Abstract
Uncertainty is poorly represented in existing studies of climate-change impacts. Methods that have been used to characterize uncertainty in the literature are described and the limitations of each discussed. It is found that two broad characterizations are useful. A large number of studies are based on several specific scenarios or attempt to randomize selected variables in their deterministic economic models. Other studies describe individual or collective reaction to risk. The first category falls short of an adequate representation of uncertainty by focusing primarily on a few values of variables included to capture variability. The second group of studies tend to focus more on behavior than impacts. What is needed are Monte Carlo type simulations where randomness is apparent in a series of independent draws from a distribution suitably adjusted for climate change. Some of the benefits of improvements in the characterization of uncertainty are discussed.
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I am thankful to my colleagues at the Economic Research Service (ERS), Linda Atkinson, George Frisvold, Charlie Hallahan, Betsey Kühn, Jan Lewandrowski, John Reilly, Keith Weibe and Olivia Wright for production assistance; to fellow members of the JPCC Working Group II chapter on Agriculture and participants in the IPCC seminar held at ERS for helpful discussions; to Jim Titus and an anonymous reviewer. The views expressed are those of the author and not necessarily those of the U.S. Department of Agriculture.
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Schimmelpfennig, D. Uncertainty in economic models of climate-change impacts. Climatic Change 33, 213–234 (1996). https://doi.org/10.1007/BF00140247
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DOI: https://doi.org/10.1007/BF00140247