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Robust Control in Global Warming Management: An Analytical Dynamic Integrated Assessment

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  • Hennlock, Magnus

Abstract

Imperfect measurement of uncertainty (deeper uncertainty) in climate sensitivity is introduced in a two-sectoral integrated assessment model (IAM) with endogenous growth, based on an extension of DICE. The household expresses ambiguity aversion and can use robust control via a `shadow ambiguity premium' on social carbon cost to identify robust climate policy feedback rules that work well over a range such as the IPCC climate sensitivity range (IPCC, 2007a). Ambiguity aversion, in combination with linear damage, increases carbon cost in a similar way as a low pure rate of time preference. However, ambiguity aversion in combination with non-linear damage would also make policy more responsive to changes in climate data observations. Perfect ambiguity aversion results in an infinite expected shadow carbon cost and a zero carbon consumption path. Dynamic programming identifies an analytically tractable solution to the IAM.

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  • Hennlock, Magnus, 2009. "Robust Control in Global Warming Management: An Analytical Dynamic Integrated Assessment," RFF Working Paper Series dp-09-19, Resources for the Future.
  • Handle: RePEc:rff:dpaper:dp-09-19
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    File URL: http://www.rff.org/RFF/documents/RFF-DP-09-19.pdf
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    Cited by:

    1. 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 Business School.
    2. Xin Li & Borghan N. Narajabad & Ted Temzelides, 2014. "Robust Dynamic Optimal Taxation and Environmental Externalities," Finance and Economics Discussion Series 2014-75, Board of Governors of the Federal Reserve System (U.S.).
    3. Anderson, Evan W. & Brock, William & Sanstad, Alan H., 2016. "Robust Consumption and Energy Decisions," 2017 Allied Social Sciences Association (ASSA) Annual Meeting, January 6-8, 2017, Chicago, Illinois 250117, Agricultural and Applied Economics Association.
    4. Tol, Richard S.J., 2013. "Targets for global climate policy: An overview," Journal of Economic Dynamics and Control, Elsevier, vol. 37(5), pages 911-928.
    5. Mark Kagan, 2012. "Climate Change Skepticism in the Face of Catastrophe," Tinbergen Institute Discussion Papers 12-112/VIII, Tinbergen Institute, revised 29 Sep 2014.
    6. Peter von zur Muehlen, 2022. "Prices and Taxes in a Ramsey Climate Policy Model under Heterogeneous Beliefs and Ambiguity," Economies, MDPI, vol. 10(10), pages 1-56, October.
    7. Brock, William & Xepapadeas, Anastasios, 2021. "Regional climate policy under deep uncertainty: robust control and distributional concerns," Environment and Development Economics, Cambridge University Press, vol. 26(3), pages 211-238, June.
    8. In Chang Hwang & Richard S.J. Tol & Marjan W. Hofkes, 2013. "Active Learning about Climate Change," Working Paper Series 6513, Department of Economics, University of Sussex Business School.
    9. Lemoine, Derek & Traeger, Christian P., 2016. "Ambiguous tipping points," Journal of Economic Behavior & Organization, Elsevier, vol. 132(PB), pages 5-18.
    10. In Chang Hwang, 2017. "A Recursive Method for Solving a Climate–Economy Model: Value Function Iterations with Logarithmic Approximations," Computational Economics, Springer;Society for Computational Economics, vol. 50(1), pages 95-110, June.
    11. In Hwang & Frédéric Reynès & Richard Tol, 2013. "Climate Policy Under Fat-Tailed Risk: An Application of Dice," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 56(3), pages 415-436, November.
    12. David Anthoff & Richard Tol, 2014. "Climate policy under fat-tailed risk: an application of FUND," Annals of Operations Research, Springer, vol. 220(1), pages 223-237, September.
    13. 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.

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    More about this item

    Keywords

    climate policy; carbon cost; robust control; Knightian uncertainty; ambiguity aversion; integrated asssessment;
    All these keywords.

    JEL classification:

    • C73 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Stochastic and Dynamic Games; Evolutionary Games
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming

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