The effect of learning on climate policy under fat-tailed risk

In Chang Hwang*, Frédéric Reynès, Richard S.J. Tol

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review

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.

Original languageEnglish
Pages (from-to)1-18
Number of pages18
JournalResource and Energy Economics
Volume48
DOIs
Publication statusPublished - 2017

Keywords

  • Bayesian learning
  • Climate policy
  • Dynamic programming
  • Fat tailed risk
  • Integrated assessment

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