Comprehensive spatial LCA framework for urban scale net zero energy buildings in Canada using GIS and BIM
Bibliographic record
Abstract
The Canadian federal government has set ambitious targets for achieving net-zero emissions by 2050, with buildings contributing 12 % of the country's total greenhouse gas (GHG) emissions. To reduce building GHG emissions, assessing the life cycle energy and carbon impacts of urban buildings is a critical first step. However, the lack of spatial Life Cycle Assessment (LCA) frameworks tailored for urban-level analysis complicates efforts to achieve these sustainability goals. This study develops a novel spatialized LCA framework, integrating GIS (Geographic Information Systems), BIM (Building Information Modeling), and LCA methodologies, to evaluate the life cycle impacts of Canadian urban buildings. The framework adheres to ISO 14044, ISO 14040, and EN 15978 standards and covers the entire building life cycle, including manufacturing, construction, operation, and end-of-life phases. A case study of Richmond BC, Canada, using a LoD101 city model, demonstrates that UNZEB scenarios achieve lower environmental impacts compared to Business-as-Usual (BAU) urban development. The findings identify low-rise apartments and mixed-use commercial buildings as impact hotspots, particularly in operational phases. Implementing Urban Net Zero Energy Building (UNZEB) strategies results in significantly cutting total life cycle emissions by 40 %, but highlights burden-shifting to upstream and downstream processes. This research supports urban sustainability and net-zero energy targets while informing policy and decision-making for large-scale urban planning. • First spatialized LCA for Canadian Urban Net Zero Energy Buildings (UZNEB). • New GIS-BIM-LCA framework for Canadian urban buildings and UNZEBs. • Life cycle impact comparison: Business-as-Usual (BAU) vs. UNZEB scenarios. • BIM-integrated urban LCA for life cycle energy simulation. • Established framework to evaluate environmental impact profile of Canadian cities.
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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 | 0.000 | 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".