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Uncertainty in Integrated Assessment Models of Climate Change: Alternative Analytical Approaches

Author

Listed:
  • Alexander Golub

    (Environmental Defense Fund)

  • Daiju Narita

    (Kiel Institute for the World Economy)

Abstract

Uncertainty plays a key role in the economics of climate change, and the discussions surrounding its implications for climate policy are far from settled. We give an overview of the literature on uncertainty in integrated assessment models of climate change and identify some future research needs. In the paper, we pay particular attention to three different and complementary approaches that model uncertainty in association with integrated assessment models: the discrete uncertainty modeling, the most common way to incorporate uncertainty in complex climate-economy models: the real options analysis, a simplified way to identify and value flexibility: the continuous-time stochastic dynamic programming, which is computationally most challenging but necessary if persistent stochasticity is considered.

Suggested Citation

  • Alexander Golub & Daiju Narita, 2011. "Uncertainty in Integrated Assessment Models of Climate Change: Alternative Analytical Approaches," Working Papers 2011.02, Fondazione Eni Enrico Mattei.
  • Handle: RePEc:fem:femwpa:2011.02
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    Cited by:

    1. Alexander Golub & Michael Brody, 2017. "Uncertainty, climate change, and irreversible environmental effects: application of real options to environmental benefit-cost analysis," Journal of Environmental Studies and Sciences, Springer;Association of Environmental Studies and Sciences, vol. 7(4), pages 519-526, December.
    2. Daiju Narita & Martin F. Quaas, 2014. "Adaptation To Climate Change And Climate Variability: Do It Now Or Wait And See?," Climate Change Economics (CCE), World Scientific Publishing Co. Pte. Ltd., vol. 5(04), pages 1-28.
    3. Halkos, George, 2014. "The Economics of Climate Change Policy: Critical review and future policy directions," MPRA Paper 56841, University Library of Munich, Germany.
    4. Yu-Fu Chen & Michael Funke & Nicole Glanemann, 2011. "Time is Running Out: The 2°C Target and Optimal Climate Policies," Dundee Discussion Papers in Economics 262, Economic Studies, University of Dundee.
    5. Chen, Yu-Fu & Funke, Michael & Glanemann, Nicole, 2011. "Dark Clouds or Silver Linings? Knightian Uncertainty and Climate Change," SIRE Discussion Papers 2011-64, Scottish Institute for Research in Economics (SIRE).
    6. Barry Anderson & Emanuele Borgonovo & Marzio Galeotti & Roberto Roson, 2014. "Uncertainty in Climate Change Modeling: Can Global Sensitivity Analysis Be of Help?," Risk Analysis, John Wiley & Sons, vol. 34(2), pages 271-293, February.
    7. Gren, Ing-Marie & Carlsson, Mattias & Elofsson, Katarina & Munnich, Miriam, 2012. "Stochastic carbon sinks for combating carbon dioxide emissions in the EU," Energy Economics, Elsevier, vol. 34(5), pages 1523-1531.
    8. 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.

    More about this item

    Keywords

    Uncertainty; Learning; Economics of Climate Change; Integrated Assessment Models; Real Options;
    All these keywords.

    JEL classification:

    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis

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