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In recent years, most of the studies used the disaggregated smart meter data for demand modeling and identifying factors affecting residential water end-uses. Almost all of these studies tried to fit the data to known probability distributions while acknowledging that such distribution may not be applicable for a different dataset as water end-use varies with region, season, demography, climate, culture, etc. Few studies tried to utilize the high-frequency disaggregated end-use data as a feedback tool with the assumption that providing just the water-use data may help the users change their behavior. But behavioral transformation theories state that understanding users' intention towards environment, building conservation attitude, showing peers' conservation behaviors, and regular communication between the users and the water managers are essential factors for behavioral change which act in combination with the self-observation data supplied through the feedbacks (Ajzen 1985, Bandura 1991, Thibodeau et al., 1992, De Young, 1993). To fill the gap of prior studies, this research proposes to use a three-step approach for conservation practices. Steps include developing a set of baseline questions for understanding users' perception of conservation and environment, followed by segmentation of water-users based on users' separate and combined technological and behavioral potential by ranking water-use, technical efficiency (flowrate), and behavior (intensity and duration of use), and finally developing customized messages to encourage high water users to conserve by reviewing leading behavioral transformation theories.
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World Bank Population, Income and Carbon Emission Data
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