Abstract
Estimation of the demand of an urban precinct, related to Electricity, Transport, Waste and Water (ETWW), is a necessary step toward the delivery of quality living environments where daily activities can be conducted in a sustainable manner. A forecasting model that concurrently links demand in all four aforementioned domains to carbon emissions can assist planning agencies, infrastructure providers, operators and private developers to deliver low-carbon urban precincts in the future. Integration of modelling methodologies delivers improved ability, accuracy and flexibility when compared to typical forecasting approaches. This chapter details the outcomes of recent research efforts on the development of an integrated ETWW demand estimation tool with detailed scenario forecasting abilities. Focusing on the residential components of the precinct, modelling outputs provide detailed estimations of household demands and resulting carbon impacts across the four domains. Impacts of non-residential land uses including high-value industry, retail, commercial and open space are also considered and reported on. Model users can estimate the carbon impact of resident population changes, various household structure types, carbon-friendly technologies and climate change for precinct locations across Australia . In addition, the tool accounts for interactions with external infrastructure such as transport networks, off-site waste disposal, water supply locations and grid-based energy supply. Forecasting abilities of the model are demonstrated through case-study applications that reflect of ‘what-if’ type scenario investigations, important to policymaking and planning for future urban development. The user is ultimately able to explore combinations to achieve a low-carbon precinct development.
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Holyoak, N., Taylor, M., Hadjikakou, M., Percy, S. (2017). An Integrated Demand and Carbon Impact Forecasting Approach for Residential Precincts. In: Geertman, S., Allan, A., Pettit, C., Stillwell, J. (eds) Planning Support Science for Smarter Urban Futures. CUPUM 2017. Lecture Notes in Geoinformation and Cartography. Springer, Cham. https://doi.org/10.1007/978-3-319-57819-4_17
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