Embodied Carbon Optimization for Residential Building Design through Strategic Low-Carbon Material Configurations
Bibliographic record
Abstract
Embodied carbon (EC) is becoming a dominant factor in energy-efficient buildings, often accounting for over 50% of their life-cycle emissions in new construction.Therefore, minimizing building EC emissions is imperative to meeting Canada's climate targets under the Paris Agreement and supporting the transition to a low-carbon built environment.Early design decisions, particularly in the selection and configuration of materials, play a critical role in achieving significant reductions in EC.Building on this premise, this paper investigates how EC performance can be optimized through the strategic use of lowcarbon material combinations, selected from a curated database of low-carbon alternatives.To illustrate this approach, a case study is conducted on a single-family residential building in British Columbia (BC), Canada.The study explores a range of possibilities for material usage, performing EC assessments for various scenarios and analyzing the resulting configurations to identify the optimal design.The results of the case study reveal that strategic combinations of low-carbon materials in key building components, such as foundations, and sheathing, can reduce EC by 24.19% compared to conventional construction methods.The carbon intensity decreases from 277.17 kg CO2e/m² to 210 kg CO2e/m², nearly meeting the benchmark for EC in new Part 9 homes in Vancouver, highlighting the potential to reduce building EC through strategic integration of low-carbon materials during early design phases.Through computational tools and life cycle assessment (LCA), it offers practical strategies to reduce EC while maintaining performance and aesthetics, supporting sustainable construction and climate goals.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".