Fostering Human Wellbeing in Africa through Solar Home Systems: A Systematic and a Critical Review
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".