Implications of Charcoal Briquette Produced by Local Communities on Livelihoods and Environment in Nairobi- Kenya
Bibliographic record
Abstract
The residents of Nairobi, Kenya, use 700 tonnes of charcoal per day, producing about88 tonnes of charcoal dust that is found in most of the charcoal retailing stalls that is disposed of inwater drainage systems or in black garbage heaps. The high costs of cooking fuel results in poorhouseholds using unhealthy materials such as plastic waste. Further, poor households are opting tocook foods that take a short time to prepare irrespective of their nutritional value. This articlepresents experiences with community self-help groups producing charcoal fuel briquettes fromcharcoal dust in poorer nieghbourhoods of Nairobi for home use and sale. Households thatproduced charcoal fuel briquettes for own use and those that bought them saved 70% and 30% ofmoney spent on cooking energy respectively. The charcoal fuel briquettes have been found to beenvironmentally beneficial since they produce less smoke and increase total cooking energy bymore than 15%, thereby saving an equivalent volume of trees that would be cut down for charcoal.Charcoal briquette production is a viable opportunity for good quality and affordable cooking fuel.Bioenergy and waste management initiatives should promote recovery of organic by-products forcharcoal briquette production.
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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 | 0.009 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".