Machine learning has pulled back the curtain on one of the most contested questions in modern finance: what actually drives a company’s environmental, social, and governance score? A new study published in Heliyon applies an explainable artificial intelligence framework to more than 2,600 firm-year observations from the S&P 1500 index, spanning the Energy, Utilities, Basic Materials, Industrials, and Financials sectors between 2019 and 2022. The verdict is striking. Across every sector analyzed, one variable towers above all others in predicting ESG performance: sheer company size.
The research team, led by José Alejandro Fernández Fernández with Renata Kubus and Inés Martín de Santos, drew its data from the Refinitiv Eikon database and constructed a balanced panel of 666 unique firms, yielding 2,612 estimation observations after excluding a small number of records with missing ESG outcomes. Rather than relying on conventional linear econometrics, which the authors argue can obscure threshold-dependent and nonlinear relationships, they deployed random forests built from 500 regression trees, each constrained to a maximum depth of eight, alongside regression-tree benchmarks, permutation importance, SHAP values, and partial dependence plots.
The methodological care is notable. All preprocessing, including winsorization of extreme values at the first and ninety-ninth percentiles, median imputation of missing predictors, and a logarithmic transformation of total assets, was fitted strictly within each training fold and then applied to validation data, preventing information leakage. The train-test split was grouped by firm, so no company’s observations appeared in both partitions. A separate temporal validation trained the models on 2019 to 2021 and tested them on 2022, and out-of-bag estimates provided an additional ensemble-based diagnostic.
Predictive performance varied meaningfully across industries. Financials proved the most predictable sector, achieving a grouped cross-validated R-squared of 0.415 and a temporal test score of 0.417, with out-of-bag performance reaching 0.571. Utilities followed with a temporal R-squared of 0.363, while Industrials and Basic Materials landed in the mid-range. Energy showed the least stable results, with a negative single grouped holdout but positive five-fold and temporal scores, a dispersion the authors attribute to the sector’s smaller sample and warrant cautious interpretation.
When the researchers jointly permuted six analytical blocks of predictors on the 2022 holdout, the size signal proved overwhelming. Disrupting the size block degraded test performance by a mean R-squared decline of 0.650 in Financials, 0.566 in Utilities, 0.488 in Energy, and 0.487 in Industrials, with Basic Materials at 0.348. Leverage ranked second in four of the five sectors, while profitability took that position in Financials. This block-level evidence matters because correlated accounting ratios can redistribute individual importance scores; by permuting whole blocks at once, the team confirmed that the dominance of firm size is not an artifact of importance being split among overlapping variables.
Beyond the shared size effect, the sectors diverged sharply. In Energy and Utilities, the predictive structure was comparatively concentrated, centered on firm scale and capital expenditure intensity. Intriguingly, capital expenditure showed predominantly negative nonlinear associations with predicted ESG scores beyond moderate thresholds in both sectors, suggesting that spending more on plant and equipment does not automatically translate into stronger sustainability ratings. The authors caution that this pattern may reflect differences in the orientation and efficiency of capital allocation rather than investment volume itself, though the observational design cannot establish the mechanism.
Industrials and Basic Materials displayed the most heterogeneous and nonlinear configurations. In Industrials, the assets-to-equity ratio emerged as the second most relevant predictor, with free operating cash flow showing negative associations beyond levels near 0.14 and debt service contributing threshold-dependent effects across firm-size regions. Basic Materials exhibited a U-shaped relationship between EBITDA-to-equity and predicted ESG scores among smaller firms, with the direction changing around values close to 0.28. Financials, by contrast, presented the most concentrated and stable structure of all, with size dominating and profitability indicators such as EBITDA-to-equity and EBITDA-to-total-assets providing secondary contributions, while debt variables played a comparatively minor role.
The study also documents a broader convergence story in the raw ESG data. Average scores rose across all five sectors between 2019 and 2022, with Energy posting the strongest growth at 39 percent, followed by Basic Materials at 27 percent and Industrials at 24 percent, while Utilities and Financials improved more modestly at 18 and 17 percent. Meanwhile, the coefficient of variation declined across sectors, indicating that ESG practices are converging, even as Financials and Energy retain higher internal dispersion, pointing to uneven adoption within those industries.
The policy implications are interpretive rather than causal, as the authors emphasize, but they are consequential. The dominance of firm size suggests that ESG integration may be structurally easier for large corporations with sophisticated governance and reporting infrastructure, implying that smaller and medium-sized firms face proportionally higher compliance costs and could benefit from more proportional disclosure requirements. The sectoral heterogeneity in how leverage, profitability, and investment relate to ESG scores further argues against one-size-fits-all regulation, particularly for capital-intensive industries where financing structure matters most. And the finding that higher investment intensity sometimes accompanies weaker ESG outcomes underscores the need to distinguish investment quality from quantity when designing sustainability incentives.
Perhaps the most sobering takeaway for investors is that market valuation signals, captured through the historic earnings-to-price ratio, showed weak and often negative alignment with ESG performance in several sectors, hinting that sustainability information may not be fully priced into American equities. The authors argue that strengthening ESG transparency, comparability, and interpretability could improve market efficiency and channel capital more effectively. What the study ultimately delivers is a nuanced map: ESG performance carries a common scale-related backbone, but the financial characteristics that matter beyond scale depend fundamentally on the economic structure of each industry, a conclusion that resists the temptation of a single homogeneous financial profile for sustainability success.
Subject of Research: Machine learning analysis of the relationship between financial health indicators and ESG scores in US firms
Article Title: “Machine learning analysis of ESG score and financial health in the USA”
Article References: “Machine learning analysis of ESG score and financial health in the USA”. (n.d.). Original publication
Image Credits: AI Generated
DOI: Not provided
Keywords: ESG, machine learning, random forests, SHAP values, S&P 1500, firm size, financial health, sustainable finance, corporate governance, sectoral analysis, explainable AI, Refinitiv Eikon
News Source: Drew Townsend. (October 6, 2026). AI Reveals Firm Size Dominates ESG Scores Across Five US Sectors. Scienmag.
