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The Effect of Learning on Climate Policy under Fat-tailed Uncertainty

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  • Richard S. J. Tol

    ()
    (Department of Economics, University of Sussex, Brighton, United Kingdom
    Institute for Environmental Studies, Vrije Universiteit, Amsterdam, Netherlands
    Department of Spatial Economics, Vrije Universiteit, Amsterdam, Netherlands)

  • In Chang Hwang

    (Institute for Environmental Studies, Vrije Universiteit, Amsterdam, Netherlands)

  • Frédéric Reynès

    ()
    (OFCE Sciences Po’s Economic Research Centre, Paris, France
    Institute for Environmental Studies, Vrije Universiteit, Amsterdam, Netherlands
    TNO - Netherlands Organisation for Applied Scientific Research, Delft, The Netherlands)

Abstract

The effect of learning on climate policy is not straightforward when climate policy is concerned. It depends not only on the ways that climate feedbacks, preferences, and economic impacts are considered, but also on the ways that uncertainty and learning are introduced. Deep (or fat-tailed) uncertainty does matter for the optimal climate policy in that it requires more stringent efforts to reduce carbon emissions. However, learning may reveal thin-tailed uncertainty, weakening the case for emission abatement: learning reduces the stringency of the optimal abatement efforts relative to the no learning case even when we account for deep uncertainty. In order to investigate this hypothesis, we construct an endogenous (Bayesian) learning model with fat-tailed uncertainty on climate change and solve the model with stochastic dynamic programming. In our model a decision maker updates her belief on the total feedback factors through temperature observations each period and takes a course of action (carbon reductions) based on her belief. With various scenarios, we find that the uncertainty is partially resolved over time, although the rate of learning is relatively slow, and this materially affects the optimal decision: the decision maker with a possibility of learning lowers the effort to reduce carbon emissions relative to the no learning case. This is because the decision maker fully utilizes the information revealed to reduce uncertainty, and thus she can make a decision contingent on the updated information. In addition, with incorrect belief scenarios, we find 2 that learning enables the economic agent to have less regrets (in economic terms, sunk benefits or sunk costs) for her past decisions after the true value of the uncertain variable is revealed to be different from the initial belief.

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Bibliographic Info

Paper provided by Department of Economics, University of Sussex in its series Working Paper Series with number 5312.

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Date of creation: Dec 2012
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Handle: RePEc:sus:susewp:5312

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Keywords: Climate policy; deep uncertainty; fat-tails; Bayesian learning; integrated assessment; stochastic modeling; dynamic programming;

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Cited by:
  1. Hwang, In Chang, 2014. "A recursive method for solving a climate-economy model: value function iterations with logarithmic approximations," MPRA Paper 54782, University Library of Munich, Germany.
  2. Hwang, In Chang, 2014. "Fat-tailed uncertainty and the learning-effect," MPRA Paper 53671, University Library of Munich, Germany.

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