Linkages between environmental sustainability, disaggregated emission, renewable energy, and energy efficiency: An evidence from BRI countries
Bibliographic record
Abstract
This paper examines the long-term and short-run causative relationship among environmental sustainability, energy efficiency, renewable energy and carbon emissions from all over sources (coal, oil and fossil fuels) and sector wise division (heat and power, transportation, residential, manufacturing and other sectors. The empirical evidence presented in this study is derived from a balanced panel dataset spanning the annual periods from 2000 to 2021. The dataset specifically focuses on a selection of BRI Countries. The Kao test demonstrates the presence of cointegration across variables such as carbon dioxide emissions, environmental suitability, energy efficiency and renewable energy. The Panel Pooled Mean Group-Autoregressive Distributed Lag (PMG-ARDL) model indicates a statistically significant positive association between the environmental sustainability and disaggregated CO2 emissions over a long-term period. The study found a positive relationship between disaggregated CO2 emissions and environmental sustainability and energy efficiency, with renewable energy sources reducing emissions. It suggests a need for a structural transition from an energy-intensive economy to a decarbonized one, with sectors like heat and power positively impacting sustainability. Implementing measures to reduce emissions is crucial for tackling climate change.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".