State-of-play in international carbon markets 2025
Bibliographic record
Abstract
- This policy brief gives an overview of existing carbon pricing mechanisms and outlines the trends of mandatory and voluntary carbon markets (VCMs) in 2023. It also reviews the integration of carbon markets. - As of April 2023, 73 carbon taxes and emissions trading systems (ETSs) were in operation, covering approximately 23% of global GHG emissions. - 28 of these compliance carbon pricing instruments were ETSs at regional, national or subnational levels and covered about 17% of global GHG emissions. The number of ETSs in force will likely rise in the coming years as 8 systems are currently under development and 11 are under consideration. - After growing rapidly in 2020 and 2021, the issuance of offset credits declined slightly in 2022. Several factors contributed to this decline, including the challenging macroeconomic conditions, public skepticism about the quality of credits, and the absence of commonly accepted guidance on best-practice for the use of credits to support net-zero claims. - Linked ETSs include: the EU and Swiss ETSs since 2020, the California and Québec Cap-and-Trade Programs since 2014, an evolving set of US states participating in the Regional Greenhouse Gas Initiative (RGGI) since 2009, and the Tokyo Cap-and-Trade Program and the Saitama ETS since 2011. - Progress on the integration of compliance carbon markets via linking has not been rapid. Each system is tailored to its domestic circumstances which makes the required level of alignment for successful links difficult to achieve. Moreover, the potential increase in regulatory uncertainty and the expected negative impacts on the robustness of each system act as strong barriers to linking. - Connecting ETSs with VCMs should be treated with great caution due to concerns about credit quality as well as monitoring, reporting and verification issues connected with offsets.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.015 | 0.018 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.038 | 0.004 |
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".