Low Embodied Carbon and Energy Materials in Building Systems: A Case Study of Reinforcing Clay Houses in Desert Regions
Bibliographic record
Abstract
Over 40% of the world's energy consumption occurs in the construction sector. However, some countries do not address environmental criteria as design requirements in their construction codes. Accordingly, this research aims to provide a solution that reduces embodied energy and carbon while preserving historical and traditional textures of Iran. The comparison of embodied carbon and energy between new concrete and traditional buildings was performed by calculating the amount of construction materials. By examining both types of buildings, the reduction of embodied carbon and energy in a combined building system was evaluated. In the following, using SWOT analysis, the strategies of this combination were investigated. Clay building has less embodied energy and carbon than concrete one despite containing more mass of materials. According to SWOT analysis, the strategy of integrating clay and concrete systems is presented. The proposed system in compare to the concrete structure resulted in around 40% and 35% reduction in embodied carbon and energy, respectively. Extending this strategy throughout the country saves 13 million tons of embodied carbon and 130 million GJ of embodied energy. Finding a solution based on sustainability considerations to preserve historical texture is one of the basic concerns of countries where these textures form a part of their identity. The presented combined system, while paying attention to sustainable building and urban development, is a desirable solution to reduce buildings' embodied carbon and energy.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".