ESG Policy–Practice Decoupling: A Measurement Framework and Empirical Validation
Bibliographic record
Abstract
As sustainability becomes more critical to corporate strategy and performance, firms, investors, and researchers must continue to refine methods for measuring and addressing the gap between rhetoric and reality. Closing this gap is crucial to ensuring that externally oriented ESG claims are supported by genuine internal actions that benefit both the firm and society at large. To address this issue, this study introduces a theoretically driven framework to assess the alignment (or lack thereof) between firms’ ESG policies and their actual implementation. By proposing a more granular and objective measure, we address a gap in the existing literature. Additionally, we empirically validate this framework using data from ASSET4, providing insights into the extent and persistence of this phenomenon using a sample of S&P 1500 firms from 2016 to 2022. Our results reveal that misalignment between internal actions and external endorsements in managing environmental and social issues is both significant and persistent across the years analyzed. Over 80% of the sample firms exhibit this misalignment, underscoring its prevalence within the sample. In more recent years, however, firms have shown a clear tendency to prioritize internal actions over initiatives aimed at externally endorsing their efforts. Building on the framework we propose to measure ESG policy–practice decoupling, along with the empirical analysis we conducted, we discuss its broader implications and outline several opportunities for future research.
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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 | 0.118 | 0.178 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".