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Record W4285005784 · doi:10.3390/su14148382

Fostering Human Wellbeing in Africa through Solar Home Systems: A Systematic and a Critical Review

2022· review· en· W4285005784 on OpenAlexaff
Nathanael Ojöng

Bibliographic record

VenueSustainability · 2022
Typereview
Languageen
FieldEnvironmental Science
TopicEnergy and Environment Impacts
Canadian institutionsYork University
Fundersnot available
KeywordsRenewable energyProductivityElectrificationPopulationBusinessEconomic growthEnvironmental economicsEconomicsPublic economicsElectricitySociologyEngineering

Abstract

fetched live from OpenAlex

Solar home systems are being increasingly used for energy access in Africa, and claims have been made about their ability to enhance human wellbeing. Therefore, this paper systematically and critically assesses the human wellbeing effects of these systems in Africa. According to the systematic review, these small-scale renewable energy systems have positive effects in terms of education, health, safety and security, entertainment, and social connectedness. In the realms of income and firm productivity, the results were mixed, with some studies showing that the adoption of solar home systems contributed to increases in income and firm productivity, and others finding little or no evidence to support this view. However, a critical review indicates that some of the positive effects are often based on self-reporting, and rigorous evidence regarding the nature and the magnitude of the wellbeing effects of these systems is currently scarce and at times inconclusive. These systems will continue to play a role in Africa’s energy landscape in the foreseeable future due to limited access to and uncertainties related to centralised grid electrification for a significant segment of the population; but, based on the weak evidence base, we are daydreaming if we think that solar home systems can improve human wellbeing in a significant way due to their low energy-generation capacity. Accordingly, future research opportunities are suggested, which could help to address some of the shortcomings in the evidence base.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.825
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.002
Research integrity0.0000.001
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 head0.074
GPT teacher head0.338
Teacher spread0.264 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations5
Published2022
Admission routes1
Has abstractyes

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