What matters the most in designing low-carbon buildings in Canada? Exploring the tradeoff between embodied and operational carbon in early stage design
Bibliographic record
Abstract
• Results show the high impact of local carbon intensity in the extent of design parameters impact on building carbon footprints. • In Canadian cities with LGC, the most significant design parameters are associated with structure and HVAC, followed by WWR, geometry and level-of-insulation. • In cities with HGC, design efforts should focus on HVAC, structure WWR, level-of insulation and geometry. • Regardless of the location, informed decisions in designing HVAC and structural systems can reduce TC significantly. Reducing global greenhouse gas emissions is a crucial sustainability objective around the world. The ambitious sustainability targets that have been defined for the building sector can only be achieved through carbon-sensitive design from the beginning. Multiple design parameters, their impacts on building performance, along with embodied and operational carbon tradeoffs makes it difficult for architects to compare design alternatives in the uncertain context of early-stage design. Tools and methods often focus more on either operating efficiency or material selection rather than assisting architects in making holistic carbon-sensitive design decisions. In this research, a design-compatible methodology for low-carbon buildings was developed by using design exploration methods and parametric calculations of whole-life carbon emissions. The model was run for seven Canadian cities, resulting in over 20,000 design scenarios to capture a broad range of potential solutions. The study investigates the most influential design parameters in relation to the varying carbon intensity of local grid electricity. The results show that despite the significant impact of climate, local grid carbon emission intensity plays the most crucial role in defining building design priorities in order to optimize for total life carbon impacts. The results also indicate that mechanical and structural systems play a significant role in building carbon footprint. The results also show that in cities with high grid carbon factors, optimizing window-to-wall ratio, level of insulation and geometry can contribute the most to reducing carbon footprint. Conversely, in cities with low local grid carbon emission intensity, structural material and mechanical systems selection are highly impactful and all other factors play a marginal role.
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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 | 0.000 |
| 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".