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Climate Sensitivity Uncertainty: When is Good News Bad?

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  • Mark C. Freeman
  • Gernot Wagner
  • Richard J. Zeckhauser

Abstract

Climate change is real and dangerous. Exactly how bad it will get, however, is uncertain. Uncertainty is particularly relevant for estimates of one of the key parameters: equilibrium climate sensitivity—how eventual temperatures will react as atmospheric carbon dioxide concentrations double. Despite significant advances in climate science and increased confidence in the accuracy of the range itself, the “likely” range has been 1.5-4.5°C for over three decades. In 2007, the Intergovernmental Panel on Climate Change (IPCC) narrowed it to 2-4.5°C, only to reverse its decision in 2013, reinstating the prior range. In addition, the 2013 IPCC report removed prior mention of 3°C as the “best estimate.” We interpret the implications of the 2013 IPCC decision to lower the bottom of the range and excise a best estimate. Intuitively, it might seem that a lower bottom would be good news. Here we ask: When might apparently good news about climate sensitivity in fact be bad news? The lowered bottom value also implies higher uncertainty about the temperature increase, a definite bad. Under reasonable assumptions, both the lowering of the lower bound and the removal of the “best estimate” may well be bad news.

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  • Mark C. Freeman & Gernot Wagner & Richard J. Zeckhauser, 2015. "Climate Sensitivity Uncertainty: When is Good News Bad?," NBER Working Papers 20900, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:20900
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    References listed on IDEAS

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    Cited by:

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    2. Robert S. Pindyck, 2021. "What We Know and Don’t Know about Climate Change, and Implications for Policy," Environmental and Energy Policy and the Economy, University of Chicago Press, vol. 2(1), pages 4-43.
    3. Qingquan Jiang & Jinhuang Lin & Qianqian Wei & Rui Zhang & Hongzhen Fu, 2023. "Demystifying the Economic Growth and CO 2 Nexus in Fujian’s Key Industries Based on Decoupling and LMDI Model," Sustainability, MDPI, vol. 15(4), pages 1-23, February.
    4. Elettra Agliardi & Anastasios Xepapadeas, 2018. "Optimal Scheduling of Greenhouse Gas Emissions under Carbon Budgeting and Policy Design," DEOS Working Papers 1808, Athens University of Economics and Business.
    5. Felix Kapfhammer & Vegard H. Larsen & Leif Anders Thorsrud, 2020. "Climate Risk and Commodity Currencies," Working Papers No 10/2020, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    6. Johanna Etner & Meglena Jeleva & Natacha Raffin, 2021. "Climate policy: How to deal with ambiguity?," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 72(1), pages 263-301, July.
    7. Robert S. Pindyck, 2017. "The Use and Misuse of Models for Climate Policy," Review of Environmental Economics and Policy, Association of Environmental and Resource Economists, vol. 11(1), pages 100-114.
    8. Fonseca, Jimeno A. & Nevat, Ido & Peters, Gareth W., 2020. "Quantifying the uncertain effects of climate change on building energy consumption across the United States," Applied Energy, Elsevier, vol. 277(C).
    9. John E. Bistline, 2015. "Fat-Tailed Uncertainty, Learning, And Climate Policy," Climate Change Economics (CCE), World Scientific Publishing Co. Pte. Ltd., vol. 6(02), pages 1-21.

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    More about this item

    JEL classification:

    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming

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