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Scientific uncertainty and climate change: Part II. Uncertainty and mitigation

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  • Stephan Lewandowsky
  • James Risbey
  • Michael Smithson
  • Ben Newell

Abstract

In public debate surrounding climate change, scientific uncertainty is often cited in connection with arguments against mitigative action. This article examines the role of uncertainty about future climate change in determining the likely success or failure of mitigative action. We show by Monte Carlo simulation that greater uncertainty translates into a greater likelihood that mitigation efforts will fail to limit global warming to a target (e.g., 2 °C). The effect of uncertainty can be reduced by limiting greenhouse gas emissions. Taken together with the fact that greater uncertainty also increases the potential damages arising from unabated emissions (Lewandowsky et al. 2014 ), any appeal to uncertainty implies a stronger, rather than weaker, need to cut greenhouse gas emissions than in the absence of uncertainty. Copyright Springer Science+Business Media Dordrecht 2014

Suggested Citation

  • Stephan Lewandowsky & James Risbey & Michael Smithson & Ben Newell, 2014. "Scientific uncertainty and climate change: Part II. Uncertainty and mitigation," Climatic Change, Springer, vol. 124(1), pages 39-52, May.
  • Handle: RePEc:spr:climat:v:124:y:2014:i:1:p:39-52
    DOI: 10.1007/s10584-014-1083-6
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    References listed on IDEAS

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    2. Garnaut,Ross, 2011. "The Garnaut Review 2011," Cambridge Books, Cambridge University Press, number 9781107691681.
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    Cited by:

    1. 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.
    2. Freeman, Mark C. & Wagner, Gernot & Zeckhauser, Richard J., 2015. "Climate Sensitivity Uncertainty: When Is Good News Bad?," Working Paper Series rwp15-002, Harvard University, John F. Kennedy School of Government.
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    4. 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.
    5. 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.
    6. repec:cup:judgdm:v:12:y:2017:i:5:p:430-444 is not listed on IDEAS
    7. 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.
    8. 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.
    9. Duan, Hongbo & Mo, Jianlei & Fan, Ying & Wang, Shouyang, 2018. "Achieving China's energy and climate policy targets in 2030 under multiple uncertainties," Energy Economics, Elsevier, vol. 70(C), pages 45-60.
    10. Bernadette Hyland-Wood & John Gardner & Julie Leask & Ullrich K. H. Ecker, 2021. "Toward effective government communication strategies in the era of COVID-19," Palgrave Communications, Palgrave Macmillan, vol. 8(1), pages 1-11, December.

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