Decarbonization of Modular Construction and LEED v5 Certification
Bibliographic record
Abstract
Modular and offsite construction (MOC) provides several advantages such as reducing cost and construction time as well as enhancing safety and quality. Decarbonization which is concerned with reducing or eliminating carbon emissions for manufacturing and construction is another advantage of MOC compared to traditional construction. MOC offers a promising approach to climate change mitigation by reducing greenhouse gas emissions, resource consumption, waste, and energy use. Manufactured prefabricated 2D or 3D modules in controlled manufacturing facilities allow modular construction to optimize material usage and minimize waste while reducing transportation of raw materials to construction sites, which reduces embodied carbon emission. For reducing operational carbon footprints, MOC also can be designed to be highly energy-efficient, while incorporating sustainable technologies and materials. Many studies conducted life cycle assessments (LCA) to evaluate the sustainability and decarbonization potential of MOC which are related to the total amount of carbon emissions through different life cycle phases including material production, construction, use of buildings, operation and maintenance, end-of-life stage, demolition and disposal. Green Building Certifications for decarbonized buildings can help in recognizing and assessing construction projects and buildings regarding their energy efficiency and sustainability. Many voluntary programs for certifications of green buildings exist in different countries such as the Leadership in Energy and Environmental Design (LEED), Green Star program, Building Research Establishment Environmental Assessment Method (BREEAM), etc. However, there is a lack of studies focusing on application of LEED certification for MOC. Hence, this study will investigate the practical application of LEED green building rating system for MOC.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".