Abstract
A proliferation of energy models has been developed across disciplines to explore energy and greenhouse gas (GHG) emissions-reduction strategies in cities. Hybrid models are especially useful because they incorporate more dynamics to simulate realistic results informed by relevant high-level policy decisions and building-level factors. Spatial and aspatial energy models, however, are not often linked, which overlooks the spatial impact of energy and emissions policies in urban environments. A new method is presented that links these types of models to understand how building stocks change over time in response to policies. This approach integrates outputs from an aspatial economic model, CIMS, with buildings in a spatially explicit urban building energy model (UBEM), UMI. The energy–economy model is parameterised against the UBEM using identified baseline condition and proposed future policy interventions. Building stock replacement and retrofit change are downscaled and disaggregated to individual buildings based on existing stock age and a probability-based Markov chain model (MCM). This integration enables simulations of cross-scale policy interventions that are sensitive to both economically and mechanically driven factors. An application of this approach shows how it can be used to evaluate how different policies interact with and influence building energy demand and GHG emissions. Practice relevance The results are integrated as a series spatially explicit energy modeling procedure (UMI) at the neighborhood scale. This process enables local assessments of efficacy of the proposed city scale and even regional policies in municipalities with various energy and GHG emission agendas. In the presented case study (of the Sunset neighborhood of Vancouver, BC, Canada) this method can quantify the elasticity of emission reductions from various urban form changes (e.g. infill, transportation-oriented development, etc.), new building code (i.e. BC Energy Step Code), active transportation and retrofit strategies from 2020 to 2050.
Highlights
IntroductionEnergy–economy models understand energy output in relation to high-level economic variables, market behavior, and sectoral relationships
This paper demonstrates the potential of the geographic information system (GIS)-integrated Markov chain model (MCM) as a way of disaggregating aspatial economic parameters and market shares of building technologies to individual building shells for calculating energy use and emissions in a urban building energy model (UBEM)
This paper presented a new methodology for integrating downscaled simulation of an energy– economy model to a spatially explicit community-level urban building energy model (UBEM) that enables one to iteratively test cross-scale what-if policy interventions
Summary
Energy–economy models understand energy output in relation to high-level economic variables, market behavior, and sectoral relationships Due to their scale and resolution, energy– economy models are primarily used to model and assess provincial or federal policy strategies. At a much finer resolution, design–planning models encompass a physical building energy model (BEM) or urban building energy model (UBEM), a category of energy simulators that are often spatially explicit and heavily reliant on engineering methodologies to simulate energy usage (Reinhart & Cerezo Davila 2016) Both BEM and UBEM are common tools among architects, engineers, and planners to assess the energy impacts of design decisions. They vary conceptually and methodologically, energy–economy and design–planning models capture intertwined drivers of energy and emissions at different scales
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