Beyond ESG Disclosure:Measuring Real-World Outcomes and Disclosure–Outcome Misalignment
Bibliographic record
Abstract
Environmental, social, and governance (ESG) reporting has expanded rapidly across global capital markets. Despite this growth, persistent concerns remain that disclosure quality and ESG scores are imperfect proxies for real-world environmental and social outcomes. Disclosure reflects what firms say, not necessarily what they change. This study develops a transparent and replicable measurement framework that shifts ESG assessment from “ESG-as-disclosure” to “ESG-as-impact” by explicitly linking firm-level ESG disclosures to externally verifiable outcome indicators. The framework distinguishes three analytically separate layers: (i) disclosure and managerial inputs, (ii) operational outputs, and (iii) real-world outcomes. Building on theories of decoupling and greenwashing, the paper operationalizes disclosure–outcome misalignment as a measurable indicator of greenwashing risk. Using established disclosure standards and publicly available outcome datasets, the study provides a structured data architecture, validation logic, and reporting templates that enhance comparability, materiality, and auditability. The contribution is both methodological and practical: a rigorous approach for evaluating ESG performance beyond narrative reporting, without reliance on proprietary ESG ratings.
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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 | 0.050 | 0.173 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".