The Convergence in Greenhouse Gas Emissions Across G-7 Countries
Bibliographic record
Abstract
Environmental degradation, such as climate crisis, global warming, etc., is one of the crucial issues for countries. Studies in the literature analyze the convergence in environmental degradation regarding the environmental convergence hypothesis using different indicators such as carbon dioxide emissions, ecological footprint, etc. to identify the differences in environmental quality across countries. This study tests the environmental convergence hypothesis for G-7 countries over the period 1997-2018. To do so, we use greenhouse gas emissions per capita as an indicator of environmental degradation and apply non-linear dynamic factor model developed by Phillips and Sul (2007). According to the results, countries do not converge to a single equilibrium point. However, Phillips and Sul (2007) convergence methodology allows us to identify possible convergence clubs. The club clustering algorithm identifies three convergence clubs, each converging to a different steady-state. Club 1, which converges to higher greenhouse gas emissions per capita level, includes Canada and United States, whereas Club 2 includes Germany and Japan, and Club 3 includes France, Italy, and the United Kingdom. The results confirm that the that the environmental convergence hypothesis does not hold for G-7 countries.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.013 |
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 teacher head, 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".