Scaling urban energy use and greenhouse gas emissions through LiDAR
Bibliographic record
Abstract
Although models to quantify CO₂e emissions in urban areas exist, they are within isolated disciplines, and are targeted at specific scales, emissions processes, and end-users — not a priori compatible with planning needs. Furthermore, the majority of existing models rely on inventory data, which is typically only available at aggregate space and time scales. It is necessary however, that neighborhood-scale CO₂e emissions estimates are provided to determine the key relationships between urban form and emissions — which can than be applied to future planning strategies. This thesis developed a new methodology to integrate LiDAR data, building simulation software and a building typology database to rapidly model energy and emissions for a large number of buildings. To adjust building energy demand to local urban-context, building morphology, and population density a scaling approach is proposed. This methodology was applied to a study area of 7.4 km² in Vancouver, BC, consisting of 7812 buildings ranging in moderate to high density. Modeled building energy use in this transect was sensitive to local conditions (average variation in building energy use due to urban-context 2.8%, building morphology 2.8%, and population density 3.2%) resulting CO₂e emissions of 14.2 kg CO₂e m⁻²yr⁻¹ (1309 kg CO₂e Inh.⁻¹ yr⁻¹) varying dramatically between the central business district (40.1), mixed-use (12.7), and residential (9.0) neighbourhoods. Spatial and temporal patterns of building energy use, CO₂e emissions and anthropogenic heat release by buildings are presented and discussed in relation to urban form.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".