Beyond Green: Integrating Economic and Social Aspects to Environmental Life Cycle Assessments in Canadian Housing
Bibliographic record
Abstract
Abstract Ensuring sustainability in the Architecture, Engineering, and Construction (AEC) industry is essential in the 21st century. This requires addressing the three pillars of sustainability - environment, economy, and society. However, existing evaluation methods, such as Life Cycle Assessment (LCA), primarily focuses on environmental impacts, inadvertently downplaying the significance of economic and social aspects. This study aims to comprehensively assess the environmental performance of residential buildings in Canada while integrating economic and social aspects. Firstly, following the LCA methodology, an environmental analysis was made of a representative model (RM) of Canadian housing to emphasize the circular economy strategies possible within Canada’s AEC industry. The study investigates two scenarios: the CE scenario, in which selective deconstruction strategies are applied to the RM, and the Linear Landfill scenario, in which conventional landfill practices are applied. A life cycle impact assessment is conducted to understand the environmental impacts of the two scenarios, comparing namely carbon footprint, water usage, and fossil resource use. Subsequently, an economic evaluation and cost-benefit analysis, encompassing a Life Cycle Cost Assessment (LCCA), are performed for each scenario. In the CE scenario, secondary materials from reusable or recycled components are incorporated; in the Linear Landfill scenario, only virgin materials are used. To address the social aspect of sustainability, both scenarios are assessed based on the affordability of the RM in three Canadian cities: Toronto, Montreal, and Vancouver. The findings highlight essential trade-offs between the three sustainability pillars, indicating positive results in the CE scenario. In the environmental analysis, the CE scenario saves 63% of CO2, 53% of water use, and 48% of fossil resource use when compared to the Linear Landfill scenario. In the economic analysis, the CE scenario is 1.6% higher than the Linear Landfill scenario when two life cycles are considered. Finally, in the social analysis, the average affordability index points out the CE scenario offers 5.23% more affordable housing. This research underscores the significance of a sustainability comprehensive evaluation aspects to provide an initial holistic understanding of the interconnected sustainability pillars, and its findings may have implications by encouraging the use of reused and recycled materials, promoting green financing approaches as better mortgage rates or financial support for sustainable buildings.
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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 | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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 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".