| Summary: | 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. === Arts, Faculty of === Geography, Department of === Graduate
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