Assessing the Contribution of Carbon Emissions Trading in China to Carbon Intensity Reduction
Bibliographic record
Abstract
This paper assesses the impacts of emissions trading between Jiangxi Province and the Rest of China on the carbon prices, total cost of carbon reduction and GDP loss using a two-region provincial Computable General Equilibrium model developed for China. The results reveal that without emissions trading, the carbon prices in 2020 would be 46.8 US$ in Jiangxi Province and 23.2 US$ in the rest of China, leading to GDP loss of 1.07% and 0.79% respectively. However, if emissions trading is allowed between provinces, Jiangxi Province needs to import CO 2 emissions allowance from the rest of China. In 2013, the trading amount is 14.30 million ton or 7.84% of total CO 2 emissions of Jiangxi Province. In 2020, the trading amount triples as compared to 2013, to a level of 44.85 million ton, accounting for 19.37% of Jiangxi’s total emissions. The results also reveal that the total costs of Jiangxi Province and the whole China would fall due to emissions trading, which is consistent with the theoretical implications. It is found that in the case of emissions trading, the GDP loss in 2020 would be lower for Jiangxi Province, at 0.36% instead of 1.07%. Key words: Domestic carbon emissions trading; 2-regional CGE model; China
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".