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

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 NO_x 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 NO_x reductions and $59.64 from SO₂ reductions.