Corporate Governance and Environmental Performance: Industry and Country Effects
Bibliographic record
Abstract
Portfolio investment managers and institutional investor clients are becoming more sensitive to the investment risks and opportunities that corporate governance and corporate environmental performance pose because of the growth in understanding of their potential financial repercussions. However, while both corporate governance and corporate environmental performance are increasingly examined within the financial marketplace, there is very limited empirical research that examines them together. In this paper, an empirical analysis utilizing proprietary quantitative data from two non-financial rating agencies is conducted in order to develop an understanding of the relationship between these two types of corporate performance, their causes, and their consequences. The findings of this paper do not suggest that there is a direct correlation between corporate governance and environmental performance. However, it is established that each has been improving over time, and that a convergence in standards is occurring between poor and strong performing firms. Perhaps the most salient finding of this research is that both the corporate governance and corporate environmental performance share a common predictor - disclosure. The discovery of a significant relationship between disclosure and performance is very important as it suggests that when a firm discloses non-financial performance information, actors within the firm become increasingly concerned with managing those revealed areas. Therefore, global standards of corporate governance and environmental performance are likely to be improved by of the recent explosion in demand for disclosure by institutional investors in these areas.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".