Corporate environmental sustainability disclosures and environmental risk
Bibliographic record
Abstract
Purpose – This paper aims to propose and apply a novel risk-based approach to explore whether socio-political theories explain the level of corporate environmental disclosures given inconclusive evidence on the relation between environmental disclosure and environmental performance. Design/methodology/approach – Based on content analysis of corporate risk reporting, the paper develops measures of environmental risk to proxy for a firm’s exposure to public pressure in regard to environmental concerns that should be positively associated with the level of corporate environmental disclosures according to socio-political theories. Multiple regressions are used to test the predictions of socio-political theories for US Standards and Poor’s 500 constituents from polluting sectors. Findings – The level of environmental disclosures is found to be positively associated with a firm’s environmental risk while unrelated to its environmental performance. The findings suggest that firms tend to provide higher levels of environmental disclosures in response to greater exposure to public pressure as depicted by broad environmental indicators. The results are robust to alternative measures of environmental disclosures, environmental risk and environmental performance, alternative specifications of the economic model and additional sensitivity checks. Research limitations/implications – This study is limited to US firms in polluting sectors. The risk-based approach proposed may not be appropriate to cover sectors where corporate risk reporting is less likely to address environmental risk, but it could potentially be adopted in other countries with advanced risk reporting regulation or practice. Practical implications – Findings are important to understand a firm’s incentives to disclose environmental information. Cross-sectional differences found in environmental disclosures, risk and performance, highlight the importance of considering industry affiliation when analyzing environmental data. Originality/value – This paper is the first to use firm-level environmental risk variables to explain the level of corporate environmental disclosures. The risk-based approach taken suggests opportunities for research at the multi-country level and in countries where corporate environmental performance data are not publicly available.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".