How Can Government Regulation Reinforce the Low-Carbon Effects of Green Finance in China? Heterogeneity of Resource-Based Cities and High-Energy-Consuming Cities
Bibliographic record
Abstract
Based on panel data of 273 prefecture-level cities in China from 2006 to 2022, this study empirically examines the impact and mechanism of green finance on urban low-carbon development from the perspective of government regulation. The study yields the following key results: First, green finance significantly promotes urban low-carbon development, and this finding remains valid after addressing endogeneity issues and conducting a series of robustness tests. Second, government regulation strengthens the low-carbon effect of green finance through two pathways: market-oriented reform and urban land planning. Third, the low-carbon effect of green finance is more pronounced in resource-based cities and low-energy-consuming cities, which corresponds to urban disparities in development stages and resource constraints. Given these results, this study proposes two targeted recommendations: institutionalizing the coordination mechanism between land use and carbon markets and implementing context-specific green finance strategies via urban differentiation approaches.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".