Spatiotemporal Changes of China's Carbon Emissions
Bibliographic record
Abstract
Abstract Spatiotemporal changes in China's carbon emissions during the 11th and 12th Five‐Year Plan periods are quantified for the first time through a reconstructed nationwide high‐resolution gridded data set. The hot spots of carbon emissions in China have expanded by 28.5% (toward the west) in the north and shrunk by 18.7% in the south; meanwhile, the emission densities in North and South China have increased by 15.7% and 49.9%, respectively. This suggests a clear transition to a more intensive economic growth model in South China as a result of the energy conservation and emission reduction policies, while the expanded carbon hot spots in North China are mainly dominated by the Grand Western Development Program. The results also show that China's carbon emissions exhibit a typical spatially intensive, high‐emission pattern, which has undergone a slight relaxation (up to 3%) from 2007 to 2012 due to a typical urbanization process.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".