Prevalence of Sustainability Metrics within Executive Compensation Schemes in Large US Companies
Bibliographic record
Abstract
This study is a descriptive qualitative assessment that examines the inclusion and extent of sustainability metrics into executive compensation packages among Dow Jones Industrial Average (DJIA) firms based on 2023 proxy statements. With growing stakeholder pressure, companies increasingly link executive pay to environmental, social, and governance (ESG) goals, which has been linked to improved corporate social responsibility, firm value, and lending accountability to strategic goals. Enhanced sustainability disclosures have been shown to be beneficial for most firms, yet challenges remain in balancing financial and ESG objectives. While the prevalence and form of sustainability metrics differ by industry, most DJIA firms include sustainability metrics in short-term incentive pay, even though sustainability practices are associated with achieving long-term success. Furthermore, recent political and corporate shifts have led some firms to scale back diversity, equity and inclusion (DEI) and sustainability efforts, raising concerns about the future of ESG-linked incentives. Given the rapid growth of ESG-related compensation and evolving regulations, this analysis provides timely insights into the role of executive incentives in driving corporate sustainability.
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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 | 0.017 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".