Estimating co-pollutant benefits from climate change policies in the electricity sector: A regression approach

Nikolaos Zirogiannis, Daniel H. Simon and Alex Hollingsworth ()

Energy Economics, 2020, vol. 90, issue C

Abstract: We use data from US power plants and a regression based approach to empirically estimate the marginal rate of co-pollutant emission reductions resulting from a mass-based carbon reduction policy for electricity producers. The standard approach to estimating co-pollutant reductions uses Linear Programming Models. These models require millions of input variables and constraints, resulting in long computational times and an opaque simulation process, while yielding only point estimates for key variables of interest. Our regression-based approach has far fewer data requirements, needs less computational resources, and produces estimates with confidence intervals that capture estimation uncertainty. Moreover, it is straightforward and transparent to implement and provides a larger range of potential outcomes for policy makers to consider. Our results indicate that a 1% decrease in electricity output from coal (gas) power plants would reduce SO2 by 0.6% and NOx by 0.8% (0.7%). These are not statistically significant different than estimates reported by the Environmental Protection Agency (EPA). We estimate that reducing electricity output enough to reduce CO2 emissions by one ton yields health benefits of $15.33 from NOx reductions and $59.64 from SO2 reductions.

Keywords: Co-pollutants; Health co-benefits; Climate change; Electricity generation (search for similar items in EconPapers)
JEL-codes: Q40 Q48 Q53 Q54 Q56 (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:eee:eneeco:v:90:y:2020:i:c:s0140988320302036

DOI: 10.1016/j.eneco.2020.104863

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