Biomass or LPG? A case study for unraveling cooking fuel choices and motivations of rural users in Maheshkhali Island, Bangladesh
Bibliographic record
Abstract
Biomass fuel could effectively address the existing energy crisis in developing countries, including Bangladesh, yet its potential remains largely overlooked in scholarly and policy discussions. The objective of this study was to understand the people's perception of fuelwood, LPG, and cow dung as well as to identify factors influencing the choices of solid cooking fuels and the extent of daily fuelwood consumption in Maheshkhali, a secluded island off the coast of the Bay of Bengal in Bangladesh, characterized by its diverse landscapes. Primary data was collected through a questionnaire survey and focus group discussions and were analyzed using descriptive statistics, binomial logistic regression, and ordinary least squares regression (OLSR) to identify key determinants. Our findings suggest a pronounced preference for biomass fuel, as indicated by the odds ratio and user perceptions grounded in the central capability approach. The OLSR results indicate that cooking time, quantity collection, the number of school-going children, and educational score explain 82.5% of the total variance in fuelwood consumption, making them major driving factors. The household survey revealed a stark reliance on biomass fuel, with 87% of families using it exclusively, while only 4% rely solely on LPG. Fuelwood collection, primarily a task for women and children, also involved men who spent approximately five hours to traverse 1.5 km to collect 23 kg of biomass per trip. The strong biomass preference for fuel, in terms of central capability, underscores the challenges in motivating users to cleaner alternatives like LPG.
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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 | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".