Board Gender Diversity and Carbon Proactivity: The Influence of Cultural Factors
Bibliographic record
Abstract
Due to inadequate studies, our knowledge of the effect of female directors and national culture on the corporate response to climate change is still limited. To address this gap, the purpose of this paper is to investigate the dynamic relationship between gender diversity on the board of directors and corporate carbon proactivity and how two dimensions of national culture (individualism and indulgence) moderate this relationship. This study focuses on large companies that disclosed carbon-related information via the CDP survey in 2011–2017. Our findings show that gender diversity promotes corporate carbon proactivity. Furthermore, the positive effect of gender diversity on carbon proactivity is weaker when firms are in countries marked by a higher level of individualism and indulgence. As far as we know, this study is the first to explore and document the empirical evidence on the dynamic impact of gender diversity in the corporate governance body and national culture on managers’ climate change behaviors in terms of green proactivity.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".