Optimal regulation of pollution with stochastic exposure, acute damages, and chronic damages

Epidemiologists have documented both acute short-term and chronic long-term damages associated with exposure to air pollution, while atmospheric scientists have demonstrated that air pollution exposure depends on stochastic meteorological conditions. We analyze the implications of these features for the optimal regulation of pollution emissions. Our model incorporates abatement costs, separate acute and chronic damage components, and stochastic pollution exposure. We characterize the optimal path of pollution regulation and total expected costs under three different scenarios regarding the regulator's ability to update policy and forecast meteorological conditions. We also present a numerical example of sulfur dioxide emissions and small particulate matter concentrations from electric power plants in northeast Ohio. In the example, dynamic updating of regulation provides significantly more benefits when combined with accurate forecasts of current meteorological conditions.