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Анализ Неопределенности В Интегрированных Моделях Климата И Экономики: Обзор Литературы
[Uncertainty analysis in integrated assessment models of the economics of climate change: a literature survey]

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  • Shumilov, Andrei

Abstract

This paper presents a survey of studies analyzing various uncertainties in integrated assessment models of the economics of climate change. Applications of techniques for both deterministic models (Monte Carlo simulations, sensitivity analysis) and stochastic IAMs (stochastic dynamic programming) are reviewed.

Suggested Citation

  • Shumilov, Andrei, 2021. "Анализ Неопределенности В Интегрированных Моделях Климата И Экономики: Обзор Литературы [Uncertainty analysis in integrated assessment models of the economics of climate change: a literature survey," MPRA Paper 110171, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:110171
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    References listed on IDEAS

    as
    1. Valentina Bosetti & Carlo Carraro & Marzio Galeotti & Emanuele Massetti & Massimo Tavoni, 2006. "WITCH. A World Induced Technical Change Hybrid Model," Working Papers 2006_46, Department of Economics, University of Venice "Ca' Foscari".
    2. Derek Lemoine & Christian Traeger, 2014. "Watch Your Step: Optimal Policy in a Tipping Climate," American Economic Journal: Economic Policy, American Economic Association, vol. 6(1), pages 137-166, February.
    3. Johannes Emmerling & Laurent Drouet & Lara Aleluia Reis & Michela Bevione & Loic Berger & Valentina Bosetti & Samuel Carrara & Enrica De Cian & Gauthier De Maere D'Aertrycke & Tom Longden & Maurizio M, 2016. "The WITCH 2016 Model - Documentation and Implementation of the Shared Socioeconomic Pathways," Working Papers 2016.42, Fondazione Eni Enrico Mattei.
    4. Crost, Benjamin & Traeger, Christian P., 2013. "Optimal climate policy: Uncertainty versus Monte Carlo," Economics Letters, Elsevier, vol. 120(3), pages 552-558.
    5. 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.
    6. Henri Waisman & Céline Guivarch & Fabio Grazi & Jean Hourcade, 2012. "The I maclim-R model: infrastructures, technical inertia and the costs of low carbon futures under imperfect foresight," Climatic Change, Springer, vol. 114(1), pages 101-120, September.
    7. Anthoff, David & Tol, Richard S.J., 2010. "On international equity weights and national decision making on climate change," Journal of Environmental Economics and Management, Elsevier, vol. 60(1), pages 14-20, July.
    8. Valentina Bosetti, Carlo Carraro, Marzio Galeotti, Emanuele Massetti, Massimo Tavoni, 2006. "A World induced Technical Change Hybrid Model," The Energy Journal, International Association for Energy Economics, vol. 0(Special I), pages 13-38.
    9. David Anthoff & Richard Tol, 2013. "The uncertainty about the social cost of carbon: A decomposition analysis using fund," Climatic Change, Springer, vol. 117(3), pages 515-530, April.
    10. Borgonovo, E., 2010. "Sensitivity analysis with finite changes: An application to modified EOQ models," European Journal of Operational Research, Elsevier, vol. 200(1), pages 127-138, January.
    11. Kenneth Gillingham & William Nordhaus & David Anthoff & Geoffrey Blanford & Valentina Bosetti & Peter Christensen & Haewon McJeon & John Reilly, 2018. "Modeling Uncertainty in Integrated Assessment of Climate Change: A Multimodel Comparison," Journal of the Association of Environmental and Resource Economists, University of Chicago Press, vol. 5(4), pages 791-826.
    12. Manne, Alan & Mendelsohn, Robert & Richels, Richard, 1995. "MERGE : A model for evaluating regional and global effects of GHG reduction policies," Energy Policy, Elsevier, vol. 23(1), pages 17-34, January.
    13. Borgonovo, E., 2007. "A new uncertainty importance measure," Reliability Engineering and System Safety, Elsevier, vol. 92(6), pages 771-784.
    14. William Nordhaus, 2014. "Estimates of the Social Cost of Carbon: Concepts and Results from the DICE-2013R Model and Alternative Approaches," Journal of the Association of Environmental and Resource Economists, University of Chicago Press, vol. 1(1), pages 000.
    15. 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.
    16. David Anthoff & Richard Tol, 2013. "Erratum to: The uncertainty about the social cost of carbon: A decomposition analysis using fund," Climatic Change, Springer, vol. 121(2), pages 413-413, November.
    17. Kelly, David L. & Kolstad, Charles D., 1999. "Bayesian learning, growth, and pollution," Journal of Economic Dynamics and Control, Elsevier, vol. 23(4), pages 491-518, February.
    18. Jensen, Svenn & Traeger, Christian P., 2014. "Optimal climate change mitigation under long-term growth uncertainty: Stochastic integrated assessment and analytic findings," European Economic Review, Elsevier, vol. 69(C), pages 104-125.
    19. P. Capros & Denise Van Regemorter & Leonidas Paroussos & P. Karkatsoulis & C. Fragkiadakis & S. Tsani & I. Charalampidis & Tamas Revesz, 2013. "GEM-E3 Model Documentation," JRC Research Reports JRC83177, Joint Research Centre.
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    More about this item

    Keywords

    greenhouse gases emissions; global warming; integrated assessment models; uncertainty;
    All these keywords.

    JEL classification:

    • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling
    • 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

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