Influencing Factors of Energy-Related CO2 Emissions in China: A Decomposition Analysis
Bibliographic record
Abstract
China is the largest CO2 emitter in the world and is still reliant on energy consumption for economic growth. Research has focused on effective approaches of reducing and mitigating CO2 emissions. This paper undertakes a decomposition study of energy-related CO2 emissions from the industrial and household sectors during the period 1996–2012, with the objectives of investigating trends of the changes in energy-related CO2 emissions, the driving forces of these changes, and approaches of mitigating CO2 emission. Results show the following: (1) the expansion of economic activity is the dominant stimulatory factor of the increase of CO2 emissions in China and that a sustained increase in CO2 emissions can be expected; (2) the decline in energy intensity and the adjustment of energy mix and industrial structure effectively mitigate CO2 emissions; and (3) the government should give more attention to enhancing the energy utility efficiency and reducing CO2 emissions in rural households.
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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 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".