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<p>Integrating financial value creation with non-financial issues is becoming increasingly important. This paper explores how different aspects of financial performance (growth, operations, solvency, and profitability) impact businesses’ Environment, Social, and Governance (ESG) performance. This study uses ensemble learning in machine learning for system analysis. The results show that the random forest (RF) model has significantly higher prediction accuracy than traditional models. It highlights that growth ability is more crucial for predicting ESG performance. Furthermore, the analysis identifies significant predictive effects from variables like corporate size, changing rate of net assets, net profit growth rate per share, and working capital on ESG performance. The study also uses accumulated local effects and individual conditional expectation plots to visually analyze the impact patterns of these critical variables on ESG. This research provides insights for businesses to understand the financial-ESG performance relationship and strategically optimize financial resource allocation for sustainable development.</p>

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Using Machine Learning To Decode the Impact of Financial Performance on ESG: Evidence from China

  • 期刊论文

  • 发表日期：

    2025年9月25日

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  • Zhenghao Chang

摘要

Integrating financial value creation with non-financial issues is becoming increasingly important. This paper explores how different aspects of financial performance (growth, operations, solvency, and profitability) impact businesses’ Environment, Social, and Governance (ESG) performance. This study uses ensemble learning in machine learning for system analysis. The results show that the random forest (RF) model has significantly higher prediction accuracy than traditional models. It highlights that growth ability is more crucial for predicting ESG performance. Furthermore, the analysis identifies significant predictive effects from variables like corporate size, changing rate of net assets, net profit growth rate per share, and working capital on ESG performance. The study also uses accumulated local effects and individual conditional expectation plots to visually analyze the impact patterns of these critical variables on ESG. This research provides insights for businesses to understand the financial-ESG performance relationship and strategically optimize financial resource allocation for sustainable development.