Linking Emissions Trading Schemes: Analysis and Recommendations for EU-Australia and Quebec-California Linkages
Bibliographic record
Abstract
Since the introduction of international emissions trading by the Kyoto Protocol, the emissions trading mechanism used to reduce greenhouse gas (GHG) emissions appears to regain attention at both, the national and the regional levels. Currently, the European Union (EU), Australia, Japan, some United States (US) states and Canadian provinces, New Zealand, South Korea and China, have already established or are currently developing their emissions trading schemes (ETSs). Considerations for establishing further ETSs are also in progress in Brazil, Chile, Mexico, Ukraine and Turkey. This paper aims at examining the following question: Will the EU-Australia and Quebec-California be able to achieve an effective linkage with each other? In addressing this question, this paper will first discuss design elements that were identified in the literature review as crucial for the linking of different ETSs, and then consider how each design feature is addressed by the potential linking partners, identifying potential incompatibilities, if any, and outlining what adjustments, if any, might be made to facilitate effective linkages between them.
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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 | 0.019 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.015 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".