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Transient temperature response modeling in IAMs: The effects of over simplification on the SCC

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  • Marten, Alex L.

Abstract

Integrated Assessment Models (IAMs) couple representations of the natural climate system with models of the global economy to evaluate climate and energy policies. Such models are currently used to derive the benefits of carbon mitigation policies through estimates of the social cost of carbon (SCC). To remain tractable these models often utilize highly simplified representations of complex natural, social, and economic systems. The authors consider three prominent IAMs, DICE, FUND, and PAGE, and compare their highly simplified temperature response models to two upwelling diffusion energy balance models that better reflect the progressive heat uptake of the deep ocean. They find that all three IAMs fail to fully capture important characteristics in the dynamics of temperature response, especially for high equilibrium climate sensitivities. This has serious implications given these models are often run with distributions for the equilibrium climate sensitivity which have a positive probability for such states of the world. The authors find that, all else equal, the temperature response model in FUND can lead to estimates of the expected SCC that are 1075% lower than those derived using more realistic climate models, while the models in DICE and PAGE lead to expected SCC estimates that are 10110% and 40260% higher, respectively.

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  • Marten, Alex L., 2011. "Transient temperature response modeling in IAMs: The effects of over simplification on the SCC," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 5, pages 1-42.
  • Handle: RePEc:zbw:ifweej:201118
    DOI: 10.5018/economics-ejournal.ja.2011-18
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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. Kevin D. Dayaratna & Ross McKitrick & Patrick J. Michaels, 2020. "Climate sensitivity, agricultural productivity and the social cost of carbon in FUND," Environmental Economics and Policy Studies, Springer;Society for Environmental Economics and Policy Studies - SEEPS, vol. 22(3), pages 433-448, July.
    3. Newbold, Stephen C. & Marten, Alex L., 2014. "The value of information for integrated assessment models of climate change," Journal of Environmental Economics and Management, Elsevier, vol. 68(1), pages 111-123.
    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. Louise Kessler, 2017. "Estimating The Economic Impact Of The Permafrost Carbon Feedback," Climate Change Economics (CCE), World Scientific Publishing Co. Pte. Ltd., vol. 8(02), pages 1-23, May.
    6. 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.
    7. Louise Kessler, 2015. "Estimating the economic impact of the permafrost carbon feedback," GRI Working Papers 219, Grantham Research Institute on Climate Change and the Environment.
    8. Alex L. Marten, 2014. "The Role Of Scenario Uncertainty In Estimating The Benefits Of Carbon Mitigation," Climate Change Economics (CCE), World Scientific Publishing Co. Pte. Ltd., vol. 5(03), pages 1-29.
    9. Kögel, Tomas, 2011. "The social cost of carbon on an optimal balanced growth path," Economics Discussion Papers 2011-35, Kiel Institute for the World Economy (IfW Kiel).
    10. Raphael Calel & David Stainforth & Simon Dietz, 2015. "Tall tales and fat tails: the science and economics of extreme warming," Climatic Change, Springer, vol. 132(1), pages 127-141, September.
    11. Hwang, In Chang & Reynès, Frédéric & Tol, Richard S.J., 2017. "The effect of learning on climate policy under fat-tailed risk," Resource and Energy Economics, Elsevier, vol. 48(C), pages 1-18.
    12. Alex Marten & Robert Kopp & Kate Shouse & Charles Griffiths & Elke Hodson & Elizabeth Kopits & Bryan Mignone & Chris Moore & Steve Newbold & Stephanie Waldhoff & Ann Wolverton, 2013. "Improving the assessment and valuation of climate change impacts for policy and regulatory analysis," Climatic Change, Springer, vol. 117(3), pages 433-438, April.
    13. Stephen C. Newbold & Charles Griffiths & Chris Moore & Ann Wolverton & Elizabeth Kopits, 2013. "A Rapid Assessment Model For Understanding The Social Cost Of Carbon," Climate Change Economics (CCE), World Scientific Publishing Co. Pte. Ltd., vol. 4(01), pages 1-40.
    14. 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.
    15. Richard S.J. Tol, 2021. "Estimates of the social cost of carbon have not changed over time," Working Paper Series 0821, Department of Economics, University of Sussex Business School.
    16. In Chang Hwang, 2016. "Active learning and optimal climate policy," EcoMod2016 9611, EcoMod.
    17. Kopp, Robert E. & Mignone, Bryan K., 2012. "The US government's social cost of carbon estimates after their first two years: Pathways for improvement," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 6, pages 1-41.
    18. Richard S. J. Tol, 2021. "Estimates of the social cost of carbon have increased over time," Papers 2105.03656, arXiv.org, revised Aug 2022.
    19. 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.

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    More about this item

    Keywords

    social cost of carbon; integrated assessment; transient temperature response;
    All these keywords.

    JEL classification:

    • 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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