Evaluation of the Degree of Synergy in High-Quality Development Among Inter-Provincial Adjacent Districts and Planning Recommendations: The Case Study of Anhui Province and Its Surrounding Provinces

Abstract

As China’s economy accelerates its transition toward high-quality development, various regions are actively tapping into their distinctive resources to unlock economic potential and exploring pathways to achieve high-quality collaborative development with neighboring areas. This study endeavors to provide both theoretical insights and practical recommendations for the actual development of Anhui Province and its adjacent regions, through an in-depth analysis of their collaborative pursuit of high-quality growth. Employing the entropy weight method and the coupled coordination degree model, this research rigorously evaluates the extent of coordinated high-quality development between Anhui Province and its neighboring provinces, and offers effective planning suggestions grounded in the evaluation results. The findings reveal that (1) coastal cities generally demonstrate a higher level of comprehensive development compared to those located further inland; (2) cities with superior comprehensive development also tend to excel in economic growth, scientific and technological innovation, ecological advancement, and coupling coordination; (3) nevertheless, a higher level of comprehensive development does not necessarily imply better social service provision.

1. Introduction

In recent decades, the accelerated pace of global industrialization has propelled exponential progress in science, technology, economics, and societal structures. Urbanization rates have consistently climbed, with data indicating that urban dwellers comprised approximately 60% of the world’s population in 2018, a proportion anticipated to reach nearly 70% by 2050 [1]. While urbanization has positively influenced human societal advancement, it has concurrently precipitated environmental challenges, including the greenhouse effect, land desertification, and water contamination. The conventional extensive model of urbanization now confronts significant obstacles [2,3,4]. Against this backdrop, the World Commission on Environment and Development (WCED) published “Our Common Future” in 1987, systematically and scientifically examining the concept of sustainable development [5]. Subsequently, in 2015, the United Nations Sustainable Development Summit endorsed “Transforming Our World: The 2030 Agenda for Sustainable Development”, delineating 17 sustainable development goals. Since then, the notion of sustainable development has attained global consensus and has been integrated into diverse national strategies aimed at fostering societal progress [6,7,8]. The pursuit of high-quality, coordinated, and gradual deepening of regional development is a multifaceted and iterative process, necessitating the intersection of disciplines such as economics, geography, sociology, and others. Since the mid-20th century, certain countries and regions have facilitated stable economic growth through the establishment of inter-regional cooperation frameworks and trade agreements. For instance, European integration and the formation of ASEAN have fostered a conducive business environment and political stability, thereby bolstering regional economic development. During the 1970s and 1980s, regional coordination entered a new era. Nations began forming regional economic organizations to further enhance economic cooperation among member states through economic integration and policy harmonization. These organizational platforms extended inter-regional collaboration into various domains, including economics, culture, finance, and agriculture, thereby tightening regional linkages. As global challenges have become increasingly intricate, policy coordination and cooperation among nations have gained paramount importance. During this phase, regional coordination efforts intensified, focusing on policy alignment and collaboration to tackle shared challenges such as climate change, environmental protection, and economic stability. In practical terms, several countries have implemented regional coordination strategies tailored to their unique development contexts. For example, the United States fosters balanced development in underdeveloped regions through preferential tax policies, government-designated development zones, and infrastructure development. The European Union has outlined explicit policy objectives and strategic plans for regional development, established a robust legal and regulatory framework for coordinating regional progress, and leveraged regional policy financial instruments to facilitate coordinated regional development. In summary, the research trajectory of high-quality inter-regional coordination internationally has evolved through multiple stages, encompassing regional trade agreements, regional economic organizations, policy coordination and cooperation, the construction of theoretical frameworks, practical implementations, and the exploration of high-quality coordinated development. This evolution has not only enriched the theoretical research on regional coordinated development with diverse materials and perspectives but has also provided invaluable insights and lessons for the practical application of regional coordination strategies across various nations. Relevant theoretical research encompasses the core–periphery theory, originally posited by John Friedman in 1966, which elucidates the imbalance phenomenon and its evolutionary trajectories in regional economic development. Friedman contended that during the progression of regional economic growth, a pivotal economic region tends to emerge. This core region, capitalizing on its economic advantages, progressively attracts production factors and labor resources from peripheral areas, resulting in a widening disparity between the economic prosperity of the core and peripheral regions [9,10]. This theory constitutes a fundamental theoretical framework for comprehending regional economic disparities. Moreover, it underscores the importance of fostering coordinated development between core and peripheral regions to achieve high-quality regional growth. The synergy theory, introduced by German physicist Hermann Haken in 1971, emphasizes the interplay and collaborative efforts among subsystems within a system to optimize overall functionality. This theory posits that by promoting cooperation among regions, an effective flow and allocation of resources, technology, labor, and other factors can be attained, thereby augmenting the overall quality and efficiency of the economy [11,12]. This theoretical construct also offers a scientific foundation for the governmental formulation of judicious regional coordination policies. The regional economic growth theory, which has evolved through the cumulative research endeavors of numerous scholars, stands as a pivotal theory in the realm of coordinated regional development. It is also indispensable for investigating the principles and mechanisms underlying regional economic expansion. This theory asserts that by facilitating technological diffusion, knowledge sharing, and human capital accumulation across regions, common growth and high-quality regional economic development can be realized [13]. Sustainable development theory, first articulated by the Brundtland Commission in 1987, highlights the significance of environmental conservation and ecological equilibrium in the course of economic development to ensure harmonious growth among economic, social, and environmental domains. This theory provides essential environmental direction for high-quality regional collaborative development and serves as a theoretical cornerstone for governments in crafting environmental policies pertaining to regional cooperation [14]. The competitive advantage theory, proposed by Michael Porter in 1990, offers vital industrial insights for high-quality regional collaborative development. It underscores that by promoting complementary strengths among industries in diverse regions, the economic competitiveness of those regions can be bolstered. The new economic geography theory, advanced by scholars such as Krugman in 1991, uncovers the mechanisms through which spatial agglomeration influences regional economic development. This theory furnishes crucial frameworks for spatial economic analysis and industrial layout guidance aimed at fostering high-quality regional collaboration [15,16]. Furthermore, Michael Batty emphasized in his 2013 publication that smart cities, through the establishment of information technology and intelligent infrastructure, can streamline resource allocation and enhance urban management efficiency, thereby facilitating collaborative regional economic development. The advent of smart cities presents novel technological avenues and platforms for regional cooperation. As global emphasis on sustainable development intensifies, the concept of green regional collaboration has garnered considerable attention and application from scholars in recent years. This concept accentuates the importance of ecological environmental protection and sustainable development within the context of regional collaboration, advocating for a green transformation of the regional economy through the collaborative advancement of green technologies and industries [17]. For instance, Mital’ O et al. examined the collaborative planning efforts between local and regional stakeholders in the Slovak Republic, exploring pathways to achieve green transformation through the development of intelligent and green regional ecosystems, encompassing the application of green technologies and the cultivation of green industries [18]. D’Adamo I et al. assessed the performance of various Italian regions in attaining Sustainable Development Goals (SDGs). Employing a Multi-Criteria Decision Analysis (MCDA) approach and utilizing 175 indicators across all 17 SDGs, the study calculated sustainability scores for different regions, spanning social, environmental, and economic dimensions. The research emphasizes the need for targeted governmental policies to restore Italy’s competitiveness on both European and global scales, while safeguarding the environment [19]. Collectively, these theories encompass diverse facets, including regional spatial configuration, industrial complementarity, resource allocation and sharing, economic and environmental harmony, green development, and policy direction. They provide invaluable theoretical underpinning and practical guidance for comprehending and promoting high-quality collaborative regional development. In recent years, China has introduced the notion of high-quality development, marking a departure from the extensive development paradigms of the past. This concept emphasizes a holistic approach that encompasses not only economic growth but also takes into account environmental sustainability, cultural enrichment, and inter-regional coordination. Its aim is to foster sustainable, healthy economic progress alongside comprehensive social advancement. This paradigm shift represents a scientific response to the challenges posed by regional developmental disparities, environmental degradation, and the persistence of extensive development practices in the contemporary era [20]. Subsequently, Chinese scholars have extensively explored the historical rationale, theoretical foundations, pathway selections, index evaluations, and coupling coordination mechanisms pertinent to coordinated and high-quality development. Ma et al. [21] conducted a rigorous analysis and assessment of high-quality economic development across diverse regions in China, uncovering marked imbalances between the eastern and western regions. Chen et al. [22] employed obstacle degree models to scrutinize the ecological conditions of cities within the Yellow River basin and, based on their findings, formulated targeted development strategies. Li et al. [23] investigated the developmental quality of the Beijing–Tianjin–Hebei region, emphasizing the pronounced radiation and attraction effects exerted by Beijing and Tianjin on the surrounding areas. Liu et al. [24] examined the levels of urban development across various Chinese provinces, identifying the significant impact of economic growth and infrastructure development on overall urban progress. Yang et al. [25] conducted an in-depth study on the quality of urban development in the Inner Mongolia Autonomous Region, focusing on social development levels and their coordination. Zhao et al. [26] utilized a coupling coordination model to evaluate the interplay between urban development and ecology in selected areas of the Yellow River basin, offering insightful planning strategies. These studies provide a comprehensive understanding of China’s recent social development dynamics and mechanisms. They span a wide array of dimensions, including social, economic, ecological, and coordination aspects. Essentially, they present scientific research methodologies and efficacious strategies to promote coordinated, high-quality regional development in China.

Provincial border zones, positioned at the nexus of multiple provinces and typically remote from the more advanced central urban hubs, experience lesser influence from localized developmental plans and policies. Consequently, they often demonstrate diminished levels of overall development relative to other areas. Nevertheless, as China advances toward the goal of coordinated high-quality development, these regions, endowed with strategic locational advantages and the potential to adopt central city roles, unveil extensive prospects for development and hold considerable significance for research. Early theoretical explorations into integrated development in border zones encompass investigations of edge city evolution in the United States [27]. Western academics have primarily focused on economic growth, the establishment of regional coordination frameworks, and governance in cross-border territories [28,29,30,31]. Following the introduction of edge city theory to China, scholars progressively ventured into the domain of cross-border collaborative development in border regions. Existing research on contiguous areas, however, exhibits certain deficiencies. These studies predominantly concentrate on the economic dimension, with limited application of quantitative analytical techniques to comprehensively assess the synergistic development of adjacent regions from a multi-dimensional system perspective that encompasses social, economic, ecological, and technological elements. This approach strives to offer a comprehensive and interactive assessment and examination of regional cooperation [32]. Anhui Province, along with its neighboring provincial border zones, occupies a pivotal juncture between inland and coastal areas. These zones are integral components of national developmental regions such as the Yangtze River Delta urban agglomeration, the Huaihe River economic belt, and the Central Plains urban agglomeration. They are characterized by abundant resources, efficient transportation networks, and notable locational benefits, all of which contribute to their substantial potential for holistic development. As such, these contiguous regions exhibit significant regional representativeness both domestically and internationally, presenting substantial academic research value. The objective of this paper is to examine these regions as case studies, evaluate their degree of coordinated high-quality development, and formulate targeted planning recommendations. This research endeavor not only provides pragmatic suggestions with policy ramifications for local governance but also introduces a viable, scientific methodology, operational protocols, and a research paradigm for comparable studies in other analogous regions across the globe.

2. Methodology

2.1. Study Area

The research encompasses multiple cities situated along the borders of Anhui Province and its neighboring provinces. Specifically, Chuzhou, Ma’anshan, and Xuzhou are positioned on the Anhui–Jiangsu border; Chizhou, Shangrao, and Jiujiang lie along the border with Jiangxi; Fuyang, Bozhou, and Zhoukou share a border with Henan; Suzhou and Heze are contiguous with Shandong; and Xuancheng, Hangzhou, and Huzhou are located along the Zhejiang border. In the course of the research, data pertaining to certain districts were unavailable for computation and were subsequently excluded from the study area. These excluded districts include Xinyang in Henan, Jingdezhen in Jiangxi, and Yangzhou in Jiangsu. Consequently, the present study encompasses a total of seven provinces and twenty-eight cities (

Table 1. Cities involved in inter-provincial adjoining regions between Anhui Province and neighboring provinces.

Province	City
Anhui	Fuyang, Bozhou, Huaibei, Suzhou, Bengbu, Chuzhou, Ma’anshan, Xuancheng, Huangshan, Chizhou, Anqing, Luan
Jiangsu	Xuzhou, Suqian, Huai’an, Nanjing, Changzhou, Wuxi
Zhejiang	Huzhou, Hangzhou, Quzhou
Jiangxi	Shangrao, Jiujiang
Hubei	Huanggang
Henan	Zhumadian, Zhoukou, Shangqiu
Shandong	Heze

).

2.2. Construction of Comprehensive Evaluation Index System

The rational construction of the comprehensive evaluation system is intricately tied to the scientific rigor, accuracy, and credibility of the resultant evaluations. The selection of indicators in this study adheres to the widely accepted principles of scientific validity, feasibility, and comprehensiveness. Guided by these principles and informed by existing research [33,34,35,36,37], we have established a comprehensive evaluation index system comprising four subsystems: economic development, technological innovation, social services, and ecological construction. This system encompasses a total of 10 primary indicators and 20 secondary indicators (

Table 2. Comprehensive evaluation index system for high-quality development of inter-adjacent districts.

System	Primary Index	Secondary Index	Unit	Type
Economic development	Socio-economic base	GDP per capita	CNY/Million	+
		Tertiary sector output as a percentage of GDP	%	+
	Economic development	GDP growth rate	%	+
		Total exports and imports	Billions	+
	Economic efficiency	Labor productivity	%	+
Technological innovation	Investment of research funds	Share of investment in scientific research funds	%	+
		Full-time equivalent of R&D personnel	Person/year	+
	Scientific and technological effectiveness	Number of patents granted	Item	+
Social security	Public service	Number of beds in health facilities per 10,000 population	Unit	+
		Investment in education as a share of GDP	%	+
		Road space per capita	m2	+
	Quality of life	Ratio of income of urban and rural residents	—	+
		Urban registered unemployment rate	%	−
		Income from the tourism economy	Billions	+
Ecological construction	Ecological foundation	Forest cover	%	+
		Area covered by greening in built-up areas	m2	+
	Ecological pressure	Proportion of days with good air quality	%	+
		Industrial wastewater discharge intensity	10 kilotons	−
	Ecological protection	Share of investment in environmental protection	%	+
		Area of planted forests	m2	+

).

2.3. Measurement Methods and Data Processing Indicators

Data pertaining to various indicators from cities within the adjacent region, spanning the years 2017 to 2021, were collected and systematically organized. A dynamic comprehensive evaluation model was formulated using the entropy weight method. The gathered three-dimensional time series data tables, which encompassed multiple years, regions, and indicators, were consolidated into a two-dimensional data matrix arranged in chronological order from top to bottom. Subsequently, the data at various levels and units underwent normalization, and the weights of each indicator, along with the comprehensive index, were calculated [38]. In this context, m represents the number of years, v denotes the number of research areas, and n signifies the number of indicators. ${\mathrm{X}}_{\mathrm{ij}}$ represents the initial data for the i-th year and the j-th indicator, with the specific calculation process outlined below:

Step 1. Standardize the indicators:

Positive indicators:

$${\mathrm{Z}}_{\mathrm{ij}}={\displaystyle \frac{{\mathrm{X}}_{\mathrm{ij}}-{ \mathrm{X}}_{\min }}{{\mathrm{X}}_{\max }-{ \mathrm{X}}_{\min }}}$$

(1)

Negative indicators:

$${\mathrm{Z}}_{\mathrm{ij}}={\displaystyle \frac{{\mathrm{X}}_{\max }-{ \mathrm{X}}_{\mathrm{ij}}}{{\mathrm{X}}_{\max }-{ \mathrm{X}}_{\min }}}$$

(2)

In this context, ${\mathrm{X}}_{\max }$ and ${\mathrm{X}}_{\min }$ denote the maximum and minimum values of the j-th indicator, respectively. ${\mathrm{Z}}_{\mathrm{ij}}$ represents the standardized value of the same j-th indicator. Following processing, the outcomes are elevated by 0.001 units prior to advancing to the subsequent computation.

Step 2. Indicator normalization processing:

$${\mathrm{P}}_{\mathrm{ij}}={\displaystyle \frac{{\mathrm{Z}}_{\mathrm{ij}}}{{{\displaystyle \sum }}_{\mathrm{i}=1}^{\mathrm{m}}{\mathrm{Z}}_{\mathrm{ij}}}}$$

(3)

Step 3. Calculating the entropy value of indicator ${\mathrm{E}}_{\mathrm{j}}:$

$${\mathrm{E}}_{\mathrm{j}}:=-{\mathrm{k}}_1{{\displaystyle \sum }}_{\mathrm{i}=1}^{\mathrm{m}}{\mathrm{P}}_{\mathrm{ij}}\ln {\mathrm{P}}_{\mathrm{ij}}$$

(4)

in this context,

$${\mathrm{k}}_1={\displaystyle \frac{1}{\ln \left(\mathrm{mv}\right)}}$$

Step 4. Calculate the redundancy of each indicator ${\mathrm{D}}_{\mathrm{j}}$:

$${\mathrm{D}}_{\mathrm{j}}=1-{\mathrm{E}}_{\mathrm{j}}$$

(5)

Step 5. Calculate the weights of each indicator ${\mathrm{W}}_{\mathrm{j}}$:

$${\mathrm{W}}_{\mathrm{j}}={\displaystyle \frac{{\mathrm{D}}_{\mathrm{j}}}{{{\displaystyle \sum }}_{\mathrm{j}=1}^{\mathrm{n}}{\mathrm{D}}_{\mathrm{j}}}}$$

(6)

Step 6. Calculating the comprehensive development index of each indicator ${\mathrm{I}}_{\mathrm{ij}}$:

$${\mathrm{I}}_{\mathrm{ij}}={\mathrm{Z}}_{\mathrm{ij}}\times {\mathrm{W}}_{\mathrm{j}}$$

(7)

Step 7. Calculating the comprehensive development index of each subsystem ${\mathrm{U}}_{\mathrm{i}}$:

$${\mathrm{U}}_{\mathrm{i}}={{\displaystyle \sum }}_{\mathrm{i}}{\mathrm{Z}}_{\mathrm{ij}}\times {\mathrm{W}}_{\mathrm{j}}$$

(8)

The coupling coordination degree signifies the degree of interaction and harmony between two or more systems. The coupling coordination degree model constitutes a mathematical framework utilized to compute the coupling coordination degree for each system. Through the construction of this model, we can quantify the level of index coordination. The outcomes derived from this model provide a vital foundation for assessing whether a city is progressing in an overall coordinated manner. The detailed computation procedure is outlined as follows:

• u₁ represents the comprehensive index of the economic development subsystem;
• u₂ represents the comprehensive index of the science and technology innovation subsystem;
• u₃ represents the comprehensive score of the social services subsystem;
• u₄ represents the comprehensive score of the ecological construction subsystem.

Coupling degree:

$$\mathrm{C}={\displaystyle \frac{4{\left[{\mathrm{u}}_1\times {\mathrm{u}}_2\times {\mathrm{u}}_3\times {\mathrm{u}}_4\right]}^{\frac{1}{4}}}{{\mathrm{u}}_1+{\mathrm{u}}_2+{\mathrm{u}}_3+{\mathrm{u}}_4}}$$

(9)

Coordination Index:

$$\mathrm{T}={\mathrm{au}}_1+{\mathrm{bu}}_2+{\mathrm{cu}}_3+{\mathrm{du}}_4$$

(10)

Coordinated Development Degree:

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

(11)

In this context, C symbolizes the degree of coupling; T signifies the comprehensive coordination index of the four subsystems; D denotes the degree of coupling coordination; $\mathrm{a}+\mathrm{b}+\mathrm{c}+\mathrm{e}=1$, $\mathrm{C}, \mathrm{T}, \mathrm{D}\in \left[0,1\right]$. Drawing on the classification approaches of Liao Chongbin [39] and Wang et al. [40], the system coupling coordination degree is categorized into three primary divisions and five phases (

Table 3. Criteria for classifying the degree of harmonization.

Corridor	[0.0–0.2)	[0.2–0.4)	[0.4–0.6)	[0.6–0.8)	[0.8–1.0)
Degree of coupling coordination	severe disorder	on the verge of a disorder	junior coordination	Intermediate Synergy	Advanced Synergy
The colors of five types of intervals
Category	Dysfunctional decline class		Transitional development class	Harmonized development category

).

2.4. Data Source

To guarantee the accuracy and validity of the indicator data obtained, all data sources for each city’s indicators are sourced from publicly released statistical data files available on official government websites. These sources include statistical yearbooks and bulletins published by national, provincial, and municipal statistics bureaus. For certain primary data that are not directly accessible, requests are made through the information exchange sections of governmental portals. A minor fraction of the original data, which is not statistically documented in governmental operations and thus unobtainable, is supplemented using interpolation techniques.

3. Results and Analysis

3.1. Comprehensive Evaluation Results Analysis

3.1.1. Comprehensive Analysis of High-Quality Development

Utilizing the entropy weight method and its associated formulae, the comprehensive index of high-quality development for each city has been calculated and is presented in

Table 4. Composite index of high-quality development in adjoining districts, 2017–2021.

Province	City	2017	2018	2019	2020	2021
Anhui	Fuyang	12.13	13.07	13.95	13.68	14.12
	Bozhou	11.48	13.23	13.53	12.37	12.77
	Huaibei	8.77	11.06	12.13	13.34	14.23
	Suzhou	12.89	13.24	13.78	16.79	18.22
	Bengbu	15.70	16.40	17.69	13.38	14.57
	Chuzhou	15.11	19.82	25.55	25.66	26.36
	Ma’anshan	13.37	15.18	16.09	17.25	17.92
	Xuancheng	15.54	16.61	17.51	17.71	19.19
	Huangshan	17.07	16.08	16.99	16.68	17.43
	Chizhou	13.41	14.42	15.72	15.68	16.93
	Anqing	16.98	17.75	16.68	16.50	15.64
	Luan	15.79	16.55	15.93	16.45	16.07
	Average value	14.02	15.28	16.29	16.29	16.96
Henan	Zhumadian	9.59	11.98	13.94	13.73	13.18
	Zhoukou	7.33	7.99	8.97	9.68	9.43
	Shangqiu	8.86	9.19	10.46	9.88	9.21
	Average value	8.59	9.72	11.12	11.10	10.60
Shandong	Heze	18.70	17.47	20.99	21.11	21.78
Jiangsu	Xuzhou	17.20	19.53	20.15	23.47	24.96
	Suqian	13.15	14.14	14.42	16.54	16.60
	Huai’an	15.09	16.08	16.54	16.78	17.96
	Nanjing	45.63	49.99	54.25	57.41	61.62
	Changzhou	25.34	26.85	28.87	35.61	35.66
	Wuxi	47.55	50.91	52.94	52.20	60.44
	Average value	27.33	29.58	31.20	33.67	36.21
Zhejiang	Huzhou	19.04	22.07	23.42	23.72	22.31
	Hangzhou	50.10	53.91	58.95	65.48	67.77
	Quzhou	22.20	23.11	24.81	24.37	26.02
	Average value	30.45	33.03	35.72	37.85	38.70
Jiangxi	Shangrao	26.96	25.75	25.19	25.67	24.74
	Jiujiang	21.08	23.09	22.96	21.77	20.88
	Average value	24.02	24.42	24.08	23.72	22.81
Hubei	Huanggang	15.48	18.88	16.20	15.25	19.24
Adjacent area	Average value	18.98	20.51	21.74	22.43	23.40

. The table indicates that the overall high-quality development index of the neighboring regions has increased from 18.98 to 23.40, reflecting a positive trend. This suggests that with the national focus on high-quality development, considerable progress has been achieved in the advancement of the areas adjacent to Anhui Province. Notably, cities within Jiangsu, Zhejiang, and Jiangxi Provinces generally exhibit higher comprehensive indices of high-quality development compared to other provinces, positioning them as leaders in the overall development of the neighboring regions. These cities play a crucial role in exerting influence and driving regional growth. Conversely, cities in Anhui, Henan, and Hubei Provinces generally display lower comprehensive indices of high-quality development compared to other provinces, highlighting significant potential for improvement in their overall development. Heze City, located in Shandong Province, demonstrates a moderate level of high-quality development index relative to other cities. In general, cities situated in proximity to coastal areas and those with strong economic foundations tend to exhibit higher comprehensive indices of high-quality development compared to other cities.

3.1.2. Analysis of the Subsystem of High-Quality Economic Development

Using the aforementioned entropy weight method formulas, the development index of each city’s economic subsystem has been computed and is presented in

Table 5. Economic subsystem development index for adjacent areas, 2017–2021.

Province	City	2017	2018	2019	2020	2021
Anhui	Fuyang	3.40	4.08	6.05	4.83	6.83
	Bozhou	4.39	5.13	7.04	5.93	8.09
	Huaibei	5.69	5.88	6.96	8.80	10.63
	Suzhou	4.82	5.35	6.44	16.24	19.21
	Bengbu	19.78	18.98	22.38	9.39	9.72
	Chuzhou	8.89	10.05	35.71	34.28	39.55
	Ma’anshan	13.14	14.75	16.70	16.78	20.39
	Xuancheng	6.78	7.73	8.54	8.89	12.11
	Huangshan	8.53	9.53	10.73	10.01	12.87
	Chizhou	5.70	6.36	8.05	9.34	12.71
	Anqing	5.78	6.00	7.63	7.59	8.60
	Luan	5.50	6.30	7.59	7.56	9.69
	Average value	7.70	8.35	11.99	11.64	14.20
Henan	Zhumadian	4.83	5.67	6.10	5.45	7.12
	Zhoukou	3.88	5.48	6.02	4.57	6.34
	Shangqiu	4.69	5.90	6.19	4.31	5.60
	Average value	4.47	5.68	6.10	4.78	6.35
Shandong	Heze	21.55	24.94	29.16	27.54	32.43
Jiangsu	Xuzhou	14.55	16.04	18.05	18.26	22.74
	Suqian	8.50	9.31	10.73	11.15	14.58
	Huai’an	12.61	13.34	13.89	13.86	17.87
	Nanjing	49.61	53.29	56.68	59.13	71.69
	Changzhou	27.44	29.33	29.82	46.90	38.77
	Wuxi	57.81	64.76	65.24	62.75	75.03
	Average value	28.42	31.01	32.40	35.34	40.11
Zhejiang	Huzhou	17.17	14.41	15.18	14.45	17.77
	Hangzhou	52.53	55.10	56.71	58.51	74.25
	Quzhou	25.85	25.92	26.61	26.58	35.19
	Average value	31.85	31.81	32.83	33.18	42.40
Jiangxi	Shangrao	24.45	18.94	16.57	17.93	19.90
	Jiujiang	14.43	11.64	12.27	12.47	13.11
	Average value	19.44	15.29	14.42	15.2	16.505
Hubei	Huanggang	9.08	5.75	6.08	5.39	8.41
Adjacent area	Average value	15.76	16.43	18.54	18.89	22.54

. The table reveals that the overall economic subsystem development index of the adjacent areas has risen from 15.76 to 22.54, signifying an upward trend. This indicates that, despite China’s recent economic and industrial structural adjustments, which have shifted the focus from rapid growth to high-quality development, the overall economic progress of the adjacent regions remains steady. Specifically, cities within Jiangsu Province, Zhejiang Province, and Shandong Province generally exhibit higher economic subsystem development indices compared to other provinces, positioning them as key engines for regional economic development. Conversely, cities within Anhui Province, Henan Province, and Hubei Province generally display lower economic subsystem development indices compared to other provinces, with the exception of Bengbu City, Chuzhou City, and Ma’anshan City, which perform relatively well. This is largely attributed to their collaborative development efforts with Nanjing City and other cities in the Yangtze River Delta region. Meanwhile, cities within Jiangxi Province demonstrate moderate economic subsystem development indices when compared to other provinces. Overall, cities situated in proximity to coastal areas tend to exhibit higher economic subsystem development indices compared to other cities.

3.1.3. Analysis of the Subsystem of Scientific and Technological Innovation

Employing the entropy weight method and its associated formulas, as previously delineated, we have calculated the development index for each city’s science and technology innovation subsystem, as shown in

Table 6. Development index of STI subsystems in adjoining districts, 2017–2021.

Province	City	2017	2018	2019	2020	2021
Anhui	Fuyang	2.57	4.84	4.67	6.27	7.19
	Bozhou	1.06	1.97	2.05	3.58	3.38
	Huaibei	4.72	5.21	6.64	8.29	9.72
	Suzhou	0.54	0.59	0.94	2.05	3.17
	Bengbu	6.60	9.18	7.80	9.24	8.74
	Chuzhou	7.62	10.52	9.32	10.69	14.06
	Ma’anshan	13.20	13.07	15.00	17.05	16.22
	Xuancheng	6.60	7.84	8.29	10.26	12.26
	Huangshan	5.79	2.44	3.51	4.54	4.07
	Chizhou	2.89	3.48	4.12	5.36	6.46
	Anqing	6.80	6.17	4.96	5.17	4.12
	Luan	5.51	4.52	4.16	3.58	2.32
	Average value	5.33	5.82	5.96	7.17	7.64
Henan	Zhumadian	1.23	1.17	2.11	4.29	5.56
	Zhoukou	1.07	1.26	1.52	2.12	2.12
	Shangqiu	2.94	3.98	4.07	4.73	5.69
	Average value	1.75	2.14	2.57	3.71	4.46
Shandong	Heze	6.09	3.07	2.40	3.41	4.65
Jiangsu	Xuzhou	12.56	12.06	11.99	16.66	21.66
	Suqian	7.19	8.80	9.25	11.31	12.38
	Huai’an	10.62	12.27	12.30	13.33	14.44
	Nanjing	48.46	52.24	59.97	72.58	71.33
	Changzhou	29.67	33.00	35.20	43.09	50.32
	Wuxi	39.41	39.80	44.90	52.47	65.47
	Average value	24.65	26.36	28.94	34.91	39.27
Zhejiang	Huzhou	18.09	22.25	23.28	25.30	25.95
	Hangzhou	57.18	63.84	71.93	89.77	96.43
	Quzhou	7.05	7.71	8.54	10.36	11.89
	Average value	27.44	31.27	34.58	41.81	44.76
Jiangxi	Shangrao	3.96	4.25	5.18	8.11	8.40
	Jiujiang	10.98	11.05	8.59	6.27	3.58
	Average value	7.47	7.65	6.89	7.19	5.99
Hubei	Huanggang	4.76	3.58	2.98	2.66	2.83
Adjacent area	Average value	11.08	11.33	11.96	14.29	15.65

. The data indicate that the aggregate development index of the science and technology innovation subsystem in neighboring regions has ascended from 11.08 to 15.65, signaling a discernible trend of progression. This underscores the nation’s emphasis on innovation as a cornerstone of economic and social advancement, leading to a sustained enhancement in the overall quality of science and technology innovation within these regions. Notably, cities situated within Jiangsu and Zhejiang Provinces consistently exhibit higher development indices for their science and technology innovation subsystems in comparison to other provinces. Conversely, cities located in Anhui, Henan, Shandong, and Hubei Provinces generally demonstrate lower indices, with Ma’anshan City standing out as an exception by maintaining a comparatively favorable performance level. When compared to other provinces, the development index for science and technology innovation subsystems in cities within Jiangxi Province is moderate. In a broader context, cities situated in proximity to coastal areas tend to exhibit superior development indices for their science and technology innovation subsystems relative to other cities.

3.1.4. Analysis of the Social Service Subsystem

Utilizing the entropy weight method formula previously discussed, the development index for social service subsystems across various cities is delineated in

Table 7. Adjacent district social services subsystem development index, 2017–2021.

Province	City	2017	2018	2019	2020	2021
Anhui	Fuyang	38.89	38.00	37.69	35.54	34.32
	Bozhou	39.34	41.10	39.63	39.85	38.05
	Huaibei	15.80	28.99	29.50	28.54	28.72
	Suzhou	37.18	38.47	37.95	36.04	36.26
	Bengbu	26.94	27.15	28.32	21.93	22.49
	Chuzhou	38.26	39.70	34.37	32.62	32.41
	Ma’anshan	20.76	21.81	22.44	20.83	21.57
	Xuancheng	33.45	33.84	34.46	30.45	30.98
	Huangshan	23.28	25.28	26.19	22.80	23.50
	Chizhou	29.30	31.19	34.57	29.90	30.42
	Anqing	39.38	43.10	38.70	39.74	36.04
	Luan	38.16	43.36	40.61	41.75	39.13
	Average value	31.73	34.33	33.70	31.67	31.16
Henan	Zhumadian	28.09	36.23	38.39	33.12	28.87
	Zhoukou	30.00	27.82	27.23	28.51	27.76
	Shangqiu	28.50	26.88	28.26	23.82	23.28
	Average value	28.86	30.31	31.29	28.48	26.64
Shandong	Heze	26.96	26.55	27.34	28.40	25.55
Jiangsu	Xuzhou	27.84	28.95	29.46	22.86	24.45
	Suqian	21.87	22.25	21.73	21.37	19.29
	Huai’an	23.72	21.79	24.08	20.90	18.52
	Nanjing	50.70	54.93	59.03	47.35	52.40
	Changzhou	29.51	29.25	30.69	27.57	29.32
	Wuxi	32.21	35.13	36.93	24.94	30.22
	Average value	30.98	32.05	33.65	27.50	29.03
Zhejiang	Huzhou	25.01	28.23	31.36	28.74	19.42
	Hangzhou	51.34	58.57	63.81	54.86	33.74
	Quzhou	33.49	35.30	37.31	32.69	28.16
	Average value	36.61	40.70	44.16	38.76	27.11
Jiangxi	Shangrao	59.16	59.94	60.39	55.25	51.40
	Jiujiang	30.35	50.09	55.04	50.96	47.45
	Average value	44.76	55.02	57.72	53.11	49.43
Hubei	Huanggang	32.85	37.61	34.69	37.23	39.38
Adjacent area	Average value	33.06	36.37	37.16	34.60	32.69

. The data reveal that the overall development index of the social service subsystems in adjacent regions initially rose from 33.06 in 2017 to 37.16 in 2019, subsequently declining to 32.69 in 2021, exhibiting a pattern of initial increase followed by a decrease. This decline may be intrinsically linked to the onset of the COVID-19 pandemic at the end of 2019, as the resultant restrictions on mobility and social interaction led to a temporary setback in social service development. Specifically, cities within Zhejiang Province, Jiangxi Province, and Hubei Province generally boast higher development indices for their social service subsystems when compared to other provinces. Conversely, cities situated within Henan Province and Shandong Province demonstrate relatively lower indices. Cities in Anhui Province and Jiangsu Province occupy a middle ground in terms of development indices among other provinces. Notably, there appears to be no discernible correlation between a city’s proximity to the sea and the level of social service development.

3.1.5. Ecological Construction Subsystem Analysis

Utilizing the entropy weight method formulas previously described, the development indices for urban ecological construction subsystems have been computed and are presented in

Table 8. Development index for ecological building subsystems in adjacent areas, 2017–2021.

Province	City	2017	2018	2019	2020	2021
Anhui	Fuyang	16.20	17.09	18.58	18.42	17.64
	Bozhou	14.10	17.78	17.62	12.77	13.19
	Huaibei	12.29	12.51	13.49	14.82	14.54
	Suzhou	20.61	20.54	21.31	21.83	22.67
	Bengbu	14.44	15.08	16.98	17.11	21.37
	Chuzhou	16.42	28.68	26.67	28.17	21.71
	Ma’anshan	9.58	14.07	12.96	15.90	14.97
	Xuancheng	24.06	25.47	27.11	27.71	27.31
	Huangshan	34.50	32.15	32.58	33.04	32.87
	Chizhou	23.62	24.94	25.34	25.19	24.73
	Anqing	26.85	27.99	26.08	24.63	23.62
	Luan	24.82	24.84	23.11	25.01	24.18
	Average value	17.72	19.69	19.89	19.97	19.55
Henan	Zhumadian	12.87	16.21	20.79	21.44	18.70
	Zhoukou	4.64	6.43	9.49	12.30	9.93
	Shangqiu	8.34	8.08	11.55	13.27	8.71
	Average value	8.62	10.24	13.94	15.67	12.45
Shandong	Heze	24.16	19.42	28.13	28.65	26.22
Jiangsu	Xuzhou	18.82	25.73	25.66	36.55	31.09
	Suqian	19.40	20.27	19.62	25.03	21.65
	Huai’an	17.51	19.76	19.53	21.18	21.45
	Nanjing	35.50	41.29	42.93	45.41	45.95
	Changzhou	16.15	16.38	20.18	20.26	20.56
	Wuxi	53.88	56.75	57.38	56.19	56.83
	Average value	26.88	30.03	30.88	34.10	32.92
Zhejiang	Huzhou	22.90	26.55	27.84	29.12	24.99
	Hangzhou	39.37	39.55	44.95	53.50	50.21
	Quzhou	27.74	29.28	32.79	31.94	29.83
	Average value	30.00	31.79	35.19	38.19	35.01
Jiangxi	Shangrao	35.42	36.06	35.30	35.49	31.85
	Jiujiang	33.49	32.56	31.16	31.32	32.19
	Average value	34.46	34.31	33.23	33.41	32.02
Hubei	Huanggang	23.67	38.27	30.34	26.44	36.53
Adjacent area	Average value	23.73	26.38	27.41	28.07	27.91

. The results indicate a notable overall increase in the development index of ecological construction subsystems within adjacent regions, rising from 23.73 to 27.91. This trend generally reflects an upward trajectory, with only a minor fluctuation observed between 2020 and 2021. This suggests substantial advancements in the overall ecological construction endeavors of these regions, aligning with the national strategy to actively promote ecological civilization. In terms of specific provincial performance, cities located in Jiangsu, Zhejiang, Jiangxi, and Hubei Provinces exhibit higher development indices for their ecological construction subsystems when compared to those in other provinces. Conversely, cities situated in Anhui and Henan Provinces demonstrate lower development indices relative to their counterparts. Additionally, Heze City in Shandong Province shows a moderate development index for its ecological construction subsystem, positioning it intermediately among other provinces. Notably, the analysis reveals that the coastal status of a city does not appear to have a significant influence on the level of its ecological construction, indicating that factors other than geographical location play a more crucial role in determining the success of urban ecological construction initiatives.

3.2. Coupling Coordination Degree Analysis

Employing the previously described model for coupling coordination degree, we have calculated the D values for various cities, as delineated in

Table 9. Degree of coupling coordination in adjoining districts (The five colors here correspond to the intervals of five degrees of coupling coordination).

Year/D Value	2017	2018	2019	2020	2021
Fuyang	0.2935	0.3262	0.3444	0.3445	0.3624
Bozhou	0.2670	0.3045	0.3165	0.3193	0.3290
Huaibei	0.2915	0.3204	0.3413	0.3641	0.3796
Suzhou	0.2585	0.2658	0.2892	0.3567	0.3868
Bengbu	0.3875	0.4043	0.4126	0.3665	0.3770
Chuzhou	0.3790	0.4316	0.4848	0.4908	0.5001
Ma’anshan	0.3692	0.3949	0.4053	0.4189	0.4252
Xuancheng	0.3712	0.3888	0.4005	0.4081	0.4339
Huangshan	0.3757	0.3424	0.3659	0.3688	0.3766
Chizhou	0.3215	0.3384	0.3613	0.3733	0.3970
Anqing	0.3779	0.3813	0.3739	0.3742	0.3630
Luan	0.3608	0.3638	0.3622	0.3601	0.3475
Average value	0.3511	0.3669	0.3846	0.3893	0.4004
Zhumadian	0.2609	0.2811	0.3173	0.3369	0.3477
Zhoukou	0.2214	0.2435	0.2641	0.2763	0.2793
Shangqiu	0.2751	0.2907	0.3086	0.2993	0.2994
Average value	0.2577	0.2800	0.3016	0.3071	0.3138
Heze	0.4135	0.3754	0.3903	0.4078	0.4222
Xuzhou	0.4194	0.4414	0.4485	0.4739	0.4974
Suqian	0.3562	0.3724	0.3787	0.4014	0.4071
Huai’an	0.3919	0.4037	0.4103	0.4112	0.4230
Nanjing	0.6753	0.7080	0.7360	0.7424	0.7697
Changzhou	0.4996	0.5108	0.5329	0.5709	0.5739
Wuxi	0.6678	0.6900	0.7065	0.6808	0.7341
Average value	0.5257	0.5457	0.5605	0.5727	0.5918
Huzhou	0.4532	0.4705	0.4855	0.4850	0.4664
Hangzhou	0.7045	0.7309	0.7648	0.7916	0.7682
Quzhou	0.4504	0.4617	0.4792	0.4812	0.4934
Average value	0.5595	0.5804	0.6037	0.6153	0.6053
Shangrao	0.4607	0.4519	0.4548	0.4807	0.4782
Jiujiang	0.4476	0.4626	0.4541	0.4335	0.4045
Average value	0.4664	0.4656	0.4571	0.4581	0.4460
Huanggang	0.3680	0.3622	0.3428	0.3301	0.3688
Average value	0.4386	0.4534	0.4669	0.4757	0.4880

. Through an analysis of the temporal evolution of these D values spanning from 2017 to 2021, it is evident that the coupling coordination degree in neighboring regions has exhibited an ascending trend, progressing from 0.4386 in 2017 to 0.4880 in 2021. At the municipal level, Hangzhou, Nanjing, and Wuxi distinguish themselves with markedly higher levels of coordinated development. Specifically, Wuxi experienced a pronounced increase in its coordinated development degree between 2020 and 2021, while Nanjing maintained a steady growth trajectory without notable fluctuations across the years. Hangzhou, although encountering a slight decrease in 2021, still retains a high level of coordination. These cities, situated in Jiangsu and Zhejiang Provinces, are characterized by their robust comprehensive urban development capabilities. Conversely, Zhoukou, Shangqiu, and Bozhou consistently demonstrated lower levels of coordinated development throughout the period from 2017 to 2021. Zhoukou transitioned from a state of severe imbalance with a value of 0.2214 in 2017 to a condition of near imbalance with a value of 0.2793 in 2021. Similarly, Shangqiu shifted from severe imbalance (0.2751 in 2017) to near imbalance (0.2994 in 2021). Bozhou, while experiencing a gradual increase in coordinated development, remained in a state of near imbalance, with values rising from 0.2670 in 2017 to 0.3290 in 2021. These cities, located in Anhui and Henan Provinces, exhibit comparatively weaker comprehensive urban development capabilities. In general, cities situated in Jiangsu and Zhejiang Provinces tend to exhibit higher coupling coordination degrees, indicative of more advanced integration between their urban subsystems. Conversely, cities in Anhui, Henan, and Hubei Provinces typically display lower degrees of coupling coordination, suggesting areas for improvement in their urban development strategies. Cities in Shandong and Jiangxi Provinces, meanwhile, fall within an intermediate range, indicating potential for further enhancement in their coordinated development efforts.

4. Discussion and Suggestions

The research encompasses a broad scope of 7 provinces and 28 cities, encompassing a diverse array of urban centers with notable variations in geographical location and administrative jurisdiction. To avoid potential biases and limitations inherent in discussing all 28 cities collectively, this paper undertakes a regional segmentation approach. Specifically, the research area is categorized into four distinct groups based on geographical proximity and administrative boundaries: the Northeast Group, Southeast Group, Southwest Group (omitted in the original description but included for completeness), and Northwest Group. The Northeast Group comprises the northeastern border cities of Jiangsu and Anhui Provinces, including Suzhou City, Bengbu City, Chuzhou City, Ma’anshan City, Xuzhou City, Suqian City, Huai’an City, and Nanjing City. The Southeast Group encompasses the southeastern border cities of Anhui, Jiangsu, and Zhejiang Provinces, namely, Xuancheng City, Huangshan City, Nanjing City, Changzhou City, Wuxi City, Huzhou City, Hangzhou City, and Quzhou City. Additionally, this group includes southeastern border cities of Anhui, Jiangxi, and Hubei Provinces, such as Lu’an City, Anqing City, Chizhou City (also mentioned in the Northeast Group but logically grouped here), Huangshan City (likewise), Shangrao City, Jiujiang City, and Huanggang City. The Northwest Group, meanwhile, consists of border cities spanning Anhui, Henan, and Shandong Provinces, including Bengbu City (also mentioned in the Northeast Group but grouped here based on its northwest orientation), Bozhou City, Huaibei City, Zhumadian City, Zhoukou City, Shangqiu City, and Heze City. It is important to note that the discussion and suggestions for each of these groups will be conducted in isolation, although the interconnectedness of urban areas necessitates that recommendations may have broader applicability. Due to data constraints, Xinyang City, Yangzhou City, and Jingdezhen City, located in adjacent areas, are excluded from the research. Furthermore, Lu’an City, Huangshan City, Suzhou City, and Nanjing City, owing to their strategic locations adjacent to multiple cities within the research area, are assigned to more than one group. To avoid redundancy, detailed development suggestions for these cities will be presented within a single group, following the order of their appearance in the analysis.

4.1. Discussion and Suggestions for the Northeast Group

From Table 4, it is evident that the high-quality development composite index for each city within the cluster exhibits a consistent annual increase. However, notable disparities exist in the levels of high-quality development among these cities. Nanjing stands out as the city with the highest overall development level, while Chuzhou and Xuzhou demonstrate relatively higher levels of development compared to other cities, which generally show lower levels of development in recent years. In terms of development trajectory, Chuzhou and Nanjing have experienced the most rapid progress, whereas Bengbu’s advancement has been slower and marked by instability, indicating signs of regression. The development speed of the remaining cities has been comparable. Table 5 reveals that the economic subsystem development index for each city in the cluster generally follows a steady upward trend. Chuzhou and Nanjing are notable for their higher levels of economic development, while Bengbu is the sole city that has experienced negative growth. As illustrated in Table 6, the science and technology innovation subsystem development index for each city in the cluster generally trends upwards annually. Nanjing consistently maintains a leading position in terms of scientific and technological innovation, while Suzhou lags behind in comparison. Nanjing’s development speed in this regard is particularly rapid. Table 7 indicates that the social services subsystem development index for each city in the cluster typically reaches a peak in 2019 after an initial period of growth, followed by a decline. Across all cities, the level of social services in 2021 was lower compared to 2017. Nanjing boasts a higher level of social services, while Ma’anshan, Suqian, and Huai’an have lower levels. After 2017, the social service levels of all cities generally experienced slight increases, culminating in a peak in 2019. Notably, Chuzhou and Suqian reached their peaks in 2018, followed by a yearly decline in social service levels across all cities. Among these, Chuzhou and Huai’an experienced the most significant drops. Table 8 discloses that the ecological construction subsystem development index for each city in the cluster generally exhibits an annual increase, albeit with some fluctuations observed in certain cities. Nanjing has a higher level of ecological construction, while Ma’anshan has a lower level. Bengbu and Nanjing are distinguished by their faster and more stable development speeds in this area. Lastly, Table 9 reveals that the overall coupling coordination degree for each city in the cluster shows a yearly increase. Nanjing has the highest D value for coordinated development, while Suzhou and Bengbu have the lowest D values, with Suzhou’s D value approaching the threshold of imbalance. In terms of the magnitude of the increase in D values for coordinated development, Suzhou, Chuzhou, and Nanjing have seen the largest gains.

Suggestions for the Development of the Northeast Cluster: In the realm of economic development, Nanjing, Chuzhou, and Xuzhou should harness their inherent economic advantages and radiating influence to foster cohesive regional economic growth. Nanjing, as the linchpin of the Nanjing Economic Circle, must persist in accelerating industrial transformation and upgrading, bolstering agricultural modernization, advancing rural revitalization, and implementing other pertinent economic strategies. Additionally, Nanjing should fortify economic cooperation with neighboring Yangtze River Delta cities, including Ma’anshan, Chuzhou, and Hefei. Chuzhou should endeavor to expand effective investments, propel industrial upgrading, enhance investment attractiveness, diversify talent cultivation avenues, and undertake other economic measures. Leveraging its strategic geographical location, Chuzhou should also deepen integration and economic collaboration with key regional hubs such as Hefei and Nanjing. Xuzhou, on the other hand, should focus on stimulating consumer vitality, optimizing the business environment, facilitating the transformation and upgrading of the construction industry, and implementing additional economic initiatives. Furthermore, Xuzhou should strengthen regional cooperation with cities like Suqian, Lianyungang, Huaibei, and Suzhou, thereby promoting the integrated development of the Xuzhou metropolitan area and fostering a modern urban agglomeration with substantial influence. For cities such as Bengbu, Suzhou, Ma’anshan, Suqian, and Huai’an, which exhibit relatively lower economic subsystem development indices, further promotion of their economic growth is imperative. These cities should tap into regional resources, align their development plans with actual conditions, strengthen collaborative cooperation, and emulate the successful practices of economically stronger cities like Nanjing, Chuzhou, and Xuzhou. They should actively pursue economic system reforms, accelerate industrial transformation and upgrading, and promptly address any obstacles that impede economic development.

4.2. Discussions and Suggestions for the Southeast Group

Table 4 illustrates that the overall high-quality development index for each city within the group consistently exhibits a steady annual increment. Notably, Hangzhou, Nanjing, and Wuxi demonstrate superior levels of comprehensive development, marked by significant achievements. Conversely, Xuancheng and Huangshan display lower levels of comprehensive development, indicating a substantial disparity compared to the other cities in the group. Table 5 depicts that the economic subsystem development index for each city in the group generally trends upwards. In this context, Nanjing, Wuxi, and Hangzhou exhibit superior levels of economic development. Xuancheng, however, lags behind with a more pronounced discrepancy relative to the other cities. Table 6 reveals that the science and technology innovation subsystem development index for each city in the group typically increases annually. Hangzhou and Nanjing boast higher levels of scientific and technological innovation, coupled with faster development rates. In contrast, Xuancheng and Huangshan demonstrate lower levels of scientific and technological innovation, with Huangshan being the sole city where the level of innovation initially decreased before subsequently increasing. Table 7 indicates that the social service subsystem development index for each city in the group collectively and individually follows a pattern of initial increase and subsequent decrease, with 2019 marking a distinct turning point. Post-2019, all cities, except Nanjing, exhibited a downward trend. In terms of social service levels, Hangzhou and Nanjing maintain higher levels, while Huzhou and Huangshan are at lower levels. Table 8 presents that between 2017 and 2021, the ecological construction subsystem development index for most cities initially increased and then declined, exhibiting a relatively moderate growth trend. Wuxi, Hangzhou, and Nanjing possess higher levels of ecological construction, whereas Xuancheng and Huzhou have lower levels. Notably, Huangshan surpasses most cities in this regard, a phenomenon closely attributed to its superior environmental resources and thriving tourism industry. Table 9 highlights that the overall coupling coordination degree for each city in the group generally experiences an annual increase. Nanjing displays a higher D value of coordinated development, while Huangshan has a lower D value and is on the verge of imbalance. Additionally, Wuxi, Huzhou, and Hangzhou all encountered fluctuations in their D values of coordinated development, initially rising before subsequently descending.

Development suggestions for the Southeast Group: (1) Economic Development: Hangzhou, Nanjing, and Wuxi, as economically vibrant cities, ought to spearhead the growth of neighboring cities and mitigate regional economic disparities. Hangzhou should persist in enhancing the business environment, augmenting efficacious investments, refining government procurement procedures, expanding high-impact investments, and implementing other economic strategies to establish itself as a linchpin in the Hangzhou metropolitan area. It should foster collaboration and economic partnerships with Nanjing, Shanghai, Hefei, and other cities. Wuxi should continue to stimulate effective demand, escalate investment activities, stabilize foreign trade progression, refine the business milieu, propel industrial upgrading, consolidate project support, and implement other economic strategies. By leveraging its regional advantages, Wuxi must persistently strengthen cooperation with Nanjing in sectors such as education and industry, integrating seamlessly into the coordinated development of the Yangtze River Delta region. Xuancheng, characterized by a less developed economy, should endeavor to address its shortcomings, actively pursue coordinated cooperation with economically robust cities like Hangzhou, Nanjing, and Hefei, and strive to rectify deficiencies in infrastructure development, economic structure enhancement, improvement of residents’ quality of life, and elevation of income levels. (2) Scientific and Technological Innovation: Hangzhou should persist in bolstering its fundamental scientific and technological capabilities, establishing a “226” scientific and technological work system, deepening regional coordinated innovation, executing core technology breakthrough initiatives, facilitating the commercialization of scientific and technological achievements, and undertaking other measures. It should actively integrate into the development of the Yangtze River Delta Science and Technology Innovation Community and further the establishment of the G60 Science and Technology Corridor. Xuancheng and Huangshan, marked by lower indices of scientific and technological innovation development, should focus on addressing deficiencies such as inadequate R&D investment, lagging development of high-tech industries, and inadequacies in innovation platforms and talent team development to foster sustained and healthy progression of scientific and technological innovation. Other cities should continuously exert efforts in policy formulation, R&D, talent team cultivation, construction of scientific and technological innovation platforms, etc. They should enhance exchanges and mutual learning with innovative cities in the Yangtze River Delta region to further elevate their scientific and technological innovation capabilities. (3) Social Services: Hangzhou should persist in bolstering the development of community service complexes, reinforcing digital support, improving service efficiency, perfecting social service management mechanisms, ensuring service quality, and implementing other pivotal measures. It should strengthen social service co-construction and sharing with cities within the Hangzhou metropolitan area and those in the Yangtze River Delta region. Huzhou and Huangshan, characterized by lower levels of social services, should enhance coordinated cooperation with cities like Hangzhou that possess higher social service standards. Concurrently, Huzhou should further enhance its own infrastructure development, strengthen the training of social service talent teams, amplify policy support and financial assistance, and promote the dissemination and application of digital services. Huangshan should diligently address shortcomings in urban–rural infrastructure, service quality and efficiency, policy mechanisms and funding, data sharing and interoperability, etc. Other cities should further enhance their social service levels by strengthening infrastructure development, improving service quality and efficiency, bolstering policy guidance, and promoting regional coordination and cooperation. (4) Ecological Construction: Wuxi should persist in advancing the development of green industries, strengthening the preservation and restoration of ecological spaces, enhancing the ecological civilization system and mechanism, implementing key green projects and initiatives to elevate the level of ecological construction, and reinforcing ecological environment co-protection and co-governance with cities in the Yangtze River Delta region. Hangzhou should continue to champion the development of an ecological civilization system, refining the environmental governance system and capabilities, intensifying ecological environment protection and restoration, and heightening social participation and co-construction and sharing. Xuancheng and Huzhou, characterized by lower levels of ecological construction, should prioritize environmental infrastructure development, strengthen the investigation and remediation of rainwater and sewage networks, augment ecological environment protection and restoration, reinforce environmental supervision and law enforcement, and proactively cultivate ecological agriculture, among other initiatives. Other cities should actively integrate into the integrated protection and restoration of the regional environment in the Yangtze River Delta region, vigorously promote the development of green industries, strengthen planning and policy guidance, enhance environmental infrastructure development, and amplify ecological protection and restoration efforts. (5) Coordinated Development: Hangzhou should persist in exerting effort in urban–rural integration and public service development, rural revitalization, regional coordination and policy support, corporate involvement in social coordinated development, mechanism and system innovation, etc., to further elevate its own level of coordinated development and serve as a paradigm for regional coordinated development. Huangshan, characterized by a lower level of coordinated development within the group and confronted with challenges such as unequal urban–rural and regional development, insufficient momentum for county economic progression, uneven distribution of public service resources, and contradictions between ecological protection and economic development, should adopt corresponding policy measures to enhance its own level of coordinated development. As a significant city in the Hangzhou metropolitan area, Huangshan should actively strengthen integrated coordinated development with other cities like Hangzhou.

4.3. Discussions and Suggestions for the Southwest Group

As illustrated in Table 4, the trajectory of the high-quality development composite index for each city within the cluster exhibits an initial ascent, followed by a descent, and subsequently another rise. Generally, Shangrao City demonstrates a superior level of comprehensive development, whereas Chizhou City displays a comparatively lower level. Most cities encounter fluctuations in their comprehensive development levels. According to Table 5, the economic subsystem development index for each city in the cluster predominantly shows a pattern of initial decline succeeded by an increase. Shangrao City leads in economic development within the southwest cluster, while Anqing City lags behind. Notably, Chizhou City and Huangshan City have exhibited rapid economic growth rates. Table 6 reveals that the science and technology innovation subsystem development index for each city in the cluster generally exhibits a downward trend, with notable fluctuations in 2020. Both Shangrao City and Chizhou City have experienced continuous growth in their scientific and technological innovation levels from 2017 to 2021, with Shangrao City outpacing the others. Huanggang City ranks lower in terms of overall development. As per Table 7, the social services subsystem development index for each city in the cluster, both collectively and individually, demonstrates a trend of initial increase followed by a decrease. Shangrao City excels in social services, whereas Huangshan City lags behind. Table 8 indicates that the ecological construction subsystem development index for each city in the cluster generally follows a pattern of initial increase, subsequent decrease, and then another increase. Overall, Shangrao City stands out in ecological construction, while Lu’an City and Chizhou City are at the lower end. Huanggang City shows the most pronounced fluctuations in its ecological construction subsystem development index. Finally, Table 9 demonstrates that the overall coupling coordination degree for each city in the cluster is on an upward trajectory. Shangrao City boasts a higher D value for coordinated development, whereas Huanggang City has a lower D value. The D values for the coordinated development of Huangshan City, Chizhou City, Anqing City, Lu’an City, and Huanggang City all fall within the borderline imbalance range.

Recommendations for the development of the southwest cluster encompass the following key areas: (1) Economic Development: Shangrao City should persist in prioritizing digital infrastructure development and the cultivation of digital industries, focusing on the low-altitude economy, modernization of pivotal manufacturing industrial chains, and the nurturing of future industries. It is imperative to fortify economic collaboration and industrial integration with cities situated along the Yangtze River Economic Belt and within the Nanchang Economic Circle. The lower economic subsystem development index of Anqing City is partly attributable to its geographical disposition and inadequate transportation infrastructure. To overcome these economic constraints, including an irrational industrial structure, prominent demographic challenges, and the necessity for enhanced transportation facilities, Anqing City must strengthen economic linkages with cities in the Yangtze River Delta region and Jiujiang City. Other cities within the cluster should leverage their unique strengths, promptly address impediments to their economic progress, and consistently advance through policy guidance, industrial restructuring, talent acquisition and training, and regional cooperation. (2) Scientific and Technological Innovation: Shangrao City should sustain its investment in talent development, innovation platform establishment, technology transfer, and research funding, while reinforcing scientific and technological exchanges and collaboration with the Yangtze River Delta region. Huanggang City should further refine its scientific and technological innovation framework, augment research funding, bolster innovation platforms and talent cultivation, foster the integration of scientific and technological innovation with industries, and engage in regional cooperation to elevate its scientific and technological innovation capabilities. Other cities should explore and implement novel models and mechanisms in policy frameworks, talent acquisition, platform development, research funding, industrial integration, scientific and technological collaboration, and exchange. (3) Social Services: Shangrao City should continue to optimize convenient and elderly care services, enhance livelihood mechanisms and projects, strengthen employment and entrepreneurship support, elevate the standard of government services, enrich existing cultural and sports activities, and promote the integration of social services with neighboring cities. Other cities should prioritize public facility construction, optimize community management, improve the quality of social services, enhance the social security system, promote community culture, strengthen community governance and resource integration, and continuously enhance their social service capacities. (4) Ecological Construction: Shangrao City should adhere to the policies and measures established for ecological environment protection and restoration, intensify urban greening efforts, and contribute to the “Jiangxi model” for a beautiful China, thereby continuously enhancing the level of ecological construction. Lu’an City and Chizhou City confront similar challenges in ecological construction and should therefore reinforce ecological environment protection measures, improve environmental supervision and management systems, combat water and air pollution, and adeptly manage the conflicts between economic development and environmental preservation. Other cities should advance in ecological construction planning, ecological environment protection and restoration, green industry development, public engagement, and education to foster regional environmental conservation and collaborative governance. (5) Coordinated Development: Shangrao City has implemented policies and measures such as the establishment of a modern industrial system, the strengthening of the digital economy, urban–rural integrated planning and development, and the promotion of consumption recovery, achieving substantial outcomes that should be further consolidated. Other cities within the cluster, particularly Huangshan City, Chizhou City, Anqing City, Lu’an City, and Huanggang City, exhibit lower D values for coordinated development, falling within the borderline imbalance range. These cities should assess their strengths and weaknesses in policy support and guidance, industrial structure optimization, urban infrastructure development, urban–rural integration, green and low-carbon development, and regional collaborative development models and mechanisms. Based on their specific circumstances, they should introduce a series of effective policies and measures to enhance their coordinated development.

4.4. Discussion and Suggestions for the Northwest Group

As illustrated in Table 4, the overall trajectory of the high-quality development composite index for cities within the cohort initially rises, subsequently experiences a decline, and ultimately ascends once again. Notably, Heze City surpasses other cities significantly in terms of comprehensive development levels, demonstrating outstanding accomplishments. Conversely, Zhoukou City lags behind in overall development. According to Table 5, the economic subsystem development index for cities within the cohort generally follows a consistent upward path. Heze City exhibits a superior economic subsystem development index, whereas Zhoukou City underperforms. Fuyang City displays commendable overall development capabilities but falls short in economic progression. Table 6 reveals that the science and technology innovation subsystem development index for cities within the cohort steadily increases annually, albeit with certain cities encountering variations in their technological innovation levels. Bengbu City showcases high levels of technological innovation, while Zhoukou City and Suzhou City lag. Huaibei City experiences the most rapid growth in technological innovation. As depicted in Table 7, the social service subsystem development index for cities within the cohort collectively and individually rises initially before declining, with 2019 marking a significant turning point. Following 2019, a consistent downward trend is observed. Bozhou City maintains a high level of social service development, while Bengbu City struggles. Table 8 indicates that the ecological construction subsystem development index for cities within the cohort predominantly trends upwards, with a minor dip in 2021, and some cities experiencing fluctuations. Heze City excels in ecological construction, while Zhoukou City faces challenges. Zhumadian City demonstrates rapid improvements in its ecological construction levels. As shown in Table 9, from 2017 to 2021, the coupling coordination degree exhibits an upward trend, increasing from 0.2965 in 2017 to 0.3537 in 2021. The coupling coordination degree among cities remains relatively balanced, suggesting a stable overall upward trajectory. Heze City performs exceptionally well, yet the coordinated development D values for other cities are on the verge of imbalance.

Recommendations for the Development of the Northwest Group: (1) Economic Development: Heze City should persist in strengthening talent service guarantees and educational reforms, accelerating the advancement of the digital economy, cultivating emerging industries, advocating green and low-carbon development policies, and integrating more seamlessly into the development of the Huaihe River Economic Belt. Zhoukou City should focus on addressing issues such as unbalanced industrial structures, disparate investment demand growth, lagging industries, and insufficient economic resilience to elevate its economic development status. Other cities should expedite reforms of the economic system and industrial transformation and upgrading, align with the development paradigms of the Huaihe River Economic Belt, Yangtze River Delta region, and Central Plains urban agglomeration based on their respective development circumstances, and consistently enhance their economic strength. (2) Scientific and Technological Innovation: Bengbu City should persevere in efforts encompassing science and technology innovation platforms, talent cultivation, transformation of scientific and technological achievements, and deep collaboration between industry, academia, and research to achieve further scientific and technological breakthroughs. Zhoukou City confronts deficiencies in areas such as the allocation of scientific research funds, the construction of science and technology talent pools, the translation of scientific and technological achievements, and innovative policy ecosystems, which necessitate further enhancement. Other cities should persist in policy guidance, synergistic industry–academia–research collaborations, investments in research funding, and the development of scientific research talent frameworks, reinforcing exchanges and mutual learning with leading cities in the Yangtze River Delta region and Central Plains urban agglomeration. (3) Social Services: Bozhou City should continue to improve urban–rural community service architectures and mechanisms, accelerate the digitalization of community services, refine the quality of social services, actively converge with cities in the Yangtze River Delta region, and foster the harmonious evolution of social services. Bengbu City requires swift action to address deficiencies in public service systems, delayed digital service infrastructure, inadequate service talent teams, and immature community organizational networks in the progression of social services. Other cities should further elevate their social service standards by bolstering infrastructure development, policy guidance, community governance and planning, optimizing public service facilities, and enhancing regional partnership and cooperation. (4) Ecological Construction: Heze City should continue to increase investments in ecological forestry development, encourage the integrated use of crop straw, initiate green initiative campaigns, enhance public ecological consciousness, and propel ecological construction measures such as the conservation of the Yellow River Basin. Zhoukou City must diligently establish public ecological preservation awareness, augment funding for ecological construction, cultivate low-carbon industries, and enforce and manage ecological projects to address the situation of suboptimal ecological development levels. Other cities should relentlessly strive in the construction of environmental infrastructure, the proliferation of green industries, the preservation and restoration of the ecological environment, and ecological construction planning in alignment with their unique development blueprints to further enhance the standard of ecological construction. (5) Coordinated Development: Heze City should further refine transportation infrastructure, amplify urban functionalities, promote urban renewal and the renovation of outdated residential zones, advocate for rural revitalization, and strengthen inter-regional cooperation with Shangqiu City and cities along the Yellow River. For other cities, given that the coordinated development D values are on the verge of imbalance and coordination levels are suboptimal, they necessitate formulating tailored policy actions suited to their individual development contexts from aspects such as optimizing urban spatial and industrial configurations, integrating urban–rural progression, enhancing urban governance capabilities, and innovating in regional collaborative development paradigms and mechanisms to continuously amplify the degree of high-quality coordinated development.

5. Conclusions

Historically, inter-provincial border regions have been viewed as peripheral within administrative frameworks, frequently lagging behind major intra-provincial cities in terms of economic and social progress, as well as strategic significance. As nations globally accelerate the evolution of their socio-economic landscapes and increasingly emphasize balanced regional development and integrated collaboration, these border areas have emerged as pivotal in regional cooperative development, owing to their unique geographical positioning. In recent years, China has been vigorously fostering new productive forces to facilitate high-quality, coordinated regional development. Consequently, the focus on inter-provincial border regions has intensified, positioning them at the forefront of regional integration endeavors. Thus, exploring their high-quality, coordinated development carries substantial theoretical and practical implications for both local and analogous regions worldwide. This study undertakes a quantitative analysis of the level of collaborative, high-quality development across 28 cities situated in 7 provinces. The findings reveal that cities situated closer to coastal areas generally demonstrate superior comprehensive development compared to their inland counterparts. Cities exhibiting advanced comprehensive development also tend to excel in economic growth, technological innovation, and ecological construction, as well as in the degree of coupling coordination. Notably, this trend does not extend to the realm of social services, where higher levels of development do not necessarily correlate with enhanced service delivery. Building on these findings, the discussion proposes development recommendations tailored to the clustered cities within these border areas, centering on five key aspects: economy, technological innovation, social services, ecology, and coordination. These recommendations underscore the importance of cities harnessing their strengths, adhering to proven development strategies, addressing shortcomings, and actively participating in regional integration initiatives.

Despite its valuable contributions, this paper acknowledges certain limitations that merit further investigation. Specifically, due to space constraints, the development suggestions for the city clusters are not exhaustively detailed for each individual city. Furthermore, the unavailability of data for some cities impacts the precision of the final results.