Supply Considerations for Scaling Up Clean Cooking Fuels for Household Energy in Low‐ and Middle‐Income Countries
Bibliographic record
Abstract
Promoting access to clean household cooking energy is an important subject for policy making in low- and middle-income countries, in light of urgent and global efforts to achieve universal energy access by 2030 (Sustainable Development Goal 7). In 2014, the World Health Organization issued "Guidelines for Indoor Air Quality: Household Fuel Combustion", which recommended a shift to cleaner fuels rather than promotion of technologies that more efficiently combust solid fuels. This study fills an important gap in the literature on transitions to household use of clean cooking energy by reviewing supply chain considerations for clean fuel options in low- and middle-income countries. For the purpose of this study, we consider electricity, liquefied petroleum gas (LPG), alcohol fuels, biogas, and compressed biomass pellets burned in high performing gasifier stoves to be clean fuel options. Each of the clean fuels reviewed in this study, as well as the supply of electricity, presents both constraints and opportunities for enhanced production, supply, delivery, and long-term sustainability and scalability in resource-poor settings. These options are reviewed and discussed together with policy and regulatory considerations to help in making these fuel and energy choices available and affordable. Our hope is that researchers, government officials and policy makers, and development agencies and investors will be aided by our comparative analysis of these clean household energy choices.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".