The Spatiotemporal Evolution Characteristics and Improvement Paths of China’s Green Finance Level——Empirical Study on Panel Data Based on Dynamic QCA and NCA Methods
Bibliographic record
Abstract
Green finance (GF) is the core driving force in solving environmental problems. Under the digital background, how to improve the GF with the help of digital technology is a topic worthy of further study. Based on data from 29 provinces from 2014 to 2021, this study uses the entropy weight method to estimate the level of GF in China,and uses Dagum Gini coefficient and Kernel density estimation method to explore the spatio-temporal evolution characteristics of green finance level. Finally, based on the theory of the digital innovation ecosystem (DIE), we use NCA and dynamic QCA methods to explore the configuration effects of various elements within the DIE over time. The results show that the overall level of GF in China has a steady upward trend, achieving nearly double growth, yet there are significant regional differences, and the level of GF in Northeast China fluctuates unsteadily; From the perspective of regional differences, the level of GF in different regions of China is quite different, among which the western region has the largest regional difference, and super-variable density is the main source of regional differences. From the perspective of dynamic evolution, the overall level of GF in China is on the rise, among which, there is a “catch-up effect” among provinces. A single factor does not constitute the necessary conditions to improve the level of GF, and then through linkage matching, three promotion paths are obtained, and further divided into two models: environmental support model and multi-agent comprehensive development model. Deepening the rational understanding of the complex interaction of multiple factors behind the improvement of the level of GF has important implications for sustainable economic development (SED).
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".