The Impact of Low-Carbon City (LCC) on Elderly People’s Health: Evidence from a Natural Experiment in China
Bibliographic record
Abstract
Rapid urbanization has increased haze pollution, affecting the health of elderly people. This study uses low-carbon city (LCC) data and examines the effects of LCCs on improving the health of elderly residents. Our main purpose is to explore the following question: Can the new urbanization model presented by the LCC alleviate haze pollution and enhance the health of middle-aged and elderly people? This study uses data from the China Health and Retirement Longitudinal Study (CHARLS) and the 2012 LCC pilot to explore whether the LCC can alleviate haze pollution and improve elderly people's health. The study found that the building of LCCs can reduce blood pressure, improve vital capacity, decrease obesity, and improve memory levels, including short-term and long-term memory. The building of LCCs also reduces the probability of being exposed to haze pollution by increasing the city's green total factor productivity and the use of green technologies. The study concludes that elderly people received health dividends as a result of the enhancement of living conditions, transportation, and medical support in the LCCs.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| 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".