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Uncertainty and Climate Change: The IPCC approach vs Decision Theory

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  • Anastasios Xepapadeas

Abstract

One of the most important challenges in the study of climate change and its interactions with the economy is uncertainty. The present paper looks at this uncertainty from two different points of view. The first is the way that IPCC deals with uncertainty at its report and the way that uncertainty is communicated. The IPCC approach is implemented by a combination of qualitative and qualitative methods and the use of heuristics. IPCC studies climate change its evolution and its impact in a context, which in terms of decision making approach is akin to analysis under risk. The second is the point of view of the part of decision theory that deals with uncertainty in the Knightian sense and more specially with uncertainty which is manifested in multiple probabilistic models or priors. The presence of multiple priors is associated with ambiguity aversion and misspecification concerns that necessitate the use of maxmin optimizing approaches. The IPCC and the decision theory approaches are briefly reviewed and compared seeking ways to accommodate the concept of risky parameters or impacts of the IPCC framework, to the framework of optimization under uncertainty under multiple probabilistic models.

Suggested Citation

  • Anastasios Xepapadeas, 2023. "Uncertainty and Climate Change: The IPCC approach vs Decision Theory," DEOS Working Papers 2315, Athens University of Economics and Business.
  • Handle: RePEc:aue:wpaper:2315
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    References listed on IDEAS

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    More about this item

    Keywords

    Climate change; IPCC; risk; uncertainty; misspecification; ambiguity aversion;
    All these keywords.

    JEL classification:

    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty

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