Emissions Trading in Practice, Second Edition : A Handbook on Design and Implementation
Bibliographic record
Abstract
Currently, about 46 national \n jurisdictions and 35 cities, states, and regions, \n representing almost a quarter of global greenhouse gas (GHG) \n emissions, are putting a price on carbon as a central \n component of their efforts to reduce emissions and place \n their growth trajectory on a more sustainable footing. An \n increasing number of these jurisdictions are approaching \n carbon pricing through the design and implementation of \n Emissions Trading Systems (ETS). As of 2021, ETSs were \n operating across four continents in 38 countries, 18 states \n or provinces, and six cities covering over 40 percent of \n global gross domestic product (GDP), and additional systems \n are under development. This handbook sets out a 10-step \n process for designing and implementing an ETS. These steps \n are interdependent, and the choices made at each step will \n have important repercussions for decisions in the other \n steps. In practice the process of ETS design will be \n iterative rather than linear. The need to adjust and adapt \n policies over time is reflected in the update of this \n handbook, which was first released in 2016. New insights, \n approaches, and designs have proliferated adjusting the way \n ETSs operate and further developing our understanding of them.
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 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 | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.051 | 0.053 |
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".