Board gender diversity and emissions performance: Insights from panel regressions, machine learning, and explainable AI

With European Union initiatives mandating gender quotas on corporate boards, a key question arises: Is greater board gender diversity (BGD) associated with better emissions performance (EP)? To answer this question, we examine the influence of BGD on EP across a sample of European firms from 2016 to 2022. Using panel regressions, advanced machine learning algorithms, and explainable AI, we reveal a non-linear relationship. Specifically, EP improves with BGD up to an optimal level of approximately 35 %, beyond which further increases in BGD yield no additional improvement in EP. A minimum BGD threshold of 22 % is necessary for meaningful improvements in EP. To assess the legitimacy of EP outcomes, this study examines whether ESG controversies weaken the BGD-EP relationship. The results show no significant effect, suggesting that BGD’s impact is driven by governance mechanisms rather than symbolic actions. Additionally, path analysis indicates that while environmental innovation contributes to EP, it is not the mediating channel through which BGD promotes EP. The results have implications for academics, businesses, and regulators.

💡 Research Summary

This paper investigates whether board gender diversity (BGD) improves firms’ emissions performance (EP) in Europe, using a novel combination of panel econometrics, advanced machine learning (ML), and explainable artificial intelligence (XAI). The authors assemble an unbalanced panel of 463 non‑financial firms from the STOXX Europe 600 index covering 2016‑2022. Emissions performance is measured by a percentile score from LSEG Workspace (higher = better), BGD by the share of female directors, ESG controversies (ESGC) by LSEG’s controversy index, and environmental innovation (EI) by an LSEG‑derived innovation score. Standard corporate‑governance and financial controls (board size, compensation, tenure, CEO duality, Tobin’s Q, leverage, etc.) are included.

Methodologically, three econometric specifications—fixed effects, random effects, and correlated random effects—are estimated to obtain average marginal effects while accounting for firm‑specific heterogeneity and clustered standard errors (to address heteroskedasticity). To capture higher‑order non‑linearities without imposing a functional form, three ML algorithms (XGBoost, Random Forest, and a residual neural network) are trained on the same data. Post‑hoc XAI tools—SHapley Additive exPlanations (SHAP) for feature importance and Partial Dependence (PD) plots for marginal effect visualization—translate the black‑box predictions into interpretable insights that can be directly compared with the panel results.

The empirical findings converge across all methods. BGD exhibits a positive but diminishing impact on EP: a minimum “critical mass” of roughly 22 % female representation is required before any statistically significant improvement in emissions performance is observed, and the marginal benefit peaks around 35 % female directors. Beyond this upper threshold, additional gender diversity does not translate into further EP gains, indicating a saturation effect. ESG controversies do not moderate the BGD‑EP link; the interaction term is insignificant, suggesting that the effect of gender‑diverse boards is driven by genuine governance mechanisms rather than symbolic compliance. Path analysis shows that while EI positively influences EP, it does not mediate the BGD‑EP relationship, implying that female directors affect emissions outcomes through channels other than direct innovation promotion (e.g., monitoring, stakeholder engagement, resource linkage).

The study extends “critical mass theory” by identifying both lower and upper bounds for effective gender diversity, and demonstrates that advanced ML combined with XAI can uncover nuanced, policy‑relevant thresholds that traditional linear models might miss. The authors discuss practical implications: regulators should aim for a BGD range of 22‑35 % to maximize environmental benefits, and firms should view gender quotas as a substantive governance tool rather than a mere compliance checkbox. Limitations include the Europe‑centric sample, reliance on proprietary scores for EP and ESGC, and potential omitted variables such as country‑level environmental regulation intensity. Future research could test the identified thresholds in other regions, employ alternative EP metrics, and explore causal mechanisms with structural equation modeling or natural experiments. Overall, the paper provides robust, multi‑method evidence that moderate levels of board gender diversity meaningfully enhance corporate emissions performance, independent of ESG controversy exposure and without relying on environmental innovation as an intermediary.