Linking the Emissions Trading Systems in EU and California
Bibliographic record
Abstract
The EU vision of creating a transatlantic carbon market took an important step forward when commissioner on Climate Action, Connie Hedegaard, met with California´s governor Jerry Brown in 2011 and confirmed plans to link the EU emission trading system (EU ETS) with California´s emerging carbon market. The objective of this paper is to investigate the prospects of linking the EU ETS with the California ETS with regard to relevant design features of the two systems. We find that linking the EU ETS with the California scheme is not likely, at least not in the short term. Since the high level meeting between Hedegaard and Brown, California has moved its attention away from the EU and has announced plans to link with Quebec. In addition, a major obstacle to linking the EU ETS with the California scheme concerns the use of off-sets. California allows the use of forest credits and does not acknowledge off-sets from the Clean Development Mechanism, (CDM). In contrast, EU relies on CDM credits, and doesn't recognize forest credits. Both parties signal concerns that linking will lead to losing control of allowance price. Paradoxically, the difference in abatement costs, reflected in allowance price, is an important economic motive for linking two emission trading systems, but may also constitute a significant political barrier. There is however, some common ground that could facilitate future linking. Both parties are positive to creating a larger carbon market through off-set markets and linking. Both parties appear to have compatible levels of ambition with comparably stringent caps on emissions. California will adopt a price ceiling, which could be an obstacle since the EU directive only allows linkage with systems that have absolute caps on emissions. But the California price cap is limited in volume and would probably from an EU perspective not create an insurmountable problem. Regarding allocation, while free allocation is the main method to distribute allowances initially, both systems aim at using auction in the long-term. Finally, both systems provide mechanisms for overview and adjustment of the rules, which could help the calibration of critical features like off-sets, price management mechanisms and legislative differences. With political will, the current barriers to linking the EU ETS and the emerging California scheme could probably be solved.Financed by:Mistra IndigoFORES
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".