The Influence of Carbon Emission Disclosure On Enterprise Value Under Ownership Heterogeneity: Evidence From The Heavy Polluting Corporations
Bibliographic record
Abstract
Abstract China has put forward the goals of “Emission Peak” and “Carbon Neutrality”. Therefore, whether the restriction of carbon information disclosure could promote the transformation and upgrading of heavily polluting corporations to raise their value and realize the “double carbon” goal is a problem worthy of in-depth study. This article analyzed the impact of carbon information disclosure on the enterprise value of heavily polluting corporations based on the perspective of ownership structure heterogeneity. The paper discussed the moderating effect of carbon information disclosure on enterprise value taking into account government environmental regulations, media evaluation, and corporate image management based on the Gatekeeper Theory. This article found that carbon information disclosure and enterprise value have a “U-shaped” relationship in the short term and a positive correlation in the long term. The government environmental regulation, media evaluation, and corporate image management produced different moderating effects under the difference in ownership structure. Government environmental regulations, media evaluation, and corporate image management made significant moderating effects on state-owned corporations in the short term, while they have no significant moderating effect in the long term. The results also showed that there were no significant effects on private corporations. In order to push heavily polluting corporations to implement environmental protection measures, government and corporations are necessary to promote the long-term mechanism of sustainable cleaner production.
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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 | 0.003 | 0.016 |
| 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) | 0.002 | 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".