The Impact of Corporate Characteristics on Climate Governance Disclosure
Bibliographic record
Abstract
This study examines the impact of corporate characteristics on climate change governance among 100 of the world’s largest companies, with 1400 observations in the fiscal year 2020. We consider variables such as company location, size, profitability, female board representation, years of reporting using Task Force on Climate-Related Financial Disclosures (TCFD) guidelines, the inclusion of UN Global Compact and Global Reporting Initiative (GRI) information, Dow Jones Sustainability Index (DJSI) membership, MSCI ESG ratings, and the presence of a climate transition plan, a sustainability executive, and a sustainability board committee. Applying a multi-theoretical framework, we employ correlation analysis and univariate and multiple linear regressions to assess the relationships. Our findings reveal positive correlations between climate governance and the presence of a climate transition plan, MSCI ratings, DJSI membership, and the existence of a sustainability executive. Additionally, companies located in developed countries exhibit significantly higher levels of climate change governance. These results hold across various scenarios, offering valuable insights for researchers, academics, business leaders, practitioners, and regulators. With the growing importance of climate change reporting, understanding the key contributing factors for effective climate governance is crucial for organizations seeking to address this critical issue.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".