Biogas Stoves Reduce Firewood Use, Household Air Pollution, and Hospital Visits in Odisha, India
Bibliographic record
Abstract
Traditional cooking using biomass is associated with ill health, local environmental degradation, and regional climate change. Clean stoves (liquefied petroleum gas (LPG), biogas, and electric) are heralded as a solution, but few studies have demonstrated their environmental health benefits in field settings. We analyzed the impact of mainly biogas (as well as electric and LPG) stove use on social, environmental, and health outcomes in two districts in Odisha, India, where the Indian government has promoted household biogas. We established a cross-sectional observational cohort of 105 households that use either traditional mud stoves or improved cookstoves (ICS). Our multidisciplinary team conducted surveys, environmental air sampling, fuel weighing, and health measurements. We examined associations between traditional or improved stove use and primary outcomes, stratifying households by proximity to major industrial plants. ICS use was associated with 91% reduced use of firewood ( p < 0.01), substantial time savings for primary cooks, a 72% reduction in PM 2.5, a 78% reduction in PAH levels, and significant reductions in water-soluble organic carbon and nitrogen ( p < 0.01) in household air samples. ICS use was associated with reduced time in the hospital with acute respiratory infection and reduced diastolic blood pressure but not with other health measurements. We find many significant gains from promoting rural biogas stoves in a context in which traditional stove use persists, although pollution levels in ICS households still remained above WHO guidelines.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".