Evaluation of Multi-Dimensional Coordinated Development in the Yangtze River Delta Urban Agglomeration Under the SDGs Framework

Abstract

The scientific evaluation of the coordinated development level of the Yangtze River Delta Urban Agglomeration is crucial for promoting the localization of the Sustainable Development Goals (SDGs). This study, based on the SDGs framework, utilizes data from 41 prefecture-level cities in the Yangtze River Delta from 2013 to 2023 to establish a five-dimensional evaluation index system, covering urban–rural integration (SDG 10), scientific and technological innovation (SDG 9), infrastructure (SDG 9.1), ecological environment (SDG 13/14/15), and public services (SDG 3/4/11). By applying the coupling coordination degree model, kernel density estimation, and the standard deviation ellipse method, the study systematically assesses the regional coordinated development level and its spatio-temporal evolution patterns. The findings reveal that from 2013 to 2023, the development indices of the five subsystems showed a fluctuating upward trend, with significant disparities in growth rate and stability. The overall regional coordination degree continuously improved, and differences diminished, with the coupling degree and coupling coordination degree exhibiting a “polarization followed by an overall leap” pattern. The coupling coordination degree evolved in three stages: “imbalance in mutual feedback among elements, strengthening of coordination mechanisms, and deepening of policy innovation”, with spatial differentiation and clustered development coexisting. Spatially, the distribution center shifted through three phases: “policy-driven”, “market-regulated”, and “technology-led”, forming an axial reconstruction from northwest to southeast, ultimately establishing a multi-center coordinated development system.

1. Introduction

Amid the profound restructuring of the global economic landscape and China’s ongoing pursuit of high-quality and sustainable development, the Yangtze River Delta Urban Agglomeration (hereinafter referred to as the Yangtze River Delta) plays a pivotal role as a core platform for implementing SDG 11 (Sustainable Cities and Communities) and SDG 9 (Industry, Innovation, and Infrastructure). Its level of multi-dimensional coordinated development is closely linked to the achievement of China’s broader development strategy [1].

The report of the 20th National Congress of the Communist Party of China highlights “promoting coordinated regional development” alongside “advancing green development”, offering critical policy guidance for the Yangtze River Delta in localizing the SDGs. In October 2024, during an inspection tour in Anhui Province, General Secretary Xi Jinping stressed that “the coordinated development of the Yangtze River Delta should drive balanced provincial development”, further emphasizing the importance of regional integration in addressing SDG 10 (Reduced Inequalities).

Notable progress has been made in implementing the SDGs in this region. By the end of 2023, the operational mileage of high-speed railways exceeded 6500 kilometers, enabling one-hour travel between core cities (SDG 9.1). In the realm of public services (SDGs 3.8 and 4.4), over 8 million cross-provincial medical settlements were recorded in 2022. On the environmental front (SDG 13.2), energy consumption per unit of GDP dropped by 28.6% compared with 2013.

However, several bottlenecks persist. In 2023, a 2.47-fold per capita GDP gap remained between Shanghai and Anhui, reflecting persistent regional disparities (SDG 10.1). The industrial isomorphism coefficient reached 0.68 in 2021, alongside fragmented R&D resource allocation (SDG 9.5). Moreover, environmental protection standards remained inconsistent across 20% of inter-provincial border areas in 2022 [2], indicating institutional segmentation (SDG 17.6).

Against this backdrop, this study seeks to address the following questions:

RQ1: How can an evaluation framework for the Yangtze River Delta’s multi-dimensional coordinated development be constructed based on the SDGs?

RQ2: Does a coordination trap exist within the five-dimensional system comprising urban–rural integration, innovation, infrastructure, environment, and public services?

RQ3: Which policy instruments can effectively facilitate the regional governance transformation required under SDG 17 (Partnerships for the Goals)?

Grounded in the SDGs framework, this study constructs a multidimensional evaluation system for coordinated development in the Yangtze River Delta, aiming to contribute theoretical insights to high-quality sustainable development and provide practical tools for localizing the SDGs. Unlike the market-oriented regional integration models in Europe and the United States, China’s approach is characterized by strong institutional features. The coordinated development of the Yangtze River Delta is primarily guided by national strategic planning, intergovernmental policy coordination, and infrastructure-led spatial integration. This state-led model—marked by government leadership, policy alignment, and institutional innovation—reflects the distinctive logic of regional governance in China and offers essential context for interpreting the collaborative dynamics analyzed in this study.

2. Literature Review

Research on regional coordinated development has evolved from a single economic perspective to a multi-dimensional, integrated systems approach. Early studies, such as the market barrier elimination theory proposed by Balassa (1961) [3], focused on the effects of customs unions on resource allocation efficiency and laid the economic groundwork for regional integration. In the 1990s, the rise of the new economic geography school, led by Krugman (1991) [4], incorporated spatial dimensions into the analysis and introduced theories of industrial agglomeration and diffusion, providing new insights into the spatial logic of regional development.

Entering the 21st century, the growing emphasis on sustainable development has brought ecological co-governance into the spotlight, contributing to the formation of a composite theoretical framework encompassing market integration, spatial restructuring, institutional coordination, and ecological co-governance [5,6]. Within the context of China’s development trajectory, Lu’s (1988) [7] “point-axis system” was the first to propose a spatial development model tailored to China’s geographic realities. Later, research on “administrative region economies” delved into how administrative boundaries constrain regional cooperation [8].

In recent years, under the guidance of China’s new development philosophy, regional coordination research has undergone three major transformations. These include governance model innovation driven by the digital economy (Li et al., 2025) [9], green transformation aligned with the “dual carbon” goals (Zhang et al., 2021; Shao et al., 2022) [10,11], and the development of collaborative mechanisms in national strategic regions such as the Greater Bay Area (Cui et al., 2025) [12]. Notably, the introduction of the United Nations’ 2030 Agenda for Sustainable Development Goals (SDGs) has injected renewed theoretical vitality into regional collaboration studies, promoting their evolution toward a more systematic, goal-oriented, and globally aligned paradigm.

The Strategic Environmental Assessment (SEA) is a crucial tool for supporting regional collaborative decision-making. Its core value lies in systematically evaluating the potential impacts across the economic, environmental, and social dimensions at the early stages of policy, plan, and program (PPP) decision-making (Therivel, 2012) [13]. Methodologically, SEA has evolved from early environmental impact inventories, such as the Leopold Matrix [14], into a more comprehensive assessment tool that integrates the triple-bottom-line framework for sustainable development.

However, existing SEA practices face several significant challenges: First, many methodologies still rely heavily on static case analyses, accounting for 45% of studies, and only 7% of the studies address cross-regional collaborative effects, which limits their ability to capture dynamic cumulative impacts (Fischer et al., 2012) [15]. Second, the assessment system remains fragmented, failing to adequately address cross-domain issues like climate change. There is a pressing need to facilitate multi-sector collaboration through “system alternatives” (Stoeglehner, 2020) [16]. Finally, the institutional design is often constrained by a compliance-driven model that is disconnected from policy planning, weakening its effectiveness as a strategic decision-making tool (Noble, 2020) [17].

These limitations are particularly pronounced in rapidly urbanizing areas, such as the Yangtze River Delta, where there is insufficient capacity to capture spatial heterogeneity, temporal dynamics, and imperfect cross-system coupling mechanisms, all of which weaken the decision-support functions.

To address these challenges, SEA tools can be enhanced by integrating “system alternatives” [16] and advanced intelligent algorithms (such as the LSTM neural network) [18]. This improvement would bolster SEA’s ability to perform dynamic simulations and multi-objective collaborative analyses, providing a more robust and scientifically grounded basis for decision-making in regional sustainable development.

With the deepening of SDG-related research, urban agglomerations—as advanced forms of regional coordinated development—have witnessed significant progress in both theoretical exploration and methodological innovation. Theoretically, researchers have integrated diverse perspectives—such as new economic geography [19], regional governance theory [20], and complex systems theory [21]—to construct an interdisciplinary analytical framework. Methodologically, various tools including the gravity model [22], social network analysis [23], spatial econometrics [24], and system dynamics modeling [25] have been employed. The incorporation of GIS technologies [26] and machine learning techniques [27] has further advanced the research toward greater precision and intelligence.

Internationally, the SDG assessment framework developed by Sachs et al. (2022) [28] and the urban monitoring guidelines published by UN-Habitat (2021) [29] have provided essential methodological support for analyzing the synergies among Goal 9 (Industry, Innovation and Infrastructure), Goal 11 (Sustainable Cities and Communities), and Goal 17 (Partnerships for the Goals). In the Chinese context, Qing et al. (2025) [30] developed a localized SDG-based evaluation model and demonstrated the utility of the coupling coordination method in regional sustainable development analysis. Collectively, these theoretical and methodological innovations provide a robust foundation for systematically evaluating the multi-dimensional, SDG-aligned development of the Yangtze River Delta Urban Agglomeration.

Based on the above literature review, the limitations of existing studies can be summarized as follows: (1) Most current research emphasizes specific dimensions such as the economy or the environment, while paying insufficient attention to social aspects such as urban–rural integration (SDG 10) and public service equalization (SDGs 3–4). Moreover, a comprehensive multi-dimensional evaluation framework is generally lacking. (2) Many SDG-related studies focus on the attainment of individual goals, with limited exploration of the dynamic coupling mechanisms between goals (e.g., synergy thresholds between SDG 9 and SDG 11) and their spatial differentiation patterns.

To address these gaps, this study proposes a five-dimensional evaluation system encompassing urban–rural integration, technological innovation, infrastructure, ecological environment, and public services. Using the coupling coordination model and GIS-based spatial analysis, it aims to reveal the dynamic patterns of SDG-coordinated development in the Yangtze River Delta, thereby providing scientific evidence and policy recommendations for regional sustainable development.

The potential marginal contributions of this research are twofold: (1) Theoretically, it seeks to integrate the UN Sustainable Development Goals (SDGs) framework into regional coordination studies by constructing a multi-dimensional evaluation system involving Goals 9, 11, and others, thereby exploring the synergistic mechanisms among SDGs. (2) Practically, it develops a localized SDG assessment tool based on the unique characteristics of the Yangtze River Delta, offering actionable insights for the regional implementation of the 2030 Agenda for Sustainable Development.

3. Mechanisms of Coupling and Coordination in the Five-Dimensional Collaborative Development of the Yangtze River Delta Urban Agglomeration

The coupling and coordination mechanism refers to the collaborative evolution of multiple subsystems within a complex system through nonlinear interactions [31]. Its core characteristics are as follows: (1) Mutual feedback of elements—each subsystem maintains a reciprocal relationship through the exchange of matter, energy, and information [32]. (2) Dynamic equilibrium—the system remains in a relatively coordinated state despite fluctuations [33]. (3) Threshold effects—when the coupling degree exceeds a critical threshold, it can trigger qualitative transformations in the system [34]. In the context of regional development, this mechanism provides a solid theoretical basis for analyzing the multi-dimensional coordination of the economy, society, and environment. Building on this theoretical framework, the paper focuses on the coupling and coordination mechanism of a five-dimensional system encompassing urban–rural integration (SDG 10), scientific and technological innovation (SDG 9), infrastructure (SDG 9.1), ecological environment (SDG 13/14/15), and public services (SDGs 3/4/11) in the Yangtze River Delta. By constructing an analytical framework that integrates the mutual feedback mechanism, dynamic equilibrium mechanism, and threshold transition mechanism, this study aims to systematically explore the internal dynamics and coordination laws in the process of achieving the Sustainable Development Goals (SDGs).

3.1. Element Mutual Feedback Mechanism: Synergy and Spatial Differentiation of SDG Goals

The five subsystems—urban–rural integration, scientific and technological innovation, infrastructure, ecological environment, and public services—in the Yangtze River Delta form a multi-level feedback network through nonlinear interactions involving matter, energy, and information. Urban-rural integration (SDG 10) provides human capital and real-world testing grounds for scientific and technological innovation (SDG 9) through reforms in the household registration system and the marketization of production factors. For example, the “USTC Silicon Valley” in Hefei has attracted a significant number of returning science and technology professionals. Moreover, the diffusion of digital technologies into rural areas (e.g., rural e-commerce) helps narrow the urban–rural income gap.

The high-speed rail network (SDG 9.1) reduces temporal and spatial barriers to cross-provincial medical services, thereby promoting the equalization of public services (SDG 3/4). In 2023, more than 8 million cross-regional medical insurance settlements were recorded in the Yangtze River Delta. Shared educational platforms, such as the “Yangtze River Delta University MOOC Alliance”, have further facilitated the digital upgrading of educational infrastructure.

The ecological environment (SDG 13/14/15) achieves multi-objective synergy. For instance, the ecological compensation mechanism in the Xin’an River Basin (SDG 14.1/15.1) improves water quality and protects biodiversity. Meanwhile, the reduction in energy consumption per unit of GDP (SDG 13.2) and the improvement in air quality (SDG 15.1) jointly drive the green and low-carbon transformation, establishing a positive feedback loop of “environmental improvement—industrial upgrading—sustainable development”.

However, due to limited infrastructure coverage and the outflow of innovation resources in peripheral cities such as Bozhou and Suzhou (Anhui), the intensity of mutual feedback among subsystems is significantly weaker than in core cities like Shanghai and Hangzhou. This spatial imbalance undermines the overall regional coordination and highlights the need for targeted policy interventions.

3.2. Dynamic Equilibrium Mechanism: Policy Intervention and Adaptive Adjustment

The five-dimensional collaborative system in the Yangtze River Delta maintains a dynamic equilibrium amid ongoing regional disparities through the combined effects of policy instruments and market mechanisms.

At the policy level, gradient-based compensation mechanisms—such as the Yangtze River Delta Ecological Compensation Fund, which exceeded CNY 5 billion in 2023—help address disparities in environmental governance costs between southern Anhui and southern Jiangsu, thereby mitigating ecological governance lag in underdeveloped areas. The “Nine-City Integration” policy under the G60 Science and Technology Innovation Corridor (launched in 2016) has increased regional R&D investment intensity from 2.4% in 2013 to 2.8% in 2023, enhancing the synergy within the scientific and technological innovation subsystem.

At the market level, Shanghai Zhangjiang Science City has leveraged technology spillover effects (with a technology contract turnover surpassing 30% in 2023) to stimulate cities like Suzhou (Jiangsu) and Wuxi to develop complementary R&D-to-application functions, thereby optimizing the regional innovation value chain.

In addition, the system demonstrates significant adaptive resilience. For instance, in the aftermath of the COVID-19 pandemic, Hangzhou upgraded its “Health Code” platform into a “Digital Twin Hospital”, enabling the rapid restoration of equitable delivery of public health services.

This dynamic mechanism of policy guidance—market adjustment—resilient adaptation enables the Yangtze River Delta to maintain overall coordination in response to episodic imbalances, ensuring sustained momentum toward integrated and sustainable regional development.

3.3. Threshold Transition Mechanism: Critical Points and Spatial Reconstruction

When key parameters exceed critical thresholds, the coordinated development system in the Yangtze River Delta exhibits nonlinear leapfrogging behavior. The science and technology innovation subsystem reached a significant inflection point following the launch of the Science and Technology Innovation Board in 2019. The R&D intensity in Hefei’s quantum industry surged from 3.1% to 4.5% by 2023, signaling a qualitative shift from technological lock-in to cluster-driven breakthroughs.

In the infrastructure subsystem, once high-speed rail density surpassed 8.5 stations per 10,000 square kilometers (in 2021), peripheral cities such as Nantong and Ningbo were absorbed into a three-hour intercity mobility zone, promoting the formation of a polycentric regional network.

In the ecological environment dimension (SDG 13/14/15), when the per capita GDP of cities like Suzhou (Jiangsu) and Wuxi exceeded USD 15,000 in 2020, a 28% reduction in PM2.5 concentration was observed (a reverse indicator of SDG 15.1), the proportion of water sections meeting Class III water quality standards increased to 92% (SDG 14.1), and energy consumption per unit of GDP decreased by 32% (SDG 13.2). These changes collectively contributed to the coordinated improvement of climate action, water protection, and terrestrial ecology, marking a transition from single-point governance to a stage of “climate-water-land” coordinated value addition.

These threshold effects have triggered a stage-wise spatial restructuring. During the policy-driven phase (2013–2016), the spatial center of development shifted southeastward (e.g., the expansion of the Nanjing metropolitan area). In the market-regulated phase (2016–2020), freer factor mobility facilitated a more balanced development along the northwest–southeast axis. Since 2020, in the technological leapfrogging phase, the rise in the digital economy has led to a polycentric collaborative structure—with Shanghai as a financial hub, Hangzhou as a digital innovation engine, and Hefei as a science and technology nucleus.

Ultimately, through policy direction, market coordination, and technological propulsion, the Yangtze River Delta is establishing a new regional development paradigm characterized by high-efficiency spatial linkages and multi-dimensional synergy.

4. Materials and Methods

4.1. Study Area

The Yangtze River Delta (YRD) Urban Agglomeration is located in eastern China along the Yangtze River Delta region (Figure 1), with geographical coordinates ranging from 118°19′ to 123°10′ E and 29°20′ to 32°34′ N. As a strategic intersection of China’s Belt and Road Initiative and the Yangtze River Economic Belt, the study area covers 41 prefecture-level cities across Shanghai Municipality and the provinces of Jiangsu, Zhejiang, and Anhui, spanning a total area of approximately 358,000 square kilometers. As one of the most economically dynamic regions in China, the YRD plays a pivotal role in regional integration, sustainable development, and national strategic planning, making it a representative case for analyzing high-quality and coordinated regional development.

The region features a large-scale economy, robust scientific and technological innovation capacity, and advanced infrastructure and resource integration. In recent years, with the deepening of the YRD regional integration strategy, inter-city coordination has been significantly enhanced. Policy alignment and cross-city collaboration have become key drivers of high-quality regional development. Against this backdrop, selecting the Yangtze River Delta Urban Agglomeration as the study area offers both theoretical and practical relevance.

4.2. Indicator System Construction for Multi-Dimensional Evaluation

Based on the framework of the United Nations Sustainable Development Goals (SDGs) and the connotation of integrated and high-quality development in the Yangtze River Delta, a comprehensive evaluation system has been established across five key dimensions: urban–rural integration, scientific and technological innovation, infrastructure, ecological environment, and public services. This system aligns both with the local implementation imperatives of the SDGs—where urban–rural integration corresponds to SDG 10, scientific and technological innovation to SDG 9, infrastructure to SDG 9.1, the ecological environment to SDG 13/14/15, and public services to SDG 3/4/11—and with the strategic goals outlined in the Outline of the Yangtze River Delta Regional Integration Development Plan.

Drawing on the relevant literature and indicator systems [35,36], each dimension is matched with representative, measurable indicators.

• For urban–rural integration, the urban–rural income ratio, per capita GDP, and the urbanization rate of the permanent population reflect income equity, economic development level, and factor mobility, respectively.
• For scientific and technological innovation, R&D expenditure as a percentage of GDP, the patent authorization rate, and the proportion of high-tech industries assess innovation input, output, and transformation capacity.
• For infrastructure, expressway density, fixed asset investment, and per capita postal and telecommunications business volume capture transport coverage, investment intensity, and service efficiency.
• For ecological environment, this study highlights the dual goals of ecological protection and climate action. Three core indicators are selected, as follows: energy consumption per unit of GDP (SDG 13), surface water quality compliance rate (SDG 14), and the percentage of days with good air quality (SDG 15). An evaluation system is developed from three perspectives: energy utilization, aquatic ecosystems, and air quality. This system not only emphasizes the core objectives of climate action (SDG 13) but also incorporates the ecological protection goals for water bodies (SDG 14) and land ecosystems (SDG 15), systematically assessing the synergistic effects of sustainable regional ecological development.
• For public services, per capita fiscal expenditure, the gross enrollment ratio in upper secondary education, and the number of hospital beds per 1000 people indicate fiscal input, educational accessibility, and healthcare resource allocation.

These indicators were selected based on data availability, operability, and representativeness, and are capable of comprehensively capturing the multi-dimensional performance of the Yangtze River Delta in the course of advancing the SDGs.

In conclusion, this study constructs a five-dimensional indicator system comprising 15 measurable indicators (

Table 1. Comprehensive evaluation index system for urban–rural integration, technological innovation, infrastructure, ecological environment, and public services in the Yangtze River Delta.

Evaluation Level	Evaluation Indicators	Computing Method	Indicator Attribute
Urban–Rural Integration	Urban–Rural Income Gap (Times)	Per capita disposable income of urban residents/Per capita disposable income of rural residents	−
	Per Capita GDP (CNY per Capita)	GDP/Number of permanent residents	+
	Urbanization Rate of Permanent Population (%)	Urban population/Permanent resident population	+
Technological Innovation	R&D Investment Intensity (%)	Total R&D investment of the whole society/GDP	+
	Patent Authorization Rate (%)	Number of authorized patents/Number of patent applications	+
	Proportion of High-Tech Industry Output in Total Industrial Output (%)	Output value of high-tech industries/Gross output value of industrial enterprises above designated size	+
Infrastructure	Expressway Density (%)	Total mileage of expressways/Land area of the region	+
	Fixed Asset Investment Rate (%)	Fixed asset investment/GDP	+
	Per Capita Postal and Telecommunications Business Volume (CNY per Capita)	Total postal and telecommunications business volume/Number of permanent residents	+
Ecological Environment	Excellent and Good Air Quality Rate (%)	Days when AQI reaches or exceeds the national second-level quality standard/Total number of days	+
	Surface Water Quality Compliance Rate (%)	Number of surface water monitoring sections meeting Class III or above water quality standards/Total number of monitoring sections	+
	Energy Consumption per Unit of GDP (Tons of Standard Coal per 10,000 CNY)	Total energy consumption/GDP	−
Public Services	Per Capita Public Fiscal Expenditure (CNY per Capita)	General public budget expenditure of the region/Number of permanent residents in the region	+
	Number of Hospital Beds per Thousand People (Beds per Thousand People)	(Number of hospital beds in medical and health institutions/Total population) * 1000	+
	Senior High School Education Enrollment Rate (%)	(Number of students in regular senior high schools + Number of students in adult senior high schools + Number of students in secondary vocational schools)/Total population in the eligible age group for senior high school education	+

), forming the basis for evaluating the degree of coupling and coordination across key domains of sustainable regional development.

4.3. Data Sources

This study uses panel data from 41 prefecture-level cities in the Yangtze River Delta region, covering the period from 2013 to 2023. The data are primarily obtained from the Shanghai Statistical Yearbook, Jiangsu Statistical Yearbook, Zhejiang Statistical Yearbook, and Anhui Statistical Yearbook, as well as from the statistical yearbooks, statistical bulletins, and official websites of the statistics bureaus of each prefecture-level city.

To address the issue of missing values in certain indicators for some cities, the study employs interpolation and geometric mean methods based on adjacent-year data to ensure the completeness and consistency of the dataset.

4.4. Research Methods

4.4.1. Methodological Framework

To clearly illustrate the logical structure of the study, a methodological framework is presented in Figure 2, which outlines the major research phases, methods employed, and indicator system under the SDGs framework.

4.4.2. Max–Min Normalization

To address issues related to dimensional differences and inconsistent indicator directions in the original data, this study applies the range normalization method to standardize the data [37]. The method is defined as follows:

$$\mathrm{For} \mathrm{a} \mathrm{positive} \mathrm{indicator}: x_{ij}^{\prime }=\left(x_{ij}-min\left(x_{ij}\right)\right)/\left(max\left(x_{ij}\right)-min\left(x_{ij}\right)\right)$$

(1)

$$\mathrm{For} \mathrm{a} \mathrm{negative} \mathrm{indicator}: x_{ij}^{\prime }=\left(max\left(x_{ij}\right)-x_{ij}\right)/\left(max\left(x_{ij}\right)-min\left(x_{ij}\right)\right)$$

(2)

where $x_{ij}^{\prime }$ represents the original value of the j-th indicator for the i-th city (i = 1, 2, …, m; j = 1, 2, …, n), and the $max\left(x_{ij}\right)$ and $min\left(x_{ij}\right)$ are the maximum and minimum values of the $x_{ij}$ indicator, respectively. $x_{ij}^{\prime }$ is the standardized value of the data.

4.4.3. CRITIC-Entropy Weight Method Combined Weight Model

When constructing a multi-index comprehensive evaluation system, the scientific determination of weights is crucial. The entropy weight method, based on information entropy theory, objectively assigns weights according to the degree of data dispersion. However, it does not account for the correlation between indicators. The CRITIC method addresses this limitation by incorporating the coefficient of variation and conflict analysis of the indicators. This study combines the CRITIC method (Equation (3)) with the entropy weight method (Equation (4)) to construct a combined weight model (Equation (5)). By linearly weighting both data dispersion and indicator correlation, a comprehensive evaluation index (Equation (6)) is derived [38]. This method aims to provide a more scientific and accurate multi-index evaluation framework, facilitating regional development research and decision-making [39].

$$W_j^1={\displaystyle \frac{{\sigma }_j{\sum }_{h=1}^n\left(1-\left|r_{hj}\right|\right)}{{\sum }_{j=1}^n\left[{\sigma }_j{\sum }_{h=1}^n\left(1-\left|r_{hj}\right|\right)\right]}}$$

(3)

$$W_j^2={\displaystyle \frac{1-e_j}{{\sum }_{j=1}^n\left(1-e_j\right)}},\hspace{1em}{e}_j={-\left(lnm\right)}^{-1}{\sum }_{i=1}^m{p}_{ij}ln{p}_{ij},\hspace{1em}{p}_{ij}={\displaystyle \frac{x_{ij}^{\prime }}{{\sum }_{i=1}^m{x}_{ij}^{\prime }}}$$

(4)

$$W_j=\left(W_j^1+W_j^2\right)/2$$

(5)

$$I_k={\sum }_{j=1}^n{W}_j\times x_{ij}^{\prime }$$

(6)

where

• $W_j^1$ is the weight of the j-th indicator, calculated using the CRITIC method;
• ${\sigma }_j$ is the standard deviation of the j-th indicator;
• $r_{hj}$ is the correlation coefficient between the h-th and j-th indicators;
• $W_j^2$ is the weight of the j-th indicator determined by the entropy weight method;
• $p_{ij}$ is the proportion of the data for the i-th city under the j-th indicator relative to the total value of this indicator;
• $e_j$ is the entropy value of the j-th indicator;
• $W_j$ is the combined weight of the j-th indicator derived from both the CRITIC and entropy weight methods;
• $I_k (k=1,2,3,4,5)$ is the evaluation index of the system after linear weighting, representing the development index of each system.

4.4.4. Revised Coupling Coordination Degree Model

With the continuous advancement of social science research, the coupling coordination degree model has become a key tool for evaluating regional coordinated development. However, as its application has become more widespread, certain limitations have gradually emerged. To enhance the model’s discriminant validity, address the calculation boundary issues inherent in traditional models, and more accurately reflect the true meaning of the coupling coordination degree, this study reviews existing coupling coordination models and their revised versions. Based on this review and the research requirements, the revised model proposed by Fan Dongjun et al. [40] is selected for empirical analysis. The model is expressed in Formulas (7)–(9), as follows:

$$C=1-2\sqrt{{\displaystyle \frac{5{\sum }_{k=1}^5{I}_k^2-{\left({\sum }_{k=1}^5{I}_k\right)}^2}{5^2}}}$$

(7)

$$T={\sum }_{k=1}^5{\alpha }_k\times I_k,\hspace{1em}{\sum }_{k=1}^5{\alpha }_k=1$$

(8)

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

(9)

where

• C represents the coupling degree;
• T represents the coordination degree;
• D represents the coupling coordination degree;
• ${\alpha }_k (k=1,2,3,4,5)$ are specific weights.

In this study, we assume equal importance of the five dimensions—urban–rural integration, technological innovation, infrastructure, ecological environment, and public services—in the coordinated development of the Yangtze River Delta. Therefore, the weights are assigned as follows: ${\alpha }_k=0.2 (k=1,2,3,4,5)$.

To address the issue of uneven distribution inherent in the traditional classification method for the coupling coordination degree—and the increasing complexity and judgment deviation that arise as the number of subsystems grows—this paper adopts an improved approach. Drawing on the model proposed by Fan Dongjun et al. [40] and considering the characteristics of the research data, the Monte Carlo simulation method is employed to achieve equiprobable classification of the coupling coordination degree. The specific steps are as follows:

• Parameter Setting: Based on 451 sets of observational data from 41 cities in the Yangtze River Delta between 2013 and 2023, the theoretical range of the coupling coordination degree is set to [0, 1].
• Random Sampling: Using Matlab R2022a, the mvnrnd function is employed to generate 105 sets of virtual samples following a joint normal distribution.
• Probability Classification: The empirical cumulative distribution function (ECDF) is calculated, and classification thresholds are determined based on 20% equiprobable intervals. This process is iterated 100 times, and the mean values are taken to mitigate random errors.

The Kolmogorov–Smirnov (K–S) test confirms that there is no significant difference between the simulated and empirical distributions (D = 0.032, p = 0.15). The final classification results are presented in

Table 2. Classification of coupling coordination degree.

Coupling Coordination Degree Range	Coupling Coordination Degree Level
[0.0000,0.4615)	Barely Coupled and Coordinated
[0.4615,0.5477)	Low-Level Coupling and Coordination
[0.5477,0.6254)	Moderate-Level Coupling and Coordination
[0.6254,0.7358)	High-Level Coupling and Coordination
[0.7358,1.0000]	Extremely Coupling and Coordinated

. The standard errors of the critical thresholds are all within ±0.008.

It is worth noting that the Monte Carlo simulation was employed exclusively for the classification of the coupling coordination degree, whereas the subsequent kernel density estimation was used solely for descriptive distributional analysis. All data from 2013 to 2023 were treated as a pooled cross-sectional sample, and temporality was not modeled as a separate factor during the simulation process.

4.4.5. Kernel Density Estimation

Kernel Density Estimation (KDE), as a non-parametric method, constructs a smooth and continuous probability density curve without assuming any specific data distribution. This approach allows for an objective reflection of the true distribution characteristics of variables. By adopting a data-driven modeling technique, KDE effectively avoids the biases associated with traditional parametric methods, which are constrained by pre-specified distribution forms. It is particularly well suited for revealing the spatial agglomeration characteristics, dynamic evolution patterns, and multi-peak structures of coordinated development levels in the Yangtze River Delta.

In this study, the Gaussian kernel function is used for continuous modeling (Equations (10) and (11)). The goal is to uncover the underlying patterns of regional coordination and differentiation mechanisms through density curve analysis, providing a non-parametric quantitative basis for policy evaluation.

$$f\left(x\right)={\displaystyle \frac{1}{nh}}{\sum }_{i=1}^{n}K({\displaystyle \frac{X_i-\overline{x}}{h}})$$

(10)

$$K\left(x\right)={\displaystyle \frac{1}{\sqrt{2\pi }}}exp(-{\displaystyle \frac{x^2}{2}})$$

(11)

where

• $f\left(x\right)$ represents the probability density function.
• n is the number of cities.
• h is the bandwidth, or smoothing parameter of the curve.
• $X_i$ denotes the sample observation.
• $\overline{x}$ is the sample mean.
• $K\left(x\right)$ is the Gaussian kernel function.

4.4.6. Standard Deviational Ellipse

The standard deviational ellipse, first proposed by American sociology professor Lefever [41], is a classic method for characterizing the directional features of spatial distribution. This method calculates the standard deviation and covariance of the data points to generate an ellipse that describes both the spatial distribution range and directional features. Parameters such as the centroid, major and minor axes, area, azimuth, and flattening ratio of the ellipse can effectively reveal the spatial distribution trends and directional characteristics of the variables. The specific calculation steps are outlined in Formulas (12)–(18), as follows:

Centroid Coordinates:

$$G\left(X,Y\right)=[{\sum }_{i=1}^n{\omega }_i\times x_i/{\sum }_{i=1}^n{\omega }_i, {\sum }_{i=1}^n{\omega }_i\times y_i/{\sum }_{i=1}^n{\omega }_i]$$

(12)

${\omega }_i$ represents the coupling coordination degree of each city

Major and Minor Axes:

$${\sigma }_x=\sqrt{{\sum }_{i=1}^n{{\omega }_i{\overline{x}}_i\mathit{cos}\theta -{\omega }_i{\overline{y}}_i\mathit{sin}\theta )}^2/{\sum }_{i=1}^n{\omega }_i^2}$$

(13)

$${\sigma }_y=\sqrt{{\sum }_{i=1}^n{{(\omega }_i{\overline{x}}_i\mathit{cos}\theta -{\omega }_i{\overline{y}}_i\mathit{sin}\theta )}^2/{\sum }_{i=1}^n{\omega }_i^2}$$

(14)

$$\overline{x}=x_i-X, \overline{y}=y_i-Y,$$

(15)

where $x_i$ and $y_i$ are the longitude and latitude of each city, respectively

Area:

$$S=\pi {\sigma }_x{\sigma }_y$$

(16)

Azimuth:

$$tan\theta =(\left({\sum }_{i=1}^n{\omega }^2{{\overline{x}}_i}^2-{\sum }_{i=1}^n{\omega }^2{{\overline{y}}_i}^2\right)+\sqrt{{\left({\sum }_{i=1}^n{\omega }^2{{\overline{x}}_i}^2-{\sum }_{i=1}^n{\omega }^2{{\overline{y}}_i}^2\right)}^2+4{\sum }_{i=1}^n{\omega }_i{{\overline{x}}_i}^2{{\overline{y}}_i}^2})/2{\sum }_{i=1}^n{\omega }_i^2{\overline{x}}_i{\overline{y}}_i$$

(17)

where

• $\theta$ is the azimuth;
• ${\overline{x}}_i$ and ${\overline{y}}_i$ are the coordinate deviations of the geographical coordinates of each city from the centroid of the ellipse, respectively.

Flattening Ratio:

$$e=({\sigma }_x-{\sigma }_y)/{\sigma }_x$$

(18)

5. Empirical Analysis

5.1. Evaluation and Analysis of Development Indices in the Yangtze River Delta Urban Agglomeration

Based on the aforementioned research methods and indicator system, the average development indices of urban–rural integration, scientific and technological innovation, infrastructure, ecological environment, and public services in the Yangtze River Delta Urban Agglomeration were calculated and evaluated. The results are summarized in

Table 3. Average development index for urban–rural integration, technological innovation, infrastructure, ecological environment, and public services in the Yangtze River Delta Urban Agglomeration (2013–2023).

Year	Urban–Rural Integration Development Index	Infrastructure Development Index	Science and Technology Innovation Development Index	Ecological and Environmental Development Index	Public Service Development Index
2013	0.0360	0.2171	0.1141	0.3094	0.0756
2014	0.2848	0.2842	0.1979	0.3913	0.1822
2015	0.3146	0.3750	0.3073	0.3493	0.3286
2016	0.3756	0.2901	0.3752	0.4357	0.3452
2017	0.4402	0.2887	0.4106	0.3834	0.4302
2018	0.5114	0.3659	0.4556	0.4679	0.5453
2019	0.5852	0.4757	0.5515	0.5557	0.6570
2020	0.6934	0.6285	0.6224	0.8084	0.7176
2021	0.7924	0.6370	0.5303	0.8652	0.7723
2022	0.8862	0.7056	0.6527	0.8093	0.8640
2023	0.9923	0.7897	0.6939	0.8445	0.9101
Mean Value	0.5375	0.4598	0.4465	0.5655	0.5298

, while Figure 3 visually illustrates the temporal trends of these five dimensions from 2013 to 2023.

The urban–rural integration development index demonstrated exponential growth, rising from 0.0360 in 2013 to 0.9923 in 2023, with a compound annual growth rate (CAGR) of 38.9%. A notable inflection point occurred in 2014, with a year-on-year increase of 691%, which closely aligns with the launch of the National New Urbanization Plan (2014–2020). Thereafter, the index exhibited stable growth at an average annual rate of 15.2%, reflecting the continued influence of institutional reforms such as household registration liberalization and the market-oriented allocation of urban–rural factors. By 2023, the index had approached its theoretical maximum value of 1.0000 (0.9101), suggesting that urban–rural integration in the Yangtze River Delta region has entered a mature phase.

In parallel, the public service development index also exhibited a steady upward trend, recording a CAGR of 28.1%. Three distinct periods showed notable surges: in 2015 (+80.4% year-on-year), 2019 (+20.6%), and 2022 (+13.2%), corresponding, respectively, to the implementation of the National Plan for Equalizing Basic Public Services, the launch of a cross-provincial medical insurance settlement within the Yangtze River Delta, and the post-pandemic strengthening of public health services. By 2023, the index reached a high of 0.9101, making it one of the most advanced areas in terms of coordinated development in the region.

Infrastructure development has exhibited distinct phase-based evolutionary characteristics. The rapid expansion phase from 2013 to 2015 (CAGR = 31.4%) was primarily driven by the concentrated release of infrastructure investments during the final stage of the Twelfth Five-Year Plan. A short-term adjustment followed between 2016 and 2017 (annual decline of 6.1%), reflecting cyclical fluctuations typically associated with large-scale projects. Since 2018, as the coordinated development of the Yangtze River Delta was elevated to a national strategy, infrastructure development has entered a high-quality growth phase (CAGR = 22.3% from 2018 to 2023), reaching 0.7897 by the end of the study period and marking a strategic transition from scale expansion to quality improvement.

Scientific and technological innovation followed a typical “S”-shaped growth trajectory. A steady upward trend from 2013 to 2018 (CAGR = 26.0%) showed a strong positive correlation with increasing R&D investment intensity (R&D expenditure rising from 2.4% to 2.8%). However, between 2019 and 2021, disruptions such as the global semiconductor supply chain shock led to a volatile phase, with a sharp 14.8% decline in 2021. Although a recovery phase emerged from 2022 to 2023 (average annual growth of 12.3%), the evident slowdown suggests that innovation-driven development is facing new bottlenecks.

The development of the ecological environment shows significant volatility. During the study period, the index steadily increased from 0.3094 in 2013 to 0.8084 in 2020, with an average annual growth rate of 17.1%, reflecting notable progress in regional ecological governance. However, a temporary decline occurred between 2020 and 2022. In 2022, the index dropped by 6.5% compared to 2020, likely due to the environmental pressures associated with the rapid recovery of industrial production during the post-pandemic economic rebound. As the collaborative governance mechanism strengthened, the index rebounded to a record high of 0.8445 in 2023. This fluctuating trend not only illustrates the dynamic balance between economic growth and ecological protection but also highlights the long-term and complex nature of ecological governance. Therefore, it is essential to establish a dynamic regulatory mechanism that adapts to economic fluctuations.

Based on the empirical analysis of the five development dimensions in the Yangtze River Delta from 2013 to 2023, each dimension exhibited a differentiated upward trend, with varying degrees of fluctuation. In terms of average levels, the ecological environment development index ranked first at 0.5655, underscoring the effectiveness of the region’s ecological priority strategy. The urban–rural integration index (0.5375) and public service index (0.5298) followed closely, reflecting notable progress in improving people’s livelihoods. In contrast, the infrastructure index (0.4598) and the scientific and technological innovation index (0.4465) lagged behind, highlighting evident shortcomings in cross-regional coordination and innovation capacity enhancement.

5.2. Spatiotemporal Evolution of the Coupling Coordination Degree of Five-Dimensional Coordinated Development in the Yangtze River Delta Urban Agglomeration

5.2.1. Temporal Evolution Characteristics Based on the Synergy Among SDGs

This study employs MATLAB R2022 to generate three-dimensional kernel density estimation surface plots for the coordination degree, coupling degree, and coupling coordination degree associated with five-dimensional coordinated development—namely urban–rural integration, scientific and technological innovation, infrastructure, ecological environment, and public services—in the Yangtze River Delta region from 2013 to 2023. These visualizations illustrate the temporal evolution characteristics of regional sustainable development (Figure 4).

(1)

Coordination Degree

Based on the annual data of 41 cities in the Yangtze River Delta from 2013 to 2023, the kernel density curve of the coordination degree consistently displayed a unimodal shape throughout the entire study period. This indicates that there was no significant polarization in regional coordination, and the development level remained concentrated.

From the perspective of distribution evolution, the curve gradually shifted to the right, reflecting a steady improvement in regional coordination. This trend can be attributed to the deepening policy synergies and infrastructure connectivity in the Yangtze River Delta.

The peak height of the curve fluctuated in phases, showing a pattern of “rising—falling—rising again,” suggesting that the concentration of regional coordination was influenced by both macroeconomic cycles and policy interventions. Particularly, after the integration of the Yangtze River Delta was elevated to a national strategy in 2018, the second rise in the peak was especially noticeable.

Regarding distribution width, the curve exhibits a dynamic process of “slight contraction—widening—significant contraction,” indicating that regional disparities initially expanded but later converged, with internal coordination gradually improving. In terms of the tail shape, the curve evolved from an initial structure with a long left tail and a short right tail to a narrowing right-tail structure in the later stages. This reflects an increasing number of cities with high coordination levels and highlights the “the superior get better” agglomeration effect, further emphasizing regional development advantages.

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Coupling Degree

From 2013 to 2023, the kernel density curve of the coupling degree in the Yangtze River Delta Urban Agglomeration gradually evolved from an initial “flat unimodal” structure to a later “bimodal” shape, indicating a degree of polarization in the regional coupling level. Specifically, the continuous increase in the height difference between the primary and secondary peaks reflects that the core urban agglomeration has been strengthening its synergistic effects through innovation spillovers and industrial chain integration. In contrast, peripheral cities are constrained by barriers to factor flow and differences in development foundations, leading to a lag in the improvement of their coupling degree, which has intensified the “Matthew effect” within the region.

From a dynamic evolutionary perspective, the curve shifts gradually to the right, with its coverage stabilizing between 0.3 and 1, signaling a continuous rise in the overall coupling degree and an ongoing strengthening of synergy between systems. Notably, the height of the main peak saw three significant surges in 2016, 2019, and 2023, peaking in 2023. Considering the key milestones of the Outline of the Yangtze River Delta Regional Integration Development Plan, this suggests that policy interventions have effectively promoted the centralized development of functional coupling among cities.

Meanwhile, the narrowing width of the main peak indicates that the disparities in the coupling levels between cities have gradually diminished, and the system as a whole is converging toward a high-coupling state.

The evolution of the tail shape further corroborates this conclusion; in the later stages of the study, the contrast between the elongation of the left-hand tail and the contraction of the right-hand tail not only reinforces the “core-periphery” structure but also reveals the emergence of a collaborative agglomeration belt, centered around the G60 Science and Technology Innovation Corridor, within the region’s development.

(3)

Coupling Coordination Degree

The coupling coordination degree, which is derived from both the coupling degree and the coordination degree, is represented by a kernel density curve that integrates the features of both. The analysis shows that the distribution of the coupling coordination degree has evolved from an initial unimodal pattern to a bimodal structure, with the gap between the two peaks widening over time. This indicates a clear “core-periphery” differentiation in the region’s development. Notably, this polarization is accompanied by a pronounced gradient improvement: the center of the distribution has consistently shifted to the right, and the proportion of cities with high coupling coordination has increased year by year. This suggests a substantial improvement in the overall regional coordination level, driven by the core cities.

In terms of the evolution process, the improvement in coupling coordination degree exhibits three distinct stages: from 2013 to 2016, a period of slow growth; from 2017 to 2019, a period of accelerated improvement; and from 2020 to 2023, a period of high-quality development. This non-linear progression closely aligns with the implementation of major policies. In particular, following the release of the Outline of the Yangtze River Delta Regional Integrated Development Plan in 2019, the catalytic effect of core cities has been significantly strengthened, driving rapid improvements in the coordination levels of surrounding cities.

Finally, the spatial distribution of coupling coordination degree shows a pattern of gradient convergence. By 2023, the kernel density curve features a “high and narrow main peak” and a tail that is “wider on the left and narrower on the right”. This suggests that disparities among cities within the core area have diminished. Meanwhile, the coupling and coordination levels of peripheral cities have generally increased, validating the effectiveness of the policy approach of “driving the entire region with key points”.

5.2.2. Spatial Evolution Characteristics

(1)

Spatial Pattern Evolution

To further investigate the spatial differentiation characteristics of synergy among the Sustainable Development Goals (SDGs) in the Yangtze River Delta, this study selects key milestone years during the 12th to 14th Five-Year Plan periods (2013, 2016, 2020, and 2023). Using ArcGIS 10.8, spatial distribution maps of the five-dimensional coupling coordination degree are generated to visualize the dynamic spatial evolution patterns across the region (Figure 5).

From 2013 to 2016, the Yangtze River Delta entered the initial stage of SDG-oriented synergistic development, with the coupling coordination degree gradually improving from a barely coordinated status. In 2013 (mid-Twelfth Five-Year Plan period), all 41 cities in the region were at the primary level of coordination, facing systemic constraints such as industrial structure lock-in due to the dominance of traditional manufacturing, urban–rural dualism, insufficient investment in technological innovation, fragmented infrastructure development, uncoordinated ecological governance, and imbalanced public service provision.

With the conclusion of the Twelfth Five-Year Plan and the rollout of the Thirteenth Five-Year Plan, differentiated development trajectories began to emerge, as follows:

• As a typical city with ecological advantages, Quzhou has capitalized on its abundant natural resources—boasting a forest coverage rate of 82% and an excellent air quality rate of 96% in 2016—to achieve coordinated development across its economy, society, and ecological environment through a comprehensive green development strategy. Between 2013 and 2016, the municipal government implemented several key measures to support this transformation. These included formulating the “Implementation Measures for Ecological Compensation in Quzhou City,” which led to a total investment of CNY 6.9 billion in ecological compensation for water conservation areas. In addition, the city promoted the green transformation of industries by shutting down 37 highly polluting enterprises and renovating 89 traditional industries, which resulted in an 18.6% reduction in energy consumption per unit of GDP. Moreover, Quzhou developed ecological agriculture, establishing 56 organic agricultural product bases that saw an average annual output value growth of 24.3%. The coordinated implementation of these policies contributed to an increase in the city’s coupling coordination degree, which rose from 0.49 in 2013 to 0.62 in 2016. Notably, during the industrial transformation, the circular economy initiatives at the Qianjiangyuan Chemical Industrial Park led to a significant increase in waste heat utilization, rising from 62% in 2013 (before the transformation) to 85% in 2016 (Annual Environmental Monitoring Report of the Park, 2016). This outcome strongly supports the effectiveness of the ecological value transformation mechanism in practice.
• Twenty resource-based or old industrial cities remained trapped by the “resource curse” and “path dependence”. Characterized by a narrow industrial base, weak innovation capacity, continuous talent outflow, aging infrastructure, and persistent environmental degradation, these cities exhibited only marginal improvements in coupling coordination and remained at low development levels.
• Thirteen transitional cities actively pursued industrial upgrading and regional coordination by undertaking institutional reforms and reorganizing production factors. These efforts gradually moved them toward a medium level of coupling coordination.
• Seven relatively underdeveloped cities—including Huaibei, Suzhou (Anhui), and Bozhou—lacked development momentum due to marginal geographic positions, limited policy benefits, sluggish industrial transformation, and a weak ability to attract innovation factors. These cities faced the dual dilemma of declining traditional industries and immature emerging sectors, coupled with poor infrastructure connectivity that hindered integration with core urban areas.

This phase underscores the critical importance of ecological value realization and innovation-driven investment in breaking through the low-level development equilibrium and laying a solid foundation for the next stage of regional coordinated growth.

From 2016 to 2020, the Yangtze River Delta entered a phase of mechanism enhancement for the synergistic development of the SDGs, with a notable overall improvement in coupling coordination. Driven by national strategies, regional policy innovations in industrial collaboration (SDG 9) and ecological co-governance (SDG 15/17) raised the proportion of cities reaching high or above-high levels of coordination from 14.6% in 2016 to 82.9% in 2020.

Spatially, with the exception of Huangshan—limited by its ecological conservation priority—and Wuxi, Huainan, and Bozhou—hindered by rigid industrial structures and innovation constraints and thus remaining at a moderate coordination level—most cities progressed to a high level of coupling coordination or above.

This stage of regional development was marked by multiple driving forces. Cities in Zhejiang Province, such as Shaoxing and Wenzhou, capitalized on their vibrant private economies and first-mover advantages in digital transformation to promote the green and digital upgrading of traditional industries, achieving deep urban-rural industrial integration. Shanghai leveraged its strong resource allocation capacity and core metropolitan functions to lead regional coordination in industrial and supply chains, as well as public service sharing. In Jiangsu, cities like Changzhou and Yancheng actively participated in regional industrial division and collaborative innovation, forming distinctive industrial clusters and driving the collective advancement of northern Jiangsu. Meanwhile, Anhui Province used Hefei’s role as an innovation hub and the construction of a national science center to facilitate the transformation and application of scientific achievements in neighboring cities, creating a highly integrated development pattern where innovation and industrial chains were closely aligned.

This phase demonstrates that differentiated functional positioning combined with institutional coordination mechanisms was critical for accelerating SDG implementation.

From 2020 to 2023, the Yangtze River Delta entered a deepening phase of coordinated SDG development. By 2023—the mid-point of the 14th Five-Year Plan—the region’s coupling coordination level reached a historical peak, with an average value of 0.78, and 32 cities (78%) achieving extremely high coordination, signaling the basic formation of a high-quality integrated development pattern. However, significant internal disparities persisted. Nanjing, despite its core city status, experienced a decline in coordination level, primarily due to delays in implementing mixed land-use policies under the “One Core, Three Poles” spatial strategy, resulting in misalignment between new industrial land allocations and innovation infrastructure. Suqian’s index dropped from 0.65 (2020) to 0.58 (2023), mainly constrained by its reliance on traditional sectors such as textiles and building materials, underdeveloped high-tech industries, and below-average R&D investment, which hampered its position in the regional division of labor.

In contrast, cities like Huzhou and Jiaxing achieved notable progress through institutional innovation. Huzhou pioneered an inter-provincial GEP accounting and trading system and implemented the “Standard Land + Industrial Enclave” development model, significantly enhancing the annual ecological product value conversion rate. Jiaxing accelerated industrial upgrading through digital economy strategies. These initiatives illustrate diverse paths toward regional coordination.

Overall, from 2013 to 2023, with the exception of Nanjing, all cities in the Yangtze River Delta exhibited a generally upward yet fluctuating trend in coordination levels. Spatial differentiation and clustering coexisted, ultimately shaping a diversified and balanced regional development landscape centered on Shanghai, Hangzhou, and Hefei. This trajectory reflects both the effectiveness of regional coordination mechanisms and the multiplicity of urban transformation strategies.

(2)

Characteristics of Spatial Distribution Trends

Table 4. Centroid coordinates and movement distances of the five-dimensional coupling coordination degree (urban–rural integration, technological innovation, infrastructure, ecological environment, and public services) in the Yangtze River Delta Urban Agglomeration (2013–2023).

Year	Longitude	Latitude	Moving Distance (km)
2013	118.8473	31.5230	——
2014	118.9061	31.4693	8.8373
2015	118.9079	31.4872	1.9985
2016	118.8827	31.5160	4.2558
2017	118.9489	31.4573	9.8332
2018	118.8772	31.5177	10.4076
2019	118.9295	31.4712	7.7675
2020	118.9156	31.5062	4.1834
2021	118.9987	31.4349	12.1622
2022	118.9545	31.4424	4.9791
2023	118.9460	31.4439	0.9610

presents the centroid coordinates and spatial displacement values of the coupling coordination degree among the five-dimensional systems—urban–rural integration, scientific and technological innovation, infrastructure, ecological environment, and public services—in the Yangtze River Delta from 2013 to 2023. Utilizing the geographic distribution module of ArcGIS 10.8 and adopting the first standard deviation as the benchmark, this study selects the key years 2013, 2016, 2020, and 2023 to construct standard deviation ellipses and centroid migration trajectory maps (Figure 6), thereby offering an in-depth depiction of the spatiotemporal evolution patterns in regional coordinated development under the framework of the Sustainable Development Goals (SDGs).

(1)

The Migration of the Coupling Coordination Center of Gravity in the Yangtze River Delta Urban Agglomeration (2013–2023)

Research on the migration of the coupling coordination center of gravity in the Yangtze River Delta Urban Agglomeration from 2013 to 2023 reveals that the center of gravity shifted from Bowang District in Ma’anshan City (31.5230° N, 118.8473° E) to Lishui District in Nanjing City (31.4439° N, 118.9460° E), covering a total distance of 65.3856 km (a southward shift of 0.0791° and an eastward shift of 0.0986°). This movement reflects a clear three-stage evolutionary pattern, as follows:

• 2013–2016: Policy-Driven Phase

This phase was characterized by a complex spatial trajectory of “southeast breakthrough—northeast consolidation—northwest adjustment”. Between 2013 and 2014, driven by the national strategy for integrated development in the Yangtze River Delta, the regional coupling coordination center of gravity moved southeastward by 8.8373 km into Nanjing’s jurisdiction. This shift was primarily due to Nanjing’s agglomeration of technological innovation resources and the establishment of cross-regional cooperation platforms. From 2014 to 2015, as regional coordinated development deepened, the center of gravity adjusted slightly northeastward by 1.9985 km, reinforcing Nanjing’s role as the region’s core growth pole. From 2015 to 2016, under the strategic push for ecological civilization and industrial restructuring, the center of gravity shifted northwestward by 4.2558 km back toward Ma’anshan. This shift reflected Ma’anshan’s success in functional upgrading through the green transformation of its steel industry and environmental governance, positioning it as a secondary growth center in the region.

• 2016–2019: Market-Adjustment Dominated Phase

This phase was marked by “oscillation-convergence” along the northwest–southeast axis. As integration within the Nanjing Metropolitan Circle deepened, transportation infrastructure and collaborative environmental governance improved, facilitating the free flow of production factors. Ma’anshan leveraged its geographical advantages to integrate into regional industrial chains and innovation systems, establishing a complementary development pattern with Nanjing. During this period, the regional coupling coordination center of gravity exhibited periodic fluctuations (cumulative deviation: 0.0448° southeastward and 0.0468° northwestward) before stabilizing within Nanjing’s jurisdiction. This shift indicated the dynamic equilibrium process of optimal resource allocation under market mechanisms.

• 2020–2023: Technology-Driven Phase

The final phase, characterized by technological advancements, followed an evolutionary trajectory of “retraction—leap—convergence”. In 2020, due to the global economic downturn and the impact of the COVID-19 pandemic, the regional center of gravity shifted northwestward by 4.1834 km. Starting in 2021, emerging areas such as Lishui District in Nanjing propelled the center of gravity southeastward by 12.1622 km, driven by digital technology and ecological transformation. From 2022 to 2023, the movement continued in the same direction, shifting by another 5.9401 km. With the integration of digital technology, ecological innovation, and urban–industrial development, Lishui District emerged as a new growth pole in the Yangtze River Delta. This period underscores the transformative role of technological innovation in reshaping spatial patterns.

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Spatial Evolution of the Coupling Coordination Degree in the Yangtze River Delta (2013–2023): A Standard Deviation Ellipse Analysis

The spatial pattern of the Yangtze River Delta’s coupling coordination degree from 2013 to 2023, based on standard deviation ellipse analysis, exhibited a distinct “northwest-southeast” axial characteristic. From the perspective of spatial morphological evolution, the long semi-axis of the standard deviation ellipse extended by 0.0980 km, while the short semi-axis expanded by 0.0623 km, resulting in a 15.7% increase in the elliptical area. This trend of spatial dispersion reflects the nonlinear feedback mechanisms between the five subsystems.

• 2013–2016: Transitional Phase

During this phase, the major axis of the ellipse extended by 0.0951 km (with a shift of 8.3699° westward), indicating positive feedback from the industrial gradient transfer and infrastructure synergy that drove western development. The expansion of the ellipse’s major axis reflected the influence of regional policy and industrial restructuring aimed at fostering development in the western parts of the region.

• 2016–2020: Axial Reorganization

Between 2016 and 2020, the standard deviation ellipse underwent an “axial reorganization”. The major axis contracted by 0.0180 km, while the minor axis expanded by 0.0196°. This reorganization was driven by the combined effects of innovation agglomeration in the eastern parts of the region and the tightening of environmental regulations, thus supporting the validity of the “Porter Hypothesis” in the regional context.

• 2020–2023: Technology-Driven Expansion

From 2020 to 2023, the ellipse’s axis shifted eastward at an annual rate of 1.4412°, expanding uniformly in all directions. This spatial evolution was mainly driven by breakthroughs in digital technology that surpassed critical thresholds. These technological advances reduced transaction costs and enhanced network effects, which in turn reconstructed the spatial path of regional coordinated development. This phase confirms the system evolutionary law of “factor gradient → network effect → spatial leap”.

In summary, the spatial evolution of the coupling coordination degree in the Yangtze River Delta is shaped by a combination of policy guidance, market regulation, and technological innovation. When the coupling coordination degree of the subsystems surpasses critical thresholds, the system experiences leapfrog development, driven by self-organizing mechanisms, and spatial patterns evolve accordingly.

6. Conclusions and Recommendations

6.1. Conclusions

This paper analyzes data from 41 cities in the Yangtze River Delta region between 2013 and 2023, focusing on the spatiotemporal evolution of development indices across five dimensions. The key conclusions are as follows:

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Methodological Innovation and Optimization of the Evaluation System

This study enhances the accuracy and robustness of regional coordinated development evaluation through methodological innovation. The improved coupling coordination degree model, incorporating the Monte Carlo simulation method, effectively addresses the issue of ambiguous boundary delineation for critical values in traditional multi-system comprehensive evaluations. The combined CRITIC-entropy weighting method integrates indicator dispersion (entropy) and conflict (CRITIC), significantly reducing weight deviation in the dimensions of scientific and technological innovation (SDG 9) and infrastructure (SDG 9.1), thereby improving the stability and objectivity of the evaluation system.

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Spatiotemporal Heterogeneity of Multidimensional Coordinated Development

From 2013 to 2023, the development indices of the five subsystems—urban–rural integration, scientific and technological innovation, infrastructure, ecological environment, and public services—showed an overall upward trend with fluctuations. However, significant differences in growth rates and stability were observed across dimensions. For instance, Nanjing’s coupling coordination degree experienced a “decline from a high level” between 2020 and 2023, primarily due to spatial mismatches in the allocation of industrial space and innovation infrastructure. This highlights the deep-seated structural contradictions in the coordinated promotion of SDG 9 and SDG 11.

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Reconstruction of the Spatial Pattern

Standard deviational ellipse analysis reveals that the spatial centroid of the coupling and coordination degree in the Yangtze River Delta Urban Agglomeration has shifted from Ma’anshan City to Lishui District in Nanjing along the “northwest-southeast” axis, with a cumulative displacement of 65.39 km. This shift reflects the emergence of a multi-centered, balanced spatial structure centered around Shanghai, Hangzhou, and Hefei.

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Typical City Cases and Differentiated Development Paths

The study identifies three differentiated development paths: ① Innovation-driven (e.g., Zhangjiang Science City in Shanghai); ② ecological transformation-oriented (e.g., GEP accounting system in Huzhou); and ③ gradient catch-up (e.g., Suqian’s “dual-engine drive” plan). These paths provide actionable frameworks and policy references for the localization of the SDGs.

6.2. Recommendations

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Promoting the Implementation Path and Governance Innovation of SDGs through Coordinated Development in the Yangtze River Delta

Firstly, to ensure coordinated advancement in key areas, it is recommended that differentiated policies be implemented, focusing on science and technology innovation (SDG 9), infrastructure (SDG 9.1), urban–rural integration (SDG 10), ecological environment (SDG 13/14/15), and public services. Specific measures include gradually increasing research funding, particularly in frontier fields, and providing tax reductions for enterprises with significant R&D expenditures. Additionally, cross-regional coordination committees should be established to oversee infrastructure development, such as transportation networks and 5G deployment. The urban–rural gap can be narrowed by establishing specialized industrial parks and implementing rotation systems for teachers and healthcare professionals. Ecological protection should be strengthened through the formation of joint law enforcement teams and the creation of green industry funds. Public service resources should be optimized based on population mobility data, and an integrated online service platform should be built to improve service accessibility.

Secondly, in terms of institutional and mechanism innovation, three key supporting systems should be established. First, a Yangtze River Delta SDGs Special Fund should be created to integrate various sources of fiscal funding and social capital, providing sustainable financial support for key projects. Second, administrative barriers must be eliminated to establish a unified market for talent, technology, and data, thereby promoting the efficient flow of resources. Third, differentiated incentive policies, such as the “Innovation Leadership Award” and the “Industrial Revitalization Plan”, should be introduced to encourage advanced regions to lead less-developed areas in their coordinated development.

Finally, to ensure effective dynamic governance and evaluation, a scientific monitoring and evaluation mechanism should be established. Comprehensive assessments of coupling coordination should be conducted every five years, with action plans being dynamically adjusted based on these evaluations. A real-time data monitoring platform should be implemented to promptly identify imbalances in development, and the linkage between evaluation results, policy adjustments, and resource allocation should be strengthened. This would form a governance closed-loop of “monitoring-evaluation-optimization”, ensuring the effectiveness and adaptability of policies.

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Three-Dimensional Path to Resolving the Collaborative Development Dilemma between Nanjing and Suqian: Spatial Optimization, Industrial Transformation, and Regional Linkage

Firstly, to address the issue of spatial resource misallocation in Nanjing, institutional innovation should be leveraged to unlock development potential. Specific measures include issuing the “Implementation Rules for Mixed-use Industry-Innovation Land”, piloting a mixed development model combining “Innovation Units + Industrial Communities” in Jiangbei New District, and establishing an elastic mechanism for converting land use between R&D and industrial purposes. Additionally, the Yangtze River shoreline should be utilized to create an innovation corridor that facilitates the commercialization of university research outcomes and enhances spatial efficiency. This initiative aligns with SDG Target 11.a, focusing on sustainable urban development.

Secondly, Suqian’s transformation requires a “dual-wheel drive” strategy. On one hand, a special green transformation fund should be established to support the low-carbon upgrading of traditional industries. On the other hand, a base for transforming scientific and technological achievements should be set up in the Suzhou (Jiangsu)-Suqian Industrial Park, leveraging innovative resources from Nanjing and Suzhou (Jiangsu) through the “talent enclave” policy. This “local upgrading plus external integration” model will accelerate Suqian’s integration into the regional innovation chain, contributing to the inclusive development goals outlined in SDG 10.2.

Finally, at the regional collaboration level, it is crucial to establish a multi-level coordination mechanism. Spatially, the G60 Science and Technology Innovation Corridor should be aligned with the Shanghai-Nanjing-Hefei industrial innovation belt, forming a development pattern where core cities radiate outward, and node cities act as recipients. Institutionally, pilot GDP and tax revenue-sharing mechanisms should be implemented within the Nanjing–Suqian Demonstration Zone, along with the creation of an industry transfer project database and mutual talent recognition systems. Furthermore, a dynamic monitoring system should be established to regularly assess coupling and coordination levels, providing evidence-based support for policy adjustments. This three-dimensional “space-institution-evaluation” framework can serve as a model for the paired development of similar cities.

7. Limitations and Future Research Directions

This paper has the following limitations: Firstly, the coordinated development process of the Yangtze River Delta is inherently complex and dynamic. This study primarily utilizes qualitative methods, guided by the literature and policies, to select indicators. While this approach provides valuable insights, future research could enhance the scientific rigor and comprehensiveness of the indicator system by incorporating more data-driven, quantitative approaches, such as machine learning. These methods could better capture the complexities and nuances of regional development.

Secondly, the correlations among various indicators and the heterogeneity of data characteristics present challenges in accurately reflecting the regional development state. This paper combines the CRITIC method with the entropy weight method to improve the accuracy of comprehensive weighting. However, future studies could extend this research by conducting comparative analyses with other weighting methods and integrating spatial econometric techniques to explore spatial interaction effects in the coordinated development of the Yangtze River Delta in greater depth.