Energy production-income-carbon emissions nexus in the perspective of N.A.F.T.A. and B.R.I.C. nations: a dynamic panel data approach
Bibliographic record
Abstract
This paper has attempted to examine the impact of innovation and energy production (i. e., oil, natural gas, and coal) on carbon dioxide emissions (CO2e) in the context of the Environmental Kuznets Curve (E.K.C.) hypothesis. Data were analysed for economies in B.R.I.C. (Brazil, India, Russia, and China) and North American Free Trade Agreement (N.A.F.T.A.) (the U.S., Canada, and Mexico) from 1992 to 2016. Based on the Hausman specification test, the panel mean group (P.M.G.) estimation approach was adopted. The empirical results suggested that an upsurge in coal and oil production has increased, while the gas production has disrupted CO2e in the long run. An insignificant yet positive relationship was observed between innovation and CO2e. The positive effect of per capita income and the negative effect of per capita income (square) on CO2e validated the presence of the E.K.C. hypothesis in the sampled economies. With the results showing an acute over-dependency on carbon-intensive energy sources (coal and oil), an imminent need exists for production of natural gas; at the same time, more investments are needed for exploration of low carbon-intensive renewable energy sources for environmental sustainability.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".