The Impact of Urban Regeneration, Air Pollution, Green Space, and Paved Roads on Problematic Alcohol Use: A Population-Based Study Across 43 Cities in China
Bibliographic record
Abstract
Background Evidence on the association between urban regeneration and alcohol use remains limited. This study examines the impact of urban environmental factors, specifically air pollution (measured by PM2.5, a common indicator of fine particulate matter), traffic congestion, and limited green space, on problematic alcohol use, and explores potential social and behavioral mechanisms underlying these relationships.Methods A cross-sectional survey was conducted among 11,954 students from 50 universities across 43 Chinese cities. Individual-level data were collected via self-report questionnaires, while regional environmental data were obtained from the National Bureau of Statistics. Structural equation modeling (SEM) was applied to analyze the mediating pathways.Results The prevalence of problematic alcohol use was 7.3%. Multilevel logistic regression showed that higher PM2.5 levels were positively associated with alcohol use (ORs = 2.98, 3.48), while more green space (ORs = 0.55, 0.23) and a higher proportion of paved roads (OR = 0.37) were protective factors. SEM results indicated that PM2.5 exerted both a direct effect on alcohol use (β = 0.358, p < 0.01) and an indirect effect mediated by uncertainty stress (indirect β = 0.011). Paved road area had a direct effect (β = −0.009, p < 0.01) and indirect effects through uncertainty stress (indirect β = −0.007) and life stress (indirect β = −0.001). Green space directly reduced alcohol use (β = −0.188, p < 0.01) and also indirectly via lower uncertainty stress (indirect β = −0.011).Conclusion Improving urban environmental quality, especially reducing air pollution and expanding green infrastructure, may help mitigate problematic alcohol use and promote mental health.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".