Board Characteristics and Climate Commitment: A Comprehensive Analysis Unraveling Linear and Nonlinear Relationships
Bibliographic record
Abstract
ABSTRACT In an era of increasing environmental awareness, this study analyzes the relationship between board characteristics and corporate climate commitment across various industries. Our empirical analysis examines a comprehensive dataset that includes 4027 firms from the United States and Canada over the period from 2010 to 2022 and employs panel data regression models and panel quantile regressions. Our findings indicate that board meeting frequency (BMTG) exhibits a curvilinear relationship with climate commitment, with positive effects peaking at mid‐quantiles and diminishing at higher levels. Board gender diversity (BGEN), size (BSIZE), and independence (BIND) show increasing positive impacts at higher quantiles. Conversely, board experience (BEXP) shows a negative effect, particularly at middle quantiles. These results highlight the importance of tailoring board composition to optimize environmental outcomes. This study also uniquely resolves conflicting evidence from prior research regarding the impact of corporate board characteristics on carbon emissions performance. It reveals that the effect of board characteristics on emissions varies depending on the industry's carbon emission levels and its environmental consciousness. This study enhances our understanding of how corporate governance shapes environmental stewardship and provides valuable insights for policymakers, practitioners, and scholars.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".