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

Abstract: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.

General Finance (q-fin.GN); Computers and Society (cs.CY); Machine Learning (cs.LG)

arXiv:2510.00244 [q-fin.GN]

(or arXiv:2510.00244v3 [q-fin.GN] for this version)

https://doi.org/10.48550/arXiv.2510.00244 Focus to learn more arXiv-issued DOI via DataCite

10.1016/j.jenvman.2026.128776

https://doi.org/10.1016/j.jenvman.2026.128776 Focus to learn more DOI(s) linking to related resources

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