ESG performance and corporate adaptability: Evidence from listed companies in China

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Type of the article: Research Article

Abstract
Strengthening environmental, social, and governance (ESG) practices is a keyway for corporations to improve their adaptability and cope with uncertainty. This study explores how ESG performance affects the adaptability of listed companies in China. The study used an observation data set of 45,031 Chinese listed companies from 2009 to 2024, and the fixed effect regression model was used to reveal the positive impact of ESG performance on corporate adaptability. The benchmark regression results show that the overall ESG performance has significantly improved corporate adaptability. In-depth analysis shows a significant U-shaped relationship between environmental performance and corporate adaptability. This shows that environmental investment will initially inhibit corporate adaptability due to cost pressure, but once it exceeds a certain threshold, it will have a positive impact. At the same time, both the social and governance dimensions show a continuous linear improvement effect. Heterogeneity analysis shows that higher internal agency costs will weaken the positive impact of ESG, while higher levels of external supervision will enhance these impacts. The economic consequence test corroborates that ESG performance indirectly has a positive impact on corporate value by enhancing adaptability. The research results show that for corporates operating in a dynamic environment, ESG development should be regarded as a strategic investment, especially through long-term continuous improvement of social and governance performance to overcome the threshold of environmental management costs, which can effectively enhance the corporate resilience and corporate adaptability of corporates and ultimately enhance the corporate value.

Acknowledgments
This research was funded by the General Program of the National Social Science Fund of China [Grant 25BGL022], The Impact Effect and Mechanism of Generative Artificial Intelligence on Organizational Deviant Innovation.

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    • Figure 1. Research framework
    • Table 1. Variable definition
    • Table 2. Summary statistics
    • Table 3. Model benchmark regression results
    • Table 4. Partial regression results
    • Table 5. Test results for lagging models
    • Table 6. Results of robustness tests
    • Table 7. Heterogeneity analysis results
    • Table 8. Regional heterogeneity differences
    • Conceptualization
      Haixia Ren, Dana Kangalakova, Yanliang Chen, Hao Xu
    • Data curation
      Haixia Ren, Yanliang Chen, Hao Xu
    • Formal Analysis
      Haixia Ren, Dana Kangalakova, Yanliang Chen
    • Investigation
      Haixia Ren, Dana Kangalakova
    • Methodology
      Haixia Ren, Dana Kangalakova
    • Project administration
      Haixia Ren, Dana Kangalakova
    • Resources
      Haixia Ren, Dana Kangalakova, Yanliang Chen, Hao Xu
    • Software
      Haixia Ren, Hao Xu
    • Supervision
      Haixia Ren, Yanliang Chen
    • Validation
      Haixia Ren, Dana Kangalakova, Yanliang Chen, Hao Xu
    • Visualization
      Haixia Ren, Dana Kangalakova, Yanliang Chen, Hao Xu
    • Writing – original draft
      Haixia Ren, Dana Kangalakova, Yanliang Chen, Hao Xu
    • Writing – review & editing
      Haixia Ren, Dana Kangalakova, Yanliang Chen, Hao Xu
    • Funding acquisition
      Yanliang Chen