Exploring ESG Controversies: The Role of Corporate Governance mechanism, Economic Performance, and Legal Systems.
Bibliographic record
Abstract
This study examines the association between corporate governance mechanisms (with particular reference to the structure of the BoD) and ESG controversies in publicly traded companies from the United States, Italy, Spain, the United Kingdom, France, and Canada. Specifically, the mediating role of economic performance and the moderating effect of the legal context are analyzed to understand their influence on these controversies. This topic is particularly relevant for investors and policymakers given the increasing importance of ESG issues in decision-making processes. The dataset includes 300 companies, with 50 firms from each country, representing both Common Law and Civil Law systems. The analysis covers the period from 2021 to 2023, using panel data and fixed effects models to minimize bias. The results demonstrate that the presence of ESG committees on boards is strongly associated with a reduction in ESG controversies. However, other board-level factors, including board gender diversity, CEO duality, and independent directors, were not found to have statistically significant effects. These findings provide valuable insights into how corporate governance structures can mitigate ESG controversies and open up avenues for future research, as well as offering practical recommendations for enhancing sustainable and responsible management practices.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".