Financing renewable electricity in the resource-rich countries of the Middle East and North Africa
Bibliographic record
Abstract
Renewables in the resource-rich countries of the Middle East and North Africa (MENA) are inconsequential contributors to regional total primary energy supply, but recent project developments and overt support from a range of influential regional actors suggest a general trend towards a more environmentally sustainable electricity supply.This trend is driven just as much by economics as other factors, as rapidly falling renewable energy capital costs are complementing favourable policy environments, technical suitability, and concerns around the impacts of anthropogenic climate change.Finance is an especially important consideration in this transition, yet it receives insufficient coverage.This paper seeks to remedy this deficiency of academic inquiry.At the root of our inquiry lies a simple pair of questions: what makes a project financeable, and what can the resource-rich nations of the region do to create vibrant clean electricity financing markets for renewables?We outline the factors that affect the financeability of projects, review the latest developments in renewable energy finance in the region, and present policy recommendations going forward.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".