Renewable energy financing - what can we learn from experience in developing countries?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it