The impact of corporate governance on sustainability performance
Bibliographic record
Abstract
We examine the relationship between corporate governance and sustainability, using the extensive Bloomberg Environmental, Social and Governance (ESG) data universe. Eccles, Ioannou, and Serafeim [2012. The Impact of a Corporate Culture of Sustainability on Corporate Behavior and Performance. National Bureau of Economic Research, Inc., NBER Working Papers: 17950] argued that a corporate culture of sustainability plays an important role in various facets of a firm's corporate behaviour and performance. We argue that quality corporate governance itself can engender high sustainability performance. We also build on the work of Aras and Crowther [2008. “Governance and Sustainability: An Investigation into the Relationship Between Corporate Governance and Corporate Sustainability.” Management Decision 46 (3): 433–448] by investigating the relationship between specific corporate governance and sustainability characteristics of S&P 100 companies in the USA. Our initial exploratory findings suggest that environmental disclosure scores and ESG disclosure scores are strongly influenced by governance disclosure scores. Board meeting attendance is an important predictor of both scores, suggesting that more disciplined boards result in better sustainability performance. Boards with a higher percentage of independent directors also have higher disclosure scores and are more likely to have climate change and an environmental supply chain management policy in place. They are also more likely to be Global Reporting Initiative compliant, to have a green building policy and social supply chain management. A disturbing pattern emerges, however, when assessing firms' follow-through on declared commitments. It turns out that few firms that purport to have climate change policies in place have actually discussed climate change risks or opportunities. We discuss some implications of these preliminary findings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".