A quantitative analysis of ESG disclosure and financial performance in renewable energy companies: a two-step approach using unsupervised machine learning

Purpose In the era of Industry 5.0, understanding the balance between environmental, social, and governance (ESG) and firm performance is crucial for mitigating climate change and enhancing financial outcomes. This paper aims to analyze the effect of ESG disclosure on the financial performance (FP) of renewable and clean energy (RCE) companies, focusing on the combined ESG disclosure and individual E, S, and G disclosure scores. Design/methodology/approach The study analyzed a panel data sample from 2015–2021, covering 41 RCE companies. By applying the K-means++ clustering technique, the research also explored how firm-specific features influence the relationship between ESG disclosure and FP. The Bloomberg database and audited financial reports were used to gather the data for the study. Findings The findings indicate that increased ESG disclosure positively influences FP. Further, a significant positive relationship exists between FP and a company’s E and S disclosure. However, firm-specific characteristics significantly influence this relationship. Findings suggest that a company’s commitment to comprehensive ESG efforts enhances financial efficiency rather than increasing costs. Originality/value This study adds to the ESG-FP literature by emphasizing global RCE companies, a key player in sustainability. Further, to the best of the author’s knowledge, the study’s uniqueness is attributed to its application of a two-step approach, combining ESG-FP analysis with K-means++ clustering to account for firm-specific characteristics. It also uniquely examines the individual impact of E, S, and G disclosure on the FP of global RCE companies. The findings offer valuable insights for businesses and policymakers in developing strategies that improve profitability while addressing climate change risks.