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The Curious Role of "Learning" in Climate Policy: Should We Wait for More Data?

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  • Mort Webster

Abstract

Given the large uncertainties regarding potential damages from climate change and the significant but also uncertain costs of reducing greenhouse emissions, the debate over a policy response is often framed as a choice of acting now or waiting until the uncertainty is reduced. Implicit in the "wait to learn" argument is the notion that the ability to learn in the future necessarily implies that less restrictive policies should be chosen in the near term. I demonstrate in the general case that the ability to learn in the future can lead to either less restrictive or more restrictive policies today. I also show that the initial decision made under uncertainty will be affected by future learning only if the actions taken today change the marginal costs or marginal damages in the future. Results from an intermediate-scale integrated model of climate and economics indicate that the choice of current emissions restrictions is independent of whether or not uncertainty is resolved before future decisions, because, like most models, the cross-period interactions are minimal. With stronger interactions, the effect of learning on initial period decisions can be more important.

Suggested Citation

  • Mort Webster, 2002. "The Curious Role of "Learning" in Climate Policy: Should We Wait for More Data?," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2), pages 97-119.
  • Handle: RePEc:aen:journl:2002v23-02-a05
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    Cited by:

    1. Michael Funke & Michael Paetz, 2011. "Environmental policy under model uncertainty: a robust optimal control approach," Climatic Change, Springer, vol. 107(3), pages 225-239, August.
    2. Kenneth Gillingham & William D. Nordhaus & David Anthoff & Geoffrey Blanford & Valentina Bosetti & Peter Christensen & Haewon McJeon & John Reilly & Paul Sztorc, 2015. "Modeling Uncertainty in Climate Change: A Multi-Model Comparison," NBER Working Papers 21637, National Bureau of Economic Research, Inc.
    3. Nordhaus, William, 2013. "Integrated Economic and Climate Modeling," Handbook of Computable General Equilibrium Modeling, Elsevier.
    4. Baker, Erin & Shittu, Ekundayo, 2006. "Profit-maximizing R&D in response to a random carbon tax," Resource and Energy Economics, Elsevier, vol. 28(2), pages 160-180, May.
    5. Baker, Erin & Adu-Bonnah, Kwame, 2008. "Investment in risky R&D programs in the face of climate uncertainty," Energy Economics, Elsevier, vol. 30(2), pages 465-486, March.
    6. Alejandro Bonvecchi & Carlos Scartascini, 2011. "The Presidency and the Executive Branch in Latin America: What We Know and What We Need to Know," Research Department Publications 4756, Inter-American Development Bank, Research Department.
    7. In Chang Hwang, 2016. "Active learning and optimal climate policy," EcoMod2016 9611, EcoMod.
    8. Mort Webster & Nidhi Santen & Panos Parpas, 2012. "An approximate dynamic programming framework for modeling global climate policy under decision-dependent uncertainty," Computational Management Science, Springer, vol. 9(3), pages 339-362, August.
    9. Peterson, Sonja, 2006. "Uncertainty and economic analysis of climate change: a survey of approaches and findings," Open Access Publications from Kiel Institute for the World Economy 3778, Kiel Institute for the World Economy (IfW).
    10. repec:eee:resene:v:48:y:2017:i:c:p:1-18 is not listed on IDEAS
    11. Santen, Nidhi R. & Anadon, Laura Diaz, 2016. "Balancing solar PV deployment and RD&D: A comprehensive framework for managing innovation uncertainty in electricity technology investment planning," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 560-569.
    12. Jorge Fernández & Sebastián J. Miller, 2011. "When Should Developing Countries Announce Their Climate Policy?," IDB Publications (Working Papers) 3960, Inter-American Development Bank.
    13. Richard S. J. Tol & In Chang Hwang & Frédéric Reynès, 2012. "The Effect of Learning on Climate Policy under Fat-tailed Uncertainty," Working Paper Series 5312, Department of Economics, University of Sussex.
    14. Matthias Schmidt & Hermann Held & Elmar Kriegler & Alexander Lorenz, 2013. "Climate Policy Under Uncertain and Heterogeneous Climate Damages," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 54(1), pages 79-99, January.
    15. repec:wsi:ccexxx:v:08:y:2017:i:04:n:s2010007817500142 is not listed on IDEAS
    16. Haim, David & Plantinga, Andrew J. & Thomann, Enrique, 2014. "The optimal time path for carbon abatement and carbon sequestration under uncertainty: The case of stochastic targeted stock," Resource and Energy Economics, Elsevier, vol. 36(1), pages 151-165.
    17. Iverson, Terrence, 2012. "Communicating Trade-offs amid Controversial Science: Decision Support for Climate Policy," Ecological Economics, Elsevier, vol. 77(C), pages 74-90.
    18. Peterson, Sonja, 2004. "The contribution of economics to the analysis of climate change and uncertainty: a survey of approaches and findings," Kiel Working Papers 1212, Kiel Institute for the World Economy (IfW).
    19. Olaleye, Olaitan & Baker, Erin, 2015. "Large scale scenario analysis of future low carbon energy options," Energy Economics, Elsevier, vol. 49(C), pages 203-216.
    20. Hwang, In Chang, 2014. "Fat-tailed uncertainty and the learning-effect," MPRA Paper 53671, University Library of Munich, Germany.
    21. Erin Baker, 2009. "Optimal Policy under Uncertainty and Learning about Climate Change: A Stochastic Dominance Approach," Journal of Public Economic Theory, Association for Public Economic Theory, vol. 11(5), pages 721-747, October.

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    JEL classification:

    • F0 - International Economics - - General

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