ESG and corporate performance: The moderating role of government subsidies and mediating effect of analyst coverage in Chinese A-share listed companies
Bibliographic record
Abstract
In recent years, the heightened emphasis on sustainable and high-quality economic development has garnered substantial investor interest in corporate ESG performance, significantly influencing the long-term operational stability of firms. This study, based on data from A-share listed companies from 2015 to 2022, explores the relationship between corporate ESG performance and corporate financial performance. This research employed regression analysis to examine this relationship and found that improved ESG performance significantly enhances financial performance, especially in state-owned enterprises and the manufacturing sector. Additional analysis shows that government subsidies positively influence the relationship between ESG performance and corporate financial performance, suggesting that subsidies amplify the positive effects of ESG initiatives on performance. Mechanism tests suggest that increased analyst coverage is a key pathway through which ESG performance boosts corporate financial performance. These findings underscore the importance of ESG initiatives for companies and provide empirical evidence supporting the role of government subsidies and analyst coverage in amplifying the positive impact of ESG performance on financial outcomes.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".