Can the enforcement regime of China’s emissions trading schemes effectively ensure compliance?
Bibliographic record
Abstract
To control greenhouse gas (GHG) emissions, China has been exploring emissions trading schemes (ETSs) for over a decade. In general, whether or not an ETS can control emissions efficiently and cost-effectively depends on the compliance behaviour of its covered entities, which is influenced by its enforcement regime. This research aims to evaluate the effectiveness of the enforcement strategies of the seven pilot ETSs and the national ETS in China (China’s ETSs) in ensuring compliance from a law and economics perspective. It explores how effective enforcement strategies for an ETS can be derived from the law and economics literature. It then systematically describes the current compliance requirements and enforcement regimes of China’s ETSs. Next, it evaluates the extent to which China’s ETS enforcement designs and practices align with or deviate from effective ETS enforcement strategies in theory. This thesis concludes that the enforcement designs and practices of China’s pilot and national ETSs do not always appear to be effective in incentivising the regulated entities to comply with ETS regulations.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".