Pre-print: https://www.researchsquare.com/article/rs-9087554/latest
Housing development planning demands methods that can balance its many potential environmental and economic impacts. Existing tools are custom-built for particular cities, and use data models that don’t do a good job representing the actual geography of these cities. In this work, we develop a generalizable housing development optimization tool that uses global data and models individual development opportunities. We show the adaptability of this framework by finding optimal “gentle density” housing development plans in Toronto (Canada), Houston (USA), and Perth (Australia) under diverse sustainability objectives. We focus on minimizing embodied greenhouse gas (GHG) emissions from housing construction, and find that these cities can theoretically house their projected populations with minimums of 3.0, 12.4, and 15.7 Mt CO2-eq of embodied GHG emissions (respectively) from housing architectural and structural materials. We examine the trade off between embodied carbon emissions and spatial accessibility (Toronto), flood risk (Houston), and climate damage risk (Perth), and find that these multi-objective optimization scenarios can increase the embodied carbon emissions up to 166% above the single-objective scenarios. This work advances data-driven housing development optimization models by improving both spatial resolution and cross-city applicability.
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