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Record W2025430667 · doi:10.14710/ijred.2.1.19-29

Implications of Charcoal Briquette Produced by Local Communities on Livelihoods and Environment in Nairobi- Kenya

2013· article· en· W2025430667 on OpenAlexfundno aff
Mary Njenga, Aya Yonemitsu, Nancy Karanja, Miyuki Iiyama, Jacob Kithinji, Marielle Dubbeling, Cecilia Sundberg, Ramni Jamnadass

Bibliographic record

VenueInternational Journal of Renewable Energy Development · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicEnergy and Environment Impacts
Canadian institutionsnot available
FundersJapan International Research Center for Agricultural SciencesWorld Agroforestry CentreInternational Development Research Centre
KeywordsBriquetteCharcoalWaste managementTonneLivelihoodEnvironmental scienceWood fuelBusinessCoalEngineeringGeographyAgricultureChemistry

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.589
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.008
GPT teacher head0.204
Teacher spread0.196 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations26
Published2013
Admission routes1
Has abstractyes

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