Assessing the carbon peak challenge in extremely cold regions: Scenario analysis of building emissions via an integrated Kaya-LMDI-SD framework
Bibliographic record
Abstract
The building sector is a major contributor to energy consumption and carbon emissions in China, playing a pivotal role in achieving the dual carbon goals. In extremely cold regions, building carbon emission intensity substantially surpasses that of other areas, yet the underlying mechanisms remain underexplored. This study develops an integrated framework combining the Kaya identity, the logarithmic mean Divisia index (LMDI), and a system dynamics (SD) model. Jilin Province, China, serves as a case study to identify the drivers of building carbon emissions and forecast future trends under multiple scenarios. Results indicate that between 2006 and 2022, per capita GDP and per capita urban building floor area were the primary drivers of emission growth, whereas the energy intensity of the tertiary sector and per capita energy consumption had significant inhibitory effects. Scenario analysis projects peak emissions of 73.35 MtCO 2 in 2031 under the low-carbon scenario, 86.90 MtCO 2 in 2034 under the baseline scenario, and 106.09 MtCO 2 in 2037 under the high-carbon scenario. Achieving the 2030 carbon peak target for Jilin’s building sector remains a formidable challenge. This study proposes targeted carbon reduction pathways, offering critical insights for evidence-based low-carbon policy development in extremely cold regions.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".