STOCHASTIC CONVERGENCE OF PER CAPITA GREENHOUSE GAS EMISSIONS AMONG G7 COUNTRIES: AN EVIDENCE FROM STRUCTURAL BREAKS
Bibliographic record
Abstract
This paper tests the stochastic convergence hypothesis of per capita greenhouse gas emissions among G7 countries over the period from 1990 through 2014. In testing stochastic convergence, we transfer per capita greenhouse gas emissions in level, relative to the average by using methodology of Carlino and Mills (1993, 1996) and investigate unit root properties of these obtained relative series by using recently developed unit root test of Narayan and Popp(2010) besides conventional unit root tests. Conventional unit root test results indicate that stochastic convergence hypothesis is supported only for France and United States. On the other hand, when we take into account existence of possible structural breaks, the results provide significant support for stochastic convergence of relative per capita greenhouse gas emissions for France, Japan, United Kingdom and United States and divergence for Canada, Germany and Italy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".