Economic prosperity in the presence of green energy: A global perspective and regulation
Bibliographic record
Abstract
This article investigates the connection between renewable energy (RE) and economic development, in selected developed countries such as Japan, France, China, the US, Italy, Canada, and the UK, and developing countries including South Asia, Bangladesh, Indonesia, Saudi Arabia, Ghana, Vietnam, Pakistan, Rwanda, Morocco, and the Philippines. The entire review process was conducted using a PRISMA flow chart. A total of 533 papers were identified in the Scopus database, with 118 articles subjected to eligibility assessment. Among these, 173 articles were excluded for various reasons. The analysis revealed several key findings regarding the relationship between renewable energy and economic development. Firstly, in developed countries, the integration of renewable energy sources has led to substantial economic benefits, including job creation, technological innovation, and reduced reliance on fossil fuels. These countries have demonstrated that a transition towards green energy can stimulate economic development while achieving environmental sustainability. Secondly, in developing countries, the research found that the application of renewable energy technologies is crucial for attaining sustainable economic development. These countries face unique challenges, including energy poverty, environmental contamination, and volatile energy markets. However, the findings suggest that investing in renewable energy infrastructure can address these challenges while promoting inclusive growth and poverty alleviation.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".