Drivers of household demand for cooking energy: A case of Central Uganda
Bibliographic record
Abstract
In Uganda, the total primary household energy consumption is mainly biomass. Lack of empirical information remains a daunting challenge to operationalization of strategies and policies aimed at reducing unsustainable energy use. This study specifically determined household demand for different cooking energy sources (briquettes, charcoal and firewood). Data were collected from a sample of 235 households with the help of pretested structured questionnaires. Data were analyzed using descriptive statistics and Seemingly Unrelated Regression (SUR). From the econometric analysis, results revealed that briquettes demand was significantly influenced by fuel expenditure, briquette price, gender of household head, main occupation and source of fuel. Further, charcoal demand was influenced by fuel expenditure, household size, charcoal price, district and fuel restriction. On the other hand, demand for firewood was influenced by household income, firewood price, fuel expenditure, household size and source of the fuel. From our findings, it is recommended that tailored trainings on sustainable exploitation of biomass resources for energy supply should be conducted across the country. In addition, there is need for such trainings to target women, who are the decision makers with respect to household energy supply. Lastly the study recommends the need for low-cost briquetting technologies that would reduce its price to levels that are competitive to charcoal and firewood.
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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 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".