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Record W4220996424 · doi:10.1016/j.heliyon.2022.e09118

Drivers of household demand for cooking energy: A case of Central Uganda

2022· article· en· W4220996424 on OpenAlexfundno aff
Edmond Nyuyki Mainimo, Daniel Micheal Okello, Wilson Mambo, Basil Mugonola

Bibliographic record

VenueHeliyon · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicEnergy and Environment Impacts
Canadian institutionsnot available
FundersMastercard FoundationGulu University
KeywordsFirewoodBriquetteAgricultural economicsDescriptive statisticsConsumption (sociology)EconomicsWood fuelEnergy sourceSolid fuelBusinessCharcoalRenewable energyWaste managementEngineeringCoal

Abstract

fetched live from OpenAlex

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.835
Threshold uncertainty score0.709

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 head0.013
GPT teacher head0.204
Teacher spread0.190 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations27
Published2022
Admission routes1
Has abstractyes

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