Published in Environmental Economics and Policy Studies, 29 April 2023
The clean air interstate rule (CAIR) was a regional cap-and-trade program announced in 2005 which covered 27 eastern US states and sought to reduce sulfur dioxide emissions from coal-fired power plants. The rule was later vacated after a court found that the non-targeted design of the program did not comply with the Clean Air Act provision to regulate interstate air pollution. Using a custom air pollution dispersion model, I calculate the interstate SO2 pollution from 493 coal-fired power plants across the United States between 1997 and 2020. In a difference-in-differences setup with plants not covered by CAIR in the control group, I estimate the treatment effect of the program on overall- and cross-border SO2 emissions and find a 24% reduction in overall emissions and reduces the risk that a plant violates air quality standards across state borders by 2–4%. I report evidence of heterogeneous treatment effects where the reduction in overall emissions attributed to CAIR is lower among plants transporting SO2 in excess of 1% of the National Air Quality Standards to another state.
Published in AMS: Weather, Climate, and Society, 1 April 2021
with Tobias Dalhaus and Carl-Johan Lagerkvist
Extreme heat events cause periodic damage to crop yields and may pose a threat to the income of farmers. Weather index insurance provides payouts to farmers in case of measurable weather extremes to keep production going. However, its viability depends crucially on the accuracy of local weather indices to predict yield damages from adverse weather conditions. So far extreme heat indices are poorly represented in weather index insurance. In this study we construct indices of extreme heat using observations at the nearest weather station and estimates for each county using three interpolation techniques: Inverse-distance weighting, ordinary kriging, and regression kriging. Applying these indices to insurance against heat damage to corn in Illinois and Iowa, we show that heat index insurance reduces relative risk premiums by 27-29% and that interpolated indices outperform the nearest-neighbor index by around 2-3% in terms of relative risk reduction. Further, we find that the advantage of interpolation over a nearest-neighbor index in terms of relative risk reduction increases as the sample of weather stations is reduced. These findings suggest that heat index insurance can work even when weather data is spatially sparse, which delivers important implications for insurance practice and policy makers. Further, our public code repository provides a rich toolbox of methods to be used for other, perils, crops and regions. Our results are therefore not only replicable but also constitute a cornerstone for projects to come.
(c) Daniel Leppert 2020