Environmental Protection Tax, Green Management Innovation and Corporate <scp>ESG</scp> Performance: The Moderating Effect of Media Attention and Top Management Team's Environmental Attention
Bibliographic record
Abstract
ABSTRACT Based on the organizational legitimacy theory, we examine the Environmental Protection Tax (EPT) Law in China as an exogenous shock to investigate its effects on companies' environmental, social, and governance (ESG) outcomes. Taking China's A‐share listed firms as the sample, a difference‐in‐difference (DID) with multiple time periods model is used for empirical testing. The results indicate that the introduction of EPT can promote the improvement of ESG performance, and this positive effect can be achieved by encouraging companies to implement initiatives for innovation in green management. The research finding shows that the effect of EPT on firms' ESG achievements is more prominent in the context of greater media attention and environmental attention from the senior management team. Moreover, the research conclusions have passed a series of robustness tests. This study not only expands the application of organizational legitimacy theory in corporate sustainable development but also provides a useful reference for improving the environmental regulatory system.
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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 | 0.006 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".