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Carbon Reduction Strategies Based on an NW Small-World Network with a Progressive Carbon Tax

Author

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  • Bin Wu

    (School of Economics and Management, Southeast University, No. 2 Southeast University Road, Nanjing 211189, China)

  • Wanying Huang

    (School of Economics and Management, Southeast University, No. 2 Southeast University Road, Nanjing 211189, China)

  • Pengfei Liu

    (School of Economics and Management, Southeast University, No. 2 Southeast University Road, Nanjing 211189, China)

Abstract

There is an increasingly urgent need to reduce carbon emissions. Devising effective carbon tax policies has become an important research topic. It is necessary to explore carbon reduction strategies based on the design of carbon tax elements. In this study, we explore the effect of a progressive carbon tax policy on carbon emission reductions using the logical deduction method. We apply experience-weighted attraction learning theory to construct an evolutionary game model for enterprises with different levels of energy consumption in an NW small-world network, and study their strategy choices when faced with a progressive carbon tax policy. The findings suggest that enterprises that adopt other energy consumption strategies gradually transform to a low energy consumption strategy, and that this trend eventually spreads to the entire system. With other conditions unchanged, the rate at which enterprises change to a low energy consumption strategy becomes faster as the discount coefficient, the network externality, and the expected adjustment factor increase. Conversely, the rate of change slows as the cost of converting to a low energy consumption strategy increases.

Suggested Citation

  • Bin Wu & Wanying Huang & Pengfei Liu, 2017. "Carbon Reduction Strategies Based on an NW Small-World Network with a Progressive Carbon Tax," Sustainability, MDPI, vol. 9(10), pages 1-22, September.
  • Handle: RePEc:gam:jsusta:v:9:y:2017:i:10:p:1747-:d:113547
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    2. Hong, Zitao & Peng, Zhen & Zhang, Liumei, 2022. "Game analysis on the choice of emission trading among industrial enterprises driven by data," Energy, Elsevier, vol. 239(PE).
    3. Meihui Jiang, 2022. "Locating the Principal Sectors for Carbon Emission Reduction on the Global Supply Chains by the Methods of Complex Network and Susceptible–Infective Model," Sustainability, MDPI, vol. 14(5), pages 1-13, February.
    4. Xiao Yu & Yingdong Xu & Meng Sun & Yanzhe Zhang, 2021. "The Green-Innovation-Inducing Effect of a Unit Progressive Carbon Tax," Sustainability, MDPI, vol. 13(21), pages 1-18, October.
    5. Changping Zhao & Xiaojiang Xu & Yu Gong & Houming Fan & Haojia Chen, 2019. "Blue Carbon Cooperation in the Maritime Silk Road with Network Game Model and Simulation," Sustainability, MDPI, vol. 11(10), pages 1-27, May.
    6. An, Yunfei & Zhou, Dequn & Wang, Qunwei & Shi, Xunpeng & Taghizadeh-Hesary, Farhad, 2022. "Mitigating size bias for carbon pricing in small Asia-Pacific countries: Increasing block carbon tax," Energy Policy, Elsevier, vol. 161(C).
    7. Zhen Li & Hongming Zhu & Qingfeng Meng & Changzhi Wu & Jianguo Du, 2019. "Manufacturers’ Green Decision Evolution Based on Multi-Agent Modeling," Complexity, Hindawi, vol. 2019, pages 1-14, April.
    8. Mingjun Sun & Hongjun Peng & Shuai Wang, 2018. "Cost-Sharing Mechanisms for A Wood Forest Product Supply Chain under Carbon Cap-and-Trade," Sustainability, MDPI, vol. 10(12), pages 1-19, November.

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