Technological innovation to achieve sustainable development—Renewable energy technologies diffusion 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
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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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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