Do capital goods imports improve the quality of regional development? Evidence from Chinese cities
Bibliographic record
Abstract
For developing countries with advanced societies and growing economies, it is essential to accurately assess the technological innovation effect of capital goods imports on regional development quality. This study explores the path of high-quality urban development from the perspective of international trade. Examining city-level panel data on China from 2003 to 2013, the study applies various econometric analysis methods, including fixed effects, quantile regression, two-stage least squares regression and mediating and moderating effects models, to investigate the impact of capital goods imports on regional development quality and the mechanism of action. The findings demonstrate that capital goods imports have an inverted U-shaped, non-linear effect on high-quality urban development, whereas the effect on regional development is characterised by urban heterogeneity. Regarding technological innovation, the primary reason for the inverted U-shaped relationship is the combined effects of technology dependence and technology upgrading. In terms of institutional economics, policymakers can transform the pressure of economic growth into a driving force through initiatives to enhance the economic development effect of capital goods imports. Transitioning this pressure can mitigate the hindering effect of excessive capital goods imports on improving the quality of regional development.
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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 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".