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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 capture interactions that are important for the evaluation of potential climate and energy policies. The U.S. federal government currently uses such models 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. This makes IAMs susceptible to oversimplification by failing to capture key features of the underlying system that are important for policy analysis. In this paper we focus on one area in which these models appear to have fallen into such a trap. We consider three prominent IAMs, DICE, FUND, and PAGE, and examine the way in which these models represent the transient temperature response to increases in radiative forcing. We compare the highly simplified temperature response models in these IAMs to two upwelling diffusion energy balance models that better reflect the progressive uptake of heat by the deep ocean. We find that all three IAMs are unable to fully capture important characteristics in the temporal dynamics of temperature response, especially in the case of high equilibrium climate sensitivity. This has serious implications given that these models are often run with distributions for the equilibrium climate sensitivity that contain a positive probability for such states of the world. We find that all else equal the temperature response function utilized in the FUND model results in estimates of the expected SCC that are up to 25% lower than those derived with the more realistic climate models, while the functions used in DICE and PAGE lead to expected SCC estimates up to 40% and 50% higher, respectively.

Suggested Citation

  • Marten, Alex L., 2011. "Transient temperature response modeling in IAMs: the effects of over simplification on the SCC," Economics Discussion Papers 2011-11, Kiel Institute for the World Economy (IfW).
  • Handle: RePEc:zbw:ifwedp:201111
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    References listed on IDEAS

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    1. Martin L. Weitzman, 2012. "GHG Targets as Insurance Against Catastrophic Climate Damages," Journal of Public Economic Theory, Association for Public Economic Theory, vol. 14(2), pages 221-244, March.
    2. Nordhaus, William D., 2007. "Two Centuries of Productivity Growth in Computing," The Journal of Economic History, Cambridge University Press, vol. 67(01), pages 128-159, March.
    3. Stephen Newbold & Adam Daigneault, 2009. "Climate Response Uncertainty and the Benefits of Greenhouse Gas Emissions Reductions," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 44(3), pages 351-377, November.
    4. Detlef Vuuren & Jason Lowe & Elke Stehfest & Laila Gohar & Andries Hof & Chris Hope & Rachel Warren & Malte Meinshausen & Gian-Kasper Plattner, 2011. "How well do integrated assessment models simulate climate change?," Climatic Change, Springer, vol. 104(2), pages 255-285, January.
    5. Plambeck, Erica L & Hope, Chris, 1996. "PAGE95 : An updated valuation of the impacts of global warming," Energy Policy, Elsevier, vol. 24(9), pages 783-793, September.
    6. Partha Dasgupta, 2008. "Discounting climate change," Journal of Risk and Uncertainty, Springer, vol. 37(2), pages 141-169, December.
    7. Newell, Richard G. & Pizer, William A., 2003. "Discounting the distant future: how much do uncertain rates increase valuations?," Journal of Environmental Economics and Management, Elsevier, vol. 46(1), pages 52-71, July.
    8. Martin L. Weitzman, 2009. "On Modeling and Interpreting the Economics of Catastrophic Climate Change," The Review of Economics and Statistics, MIT Press, vol. 91(1), pages 1-19, February.
    9. Marten, Alex L. & Newbold, Stephen C., 2012. "Estimating the social cost of non-CO2 GHG emissions: Methane and nitrous oxide," Energy Policy, Elsevier, vol. 51(C), pages 957-972.
    10. Daiju Narita & Richard Tol & David Anthoff, 2010. "Economic costs of extratropical storms under climate change: an application of FUND," Journal of Environmental Planning and Management, Taylor & Francis Journals, vol. 53(3), pages 371-384.
    11. Weitzman, Martin L., 1998. "Why the Far-Distant Future Should Be Discounted at Its Lowest Possible Rate," Journal of Environmental Economics and Management, Elsevier, vol. 36(3), pages 201-208, November.
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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.
    2. 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.
    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. repec:eee:resene:v:48:y:2017:i:c:p:1-18 is not listed on IDEAS
    5. Alex L. Marten, 2014. "The Role of Scenario Uncertainty in Estimating the Benefits of Carbon Mitigation," NCEE Working Paper Series 201404, National Center for Environmental Economics, U.S. Environmental Protection Agency, revised Mar 2014.
    6. 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.
    7. repec:wsi:ccexxx:v:04:y:2013:i:01:n:s2010007813500012 is not listed on IDEAS
    8. 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.
    9. repec:wsi:ccexxx:v:08:y:2017:i:02:n:s2010007817500063 is not listed on IDEAS
    10. 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.
    11. 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).
    12. In Chang Hwang, 2016. "Active learning and optimal climate policy," EcoMod2016 9611, EcoMod.
    13. 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, Kiel Institute for the World Economy (IfW), vol. 6, pages 1-41.
    14. 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.
    15. 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.

    More about this item

    Keywords

    social cost of carbon; integrated assessment; transient temperature response;

    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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