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A Dynamic Game of Emissions Pollution with Uncertainty and Learning

Author

Listed:
  • Nahid Masoudi
  • Marc Santugini
  • Georges Zaccour
Abstract
We introduce learning in a dynamic game of international pollution, with ecological uncertainty. We characterize and compare the feedback non-cooperative emissions strategies of players when the players do not know the distribution of ecological uncertainty but they gain information (learn) about it. We then compare our learning model with the benchmark model of full information, where players know the distribution of ecological uncertainty. We find that uncertainty due to anticipative learning induces a decrease in total emissions, but not necessarily in individual emissions. Further, the effect of structural uncertainty on total and individual emissions depends on the beliefs distribution and bias. Moreover, we obtain that if a player’s beliefs change toward more optimistic views or if she feels that the situation is less risky, then she increases her emissions while others react to this change and decrease their emissions.

Suggested Citation

  • Nahid Masoudi & Marc Santugini & Georges Zaccour, 2015. "A Dynamic Game of Emissions Pollution with Uncertainty and Learning," Cahiers de recherche 1501, CIRPEE.
    • Handle: RePEc:lvl:lacicr:1501
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    References listed on IDEAS

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    Cited by:

    1. Shimai Su & Anna Tur, 2022. "Estimation of Initial Stock in Pollution Control Problem," Mathematics, MDPI, vol. 10(19), pages 1-11, September.
    2. Mao, Liang, 2017. "Designing International Environmental Agreements under Participation Uncertainty," MPRA Paper 79145, University Library of Munich, Germany.
    3. Wang, Zhiyu, 2018. "Permit trading with flow pollution and stock pollution," Journal of Environmental Economics and Management, Elsevier, vol. 91(C), pages 118-132.
    4. Mao, Liang, 2017. "Designing International Environmental Agreements under Participation Uncertainty," MPRA Paper 86248, University Library of Munich, Germany.
    5. Shuhua Chang & Suresh P. Sethi & Xinyu Wang, 2018. "Optimal Abatement and Emission Permit Trading Policies in a Dynamic Transboundary Pollution Game," Dynamic Games and Applications, Springer, vol. 8(3), pages 542-572, September.
    6. Li, Liming & Chen, Weidong, 2021. "The impact of subsidies in a transboundary pollution game with myopic players," Omega, Elsevier, vol. 103(C).
    7. Francisco J André & Michael Finus, 2024. "Endogenous learning in international environmental agreements: the impact of research spillovers and the degree of cooperation," Oxford Economic Papers, Oxford University Press, vol. 76(3), pages 877-900.

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    More about this item

    Keywords

    Pollution emissions; Dynamic games; Uncertainty; Learning;
    All these keywords.

    JEL classification:

    • Q50 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - General
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • C73 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Stochastic and Dynamic Games; Evolutionary Games

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