The effect of environment, society, and governance (ESG) information disclosure on high-quality development of Chinese companies: Investigating the mediating role of green technology innovation
Bibliographic record
Abstract
China listed companies play a significant role in fostering the economy's quality development and are leaders in the application of ESG principles by businesses. The application of ESG principles by listed companies is crucial to achieving a ‘win-win’ situation of social benefits and corporate economic benefits and raising the bar for high-quality development. Nevertheless, studies on the connection between corporate high-quality development and ESG information disclosure by China listed companies are few and contentious. This paper examines the role that ESG information disclosure plays in the development of corporate high-quality and the intermediary mechanism of green technology innovation in enterprises using panel data of China A-share non-financial listed companies from 2013 to 2022. The empirical results show that ESG disclosure and its three dimensions can significantly promote high-quality enterprise development. The study also discovers that high-quality enterprise development and ESG information disclosure are partially mediated by green technology innovation. The article’s findings serve as a guide for businesses, investors, and governments looking to adopt ESG practices.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".