The Urban Building Energy Retrofitting Tool: An Open-Source Framework to Help Foster Building Retrofitting Using a Life Cycle Costing Perspective. First Results for Montréal
Bibliographic record
Abstract
Building decarbonization is a major challenge for cities. Deciding what, when, and how to retrofit a building is difficult, given the complex interaction between energy costs and investment requirements. Several tools have been developed in the last years to help public and private stakeholders with these decisions, but none cover aspects the authors think are fundamental. For this reason, an urban building retrofit tool was developed and is presented in this article. This new tool is based on a bottom-up approach, with all buildings simulated individually, considering aspects such as shading or adjacencies. As a second step, three scenarios with different levels of ambition have been implemented in the tool, and the energy demand and emissions resulting from these scenarios have been calculated. As a third step, the retrofitting scenarios' initial investment and operational costs have been implemented via a detailed Life Cycle Costs (LCC) approach. The authors describe the robust and scalable structure that has been developed and the application of this structure to calculate the LCCs of different retrofitting scenarios in Montréal.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.030 | 0.009 |
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 source (direct Gemma or distilled Codex), 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".