Bayesian Environmental Policy Decisions: Two Case Studies

Statistical decision theory can be a valuable tool for policy-making decisions. In particular, environmental problems often benefit from the application of Bayesian and decision-theoretic techniques that address the uncertain nature of problems in the environmental and ecological sciences. This paper discusses aspects of implementing statistical decision-making tools in situations where uncertainty is present, looking at issues such as elicitation of prior distributions, covariate allocation, formulation of loss functions, and minimization of expected losses subject to cooperation constraints. These ideas are illustrated through two case studies in environmental remediation.