How does environmental performance map into environmental disclosure?
Bibliographic record
Abstract
Purpose Focusing on a sample of firms from environmentally sensitive industries over several years, this study aims to reexamine the association between environmental disclosure and environmental performance. Design/methodology/approach The authors use a panel data analysis to examine how the interaction between environmental performance and economic and legitimacy factors influence firms’ environmental disclosures. Findings Results suggest that environmental performance moderates the effect of economic and legitimacy incentives on firms’ propensity to provide proprietary environmental disclosure, with both sets of incentives being influential. More specifically, there appears to be a reporting bias based on the firm’s environmental performance whereas the high-performers disclose more environmental information in the three following vehicles: annual report, 10-K and sustainability reports combined. Results also show that economic and legitimacy factors influence the disclosure decisions of the low and high environmental performers differently. Practical implications Understanding the determinants of environmental disclosure for high and low environmental performers helps regulators to close the reporting gap between these firms. Social implications There is little evidence to suggest that firms with low-environmental performance attempt to use their disclosures to legitimize their environmental operations. Originality/value The study examines environmental disclosures of 78 firms over a period of 14 years in annual, 10-K and sustainability reports. The panel data analysis controls for significant cross-sectional and period effects.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".