Bibliographic record
Abstract
Two emissions trading systems ( ETS ) are linked if a participant in one system can use an allowance or a credit issued by either system for compliance. Linking ETS offers a number of potential benefits including, lower overall compliance cost, price protection, greater liquidity in the allowance market, and reduced emissions leakage. It is useful to distinguish: (a) A unilateral link—one ETS accepts the allowances of another ETS for compliance purposes, but not vice versa. Any link to an offset system, such as the Clean Development Mechanism ( CDM ), is a unilateral link for the ETS that accepts those credits. (b) A bilateral link—each ETS accepts the allowances of the other ETS for compliance purposes. Another way to implement a bilateral link is to adopt a common compliance instrument. The European Union ETS ( EU ETS ) has a single compliance instrument—the EU allowance—that is used in all 31 participating countries. Several national and subnational jurisdictions have established an ETS for one or more greenhouse gases ( GHGs ). Some of these ETS also issue offset credits for GHG emission reductions achieved by specified sources. In addition, the international CDM and Joint Implementation ( JI ) mechanisms issue offset credits for GHG emission reductions. Most ETS have established unilateral links, mainly to the CDM and JI , but also to other ETS . Apart from the systems that are part of the EU ETS and Regional Greenhouse Gas Initiative ( RGGI ) only one bilateral link, between the California and Quebec ETS , has been established. This study summarizes the experience with linking GHG ETS . WIREs Energy Environ 2016, 5:246–260. doi: 10.1002/wene.191 This article is categorized under: Energy and Climate > Economics and Policy Energy and Climate > Systems and Infrastructure Energy Policy and Planning > Climate and Environment
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".