An Empirical Study on Impacts of Environmental Regulation on Environmental Information Disclosure of Listed Companies of China: Based on Researches on Listed Companies in Nonferrous Metal Industry
Bibliographic record
Abstract
With rapid development of economy, environmental pollution becomes increasingly serious, which not only affects quality of our life but also threatens our living conditions. Since the 1990s, the percentage that Chinese listed companies’ voluntary environmental information disclosure has increased to a larger extent and the content disclosed by many enterprises has gone beyond scope required by laws. Under this background, this paper combines with current status of environment information disclosure of Chinese listed companies, takes listed companies of non-ferrous metal industry in Shanghai and Shenzhen from 2006 to 2011 as samples, and studies impacts of environmental information disclosure system and environmental regulation intensity on environmental information disclosure. In addition, it carries out empirical tests on sample data of companies and implements robustness tests on regression results to exclude influence of differences between new and old criterion on comparability of accounting data. By virtue of positive analysis, it is found that corporate features like scale of company, financial risk, growth ability and profitability have great influence on environment information disclosure, and implementation of environment information disclosure and environmental regulation intensity have significant impacts on environment information disclosure.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| 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".