Economic and Environmental Effects of Energy Consumption in High-Consumption Countries (Evidence of Vector Regression with Nonlinear Panel Distribution Intervals)
Bibliographic record
Abstract
The aim of this study was to estimate the nonlinear effect of oil, gas, electricity, and coal energy consumption on carbon dioxide emissions in ten energy-intensive countries (Iran, South Korea, Japan, Germany-Russia-USA-India-Canada-Brazil and China) in the world. Statistics and information used to estimate the nonlinear autoregressive panel model with distributed intervals (PANEL NARDL) have been extracted from the database of the World Bank and the World Energy Organization for the period 1985-2019. The results show that increased consumption of gas, electricity, coal, and oil leads to increased carbon dioxide emissions, while a decrease in their consumption reduces carbon dioxide emissions in the long run. Also, the nonlinear relationship between the per capita of consumption of these four types of energy and the emission of carbon dioxide in high-consumption countries was confirmed by the parent test in the long run. Therefore, reducing the use of fossil fuels and shifting the focus to clean and renewable energy consumption is proposed for the five selected countries, especially Iran, and economic policymakers should prioritize environmental protection by enacting applicable laws. In this way, the creation and development of intelligent infrastructure for the carbon economy and industry are essential.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".