Rural electrification in Ghana : issues of photovoltaic energy technology utilisation
Bibliographic record
Abstract
Energy plays a pivotal role in human development. Not only is it sine qua non for national economic development, but it also provides services that enhance social development including, health and sanitation, education, potable water, cooking. In spite of this, at present, there are about two billion people without access to modern sources of energy, most of them in the rural areas of the developing world. Consequentially, the social and economic development of these two billion people hangs in the balance. In recent times, however, considerable advocacy has taken place in the academic and policy studies, environmental fora, and national agenda about solar PV energy technology serving as a panacea to the energy problems of rural populations in developing countries, especially Sub-Sahara Africa, whilst also helping to reduce greenhouse gas emissions. Notwithstanding this great advocacy, the literature on the dissemination of this technology has been incomplete in fostering understanding on the discourses surrounding its low dissemination rates in rural Ghana compared to countries such as Kenya and Zimbabwe; the sustainability of installed solar PV systems; and the usefulness of solar PV in serving the needs of the rural poor. In resorting to an interdisciplinary approach (methodology and theoretical foundation), this study has explored the energy perspectives of Ghana, the dynamics of rural electrification and energy needs, and the interplay of processes and forces underpinning the adoption and non-adoption of solar PV in rural Ghana. Results of this study show that, Ghana has abundant renewable energy resources, especially solar radiation. However, the study further reveals that the resource base alone of solar PV technology is not the panacea to its successful dissemination and the energy needs of all in rural Ghana. Significantly, this study has shown that the adoption and non-adoption perspectives of solar PV in rural Ghana and the sustainability of installed solar PV systems, as well as the disparate levels of solar PV dissemination in Ghana, Kenya and Zimbabwe, are contingent on multi-dimensional circumstances. This stands in contrast to the majority of literature that often emphasise cost as the sole determining factor of the non-adoption of solar PV in most developing countries. Results of this study therefore have implications for rural energy supply policy approaches and other institutional arrangements on solar PV issues in Ghana.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".