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Environmental policy under model uncertainty: a robust optimal control approach

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  • Michael Funke

  • Michael Paetz

Abstract

The design of optimal environmental policy inherits model uncertainty.We investigate the consequences in a simple linear model, where the aim of the policymaker is to stabilise the atmospheric content of carbon. We study how decisionmakers’ concerns about robustness alters policy using the Hansen and Sargent (2003, 2008) approach. The analysis shows that a policymaker, who fears about model misspecification should react more aggressively to changes in the stock of atmospheric carbon and reduce emissions stronger.

Suggested Citation

  • Michael Funke & Michael Paetz, 2011. "Environmental policy under model uncertainty: a robust optimal control approach," Quantitative Macroeconomics Working Papers 21108, Hamburg University, Department of Economics.
    • Handle: RePEc:ham:qmwops:21108
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    File URL: http://www.uni-hamburg.de/fachbereiche-einrichtungen/fb03/iwwt/makro/PaperRobustControl.pdf
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    Cited by:

    1. Athanassoglou, Stergios & Xepapadeas, Anastasios, 2012. "Pollution control with uncertain stock dynamics: When, and how, to be precautious," Journal of Environmental Economics and Management, Elsevier, vol. 63(3), pages 304-320.
    2. Muhammed Sütçü, 2024. "Parameter uncertainties in evaluating climate policies with dynamic integrated climate-economy model," Environment Systems and Decisions, Springer, vol. 44(1), pages 69-84, March.
    3. Amegashie, J. Atsu & Ouattara, Bazoumanna & Strobl, Eric, 2007. "Moral Hazard and the Composition of Transfers: Theory with an Application to Foreign Aid," MPRA Paper 3158, University Library of Munich, Germany, revised 06 May 2007.
    4. William A. Brock & Steven N. Durlauf, 2015. "On Sturdy Policy Evaluation," The Journal of Legal Studies, University of Chicago Press, vol. 44(S2), pages 447-473.
    5. Robert Lempert & Shawn McKay, 2011. "Some thoughts on the role of robust control theory in climate-related decision support," Climatic Change, Springer, vol. 107(3), pages 241-246, August.
    6. Chen, Yu-Fu & Funke, Michael & Glanemann, Nicole, 2011. "Dark Clouds or Silver Linings? Knightian Uncertainty and Climate Change," SIRE Discussion Papers 2011-64, Scottish Institute for Research in Economics (SIRE).
    7. Yiyong Cai & Warwick McKibbin, 2015. "Uncertainty and International Climate Change Negotiations," Italian Economic Journal: A Continuation of Rivista Italiana degli Economisti and Giornale degli Economisti, Springer;Società Italiana degli Economisti (Italian Economic Association), vol. 1(1), pages 101-115, March.
    8. Ted Temzelides & Borghan Narajabad, 2014. "Robust Dynamic Optimal Taxation and Environmental Externalities," 2014 Meeting Papers 59, Society for Economic Dynamics.
    9. Fidel Gonzalez, 2018. "Pollution Control with Time-Varying Model Mistrust of the Stock Dynamics," Computational Economics, Springer;Society for Computational Economics, vol. 51(3), pages 541-569, March.

    More about this item

    Keywords

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    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • Q25 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Renewable Resources and Conservation - - - Water
    • Q28 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Renewable Resources and Conservation - - - Government Policy
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty

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