Satellite-derived local air pollution impacts of the household ‘coal-to-clean energy’ program in Beijing
Bibliographic record
Abstract
Background: In 2015 Beijing started a coal-to-clean energy program that banned coal stoves and subsidized electric- or gas-powered heaters and electricity costs in thousands of peri-urban villages. Our objective was to estimate the effect of this program on satellite-derived PM₂.₅. Method: We geolocated villages in Beijing and assigned participation status in the program between 2015 and 2018. Average monthly outdoor PM₂.₅ data at a spatial resolution of 0.01°x0.01° grid cell were obtained for heating months (Dec-Feb) from Dec. 2014 to Dec. 2018. We estimated the number of households in each grid cell participating in the program ('treated’) for each study month. We used a Bayesian spatiotemporal model to estimate the local effect of household participation in the program on satellite-derived PM₂.₅, adjusting for meteorological conditions (temperature/RH, precipitation, and wind vectors), elevation, imperious surface, presence of villages, and eligibility to participate in the program. We assessed linearity using linear splines. Results: In the 1768 grid cells with at least one village participating in the program by the end of 2018 (out of 17353 grid cells total), the number of treated households ranged from 1-5290 (median: 155). We observed a small but consistent effect of participating in the coal-to-clean energy program on decreases in local PM₂.₅ whereby for every 10 households treated there was an accompanying 0.03μg/m³ decrease [95%CI:-0.04,-0.02] in grid-cell PM₂.₅ up to 155 households. There was no effect of treatment on PM₂.₅ in grid cells with over 155 treated households. Conclusion: In less-populated areas, we observed modest reductions in satellite-derived outdoor PM₂.₅ at the grid cell level in Beijing after participation in the program. The lack of effect in areas with households is likely due to the presence of other sources of local outdoor PM₂.₅ that masks any PM₂.₅ benefit of the program. Keywords: Outdoor air pollution; residential coal burning
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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 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.000 | 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".