Residential energy and water consumption depend on dwelling structure and the behaviour of residents. Aspects of residential behaviour can be derived from census data. Dwelling information is harder to obtain. Using both aerial and street-level views from Google mapping products, exterior dwelling characteristics were captured in each of 40 postal areas in and around Melbourne, Australia. This approach saved the time and cost of travelling to the widely spread suburbs and provided data not otherwise available. The census and dwelling data were compared with resource usage statistics in linear regression models. It was found that energy and water use are highly correlated, with socio-economic variables better explaining water consumption and dwelling structure factors better explaining energy consumption. Nevertheless, the proportions of households that include a couple with children and have a swimming pool provided useful models of variations in both energy and water use. Applications to planning through spatially explicit scenario testing were developed in ArcGIS ModelBuilder.
TopIntroduction
Rapid urban population growth and the consequent increase in urban resource demand are putting pressure on limited natural resources. Water use has been restricted in many parts of the world (Kenney, Goemans, Klein, Lowrey & Reidy, 2008; Worthington & Hoffman, 2008) and the broader implications of energy use are now widely recognised (Gram-Hanssen, 2014; Newton & Meyer, 2010; O'Callaghan, Green, Hyde, Wadley & Upadhyay, 2012). Over the past decade, a nexus between energy and water resources has been increasingly described (Marsh, 2008; Stillwell, King, Webber, Duncan & Hardberger, 2011; U.S. Department of Energy, 2006) and governments are devising policies and beginning to take action (DHI, 2008; Fisher & Ackerman, 2011; Proust, Dovers, Foran, Newell, Steffen & Troy, 2007; Stillwell, A. S., King, C. W., Webber, M. E., Duncan, I. J., & Hardberger, A., 2011) targeting the factors influencing the consumption of both energy and water in various sectors of the economy. This chapter focuses on the residential sector that, in Australia, consumes 11.7% of energy (Syed, Melanie, Thorpe & Penney, 2007) and 10.7% of non-agricultural water (ABS, 2013).
Saydi, Bishop and Rajabifard (2015) reported their use of online resource instruments to capture dwelling structure data for urban resource management, in particular residential energy and water demand modelling. The purpose of that research was to develop a novel Virtual Identification of Dwelling Characteristics Online (VIDCO) technique to support analysis of the influence of dwelling structure characteristics on urban resource consumption. In this chapter, we summarize that development and then describe how it was combined with other data sources and modelling to support a scenario testing platform for e-planning. In particular, we provide an overview of our data collection methods – especially VIDCO, and then discuss VIDCO application issues and evaluate the data produced. We then bring the VIDCO derived dwelling data together with socio-economic data and water and energy consumption data to model the respective influences of dwelling and behavioural characteristics on urban residential resource consumption. Finally, we illustrate the use of the derived relationships in spatially explicit scenario testing for resource planning purposes and report some conclusion.
TopBackground
Urban Resource Consumption
Residential energy and water consumption depend on both the structure of the dwelling (and any associated garden) and the number the residents, and their behaviour: we refer to these as dwelling characteristics and socio-economic characteristics respectively. The influence of dwelling characteristics on residential consumption of energy and water has emerged as an essential component of urban resource reduction strategies. For example, separate dwellings consume more residential energy and water than other dwelling types such as semi-detached, or flats and apartments (Crawford & Fuller, 2011; Druckman & Jackson, 2008; Fox, McIntosh & Jeffrey, 2009; Troy & Holloway, 2004). While social-economic factors are also influential in household resource consumption (Arbués, Villanúa, & Barberán, 2010; Domene, Saurí & Parés, 2005; Druckman & Jackson, 2008; O'Callaghan, B., Green, H. J., Hyde, R. A., Wadley, D., & Upadhyay, A., 2012), dwelling characteristics are a key element for planners and policy makers to devise urban resource management strategies (Guerra Santin, Itard & Visscher, 2009). Also, while the socio-economic characteristics of an area can commonly be extracted from census data, identifying the most significant dwelling structure factors is more problematic and these are not routinely surveyed. However, analysis in support of policy development requires detailed and high-quality data about the structure of dwellings.