Effects of feedback about community water consumption on residential water conservation

Although the quantity of household water consumption does not account for a huge proportion of the total water consumption amidst socioeconomic development, there has been a steadily increasing trend due to population growth and improved urbanization standards. As such, mastering the mechanisms of household water demand, scientifically predicting trends of household water demand, and implementing reasonable control measures are key focuses of current urban water management. Based on the categorization and characteristic analysis of household water, this paper used Maslow's Hierarchy of Needs to establish a level and grade theory of household water demand, whereby household water is classified into three levels (rigid water demand, flexible water demand, and luxury water demand) and three grades (basic water demand, reasonable water demand, and representational water demand). An in-depth analysis was then carried out on the factors that influence the computation of household water demand, whereby equations for different household water categories were established, and computations for different levels of household water were proposed. Finally, observational experiments on household water consumption were designed, and observation and simulation computations were performed on three typical households in order to verify the scientific outcome and rationality of the computation of household water demand. The research findings contribute to the enhancement and development of prediction theories on water demand, and they are of high theoretical and realistic significance in terms of scientifically predicting future household water demand and fine-tuning the management of urban water resources.

mentioning

confidence: 99%

Conceptual Framework and Computational Research of Hierarchical Residential Household Water Demand

Pan

Hou

Yang

et al. 2018

“…In presented by [26] participants with high water consumption achieved decreases when receiving feedback of their consumption via emoticons. Similarly, in our study, the use of graphical material reduced water consumption by 76.7%, being the only intervention strategy that presented a significant decrease in consumption, as well as a positive change in behavior in self-report techniques.…”

Section: Graphical Toolsmentioning

confidence: 99%

“…Educational campaigns encourage voluntary conservation of water, either by altering behavior [22] or the adoption of devices that are more efficient in the use of water [23]. To achieve behavioral changes, several strategies are effective, such as, among others, the use of informative material, training on proper water management, guidelines in the media, multimedia applications, and feedback on consumption levels [15,[24][25][26]. Geller [14] indicated that the provision of informative material raises awareness and efficiently communicates knowledge about the behaviors necessary to conserve water.…”

Section: Educational Programs and Behavioral Changesmentioning

confidence: 99%

Voluntary Management of Residential Water Demand in Low and Middle-Low Income Homes: A Pilot Study of Soacha (Colombia)

Alarcón

Rodríguez

Kua

2019

The efficient use of water in urban contexts becomes a priority in the face of population growth and the potential vulnerability of water supply as a result of the impacts of climate change. This pilot study focuses on the use of educational strategies to promote the voluntary management of residential water demand. Three schools in the municipality of Soacha (Colombia) were involved, where students from 12 to 15 years old participated as promoters of water consumption educational campaigns within their families, covering a total of 120 low and middle-income families. Three intervention strategies (that is, a virtual platform, learning activities, and graphical tools) were carried out. The effects of these intervention strategies on changes in the water consumption were analyzed to establish the dependence of this variable with socio-demographic, economic, environmental and quality of life factors. Different information gathering tools were used, such as validated local water utility bills, surveys and self-reports. The graphical tools reduced consumption by 14%, but other strategies did not show significant reductions. However, according to self-reported data, all educational campaigns did have positive outcomes on the participants' behavior. It was found that socioeconomic factors, such as type of housing, socioeconomic strata, living in a rented home, and quality of life variables (such as enjoying nature and culture, good and fluid family relationships, and material possessions) significantly affected the changes in water consumption behavior.

“…Many studies seeking to unlock the full potential of detailed paper-based water end-use feedback leveraged from digital water data have been undertaken [6,15,[33][34][35][36]. Although the ultimate goals of these studies are similar in nature, they fall into different categories based on their unique approach.…”

Section: Paper-based Feedbackmentioning

confidence: 99%

“…The literature has mainly focused on areas where water resources are under stress. However, one study was undertaken by Otaki et al [33] to identify the most effective type of feedback in promoting water conservation in Tokyo, Japan, where water shortages are not yet a concern. After employing three types of feedback information-actual and mean consumption, water consumption rankings, and emoticons-with three groups of households, the study revealed that the effectiveness of feedback information varies for high and low water consumers.…”

Section: Paper-based Feedbackmentioning

confidence: 99%

Machine Learning and Data Analytic Techniques in Digital Water Metering: A Review

Rahim

Nguyen

Stewart

et al. 2020

Digital or intelligent water meters are being rolled out globally as a crucial component in improving urban water management. This is because of their ability to frequently send water consumption information electronically and later utilise the information to generate insights or provide feedback to consumers. Recent advances in machine learning (ML) and data analytic (DA) technologies have provided the opportunity to more effectively utilise the vast amount of data generated by these meters. Several studies have been conducted to promote water conservation by analysing the data generated by digital meters and providing feedback to consumers and water utilities. The purpose of this review was to inform scholars and practitioners about the contributions and limitations of ML and DA techniques by critically analysing the relevant literature. We categorised studies into five main themes: (1) water demand forecasting; (2) socioeconomic analysis; (3) behaviour analysis; (4) water event categorisation; and (5) water-use feedback. The review identified significant research gaps in terms of the adoption of advanced ML and DA techniques, which could potentially lead to water savings and more efficient demand management. We concluded that further investigations are required into highly personalised feedback systems, such as recommender systems, to promote water-conscious behaviour. In addition, advanced data management solutions, effective user profiles, and the clustering of consumers based on their profiles require more attention to promote water-conscious behaviours.