An Evaluation and Correlation Analysis of Regional Development Under the Background of Chinese-Style Modernization

Abstract

Regional development represents a pivotal component in advancing the Chinese path to modernization. The pace of modernization is intrinsically linked to the effectiveness of regional development strategies. In this context, the innovative evaluation of regional development assumes critical importance. Understanding the status and dynamics of regional development offers valuable insights into the progress of Chinese-style modernization. This study, grounded in a scientific interpretation of the concept and distinctive characteristics of Chinese-style modernization, proposes an enhanced regional development evaluation indicator system within this conceptual framework. By employing dynamic comprehensive evaluation and gravitational models, this study examines the regional modernization process in China from 2012 to 2021, exploring development patterns and inter-regional relationships. The findings reveal that regional modernization has evolved into four distinct types of agglomerated areas, with a strong correlation observed between regional development patterns and geographic location. Based on these findings, targeted recommendations for optimizing regional development are provided.

1. Introduction

The report of the 20th National Congress of the Communist Party of China highlights that the central task of the party in the new era is to lead people of all ethnic groups in fully building a modern socialist power and advancing the great rejuvenation of the Chinese nation through Chinese-style modernization. As fundamental components of the nation, regions play a dual role in this process, serving both as the implementation sites and as reflections of national modernization efforts at the local level. Given China’s status as a developing country with pronounced regional disparities, adopting a “regional advancement” approach to modernization is imperative [1]. Since the Reform and Opening-up period, China has pursued a strategy of “leading development by early starters” as a key pathway to modernization. However, with evolving domestic and international circumstances, the earlier emphasis on prioritizing regional modernization has proven insufficient to meet the demands of the new era. Under the emerging development paradigm, China’s regional modernization strategy must shift from a model characterized by regional prioritization to one that fosters regional coordination [2].

Regional coordinated development serves as a critical pillar of Chinese-style modernization. Bridging regional disparities is essential for achieving high-quality economic growth at the national level. However, persistent challenges such as the pronounced development gap between eastern and western regions and the uneven distribution of industries continue to impede the modernization process, thereby hindering the attainment of the goal of “common prosperity”. Addressing these challenges requires a comprehensive understanding of regional development patterns and diffusion relationships, followed by the formulation of targeted solutions to ensure that all regions can equitably share the benefits of national modernization. This approach is not only a prerequisite for sustained economic progress but also a fundamental cornerstone for ensuring social stability and enhancing the well-being of the population.

Over decades of development, different regions in China have established diverse models and pathways for growth, reflecting variations in economic strength, social conditions, technological foundations, industrialization, and urbanization. As a result, a uniform standard for modernization cannot be applied across all regions. To accelerate the realization of Chinese-style modernization, it is crucial to create an evaluation index system for regional development that captures the unique characteristics and core principles of Chinese-style modernization. Such a system will enable the assessment of the developmental status and positioning of various regions, providing valuable insights for future strategic planning. In addition, the establishment of a regional association network among Chinese regions offers a framework for analyzing interconnections in regional development. This network facilitates the identification of patterns and relationships, providing critical insights for designing targeted strategies to promote coordinated regional development.

The literature on regional development evaluations can be broadly categorized into three types. The first type encompasses an overall assessment of regions or multiple agglomerated areas [3,4,5,6,7,8]. The second type primarily examines the factors influencing regional development. For instance, Liang and Si established an evaluation index system to assess the sustainable development capacity of Sichuan [9]. Rodríguez-Pose and Gill explored the role of regional development policies in promoting innovation and economic growth [10]. Klagge and Martin analyzed the impact of regional networks on innovation [11]. Bottazzi and Secchi discussed measurement methods for innovation and entrepreneurship in the context of regional development evaluation [12]. Tang [13] along with Graham and Zook [14] investigated the role of digital technologies in shaping regional development. The third type examines the evaluation of certain aspects of the region. Litavniece, for instance, evaluated the attractiveness of cities [15]. Alekhina et al. considered the development of innovational infrastructure in the regions of the RF [16].

Existing research has significantly enhanced the understanding of regional modernization and development in China, offering valuable insights and intuitive knowledge. However, most studies have primarily focused on static evaluations of regional concepts, categories, and characteristics, while neglecting the dynamic processes of evolution [17,18,19,20,21]. To address this gap, this study seeks to advance the evaluation of regional development within the framework of Chinese-style modernization by analyzing inter-regional development patterns and their interconnections. The objective is to explore strategies for facilitating the progress of Chinese-style modernization. This study contributes to the existing literature in several ways: First, it constructs a regional development evaluation index system tailored to the context of Chinese-style modernization. This system is grounded in a detailed analysis of the scientific connotations, distinctive characteristics, and innovation-driven development demands of this model. It expands the scope of regional evaluations by integrating not only quality and innovation assessments but also relational and interactive evaluations. Second, existing evaluation methods, which largely rely on objective data and subjective expert opinions, are predominantly static. However, there is a growing academic consensus that regional modernization is a dynamic process involving continuous changes in economic, political, social, technological, cultural, and demographic dimensions within specific geographical contexts. These changes are characterized by both temporal dynamics and spatial interconnectivity [22]. To address this, this study incorporates temporal effects into indicator selection and applies a dynamic comprehensive evaluation analysis method to calculate indicator weights, capturing the evolving nature of regional modernization. Third, based on the evaluation of regional development within the framework of Chinese-style modernization, this study designs methodologies to calculate and analyze regional development patterns. By identifying and clustering distinct modes of regional development, it enhances the understanding of regional relationships and the interconnected network of regions across China. Fourth, this paper revises the traditional gravity model to analyze the mutual influences among regions with similar development patterns. This allows for a deeper exploration of inter-regional effects and interactions, shedding light on the dynamic relationships and synergies that shape the progress of regional modernization.

The structure of this study is as follows. Section 2 presents the research design and data source. Section 3 details the methods. Section 4 shows the results of the empirical study. Section 5 discusses the study and summarizes the full text.

2. Research Design and Data Source

2.1. Evaluation Index System

2.1.1. Basis for Constructing Indicator System

In the context of Chinese-style modernization, the development of a regional evaluation indicator system must be rooted in the profound theoretical connotations of Chinese-style modernization, the historical trajectories of regional development, and the distinctive trends of social progress in the new era. Furthermore, the system should incorporate scientifically grounded and quantitatively measurable indicators to effectively guide and inform regional development evaluation.

Chinese-style modernization refers to a form of socialist modernization under the leadership of the Communist Party of China, distinguished by characteristics uniquely tailored to China’s national conditions. It emphasizes a modernization model that accommodates a large population, promotes common prosperity for all, integrates material and spiritual advancement, fosters a harmonious coexistence between humanity and nature, and adheres to a path of peaceful development.

The Internet of Things (IoT), big data, and artificial intelligence represent critical drivers of future innovation. In response, China is advancing a new infrastructure construction plan aimed at promoting digital industrialization, accelerating the digital transformation of industries, and creating intelligent scenarios. These efforts focus on building a Digital China and a Smart Society while fostering sustained innovation capacity. The widespread adoption of digital technologies, platforms, infrastructure, and data is fundamentally reshaping the relationships among innovation elements and stakeholders [23]. As digitization and intelligence are poised to profoundly transform future regional development patterns, the evaluation indicator system must reflect emerging trends in intelligence-driven development within the context of Chinese-style modernization. Furthermore, the selected indicators must be scientifically measurable and account for regional disparities, including variations in area and population, to ensure a balanced and accurate evaluation.

2.1.2. Construction Ideas for Indicator System

Scholars have examined various dimensions for measuring and evaluating regional modernization development. While different systems of modernization evaluation indicators offer valuable insights for assessing regional development within the context of Chinese-style modernization, they also exhibit notable limitations. Two primary issues can be identified: First, there is no internationally standardized set of evaluation indicators, and the limitations of data sources often result in overly generalized indicators that fail to adequately capture the unique characteristics of the Chinese context [24,25,26]. Second, many domestic modernization evaluation systems rely heavily on traditional indicators, neglecting to integrate the innovative features and distinctive characteristics of contemporary Chinese development. This omission limits their ability to reflect evolving developmental needs and to anticipate future trends [27,28].To address these gaps, the indicator system proposed in this paper fully considers the definition and connotations of Chinese-style modernization, ensuring alignment with China’s realities. Additionally, it incorporates digital indicators that reflect the demands of contemporary development, enhancing its capacity to capture the dynamic and innovative aspects of modern regional development.

This study proposes a regional development evaluation indicator system within the framework of Chinese-style modernization, comprising four primary indicators, twelve secondary indicators, and twenty-eight tertiary indicators (

Table 1. The evaluation index system of regional development in the context of China’s path to modernization.

Primary Indicator	Secondary Indicator	Tertiary Indicator	Indicator Explanation
Economic Modernization	Economic Progress	Per Capita GDP (+)	Reflects regional economic level: GDP/Total Population
		Labor Productivity (+)	Reflects economic efficiency: Industrial Value Added/Total Employment
		Level of Digital Finance Digitization (+)	Reflects the degree of economic digitalization: Data from Peking University Digital Finance Research Center
	Industrial Modernization	Rationality of Industrial Structure (+)	Measured using the Theil Index
		Level of Enterprise Digitization (+)	Measured by the number of websites owned by enterprises
	Degree of Openness	International Trade (+)	Total Trade (Imports + Exports)/GDP
		International Investment (+)	Total Foreign Investment/GDP
	Coordinated Development	Urban–Rural Income Gap (−)	Measured by the ratio of disposable income between urban and rural residents
		Urbanization Level (+)	Measured by the urbanization rate: Urban Population/Total Population
Social Modernization	Infrastructure Modernization	Breadth of Information Transmission (+)	Reflects digital infrastructure: measured by fiber optic cable density
		Accessibility of Transportation (+)	Reflects physical infrastructure: (Road Length + Rail Length + Waterway Length)/Total Population
	Modernization of People’s Lives	Internet Penetration Rate (+)	Measured by the proportion of Internet users to the total population
		Per Capita Disposable Income (+)	Reflects the standard of living
	Spiritual and Cultural Wealth	Physician Density (+)	Reflects the regional healthcare level: Number of Practicing Physicians per 10,000 Population
		Per Capita Library Collection (+)	Reflects the regional cultural level
		Coverage Rate of Social Pension Insurance (+)	Reflects regional social security level: Number of Insured Urban and Rural Residents/Total Population
Technological Modernization	Technological Development	R&D Expenditure as a Percentage of GDP (+)	Reflects the emphasis on technology in the region
		Per Capita Number of Invention Patents (+)	Reflects the regional innovation level
		Per Capita Transaction Value in Technology (+)	Reflects the conversion of technological achievements
	Educational Assurance	Education Expenditure Intensity (+)	Measured by the proportion of education spending to GDP
		Number of Full-time Faculty in Higher Education (+)	Reflects the strength of educational resources
	Talent Assurance	R&D Personnel Full-time Equivalent per 10,000 (+)	Reflects the availability of scientific talent
		Average Number of Students in Higher Education per 100,000 (+)	Reflects the potential for talent development
Ecological Modernization	Energy Conservation and Emission Reduction	Electricity Consumption per Unit of GDP (−)	Reflects energy consumption per unit of GDP
		Sulfur Dioxide Emissions per Unit of GDP (−)	Reflects regional pollution levels
	Green Development	Forest Coverage Rate (+)	Reflects the current state of forest conservation
		Rate of Non-hazardous Treatment of Urban Waste (+)	Reflects how residents manage environmental pollution
		Per Capita Urban Green Space (+)	Reflects the ecological environment of residents’ lives

). The economic modernization dimension includes indicators for economic progress, industrial modernization, openness, and coordinated development. These indicators reflect the foundational realities of regional development and the modernization goal of common prosperity for all people, while also addressing the necessity of managing external relations effectively. The social modernization dimension consists of indicators related to infrastructure modernization, living standards, and spiritual wealth. This dimension emphasizes the modernization of population scale and the coordination between material and spiritual civilization, aligning with the core principles of Chinese-style modernization. The technological modernization dimension integrates indicators for technological advancement, educational assurance, and talent development, in line with the comprehensive strategies outlined in the report of the 20th National Congress regarding education, technology, and talent development [29]. This dimension highlights the pivotal role of innovation in Chinese-style modernization, with a focus on fostering self-reliance and strength. Finally, the ecological modernization dimension incorporates indicators for energy conservation, emission reduction, and green development, reflecting the principle of harmonious coexistence between humanity and nature that is central to Chinese-style modernization. Furthermore, the design of tertiary indicators emphasizes the significant role of digital intelligence in driving regional modernization in the new era, ensuring that the evaluation system is aligned with contemporary technological trends and development needs.

2.2. Data Source

The data used in this study were sourced from the National Bureau of Statistics [30]. The rationality of the industrial structure is measured using the Theil index. Due to substantial missing data for Tibet, the analysis is limited to the remaining 30 provincial regions. The study examines data spanning the period from 2012 to 2021, with 2021 being the most recent year available at the time of data collection. The indicator system comprises both positive and negative indicators, where (+) denotes positive indicators and (−) represents negative indicators, as detailed in Table 1. Negative indicators were transformed into positive indicators using reciprocal transformation.

3. Method

3.1. Dynamic Comprehensive Evaluation Method Based on Quadratic Weighting

Scholars predominantly utilize static evaluation methods based on cross-sectional or time-series data to assess regions [31,32]. However, these approaches are limited in their ability to comprehensively analyze the dynamic trends and evolving characteristics of the evaluation subjects from a spatiotemporal perspective. To address this limitation, the dynamic comprehensive evaluation method is employed. This method, grounded in the principle of maximizing the differences between evaluation objects, determines indicator weights while incorporating a secondary weighting mechanism that emphasizes the “present over the past” [33], thereby integrating the temporal dimension into the analysis. Ultimately, this approach generates a comprehensive evaluation score for each object over a specified time period. The detailed steps are outlined as follows:

To eliminate the dimensional effects of the original data, a normalization process is applied:

$$\begin{array}{l}{y}_{ij}\left(t_k\right)={\displaystyle \frac{x_{ij}\left(t_k\right)-\overline{x_j\left(t_k\right)}}{s_j\left(t_k\right)}}\hspace{0.33em}\\ i=1,2,\dots ,n;j=1,2,\dots ,m;k=1,2,\dots ,N\end{array}$$

(1)

where $x_{ij}\left(t_k\right)$ denotes the value of indicator $j$ for region $i$ at time $t_k$, $\overline{x_j\left(t_k\right)}$ denotes the sample mean, $s_j\left(t_k\right)$ is the corrected sample standard deviation, and $y_{ij}\left(t_k\right)$ represents the standardized data.

For the comprehensive evaluation at time $t_k$, a linear weighted evaluation model is employed:

$$z_i\left(t_k\right)={\displaystyle \sum _{j=1}^m{y}_{ij}\left(t_k\right){\omega }_j}$$

(2)

where $z_i\left(t_k\right)$ is the comprehensive evaluation value for region $i$ at time $t_k$, and ${\omega }_j$ represents the weight of indicator $j$.

To maximize the differentiation across both horizontal and vertical dimensions, the value of ${\omega }_j$ should be selected to maximize the differences among the evaluation objects, which means the variance ${\sigma }^2$ should be the largest.

$${\sigma }^2={\displaystyle \sum _{k=1}^N}{\displaystyle \sum _{i=1}^n}{\left(z_i\left(t_k\right)-\overline{z}\right)}^2$$

(3)

Since the sample mean of the standardized matrix $y_{ij}\left(t_k\right)$ is 0 and its variance is 1, Formular (3) can be transformed as follows:

$$\begin{array}{ll}{\sigma }^2& ={\displaystyle \sum _{k=1}^N}{\displaystyle \sum _{i=1}^n}{\left(z_i\left(t_k\right)-\overline{z}\right)}^2={\displaystyle \sum _{k=1}^N}{\displaystyle \sum _{i=1}^n}{\left(z_i\left(t_k\right)\right)}^2\\ & ={\displaystyle \sum _{k=1}^N\left[{\omega }^T{H}_k\omega \right]}={\omega }^T{\displaystyle \sum _{k=1}^N{H}_k\omega }={\omega }^{T}H\omega \end{array}$$

(4)

where $\omega ={\left({\omega }_1,{\omega }_2,\dots ,{\omega }_m\right)}^T$, $H={\displaystyle \sum _{k=1}^N{H}_k}$ is a symmetric matrix, $H_k={Y_k}^T{Y}_k\hspace{1em}\left(k=1,2,\dots ,N\right)$, and

$$Y_k=\left(\begin{array}{ccc}{y}_{11}\left(t_k\right)& \dots & y_{1m}\left(t_k\right)\\ ⋮& \ddots & ⋮\\ y_{n1}\left(t_k\right)& \cdots & y_{nm}\left(t_k\right)\end{array}\right).$$

(5)

Thus, the solution for $\omega$ can be transformed into the following optimization problem:

$$\begin{array}{l}\max \hspace{0.33em}{\omega }^{T}H\omega \\ \mathrm{s}.\mathrm{t}.\hspace{0.33em}{{\displaystyle \{}}_{\omega >0}^{{\omega }^T\omega =1}\end{array}$$

(6)

When $\omega$ is the eigenvector corresponding to the maximum eigenvalue of matrix $H$, the value of ${\sigma }^2$ is maximized.

Through the above steps, the evaluation values for each region at time $t_k$ can be obtained; however, the overall evaluation value for the period $\left[t_1,t_N\right]$ cannot be derived. To address this, a secondary weighting based on time is applied to emphasize temporal influence, assigning greater weight to more recent periods and lesser weight to earlier ones. Thus, the time weight at time $t_k$ is given by the following:

$${\omega }_k=k/{\displaystyle \sum _{k=1}^{N}k}\hspace{1em}\left(k=1,2,\dots ,N\right)$$

(7)

where ${\omega }_k>0,\hspace{0.33em}{\displaystyle \sum _{k=1}^N{\omega }_k}=1$. Consequently, the total evaluation value for region $i$ over the period $\left[t_1,t_N\right]$ is calculated as follows:

$$v_i={\displaystyle \sum _{i=1}^N{\omega }_k}{z}_i\left(t_k\right)$$

(8)

This method determines indicator weights based on the principle of maximizing the differences among evaluation objects. By utilizing three-dimensional time-series data, the weights inherently incorporate temporal factors, enabling comparisons of evaluation objects across different time points. Furthermore, since the weight information is directly derived from the original data, the objectivity of the weight coefficients is ensured.

3.2. Regional Development Model Association

First, the data are standardized to ensure that all values are converted into non-negative numbers. The similarity in development patterns between region $p$ and region $q$ at time $t_k$ is defined as follows:

$$D_{pq}\left(t_k\right)=1-{\displaystyle \frac{{\displaystyle \sum _{j=1}^m{z}_{pj}{z}_{qj}}}{\sqrt{{\displaystyle \sum _{j=1}^m{z}_{pj}^2}}\sqrt{{\displaystyle \sum _{j=1}^m{z}_{qj}^2}}}}$$

(9)

The distance between region $p$ and region $q$ at time $t_k$ is quantified using the cosine of the angle between the 28 indicators constructed within the framework of Chinese -style modernization. The similarity values range within [0, 1], with higher values indicating a stronger alignment between the development patterns of the two regions, while values closer to 0 signify greater dissimilarity.

3.3. Gravity Model

Building on the gravity model described by Luo et al. [34], this study modifies the gravitational constant by incorporating the ratio of the development quality of one region to the total development quality of both regions:

$$H_{ij}={\displaystyle \frac{v_i}{v_i+v_j}},\hspace{1em}{H}_{ji}={\displaystyle \frac{v_j}{v_i+v_j}}$$

(10)

Here, $H_{ij}$ represents the contribution rate of region $i$ to the development of both regions $i$ and $j$, while $H_{ji}$ represents the contribution rate of region $j$ to the development of these two regions. $v_i$ denotes the comprehensive development quality score of region $i$, and $v_j$ denotes the comprehensive development quality score of region $j$. The modified gravity model is expressed as follows:

$$R_{ij}=H_{ij}{\displaystyle \frac{v_i{v}_j}{d_{ij}^2}},\hspace{1em}{R}_{ji}=H_{ji}{\displaystyle \frac{v_i{v}_j}{d_{ij}^2}}$$

(11)

$$O_i={\displaystyle \sum _j{R}_{ij},\hspace{1em}{I}_i}={\displaystyle \sum _j{R}_{ji}}$$

(12)

where $R_{ij}$ indicates the spatial interaction intensity of region $i$ on region $j$, while $R_{ji}$ denotes the spatial interaction intensity of region $j$ on region $i$. The term $O_i$ represents the influence of region $i$, signifying the “output” of region $i$ to other regions, whereas $I_i$ reflects the sensitivity of region $i$, indicating its “input” from other regions. The variable $d_{ij}$ represents the straight-line distance between the capital cities of regions $i$ and $j$, with the collected data standardized through decimal scaling.

3.4. Theil Index

The Theil index is a metric used within the indicator system to assess the rationality of regional industrial structure (RIS). The formula is expressed as follows:

$$RIS={\displaystyle \sum _{b=1}^3\left(\frac{G_b}{G}\right)}\ln \left({\displaystyle \frac{\frac{G_b}{F_b}}{\frac{G}{F}}}\right)={\displaystyle \sum _{b=1}^3\left(\frac{G_b}{G}\right)}\ln \left({\displaystyle \frac{\frac{G_b}{G}}{\frac{F_b}{F}}}\right)$$

(13)

where $b$ represents the primary, secondary, and tertiary industries, $G$ denotes the Gross Domestic Product of the region, $G_b$ represents the industrial output value of industry $b$, $F$ indicates the total number of jobs in the region, and $F_b$ signifies the number of jobs within industry $b$.

4. Empirical Results

4.1. Analysis of Regional Development Quality

Between 2012 and 2021, the process of Chinese-style modernization was most prominent in six regions: Beijing, Tianjin, Shanghai, Jiangsu, Zhejiang, and Guangdong. In contrast, provinces such as Hebei, Jiangxi, Anhui, Henan, Hubei, Hunan, Sichuan, and Guizhou demonstrated noticeable improvements. However, regions including Jilin, Guangxi, Shandong, Yunnan, Shaanxi, and Gansu exhibited relatively little progress during this period.

By incorporating the indicator weight coefficients and time weight coefficients into Equation (8), the overall development rankings of 30 regions in China for the decade spanning 2012 to 2021 are derived, as presented in

Table 2. Comprehensive evaluation and classification results of development quality in 30 regions of China.

Region	Score	Rank	Region	Score	Rank
Beijing	8.210	1	Inner Mongolia	−1.003	16
Shanghai	7.240	2	Henan	−1.156	17
Jiangsu	3.386	3	Jiangxi	−1.253	18
Guangdong	3.169	4	Sichuan	−1.262	19
Tianjin	3.126	5	Hebei	−1.421	20
Zhejiang	2.590	6	Hainan	−1.431	21
Shandong	0.779	7	Shanxi	−1.590	22
Fujian	0.440	8	Ningxia	−1.612	23
Hubei	0.269	9	Heilongjiang	−1.681	24
Liaoning	0.141	10	Qinghai	−2.024	25
Shaanxi	−0.056	11	Xinjiang	−2.077	26
Jilin	−0.422	12	Guangxi	−2.288	27
Chongqing	−0.490	13	Gansu	−2.391	28
Hunan	−0.931	14	Yunnan	−2.642	29
Anhui	−0.977	15	Guizhou	−2.643	30

. Using hierarchical clustering methods, these 30 regions are categorized into four groups. The first group includes Beijing and Shanghai, which demonstrate significant advantages over other regions in economic modernization, social modernization, technological modernization, and ecological modernization, positioning them as leading areas in the process of Chinese-style modernization. The second group comprises Jiangsu, Guangdong, Tianjin, and Zhejiang, which rank third to sixth in the overall rankings and stand out as frontrunners in the modernization process. These regions excel particularly in economic and technological modernization. From 2012 to 2021, Tianjin, Jiangsu, and Guangdong consistently ranked second to fourth in economic modernization, while Zhejiang fluctuated between fifth and sixth. In technological modernization, Tianjin, Jiangsu, and Guangdong remained within the second to fifth positions, while Zhejiang oscillated between fifth and eight. The third group includes Shandong, Shaanxi, Fujian, Hubei, Liaoning, Chongqing, and Jilin, while the fourth group consists of regions such as Henan, Sichuan, and Jiangxi. Both the third and fourth groups demonstrate strong performance in social and ecological modernization.

4.2. Analysis of Regional Development Relationships

Using the indicator data from 2021, this study identifies the development patterns of Chinese-style modernization across various regions, highlighting the interconnections in their development trajectories. Based on the clustering results, regions with strong development relationships within the framework of Chinese-style modernization can be categorized into four groups, each characterized by distinct attributes:

(1)

Regional Development Quality and Relationships: A comparison between the classifications of regional development quality (

Table 3. Classification results of comprehensive evaluation of development quality in 30 regions of China.

Category	Regions
First Class	Beijing, Shanghai
Second Class	Tianjin, Guangdong, Jiangsu, Zhejiang
Third Class	Shandong, Shaanxi, Fujian, Hubei, Liaoning, Chongqing, Jilin
Fourth Class	Henan, Sichuan, Jiangxi, Hunan, Anhui, Inner Mongolia, Heilongjiang, Ningxia, Shanxi, Hainan, Hebei, Qinghai, Gansu, Guangxi, Yunnan, Guizhou, Xinjiang

) and development relationships (

Table 4. Classification results of development associations among 30 regions in China.

Category	Regions
First Class	Beijing, Shanghai, Guangdong, Jiangsu, Zhejiang, Tianjin
Second Class	Fujian, Hubei, Chongqing, Anhui, Shaanxi, Hunan, Shandong, Henan, Hebei, Sichuan, Jiangxi, Jilin, Heilongjiang, Liaoning
Third Class	Guizhou, Yunnan, Guangxi, Hainan
Fourth Class	Shanxi, Inner Mongolia, Ningxia, Qinghai, Gansu, Xinjiang

) reveals a high degree of consistency between the first and second categories, as well as between the third and fourth categories. This indicates a strong correlation between regional development patterns and the process of Chinese-style modernization. The selection of development model significantly influences the quality of regional development. Furthermore, improvements or declines in regional development quality can trigger adjustments in development models, resulting in a dynamic, interconnected process of either enhancement or deterioration in both development patterns and quality.

(2)

Geographic Influence on Regional Development: Regional development patterns exhibit a notable correlation with geographic location. Based on the classification of development models, regions such as Beijing, Tianjin, Shanghai, Jiangsu, Zhejiang, and Guangdong, located in the southeastern coastal area, serve as key hubs for connectivity and expansion. Fujian and Shandong, which demonstrate slightly lower development quality, are grouped with central regions such as Hubei, Hunan, and Chongqing. In contrast, Guizhou, Yunnan, Guangxi, and Hainan are located in the southern part of China, while Inner Mongolia, Ningxia, Qinghai, Gansu, and Xinjiang are situated in the northwest, sharing geographical borders.

(3)

Strength of Inter-Regional Development Relationships: Regarding the strength of development relationships among the 30 provincial regions, Hubei, Hunan, Anhui, and Sichuan demonstrate the strongest interconnections. Notably, these regions are all situated within the Yangtze River Basin.

4.3. Analysis of Regional Development Influence and Diffusion Relationships

In regions with similar development patterns, disparities in regional strength give rise to spillover or absorption effects during the process of Chinese-style modernization. To further explore the influence and diffusion relationships among different regions, a modified gravity model was applied to analyze the comprehensive “inputs” and “outputs” within each category of regions from 2012 to 2021. The measurement results, presented in

Table 5. Overall connections and net influence within regions.

Category 1			Category 2
Region	Total Connections	Net Influence	Region	Total Connections	Net Influence
Beijing	5095.880	1468.240	Hubei	372.120	75.440
Tianjin	5015.850	−1434.390	Shandong	261.370	74.210
Zhejiang	2788.260	−660.800	Jiangxi	224.540	−49.320
Jiangsu	1717.560	−188.080	Anhui	198.210	−28.990
Guangdong	136.550	−15.130	Hunan	194.830	−21.670
Category 3			Jilin	189.040	7.340
Region	Total Connections	Net Influence	Henan	177.870	−34.170
Guangxi	9.500	−2.240	Liaoning	174.020	20.360
Hainan	9.000	3.740	Shanxi	154.690	16.790
Guizhou	2.730	−0.890	Hebei	147.430	−45.650
Yunnan	2.030	−0.610	Chongqing	143.570	9.510
Category 4			Fujian	120.570	23.230
Region	Total Connections	Net Influence	Sichuan	101.770	−19.270
Inner Mongolia	37.950	7.630	Heilongjiang	81.930	−27.790
Shanxi	33.740	−3.300
Ningxia	30.580	2.380
Qinghai	26.710	1.530
Gansu	26.370	−7.990
Xinjiang	1.940	−0.240

, reveal the following characteristics of their development relationships:

(1)

Significant differences in regional network density are observed, accompanied by asymmetrical development relationships. Among the four categorized groups, the first group demonstrates the highest network density, followed by the second, fourth, and third groups. This indicates a clear gradient distribution of network density. Regions within the first classification demonstrate markedly superior levels of information flow, resource connectivity, and collaborative synergy compared to those in the other groups. Additionally, within each model, certain regions exert a stronger influence on others, highlighting a noticeable asymmetry in their mutual development relationships.

(2)

The head effect is observed, with benchmark regions identified within the networks. The overall connection values among the four groups reveal substantial disparities. Development connections in the eastern region, classified within the first group, are significantly higher than those in the central, southern, and northwestern regions, represented by the second, third, and fourth groups, respectively, clearly demonstrating the presence of the head effect. Within each group, benchmark cities have been identified. In the first group, the average total connection value is 2950.82, with Beijing and Tianjin exhibiting total connection values nearly double this average, positioning them as benchmark regions within this development model. In the second group, the average total connection value is 181.57, with Hubei serving as the benchmark region. In the third group, the average value is 5.82, with Guangxi identified as the benchmark. Finally, in the fourth group, the average total connection value is 26.22, with Inner Mongolia recognized as the benchmark region.

(3)

Regional development relationships exhibit three distinct patterns: “high-point overflow”, “five-point distribution”, and “three-point divergence”. The concept of net influence reflects the relative position of a region within the network. A positive net influence indicates that a region exerts a stronger developmental impact on others than it receives, positioning it as a driving force; the greater the positive value, the more central its role within the network. Conversely, a negative net influence signifies that the region is predominantly influenced by the developmental spillovers of others. In the first and third groups, the “high-point overflow” pattern is evident, with Beijing and Hainan serving as the primary sources of developmental overflow within their respective groups. Beijing exerts a positive influence on regions such as Tianjin, Zhejiang, Jiangsu, and Guangdong, while Hainan positively impacts Guangxi, Guizhou, and Yunnan. The second group exemplifies the “five-point distribution” pattern, characterized by Hubei, Shandong, Liaoning, Shaanxi, and Chongqing emerging as key sources of developmental spillover. These regions generate significant radiating effects on the surrounding areas within the central region. The fourth group reflects the “three-point divergence” pattern, with Inner Mongolia, Ningxia, and Qinghai acting as primary divergence points. These regions facilitate developmental spillovers to neighboring regions.

5. Conclusions and Discussion

5.1. Discussion

This study advances previous research in several key aspects. First, it integrates digitalization indicators into the evaluation index system, capturing the characteristics of contemporary development and offering a forward-looking perspective on future trends, thereby building on the work of Lu and Guo [28]. Second, it introduces a dynamic comprehensive evaluation method for calculating indicator weights, representing a significant improvement over traditional static evaluation methods [27]. Third, it investigates regional development and interconnection patterns within the framework of Chinese-style modernization, addressing existing gaps in the current literature.

This study offers valuable insights for government management and decision-making. While significant progress has been achieved in the evolution of regional development within the framework of Chinese-style modernization, pressing challenges remain as China enters the critical period of formulating its 15th Five-Year Plan. Optimizing regional development models, improving development efficiency, and ultimately enhancing development quality are key priorities for the Chinese government. The findings of this study indicate that current regional development models are still heavily influenced by geographical constraints. Overcoming the limitations of spatial divisions to foster broader inter-regional interactions and the diffusion of development influences could serve as a pivotal strategy for enabling China’s regions to transcend their current development stage and advance toward higher levels of modernization.

From the perspective of the established regional development strategies, such as the Beijing–Tianjin–Hebei Coordinated Development Plan, the Guangdong–Hong Kong–Macao Greater Bay Area, and the Chengdu–Chongqing Economic Circle, as well as river basin-focused initiatives like the Yangtze River Economic Belt and the Yellow River Basin Ecological Protection and High-Quality Development Plan, it is evident that the Chinese government has demonstrated remarkable foresight in its top-level planning and design. However, the evaluation results reveal that while the Yangtze River Basin exhibits relatively strong regional connectivity and effective coordinated development, other regions require further efforts to strengthen inter-regional coordination.

Given the diversity of regional development models, nationwide uniform policies may not be as effective as differentiated and tailored approaches that account for the specific characteristics of each region. Contemporary innovation trends increasingly demonstrate multi-technological, dynamic, science-driven, and organization-based features [35]. Universities, research institutions, enterprises, and other key innovation entities can play a pivotal role in fostering organizational collaborative innovation, enhancing inter-regional innovation coordination, and ultimately driving cross-regional, holistic, and coordinated development. For instance, initiatives such as cross-regional industrial park development can facilitate the establishment of industry collaboration mechanisms among enterprises in closely connected regions, with a focus on leading enterprises as the central hubs. Such approaches have the potential to amplify synergies across regions, laying a solid foundation for higher-quality modernization and more balanced regional development.

A defining characteristic of Chinese-style modernization is the harmonious coexistence between humanity and nature. Within the framework of the “dual carbon” goals (carbon peaking and carbon neutrality), addressing cross-regional ecological challenges, such as air pollution control, has exceeded the capacity of individual regions to resolve independently. To address these cross-regional issues effectively, it is imperative to establish an optimized coordination framework that leverages China’s institutional strengths. This necessitates the development of mechanisms at the top-level design stage to promote inter-regional coordination and collaboration. Moreover, the evaluation framework for regional development should shift its focus away from solely assessing the independent progress of individual regions. Instead, it should align with the principles of Chinese-style modernization, emphasizing the effectiveness and success of regional coordination and cross-regional cooperation in achieving sustainable and harmonious development.

From the perspective of inter-regional influence and diffusion dynamics, China has already formed distinct clusters of regions with similar development patterns, within which certain benchmark regions have emerged. These benchmark regions demonstrate significant advantages in resource concentration, information flow, and efficient factor allocation. Effectively harnessing these advantages can amplify the influence of benchmark regions on neighboring areas with similar development patterns, thereby fostering broader regional synergies. For example, regions such as Beijing, Tianjin, Hubei, Guangxi, and Inner Mongolia could leverage their unique strengths to serve as initial drivers of regional development. Based on comprehensive research, the national government could implement pilot policies aimed at strengthening inter-regional interaction and coordination. By capitalizing on the leadership role of benchmark regions, this approach would enhance connectivity and collaboration among regions, promoting a more integrated and coordinated regional development process aligned with the objectives of Chinese-style modernization.

The limitations of this study primarily stem from its focus on evaluating the quality of regional development in China from a macro perspective, without exploring the underlying driving factors contributing to regional disparities. Additionally, the determination of indicator weights in this study was based exclusively on objective evaluation methods, without integrating expert opinions through interviews. This omission may have overlooked potential biases inherent in the weight allocation process.

5.2. Conclusions

This study establishes a regional development evaluation index system tailored to the context of Chinese-style modernization, incorporating the current trends and characteristics of regional development in the country. By considering the temporal influence on regional growth, this study innovatively employs a dynamic comprehensive evaluation method and a modified gravitational model to assess the development of 30 regions in China from 2012 to 2021, filling the gap in existing research by exploring the relationships and mutual influences among regional development. The findings reveal a clear modernization trajectory in regions such as Beijing, Tianjin, Shanghai, Jiangsu, Zhejiang, and Guangdong. A high degree of consistency is observed between the classification of regional development quality and development patterns, and a strong correlation is evident between regional development models and geographical location. Moreover, networks formed by regions with similar development patterns exhibit significant variations in network density. Eastern regions, exemplified by Beijing, demonstrate a pronounced head effect, with benchmark areas identified across various regional categories. Additionally, regional development relationships manifest distinct patterns, including “high-point spillover”, “five-edge distribution”, and “three-point divergence”.

The uniqueness of this study lies in the development of a regional evaluation index system from the perspective of Chinese-style modernization, combined with the application of a dynamic comprehensive evaluation method for regional development assessment. Despite certain methodological limitations, it is hoped that the findings of this study will provide valuable methodological references for regional modernization evaluation.

The regional assessment discussed in this paper offers governments a deeper understanding of the current status, development trends, and potential of different regions, enabling the formulation of more scientific and targeted policy measures. Furthermore, these regional evaluations provide global audiences with insights into the resource endowments, developmental potential, and economic characteristics of various regions within a country. This information can aid multinational corporations, investors, and international organizations in making informed decisions, supporting more precise resource allocation and investment optimization on a global scale. Ultimately, such evaluations contribute to promoting the efficient and equitable flow of global resources.