Method for Calculating CO2\n\t\t\t\t Emissions from the Power Sector at the Provincial Level in China
Bibliographic record
Abstract
Based on the detailed origins of each province’s electricity consumption, a new method for calculating CO2 emissions from the power sector at the provincial level in China is proposed. With this so-called consumer responsibility method, the emissions embodied in imported electricity are calculated with source-specific emission factors. Using the new method, we estimate CO2 emissions in 2005 and 2010. Compared with those derived from the producer responsibility method, the power exporters’ emissions decreased sharply. The emissions from the power sector in Inner Mongolia, the largest power exporter of China, decreased by 109 Mt in 2010. The value is equivalent to those from Shaanxi’s power production and Canada’s power and heat production. In contrast, the importers’ emissions increased substantially. The emissions from the power sector in Hebei, the largest power importer of China, increased by 74 Mt. Emissions of Beijing, increased by 60 Mt (320%), in 2010. Thus, we suggest that the Chinese government should take the emissions, as calculated from the consumption perspective, into account when formulating and assessing local CO2 emission reduction targets. Ma, C.-M., Ge, Q.-S., 2014. Method for calculating CO2 emissions from the power sector at the provincial level in China. Adv. Clim. Change Res. 5(2), doi: 10.3724/SP.J.1248.2014.092.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".