Does the combining effects of energy and consideration of financial development lead to environmental burden: social perspective of energy finance?
Bibliographic record
Abstract
Abstract In light of the rapidly growing industrialization in BRICS and G7 regions, thorough energy, financials, and environmental analyses are essential for sustainable financial development in these countries. In this context, this work analyzes the relationship between energy, financial, and environmental sustainability and the regions' social performance. Data from 2000 to 2017 is analyzed through a data envelopment analysis (DEA) like a composite index. Results show China and Brazil's better performance in the region, with a sustainability score of 0.96, India was the third, followed by South Africa and Russia. Japan, the UK, and the USA were the most energy-efficient countries for five consecutive years. A 0.18%, 0.27%, 0.22%, 0.09%, 0.31%, and 0.32% reduction in carbon emission is observed with a 1% increase in R&D costs by Canada, France, Germany, Italy, Japan, and the USA, respectively. This work contributes to the existing literature regarding an eco-friendly sustainable policy design for the G7 countries based on multiple indicators.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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".