Geospatial mapping of biomass supply and demand for household energy management in Nepal
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
This paper presents a geospatial mapping model for assessing spatial distribution and demand of biomass sources for household energy use in Nepal. In the context of rural households, correlation between supply and demand of biomass is crucial for designing effective rural energy programs. Three districts were considered to represent the country's main topographical regions: lowlands, hills, and mountains, where geospatial distribution and demand of biomass are different. The supply potential of fuelwood was assessed using Geographical Information System (GIS) tool, and the potential of crop residues and dung and household energy demands were determined by field surveys and experiments. The results showed that households with secure access to biomass sources in lowlands, hills and mountains were 57%, 50% and 3% respectively. In lowlands, crop residues and dung were extensively used due to lack of forest biomass, whereas forest biomass was extensively used in hills and mountains, with negligible use of crop residues and animal dung. The results indicate that use of improved cooking stoves and biogas was negligible and thus cleaner biomass energy conversion and cooking technologies are needed to achieve universal target of clean cooking for all. The GIS model provided better estimation of biomass energy supply potential in the communities, which is crucial in the design of energy policies for sustainable clean cooking solutions. It is anticipated that this geospatial mapping model is also applicable to the cases of other developing countries, which have dominant biomass consumption for household energy use.
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