A Linkage Framework for the China National Emission Trading System (CETS): Insight from Key Global Carbon Markets
Bibliographic record
Abstract
Given that international collaborative efforts to reduce greenhouse gas (GHG) emissions are urgent and crucial, a critical understanding of challenges and opportunities of linking China’s newly established national ETS with existing domestic or regional ETSs is essential in order to achieve global emission targets, and may attract other jurisdictions to join in global carbon market development. In this backdrop, we analyzed the experiences, lessons, and insights from three key global carbon markets, namely North America, the EU and China, in terms of the barriers to linking the global carbon market, with a focus on China, using thematic analysis. The four most commonly cited linkage design elements (barriers) were the legal basis; monitoring, reporting, and verification; political feasibility; and the price-management mechanism. Like-minded jurisdictions with similar political views and design features will have a higher chance of linking. Additionally, sustaining market liquidity, widening sectoral coverage, minimizing carbon leakage, ensuring offset quality, and a transparent allowance and cap setting rules are crucial steps towards linkage. These outcomes can be used as an ETS linkage-ready design framework for CETS and ETS under development to overcome barriers to future international ETS linkages.
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How this classification was reachedexpand
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 | 0.001 | 0.003 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".