董事会性别多样性与排放绩效：面板回归、机器学习与可解释 AI 的洞察
Board gender diversity and emissions performance: Insights from panel regressions, machine learning, and explainable AI
一项针对 2016 至 2022 年欧洲企业的研究发现，董事会性别多样性（BGD）与排放绩效（EP）呈非线性关系，BGD 约 35% 时 EP 达到最优，超过该水平不再改善，而实现有意义改善的最低 BGD 门槛为 22%。研究结合面板回归、机器学习与可解释 AI，并发现 ESG 争议对这一关系无显著削弱作用。该论文已因版权问题被作者撤回。
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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.
| Comments: | Copyright issue |
| Subjects: | General Finance (q-fin.GN); Computers and Society (cs.CY); Machine Learning (cs.LG) |
| MSC classes: | 62P20 |
| Cite as: | arXiv:2510.00244 [q-fin.GN] |
| (or arXiv:2510.00244v3 [q-fin.GN] for this version) | |
| https://doi.org/10.48550/arXiv.2510.00244 arXiv-issued DOI via DataCite |
|
| Journal reference: | 10.1016/j.jenvman.2026.128776 |
| Related DOI: | https://doi.org/10.1016/j.jenvman.2026.128776
DOI(s) linking to related resources |
Submission history
From: Mohammad Hassan Shakil [view email]
[v1]
Tue, 30 Sep 2025 20:16:56 UTC (1,011 KB)
[v2]
Fri, 6 Feb 2026 09:20:47 UTC (3,868 KB)
[v3]
Fri, 9 Oct 2026 16:48:20 UTC (1 KB) (withdrawn)
来源：arXiv cs.CY · arxiv.org