Uncertainty, imprecision, and the precautionary principle in climate change assessment.
Statistical decision theory can provide useful support for climate change decisions made under conditions of uncertainty. However, the probability distributions used to calculate expected costs in decision theory are themselves subject to uncertainty, disagreement, or ambiguity in their specification. This imprecision can be described using sets of probability measures, from which upper and lower bounds on expectations can be calculated. However, many representations, or classes, of probability measures are possible. We describe six of the more useful classes and demonstrate how each may be used to represent climate change uncertainties. When expected costs are specified by bounds, rather than precise values, the conventional decision criterion of minimum expected cost is insufficient to reach a unique decision. Alternative criteria are required, and the criterion of minimum upper expected cost may be desirable because it is consistent with the precautionary principle. Using simple climate and economics models as an example, we determine the carbon dioxide emissions levels that have minimum upper expected cost for each of the selected classes. There can be wide differences in these emissions levels and their associated costs, emphasizing the need for care when selecting an appropriate class.
Duke Scholars
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Related Subject Headings
- Uncertainty
- Temperature
- Risk Assessment
- Models, Theoretical
- Greenhouse Effect
- Environmental Engineering
- Costs and Cost Analysis
- Carbon Dioxide
- Air Pollution
Citation
Published In
DOI
EISSN
ISSN
Publication Date
Volume
Issue
Start / End Page
Related Subject Headings
- Uncertainty
- Temperature
- Risk Assessment
- Models, Theoretical
- Greenhouse Effect
- Environmental Engineering
- Costs and Cost Analysis
- Carbon Dioxide
- Air Pollution