How Do Household Energy Transitions Work?
Bibliographic record
Abstract
INTRODUCTION: Since 2015, thousands of rural and peri-urban villages across Beijing and northern China have been treated by a household Clean Heating Policy (CHP) that banned household coal burning and subsidized the costs of electric heaters and electricity. Whether this large-scale policy was successful in improving air quality and health remains an important and unresolved question. We estimated the effects of the CHP policy on air quality and cardiopulmonary health in Beijing villages and quantified how much of the policy's effects on health were mediated by changes in air pollution and indoor temperature. METHODS: filters were analyzed for their mass, black carbon (BC), and chemical composition, which were used for source apportionment. To estimate the impacts of the policy, we used a difference-in-differences design that accommodated the staggered rollout of the CHP. We used "extended" two-way fixed effects models and marginal effects to quantify the effect of the policy on air pollution and health outcomes. We further evaluated whether villages treated by the policy in different years responded differently to the policy and whether the observed health impacts of the policy were mediated through changes in air pollution or home (indoor) temperature. RESULTS: or on biomarkers of inflammation and oxidative stress. CONCLUSIONS: and improvements in home temperature, providing empirical evidence that clean household energy policies can provide population health benefits.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.001 |
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".