Health and Climate-Relevant Pollutant Concentrations from a Carbon-Finance Approved Cookstove Intervention in Rural India
Bibliographic record
Abstract
Efforts to introduce more efficient stoves increasingly leverage carbon-finance to scale up dissemination of interventions. We conducted a randomized intervention study to evaluate a Clean Development Mechanism approved stove replacement impact on fuelwood usage, and climate and health-relevant air pollutants. We randomly assigned 187 households to either receive the intervention or to continue using traditional stoves. Measurements of fine particulate matter (PM2.5) and absorbance were conducted in cooking areas, village center and at upwind background site. There were minor and overlapping seasonal differences (post- minus preintervention change) between control and intervention groups for median (95% CI) fuel use (-0.60 (-1.02, -0.22) vs -0.52 (-1.07, 0.00) kg day(-1)), and 24 h absorbance (35 (18, 60) vs 36 (22, 50) × 10(-6) m(-1)); for 24 h PM2.5, there was a higher (139 (61,229) vs 73(-6, 156) μg m(-3))) increase in control compared to intervention homes between the two seasons. Forty percent of the intervention homes continued using traditional stoves. For intervention homes, absorbance-to-mass ratios suggest a higher proportion of black carbon in PM2.5 emitted from intervention compared with traditional stoves. Absent of field-based evaluation, stove interventions may be pursued that fail to realize expected carbon reductions or anticipated health and climate cobenefits.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".