Coordinated Development of the Digital Economy and Urban–Rural Integration in the Yangtze River Delta and Its Spatial Correlation Structure

Abstract

The digital economy plays a crucial role in facilitating the flow of resources between urban and rural areas and addressing developmental imbalances. This study analyzed the coordinated development of the digital economy (DE) and urban–rural integration (URI) across 41 cities in the Yangtze River Delta (YRD) from 2010 to 2022. Using the entropy-weighted TOPSIS method and the coupling coordination degree model, we evaluated the spatial distribution of coordination levels. A modified gravity model and social network analysis revealed spatial correlation structures, while geographic detectors identified dominant influencing factors. The results showed that: (1) the digital economy has advanced faster than urban–rural integration, leading to regional disparities; (2) the coupling coordination level improved over the study period, with cities exhibiting higher coordination concentrated in the eastern and southeastern parts of the region, showing an M-shaped distribution; (3) the coupling coordination degree displayed a spatial distribution pattern from northwest to southeast, with the center shifting southeastward, while overall the central area outperformed the northern and southern areas and the eastern area outperformed the western area; (4) core cities in the spatial correlation network have expanded from Shanghai, Suzhou, and Wuxi to include Nanjing and Changzhou, with increasing influence, but uneven mediating roles; (5) the coordinated development of the digital economy and urban–rural integration was influenced by different factors over various time periods, with the flow of urban–rural resources, agricultural modernization, and social equalization being the primary contributors. Targeted optimization strategies using the geographic detector model would provide a reference for regional urban–rural development.

1. Introduction

The relationship between urban and rural areas is critical for a country’s economic and social development. A strong urban–rural relationship plays a crucial positive role in effective national modernization. As urbanization continues globally, both developed and developing countries need to explore and implement strategies suitable for their national contexts to foster positive urban–rural relationships and better support vigorous urban development [1]. As the world’s largest developing country, China has a unique dual economic structure in relation to its urban and rural areas [2]. China’s cities and villages are broadly defined as the municipal districts and surrounding areas within the city’s administrative boundaries. However, as China’s social and economic development has entered a new era, the significant urban–rural gap has become a pressing issue that must be addressed [3]. The urban–rural gap extends beyond economic disparities to include imbalances in public services, culture, and the ecological environment. Integrated urban–rural development helps to eliminate dual structures and promotes the free flow of resources and the sharing of infrastructure and services between cities and neighboring rural areas [4]. Establishing a robust mechanism for urban–rural integration (URI) is essential for achieving high-quality modernization.

Since the beginning of the 21st century, the Chinese government has prioritized reconciling tensions within the dual urban–rural structure as one of its main development goals by implementing targeted policies in rural and urban areas to eliminate these tensions and promote integrated development [5]. The Digital Rural Strategy was first proposed in the Central Document No. 1 of 2018, attracting widespread attention [6]. As a new form of economic activity, the digital economy (DE) has gradually become a key driving force and core focus of economic development and transformation in this new era [7]. Characterized by high innovation, broad applicability, and extensive coverage, the DE simultaneously enhances productivity and consumption, facilitates low-cost accumulation and the efficient flow of resources critical to urban and rural development, breaks down traditional barriers affecting the movement of such resources, and serves as an important pathway for promoting the bidirectional flow of urban- and rural-development resources to achieve high-quality development [8,9,10,11].

The rapid development of the DE has significantly affected various aspects of urban and rural development, and the interactions between them have become increasingly complex. Research shows a significant correlation between the DE and URI [12]. The DE influences rural labor mobility and social capital allocation [13], reduces the segmentation of urban and rural markets [14], balances the distribution of factors among urban and rural residents [15], promotes green and high-quality development [16], drives URI, and narrows the urban–rural gap [17]. Research has shown that the DE has greatly boosted employment and income growth in Canada’s service sector [18], with positive effects also reported on rural household income in Ghana [19].

Urban–rural integration is not only a stage in the evolution of urban–rural relations but also an important manifestation of the restructuring of the regional economic and social system [20]. In essence, the process of urban–rural integration reflects the restructuring of the regional spatial structure, which is manifested in the transformation of the traditional “center–periphery” pattern into a networked spatial interaction pattern under the dynamic flow of factors [21]. In his early research, Carey H C first systematically introduced the concept of “gravity” into the framework of analyzing socioeconomic interactions. He argued that there is a gravitational-like economic link between cities and villages and between regions, and that this force promotes spatial agglomeration and market expansion [22]. Subsequent studies have further extended the framework to the study of the spatial structure of networks formed by the “gravitational pull” between nodes, which explains development patterns in different areas, countries, and regions. For example, David Bogataj et al. analyzed the spatial distribution structure of existing intercontinental freight hubs through a gravity model and elaborated on the impact of the location of new ports on the intercontinental supply chain [23]. Tiantian Li et al. compared the network structure of high-quality economic development in China’s Yellow River Basin and Yangtze River Basin based on the context of regional integration and by means of social network analysis and found that the two network relationships are more stable, but the correlation of the nodes is not high [24]. Bensassi et al. analyzed the impact of the spatial distribution of logistics infrastructure on the export trade of the Spanish region by extending the gravity model [25].

Currently, there is limited research on the spatial correlation structure of the mutual influences between the DE and URI. Most scholars have focused on studying the interactions between the DE and URI or their spatial evolution processes [26,27], with few addressing the spatial correlation structure of their coordinated development. Given this context, this study aimed to systematically assess the development level of the digital economy (DE) and urban–rural integration (URI) in 41 cities in the Yangtze River Delta (YRD), reveal the spatial and temporal characteristics and spatial correlation structure of the coupling of the DE and URI, and explore the main factors affecting the development of the DE and URI in these cities. On this basis, we propose targeted development policy recommendations, which are expected to provide theoretical references and practical reference for achieving high-quality and sustainable development of urban–rural integration in the Yangtze River Delta region and other regions. First, this paper provides an evidence-based evaluation of the development levels and spatial evolution of the coupling coordination between the DE and URI in the Yangtze River Delta. Second, it constructs a spatial correlation network based on a modified gravity model and employs social network analysis (SNA) to explore the roles and contributions of different cities within this network. Third, it utilizes geographic detectors to analyze the factors influencing the coordinated development of the DE and URI.

As a template for URI in China and a crucial hub for digital economic development, studying the synergistic growth of the DE and URI in the Yangtze River Delta is of significant theoretical and practical importance for promoting high-quality development [28]. Moreover, the findings can provide a practical reference for research on the DE and URI in other regions and even other developing countries.

2. Data Sources and Research Methods

2.1. Research Area

The Yangtze River Delta (YDR) region (27°12′–35°20′ N, 114°54′–122°12′ E), located in eastern China, is one of the country’s most economically developed areas [29]. It encompasses Shanghai, Jiangsu, Zhejiang, and Anhui, comprising 41 prefecture-level cities and is directly governed by municipalities, as shown in Figure 1. The region covers approximately 360,000 square kilometers and has a population exceeding 200 million, making it one of the most densely populated areas in China. The YDR plays a pivotal role in China’s economy, with a regional GDP exceeding RMB 20 trillion in 2019, contributing approximately 20% to the national GDP [30].

As the earliest region in China to achieve large-scale urbanization, the Yangtze River Delta region has a high degree of urbanization and high urban population density. It also has a well-developed industrial structure and a public service system of colleges and universities. However, the rural areas attached to it have also suffered serious population loss and a significant urban–rural income gap due to the agglomeration effect of cities. In 2010, the Chinese government proposed the “Integration Development of the YDR” policy, which has significantly advanced the development of transportation, economy, and society within the region. Under this policy, regional resources have been circulated effectively, greatly enhancing the level of URI [31]. With a well-established transportation network and advanced economic conditions, the region has become a key area for URI in China, reflecting the ambition of a 2012 proposal emphasizing the need for “people-centered new-type urbanization, deepening the coordination of urban–rural relations, and promoting sustainable urban–rural development” [32]. Therefore, this study focused on the YDR region to reveal the spatiotemporal evolution and spatial correlation structure of coordinated development between the DE and URI to provide insights for other countries and regions.

2.2. Indicator System Construction

URI refers to the final stage of the development of urban–rural relationships [33]. This concept encompasses mutual interactions and connections between material and non-material factors affecting urban and rural areas [34,35]. Therefore, this study sought to construct an indicator system for URI in terms of four dimensions: spatial integration, economic integration, social integration, and ecological integration. Concerning spatial integration, the focus is on the level of aggregation of material resources between urban and rural areas and accessibility in relation to resource circulation. Regarding economic integration, structural differences in industries between urban and rural areas as well as their development levels are considered, as imbalances in industrial development can lead to disparities in the living standards of urban and rural residents. In the context of social integration, appropriately balanced urban and rural infrastructure and the universality of social security reflect to some extent whether urban and rural residents enjoy the benefits of development equally. Ecological integration is manifested in the quality of living environments and the capacity for pollution control in urban and rural areas.

For specific indicator selection, this study followed Zhang Aiting [36] and Zhang Xinling [37] on indicator systems for URI development. A total of 19 indicators reflecting URI development were identified from the four dimensions previously noted, as presented in

Table 1. URI indicator system.

Primary Indicator	Secondary Indicator	Tertiary Indicator	Indicator Explanation	Attribute	Weight
Urban–rural spatial integration	Urban–rural spatial aggregation	Urbanization rate	Urban population/total resident population	+	0.019
		Land urbanization rate	Built-up area/administrative area	+	0.091
	Urban–rural accessibility	Urban–rural transportation accessibility	Total road length/administrative area	+	0.017
		Urban–rural information accessibility	Number of internet households/total urban–rural households	+	0.051
Urban–rural economic integration	Living standards of urban and rural residents	Per capita disposable income ratio	Urban per capita disposable income/rural per capita disposable income (comparison)	−	0.002
		Per capita consumption ratio	Urban household per capita consumption/rural household per capita consumption (comparison)	−	0.03
	Urban–rural industrial structure	Urban–rural output ratio	Value of primary industry/value of secondary and tertiary industries	+	0.068
		Urban–rural dual coefficient	(value of primary industry/employment in primary industry)/(value of secondary and tertiary industries/employment in secondary and tertiary industries) (comparison)	+	0.03
	Agricultural development level	Agricultural technology level	Total power of agricultural machinery/area of grain cultivation	+	0.205
		Per unit area yield of grain	Total yield of grain crops/area of grain cultivation	+	0.079
Urban–rural social integration	Equity of urban–rural facilities	Student–teacher ratio in general secondary schools	Urban secondary school student-teacher ratio/rural secondary school student–teacher ratio (comparison)	−	0.016
		Number of hospital beds in medical institutions	Ratio of beds per ten thousand people in urban hospitals to those in rural hospitals (comparison)	−	0.083
		Number of healthcare workers per thousand people	Ratio of doctors per thousand people in urban areas to those in rural areas (comparison)	−	0.082
	Level of social security	Medical insurance coverage ratio	Urban basic pension insurance coverage rate/new rural social pension insurance coverage rate (comparison)	−	0.132
		Unemployment insurance coverage rate	Number of people covered by urban–rural unemployment insurance/total resident population	−	0.065
Urban–rural ecological integration	Quality of living environment	Green coverage rate of built-up areas	Green area in built-up areas/total area of built-up areas	+	0.003
		Urban–rural air pollution index	Annual average concentration of fine particulate matter	−	0.019
	Pollution prevention and control capacity	Wastewater treatment	Rate of centralized treatment by wastewater treatment plants	+	0.006
		Solid waste treatment	Rate of harmless treatment of domestic waste	+	0.003

.

The DE can drive the upgrade of urban and rural industries, promote technological advancement, and enhance the flow of resources between urban and rural areas [38]. The DE infrastructure provides the necessary software and hardware facilities and delivers high-quality services to urban and rural residents and industries, thereby ensuring equal access to the benefits of digital economic development. Thus, this study measured the level of development of the urban–rural DE in relation to three aspects: infrastructure, business level, and digital inclusiveness.

For specific indicators, this study followed Kahin et al. [39], Zou Yueqing et al. [40], and Zhang Wang et al. [41]. Six indicators that reflect the level of DE development were selected in relation to infrastructure, business level, and digital inclusiveness, as shown in

Table 2. DE indicator system.

Primary Indicator	Secondary Indicator	Tertiary Indicator	Indicator Explanation	Attribute	Weight
Digital economy development	Infrastructure	Urban–rural broadband access rate	Number of broadband users per hundred households	+	0.105
		Number of mobile internet users	Number of mobile internet users per hundred people	+	0.231
		Number of internet-related practitioners	Number of computer and software practitioners/total number of practitioners at year-end	+	0.177
	Business level	Per capita postal volume	Total postal volume/total resident population	+	0.368
		Per capita telecommunications volume	Total telecommunications volume/total resident population	+	0.065
	Digital inclusion	Development of digital inclusive finance	China Digital Inclusive Finance Index	+	0.054

. The China Digital Inclusive Finance Index, developed by a joint research team (Peking University’s Digital Finance Research Center and the Ant Group Research Institute), served as a comprehensive indicator system to assess and measure the development of digitally inclusive finance.

2.3. Data Sources

The data used in this study were primarily derived from the China City Statistical Yearbook, the statistical yearbooks of each province and directly governed municipalities, and the statistical yearbooks of individual cities. This study analyzed data from 2010 to 2022, considering their availability and accuracy. Linear interpolation methods were used to fill small amounts of missing data.

2.4. Research Methods

2.4.1. Measurement of Development Levels

The entropy-weight TOPSIS method used in this study is a multi-criteria decision analysis approach that is an improved version of the TOPSIS method [42]. This data-driven method assigns weights to indicators based on the entropy method, thereby avoiding the influence of the subjective bias inherent in manual weighting methods. This approach objectively reflects the importance of each indicator within the corresponding indicator system during the decision-making process and highlights how the weights of the indicators change over time [43]. The specific formulas are as follows (Equations (1)–(8)).

First, the data were standardized to eliminate the influence of different dimensions on the computational results.

For positive indicators, the following equation was used:

$$x_{ij}={\displaystyle \frac{a_{ij}-\underset{i}{\min }\left(a_{ij}\right)}{\underset{i}{\max }\left(a_{ij}\right)-\underset{i}{\min }\left(a_{ij}\right)}},i=1,2,\cdots ,m,j=1,2,\cdots ,n$$

(1)

For negative indicators, the following equation was used:

$$x_{ij}={\displaystyle \frac{\underset{i}{\max }\left(a_{ij}\right)-a_{ij}}{\underset{i}{\max }\left(a_{ij}\right)-\underset{i}{\min }\left(a_{ij}\right)}},i=1,2,\cdots ,m,j=1,2,\cdots ,n$$

(2)

Next, the entropy value (e) was used to express the decision information for each indicator, where the value of e_i ranged from 0 to 1, as follows:

$$e_j=-\sum _{i=1}^m{x}_{ij}\ln {\displaystyle \frac{x_{ij}}{\ln m}},j=1,2,\cdots ,n$$

(3)

The weights of each indicator were calculated based on entropy (e), where the weight indicator of j can be expressed as follows:

$$w_j\in \left[\mathrm{0,1}\right],w_j={\displaystyle \frac{\left(1-e_j\right)}{\sum _j^n\left(1-e_j\right)}}$$

(4)

Then, a weighted decision matrix was constructed, as follows:

$$Z={\left(z_{ij}\right)}_{m\times n}{,z}_{ij}=x_{ij}{w}_{ij},i=1,2,\cdots ,m,j=1,2,\cdots ,n$$

(5)

Subsequently, using the weighted decision matrix, the positive ideal solution Z⁺ and the negative ideal solution Z⁻ can be calculated as follows:

$$\left\{\begin{array}{c}{Z}^{+}=\left\{\underset{i}{\max }{z}_{ij}|j=1,2,\cdots ,n\right\}=\left\{Z_1^{+},Z_2^{+},\cdots ,Z_n^{+}\right\}\\ Z^{-}=\left\{\underset{i}{\min }{z}_{ij}|j=1,2,\cdots ,n\right\}=\left\{Z_1^{-},Z_2^{-},\cdots ,Z_n^{-}\right\}\end{array}\right.$$

(6)

The Euclidean distances from each year to the positive and negative ideal solutions were computed as follows:

$$\left\{\begin{array}{c}{D}_i^{+}=\sqrt{{\displaystyle \sum _{j=1}^n}{\left(Z_j^{+}-z_{ij}\right)}^2},i=1,2,\cdots m\\ D_i^{-}=\sqrt{{\displaystyle \sum _{j=1}^n}{\left(Z_j^{-}-z_{ij}\right)}^2},i=1,2,\cdots m\end{array}\right.$$

(7)

Finally, the relative closeness C was calculated, representing the comprehensive development level of the DE and URI, as follows:

$$C_i={\displaystyle \frac{D_i^{-}}{D_i^{+}+D_i^{-}}},i=1,2,\cdots ,m$$

(8)

Through these calculations, relevant detail on the development levels of the DE and URI for different years and cities were obtained, with values ranging from 0 to 1.

2.4.2. Measurement of Coordination Levels

The coupling coordination degree indicates the degree of interaction and mutual influence between two subsystems. The coupling interaction and coordination degree between the subsystems are the core factors that determine the evolution of the coupling system [44]. The coupling coordination degree model for DE–URI development constructed in this study represents the complex nonlinear relationship of interaction between the two subsystems, expressed as follows (Equations (9)–(11)):

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

(9)

$$C=\sqrt{\left[D_f+C_r/2\right]}$$

(10)

$$T=\alpha D_f+\beta C_r$$

(11)

where C represents the coupling degree between the two systems, T represents the development indices of the two systems, and D represents the coupling coordination degree between the systems. The coefficients α and β are undetermined, and for the purposes of this study, the DE development level D_f and URI degree C_r are considered equally important, with the coefficients both set to 0.5.

Based on previous research [45] and the actual development of the DE and URI, the coupling coordination types were divided into five categories (

Table 3. Coordination types categories.

D	Categories
[0.00–0.30)	near collapse
[0.30–0.40)	initial coordination
[0.40–0.50)	moderate coordination
[0.50–0.60)	good coordination
[0.60–1.00)	high-quality coordination

).

2.4.3. Modified Gravity Model

This study employed a gravity model to determine the spatial correlation between the development of the DE and URI. Per capita GDP was introduced to comprehensively measure the impact of economic and geographical distances on this correlation. The coupling coordination degree of a specific city was expressed as a proportion of the total coupling coordination degree of a pair of associated cities, which served as a moderating factor for adjusting the gravity coefficient [46]. The calculation method is as follows (Equation (12)):

$$R_{ij}=k_{ij}\times {\displaystyle \frac{U_i\times U_j}{{\left[D_{ij}/\left(g_i-g_j\right)\right]}^2}},k_{ij}={\displaystyle \frac{U_i}{U_i+U_j}}$$

(12)

where R_ij denotes the spatial correlation degree of new urbanization between cities i and j; D_ij represents the geographical distance between the two cities; U and g are the coupling coordination degrees and per capita GDP of cities i and j, respectively; and k_ij is the moderating factor, indicating the contribution of city i to the spatial correlation of coupling coordination between cities i and j. Based on the spatial correlation matrix of the coupling coordination degrees of each city, this study applied the “mean principle method” to binarize the matrix, ultimately deriving the spatial correlation matrix of coupling coordination between the DE and URI development [47].

2.4.4. Social Network Analysis Model

The coordinated development of the DE and URI not only emphasizes the enhancement of individual levels but also highlights the regional division of labor, cooperation, and coordinated development. This study employed a social network analysis model to quantitatively analyze the connection pathways among nodes and identify their roles, revealing the interrelationships among relevant units and members and their spillover effects [48].

The overall network characteristics include network density, connectivity, centrality, and efficiency [49]. Specifically, network density measures the closeness of spatial correlation relationships in the coupling coordination degree among units, network connectivity assesses the extent to which network members are directly or indirectly reachable, network centrality gauges the asymmetrical reachability of nodes, and network efficiency evaluates the redundancy of connection pathways within the network. The calculation methods for these network characteristics are as follows (Equations (13)–(16)):

$$D=L/\left[N\times \left(N-1\right)\right]$$

(13)

$$C=1-2V/\left[N\times \left(N-1\right)\right]$$

(14)

$$H=1-K/\mathit{max}\left(K\right)$$

(15)

$$E=1-M/\mathit{max}\left(M\right)$$

(16)

where D, C, H, and E denote network density, connectivity, centrality, and efficiency, respectively, N represents the number of members in the spatial correlation network, L is the actual number of relational connections among members, V is the number of unreachable member pairs in the network, K indicates the number of symmetrically reachable member pairs, and M refers to the number of redundant connections in the network.

Individual network characteristics include degree, closeness, and betweenness centralities [50]. Degree centrality quantifies the number of direct connections a specific individual has with others in the network, closeness centrality describes the sum of shortcut distances established between a specific member and other members, and betweenness centrality measures the “bridging” role of a specific member within the network structure [51]. The calculation methods for these centrality metrics are as follows (Equations (17)–(19)):

$$DC=n/\left(N-1\right)$$

(17)

$$CC=\sum _{j=1}^k{d}_{ij}$$

(18)

$$BC=2\sum _i^N\sum _j^N{b}_{ik}\left(i\right)/(N^2-3N+2),b_{jk}\left(i\right)=g_{jk}\left(i\right)/g_{jk}$$

(19)

where DC, CC, and BC represent degree centrality, closeness centrality, and betweenness centrality, respectively; n is the number of members associated with a specific member in the network; d_ij is the shortcut distance between cities i and j; g_jk denotes the number of shortcuts between cities i and k, with g_jk(i) being the number of shortcuts between cities i and k that pass through city i; and b_jk(i) indicates the probability of city i being located on a shortcut between cities i and k, where j ≠ k ≠ i and j < k.

2.4.5. Measurement of Influencing Factors

This study primarily used a geographic detector to explore the factors influencing the coupling coordination degree of the DE and URI in the YDR and to reveal the driving forces behind it [52,53]. The specific model is expressed as follows (Equation (20)):

$$q=1-{\displaystyle \raisebox{1ex}{${\sum }_{i=1}^m{N}_i{\delta }_i^2$}\!\left/ \!\raisebox{-1ex}{$N{\delta }^2$}\right.}$$

(20)

where q represents the impact of the detection factor on the coupling coordination degree of the DE and URI; N and N_i are the sample sizes of the entire study area and the sub-study area, respectively; m is the number of influencing factors; and δ and δ_i are the overall variance of the entire area and the variance in the sub-study area samples, respectively. The q value ranges from 0 to 1, with higher q values indicating a greater influence of the detection factor on the degree of coupling coordination.

3. Results and Discussion

3.1. Temporal Characteristics

3.1.1. Temporal Characteristics of Development

Based on panel data from cities in the YDR from 2010 to 2022, the development-level indices of the DE (U1) and URI (U2) systems were calculated. Because of the large amount of data, as well as the fact that both urban–rural integration and digital economic development are complex socioeconomic development phenomena that require a great deal of time to accumulate before they can be reflected, in order to visualize the changes between the two, the results for 2010, 2014, 2018, and 2022 were selected to construct a comparison matrix (

Table 4. Comparison of DE and URI development level indices.

City	2010		2014		2018		2022		Average		Rank
	U1	U2	U1	U2	U1	U2	U1	U2	U1	U2	U1	U2
Shanghai	0.108	0.355	0.206	0.400	0.279	0.458	0.751	0.470	0.350	0.426	2	2
Nanjing	0.130	0.408	0.294	0.228	0.396	0.250	0.414	0.262	0.322	0.280	3	3
Wuxi	0.103	0.141	0.141	0.151	0.232	0.163	0.265	0.194	0.193	0.160	5	11
Xuzhou	0.039	0.122	0.065	0.116	0.118	0.119	0.130	0.114	0.091	0.117	36	25
Changzhou	0.079	0.151	0.108	0.166	0.194	0.261	0.233	0.279	0.155	0.226	8	5
Suzhou	0.093	0.073	0.162	0.135	0.274	0.156	0.314	0.321	0.220	0.155	4	14
Nantong	0.058	0.088	0.072	0.088	0.134	0.093	0.186	0.125	0.113	0.096	23	36
Lianyungang	0.051	0.209	0.075	0.132	0.116	0.128	0.229	0.181	0.124	0.143	15	18
Huaian	0.036	0.152	0.060	0.124	0.117	0.131	0.149	0.120	0.091	0.129	34	20
Yancheng	0.046	0.118	0.058	0.101	0.110	0.098	0.142	0.134	0.092	0.108	32	29
Yangzhou	0.078	0.128	0.082	0.114	0.141	0.112	0.169	0.121	0.118	0.116	18	26
Zhenjiang	0.042	0.099	0.083	0.100	0.141	0.109	0.184	0.124	0.129	0.106	14	32
Taizhou	0.069	0.076	0.067	0.085	0.117	0.094	0.167	0.116	0.105	0.090	27	38
Suqian	0.053	0.133	0.077	0.106	0.112	0.102	0.167	0.110	0.117	0.108	20	28
Hangzhou	0.133	0.181	0.252	0.255	0.494	0.329	0.592	0.302	0.366	0.265	1	4
Ningbo	0.091	0.136	0.130	0.147	0.209	0.159	0.213	0.229	0.163	0.159	7	12
Wenzhou	0.074	0.076	0.115	0.083	0.176	0.093	0.241	0.122	0.152	0.090	9	39
Jiaixng	0.076	0.091	0.128	0.099	0.157	0.103	0.164	0.155	0.135	0.108	12	30
Huzhou	0.038	0.102	0.125	0.103	0.184	0.119	0.244	0.191	0.151	0.119	10	23
Shaoxing	0.067	0.077	0.102	0.115	0.134	0.117	0.151	0.202	0.116	0.121	21	22
Jinhua	0.072	0.073	0.123	0.087	0.180	0.096	0.140	0.123	0.131	0.089	13	40
Quzhou	0.041	0.093	0.087	0.088	0.126	0.088	0.170	0.116	0.114	0.093	22	37
Zhoushan	0.103	0.322	0.151	0.532	0.150	0.518	0.173	0.440	0.144	0.447	11	1
Taizhou	0.070	0.089	0.096	0.102	0.134	0.101	0.200	0.146	0.119	0.102	17	35
Lishui	0.087	0.077	0.106	0.078	0.134	0.075	0.143	0.110	0.118	0.079	19	41
Hefei	0.087	0.157	0.111	0.168	0.186	0.190	0.267	0.232	0.172	0.175	6	9
Wuhu	0.020	0.140	0.069	0.130	0.121	0.121	0.169	0.135	0.108	0.135	24	19
Bengbu	0.043	0.203	0.060	0.162	0.096	0.131	0.117	0.141	0.080	0.154	37	15
Huainan	0.016	0.235	0.055	0.246	0.085	0.137	0.117	0.131	0.079	0.189	38	7
Maanshan	0.019	0.118	0.084	0.123	0.119	0.117	0.145	0.124	0.094	0.122	30	21
Huaibei	0.015	0.348	0.063	0.189	0.097	0.123	0.134	0.134	0.079	0.187	39	8
Tongling	0.032	0.227	0.079	0.163	0.090	0.229	0.126	0.138	0.094	0.206	31	6
Anqing	0.063	0.126	0.084	0.110	0.106	0.097	0.146	0.123	0.098	0.107	29	31
Huangshan	0.078	0.120	0.090	0.103	0.116	0.104	0.151	0.110	0.120	0.105	16	33
Chuzhou	0.070	0.156	0.098	0.132	0.126	0.103	0.110	0.117	0.107	0.119	25	24
Fuyang	0.022	0.219	0.053	0.180	0.084	0.144	0.100	0.121	0.069	0.161	41	10
Suzhou	0.035	0.206	0.072	0.175	0.099	0.133	0.117	0.124	0.091	0.157	35	13
Luan	0.003	0.167	0.083	0.145	0.090	0.136	0.130	0.130	0.070	0.144	40	17
Bozhou	0.049	0.203	0.057	0.164	0.089	0.128	0.129	0.118	0.092	0.150	33	16
Chizhou	0.077	0.138	0.093	0.118	0.107	0.099	0.144	0.103	0.100	0.114	28	27
Xuancheng	0.043	0.119	0.086	0.100	0.106	0.088	0.144	0.114	0.106	0.103	26	34

) for the analysis.

When comparing the development levels of the DE and URI, it was found that from 2010 to 2014, the DE in the YDR generally lagged behind URI. Particularly in cities such as Xuzhou, Nantong, Zhenjiang, and Wenzhou, there was a significant mismatch between the DE and URI. An analysis of the primary, secondary, and tertiary industry outputs of these cities revealed that their economic development relied heavily on secondary and tertiary industries, with a low proportion of primary industry output. Benefiting from policies that promoted DE development and industrial digital transformation, from 2014 to 2022, most cities began to outperform in terms of DE development compared with URI owing to policy support. The DE has developed rapidly in cities such as Hangzhou, Nanjing, Shanghai, Hefei, Suzhou, and Wuxi. Notably, thanks to the regional linkage development strategy of digital industry in Shanghai and Hangzhou, the DE development level exceeded 0.60 in 2019 and 2017, respectively, with Shanghai reaching 0.751 by the end of the study period.

3.1.2. Temporal Characteristics of Coordination

Based on the results calculated from the coupling coordination degree model, ArcGIS 10.8 software was used to create spatial distribution evolution maps of the coupling coordination degree between the DE and URI across cities in the YDR (Figure 2). As illustrated in Figure 2, owing to the mismatch between regional DE development and URI, the coupling degree in most cities of the YDR has been relatively low, primarily ranging between 0.2 and 0.5, indicating that preliminary and moderate coordination has predominated. Specifically, during the period from 2010 to 2014, the coordination level between DE development and URI was extremely low, with most areas showing minimal to no coordination. Only a few cities, mainly provincial capitals and their surrounding areas, had achieved preliminary coordination. As the regional political and economic center, the provincial capital city is at the forefront of regional industry and digital upgrading. From 2014 to 2018, preliminary coordination became the norm, with some provincial capitals achieving moderate or high coordination. Cities along the Nanjing–Shanghai axis benefiting from the radiative effects of Shanghai and Nanjing displayed good coordination. From 2018 to 2022, preliminary and moderate coordination became dominant, with some cities achieving good coordination. Cities with higher levels of coordination were predominantly located along the Nanjing–Shanghai axis and in the southeastern coastal region, with the overall distribution forming an “M” shape. In summary, from 2010 to 2022, the degree of coupling coordination of the YDR was primarily in the preliminary coordination stage. After 2018, with the implementation of the YDR integration strategy, cities such as Nanjing, Shanghai, and Hangzhou played significant roles in promoting growth, leading to an increase in cities with moderate and good coordination.

3.1.3. Spatial Characteristics

From 2010 to 2022, the standard deviation ellipsoid of the coupling coordination degree between the DE and URI in the YDR showed a distinct northwest–southeast distribution pattern. The parameters of the standard deviation ellipsoid generally remained stable (see

Table 5. Standard deviation ellipsoid parameters for the coupling coordination degree between DE and URI.

Year	Major Axis (km)	Minor Axis (km)	Azimuth Angle (°)	Centroid Coordinates	Shape Index	Area (km2)
2010	2.816	1.728	135.195	118°59′58.294″ E, 31°27′50.281″ N	0.386	15.286
2014	2.815	1.719	133.976	119°1′46.124″ E, 31°26′39.966″ N	0.390	15.199
2018	2.778	1.714	134.894	119°4′10.186″ E, 31°26′26.254″ N	0.383	14.958
2022	2.768	1.736	135.293	119°6′43.114″ E, 31°25′52.41″ N	0.373	15.090

), with slight fluctuations. The shape index of the ellipsoid displayed a continuous fluctuating trend, with the major axis showing a decreasing trend and the minor axis first decreasing and then increasing. The amplitude of the change in the major axis was greater than that in the minor axis. This indicates that the concentration of the coupling coordination degree increased over the 2010–2022 period and the distribution direction became more pronounced. The overall azimuth angle of the ellipsoid showed a “first decreasing, then increasing” trend, suggesting that the directional characteristics of the distribution of the coupling coordination degree in the YDR changed slightly. The centroid of the standard deviation ellipsoid was located in the southern part of Nanjing (119°6′ E–118°59′ E, 31°25′ N–31°27′ N), primarily because the central and southeastern cities of the YDR had higher levels of coupling coordination than the northwestern regions, causing the centroid to be positioned slightly southward in the central part of the delta. The centroid of the standard deviation ellipsoid shifted southeastward during the study period, indicating an increasing concentration of the coupling coordination degree in the southeastern part of the YDR, which also reflected the growing relative advantage of the central and southeastern regions of the delta.

3.1.4. Spatial Development Trends

Trend analysis was employed to explore the developmental mechanisms and evolutionary processes of coupling coordination between the DE and URI in the YDR. According to the trend surface fitting results (Figure 3), in the early years, the distribution of the coupling coordination degree across the X-axis (east–west) displayed a U-shape with “high ends and a low center”, while along the Y-axis (north–south), it exhibited an inverted triangle pattern with a “high center and low ends”. Over time, the X-axis (east–west) trend gradually evolved into a “high east, low west” pattern, indicating that the advantages in the coupling coordination degree of eastern cities became more pronounced. Along the Y-axis (north–south), the overall curve showed a downward trend with more pronounced declines at both ends, indicating an expanding advantage for the central cities. Notably, the changing trend along the east–west direction was steeper than that along the north–south direction, indicating more pronounced and intense distribution differences along the east–west axis. In summary, during coupling coordination development in the YDR, the central region outperformed the southern and northern regions, and the eastern region outperformed the western region.

3.2. Spatial Correlation Network

Figure 4 illustrates that the coupling coordination degree between the DE and URI in the YDR had a distinct network structure, indicating that the coupling coordination degree transcended traditional geographical constraints and that its spatial effects were no longer confined to neighboring units. In 2010, Shanghai, Suzhou, and Wuxi occupied core positions within the coupling coordination network, with surrounding cities, such as Nanjing, Zhenjiang, and Changzhou, showing significant connectivity. After 2014, Nanjing, Changzhou, Wuxi, and Suzhou leveraged their strategic locations in the core region and emerged as central areas, while the number of surrounding secondary cores gradually increased over time. The spatial interactions between Jiangsu province (centered on Nanjing and Suzhou) and Shanghai became more frequent, whereas the interactions between Anhui and Zhejiang provinces remained relatively sluggish. Overall, the spatial interactions exhibited unbalanced characteristics, with a dense central area and sparse connections in the northwest and southeast. By 2022, Jiangsu’s position in the network was robust, with Hangzhou and Yangzhou connecting to adjacent cities and fostering a multipolar network in the central YDR. Suqian and Xuancheng had also emerged as significant gateways in the northwest and southwest regions; however, the overall network pattern still reflected an unbalanced structure that requires improvement.

3.2.1. Analysis of Overall Network Characteristics

Overall, the network connection strength of the DE and URI integration-coupled coordination in the YDR remained stable. The number of network relationships initially declined and then increased, whereas network efficiency and density exhibited only slight fluctuations (Figure 5). Specifically, the network correlation remained constant at 1, indicating the absence of “isolated nodes”. From 2010 to 2014, the number of network relationships decreased from 461 to 340, reflecting a reduction in interactions between cities. A possible reason is that the competition in the digital industry layout between the early cities weakened. After 2014, the number of network relationships increased and stabilized at approximately 430, suggesting the restoration of spatial connections in coupling coordination among cities. However, a significant gap remains in terms of the maximum possible relationship count (1640), indicating that cities should continue to enhance cooperation and collaborative construction in the DE and in URI to improve the density and compactness of spatial connections. In terms of network hierarchy, the structure had transitioned from a hierarchical model to a flat one by 2014, with the network density fluctuating around 0.270, suggesting a stable spatial correlation. The average network efficiency was maintained at a high level of 0.670, implying that the intercity connection paths were still relatively singular and the degree of network connectivity and stability required enhancement.

3.2.2. Analysis of Node Centrality Characteristics

By calculating the centrality of each city, we assessed its role and position within the spatial correlation network. As shown in

Table 6. Centrality characteristics of spatial correlation network for coupling coordination degree.

City	Degree of Centrality				Closeness Centrality				Betweenness Centrality
	2010	2014	2018	2022	2010	2014	2018	2022	2010	2014	2018	2022
Shanghai	75.000	67.500	70.000	70.000	50.000	53.000	53.000	52.000	8.674	3.846	6.318	11.377
Nanjing	62.500	75.000	82.500	77.500	55.000	50.000	50.000	49.000	4.887	5.963	7.908	7.877
Wuxi	92.500	87.500	90.000	85.000	43.000	45.000	45.000	46.000	7.078	7.224	7.929	8.782
Xuzhou	25.000	22.500	22.500	30.000	70.000	71.000	71.000	69.000	0.088	0.266	0.306	0.248
Changzhou	65.000	75.000	85.000	77.500	54.000	50.000	50.000	49.000	2.653	7.355	8.161	5.618
Suzhou	87.500	90.000	90.000	75.000	45.000	44.000	44.000	50.000	5.972	4.888	7.839	5.346
Nantong	10.000	12.500	22.500	25.000	78.000	77.000	77.000	70.000	0.021	0.103	0.145	1.391
Lianyungang	32.500	27.500	27.500	25.000	67.000	69.000	69.000	71.000	0.000	0.106	0.087	0.108
Huaian	27.500	20.000	27.500	30.000	69.000	73.000	73.000	68.000	0.823	0.325	0.895	0.283
Yancheng	22.500	20.000	22.500	27.500	72.000	74.000	74.000	69.000	1.076	0.145	0.530	0.229
Yangzhou	37.500	45.000	42.500	42.500	65.000	62.000	62.000	63.000	1.205	4.626	6.638	5.019
Zhenjiang	50.000	65.000	55.000	52.500	60.000	54.000	54.000	59.000	2.744	10.022	2.846	2.168
Taizhou	17.500	22.500	27.500	42.500	73.000	71.000	71.000	63.000	0.129	0.196	0.667	4.015
Suqian	27.500	25.000	22.500	25.000	69.000	71.000	71.000	71.000	0.439	0.349	0.366	0.407
Hangzhou	57.500	57.500	62.500	45.000	58.000	61.000	61.000	63.000	8.079	15.516	13.061	7.072
Ningbo	37.500	40.000	35.000	27.500	65.000	64.000	64.000	81.000	2.956	3.097	4.930	2.437
Wenzhou	22.500	27.500	22.500	25.000	72.000	69.000	69.000	70.000	0.177	1.493	1.572	0.314
Jiaixng	20.000	20.000	20.000	20.000	72.000	72.000	72.000	72.000	0.867	1.356	1.548	1.905
Huzhou	22.500	25.000	20.000	22.500	71.000	70.000	70.000	71.000	0.277	0.866	0.389	0.748
Shaoxing	37.500	37.500	32.500	27.500	65.000	65.000	65.000	69.000	2.234	0.732	0.597	0.792
Jinhua	17.500	22.500	22.500	22.500	74.000	72.000	72.000	71.000	0.613	0.525	1.789	2.696
Quzhou	25.000	25.000	17.500	25.000	70.000	70.000	70.000	70.000	0.767	0.609	0.294	0.764
Zhoushan	52.500	42.500	42.500	30.000	59.000	63.000	63.000	73.000	4.677	0.976	1.732	2.346
Taizhou	17.500	22.500	22.500	22.500	74.000	72.000	72.000	71.000	0.129	1.359	0.101	1.019
Lishui	25.000	27.500	22.500	22.500	70.000	69.000	69.000	71.000	0.767	1.493	1.572	1.566
Hefei	40.000	35.000	35.000	37.500	64.000	66.000	66.000	65.000	3.763	2.075	2.932	4.193
Wuhu	30.000	27.500	30.000	32.500	68.000	69.000	69.000	67.000	0.361	0.679	1.035	1.755
Bengbu	42.500	30.000	27.500	32.500	63.000	68.000	68.000	69.000	1.968	0.944	0.842	1.734
Huainan	35.000	30.000	32.500	37.500	66.000	68.000	68.000	66.000	0.184	1.397	1.578	1.934
Maanshan	42.500	22.500	17.500	22.500	63.000	71.000	71.000	71.000	4.365	0.833	0.333	0.120
Huaibei	32.500	35.000	30.000	30.000	67.000	66.000	66.000	69.000	0.278	0.251	0.365	0.247
Tongling	45.000	50.000	27.500	25.000	67.000	64.000	64.000	70.000	2.869	2.821	0.901	0.791
Anqing	35.000	30.000	25.000	25.000	66.000	68.000	68.000	72.000	1.221	0.589	0.495	0.389
Huangshan	35.000	27.500	27.500	32.500	66.000	69.000	69.000	67.000	9.594	3.133	2.985	4.879
Chuzhou	40.000	37.500	37.500	22.500	64.000	65.000	65.000	74.000	2.739	3.995	3.355	0.976
Fuyang	35.000	35.000	27.500	32.500	66.000	66.000	66.000	69.000	0.686	1.060	0.632	0.368
Suzhou	40.000	37.500	40.000	45.000	64.000	65.000	65.000	63.000	2.481	4.748	5.562	7.457
Luan	27.500	37.500	32.500	32.500	69.000	65.000	65.000	68.000	0.286	1.736	2.012	1.893
Bozhou	37.500	32.500	27.500	32.500	65.000	67.000	67.000	69.000	3.659	1.439	0.472	0.819
Chizhou	35.000	32.500	30.000	27.500	66.000	67.000	67.000	69.000	1.593	1.170	1.297	0.499
1ancheng	40.000	37.500	40.000	62.500	64.000	65.000	65.000	55.000	2.776	2.128	1.408	3.958

, the overall degree of centrality has declined. In 2010, Shanghai, Wuxi, and Suzhou had significantly higher degree centralities than the other cities, positioning them as leaders with a notable radiative effect on surrounding cities. Conversely, cities such as Nantong, Taizhou, and Jinhua had lower degree centralities, categorizing them as peripheral actors. With an overall increase in coupling coordination, the degree centralities of Nanjing and Changzhou also increased, enhancing the influence of Jiangsu and Shanghai within the YDR network structure. The closeness centrality displayed an overall upward trend. Nantong, Yancheng, Jinhua, and Taizhou formed the first tier, whereas Huai’an, Suqian, and Wenzhou constituted the second tier, becoming intermediaries for the diffusion of advantages owing to their favorable transportation positions. Most provinces exhibited low betweenness centrality values. However, cities such as Shanghai, Wuhu, and Suqian showed gradual increases in betweenness centrality, indicating the effective mediation role played by cities surrounding the core area. Conversely, cities such as Ma’anshan, Anqing, and Tongling have experienced declining betweenness centralities over the years, contributing to the dispersive pattern of multiple intermediaries. Overall, the DE and URI coupled coordination network has resulted in a limited number of unevenly distributed communication intermediaries, which hinders the improvement of network accessibility and stability.

3.3. Analysis of the Driving Factors of Coupling Coordination Development

The coordinated development of the DE and URI is influenced by multiple factors. Based on prior research, four influencing driving-force factors were identified for analysis using a geographical detector to determine relevant developmental characteristics, as follows.

Resource mobility: This factor reflected the convenience of material and information flow between urban and rural areas. The selected detection factors included urban–rural transportation accessibility (X1), internet penetration rate (X2), and the number of mobile phone users per 100 people (X3).

Industrial development: This factor reflected the development level of dominant urban and rural industries. The selected detection factors included the value-added ratio of primary, secondary, and tertiary industries (X4) and the level of agricultural technology (X5).

Social equalization: This reflected the extent of equal access for urban and rural residents to social development. The selected detection factors included the number of hospital beds per 1000 people in urban and rural areas (X6) and the urban–rural medical insurance ratio (X7).

Living environment: This reflected the quality of urban and rural living environments and pollution prevention measures. The selected detection factors included the green coverage rate in built-up areas (X8) and the centralized treatment rate of sewage treatment plants (X9).

After excluding strong correlations between these driving factors using the variance inflation factor, the results showed that none of the variables exhibited strong multicollinearity, thus eliminating the potential issue of strong correlations among the variables.

GeoDetector 2018 software was used to calculate the influence of different detection factors on the coupling coordination degree of the DE and URI for each year (

Table 7. Impact of various exploration factors on the coupling coordination degree of DE and URI.

Year	X1	X2	X3	X4	X5	X6	X7	X8	X9
2010	0.042	0.282	0.115	0.229	0.476	0.338	0.142	0.006	0.156
2014	0.091	0.352	0.285	0.277	0.567	0.392	0.102	0.067	0.197
2018	0.158	0.487	0.355	0.215	0.342	0.372	0.245	0.195	0.082
2022	0.066	0.271	0.459	0.293	0.406	0.255	0.108	0.116	0.063

). From a time-series perspective, the impact of each detection factor on the coupling coordination degree varied across different periods. From 2010 to 2014, the main influencing factors were the internet penetration rate (X2), the number of mobile phone users per 100 people (X3), the value-added ratio of primary to secondary and tertiary industries (X4), the agricultural technology level (X5), and the number of hospital beds per 1000 people in urban and rural areas (X6). From 2014 to 2022, the influence of the internet penetration rate (X2) and the number of mobile phone users per 100 people (X3) increased annually, becoming the main influencing factors, along with the level of agricultural technology (X5).

4. Conclusions and Recommendations

4.1. Conclusion

This study identified various indicators to assess the development levels of the DE and URI in the YDR, after which the coupling coordination degree between these two components was calculated and their coordinated development characteristics and spatiotemporal evolution analyzed, which was then followed by a geographic detector analysis to identify the driving factors influencing the coupling coordination degree. The main conclusions are as follows.

(1)

The DE and URI in the YDR exhibited different characteristics across various time periods. From 2010 to 2014, the DE lagged behind URI, but experienced rapid growth. Between 2014 and 2022, most cities witnessed their digital economies begin to surpass the levels of URI, with Shanghai and Hangzhou achieving DE development levels of 0.60 in 2019 and 2017, respectively.

(2)

The coupling coordination degree of the DE and URI in the YDR was relatively low, with most cities recording coupling degrees below 0.40. This discrepancy can be primarily attributed to a mismatch between the two types of development. By the end of the study period, cities with higher levels of coordination were concentrated in the eastern and southeastern regions of the YDR, displaying an M-shaped distribution.

(3)

The standard deviation ellipse of the coupling coordination degree in the YDR showed clear directional characteristics, with a northwest–southeast distribution pattern. The parameters of the standard deviation ellipse remained stable, with its centroid located in the southern area of Nanjing, exhibiting a movement pattern of “first southeast, then northwest”.

(4)

Throughout the study period, the distribution of the coupling coordination degree along the X-axis (east–west) evolved from a U-shaped pattern—characterized as “high at both ends, low in the middle”—to “high in the east, low in the west”. Along the Y-axis (north–south), it maintained a “reverse triangular” shape, characterized as “high in the middle, low at both ends”, but the trend became steeper. Overall, the development trend indicated that the central region was outperforming the southern and northern regions and that the eastern region was outperforming the western region.

(5)

The coupling coordination degree formed a complex network structure, with core cities expanding from Shanghai, Suzhou, and Wuxi to Nanjing and Changzhou, promoting multipolar development. Although the overall network connection strength remained stable, collaboration among cities needs to be enhanced. The network structure transitioned from hierarchical to flat, with centrality analysis indicating an increase in influence for Nanjing and Changzhou. However, the distribution of mediation roles remained uneven, affecting network accessibility and stability.

(6)

The factors influencing the coordinated development of the DE and URI were found to be complex, with varying influences across different periods. From 2010 to 2014, industrial development was the dominant driving force. Between 2014 and 2022, household internet penetration rates and mobile phone user numbers gradually became the primary influencing factors.

4.2. Recommendations

Based on the research conclusions, the following recommendations are proposed.

(1)

Enhancing Digital Infrastructure to Reduce Regional Disparities

The study highlights the uneven development of digital infrastructure across different regions. Governments should prioritize investment in rural broadband expansion, 5G deployment, and smart infrastructure, ensuring equal access to digital services. Additionally, financial incentives and public–private partnerships should be encouraged to accelerate the construction of digital public service platforms in rural areas.

(2)

Strengthening the Bidirectional Flow of Resources between Urban and Rural Areas

The results show that digital economic benefits are not evenly distributed across urban and rural areas. To bridge this gap, policies should promote digital platforms for agricultural supply chains, e-commerce integration, and remote education and healthcare services. Supporting rural entrepreneurship in the digital sector can further facilitate the diffusion of digital economic advantages to underdeveloped areas.

(3)

Establishing Targeted Policy Interventions to Improve Coordination Mechanisms

Given the varying levels of urban–rural coordination across different cities, a regional policy differentiation approach should be implemented. Coastal cities with advanced digital economies should focus on integrating smart technologies with urban–rural governance, while inland regions should prioritize digital infrastructure investment and capacity-building programs to support lagging areas.

(4)

Developing a Monitoring and Evaluation Framework for Digital-Economy-Driven Urban–Rural Integration

To ensure effective implementation, a real-time data monitoring system should be developed to track the progress of digital economy initiatives and their impact on urban–rural integration. Periodic assessments should be conducted to refine policies using data-driven decision-making tools such as geospatial analysis, machine learning, and impact evaluation models.

(5)

Incorporating Digital Economy Development into Urban–Rural Sustainability Goals

Digital transformation should be aligned with sustainability objectives, ensuring that urban–rural digital development supports carbon reduction, smart energy solutions, and low-carbon transportation. Policy frameworks should integrate green digital finance initiatives, renewable energy projects, and intelligent urban planning, fostering a sustainable and resilient urban–rural development model.

4.3. Discussion

This study explored the coordinated development of the DE and URI within the YDR region alongside its spatial correlation structure, employing various quantitative analysis methods such as the entropy-weighted TOPSIS method, coupling coordination model, improved gravity model, and geo-detector. Several key findings merit further discussion. First, in terms of the evolution of the network structure, there has been a significant change in the core cities of the Yangtze River Delta, with cities such as Hangzhou and Suzhou gradually increasing their centrality. This change may be attributed to the significant improvements in digital infrastructure, innovation capacity, and expansion of the digital economy in these cities, which have enhanced their ability to drive the development of neighboring regions. Second, the change in the number of network relationships also reflects the dynamic adjustment of intraregional cooperation. Overall, the frequency of intercity factor mobility increased as the digital economy and the level of urban–rural integration improved, leading to higher network density; however, the number of connections declined in some cities in some years, which may be related to macroeconomic fluctuations, the impact of the new crown epidemic, or regional policy adjustments. Future research should adopt a multidimensional perspective, including economic, social, and political factors.

However, the study still has some shortcomings. Firstly, we used economic and social data to construct an indicator system for the development of DE and URI based on previous studies, but it may not accurately reflect the development level of DE and URI, because there is no uniform standard for measuring the development level of the two and the differences in the statistical methods of the data in different regions. Second, the study focused on 41 cities in the YDR region, and despite the fact that the region has a developed economy and relatively complete data, the results may be difficult to fully apply to other regions in China with different levels of economic development and urban–rural structures. Finally, the methodology used in the study may have certain simplifications and assumptions when dealing with multidimensional interactions and dynamic changes, only describing the temporal evolution of the URI and the coordinated development of DE in the YDR, but with a lack of in-depth relationships. In the future, research can combine remote sensing images, travel data, and other big data combined with instrumental variables such as government investment and infrastructure construction to explore the system and mechanisms of the impact of DE on urban and rural integration development.