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Dynamic optimal strategies in transboundary pollution game under learning by doing

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  • Chang, Shuhua
  • Qin, Weihua
  • Wang, Xinyu

Abstract

In this paper, we present a transboundary pollution game, in which emission permits trading and pollution abatement costs under learning by doing are considered. In this model, the abatement cost mainly depends on the level of pollution abatement and the experience of using pollution abatement technology. We use optimal control theory to investigate the optimal emission paths and the optimal pollution abatement strategies under cooperative and noncooperative games, respectively. Additionally, the effects of parameters on the results have been examined.

Suggested Citation

  • Chang, Shuhua & Qin, Weihua & Wang, Xinyu, 2018. "Dynamic optimal strategies in transboundary pollution game under learning by doing," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 139-147.
    • Handle: RePEc:eee:phsmap:v:490:y:2018:i:c:p:139-147
    DOI: 10.1016/j.physa.2017.08.010
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    References listed on IDEAS

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    Cited by:

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    3. Fangju Jia & Dong‐dong Wang & Kun Zhou & Lianshui Li, 2022. "Differential decision analysis of transboundary pollution considering the participation of the central government," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 43(6), pages 1684-1703, September.
    4. Jia Xue & Youshi He & Peng Gao & Yin Tang & Hanyang Xu, 2022. "Multi-Agent Evolutionary Game Model: Corporate Low-Carbon Manufacturing, Chinese Government Supervision, and Public Media Investigation," Sustainability, MDPI, vol. 14(9), pages 1-24, May.
    5. Li, Shoude & Fu, Tong, 2022. "Abatement technology innovation, worker productivity and firm profitability: A dynamic analysis," Energy Economics, Elsevier, vol. 115(C).
    6. Wang, Xinyu & Zhang, Shuhua & Hao, Wenwei, 2022. "Myopic vs. foresighted behaviors in a transboundary pollution game with abatement policy and emission permits trading," Chaos, Solitons & Fractals, Elsevier, vol. 156(C).
    7. Ni, Jian & Huang, Hongzhi & Wang, Peipei & Zhou, Wei, 2020. "Capacity investment and green R&D in a dynamic oligopoly under the potential shift in environmental damage," Economic Modelling, Elsevier, vol. 88(C), pages 312-319.
    8. Weixin Yang & Yunpeng Yang, 2020. "Research on Air Pollution Control in China: From the Perspective of Quadrilateral Evolutionary Games," Sustainability, MDPI, vol. 12(5), pages 1-23, February.
    9. Li, Shoude & Zhang, Yingxuan, 2023. "Abatement technology innovation and pollution tax design: A dynamic analysis in monopoly," Energy Economics, Elsevier, vol. 119(C).
    10. Enquan Luo & Zuopeng Hu & Shuwen Xiang & Yanlong Yang & Zhijun Hu, 2024. "The Design of Ecological Compensation for Air Pollution Based on Differential Game," Sustainability, MDPI, vol. 16(3), pages 1-14, January.
    11. Li, Liming & Chen, Weidong, 2021. "The impact of subsidies in a transboundary pollution game with myopic players," Omega, Elsevier, vol. 103(C).
    12. Zhonghao Zhang & Tiantian Nie & Yingtao Wu & Jiahui Ling & Danhuang Huang, 2022. "The Temporal and Spatial Distributions and Influencing Factors of Transboundary Pollution in China," IJERPH, MDPI, vol. 19(8), pages 1-15, April.
    13. Li, Huiquan & Guo, Genlong, 2019. "A differential game analysis of multipollutant transboundary pollution in river basin," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).
    14. Xiao, Lu & Liu, Jianyue & Ge, Jinwen, 2021. "Dynamic game in agriculture and industry cross-sectoral water pollution governance in developing countries," Agricultural Water Management, Elsevier, vol. 243(C).

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