Knowledge Ecology and Policy Governance of Green Finance in China—Evidence from 2469 Studies
Bibliographic record
Abstract
CiteSpace was used to visualize the knowledge ecology of the green finance research literature in CNKI and WOS, and NVivo software was used to root the code analysis of the current green finance policies in China. From the analysis of the research hotspots, both in China and internationally, great importance is attached to the research on green finance, and the research on green financialization has broad prospects. The core group of authors on green finance research in China has taken shape, whereas the core group of authors of green finance research in the rest of the world has not yet taken shape. There is a lack of close cooperation and a relatively low level of communication among important domestic green finance research institutions, and a certain scale of cooperation network has been formed among influential international institutions. The major countries for influential international green finance research are Singapore, France, Switzerland, Canada and Saudi Arabia, and the international influence of China's green finance needs to be improved. Both domestic and foreign countries attach great importance to the balance between economic growth and the low-carbon green transition. China attaches more importance to macroeconomic development and strategic transition, but internationally, the trend is toward microcorporate green performance, policy optimization and market innovation. The research focus of green finance has achieved in three stages of evolution, namely, green industry in the early stage, green services in the middle stage and green strategy in the near future. International green finance research focuses on climate change, market players, government performance, social responsibility sharing, etc. In particular, reducing the cost of green development is the focus of international green finance. The domestic focus is on climate risk, carbon neutrality, carbon peak, low-carbon transition, carbon reduction, and green transition themes. Internationally, the focus is on financial performance, decisions, green finance, credit, drivers, quality, socially responsible investment and other topics. Considering the practical implementation of green finance in China, the governance logic of China's green finance policy consists of five main categories: policy belief, policy objective, policy tool, policy feedback and policy cycle. In the future, the development and improvement of China's green finance policy should achieve breakthroughs in the following aspects: first, guiding the main body of green finance policy to firmly establish policy beliefs; second, improving the clarity of green finance policy objectives; third, enhancing the overall effectiveness of the governance of green financial policy instruments; fourth, strengthening the green finance incentive policy feedback system construction; and fifth, improving the quality of green finance policy cycles.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".