Coupling Coordination Between New-Type Urbanization and Water Use Efficiency: A CAS-Based Feedback Perspective Analysis of Jiangxi and Hunan Provinces, China

Abstract

Under tightening resource and environmental constraints, exploring the synergy between new-type urbanization and water use efficiency is key to high-quality development in Central China. Using panel data for cities in Jiangxi (11) and Hunan (13) from 2013 to 2022, this study measures the spatiotemporal evolution characteristics of water-coupling coordination in the two provinces. It also incorporates feedback mechanisms from Complex Adaptive Systems (CAS) theory to explain the differences in the evolutionary trajectories of the two provinces from three dimensions: signal strength, transmission efficiency, and bottleneck nodes. The results show the following: (1) The urbanization processes in these two provinces have shifted from a phase of rapid growth to one of steady optimization, and water use efficiency has improved in both. Since 2016, Jiangxi Province has experienced fluctuations, moving from a decline to a recovery, while Hunan Province has seen a steady increase; however, disparities among regions within the provinces persist. (2) The evolutionary trajectories of coupling coordination between the two provinces show marked divergence: Hunan exhibits a pattern of “core responsiveness and peripheral lag,” while Jiangxi displays a “multi-point gradual response”. (3) The barriers in the two provinces are highly similar in nature, focusing on economic scale, capital investment, and labor. Although the same constraints exist, different response pathways lead to distinct patterns—Jiangxi’s passive adaptation results in a diffuse equilibrium, while Hunan’s proactive adjustment results in a polarized gradient. These findings identify two distinct pathways for urban-water system evolution in central China’s water-rich areas, advancing theoretical understanding of system adaptation while providing targeted policy references for regional high-quality development.

1. Introduction

China is one of the fastest urbanizing countries in the world; its urbanization rate reached 65.2% in 2022. In response, the country has proposed a “new-type urbanization” strategy centered on “humanization, intensification, and greening” to promote urban–rural integration and high-quality development. However, mounting resource and environmental pressures—particularly water scarcity, pollution, and ecological strain—pose critical challenges. As urbanization drives population, industrial, and spatial expansion, water resources emerge as both a fundamental constraint and a strategic focus. The 14th Five-Year Plan’s New-Type Urbanization Implementation Plan clearly states that China must adhere to the principle of “water determines the city, water determines the land, water determines the population, and water determines the production.” This means optimizing urban spatial layout, industrial structure, and population size in accordance with the carrying capacity of water resources. Thus, the achievement of efficient water resource utilization during urbanization has become a key focus for academia [1].

Previous research on this topic has focused on three areas: water use efficiency measurement, new-type urbanization evaluation, and the coordination between urbanization and water systems. Both domestic and international scholars have established a relatively systematic research foundation in the study of water resource utilization efficiency. Early studies mainly used stochastic frontier analysis (SFA) and data envelopment analysis (DEA) to measure efficiency in agricultural [2,3], industrial [4], and urban water systems [5]. With the development of green concepts, research has shifted from traditional single-output efficiency to green efficiency that incorporates resource use, pollution, and overall performance. Compared to traditional DEA and SFA models, the Super-Efficient SBM model incorporates unintended outputs, allowing the efficiency values of efficient units to exceed 1. Its core advantage lies in its ability to distinguish and rank multiple “efficient” decision units. The distance function (DDF) allows for different directions of change to be set for different outputs. EBM not only reflects the radial proportional characteristics of inputs and outputs but also accommodates slack improvements for each factor. Clearly, these methods offer greater advantages in handling unanticipated outputs, multiple inputs and outputs, and slack variables, and are therefore widely used in measuring regional water use efficiency [6,7,8,9]. Some researchers have expanded from static efficiency measurement to policy effect identification [10]. And other scholars, from the perspective of high-quality development, have constructed indicator systems covering high-quality economic development, water use efficiency, and green finance, revealing that water use efficiency plays a partial mediating role between green finance and the promotion of high-quality economic development [11]. Research on water use efficiency is no longer limited to the use of resources but is progressively linked to the institutional environment, green transformation, and regional high-quality development [12,13,14].

Scholars who research new-type urbanization have largely moved beyond the traditional understanding centered on scale expansion and population agglomeration, placing greater emphasis on a quality-oriented approach characterized by “people-orientation, green and low-carbon development, and coordinated and shared benefits”. International studies often understand high-quality urbanization from perspectives such as social equity [15,16], livability [17,18,19], environmental friendliness [20,21,22], risk resilience [23,24] and smart governance [25,26]. Domestic research in China, by contrast, focuses mainly on people orientation [27,28], sustainability [29], and synergy [30], arguing that new-type urbanization is not merely a process of population concentration in cities and towns, but rather the outcome of the coordinated evolution of multiple dimensions—population, economy, society, space, and ecology. Based on this understanding, research has gradually shifted from single-indicator evaluations of urbanization to multi-dimensional comprehensive assessment frameworks, and an increasing number of scholars are paying attention to the interactive relationship between new-type urbanization and the resource–environment system [31,32].

The relationship between urbanization and water use efficiency is one of the core issues addressed by the United Nations Sustainable Development Goals (SDG 6.4 and SDG 11.3). Studies on the relationship between urbanization and water resources generally agree that there is a significant two-way relationship between them. On the one hand, water serves as a fundamental resource underpinning urban production, daily life, and ecosystem functioning; its availability, quality, and utilization efficiency directly shape a region’s capacity to sustain urbanization [33,34]. On the other hand, limited water resources and low environmental carrying capacity can severely constrain the pace and scale of urbanization [35,36,37]. Rapid urbanization may not only increase water demand and exacerbate water environment pollution, it may also promote intensive water resource utilization through technological progress, industrial upgrading, and improved governance capacity [38,39]. Therefore, the relationship between urbanization and water resources is not simply one of promotion or constraint, but rather a compound interactive process involving both synergy and conflict. On this basis, the coupling coordination degree model [40,41,42] has gradually become an important tool for analyzing the relationship between the two. In recent years, scholars have further extended from “coupling state identification” to “obstacle factor diagnosis” [43,44,45]. Compared with studies that only depict the level of coupling coordination, this research further reveals the underlying reasons for hindered system synergy, enhancing its explanatory power and practical relevance, and provides effective methodological support for identifying key bottlenecks in regional coordinated development.

Overall, existing research has made considerable progress in terms of methodological frameworks, indicator construction, and the identification of systemic relationships, thereby providing a solid foundation for this study. However, further expansion is still needed. First, with regard to research subjects, most existing studies have focused on regions such as the Yellow River Basin, the Yangtze River Delta, the Yangtze River Economic Belt, and the arid and semi-arid areas of Northwest China, while comparative studies of Central China and its neighboring provinces remain relatively scarce. Internationally, there is a relative lack of research on typical regions where rapid urbanization coexists with water resource conflicts. Second, in terms of research content, the existing literature has devoted greater attention to the relationships between urbanization and water resource carrying capacity, water resource development and utilization, or the water environment. Such studies often “emphasize measurement over mechanisms” or “prioritize theory over empirical evidence”. Furthermore, there is still a noticeable gap in research that directly incorporates both “new-type urbanization” and “water use efficiency” into a unified framework for coupling and coordination analysis. Third, in terms of research depth, although the application of the coupling and coordination model is relatively mature, most studies remain limited to describing the level of coordination. They lack a mechanistic explanation of how changes in the system’s state feed back to the actors driving urbanization and influence their subsequent decisions, which hinders the identification of the root causes of regional divergence and the development of differentiated governance strategies. Fourth, in terms of practical needs, against the policy backdrop of the national strategy of “using water to determine city scale, land use, population size, and industrial output,” there remains a lack of systematic empirical research focusing on Hunan and Jiangxi Provinces regarding how to optimize the urbanization layout and development pathways of the central region based on water resource carrying capacity.

Both the new urbanization system and the water resource utilization system are dynamic and adaptive; the entities within each subsystem can adjust their behavior in response to changes in water resource conditions. Based on this, this paper introduces feedback mechanisms from the theory of complex adaptive systems and incorporates the results of identifying coupling coordination levels and obstructing factors into a framework for analyzing the strength of feedback loops, with the aim of advancing our understanding of the system’s evolutionary mechanisms based on a characterization of its state. Therefore, this study takes Hunan and Jiangxi Provinces as its research subjects. Building upon an evaluation system for new-type urbanization and water resource utilization efficiency, the study introduces a coupling coordination model and an obstruction model. From the perspective of feedback mechanisms, it systematically examines the temporal evolution characteristics, regional differences, and key constraining factors of these two systems. The aim is to reveal the differentiated pathways for the coordinated development of new-type urbanization and water use efficiency under resource and environmental constraints in central China, with the goal of advancing understanding of the mechanisms underlying system evolution based on state characterization, and providing empirical evidence and policy references for promoting regional high-quality development and ecological civilization construction.

2. Overview of the Study Area and Data Sources

2.1. Overview of the Study Area

Jiangxi and Hunan Provinces are located in Central China at the core of the Middle Yangtze River Urban Agglomeration, and serve as key pillars in China’s “Rise of Central China” strategy and the high-quality development of the Yangtze River Economic Belt. Jiangxi Province is located on the southern bank of the middle and lower reaches of the Yangtze River, between latitudes 24°29′–30°04′ N and longitudes 113°34′–118°28′ E. The region’s topography is dominated by mountains and hills, with the terrain generally higher in the southeast and lower in the northwest. The province is home to Poyang Lake, a critical ecological hub. Five major rivers—the Gan, Fu, Xin, Rao, and Xiu—flow into Poyang Lake. While the total amount of water resources is relatively high, the region is significantly influenced by a monsoon climate, resulting in substantial interannual fluctuations and pronounced spatial and temporal disparities in water distribution. Hunan Province is located on the southern bank of the middle reaches of the Yangtze River, between latitudes 24°38′–30°08′ N and longitudes 108°47′–114°15′ E. It has a basin-like topography that is “enclosed on three sides by mountains and open to the north.” The “Four Rivers”—the Xiang, Zi, Yuan, and Li—flow into Dongting Lake, creating a complex water resource system. The region experiences alternating floods and droughts, resulting in significant pressure on water resource regulation and utilization. Overall, the geographical configurations of the two provinces are highly similar, characterized by mountains on three sides, an opening to the north, and a river–lake system at the center. Each province has a major river running north to south (the Gan River in Jiangxi and the Xiang River in Hunan) that flows into a lake and then into the Yangtze River. The provincial capitals of Changsha and Nanchang are both located in the north-central part of their respective provinces, on the edge of the plains downstream of the main rivers. Both provinces have seen steady progress in industrialization and urbanization in recent years. There has been a clear trend of population concentration in urban areas, continuous optimization and upgrading of industrial structures, and steady improvement in the level of new-type urbanization. By 2022, the urbanization rates of Jiangxi and Hunan had reached 64.8% and 59.96%, respectively, and the share of the tertiary sector in both provinces did not exceed 52%. However, amid rapid urbanization, constraints on resources and factors of production have gradually tightened. In particular, the imbalance between water supply and demand has become increasingly pronounced, the pressure on water environmental capacity has continued to grow, and traditional extensive development models face pressure to transform.

Jiangxi Province and Hunan Province share strong similarities in terms of their stages of development, geographical location, policy environments, and natural endowment of water resources; however, they exhibit certain differences in terms of spatial differentiation and conditions for development and utilization (

Table 1. Comparison of Main Characteristics between Hunan and Jiangxi Provinces

		Indicator		Hunan Province			Jiangxi Province
Similarities		Geographic Location		As landlocked provinces located in central China, neither of the two provinces borders the sea nor adjoins international frontiers, and consequently they face comparable geographical conditions.
		Policy Background		Both provinces undertake national strategies including new-type urbanization, Yangtze Economic Belt development, and Yangtze River protection, and serve as key ecological function zones.
		History and Culture		Both are old revolutionary base areas with abundant revolutionary heritage.
		Geographic Factors		The two provinces are separated by the Luoxiao Mountains, exhibiting a highly symmetrical, semi-enclosed geomorphological configuration. Both are flanked by mountain ranges on their eastern, western, and southern peripheries, while opening northward toward the Yangtze River. They are both situated within the Jiangnan Hills region and share a common southern border with Guangdong Province.
		Resource Endowment	Water Resources	Both have a mid-subtropical humid monsoon climate with abundant rainfall and water resources. Hunan has Dongting Lake; Jiangxi has Poyang Lake—both are among China’s largest freshwater lakes. The Xiang, Zi, Yuan, and Li rivers flow into Dongting; the Gan, Fu, Xin, Rao, and Xiu rivers flow into Poyang.
			Mineral Resources	Both are famous “nonferrous metal bases” in China, rich in mineral resources.
			Agricultural Resources	Both provinces are well-known as “lands of fish and rice” with abundant produce, and rice serves as the primary staple crop, holding a comparative advantage in their agricultural production.
			Forest Resources	Both have rich forest resources, ranking high nationally in forest biomass, and serve as similar green ecological barriers.
Heterogeneity		Indicator		Hunan Province		Jiangxi Province			China
				2013 year	2022 year	2013 year		2022 year	2013 year	2022 year
		Urban Population		32.09 million	39.83 million	22.10 million		28.11 million	731.11 million	920.71 million
		Urbanization Rate		47.96%	60.31%	48.87%		62.07%	53.73%	65.22%
		GDP per Capita		CNY 36,763	CNY 73,598	CNY 31,771		CNY 70,923	CNY 41,908	CNY 85,698
		Built-up Area		1504.95 km2	2104.99 km2	1151.42 km2		1789.49 km2	47,855.28 km2	63,676.40 km2
		Green Coverage		11.73%	42.32%	10.53%		46.63%	12.72%	42.96%
		Water per Capita		2373.6 m3/person	2546.2 m3/person	3155.3 m3/person		3441.0 m3/person	2059.7 m3/person	1918.2 m3/person

). This “heterogeneity on a homogeneous foundation”—that is, similar natural conditions but vastly different utilization efficiencies—stems primarily from differences in human factors. Its essence lies in the differing feedback sensitivities and response pathways of the two provinces’ urbanization systems toward water resource conditions, which have led to the evolution of divergent patterns of coupling and coordination under similar resource endowments. Consequently, the two provinces serve as typical research areas for exploring the coupling and coordination between new-type urbanization and water resource utilization efficiency, as well as the factors hindering this relationship. They offer valuable insights for similar regions in Central China and across the nation; furthermore, the comparative approach based on differences in feedback sensitivity can also provide a research reference for regions undergoing rapid urbanization worldwide.

2.2. Data Sources

This study investigates 11 prefecture-level cities in Jiangxi Province and 13 prefecture-level cities in Hunan Province (Figure 1) from 2013 to 2022. An evaluation indicator system for new-type urbanization and water use efficiency is constructed. Based on this, coupling coordination and obstacle factors are analyzed. The research data are sourced from the China City Statistical Yearbook, Jiangxi Statistical Yearbook, Hunan Statistical Yearbook, and the statistical bulletins on national economic and social development of the respective prefecture-level cities, as well as the Jiangxi Water Resources Bulletin and Hunan Water Resources Bulletin. Due to significant gaps in the statistical data for the Xiangxi Tujia and Miao Autonomous Prefecture in Hunan Province, this region was excluded from the scope of this study. Linear interpolation was used to fill in missing values in specific years in other study units.

3. Theoretical Framework and Indicator System Construction

3.1. Theoretical Foundation and Research Framework

The concept of Complex Adaptive Systems (CAS) was formally proposed in 1994 by John Holland (J.H. Holland), a scientist at the Santa Fe Institute in the United States. The theory views the members of a system as adaptive agents; in order to adapt to the environment and the characteristics of other members around them, these agents interact with one another and continuously alter their own structures and compositions, ultimately evolving into a new system [46]. Starting from the systemic adaptability of various elements, CAS theory provides a deeper understanding of the interactions and influences between the behavior of these elements and their environment, opening up new perspectives for systems research. CAS are characterized by four fundamental properties—aggregation, diversity, flows, and nonlinearity—and operate through three basic mechanisms: tagging, internal models, and building blocks [47]. The feedback mechanism is a core concept of CAS theory, referring to the continuous, cyclical interaction between an agent’s behavior and the state of the system. In the city–water relationship, the water use behavior of urbanization actors alters the efficiency of water resource utilization, and changes in efficiency, in turn, serve as signals that influence subsequent behavior. Specifically, there is a classic bidirectional coupling relationship between new-type urbanization and water resource use efficiency. New-type urbanization exerts selective pressure on the water resource system through population concentration, industrial development, spatial expansion, and social governance, while changes in the state of the water resource system, in turn, influence the urbanization process through feedback loops; the two exhibit distinct characteristics at different stages of development. In the early stages of urbanization, positive feedback from factor expansion dominated the system—population and industrial agglomeration drove growth in water demand, while investment expansion further reinforced the path of resource consumption, leading to a decline in water use efficiency under mounting pressure. As urbanization advances toward a stage of high-quality development, negative feedback signals—such as water scarcity and tightening environmental carrying capacity—are gradually intensifying. These pressures are driving industrial upgrading, technological innovation, and governance optimization, propelling water use efficiency to achieve an adaptive leap forward. At the same time, water resource utilization efficiency exerts a constraining feedback on the urbanization pattern through pathways such as resource security, ecological support, and governance regulation—improved efficiency alleviates the constraints that resource scarcity places on population agglomeration and mitigates the limitations that environmental carrying capacity imposes on spatial expansion. Conversely, inefficiency reinforces the rigid constraints that resources and the environment place on urbanization, exposing the system to the risk of stagnation and lock-in. Consequently, under the dual influence of scale expansion driven by positive feedback and efficiency constraints moderated by negative feedback, the two have formed a dynamic coupling relationship [48]. The level of coordination within this coupling not only reflects the quality of regional development but also, to a certain extent, reveals the strength and transmission efficiency of the feedback loop in the urban-water system—that is, whether signals of water resource constraints can be effectively perceived by urbanization actors and translated into behavioral adjustments determines whether the system will evolve toward coordination or fall into an imbalance lock-in (Figure 2 and Figure 3).

3.2. Construction of Indicator System

This study builds upon previous studies on the practical development status of new-type urbanization advancement and water resource utilization in Jiangxi and Hunan Provinces [49,50]. The study aims to comprehensively characterize the operational conditions and interactive correlation between the two major systems, using indicators selected based on scientific, systematic, and data accessibility criteria. Specifically, the new-type urbanization system was quantified with five primary indicators, namely population urbanization, economic urbanization, social urbanization, spatial urbanization, and eco-environmental urbanization. In view of the characteristics of input, desirable output, and undesirable output, the water resources utilization efficiency system was constructed by selecting indicators including total water consumption, fixed asset investment, labor input, regional gross domestic product and urban sewage discharge capacity. Among them, the environmental indicators included in the new-type urbanization system reflect the city’s environmental governance capacity and sustainable development level, while the urban sewage discharge within the water use efficiency system represents the environmental pressure generated during the process of water resource utilization and is treated as an undesirable output. These indicators represent different dimensions and do not constitute conceptual duplication (

Table 2. Evaluation index system for new-type urbanization and water use efficiency.

Coupled System	Primary Indicators	Secondary Indicators	Unit	Directionality	Weight
New-type urbanization	population urbanization	urbanization rate of population (U1)	%	+	0.049
		population density (U2)	People/km2	+	0.054
		Share of employment in the tertiary industry (U3)	%	+	0.007
	economic urbanization	per capita gross regional product (U4)	CNY	+	0.032
		local general public budget revenue (U5)	10,000 CNY	+	0.041
		share of secondary and tertiary industry output in Gross Domestic Product (GDP) (U6)	%	+	0.028
	social urbanization	number of hospital and health center beds (U7)	number	+	0.039
		science and technology expenditure (U8)	10,000 CNY	+	0.024
		number of internet users (U9)	number	+	0.055
		total retail sales of consumer goods (U10)	10,000 CNY	+	0.132
		gas penetration rate (U11)	%	+	0.100
	spatial urbanization	completed real estate development investment (U12)	10,000 CNY	+	0.151
		per capita urban road area (U13)	m2/capita	+	0.110
	environmental urbanization	green coverage rate of built-up areas (U14)	%	+	0.068
		per capita park green space area (U15)	m2/capita	+	0.088
		comprehensive utilization rate of general industrial solid waste (U16)	%	+	0.018
		harmless treatment rate of domestic waste (U17)	%	+	0.003
Water use efficiency	input	total water consumption (X1)	100 million m3	+	0.181
		fixed asset investment (X2)	10,000 CNY	+	0.224
		labor input (X3)	person	+	0.267
	output	gross regional product (Y1)	10,000 CNY	+	0.310
		municipal waste water discharge (Y2)	100 million m3	-	0.019

).

Within the framework of the feedback mechanism, indicators across various dimensions of new-type urbanization reflect the selective pressures exerted by the subject on the water resources system, while input–output indicators of water use efficiency capture the efficiency feedback signals generated by the system’s response. The combination of these two types of indicators provides a data foundation for subsequently interpreting the results of the coupling coordination degree calculations from a feedback perspective.

4. Research Methods

4.1. Entropy Weight Method

To mitigate the influence of subjective human bias inherent in subjective weighting approaches, this study employs the entropy weight method to measure the level of new-type urbanization. Assuming m evaluation objects and n indicators within the index system, the original data matrix is $A={\left(a_{ij}\right)}_{m\times n}$. Given that the original indicator data differ in attribute orientations and dimensional units, direct comparative analysis cannot be conducted. Hence, raw data were preprocessed using the range standardization method.

Formula for positive indicators:

$$Z_{ij}={\displaystyle \frac{a_{ij}-min{(a}_j)}{max\left(a_j\right)-min\left(a_j\right)}}$$

(1)

Formula for negative indicators:

$$Z_{ij}={\displaystyle \frac{max{(a}_j)-a_{ij}}{max\left(a_j\right)-min\left(a_j\right)}}$$

(2)

Here, max(a_j) represents the maximum value of the j-th indicator, min(a_j) denotes the minimum value of the j-th indicator, and Z_ij stands for the standardized value of a_ij. If some standardized values were equal to zero, data were normalized using Z_ij = Z_ij + d, with d set to 0.0001. Following the normalization of the evaluation indicators, the entropy values, weights and the level of new-type urbanization are calculated using the following formulas:

$$e_j=\left(-{\displaystyle \frac{1}{\ln m}}\right)\sum _{i=1}^m\left(b_{ij}\ln b_{ij}\right), j=1,2,3,\dots ,n, 0\le e_j\le 1$$

(3)

$$w_j={\displaystyle \frac{1-e_j}{{\displaystyle {\sum }_{j=1}^n}\left(1-e_j\right)}}$$

(4)

$$S_j={\sum }_{j=1}^n{w}_j\times Z_{ij}$$

(5)

4.2. Super-Efficiency SBM Model

When using the super-efficiency SBM model to measure water use efficiency, each city was considered as a decision-making unit (DMU). Suppose there are n decision-making units, each with m input indicators, s₁ expected outputs, and s₂ undesirable outputs. x: input matrix; y^g: expected output matrix; y^b: unexpected output matrix; ρ: water use efficiency of the k-th DMU. The larger the ρ value, the higher the level of water use efficiency. The model is as follows:

$${\rho }^{*}=min{\displaystyle \frac{\frac{1}{m}{\sum }_{i=1}^m\frac{\overline{x_i}}{x_{i0}}}{\frac{1}{s_1+s_2}\left({\displaystyle {\sum }_{l=1}^{s_1}}\frac{{\overline{y}}_l^g}{y_{l0}^g}+{\displaystyle {\sum }_{k=1}^{s_2}}\frac{{\overline{y}}_k^b}{y_{k0}^b}\right)}}$$

(6)

$$s.t.\left\{\begin{array}{c}\overline{x}\ge {\displaystyle \sum _{j=1,\ne 0}^{\mathit{n}}}{\beta }_j{x}_{ij}\\ {\overline{y}}^g\le {\displaystyle \sum _{j=1,\ne 0}^{\mathit{n}}}{\beta }_j{y}_{lj}^g\\ {\overline{y}}^b\ge {\displaystyle \sum _{j=1,\ne 0}^{\mathit{n}}}{\beta }_j{y}_{kj}^b\\ \overline{x}\ge x_0,{\overline{y}}^g\le y_0^g,{\overline{y}}^b\ge y_0^b,{\overline{y}}^g\ge 0\\ {\beta }_j\ge 0,i=1,2,\dots \dots ,m;j=1,2,\dots \dots ,n;l=1,2,\dots \dots ,s_1;k=1,2,\dots \dots ,s_2\end{array}\right.$$

(7)

4.3. Coupling Coordination Model

The coupling coordination model was employed to measure the interactive relationships and interactions among multiple systems. Its core function is to quantify both the degree of coupling and the level of coordination between systems. The degree of coupling reflects the strength of interaction within a given system or the extent of interdependence among its internal elements, whereas the coupling coordination degree characterizes the quality or harmonization level of such interactions. Based on existing studies [51], considering the characteristics of the Coupling Coordination Degree (CCD )and the comprehensive evaluation index of each system, the coupling coordination types between new-type urbanization and water use efficiency in Jiangxi and Hunan Provinces are comprehensively characterized. A coupling coordination model was then constructed to evaluate the coordinated development level between new-type urbanization and water use efficiency in Jiangxi and Hunan Provinces. The calculation formulas are as follows:

$$C=\sqrt{{\displaystyle \frac{s_j\times {\rho }^{*}}{{\left[\left(s_j+{\rho }^{*}\right)/2\right]}^2}}} T=\alpha S_j+\beta {\rho }^{*}$$

(8)

$$D=\sqrt{C\times T}$$

(9)

where $s_j$ and ${\rho }^{*}$ represent the comprehensive evaluation values of new-type urbanization and water use efficiency, respectively; $C$ is the coupling degree between the two systems; $T$ is the comprehensive evaluation index of the systems; $\mathrm{D}$ is the coupling coordination degree; and $\alpha$ and $\beta$ are the weight coefficients corresponding to new-type urbanization and water use efficiency. Due to the equivalence of the coordinated development of new-type urbanization and water use efficiency, the values of $\alpha$ and $\beta$ are set to 0.5. To establish a classification standard, this study draws on existing research results to define the threshold intervals for the coupling coordination degree [52,53,54], Furthermore, through an in-depth analysis of the characteristics of the system comprehensive evaluation indices, the CCD can be classified into three categories: when D ≤ 0.4, the coupling coordination type is classified as the disorderly recession period; when 0.4 < D ≤ 0.6, the coupling coordination type is classified as the transitional transformation period; and when 0.6 < D ≤ 1, the coupling coordination type is classified as the coordinated development period, as shown in

Table 3. Classification of coupling coordination degree types.

Stage	CCD (D)	Coupling Coordination Types
dysfunctional decline stage	(0–0.1]	extreme dysfunction
	(0.1–0.2]	severe dysfunction
	(0.2–0.3]	moderate dysfunction
	(0.3–0.4]	mild dysfunction
transitional stage	(0.4–0.5]	on the verge of dysfunction
	(0.5–0.6]	barely coordinated
coordinated development stage	(0.6–0.7]	primary coordinated stage
	(0.7–0.8]	intermediate coordinated stage
	(0.8–0.9]	good coordination stage
	(0.9–1.0]	high-quality coordinated stage

below.

4.4. Obstacle Degree Model

This study adopts the obstacle degree model to identify the core constraining factors that hinder the coupling coordination level between new-type urbanization and water resources utilization efficiency. The relevant calculation formulas are presented below.

$$I_{ij}=1-Z_{ij}^{\prime }$$

(10)

$$O_{ij}={\displaystyle \frac{I_{ij}{w}_j}{{\sum }_{j=1}^n{I}_{ij}{w}_j}}\times 100$$

(11)

where $O_{ij}$ represents the obstacle degree (%) of the j-th indicator in the i-th year to the coordinated development of new-type urbanization and water use efficiency in that year; $I_{ij}$ represents the indicator deviation degree of the j-th indicator in the i-th year; and $w_j$ is the weight value of the single indicator, indicating the degree of influence of the single indicator j on the overall target.

5. Analysis of Results

5.1. Time-Series Trends in the Comprehensive Evaluation Index of New-Type Urbanization

The comprehensive evaluation score for new-type urbanization shows an overall upward trend (

Table 4. New Urbanization Index for Hunan and Jiangxi Provinces by City and Year, 2013–2022.

Province	District	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022
Hunan	Changsha	0.448	0.493	0.504	0.561	0.603	0.658	0.729	0.764	0.854	0.911
	Zhuzhou	0.184	0.200	0.228	0.245	0.264	0.290	0.316	0.337	0.357	0.384
	Xiangtan	0.152	0.180	0.190	0.202	0.236	0.255	0.276	0.281	0.288	0.313
	Hengyang	0.163	0.174	0.195	0.213	0.218	0.218	0.263	0.290	0.325	0.346
	Shaoyang	0.119	0.147	0.165	0.177	0.183	0.213	0.225	0.249	0.265	0.266
	Yueyang	0.157	0.173	0.186	0.203	0.211	0.233	0.271	0.294	0.322	0.343
	Chande	0.159	0.180	0.189	0.207	0.214	0.242	0.267	0.281	0.295	0.318
	Zhangjiajie	0.093	0.104	0.103	0.119	0.128	0.135	0.120	0.134	0.152	0.159
	Yiyang	0.112	0.132	0.125	0.138	0.148	0.168	0.178	0.191	0.222	0.254
	Chenzhou	0.151	0.177	0.188	0.201	0.203	0.209	0.228	0.260	0.285	0.301
	Yongzhou	0.095	0.123	0.144	0.151	0.163	0.179	0.194	0.208	0.233	0.252
	Huaihua	0.113	0.127	0.136	0.156	0.171	0.193	0.208	0.222	0.236	0.245
	Loudi	0.118	0.125	0.133	0.141	0.162	0.199	0.190	0.200	0.212	0.220
Jiangxi	Nanchang	0.408	0.446	0.490	0.540	0.623	0.695	0.730	0.773	0.825	0.836
	Jingdezhen	0.167	0.175	0.187	0.222	0.216	0.228	0.259	0.275	0.292	0.311
	Pingxiang	0.170	0.183	0.200	0.216	0.219	0.274	0.274	0.275	0.282	0.292
	Jiujiang	0.269	0.293	0.318	0.338	0.317	0.348	0.380	0.405	0.464	0.462
	Xinyu	0.202	0.205	0.213	0.219	0.222	0.233	0.234	0.245	0.263	0.276
	Yingtan	0.112	0.136	0.160	0.167	0.210	0.188	0.219	0.256	0.294	0.331
	Ganzhou	0.253	0.290	0.342	0.349	0.415	0.440	0.506	0.550	0.607	0.648
	Ji an	0.153	0.183	0.204	0.216	0.226	0.260	0.287	0.307	0.352	0.364
	Yichun	0.199	0.228	0.250	0.268	0.300	0.353	0.407	0.432	0.472	0.519
	Fuzhou	0.170	0.187	0.203	0.218	0.232	0.259	0.283	0.316	0.342	0.375
	Shangrao	0.232	0.254	0.295	0.308	0.332	0.376	0.421	0.462	0.513	0.561

, Figure 4). From 2013 to 2022, the scores of all prefecture-level cities in Jiangxi and Hunan Provinces increased to varying degrees, indicating that new-type urbanization efforts in both provinces are progressing steadily. However, within the provinces, regional disparities have remained pronounced. In particular, Nanchang and Changsha have maintained significant leads within their respective provinces, reflecting the strong driving role of core cities in their surrounding areas.

(1)

Initial Accumulation Stage (2013–2016): The new urbanization evaluation scores of cities across both provinces showed overall slow growth, with a regional gradient pattern beginning to take shape. Within Jiangxi Province, Nanchang’s evaluation score was significantly higher than that of other prefecture-level cities, while cities such as Ganzhou and Jiujiang also demonstrated some growth potential. In Hunan Province, Changsha served as the absolute core, with its evaluation score holding a clear lead. Cities such as Zhuzhou, Xiangtan, and Hengyang formed the second tier, indicating a pronounced imbalance in provincial development.

(2)

Accelerated Improvement Phase (2017–2019): Most cities in both provinces entered a period of rapid growth, with the rate of increase in new urbanization evaluation scores expanding significantly. In Jiangxi Province, Nanchang has shown the most remarkable growth momentum, with its polarization effect continuing to manifest; at the same time, cities such as Ganzhou, Fuzhou, Yichun, and Shangrao are gradually accelerating their development, indicating that the provincial capital’s radiating influence is gradually spreading to surrounding areas. A relatively pronounced catch-up trend is emerging among cities across the province, and the overall spatial pattern is moving toward more balanced development. In Hunan Province, Changsha continued its rapid ascent, with cities such as Zhuzhou, Hengyang, Yueyang, and Chenzhou improving in tandem, indicating that Hunan’s new-type urbanization is evolving from single-core leadership toward polycentric development. The differences in the spatial patterns of the two provinces may be related to factors such as the development capacity of central cities, transportation accessibility, and regional coordination policies.

(3)

Steady Optimization Phase (2020–2022): The level of new-type urbanization continued to rise across all prefectures and cities in both provinces, but the pace of growth began to diverge, with the overall focus shifting from quantitative expansion to qualitative improvement. The overall level of urbanization in Jiangxi Province has improved, with Nanchang further extending its lead, Ganzhou showing particularly strong growth momentum, and the remaining cities generally exhibiting a steady upward trend. Geographically, this has resulted in a differentiated pattern characterized by “core cities leading the way, with peripheral cities following suit.” In Hunan Province, Changsha’s evaluation score rose significantly, widening the gap with other cities, while Zhuzhou and Hengyang maintained steady growth. In contrast, cities such as Zhangjiajie and Yiyang saw relatively limited improvements, indicating that a distinct gradient differentiation still exists within Hunan Province.

Overall, both Jiangxi and Hunan Provinces demonstrate a positive trend of year-on-year improvement in their comprehensive new urbanization evaluation scores. However, both provinces exhibit distinct “core–periphery” structural characteristics. In comparison, while the disparities among cities in Jiangxi are relatively pronounced, the overall trend of catch-up is stronger. In Hunan, the single-core agglomeration effect centered on Changsha is more pronounced, and the provincial capital plays a more dominant role in driving the province’s new urbanization progress. This pattern indicates that the transmission of feedback signals during the urbanization process in the two provinces follows different spatial pathways: Jiangxi exhibits diffuse transmission characterized by gradual responses at multiple points, while Hunan exhibits polarized transmission characterized by reinforcement at the core and lag at the periphery.

5.2. Temporal Evolution Characteristics of Water Use Efficiency

Using the super-efficiency SBM model with undesirable outputs, the water use efficiency of 11 prefecture-level cities in Jiangxi and Hunan Provinces from 2013 to 2022 was measured, as shown in Figure 5 and

Table 5. Statistical Summary of Water Use Efficiency in Hunan and Jiangxi Provinces, 2013–2022.

Province	District	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022
Hunan	Changsha	0.709	0.728	0.769	0.829	0.867	0.889	0.880	0.882	0.955	1.022
	Zhuzhou	0.431	0.444	0.481	0.489	0.484	0.492	0.561	0.600	0.632	0.666
	Xiangtan	0.392	0.362	0.361	0.394	0.454	0.496	0.484	0.512	0.552	0.579
	Hengyang	0.424	0.437	0.456	0.485	0.491	0.506	0.588	0.604	0.648	0.688
	Shaoyang	0.308	0.298	0.315	0.359	0.370	0.395	0.533	0.549	0.585	0.565
	Yueyang	0.480	0.501	0.512	0.549	0.557	0.619	0.685	0.744	0.883	1.056
	Chande	0.615	0.622	1.122	0.740	0.636	0.659	0.641	0.657	0.676	0.706
	Zhangjiajie	0.467	0.477	0.527	0.561	0.573	0.592	0.524	0.505	0.511	0.497
	Yiyang	0.383	0.395	0.414	0.461	0.484	0.527	0.512	0.497	0.500	0.512
	Chenzhou	0.470	0.523	0.485	0.531	0.503	0.518	0.521	0.598	0.662	0.697
	Yongzhou	0.389	0.344	0.362	0.390	0.389	0.418	0.457	0.465	0.475	0.514
	Huaihua	0.525	0.469	0.503	0.527	0.476	0.521	0.541	0.538	0.570	0.573
	Loudi	0.461	0.453	0.454	0.488	0.504	0.544	0.557	0.657	0.679	1.013
Jiangxi	Nanchang	0.464	0.465	0.462	0.483	0.513	0.539	0.557	0.588	0.922	1.042
	Jingdezhen	0.594	0.510	0.491	0.505	0.477	0.460	0.494	0.529	0.617	0.652
	Pingxiang	0.530	0.549	0.570	0.643	0.598	0.585	0.462	0.499	0.550	0.590
	Jiujiang	0.478	0.492	0.502	0.531	0.590	0.680	0.842	0.805	0.864	0.866
	Xinyu	0.665	0.665	0.617	0.648	0.631	0.616	0.566	0.586	0.680	1.011
	Yingtan	1.034	0.852	0.667	0.691	0.833	0.630	0.761	0.806	0.890	1.054
	Ganzhou	0.599	0.456	0.452	0.455	0.486	0.518	0.642	0.647	0.706	0.724
	Ji an	0.590	0.547	0.548	0.592	0.456	0.644	0.849	0.690	0.922	1.052
	Yichun	1.019	0.732	0.552	0.590	0.648	0.645	1.025	1.020	0.878	1.012
	Fuzhou	0.463	0.434	0.398	0.404	0.403	0.413	0.457	0.451	0.487	0.514
	Shangrao	0.993	1.018	0.506	0.533	0.693	0.561	0.860	0.787	0.867	1.025

. From an overall perspective, during the period from 2013 to 2022, the water use efficiency in both provinces showed a fluctuating upward trend, but there were significant inter-provincial and intra-provincial differences. In contrast, Jiangxi Province exhibited a more pronounced decline in the early period, followed by a significant rebound in the later period. Hunan Province’s overall improvement process was smoother, but cities like Changsha and Changde showed more notable performance. Based on the measurement results from each year, the trends can be broadly divided into three phases: 2013–2015, 2016–2018, and 2019–2022.

Specifically, from 2013 to 2015, water use efficiency in Jiangxi Province generally followed a fluctuating downward trend, with significant disparities in efficiency among cities. Although cities such as Nanchang, Shangrao, Yichun, and Yingtan maintained relatively high levels of efficiency, most cities experienced varying degrees of decline during this period. This indicates that the overall water use efficiency in Jiangxi Province remained under pressure during this period, and the coordination among resource utilization patterns, input–output structures, and environmental constraints still needs to be strengthened. During the same period, Hunan Province as a whole showed a trend of slow growth, with Changsha consistently in an absolute leading position. Changde experienced a significant peak in 2015, suggesting that efficiency in certain local areas may have improved rapidly in the short term due to factors such as industrial restructuring, increased investment in technology, or optimized resource utilization; however, most cities remained concentrated in the lower range, and regional disparities within the province are also quite pronounced.

From 2016 to 2018, water use efficiency in the two provinces entered a phase of adjustment and recovery. After an initial decline, efficiency in Jiangxi Province began to gradually stabilize and rebound. Efficiency values improved in most cities, with Nanchang and Ganzhou showing a relatively steady upward trend. However, the growth in cities like Jingdezhen and Fuzhou was relatively limited, indicating that while the capacity for water conservation and intensive use has strengthened in some areas, the foundation for overall improvement remains insufficiently solid. Hunan Province, on the other hand, demonstrated a more sustained upward trend. Changsha continued to improve steadily, while Cities such as Zhuzhou, Hengyang, and Yueyang experienced coordinated growth, with improvements in efficiency spreading along the Xiangjiang River basin and being transmitted more smoothly. These spatial differences may be related to variations in the pace of industrial transformation in central cities and differences in the capacity for technology diffusion across regions.

From 2019 to 2022, the water use efficiency in the two provinces generally entered a phase of rapid improvement, but the differentiation remained prominent. In Jiangxi Province, Nanchang and Yingtan showed the most significant increase in the later period, reaching a relatively high level by 2022. Ganzhou also continued to grow and maintained a strong development momentum, while cities like Fuzhou and Jingdezhen experienced relatively mild improvements. In Hunan Province, Changsha continues to maintain high growth, while the improvements in Yueyang and Loudi in the later period are particularly notable, approaching 1.0 in 2022, indicating strong catching-up ability. After experiencing significant fluctuations in the early period, Changde generally remained at a high level. Cities such as Zhangjiajie and Yiyang, however, remain at relatively low levels, reflecting that efficiency improvements in Western Hunan and parts of the area surrounding Lake Dongting still face obstacles in being fully realized. This marked spatial differentiation in Hunan Province may be related to the spillover effects of the Changsha–Zhuzhou–Xiangtan (CZX) metropolitan area, differences in water resources across the Dongting Lake region, and the relatively slow pace of industrial transformation in western Hunan.

During the study period, water use efficiency in both provinces showed an improving trend, but their trajectories differed. Jiangxi exhibited a “decline followed by an increase” pattern, with the inflection point occurring around 2015—a timeframe that closely coincided with the implementation of the most stringent water resources management system and the pilot program for ecological civilization—as concentrated external institutional interventions prompted efficiency to shift from decline to growth. The initial decline in Jiangxi was partly due to its high proportion of agricultural water use, where improving irrigation efficiency presented greater marginal challenges, resulting in a lower starting point for efficiency improvements compared to Hunan. Hunan has shown a steady upward trend, with the rate of improvement accelerating slightly after 2018. This period largely coincides with the deepening integration of the CZX metropolitan area and the acceleration of industrial upgrading; the sustained release of institutional dividends has made efficiency improvements more consistent. This comparison indicates that the timing, intensity, and continuity of institutional intervention are key factors determining the differences in the evolution of water use efficiency.

5.3. Spatiotemporal Evolution of the Coupling Coordination Index Between New-Type Urbanization and Water Use Efficiency

5.3.1. Temporal Changes in the Coupling Coordination Index

The degree of coupling and coordination varies considerably between Jiangxi and Hunan. Drawing on the average values for 2013–2022 (Figure 6), the two provinces can be divided into three tiers according to their coordination levels: Tier I (0.7–1.0), Tier II (0.35–0.7), and Tier III (0–0.35). The classifications are summarized in

Table 6. Coupling Coordination Index Summary for Hunan and Jiangxi Provinces, 2013–2022.

Province	District	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022
Hunan	Changsha	0.649	0.694	0.697	0.743	0.777	0.826	0.826	0.819	0.863	0.908
	Zhuzhou	0.332	0.359	0.402	0.426	0.455	0.485	0.516	0.539	0.558	0.584
	Xiangtan	0.270	0.278	0.277	0.341	0.417	0.441	0.473	0.476	0.481	0.510
	Hengyang	0.293	0.313	0.350	0.380	0.386	0.386	0.446	0.479	0.518	0.538
	Shaoyang	0.115	0.020	0.158	0.271	0.293	0.341	0.396	0.429	0.449	0.451
	Yueyang	0.287	0.316	0.337	0.364	0.377	0.405	0.450	0.472	0.484	0.468
	Chande	0.297	0.330	0.256	0.366	0.380	0.415	0.447	0.464	0.479	0.503
	Zhangjiajie	0.025	0.157	0.161	0.216	0.238	0.253	0.214	0.245	0.281	0.292
	Yiyang	0.169	0.224	0.211	0.248	0.271	0.309	0.325	0.346	0.392	0.438
	Chenzhou	0.275	0.324	0.340	0.360	0.364	0.372	0.400	0.442	0.469	0.485
	Yongzhou	0.084	0.191	0.248	0.267	0.292	0.323	0.349	0.372	0.410	0.435
	Huaihua	0.196	0.226	0.249	0.289	0.311	0.348	0.372	0.391	0.411	0.423
	Loudi	0.203	0.218	0.236	0.256	0.297	0.359	0.346	0.360	0.375	0.340
Jiangxi	Nanchang	0.437	0.436	0.430	0.450	0.475	0.491	0.503	0.527	0.840	0.949
	Jingdezhen	0.311	0.319	0.339	0.392	0.384	0.403	0.447	0.466	0.480	0.501
	Pingxiang	0.312	0.335	0.359	0.381	0.387	0.460	0.444	0.469	0.474	0.483
	Jiujiang	0.463	0.483	0.494	0.530	0.515	0.541	0.553	0.586	0.637	0.635
	Xinyu	0.362	0.366	0.379	0.385	0.390	0.406	0.408	0.422	0.440	0.408
	Yingtan	0.164	0.249	0.300	0.311	0.361	0.343	0.379	0.420	0.452	0.456
	Ganzhou	0.432	0.434	0.425	0.429	0.464	0.502	0.633	0.631	0.681	0.691
	Ji an	0.286	0.333	0.365	0.383	0.400	0.439	0.451	0.493	0.510	0.492
	Yichun	0.311	0.393	0.431	0.453	0.488	0.552	0.544	0.571	0.643	0.659
	Fuzhou	0.309	0.337	0.347	0.356	0.354	0.371	0.436	0.424	0.471	0.503
	Shangrao	0.360	0.380	0.497	0.510	0.521	0.561	0.594	0.650	0.686	0.698

and illustrated in Figure 5.

Changsha is the only city in Tier I, with an average coupling coordination index of 0.78. It is the sole city among the 24 cities in Jiangxi and Hunan to exceed the 0.7 threshold, and has maintained high-level growth since 2013, achieving intermediate coordination by 2022. This is primarily attributable to Changsha’s status as a provincial capital, which endows it with strong economic agglomeration capabilities, a solid foundation for industrial upgrading, support from scientific and technological innovation, and efficient resource allocation. Driven by policies such as the development of the CZX urban cluster, the Yangtze River Economic Belt, and the construction of water-conserving cities, infrastructure has been continuously improved and public services have been consistently optimized, leading to accelerated industrial restructuring, and improved levels of resource conservation and intensive utilization. These efforts have propelled the coupling coordination index toward “good coordination,” demonstrating a distinct role as a core growth pole with significant radiating and driving effects.

Tier II includes Nanchang, Jiujiang, Shangrao, Ganzhou, Yichun, Ji’an, Pingxiang, Jingdezhen, Fuzhou, and Xinyu, Zhuzhou, Hengyang, Xiangtan, Yueyang, Changde, and Chenzhou. This tier constitutes the primary driving force for the coordinated development of Jiangxi and Hunan. Most of Jiangxi’s cities fall into this tier. Although none have yet formed a high-value core comparable to Changsha, most cities have moved beyond their previous state of imbalance and are now entering a phase of initial coordination, reflecting generally sound coordination. As shown in Figure 5, Nanchang, Jiujiang, Shangrao, Ganzhou, and Yichun exhibit the strongest upward momentum. Nanchang, in particular, has shown significant growth in recent years despite its average value remaining in Tier II. Its coupled coordination level surged rapidly from 2020 to 2022 and now far exceeds that of other cities in the province. Jiujiang, Shangrao, and Ganzhou have also followed a steady upward trajectory, reflecting their growing capacity in industrial transfer, transportation infrastructure, urban development, and resource utilization. Hunan’s Tier II comprises Zhuzhou, Hengyang, Xiangtan, Yueyang, Changde, and Chenzhou. These cities are generally shifting from near dysfunction to barely coordinated levels. Hengyang, Xiangtan, Yueyang, Changde, and Chenzhou, on the other hand, showed an overall trend of fluctuating growth, with their coupling coordination gradually approaching 0.5 in the later stages. These trends suggest that ongoing improvements in infrastructure, industrial restructuring, and ecological governance are steadily enhancing the alignment between new-type urbanization and water use efficiency in these cities, although there is still a notable gap relative to Changsha.

Tier III includes Yingtan in Jiangxi, as well as Shaoyang, Zhangjiajie, Yiyang, Yongzhou, Huaihua, and Loudi in Hunan. This tier represents the weakest link in the coordinated development of the two provinces. Although only Yingtan falls into Tier III, it has shown a clear upward trend rather than remaining consistently low. As can be seen from the figure, its coupling coordination index rose gradually from 0.164 in 2013 to 0.456 in 2022, indicating a trajectory converging toward Tier II. In Hunan, the number of cities in Tier III has increased significantly, including Shaoyang, Zhangjiajie, Yiyang, Yongzhou, Huaihua, and Loudi. However, many cities in this tier remain on the verge of imbalance. Zhangjiajie’s average coupling coordination index stands at 0.208, consistently ranking last in the province. Constrained by topographical conditions, Zhangjiajie has a relatively weak foundation for coordinated development and has yet to establish a stable mechanism for synergistic evolution. Although Shaoyang, Yiyang, Yongzhou, Huaihua, and Loudi all showed improvement in the later stages, their overall average coupling coordination index has yet to exceed 0.35, indicating that these cities still have significant shortcomings in terms of economic foundations, industrial tiers, and comprehensive support capabilities.

Overall, the evolution of the two provinces in terms of their levels of coupling and coordination shows a clear divergence. This divergence indicates that improvements in coordination are not only constrained by a shared factor-driven development model but also depend on differences in institutional factors, the development capacity of core cities, and regional coordination policies.

5.3.2. Analysis of the Spatial Evolution of Coupling Coordination

Spatial Patterns of Coupling Coordination

To facilitate a more intuitive analysis of the spatial patterns of the coupling coordination index between new-type urbanization and water use efficiency in Jiangxi and Hunan, ArcGIS was employed to map the index values for 2013, 2018, and 2022 (see the corresponding figures). Following the classification scheme presented in Table 2, the index was categorized into three tiers: 0.0–0.4 (low-level stage, indicating imbalance and decline), 0.4–0.6 (medium-level stage, indicating a transitional phase), and 0.6–1.0 (high-level stage, indicating coordinated development) (Figure 7).

In 2013, the overall degree of coupling coordination between new urbanization and water use efficiency in Hunan and Jiangxi Provinces was in a period of imbalance and decline, but the different spatial patterns in the two provinces had already emerged. Hunan exhibits a difference between the contiguous areas of the western and central-southern regions, with a single-core breakthrough in the CZX area. Most cities, such as Huaihua, Shaoyang, and Changde, are in a state of disorder. Overall, Jiangxi is in better condition than Hunan; its spatial structure is relatively stable in the north and center, and gradually rising in the south. Most cities have moved from the period of imbalance and decline toward the transitional period, with some cities reaching a higher level within the transitional period. The reason for this pattern lies in the fact that most cities in Hunan are still in the stage of urbanization driven primarily by population agglomeration and construction expansion. The proportion of traditional water-consuming industries such as steel, non-ferrous metals, building materials, and chemicals is relatively high. Cities like Nanchang and Jiujiang, on the other hand, have superior geographical conditions, better water conservancy infrastructure, and stronger capacity to support industries and populations, resulting in a significantly smaller range of imbalance and decline compared to Hunan. In 2013, though both provinces remained largely in imbalance and decline, regional differentiation was already emerging—CZX led in coordinated breakthroughs, while Jiangxi’s riverside cities saw milder decline, reflecting the deep impact of resource endowments, industrial structure, and policy on the urbanization-water environment nexus.

In 2018, the coupled coordination pattern of the two provinces shifted from one dominated by imbalance and decline to one primarily characterized by a transitional phase. Spatially, this shift was reflected in the diffusion from core cities to surrounding areas. In Hunan, the extent of the imbalance and decline stage narrowed significantly, with low-value areas previously distributed in the western and central-southern parts beginning to shrink. Cities such as Changde, Yiyang, and Chenzhou successively entered the transitional period, while the CZX region continued to capitalize on its strengths during the coordinated development phase, forming a pattern of stable, high-value growth in the core area, with tiered development in the surrounding areas. In contrast, Jiangxi experienced more pronounced area-wide improvement. Hunan, propelled by the CZX urban clusters initiative and the comprehensive rehabilitation of the Xiangjiang River basin, alongside Jiangxi, facilitated by the Chang-Jiu integration strategy, the Poyang Lake Ecological Economic Zone, and its role in receiving industrial relocation from the eastern coastal regions, have collectively advanced industrial restructuring and enhanced the efficiency of resource allocation. Zhangjiajie, by contrast, remains encumbered by its mountainous topography, inadequate transport connectivity, underdeveloped urban hierarchy, and constrained industrial carrying capacity, thereby perpetuating its state of dysfunctional decline and rendering it the most conspicuous low-value enclave within the provincial landscape.

In 2022, the coupling coordination degree between Hunan and Jiangxi Provinces optimized further, with the spatial pattern evolving from the “expansion of the transitional period” to the “enhanced aggregation of the coordinated development period.” Hunan is characterized by a “stable high-value core in the CZX, a persistent low-value tail in the west, and a continuous uplift in the central and eastern regions.” Changsha, Zhuzhou, and Xiangtan remain in the coordinated development phase, while Changde and Hengyang have also entered a higher level of development. Huaihua, Shaoyang, Yongzhou, and Chenzhou have stabilized at a stage of transition from the transitional phase to the coordinated development phase; however, Zhangjiajie remains in the phase of imbalance and decline, representing the most prominent low-performing area in the region. Jiangxi, on the other hand, exhibits a more pronounced pattern of high-value contiguous areas in northern and northeastern areas, diffusion in central areas, and steady progress in the south. Nanchang, Jiujiang, Shangrao, and Ganzhou have generally entered a phase of coordinated development, while Yichun, Ji’an, and Yingtan have also clearly moved from a transitional phase toward a higher level of development, resulting in significantly enhanced connectivity among the province’s medium- and high-value regions. The mechanism behind its formation lies in the gradual emergence of results in the core cities of the two provinces in areas such as digital water management, the renovation of water supply and drainage networks, the resource utilization of wastewater, the green transformation of industries, and ecological environment governance, following prior accumulation. These achievements have driven the continuous optimization of the interrelationships among population, industry, spatial layout, and the ecosystem. In particular, Nanchang, Jiujiang, and Shangrao have rapidly entered a phase of coordinated development, leveraging their comprehensive transportation hubs, industrial upgrades, and enhanced capacity for openness and connectivity. Ganzhou, on the other hand, has leveraged the construction of provincial sub-centers, infrastructure improvements, and enhanced industrialization. In contrast, Zhangjiajie has long been constrained by its isolated geography, underdeveloped industries, limited fiscal investment, and weak infrastructure. Consequently, the role of urbanization in promoting the efficient use of water resources remains limited, and the region has thus been unable to escape a state of imbalance and decline.

From an overall evolutionary perspective, the spatial pattern of the coupling and coordination between new-type urbanization and water use efficiency in Hunan and Jiangxi Provinces from 2013 to 2022 underwent a process of evolution: from widespread distribution during the period of imbalance and decline, to extensive expansion during the transitional period, and finally to increased concentration and strengthening during the period of coordinated development. Hunan’s spatial characteristics were primarily marked by the strong driving force of the CZX core region and the prominent stagnation of low values in Zhangjiajie, exhibiting a distinct core–periphery gradient. Jiangxi, on the other hand, was characterized by breakthroughs in northern and northeastern Jiangxi followed by diffusion to central and southern Jiangxi, with a more balanced overall improvement and a more continuous expansion during the coordinated development phase. Overall, the level of coupling and coordination between the two provinces continues to improve. However, distinct spatial divergences still exist between western Hunan and northeastern Jiangxi, as well as between core cities and peripheral areas. This indicates that the enhancement of coupling and coordination depends not only on the pace of urbanization but also on the green transformation of industries, the improvement of water conservancy facilities, the strengthening of ecological governance capabilities, and the sustained release of the diffusion effects of core cities.

Directional Characteristics of Coupling Coordination Distribution

We utilized the spatial statistics tools in ArcGIS 10.8 software to construct the standard deviation ellipse and the trajectory of the center of gravity for coupling coordination. We then analyzed the migration direction of the center of gravity for coupling coordination in 2013, 2018, and 2022, as shown in

Table 7. Parameters of the Standard Deviation Ellipse for Coupling Coordination Degree.

Year	Province	Ellipse Area (104 km2)	Major Axis Length (km)	Minor Axis Length (km)	Azimuth (°)	Axis Ratio (Major/Minor)
2013	Jiangxi	7.12	194.48	116.59	33.07	1.67
	Hunan	6.82	163.28	132.89	164.65	1.23
2018	Jiangxi	7.05	193.12	116.25	38.75	1.66
	Hunan	7.78	173.07	143.14	162.18	1.21
2022	Jiangxi	6.88	191.33	114.48	35.80	1.67
	Hunan	7.90	176.32	142.65	163.53	1.24

and Figure 8.

From 2013 to 2022, the spatial distribution pattern of the coupling coordination between new-type urbanization and water use efficiency in Jiangxi Province remained generally stable; however, its spatial agglomeration characteristics and development focus exhibited certain dynamic evolutionary trends. The standard deviation of the elliptical area decreased by 3.40%, indicating that the spatial distribution range of the coupling coordination index has slightly converged. Regional development has gradually shifted from relative dispersion toward concentration in core areas, and the degree of spatial agglomeration has increased. In terms of changes in the ellipse’s axis lengths, the major axis shortened from 194.48 km to 191.33 km, and the minor axis from 116.59 km to 114.48 km, both exhibiting a slight shortening trend. This indicates that the spatial extent of Jiangxi Province’s coupling coordination has converged in both east–west and north–south directions, and that spatial disparities between regions have gradually diminished. Meanwhile, the azimuth increased from 33.07 to 35.80°, maintaining a northeast–southwest orientation with a slight northeastward shift. The elliptical flattening ratio remained largely stable between 1.66 and 1.67, suggesting that the directionality of the spatial distribution of Jiangxi’s coupling coordination remains relatively stable and that the overall spatial structure has not undergone significant changes.

The center of gravity for Jiangxi Province’s coupling coordination has generally moved in a northeasterly direction. In 2013, it was located at coordinates (115.72° E, 28.02° N), and by 2018, it had shifted to (115.74° E, 28.04° N). Based on Euclidean distance calculations using projected coordinates, the distance of this shift was approximately 3.04 km. Subsequently, by 2022, it continued to shift to (115.81° E, 28.05° N), with a displacement of approximately 7.28 km. Overall, the cumulative shift in the center of gravity from 2013 to 2022 was approximately 10.32 km. This change indicates that, during the study period, regions with higher levels of coupling coordination in Jiangxi Province gradually became concentrated in the northeastern part of the province, with the regional development center showing a trend of shifting toward the Jingdezhen–Shangrao–Nanchang area.

The spatial distribution pattern of the coupling coordination between new-type urbanization and water use efficiency in Hunan Province from 2013 to 2022 exhibited distinct dynamic evolutionary characteristics. The area of the standard deviation ellipse increased by approximately 15.84%, indicating that the spatial distribution range of coupling coordination continued to expand, inter-regional disparities intensified, and the spatial pattern shifted from relative concentration to a certain degree of dispersion. In terms of changes in the ellipse’s axis lengths, the major axis increased from 163.28 km to 176.32 km, and the minor axis increased from 132.89 km to 142.65 km. Both axes showed a clear trend of growth, indicating that the coupling coordination in Hunan Province is expanding in both the east–west and north–south directions, with the scope of spatial connectivity continuously widening. The azimuth shifted slightly from 164.65° to 163.53°, maintaining an overall northwest–southeast orientation with minimal variation, indicating that the directionality of the spatial distribution of Hunan Province’s coupling coordination remains relatively stable. The elliptical flattening ratio increased slightly from 1.23 to 1.24, with no significant overall change. This suggests that, although the spatial structure has expanded, its morphological stability remains strong, and no significant directional restructuring has occurred.

From the perspective of the spatial centroid migration characteristics, the coupling coordination degree centroid of Hunan Province has generally been migrating continuously toward the southwest. In 2013, the centroid was located at (112.46° E, 27.75° N), and in 2018 it moved to (112.24° E, 27.72° N), a migration distance of approximately 22.05 km. From 2018 to 2022, it shifted further to (112.22° E, 27.68° N), a distance of approximately 5.25 km. Overall, the cumulative shift in the center of gravity from 2013 to 2022 was approximately 27.30 km, representing a significant change. This indicates that the spatial pattern of coupling coordination in Hunan Province is undergoing active changes, and the center of regional development is undergoing a distinct process of spatial shift.

In summary, there are marked differences in the direction and magnitude of shifts between the two provinces. The center of gravity in Jiangxi Province has shifted generally toward the northeast, and the magnitude of this shift is relatively small, indicating that its spatial pattern is relatively stable and that the agglomeration effect in its core areas is gradually strengthening. Policy resources are prioritized here, but the spillover effects to the south are limited. In contrast, the center of gravity in Hunan Province has continued to move toward the southwest, with a cumulative shift of approximately 27.30 km—a magnitude significantly greater than that of Jiangxi—suggesting that its spatial expansion is more pronounced and that the effects of policy measures have begun to extend to outlying areas. Spatial restructuring has become more dynamic, and there has been a marked shift in the regional development center of gravity.

5.4. Analysis of Obstacle Factors Constraining the Coupling Coordination Degree

Using the obstacle degree model, this study identifies and ranks the primary urbanization-level obstacle indicators affecting the coupling coordination between new-type urbanization and water use efficiency across cities in Jiangxi and Hunan from 2013 to 2022. The top three barriers in each city were extracted and aggregated. The results reveal a pronounced structural concentration of obstacle indicators across both provinces. Completed investment in real estate development (U₁₂) and total retail sales of consumer goods (U₁₀) emerge as the most frequent impediments, while gas penetration rate (U₁₁), per capita urban road area (U₁₃), and population density (U₂) constitute additional constraining factors in selected cities (

Table 8. Major Obstacle Factors of New-Type Urbanization at the Indicator Level in Jiangxi Province, 2013–2022.

No.	Nanchang	Jingdezhen	Pingxiang	Jiujiang	Xinyu	Yingtan	Ganzhou	Ji’an	Yichun	Fuzhou	Shangrao
1	U12/ 0.167	U12/ 0.183	U12/ 0.182	U12/ 0.190	U12/ 0.186	U12/ 0.179	U12/ 0.186	U12/ 0.180	U12/ 0.183	U12/ 0.180	U12/ 0.189
2	U10/ 0.153	U10/ 0.157	U10/ 0.152	U10/ 0.155	U10/ 0.159	U10/ 0.150	U10/ 0.141	U10/ 0.142	U10/ 0.135	U10/ 0.145	U10/ 0.158
3	U11/ 0.113	U13/ 0.129	U13/ 0.130	U13/ 0.124	U13/ 0.132	U13/ 0.129	U13/ 0.122	U13/ 0.123	U13/ 0.128	U13/ 0.127	U13/ 0.125

and

Table 9. Major Obstacle Factors of New-Type Urbanization at the Indicator Level in Hunan Province, 2013–2022.

No.	Changsha	Zhuzhou	Xiangtan	Hengyang	Shaoyang	Yueyang	Changde	Zhangjiajie	Yiyang	Chenzhou	Yongzhou	Huaihua	Loudi
1	U10/ 0.158	U12/ 0.175	U12/ 0.176	U12/ 0.179	U12/ 0.175	U12/ 0.184	U12/ 0.174	U12/ 0.166	U12/ 0.172	U12/ 0.176	U12/ 0.174	U12/ 0.172	U12/ 0.173
2	U2/ 0.139	U10/ 0.136	U10/ 0.152	U10/ 0.163	U10/ 0.161	U10/ 0.155	U10/ 0.158	U10/ 0.151	U10/ 0.151	U10/ 0.152	U10/ 0.149	U10/ 0.154	U10/ 0.155
3	U12/ 0.107	U13/ 0.123	U13/ 0.127	U11/ 0.116	U11/ 0.119	U11/ 0.119	U11/ 0.115	U11/ 0.114	U11/ 0.116	U11/ 0.112	U11/ 0.113	U11/ 0.117	U11/ 0.116

).

Looking at the 11 prefectures in Jiangxi Province, the top-ranked obstacle in each case was the value of completed real estate development investment (U₁₂), indicating that issues related to the scale and structure of real estate investment have become the core constraining variable hindering the coordinated development of Jiangxi’s urbanization subsystem. Real estate investment’s excessive reliance on land-based fiscal revenue and an expansion-driven model tends to drive up urban construction water demand and infrastructure operating costs, while simultaneously exacerbating spatial sprawl and resource misallocation, thereby undermining the path toward intensive urbanization. This suggests that imbalances in the structure of spatial capital investment indirectly affect water use efficiency and the level of coordination within the urbanization system through changes in land development intensity and resource allocation efficiency. Total retail sales of consumer goods (U₁₀) ranks among the top two obstacle factors across all cities in Jiangxi, which reflects urban consumption vitality and domestic demand scale. The lagged upgrading of consumption structure leads to insufficient industrial chain extension and inadequate development of service industries, making it difficult to establish a water-saving economic system dominated by modern service industries. This reveals that insufficient domestic demand not only weakens economic vitality but also transmits negative impacts to water resources utilization efficiency through industrial structural changes, thereby restricting the progress of green development transformation. In terms of the third dominant obstacle factor, gas popularization rate (1) is identified in Nanchang, while per capita urban road area (U₁₃) predominates in other cities. Low per capita road area reflects insufficient urban traffic carrying capacity and irrational spatial layout, which easily triggers traffic congestion and increased energy consumption. Meanwhile, low gas popularization rate implies the slow progress of clean energy substitution and rigid defects in urban energy structure. In conclusion, infrastructure construction quality and green energy supply capacity are crucial factors restricting the high-quality development of urbanization in Jiangxi Province.

From the perspective of the 14 cities in Hunan Province, fixed asset investment in real estate development (U₁₂) also ranks first or second in most cities, with the highest frequency among all indicators. This result suggests that Hunan and Jiangxi share similar spatial capital-driven urbanization pathways, where the expansion of real estate investment remains a key structural factor influencing the level of coupling coordination. However, excessive reliance on real estate investment tends to generate a development pattern characterized by high water and energy consumption, thereby intensifying pressure on the water resource system and weakening overall system coordination. Total retail sales of consumer goods (U₁₀) also consistently rank within the top two obstacle factors across many cities in Hunan Province, indicating that consumption structure and industrial upgrading remain critical constraints on the high-quality development of urbanization in the province. If consumption growth is not accompanied by a simultaneous green transformation, it may exert a crowding-out effect on water use efficiency through increased water demand in commercial activities and higher energy consumption in the service sector. Notably, population density (U₂) emerges as a leading obstacle factor in Changsha, suggesting that resource carrying capacity pressures associated with population concentration in mega-cities have already become evident. Excessively high population density increases domestic water demand and infrastructure operational burdens; if public resource allocation is not correspondingly optimized, it may lead to a decline in resource utilization efficiency. In addition, the gas penetration rate (U₁₁) ranks as the third most important obstacle factor in most cities, indicating that the level of clean energy adoption still requires improvement. A relatively low gas penetration rate implies a continued reliance on traditional, more polluting energy sources, which is unfavorable for promoting coordinated progress in urban green transformation and water-saving emission reduction.

A comprehensive comparison between the two provinces reveals that the major obstacle factors at the urbanization indicator level are highly concentrated in real estate investment and consumption scale, indicating that the current urbanization development is still predominantly driven by capital input and demand expansion. Second, Jiangxi is more prominently constrained by road infrastructure, while Hunan presents distinct differences in energy structure and population density. Third, both provinces face structural pressure in the transformation from space-expansion-oriented urbanization to quality-improvement-oriented urbanization. In the future, it is essential to optimize the structure of real estate investment, curb disorderly urban expansion, and accelerate the green upgrading of consumption structure. Meanwhile, efforts should be made to improve the quality of urban infrastructure and the clean utilization level of energy, and strengthen the dynamic balance between population agglomeration capacity and resource carrying capacity, so as to realize the coordinated improvement of water resources utilization efficiency and urbanization quality.

Based on the obstacle degree model, this study statistically analyzes the main obstacle factors at the water use efficiency indicator level affecting the coupling coordination between new-type urbanization and water use efficiency in cities of Jiangxi and Hunan from 2013 to 2022, and extracts the top three obstacle indicators for each city. Overall, the obstacle structures at the water use efficiency indicator layer in the two provinces are highly convergent. Gross regional product (Y₁), fixed asset investment (X₂), and labor input (X₃) show the highest frequency of occurrence, forming a tripartite high-frequency constraint pattern of “economic scale—capital input—labor factors.” In contrast, total water use is prominent only in a few core cities and has not yet become a universal dominant obstacle. This indicates that the core issue currently constraining the coupling coordination between water use efficiency and urbanization in the two provinces is not an absolute shortage of water resources, but rather the systematic constraints imposed by the economic development model and factor allocation structure on the improvement of water use efficiency (

Table 10. Major Obstacle Factors of Water Use Efficiency at the Indicator Level in Jiangxi Province, 2013–2022.

No.	Nanchang	Jingdezhen	Pingxiang	Jiujiang	Xinyu	Yingtan	Ganzhou	Ji’an	Yichun	Fuzhou	Shangrao
1	Y1/ 0.475	Y1/ 0.324	Y1/ 0.325	Y1/ 0.358	Y1/ 0.319	Y1/ 0.319	Y1/ 0.384	Y1/ 0.374	Y1/ 0.403	Y1/ 0.358	Y1/ 0.371
2	X2/ 0.188	X3/ 0.272	X3/ 0.275	X3/ 0.282	X3/ 0.281	X3/ 0.275	X3/ 0.275	X3/ 0.293	X3/ 0.307	X3/ 0.271	X3/ 0.283
3	X1/ 0.175	X2/ 0.222	X2/ 0.211	X2/ 0.212	X2/ 0.217	X2/ 0.224	X2/ 0.241	X2/ 0.235	X2/ 0.258	X2/ 0.238	X2/ 0.240

and

Table 11. Major Obstacle Factors of Water Use Efficiency at the Indicator Level in Hunan Province, 2013–2022.

No.	Changsha	Zhuzhou	Xiangtan	Hengyang	Shaoyang	Yueyang	Changde	Zhangjiajie	Yiyang	Chenzhou	Yongzhou	Huaihua	Loudi
1	X1/ 0.315	Y1/ 0.354	Y1/ 0.347	Y1/ 0.370	Y1/ 0.362	Y1/ 0.358	Y1/ 0.368	Y1/ 0.315	Y1/ 0.345	Y1/ 0.351	Y1/ 0.353	Y1/ 0.335	Y1/ 0.334
2	Y1/ 0.300	X3/ 0.279	X3/ 0.273	X3/ 0.278	X3/ 0.277	X3/ 0.292	X3/ 0.307	X3/ 0.274	X3/ 0.287	X3/ 0.286	X3/ 0.282	X3/ 0.275	X3/ 0.269
3	X2/ 0.162	X2/ 0.205	X2/ 0.223	X2/ 0.245	X2/ 0.240	X2/ 0.259	X2/ 0.248	X2/ 0.226	X2/ 0.236	X2/ 0.215	X2/ 0.236	X2/ 0.234	X2/ 0.229

).

Gross domestic product (Y₁) emerges as a dominant obstacle factor across all cities in both provinces. In Jiangxi Province, cities such as Nanchang, Ganzhou, and Yichun exhibit GDP as the most significant constraint. In Hunan Province, with the exception of Changsha, GDP ranks as the primary obstacle factor in the remaining 12 cities. This pattern indicates that economic growth in most prefecture-level cities in the two provinces continues to be driven primarily by extensive scale expansion, while the reduction in water consumption per unit of output lags behind the pace of economic growth. As a result, resource and environmental constraints have not yet been fully internalized into development incentives. In several cities characterized by a relatively high proportion of resource-based and traditional manufacturing industries, progress in industrial structure upgrading and water-saving technological transformation remains limited, leading to sustained pressure on water resources induced by economic expansion. This further demonstrates that an economic structure biased toward extensive development has created a structural tension between “output expansion” and “efficiency improvement”, thereby constraining the coordinated enhancement of water use efficiency and the quality of urbanization development.

Fixed asset investment (X₂) appears 11 times in Jiangxi Province and 13 times in Hunan Province, acting as a universal restrictive factor. In most cities, fixed asset investment is still mainly concentrated on traditional infrastructure and real estate sectors, while insufficient investment is allocated to reclaimed water utilization projects, smart water affairs system construction and water-saving industrial upgrading, with capital allocation prioritizing short-term economic growth goals. Particularly in Hunan Province, fixed asset investment ranks among the top three obstacle factors in all prefecture-level cities, suggesting that the lagging optimization of investment structure has become a prevalent regional issue. It is indicated that if capital factors fail to incline toward water-saving technological innovation and industrial structure optimization, long-term driving forces for the improvement of water resources utilization efficiency cannot be formed, which will further hinder the high-quality development of urbanization.

Labor input (X₃) appears 10 times in Jiangxi and 12 times in Hunan, reflecting the profound impact of human resource structure on the improvement of water resource efficiency. In certain cities, the labor force remains concentrated in traditional manufacturing and resource processing industries, while high-skilled water-saving technical personnel and modern water management professionals are relatively scarce, resulting in slow progress in technological advancement and management efficiency improvement. Meanwhile, the synchronous increase in domestic and production water demand driven by labor force expansion further intensifies the pressure on the water resource system, given that water-saving technologies and management standards have not improved correspondingly. This indicates a structural imbalance between quantitative expansion and quality enhancement of labor factors, which constrains the endogenous growth capacity of water use efficiency.

Total water consumption (X₁) appears as a prominent obstacle factor only in Nanchang and Changsha, with Changsha ranking first while Nanchang ranks within the top three. As provincial capital cities, both exhibit a high degree of population and industrial agglomeration, resulting in rigid and continuously increasing water demand from industrial, service, and residential sectors. Consequently, the scale effect amplifies pressure on water resources, making it more pronounced in these urban centers. If upgrades to water supply systems and demand-side management lag behind, total water consumption will directly translate into a binding constraint on improvements in utilization efficiency. This finding indicates that, in major regional growth poles, environmental carrying capacity is gradually emerging as a critical limiting factor for enhancing the quality of urbanization development.

In summary, the obstacle factors at the water use efficiency indicator layer in Jiangxi and Hunan Provinces exhibit distinct structural characteristics: the pressure of economic scale expansion is widespread, the structure of capital investment has yet to be optimized, and the improvement of labor quality lags behind, while core cities face additional rigid constraints on total water use. The essence lies in the fact that the development model remains in a transitional phase from factor-driven to efficiency-driven growth. Without systematic adjustments through industrial upgrading, optimization of investment structure, and enhancement of human capital, the level of coupling coordination between water use efficiency and urbanization will hardly achieve sustained improvement.

5.5. Analysis of the Mechanisms of Coupled Evolution in Jiangxi and Hunan Provinces

Based on the spatiotemporal evolution characteristics of the coupling coordination degree and the diagnostic results of obstacle factors presented above, this section further introduces the concept of feedback mechanisms from CAS theory to provide a mechanistic explanation of the evolutionary differences in coupling coordination degree between the two provinces, from the perspectives of feedback signals, transmission efficiency, and blocking nodes. Among them, “feedback signal” refers to the information released by changes in water resource utilization efficiency; “signal transmission” refers to the spatial process through which this information is conveyed between core and peripheral cities and is perceived and responded to by different actors; “transmission efficiency” refers to the extent to which this process can be effectively received and responded to by different cities; and “blocking node” refers to the factors that hinder the transmission and reception of signals during the conduction process.

The positive interaction between the new urbanization system and the water use efficiency system drives them toward high-level coupling, promotes the synergistic evolution of the urban-water system, and enhances the urbanization system’s adaptability to changing water resource conditions. In this process, urbanization exerts selective pressure on water resources through population agglomeration, industrial expansion, and spatial development; in turn, the water resources system provides feedback to urbanization actors via changes in supply capacity, environmental carrying capacity, and efficiency levels, guiding them to adjust their development strategies. In essence, the water use behavior of urbanization actors alters the state of the water resources system, and the resulting changes in system state are fed back as signals to influence subsequent decisions. This coupled evolution mechanism is illustrated in Figure 9.

In terms of transmission efficiency, Hunan exhibits a polarized pattern of “core responsiveness and peripheral lag,” with efficiency signals concentrated in Changsha and significantly attenuated in peripheral areas. As the provincial capital, Changsha benefits from industrial agglomeration, policy resources, and technology diffusion—competitive industries like construction machinery sustain high production efficiency, while policy concentration further enhances water use efficiency, continuously reinforcing the positive feedback loop. In contrast, peripheral cities, such as those in Western and Southern Hunan, lag behind due to weak industrial foundations, high costs of water-saving technology diffusion, and limited information access. As efficiency signals propagate outward, they attenuate step by step, forming a spatial gradient pattern with Changsha as the high-value center decreasing toward the periphery.

However, Jiangxi exhibits a diffuse pattern of “multi-point gradual response,” with overall signal intensity relatively weak but spatial coverage more evenly distributed. Its abundant water resources weaken price and administrative constraints—enterprises face long payback periods for water-saving retrofits, residents are insensitive to water price changes, and local governments experience lenient water resource assessment pressure. These three factors collectively hinder efficiency decline signals from entering the decision-making agendas of various actors, resulting in a slow initial triggering of the feedback loop. Consequently, the widespread improvement in coordination degree across most Jiangxi cities does not stem from internal system feedback, but rather from sustained external institutional intervention. Throughout the study period, the most stringent water resources management system remained in effect, with total water use and efficiency controls directly constraining urban water use through “external imposition”—a top-down administrative enforcement that did not rely on local governments’ proactive responses. This enabled cities to achieve passive efficiency improvements even without intrinsic motivation. These external constraints exerted relatively uniform influence across cities, preventing extreme core–periphery polarization and producing an evenly diffuse pattern.

Furthermore, the differing shifts in the centers of gravity between the two provinces reflect distinct pathways of feedback signal amplification and diffusion. Jiangxi’s northeastward shift indicates signal amplification mainly concentrated in the northern and northeastern regions, with weak transmission to central and southern areas—a “core-concentrated” pattern. Hunan’s southwestward shift indicates gradual signal spread from the CZX core to southwestern Hunan—a “core-diffusion” pathway. Structurally, Jiangxi exhibits a single-core pattern with Nanchang as the sole high-value coordination center and no secondary receiving nodes; feedback signals attenuate rapidly beyond Nanchang, hindering propagation to southern and western areas. Moreover, the Beijing–Kowloon Corridor’s lower efficiency compared to the Beijing–Guangzhou Corridor further restricts southward signal spread. In contrast, Hunan relies on the multi-centered Chang–Zhu–Tan urban agglomeration, where signals spread outward simultaneously from the triangular region, ensuring smoother transmission. The direction and extent of the center-of-gravity shifts thus reflect spatial attenuation differences as feedback signals propagate from core to periphery.

At the bottleneck level, the structural obstacles to water resource utilization efficiency in the two provinces show a high degree of convergence, with regional GDP, fixed-asset investment, and labor input appearing most frequently, revealing structural characteristics where economic scale plays a leading role while capital and labor act as synergistic constraints. This indicates that the bottleneck lies not in water endowment but in a shared factor-input-driven development model—where economic growth is highly dependent on water consumption and capital expansion, while approaches to improving efficiency through factor substitution have not yet become widespread, and changes in efficiency have not been effectively incorporated into the policy agenda. However, despite similar structural bottlenecks, the two provinces have diverged in their paths of coupled coordination: Jiangxi, driven by external institutional pressures, exhibits a passive and fragmented pattern; Hunan, propelled by the endogenous dynamics of its core cities, displays a proactive and polarized trend. This divergence is closely linked to the development capacity of core cities, industrial transformation, and regional coordination policies—under identical bottleneck conditions, different response strategies lead to different outcomes of coupling.

6. Conclusions and Recommendations

6.1. Research Conclusions

(1)

Urbanization in the two provinces has transitioned from a phase of accelerated growth to one of steady optimization. While overall urbanization levels continue to rise, imbalances in development within the provinces persist, and there remain significant disparities in the quality of development and the capacity to attract production factors across different regions. The divergence between core and peripheral cities reflects a gradual attenuation of growth momentum as it spreads across different levels; continued structural divergence may exacerbate the risk of weakened development momentum in peripheral cities and widening interprovincial disparities.

(2)

The water resource utilization efficiency in both provinces showed an improving trend, yet their evolutionary paths differed significantly, with regional imbalance remaining prominent. Jiangxi Province exhibited a clear “decline-first, rise-later” pattern, with relatively large fluctuations during the period. In its early years, the province was dominated by high water-consuming industries and lagged behind in water-saving technologies, which dragged down its efficiency. However, after the National Ecological Civilization Pilot Zone promoted industrial green transformation in 2016, its efficiency gradually recovered. In contrast, Hunan Province demonstrated a more stable and steadily increasing trend. Benefiting from the institutional dividends of the Chang–Zhu–Tan pilot zones, which were released earlier, Hunan achieved more consistent and smoother efficiency improvements, with notable demonstration and spillover effects from its core cities. Nevertheless, intra-provincial disparities remained evident.

(3)

The degree of coupling and coordination between new-type urbanization and water resource utilization efficiency has generally shown an upward trend, but significant spatial disparities exist, and the two provinces have followed different evolutionary paths. The differences in the spatial evolution paths of the two provinces reflect distinct institutional drivers and spatial transmission mechanisms. Hunan shows a “high-value agglomeration in CZX” pattern, aligned with the integration strategy—policy dividends accumulate in core cities, forming high-value zones. In later stages, some southwestern cities began to follow, supported by abundant water resources. Jiangxi shows a “pioneering breakthroughs in northern and northeastern” pattern, aligned with its spatial development strategy—as the forefront of industrial transfer from the Yangtze River Delta, policy concentration in this region drives gradual southward diffusion to central and southern Jiangxi.

(4)

The obstacle factors restricting the improvement of coupling coordination degree in the two provinces present prominent structural characteristics and obvious concentration, and the urbanization expansion pattern and factor allocation efficiency constitute the core. At the urbanization indicator level, completed real estate development investment and total retail sales of consumer goods are the dominant common obstacle factors for both provinces, which demonstrates that current urbanization development still relies heavily on capital expansion and demand-driven growth to a certain extent. Specifically, Jiangxi is more restricted by road infrastructure conditions, whereas Hunan faces greater pressure in terms of population density and clean energy development. From the perspective of water resources utilization efficiency indicators, regional gross domestic product, fixed asset investment and labor input are the most frequent obstacle factors. This reveals that most cities in the two provinces are still in the critical transition period from scale expansion to efficiency improvement, and the economic growth pattern, capital investment orientation, and human resource structure have not yet fully adapted to the constraints of resources and environment. Meanwhile, core cities such as Nanchang and Changsha are additionally confronted with rigid constraints on total water consumption, indicating that these growth pole cities have begun to encounter increasingly prominent resource carrying capacity limits.

6.2. Policy Recommendations

(1)

Adhere to region-specific strategies and build a differentiated and coordinated development pattern. Given the pronounced spatial heterogeneity in the coupling coordination level between the two provinces, targeted policy interventions should be formulated in accordance with regional development foundations, resource endowments, and dominant obstacle factors. In Jiangxi Province, greater emphasis should be placed on strengthening the radiating and driving role of Nanchang, while further enhancing the function of secondary growth poles such as Jiujiang, Shangrao, and Ganzhou. Efforts should be made to promote the outward diffusion of high-value areas toward central and southern Jiangxi, thereby facilitating more balanced regional development. In Hunan Province, continued efforts should focus on reinforcing the core leadership role of the CZX urban agglomeration, while simultaneously increasing policy support and capacity-building investment in lower-performing cities such as Zhangjiajie, Shaoyang, Yongzhou, and Huaihua. This approach aims to foster a gradient-coordinated development structure characterized by core cities as drivers, node cities as supports, and lagging regions as complementary catch-up zones.

(2)

Transform the factor-driven model and improve water resource utilization efficiency. Based on the obstacle identification results of the water resource utilization efficiency indicator layer, economic scale expansion, capital input intensity, and labor factor allocation are the main constraints on improving water resource utilization efficiency in the two provinces. Therefore, the development model should be shifted from factor-input-driven to technology-innovation- and efficiency-improvement-driven. Nanchang and Ganzhou should optimize the structure of fixed asset investment, accelerate the development of advanced manufacturing, electronic information, new energy, and water-conserving industries, strengthen the cultivation of skilled talents, and improve the efficiency of capital and labor input. Changsha, Zhuzhou, and Xiangtan should leverage their advantages in scientific and technological innovation and advanced manufacturing foundation, promote the digital and green transformation of industries such as construction machinery and rail transit, improve industrial water quota management and smart water affairs construction, and enhance the alignment between capital input and water resource utilization efficiency.

(3)

Optimize the urbanization development model and enhance the comprehensive carrying capacity of cities. In response to the main obstacles to urbanization development in the two provinces, urbanization should be promoted to shift from scale expansion to quality improvement. Ganzhou, Yichun, and Shangrao should address the shortcomings in public service facilities such as road transportation and gas pipeline networks, improve the comprehensive carrying capacity of cities, and promote the transformation of real estate investment from incremental development to urban renewal and affordable housing construction. Changsha, Yueyang, and Hengyang should strengthen the interconnection of infrastructure within metropolitan areas and urban agglomerations, optimize the spatial distribution of population and industries, cultivate new growth points such as modern service consumption and digital consumption, and enhance the supporting role of consumption in the development of new-type urbanization.

(4)

Cross-regional collaborative governance and dynamic monitoring mechanisms should be improved. Jiangxi and Hunan, both situated in the middle reaches of the Yangtze River, are closely interconnected in terms of resources and the environment. Coupling coordination in this context transcends the internal affairs of individual cities, encompassing instead a broader systematic endeavor that involves urban agglomerations, river basins, and cross-regional factor mobility. Accordingly, collaborative ties between the two provinces should be strengthened in water resource allocation, ecological protection, pollution control, and industrial planning, with a view to gradually institutionalizing cross-regional mechanisms for information sharing, joint governance, and policy coordination. Moreover, building on the coupling coordination degree and obstacle degree models, a regular monitoring and evaluation framework can be established to identify stage- and region-specific constraints in real time, thus informing evidence-based policy adjustments.

6.3. Comparative Analysis and Research Limitation

There is a complex, nonlinear, and multiscale feedback relationship between new-type urbanization and water use efficiency; current research still faces limitations in terms of data, indicators, and models. The model used in this study focuses on descriptive diagnosis and lacks sufficient explanation of underlying mechanisms; therefore, we introduced CAS feedback mechanisms to enhance the explanation of these mechanisms. However, the explanation of these mechanisms remains primarily qualitative and fails to fully quantify the contributions of each feedback pathway. Due to the limited sample scope of the two provinces, the generalizability of the conclusions remains to be verified. Future research should expand the coverage to more provinces and incorporate system dynamics or multi-agent simulation to optimize dynamic modeling.