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Iterative ‘what-if’ neighborhood simulation: energy and emissions impacts

Authors: Jonathan Salter; Yuhao Lu; Ju Chan Kim; Ronald Kellett; Cynthia Girling; Fausto Inomata; Alix Krahn;

Iterative ‘what-if’ neighborhood simulation: energy and emissions impacts

Abstract

As efforts to address climate change shift to action at local scales, municipalities are called upon to develop locally specific action plans. Many municipalities lack the resources to develop energy and emissions-reducing policy interventions appropriate to their characteristics. This research synthesises urban form, scenario analysis and energy simulation into a cohesive workflow for evaluating energy and emissions policy interventions across a range of urban forms. A geospatial and census analysis of six cities across British Columbia, Canada, led to the development of seven urban neighborhood patterns. These represent neighborhood forms and densities found in cities of various sizes, densities, forms and climates. To test the approach of an urban built environment model (UBEM), retrofit and infill redevelopment ‘what-if’ scenarios were applied iteratively to two sample patterns comparing the relative efficacy of building technology-improvement policies versus land-use intensification policies. The future ‘what-if’ policy scenarios were spatially tested and validated using relevant policy. The simplified UBEM methods applied to typical patterns and development demonstrates a step towards an accessible and flexible modeling approach. Small and medium-sized municipalities can use this approach to assess and compare potential energy and emissions policy options and outcomes at building and neighborhood scales. 'Practice relevance' A new, simple method has been created for municipalities to understand multiple ‘what-if’ scenarios for reducing energy demand and emissions from buildings. This is based on profiles from census data, geospatial analysis and energy data that characterise urban neighborhood patterns. The approach integrates building-scale and neighborhood-scale energy and greenhouse gas simulations. It can simulate a variety of policy scenarios and strategy interventions in order to show the interactions between and among urban form and retrofit options. This enables planners and decision-makers to compare the relative magnitudes of different interventions at the neighborhood or city level for energy and emissions performance. The model was developed for use by a variety of communities in British Columbia, Canada. There is potential for adapting this method for use in other locations.

Keywords

energy model, energy demand, Architectural engineering. Structural engineering of buildings, buildings, urban morphology, TH845-895, energy policy, ‘what-if’ scenarios, neighborhood

  • BIP!
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    citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    11
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
11
Top 10%
Average
Top 10%
gold