How does green credit policy improve corporate social responsibility in China? An analysis based on carbon‐intensive listed firms
Bibliographic record
Abstract
Abstract For China to become carbon neutral, green financing is seen as a crucial avenue, wherein the green credit policy, which was introduced in 2012, is a crucial tool. However, whether the policy would work and how to improve its effectiveness remain unknown. This study attempts to analyze the policy's effects on carbon performance, a crucial measure of Corporate Social Responsibilities (CSR), particularly in carbon‐intensive industries, using a sample of Chinese listed companies from 2009 to 2018. As a result, first, it's confirmed that the policy can boost carbon performance of carbon‐intensive firms. Additionally, this study has verified that firm's R&D investment intensity has no mediating role in the relationship between the policy and carbon performance, while debt financing cost partly mediating the relationship, implying the policy fails to stimulate technological innovation. Moreover, firms with stronger environmental regulation intensity, weaker financing constraints, poorer corporate governance and more analyst following have greater promotion in carbon performance after the policy execution. Finally, the policy significantly improves the quality of corporate environment information disclosure. Briefly, this study enriches theoretical grounding regarding strategies of green reform in carbon‐intensive industries and provides implications for emerging economies to improve green finance via enhancing CSR.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".