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Modeling Uncertainty in Climate Change: A Multi-Model Comparison

Author

Listed:
  • Kenneth Gillingham

    (Yale University, New Haven, CT, USA)

  • William Nordhaus

    (Yale University, New Haven, CT, USA)

  • David Anthoff

    (University of California, Berkeley, USA)

  • Valentina Bosetti

    (Bocconi University, Milan, Italy)

  • Haewon McJeon

    (PNNL, College Park, Maryland, USA)

  • Geoffrey Blanford

    (EPRI, USA)

  • Peter Christensen

    (Aarhus University, Denmark)

  • John Reilly

    (MIT, Cambridge, MA, USA)

  • Paul Sztorc

    (Yale University, New Haven, USA)

Abstract

The economics of climate change involves a vast array of uncertainties, complicating both the analysis and development of climate policy. This study presents the results of the first comprehensive study of uncertainty in climate change using multiple integrated assessment models. The study looks at model and parametric uncertainties for population, total factor productivity, and climate sensitivity. It estimates the pdfs of key output variables, including CO2 concentrations, temperature, damages, and the social cost of carbon (SCC). One key finding is that parametric uncertainty is more important than uncertainty in model structure. Our resulting pdfs also provide insights on tail events.

Suggested Citation

  • Kenneth Gillingham & William Nordhaus & David Anthoff & Valentina Bosetti & Haewon McJeon & Geoffrey Blanford & Peter Christensen & John Reilly & Paul Sztorc, 2016. "Modeling Uncertainty in Climate Change: A Multi-Model Comparison," Working Papers 2016.13, Fondazione Eni Enrico Mattei.
  • Handle: RePEc:fem:femwpa:2016.13
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    References listed on IDEAS

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    More about this item

    Keywords

    Climate Change; Integrated Assessment Models;

    JEL classification:

    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming

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