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

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  • Golub, Alexander
  • Narita, Daiju
  • Schmidt, Matthias G.W.

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

  • Golub, Alexander & Narita, Daiju & Schmidt, Matthias G.W., 2011. "Uncertainty in Integrated Assessment Models of Climate Change: Alternative Analytical Approaches," Sustainable Development Papers 99638, Fondazione Eni Enrico Mattei (FEEM).
    • Handle: RePEc:ags:feemdp:99638
    DOI: 10.22004/ag.econ.99638
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    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. Yu-Fu Chen & Michael Funke & Nicole Glanemann, 2011. "Dark Clouds or Silver Linings? Knightian Uncertainty and Climate Change," CESifo Working Paper Series 3516, CESifo.
    6. 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.
    7. 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.
    8. Halkos, George, 2014. "The Economics of Climate Change Policy: Critical review and future policy directions," MPRA Paper 56841, University Library of Munich, Germany.

    More about this item

    Keywords

    Environmental Economics and Policy;

    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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