, Volume 37, Issue 3, pp 539-563

Bene-Eia: A Bayesian Approach to Expert Judgment Elicitation with Case Studies on Climate Change Impacts on Surface Waters

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Abstract

Climatic change impact studies are among the most complicated environmental assessments scientists have ever faced. The questions that policy makers face are enormous. There is plenty of experience and systematization in the environmental impact assessment (EIA) practice, especially at project level studies, but it has not been fully utilized in climatic change studies, we argue. Screening and scoping in EIA are typical examples. Beset by uncertainty and interdisciplinary divisions, climatic change impact analyses and policy assessments have been dominated by very detailed studies without the prior cross-sectorial, integrative phases that would aid in focusing the issues. Here, we present a probabilistic, Bayesian impact matrix approach (BeNe-EIA) for expert judgment elicitation, using belief networks from artificial intelligence. One or more experts are used to define a Bayesian prior distribution to each of the selected attributes, and the interattribute links, of the system under study. Posterior probabilities are calculated interactively, indicating consistency of the assessment and allowing iterative analysis of the system. Illustration is given by 2 impact studies of 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.