Proceedings of the 4th International Conference on Economic Management and Model Engineering, ICEMME 2022, November 18-20, 2022, Nanjing, China

Research Article

The Impact on Chinese Energy Enterprises in the Context of Carbon Neutrality: Based on the ESG and Carbon Disclosure Index

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  • @INPROCEEDINGS{10.4108/eai.18-11-2022.2326893,
        author={Hao  Du and Xiaohan  Lyu and Mengyao  Zhao},
        title={The Impact on Chinese Energy Enterprises in the Context of Carbon Neutrality: Based on the ESG and Carbon Disclosure Index},
        proceedings={Proceedings of the 4th International Conference on Economic Management and Model Engineering, ICEMME 2022, November 18-20, 2022, Nanjing, China},
        publisher={EAI},
        proceedings_a={ICEMME},
        year={2023},
        month={2},
        keywords={carbon neutrality; corporate value; esg; carbon disclosure},
        doi={10.4108/eai.18-11-2022.2326893}
    }
    
  • Hao Du
    Xiaohan Lyu
    Mengyao Zhao
    Year: 2023
    The Impact on Chinese Energy Enterprises in the Context of Carbon Neutrality: Based on the ESG and Carbon Disclosure Index
    ICEMME
    EAI
    DOI: 10.4108/eai.18-11-2022.2326893
Hao Du1,*, Xiaohan Lyu2, Mengyao Zhao3
  • 1: University of Toronto Toronto, Ontario, Canada
  • 2: Business College, Shandong University Weihai, Shandong, China
  • 3: Western University London, Ontario, Canada
*Contact email: daniel.du@mail.utoronto.ca

Abstract

This article collected 2020 data from wind and Cninfo, used them as samples, and used a multiple regression model to explore the impact of carbon neutrality on Chinese energy companies. The final result found that carbon information disclosure is positively correlated with corporate value, ESG, and corporate, concluding that value is negatively correlated. At the same time, this article also finds a strong correlation between better financial performance and better climate change disclosure and performance. However, the data used in this article is during the COVID-19 period, so the conclusion may be different from other periods. It is recommended that future studies select data that is not during the COVID-19 period for research to avoid the impact of COVID-19 on the problems we are studying.