Analysis of spatiotemporal evolution and influencing factors of water poverty and ecological resilience coupling coordination in Chinese mega cities

Analysis of spatiotemporal evolution and influencing factors of water poverty and ecological resilience coupling coordination in Chinese mega cities | 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 Research Article Analysis of spatiotemporal evolution and influencing factors of water poverty and ecological resilience coupling coordination in Chinese mega cities haiqi zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4534232/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 3 You are reading this latest preprint version Abstract Water poverty and ecological resilience in mega cities are not only related to the health and well-being of residents, but also important aspects of achieving sustainable development goals, promoting environmental protection, and international cooperation. This article selects data from 10 mega cities in China from 2013 to 2022 and constructs a coupling coordination model to analyze the coupling relationship and spatiotemporal evolution characteristics between water poverty and ecological resilience in mega cities. In addition, the Tobit model was used to explore the main influencing factors of the above two systems, and the following conclusions were drawn: firstly, from 2013 to 2022, the comprehensive evaluation index of water poverty and ecological resilience in Chinese mega cities showed a dynamic upward trend. Secondly, during the period of 2013-2022, the degree of coupling and coordination between water poverty and ecological resilience in China's mega cities has become increasingly high, with Wuhan being in a highly coordinated area in 20222. Thirdly, in terms of influencing factors, economic development level, industrial structure, and technological innovation have a positive impact on the coupling and coordination level of water poverty and ecological resilience. The negative impact of population density and urbanization level on the coupling and coordination level of water poverty and ecological resilience. mega cities Water poverty Ecological resilience Coupling coordination level influence factor Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction The current situation of water resources in Chinese cities is not optimistic, with water scarcity and environmental pollution becoming increasingly prominent. According to statistics, nearly 60 cities in China suffer from an annual water shortage of 6 billion cubic meters, resulting in economic losses of up to 200 billion yuan each year. The expansion of urban scale has led to an increase in sewage discharge and a deterioration in water resource quality. In 2017, the water quality problems of surface water and groundwater in China were prominent, and the deterioration of the water environment seriously affected the quality of water supply, exacerbating the shortage of water resources. In China's mega cities, such as Beijing, Shanghai, Guangzhou, and Shenzhen, due to population density and concentration of economic activities, they face the dual challenges of water resource scarcity and water environment degradation (Chang et al. 2020). The development status of these cities shows some common characteristics and problems in terms of water resources. The rapid development of super urbanization has led to high demand and consumption of water resources. With population growth and economic expansion, the demand for urban water continues to rise, which directly increases the pressure on water resources. For example, although Shanghai has the advantage of supplying Yangtze River water resources, it still faces the threat of water scarcity. Megacities typically have a high water resource development rate, but this development often comes at the cost of sacrificing water environment quality and ecological balance. The excessive development and utilization of water resources have led to water pollution and ecosystem degradation, thereby affecting the sustainable utilization of water resources. Meanwhile, mega cities face complexity in water resource management and protection (Li et al. 2015) . In the process of urbanization, the improvement of urban water use efficiency often lags behind the growth of water demand, resulting in generally low water resource utilization efficiency (Jiang et al. 2013) . In the context of sustainable development, the sustainable utilization of water resources in mega cities is of great significance for the sustainable development of China and even the world. Water resources are not only the source of life, but also an important foundation for social and economic development and ecological environment protection. As the engine of national economic development, the water resources of mega cities are directly related to the economic and ecological security of the country. However, due to the impact of climate change and human activities, mega cities are facing dual pressures of water resource scarcity and water environment pollution, which poses a serious threat to the sustainable development of cities (Wei et al. 2020). Therefore, strengthening the sustainable utilization of water resources in mega cities is of great strategic significance for ensuring the quality of life of urban residents, promoting green economic development, maintaining the health of ecosystems, and achieving the United Nations 2030 Sustainable Development Goals. Ecological resilience is a concept originating from ecology, originally proposed by C S. Holling proposed in the 1970s to describe the ability of ecosystems to maintain their function and structure in the face of disturbances and changes (Holling and C.S 1996) . Ecological resilience emphasizes the ability of a system to recover to its original state after being disturbed, as well as the ability to transition to a new state when necessary. Existing research has different definitions of ecological resilience. For example,( Gunder and sonL.H 2000) believe that ecological resilience is closely related to the stability and resistance of ecosystems, which involves the response and recovery process of ecosystems in the face of natural or human interference. (Adger and W.N. 2000) regard ecological resilience as the ability of a system to maintain its core functions and identity in long-term changes, even in the face of pressure and shocks, to adapt and evolve. The research on ecological resilience has achieved rich results internationally. For example, Walker et al. (2004) emphasized the resilience, adaptability, and variability of social ecosystems in their 2004 study. In China, scholars have also begun to pay attention to the issue of ecological resilience in the process of urbanization, such as the coupling and coordination research between urbanization and ecological resilience in the Pearl River Delta region (Wang et al. 2022). Through literature review of ecological resilience, it can also be seen that researchers typically evaluate and construct ecological resilience from multiple dimensions, including but not limited to biodiversity, ecosystem services, landscape patterns, and environmental changes. In the study of urban ecological resilience, scholars focus on how the complexity of urban structure and the diversity of functions are coupled with natural environment, economy, and social needs.In summary, ecological resilience is a multidimensional and interdisciplinary concept that involves the stability, adaptability, and transformative ability of ecosystems in the face of change (Peterson et al. 1998) . The study of ecological resilience is not only crucial for ecology and environmental science, but also has important guiding significance for urban planning, resource management, and sustainable development. Water poverty and ecological resilience are two very important concepts in the fields of sustainable development and environmental management. Due to population density and concentration of economic activities, mega cities have a huge demand for water resources. At the same time, land use changes and environmental pollution in the process of urbanization have had a significant impact on water ecosystems. The emergence of water poverty not only limits the quality of life of urban residents, but also restricts the economic development and social stability of citie s (Tian et al. 2024) . The existence of water poverty means that there are imbalanced and unsustainable issues in the acquisition, distribution, and utilization of water resources in cities. If these problems are not properly addressed, they will lead to water scarcity in cities, threatening urban ecological security and the health of residents. Ecological resilience reflects the adaptability and resilience of cities in the face of water resource challenges, and is an important attribute for cities to cope with climate change, natural disasters, and the impact of human activities (Pickett et al. 2014) . Cities with strong ecological resilience can better cope with water crises, maintain the health and stability of water ecosystems, and thus ensure the sustainable use of water resources. Studying the coupling and coordination between water poverty and ecological resilience in mega cities can help formulate scientific water resource management and urban planning strategies. Through coupling coordination analysis, the risk points of urban water resource shortage can be identified, and the ecological resilience level of different regions can be evaluated, providing a basis for the rational allocation of urban water resources, water ecological protection, and restoration. At the same time, it can promote the balance between urban economic growth and ecological environment protection, improve the quality of life of urban residents, and achieve a win-win situation between economic and social development and ecological environment protection. In addition, coupled coordination research can help the international community understand the challenges and efforts of Chinese mega cities in water resource management, promote international cooperation and knowledge sharing, and jointly respond to the global water crisis. Therefore, this article selects data from 2013 to 2022 to evaluate the water poverty and ecological resilience levels of 10 mega cities in China, and further analyzes their spatiotemporal evolution characteristics in 2013, 2016, 2019, and 2022. Meanwhile, evaluate the degree of coupling between water poverty and ecological resilience, and analyze their changing trends in 2013, 2016, 2019, and 2022. Finally, this article selects five influencing factors: economic development level, industrial structure, technological innovation, population density, and urbanization level, to analyze which factors are the main factors affecting the coupling degree of urban water poverty and ecological resilience. 2. Research methods and data sources 2.1Overview of the research area Megacities are one of the classification criteria for the size of cities in China. According to the Notice on Adjusting the Standards for Urban Scale Classification issued by the State Council in 2014, cities with a permanent population of more than 10 million in urban areas are considered mega cities, while cities with a permanent population of more than 5 million but less than 10 million in urban areas are considered mega cities. The 2019 Urban Construction Statistical Yearbook released by the Chinese government in 2020 stipulates that six cities in China, namely Shanghai, Beijing, Chongqing, Guangzhou, Shenzhen, and Tianjin, meet the standards for "mega cities". The "2022 Urban Construction Statistical Yearbook" released by the Chinese government in October 2023 pointed out that there are a total of 10 mega cities in China, namely Shanghai, Beijing, Shenzhen, Chongqing, Guangzhou, Chengdu, Tianjin, Dongguan, Wuhan, and Hangzhou. Based on this, this article selects 10 mega cities in China, including Shanghai, Beijing, Shenzhen, Chongqing, Guangzhou, Chengdu, Tianjin, Dongguan, Wuhan, and Hangzhou, as the main research areas. The above 10 cities, Beijing and Tianjin are located in the northern region of China, Guangzhou, Dongguan, and Shenzhen are located in the southern region of China, and the other 5 cities are located in the central and eastern regions of China. The specific locations are shown in Figure 1. 2.2 Data sources The data source of this article is the China Statistical Yearbook(https://www.stats.gov.cn/sj/ndsj/); China Environmental Statistical Yearbook(https://www.mee.gov.cn/); EPS data platform(https://www.epsnet.com.cn/); Statistical yearbooks for various cities, such as the Beijing Statistical Yearbook(https://tjj.beijing.gov.cn/). In addition, some data is sourced from water resource bulletins of various cities, such as the Beijing Water Resources Bulletin(https://swj.beijing.gov.cn/). 2.3 Index system construction 2.3.1Construction of urban water poverty indicator system With the deepening of research on water poverty, the evaluation index system has gradually become diversified and comprehensive. This article refers to (Sullivan and C 2002; Forouzani et al. 2011; Shaban and Abdul 2008; Sullivan and C 2001 ). At the same time, based on the development status and characteristics of 10 mega cities in China, following the principles of scientificity, objectivity, and comprehensiveness, the urban water poverty indicator system is divided into five major aspects: resources, facilities, use, capacity and environment. The details are shown in Table 1 below. Table 1: Urban Water Poverty Indicator System Criterion layer Indicator layer unit attribute resource Annual precipitation millimeter + Surface water resources Billion cubic meters + groundwater resources Billion cubic meters + Total water resources Billion cubic meters + facilities Urban drainage pipeline length kilometre + Total water supply Billion cubic meters + Length of water supply pipeline kilometre + Total Highway Mileage kilometre + use 10000 yuan industrial value-added water consumption cubic meter - Per capita domestic water consumption of urban and rural residents L/d - Per capita water consumption L/d - Water consumption per 10000 yuan of gross domestic product cubic meter - capacity Fiscal self-sufficiency rate % + Urban per capita disposable income yuan + Per capita medical expenditure of residents yuan + Per capita education expenditure yuan + Regional Gross Domestic Product RMB100mn + environment Per capita park green space area square meter + Urban sewage treatment efficiency % + Number of public toilets seat + 2.3.2Construction of Urban Ecological Resilience Evaluation Index System Urban ecological resilience refers to the ability of a city to maintain its ecological functions and services, recover and adapt to changes in the face of natural and human disturbances. The construction of urban ecological resilience assessment models usually includes three aspects: resistance, adaptability, and resilience. Based on the research of ( Suárez et al. 2019; Kotzee et al. 2016; Alberti and Marzluff 2004) ,this article constructs an indicator system from three aspects: resistance, adaptability, and resilience to evaluate the ecological resilience.The details are shown in Table 2 below. Table 2: Urban Ecological Resilience Index System Criterion layer Indicator layer unit attribute Resistance Value added of the secondary industry/GDP % + Value added of the tertiary industry/GDP % + Per capita GDP yuan + Natural population growth rate % + The proportion of science and technology expenditure to general public budget expenditure % + Adaptability Industrial smoke and dust emissions ton - Industrial sulfur dioxide emissions ton - Centralized treatment rate of sewage treatment plants % + Harmless treatment rate of household waste % + Resilience Drainage pipeline length Kilometers + Urban solid waste removal volume 10000 tons + Garden green space area hectare + Green coverage rate in built-up areas cubic meter + Fiscal self-sufficiency rate % + Per capita park green space area hectare + 2.4model building 2.4.1Entropy method Entropy method is an objective weighting method that can objectively and truthfully reflect the information hidden in indicator data. This article uses the entropy method to evaluate the water poverty and ecological resilience levels of 10 mega cities in China. 2.4.2Water poverty measurement model（WPI） WPI is a comprehensive set of indicators that can quantitatively evaluate the relative water scarcity level between countries or regions. This indicator can not only reflect the background situation of regional water resources, but also reflect the engineering, management, economy, human welfare, and environmental conditions.The water poverty evaluation index system constructed in this article is composed of five subsystems: resources, facilities, capabilities, usage, and environment. Several evaluation indicators are set up within each subsystem. 2.4.3Coupling coordination model Coupling is widely used to measure the degree of interaction between systems. The coupling coordination model is applied to this article to measure the interaction relationship between water poverty and ecological resilience systems. Based on existing research, this article divides the calculation results of the coupling coordination degree between water poverty and ecological resilience into levels. The details are shown in Table 3 below. Table 3: Coupling Coordination Level Development stage Coupled co scheduling gradation Dysfunction stage [0-0.2） Complete dysregulation [0.2-0.4） Moderate imbalance Transition phase [0.4-0.6) Basic coordination Coordination stage [0.6-0.8) Moderate coordination [0.8-1.0) Highly coordinated 2.4.4Panel Tobit model The coupling coordination degree is within the range of 0 and 1. Due to the limitation of the dependent variable and the avoidance of bias caused by OLS estimation, the Tobit model is usually used for estimation. In this paper, the Tobit stochastic model is selected to analyze the influencing factors of the coupling coordination degree between water poverty and ecological resilience. Based on the research of ( Shi et al. 2022; Zhang et al. 2019; You et al. 2022; Lee et al. 2024) ,this article selects the following main influencing factors for analysis. (1) Economic development level. The expansion of economic activities often accompanies the significant consumption of water resources and water pollution, affecting water poverty and ecological resilience. Cities with higher levels of economic development may have more resources and funds to invest in water resource management and protection, thereby improving water resource utilization efficiency and ecological resilience (Wang et al. 2018) . (2) Industrial structure. Different industrial structures have varying demands and impacts on water resources. The increasing proportion of high water consuming industries such as heavy industry and agriculture may lead to excessive development and pollution of water resources, while service and high-tech industries may adopt more water-saving and environmentally friendly production methods to reduce pressure on water resources (Tang et al. 2023) . (3) Technological innovation: Technological innovation can improve the efficiency of water resource utilization, develop water-saving technologies and clean energy, and reduce dependence and pollution on water resources. Technological innovation can also promote ecological restoration and environmental protection, enhancing the ecological resilience of cities. (4) Population density. The increase in population size will increase the demand for water resources, especially in terms of water supply, sanitation, and domestic use. The increase in population density may also exacerbate the shortage of water resources and affect the water poverty status of cities (Zhu et al. 2023) . (5) Urbanization level. Rapid urbanization has led to population growth and land use changes, increasing the demand for water resources and potentially disrupting natural water cycles and ecological balance. The construction of infrastructure in the process of urbanization, such as water supply systems and sewage treatment facilities, has a direct impact on the supply and quality of water resources (Gao et al. 2021) . Based on this, this article selects per capita GDP to represent the level of economic development; The proportion of scientific and technological expenditure to general public budget expenditure represents scientific and technological innovation; The proportion of the added value of the secondary industry to GDP represents the industrial structure; The year-end permanent population represents the population size; Urban population/total population represents the level of urbanization. 3. Result analysis 3.1 Evaluation and spatiotemporal evolution characteristics of water poverty This article uses the entropy method to evaluate water poverty in 10 mega cities in China, and the final results are shown in Figure 1. According to the data in Figure 2 it can be seen that in 2013, the water poverty index of 10 mega cities in China was 2.48, and in 2022 it was 6.60. The water poverty index has achieved a significant increase, with an overall increase of 166.13%, indicating that the water poverty level of 10 mega cities in China is gradually improving. The analysis of the reasons for the above results can be summarized into the following three aspects: first, the improvement of water resource management policies. The Chinese government has successively introduced policies to strengthen water resource management in 2013, 2013, 2015, 2017, 2019, and other years. Major cities have introduced their own water resource management policies, such as the Beijing Water Conservation Regulations issued in 2022, which clearly stipulate the improvement of water resource utilization efficiency, the formation of water-saving production and lifestyle, the guarantee of water safety, and the promotion of high-quality economic and social development. The above policies will promote the rational allocation and effective utilization of water resources, thereby improving the efficiency of water resource utilization. Secondly, the improvement of urban water resource utilization efficiency. With technological progress and increased awareness of water conservation, China's 10 mega cities may have adopted more efficient water resource utilization technologies, such as improving irrigation systems, increasing industrial water recycling rates, and promoting household water-saving appliances, all of which help reduce water waste. Thirdly, environmental protection and ecological restoration measures. With the increasing emphasis of the Chinese government on ecological civilization, major cities have increased their efforts in controlling water pollution and implemented ecological projects such as river and lake remediation, wetland protection, and restoration. These measures help improve the quality of water environment and enhance the sustainability of water resources. This article uses ArcGis10.8 to classify the natural breakpoints of 10 mega cities in China in 2013, 2016, 2019, and 2022. From low to high, they are defined as low-level areas, low-level areas, general level areas, high-level areas, and high-level areas. The final result is shown in Figure 3. In 2013, there were three cities in the low-level areas: Chengdu, Wuhan, and Dongguan. Lower level areas include Tianjin. There are two general level areas: Beijing and Guangzhou. There are two high-level areas: Chongqing and Hangzhou. There are two high-level areas: Shenzhen and Shanghai. In 2016, there were three cities in the low-level area: Tianjin, Chongqing, and Guangzhou. There are four cities in the lower level areas: Beijing, Chengdu, Wuhan, and Dongguan. The general level area is Hangzhou. The higher level area is Shanghai. The high-level area is Shenzhen. In 2019, low-level areas included Tianjin and Chongqing. There are two cities in the lower level area, Beijing and Wuhan. The general level areas are Hangzhou, Shanghai, Chengdu, and Dongguan. The higher level area is Guangzhou. The high-level area is Shenzhen. In 2022, there will be Chongqing in the low-level area. There are three cities in the lower level areas: Tianjin, Shanghai, and Hangzhou. The general level areas are Beijing and Chengdu. The high-level areas are Guangzhou, Dongguan, and Wuhan. The high-level area is Shenzhen. From the graph, it can be seen that from 2013 to 2016, the water poverty index of China's 10 mega cities has been decreasing, with 7 cities in low-level and lower level areas, indicating a higher level of water poverty in China's mega cities. From 2016, 2019, and 2022, the level of water poverty in 10 mega cities in China has gradually increased, indicating that the utilization of water resources in these cities is gradually improving. Of course, from Figure 3, it can also be seen that in 2013, 2016, 2019, and 2022, the water poverty index in Shenzhen was relatively high, and the degree of water poverty was relatively low. This is because, firstly, Shenzhen has abundant precipitation: it is located in the southern region of China, with relatively high precipitation, and the average annual rainfall is much higher than the national average level. This provides it with a relatively rich water resource foundation. Secondly, efficient water resource management and utilization: Despite the limited total water resources in Shenzhen, the municipal government has been committed to improving the efficiency and management level of water resource utilization. By implementing scientific water resource scheduling, optimizing water supply structure, and promoting water-saving technologies, the supply-demand contradiction of water resources has been effectively alleviated. Thirdly, strong economic strength and technological support: Shenzhen, as one of China's special economic zones, has strong economic strength and technological support. This provides more funding and technical support for its water resource management and utilization, which helps to improve its water resource utilization efficiency and management level. 3.2 Ecological resilience evaluation and spatiotemporal evolution characteristics This article uses the entropy method to evaluate the ecological resilience of 10 mega cities in China, and the final results are shown in Figure 4. From the data in the graph, it can be seen that the ecological resilience index of 10 mega cities in China has been increasing year by year from 2013 to 2022. In 2013, the ecological resilience index of 10 mega cities in China was 2.22, and in 2022 it was 7.09, an increase of 204.29%. This indicates that the 10 mega cities in China have made significant progress in ecological environment protection, resource utilization, disaster response, urban planning and management, and public participation. From the perspective of spatial distribution, as shown in Figure5, it can be seen that in 2013, cities with low ecological resilience levels were Hangzhou, Wuhan, and Dongguan. Cities with lower levels of ecological resilience include Hangzhou. Cities with average ecological resilience include Beijing. Tianjin, Chongqing, Chengdu, and Guangzhou have higher levels of ecological resilience. Cities with high levels of ecological resilience include Shenzhen and Shanghai. Overall, in 2013, among the 10 mega cities in China, there were relatively more cities with good ecological resilience development, with 6 cities accounting for 60%. In 2016, cities with low levels of ecological resilience included Dongguan and Wuhan. Cities with lower levels of ecological resilience include Beijing. Cities with average levels of ecological resilience include Chongqing, Chengdu, and Hangzhou. Tianjin, Shanghai, and Guangzhou have higher levels of ecological resilience. Shenzhen has a high level of ecological resilience. Compared to 2013, in 2016, there were 4 out of 10 mega cities in China with good ecological resilience development, a decrease. In 2019, cities with low levels of ecological resilience included Beijing and Shenzhen. Dongguan is a city with a low level of ecological resilience. Cities with average levels of ecological resilience include Tianjin and Guangzhou. Wuhan has a higher level of ecological resilience. Cities with high levels of ecological resilience include Chengdu, Chongqing, Hangzhou, and Shanghai. From Figure 3, it can also be seen that in 2019, the high level of ecological resilience was mainly concentrated in the central region, showing a "horizontal line" distribution. In 2022, Tianjin has a low level of ecological resilience. Cities with lower levels of ecological resilience include Beijing, Guangzhou, and Shanghai. Tianjin is a city with an average level of ecological resilience. Chengdu, Chongqing, Hangzhou, and Dongguan have higher levels of ecological resilience. Wuhan is a city with a high level of ecological resilience. From Figure5, it can also be seen that in 2022, the ecological resilience level of 10 mega cities also shows a "horizontal line" distribution. 3.3 The coupling and coordination level and spatiotemporal evolution characteristics of water poverty and ecological resilience From Figure 6 below, it can be seen that overall, from 2013 to 2022, the coupling coordination degree range of water poverty and ecological resilience in 10 mega cities in China is 1.603 to 9.305. From 2013 to 2021, there has been a trend of increasing year by year, and starting to decline in 2022. The coupling coordination between water poverty and ecological resilience in 10 mega cities in China has shifted from severe imbalance to good coordinated development. From Figure 7, it can be seen that in 2013, Chongqing, Hangzhou, and Tianjin had a high degree of coupling coordination between water poverty and ecological resilience, belonging to mild, moderate, and moderate imbalance states, respectively. The numerical results in other cities are relatively low, indicating a serious imbalance. Meanwhile, overall, from 2013 to 2022, Chengdu, Guangzhou, Shanghai, and Shenzhen cities showed a high degree of coupling and coordination between water poverty and ecological resilience. This indicates that they have invested more funds and technology in water resource management and ecological protection, thereby improving the coupling and coordination between water poverty and ecological resilience. However, the coupling coordination between water poverty and ecological resilience is relatively low in Beijing, Chongqing, and Dongguan. From the perspective of spatial changes (Figure 8), from 2013 to 2022, the coupling coordination level of water poverty and ecological resilience in 10 mega cities in China has been increasing. In 2013, most cities were located in completely or moderately dysregulated areas. For example, Chengdu, Wuhan, and Hangzhou are located in completely imbalanced areas, while the other seven cities are located in moderately imbalanced areas. Overall, the complete imbalance in 2013 presented a "one" shape. In 2016, most cities were in areas of moderate imbalance and basic coordination. Tianjin, Shanghai, Chengdu, and Guangzhou are located in the basic coordination area, while other cities are located in the moderate imbalance area. The basic coordination area presents an irregular quadrilateral shape. In 2019, most cities withdrew from moderately imbalanced areas, with only one city being Chongqing. Beijing, Chengdu, Shanghai, and Hangzhou are located in a moderately coordinated area, presenting a triangular shape. Tianjin, Wuhan, Guangzhou, Dongguan, and Shenzhen are located in the basic coordination area, presenting a "1" shape. In 2022, China's 10 mega cities are mainly located in three major regions: basic coordination, moderate coordination, and high coordination. There are four cities in the basic coordination area, namely Tianjin, Hangzhou, Guangzhou, and Shenzhen. There are 5 cities with moderate coordination, namely Beijing, Chengdu, Chongqing, Shanghai, and Dongguan. Only Wuhan is located in a highly coordinated area. Overall, from 2013 to 2022, the coupling and coordination level between water poverty and ecological resilience in Wuhan City has been increasing, from being located in a low coordination area to being in a high coordination area by 2022. There are three main reasons: firstly, water resource management and protection policies. During this period, the Wuhan Municipal Government formulated and implemented a series of water resource management and protection policies, such as the Wuhan Water Pollution Prevention and Control Action Plan, which played a positive role in reducing water pollution and protecting water resources. Secondly, ecological restoration projects. The government has invested a large amount of funds in ecological restoration projects, such as lake management and wetland protection. For example, the water quality of lakes such as East Lake and South Lake has significantly improved, not only enhancing the water environment quality of cities, but also enhancing their ecological resilience. Thirdly, the public's awareness of environmental protection should be enhanced. With the strengthening of environmental protection publicity, the environmental awareness of Wuhan citizens is gradually increasing, and people are paying more attention to water resource protection and water conservation. The formation of this social atmosphere has played a positive role in enhancing the coupling and coordination level between water poverty and ecological resilience. In addition, the coupling and coordination level between water poverty and urban ecological resilience in Chengdu and Dongguan is increasing, mainly due to the joint efforts of policy guidance and strict implementation, ecological environment protection and restoration, technological innovation and application, and industrial structure optimization and upgrading. The implementation of these measures not only improves the efficiency of water resource utilization and water environment quality in cities, but also enhances the ecological resilience of cities, laying a solid foundation for their sustainable development. 3.4Identification of influencing factors on the coupling coordination between water poverty and ecological resilience This article uses the Tobit regression model to analyze the coupling and coordinated influencing factors of water poverty and ecological resilience in 10 mega cities in China, as shown in Table 4. From the data in the table, it can be seen that all five factors have passed the significance test. The level of economic development, industrial structure, and technological innovation have a positive impact on the coupling of water poverty and ecological resilience, while population size and urbanization level have a negative impact on the coupling of water poverty and ecological resilience. The reasons for the above results are: firstly, with the improvement of economic development level, cities have more resources invested in water resource management and ecological protection, thereby improving water poverty. Meanwhile, the optimization of industrial structure, especially the transformation towards low water consumption and low pollution industries, helps to reduce pressure on water resources and enhance ecological resilience. Meanwhile, technological innovation plays an important role in areas such as water resource management, pollution control, and ecological restoration. By introducing new technologies and methods, water resources can be more effectively utilized, waste and pollution can be reduced, and the resilience and resilience of ecosystems can be improved. Secondly, in terms of population size. With the increase of population, the demand for water resources will also correspondingly increase, which may lead to excessive development and utilization of water resources, exacerbating water poverty. At the same time, the environmental problems brought about by population growth, such as household waste and industrial wastewater, will also put pressure on the ecosystem and reduce ecological resilience. In terms of improving urbanization level. In the process of urbanization, urban expansion and infrastructure construction require a large amount of water resources, which may lead to water scarcity and water pollution problems. In addition, phenomena such as urban heat island effect and ground hardening can also affect the stability of urban ecosystems and reduce ecological resilience. In addition, among the 10 mega cities in China, the main influencing factor for the coupling and coordination of water poverty and ecological resilience in Beijing, Hangzhou, and Shanghai is technological innovation. The main influencing factor for the coupling and coordination of poverty and ecological resilience in Dongguan, Shenzhen, Tianjin, and Wuhan is the industrial structure. The main influencing factor for the coupling and coordination of poverty and ecological resilience in Chongqing and Chengdu is the level of urbanization. Table 4: Analysis results of influencing factors city lnEDL lnIS lnTI lnPD lnUL beijing 0.001 ** (4.174) 0.043 ** (0.038) 0.241 ** (3.741) -0.101 * (2.381) -0.062 * (12.447) chengdu 0.001 ** (3.406) 0.014 ** (2.648) 0.207 ** (8.289) -0.001 * (0.159) -0.059 ** (8.091) chongqing 0.001 * (0.363) 0.021 * (1.411) 0.006 ** (0.385) -0.000 * (0.026) -0.079 ** (1.385) dongguan 0.001 ** (4.163) -0.226 ** (-3.497) -0.021 * (-2.502) -0.003 * (2.465) -0.058 * (2.052) hanzghou 0.000 ** (1.528) 0.009 ** (5.806) 0.013 * (2.478) -0.002 * (1.695) -0.005 ** (0.675) shanghai 0.000 * (2.576) 0.014 ** (1.591) 0.023 * (1.230) -0.013 ** (2.842) -0.006 * (-0.695) shenzhen 0.000 * (1.775) 0.203 * (0.517) 0.002 ** (10.614) -0.000 * (2.554) -0.004 * (0.588) tianjin 0.000 ** (0.360) 0.108 ** (0.876) 0.008 ** (0.138) -0.001 * (0.420) -0.038 * (2.904) wuhan 0.000 * (1.941) 0.018 ** (3.163) 0.015 * (2.066) -0.001 ** (8.979) -0.017 * (1.663) *P<0.05 * * p<0.01 The values in parentheses are z values 4. Conclusion This article is based on panel data from 10 mega cities in China from 2013 to 2022, and uses a coupled coordination model to explore the development level and spatiotemporal evolution characteristics of water poverty and ecological resilience coupling. At the same time, the Tobit model was used to analyze the degree of influence of five main influencing factors on the coupling of water poverty and ecological resilience in mega cities. The conclusions obtained are as follows: (1)From 2023 to 2022, the water poverty index in 10 mega cities in China has significantly increased, with an overall increase of 166.13%, indicating that the water poverty level in these 10 mega cities is gradually improving. During the period of 2013-2022, the ecological resilience index of 10 mega cities in China has been increasing year by year, with an overall increase of 204.29%. This indicates that the 10 mega cities in China have made significant progress in ecological environment protection, resource utilization, disaster response, urban planning and management, and public participation. (2)The coupling coordination model shows that from 2013 to 2022, the coupling coordination range of water poverty and ecological resilience in 10 mega cities in China is 1.603 to 9.305. The coupling coordination between water poverty and ecological resilience in 10 mega cities in China has shifted from severe imbalance to good coordinated development. In terms of spatiotemporal evolution characteristics, the complete balance in 2013 presented a "one" shape. In 2016, the basic coordination area presented an irregular quadratic shape. In 2019, Tianjin, Wuhan, Guangzhou, Dongguan, and Shenzhen were located in the basic coordination area, presenting a "1" shape. (3) The results of the Tobit model show that economic development level, industrial structure, and technological innovation have a positive impact on the coupling of water poverty and ecological resilience, while population size and urbanization level have a negative impact on the coupling of water poverty and ecological resilience. The main influencing factor for the coupling and coordination of water poverty and ecological resilience in Beijing, Hangzhou, and Shanghai is technological innovation. The main influencing factor for the coupling and coordination of poverty and ecological resilience in Dongguan, Shenzhen, Tianjin, and Wuhan is the industrial structure. The main influencing factor for the coupling and coordination of poverty and ecological resilience in Chongqing and Chengdu is the level of urbanization. Declarations Funding Declaration ： This article did not receive any funding support Data availability ： All generated or analyzed data is included in the Supplementary. Code availability ： Not applicable Ethics approval ：Not applicable Consent to participate ：Not applicable Consent for publication ： By submitting this manuscript for publication, we, the authors, provide our consent for its publication in the designated journal. We affirm that this manuscript is original, has not been previously published, and is not under consideration for publication elsewhere. We take responsibility for the content and integrity of the manuscript and declare that all the listed authors have made substantial contributions to the study. Conflict of interest ：The authors declare no competing interests. Author's contribution: Author's contribution: Zhang Haiqi's main contributions include collecting and organizing data, calculating data, writing the first draft of the paper, and making later revisions. References Adger, W. N. (2000). Social and ecological resilience: are they related?. Progress in human geography, 24(3), 347-364. Alberti, M., & Marzluff, J. M. (2004). Ecological resilience in urban ecosystems: Linking urban patterns to human and ecological functions. Urban ecosystems, 7, 241-265. Chang, I. S., Zhao, M., Chen, Y., Guo, X., Zhu, Y., Wu, J., & Yuan, T. (2020). Evaluation on the integrated water resources management in China’s major cities--Based on City Blueprint® Approach. Journal of Cleaner Production, 262, 121410. Gao, Y., & Chen, W. (2021). Study on the coupling relationship between urban resilience and urbanization quality—A case study of 14 cities of Liaoning Province in China. PLoS One, 16(1), e0244024. Gunderson, L. H. (2000). Ecological resilience—in theory and application. Annual review of ecology and systematics, 31(1), 425-439. Holling, C. S. (1996). Engineering resilience versus ecological resilience. Engineering within ecological constraints, 31(1996), 32. Jiang, Y., Ka Shun Chan, F., Holden, J., Zhao, Y., & Guan, D. (2013). CHINA'S WATER MANAGEMENT--CHALLENGES AND SOLUTIONS. Environmental Engineering & Management Journal (EEMJ), 12(7). Lee, C. C., Yan, J., & Li, T. (2024). Ecological resilience of city clusters in the middle reaches of Yangtze river. Journal of Cleaner Production, 141082. Li, E., Endter‐Wada, J., & Li, S. (2015). Characterizing and contextualizing the water challenges of megacities. JAWRA Journal of the American Water Resources Association, 51(3), 589-613. Peterson, G., Allen, C. R., & Holling, C. S. (1998). Ecological resilience, biodiversity, and scale. Ecosystems, 1, 6-18. Pickett, S. T., McGrath, B., Cadenasso, M. L., & Felson, A. J. (2014). Ecological resilience and resilient cities. Building Research & Information, 42(2), 143-157. Shaban, A. (2008, June). Water poverty in urban India: a study of major cities. In Seminar Paper UGC Summer Programme June–July. Shi, C., Zhu, X., Wu, H., & Li, Z. (2022). Assessment of urban ecological resilience and its influencing factors: a case study of the Bei**g-Tian**-Hebei urban agglomeration of China. Land, 11(6), 921. Suárez, M., Gómez-Baggethun, E., & Onaindia, M. (2019). Assessing socio-ecological resilience in cities. In The Routledge handbook of urban resilience (pp. 197-216). Routledge. Tang, D., Li, J., Zhao, Z., Boamah, V., & Lansana, D. D. (2023). The influence of industrial structure transformation on urban resilience based on 110 prefecture-level cities in the Yangtze River. Sustainable Cities and Society, 96, 104621. Tian, Y., Hua, C., Zhu, M., Fang, Z., Yong, X., Yang, J., ... & Ren, L. (2024). Research on urban water security based on water poverty theory: a case study of lower yellow river cities. Stochastic Environmental Research and Risk Assessment, 38(2), 407-422. Walker, B., Holling, C. S., Carpenter, S. R., & Kinzig, A. (2004). Resilience, adaptability and transformability in social–ecological systems. Ecology and society, 9(2). Wang, S., Cui, Z., Lin, J., **e, J., & Su, K. (2022). The coupling relationship between urbanization and ecological resilience in the Pearl River Delta. Journal of Geographical Sciences, 32(1), 44-64. Wang, Z., Deng, X., Wong, C., Li, Z., & Chen, J. (2018). Learning urban resilience from a social-economic-ecological system perspective: A case study of Bei**g from 1978 to 2015. Journal of Cleaner Production, 183, 343-357. Wei, F., Zhang, X., Xu, J., Bing, J., & Pan, G. (2020). Simulation of water resource allocation for sustainable urban development: An integrated optimization approach. Journal of cleaner production, 273, 122537. Wu, J., & Wu, T. (2012). Ecological resilience as a foundation for urban design and sustainability. In Resilience in ecology and urban design: Linking theory and practice for sustainable cities (pp. 211-229). Dordrecht: Springer Netherlands. You, X., Sun, Y., & Liu, J. (2022). Evolution and analysis of urban resilience and its influencing factors: a case study of Jiangsu Province, China. Natural Hazards, 113(3), 1751-1782. Zhang, M., Chen, W., Cai, K., Gao, X., Zhang, X., Liu, J., ... & Li, D. (2019). Analysis of the spatial distribution characteristics of urban resilience and its influencing factors: a case study of 56 cities in China. International Journal of Environmental Research and Public Health, 16(22), 4442. Zhu, Q., **e, C., & Liu, J. B. (2023). The impact of population agglomeration on ecological resilience: evidence from China. Math. Biosci. Eng, 20, 15898-15917. Additional Declarations No competing interests reported. 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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-4534232","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":312896598,"identity":"cdf61ac5-066d-45f2-99e6-51d117687d99","order_by":0,"name":"haiqi zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuklEQVRIiWNgGAWjYBACAwbGBoYEIIOfmfnwA9K0SLazpRkQqQXGOM+jIEGUFnP2w403HlTckzc+zAPUX2MTTVCLZU9is0XCmWLDbYd5DzxgOJaW20DQYQcS2yQS2xIYtx3mSzBgbDhMhJbzD4Fa/iXYb27mMZAgTssNkC0NCYkbmInX8hDol2MJyTMOAwM5gSi/nE9/ePNHTYJtf//hww8+1NgQ1gICiOhIIEY5qpZRMApGwSgYBdgAAOw9QdxauFyjAAAAAElFTkSuQmCC","orcid":"","institution":"Central Party School of the Communist Party of China","correspondingAuthor":true,"prefix":"","firstName":"haiqi","middleName":"","lastName":"zhang","suffix":""}],"badges":[],"createdAt":"2024-06-05 12:52:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4534232/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4534232/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":58993165,"identity":"114cc94b-ece1-4aa4-b66b-adedef3173da","added_by":"auto","created_at":"2024-06-25 05:30:50","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":192238,"visible":true,"origin":"","legend":"\u003cp\u003eGeographical Location Map of 10 Megacities in China\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4534232/v1/358a306ac44e23c1beb79bdc.png"},{"id":58993162,"identity":"f6c96fa4-e7ed-45e3-8084-44c5abf3f4bc","added_by":"auto","created_at":"2024-06-25 05:30:50","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":105123,"visible":true,"origin":"","legend":"\u003cp\u003eWater Poverty Index of Chinese Megacities\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4534232/v1/f2cc631280aef4faa5420161.png"},{"id":58993694,"identity":"db446838-b6c5-4fa3-bd47-57b40e7fe361","added_by":"auto","created_at":"2024-06-25 05:38:50","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":700008,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial and temporal evolution of water poverty in mega cities in China\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4534232/v1/4af623a7cb9bab951b49f5e6.png"},{"id":58993167,"identity":"7f4dcb3d-8b49-4b32-a488-6a3fb767c6d3","added_by":"auto","created_at":"2024-06-25 05:30:50","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":31485,"visible":true,"origin":"","legend":"\u003cp\u003eFrom 2013 to 2022, the ecological resilience level of 10 mega cities in China\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4534232/v1/c885906415128692c9368b3a.png"},{"id":58993163,"identity":"8c7848f0-4b0d-46ad-9a99-2b9bbdf76b5c","added_by":"auto","created_at":"2024-06-25 05:30:50","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":531855,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial and temporal evolution map of ecological resilience level\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4534232/v1/0c4116cc2825c7f1f62a5fe5.png"},{"id":58993168,"identity":"35c4f767-5205-4918-97bd-08580621f2fe","added_by":"auto","created_at":"2024-06-25 05:30:50","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":118481,"visible":true,"origin":"","legend":"\u003cp\u003eCoupling Coordination Diagram from 2013 to 2022\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-4534232/v1/20957486194e52b1e2f3d935.png"},{"id":58993166,"identity":"a01f730c-d325-4842-81cc-97ff6058451d","added_by":"auto","created_at":"2024-06-25 05:30:50","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":415099,"visible":true,"origin":"","legend":"\u003cp\u003eCoupling and coordinated scheduling of different cities from 2013 to 2022\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-4534232/v1/003855f9f52a76038b8d19d0.png"},{"id":58993170,"identity":"b309e690-9cf7-4aa5-9815-65175808f628","added_by":"auto","created_at":"2024-06-25 05:30:50","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":699578,"visible":true,"origin":"","legend":"\u003cp\u003eCoupling coordination spatiotemporal evolution diagram\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-4534232/v1/00bbd0e2aa3cd940286b594a.png"},{"id":58994229,"identity":"4b20549b-ef07-4910-a1ca-1145110498e1","added_by":"auto","created_at":"2024-06-25 05:46:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3409248,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4534232/v1/02e9a784-1400-4316-96a6-72db0624c7b0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Analysis of spatiotemporal evolution and influencing factors of water poverty and ecological resilience coupling coordination in Chinese mega cities","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe current situation of water resources in Chinese cities is not optimistic, with water scarcity and environmental pollution becoming increasingly prominent. According to statistics, nearly 60 cities in China suffer from an annual water shortage of 6 billion cubic meters, resulting in economic losses of up to 200 billion yuan each year. The expansion of urban scale has led to an increase in sewage discharge and a deterioration in water resource quality. In 2017, the water quality problems of surface water and groundwater in China were prominent, and the deterioration of the water environment seriously affected the quality of water supply, exacerbating the shortage of water resources. In China\u0026apos;s mega cities, such as Beijing, Shanghai, Guangzhou, and Shenzhen, due to population density and concentration of economic activities, they face the dual challenges of water resource scarcity and water environment degradation\u003cstrong\u003e(Chang et al. 2020).\u003c/strong\u003e The development status of these cities shows some common characteristics and problems in terms of water resources. The rapid development of super urbanization has led to high demand and consumption of water resources. With population growth and economic expansion, the demand for urban water continues to rise, which directly increases the pressure on water resources. For example, although Shanghai has the advantage of supplying Yangtze River water resources, it still faces the threat of water scarcity. Megacities typically have a high water resource development rate, but this development often comes at the cost of sacrificing water environment quality and ecological balance. The excessive development and utilization of water resources have led to water pollution and ecosystem degradation, thereby affecting the sustainable utilization of water resources. Meanwhile, mega cities face complexity in water resource management and protection\u003cstrong\u003e(Li et al. 2015)\u003c/strong\u003e. In the process of urbanization, the improvement of urban water use efficiency often lags behind the growth of water demand, resulting in generally low water resource utilization efficiency\u003cstrong\u003e(Jiang et al. 2013)\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eIn the context of sustainable development, the sustainable utilization of water resources in mega cities is of great significance for the sustainable development of China and even the world. Water resources are not only the source of life, but also an important foundation for social and economic development and ecological environment protection. As the engine of national economic development, the water resources of mega cities are directly related to the economic and ecological security of the country. However, due to the impact of climate change and human activities, mega cities are facing dual pressures of water resource scarcity and water environment pollution, which poses a serious threat to the sustainable development of cities\u003cstrong\u003e(Wei et al. 2020).\u003c/strong\u003eTherefore, strengthening the sustainable utilization of water resources in mega cities is of great strategic significance for ensuring the quality of life of urban residents, promoting green economic development, maintaining the health of ecosystems, and achieving the United Nations 2030 Sustainable Development Goals.\u003c/p\u003e\n\u003cp\u003eEcological resilience is a concept originating from ecology, originally proposed by C S. Holling proposed in the 1970s to describe the ability of ecosystems to maintain their function and structure in the face of disturbances and changes\u003cstrong\u003e(Holling and C.S 1996)\u003c/strong\u003e. Ecological resilience emphasizes the ability of a system to recover to its original state after being disturbed, as well as the ability to transition to a new state when necessary. Existing research has different definitions of ecological resilience. For example,(\u003cstrong\u003eGunder and sonL.H 2000)\u0026nbsp;\u003c/strong\u003ebelieve that ecological resilience is closely related to the stability and resistance of ecosystems, which involves the response and recovery process of ecosystems in the face of natural or human interference.\u003cstrong\u003e(Adger and W.N. 2000)\u003c/strong\u003eregard ecological resilience as the ability of a system to maintain its core functions and identity in long-term changes, even in the face of pressure and shocks, to adapt and evolve. The research on ecological resilience has achieved rich results internationally. For example,\u003cstrong\u003eWalker et al. (2004)\u003c/strong\u003eemphasized the resilience, adaptability, and variability of social ecosystems in their 2004 study. In China, scholars have also begun to pay attention to the issue of ecological resilience in the process of urbanization, such as the coupling and coordination research between urbanization and ecological resilience in the Pearl River Delta region\u003cstrong\u003e(Wang et al. 2022).\u003c/strong\u003e Through literature review of ecological resilience, it can also be seen that researchers typically evaluate and construct ecological resilience from multiple dimensions, including but not limited to biodiversity, ecosystem services, landscape patterns, and environmental changes. In the study of urban ecological resilience, scholars focus on how the complexity of urban structure and the diversity of functions are coupled with natural environment, economy, and social needs.In summary, ecological resilience is a multidimensional and interdisciplinary concept that involves the stability, adaptability, and transformative ability of ecosystems in the face of change\u003cstrong\u003e(Peterson et al. 1998)\u003c/strong\u003e. The study of ecological resilience is not only crucial for ecology and environmental science, but also has important guiding significance for urban planning, resource management, and sustainable development.\u003c/p\u003e\n\u003cp\u003eWater poverty and ecological resilience are two very important concepts in the fields of sustainable development and environmental management. Due to population density and concentration of economic activities, mega cities have a huge demand for water resources. At the same time, land use changes and environmental pollution in the process of urbanization have had a significant impact on water ecosystems. The emergence of water poverty not only limits the quality of life of urban residents, but also restricts the economic development and social stability of citie\u003cstrong\u003es\u003c/strong\u003e\u003cstrong\u003e(Tian et al. 2024)\u003c/strong\u003e. The existence of water poverty means that there are imbalanced and unsustainable issues in the acquisition, distribution, and utilization of water resources in cities. If these problems are not properly addressed, they will lead to water scarcity in cities, threatening urban ecological security and the health of residents. Ecological resilience reflects the adaptability and resilience of cities in the face of water resource challenges, and is an important attribute for cities to cope with climate change, natural disasters, and the impact of human activities\u003cstrong\u003e(Pickett et al. 2014)\u003c/strong\u003e. Cities with strong ecological resilience can better cope with water crises, maintain the health and stability of water ecosystems, and thus ensure the sustainable use of water resources. Studying the coupling and coordination between water poverty and ecological resilience in mega cities can help formulate scientific water resource management and urban planning strategies. Through coupling coordination analysis, the risk points of urban water resource shortage can be identified, and the ecological resilience level of different regions can be evaluated, providing a basis for the rational allocation of urban water resources, water ecological protection, and restoration. At the same time, it can promote the balance between urban economic growth and ecological environment protection, improve the quality of life of urban residents, and achieve a win-win situation between economic and social development and ecological environment protection. In addition, coupled coordination research can help the international community understand the challenges and efforts of Chinese mega cities in water resource management, promote international cooperation and knowledge sharing, and jointly respond to the global water crisis.\u003c/p\u003e\n\u003cp\u003eTherefore, this article selects data from 2013 to 2022 to evaluate the water poverty and ecological resilience levels of 10 mega cities in China, and further analyzes their spatiotemporal evolution characteristics in 2013, 2016, 2019, and 2022. Meanwhile, evaluate the degree of coupling between water poverty and ecological resilience, and analyze their changing trends in 2013, 2016, 2019, and 2022. Finally, this article selects five influencing factors: economic development level, industrial structure, technological innovation, population density, and urbanization level, to analyze which factors are the main factors affecting the coupling degree of urban water poverty and ecological resilience.\u003c/p\u003e"},{"header":"2. Research methods and data sources","content":"\u003ch2\u003e2.1Overview of the research area\u003c/h2\u003e\n\u003cp\u003eMegacities are one of the classification criteria for the size of cities in China. According to the Notice on Adjusting the Standards for Urban Scale Classification issued by the State Council in 2014, cities with a permanent population of more than 10 million in urban areas are considered mega cities, while cities with a permanent population of more than 5 million but less than 10 million in urban areas are considered mega cities. The 2019 Urban Construction Statistical Yearbook released by the Chinese government in 2020 stipulates that six cities in China, namely Shanghai, Beijing, Chongqing, Guangzhou, Shenzhen, and Tianjin, meet the standards for \u0026quot;mega cities\u0026quot;. The \u0026quot;2022 Urban Construction Statistical Yearbook\u0026quot; released by the Chinese government in October 2023 pointed out that there are a total of 10 mega cities in China, namely Shanghai, Beijing, Shenzhen, Chongqing, Guangzhou, Chengdu, Tianjin, Dongguan, Wuhan, and Hangzhou. Based on this, this article selects 10 mega cities in China, including Shanghai, Beijing, Shenzhen, Chongqing, Guangzhou, Chengdu, Tianjin, Dongguan, Wuhan, and Hangzhou, as the main research areas. The above 10 cities, Beijing and Tianjin are located in the northern region of China, Guangzhou, Dongguan, and Shenzhen are located in the southern region of China, and the other 5 cities are located in the central and eastern regions of China. The specific locations are shown in Figure 1.\u003c/p\u003e\n\u003ch2\u003e2.2 Data sources\u003c/h2\u003e\n\u003cp\u003eThe data source of this article is the China Statistical Yearbook(https://www.stats.gov.cn/sj/ndsj/); China Environmental Statistical Yearbook(https://www.mee.gov.cn/); EPS data platform(https://www.epsnet.com.cn/); Statistical yearbooks for various cities, such as the Beijing Statistical Yearbook(https://tjj.beijing.gov.cn/). In addition, some data is sourced from water resource bulletins of various cities, such as the Beijing Water Resources Bulletin(https://swj.beijing.gov.cn/).\u003c/p\u003e\n\u003ch2\u003e2.3 Index system construction\u003c/h2\u003e\n\u003ch3\u003e2.3.1Construction of urban water poverty indicator system\u003c/h3\u003e\n\u003cp\u003eWith the deepening of research on water poverty, the evaluation index system has gradually become diversified and comprehensive. This article refers to \u003cstrong\u003e(Sullivan and C 2002;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Forouzani et al. 2011; Shaban and Abdul 2008;\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eSullivan and C 2001\u003c/strong\u003e\u003cstrong\u003e).\u003c/strong\u003eAt the same time, based on the development status and characteristics of 10 mega cities in China, following the principles of scientificity, objectivity, and comprehensiveness, the urban water poverty indicator system is divided into five major aspects: resources, facilities, use, capacity and environment. The details are shown in Table 1 below.\u003c/p\u003e\n\u003cp\u003eTable 1: Urban Water Poverty Indicator System\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"99%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.242424242424242%\" valign=\"top\"\u003e\n \u003cp\u003eCriterion layer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.252525252525253%\" valign=\"top\"\u003e\n \u003cp\u003eIndicator layer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.252525252525253%\" valign=\"top\"\u003e\n \u003cp\u003eunit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.252525252525253%\" valign=\"top\"\u003e\n \u003cp\u003eattribute\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.242424242424242%\" rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eresource\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.252525252525253%\" valign=\"top\"\u003e\n \u003cp\u003eAnnual precipitation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.252525252525253%\" valign=\"top\"\u003e\n \u003cp\u003emillimeter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.252525252525253%\" valign=\"top\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eSurface water resources\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eBillion cubic meters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003egroundwater resources\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eBillion cubic meters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eTotal water resources\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eBillion cubic meters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.242424242424242%\" rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003efacilities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.252525252525253%\" valign=\"top\"\u003e\n \u003cp\u003eUrban drainage pipeline length\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.252525252525253%\" valign=\"top\"\u003e\n \u003cp\u003ekilometre\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.252525252525253%\" valign=\"top\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eTotal water supply\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eBillion cubic meters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eLength of water supply pipeline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003ekilometre\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eTotal Highway Mileage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003ekilometre\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.242424242424242%\" rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003euse\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.252525252525253%\" valign=\"top\"\u003e\n \u003cp\u003e10000 yuan industrial value-added water consumption\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.252525252525253%\" valign=\"top\"\u003e\n \u003cp\u003ecubic meter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.252525252525253%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003ePer capita domestic water consumption of urban and rural residents\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eL/d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003ePer capita water consumption\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eL/d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eWater consumption per 10000 yuan of gross domestic product\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003ecubic meter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.242424242424242%\" rowspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003ecapacity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.252525252525253%\" valign=\"top\"\u003e\n \u003cp\u003eFiscal self-sufficiency rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.252525252525253%\" valign=\"top\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.252525252525253%\" valign=\"top\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eUrban per capita disposable income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eyuan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003ePer capita medical expenditure of residents\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eyuan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003ePer capita education expenditure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eyuan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eRegional Gross Domestic Product\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eRMB100mn\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.242424242424242%\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eenvironment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.252525252525253%\" valign=\"top\"\u003e\n \u003cp\u003ePer capita park green space area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.252525252525253%\" valign=\"top\"\u003e\n \u003cp\u003esquare meter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.252525252525253%\" valign=\"top\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eUrban sewage treatment efficiency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eNumber of public toilets\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eseat\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003ch3\u003e2.3.2Construction of Urban Ecological Resilience Evaluation Index System\u003c/h3\u003e\n\u003cp\u003eUrban ecological resilience refers to the ability of a city to maintain its ecological functions and services, recover and adapt to changes in the face of natural and human disturbances. The construction of urban ecological resilience assessment models usually includes three aspects: resistance, adaptability, and resilience. Based on the research of \u003cstrong\u003e(\u003c/strong\u003e\u003cstrong\u003eSu\u0026aacute;rez et al. 2019; Kotzee et al. 2016; Alberti and Marzluff 2004)\u003c/strong\u003e,this article constructs an indicator system from three aspects: resistance, adaptability, and resilience to evaluate the ecological resilience.The details are shown in Table 2 below.\u003c/p\u003e\n\u003cp\u003eTable 2: Urban Ecological Resilience Index System\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"99%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.242424242424242%\" valign=\"top\"\u003e\n \u003cp\u003eCriterion layer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.252525252525253%\" valign=\"top\"\u003e\n \u003cp\u003eIndicator layer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.252525252525253%\" valign=\"top\"\u003e\n \u003cp\u003eunit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.252525252525253%\" valign=\"top\"\u003e\n \u003cp\u003eattribute\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.242424242424242%\" rowspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003eResistance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.252525252525253%\" valign=\"top\"\u003e\n \u003cp\u003eValue added of the secondary industry/GDP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.252525252525253%\" valign=\"top\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.252525252525253%\" valign=\"top\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eValue added of the tertiary industry/GDP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003ePer capita GDP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eyuan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eNatural population growth rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eThe proportion of science and technology expenditure to general public budget expenditure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.242424242424242%\" rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eAdaptability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.252525252525253%\" valign=\"top\"\u003e\n \u003cp\u003eIndustrial smoke and dust emissions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.252525252525253%\" valign=\"top\"\u003e\n \u003cp\u003eton\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.252525252525253%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eIndustrial sulfur dioxide emissions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eton\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eCentralized treatment rate of sewage treatment plants\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eHarmless treatment rate of household waste\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.242424242424242%\" rowspan=\"6\" valign=\"top\"\u003e\n \u003cp\u003eResilience\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.252525252525253%\" valign=\"top\"\u003e\n \u003cp\u003eDrainage pipeline length\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.252525252525253%\" valign=\"top\"\u003e\n \u003cp\u003eKilometers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.252525252525253%\" valign=\"top\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eUrban solid waste removal volume\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e10000 tons\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eGarden green space area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003ehectare\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eGreen coverage rate in built-up areas\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003ecubic meter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eFiscal self-sufficiency rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003ePer capita park green space area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003ehectare\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e2.4model building\u003c/h2\u003e\n\u003ch3\u003e2.4.1Entropy method\u003c/h3\u003e\n\u003cp\u003eEntropy method is an objective weighting method that can objectively and truthfully reflect the information hidden in indicator data. This article uses the entropy method to evaluate the water poverty and ecological resilience levels of 10 mega cities in China.\u003c/p\u003e\n\u003ch3\u003e2.4.2Water poverty measurement model（WPI）\u003c/h3\u003e\n\u003cp\u003eWPI is a comprehensive set of indicators that can quantitatively evaluate the relative water scarcity level between countries or regions. This indicator can not only reflect the background situation of regional water resources, but also reflect the engineering, management, economy, human welfare, and environmental conditions.The water poverty evaluation index system constructed in this article is composed of five subsystems: resources, facilities, capabilities, usage, and environment. Several evaluation indicators are set up within each subsystem.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003e2.4.3Coupling coordination model\u003c/h3\u003e\n\u003cp\u003eCoupling is widely used to measure the degree of interaction between systems. The coupling coordination model is applied to this article to measure the interaction relationship between water poverty and ecological resilience systems. Based on existing research, this article divides the calculation results of the coupling coordination degree between water poverty and ecological resilience into levels. The details are shown in Table 3 below.\u003c/p\u003e\n\u003cp\u003eTable 3: Coupling Coordination Level\u003c/p\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eDevelopment stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eCoupled co scheduling\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003egradation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eDysfunction stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e[0-0.2）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eComplete dysregulation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e[0.2-0.4）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eModerate imbalance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eTransition phase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e[0.4-0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eBasic coordination\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eCoordination stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e[0.6-0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eModerate coordination\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e[0.8-1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eHighly coordinated\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003ch3\u003e2.4.4Panel Tobit model\u003c/h3\u003e\n\u003cp\u003eThe coupling coordination degree is within the range of 0 and 1. Due to the limitation of the dependent variable and the avoidance of bias caused by OLS estimation, the Tobit model is usually used for estimation. In this paper, the Tobit stochastic model is selected to analyze the influencing factors of the coupling coordination degree between water poverty and ecological resilience.\u003c/p\u003e\n\u003cp\u003eBased on the research of (\u003cstrong\u003eShi et al. 2022; Zhang et al. 2019; You et al. 2022; Lee et al. 2024)\u003c/strong\u003e,this article selects the following main influencing factors for analysis. (1) Economic development level. The expansion of economic activities often accompanies the significant consumption of water resources and water pollution, affecting water poverty and ecological resilience. Cities with higher levels of economic development may have more resources and funds to invest in water resource management and protection, thereby improving water resource utilization efficiency and ecological resilience\u003cstrong\u003e(Wang et al. 2018)\u003c/strong\u003e. (2) Industrial structure. Different industrial structures have varying demands and impacts on water resources. The increasing proportion of high water consuming industries such as heavy industry and agriculture may lead to excessive development and pollution of water resources, while service and high-tech industries may adopt more water-saving and environmentally friendly production methods to reduce pressure on water resources\u003cstrong\u003e(Tang et al. 2023)\u003c/strong\u003e. (3) Technological innovation: Technological innovation can improve the efficiency of water resource utilization, develop water-saving technologies and clean energy, and reduce dependence and pollution on water resources. Technological innovation can also promote ecological restoration and environmental protection, enhancing the ecological resilience of cities. (4) Population density. The increase in population size will increase the demand for water resources, especially in terms of water supply, sanitation, and domestic use. The increase in population density may also exacerbate the shortage of water resources and affect the water poverty status of cities\u003cstrong\u003e(Zhu et al. 2023)\u003c/strong\u003e. (5) Urbanization level. Rapid urbanization has led to population growth and land use changes, increasing the demand for water resources and potentially disrupting natural water cycles and ecological balance. The construction of infrastructure in the process of urbanization, such as water supply systems and sewage treatment facilities, has a direct impact on the supply and quality of water resources\u003cstrong\u003e(Gao et al. 2021)\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eBased on this, this article selects per capita GDP to represent the level of economic development; The proportion of scientific and technological expenditure to general public budget expenditure represents scientific and technological innovation; The proportion of the added value of the secondary industry to GDP represents the industrial structure; The year-end permanent population represents the population size; Urban population/total population represents the level of urbanization.\u003c/p\u003e"},{"header":"3. Result analysis","content":"\u003ch2\u003e3.1 Evaluation and spatiotemporal evolution characteristics of water poverty\u003c/h2\u003e\n\u003cp\u003eThis article uses the entropy method to evaluate water poverty in 10 mega cities in China, and the final results are shown in Figure 1. According to the data in Figure 2 it can be seen that in 2013, the water poverty index of 10 mega cities in China was 2.48, and in 2022 it was 6.60. The water poverty index has achieved a significant increase, with an overall increase of 166.13%, indicating that the water poverty level of 10 mega cities in China is gradually improving. The analysis of the reasons for the above results can be summarized into the following three aspects: first, the improvement of water resource management policies. The Chinese government has successively introduced policies to strengthen water resource management in 2013, 2013, 2015, 2017, 2019, and other years. Major cities have introduced their own water resource management policies, such as the Beijing Water Conservation Regulations issued in 2022, which clearly stipulate the improvement of water resource utilization efficiency, the formation of water-saving production and lifestyle, the guarantee of water safety, and the promotion of high-quality economic and social development. The above policies will promote the rational allocation and effective utilization of water resources, thereby improving the efficiency of water resource utilization. Secondly, the improvement of urban water resource utilization efficiency. With technological progress and increased awareness of water conservation, China\u0026apos;s 10 mega cities may have adopted more efficient water resource utilization technologies, such as improving irrigation systems, increasing industrial water recycling rates, and promoting household water-saving appliances, all of which help reduce water waste. Thirdly, environmental protection and ecological restoration measures. With the increasing emphasis of the Chinese government on ecological civilization, major cities have increased their efforts in controlling water pollution and implemented ecological projects such as river and lake remediation, wetland protection, and restoration. These measures help improve the quality of water environment and enhance the sustainability of water resources.\u003c/p\u003e\n\u003cp\u003eThis article uses ArcGis10.8 to classify the natural breakpoints of 10 mega cities in China in 2013, 2016, 2019, and 2022. From low to high, they are defined as low-level areas, low-level areas, general level areas, high-level areas, and high-level areas. The final result is shown in Figure 3. In 2013, there were three cities in the low-level areas: Chengdu, Wuhan, and Dongguan. Lower level areas include Tianjin. There are two general level areas: Beijing and Guangzhou. There are two high-level areas: Chongqing and Hangzhou. There are two high-level areas: Shenzhen and Shanghai. In 2016, there were three cities in the low-level area: Tianjin, Chongqing, and Guangzhou. There are four cities in the lower level areas: Beijing, Chengdu, Wuhan, and Dongguan. The general level area is Hangzhou. The higher level area is Shanghai. The high-level area is Shenzhen. In 2019, low-level areas included Tianjin and Chongqing. There are two cities in the lower level area, Beijing and Wuhan. The general level areas are Hangzhou, Shanghai, Chengdu, and Dongguan. The higher level area is Guangzhou. The high-level area is Shenzhen. In 2022, there will be Chongqing in the low-level area. There are three cities in the lower level areas: Tianjin, Shanghai, and Hangzhou. The general level areas are Beijing and Chengdu. The high-level areas are Guangzhou, Dongguan, and Wuhan. The high-level area is Shenzhen. From the graph, it can be seen that from 2013 to 2016, the water poverty index of China\u0026apos;s 10 mega cities has been decreasing, with 7 cities in low-level and lower level areas, indicating a higher level of water poverty in China\u0026apos;s mega cities. From 2016, 2019, and 2022, the level of water poverty in 10 mega cities in China has gradually increased, indicating that the utilization of water resources in these cities is gradually improving.\u003c/p\u003e\n\u003cp\u003eOf course, from Figure 3, it can also be seen that in 2013, 2016, 2019, and 2022, the water poverty index in Shenzhen was relatively high, and the degree of water poverty was relatively low. This is because, firstly, Shenzhen has abundant precipitation: it is located in the southern region of China, with relatively high precipitation, and the average annual rainfall is much higher than the national average level. This provides it with a relatively rich water resource foundation. Secondly, efficient water resource management and utilization: Despite the limited total water resources in Shenzhen, the municipal government has been committed to improving the efficiency and management level of water resource utilization. By implementing scientific water resource scheduling, optimizing water supply structure, and promoting water-saving technologies, the supply-demand contradiction of water resources has been effectively alleviated. Thirdly, strong economic strength and technological support: Shenzhen, as one of China\u0026apos;s special economic zones, has strong economic strength and technological support. This provides more funding and technical support for its water resource management and utilization, which helps to improve its water resource utilization efficiency and management level.\u003c/p\u003e\n\u003ch2\u003e3.2 Ecological resilience evaluation and spatiotemporal evolution characteristics\u003c/h2\u003e\n\u003cp\u003eThis article uses the entropy method to evaluate the ecological resilience of 10 mega cities in China, and the final results are shown in Figure 4. From the data in the graph, it can be seen that the ecological resilience index of 10 mega cities in China has been increasing year by year from 2013 to 2022. In 2013, the ecological resilience index of 10 mega cities in China was 2.22, and in 2022 it was 7.09, an increase of 204.29%. This indicates that the 10 mega cities in China have made significant progress in ecological environment protection, resource utilization, disaster response, urban planning and management, and public participation.\u003c/p\u003e\n\u003cp\u003eFrom the perspective of spatial distribution, as shown in Figure5, it can be seen that in 2013, cities with low ecological resilience levels were Hangzhou, Wuhan, and Dongguan. Cities with lower levels of ecological resilience include Hangzhou. Cities with average ecological resilience include Beijing. Tianjin, Chongqing, Chengdu, and Guangzhou have higher levels of ecological resilience. Cities with high levels of ecological resilience include Shenzhen and Shanghai. Overall, in 2013, among the 10 mega cities in China, there were relatively more cities with good ecological resilience development, with 6 cities accounting for 60%. In 2016, cities with low levels of ecological resilience included Dongguan and Wuhan. Cities with lower levels of ecological resilience include Beijing. Cities with average levels of ecological resilience include Chongqing, Chengdu, and Hangzhou. Tianjin, Shanghai, and Guangzhou have higher levels of ecological resilience. Shenzhen has a high level of ecological resilience. Compared to 2013, in 2016, there were 4 out of 10 mega cities in China with good ecological resilience development, a decrease. In 2019, cities with low levels of ecological resilience included Beijing and Shenzhen. Dongguan is a city with a low level of ecological resilience. Cities with average levels of ecological resilience include Tianjin and Guangzhou. Wuhan has a higher level of ecological resilience. Cities with high levels of ecological resilience include Chengdu, Chongqing, Hangzhou, and Shanghai. From Figure 3, it can also be seen that in 2019, the high level of ecological resilience was mainly concentrated in the central region, showing a \u0026quot;horizontal line\u0026quot; distribution. In 2022, Tianjin has a low level of ecological resilience. Cities with lower levels of ecological resilience include Beijing, Guangzhou, and Shanghai. Tianjin is a city with an average level of ecological resilience. Chengdu, Chongqing, Hangzhou, and Dongguan have higher levels of ecological resilience. Wuhan is a city with a high level of ecological resilience. From Figure5, it can also be seen that in 2022, the ecological resilience level of 10 mega cities also shows a \u0026quot;horizontal line\u0026quot; distribution.\u003c/p\u003e\n\u003ch2\u003e3.3 The coupling and coordination level and spatiotemporal evolution characteristics of water poverty and ecological resilience\u003c/h2\u003e\n\u003cp\u003eFrom Figure 6 below, it can be seen that overall, from 2013 to 2022, the coupling coordination degree range of water poverty and ecological resilience in 10 mega cities in China is 1.603 to 9.305. From 2013 to 2021, there has been a trend of increasing year by year, and starting to decline in 2022. The coupling coordination between water poverty and ecological resilience in 10 mega cities in China has shifted from severe imbalance to good coordinated development.\u003c/p\u003e\n\u003cp\u003eFrom Figure 7, it can be seen that in 2013, Chongqing, Hangzhou, and Tianjin had a high degree of coupling coordination between water poverty and ecological resilience, belonging to mild, moderate, and moderate imbalance states, respectively. The numerical results in other cities are relatively low, indicating a serious imbalance. Meanwhile, overall, from 2013 to 2022, Chengdu, Guangzhou, Shanghai, and Shenzhen cities showed a high degree of coupling and coordination between water poverty and ecological resilience. This indicates that they have invested more funds and technology in water resource management and ecological protection, thereby improving the coupling and coordination between water poverty and ecological resilience. However, the coupling coordination between water poverty and ecological resilience is relatively low in Beijing, Chongqing, and Dongguan.\u003c/p\u003e\n\u003cp\u003eFrom the perspective of spatial changes (Figure 8), from 2013 to 2022, the coupling coordination level of water poverty and ecological resilience in 10 mega cities in China has been increasing. In 2013, most cities were located in completely or moderately dysregulated areas. For example, Chengdu, Wuhan, and Hangzhou are located in completely imbalanced areas, while the other seven cities are located in moderately imbalanced areas. Overall, the complete imbalance in 2013 presented a \u0026quot;one\u0026quot; shape. In 2016, most cities were in areas of moderate imbalance and basic coordination. Tianjin, Shanghai, Chengdu, and Guangzhou are located in the basic coordination area, while other cities are located in the moderate imbalance area. The basic coordination area presents an irregular quadrilateral shape. In 2019, most cities withdrew from moderately imbalanced areas, with only one city being Chongqing. Beijing, Chengdu, Shanghai, and Hangzhou are located in a moderately coordinated area, presenting a triangular shape. Tianjin, Wuhan, Guangzhou, Dongguan, and Shenzhen are located in the basic coordination area, presenting a \u0026quot;1\u0026quot; shape. In 2022, China\u0026apos;s 10 mega cities are mainly located in three major regions: basic coordination, moderate coordination, and high coordination. There are four cities in the basic coordination area, namely Tianjin, Hangzhou, Guangzhou, and Shenzhen. There are 5 cities with moderate coordination, namely Beijing, Chengdu, Chongqing, Shanghai, and Dongguan. Only Wuhan is located in a highly coordinated area.\u003c/p\u003e\n\u003cp\u003eOverall, from 2013 to 2022, the coupling and coordination level between water poverty and ecological resilience in Wuhan City has been increasing, from being located in a low coordination area to being in a high coordination area by 2022. There are three main reasons: firstly, water resource management and protection policies. During this period, the Wuhan Municipal Government formulated and implemented a series of water resource management and protection policies, such as the Wuhan Water Pollution Prevention and Control Action Plan, which played a positive role in reducing water pollution and protecting water resources. Secondly, ecological restoration projects. The government has invested a large amount of funds in ecological restoration projects, such as lake management and wetland protection. For example, the water quality of lakes such as East Lake and South Lake has significantly improved, not only enhancing the water environment quality of cities, but also enhancing their ecological resilience. Thirdly, the public\u0026apos;s awareness of environmental protection should be enhanced. With the strengthening of environmental protection publicity, the environmental awareness of Wuhan citizens is gradually increasing, and people are paying more attention to water resource protection and water conservation. The formation of this social atmosphere has played a positive role in enhancing the coupling and coordination level between water poverty and ecological resilience. In addition, the coupling and coordination level between water poverty and urban ecological resilience in Chengdu and Dongguan is increasing, mainly due to the joint efforts of policy guidance and strict implementation, ecological environment protection and restoration, technological innovation and application, and industrial structure optimization and upgrading. The implementation of these measures not only improves the efficiency of water resource utilization and water environment quality in cities, but also enhances the ecological resilience of cities, laying a solid foundation for their sustainable development.\u003c/p\u003e\n\u003ch2\u003e3.4Identification of influencing factors on the coupling coordination between water poverty and ecological resilience\u003c/h2\u003e\n\u003cp\u003eThis article uses the Tobit regression model to analyze the coupling and coordinated influencing factors of water poverty and ecological resilience in 10 mega cities in China, as shown in Table 4. From the data in the table, it can be seen that all five factors have passed the significance test. The level of economic development, industrial structure, and technological innovation have a positive impact on the coupling of water poverty and ecological resilience, while population size and urbanization level have a negative impact on the coupling of water poverty and ecological resilience. The reasons for the above results are: firstly, with the improvement of economic development level, cities have more resources invested in water resource management and ecological protection, thereby improving water poverty. Meanwhile, the optimization of industrial structure, especially the transformation towards low water consumption and low pollution industries, helps to reduce pressure on water resources and enhance ecological resilience. Meanwhile, technological innovation plays an important role in areas such as water resource management, pollution control, and ecological restoration. By introducing new technologies and methods, water resources can be more effectively utilized, waste and pollution can be reduced, and the resilience and resilience of ecosystems can be improved. Secondly, in terms of population size. With the increase of population, the demand for water resources will also correspondingly increase, which may lead to excessive development and utilization of water resources, exacerbating water poverty. At the same time, the environmental problems brought about by population growth, such as household waste and industrial wastewater, will also put pressure on the ecosystem and reduce ecological resilience. In terms of improving urbanization level. In the process of urbanization, urban expansion and infrastructure construction require a large amount of water resources, which may lead to water scarcity and water pollution problems. In addition, phenomena such as urban heat island effect and ground hardening can also affect the stability of urban ecosystems and reduce ecological resilience.\u003c/p\u003e\n\u003cp\u003eIn addition, among the 10 mega cities in China, the main influencing factor for the coupling and coordination of water poverty and ecological resilience in Beijing, Hangzhou, and Shanghai is technological innovation. The main influencing factor for the coupling and coordination of poverty and ecological resilience in Dongguan, Shenzhen, Tianjin, and Wuhan is the industrial structure. The main influencing factor for the coupling and coordination of poverty and ecological resilience in Chongqing and Chengdu is the level of urbanization.\u003c/p\u003e\n\u003cp\u003eTable 4: Analysis results of influencing factors\u003c/p\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ecity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003elnEDL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003elnIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003elnTI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003elnPD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003elnUL\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ebeijing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.001\u003csup\u003e**\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(4.174)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.043\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;(0.038)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.241\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;(3.741)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.101\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;(2.381)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.062\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e(12.447)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003echengdu\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.001\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;(3.406)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.014\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;(2.648)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.207\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;(8.289)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e(0.159)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.059\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e(8.091)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003echongqing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e(0.363)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.021\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e(1.411)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.006\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;(0.385)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.000 \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e(0.026)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.079\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e(1.385)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003edongguan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.001\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;(4.163)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.226\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;(-3.497)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.021\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;(-2.502)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.003\u003csup\u003e*\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(2.465)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.058\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e(2.052)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ehanzghou\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.000\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e(1.528)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.009\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;(5.806)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.013\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;(2.478)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.002\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;(1.695)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.005\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e(0.675)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eshanghai\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.000\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;(2.576)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.014\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;(1.591)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.023\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;(1.230)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.013\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;(2.842)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.006\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;(-0.695)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eshenzhen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.000\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;(1.775)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.203\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e(0.517)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.002\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;(10.614)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.000\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;(2.554)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.004\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e(0.588)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003etianjin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.000 \u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e(0.360)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.108\u003csup\u003e\u0026nbsp;**\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e(0.876)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.008\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e(0.138)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;(0.420)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.038\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e(2.904)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ewuhan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.000\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;(1.941)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.018\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;(3.163)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.015\u003csup\u003e*\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(2.066)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.001\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;(8.979)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.017\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;(1.663)\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\u003e*P\u0026lt;0.05 * * p\u0026lt;0.01 The values in parentheses are z values\u003c/p\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eThis article is based on panel data from 10 mega cities in China from 2013 to 2022, and uses a coupled coordination model to explore the development level and spatiotemporal evolution characteristics of water poverty and ecological resilience coupling. At the same time, the Tobit model was used to analyze the degree of influence of five main influencing factors on the coupling of water poverty and ecological resilience in mega cities. The conclusions obtained are as follows:\u003c/p\u003e\n\u003cp\u003e(1)From 2023 to 2022, the water poverty index in 10 mega cities in China has significantly increased, with an overall increase of 166.13%, indicating that the water poverty level in these 10 mega cities is gradually improving. During the period of 2013-2022, the ecological resilience index of 10 mega cities in China has been increasing year by year, with an overall increase of 204.29%. This indicates that the 10 mega cities in China have made significant progress in ecological environment protection, resource utilization, disaster response, urban planning and management, and public participation.\u003c/p\u003e\n\u003cp\u003e(2)The coupling coordination model shows that from 2013 to 2022, the coupling coordination range of water poverty and ecological resilience in 10 mega cities in China is 1.603 to 9.305. The coupling coordination between water poverty and ecological resilience in 10 mega cities in China has shifted from severe imbalance to good coordinated development. In terms of spatiotemporal evolution characteristics, the complete balance in 2013 presented a \u0026quot;one\u0026quot; shape. In 2016, the basic coordination area presented an irregular quadratic shape. In 2019, Tianjin, Wuhan, Guangzhou, Dongguan, and Shenzhen were located in the basic coordination area, presenting a \u0026quot;1\u0026quot; shape.\u003c/p\u003e\n\u003cp\u003e(3) The results of the Tobit model show that economic development level, industrial structure, and technological innovation have a positive impact on the coupling of water poverty and ecological resilience, while population size and urbanization level have a negative impact on the coupling of water poverty and ecological resilience. The main influencing factor for the coupling and coordination of water poverty and ecological resilience in Beijing, Hangzhou, and Shanghai is technological innovation. The main influencing factor for the coupling and coordination of poverty and ecological resilience in Dongguan, Shenzhen, Tianjin, and Wuhan is the industrial structure. The main influencing factor for the coupling and coordination of poverty and ecological resilience in Chongqing and Chengdu is the level of urbanization.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding Declaration\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003e\u003cstrong\u003eThis article did not receive any funding support\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003eAll generated or analyzed data is included in the Supplementary.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e：Not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e：Not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003eBy submitting this manuscript for publication, we, the authors, provide our consent for its publication in the designated journal. We affirm that this manuscript is original, has not been previously published, and is not under consideration for publication elsewhere. We take responsibility for the content and integrity of the manuscript and declare that all the listed authors have made substantial contributions to the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e：The authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u0026apos;s contribution:\u003c/strong\u003eAuthor\u0026apos;s contribution: Zhang Haiqi\u0026apos;s main contributions include collecting and organizing data, calculating data, writing the first draft of the paper, and making later revisions.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAdger, W. N. (2000). Social and ecological resilience: are they related?. Progress in human geography, 24(3), 347-364.\u003c/li\u003e\n \u003cli\u003eAlberti, M., \u0026amp; Marzluff, J. M. (2004). Ecological resilience in urban ecosystems: Linking urban patterns to human and ecological functions. Urban ecosystems, 7, 241-265.\u003c/li\u003e\n \u003cli\u003eChang, I. S., Zhao, M., Chen, Y., Guo, X., Zhu, Y., Wu, J., \u0026amp; Yuan, T. (2020). Evaluation on the integrated water resources management in China\u0026rsquo;s major cities--Based on City Blueprint\u0026reg; Approach. Journal of Cleaner Production, 262, 121410.\u003c/li\u003e\n \u003cli\u003eGao, Y., \u0026amp; Chen, W. (2021). Study on the coupling relationship between urban resilience and urbanization quality\u0026mdash;A case study of 14 cities of Liaoning Province in China. PLoS One, 16(1), e0244024.\u003c/li\u003e\n \u003cli\u003eGunderson, L. H. (2000). Ecological resilience\u0026mdash;in theory and application. Annual review of ecology and systematics, 31(1), 425-439.\u003c/li\u003e\n \u003cli\u003eHolling, C. S. (1996). Engineering resilience versus ecological resilience. 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Eng, 20, 15898-15917.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"urban-ecosystems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ueco","sideBox":"Learn more about [Urban Ecosystems](https://www.springer.com/journal/11252)","snPcode":"11252","submissionUrl":"https://submission.nature.com/new-submission/11252/3","title":"Urban Ecosystems","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"mega cities, Water poverty, Ecological resilience, Coupling coordination level, influence factor","lastPublishedDoi":"10.21203/rs.3.rs-4534232/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4534232/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWater poverty and ecological resilience in mega cities are not only related to the health and well-being of residents, but also important aspects of achieving sustainable development goals, promoting environmental protection, and international cooperation. This article selects data from 10 mega cities in China from 2013 to 2022 and constructs a coupling coordination model to analyze the coupling relationship and spatiotemporal evolution characteristics between water poverty and ecological resilience in mega cities. In addition, the Tobit model was used to explore the main influencing factors of the above two systems, and the following conclusions were drawn: firstly, from 2013 to 2022, the comprehensive evaluation index of water poverty and ecological resilience in Chinese mega cities showed a dynamic upward trend. Secondly, during the period of 2013-2022, the degree of coupling and coordination between water poverty and ecological resilience in China's mega cities has become increasingly high, with Wuhan being in a highly coordinated area in 20222. Thirdly, in terms of influencing factors, economic development level, industrial structure, and technological innovation have a positive impact on the coupling and coordination level of water poverty and ecological resilience. The negative impact of population density and urbanization level on the coupling and coordination level of water poverty and ecological resilience.\u003c/p\u003e","manuscriptTitle":"Analysis of spatiotemporal evolution and influencing factors of water poverty and ecological resilience coupling coordination in Chinese mega cities","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-25 05:30:46","doi":"10.21203/rs.3.rs-4534232/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorAssigned","content":"","date":"2024-06-11T02:30:29+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-06-10T11:11:23+00:00","index":"","fulltext":""},{"type":"submitted","content":"Urban Ecosystems","date":"2024-06-05T12:51:05+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"urban-ecosystems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ueco","sideBox":"Learn more about [Urban Ecosystems](https://www.springer.com/journal/11252)","snPcode":"11252","submissionUrl":"https://submission.nature.com/new-submission/11252/3","title":"Urban Ecosystems","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"4f93222a-6481-4b61-8f28-434f729277ea","owner":[],"postedDate":"June 25th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-06-25T05:30:46+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-25 05:30:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4534232","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4534232","identity":"rs-4534232","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}