Will solar energy escape the natural “resource curse”?
Bibliographic record
Abstract
The resource curse haunts countries whose economies have become dangerously specialized in the exploitation of a single resource. This curse threatens countries whose economies are poorly diversified and oriented mainly towards the export of their non-renewable natural resources, such as oil. What about the exploitation of an abundant renewable natural resource such as solar energy? Based on a case study of six solar power plants in six African countries (Burkina Faso, Madagascar, Morocco, Rwanda, Senegal, and South Africa), this paper analyzes the extent to which the impacts of the exploitation of these energy systems contribute to this curse. The research method is qualitative (296 interviews) and quantitative (use of a sustainability index), making it possible to analyze the impacts of solar power plants on four levels (local, regional, national, and international). Our results reveal four findings symptomatic of the resource curse: (i) the emergence of conflict situations, (ii) fragile local development, (iii) latent financial risk, and (iv) limited economic development leverage. In short, the resource curse linked to the use of renewable energies seems to bring another challenge to the landscape of energy transition.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".