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

Paper: arXiv 2510.00244

Authors: Mohammad Hassan Shakil, Arne Johan Pollestad, Khine Kyaw, Ziaul Haque Munim

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.

Complexity vs Empirical Score

• Math Complexity: 3.0/10
• Empirical Rigor: 7.5/10
• Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper relies on empirical corporate finance methods, panel regressions, and basic machine learning (Explainable AI/Partial Dependence plots) rather than complex mathematical derivations. It provides strong empirical rigor by identifying specific non-linear thresholds (22% and 35%) for board gender diversity and testing moderation/mediation effects, offering actionable insights for ESG governance.

Research Flowchart

  flowchart TD
    A[Research Goal: Impact of Board Gender Diversity on Emissions Performance] --> B[Dataset: European Firms 2016-2022]
    B --> C[Methodology: Panel Regressions, ML & Explainable AI]
    C --> D[Path Analysis & Moderation: ESG Controversies & Eco-Innovation]
    D --> E[Key Finding: Non-Linear Thresholds]
    D --> F[Key Finding: Robust Governance Driver]

    E --> E1[Min 22% BGD for impact<br>Optimal 35% BGD ceiling]
    F --> F1[No ESG controversy dilution<br>Not mediated by eco-innovation]