Assessing the life cycle environmental impacts of modular construction: a US case study of a prototype housing unit
Bibliographic record
Abstract
The construction industry is a major contributor to greenhouse gas emissions, resource depletion, and waste generation. Modular construction (MC), which involves prefabricating building components off-site and assembling them on-site, has emerged as a promising strategy to reduce project time, cost, and environmental impacts. However, there is still limited comparative life cycle data available when measuring against traditional construction (TC). This study evaluates the environmental performance of MC versus TC in the United States through life cycle assessment (LCA). A mixed-methods approach combines a literature review with a comparative case study analyzed through BIM-integrated LCA tools. Results indicate that MC can achieve up to 54% lower embodied carbon per square foot and 45% lower emissions per kilogram of material, mainly due to efficient material use, prefabrication, and minimized on-site waste. Controlled factory-based production also enhances optimization and reduces environmental burdens during the construction process. These benefits, alongside faster project delivery, position MC as a viable pathway toward sustainable construction, particularly for affordable housing and post-disaster recovery. Key limitations include data variability and the restricted scope of current LCA datasets, highlighting the need for broader, multi-regional studies and diverse building typologies to strengthen future assessments.
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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) | 0.000 | 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".