The impact of China's low-carbon city pilot program on the health of children
Bibliographic record
Abstract
Global climate change poses a significant threat to public health in general and to the health of children, in particular. In response to this threat, many countries have implemented a series of policies to mitigate climate change, among which China's low-carbon city pilot program has attracted widespread attention. This study used longitudinal data drawn from the China Family Panel Studies between 2012 and 2018 to evaluate the impact of China's low-carbon city pilot program on the health of children. A difference-in-differences model was employed to investigate the effects of the policy, with further exploration of potential impact mechanisms. The results demonstrated that China's low-carbon city pilot program substantially improved the health of children by fostering environmental quality and promoting slow mobility (i.e., travel on foot or by bicycle). The study also showed that the impact of the pilot program on the health of children was proportional to their proximity to school. Our findings are significant not only for the expansion of China's pilot policy, but also generally for low- and middle-income countries in their efforts to combat air pollution and understand the scale of its impact on the health of children. • The pilot program improves the health of children in the pilot areas. • The pilot program improves the health of children by enhancing environmental quality. • The pilot program promotes slow mobility, thereby improving the health of children. • The health impact of pilot program is proportional to children's proximity to school.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".