Quantitative Analysis of China’s Carbon Emissions Trading Policies: Perspectives of Policy Content Validity and Carbon Emissions Reduction Effect
Bibliographic record
Abstract
Carbon emissions trading (CET) is now one of China’s key policy tools for achieving the goals of carbon peak and carbon neutrality. To comprehensively explore the consequences of China’s CET policy, the study first evaluated the content validity of CET policies across China’s 31 provinces, autonomous regions, and municipalities falling directly under the central government using policy strengths, tools, and measures from 2011 to 2020. The spatiotemporal drivers of regional carbon efficiency improvements from aspects of actual policy effect, average policy content validity effect, and policy quantity effect were also evaluated via the logarithmic mean Divisia index. This analysis revealed that the policy content validity was increasing in general and was higher in pilot regions. On average, the policy quantity effect was the primary driver of carbon efficiency improvements in both pilot and non-pilot regions, but the actual policy effect failed to promote carbon efficiency in both regions. Beijing’s carbon emissions reduction effect was superior to that of other pilot regions, and where actual policy effect and policy quantity effect were the primary and secondary drivers of local carbon efficiency improvements, respectively. These findings suggest that when formulating CET policies, each region should not only focus on improving policy content validity, but also pay attention to the actual carbon emissions reduction effects produced by policies as well.
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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 | 0.025 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".