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Learning and climate change

Author

Listed:
  • Brian C. O'Neill
  • Paul Crutzen
  • Arnulf Grübler
  • Minh Ha-Duong

    (CIRED - centre international de recherche sur l'environnement et le développement - Cirad - Centre de Coopération Internationale en Recherche Agronomique pour le Développement - EHESS - École des hautes études en sciences sociales - AgroParisTech - ENPC - École nationale des ponts et chaussées - CNRS - Centre National de la Recherche Scientifique)

  • Klaus Keller
  • Charles Kolstad
  • Jonathan Koomey
  • Andreas Lange
  • Michael Obersteiner
  • Michael Oppenheimer
  • William Pepper
  • Warren Sanderson
  • Michael Schlesinger
  • Nicolas Treich

  • Alistair Ulph
  • Mort Webster
  • Chris Wilson

Abstract

Learning – i.e. the acquisition of new information that leads to changes in our assessment of uncertainty – plays a prominent role in the international climate policy debate. For example, the view that we should postpone actions until we know more continues to be influential. The latest work on learning and climate change includes new theoretical models, better informed simulations of how learning affects the optimal timing of emissions reductions, analyses of how new information could affect the prospects for reaching and maintaining political agreements and for adapting to climate change, and explorations of how learning could lead us astray rather than closer to the truth. Despite the diversity of this new work, a clear consensus on a central point is that the prospect of learning does not support the postponement of emissions reductions today.

Suggested Citation

  • Brian C. O'Neill & Paul Crutzen & Arnulf Grübler & Minh Ha-Duong & Klaus Keller & Charles Kolstad & Jonathan Koomey & Andreas Lange & Michael Obersteiner & Michael Oppenheimer & William Pepper & Warre, 2006. "Learning and climate change," Post-Print halshs-00134718, HAL.
    • Handle: RePEc:hal:journl:halshs-00134718
    Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-00134718v1
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    • Brian C. O'Neill & Paul Crutzen & Arnulf Gr�bler & Minh Ha Duong & Klaus Keller & Charles Kolstad & Jonathan Koomey & Andreas Lange & Michael Obersteiner & Michael Oppenheimer & William Pepper & Warre, 2006. "Learning and climate change," Climate Policy, Taylor & Francis Journals, vol. 6(5), pages 585-589, September.

    References listed on IDEAS

    as
    1. Minh Ha-Duong & Patrice Dumas, 2004. "An abrupt stochastic damage function to analyse climate policy benefits," Post-Print halshs-00002451, HAL.
    2. repec:aen:journl:2006se-a01 is not listed on IDEAS
    3. Gritsevskyi, Andrii & Nakicenovi, Nebojsa, 2000. "Modeling uncertainty of induced technological change," Energy Policy, Elsevier, vol. 28(13), pages 907-921, November.
    4. Keller, Klaus & Bolker, Benjamin M. & Bradford, D.F.David F., 2004. "Uncertain climate thresholds and optimal economic growth," Journal of Environmental Economics and Management, Elsevier, vol. 48(1), pages 723-741, July.
    5. repec:aen:journl:1997v18-01-a01 is not listed on IDEAS
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    Citations

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    Cited by:

    1. Shurojit Chatterji & Sayantan Ghosal & Sean Walsh & John Whalley, 2014. "Unilateral Emissions Mitigation, Spillovers, And Global Learning," Climate Change Economics (CCE), World Scientific Publishing Co. Pte. Ltd., vol. 5(03), pages 1-16.
    2. van Wijnbergen, Sweder & Willems, Tim, 2015. "Optimal learning on climate change: Why climate skeptics should reduce emissions," Journal of Environmental Economics and Management, Elsevier, vol. 70(C), pages 17-33.
    3. Pizer, William A., 1997. "Optimal Choice of Policy Instrument and Stringency Under Uncertainty: The Case of Climate Change," Discussion Papers 10582, Resources for the Future.

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