avoided damages by 25% compared to 2017 factors. Our assessment of PJM summer load further suggests that fossil-only average emissions factors <i>overestimate</i> damages by 63% compared to average factors incorporating nonemitting generation. We recommend that energy modelers carefully select appropriate emissions metrics when performing their analyses. Furthermore, since the U.S. electric grid is rapidly changing, we urge decision-makers to frequently update (and consider forecasting) grid emissions factors.