 The effect of learning on climate policy under fat-tailed riskIn Chang Hwang, Frédéric Reynès () and Richard Tol Resource and Energy Economics, 2017, vol. 48, issue C, 1-18 Abstract: This paper investigates the effect of learning on climate policy under fat tailed risk about climate change. We construct an endogenous learning model with fat-tailed uncertainty about the equilibrium climate sensitivity. We find that a decision maker with a possibility of learning lowers efforts to reduce carbon emissions relative to the no-learning case. The larger the tail effect, the larger the counteracting learning effect because learning reduces the marginal benefit of emissions control compared to the case where there is no learning. The optimal decisions (summarized by the carbon tax level) in the learning case are less sensitive to the true value of the uncertain variable than the decisions in the uncertainty case. Learning lets uncertainty converge to the true value of the state in the sense that the variance approaches zero as information accumulates. Keywords: Climate policy; Fat tailed risk; Bayesian learning; Integrated assessment; Dynamic programming (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:resene:v:48:y:2017:i:c:p:1-18 DOI: 10.1016/j.reseneeco.2017.01.001 Access Statistics for this article Resource and Energy Economics is currently edited by J. F. Shogren and S. Smulders More articles in Resource and Energy Economics from Elsevier |
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