Board Gender Diversity and Carbon Emissions Performance: Insights from Panel Regressions, Machine Learning and Explainable AI
This research addresses the problem of understanding how board gender diversity influences corporate environmental performance for academics, businesses, and regulators, with incremental insights using existing methods on new data.
This study investigated the impact of board gender diversity on firms' carbon emission performance in European firms, finding a non-linear relationship with an optimal level of approximately 35% diversity and a minimum threshold of 22% for meaningful improvements, while showing that ESG controversies do not affect this relationship.
With the European Union introducing gender quotas on corporate boards, this study investigates the impact of board gender diversity (BGD) on firms' carbon emission performance (CEP). Using panel regressions and advanced machine learning algorithms on data from European firms between 2016 and 2022, the analyses reveal a significant non-linear relationship. Specifically, CEP improves with BGD up to an optimal level of approximately 35 percent, beyond which further increases in BGD yield no additional improvement in CEP. A minimum threshold of 22 percent BGD is necessary for meaningful improvements in CEP. To assess the legitimacy of CEP outcomes, this study examines whether ESG controversies affect the relationship between BGD and CEP. The results show no significant effect, suggesting that the effect of BGD is driven by governance mechanisms rather than symbolic actions. Additionally, structural equation modelling (SEM) indicates that while environmental innovation contributes to CEP, it is not the mediating channel through which BGD promotes CEP. The results have implications for academics, businesses, and regulators.