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Optimal climate policy: Uncertainty versus Monte Carlo

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  • Crost, Benjamin
  • Traeger, Christian P.

Abstract

The integrated assessment literature frequently replicates uncertainty by averaging Monte Carlo runs of deterministic models. This Monte Carlo analysis is, in essence, an averaged sensitivity analyses. The approach resolves all uncertainty before the first time period, drawing parameters from a distribution before initiating a given model run. This paper analyzes how closely a Monte Carlo based derivation of optimal policies is to the truly optimal policy, in which the decision maker acknowledges the full set of possible future trajectories in every period. Our analysis uses a stochastic dynamic programming version of the widespread integrated assessment model DICE, and focuses on damage uncertainty. We show that the optimizing Monte Carlo approach is not only off in magnitude, but can even lead to a wrong sign of the uncertainty effect. Moreover, it can lead to contradictory policy advice, suggesting a more stringent climate policy in terms of the abatement rate and a less stringent one in terms of the expenditure on abatement.

Suggested Citation

  • Crost, Benjamin & Traeger, Christian P., 2013. "Optimal climate policy: Uncertainty versus Monte Carlo," Economics Letters, Elsevier, vol. 120(3), pages 552-558.
  • Handle: RePEc:eee:ecolet:v:120:y:2013:i:3:p:552-558
    DOI: 10.1016/j.econlet.2013.05.019
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    References listed on IDEAS

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    Cited by:

    1. Ton S. van den Bremer & Rick van der Ploeg, 2018. "Pricing Carbon Under Economic and Climatic Risks: Leading-Order Results from Asymptotic Analysis," OxCarre Working Papers 203, Oxford Centre for the Analysis of Resource Rich Economies, University of Oxford.
    2. van den Bijgaart, Inge & Gerlagh, Reyer & Liski, Matti, 2016. "A simple formula for the social cost of carbon," Journal of Environmental Economics and Management, Elsevier, vol. 77(C), pages 75-94.
    3. Lemoine, Derek & Traeger, Christian P., 2016. "Ambiguous tipping points," Journal of Economic Behavior & Organization, Elsevier, vol. 132(PB), pages 5-18.
    4. Jensen, Svenn & Traeger, Christian P., 2014. "Optimal climate change mitigation under long-term growth uncertainty: Stochastic integrated assessment and analytic findings," European Economic Review, Elsevier, vol. 69(C), pages 104-125.
    5. Sturla Furunes Kvamsdal & Diwakar Poudel & Leif Kristoffer Sandal, 2016. "Harvesting in a Fishery with Stochastic Growth and a Mean-Reverting Price," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 63(3), pages 643-663, March.
    6. Armon Rezai & Frederick Van der Ploeg, 2016. "Intergenerational Inequality Aversion, Growth, and the Role of Damages: Occam's Rule for the Global Carbon Tax," Journal of the Association of Environmental and Resource Economists, University of Chicago Press, vol. 3(2), pages 493-522.
    7. Frederick Van der Ploeg & Armon Rezai, 2016. "Stranded Assets, the Social Cost of Carbon, and Directed Technical Change: Macroeconomic Dynamics of Optimal Climate Policy," CESifo Working Paper Series 5787, CESifo Group Munich.
    8. Christian Traeger, 2014. "A 4-Stated DICE: Quantitatively Addressing Uncertainty Effects in Climate Change," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 59(1), pages 1-37, September.
    9. Baker, Erin & Olaleye, Olaitan & Aleluia Reis, Lara, 2015. "Decision frameworks and the investment in R&D," Energy Policy, Elsevier, vol. 80(C), pages 275-285.
    10. repec:wsi:ccexxx:v:08:y:2017:i:02:n:s2010007817500063 is not listed on IDEAS
    11. Hess, Joshua & Manning, Dale & Iverson, Terry & Cutler, Harvey, 2016. "Uncertainty, Learning, and Local Opposition to Hydraulic Fracturing," MPRA Paper 79238, University Library of Munich, Germany.
    12. van den Bijgaart, Inge, 2016. "Essays in environmental economics and policy," Other publications TiSEM 298bee2a-cb08-4173-9fe1-8, Tilburg University, School of Economics and Management.
    13. repec:wsi:ccexxx:v:08:y:2017:i:04:n:s2010007817500142 is not listed on IDEAS
    14. Rick Van der Ploeg & Armon Rezai, 2015. "Intergenerational Inequality Aversion, Growth and the Role of Damages: Occam's rule for the global tax," Economics Series Working Papers OxCarre Research Paper 15, University of Oxford, Department of Economics.
    15. KEVIN DAYARATNA & ROSS McKITRICK & DAVID KREUTZER, 2017. "Empirically Constrained Climate Sensitivity And The Social Cost Of Carbon," Climate Change Economics (CCE), World Scientific Publishing Co. Pte. Ltd., vol. 8(02), pages 1-12, May.
    16. J. Farmer & Cameron Hepburn & Penny Mealy & Alexander Teytelboym, 2015. "A Third Wave in the Economics of Climate Change," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 62(2), pages 329-357, October.
    17. Charles F. Mason & Neil Wilmot, 2015. "Modeling Damages in Climate Policy Models: Temperature-Based or Carbon-Based?," CESifo Working Paper Series 5287, CESifo Group Munich.

    More about this item

    Keywords

    Climate change; Uncertainty; Integrated assessment; Monte Carlo; Risk aversion; DICE;

    JEL classification:

    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming
    • Q00 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - General - - - General
    • D61 - Microeconomics - - Welfare Economics - - - Allocative Efficiency; Cost-Benefit Analysis
    • D90 - Microeconomics - - Micro-Based Behavioral Economics - - - General
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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