End use approach

A large amount of literature has been carried out in order to estimate how residential demand will respond to demand management policies. As mentioned earlier, residential end-use studies are directed to collect where and how much water is consumed in a residency unit (Giurco et al., 2008).

Researchers who used end use methods have identified four main data collection methods: metering, data logging, surveys, and diaries (Turner et al., 2008). A comprehensive description for conducting end use studies for residential water demand can be found from Turner et al. (2008), Beal et al. (2010), and Giurco et al. (2008). A thorough investigation of these guidebooks reveals that most end use studies follow these steps:

(1) Define the objectives: This step is concerned with the clarification of the purpose of the study. Reported objectives in the literature regarding end use modeling can range from improving urban water planning through developing accurate end use water demand, designing water management programs by understanding the potential savings of devices at end uses, monitoring demand management programs, and leak detection.

(2) Defining data requirements: Once the objective is defined, data should be collected accordingly so that objectives can be achieved. Example of data could be single family end-use, multi-family, and data within certain geographic regions.

(3) Choosing a technology: This step concerns selecting the most convenient tool to capture the required data. It should be noted that the selected technology is subject to many constraints such as time, cost of technology, and resolution of data.

31 (4) Define the sample size: To ensure the model is representative of the real

situation, the analyst should define the population and acceptable error.

As noted above, since the methodological approach for conducting end use analysis follow the same steps, the following paragraphs present some well-cited end use studies. The purpose is to show how different end-use appliances can reduce demand.

Willis et al. (2013) surveyed 151 households in Gold Coast, Australia, to estimate per capita and household water demand, and to examine if there is a correlation between social and demographic factors and water saved. They followed a mixed method approach, where the design of the study followed both a survey questionnaire and a stock inventory of water devices within each household. This study used smart metering to record where and how much water was being used.

Variables such as income, lot size, and the possession of rain water tanks have been linked with the amount of water saved at each end use when installing water efficient appliances. It has been found that the smaller the lot size the more water will be saved by irrigation, especially when a rainwater tank is installed.

The analysis of other devices follow this trend: the amount of water used by devices before retrofitting is recorded then compared with the water used after the devices at end uses are retrofitted. For instance, installation of an efficient clothes washer has demonstrated a per capita savings of 14kL/p/a. The authors concluded that there is positive correlation between the income level, lot size, and amount of water used.

Beal et al. (2010) carried out a survey for 250 single family households in South East Queensland (SEQ) to estimate per capita and household water demand at all end uses. Moreover, the study was developed to compare water consumption between different regions within SEQ and assess the efficiency of different water efficient appliances. The survey revealed some regional differences in water use

32 at both scales, per capita and household levels. On average, it was found that the

shower comprises 29% of total indoor consumption, while taps account for 24%.

Turner et al. (2005) reported on the results from the largest conservation program applied in Australia, “Every Drop Counts,” to retrofit residential units with water efficient appliances at all end uses. The report claimed that on average a single family household can save 20.9±2.5 kL/hh/a.

A recent study in the U.S. was conducted by Lee et al. (2013) in Miami-Dade County, Florida, with the objective of analyzing the long term savings in residential water demand of low-income senior families. They noted that over 3 years, average per capita saving reached 200 l/d. Moreover, their analysis showed that monetary savings can be maximized by adopting multiple efficient devices.

One clear observation could be made here. While many studies have investigated end use consumption and have defined potential savings in water, there is a lot more to do in this area. Inman and Jeffrey (2006) questioned the efficiency of demand management studies when they claimed that most demand management studies show a lack of willingness to participate in demand management practices.

They stated that more variables should be investigated to determine the effectiveness of demand management more accurately. In this regards, this research will address the behavioral aspect of consumers to understand the frequency (number of times) of using a certain water appliance.

Jorgensen et al. (2009) shed light on the fact that successful demand management policies should follow an in-depth understanding of how consumers perceive water and use it. In fact, studies on household water demand and conservation revealed different variables that have different impacts on water use.

Along with the need for more studies and variables in the context of residential demand management, the use of variables should also shift from aggregate scale to individual scale. In other words, although studies shown above have combined

33 socioeconomic variables with end use consumption, the dominant approach was

to cluster consumers into income levels, for example, and record their end use consumption. To this end, while this approach might reveal some trend in the use of water at specific end uses, it lacks an understanding of how these socioeconomic variables can affect the belief in the need for water conservation and thus participation in demand management.

Therefore, this research will take analysis on end use consumption further in an attempt to capture the role of income and education, for example, in participating in demand management activities.