Prediction of nexus among ESG disclosure and firm Performance: Applicability, explainability and implications

Joel Victor Dossa, Chiagoziem C. Ukwuoma, Dara Thomas, James Mhoja Dossa and Aamir Ali Gopang

Innovation and Green Development, 2025, vol. 4, issue 4

Abstract: This study investigates the nexus between ESG disclosure and firm performance using advanced machine learning models (MLs) to capture complex, non-linear interactions. Analyzing data from Chinese A-share firms (2012–2022), it employs Explainable AI (XAI) tools such as SHAP, heat maps, and Williams plots to enhance model transparency and interpretability. Among several models, the Extra Trees model demonstrated the best predictive performance, revealing that ESG disclosure positively correlates with firm performance, with environmental disclosure exerting the strongest influence. Policymakers are urged to promote standardized, transparent ESG disclosures, particularly focusing on environmental practices while addressing greenwashing to enhance credibility. Investors can prioritize firms with strong environmental practices and use predictive models to refine decision-making. Corporate managers are encouraged to embed sustainability into long-term strategies and utilize ML techniques for improved governance. The study contributes by showcasing the utility of MLs in exploring ESG-performance relationships, offering actionable insights for stakeholders, and providing a foundation for future research. Researchers are encouraged to investigate non-linear ESG impacts across diverse contexts, using broader samples and incorporating market-based measures and ESG rating agencies to improve generalizability. This approach advances understanding of ESG's role in driving firm performance while addressing methodological gaps.

Keywords: Machine learning; ESG disclosure; Extra tree; Williams plots (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S294975312500058X
Open-access

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:ingrde:v:4:y:2025:i:4:s294975312500058x

DOI: 10.1016/j.igd.2025.100261

Access Statistics for this article

Innovation and Green Development is currently edited by Hai-Jie Wang and Chun-Ping Chang

More articles in Innovation and Green Development from Elsevier
Bibliographic data for series maintained by Catherine Liu ().