Developing Supra-European Emissions Trading Schemes: An Efficiency and International Trade Analysis
Bibliographic record
Abstract
Given the coexistent EU priorities concerning the competitiveness of European industries and international emissions regulation at the company level, this paper assesses the efficiency and competitiveness implications of linking the EU Emissions Trading Scheme (ETS) to emerging trading schemes outside Europe. Currently, countries like Canada, Japan or Australia are contemplating the set up of domestic ETS with the intention of linking up to the European scheme. While a stylized partial-market analysis suggests that the integration of trading systems is always beneficial in efficiency terms, our applied general equilibrium approach shows that the aggregate welfare impacts of linking the EU ETS are rather limited. We further find that the trade-based competitiveness effects of linking the European ETS crucially depend on the linked trading system: Although EU economy-wide competitiveness varies only moderately across linking scenarios, the sectoral decomposition of these aggregate effects shows that European industries are much more sensitive to the linking constellation. Similarly, the incentives for non-EU regions to join the European system display considerable heterogeneity. A stricter allowance allocation within domestic ETS can, however, substantially improve the overall prospects for establishing supra-European emissions trading schemes.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".