Embodied Carbon Impacts of Building Envelope Systems
Bibliographic record
Abstract
Abstract As buildings become more energy efficient due to stringent building codes, the emphasis on reducing the embodied carbon (EC) in the built environment has become significant. Studies predict that between 2020 and 2050, new buildings will surpass operational emissions with higher embodied emissions in their initial decade and throughout their life cycle. To meet decarbonization targets, reducing EC through low-carbon, high-performance building designs and grid decarbonization is crucial. This research analyzes the embodied carbon intensity of 26 commonly used envelope systems of large buildings in Ontario’s Greater Toronto and Hamilton Area (GTHA) using the attributional life cycle assessment (ALCA) methodology. Results highlight the impact of cladding materials, insulation layers, and backup structures on EC. The study ranks envelope assemblies based on their EC intensity (ECI), providing insights for designers to balance operational and embodied emissions. Noteworthy findings include the relatively high ECI of floor systems compared to exterior wall and roof systems. It also emphasized the challenges in assessing the environmental impacts of wood-based products and the need for uncertainty analysis and transparent reporting. The study contributes to understanding EC in building envelopes, guiding design teams toward low-carbon structures and more informed decision-making. As research progresses, recommendations include consequential analysis, assessing biogenic carbon impacts, and material durability studies for a deeper insight on the environmental impacts of building envelope materials.
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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 | 0.001 | 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 | 0.004 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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 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".