What you see is what you get? Building confidence in <scp>ESG</scp> disclosures for sustainable finance through external assurance
Bibliographic record
Abstract
Abstract The main objective of this study is to understand the value of environmental, social, and governance (ESG) disclosure assurance in the context of the development of sustainable finance standards and laws. This study is based on an analysis of 188 comment letters submitted by such actors in the context of public consultations on the development of three new sustainable finance initiatives (the CFA Institute, the Financial Conduct Authority in the UK, and the New Zealand parliament). The study shows these actors' nuanced and often quite critical perceptions of the effectiveness of external assurance in preventing greenwashing and their reservations about its mandatory nature. These actors have raised various criticisms, including concerns about the vagueness surrounding verification practices; the lack of expertise available to conduct assurance in a new, specialized, and complex field; the costs of the assurance process, particularly for small players; and the lack of control over the reliability of the ESG data used. This article contributes to several emerging trends in the literature—in particular, research on governance practices to prevent greenwashing, on the institutionalization of sustainable finance standards and laws, and on the role of rational myths in the assurance process for ESG disclosures.
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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 | 0.042 | 0.144 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".