STOCHASTIC CONVERGENCE OF PER CAPITA GREENHOUSE GAS EMISSIONS AMONG G7 COUNTRIES: AN EVIDENCE FROM STRUCTURAL BREAKS
Bibliographic record
Abstract
This paper tests the stochasticconvergence hypothesis of per capita greenhouse gas emissions among G7countries over the period from 1990 through 2014. In testing stochasticconvergence, we transfer per capita greenhouse gas emissions in level, relativeto the average by using methodology of Carlino and Mills (1993, 1996) andinvestigate unit root properties of these obtained relative series by usingrecently developed unit root test of Narayan and Popp(2010) besidesconventional unit root tests. Conventional unit root test results indicate thatstochastic convergence hypothesis is supported only for France and UnitedStates. On the other hand, when we take into account existence of possiblestructural breaks, the results provide significant support for stochasticconvergence 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 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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".