Overview and comparison of existing carbon crediting schemes
Bibliographic record
Abstract
At COP24 in December 2018, Parties adopted a large part of the so-called “rulebook” operationalizing the articles of the Paris Agreement and the accompanying decision 1/CP.21. Due to lack of consensus on several contentious topics surrounding accounting, integrity and ambition, the rules for the market mechanisms under Article 6 have been postponed to COP25. Even if the guidelines for transfers of international emission reduction credits in cooperative approaches (Article 6.2) and the rules, modalities and procedures for the UNFCCC-supervised crediting mechanism (Article 6.4) are adopted as planned at COP25, the full operationalization of these mechanisms is expected to take several years. In this context, the objective of this study is to provide a comprehensive overview of key design elements implemented in existing “baseline and credit” carbon crediting schemes and to draw lessons that can inform the negotiations on Article 6. In a first step, the paper identifies the most important carbon crediting schemes at different levels of governance and of different geographical focus for analysis and subsequently compares them along six main dimensions: Governance and accounting; scope and eligibility; environmental integrity; monitoring, reporting and verification (MRV); sustainable development (SD) contributions; and linkages with other carbon pricing instruments. While international crediting schemes have suffered from a lack of demand since the early 2010s, domestic crediting schemes are spreading at national and subnational levels. At the international level, the study reviews key features of the international crediting schemes under the Kyoto Protocol, notably the Clean Development Mechanism, Joint Implementation and Green Investment Schemes for International Emissions Trading. As an example for bilaterally implemented schemes, the Joint Crediting Mechanism is included. At a (sub)national level, schemes from Australia, California, Canadian provinces, China, Spain and Switzerland were selected. Finally, the voluntary offset standards Gold Standard and Verra are discussed. The analysis is of common features and differences is completed by discussing alternative implementation approaches (see the Table below).
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.014 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".