Multi-year Evaluation of a Clean Heating Policy on Residents’ Air Pollution Exposures in Beijing, China
Bibliographic record
Abstract
China’s Clean Heating Policy (CHP), aimed at shifting households from coal to electricity for space heating, represents a major residential energy transition initiative. We evaluated its multi-year impacts on outdoor, indoor, and personal exposures to PM 2.5 and black carbon (BC) across 50 villages and 1,236 households in rural Beijing. Using a difference-in-differences (DiD) design, we observed a substantial (31 μg/m 3 ) reduction in winter (3 month) indoor PM 2.5 (95% CI: −53, −9), but an increase in 24-h indoor BC by 2.6 (0.4, 4.7) μg/m 3 . CHP-driven reductions in personal exposures were limited, emphasizing the limitations of using single 24-h measurements to estimate “usual” exposure. Outdoor air quality improved in all villages, with no difference between treated versus untreated villages. Exposure-energy trade-off analysis showed that untreated households achieved similar personal PM 2.5 reductions at lower cost, with smaller coal use reductions and less electricity expenditures. The CHP significantly reduced seasonal indoor PM 2.5, but continued burning of biomass, which was accessible at no cost, limited air quality improvements and may have contributed to the observed increase in 24-h indoor BC. This illustrates how behavioral choices, economic feasibility, and selection of exposure metrics influence the measured impact of household energy transitions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".