Technological innovation to achieve sustainable development—Renewable energy technologies diffusion in developing countries
Bibliographic record
Abstract
Abstract There is no dispute about the importance of speeding up the development, spread, and implementation of renewable energy technologies (RETs). RETs are the best means to address the current wasteful and dangerous effects of existing energy systems. In addition to the environmental aspects, the renewable energy industry is an exciting opportunity for investment. Nations that embrace the value of reinforcing renewables infrastructures will achieve competitive advantages in the worldwide marketplace. To accomplish that, however, one needs insight into the elements that make renewable energy development and diffusion move slowly. A variety of reasons causes the slow spread, but we would like to address the barriers from the economic theory perspective. Then, we will scrutinize the specific elements of the developing countries market that make the diffusion move slowly. We believe that by examining the factors that result in market failure and by taking into account the specific characteristics of the renewables industry, especially in developing countries, governments can enable their national infant market to be a competitor in the worldwide marketplace. To study the reason for the slow diffusion of RETs in developing countries, we need to examine the facts through the lens of the innovation system. The innovation ecosystem takes into account the socioeconomic factors that shape the capability for innovation in each specific country. This paper peered a meaningful link between innovation systems and the problem of poverty and inequality through a well‐researched and planned innovation system.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".