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China's ESG scorecard: A predictive machine learning model

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  • Lemuel Kenneth David
  • Jianling Wang
  • Vanessa Angel
  • Meiling Luo

Abstract

In today's globalized business environment, the intersection of Environmental, Social, and Governance (ESG) factors has come to the forefront, shaping the paradigms of Corporate Social Responsibility (CSR) and environmental stewardship. As China, with its economic prowess and distinct socio‐cultural milieu, takes center stage, deciphering its ESG dynamics becomes imperative for both local and global stakeholders. This groundbreaking research unveils a pioneering multidimensional ESG scoring system tailored for the Chinese corporate landscape. Drawing on an extensive dataset from Bloomberg, spanning an 11‐year period and encompassing 1496 companies, this study stands as a seminal contribution to the ESG literature. By harnessing advanced mathematical explorations coupled with state‐of‐the‐art machine learning techniques, the model's predictive prowess is heightened, ensuring its adaptability and robustness. Venturing beyond mere metrics, this research accentuates the practical implications of ESG in shaping sustainable and responsible business practices in China, thereby catalyzing a more ethically aligned corporate trajectory in the region.

Suggested Citation

  • Lemuel Kenneth David & Jianling Wang & Vanessa Angel & Meiling Luo, 2024. "China's ESG scorecard: A predictive machine learning model," Corporate Social Responsibility and Environmental Management, John Wiley & Sons, vol. 31(4), pages 3468-3486, July.
    • Handle: RePEc:wly:corsem:v:31:y:2024:i:4:p:3468-3486
    DOI: 10.1002/csr.2746
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    References listed on IDEAS

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    1. Valeria D’Amato & Rita D’Ecclesia & Susanna Levantesi, 2022. "ESG score prediction through random forest algorithm," Computational Management Science, Springer, vol. 19(2), pages 347-373, June.
    2. Caterina De Lucia & Pasquale Pazienza & Mark Bartlett, 2020. "Does Good ESG Lead to Better Financial Performances by Firms? Machine Learning and Logistic Regression Models of Public Enterprises in Europe," Sustainability, MDPI, vol. 12(13), pages 1-29, July.
    3. Manuel Arellano & Stephen Bond, 1991. "Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 58(2), pages 277-297.
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