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Record W3093083812 · doi:10.5539/jsd.v13n6p11

Profile and Determinants of the Middle Classes in Ghana: Energy Use and Sustainable Consumption

2020· article· en· W3093083812 on OpenAlexvenueno aff
Bernardin Senadza, Babette Never, Sascha Kuhn, Felix Asante

Bibliographic record

VenueJournal of Sustainable Development · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicEnergy and Environment Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsConsumption (sociology)Middle classEnergy consumptionAsset (computer security)EconomicsCapital (architecture)BusinessNatural resource economicsAgricultural economicsGeographyMarket economy

Abstract

fetched live from OpenAlex

High and sustained economic growth rates of the Ghanaian economy in the past two to three decades have been accompanied by a growing urban middle class. With a rapidly growing middle class, overall consumption is not only increasing but changing too. This paper analyses the asset ownership patterns among the Ghanaian middle class, and examines the effect of household wealth, environmental concern and environmental knowledge on carbon dioxide emissions emanating from energy use and transport based on urban household survey data collected in Accra, the capital city, in 2018. We find that middle class households consume a variety of energy intensive consumer goods, and the intensity of consumption increases with household wealth. Regression results reveal statistically significant relationship between household wealth and carbon emissions from energy and transport use. We also find that environmental knowledge has a statistically negative effect on carbon emissions from transport. The policy implications are discussed.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.328

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.025
GPT teacher head0.210
Teacher spread0.186 · 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

Citations4
Published2020
Admission routes1
Has abstractyes

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