Full length articleRegional differences in the industrial water use efficiency of China: The spatial spillover effect and relevant factors
Introduction
Water resources are essential to the economic development of a country or region. Amid the profound development of economic globalization and a continual increase in the global population, water scarcity has become a constraint for socioeconomic development in various countries. Since its economic reform, China has achieved substantial progress in urbanization and industrialization (Yin, Liu, and Wang, 2018); however, this progress has been achieved at the cost of massive resource consumption and intensified environmental pollution(Wang and Yang, 2016). According to China Statistical Yearbook, the industrial water consumption in China increased from 52.3 to 126.1 billion m3 between 1978 and 2018. Moreover, the proportion of industrial water consumption increased from 11% to 21% of the total water consumption during the same period. Meanwhile, an increase in wastewater discharge has worsened water pollution problems (Yang et al., 2018). Concerns such as the uneven spatiotemporal distribution of water resources, water scarcity, and severe water pollution have become the main obstacles restraining the sustainable development of China's economy. Therefore, increasing water usage efficiency is essential for alleviating the pressure on water resources in China and for protecting the water environment(Shao, Tian, and Fan, 2018). In recent years, the Chinese government has proposed several policies and plans to strengthen water management and optimize the use of water resources. The Opinions of the State Council on Applying the Strictest Water Resources Control System issued by the Chinese government in 2012 suggested that the total water use should be strictly controlled to increase water efficiency. The Action Plan for the Prevention and Control of Water Pollution, which was issued in 2015, focused on enhancing the industrial water cycle, establishing water efficiency evaluation systems, and developing water efficiency evaluation indices, such as the water consumption per unit of GDP index. In 2016, the Ministry of Industry and Information Technology published the Industrial Green Development Plan (2016–2020), which suggested that the water consumption per unit of industrial added value should be reduced. It indicates the determination of Chinese government to take actions to improve the efficiency of water resources utilization and to promote the sustainable development of industrial economy.
Industrial water use efficiency serves as a major indicator of the relationship between the input and output of water resources; it also reflects the extent and effectiveness of water resource use in the industrial production process. Commonly used single-factor water consumption indicators include water consumption per 10,000 RMB of GDP (Fujii, Managi, and Kaneko, 2013) and industrial water reuse rate (Zhao, Wang, Chan, and Liu, 2016). These indicators reflect the economic benefits of water resource use, but they do not reflect the ecological and environmental benefits (Shi, Chen, Shi, Wang, and Deng, 2014). Other factors (e.g., labor and capital) should also be considered regarding the use of water resources in industrial production and consumption, which explains the growing use of total-factor water efficiency indicators (Hu, Wang, and Yeh, 2006). Presently, data envelopment analysis (DEA) is the most common method for measuring water efficiency. First proposed by Charnes, Cooper, and Rhodes (1978), DEA is a nonparametric evaluation method that evaluates the relative efficiency of decision-making units by solving mathematical programming problems. Particularly advantageous in analyzing the situation of multiple inputs and multiple outputs, DEA has been applied in fields such as agriculture (Fan, Lu, Gu, and Guo, 2020; Ren, Li, and Guo, 2016), social science (Sun, Li, and Wang, 2019), energy (Jebali, Essid, and Khraief, 2017; Song, Zhang, Liu, and Fisher, 2013), and environmental studies (Yang, Hou, Ji, and Zhang, 2020). In particular, some scholars have estimated the resource and energy utilization efficiency of crops and livestock farming in the field of agriculture (Abbas et al., 2017; Elahi, Weijun, Jha, and Zhang, 2019a, 2019b, 2020), which are in-depth and valuable work for the efficient utilization of resources and the improvement of agricultural productivity.
DEA is also a prevalent approach used globally to measure water use efficiency. Research focusing on developed countries such as the United States (Morales and Heaney, 2015; Scaratti, Michelon, and Scaratti, 2013), Canada (Ali and Klein, 2014), France (Lannier and Porcher, 2013), Germany (Zschille and Walter, 2012), and Spain (González–Gómez, García Rubio, Alcalá–Olid, and Ortega–Díaz, 2013) has mainly centered on the efficiency measurement of agricultural water use and hydraulic facilities. Many scholars have also conducted extensive and critical research on the context in developing and underdeveloped countries, such as India (Raju and Kumar, 2013; Veettil et al., 2013), Vietnam (Huong et al., 2020), Tunisia (Chemak, Boussemart, and Jacquet, 2010), Uganda (Mugisha, 2013), and Kenya (Njiraini and Guthiga, 2013), with a focus on water supply efficiency.
China is the world's largest developing country, and the contradiction between its water scarcity and increasing industrial water consumption is exacerbating (Wang and Yang, 2016); industrial development is facing the dual constraints of total water use control and water pollution control. Under such circumstances, using water resources scientifically and appropriately as well as increasing the efficiency of industrial water consumption are the fundamental resolutions to medium- and long-term problems related to industrial development in China (Shi, 2018). However, water use efficiency in industrial sector of China has not been determined and needs to be further explored. Existing Research on China's industrial water efficiency can be roughly divided into two categories. One type of research analyzes industrial water use efficiency at the regional level and uses a variety of regional statistical data to measure industrial water efficiency; provincial-level data are mostly used (Bian, Yan, and Xu, 2014; Deng, Li, and Song, 2016; Liu, Yang, and Yang, 2020; Ma, Shi, and Chou, 2016; Song, Wang, and Zeng, 2018; Wang, Bian, and Xu, 2015; Xu et al., 2019; Yao et al., 2018; Zhou et al., 2019; Zou et al., 2020). The other type of research analyzes water use efficiency in specific industrial sectors; most of these studies have analyzed water use efficiency of a certain industry or industrial sector (Fujii, Managi, and Kaneko, 2012; Liu, Zhang, and Qin, 2020; Tian, Zhang, and Lu, 2020; Wang et al., 2020; Wang, Yu, Xiong, and Chang, 2019; Zhou et al., 2018).
DEA has been verified by domestic and international studies to be an effective method for measuring water efficiency. The DEA method is particularly suitable for efficiency measurement under scenarios with multiple inputs and multiple outputs, and it does not require configurating the functions of input and output variables in advance. However, errors and statistical test problems may occur when DEA is used for efficiency evaluation of small samples. The bootstrap-DEA method, proposed by Simar and Wilson (1998), can correct such errors in efficiency evaluation and provide a confidence interval of the efficiency value. Bootstrap-DEA has been widely applied in the tourism industry (Song and Li, 2019), financial industry (Aggelopoulos and Georgopoulos, 2017; Wijesiri, Viganò, and Meoli, 2015), and energy industry (Jebali et al., 2017; Song et al., 2013); however, it has rarely been applied for measuring water use efficiency. In addition, scant research has addressed the spatial characteristics of water use efficiency. According to Tobler's First Law of Geography (Tobler, 1970), spatial phenomena are often interrelated, rather than completely independent. Conventional mathematical statistical methods cannot effectively analyze the spatial correlation characteristics of water use efficiency. By contrast, exploratory spatial data analysis (ESDA) is highly advantageous in describing the spatial distribution and revealing the spatial correlation structure; thus, it is an effective method for studying the spatial characteristics of water efficiency. This study used spatial autocorrelation and spatial Markov chain in exploratory spatial data analysis to analyze the spatial correlation structure and spatial spillover effects of industrial water use efficiency.
In consideration of the aforementioned analyses, the objectives of this study are as follows: (1) to calculate the industrial water use efficiency of 31 provinces and regions in China during 2005–2015, as well as to analyze its temporal and spatial evolution characteristics; (2) to explore the spatial correlation model and spatial spillover effects of industrial water use efficiency; and (3) to analyze the factors influencing industrial water use efficiency.
Compared with existing research, this study has made improvements in two main aspects. First, his study used the Bootstrap-DEA model to measure industrial water use efficiency. The bootstrap method was incorporated into DEA to create large amounts of sample data by simulating the data generation process. By doing so, the distribution of the original sample estimates was simulated and the deviation of the efficiency value was corrected. Second, this study performed ESDA to address the spatial correlation of industrial water use efficiency; the spatial spillover effect of water use efficiency under different regional backgrounds was also confirmed. The conclusions of this study may, to some degree, serve as a basis for different regions of China to formulate corresponding water resources utilization plans and management systems.
Section snippets
Bootstrap-DEA
In the bootstrap method, a numerical simulation is conducted on raw samples and a DEA efficiency calculation is performed on the large number of generated simulation samples (Simar and Wilson, 1998; Wilson, 1999). The estimation procedures in the bootstrap method are as follows:
- (1)
The values of the kth input and output (k = 1, 2,…, n) are substituted into each decision-making unit (DMU). The parameters Xk and Yk refer to the input and output of the kth DMU, respectively. A DEA-CCR model is used to
Estimate results for industrial water use efficiency
The results obtained for the industrial water use efficiency of each considered province during 2005–2015 are presented in Table 4. The kernel density curve of the results over time (Fig. 1) reflects the dynamic change in the industrial water use efficiency in China. As presented in Table 3, the industrial water use efficiency of all the provinces was less than 1, which implies that the industrial water use of these provinces is not optimized and can be improved. The mean industrial water use
The main conclusions
An increase in industrial water use efficiency would facilitate sustainable industrial development in China. This study employed a bootstrap DEA model to calculate the industrial water use efficiency of 31 provinces in China from 2005 to 2015 and to analyze their regional differences and spatial agglomeration features. Moreover, spatial Markov chain was used to examine the spatial spillover effect of industrial water use efficiency. A spatial Tobit model was used to conduct an empirical
Funding
This work was supported by the National Natural Science Foundation of China [grant number 71,704,097].
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgements
This work was supported by the National Natural Science Foundation of China [grant number 71704097].
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