On-Grid Solar PV versus Diesel Electricity Generation in Sub-Saharan Africa: Economics and GHG Emissions
Bibliographic record
Abstract
Many power utilities in sub-Saharan Africa (SSA) have inadequate generation capacity, unreliable services, and high costs. They also face capital constraints that restrict them from making the investments necessary for capacity expansion. Capacity shortages have compelled power utilities to use leased emergency power-generating units, mainly oil-fired diesel generators, as a short-term solution. An economic analysis is carried out to compare the economic net present value (ENPV) of fuel savings, as well as the greenhouse gas (GHG) savings, from investing capital in a solar PV power-generation plant with those from investing the same amount of funds into a diesel power plant. The results show that ENPV is negative for the solar PV plant, whereas it has a large positive value for the diesel plant. In addition, the diesel plant would be almost three times as effective in reducing GHG emissions as the same value of investment in the solar PV plant. Even with solar investment costs falling, it will take 12 to 24 years of continuous decline before solar PV becomes cost-effective for SSA. The capital cost of solar PV would need to drop to US$1058.4 per kW to yield the same level of ENPV as the diesel plant.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".