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Learning and climate change

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Author Info

  • Brian C. O'neill
  • Paul Crutzen
  • Arnulf Grübler
  • Minh Ha-Duong

    ()
    (CIRED - Centre International de Recherche sur l'Environnement et le Développement - CIRAD : UMR56 - CNRS : UMR8568 - Ecole des Hautes Etudes en Sciences Sociales (EHESS) - Ecole des Ponts ParisTech - Ecole Nationale du Génie Rural des Eaux et Forêts)

  • 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.

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File URL: http://halshs.archives-ouvertes.fr/docs/00/13/47/18/PDF/ONeill.ea-2006-SubmittedLearningClimateChange.pdf
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Bibliographic Info

Paper provided by HAL in its series Post-Print with number halshs-00134718.

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Date of creation: 2006
Date of revision:
Publication status: Published, Climate Policy, 2006, 6, 5, 1-6
Handle: RePEc:hal:journl:halshs-00134718

Note: View the original document on HAL open archive server: http://halshs.archives-ouvertes.fr/halshs-00134718/en/
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Web page: http://hal.archives-ouvertes.fr/

Related research

Keywords: Learning; Uncertainty; Climate change; Decision analysis;

References

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  1. Minh Ha-Duong & Patrice Dumas, 2004. "An abrupt stochastic damage function to analyse climate policy benefits," Post-Print halshs-00002451, HAL.
  2. William D. Nordhaus & David Popp, 1997. "What is the Value of Scientific Knowledge? An Application to Global Warming Using the PRICE Model," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1), pages 1-45.
  3. 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.
  4. Gritsevskyi, Andrii & Nakicenovi, Nebojsa, 2000. "Modeling uncertainty of induced technological change," Energy Policy, Elsevier, vol. 28(13), pages 907-921, November.
  5. Michael Grubb, Carlo Carraro and John Schellnhuber, 2006. "Technological Change for Atmospheric Stabilization: Introductory Overview to the Innovation Modeling Comparison Project," The Energy Journal, International Association for Energy Economics, vol. 0(Special I), pages 1-16.
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Cited by:
  1. Pizer, William, 1997. "Optimal Choice of Policy Instrument and Stringency Under Uncertainty: The Case of Climate Change," Discussion Papers dp-97-17, Resources For the Future.

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