An effective statistical approach for comparative risk assessment

Abstract

Comparative risk assessment is an evaluation process designed to rank environmental problems based on the severity of potential hazards. The purpose of this paper is to provide an effective statistical approach to analyze perceived environmental risks. Environmental problems, evaluative criteria, and other potential moderator variables need to be determined first, and then the risk perception data collected. Repeated measures analysis is used to first test for interactions between environmental problems and potential moderator variables. If there are no significant interactions, then the risk difference among environmental problems is tested unconditionally; otherwise the risk difference is tested conditionally. Cluster analysis for environmental problems is performed only when the risk difference is significant. The clustering results can be objectively determined by using the simultaneous T-confidence intervals. Risk-based priority setting is made according to the clusters obtained. To illustrate this approach, an empirical study of comparative socioeconomic risks in Taiwan was conducted. Socioeconomic impacts areas including social security, quality of life, production cost, investment willingness, and economic resources are used as evaluative criteria. Results indicate that selected impact areas do affect relative risk differences among 24 environmental problems, and the difference is significant for each area. Therefore, cluster analysis is conducted separately for each impact area. Risk-based priority settings for clusters of environmental problems are reported.