Biomass or LPG? A case study for unraveling cooking fuel choices and motivations of rural users in Maheshkhali Island, Bangladesh
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it