The relationship between economic growth and carbon emissions in G-7 countries: evidence from time-varying parameters with a long history
Bibliographic record
Abstract
This paper re-investigates the time-varying impacts of economic growth on carbon emissions in the G-7 countries over a long history. In doing so, the historical data spanning the period from the 1800s to 2010 (as constructed) for each country is examined using the time-varying cointegration and bootstrap-rolling window estimation approach. Unlike the previous environmental Kuznets curve (EKC) studies, using this methodology gives us avenue to detect more than one, two, or more turning points for the economic growth-carbon emissions nexus. The empirical findings show that the nexus between economic growth and carbon emission seems over a long history to be M-shaped for Canada and the UK; N-shaped for France; inverted N-shaped for Germany; and invertedM-shaped (W-shaped) for Italy, Japan, and the USA. In addition, the possible validity of EKC hypothesis is examined for both the pre-1973 and post-1973 sub-periods. Based on this investigation, we found that an inverted U-shaped is confirmed only for the pre-1973 period in France, Italy, and the USA. These empirical evidences provide new insights to policy makers to improve environmental quality using economic growth as an economic tool for the long run by observing changes in the environmental impact of this growth from year to year.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".