Coupling coordination relationship and influencing factors between green water use efficiency and new urbanization in the Huaihe River Ecological Economic Belt, China

Water resources, as one of the crucial natural resources for human society, their sustainable utilization is directly related to urban construction, public health, ecological quality, and social stability (Gain et al. 2016; He et al. 2021; Pallavi et al. 2021). Green water-use efficiency (GWUE), which incorporates water pollution into the water resource input–output analysis framework (Zhang et al. 2021; Yue et al. 2023), is a comprehensive indicator for assessing the sustainable use of water resources (Hu et al. 2023). Enhancing GWUE is an inherent prerequisite for transitioning from extensive and inefficient to green and efficient water resource utilization, as well as water security.

Over the past 40 years of reform and opening-up, China's urbanization process has evolved swiftly, becoming a major event in the history of global urban development (Zhang et al. 2022a). Nevertheless, disorderly urban sprawl, inefficient land use, and concentrated population aggregation have generated a series of water resource concerns, including inadequate water, worsening water contamination, and diminishing aquatic ecology (Bao & Fang 2012; Liu et al. 2018; Ba et al. 2023), which has strained the relationship between GWUE and urbanization. In response to this grim situation, the Central Committee of the Communist Party of China (CPC) and the State Council issued the National New Urbanization Plan (2014–2020) in 2014, vigorously promoting new urbanization (NU) construction. Compared with traditional urbanization, NU emphasizes the importance of ecological environment protection, suggesting that urban construction should not only focus on socio-economic development but also create a good living environment (Lang et al. 2016; Chen et al. 2019). The report of the 20th National Congress of the CPC pointed out that ‘we will make concerted efforts to improve aquatic environments, water resources, and aquatic ecosystems, strengthen ecological conservation of major rivers, lakes, and reservoirs’. Therefore, under the backdrop of a new era of urbanization and green development in China, exploring the spatio-temporal evolution and influencing mechanisms of the coupling coordination between GWUE and NU is of great necessity for attaining integrated development of ‘water-city’ relations.

The extant literature has performed substantial research on the interactive relationship between water resources and urbanization, mainly focusing on three aspects: first, the impact of water resources on urbanization, where most literature suggests that water resources impose constraints on urban development. For example, Bao & Fang (2007) found that water resources would represent a significant barrier to slow down the urbanization process when water resource consumption neared to exceeded its' carrying capacity. An et al. (2018) indicated that water usage had obvious regional spillover effects on the restriction of urbanization. Ren & West (2023) assessed the urbanization effect of the Clean Water Act in the US, and discovered that improving the water environment will promote the living conditions in rural regions and increase the development of rural urbanization.

Second, the impact of urbanization on water resources involves multiple perspectives. For example, in terms of water supply, Nayan et al. (2020) and Balha et al. (2020) applied land use and land cover data to discuss the impact urban expansion on surface and groundwater resources in Hyderabad and Delhi, India, respectively. They all found that disorderly urban expansion exacerbated the scarcity of urban water resources. Based on the case of Atlanta in the US, however, Kalhor & Emaminejad (2019) found that the impact of urban development on groundwater levels was unclear. In terms of water demand, Niva et al. (2020) concluded that rapid and long-term urbanization has greatly constrained the availability of water resources in China and exacerbated water inequality in food and energy sectors. Wang et al. (2022a) used the spatial Durbin panel model to explore the spillover effects of NU on regional water footprints. In terms of water environment quality, Wang et al. (2020), Yu et al. (2023), and Liu & Guo (2023) found that there was an obvious nonlinear relationship between urbanization and water quality. In the early stages of urbanization, the impact on water pollution may be significant, but in the later stages of urbanization, the impact tends to be reduced or even insignificant. In terms of water ecological services, Wu et al. (2024) concluded that the expansion of urban land use scale has spatial dependence and spillover effects on water ecosystems.

Third, coupling coordination relationship between water resources and urbanization. Coupling coordination reflects whether two or more systems are properly matched and form a virtuous cycle relationship (Dong & Li 2021; Guo et al. 2022). Several studies have utilized the coupling coordination degree (CCD) model to study the mutual link between them. For example, Ma et al. (2022) used over 30 years of temporal data from Nanjing, China, to analyze the CCD between urbanization and water environment. In addition, the CCD between urbanization and water resources use efficiency was investigated by Wang et al. (2022b). Bi et al. (2023) carried out initial research on the CCD between urbanization and water-related ecosystem services.

In summary, studies on the relationship between water resources and urbanization in different regions, such as nations, provinces, cities, urban agglomerations, arid areas, and watersheds, have yielded rich results, providing references for this study. However, there are still deficiencies: First, most important papers concentrate on the unidirectional impact of urbanization on water resources, while relatively few analyze the interactive relationship between them from a coupling coordination perspective. Second, the measurement of water resources often uses single indicators such as total volume, carrying capacity, and water ecological environment, or constructs an indicator system for comprehensive evaluation by using the entropy method, but relatively few analyze the interactive relationship between GWUE and urbanization from an efficiency perspective. Third, in terms of urbanization measurement, previous studies have mostly focused on ‘quantity’ aspects, such as urban land expansion and population aggregation. However, there is less attention paid to ‘quality’ and ‘ecological’ dimensions of ‘new’ urbanization. Fourth, existing studies lack exploration of the influencing factors and spillover effects of the coupling coordination development of GWUE and NU. Given these gaps, this paper takes the Huaihe River Ecological Economic Belt (HREEB) as the research area and analyzes the coupling coordination mechanism of GWUE and NU. Based on this, we develop an assessment index system for GWUE and NU at the city scale, and conduct a quantitative analysis of the evolutionary pattern and influencing factors of the ‘water-city’ coupling coordination relationship in the HREEB from 2011 to 2020.

The remaining sections of this paper are arranged as follows. The next section goes over the study area and data source, followed by the research methods. Next, we examine the spatio-temporal evolution characteristics of GWUE and NU, as well as conduct a CCD analysis of GWUE and NU. Finally, we present the conclusions and policy implications.

The HREEB occupies a significant place in China's economic development. By the end of 2020, its permanent population was 161 million, accounting for 11.42% of the national total, and its GDP was 8.87 trillion yuan, comprising 8.79% of the national GDP. However, the region still faces prominent issues in urban development and water ecological construction, including lagging infrastructure, slow industrial structure upgrading, and a long-term low level of urbanization rate, which was only 54.48% in 2020, far behind the national average. In addition, the region suffers from water resource scarcity and uneven distribution, substantial water contamination, and low water resource usage efficiency, emphasizing the conflict between the humans and water. In 2018, the State Council announced the Development Plan for the HREEB, China (hereinafter referred to as ‘the Plan’), which recognized it as a national demonstration belt for ecological civilization construction in river watersheds and NU. Therefore, a study on the coupling coordination of GWUE and NU in HREEB holds significant reference value for China's coordinated watershed water resource governance and high-quality urban construction.

The study covers 28 cities in the HREEB. Due to changes in the statistical caliber of chemical oxygen demand (COD) and ammoniacal nitrogen (NH3-N) from 2011, the study period is set from 2011 to 2020. Data on GWUE and NW evaluation index system and data involved in the impact model are mainly derived from the China City Statistical Yearbook, the China Urban Construction Statistical Yearbook, the China Water Resources Bulletin, and statistical yearbooks covering provinces and cities. Missing data for specific years are supplemented using interpolation and extrapolation methods. GDP, per capita GDP, and other relevant economic data are converted based on the base year of 2011.

Traditional urbanization is primarily concerned with population urbanization, as seen by the steady increase in the proportion of non-agricultural population. However, a single indicator fails to capture the rural-to-urban population transition process, nor does it measure the essence of NU. Compared with traditional urbanization, NU emphasizes a people-centered approach and the organic integration of quantity, quality, and ecology in urban development. Combining the connotation of NU, with data availability and referring to existing studies (Lin & Zhu 2021; Feng et al. 2022; Chen et al. 2023; Cheng & Wang 2023), the NU evaluation index system is constructed from six dimensions: economic urbanization, population urbanization, social urbanization, spatial urbanization, ecological urbanization, and urban–rural integration (Table 1). To ensure data comparability, we employ range standardization to remove errors caused by secondary indicators and the entropy approach to calculate weights (Table 1).

The spatial distribution maps for GWUE in the cities of the HREEB are drawn for 2011 and 2020 using critical values of 0.4, 0.6, 0.8, and 1.0, dividing them into five levels: low efficiency, relatively low efficiency, medium efficiency, relatively high efficiency, and high efficiency.

Overall, there have been some changes in the spatial distribution of GWUE, but the general trend remains stable. The downstream areas in Jiangsu, like the Yangzhou, Taizhou, and the southern Shandong region have consistently maintained high efficiency. Shandong Province has been implementing the strictest water resource management system, with policies such as ‘water allocation to counties’ and promoting water rights market reforms (Zhang et al. 2023), effectively enhancing sustainable water resource utilization. In Jiangsu Province, the river chief system was creatively proposed as early as 2007, and by 2012, the ‘Wuxi Experience’ was extended province-wide, effectively suppressing industrial water pollution (Li et al. 2020). The cities, being more vulnerable to flood threats, along the Huaihe River main course generally have lower GWUE. The flood control and storage projects in these cities often suffer from inadequate facilities, low safety standards, and lagging management, exacerbating conflicts between humans, water, and land.

The spatial distribution maps for NU in the cities of the HREEB are drawn for 2011 and 2020 using critical values of 0.4, 0.5, 0.6, and 0.7, dividing them into five levels: low, relatively low, medium, relatively high, and high.

Overall, the NU of the HREEB has significantly improved. 67.86% of the cities in the region have achieved an upward shift in their level, mainly transitioning from low and lower levels to medium (progressive) and relatively high levels (leapfrogging).

As shown in Table 3, Models 1–4 introduce four independent variables: industrial structure, economic openness, technological level, and water resource management. The R² value increases from 0.283 to 0.554, indicating that as more independent variables are included, the explanatory power of the model becomes stronger, and the significance of each variable further confirms the theoretical analysis above. Specifically, without considering spatial characteristics, the regression coefficient for industrial structure is significantly positive, suggesting that rationalizing industrial structure and increasing the proportion of the tertiary sector contribute to the coupling coordination development of GWUE and NU. The positive regression coefficient for economic openness indicates that strengthening foreign capital introduction and regional integration into the international market have a significant benefit on CCD. The positive coefficient for the technological level indicates that regional technological advancement is a crucial driver in mitigating the ‘water-city’ conflict. The significant negative coefficient for water resource management suggests that strengthening strict control and avoiding waste of water resources promotes the coordinated development of a ‘water-city’ in the HREEB.

In Table 3, Models 5–7 respectively represent the fitted results for the upper, middle, and lower reaches of the HREEB. The impact of industrial structure varies significantly across these three regions. It is not significant in the upper reaches, negatively significant in the middle reaches, and positively significant in the lower reaches. This is attributable to the fact that cities in the upper and middle reaches, being resource-based, have a relatively slow pace in upgrading their industrial structures, thereby exacerbating water shortage and pollution issues. In contrast, the lower reaches, benefiting from favorable locations, especially in the northern Jiangsu and southern Shandong regions, have continually optimized and upgraded their industrial structures after undertaking industrial transfers from the Yangtze River Delta and Jiaodong Peninsula. Economic openness only has a significant positive impact in the lower reaches, indicating that cities in the inland upper and middle reaches need to create a favorable business environment, continuously improve infrastructure conditions, and deepen economic and trade relations. The influence of technological level varies across the three regions: it is not significant in the upper reaches, positively significant in the middle reaches, and negatively significant in the lower reaches. Since the 18th CPC National Congress initiatives, Anhui Province has vigorously implemented the strategy for invigorating the province through science and education with the workforce development strategy, effectively promoting technological innovation. Consequently, the northern Anhui region in the upper reaches has seen a significant enhancement in technological capabilities, positively driving the coupling coordination of GWUE and NU. However, in the lower reaches, including the southern Shandong regions, northern Jiangsu, and the Yangzhou–Taizhou region, economic development is weaker within their respective provinces. Water resource management in these three regions has shown significant impacts. The main reason is that the central and local governments at all levels have always attached great importance to the governance and protection of water resources in the HREEB, established a rigid constraint system for water resources, and promoted collaborative governance and management of the upper and middle reaches, the left and right banks, and the main and tributaries. As a result, the entire basin has strong governance and management capabilities.

• (1) Spatial autocorrelation test. This paper employs the global Moran's I index for evaluation, considering W₁, W₂, W₃, and W₄. As shown in Table 4, under four types of weights, the global Moran's I indices for CCD of cities in the HREEB from 2011 to 2020 are significantly positive. This implies a positive spatial autocorrelation in CCD among cities, with high (or low) CCD cities showing a relatively clustered spatial distribution pattern, and the spatial spillover effect is apparent.

• (2) SDM analysis. To determine whether SDM can be simplified to spatial lag model (SLM) and spatial error model (SEM), Wald and LR tests can be utilized. As seen in Table 5, regardless of whether W₁, W₂, W₃, or W₄ is selected to construct the weight, both SLM and SEM pass the significance level test for Wald and LR, indicating that SDM cannot be simplified to SLM and SEM.

Table 6 presents the estimated results for the SDM. Compared to ordinary regression results, the only difference lies in economic openness; other independent variables remain consistent. This suggests that considering spatial factors, internal regional elements have a more pronounced impact on CCD.

Table 7 represents the direct and indirect effects of SDM. The direct effect of industrial structure is significantly positive, while the spillover effect exhibits different impact results under four types of weights. This signifies that industrial structure positively impacts CCD of local cities, but may have an uncertain impact on neighboring cities. Except for W₄, the indirect effect of economic openness is significantly positive, with an insignificant direct effect, indicating that local openness only promotes the integrated development of ‘water cities' in neighboring areas. The direct effect of technological level is significantly positive, with an uncertain indirect effect, suggesting that improvements in technological innovation primarily benefit the local area. The direct effect of water resource management is significantly positive. In contrast, its spillover effect is significantly negative, indicating that as local city water resource governance strengthens, it significantly enhances the city's integrated development level, but creates a ‘beggar-thy-neighbor’ effect on surrounding cities, hence necessitating the establishment of effective cross-regional joint defense mechanisms.

• (1) The GWUE of the HREEB is generally low and shows a slight downward trend, with 2018 being a pivotal turning point. Spatially, the GWUE displays a ‘low in the middle, and high in the periphery’ pattern. High-efficiency areas located in the lower reaches, specifically in the Yangzhou–Taizhou region and the southern Shandong region, and in the upper reaches, notably Pingdingshan and Luohe, and areas of lower efficiency are expanding towards the east and west ends of the Huaihe River mainstream.

• (2) The NU of the HREEB is showing a favorable development trend, with accelerated development in the upper and middle reaches. Spatially, NU shows a ‘high in the northeast, and low in the southwest’ pattern, with 67.86% of the cities achieving a level jump, mainly transitioning from low and lower levels to medium (sequential) and higher levels (leapfrogging).

• (3) The CCD between HREEB and NU overall presents a growing trend, transitioning from basic coordination to good coordination. A significant sequential transition trend in CCD types is observed, with the proportion of cities above the low coordination type rising from 57.14 to 96.43%. The proportion of low coordination cities has significantly decreased from 35.72 to 3.57%, with most cities at the basic coordination stage, indicating an increasingly deep integration of ‘water-city’ relations in the HREEB. Spatially, high-value areas are mainly located in the Yangzhou–Taizhou region and the Southern Shandong region, while low-value areas are concentrated in the upper and middle reaches along both sides of the mainstream. Spatial disparities still exist, suggesting that the upper and middle reaches should reasonably utilize water resources, enhance water resource allocation capabilities, maintain a healthy aquatic ecological environment, focus on ‘people-centered’ urban construction, continuously optimize industrial structure, and improve public facility services.

• (4) Factors such as industrial structure, economic openness, technological level, and water resource management all have a significant impact on CCD, but the main influencing factors vary across the upper, middle, and lower reaches. For instance, industrial structure and openness level only positively affect CCD in the lower reaches, while the technological level only positively affects CCD in the middle reaches. Due to spatial interactive effects in coupling coordination, the SDM analysis reveals that economic openness has a positive spillover effect on CCD, while water resource management exhibits ‘beggar-thy-neighbor’ effect.

First, it's imperative to deepen regional collaboration and joint governance, aiming to achieve a synergistic force for coordinated development. The boundary effects between cities and administrative limitations significantly influence the spatial clustering of the CCD, with notable negative spillover effects in water resource management. Hence, governments at all levels within the HREEB must cooperate to break down barriers and jointly advance the efficient utilization of water resources and urbanization construction.

Second, tailored development and governance strategies should be implemented in line with the unique characteristics of the upper, middle, and lower reaches. The lower reaches, located in the eastern coastal area with distinct locational advantages and better economic development, are in a stage of good coordination. Leveraging their strengths, continuous efforts should be made to enhance the training of water conservancy technical personnel and innovation in water conservancy science and technology. Emphasis should be placed on industrial wastewater treatment and purification of urban residential sewage to improve the living environment and focus on urban-quality construction. The upper and middle reaches, located in the central region with lagging urban construction and relatively poor economic conditions, are home to many resource-based cities highly dependent on resources and energy. As these areas are at a stage of basic coordination, they should optimize their industrial structure according to the local ecological carrying capacity, strengthen the development of emerging industries and modern service industries.

At last, it is crucial to actively integrate into the domestic and international dual circulation and achieve a higher level of openness. As discussed earlier, openness significantly contributes positively to coupling coordination. It is evident that on the one hand, the HREEB should actively integrate into the international market, and enhance water resource management experience and innovative technology. On the other hand, it is crucial to strengthen in-depth cooperation with surrounding regions such as the Yangtze River Delta and the Beijing–Tianjin–Hebei region, promoting the free flow of innovative elements such as talents, technology, and knowledge. Under the effective radiating drive of high-development-level areas, there should be a steady promotion of the coexistence and development of GWUE and NU in the HREEB.

The authors acknowledge the financial support granted by the Philosophy and Social Science Research Fund Project in Anhui Province (No. AHSKQ2022D049).

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.