From central directives to local actions: The effect of China's climate policy on urban diversification of green technologies
Bibliographic record
Abstract
The low-carbon city pilot (LCCP) policy in China, combining central government design with local government action, aims to foster green innovation and improve climate outcomes. This study evaluates the policy's effect on green technological diversification in pilot cities, with attention to characteristics of local technological portfolios and selection modes of pilot cities. Our findings reveal that the LCCP policy, rather than technological relatedness, plays the dominant role in driving the entry of low-quality green technologies. Specifically, this policy enhances the related diversification of low-quality green technologies and, more importantly, encourages their unrelated diversification, the latter of which drives path-breaking development in pilot cities. Interaction analysis shows that the policy's effect on unrelated diversification is amplified in cities with higher economic levels, whereas city size has no influence. When considering differences in pilot selection modes, the policy's effect on unrelated diversification is valid only in cities selected through local government self-declaration. Conversely, in cities directly designated by the central government, the policy fails to encourage unrelated diversification and even hinders the related diversification of high-quality green technologies. • We study the effect of China's climate policy on urban diversification of green technologies • The policy's effect on unrelated diversification is amplified in cities with higher economic levels, whereas city size has no influence • The policy's effect on unrelated diversification is valid only in cities selected through local government self-declaration
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".