Emissions trading in practice : a handbook on design and implementation
Bibliographic record
Abstract
As the world moves on from the climate agreement negotiated in Paris, attention is turning from the identification of emissions reduction trajectories—in the form of Nationally Determined Contributions (NDCs)—to crucial questions about how these emissions reductions are to be delivered and reported within the future international accounting framework. The experience to date shows that, if well designed, emissions trading systems (ETS) can be an effective, credible, and transparent tool for helping to achieve low-cost emissions reductions in ways that mobilize private sector actors, attract investment, and encourage international cooperation. However, to maximize effectiveness, any ETS needs to be designed in a way that is appropriate to its context. This Handbook is intended to help decision makers, policy practitioners, and stakeholders achieve this goal. It explains the rationale for an ETS, and sets out a 10-step process for designing an ETS – each step involves a series of decisions or actions that will shape major features of the policy. In doing so, it draws both on conceptual analysis and on some of the most important practical lessons learned to date from implementing ETSs around the world, including from the European Union, several provinces and cities in China, California and Quebec, the Northeastern United States, Alberta, New Zealand, Kazakhstan, the Republic of Korea, Tokyo, and Saitama.
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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 | 0.013 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.032 | 0.021 |
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".