Assessing the Impact of Prefabrication on the Carbon Footprint of Multi-Story Residential Construction
Bibliographic record
Abstract
As sustainability becomes a global priority, the construction sector faces a significant challenge: reducing its carbon footprint while meeting the growing demand for housing.Prefabrication, a method involving the manufacturing of building elements off-site to be assembled on-site, emerges as a promising solution to achieve this goal.This approach has the potential to reduce waste and carbon footprint but also offers advantages in terms of quality and speed of execution.This work is set against a backdrop where the climate crisis demands a revaluation of traditional construction practices.The choice of this topic stems from the imperative to find more environmentally friendly construction methods.Specifically, this research aims to assess the impact of prefabrication on reducing the carbon footprint in the construction sector, especially in multi-story residential buildings.Employing a mixed-methods approach, the study involved an in-depth literature review, semi-structured interviews with construction industry professionals, and a questionnaire administered to a prefabricated construction company in Quebec.The research also evaluated the Gestimat digital tool for environmental assessment of building materials.Data analysis involved thematic evaluation of qualitative inputs from interviews and the questionnaire, as well as a comparative assessment of prefabrication's environmental performance across different life-cycle stages.Findings indicate that prefabrication plays a significant role in reducing the carbon footprint, especially during the production and construction phases of multi-story residential buildings.However, factors such as the specific materials used, and the degree of prefabrication integration also influence the environmental impact.Despite its potential, widespread adoption of prefabrication is hindered by industry inertia, misperceptions about its environmental benefits, and an underestimation of its potential.This research highlights the need for targeted efforts to bridge the gap between the theoretical potential of prefabrication and its practical implementation in the Quebec context.Recommendations include improved communication strategies, pilot projects, and case studies to demonstrate the environmental and economic benefits of prefabrication in multi-story residential construction and accelerate its adoption towards a more sustainable built environment.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".