Carbon Emission Accounting and Reduction for Buildings Based on a Life Cycle Assessment: A Case Study in China’s Hot-Summer and Warm-Winter Region
Bibliographic record
Abstract
At the 75th United Nations General Assembly, China committed to peaking carbon dioxide emissions by 2030 and achieving carbon neutrality by 2060. In response, the national standard “General Specification for Building Energy Conservation and Utilization of Renewable Energy” has been adopted across 20 provinces and cities in seven major regions, including North China, Northeast China, and South China. These regions have implemented stringent energy-saving and emission reduction reviews and quota requirements. Despite this, there is limited research on comprehensive life cycle carbon emission calculations and carbon reduction designs. This study addresses this gap by focusing on economically developed regions with high population density and substantial energy-saving potential, specifically targeting the warm winter and hot summer regions of China. Using a commercial building in Shenzhen as a case study, we established a carbon emission accounting model based on the life cycle assessment (LCA) method. We calculated carbon emissions during the material phase using the project’s bill of quantities and relevant carbon emission factors. Additionally, we used the CEEB 2023 software to design energy-saving and emission reduction solutions for the building. Our comparative analysis reveals that the new design reduces the carbon emissions of the case study building by 13.5%. This reduction not only mitigates the environmental impact of construction but also contributes to the fight against the greenhouse effect, supporting the broader goal of sustainable development.
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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 | 0.000 |
| 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".