Performance Assessment and Outlook of China’s Emission-Trading Scheme
Bibliographic record
Abstract
China overtook the US as the world’s top emitter in 2007, and produced 1.5 times the emissions of the US by 2013 [1]. At present, China’s emissions make up over a quarter of the global total. China is expected to produce three times the emissions of the US by 2030 [2]. Indeed, China’s role and efforts in CO2 reductions matter greatly for the peaking of global emissions, even without further emission leakages to less-developed regions or countries. China recently announced the launch of a nation-wide emission-trading scheme (ETS) starting in 2017 [3] in order to help deliver its emission peak by 2030. A number of climate policies in China are ongoing, and require a full performance review, effective coordination, and appropriate implementation of planning and monitoring measures along with any newly added mechanisms. This paper utilizes the latest energy and emission data to explore the impact of emission trading as a policy driver toward absolute emission and emission intensity changes in China and in its seven provinces or municipalities.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".