A study on the differential impact of non-financial factors on ESG performance under different corporate natures

Nan Zou; Di Zeng

doi:10.54097/b1khj861

Authors

Nan Zou
Di Zeng

DOI:

https://doi.org/10.54097/b1khj861

Keywords:

ESG performance, firm nature, non-financial factors, machine learning.

Abstract

Investigating how non-financial elements shape ESG performance across various company types is crucial for advancing corporate governance and ensuring sustainable business practices. This research examines Chinese listed companies between 2014 and 2022 to understand these dynamics. It starts by analyzing the variance in ESG scores between state-owned and privately-owned firms using kernel density estimation. The study then employs K-means clustering to categorize companies into high, medium, and low ESG performers and tracks their progress over time. The LightGBM algorithm and Shapley value are utilized to assess how diverse non-financial factors uniquely affect ESG outcomes for state-owned and private enterprises.Findings indicate that state-owned firms tend to have more consistent ESG scores, whereas their private counterparts display greater variability. Key factors influencing ESG scores in state-owned companies include capital intensity, market concentration, and equity distribution, while in private firms, the financial inclusion index, digitalization efforts, and R&D spending are more influential. Additionally, the study highlights that formal and informal environmental policies differentially affect ESG performance in state-owned and private companies. The paper concludes with suggestions to improve ESG standards and support sustainable growth in businesses.

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