The Role of CETA on Carbon Dioxide, F-Gasses, Methane, and Nitrous Oxide
Bibliographic record
Abstract
This study empirically investigates how the presence of CETA (Comprehensive Economic and Trade Agreement) may affect per capita emissions of four air pollutants. It follows closely the empirical work of (Qirjo et al., 2019), but it focuses in each category of GHGs. It finds statistically significant evidence suggesting that trade openness between the EU and Canada could help reduce per capita emissions of CO2, CH4, and N2O in a typical CETA member, respectively. In the case of CO2, the presence of CETA may help reduce per capita emissions in almost all CETA members. However, there is empirical evidence that suggests that per capita emissions of CH4 could move from the EU towards Canada due to the implementation of CETA. There is also empirical evidence implying that there could be a shift of emissions per capita of N2O from Canada towards 8 former EU members due to the implementation of CETA. There is mainly statistically insignificant evidence of a positive relationship between the trade intensity of each EU member and Canada and per capita emissions of HFCs/PFCs/SF6. Furthermore, the study reports unambiguous empirical evidence in support of the Pollution Haven Hypothesis originating from national population density variations (PHH2) for Canada, in the case of CH4. Moreover, there is also clear evidence consistent with the Pollution Haven Hypothesis due to national income differences (PHH1) for 8 former Communist EU members, in the cases of N2O and HFCs/PFCs/SF6.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".