Is corporate disclosure of environmental performance indicators reliable or biased information? A look at the underlying drivers
Bibliographic record
Abstract
Purpose This study aims to examine the disclosure determinants of environmental performance indicators (EPIs) for a sample of US firms to understand if these disclosures are reliable or whether they are biased towards the reporting of positive information. Design/methodology/approach The study uses a panel data analysis to examine the association between firms’ EPIs disclosures and their environmental performances, and other economic and legitimacy factors. Findings The results show that firms’ disclosures are not associated with the level of environmental performance and that firms continue to provide EPI information even if they witness a decline in their environmental performance. The evidence suggests that firms’ environmental disclosures are reliable and indicative of their environmental performance. Practical implications The findings suggest that mandating EPI disclosures may increase the level of the information reported and reduce firms’ discretion over the disclosure of such information. Originality/value Reporting of EPIs is directly linked to firms’ environmental performances. By examining the association between EPI disclosures and environmental performance, the study contributes to the ongoing debate about firms’ reporting and whether it is informative to its stakeholders or whether firms use this type of information to legitimize their operations and portray it in a positive light.
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 | 0.018 | 0.145 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".