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

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  • Hwang, In Chang
  • Reynes, Frederic
  • Tol, Richard

Abstract

We construct an endogenous (Bayesian) learning model with fat-tailed uncertainty on the equilibrium climate sensitivity and solve the model with stochastic dynamic programming. In our model a decision maker updates her belief on the climate sensitivity through temperature observations each time period and takes a course of action (carbon reductions) based on her belief. We find that the uncertainty is partially resolved over time, although the rate of learning is relatively slow, and the decision maker with a possibility of learning lowers the efforts to reduce carbon emissions relative to the no-learning case. The larger the tail effect, the larger the counteracting learning effect. Learning at least partly offsets the tail-effect of deep uncertainty. This is intuitive in that 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 various scenarios, we find that learning enables the economic agent to have less regrets for her past actions after the true value of the uncertain variable turns out to be different from the initial best guess. Furthermore the optimal decisions in the learning case are less sensitive to the true value of the uncertain variable than the decisions in the uncertainty case. The reason is that learning lets uncertainty converge to the true value of the state in the sense that the variance approaches 0 as information accumulates.

Suggested Citation

  • Hwang, In Chang & Reynes, Frederic & Tol, Richard, 2014. "The effect of learning on climate policy under fat-tailed uncertainty," MPRA Paper 53681, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:53681
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    Citations

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    Cited by:

    1. Richard S. J. Tol, 2015. "Economic impacts of climate change," Working Paper Series 7515, Department of Economics, University of Sussex.
    2. repec:kap:compec:v:50:y:2017:i:1:d:10.1007_s10614-016-9583-2 is not listed on IDEAS
    3. Kelly, David L. & Tan, Zhuo, 2015. "Learning and climate feedbacks: Optimal climate insurance and fat tails," Journal of Environmental Economics and Management, Elsevier, vol. 72(C), pages 98-122.
    4. In Chang Hwang, 2016. "Active learning and optimal climate policy," EcoMod2016 9611, EcoMod.
    5. Hwang, In Chang, 2014. "Fat-tailed uncertainty and the learning-effect," MPRA Paper 53671, University Library of Munich, Germany.
    6. Matthew Adler & David Anthoff & Valentina Bosetti & Greg Garner & Klaus Keller & Nicolas Treich, 2016. "Priority for the Worse Off and the Social Cost of Carbon," CESifo Working Paper Series 6032, CESifo Group Munich.
    7. 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.
    8. Hwang, In Chang & Tol, Richard S.J. & Hofkes, Marjan W., 2016. "Fat-tailed risk about climate change and climate policy," Energy Policy, Elsevier, vol. 89(C), pages 25-35.

    More about this item

    Keywords

    Climate policy; deep uncertainty; Bayesian learning; integrated assessment; stochastic dynamic programming;

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • H23 - Public Economics - - Taxation, Subsidies, and Revenue - - - Externalities; Redistributive Effects; Environmental Taxes and Subsidies
    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming
    • Q58 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Environmental Economics: Government Policy

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