Board Directorships and Carbon Emissions: Curvilinear Relationships and Moderating Roles of Other Board Characteristics
Bibliographic record
Abstract
Our research investigates the moderating roles of various board characteristics (independence, gender diversity, tenure, duality, and size) on the curvilinear relationship between board directorships and carbon emissions using a two-step generalized method of moments (GMM) system approach. We use a total of 1582 observations from 391 firms listed in the US Standard and Poor 500 (S&P 500) index collected from 2015 to 2021. Our findings provide empirical evidence in four aspects: (1) there is a U-shaped curvilinear relationship between board directorships and carbon emissions; (2) board directors should not go over two directorships because carbon emissions are likely to increase; (3) board independence, duality, and size positively moderate curvilinear relationships between board directorships and carbon emissions; and (4) board tenure and gender diversity negatively moderate curvilinear relationships. Our study contributes to expanding the existing literature related to sustainable corporate governance in the US market, and also has implications for regulatory issues, business practice, and further research.
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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 | 0.002 | 0.001 |
| 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 | 0.000 |
| 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".