Life Cycle Assessment of Nature-Based Design Solutions for Buildings and Building Retrofit
Bibliographic record
Abstract
In the context of energy efficiency and nature-based design solutions for buildings, this manuscript-based thesis presents a comprehensive Life Cycle Assessment (LCA) framework, applied to practical case studies, that architects, designers, and engineers may consider when conducting the environmental impact assessment of a building or design strategy. \nManuscript #1 presents the LCA framework, relevant software tools, and a methodology to assess the potential carbon offset achievable by integrating tree planting areas around buildings. The net annual carbon sequestration rate of 0.575 kgCO2eq/m2 of tree cover area is considered in this part. Then, two real case study buildings are thoroughly examined and compared: one involving a recently constructed all-electric research laboratory at Concordia University, and the other focusing on a natural gas-heated single-detached house. For the all-electric laboratory, a garden fully covered with representative urban trees could offset around 17% of the life cycle carbon emissions. For the natural gas-heated single-detached house, the offset was around 3% of the total life cycle carbon emission. \nManuscript #2 expands the results from Manuscript #1, specifically focusing on the case study of the research laboratory at Concordia. This part demonstrates how to estimate and report the environmental benefits linked to wood products, biogenic carbon storage, and end-of-life treatment of materials under various scenarios. The results from this part indicate that the set of design solutions adopted on this case study can potentially offset building’s carbon footprint by 37.2% up to 83.9% when included in the LCA estimation, depending on the scenario considered. \nAfter discussing the two manuscripts, an additional chapter explores the application of LCA in the context of building/energy retrofit. This part demonstrates the connection between the local energy profile and the potential carbon offsets achieved through the retrofit process. We analyzed the case study of a Canadian school building to illustrate whether the reduction in GHG emissions from operational energy use savings can counterbalance the environmental impacts associated with manufacturing the new envelope materials and mechanical equipment added during retrofit. The findings underscore the significance of building/energy retrofit in places where the grid-electricity relies on fossil fuel, such as Nova Scotia, but opens a discussion about the extent of the benefits in locations where electricity is currently sourced from renewables. In places like Quebec, if the existent case study building already relied on electricity for space heating, the embodied emissions associated with new components might outweigh the operational emissions savings resulting from the retrofit.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".