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Research on entropy generation strategy and its application in carbon trading market

Author

Listed:
  • Miao Zhenjian
  • Kofi Baah Boamah
  • Xingle Long

Abstract

The study seeks to provide a deeper insight and strategies into the carbon trading market, the internal mechanism, and the linkage mechanism for emission reduction in the carbon trading market right from its inception. The study differs from most prior research on carbon trading, as this current paper incorporates the carbon market with the dissipative structure theory. Based on the dissipative structure theory, this study adapts the entropy generation principle as the base for the carbon trading market. The study captures the following key dimensions: the change of carbon market value, the entropy generation principle of carbon quota trading process, and the replacement process involved in production factors in carbon trading. The study revealed that the entropy generation of the carbon trading market is mainly caused by the cost gradient formed as a result of the cost difference of emission reduction and the increase of production factor input required by economic development. Moreover, the study revealed clearly the dynamics of the entropy index and the carbon market emission reduction efficiency. Thus, the lower the entropy value of the carbon market, the higher the emission reduction efficiency.

Suggested Citation

  • Miao Zhenjian & Kofi Baah Boamah & Xingle Long, 2020. "Research on entropy generation strategy and its application in carbon trading market," Business Strategy and the Environment, Wiley Blackwell, vol. 29(5), pages 1992-2000, July.
    • Handle: RePEc:bla:bstrat:v:29:y:2020:i:5:p:1992-2000
    DOI: 10.1002/bse.2483
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    References listed on IDEAS

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    5. Nathaniel O. Keohane, 2009. "Cap and Trade, Rehabilitated: Using Tradable Permits to Control U.S. Greenhouse Gases," Review of Environmental Economics and Policy, Association of Environmental and Resource Economists, vol. 3(1), pages 42-62, Winter.
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    Cited by:

    1. Xing Yang & Jun-long Mi & Jin Jiang & Jia-wen Li & Quan-shen Zhang & Meng-meng Geng, 2022. "Carbon sink price prediction based on radial basis kernel function support vector machine regression model [Chaos and order in the capital markets]," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 17, pages 1075-1084.

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