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Record W4317103742 · doi:10.3390/su15031749

Consumer Motivation behind the Use of Ecological Charcoal in Cameroon

2023· article· en· W4317103742 on OpenAlexaff
Ahmed Moustapha Mfokeu, Elie Chrysostome, Jean‐Pierre Gueyié, Olivier Ebenezer Mun Ngapna

Bibliographic record

VenueSustainability · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicEnergy and Environment Impacts
Canadian institutionsUniversité du Québec à MontréalWestern University
Fundersnot available
KeywordsDeforestation (computer science)CharcoalConsumption (sociology)Sustainable developmentEnvironmental pollutionBusinessGeographyNatural resource economicsEnvironmental planningEnvironmental economicsEnvironmental protectionEcologyEconomicsSociologySocial science

Abstract

fetched live from OpenAlex

Climate change and global warming are amplified by pollution and deforestation. For this reason, governments around the world meet every year to find ways to reduce pollution and deforestation and ensure sustainable development. The use of clean energy, particularly ecological charcoal, appears to be an appropriate solution in developing countries. The main objective of this research is to assess the motivations for the consumption of ecological charcoal in Cameroon, using a quantitative approach based on Partial Least Squares Structural Equation Modeling (PLS-SEM). Data were collected from 525 households in the cities of Yaoundé and Douala, Cameroon. The results show that the desire to protect the environment (ecological sensibility), the desire to reduce the energy costs of cooking (economic sensibility), the need to improve health and security, and the desire to enhance the quality of meals and to preserve the cleanliness of pots are all determinants in the consumers’ choice to use ecological charcoal. These results are refreshing. In Cameroon, in addition to its economic value, the massive consumption of ecological charcoal will contribute to a reduction in household waste management problems in cities and municipalities, while preserving the environment.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.072
Threshold uncertainty score0.730

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.044
GPT teacher head0.256
Teacher spread0.211 · 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 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

Citations2
Published2023
Admission routes1
Has abstractyes

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