Diffusion of solar PV in East Africa: What can be learned from private sector delivery models?
Bibliographic record
Abstract
Solar photovoltaic (PV) will play the leading role in addressing off‐grid electricity access; it can be applied almost anywhere and used in a wide range of applications for households, businesses, institutions and communities. However, to fully exploit this opportunity, off‐grid markets that need these solutions need to be effectively penetrated. This article focuses on delivery models for off‐grid solar PV solutions and how they address barriers such as awareness, acceptance, access and affordability. It is based on a survey of 13 solar PV businesses in East Africa, supported by the Energy and Environment Partnership Programme and implementing the following delivery models: Retail, Pay‐As‐You‐Go (PAYG), Consumer financing, Mini‐grid and Fee‐for‐service. The survey is complemented by supporting literature and incorporates experiences from a University of Oslo research project on a village scale energy access model in Kenya and case studies of solar PV mini‐grids in Senegal and India. Experiences from implementation of the different models are analyzed and generic descriptions provided. The models are compared to illustrate their suitability and effectiveness for delivering different levels of energy access. Retail and PAYG models are identified as effective at reaching scale, while the mini‐grid and fee‐for‐service models demonstrate good potential to affordably and sustainably deliver a wider range of electricity access. The limitations of conventional rural electrification strategies are also discussed and the potential to incorporate some delivery models into electrification programs assessed. This article is categorized under: Photovoltaics > Economics and Policy Photovoltaics > Systems and Infrastructure Energy and Development > Economics and Policy Solar Heating and Cooling > Economics and Policy
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".