Impact of regulatory policies on green bond issuances in China: policy lessons from a top-down approach
Bibliographic record
Abstract
This study examines whether the green bond policies of major Chinese financial regulators’ have a direct and positive impact on the green bond market. Using Chinese green bond issuances from 2012 to 2019, we analyze green bond issuer response to top-down regulatory policies post 2014. Using a difference-in-difference model, we find a direct positive influence of green bond regulatory policies on issuance amounts. Additional analysis shows that specific issuer characteristics like ownership type (government-owned), industry type (green industry), and sector type (financial issuer) have a stronger positive reaction to policy announcements and led to the issuance of more green bonds. Our results highlight the supporting role of financial regulators in advancing the green finance agenda in China.Key policy insights Green bond policies implemented by Chinese financial market regulators have been an effective means to increase overall green bond issuancesCertain issuer types react more positively by increasing their green bond issuances following the announcement of green bond policiesPro-active participation by key financial regulators in the form of harmonized definitions, consistent engagement, and alignment with international best practices can be beneficial for stimulating green finance growth
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.003 | 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".