Carbon Reduction Measures-Based LCA of Prefabricated Temporary Housing with Renewable Energy Systems
Bibliographic record
Abstract
Temporary housing plays an important role in providing secure, hygienic, private, and comfortable shelter in the aftermath of disaster (such as flood, fire, earthquake, etc.). Additionally, temporary housing can also be used as a sustainable form of on-site residences for construction workers. While most of the building components used in temporary housing can be manufactured in a plant, prefabrication technology improves the production efficiency of temporary housing; furthermore, integrated renewable energy systems, for example, solar photovoltaic (PV) system, offer benefits for temporary housing operations. In order to assess the environmental impacts of prefabricated temporary housing equipped with renewable energy systems, this study first divides the life cycle of temporary housing into six stages, and then establishes a life cycle assessment (LCA) model for each stage. Furthermore, with the aim of reducing the environmental impacts, life cycle carbon reduction measures are proposed for each stage of temporary housing. The proposed methodology is demonstrated using a case study in China. Based on the proposed carbon reduction measures, the LCA of a prefabricated temporary housing case study building equipped with renewable energy systems indicates a carbon emissions intensity of 35.7 kg/m2·per year, as well as a reduction in material embodied emissions of 18%, assembly emissions of 17.5%, and operational emissions of 91.5%. This research proposes a carbon reduction-driven LCA of temporary housing and contributes to promoting sustainable development of prefabricated temporary housing equipped with renewable energy systems.
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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) | 0.000 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".