Digital industry agglomeration and inclusive green growth: Synergies and path exploration
Bibliographic record
Abstract
One crucial tactical choice to accomplish high-quality economic development in China is to advocate for inclusive green growth, stimulate green and shared growth, and help achieve common prosperity for everyone. Based on the theory of endogenous economic growth and from the perspective of resource allocation and technological innovation, this paper uses China’s provincial panel data from 2012 to 2023 to systematically explore the impact mechanism and spatial heterogeneity characteristics of digital industry agglomeration on inclusive green growth through fixed effect model, mediating effect model, and threshold model. The analysis concludes that: (1) Digital industry agglomeration greatly facilitates inclusive green growth. For every additional unit of digital industry agglomeration, inclusive green growth will increase by 0.215 units. (2) Regression analysis based on regional heterogeneity, the effect was manifested as the trend of “Western region > Central region > Eastern region”. (3) The analysis of the transmission mechanism shows that resource allocation efficiency and technological innovation constitute the core intermediary path. Assuming that all other variables stay constant, each unit change in the resource allocation efficiency will significantly increase inclusive green growth by 0.055 units. For technological innovation, for every 1 unit change in digital industry agglomeration, it will indirectly promote an increase of 0.013 units in inclusive green growth. (4) Based on provincial heterogeneity, digital industry agglomeration has threshold effects on inclusive green growth. At the initial stage of digital industry agglomeration, it plays a substantial role in facilitating inclusive green growth. When the resource allocation efficiency does not reach the threshold of 0.8394, moderately improving the resource allocation efficiency can enhance the effect of regional digital industry agglomeration and significantly improve the benefits of regional inclusive green growth. In particular, every 1 unit change in digital industry agglomeration encourages inclusive green growth by 0.874 units. When the threshold variable is considered technological innovation, it has a key inflection point of 10.1339. After crossing the threshold, the regional development model changes from competitive “beggar neighbor” to cooperative “neighbor as a partner”. The research conclusion offers reference value for fostering a favorable atmosphere for digital sector expansion and inclusive, sustainable green growth.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".