Renewable energy financing - what can we learn from experience in developing countries?
Bibliographic record
Abstract
Renewable energy (RE) has been considered as one of the stronger contenders to improve the plight of nearly two billion people, mostly in rural areas, without access to modern forms of energy . Although the economics of renewable energy technologies (RETs) have yet to reach a stage where these could replace fossil fuels on a significant scale, many experts argue that technologies such as solar, wind, and small-scale hydropower are not only economically viable but also ideal for rural areas. The mismatch between the potential and actual use of RE ca n be attributed to barriers in its implementation . Among others , a lack of financing has been one of the important barriers adversely affecting the widespread use of RETs. In developing countries , a majority of initiatives have focused on financial incentives. The re are successes as well as failures from the models adopted. The paper discusses problems related to financing RETs, by focusing on small-scale off-grid RETs in developing countries , and reviews some of these model s to bring out the lesson s that we can learn to accelerate the availability of finance to RETs.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".