Board interlocks and greenhouse gas emissions
Bibliographic record
Abstract
Abstract Using resource dependence theory, we analyze board interlocks, their industry origin, and their relationship to firms' greenhouse gas (GHG) emissions. Interlocks create connections by having board members from one firm sit on other firms' boards, providing an avenue for sharing information and resources to aid in knowledge transfer and capability development. As firms face challenges for improved GHG emissions performance, they may look to their board members' connections to other firms to acquire needed resources. Using a sample of US Standard & Poor's (S&P) 1500 firms for years 2009 to 2018, we find that firms with a greater number of board interlocks achieve lower GHG emissions intensity. We also find that boards for the best performing companies have interlocks in the same industry, in other industries, and with firms leading in GHG emissions intensity, especially for firms in higher environmentally impacting industries, as they face greater emissions challenges.
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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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".