A Dynamic Game of Emissions Pollution with Uncertainty and Learning
Marc Santugini and
Cahiers de recherche from CIRPEE
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.
Keywords: Pollution emissions; Dynamic games; Uncertainty; Learning (search for similar items in EconPapers)
JEL-codes: C73 D81 D83 Q50 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ene, nep-env, nep-gth, nep-mic and nep-res
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Journal Article: A Dynamic Game of Emissions Pollution with Uncertainty and Learning (2016)
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Persistent link: https://EconPapers.repec.org/RePEc:lvl:lacicr:1501
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