Probabilistic Decision-Making of Air Emission Control Alternatives

Abstract

The polluter pays principle that has been adopted in environmental regulations worldwide places the economic burden of prevention, control, and cost of damage and mitigation on the pollution generator. The United States and Canada have established monetary penalties for air emission violations based on formulas and notions that account, among others, for the harm done by a violation to the environment and to human health, the environmental history of the violator, and the economic benefits reaped as a result of noncompliance. Despite their legal completeness these regulations do not address the probabilistic nature of air pollution. The current paper recasts the issue of air pollution penalties in a Bayesian decisionmaking framework where the prior probability distribution function from historical data on emissions or experts’ opinions can be constructed and used to establish optimal decisions regarding new emission controls. Our article advances the use of the loss function as an alternative risk analysis tool to the current deterministic penalty regulatory formulas, which can also be used as a public policy instrument to promote environmentally, friendlier air emission choices.