The spatial spillover effect of financial growth on high-quality development: Evidence from Yellow River Basin in China
Bibliographic record
Abstract
Abstract River basin cities are areas with remarkable conflicts between the human activity and the ecological environment. They are also important targets for policy implementation of sustainable and high-quality development (HD) in various countries around the world. This article exploits the panel data of 99 cities located in the Yellow River Basin (YRB) from 2006 to 2019 to empirically analyze the spatial effect of financial growth on HD. Spatial weights participated econometric models are utilized to analyze this spatial effect. Empirical results reveal that: (1) the HD in the YRB shows a strong positive spatial autocorrelation. (2) Financial growth exerts an N-shaped curve effect on the HD from a long-term perspective. When this influence spills out to the surroundings, it exhibits an inverted U-shaped characteristic. (3) Green innovation can be an important intermediary factor in the influence of financial growth on HD. (4) The influence of financial growth on HD appears stronger in regions with higher economic levels, where N-shaped effects can be transmitted to the surrounding regions. However, the backward economic development in low-economy regions prevents the spatial spillover of N-shaped effects. This study can be instrumental for countries to formulate financial policies that aim to promote HD in river basin cities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".