Emissions Trading Worldwide: Status Report 2024
Bibliographic record
Abstract
The ICAP Status Report 2024 presents the latest developments in emissions trading systems worldwide. As the world witnessed the warmest year on record, governments are increasingly turning to emissions trading, finds the International Carbon Action Partnership’s (ICAP’s) Emissions Trading Worldwide 2024 Status Report. Jurisdictions making up 58% of global GDP are using an ETS. 36 systems are now in place, with a further 22 under development or consideration. Emerging economies are increasingly turning to emissions trading, with design adaptations for local circumstances. Global revenue from ETSs surpassed USD 74 billion in 2023, marking another record year. Governments around the world are increasingly turning to emissions trading systems (ETSs) as part of their policy response to the climate crisis, with those in emerging economies in particular gaining momentum. This year’s ICAP Emissions Trading Worldwide Status Report finds a growing number of systems are under development or consideration, including in Argentina, Brazil, India, Türkiye and Vietnam, among others. Developed economies such as Canada and the European Union are also looking to create new systems to expand carbon pricing to new sectors in a bid to drive down emissions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.043 | 0.047 |
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".