Impact of National Innovative City Policy on Enterprise Green Technology Innovation—Mediation Role of Innovation Environment and R&D Investment
Bibliographic record
Abstract
This study investigates the impact of the national innovative city policy on enterprise green technology innovation amid China’s transformation from a resource-dependent to an innovation-driven economy. Working on city- and enterprise-level data from 2003 to 2018, this study employs the multi-period difference-in-differences (DID) model and the Sobel test to explore the impact of innovative city policies. The empirical results demonstrate that the innovative city policy has improved both the quantity and quality of enterprises’ green technology innovation output. This positive impact is accomplished via improving the urban innovation environment and stimulating enterprise research and development (R&D) investment. The promoting effect of the policy is stronger in attaining green utility patents by state-owned enterprises and green invention patents by non-state-owned enterprises. The positive policy impact is more pronounced for large enterprises. This study provides micro-level evidence regarding the policy’s impact on green innovation, and the results carry valuable policy implications.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 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".