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Research on low-carbon technology diffusion among enterprises in networked evolutionary game

Author

Listed:
  • Wu, Yu’e
  • Wang, Xinyu
  • Liu, Zeyun
  • Zhao, Xiukun

Abstract

The diffusion of low-carbon technologies (LCTs) is one of the important measures to mitigate carbon emissions. Local governments, enterprises, and consumers are crucial participants in the complex adaptive LCT diffusion system. In this context, this study builds an evolutionary game model based on a complex network comprised of three sub-networks, which respectively represent the connections of local governments, enterprises and consumers. Using this model, this research explores the impacts of homogeneous subsidies, heterogeneous subsidies, homogeneous carbon taxes, heterogeneous carbon taxes, targeted penalties, market demand, and policy mixes combined with these instruments on LCT diffusion. The results show that carbon taxes, subsidies, and penalties can all promote the diffusion of LCTs. The comprehensive effect of LCT diffusion brought by heterogeneous subsidies or carbon taxes is slightly better than those achieved by the fixed subsidy or the carbon tax equal to their respective mean values. Compared with pure carbon tax and subsidy policies, the mixed policy of introducing targeted fines brings a more obvious LCT diffusion effect. However, none of these policy interventions can achieve the complete spread of LCTs. Meanwhile, increasing the potential market demand for low-carbon products can achieve a very significant diffusion of LCTs. And when the proportion of white consumers reaches 0.9, the full proliferation of LCTs can be realized. This research develops a valuable framework that enriches the modeling practice of the diffusion of LCTs and provides insights for implementing well-designed policy packages.

Suggested Citation

  • Wu, Yu’e & Wang, Xinyu & Liu, Zeyun & Zhao, Xiukun, 2023. "Research on low-carbon technology diffusion among enterprises in networked evolutionary game," Chaos, Solitons & Fractals, Elsevier, vol. 174(C).
  • Handle: RePEc:eee:chsofr:v:174:y:2023:i:c:s0960077923007531
    DOI: 10.1016/j.chaos.2023.113852
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    Cited by:

    1. Zeng, Yongchao & Shi, Yingying & Shahbaz, Muhammad & Liu, Qin, 2024. "Scenario-based policy representative exploration: A novel approach to analyzing policy portfolios and its application to low-carbon energy diffusion," Energy, Elsevier, vol. 296(C).

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