Integrated demand forecasting to support urban planning of low-carbon precincts: The waste scenario
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© 2013 College Publishing. Reproduced with permission.
Waste is a symbol of inefficiency in modern society and represents misallocated resources. This paper outlines an ongoing interdisciplinary research project entitled 'Integrated ETWW demand forecasting and scenario planning for low-carbon precincts' and reports on first findings and a literature review. This large multi-stakeholder research project has been designed to develop a shared platform for integrated ETWW (energy, transport, waste, and water) planning in a low-carbon urban future, focusing on synergies and alternative approaches to urban planning. The aim of the project is to develop a holistic integrated software tool for demand forecasting and scenario evaluation for residential precincts covering the four domains (ETWW), using identified commonalities in data requirements and model formulation. The authors of this paper are overseeing the waste domain, while other researchers in the team have expertise in the remaining domains. A major component of the project will be developing a method for including the impacts of household behaviour change in demand forecasting. In this way the overall carbon impacts of urban developments or redevelopments of existing precincts can be assessed effectively and efficiently. The resulting tool will allow urban planners, municipalities, and developers to assess the future total demands for energy, transport, waste, and water while in the planning phase. The tool will also help to assess waste management performance and materials flow in relation to energy and water consumption and travel behaviour, supporting the design and management of urban systems in different city contexts.
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Lehmann, S.; Zaman, Atiq; Devlin, J.; Holyoak, N. (2013)Waste is a symbol of inefficiency in modern society and represents misallocated resources. This paper outlines an on-going interdisciplinary research project entitled "Integrated ETWW demand forecasting and scenario ...
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