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Scientific uncertainty and climate change: Part I. Uncertainty and unabated emissions

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  • Stephan Lewandowsky

  • James Risbey
  • Michael Smithson
  • Ben Newell
  • John Hunter

Abstract

Uncertainty forms an integral part of climate science, and it is often used to argue against mitigative action. This article presents an analysis of uncertainty in climate sensitivity that is robust to a range of assumptions. We show that increasing uncertainty is necessarily associated with greater expected damages from warming, provided the function relating warming to damages is convex. This constraint is unaffected by subjective or cultural risk-perception factors, it is unlikely to be overcome by the discount rate, and it is independent of the presumed magnitude of climate sensitivity. The analysis also extends to “second-order” uncertainty; that is, situations in which experts disagree. Greater disagreement among experts increases the likelihood that the risk of exceeding a global temperature threshold is greater. Likewise, increasing uncertainty requires increasingly greater protective measures against sea level rise. This constraint derives directly from the statistical properties of extreme values. We conclude that any appeal to uncertainty compels a stronger, rather than weaker, concern about unabated warming than in the absence of uncertainty. Copyright Springer Science+Business Media Dordrecht 2014

Suggested Citation

  • Stephan Lewandowsky & James Risbey & Michael Smithson & Ben Newell & John Hunter, 2014. "Scientific uncertainty and climate change: Part I. Uncertainty and unabated emissions," Climatic Change, Springer, vol. 124(1), pages 21-37, May.
    • Handle: RePEc:spr:climat:v:124:y:2014:i:1:p:21-37
    DOI: 10.1007/s10584-014-1082-7
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    2. Walter Leal Filho & Robert Stojanov & Franziska Wolf & Newton R. Matandirotya & Christian Ploberger & Desalegn Y. Ayal & Fardous Mohammad Safiul Azam & Tareq Mohammed Ali AL-Ahdal & Rebecca Sarku & No, 2022. "Assessing Uncertainties in Climate Change Adaptation and Land Management," Land, MDPI, vol. 11(12), pages 1-15, December.
    3. Arthur Kary & Guy E. Hawkins & Brett K. Hayes & Ben R. Newell, 2017. "A Bayesian latent mixture model approach to assessing performance in stock-flow reasoning," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 12(5), pages 430-444, September.
    4. Felix J. Formanski & Marcel M. Pein & David D. Loschelder & John-Oliver Engler & Onno Husen & Johann M. Majer, 2022. "Tipping points ahead? How laypeople respond to linear versus nonlinear climate change predictions," Climatic Change, Springer, vol. 175(1), pages 1-20, November.
    5. 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.
    6. Doncaster, C. Patrick & Tavoni, Alessandro & Dyke, James G., 2017. "Using Adaptation Insurance to Incentivize Climate-change Mitigation," Ecological Economics, Elsevier, vol. 135(C), pages 246-258.
    7. Astrid Kause & Wändi Bruine de Bruin & Fai Fung & Andrea Taylor & Jason Lowe, 2020. "Visualizations of Projected Rainfall Change in the United Kingdom: An Interview Study about User Perceptions," Sustainability, MDPI, vol. 12(7), pages 1-21, April.
    8. Bernadette Hyland-Wood & John Gardner & Julie Leask & Ullrich K. H. Ecker, 2021. "Toward effective government communication strategies in the era of COVID-19," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 8(1), pages 1-11, December.

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