{"paper_id":"35ef92d4-a37e-4bb4-b1c2-5f0bdec2a4dc","body_text":"Does energy poverty affect the sustainable development of water resources？—Empirical Evidence from China | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Does energy poverty affect the sustainable development of water resources？—Empirical Evidence from China Zhong Fang, Qiqi Xiao, Zi Ye This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4987800/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Energy poverty and water resources development, as one of the global Sustainable development Goals, are also one of the keys to achieving sustainable development and social welfare in China. In this study, this paper constructs a two-stage dynamic DDF model to evaluate the efficiency of water resources sustainable development, and studies the dynamic efficiency level of water resources sustainable development and its regional differences. At the same time, a multidimensional energy poverty index evaluation system was established, the entropy weight method was used to measure the energy poverty index, and energy poverty was included in the evaluation system of the efficiency of sustainable development of water resources, and the changes in the efficiency of sustainable development of water resources were observed when there was no energy poverty. energy poverty energy efficiency water resources two-stage dynamic DDF sustainable development Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction China’s economic and social development has entered a new era with its economy shifting from high-speed to high-quality growth. However, the country still faces a series of social and ecological problems such as inadequate development and ecological environment deterioration. Rapid economic advancement must meet the needs of production and livelihoods of residents, but comes mostly at the expense of resources and the environment[ 1 ]. Therefore, the concept of unsustainable development needs to be changed, and environmental quality and social livelihood should be taken into account when maintaining economic growth by emphasizing the coordinated and sustainable progress of the social economy and ecological environment. Energy resources are not only an important factor affecting rapid economic development, but are also the basic and core needs of residents. However, China is faced with the situation of single energy utilization, low energy efficiency, and low energy structure level, and there are great regional differences in energy supply. At the same time, in the contexts of global air pollution, rapid urban expansion, shortage of safe drinking water resources in the world, and improper management of sewage facilities in most enterprises, energy poverty has greatly affected resources, environment, and social livelihoods. The proper use of energy is increasingly becoming an important constraint for environmental and economic sustainable development[ 2 ]. The overall incidence of energy poverty in China is more than 40%, and the relative energy poverty of urban households is growing more serious, which is mainly reflected in the low level of energy consumption of residents, unreasonable energy consumption structure, and weak energy consumption capacity[ 3 ]. Under the background of comprehensive poverty alleviation and the goal of common prosperity, the impact of energy consumption equity on the sustainable development of water resources has important theoretical and practical significances. However, according to the International Energy Agency, hundreds of millions of people around the world still do not have access to and use the modern energy they need on a daily basis, and the global COVID-19 pandemic has delayed progress in addressing energy poverty. Energy poverty not only affects people's physical health, mental health, and quality of life, but also restricts human sustainable development and social progress (World Bank, 2017; United Nations, 2015). Promoting energy tribute is crucial to eliminating energy poverty in China and achieving common prosperity[ 4 ]. With the increasing international discussion on energy use and ecological environment, water-related topics have also received high attention from all walks of life. With the reality that the world's potable water resources are turning increasingly scarce, international organizations are paying close attention to the efficiency level of water resources management and sustainable use and are adopting appropriate forms of international cooperation to avoid serious water shortage problems in the future. In 2015, for example, the United Nations Sustainable Development Goal (SDG) made a historic commitment to end poverty forever in every corner of the world, with SDG Goal 6 calling for access to clean drinking water and water for all. With 6% of the world's fresh water resources, China has guaranteed safe water for nearly 20% of the world's population and has actively provided assistance to developing countries in water conservancy construction. However, data show in 2021 that China's total water resources are 2.95 trillion cubic meters, but per capita occupancy is only 2,200 cubic meters versus the world's per capita water resources level of 8,800 cubic meters. The amount of water resources in China is significantly low, and the situation of water resources is not optimistic. Indeed, there are still difficulties in the process of water resources development. From the perspective of the country as a whole, it is extremely important to seek sustainable use of water resources[ 5 ]. China's water resources shortage, low utilization efficiency of water resources, serious water pollution, and other phenomena are quite prominent, and the carrying capacity of water resources in some areas is close to the limit. At present, there are many problems in industrial water, such as large water demand, large sewage discharge, and low utilization efficiency, thus restricting the sustainable use of water resources and green transformation of the industrial economy[ 6 ]. From the perspective of geographical differences, the spatial and temporal distribution patterns of China's water resources do not match the regional energy structure, the imbalance of energy structure, and the frequent occurrence of water pollution events that threaten regional sustainable development and social stability. Therefore, overall planning at the government level is an important measure to eliminate energy poverty and support sustainable development of water resources. Therefore, this paper discusses the relationship between sustainable development of water resources and energy poverty within a research framework and evaluates how to create more welfare growth with less water consumption within the range of water resources carrying capacity under the social background of energy poverty in China. The remaining structure of this paper is as follows: The second chapter mainly collates relevant literature research, and conducts literature research on the efficiency of sustainable development of water resources and energy poverty; The third chapter describes the energy poverty index system constructed by this research, as well as the models and methods used. Chapter four discusses the empirical results of this paper and analyzes the impact of energy poverty as an exogenous variable on the sustainable development of water resources. Finally, the fifth chapter summarizes and puts forward policy opinions. The main contribution of this study is to put energy poverty into the sustainable development of water resources as an exogenous variable, and analyze the changes in the exogenous variable affecting the sustainable development efficiency of sewage resources. 2. Literature review 2.1. Research related to sustainable development of water resources As an important resource, water shortages have become a serious challenge faced by the whole world, and this challenge is expected to continue to intensify in the next 50 years [ 7 – 8 ]. Research on sustainable development related to water resources has attracted wide attention in the world. Li et al. established a multi-dimensional and multi-attribute water resources sustainable assessment system, which comprehensively considers three objects of human and water systems, three attributes of water resources, and three processes of water resource utilization that fully represent the sustainable development of water resources[ 9 ]. However, this index system also has some shortcomings, such as subjectivity of weights, repeatability of indicators, underrepresentation of indicators, and difficulty in obtaining index data[ 10 ]. The data envelopment analysis (DEA) model is often used in the establishment of water resource sustainable assessment systems. For example, Wang et al. applied ultra-efficient DEA to measure the resource utilization efficiency of 30 provinces in China[ 11 ]. Zhou et al. selected the relevant data indicators of Hubei in China from 2006 to 2017, calculated its agricultural water resource use efficiency based on DEA, and then studied its agricultural water resource use efficiency via the grey relationship model. The production and utilization stages of water resources are staged and periodic[ 12 ]. Most scholars employ a multi-stage DEA model to calculate the corresponding efficiency value based on the input-output index of water resources production process. For example, Zhao et al. calculated the water resource use efficiency of 31 provinces in China from 2011 to 2014 with a two-stage measurement model based on relaxation[ 13 ]. The results showed that the comprehensive efficiency of the first stage is significantly higher than that of the second stage, but overall efficiency is close to that of the second stage, which determines the overall efficiency of water resource use. Liu et al. applied the DEA model to comprehensively analyze the water resource use efficiency of the main representative urban areas in the upper, middle, and lower reaches of the Ganjiang River basin in three stages[ 14 ]. Early studies have recognized that the influencing factors of sustainable water resource development mainly focus on population growth, climate change, and economic development [ 15 ]. However, few scholars have recognized that energy poverty relates to the efficiency of water resource development. From the perspective of spatial and temporal distributions, he spatial distribution of water resource use efficiency in China is unbalanced[ 16 ]. Moreover, the mismatch between the spatial and temporal distribution patterns of water resources and economic development will also become an important factor hindering regional sustainable development[ 17 ]. 2.2. Research on energy poverty Ever since the 1990s, poor families in developed countries often cannot meet their energy needs with their current income[ 18 ]. Therefore, early studies defined energy poverty as residents' inability to get energy services. The concept of energy poverty has been further expanded and enriched in the literature. Sefa et al. believed that energy poverty refers to people who cannot get clean energy, equipment, or energy-related services equitably[ 19 ]. Bouzarovski et al. proposed that energy poverty refers to the situation in which people cannot get energy services necessary for social activities or material needs[ 20 ]. It is found that quantifying energy poverty enables policy makers and researchers to better address energy-related issues[ 21 ]. The current methods for assessing energy poverty are divided into a single energy poverty indicator (LILEE method) and a multi-dimensional energy poverty indicator (MEPI method) [ 4 ]. The single energy poverty indicator assessment estimates the number of energy-poor households mainly by establishing a low energy income standard[ 22 ]. In contrast, the multi-dimensional energy poverty assessment method is more reliable, which emphasizes the measurement of energy poverty by more energy-related indicators. Changes in energy poverty, as well as differences in energy poverty and poverty levels among different groups or regions, have been more clearly observed[ 23 ]. For example, Nussbaumer et al. built an evaluation index system covering the five aspects of energy use in cooking, lighting, household appliances, education, and communication[ 24 ]. Li Kang et al. established a four-dimensional regional energy poverty evaluation system in China to assess the current situation and changing trend of energy poverty in China on the basis of clarifying the nation’s energy concept and international energy poverty evaluation methods[ 25 ]. HG. Et al. applied the consensus method and comprehensive measurement of EU-SILC survey data to construct a four-dimensional index system that measures energy poverty, and each index was assigned a corresponding weight[ 26 ]. Salman Muhammad et al. constructed a multidimensional energy poverty index incorporating the dimensions of energy affordability, energy cleanability and energy accessibility[ 27 ]. Wang Fu et al. constructed a multidimensional energy poverty index from seven dimensions to better measure China's energy poverty situation and its dynamic changes[ 28 ]. Xie et al. selected five dimensions of household cooking fuel, lighting, home appliance services, entertainment and education, and communication to build a multidimensional energy poverty index and to examine the determinants of multidimensional energy poverty[ 29 ]. The multidimensional energy poverty assessment method subdivides a single energy poverty factor into different dimensions, thus improving the scientific and feasibility of the research[ 30 ]. From the perspective of spatial distribution, there are spatial differences in energy poverty, and research has shown that provinces with better economic conditions and higher performance are classified as the progress group in alleviating energy poverty. Conversely, provinces with lagging economies experience the most retrogression and are also the provinces with the worst performance in reducing energy poverty[ 31 ]. 2.3. Research on water resources and energy The relationship between water and energy has attracted much attention lately. In the field of energy economics, energy, like water resources, is a relevant indicator of sustainable development for the United Nations. In the study of energy and water, most scholars have targeted the impact of energy and water as a unified circulating system on sustainable development. For example, Ke et al. believed that while reducing the adverse impact on natural resources, society, and environment, energy and water can help reduce the impact on sustainable development[ 32 ]. Improving the efficiency of energy and water use is essential for sustaining socio-economic development. However, energy consumption has gradually become an important factor affecting the sustainable development of regional water resources. At the basic level of production, the extraction, production, and transportation of energy all require water. There are inputs and outputs of related resources in the water resource cycle process, and the whole process of resource utilization requires a large amount of energy input[ 33 ]. For example, Shang et al. assessed the impact of China’s coal base development on the sustainable development of regional water resources[ 34 ]. Their results showed that compared with 2012, new coal production, coal-fired power generation, and coal chemical water demand will reach 230–380 million cubic meters in 2020. Given the limited power generation capacity of water resources, this growth will intensify regional water competition among different industries. Although there are many studies on energy consumption and water use, there are scant studies on the relationship between energy poverty and sustainable water resource development. However, from the perspective of the relationship between energy and water, there is a correlation between the degree of regional energy poverty and the efficiency of sustainable use of regional water resources. Therefore, it is necessary to include energy poverty in the study of water sustainable development. 3. Research Model and Data Description 3.1. Dynamic two-stage SBM model In actual inter-provincial development, the development path of water resources obviously has stages. First, through production activities it creates economic benefits and social, industrial, and agricultural relative to the total water consumption of the value added. Second, there are pollutants emitted by the industry, and through pollution control one can derive the amount of pollutants to improve; i.e., the formation of a dynamic two-stage path of the sustainable development of water resources. According to the research purpose of this paper and in order to explore the impact of energy poverty on water resource sustainable development, the energy poverty index calculated herein is an exogenous variable that is put into the water resource sustainable development system. We thus can observe the level of change in the efficiency of water resource sustainable development with or without the exogenous variable of energy poverty. The sustainable development of water resources is fundamentally the pursuit of comprehensive and coordinated development of social life, industrial production, and ecological environment. Thus, the dynamic two-stage SBM model is applied to analyze and evaluate the system efficiency of sustainable development of water resources and the changes in the efficiency under the influence of exogenous variables at each stage and at the overall level. The dynamic two-stage sustainable development of water resources is shown in Figure I. A decision-making unit (DMU) is composed of K sub-phases. Assume there are n DMUs, denoted as \\(\\:{DMU}_{i}\\) (i = 1,2,···, n). Any DMU contains m inputs \\(\\:{X}_{ij}\\) (j = 1,2,···, m), p intermediate outputs \\(\\:{M}_{ij}\\) (j = 1,2,···, p), h re-inputs \\(\\:{H}_{ij}\\) (j = 1,2,···, h), and r final outputs \\(\\:{Y}_{ij}\\) (j = 1,2,···, r) in T periods. Two neighboring periods are linked across periods with some of the outputs in period T-1 being some of the inputs in period T, and some of the inputs in period T + 1 being some of the outputs in period T again. There are s carry-over variables for such inter-period inputs, \\(\\:{Z}_{ij}\\) (j = 1,2,···, s). Based on the dynamic two-stage SBM model described above, the overall efficiency of the qth DMU in period T is defined as: $$\\:min{\\theta\\:}_{o}=\\frac{{w}_{q1}\\left[1-\\frac{1}{m+s}\\left({\\sum\\:}_{i=1}^{m}\\frac{{s}_{mo}^{-}}{{x}_{mo}}+{\\sum\\:}_{i=1}^{s}\\frac{{s}_{so}^{-}}{{z}_{so}}\\right)\\right]+{w}_{q2}[1-\\frac{1}{p+ℎ}({\\sum\\:}_{i=1}^{p}\\frac{{s}_{po}^{-}}{{m}_{po}}+{\\sum\\:}_{i=1}^{ℎ}\\frac{{s}_{ℎo}^{-}}{{ℎ}_{ℎo}})]}{{w}_{q1}(1+\\frac{1}{p}{\\sum\\:}_{i=1}^{p}{s}_{po}^{+}/{m}_{po})+{w}_{q2}[1+\\frac{1}{r+s}\\left({\\sum\\:}_{i=1}^{r}\\frac{{s}_{ro}^{+}}{{y}_{ro}}+{\\sum\\:}_{i=1}^{s}\\frac{{s}_{so}^{+}}{{z}_{so}})\\right]}$$ $$\\:s.t.\\left\\{\\begin{array}{c}{\\sum\\:}_{j=1}^{n}{\\lambda\\:}_{j}{x}_{mj}={x}_{mo}-{s}_{mo}^{-}\\\\\\:{\\sum\\:}_{j=1}^{n}{\\lambda\\:}_{j}{z}_{sj}={z}_{so}-{s}_{so}^{-}\\\\\\:{\\sum\\:}_{j=1}^{n}{\\lambda\\:}_{j}{m}_{pj}={m}_{po}+{s}_{po}^{+}\\\\\\:{\\sum\\:}_{j=1}^{n}{\\delta\\:}_{j}{ℎ}_{ℎj}={ℎ}_{ℎo}-{s}_{ℎo}^{-}\\\\\\:{\\sum\\:}_{j=1}^{n}{\\delta\\:}_{j}{m}_{pj}={m}_{po}-{s}_{po}^{-}\\\\\\:{\\sum\\:}_{j=1}^{n}{\\delta\\:}_{j}{y}_{rj}={y}_{ro}+{s}_{ro}^{+}\\\\\\:{\\sum\\:}_{j=1}^{n}{\\delta\\:}_{j}{z}_{sj}={z}_{so}+{s}_{so}^{+}\\\\\\:{\\sum\\:}_{j=1}^{n}{\\lambda\\:}_{j}={\\sum\\:}_{j=1}^{n}{\\delta\\:}_{j}\\\\\\:{s}_{mo}^{-},{s}_{so}^{-},{s}_{po}^{+},{s}_{po}^{-},{s}_{ℎo}^{-},{s}_{ro}^{+},{s}_{so}^{+},{\\lambda\\:}_{j},{\\delta\\:}_{j}\\ge\\:0\\end{array}\\right.$$ Here, \\(\\:{\\theta\\:}_{o}\\) is the overall efficiency of the water resource sustainable development system, which is effective if the value of efficiency is 1, and vice versa; \\(\\:\\lambda\\:\\) is a non-negative multiplier for integrating the production stage; \\(\\:\\delta\\:\\) is a non-negative multiplier for integrating the perpetual stage; \\(\\:{s}_{mo}^{-}\\) denotes the slack variables for inputs, \\(\\:{s}_{so}^{+}\\:\\text{a}\\text{n}\\text{d}\\:{s}_{so}^{-}\\) denotes the slack variables for carry-over variables, \\(\\:{s}_{po}^{+}\\:\\text{a}\\text{n}\\text{d}\\:{s}_{po}^{-}\\) denotes the slack variables for intermediate outputs, \\(\\:{s}_{ℎo}^{-}\\) denotes the slack variables for re-inputs, \\(\\:{s}_{ro}^{+}\\) denotes the slack variables for final outputs. The slack variables can reflect the gap between the input and output variables and the target value of optimal allocation, which can provide a reasonable opinion for the improvement of the sustainable development of water resources in each province and the adaptation of resources in the future. Lastly, \\(\\:{w}_{q1}\\) and \\(\\:{w}_{q2}\\) denote the weights of the qth decision variable in the two sub-stages in period T, respectively. The efficiency of the production stage subsystem is defined as: $$\\:min{\\theta\\:}_{1}=\\frac{1-\\frac{1}{m+s}({\\sum\\:}_{i=1}^{m}\\frac{{s}_{mo}^{-}}{{x}_{mo}}+{\\sum\\:}_{i=1}^{s}\\frac{{s}_{so}^{-}}{{z}_{so}})}{1+\\frac{1}{p}{\\sum\\:}_{i=1}^{p}{s}_{po}^{+}/{m}_{po}}$$ $$\\:s.t.\\left\\{\\begin{array}{c}{\\sum\\:}_{j=1}^{n}{\\lambda\\:}_{j}{x}_{mj}={x}_{mo}-{s}_{mo}^{-}\\\\\\:{\\sum\\:}_{j=1}^{n}{\\lambda\\:}_{j}{z}_{sj}={z}_{so}-{s}_{so}^{-}\\\\\\:{\\sum\\:}_{j=1}^{n}{\\lambda\\:}_{j}{m}_{pj}={m}_{po}+{s}_{po}^{+}\\\\\\:{\\sum\\:}_{j=1}^{n}{\\lambda\\:}_{j}=1\\\\\\:{s}_{mo}^{-},{s}_{so}^{-},{s}_{po}^{+},{\\lambda\\:}_{j}\\ge\\:0\\end{array}\\right.$$ The efficiency of the perpetual stage subsystem is defined as: $$\\:min{\\theta\\:}_{2}=\\frac{1-\\frac{1}{p+ℎ}({\\sum\\:}_{i=1}^{p}\\frac{{s}_{po}^{-}}{{m}_{po}}+{\\sum\\:}_{i=1}^{ℎ}\\frac{{s}_{ℎo}^{-}}{{ℎ}_{ℎo}})}{1+\\frac{1}{r+s}({\\sum\\:}_{i=1}^{r}\\frac{{s}_{ro}^{+}}{{y}_{ro}}+{\\sum\\:}_{i=1}^{s}\\frac{{s}_{so}^{+}}{{z}_{so}})}$$ $$\\:\\left\\{\\begin{array}{c}{\\sum\\:}_{j=1}^{n}{\\delta\\:}_{j}{m}_{pj}={m}_{po}-{s}_{po}^{-}\\\\\\:{\\sum\\:}_{j=1}^{n}{\\delta\\:}_{j}{ℎ}_{ℎj}={ℎ}_{ℎo}-{s}_{ℎo}^{-}\\\\\\:{\\sum\\:}_{j=1}^{n}{\\delta\\:}_{j}{y}_{rj}={y}_{ro}+{s}_{ro}^{+}\\\\\\:{\\sum\\:}_{j=1}^{n}{\\delta\\:}_{j}{z}_{sj}={z}_{so}+{s}_{so}^{+}\\\\\\:{\\sum\\:}_{j=1}^{n}{\\delta\\:}_{j}=1\\\\\\:{s}_{po}^{-},{s}_{ℎo}^{-},{s}_{ro}^{+},{s}_{so}^{+},{\\delta\\:}_{j}\\ge\\:0\\end{array}\\right.$$ Here, \\(\\:{\\theta\\:}_{1}\\) and \\(\\:{\\theta\\:}_{2}\\) denote the efficiency of the assessment decision unit in the production stage subsystem and the perpetual stage subsystem, respectively. 3.2. Technology gap ratio Technology gap ratio (TGR) is an important indicator in the framework of common frontiers. This study measures the meta-frontier technical efficiency (MTE) of the kth DMU based on the meta-frontier of the 29 DMUs according to the model construction described above. This meta-frontier is made up of the most efficient DMUs in each of the 3 groups together, and based on the different groups under the boundary the group frontier technical efficiency (GTE) can be calculated. Based on GTE and MTE under different groups, it is possible to calculate the technology gap efficiency (TGR) value, which measures the technological status of each DMU and the difference in the level of technology between the groups, reflecting the gap between groups under the meta-frontier and group frontier. The specific calculation formula is: $$\\:0\\le\\:TGR=\\frac{MTE}{GTE}\\le\\:1$$ 3.3. Construction of the indicator system and data sources 3.3.1. Selection of research variables and construction of the indicator system The sustainability of water resources is affected by social life and industrial production. In order to examine the impact of energy poverty on the efficiency of water resource sustainability, this study establishes an indicator system, starting from two subsystems: the production stage subsystem and the perpetual stage subsystem. It then combines accessibility of the data and follows the principles of constructing the evaluation indicator system in a systematic, comparable, comprehensive, and scientific way, as shown in Fig. 2 . Inputs in the production stage mainly include fixed assets carried over from the previous period, labor force, and total water supply, and so the number of people employed in the region (unit: 10,000) and the total amount of water supplied in the region (unit: 100 million tons) are selected as indicators. The intermediate outputs in the production stage are the direct results of water resource development, which are reflected in GDP and the economic benefits of water resources in life, agriculture, and industry. For the economic benefits of water resources we use the entropy weighting method to calculate the weights of three indicators: per capita water consumption, the ratio of value added in agriculture to the total amount of water consumption, and the ratio of value added in industry to the total amount of water consumption. In addition to the outputs of the production stage, the inputs of the perpetuation stage require total investment in industrial treatment (wastewater, waste gas, and solid waste) and energy poverty indicators in order to realize the perpetuation of water resources in industrial production. Total investment in industrial treatment is derived by fitting three line item indicators, wastewater treatment inputs, CDC treatment inputs, and ammonia treatment inputs, through entropy weighting method. The final output of the second stage is represented by the pollution improvement index, which is derived by fitting three line items: wastewater improvement per capita, CDC improvement per capita, and ammonia improvement per capita through the entropy weighting method. The fixed assets produced in this stage are invested in the production stage of the next period as a carry-over variable. The assessment of energy poverty refers to the method of Li Kang (2014) [ 24 ]. Taking into account the data availability and the research needs of this paper, 14 indicators are finally selected from the four aspects of energy service availability, cleanliness of domestic energy use, completeness of energy management, and affordability and high efficiency of domestic energy use to establish an assessment index system. The specific indicators are shown in Table 1 . The selected indicators are standardized and weighted using the entropy weighting method. Finally, the energy poverty score of each province is calculated using the weighted sum method. Table 1 Energy Poverty Measurement System Level 1 indicator Level 2 indicators Level 3 indicators Energy Poverty Accessibility of energy services Urban gas penetration rate Per capita urban gas supply (10,000 cubic meters) Cleanliness for domestic use Share of non-fire power generation in total power generation (%) Average rural household biogas production Completeness of energy management Number of Rural Energy Management Extension Agencies (REMEAs) per million per capita in each region Per capita expenditure on rural energy inputs Affordability and efficiency of domestic energy use Ownership of air conditioners per 100 households in urban areas Ownership of refrigerators per 100 urban households Ownership of cooker hoods per 100 households in rural areas Ownership of wood-saving and coal-saving stoves per 100 rural households Proportion of rural households using biogas digesters Per capita coverage of rural solar water heaters Domestic sulphur dioxide emissions per million population (tons) Domestic smoke emissions per million population (tons) In terms of grouping indicators, this study selects 29 provinces in China as the research objects. Because the large regional span between provinces, the geographical form, the economic situation, and the use of water resources and investment in industrial governance differ greatly, this study divides the 29 provinces into east, central, and west regions according to their geographical location. The details are shown in Table 2 . Table 2 Specific Groupings Region Provinces and Cities East region Shanghai, Shandong, Tianjin, Beijing, Jilin, Jiangsu, Hebei, Hainan, Zhejiang, Heilongjiang, Fujian, Guangdong, Liaoning Central region Shanxi, Anhui, Jiangxi, Henan, Hubei, Hunan West region Yunnan, Sichuan, Gansu, Ningxia, Qinghai, Chongqing, Inner Mongolia, Guangxi, Guizhou, Shaanxi 3.3.2. Data sources In view of the data updating situation, this study collects relevant data through China Statistical Yearbook, China Environmental Statistical Yearbook, China Energy Statistical Yearbook, China Rural Statistical Data, and statistical yearbooks of provinces (municipalities and autonomous regions). It then constructs a system of input-output indicators for the period 2016–2020 for 29 provinces in China. Due to the lack of relevant data on Tibet, only 29 provinces are selected as the research objects. 4. Results and discussions 4.1. Temporal analysis As can be seen from Table 3 , under the influence of energy poverty, most provinces have low water resource sustainability efficiency values in 2016–2020 that have a fluctuating upward trend. This reflects that China's water resource sustainability level has improved, but the overall level is still low. This may be due to the fact that China's strategic water conservancy projects have effectively alleviated localized water shortages, and water use problems are not prominent in most regions. Thus, the issue of sustainable development of water resources has not been given sufficient attention. This is also evidenced by the differences in water resource sustainable development efficiency values among regions. Table 3 Average Value of Provincial Water Sustainability Efficiency in China, 2016–2020 2016 2017 2018 2019 2020 Mean Shanghai 0.519273 0.576247 0.554923 0.525211 0.565729 0.548277 Shanxi 0.355826 0.477739 0.475551 0.535338 0.478045 0.4645 Shandong 0.520711 0.54446 0.508618 0.5045 0.504907 0.516639 Yunan 0.298669 1 0.842589 0.737785 0.56091 0.687991 Tianjin 0.723007 1 1 1 1 0.944601 Beijing 1 1 1 1 1 1 Sichuan 0.509791 0.757583 0.496136 0.458784 0.435105 0.53148 Gansu 0.410341 0.649472 0.668331 0.591461 0.569247 0.57777 Ningxia 1 1 1 1 1 1 Jilin 0.851293 0.599637 0.609501 0.864962 1 0.785079 Anhui 0.390328 0.531218 0.468542 0.441179 0.407689 0.447791 Jiangxi 0.391992 0.603777 0.548207 0.619403 0.562217 0.545119 Jiangsu 0.516774 0.618298 0.506245 0.518662 0.505863 0.533168 Hebei 0.600625 0.523691 0.50935 0.551383 0.555075 0.548025 Henan 0.53331 0.56337 0.505845 0.515046 0.524159 0.528346 Qinghai 1 1 1 1 1 1 Chongqing 0.755012 0.736572 0.481909 0.5522 0.428075 0.590754 Hainan 1 0.651751 1 1 1 0.93035 Zhejiang 0.5306 0.652143 0.505902 0.544533 0.543994 0.555434 Hubei 0.399179 0.524124 0.42309 0.485237 0.365867 0.439499 Hunan 0.528332 0.694569 0.508982 0.631071 0.55083 0.582757 Heilongjiang 0.655243 0.563179 0.544166 1 1 0.752518 Fujian 0.566861 0.584386 1 0.64258 0.579035 0.674572 Inner Mongolia 1 0.561741 1 0.774085 1 0.867165 Guangxi 0.296203 0.505348 0.344414 0.388921 0.400241 0.387025 Guangdong 1 1 0.52809 0.520641 0.527238 0.715194 Guizhou 0.588417 1 0.568903 0.614415 0.569075 0.668162 Liaoning 0.434707 0.568261 0.369146 0.630568 1 0.600536 Shaanxi 0.615403 0.614887 0.568576 0.583414 0.534628 0.583382 It is not difficult to find that Beijing, Ningxia, Tianjin, and Qinghai are significantly more efficient in terms of water sustainability than other provinces. Except for Qinghai, the other three provinces are located in North China and Northwest China, where water resources are relatively scarce. It is reasonable to assume that the reality of water scarcity will force localities to improve the efficiency of sustainable water resource development. On the contrary, in areas with abundant water resources such as Anhui, Guangxi, and Hubei, there may be a general lack of awareness of water conservation and a serious waste of water resources, leading to inefficiency in sustainable development of water resources. The efficiency of sustainable development of water resources also relates to the mode of economic development. It can be found that the economic development of the three provinces of Shanxi, Shandong, and Henan, which also have the lowest average efficiency value in the period 2016–2020, is based on resource extraction, heavy industry and chemical industry, and agriculture, respectively. Their pollution of water resources is more serious, because there may be problems such as substandard industrial wastewater discharge, acid rain caused by industrial emissions, and groundwater pollution caused by seepage through irrational application of fertilizers and medicines in agriculture. Although Qinghai is one of the richest regions in China in terms of water resources, the tourism industry in this province, as a pillar industry of economic and social development in the province, has stimulated the protection and sustainable development of its water resources, thus achieving effective DEA in terms of sustainable development of water resources. The paper further plots the trend of water resource sustainable development efficiency histograms in Fig. 3 and divides the overall trend into six categories. Among them, Shanghai, Jiangxi, Zhejiang, Hunan, and Guangxi show a fluctuating upward trend in water resource sustainable development efficiency. Jiangsu, Chongqing, Hubei, and Shaanxi show a fluctuating downward trend. Shanxi, Yunnan, Sichuan, Gansu, Anhui, and Fujian show a fluctuating upward and then downward trend in efficiency value. Jilin, Hebei, and Heilongjiang show a fluctuating downward and then upward trend in efficiency value. Shandong, Tianjin, Beijing, Ningxia, Shandong, Tianjin, Beijing, Ningxia, Henan, Qinghai, and Hainan have smaller fluctuations in efficiency, basically remaining the same. Inner Mongolia, Guizhou, Guangdong, and Liaoning have larger fluctuations in efficiency. The efficiency of water resource sustainability in most provinces has overall increased significantly in 2017. The reason for this phenomenon may be that China released the National Water Resources Protection and Utilization Planning Outline (2016–2020) in 2016, which set out the overall objectives and key tasks for water resource protection and utilization in the next five years, including strengthening water resource protection, improving the efficiency of water resource utilization, and optimizing water resource allocation. Under the guidance of this document, localities are likely to pay attention to and effectively improve the efficiency of sustainable development of water resources. It can be seen that to achieve the sustainable development of water resources, the role of policy promotion is very important. This also reveals that government departments in regions with low water resource sustainable development efficiency should pay attention to this issue, actively formulate relevant policies and implement them to regulate the behavior of water use in social production and daily life, and promote the protection and sustainable development of local water resources. 4.2 Subsystem efficiency analysis by stage As can be seen from Table 4 , there is no consistency between the efficiencies of the production stage and the sustainability stage of water resources. Except for the three provinces of Beijing, Ningxia, and Qinghai, whose efficiency values of the two stages are all 1, the efficiency of the production stage in other provinces is significantly higher than that of the perpetual stage. This shows that China is currently developing and utilizing its water resources while neglecting their protection and sustainable development, meaning there is still a lot of room for progress in the stage of sustainable development of water resources domestically. Sichuan is the province with the richest water resources in China after Tibet and also the province with the highest level of economic development in the west region. The efficiency of its water resources in the production stage is 0.82, but the value of the efficiency in the perpetual stage is only 0.24, which indicates that the province's water resources are more efficient in terms of development and production, but are wasted and polluted in a more serious way. On the contrary, Ningxia, as an extremely water-scarce region in China, is effective in both the production and perpetuation stages of its water resources. Despite the fact that its natural conditions are not dominant, it is possible to see that the local government attaches great importance to the utilization and protection of water resources. Further observation reveals during the period 2016–2020 that the efficiency of the production stage of water resources in each province has a clear trend of progress, but the fluctuation of the efficiency in the perpetual stage is more random. This is because the improvement of water resource production efficiency brings immediate economic and social benefits, while efficiency improvement in the sustainable stage is beneficial in the long term. Therefore, the regions are highly motivated to improve the efficiency of the production stage, while the efficiency of the sustainable stage relates more to the degree of policy relaxation, and so the motivation is not high. In this study we also construct a water production-permanent efficiency mean matrix by taking the national water production efficiency mean value of 0.93 and the perpetual stage efficiency mean value of 0.38 as the dividing lines to separate the provinces and cities into four regions in Fig. 4 : A (high one, high two), B (low one, high two), C (low one, low two), and D (high one, low two). Most provinces are concentrated on the right side of the matrix region, indicating that the the levels of development and utilization of China's regional water resources are relatively balanced, but there is a serious regional imbalance in terms of protection and sustainability. Production efficiency is higher in all the east region, while perpetual efficiency has more pronounced provincial differences. Guangxi in the west region performs the worst. The sustainable development efficiency of water resources in most regions needs to be improved, and there is no strong correlation between the development efficiency and sustainability efficiency of provincial water resources. Provinces should actively learn from the more advantageous regional provinces and combine the characteristics of their own regions to promote the coordinated development of local water resource development and protection. 4.3 Technical efficiency analysis 4.3.1 Analysis of meta-frontier technical efficiency and group frontier technical efficiency considering exogenous variables The intertemporal frontier is employed to put the inputs and outputs of all years of the DMUs into the construction system of the production frontier surface. The results of the calculation appear in Table 5 . MTE and GTE denote the efficiency value of water resource sustainability under the meta-frontier and the group frontier, respectively. TGR is the ratio of the perpetual development gap between the potential efficiency under the meta-frontier and the actual efficiency classified by region. Table 5 presents the results of the measurements taking energy poverty as an exogenous variable and without an exogenous variable. The results of the data in Table 5 relate to energy poverty and take in the consideration of the common border. The average value of China's provincial water sustainability efficiency is 0.47 during the period 2016–2020. The average value of the east region is 0.52, the average value of the central region is 0.30, and the average value of the west region is 0.60. Thus, it can be seen that the central region has not reached the national average, and there are obvious differences between the three regions. The water sustainability efficiency values of Tianjin, Beijing, Jilin, Hainan, Heilongjiang, Fujian, and Liaoning in the east region exceed the east regional average and China’s provincial average, with Beijing's efficiency value of 1 as well as Tianjin and Hainan far exceeding the group's average and there was a large gap within the group. In the central region, only Hunan has an efficiency value that exceeds the group mean and China’s provincial mean, while Jiangxi has a higher efficiency of water resource sustainability than the group mean. The west region has the highest intra-group mean. Moreover, Yunnan, Ningxia, Qinghai, and Inner Mongolia have higher efficiency values than the intra-group mean and China’s provincial mean, with Ningxia and Qinghai both having an efficiency value of 1. All of the west region's water resource sustainability efficiency values are relatively and generally high. In terms of the efficiency values of the provinces, there are significant differences between them, and the room for upward mobility varies from province to province. The possible reasons for these results relate to the energy poverty problem and the results of pollution control. The east region, with its service-based tertiary sector as the mainstay of the economy and its more advanced energy services and innovations, has been able to alleviate the problem of energy poverty compared to the central region, which has a heavy industrial economy. The west region has rich mineral resources and is China's key ecological barrier area, but it is environmentally poor and sparsely populated, mineral mining enterprises are rund under more stringent environmental and ecological protection policies, and there is strict compliance with the rules of reasonable mining and use. Thus, the west region’s energy poverty and environmental pollution compared to the east and central regions is better, and its water resource sustainable development has higher efficiency. Under the group frontier, the mean values of technical efficiency of the east, central, and west group are 0.69, 0.97, and 0.81, respectively. Their efficiency values are more obviously improved compared to the meta-frontier, with the central region being the highest, the west region as second best, and the east region in last. In addition, a comparison between the data reveals that there are still large differences between the groups. Table 5 Technical Efficiency in 29 Provinces Based on Meta-frontier MTE GTE TGR Group Province With exogenous variables Without exogenous variables With exogenous variables Without exogenous variables With exogenous variables Without exogenous variables East Region Shanghai 0.26 0.26 0.68 0.68 0.38 0.38 Shandong 0.14 0.12 0.25 0.12 0.57 0.98 Tianjin 0.94 0.94 1 1 0.94 0.94 Beijing 1 1 1 1 1 1 Jilin 0.76 0.48 0.91 0.53 0.84 0.92 Jiangsu 0.17 0.17 0.52 0.34 0.34 0.5 Hebei 0.24 0.22 0.93 0.83 0.26 0.26 Hainan 0.89 0.89 0.9 0.9 1 1 Zhejiang 0.33 0.24 0.35 0.25 0.94 0.96 Heilongjiang 0.52 0.26 0.7 0.31 0.74 0.87 Fujian 0.55 0.53 0.66 0.6 0.83 0.89 Guangdong 0.47 0.26 0.48 0.3 0.99 0.88 Liaoning 0.53 0.49 0.64 0.62 0.83 0.8 East Region Mean 0.52 0.45 0.69 0.57 0.74 0.8 Central Region Shanxi 0.15 0.14 1 1 0.15 0.14 Anhui 0.25 0.23 0.8 0.8 0.31 0.28 Jiangxi 0.44 0.44 1 1 0.44 0.44 Henan 0.19 0.17 1 1 0.19 0.17 Hubei 0.25 0.23 1 1 0.25 0.23 Hunan 0.55 0.48 1 1 0.55 0.48 Central Region Mean 0.3 0.28 0.97 0.97 0.31 0.29 West Region Yunan 0.6 0.46 0.72 0.72 0.82 0.64 Sichuan 0.18 0.16 0.43 0.43 0.41 0.38 Gansu 0.54 0.48 0.84 0.84 0.63 0.57 Ningxia 1 1 1 1 1 1 Qinahai 1 1 1 1 1 1 Chongqing 0.58 0.57 1 1 0.58 0.57 Inner Mongolia 0.67 0.6 0.67 0.67 1 0.89 Guangxi 0.38 0.37 0.93 0.93 0.4 0.39 Guizhou 0.63 0.57 0.94 0.87 0.68 0.65 Shaanxi 0.47 0.35 0.52 0.52 0.9 0.67 West Region Mean 0.6 0.56 0.81 0.8 0.74 0.68 China Provincial Mean 0.47 0.45 0.82 0.74 0.6 0.65 4.3.2 Analysis of technology gap ratio The average meta-frontier technology efficiency, average group frontier technology efficiency, and average technology gap ratio in 2016–2020 in the east, central, and west regions are shown in Table 6 and Fig. 5 . The specific changes are as follows. (1) The average technology gap ratio in the east region is equal to or close to 1 in the time range of the study, which indicates that its meta-frontier technology efficiency is closer to its group frontier technology efficiency. (2) The data in the Table 6 show that both the meta-frontier technology efficiency and the group frontier technology efficiency are fluctuating upward in the central region, while it can be seen from Fig. 5 that the technology gap ratio in the central region also has a fluctuating increase. This shows that the gap between the meta-frontier efficiency and the group frontier efficiency in the central region is very large. It is thus necessary to increase the use of resource developers to improve the efficiency of resource use, at the same time formulate more effective incentive policies for the production, distribution, and consumption of energy, establish a perfect water resource service system, and realize the leaping upgrade of scientific and technological innovations. (3) The average technological gap ratio of the west region is close to the average national technological gap ratio. Compared with the central region, the west region's water resource sustainable development innovation efficiency is higher. This shows that there is a gap between the meta-frontier efficiency and the group frontier efficiency in the west region, but the gap is small. Compared with the east and central regions, the west region has the shortcomings of imperfect economic development and insufficient industrial economy, but this region has a vast land, sparse human traces, and abundant resources, and so it should continue to develop innovative energy on the basis of maintaining a green environment. Table 6 Average Technology Gap Ratio, 2016–2020 Year 2016 2017 2018 2019 2020 Group MTE GTE TGR MTE GTE TGR MTE GTE TGR MTE GTE TGR MTE GTE TGR East region 0.51 0.56 0.90 0.59 0.71 0.85 0.60 0.72 0.85 0.60 0.74 0.85 0.65 0.73 0.91 Central region 0.30 1.00 0.30 0.54 1.00 0.54 0.48 0.99 0.49 0.51 0.95 0.54 0.47 0.95 0.49 West region 0.59 0.90 0.64 0.75 0.89 0.85 0.65 0.91 0.72 0.62 0.82 0.76 0.58 0.85 0.69 Country 0.49 0.77 0.69 0.64 0.83 0.79 0.59 0.84 0.73 0.59 0.81 0.76 0.59 0.82 0.75 4.4. Analysis of input-output redundancy and deficiencies Redundancy and insufficiency reflect to the gap between the input-output values and the optimal allocation, and redundancy and insufficiency can help make reasonable suggestions for the improvement of the efficiency of water resource sustainable development in each province. In This study selects redundancy and insufficiency in the perpetual stage of each province in 2016 for analysis, but due to space limitations, only the input-output slack variables in the perpetual stage in 2016 are listed in Table 7 . The efficiency values of Beijing, Ningxia, Qinghai, Hainan, Inner Mongolia and Guangdong in the perpetual stage in 2016 is are all 1. Thus, the value of the slack variable is 0, which indicates that in the perpetual stage the indicators are at optimal allocation, and they are all have been effectively utilized. Ningxia, Qinghai, and Inner Mongolia are more backward than other provinces in terms of economic development level and industrial scale, but the efficiency of water resources in the sustainable development stage has reached the optimum for five consecutive years, which indicates that improving the sustainability of water resources is not a matter of blindly expanding the inputs and increasing the outputs of industrial production and investment, but that finding the appropriate optimal allocation is an important way to promote the sustainable development of water resources. Regarding the analysis of slack variables and taking Sichuan as an example, the redundancy of wastewater, exhaust gas and waste there is 39184.7, 10.4688, and 8592.63 respectively. Its imperfect industrial governance leads to a large amount of exhaust gas and wastewater entering into water resource cycle, which greatly impede the efficiency of water resource sustainable development and causes waste of other resources. Fixed assets as output should be reduced by 855.041 billion yuan, suggesting that enterprises in the region must reduce their blind expansion of factory scale and their increase of fixed assets such as ordinary production tools. Instead, they should increase investment in pollution control of industrial production and energy poverty in social life, focusing on the sustainability and future of water resource cycle development. The amount of pollution improvement should be increased by 1.03 units, which shows that the efforts of Sichuan in pollution control and improvement of the environment are not sufficient, and it can alleviate pollution emissions from the perspective of improving the environment for energy use. Table 7 2016 Perpetual Stage Slack Variables Province Volume of investment inputs in industrial governance Wastewater inputs Exhaust gas inputs Solid waste inputs Fixed asset generation (billions of yuan) Pollution improvement volume generation Shanghai 0 0 0 0 0 9.60 Shanxi 0 -36632.1 -43.7529 -38639.1 -7918.49 6.04 Shandong 0 0 0 0 0 27.09 Yunan 0 0 -33.2613 -13605.3 0 2.48 Tianjin 0 0 0 0 0 0.88 Beijing 0 0 0 0 0 0.00 Sichuan 0 -39184.7 -10.4688 -8592.63 -8550.41 1.03 Gansu 0 0 -12.1519 -5676.2 -3822.43 2.36 Ningxia 0 0 0 0 0 0.00 Jilin 0 0 -5.01553 -2448.92 -1853.21 0.47 Anhui 0 0 -7.80923 -9542.04 -7700.16 7.71 Jiangxi 0 -11197.8 -32.0948 -8655.38 0 1.62 Jiangsu 0 0 0 0 0 14.20 Hebei 0 0 -13.7873 -19154.5 0 4.98 Henan 0 0 0 0 0 13.55 Qinghai 0 0 0 0 0 0.00 Chongqing 0 -61199 -4.64723 -239.322 -6807.08 0.09 Hainan 0 0 0 0 0 0.00 Zhejiang 0 0 0 0 0 11.20 Hubei 0 0 -3.98498 -2743.16 -10594 8.30 Hunan 0 -24100.5 -11.5975 -1651.7 -7495.81 1.58 Heilongjiang 0 0 0 0 0 3.75 Fujian 0 0 -6.27355 -2183.78 0 4.01 Inner Mongolia 0 0 0 0 0 0.00 Guangxi 0 0 -1.21466 -6069.91 -7473.25 2.62 Guangdong 0 0 0 0 0 2.82 Guizhou 0 0 -28.6997 -6820.21 0 0.98 Liaoning 0 -47388.9 -30.6228 -17252.5 -8730.91 4.19 Shaanxi 0 0 0 0 0 4.14 5. Conclusions and Policy Implications Water is the source of life, the key to production, and the basis of ecology and is vital to improve the efficiency of water resource usage per unit and to achieve the sustainable use of water resources. The seventh United Nations Sustainable Development Goal aims to eliminate energy poverty and to achieve equity and sustainability in energy consumption. Therefore, under the increasingly serious situation of energy poverty in China, it is of great significance to study energy poverty to promote the sustainable use of water resources based on the new development stage. Taking energy poverty as an exogenous variable, this paper divides the process of water resource sustainable development into two associated sub-system stages, production and perpetuation, and adopts a two-stage dynamic SBM model to measure the efficiency of the sample data of 29 provinces and municipalities from 2016 to 2020. To exclude the influence caused by regional differences, this paper applies the common frontier model to group the 29 provinces and cities and to measure their technical efficiency values, so as to better explore the influence of energy poverty on the efficiency of water resources sustainable development. To get more comprehensive and precise results, this research analyzes the results from multiple perspectives as follows. (1) Time perspective: Most provinces and municipalities show a fluctuating upward trend in water resource sustainability efficiency from 2016 to 2020, but their overall efficiency is still low, suggesting that there is more room for upward mobility. In addition, there are obvious differences in regional water resource stock and water use, and there is a more obvious energy poverty situation in some regions. (2) Stage subsystem efficiency analysis: Two-stage efficiency is not consistent. Moreover, the production efficiency of some provinces is significantly higher than their perpetual efficiency. (3) Benchmarking with different production frontier functions leads to differences in efficiency evaluation. After considering meta-frontier, it is found that the overall efficiency of China's water resource sustainable development under the meta-frontier is not high during the period 2016–2020, and there are obvious differences in the efficiency level of each province and city. The efficiency situation of water resource sustainable development under the group frontier has improved, with the highest in the central region, followed by the west, and the east as last. (4) By analyzing the technical gap ratio, it can be seen that TGR in the east region is close to 1. In addition, the technical efficiency of meta-frontier is very close to the technical efficiency of the group frontier. However, the values of the two technical efficiencies in the central region show an obvious gap. Based on the above conclusions, this paper makes the following recommendations for energy poverty and sustainable development of water resources from four perspectives: institutional, economic, resource, and regional. (1) In order to consolidate the results of poverty eradication, the government urgently needs to pay more attention to the issue of residential energy services. The government needs to focus moer on residential energy poverty and gradually improve all types of energy service facilities, especially the energy use of poor households, in order to safeguard the energy needs of micro-households and to reduce the depth of residential energy poverty. In addition, the government should advocate energy and water conservation among residents and enterprises and address the issue of energy poverty through multifaceted and multilevel institutional measures, so as to ensure the prospect of sustainable development of water resources. (2) The economy and the use of water resources should be in a more balanced state of coordination. Although natural factors such as precipitation are an important factor affecting the total amount of water resources, excessive water use in human economic production is one of the major reasons seriously impacting the sustainable development of resources. Therefore, while gradually improving the modern development of water resources, provincial and municipal governments should pay attention to the concept of scientific water use, further promote the innovation of production technology and industrial structure upgrading, at the same time introduce relevant emission reduction programs for the management of corporate sewage disposal, and strengthen the supervision and management of resource allocation and use and the environment. (3) Attention should be paid to the improvement of the degree of coordination of resources. According to redundancy and insufficiency analyses, most provinces have a relatively obvious situation of low coordination of resource use between 2016 and 2020. Therefore, while achieving high production efficiency, extra attention should be paid to promoting effective resource allocation and production efficiency, rather than blindly pursuing the expansion of production scale, which results in the waste of resources. Since the slack variables of the pollution indicators in the perpetual stage also show that excessive pollution will seriously affect the sustainable development of water resources, the government should focus on the coordinated development of energy and water resources, strengthen the governance of sewage discharge in all provinces and municipalities, formulate corresponding rules for the governance of discharge in respect of the different pollution indicators, constrain the production of high-polluting enterprises, increase the construction of modern water conservancy infrastructures, strictly enforce the rigid constraints on water resource systems, actively establish an integrated water resource management system, and innovate and apply digital technology and other water control programs and measures. The development of new energy industries should further be accelerated to promote the upgrading of the energy structure, reduce the level of energy poverty in China, and give full play to the role of energy poverty reduction in fueling the sustainable development of water resources. (4) Relevant policies should be formulated according to local conditions. According to the above findings, the sustainability of China's water resource development is found to be unbalanced between the east, central, and west regions. The central region, as a region dominated by the development of heavy industry, should target strengthening new energy technologies and pollution control to ensure the sustainable development of energy and water resources. As for the west region, where the level of energy poverty is relatively serious, the scale of energy development in this region should be properly controlled while expanding energy imports, so as to effectively guarantee the coordinated security of water and energy resources. 6. Discussion This paper has certain limitations in the research process, due to data availability and other reasons, this paper only selects the relevant data of 30 provinces for research, the research period is also shorter, the sample size is insufficient; at the same time, the indicator system is not comprehensive, the accuracy of the assessment of the efficiency of sustainable development of water resources needs to be improved. In order to solve the shortcomings of this paper, the future development direction is proposed, firstly, to expand the sample capacity and extend the research period, and secondly, to be more representative when constructing the research index system, in order to more accurately portray the dynamic changes of the efficiency of water resources sustainable development of each province in China as well as the impact of energy poverty on it. Declarations Competing interests: The authors declare no conflict of interest. Authors contributions: Conceptualization, X.Z and Z.F.; Data curation,QQ.X..; Formalanalysis, Z.F.; Investigation, X.Z.; Methodology, Z.f. and Z.Y.; Visualization, X.Z.; Supervision,Z.Y. and QQ.X..; Project administration, Z.F.; Writing—original draft preparation, Z.F.; Writing—reviewand editing,X.Z. All authors have read and agreed to the published version of the manuscript. 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Applied Energy. 194: 735-50, 2017. Table 4 Table 4 is available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files rawdata.xlsx Table4.docx Cite Share Download PDF Status: Published Journal Publication published 02 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 29 Oct, 2024 Reviews received at journal 15 Oct, 2024 Reviewers agreed at journal 07 Oct, 2024 Reviews received at journal 06 Oct, 2024 Reviewers agreed at journal 26 Sep, 2024 Reviewers invited by journal 26 Sep, 2024 Editor assigned by journal 26 Sep, 2024 Editor invited by journal 26 Sep, 2024 Submission checks completed at journal 23 Sep, 2024 First submitted to journal 27 Aug, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-4987800\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Article\",\"associatedPublications\":[],\"authors\":[{\"id\":371636283,\"identity\":\"e1b1973f-9870-47de-9625-76adc0cb386b\",\"order_by\":0,\"name\":\"Zhong Fang\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0ElEQVRIiWNgGAWjYBAC9gYGNhDNw8/e2PjgAzFaeA5AtMhJ9hxuNpxBihZjgxvpbdIcRGlhP/7swccdtYkNNx82SDMw2MnpNhDSwpOQbjjzzPHExtmJDcYFDMnGZgcIaLFnSDgmzdt2LLFZOrEheQbDgcRthLTw8D9sA2tpkzzYcJiHKC0SyWxALTXGPBKMjc1EannGJjmz7YCcBE9iM+MMAyL8wsOf/kziY1sdj/3x489/fKiwkyOoBQoOQ2kD4pSDQB3xSkfBKBgFo2DkAQCZm0OKYjk2wwAAAABJRU5ErkJggg==\",\"orcid\":\"\",\"institution\":\"Xiamen Institute of Technology\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Zhong\",\"middleName\":\"\",\"lastName\":\"Fang\",\"suffix\":\"\"},{\"id\":371636284,\"identity\":\"f603ccbd-e131-4b46-9f64-94ce474ae762\",\"order_by\":1,\"name\":\"Qiqi Xiao\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Fujian Normal University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Qiqi\",\"middleName\":\"\",\"lastName\":\"Xiao\",\"suffix\":\"\"},{\"id\":371636285,\"identity\":\"fbe48fdd-3797-4963-9608-59e22bef0c45\",\"order_by\":2,\"name\":\"Zi Ye\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Fujian Normal University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Zi\",\"middleName\":\"\",\"lastName\":\"Ye\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2024-08-28 03:30:32\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-4987800/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-4987800/v1\",\"draftVersion\":[],\"editorialEvents\":[{\"content\":\"https://doi.org/10.1038/s41598-025-05240-5\",\"type\":\"published\",\"date\":\"2025-07-02T15:58:29+00:00\"}],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":69028222,\"identity\":\"33c349c0-ca3a-41bb-9684-a4fcdb92fd97\",\"added_by\":\"auto\",\"created_at\":\"2024-11-14 17:47:21\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":89748,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eDynamic Two-stage Process of Sustainable Development of Water Resources\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4987800/v1/4d64f407ea0d642c2de7e0e0.png\"},{\"id\":69028619,\"identity\":\"5dfe7e17-2817-4348-8924-b35478201579\",\"added_by\":\"auto\",\"created_at\":\"2024-11-14 17:55:21\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":186235,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eSystem of Energy Poverty Impacts on the Sustainable Development of Water Resources.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4987800/v1/96d5e731cbb787d8f8daf529.png\"},{\"id\":69028766,\"identity\":\"f6f38a49-378a-4727-b12c-eea0ef5268b1\",\"added_by\":\"auto\",\"created_at\":\"2024-11-14 18:03:21\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":95245,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eTrend Map of Sustainable development Efficiency of Water Resources\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4987800/v1/082dda4e688701727be82493.png\"},{\"id\":69028228,\"identity\":\"e26bfa20-8a2a-4222-8fb9-ae222b742ba4\",\"added_by\":\"auto\",\"created_at\":\"2024-11-14 17:47:22\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":145449,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eProductivity Matrix, 2016-2020\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4987800/v1/29cf38dc9bf13df104f02543.png\"},{\"id\":69028224,\"identity\":\"3515394c-8d3c-43d5-98a3-88d686ebb7eb\",\"added_by\":\"auto\",\"created_at\":\"2024-11-14 17:47:21\",\"extension\":\"png\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":91848,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eLine Graph of Technology Gap Ratios for Groups and the Country, 2016-2020\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"5.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4987800/v1/3e3f7b9d3b43ae6bbc918b93.png\"},{\"id\":86179191,\"identity\":\"1485afbc-7aa5-4f7c-bb1c-e12f1e2205fd\",\"added_by\":\"auto\",\"created_at\":\"2025-07-07 16:17:02\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":2444107,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4987800/v1/94b20efa-f5e7-4233-a3b2-ac85330f7e4e.pdf\"},{\"id\":69028223,\"identity\":\"46993f9c-4f18-42b7-a4fa-b3b05c0d1d25\",\"added_by\":\"auto\",\"created_at\":\"2024-11-14 17:47:21\",\"extension\":\"xlsx\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":30248,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"rawdata.xlsx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4987800/v1/9195a78a7137a10ae3910f35.xlsx\"},{\"id\":69028227,\"identity\":\"9162daa8-1a76-4bea-85db-1e296d49c86f\",\"added_by\":\"auto\",\"created_at\":\"2024-11-14 17:47:21\",\"extension\":\"docx\",\"order_by\":2,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":31723,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Table4.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4987800/v1/7d53f14eaa403c67542c0b8f.docx\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Does energy poverty affect the sustainable development of water resources？—Empirical Evidence from China\",\"fulltext\":[{\"header\":\"1. Introduction\",\"content\":\"\\u003cp\\u003eChina\\u0026rsquo;s economic and social development has entered a new era with its economy shifting from high-speed to high-quality growth. However, the country still faces a series of social and ecological problems such as inadequate development and ecological environment deterioration. Rapid economic advancement must meet the needs of production and livelihoods of residents, but comes mostly at the expense of resources and the environment[\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e]. Therefore, the concept of unsustainable development needs to be changed, and environmental quality and social livelihood should be taken into account when maintaining economic growth by emphasizing the coordinated and sustainable progress of the social economy and ecological environment. Energy resources are not only an important factor affecting rapid economic development, but are also the basic and core needs of residents. However, China is faced with the situation of single energy utilization, low energy efficiency, and low energy structure level, and there are great regional differences in energy supply. At the same time, in the contexts of global air pollution, rapid urban expansion, shortage of safe drinking water resources in the world, and improper management of sewage facilities in most enterprises, energy poverty has greatly affected resources, environment, and social livelihoods. The proper use of energy is increasingly becoming an important constraint for environmental and economic sustainable development[\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eThe overall incidence of energy poverty in China is more than 40%, and the relative energy poverty of urban households is growing more serious, which is mainly reflected in the low level of energy consumption of residents, unreasonable energy consumption structure, and weak energy consumption capacity[\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e]. Under the background of comprehensive poverty alleviation and the goal of common prosperity, the impact of energy consumption equity on the sustainable development of water resources has important theoretical and practical significances. However, according to the International Energy Agency, hundreds of millions of people around the world still do not have access to and use the modern energy they need on a daily basis, and the global COVID-19 pandemic has delayed progress in addressing energy poverty. Energy poverty not only affects people's physical health, mental health, and quality of life, but also restricts human sustainable development and social progress (World Bank, 2017; United Nations, 2015). Promoting energy tribute is crucial to eliminating energy poverty in China and achieving common prosperity[\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eWith the increasing international discussion on energy use and ecological environment, water-related topics have also received high attention from all walks of life. With the reality that the world's potable water resources are turning increasingly scarce, international organizations are paying close attention to the efficiency level of water resources management and sustainable use and are adopting appropriate forms of international cooperation to avoid serious water shortage problems in the future. In 2015, for example, the United Nations Sustainable Development Goal (SDG) made a historic commitment to end poverty forever in every corner of the world, with SDG Goal 6 calling for access to clean drinking water and water for all. With 6% of the world's fresh water resources, China has guaranteed safe water for nearly 20% of the world's population and has actively provided assistance to developing countries in water conservancy construction. However, data show in 2021 that China's total water resources are 2.95 trillion cubic meters, but per capita occupancy is only 2,200 cubic meters versus the world's per capita water resources level of 8,800 cubic meters. The amount of water resources in China is significantly low, and the situation of water resources is not optimistic. Indeed, there are still difficulties in the process of water resources development.\\u003c/p\\u003e \\u003cp\\u003eFrom the perspective of the country as a whole, it is extremely important to seek sustainable use of water resources[\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e]. China's water resources shortage, low utilization efficiency of water resources, serious water pollution, and other phenomena are quite prominent, and the carrying capacity of water resources in some areas is close to the limit. At present, there are many problems in industrial water, such as large water demand, large sewage discharge, and low utilization efficiency, thus restricting the sustainable use of water resources and green transformation of the industrial economy[\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e]. From the perspective of geographical differences, the spatial and temporal distribution patterns of China's water resources do not match the regional energy structure, the imbalance of energy structure, and the frequent occurrence of water pollution events that threaten regional sustainable development and social stability. Therefore, overall planning at the government level is an important measure to eliminate energy poverty and support sustainable development of water resources. Therefore, this paper discusses the relationship between sustainable development of water resources and energy poverty within a research framework and evaluates how to create more welfare growth with less water consumption within the range of water resources carrying capacity under the social background of energy poverty in China.\\u003c/p\\u003e \\u003cp\\u003eThe remaining structure of this paper is as follows: The second chapter mainly collates relevant literature research, and conducts literature research on the efficiency of sustainable development of water resources and energy poverty; The third chapter describes the energy poverty index system constructed by this research, as well as the models and methods used. Chapter four discusses the empirical results of this paper and analyzes the impact of energy poverty as an exogenous variable on the sustainable development of water resources. Finally, the fifth chapter summarizes and puts forward policy opinions. The main contribution of this study is to put energy poverty into the sustainable development of water resources as an exogenous variable, and analyze the changes in the exogenous variable affecting the sustainable development efficiency of sewage resources.\\u003c/p\\u003e\"},{\"header\":\"2. Literature review\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.1. Research related to sustainable development of water resources\\u003c/h2\\u003e \\u003cp\\u003eAs an important resource, water shortages have become a serious challenge faced by the whole world, and this challenge is expected to continue to intensify in the next 50 years [\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e]. Research on sustainable development related to water resources has attracted wide attention in the world. Li et al. established a multi-dimensional and multi-attribute water resources sustainable assessment system, which comprehensively considers three objects of human and water systems, three attributes of water resources, and three processes of water resource utilization that fully represent the sustainable development of water resources[\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e]. However, this index system also has some shortcomings, such as subjectivity of weights, repeatability of indicators, underrepresentation of indicators, and difficulty in obtaining index data[\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eThe data envelopment analysis (DEA) model is often used in the establishment of water resource sustainable assessment systems. For example, Wang et al. applied ultra-efficient DEA to measure the resource utilization efficiency of 30 provinces in China[\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e]. Zhou et al. selected the relevant data indicators of Hubei in China from 2006 to 2017, calculated its agricultural water resource use efficiency based on DEA, and then studied its agricultural water resource use efficiency via the grey relationship model. The production and utilization stages of water resources are staged and periodic[\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eMost scholars employ a multi-stage DEA model to calculate the corresponding efficiency value based on the input-output index of water resources production process. For example, Zhao et al. calculated the water resource use efficiency of 31 provinces in China from 2011 to 2014 with a two-stage measurement model based on relaxation[\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e]. The results showed that the comprehensive efficiency of the first stage is significantly higher than that of the second stage, but overall efficiency is close to that of the second stage, which determines the overall efficiency of water resource use. Liu et al. applied the DEA model to comprehensively analyze the water resource use efficiency of the main representative urban areas in the upper, middle, and lower reaches of the Ganjiang River basin in three stages[\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eEarly studies have recognized that the influencing factors of sustainable water resource development mainly focus on population growth, climate change, and economic development [\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e]. However, few scholars have recognized that energy poverty relates to the efficiency of water resource development. From the perspective of spatial and temporal distributions, he spatial distribution of water resource use efficiency in China is unbalanced[\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e]. Moreover, the mismatch between the spatial and temporal distribution patterns of water resources and economic development will also become an important factor hindering regional sustainable development[\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e].\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec4\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.2. Research on energy poverty\\u003c/h2\\u003e \\u003cp\\u003eEver since the 1990s, poor families in developed countries often cannot meet their energy needs with their current income[\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e]. Therefore, early studies defined energy poverty as residents' inability to get energy services. The concept of energy poverty has been further expanded and enriched in the literature. Sefa et al. believed that energy poverty refers to people who cannot get clean energy, equipment, or energy-related services equitably[\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e]. Bouzarovski et al. proposed that energy poverty refers to the situation in which people cannot get energy services necessary for social activities or material needs[\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e]. It is found that quantifying energy poverty enables policy makers and researchers to better address energy-related issues[\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eThe current methods for assessing energy poverty are divided into a single energy poverty indicator (LILEE method) and a multi-dimensional energy poverty indicator (MEPI method) [\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e]. The single energy poverty indicator assessment estimates the number of energy-poor households mainly by establishing a low energy income standard[\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e]. In contrast, the multi-dimensional energy poverty assessment method is more reliable, which emphasizes the measurement of energy poverty by more energy-related indicators. Changes in energy poverty, as well as differences in energy poverty and poverty levels among different groups or regions, have been more clearly observed[\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e]. For example, Nussbaumer et al. built an evaluation index system covering the five aspects of energy use in cooking, lighting, household appliances, education, and communication[\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e]. Li Kang et al. established a four-dimensional regional energy poverty evaluation system in China to assess the current situation and changing trend of energy poverty in China on the basis of clarifying the nation\\u0026rsquo;s energy concept and international energy poverty evaluation methods[\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e]. HG. Et al. applied the consensus method and comprehensive measurement of EU-SILC survey data to construct a four-dimensional index system that measures energy poverty, and each index was assigned a corresponding weight[\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e]. Salman Muhammad et al. constructed a multidimensional energy poverty index incorporating the dimensions of energy affordability, energy cleanability and energy accessibility[\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e]. Wang Fu et al. constructed a multidimensional energy poverty index from seven dimensions to better measure China's energy poverty situation and its dynamic changes[\\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e]. Xie et al. selected five dimensions of household cooking fuel, lighting, home appliance services, entertainment and education, and communication to build a multidimensional energy poverty index and to examine the determinants of multidimensional energy poverty[\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e]. The multidimensional energy poverty assessment method subdivides a single energy poverty factor into different dimensions, thus improving the scientific and feasibility of the research[\\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eFrom the perspective of spatial distribution, there are spatial differences in energy poverty, and research has shown that provinces with better economic conditions and higher performance are classified as the progress group in alleviating energy poverty. Conversely, provinces with lagging economies experience the most retrogression and are also the provinces with the worst performance in reducing energy poverty[\\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e].\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec5\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.3. Research on water resources and energy\\u003c/h2\\u003e \\u003cp\\u003eThe relationship between water and energy has attracted much attention lately. In the field of energy economics, energy, like water resources, is a relevant indicator of sustainable development for the United Nations. In the study of energy and water, most scholars have targeted the impact of energy and water as a unified circulating system on sustainable development. For example, Ke et al. believed that while reducing the adverse impact on natural resources, society, and environment, energy and water can help reduce the impact on sustainable development[\\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e32\\u003c/span\\u003e]. Improving the efficiency of energy and water use is essential for sustaining socio-economic development. However, energy consumption has gradually become an important factor affecting the sustainable development of regional water resources. At the basic level of production, the extraction, production, and transportation of energy all require water. There are inputs and outputs of related resources in the water resource cycle process, and the whole process of resource utilization requires a large amount of energy input[\\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e33\\u003c/span\\u003e]. For example, Shang et al. assessed the impact of China\\u0026rsquo;s coal base development on the sustainable development of regional water resources[\\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e34\\u003c/span\\u003e]. Their results showed that compared with 2012, new coal production, coal-fired power generation, and coal chemical water demand will reach 230\\u0026ndash;380\\u0026nbsp;million cubic meters in 2020. Given the limited power generation capacity of water resources, this growth will intensify regional water competition among different industries.\\u003c/p\\u003e \\u003cp\\u003eAlthough there are many studies on energy consumption and water use, there are scant studies on the relationship between energy poverty and sustainable water resource development. However, from the perspective of the relationship between energy and water, there is a correlation between the degree of regional energy poverty and the efficiency of sustainable use of regional water resources. Therefore, it is necessary to include energy poverty in the study of water sustainable development.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"3. Research Model and Data Description\",\"content\":\"\\u003cdiv id=\\\"Sec7\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.1. Dynamic two-stage SBM model\\u003c/h2\\u003e \\u003cp\\u003eIn actual inter-provincial development, the development path of water resources obviously has stages. First, through production activities it creates economic benefits and social, industrial, and agricultural relative to the total water consumption of the value added. Second, there are pollutants emitted by the industry, and through pollution control one can derive the amount of pollutants to improve; i.e., the formation of a dynamic two-stage path of the sustainable development of water resources.\\u003c/p\\u003e \\u003cp\\u003eAccording to the research purpose of this paper and in order to explore the impact of energy poverty on water resource sustainable development, the energy poverty index calculated herein is an exogenous variable that is put into the water resource sustainable development system. We thus can observe the level of change in the efficiency of water resource sustainable development with or without the exogenous variable of energy poverty. The sustainable development of water resources is fundamentally the pursuit of comprehensive and coordinated development of social life, industrial production, and ecological environment. Thus, the dynamic two-stage SBM model is applied to analyze and evaluate the system efficiency of sustainable development of water resources and the changes in the efficiency under the influence of exogenous variables at each stage and at the overall level. The dynamic two-stage sustainable development of water resources is shown in Figure I.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eA decision-making unit (DMU) is composed of K sub-phases. Assume there are n DMUs, denoted as \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{DMU}_{i}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e (i\\u0026thinsp;=\\u0026thinsp;1,2,\\u0026middot;\\u0026middot;\\u0026middot;, n). Any DMU contains m inputs \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{X}_{ij}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e(j\\u0026thinsp;=\\u0026thinsp;1,2,\\u0026middot;\\u0026middot;\\u0026middot;, m), p intermediate outputs \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{M}_{ij}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e(j\\u0026thinsp;=\\u0026thinsp;1,2,\\u0026middot;\\u0026middot;\\u0026middot;, p), h re-inputs \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{H}_{ij}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e(j\\u0026thinsp;=\\u0026thinsp;1,2,\\u0026middot;\\u0026middot;\\u0026middot;, h), and r final outputs \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{Y}_{ij}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e(j\\u0026thinsp;=\\u0026thinsp;1,2,\\u0026middot;\\u0026middot;\\u0026middot;, r) in T periods. Two neighboring periods are linked across periods with some of the outputs in period T-1 being some of the inputs in period T, and some of the inputs in period T\\u0026thinsp;+\\u0026thinsp;1 being some of the outputs in period T again. There are s carry-over variables for such inter-period inputs, \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{Z}_{ij}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e(j\\u0026thinsp;=\\u0026thinsp;1,2,\\u0026middot;\\u0026middot;\\u0026middot;, s).\\u003c/p\\u003e \\u003cp\\u003eBased on the dynamic two-stage SBM model described above, the overall efficiency of the qth DMU in period T is defined as:\\u003cdiv id=\\\"Equa\\\" class=\\\"Equation\\\"\\u003e\\u003cdiv format=\\\"TEX\\\" class=\\\"mathdisplay\\\" id=\\\"FileID_Equa\\\" name=\\\"EquationSource\\\"\\u003e\\n$$\\\\:min{\\\\theta\\\\:}_{o}=\\\\frac{{w}_{q1}\\\\left[1-\\\\frac{1}{m+s}\\\\left({\\\\sum\\\\:}_{i=1}^{m}\\\\frac{{s}_{mo}^{-}}{{x}_{mo}}+{\\\\sum\\\\:}_{i=1}^{s}\\\\frac{{s}_{so}^{-}}{{z}_{so}}\\\\right)\\\\right]+{w}_{q2}[1-\\\\frac{1}{p+ℎ}({\\\\sum\\\\:}_{i=1}^{p}\\\\frac{{s}_{po}^{-}}{{m}_{po}}+{\\\\sum\\\\:}_{i=1}^{ℎ}\\\\frac{{s}_{ℎo}^{-}}{{ℎ}_{ℎo}})]}{{w}_{q1}(1+\\\\frac{1}{p}{\\\\sum\\\\:}_{i=1}^{p}{s}_{po}^{+}/{m}_{po})+{w}_{q2}[1+\\\\frac{1}{r+s}\\\\left({\\\\sum\\\\:}_{i=1}^{r}\\\\frac{{s}_{ro}^{+}}{{y}_{ro}}+{\\\\sum\\\\:}_{i=1}^{s}\\\\frac{{s}_{so}^{+}}{{z}_{so}})\\\\right]}$$\\u003c/div\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Equb\\\" class=\\\"Equation\\\"\\u003e\\u003cdiv format=\\\"TEX\\\" class=\\\"mathdisplay\\\" id=\\\"FileID_Equb\\\" name=\\\"EquationSource\\\"\\u003e\\n$$\\\\:s.t.\\\\left\\\\{\\\\begin{array}{c}{\\\\sum\\\\:}_{j=1}^{n}{\\\\lambda\\\\:}_{j}{x}_{mj}={x}_{mo}-{s}_{mo}^{-}\\\\\\\\\\\\:{\\\\sum\\\\:}_{j=1}^{n}{\\\\lambda\\\\:}_{j}{z}_{sj}={z}_{so}-{s}_{so}^{-}\\\\\\\\\\\\:{\\\\sum\\\\:}_{j=1}^{n}{\\\\lambda\\\\:}_{j}{m}_{pj}={m}_{po}+{s}_{po}^{+}\\\\\\\\\\\\:{\\\\sum\\\\:}_{j=1}^{n}{\\\\delta\\\\:}_{j}{ℎ}_{ℎj}={ℎ}_{ℎo}-{s}_{ℎo}^{-}\\\\\\\\\\\\:{\\\\sum\\\\:}_{j=1}^{n}{\\\\delta\\\\:}_{j}{m}_{pj}={m}_{po}-{s}_{po}^{-}\\\\\\\\\\\\:{\\\\sum\\\\:}_{j=1}^{n}{\\\\delta\\\\:}_{j}{y}_{rj}={y}_{ro}+{s}_{ro}^{+}\\\\\\\\\\\\:{\\\\sum\\\\:}_{j=1}^{n}{\\\\delta\\\\:}_{j}{z}_{sj}={z}_{so}+{s}_{so}^{+}\\\\\\\\\\\\:{\\\\sum\\\\:}_{j=1}^{n}{\\\\lambda\\\\:}_{j}={\\\\sum\\\\:}_{j=1}^{n}{\\\\delta\\\\:}_{j}\\\\\\\\\\\\:{s}_{mo}^{-},{s}_{so}^{-},{s}_{po}^{+},{s}_{po}^{-},{s}_{ℎo}^{-},{s}_{ro}^{+},{s}_{so}^{+},{\\\\lambda\\\\:}_{j},{\\\\delta\\\\:}_{j}\\\\ge\\\\:0\\\\end{array}\\\\right.$$\\u003c/div\\u003e\\u003c/div\\u003e\\u003c/p\\u003e \\u003cp\\u003eHere, \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{\\\\theta\\\\:}_{o}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e is the overall efficiency of the water resource sustainable development system, which is effective if the value of efficiency is 1, and vice versa;\\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:\\\\lambda\\\\:\\\\)\\u003c/span\\u003e\\u003c/span\\u003e is a non-negative multiplier for integrating the production stage; \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:\\\\delta\\\\:\\\\)\\u003c/span\\u003e\\u003c/span\\u003e is a non-negative multiplier for integrating the perpetual stage; \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{s}_{mo}^{-}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e denotes the slack variables for inputs, \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{s}_{so}^{+}\\\\:\\\\text{a}\\\\text{n}\\\\text{d}\\\\:{s}_{so}^{-}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e denotes the slack variables for carry-over variables, \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{s}_{po}^{+}\\\\:\\\\text{a}\\\\text{n}\\\\text{d}\\\\:{s}_{po}^{-}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e denotes the slack variables for intermediate outputs, \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{s}_{ℎo}^{-}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e denotes the slack variables for re-inputs, \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{s}_{ro}^{+}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e denotes the slack variables for final outputs. The slack variables can reflect the gap between the input and output variables and the target value of optimal allocation, which can provide a reasonable opinion for the improvement of the sustainable development of water resources in each province and the adaptation of resources in the future. Lastly, \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{w}_{q1}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e and \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{w}_{q2}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e denote the weights of the qth decision variable in the two sub-stages in period T, respectively.\\u003c/p\\u003e \\u003cp\\u003eThe efficiency of the production stage subsystem is defined as:\\u003cdiv id=\\\"Equc\\\" class=\\\"Equation\\\"\\u003e\\u003cdiv format=\\\"TEX\\\" class=\\\"mathdisplay\\\" id=\\\"FileID_Equc\\\" name=\\\"EquationSource\\\"\\u003e\\n$$\\\\:min{\\\\theta\\\\:}_{1}=\\\\frac{1-\\\\frac{1}{m+s}({\\\\sum\\\\:}_{i=1}^{m}\\\\frac{{s}_{mo}^{-}}{{x}_{mo}}+{\\\\sum\\\\:}_{i=1}^{s}\\\\frac{{s}_{so}^{-}}{{z}_{so}})}{1+\\\\frac{1}{p}{\\\\sum\\\\:}_{i=1}^{p}{s}_{po}^{+}/{m}_{po}}$$\\u003c/div\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Equd\\\" class=\\\"Equation\\\"\\u003e\\u003cdiv format=\\\"TEX\\\" class=\\\"mathdisplay\\\" id=\\\"FileID_Equd\\\" name=\\\"EquationSource\\\"\\u003e\\n$$\\\\:s.t.\\\\left\\\\{\\\\begin{array}{c}{\\\\sum\\\\:}_{j=1}^{n}{\\\\lambda\\\\:}_{j}{x}_{mj}={x}_{mo}-{s}_{mo}^{-}\\\\\\\\\\\\:{\\\\sum\\\\:}_{j=1}^{n}{\\\\lambda\\\\:}_{j}{z}_{sj}={z}_{so}-{s}_{so}^{-}\\\\\\\\\\\\:{\\\\sum\\\\:}_{j=1}^{n}{\\\\lambda\\\\:}_{j}{m}_{pj}={m}_{po}+{s}_{po}^{+}\\\\\\\\\\\\:{\\\\sum\\\\:}_{j=1}^{n}{\\\\lambda\\\\:}_{j}=1\\\\\\\\\\\\:{s}_{mo}^{-},{s}_{so}^{-},{s}_{po}^{+},{\\\\lambda\\\\:}_{j}\\\\ge\\\\:0\\\\end{array}\\\\right.$$\\u003c/div\\u003e\\u003c/div\\u003e\\u003c/p\\u003e \\u003cp\\u003eThe efficiency of the perpetual stage subsystem is defined as:\\u003cdiv id=\\\"Eque\\\" class=\\\"Equation\\\"\\u003e\\u003cdiv format=\\\"TEX\\\" class=\\\"mathdisplay\\\" id=\\\"FileID_Eque\\\" name=\\\"EquationSource\\\"\\u003e\\n$$\\\\:min{\\\\theta\\\\:}_{2}=\\\\frac{1-\\\\frac{1}{p+ℎ}({\\\\sum\\\\:}_{i=1}^{p}\\\\frac{{s}_{po}^{-}}{{m}_{po}}+{\\\\sum\\\\:}_{i=1}^{ℎ}\\\\frac{{s}_{ℎo}^{-}}{{ℎ}_{ℎo}})}{1+\\\\frac{1}{r+s}({\\\\sum\\\\:}_{i=1}^{r}\\\\frac{{s}_{ro}^{+}}{{y}_{ro}}+{\\\\sum\\\\:}_{i=1}^{s}\\\\frac{{s}_{so}^{+}}{{z}_{so}})}$$\\u003c/div\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Equf\\\" class=\\\"Equation\\\"\\u003e\\u003cdiv format=\\\"TEX\\\" class=\\\"mathdisplay\\\" id=\\\"FileID_Equf\\\" name=\\\"EquationSource\\\"\\u003e\\n$$\\\\:\\\\left\\\\{\\\\begin{array}{c}{\\\\sum\\\\:}_{j=1}^{n}{\\\\delta\\\\:}_{j}{m}_{pj}={m}_{po}-{s}_{po}^{-}\\\\\\\\\\\\:{\\\\sum\\\\:}_{j=1}^{n}{\\\\delta\\\\:}_{j}{ℎ}_{ℎj}={ℎ}_{ℎo}-{s}_{ℎo}^{-}\\\\\\\\\\\\:{\\\\sum\\\\:}_{j=1}^{n}{\\\\delta\\\\:}_{j}{y}_{rj}={y}_{ro}+{s}_{ro}^{+}\\\\\\\\\\\\:{\\\\sum\\\\:}_{j=1}^{n}{\\\\delta\\\\:}_{j}{z}_{sj}={z}_{so}+{s}_{so}^{+}\\\\\\\\\\\\:{\\\\sum\\\\:}_{j=1}^{n}{\\\\delta\\\\:}_{j}=1\\\\\\\\\\\\:{s}_{po}^{-},{s}_{ℎo}^{-},{s}_{ro}^{+},{s}_{so}^{+},{\\\\delta\\\\:}_{j}\\\\ge\\\\:0\\\\end{array}\\\\right.$$\\u003c/div\\u003e\\u003c/div\\u003e\\u003c/p\\u003e \\u003cp\\u003eHere, \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{\\\\theta\\\\:}_{1}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e and \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{\\\\theta\\\\:}_{2}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e denote the efficiency of the assessment decision unit in the production stage subsystem and the perpetual stage subsystem, respectively.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.2. Technology gap ratio\\u003c/h2\\u003e \\u003cp\\u003eTechnology gap ratio (TGR) is an important indicator in the framework of common frontiers. This study measures the meta-frontier technical efficiency (MTE) of the kth DMU based on the meta-frontier of the 29 DMUs according to the model construction described above. This meta-frontier is made up of the most efficient DMUs in each of the 3 groups together, and based on the different groups under the boundary the group frontier technical efficiency (GTE) can be calculated. Based on GTE and MTE under different groups, it is possible to calculate the technology gap efficiency (TGR) value, which measures the technological status of each DMU and the difference in the level of technology between the groups, reflecting the gap between groups under the meta-frontier and group frontier. The specific calculation formula is:\\u003cdiv id=\\\"Equg\\\" class=\\\"Equation\\\"\\u003e\\u003cdiv format=\\\"TEX\\\" class=\\\"mathdisplay\\\" id=\\\"FileID_Equg\\\" name=\\\"EquationSource\\\"\\u003e\\n$$\\\\:0\\\\le\\\\:TGR=\\\\frac{MTE}{GTE}\\\\le\\\\:1$$\\u003c/div\\u003e\\u003c/div\\u003e\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec9\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.3. Construction of the indicator system and data sources\\u003c/h2\\u003e \\u003cdiv id=\\\"Sec10\\\" class=\\\"Section3\\\"\\u003e \\u003ch2\\u003e3.3.1. Selection of research variables and construction of the indicator system\\u003c/h2\\u003e \\u003cp\\u003eThe sustainability of water resources is affected by social life and industrial production. In order to examine the impact of energy poverty on the efficiency of water resource sustainability, this study establishes an indicator system, starting from two subsystems: the production stage subsystem and the perpetual stage subsystem. It then combines accessibility of the data and follows the principles of constructing the evaluation indicator system in a systematic, comparable, comprehensive, and scientific way, as shown in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eInputs in the production stage mainly include fixed assets carried over from the previous period, labor force, and total water supply, and so the number of people employed in the region (unit: 10,000) and the total amount of water supplied in the region (unit: 100\\u0026nbsp;million tons) are selected as indicators. The intermediate outputs in the production stage are the direct results of water resource development, which are reflected in GDP and the economic benefits of water resources in life, agriculture, and industry. For the economic benefits of water resources we use the entropy weighting method to calculate the weights of three indicators: per capita water consumption, the ratio of value added in agriculture to the total amount of water consumption, and the ratio of value added in industry to the total amount of water consumption.\\u003c/p\\u003e \\u003cp\\u003eIn addition to the outputs of the production stage, the inputs of the perpetuation stage require total investment in industrial treatment (wastewater, waste gas, and solid waste) and energy poverty indicators in order to realize the perpetuation of water resources in industrial production. Total investment in industrial treatment is derived by fitting three line item indicators, wastewater treatment inputs, CDC treatment inputs, and ammonia treatment inputs, through entropy weighting method. The final output of the second stage is represented by the pollution improvement index, which is derived by fitting three line items: wastewater improvement per capita, CDC improvement per capita, and ammonia improvement per capita through the entropy weighting method. The fixed assets produced in this stage are invested in the production stage of the next period as a carry-over variable.\\u003c/p\\u003e \\u003cp\\u003eThe assessment of energy poverty refers to the method of Li Kang (2014) [\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e]. Taking into account the data availability and the research needs of this paper, 14 indicators are finally selected from the four aspects of energy service availability, cleanliness of domestic energy use, completeness of energy management, and affordability and high efficiency of domestic energy use to establish an assessment index system. The specific indicators are shown in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e. The selected indicators are standardized and weighted using the entropy weighting method. Finally, the energy poverty score of each province is calculated using the weighted sum method.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eEnergy Poverty Measurement System\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"3\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLevel 1 indicator\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eLevel 2 indicators\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eLevel 3 indicators\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"13\\\" rowspan=\\\"14\\\"\\u003e \\u003cp\\u003eEnergy Poverty\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eAccessibility of energy services\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eUrban gas penetration rate\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003ePer capita urban gas supply (10,000 cubic meters)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eCleanliness for domestic use\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eShare of non-fire power generation in total power generation (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eAverage rural household biogas production\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eCompleteness of energy management\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eNumber of Rural Energy Management Extension Agencies (REMEAs) per million per capita in each region\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003ePer capita expenditure on rural energy inputs\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\" morerows=\\\"7\\\" rowspan=\\\"8\\\"\\u003e \\u003cp\\u003eAffordability and efficiency of domestic energy use\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eOwnership of air conditioners per 100 households in urban areas\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eOwnership of refrigerators per 100 urban households\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eOwnership of cooker hoods per 100 households in rural areas\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eOwnership of wood-saving and coal-saving stoves per 100 rural households\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eProportion of rural households using biogas digesters\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003ePer capita coverage of rural solar water heaters\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eDomestic sulphur dioxide emissions per million population (tons)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eDomestic smoke emissions per million population (tons)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eIn terms of grouping indicators, this study selects 29 provinces in China as the research objects. Because the large regional span between provinces, the geographical form, the economic situation, and the use of water resources and investment in industrial governance differ greatly, this study divides the 29 provinces into east, central, and west regions according to their geographical location. The details are shown in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 2\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eSpecific Groupings\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"2\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eRegion\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eProvinces and Cities\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eEast region\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eShanghai, Shandong, Tianjin, Beijing, Jilin, Jiangsu, Hebei, Hainan, Zhejiang, Heilongjiang, Fujian, Guangdong, Liaoning\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCentral region\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eShanxi, Anhui, Jiangxi, Henan, Hubei, Hunan\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eWest region\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eYunnan, Sichuan, Gansu, Ningxia, Qinghai, Chongqing, Inner Mongolia, Guangxi, Guizhou, Shaanxi\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec11\\\" class=\\\"Section3\\\"\\u003e \\u003ch2\\u003e3.3.2. Data sources\\u003c/h2\\u003e \\u003cp\\u003eIn view of the data updating situation, this study collects relevant data through China Statistical Yearbook, China Environmental Statistical Yearbook, China Energy Statistical Yearbook, China Rural Statistical Data, and statistical yearbooks of provinces (municipalities and autonomous regions). It then constructs a system of input-output indicators for the period 2016\\u0026ndash;2020 for 29 provinces in China. Due to the lack of relevant data on Tibet, only 29 provinces are selected as the research objects.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e\"},{\"header\":\"4. Results and discussions\",\"content\":\"\\u003cdiv id=\\\"Sec13\\\"\\u003e\\n \\u003ch2\\u003e4.1. Temporal analysis\\u003c/h2\\u003e\\n \\u003cp\\u003eAs can be seen from Table\\u0026nbsp;\\u003cspan\\u003e3\\u003c/span\\u003e, under the influence of energy poverty, most provinces have low water resource sustainability efficiency values in 2016\\u0026ndash;2020 that have a fluctuating upward trend. This reflects that China\\u0026apos;s water resource sustainability level has improved, but the overall level is still low. This may be due to the fact that China\\u0026apos;s strategic water conservancy projects have effectively alleviated localized water shortages, and water use problems are not prominent in most regions. Thus, the issue of sustainable development of water resources has not been given sufficient attention. This is also evidenced by the differences in water resource sustainable development efficiency values among regions.\\u003c/p\\u003e\\n \\u003cdiv\\u003e\\n \\u003ctable id=\\\"Tab3\\\" border=\\\"1\\\"\\u003e\\n \\u003ccaption language=\\\"En\\\"\\u003e\\n \\u003cdiv\\u003eTable 3\\u003c/div\\u003e\\n \\u003cdiv\\u003e\\n \\u003cp\\u003eAverage Value of Provincial Water Sustainability Efficiency in China, 2016\\u0026ndash;2020\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n \\u003c/caption\\u003e\\n \\u003ccolgroup cols=\\\"7\\\"\\u003e\\u003c/colgroup\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2016\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2017\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2018\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2019\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2020\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eMean\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/thead\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eShanghai\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.519273\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.576247\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.554923\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.525211\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.565729\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.548277\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eShanxi\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.355826\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.477739\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.475551\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.535338\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.478045\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.4645\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eShandong\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.520711\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.54446\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.508618\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.5045\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.504907\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.516639\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eYunan\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.298669\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.842589\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.737785\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.56091\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.687991\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eTianjin\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.723007\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.944601\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eBeijing\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eSichuan\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.509791\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.757583\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.496136\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.458784\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.435105\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.53148\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eGansu\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.410341\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.649472\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.668331\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.591461\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.569247\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.57777\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eNingxia\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eJilin\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.851293\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.599637\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.609501\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.864962\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.785079\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eAnhui\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.390328\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.531218\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.468542\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.441179\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.407689\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.447791\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eJiangxi\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.391992\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.603777\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.548207\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.619403\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.562217\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.545119\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eJiangsu\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.516774\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.618298\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.506245\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.518662\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.505863\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.533168\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eHebei\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.600625\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.523691\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.50935\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.551383\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.555075\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.548025\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eHenan\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.53331\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.56337\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.505845\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.515046\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.524159\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.528346\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eQinghai\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eChongqing\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.755012\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.736572\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.481909\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.5522\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.428075\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.590754\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eHainan\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.651751\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.93035\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eZhejiang\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.5306\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.652143\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.505902\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.544533\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.543994\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.555434\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eHubei\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.399179\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.524124\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.42309\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.485237\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.365867\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.439499\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eHunan\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.528332\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.694569\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.508982\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.631071\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.55083\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.582757\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eHeilongjiang\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.655243\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.563179\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.544166\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.752518\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eFujian\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.566861\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.584386\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.64258\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.579035\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.674572\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eInner Mongolia\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.561741\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.774085\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.867165\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eGuangxi\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.296203\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.505348\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.344414\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.388921\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.400241\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.387025\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eGuangdong\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.52809\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.520641\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.527238\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.715194\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eGuizhou\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.588417\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.568903\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.614415\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.569075\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.668162\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eLiaoning\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.434707\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.568261\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.369146\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.630568\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.600536\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eShaanxi\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.615403\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.614887\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.568576\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.583414\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.534628\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.583382\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003c/table\\u003e\\n \\u003c/div\\u003e\\n \\u003cp\\u003eIt is not difficult to find that Beijing, Ningxia, Tianjin, and Qinghai are significantly more efficient in terms of water sustainability than other provinces. Except for Qinghai, the other three provinces are located in North China and Northwest China, where water resources are relatively scarce. It is reasonable to assume that the reality of water scarcity will force localities to improve the efficiency of sustainable water resource development. On the contrary, in areas with abundant water resources such as Anhui, Guangxi, and Hubei, there may be a general lack of awareness of water conservation and a serious waste of water resources, leading to inefficiency in sustainable development of water resources.\\u003c/p\\u003e\\n \\u003cp\\u003eThe efficiency of sustainable development of water resources also relates to the mode of economic development. It can be found that the economic development of the three provinces of Shanxi, Shandong, and Henan, which also have the lowest average efficiency value in the period 2016\\u0026ndash;2020, is based on resource extraction, heavy industry and chemical industry, and agriculture, respectively. Their pollution of water resources is more serious, because there may be problems such as substandard industrial wastewater discharge, acid rain caused by industrial emissions, and groundwater pollution caused by seepage through irrational application of fertilizers and medicines in agriculture. Although Qinghai is one of the richest regions in China in terms of water resources, the tourism industry in this province, as a pillar industry of economic and social development in the province, has stimulated the protection and sustainable development of its water resources, thus achieving effective DEA in terms of sustainable development of water resources.\\u003c/p\\u003e\\n \\u003cp\\u003eThe paper further plots the trend of water resource sustainable development efficiency histograms in Fig.\\u0026nbsp;\\u003cspan\\u003e3\\u003c/span\\u003e and divides the overall trend into six categories. Among them, Shanghai, Jiangxi, Zhejiang, Hunan, and Guangxi show a fluctuating upward trend in water resource sustainable development efficiency. Jiangsu, Chongqing, Hubei, and Shaanxi show a fluctuating downward trend. Shanxi, Yunnan, Sichuan, Gansu, Anhui, and Fujian show a fluctuating upward and then downward trend in efficiency value. Jilin, Hebei, and Heilongjiang show a fluctuating downward and then upward trend in efficiency value. Shandong, Tianjin, Beijing, Ningxia, Shandong, Tianjin, Beijing, Ningxia, Henan, Qinghai, and Hainan have smaller fluctuations in efficiency, basically remaining the same. Inner Mongolia, Guizhou, Guangdong, and Liaoning have larger fluctuations in efficiency.\\u003c/p\\u003e\\n \\u003cp\\u003eThe efficiency of water resource sustainability in most provinces has overall increased significantly in 2017. The reason for this phenomenon may be that China released the National Water Resources Protection and Utilization Planning Outline (2016\\u0026ndash;2020) in 2016, which set out the overall objectives and key tasks for water resource protection and utilization in the next five years, including strengthening water resource protection, improving the efficiency of water resource utilization, and optimizing water resource allocation. Under the guidance of this document, localities are likely to pay attention to and effectively improve the efficiency of sustainable development of water resources. It can be seen that to achieve the sustainable development of water resources, the role of policy promotion is very important. This also reveals that government departments in regions with low water resource sustainable development efficiency should pay attention to this issue, actively formulate relevant policies and implement them to regulate the behavior of water use in social production and daily life, and promote the protection and sustainable development of local water resources.\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec14\\\"\\u003e\\n \\u003ch2\\u003e4.2 Subsystem efficiency analysis by stage\\u003c/h2\\u003e\\n \\u003cp\\u003eAs can be seen from Table\\u0026nbsp;\\u003cspan\\u003e4\\u003c/span\\u003e, there is no consistency between the efficiencies of the production stage and the sustainability stage of water resources. Except for the three provinces of Beijing, Ningxia, and Qinghai, whose efficiency values of the two stages are all 1, the efficiency of the production stage in other provinces is significantly higher than that of the perpetual stage. This shows that China is currently developing and utilizing its water resources while neglecting their protection and sustainable development, meaning there is still a lot of room for progress in the stage of sustainable development of water resources domestically.\\u003c/p\\u003e\\n \\u003cdiv\\u003e\\n \\u003c/div\\u003e\\n \\u003cp\\u003eSichuan is the province with the richest water resources in China after Tibet and also the province with the highest level of economic development in the west region. The efficiency of its water resources in the production stage is 0.82, but the value of the efficiency in the perpetual stage is only 0.24, which indicates that the province\\u0026apos;s water resources are more efficient in terms of development and production, but are wasted and polluted in a more serious way. On the contrary, Ningxia, as an extremely water-scarce region in China, is effective in both the production and perpetuation stages of its water resources.\\u003c/p\\u003e\\n \\u003cp\\u003eDespite the fact that its natural conditions are not dominant, it is possible to see that the local government attaches great importance to the utilization and protection of water resources.\\u003c/p\\u003e\\n \\u003cp\\u003eFurther observation reveals during the period 2016\\u0026ndash;2020 that the efficiency of the production stage of water resources in each province has a clear trend of progress, but the fluctuation of the efficiency in the perpetual stage is more random. This is because the improvement of water resource production efficiency brings immediate economic and social benefits, while efficiency improvement in the sustainable stage is beneficial in the long term. Therefore, the regions are highly motivated to improve the efficiency of the production stage, while the efficiency of the sustainable stage relates more to the degree of policy relaxation, and so the motivation is not high.\\u003c/p\\u003e\\n \\u003cp\\u003eIn this study we also construct a water production-permanent efficiency mean matrix by taking the national water production efficiency mean value of 0.93 and the perpetual stage efficiency mean value of 0.38 as the dividing lines to separate the provinces and cities into four regions in Fig.\\u0026nbsp;\\u003cspan\\u003e4\\u003c/span\\u003e: A (high one, high two), B (low one, high two), C (low one, low two), and D (high one, low two). Most provinces are concentrated on the right side of the matrix region, indicating that the the levels of development and utilization of China\\u0026apos;s regional water resources are relatively balanced, but there is a serious regional imbalance in terms of protection and sustainability. Production efficiency is higher in all the east region, while perpetual efficiency has more pronounced provincial differences. Guangxi in the west region performs the worst. The sustainable development efficiency of water resources in most regions needs to be improved, and there is no strong correlation between the development efficiency and sustainability efficiency of provincial water resources. Provinces should actively learn from the more advantageous regional provinces and combine the characteristics of their own regions to promote the coordinated development of local water resource development and protection.\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec15\\\"\\u003e\\n \\u003ch2\\u003e4.3 Technical efficiency analysis\\u003c/h2\\u003e\\n \\u003cdiv id=\\\"Sec16\\\"\\u003e\\n \\u003ch2\\u003e4.3.1 Analysis of meta-frontier technical efficiency and group frontier technical efficiency considering exogenous variables\\u003c/h2\\u003e\\n \\u003cp\\u003eThe intertemporal frontier is employed to put the inputs and outputs of all years of the DMUs into the construction system of the production frontier surface. The results of the calculation appear in Table\\u0026nbsp;\\u003cspan\\u003e5\\u003c/span\\u003e. MTE and GTE denote the efficiency value of water resource sustainability under the meta-frontier and the group frontier, respectively. TGR is the ratio of the perpetual development gap between the potential efficiency under the meta-frontier and the actual efficiency classified by region. Table\\u0026nbsp;\\u003cspan\\u003e5\\u003c/span\\u003e presents the results of the measurements taking energy poverty as an exogenous variable and without an exogenous variable.\\u003c/p\\u003e\\n \\u003cp\\u003eThe results of the data in Table\\u0026nbsp;\\u003cspan\\u003e5\\u003c/span\\u003e relate to energy poverty and take in the consideration of the common border. The average value of China\\u0026apos;s provincial water sustainability efficiency is 0.47 during the period 2016\\u0026ndash;2020. The average value of the east region is 0.52, the average value of the central region is 0.30, and the average value of the west region is 0.60. Thus, it can be seen that the central region has not reached the national average, and there are obvious differences between the three regions.\\u003c/p\\u003e\\n \\u003cp\\u003eThe water sustainability efficiency values of Tianjin, Beijing, Jilin, Hainan, Heilongjiang, Fujian, and Liaoning in the east region exceed the east regional average and China\\u0026rsquo;s provincial average, with Beijing\\u0026apos;s efficiency value of 1 as well as Tianjin and Hainan far exceeding the group\\u0026apos;s average and there was a large gap within the group. In the central region, only Hunan has an efficiency value that exceeds the group mean and China\\u0026rsquo;s provincial mean, while Jiangxi has a higher efficiency of water resource sustainability than the group mean. The west region has the highest intra-group mean. Moreover, Yunnan, Ningxia, Qinghai, and Inner Mongolia have higher efficiency values than the intra-group mean and China\\u0026rsquo;s provincial mean, with Ningxia and Qinghai both having an efficiency value of 1. All of the west region\\u0026apos;s water resource sustainability efficiency values are relatively and generally high.\\u003c/p\\u003e\\n \\u003cp\\u003eIn terms of the efficiency values of the provinces, there are significant differences between them, and the room for upward mobility varies from province to province. The possible reasons for these results relate to the energy poverty problem and the results of pollution control. The east region, with its service-based tertiary sector as the mainstay of the economy and its more advanced energy services and innovations, has been able to alleviate the problem of energy poverty compared to the central region, which has a heavy industrial economy. The west region has rich mineral resources and is China\\u0026apos;s key ecological barrier area, but it is environmentally poor and sparsely populated, mineral mining enterprises are rund under more stringent environmental and ecological protection policies, and there is strict compliance with the rules of reasonable mining and use. Thus, the west region\\u0026rsquo;s energy poverty and environmental pollution compared to the east and central regions is better, and its water resource sustainable development has higher efficiency.\\u003c/p\\u003e\\n \\u003cp\\u003eUnder the group frontier, the mean values of technical efficiency of the east, central, and west group are 0.69, 0.97, and 0.81, respectively. Their efficiency values are more obviously improved compared to the meta-frontier, with the central region being the highest, the west region as second best, and the east region in last. In addition, a comparison between the data reveals that there are still large differences between the groups.\\u003c/p\\u003e\\n \\u003cdiv\\u003e\\n \\u003ctable id=\\\"Tab5\\\" border=\\\"1\\\"\\u003e\\n \\u003ccaption language=\\\"En\\\"\\u003e\\n \\u003cdiv\\u003eTable 5\\u003c/div\\u003e\\n \\u003cdiv\\u003e\\n \\u003cp\\u003eTechnical Efficiency in 29 Provinces Based on Meta-frontier\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n \\u003c/caption\\u003e\\n \\u003ccolgroup cols=\\\"8\\\"\\u003e\\u003c/colgroup\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eMTE\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eGTE\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eTGR\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eGroup\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eProvince\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eWith exogenous variables\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eWithout exogenous variables\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eWith exogenous variables\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eWithout exogenous variables\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eWith exogenous variables\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eWithout exogenous variables\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/thead\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" rowspan=\\\"13\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eEast Region\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eShanghai\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.26\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.26\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.68\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.68\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.38\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.38\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eShandong\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.14\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.12\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.25\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.12\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.57\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.98\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eTianjin\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.94\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.94\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.94\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.94\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eBeijing\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eJilin\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.76\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.48\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.91\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.53\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.84\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.92\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eJiangsu\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.17\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.17\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.52\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.34\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.34\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.5\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eHebei\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.24\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.22\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.93\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.83\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.26\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.26\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eHainan\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.89\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.89\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.9\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.9\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eZhejiang\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.33\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.24\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.35\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.25\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.94\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.96\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eHeilongjiang\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.52\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.26\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.7\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.31\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd 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align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.69\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.57\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.74\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.8\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" rowspan=\\\"6\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eCentral Region\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eShanxi\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.15\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.14\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n 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align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eHenan\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.19\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.17\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.19\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.17\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eHubei\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.25\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n 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align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.55\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.48\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eCentral Region Mean\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.28\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.97\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.97\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.31\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n 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\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.54\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.48\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.84\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.84\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.63\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.57\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eNingxia\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eQinahai\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eChongqing\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.58\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.57\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.58\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.57\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eInner Mongolia\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.67\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.6\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.67\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.67\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.89\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eGuangxi\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.38\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.37\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.93\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.93\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.39\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eGuizhou\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.63\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.57\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.94\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.87\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.68\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.65\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eShaanxi\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.47\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.35\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.52\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.52\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.9\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.67\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eWest Region Mean\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.6\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.56\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.81\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.8\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.74\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.68\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eChina Provincial Mean\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.47\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.45\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.82\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.74\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.6\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.65\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003c/table\\u003e\\n \\u003c/div\\u003e\\n \\u003c/div\\u003e\\n \\u003cdiv id=\\\"Sec17\\\"\\u003e\\n \\u003ch2\\u003e4.3.2 Analysis of technology gap ratio\\u003c/h2\\u003e\\n \\u003cp\\u003eThe average meta-frontier technology efficiency, average group frontier technology efficiency, and average technology gap ratio in 2016\\u0026ndash;2020 in the east, central, and west regions are shown in Table\\u0026nbsp;\\u003cspan\\u003e6\\u003c/span\\u003e and Fig.\\u0026nbsp;\\u003cspan\\u003e5\\u003c/span\\u003e. The specific changes are as follows.\\u003c/p\\u003e\\n \\u003cp\\u003e(1) The average technology gap ratio in the east region is equal to or close to 1 in the time range of the study, which indicates that its meta-frontier technology efficiency is closer to its group frontier technology efficiency.\\u003c/p\\u003e\\n \\u003cp\\u003e(2) The data in the Table\\u0026nbsp;\\u003cspan\\u003e6\\u003c/span\\u003e show that both the meta-frontier technology efficiency and the group frontier technology efficiency are fluctuating upward in the central region, while it can be seen from Fig.\\u0026nbsp;\\u003cspan\\u003e5\\u003c/span\\u003e that the technology gap ratio in the central region also has a fluctuating increase. This shows that the gap between the meta-frontier efficiency and the group frontier efficiency in the central region is very large. It is thus necessary to increase the use of resource developers to improve the efficiency of resource use, at the same time formulate more effective incentive policies for the production, distribution, and consumption of energy, establish a perfect water resource service system, and realize the leaping upgrade of scientific and technological innovations.\\u003c/p\\u003e\\n \\u003cp\\u003e(3) The average technological gap ratio of the west region is close to the average national technological gap ratio. Compared with the central region, the west region\\u0026apos;s water resource sustainable development innovation efficiency is higher. This shows that there is a gap between the meta-frontier efficiency and the group frontier efficiency in the west region, but the gap is small. Compared with the east and central regions, the west region has the shortcomings of imperfect economic development and insufficient industrial economy, but this region has a vast land, sparse human traces, and abundant resources, and so it should continue to develop innovative energy on the basis of maintaining a green environment.\\u003c/p\\u003e\\n \\u003cdiv\\u003e\\n \\u003ctable id=\\\"Tab6\\\" border=\\\"1\\\"\\u003e\\n \\u003ccaption language=\\\"En\\\"\\u003e\\n \\u003cdiv\\u003eTable 6\\u003c/div\\u003e\\n \\u003cdiv\\u003e\\n \\u003cp\\u003eAverage Technology Gap Ratio, 2016\\u0026ndash;2020\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n \\u003c/caption\\u003e\\n \\u003ccolgroup cols=\\\"16\\\"\\u003e\\u003c/colgroup\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eYear\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" colspan=\\\"3\\\"\\u003e\\n \\u003cp\\u003e2016\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" colspan=\\\"3\\\"\\u003e\\n \\u003cp\\u003e2017\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" colspan=\\\"3\\\"\\u003e\\n \\u003cp\\u003e2018\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" colspan=\\\"3\\\"\\u003e\\n \\u003cp\\u003e2019\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" colspan=\\\"3\\\"\\u003e\\n \\u003cp\\u003e2020\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eGroup\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eMTE\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eGTE\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eTGR\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eMTE\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eGTE\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eTGR\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eMTE\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eGTE\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eTGR\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eMTE\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eGTE\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eTGR\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eMTE\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eGTE\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eTGR\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/thead\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eEast region\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.51\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.56\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.90\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.59\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.71\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.85\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.60\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.72\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.85\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.60\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.74\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.85\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.65\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.73\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.91\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eCentral region\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.30\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.00\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.30\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.54\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.00\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.54\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.48\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.99\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.49\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.51\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.95\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.54\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.47\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.95\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.49\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eWest region\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.59\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.90\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.64\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.75\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.89\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.85\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.65\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.91\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.72\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.62\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.82\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.76\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.58\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.85\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.69\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eCountry\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.49\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.77\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.69\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.64\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.83\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.79\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.59\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.84\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.73\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.59\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.81\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.76\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.59\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.82\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.75\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003c/table\\u003e\\n \\u003c/div\\u003e\\n \\u003c/div\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec18\\\"\\u003e\\n \\u003ch2\\u003e4.4. Analysis of input-output redundancy and deficiencies\\u003c/h2\\u003e\\n \\u003cp\\u003eRedundancy and insufficiency reflect to the gap between the input-output values and the optimal allocation, and redundancy and insufficiency can help make reasonable suggestions for the improvement of the efficiency of water resource sustainable development in each province. In This study selects redundancy and insufficiency in the perpetual stage of each province in 2016 for analysis, but due to space limitations, only the input-output slack variables in the perpetual stage in 2016 are listed in Table\\u0026nbsp;\\u003cspan\\u003e7\\u003c/span\\u003e. The efficiency values of Beijing, Ningxia, Qinghai, Hainan, Inner Mongolia and Guangdong in the perpetual stage in 2016 is are all 1. Thus, the value of the slack variable is 0, which indicates that in the perpetual stage the indicators are at optimal allocation, and they are all have been effectively utilized. Ningxia, Qinghai, and Inner Mongolia are more backward than other provinces in terms of economic development level and industrial scale, but the efficiency of water resources in the sustainable development stage has reached the optimum for five consecutive years, which indicates that improving the sustainability of water resources is not a matter of blindly expanding the inputs and increasing the outputs of industrial production and investment, but that finding the appropriate optimal allocation is an important way to promote the sustainable development of water resources.\\u003c/p\\u003e\\n \\u003cp\\u003eRegarding the analysis of slack variables and taking Sichuan as an example, the redundancy of wastewater, exhaust gas and waste there is 39184.7, 10.4688, and 8592.63 respectively. Its imperfect industrial governance leads to a large amount of exhaust gas and wastewater entering into water resource cycle, which greatly impede the efficiency of water resource sustainable development and causes waste of other resources. Fixed assets as output should be reduced by 855.041\\u0026nbsp;billion yuan, suggesting that enterprises in the region must reduce their blind expansion of factory scale and their increase of fixed assets such as ordinary production tools. Instead, they should increase investment in pollution control of industrial production and energy poverty in social life, focusing on the sustainability and future of water resource cycle development. The amount of pollution improvement should be increased by 1.03 units, which shows that the efforts of Sichuan in pollution control and improvement of the environment are not sufficient, and it can alleviate pollution emissions from the perspective of improving the environment for energy use.\\u003c/p\\u003e\\n \\u003cdiv\\u003e\\n \\u003ctable id=\\\"Tab7\\\" border=\\\"1\\\"\\u003e\\n \\u003ccaption language=\\\"En\\\"\\u003e\\n \\u003cdiv\\u003eTable 7\\u003c/div\\u003e\\n \\u003cdiv\\u003e\\n \\u003cp\\u003e2016 Perpetual Stage Slack Variables\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n \\u003c/caption\\u003e\\n \\u003ccolgroup cols=\\\"7\\\"\\u003e\\u003c/colgroup\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eProvince\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eVolume of investment inputs in industrial governance\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eWastewater inputs\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eExhaust gas inputs\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eSolid waste inputs\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eFixed asset generation (billions of yuan)\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003ePollution improvement volume generation\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/thead\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eShanghai\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e9.60\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eShanxi\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-36632.1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-43.7529\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-38639.1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-7918.49\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e6.04\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eShandong\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e27.09\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eYunan\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-33.2613\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-13605.3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.48\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eTianjin\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.88\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eBeijing\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.00\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eSichuan\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-39184.7\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-10.4688\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-8592.63\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-8550.41\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.03\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eGansu\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-12.1519\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-5676.2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-3822.43\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.36\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eNingxia\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.00\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eJilin\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n 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\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4.14\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003c/table\\u003e\\n \\u003c/div\\u003e\\n\\u003c/div\\u003e\"},{\"header\":\"5. Conclusions and Policy Implications\",\"content\":\"\\u003cp\\u003eWater is the source of life, the key to production, and the basis of ecology and is vital to improve the efficiency of water resource usage per unit and to achieve the sustainable use of water resources. The seventh United Nations Sustainable Development Goal aims to eliminate energy poverty and to achieve equity and sustainability in energy consumption. Therefore, under the increasingly serious situation of energy poverty in China, it is of great significance to study energy poverty to promote the sustainable use of water resources based on the new development stage.\\u003c/p\\u003e \\u003cp\\u003eTaking energy poverty as an exogenous variable, this paper divides the process of water resource sustainable development into two associated sub-system stages, production and perpetuation, and adopts a two-stage dynamic SBM model to measure the efficiency of the sample data of 29 provinces and municipalities from 2016 to 2020. To exclude the influence caused by regional differences, this paper applies the common frontier model to group the 29 provinces and cities and to measure their technical efficiency values, so as to better explore the influence of energy poverty on the efficiency of water resources sustainable development.\\u003c/p\\u003e \\u003cp\\u003eTo get more comprehensive and precise results, this research analyzes the results from multiple perspectives as follows.\\u003c/p\\u003e \\u003cp\\u003e(1) Time perspective: Most provinces and municipalities show a fluctuating upward trend in water resource sustainability efficiency from 2016 to 2020, but their overall efficiency is still low, suggesting that there is more room for upward mobility. In addition, there are obvious differences in regional water resource stock and water use, and there is a more obvious energy poverty situation in some regions.\\u003c/p\\u003e \\u003cp\\u003e(2) Stage subsystem efficiency analysis: Two-stage efficiency is not consistent. Moreover, the production efficiency of some provinces is significantly higher than their perpetual efficiency.\\u003c/p\\u003e \\u003cp\\u003e(3) Benchmarking with different production frontier functions leads to differences in efficiency evaluation. After considering meta-frontier, it is found that the overall efficiency of China's water resource sustainable development under the meta-frontier is not high during the period 2016\\u0026ndash;2020, and there are obvious differences in the efficiency level of each province and city. The efficiency situation of water resource sustainable development under the group frontier has improved, with the highest in the central region, followed by the west, and the east as last.\\u003c/p\\u003e \\u003cp\\u003e(4) By analyzing the technical gap ratio, it can be seen that TGR in the east region is close to 1. In addition, the technical efficiency of meta-frontier is very close to the technical efficiency of the group frontier. However, the values of the two technical efficiencies in the central region show an obvious gap.\\u003c/p\\u003e \\u003cp\\u003eBased on the above conclusions, this paper makes the following recommendations for energy poverty and sustainable development of water resources from four perspectives: institutional, economic, resource, and regional.\\u003c/p\\u003e \\u003cp\\u003e(1) In order to consolidate the results of poverty eradication, the government urgently needs to pay more attention to the issue of residential energy services. The government needs to focus moer on residential energy poverty and gradually improve all types of energy service facilities, especially the energy use of poor households, in order to safeguard the energy needs of micro-households and to reduce the depth of residential energy poverty. In addition, the government should advocate energy and water conservation among residents and enterprises and address the issue of energy poverty through multifaceted and multilevel institutional measures, so as to ensure the prospect of sustainable development of water resources.\\u003c/p\\u003e \\u003cp\\u003e(2) The economy and the use of water resources should be in a more balanced state of coordination. Although natural factors such as precipitation are an important factor affecting the total amount of water resources, excessive water use in human economic production is one of the major reasons seriously impacting the sustainable development of resources. Therefore, while gradually improving the modern development of water resources, provincial and municipal governments should pay attention to the concept of scientific water use, further promote the innovation of production technology and industrial structure upgrading, at the same time introduce relevant emission reduction programs for the management of corporate sewage disposal, and strengthen the supervision and management of resource allocation and use and the environment.\\u003c/p\\u003e \\u003cp\\u003e(3) Attention should be paid to the improvement of the degree of coordination of resources. According to redundancy and insufficiency analyses, most provinces have a relatively obvious situation of low coordination of resource use between 2016 and 2020. Therefore, while achieving high production efficiency, extra attention should be paid to promoting effective resource allocation and production efficiency, rather than blindly pursuing the expansion of production scale, which results in the waste of resources. Since the slack variables of the pollution indicators in the perpetual stage also show that excessive pollution will seriously affect the sustainable development of water resources, the government should focus on the coordinated development of energy and water resources, strengthen the governance of sewage discharge in all provinces and municipalities, formulate corresponding rules for the governance of discharge in respect of the different pollution indicators, constrain the production of high-polluting enterprises, increase the construction of modern water conservancy infrastructures, strictly enforce the rigid constraints on water resource systems, actively establish an integrated water resource management system, and innovate and apply digital technology and other water control programs and measures. The development of new energy industries should further be accelerated to promote the upgrading of the energy structure, reduce the level of energy poverty in China, and give full play to the role of energy poverty reduction in fueling the sustainable development of water resources.\\u003c/p\\u003e \\u003cp\\u003e(4) Relevant policies should be formulated according to local conditions. According to the above findings, the sustainability of China's water resource development is found to be unbalanced between the east, central, and west regions. The central region, as a region dominated by the development of heavy industry, should target strengthening new energy technologies and pollution control to ensure the sustainable development of energy and water resources. As for the west region, where the level of energy poverty is relatively serious, the scale of energy development in this region should be properly controlled while expanding energy imports, so as to effectively guarantee the coordinated security of water and energy resources.\\u003c/p\\u003e\"},{\"header\":\"6. Discussion\",\"content\":\"\\u003cp\\u003eThis paper has certain limitations in the research process, due to data availability and other reasons, this paper only selects the relevant data of 30 provinces for research, the research period is also shorter, the sample size is insufficient; at the same time, the indicator system is not comprehensive, the accuracy of the assessment of the efficiency of sustainable development of water resources needs to be improved. In order to solve the shortcomings of this paper, the future development direction is proposed, firstly, to expand the sample capacity and extend the research period, and secondly, to be more representative when constructing the research index system, in order to more accurately portray the dynamic changes of the efficiency of water resources sustainable development of each province in China as well as the impact of energy poverty on it.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eCompeting interests: \\u003c/strong\\u003eThe authors declare no conflict of interest.\\u003c/p\\u003e\\n\\n\\u003cp\\u003e\\u003cstrong\\u003eAuthors contributions: \\u003c/strong\\u003eConceptualization, X.Z and Z.F.; Data curation,QQ.X..; Formalanalysis, Z.F.; Investigation, X.Z.; Methodology, Z.f. and Z.Y.; Visualization, X.Z.; Supervision,Z.Y. and QQ.X..; Project administration, Z.F.; Writing\\u0026mdash;original draft preparation, Z.F.; Writing\\u0026mdash;reviewand editing,X.Z. All authors have read and agreed to the published version of the manuscript.\\u003c/p\\u003e\\n\\n\\u003cp\\u003e\\u003cstrong\\u003eAvailability of supporting data:\\u003c/strong\\u003e Data is provided within the manuscript or supplementary information files. The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.\\u003c/p\\u003e\\n\\n\\u003cp\\u003e\\u003cstrong\\u003eAcknowledgements: \\u003c/strong\\u003eThis research was funded by Major Projects of Fujian Social Science Base（FJ2020JDZ025, FJ2022JDZ022）\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003eSHUAI C.Y., JIAO L.D.,SONG X.N.,SHEN L.Y. Decoupling analysis on the relationship between economic development and environment degradation in China. PROCEEDINGS OF THE 20TH INTERNATIONAL SYMPOSIUM ON ADVANCEMENT OF CONSTRUCTION MANAGEMENT AND REAL ESTATE.1207-1216, 2017.\\u003c/li\\u003e\\n\\u003cli\\u003eBEN C.N., BEN Z.Y., NGUYEN D.K. Understanding energy poverty drivers in Europe. Energy Policy. 183,2023.\\u003c/li\\u003e\\n\\u003cli\\u003eQIN L., CHEN W.D., SUN L.C. Impact of energy poverty on household quality of life--based on Chinese household survey panel data. Journal of Cleaner Production. 366,2022.\\u003c/li\\u003e\\n\\u003cli\\u003eWEI T., DUAN Z.C., XIE P. Spatial disparities and variation sources decomposition of energy poverty in China. Journal of Cleaner Production. 421,2023.\\u003c/li\\u003e\\n\\u003cli\\u003eCAI J.L., VARIS L., YIN H. China\\u0026apos;s water resources vulnerability: a spatio-temporal analysis during 2003\\u0026ndash;2013 . Journal of cleaner production.142: 2901-10,2017.\\u003c/li\\u003e\\n\\u003cli\\u003eZHAO L.S., SUN C.Z., LIU F.C. Interprovincial two-stage water resource utilization efficiency under environmental constraint and spatial spillover effects in China. Journal of Cleaner Production. 164: 715-25, 2017.\\u003c/li\\u003e\\n\\u003cli\\u003eDONG H.J., GENG Y., FUJITA T., FUJII M., HAO D.,YU X.M. Uncovering regional disparity of China\\u0026apos;s water footprint and inter-provincial virtual water flows. Science of the total environment. 500: 120-30,2014.\\u003c/li\\u003e\\n\\u003cli\\u003eLI J.W., LIU Z.F., HE C.Y, YUE H.B., GOU S.Y. Water shortages raised a legitimate concern over the sustainable development of the drylands of northern China: Evidence from the water stress index. Science of the Total Environment. 590: 739-50, 2017.\\u003c/li\\u003e\\n\\u003cli\\u003eLI D.L., ZUO Q.T., ZHANG Z.Z. A new assessment method of sustainable water resources utilization considering fairness-efficiency-security: A case study of 31 provinces and cities in China. 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Estimating the impact of rural centralized residence policy interventions on energy poverty in China. Renewable and Sustainable Energy Reviews. 187, 2023.\\u003c/li\\u003e\\n\\u003cli\\u003eBRIDGEN P., ROBINSON C. A decade of fuel poverty in England: A spatio-temporal analysis of needs-based targeting of domestic energy efficiency obligations. Energy Research \\u0026amp; Social Science. 101, 2023.\\u003c/li\\u003e\\n\\u003cli\\u003eSSENNONO V.F., NTAYI J.M., BUYINZA F., WASSWA F., AARAKIT S.M., MUKIZA C.N. Energy poverty in Uganda: Evidence from a multidimensional approach. Energy Economics. 101,2021.\\u003c/li\\u003e\\n\\u003cli\\u003eNUSSBAUMER P., BAZILIAN M., MODI V. Measuring energy poverty: Focusing on what matters. Renewable and Sustainable Energy Reviews. 16(1), 2011.\\u003c/li\\u003e\\n\\u003cli\\u003eLI K., WANG Y.X. Comprehensive evaluation of regional energy poverty in China. Journal of Beijing Institute of Technology (Social Science Edition). 16(02): 1-12,2014.\\u003c/li\\u003e\\n\\u003cli\\u003eHALKOS G.E., GKAMPOURA E.C. Evaluating the effect of economic crisis on energy poverty in Europe. Renewable and Sustainable Energy Reviews. 144,2021.\\u003c/li\\u003e\\n\\u003cli\\u003eSALMAN M., ZHA D.L., WANG G.M. Assessment of energy poverty convergence: A global analysis. Energy. 255,2022. \\u003c/li\\u003e\\n\\u003cli\\u003eWANG F., GENG H., ZHA D.L., ZHANG C.Q. Multidimensional Energy Poverty in China: Measurement and Spatio-Temporal Disparities Characteristics. Social indicators research.168(1-3):31-34,2023. . \\u003c/li\\u003e\\n\\u003cli\\u003eXIE Y.X., XIE E. Measuring and Analyzing the Welfare Effects of Energy Poverty in Rural China Based on a Multi-Dimensional Energy Poverty Index. Sustainability. 15(18), 2023.\\u003c/li\\u003e\\n\\u003cli\\u003eADUSAH-POKU F., TAKEUCHI K. Energy poverty in Ghana: Any progress so far? Renewable and Sustainable Energy Reviews. 112: 853-64, 2019.\\u003c/li\\u003e\\n\\u003cli\\u003eLU S.F., REN J.Z., ZHANG L., LEE C.K.M. Spatial-temporal energy poverty analysis of China from subnational perspective. Journal of Cleaner Production. 341, 2022.\\u003c/li\\u003e\\n\\u003cli\\u003eKE J., KHANNA N., ZHOU N. Analysis of water\\u0026ndash;energy nexus and trends in support of the sustainable development goals: A study using longitudinal water\\u0026ndash;energy use data. Journal of Cleaner Production. 371, 2022.\\u003c/li\\u003e\\n\\u003cli\\u003eHE Y.M., GAO S.H. Energy-Water Consumption and Food Yield: An Empirical Dual Sectors Dynamic Equilibrium Model. Applied Economics. 54(50), 2022.\\u003c/li\\u003e\\n\\u003cli\\u003eSHANG Y.Z., LU S.B., LI X.F., HEI P.F., LEI X.H., GONG J.G., LIU J.H., ZHAI J.Q. WANG H. Balancing development of major coal bases with available water resources in China through 2020. Applied Energy. 194: 735-50, 2017. \\u003c/li\\u003e\\n\\u003c/ol\\u003e\"},{\"header\":\"Table 4\",\"content\":\"\\u003cp\\u003eTable 4 is available in the Supplementary Files section.\\u003c/p\\u003e\\n\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":true,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"scientific-reports\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"scirep\",\"sideBox\":\"Learn more about [Scientific Reports](http://www.nature.com/srep/)\",\"snPcode\":\"\",\"submissionUrl\":\"\",\"title\":\"Scientific Reports\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"stoa\",\"reportingPortfolio\":\"Scientific Reports\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true},\"keywords\":\"energy poverty, energy efficiency, water resources, two-stage dynamic DDF, sustainable development\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-4987800/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-4987800/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eEnergy poverty and water resources development, as one of the global Sustainable development Goals, are also one of the keys to achieving sustainable development and social welfare in China. In this study, this paper constructs a two-stage dynamic DDF model to evaluate the efficiency of water resources sustainable development, and studies the dynamic efficiency level of water resources sustainable development and its regional differences. At the same time, a multidimensional energy poverty index evaluation system was established, the entropy weight method was used to measure the energy poverty index, and energy poverty was included in the evaluation system of the efficiency of sustainable development of water resources, and the changes in the efficiency of sustainable development of water resources were observed when there was no energy poverty.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Does energy poverty affect the sustainable development of water resources？—Empirical Evidence from China\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2024-11-14 17:47:17\",\"doi\":\"10.21203/rs.3.rs-4987800/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"decision\",\"content\":\"Revision requested\",\"date\":\"2024-10-29T08:27:52+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2024-10-15T15:15:18+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"304697638596748549141691915854343407684\",\"date\":\"2024-10-08T00:37:54+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2024-10-06T17:10:20+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"149624446893482472028619815605521502678\",\"date\":\"2024-09-26T21:28:13+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2024-09-26T16:04:50+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2024-09-26T08:37:01+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvited\",\"content\":\"\",\"date\":\"2024-09-26T07:23:59+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"checksComplete\",\"content\":\"\",\"date\":\"2024-09-23T08:43:31+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"Scientific Reports\",\"date\":\"2024-08-28T03:28:57+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"scientific-reports\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"scirep\",\"sideBox\":\"Learn more about [Scientific Reports](http://www.nature.com/srep/)\",\"snPcode\":\"\",\"submissionUrl\":\"\",\"title\":\"Scientific Reports\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"stoa\",\"reportingPortfolio\":\"Scientific Reports\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"16e6c66e-d21e-444d-896b-3461ee5206fd\",\"owner\":[],\"postedDate\":\"November 14th, 2024\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"published-in-journal\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2025-07-07T16:07:21+00:00\",\"versionOfRecord\":{\"articleIdentity\":\"rs-4987800\",\"link\":\"https://doi.org/10.1038/s41598-025-05240-5\",\"journal\":{\"identity\":\"scientific-reports\",\"isVorOnly\":false,\"title\":\"Scientific Reports\"},\"publishedOn\":\"2025-07-02 15:58:29\",\"publishedOnDateReadable\":\"July 2nd, 2025\"},\"versionCreatedAt\":\"2024-11-14 17:47:17\",\"video\":\"\",\"vorDoi\":\"10.1038/s41598-025-05240-5\",\"vorDoiUrl\":\"https://doi.org/10.1038/s41598-025-05240-5\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-4987800\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-4987800\",\"identity\":\"rs-4987800\",\"version\":[\"v1\"]},\"buildId\":\"qtupq5eGEP_6zYnWcrvyt\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}