Using Urban Building Energy Modeling to Develop Carbon Reduction Pathways for Cities
Bibliographic record
Abstract
Cities have been the nexus of economic activity and growth, but they have an insatiable appetite for energy. In response to the challenges and potential impact of climate change, cities and municipalities around the world are developing climate action plans to reduce carbon emissions and enhance resilience of their built environments. However, policymakers require a data-driven method to identify the most impactful, economical, and feasible strategies – and further translate these to actionable policy levers. This research serves to democratize and facilitate the wider use of urban building energy models in cities and municipalities. First, key applications and use cases of urban building energy modeling (UBEM) are identified, and a minimum viable UBEM is introduced for each use case. This framework streamlines computational requirements, data, and calibration needs, promoting more rapid development and utilization of UBEMs. Second, a web-based framework to rapidly generate UBEMs for carbon reduction technology pathways is developed, subsequently piloted in the City of Evanston, and found to significantly reduce time and resources needed for developing and utilizing UBEMs. The approach was further validated in collaboration with policymakers and researchers in eight cities – viz. Braga (Portugal), Cairo (Egypt), Dublin (Ireland), Florianopolis (Brazil), Kiel (Germany), Middlebury, VT (USA), Montreal (Canada), and Singapore. Finally, conventional UBEMs typically only incorporate building properties and characteristics. This dissertation also presents an exploratory approach – using supervised and unsupervised data science / machine learning methods – to integrate building properties with socio-economic data from census for better inference and understanding of energy use in cities Each approach is documented with the relevant results compared against conventional modelling workflows and / or validated through real-world urban case studies. The major contribution is the development and validation of methods and frameworks that can rapidly and automatically generate UBEMs to help cities and municipalities develop carbon reduction pathways to impact.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".