How Does Sustainable Energy System, Creativity, and Green Finance affect Environment Efficiency and Sustainable Economic Growth: Evidence from Highest Emitting Economies
Bibliographic record
Abstract
In present times, sustainability is to be demanded to attain high economic growth in longer run. Thus, countries are obliged to scrutinize the factors which balance the economic growth and prove to be efficient indicator of environment. This study, knowing the significance of such indicators, the study formulates a framework in which a role of sustainable energy system, creativity, and green finance on the environmental efficiency and sustainable economic development intends to be examined in the context of high carbon emission economies such as Iran, Canada, China, Indonesia, India, Japan, Saudi Arabia, United States, Russia, and South Korea. In order to tackle the abovementioned issue, the study used the data extracted from world development indicators (WDI) covering the period from 2010 to 2020. The study employed Random fixed model to test the proposed hypotheses. Obtained results expose that sustainable energy systems, creativity and green finance share negative association with carbon emissions, hence, revealing this point that the indicators are useful to increase sustainable economic development and environmental efficiency. Thereby, in order to achieve sustainable economic growth, governments are liable to implement policies with a long-term approach. The outcomes are also helpful for the upcoming researchers and regulators while formulating the policies about environmental efficiency.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".