The relationship between financial attributes, environmental performance and environmental disclosure
Bibliographic record
Abstract
Purpose An increasing number of business organizations around the world are engaged in the accounting reporting on non-financial performance aspects, mainly within the field of environmental responsibility. The purpose of this paper is to assess the association between environmental disclosure and environmental performance and examine the financial attributes of companies using a composite disclosure index to investigate the status of the environmental disclosure practices of the top 40 companies operating in France. Design/methodology/approach The sample used in this study consists of the 40 largest companies operating in France (index CAC 40). Findings The findings of the study show that environmental disclosure is positively associated to environmental performance. Financial attributes, such as firm size, the need for capital, profitability and capital spending, are positively associated with environmental disclosure quality. Equally, a high quality of environmental disclosure will reflect the effectiveness of corporate governance and would tend to face fewer difficulties in accessing capital markets. The authors found that firms revealed on healthcare and gas oil business sector disclose more environmental information than other industries. Originality/value A web-based search was performed during the fourth quarter of 2014, locating the corporate websites of the sample firms. The sample period is 2011-2013 (108 firm-year observations).
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 | 0.003 | 0.018 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".