The Economic Relevance of Environmental Disclosure and its Impact on Corporate Legitimacy: An Empirical Investigation
Bibliographic record
Abstract
Abstract In determining its environmental disclosure strategy, a firm's management faces a tension between responding to the information needs of financial markets and maintaining its legitimacy within the community. In this paper, relying on information economics and legitimacy theory, we explore how firms resolve this tension. Results show that a firm's environmental disclosure enhances the quality of analysts' information context, which ultimately allows them to make better forecasts. Moreover, financial analysts seem to be able to decipher environmental information, discounting discourses that are inconsistent with a firm's underlying environmental performance. We find also that a firm's environmental disclosure serves another purpose, as it influences how its other stakeholders (beyond financial ones) perceive its legitimacy. Such enhanced legitimacy reduces the information uncertainty faced by financial analysts. Our results suggest also that both economic‐based environmental disclosure and sustainable development and environmental disclosure are useful to analysts in making their forecasts and enhance a firm's legitimacy. Copyright © 2013 John Wiley & Sons, Ltd and ERP Environment.
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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 | 0.013 | 0.137 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".