Construction of a “Three-Waters” Evaluation Indicator System: A Meta-Analysis

Improving water utilization efficiency can effectively alleviate the contradiction between water shortage and water demand in the process of rapid urbanization. The middle and lower reaches of the Yellow River Basin, China, are characterized by water shortage. In order to improve water use efficiency, taking the 43 prefecture-level units in this region as the study area, industrial and agricultural water use efficiency is calculated based on the undesired SBM-DEA model. Then, the Tobit model is used to explore their influencing factors. The results show that the regional average agricultural water use efficiency is greater than the industrial water use efficiency. The temporal trend indicates that the agricultural water use efficiency shows a fluctuating upward trend, while industrial water use efficiency has a fluctuating downward trend. The gravity center of industrial water use efficiency moves from northwest to southeast in a “Z” shape, while the gravity center of agricultural water use efficiency moves westward as a whole. From the perspective of spatial patterns, the standard deviation ellipse of industrial water use efficiency shows that the industrial water use efficiency is higher in the east–west direction, while the agricultural water use efficiency is higher in the northwest–southeast direction. The improvement of urbanization level is conducive to the improvement of industrial water use efficiency; however, the development of urbanization has a significant inhibitory effect on improving agricultural water use efficiency.

China is facing severe pressure on its water resources and water environments. Calculating the industrial water efficiency of each province is an important index for the central government to evaluate local governments. In the traditional water resources evaluation index, the industrial water use efficiency and pollutant discharge are evaluated separately. In this paper, we collected industrial input data, output data and pollutant discharge data with a four-stage data envelopment analysis to calculate China's industrial water use efficiency with and without considering pollutant discharge, and then analyzed the factors influencing the industrial water use efficiency. The results show that the eastern coastal provinces of China have the highest water use efficiency and are less affected by pollutant discharge than other provinces. The industrial water use efficiency of the central and western provinces is lower than that of the other provinces, and the industrial water use efficiency in the central provinces is greatly affected by pollutant discharge. Factor endowment, economic development level, scientific and technological progress, industrial structure, proportion of foreign investment, water consumption per 10000 yuan of value-added by industry, industrial sewage treatment capacity and educational investment have a significant influence on the industrial water use efficiency of China. We suggest that the government strengthen the construction of sewage plants and other related infrastructure in central provinces when conducting the industrial transfer of heavy polluting enterprises.

Consumptive fresh-water use by industry in California is estimated at about 230 million gallons per day, or about one-half of one percent of agricultural withdrawals in the State, and only about 1 percent of agricultural consumptive use. Therefore, a significant State-wide realignment of the total water resources could not be made by industrial conservation measures. Nevertheless, considerable latitude for water conservation exists in industry fresh water consumed by self-supplied industry amounts to about 40 percent of its withdrawals in California, and only about 10 to 15 percent nationally (not including powerplant use). Furthermore, where firms withdraw and consume less water there is more for others nearby to use.

Regional differences in the industrial water use efficiency of China: The spatial spillover effect and relevant factors

Comprehensive analysis of water use and pollution management plays an important role in regional water security and sustainable socio-economic development. This study applies the environmental Kuznets curve (EKC), Gini index and elasticity coefficient methods to conduct an investigation of industrial and domestic water use and pollution management in Shandong. The results show that industrial water pollution generally displayed a coordinated relationship with socio-economic development, while an uncoordinated relationship occurred between domestic water pollution and socio-economic development. Meanwhile, the Gini index between domestic water use and population in 2017 (0.101) was superior to that of 2003 (0.165), and the Gini index of industrial water use and second industry output in 2017 (0.273) was better than that of 2003 (0.292), indicating that the allocation and equity of domestic and industrial water use in Shandong kept to a good development trend. Additionally, the industrial effect is better than the domestic effect in terms of the control of wastewater emissions and the governance of typical pollutants in wastewater. Accordingly, domestic water pollution has gradually become one of the major sources of water pollution, and the allocation of industrial and domestic water use has room to improve further in Shandong. Conjunctive use of the aforementioned three methods provides an approach to investigate the integrated management of water use and water pollution control from multiple angles.

ABSTRACT: The paper outlines both the methods used and the results obtained in a study of the demand for municipal and industrial water for the Seattle region. The study was made as part of a regional water management study program, one objective of which is to “… identify, quantify, and set priorities for all current and future water uses …”. A basic concept in the study of municipal and industrial water use is that the demand for water is derived from the demand for output and the direct services that water provides. Principal characteristics of the study are: (1) Water use is studied by type ‐ residential, commercial, industrial and public ‐with identification of factors affecting each; (2) Water demands are studied by season as well as on an annual basis; (3) Projections of future water use are tied directly to projections of economic change in the service area; and (4) The effects of alternative policies on water use are estimated. Water use levels are projected under alternative regional growth assumptions provided by the Puget Sound Governmental Conference, a regional planning agency. Thus, the water use planning is consistent with other regional planning programs in this respect. The results can be varied according to changes in specific factors affecting water use. The factors considered in the present study include: single‐family residential lot size, distribution of population between single‐ and multi‐family units, per capita water use by multi‐family unit residents, and industrial and commercial water use per employee. An income elasticity of demand was estimated for single‐family residential water use.

Global inequity and the unbalance of water resources has been a critical issue for many years; and the Chinese per capita water resources are only 1/4 of the global average. Meanwhile, as the Chinese economy is growing rapidly, the demand of Chinese industrial water use is also increasing. In this case, it is important to balance the relationship between economic growth and industrial water use. In this study, a reduction model is established for the northeastern, northern coastal, eastern coastal, southern coastal, middle Yellow River, middle Yangtze River, southwestern, and northwestern regions to verify the environmental Kuznets curve (EKC) for their respective industrial water use and provide theoretical support for decision making from an economic perspective. It adopts the per capita industrial water use and GDP of the eight economic zones from 2002 to 2014. The unit root test and co-integration test were adopted to analyze the stationarity of the data, and the triple reduction model was used for the fitting of variables. The relationship between per capita industrial water use and GDP showed an inverted U-shaped curve from 2002 to 2014 for China, as well as for the eastern coastal and middle Yangtze River regions, with the coordinates of the turning points being (9.8749, 4.6735), (10.3098, 5.4783), and (9.8184, 5.0622), respectively. The per capita GDP at the turning point of the inverted U-shaped curve is 18,000–30,000 Yuan (at constant prices from 2000). This study provides important thoughts and lessons for collaborative research into the relationship between industrial water consumption and economic development. The central government should focus on the central and western regions when creating policies for water resource management and technological development to improve their industrial water use efficiency.

Over the past two decades, the industrial sector of China has experienced rapid development, which has correspondingly led to a significant increase in water resource consumption. To better understand the dynamics of industrial water use, and formulate appropriate water resource conservation and management policies, it is necessary to evaluate the evolution of industrial water use efficiency and its influencing factors in China. Given the high sensitivity and accuracy of the stochastic frontier analysis (SFA) model for efficiency assessment, the Tobit model is more suitable for regression analyses of truncated data. This study employed the SFA–Tobit panel model to evaluate the industrial water use efficiency of provinces in China from 2003 to 2021. The results indicate that national industrial water use efficiency improved from 0.41 to 0.65 during the study period. All provinces showed significant improvements, with developed provinces exhibiting higher industrial water use efficiency than undeveloped provinces. Regionally, the eastern areas demonstrated superior industrial water use efficiency compared to the western regions, with the central regions having the lowest overall water use efficiency. Moreover, the efficiency gap between regions has been narrowing. The national industrial water-saving potential is estimated at 31.306 billion cubic meters, with Jiangsu province having the highest saving potential at 3.709 billion cubic meters. In comparison, Beijing has the lowest at just 32,000 cubic meters. The Tobit regression results reveal that economic development and technological progress positively contribute to increased industrial water use efficiency. In contrast, water use intensity, openness, and urbanization levels negatively impacted the improvement of industrial water use efficiency. Therefore, it is necessary to increase investment in technological innovation, strictly control industrial water intensity, appropriately balance import and export trade with urbanization levels, and promote sustainable economic development. This study can provide effective support for the subsequent green transformation of China’s industry.

The construction of urban agglomerations around Poyang Lake is an important starting point of the strategy for the improvement of central China, but the spatial agglomeration of industry and population brings great pressure to the ecological environment. It is of great practical value to explore the impact of rapid urbanization on the water use efficiency of important ecological functional areas. Considering the undesired output of industrial production, this paper adopts the SE-SBM model to measure industrial green water use efficiency, comprehensively considers different aspects of urbanization of the urban agglomeration around Poyang Lake, empirically tests its inhibiting or boosting effect on industrial green water use and explores its spatial spillover effect with the help of a spatial metrology model. The results show that (1) the industrial green water use efficiency of urban agglomerations shows an overall upward trend, and the efficiency value of central cities is significantly higher than that of non-central cities and continues to show a state of diffusion; (2) social urbanization, environmental urbanization, and balanced urbanization can significantly improve industrial green water use efficiency, while industrial urbanization or industrialization inhibits the improvement in water use efficiency; (3) considering the spatial spillover factor, there are significant positive local effects between population urbanization and balanced urbanization, and significant positive spatial spillover effects between industrial urbanization and environmental urbanization; (4) the original model can pass the significance test by replacing the output-oriented water use efficiency with the input-oriented or non-oriented water use efficiency; the study area is extended to Jiangxi Province, and the impact of urbanization on industrial water use efficiency is basically consistent. We should adhere to the new type of urbanization that improves well-being and is friendly to the environment, rationally plan the industrial spatial pattern of urban agglomerations, adhere to the ecological and environmental threshold on undertaking industrial transfer, and promote the flow and sharing of green production factors.

The balance between water supply and demand is essential for industrial growth, affecting economic, social, and environmental sustainability. Our research employs a Gaussian process regression for demand prediction. Additionally, it takes into account water limits and policy thresholds when determining the supply, thereby defining a range of uncertainty for both the industrial demand and the supply. A pattern recognition method matches this trade-off range, identifying three patterns to support water management. The study focuses on the analysis of industrial water supply and demand dynamics under uncertain conditions in nine cities (Baiyin, Dingxi, Gannan, Lanzhou, Linxia, Pingliang, Qingyang, Tianshui, and Wuwei) in Gansu Province of China’s Yellow River Basin in 2030. The results of the study show that industrial water use in Baiyin, Linxia, Dingxi, and Tianshui cities falls into Pattern I, providing water resources to support industrial development. Industrial water use in Wuwei, Pingliang, Qingyang, and Gannan cities represents Pattern II, which maintains a balance between supply and demand while allowing flexibility in water demand. Finally, the industrial water use in Lanzhou city is characterized by Pattern III, which requires optimization through structural, technological, and management improvements to mitigate the negative impacts of water scarcity on the sustainable development of the economy and society. The results of the research can be used as a reference for policy making in water resources planning and management in the basin.

根据2003年至2014年景德镇市农业、工业、生活、生态环境等行业的用水量资料，分析景德镇市近12年来行业用水的变化发展，结合SPSS统计软件对引起行业用水变化的主要驱动力因子进行分析和说明。结果表明：景德镇市的水资源利用以农业用水为主，占用水总量的50.6%，工业用水占用水总量的34.5%，比重相对较大，生活用水和生态环境用水所占用水总量的比重较小，分别为13.9%和1.0%；景德镇市用水总量基本稳定，无整体上升或下降趋势，农业用水量及其所占用水总量的比重呈现整体上升的趋势，工业用水量及其所占用水总量的比重呈现出先上升而后下降的趋势，生活用水量及其所占用水总量的比重近10年来呈现逐年攀升的趋势，生态环境用水量及其所占用水总量的比重呈现略微上升的态势；因子分析的结果显示人口因素、经济发展因素和农田灌溉因素是景德镇市行业用水演变的主要驱动力。 According to the agricultural, industrial, life, public and ecological environment water use data of Jing-dezhen city from 2003 to 2014, the paper analyzes the change of water use in various industries in Jing-dezhen city during the last 12 years, and analyzes and explains the main driving factors of the develop-ment of water use in various industries by using SPSS statistical software. The results show that the water resources utilization in Jingdezhen city is mainly agricultural water use, which accounts for 50.6% of the total amount; the proportion of industrial water use is relatively larger, which accounts for 34.5% of the total amount; the proportion of water used for life, public and ecological environment is small, which accounts for 13.9% and 1.0% of the total amount. The total amount of water use in Jingdezhen is basically stable, and there is no overall rise or fall, agricultural water use, water use of the ecological environment are showing an overall upward trend and the proportion of total water use in agriculture and ecological environment is also increasing; industrial water use and its proportion of the total water use showed a trend of first increasing and then decreasing trend; water use for life and public and their proportion of the total water use in the past 10 years has been rising year by year. Factor analysis results show that the main driving force of the development of water use in various industries in Jingdezhen city is the population, economic development and farmland irrigation.

: This report documents over 50 substantial studies of the response of municipal and industrial water use to price in an attempt to correct the poorly understood phenomenon of the sensitivity of water use to price. Most likely price elasticity ranges are given for non-seasonal and seasonal residential water use; average residential water use; and reported elasticities for industrial and commercial water use. (Author)

Achieving carbon neutrality enables China to attain its industrial water-use target

Rational water use is the basis for sustainable development. The issue of how to use limited water resources to satisfy the high rate of economic development has attracted a great deal of attention from society. This paper presents a quantitative analysis of the intrinsic relationship between economic growth and industrial water use changes in Hubei Province based on panel data from 2004 to 2019. With the help of the Tapio decoupling model, the problem of decoupling the economic growth of Hubei Province and the water use of the three industries in 15 years was discussed. On the basis of Kaya’s extended identity, the Logarithmic Mean Divisia Index (LMDI) index decomposition method is used to evaluate the driving factors and steady state changes in the three industries’ water use. The results show that, with regard to the decoupling state, there are three decoupling states between economic growth and industrial water use in Hubei province: negative decoupling, strong decoupling, and weak decoupling, which showed a phase characteristic. From the decomposition of the factors, the industrial structure effect and the water intensity effect are the key factors that determine the decoupling of economic growth and industrial water use in Hubei Province, as well as the core driving force to promote the decoupling state. According to the development trend, Hubei Province needs to take into account the efficiency and affordability of water resources in the process of promoting social and economic development. Therefore, in line with the research outcomes, this study provides effective and feasible recommendations for promoting sustainable economic and social development in Hubei Province.

Evaluating and improving water use efficiency is considered one of the main ways of tackling water shortage challenges in water-scarce cities. A useful indicator for evaluating industrial water use efficiency is the relative water use efficiency (RWUE). In this paper, an industrial RWUE evaluation scheme is proposed based on data envelopment analysis (DEA) theory. In this scheme, the RWUE is divided into overall efficiency (OE), pure technical efficiency (PTE), and scale efficiency (SE). In order to help decision-makers specify the focal industry of efficiency improvement, direct water use is distinguished from indirect water use. By employing this industrial RWUE evaluation scheme, this research calculated the industrial RWUEs for 25 representative industries of Beijing (1990–2010). Results show that the OE, PTE, and SE of Beijing have improved significantly. In the primary industry, the scale adjustment fundamentally lifted OE. For the secondary industries, there still exists much room for water use efficiency improvement in technical innovation. Some emerging tertiary industries replaced traditional tertiary industries as the most efficient water users. This study serves as a valuable reference for the implementation of the Strictest Administration of Water Resources (SAWR) of China, and provides policy-makers worldwide with a useful framework of understating industrial water use efficiency.