scholarly journals Causes of Domestic Water Consumption Trends in the City of Alicante: Exploring the Links between the Housing Bubble, the Types of Housing and the Socio-Economic Factors

Water ◽  
2016 ◽  
Vol 8 (9) ◽  
pp. 374 ◽  
Author(s):  
Álvaro-Francisco Morote ◽  
María Hernández ◽  
Antonio-Manuel Rico
Author(s):  
Saeid Eslamian ◽  
Jorge Olcina ◽  
Antonio Manuel Rico ◽  
María Hernández ◽  
Álvaro Francisco Morote

2020 ◽  
Vol 13 (2) ◽  
pp. 82
Author(s):  
Gondo Reniko ◽  
Nametso D. Phonchi-Tshekiso ◽  
Patricia K. Mogomotsi ◽  
Goemeone E. J. Mogomotsi ◽  
Harison Chirefu

The current trends in water resources management underscore management of the resource as an economic good. Consequently, management strategies overlook demographic and social factors that influence domestic water consumption. Adopting water as an economic good conceptual framework, in a case study approach, a total of 120 household heads and two officials from Zimbabwe National Water Supply Authority (ZINWA) were selected in Karoi. While quantitative data were analysed using descriptive (frequency, percentages, etc.) and inferential statistics (t-test, ANOVA etc.), content analysis was used to analyse qualitative data. Findings revealed that while economic factors play a role in influencing domestic water consumption, demographic and social factors play equally the same in determining water consumption at the household level. The study recommends a further study to understand the role of demographic and socio-economic factors which affect water consumption.


Water ◽  
2019 ◽  
Vol 11 (12) ◽  
pp. 2620 ◽  
Author(s):  
Wenge Zhang ◽  
Xianzeng Du ◽  
Anqi Huang ◽  
Huijuan Yin

Proper water use requires its monitoring and evaluation. An indexes system of overall water use efficiency is constructed here that covers water consumption per 10,000 yuan GDP, the coefficient of effective utilization of irrigation water, the water consumption per 10,000 yuan of industrial value added, domestic water consumption per capita of residents, and the proportion of water function zone in key rivers and lakes complying with water-quality standards and is applied to 31 provinces in China. Efficiency is first evaluated by a projection pursuit cluster model. Multidimensional efficiency data are transformed into a low-dimensional subspace, and the accelerating genetic algorithm then optimizes the projection direction, which determines the overall efficiency index. The index reveals great variety in regional water use, with Tianjin, Beijing, Hebei, and Shandong showing highest efficiency. Shanxi, Liaoning, Shanghai, Zhejiang, Henan, Shanxi, and Gansu also use water with high efficiency. Medium efficiency occurs in Inner Mongolia, Jilin, Heilongjiang, Jiangsu, Hainan, Qinghai, Ningxia, and Low efficiency is found for Anhui, Fujian, Jiangxi, Hubei, Hunan, Guangdong, Guangxi, Chongqing, Sichuan, Guizhou, Yunnan, and Xinjiang. Tibet is the least efficient. The optimal projection direction is a* = (0.3533, 0.7014, 0.4538, 0.3315, 0.1217), and the degree of influence of agricultural irrigation efficiency, water consumption per industrial profit, water used per gross domestic product (GDP), domestic water consumption per capita of residents, and environmental water quality on the result has decreased in turn. This may aid decision making to improve overall water use efficiency across China.


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