An Analysis Based on SD Model for Energy-Related CO2 Mitigation in the Chinese Household Sector
Bibliographic record
Abstract
Reducing carbon dioxide (CO2) emissions has become a global consensus in response to global warming and climate change, especially to China, the largest CO2 emitter in the world. Most studies have focused on CO2 emissions from the production sector, however, the household sector plays an important role in the total energy-related CO2 emissions. This study formulates an integrated model based on logarithmic mean Divisia index methodology and a system dynamics model to dynamically simulate household energy consumption and CO2 emissions under different conditions. Results show the following: (1) the integrated model performs well in calculating the contribution of influencing factors on household CO2 emissions and analyzing the options for CO2 emission mitigation; (2) the increase in income is the dominant driving force of household CO2 emissions, and as a result of the improved standard of living in China a sustained increase in household CO2 emissions can be expected; (3) with decreasing energy intensity, CO2 emissions will decrease to 404.26 Mt-CO2 in 2020, which is 9.84% lower than the emissions in 2014; (4) the reduction potential by developing non-fossil energy sources is limited, and raising the rate of urbanization cannot reduce the household CO2 emission under the comprehensive influence of other factors.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".