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Analysis of Emergency Cooperative Strategies in Marine Oil Spill Response: A Stochastic Evolutionary Game Approach

Author

Listed:
  • Feifan He

    (Faculty of Maritime and Transportation, Ningbo University, Ningbo 315832, China)

  • Yuanyuan Xu

    (Faculty of Maritime and Transportation, Ningbo University, Ningbo 315832, China)

  • Pengjun Zheng

    (Faculty of Maritime and Transportation, Ningbo University, Ningbo 315832, China)

  • Guiyun Liu

    (Faculty of Maritime and Transportation, Ningbo University, Ningbo 315832, China)

  • Dan Zhao

    (Faculty of Maritime and Transportation, Ningbo University, Ningbo 315832, China)

Abstract

Marine oil spills significantly adversely affect the socio-economic environment and marine ecosystems. Establishing an efficient emergency cooperation mechanism that enables swift and coordinated responses from all stakeholders is crucial to mitigate the harmful consequences of such spills and protect regional security. This study uses stochastic evolutionary game theory to develop an emergency cooperation model, focusing on the strategic interactions and dynamic evolution between three main parties: the local government, port enterprises, and specialized oil spill cleanup units. The findings indicate the following: (1) The strategy choice of the local government plays a dominant role in the three-party game and has a significant guiding effect on the behavioral decisions of port enterprises and specialized oil spill cleanup units. (2) The strength of the government’s reward and punishment mechanism directly affects the cooperation tendency of the port enterprises and specialized oil spill cleanup units. (3) When the emergency response is more efficient and the cooperation effect is significant, the cleanup units may choose negative cooperation based on payoff maximization in order to prolong the cleaning time. (4) In the process of system evolution, the strategies of local governments and port enterprises are more stable and less affected by random perturbations, while the strategy fluctuations of cleanup units are more sensitive. The findings enrich the theoretical framework for handling marine oil spill emergencies and provide valuable insights for developing efficient collaborative mechanisms and formulating well-grounded regulatory incentive policies.

Suggested Citation

  • Feifan He & Yuanyuan Xu & Pengjun Zheng & Guiyun Liu & Dan Zhao, 2025. "Analysis of Emergency Cooperative Strategies in Marine Oil Spill Response: A Stochastic Evolutionary Game Approach," Sustainability, MDPI, vol. 17(11), pages 1-29, May.
  • Handle: RePEc:gam:jsusta:v:17:y:2025:i:11:p:4920-:d:1665612
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    References listed on IDEAS

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    1. Xinmin Liu & Kangkang Lin & Lei Wang & Hongkun Zhang, 2021. "Stochastic Evolutionary Game Analysis Between Special Committees and CEO: Incentive and Supervision," Dynamic Games and Applications, Springer, vol. 11(3), pages 538-555, September.
    2. Jida Liu & Yuwei Song & Shi An & Changqi Dong, 2022. "How to Improve the Cooperation Mechanism of Emergency Rescue and Optimize the Cooperation Strategy in China: A Tripartite Evolutionary Game Model," IJERPH, MDPI, vol. 19(3), pages 1-27, January.
    3. Fudenberg, Drew & Imhof, Lorens A., 2006. "Imitation processes with small mutations," Journal of Economic Theory, Elsevier, vol. 131(1), pages 251-262, November.
    4. Daniel Seaberg & Laura Devine & Jun Zhuang, 2017. "A review of game theory applications in natural disaster management research," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 89(3), pages 1461-1483, December.
    5. Lanying Du & Ling Qian, 2016. "The government’s mobilization strategy following a disaster in the Chinese context: an evolutionary game theory analysis," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 80(3), pages 1411-1424, February.
    6. Hongmei Shan & Yiyi An & Haoze Bai & Jing Shi, 2025. "Intergovernmental collaborative governance of emergency response logistics: an evolutionary game study," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 121(1), pages 705-730, January.
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