China’s emissions trading scheme in PM2.5 reduction: a meteorology-based hybrid model
Bibliographic record
Abstract
Implementing appropriate environmental economics and policies has become challenging as the global haze problem has become increasingly serious. China is one of the largest emitters of carbon in the world, necessitating the rapid development and improvement in its policies to address haze pollution national. The impact of the emissions trading scheme (ETS) on pollution caused by particulate matter in the air with a diameter of 2.5 µm or less (PM2.5) in China has been studied; however, the seasonal meteorological factors affecting PM2.5 concentrations remain largely underexplored. Therefore, this study estimated how the ETS policy reduced PM2.5 pollution using a difference-in-differences model. This study used the convergent cross-mapping method to analyse the causal relationships among the PM2.5 concentration and meteorological factors in all four seasons. The empirical results showed that China’s ETS policy substantially reduced PM2.5 concentration in 37 pilot cities by 10.9% on average. A causal relationship was found between PM2.5 concentration and various meteorological factors. In addition, the effect of the ETS regime on reducing emissions strongly correlated with urban meteorological conditions and showed seasonal differences. This study provides specific guidelines for implementing national environmental policies in China and other developing countries.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".