Revisiting Energy Consumption, CO2 Emissions, and Economic Growth between G7 and BRICS Countries: Using Bootstrap ARDL Test for Cointegration
Bibliographic record
Abstract
[[abstract]]Because greenhouse gases are closely related to climate change and the sustainable environment, this issue has drawn widespread attention from all over the world. To this end, the aim of this paper adopted the new bootstrap ARDL bound test (McNown, Sam, and Goh, 2016) and Granger causality test (Granger, 1969) is to examine the variables between G7-USA, Canada, UK, France, Germany, Italy, and Japan- and the BRICS-Brazil, Russian Federation, India, China, and South Africa- through the long-run and short-run relationships. Empirical results from bootstrap ARDL bound test indicate that these three variables are not cointegrated. Only two countries show a long-run relationship with France for CO2, and the USA for GDP. The results show that Degenerated case #1 are found for the Russian Federation and France (ENG) and Japan (both ENG and GDP), and Degenerated case #2 are found for South Africa and Japan (CO2). Some causality patterns are identified in the short-run for these countries. Thus, the empirical results do not support: one size fits all. The finding, therefore, has been provided with crucial policy implications for the authority of the G7 and the BRICS countries to further refer to.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".