Expanded S-Curve Model of Relationship between Domestic Water Usage and Economic Development: A Case Study of Typical Countries

Domestic water plays a growing role with the unprecedented economic development and rising urbanization. The lack of long-term evaluation of domestic water usage trends limits our understanding of the relationship between domestic water usage and economics. Here, we present a pragmatic approach to assess the long-term relationship between domestic water usage and economics through historical data of the last 100 years from 10 typical countries to establish an evaluation method for different economics. The relationship between domestic water usage and GDP per capita was described as an expanded S-curve model and the mathematical modeling was derived to simulate this relationship for four typical countries as case studies. The simulation results show that the expanded S-curve of different countries can be calibrated with three key points: takeoff point, turning point, and zero-growth point, and four transitional sections: slow growth, accelerated growth, decelerated growth, and zero/negative growth, corresponding to the same economic development level. In addition, other factors influencing domestic water usage are also discussed in this research, including urbanization, industrial structure, and technical progress. We hope to provide a case study of an expanded S-curve as a foundation for forecasting domestic water usage in different countries or in the same economy at different developmental stages

Expanded S-Curve Model of Relationship between Domestic Water Usage and Economic Development: A Case Study of Typical Countries

Domestic water plays a growing role with the unprecedented economic development and rising urbanization. The lack of long-term evaluation of domestic water usage trends limits our understanding of the relationship between domestic water usage and economics. Here, we present a pragmatic approach to assess the long-term relationship between domestic water usage and economics through historical data of the last 100 years from 10 typical countries to establish an evaluation method for different economics. The relationship between domestic water usage and GDP per capita was described as an expanded S-curve model and the mathematical modeling was derived to simulate this relationship for four typical countries as case studies. The simulation results show that the expanded S-curve of different countries can be calibrated with three key points: takeoff point, turning point, and zero-growth point, and four transitional sections: slow growth, accelerated growth, decelerated growth, and zero/negative growth, corresponding to the same economic development level. In addition, other factors influencing domestic water usage are also discussed in this research, including urbanization, industrial structure, and technical progress. We hope to provide a case study of an expanded S-curve as a foundation for forecasting domestic water usage in different countries or in the same economy at different developmental stages

Analysis of domestic water consumption in typical countries and its enlightenment to China

With the development of the country and the improvement of the level of science and technology, the use of water resources in China is on the rise. Among them, the domestic water consumption is increasing rapidly, and it is predicted that the domestic water consumption in China will reach two to three times that in the early 21st century in the middle of the 21st century. Based on the data of domestic water consumption in ten typical countries in the world, such as the United States, Germany and Japan, this paper studies the variation of per capita domestic water consumption in these countries with per capita GDP, urbanization rate and other factors. Taking these data as samples, the domestic water consumption in China is predicted. The prediction results can further help relevant departments to formulate reasonable policies in water resources planning

China’s Water Intensity Factor Decomposition and Water Usage Decoupling Analysis

As the most populous country in the world, China has a great shortage pressure of water resources. With the acceleration of urbanization, China’s water usage in different sectors will change significantly in next few years. In order to investigate the main reasons behind water usage change in China, the Logarithmic Mean Divisia Index (LMDI) model was adopted in this paper from 2000 to 2020 with provincial data. Three effects, including that of technology, industrial structure, and regional scale, were analyzed. In addition, the decoupling effect between water usage and economic growth was also considered. The results show that: (1) from 2000 to 2020, the technological effect, industrial structure effect, and regional scale effect are −376.54, −89.85 and 20.66, respectively; (2) the technical effect and industrial structure effect have the greatest impact on primary industry, followed by secondary industry; (3) the technical effect is greater than the industrial structure effect in most provinces; and (4) the decoupling state gradually changes from weak decoupling to strong decoupling. In the future, the key policy recommendations for water saving are the following: (1) technological innovation has the most efficient effect on the reduction of water usage in China, and (2) the optimization of industrial structure can be helpful in water-saving in the future

China’s Water Intensity Factor Decomposition and Water Usage Decoupling Analysis

As the most populous country in the world, China has a great shortage pressure of water resources. With the acceleration of urbanization, China’s water usage in different sectors will change significantly in next few years. In order to investigate the main reasons behind water usage change in China, the Logarithmic Mean Divisia Index (LMDI) model was adopted in this paper from 2000 to 2020 with provincial data. Three effects, including that of technology, industrial structure, and regional scale, were analyzed. In addition, the decoupling effect between water usage and economic growth was also considered. The results show that: (1) from 2000 to 2020, the technological effect, industrial structure effect, and regional scale effect are −376.54, −89.85 and 20.66, respectively; (2) the technical effect and industrial structure effect have the greatest impact on primary industry, followed by secondary industry; (3) the technical effect is greater than the industrial structure effect in most provinces; and (4) the decoupling state gradually changes from weak decoupling to strong decoupling. In the future, the key policy recommendations for water saving are the following: (1) technological innovation has the most efficient effect on the reduction of water usage in China, and (2) the optimization of industrial structure can be helpful in water-saving in the future