Pathways to urban net zero energy buildings in Canada: A comprehensive GIS-based framework using open data
Bibliographic record
Abstract
• Developed a GIS-based framework for urban net-zero energy planning in Canada. • Created Canadian-specific building archetypes for energy modeling and analysis. • Identified pathways for energy efficiency and renewable energy integration. • Explored the spatial potential for urban net-zero energy using open data. While policies outline ambitious Urban Net Zero Energy Buildings (UNEZB) strategies, the lack of available Canadian-specific archetypes and data complexity has limited spatial and quantitative validation of these strategies. In this study, a simplified 3D building model (LoD 100) was developed using footprint and Digital Surface Model (DSM) data. An archetype database, based on ASHRAE 90.1 and NECB 2011, was created to classify urban-level energy use intensity across various building types and HVAC systems. This research explores three pathways to net-zero energy: electrification transitions, energy efficiency retrofits, and renewable energy integration. A case study was conducted by developing the urban-scale 3D building models for the City of Richmond at BC Canada, and the spatial energy analysis revealed significant disparities in energy consumption across urban and suburban areas. Key findings from the case study indicate that electrification and solar energy adoption in commercial districts, along with targeted retrofitting in residential zones, can significantly reduce energy use. This study provides a physics-based framework and robust methodology for Canadian cities to achieve net-zero energy goals, which offers valuable insights for policymakers, urban planners, and energy engineers to support decision-making and urban sustainability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".