Emissions Trading in Practice : A Handbook on Design and Implementation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Note: this version of the Handbook has been superseded. The updated Second Edition of the Handbook can be downloaded at the link below ("Associated URLs"). \n \n \n \nAs 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 Québec, the Northeastern United States, Alberta, New Zealand, Kazakhstan, the Republic of Korea, Tokyo, and Saitama.
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.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it