The use of loss functions for constructing a statistical decision making framework is demonstrated through the case study of a former battery recycling facility in Pennsylvania. Toxic lead contamination of soil had occurred, and remediation was therefore mandated by the EPA. A Bayesian model is proposed that uses covariate and prior information to address the latent variable problem of distinguishing background soil lead concentrations from plant contamination. The results from this model are illustrated with a variety of loss functions, formulated both from the perspective of the plant and of the EPA, to create a framework that incorporates uncertainty for deciding which properties near the facility are eligible for remediation, while allowing each party in the decision-making process to understand the implications of their decisions for the other party. This approach can easily be adapted to many types of environmental risk or similar public policy problems where uncertainty is present and multiple stateholders have different perspectives on potential losses or benefits of different decisions and outcomes.