Dynamic Association between Non-Renewable Energy Matrix, Carbon Dioxide Emissions, and Economic Growth in G7 Countries: A Contribution to the Sustainable Development Goals
Bibliographic record
Abstract
This study contributes to the Sustainable Development Goals (SDGs) and the 2030 Agenda by examining CO2 emissions and economic growth in G7 countries. The primary aim is to explore connections among coal, oil, and natural gas consumption, predicting both CO2 emissions and economic growth despite external disruptions. The research employs vector autoregressive (VAR) and Bayesian autoregressive (BVAR) models, alongside the Granger causality test. The study tests hypotheses: (i) Fossil fuel consumption drives CO2 emissions; (ii) Fossil fuel consumption influences economic growth; (iii) A causal link exists between CO2 emissions and economic growth. Air pollution analysis (hypothesis i) indicates natural gas associates with CO2 emissions in Germany, the USA, and Italy; coal links to CO2 emissions in Canada, the USA, and Japan; CO2 emissions due to oil connect to Canada, the USA, France, Italy, Japan, and the UK. Hypothesis ii shows natural gas consumption in Canada, the USA, France, Italy, and coal consumption in France, Italy, and the UK correlate with GDP. No GDP correlation with oil consumption is seen. Hypothesis iii reveals a two-way relationship only in Germany CO2 emissions impact GDP and vice versa. Forecasts suggest external shocks lead to variable fluctuations up to seven periods ahead.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".