Nexus between carbon emissions, renewable energy, technological innovation, and economic growth in the G7 economies: an econometric analysis
Bibliographic record
Abstract
Abstract This research investigates the relationship among CO \(_2\) emissions, economic growth, technological innovation, renewable energy consumption, and the Environmental Kuznets Curve (EKC) in G7 countries from 1990 to 2022. The overarching objective is to uncover specific short-run and long-run associations between these variables, examining immediate impacts and long-term effects. With global concerns related to climate change and concerted international efforts to reduce CO$_2$, this study explores the critical dynamics between economic growth and environmental sustainability. The methods include panel unit root tests, cointegration analysis, causality tests, and AutoRegressive Distributed Lag (ARDL) models, chosen for their capacity to offer a thorough understanding of the relationships between the variables. The finding indicates a stable equilibrium between GDP and CO$_2$ emissions within G7 nations, signifying a crucial aspect of global climate change. Specifically, the results highlight that the relationship is statistically significant for the USA, Canada, Germany, and the UK while being deemed insignificant for Italy, France, and Japan. This study suggests a complex relationship that extends beyond short-run fluctuations. In the short term, an observed inverse correlation emphasizes the need for agile policy strategies to balance economic growth and environmental concerns. Promoting renewable energy sources and strategically increasing investment in research and development have policy implications that can help make economic growth and environmental concerns more sustainable and balanced.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 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) | 0.004 | 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".