Do women on corporate boards enhance biodiversity disclosure? Evidence from South Africa
Bibliographic record
Abstract
Purpose This study investigates whether board gender diversity enhances biodiversity disclosure among listed firms in South Africa. Drawing on legitimacy theory, resource-based view and critical mass theory, we examine the extent to which female board representation drives more comprehensive biodiversity reporting, particularly in environmentally sensitive sectors, and whether a threshold effect strengthens this relationship. Design/methodology/approach We use panel data comprising 1,016 firm-year observations from 254 Johannesburg Stock Exchange−listed firms between 2018 and 2021. The analysis relies on OLS regressions with industry and year-fixed effects, as well as firm-fixed effects models. We further test for the presence of a critical mass effect, investigate sectoral heterogeneity and perform robustness checks using alternative specifications and strategies to address endogeneity concerns. Findings The results provide strong and consistent evidence that board gender diversity is positively associated with biodiversity disclosure. This effect is stronger in firms with three or more female directors and in environmentally sensitive industries, although it is also present among non-financial firms more broadly. The positive association is concentrated in disclosure dimensions related to policies, targets, and governance, but is not evident in more technical areas such as biodiversity valuation, risk quantification, and impact measurement. This suggests that gender-diverse boards may drive strategic commitment but not necessarily technical implementation. Practical implications The findings highlight the importance of gender diversity as a governance lever for promoting biodiversity accountability, but also suggest it must be complemented by technical capacity, environmental expertise, and organizational systems. Policymakers, investors and sustainability advocates should view board gender diversity as an enabling factor that requires additional support to translate commitment into comprehensive reporting. Originality/value This study extends the literature by providing novel evidence on the governance determinants of biodiversity disclosure in an emerging market context. It unpacks the heterogeneous nature of biodiversity reporting and shows that board composition influences strategic disclosure elements more than technical ones. The findings have implications for corporate governance reform, disclosure policy and research on gender and sustainability.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".