BENE-EIA - A BAYESIAN-APPROACH TO EXPERT JUDGMENT ELICITATION WITH CASE-STUDIES ON CLIMATE-CHANGE IMPACTS ON SURFACE WATERS

Climatic change impact studies are among the most complicated environm ental assessments scientists have ever faced. The questions that polic y makers face are enormous. There is plenty of experience and systemat ization in the environmental impact assessment (EIA) practice, especia lly at project level studies, but it has not been fully utilized in cl imatic change studies, we argue. Screening and scoping in EIA are typi cal examples. Beset by uncertainty and interdisciplinary divisions, cl imatic change impact analyses and policy assessments have been dominat ed by very detailed studies without the prior cross-sectorial, integra tive phases that would aid in focusing the issues. Here, we present a probabilistic, Bayesian impact matrix approach (BeNe-EIA) for expert j udgment elicitation, using belief networks from artificial intelligenc e. One or more experts are used to define a Bayesian prior distributio n to each of the selected attributes, and the interattribute links, of the system under study. Posterior probabilities are calculated intera ctively, indicating consistency of the assessment and allowing iterati ve analysis of the system. Illustration is given by 2 impact studies o f surface waters. In addition to climatic change studies, the approach has been designed to be applicable to conventional EIA. Insufficient attention has thus far been devoted to the probabilistic nature of the assessment and potential inconsistencies in expert judgment.