Spatiotemporal Coupling Relationship between Higher Education and Economic Development in China: Based on Interprovincial Panel Data from 2012 to 2023

Abstract

This study explored the relationship between education and the economy in China, focusing on 31 provinces, municipalities, and autonomous regions from 2012 to 2023. It developed an appropriate evaluation model to assess the coupling and collaborative development of the ‘higher education and economy’ composite system. The study quantified both the quantity and quality of higher education modernization in China using the entropy-weight method and a comprehensive development- level evaluation model. The coupling-coordination degree model was applied to empirically analyze the internal logic, operating mechanisms, and coupling-coordination degree between the supply of higher education and the demand for high-quality regional economic development within the context of the new development pattern. Additionally, the obstacle degree model was introduced to identify factors hindering the coupling and coordination of higher education and regional economic development across 31 provinces and municipalities. The findings revealed that (1) the modernization levels of higher education exhibited fluctuating yet overall upward trends, with the eastern region leading. Economic development followed a similar upward trajectory, with the eastern region outperforming other areas. (2) The coupling coordination between higher education and economic development followed a ‘rising-falling-rising’ pattern. (3) The higher education system emerged as the primary obstacle to coupling coordination, with specific challenges varying across different regions.

1. Introduction

Higher education, as a crucial nexus of science, talent, and innovation, plays a pivotal role in the historical process of achieving Chinese-style modernization. It serves as a key driver of high-quality economic development and is a fundamental supporter of regional economic advancement [1,2,3,4,5,6]. As the capacity of higher education to enhance China’s economic progress continues to grow, its role in promoting sustainable development becomes increasingly interconnected [7,8]. However, the structural, typological, and resource disparities within higher education are increasingly at odds with the demands of China’s high-quality economic development. These disparities, coupled with significant differences in economic conditions and the status of higher education across China’s provinces, result in varying levels of effectiveness in how higher education supports regional economic development. Consequently, both the nation and society are placing higher expectations on higher education, stressing the need for it to maintain a service-oriented approach, proactively adapt to changes, and support high-quality economic development [9,10]. In response, this paper establishes an evaluation model for the coupling and coordinated development of a ‘higher education–economy’ tailored to China’s unique context. This model is urgently needed in the current era to promote the construction of a high-quality education system and the development of a strong educational nation [10,11]. It provides a basis for quantitatively evaluating the coordinated development of higher education and the economy and offers data references for assessing the relationship between higher education and regional economic development.

Historically, the relationship between higher education and the economy has been extensively studied, beginning with Schultz’s theory of human capital in 1961 [12]. The endogenous economic growth theory, proposed by Romer and Lucas, suggests that technological progress is the core driver of economic growth [11,12]. This technological advancement depends on well-educated, innovative, and capable individuals. Subsequent research indicates that education enhances the quality of human capital and fosters technological innovation, thereby promoting sustained economic growth. Studies such as Maneejuk (2021) highlight the positive correlation between higher education and economic development in ASEAN countries [13].

In China, the coupling-coordination degree between higher education and regional economies is a well-established research approach, revealing a mutual, yet improvable, relationship [14]. To effectively assess and enhance this coordination, it is crucial to understand the factors that influence it and how higher education can adapt to economic changes to provide high-quality talent for regional development. Empirical analysis is necessary to gain insights into how higher education supports regional economic development under the new paradigm [8,14,15,16,17].

This study follows the logic of mutual integration between education and the economy, as well as the synchronized progress of educational development and economic growth. Drawing on the literature analysis and the five major functions of universities, it utilizes official data from China’s 31 provinces and cities, sourced from official websites and statistical yearbooks. Focusing on the period from 2012 to 2023, the study establishes a “higher education–economy” composite system coupling-coordination evaluation model tailored to China. This model quantifies the “quantity” and “quality” of China’s higher education modernization using the entropy-weight method and a comprehensive development-level evaluation model.

The study employs the coupling-coordination degree model to empirically analyze how well the “supply” of higher education aligns with the “demand” for high-quality regional economic development under the new development paradigm. Additionally, the obstacle degree model is introduced to identify the factors hindering the coupling and coordination between higher education and regional economic development in the 31 provinces, municipalities, and autonomous regions. This provides a foundation for the quantitative evaluation of the coordination between higher education and economic development in China and offers data references for assessing the development of higher education and regional economies [18].

The findings will support the formulation and implementation of policies aimed at promoting coordinated and sustainable development between higher education and the economy across China’s 31 provinces, municipalities, and autonomous regions. Ultimately, this will contribute to creating a favorable scenario where high-quality economic development and higher education in China are harmoniously aligned and coordinated.

2. Literature Review

2.1. Impact of Higher Education on the Economy

Schultz’s early quantitative studies on education’s contribution to economic development used a residual analysis to underscore the significant returns on educational investment, profoundly influencing subsequent research [12,19]. Hanushek and Woessmann demonstrated that higher education positively impacts regional economies by improving workforce quality [20]. Romer found that higher education not only directly benefits regional economic development but also produces skilled labor for technological innovation, leading to higher wages and increased tax contributions [21]. Denison emphasized the dual role of education in enhancing labor quality and disseminating knowledge [22].

Scholars like Schultz argue that effective educational spending not only improves higher education but also yields positive economic effects, aligning with broader social development and industrial policies. Anna Valero noted that the impact of higher education on economic growth might exhibit a delayed effect, typically ranging from 3 to 7 years [23].

2.2. Research Area Selection

The study of the interactive relationship between higher education and high-quality economic development spans various regions [22,23], including, but not limited to, the 31 provinces, the central and western regions, the western regions, the Beijing–Tianjin–Hebei Economic Zone, and the Chengdu–Chongqing Twin City Economic Zone.

2.3. Selecting Intermediate Variables for Research

Exploring the modernization of higher education and high-quality economic development at the regional level involves examining both its direct impact on innovation and its indirect impact through variables like technological innovation and human capital. Employing instrumental variables can mitigate endogeneity issues [24]. Current research, both domestically and internationally, focuses on the bidirectional interaction between higher education and economic development, higher education and technological innovation [25,26], or technological innovation and economic development. These studies, whether theoretical or empirical, are often conducted at national, provincial, or urban levels. However, a comprehensive research framework has yet to be established.

2.4. Temporal and Spatial Dimensions of Research

In empirical research on the interaction between higher education and economic development, exploration typically occurs along two dimensions, namely temporal and spatial. Spatial autocorrelation methods are generally classified into global and local spatial autocorrelation based on their scope [27]. The most commonly used methods in research include Moran’s I, Geary’s C, Getis, join count, and spatial autocorrelation coefficient maps [28,29,30]. In China, research on the coordinated development of modern higher education and the economy primarily falls into two categories. The first focuses on the temporal dimension, where researchers utilize ArcGIS 10.2 software to validate the coupling and coordination of variables, employing global Moran’s I and local Getis–Ord Gi* coefficients.

3. Materials and Methods

3.1. Indicator Selection and Data Sources

This study builds on previous research by adopting a multidimensional evaluation perspective based on systematicity, scientific rigor, and data availability. These principles guide the construction of a comprehensive evaluation index system.

Primary data sources include the China Education Statistics Yearbook, the China Education Expenditure Statistics Yearbook, the China Science and Technology Statistics Yearbook, Ministry of Education statistics, National Bureau of Statistics data, and provincial statistical yearbooks from 2012 to 2022. Following a thorough review of the existing studies and a consideration of factors such as data continuity, availability, and scientific relevance, the following evaluation systems were implemented.

The Higher Education Modernization Evaluation Indicator System consists of 3 primary indicators, 4 secondary indicators, and 16 tertiary indicators.

The Evaluation Indicator System for Economic High-Quality Development comprises 3 primary indicators, 5 secondary indicators, and 13 tertiaries indicators [31,32].

After standardizing the indicators, their respective weights were determined using the entropy-weight method [33,34]. Detailed information is provided in

Table 1. Evaluation index system for modernization of higher education and high-quality economic development.

Coupled System	Target Layer	Criteria Layer	Indicator Layer	Unit	Weight
Modernized Higher Education System	Education Scale	School Scale 0.144	Number of Higher Education Institutions	Institution	0.055
			Number of Enrollments in Regular Higher Education Institutions	People	0.044
			Number of Students in Regular Higher Education Institutions	People	0.045
		Investment Scale 0.169	Average Education Expenditure per Student in Regular Higher Education Institutions	CNY	0.015
			Number of Full-time Faculty in Regular Higher Education Institutions	People	0.046
			Total Number of Staff in Regular Higher Education Institutions	People	0.049
			National Fiscal Education Expenditure	CNY Ten thousand	0.030
			Total Education Expenditure	CNY Ten thousand	0.029
	Education Quality	Talent Cultivation 0.165	Number of Undergraduate Students	People	0.049
			Number of Undergraduate Graduates	People	0.047
			Number of Graduate (Postgraduate) Graduates	People	0.026
			Number of Doctoral Graduates	People	0.013
			Number of Master’s Graduates	People	0.029
	Education Structure	Flow Structure 0.094	Proportion of R&D Projects	%	0.039
			Proportion of Research and Development Personnel	%	0.033
			Proportion of Research and Development Expenditure	%	0.023
High-Quality Economic Development System	Economic Scale	Output Scale 0.106	GDP	Hundred million Yuan	0.033
			GDP Growth Rate	%	0.073
	Economic Quality	Shared Development 0.031	Per Capita GDP	CNY	0.031
		Coordinated Development 0.134	Per Capita Disposable Income of Urban Residents	CNY	0.029
			Per Capita Consumption Expenditure of Urban Residents	CNY	0.031
			Per Capita Disposable Income of Rural Residents	CNY	0.036
			Per Capita Consumption Expenditure of Rural Residents	CNY	0.037
	Economic Structure	Output Structure 0.110	Value Added of the Primary Industry	CNY Hundred million	0.048
			Value Added of the Secondary Industry	CNY Hundred million	0.031
			Value Added of the Tertiary Industry	CNY Hundred million	0.032
		Flow Structure 0.140	Proportion of Value Added of the Primary Industry in Gross Domestic Product (GDP)	%	0.045
			Proportion of Value Added of the Secondary Industry in GDP	%	0.064
			Proportion of Value Added of the Tertiary Industry in GDP	%	0.032

.

3.2. Methodology

3.2.1. Entropy-Weight Method and Evaluation Model

Following dimensionless standardization processing, the weights for both primary and secondary indicators are calculated using the entropy-weight method [35]. Subsequently, the comprehensive evaluation model is employed to compute the comprehensive evaluation index for the modernization development of higher education. Specific details are provided in the table below.

$${\mathrm{U}}_1={\sum }_{\mathrm{i}=1}^{\mathrm{n}}{\mathrm{W}}_{\mathrm{i}}{\mathrm{X}}_{\mathrm{i}};{\mathrm{U}}_2={\sum }_{\mathrm{j}=1}^{\mathrm{n}}{\mathrm{W}}_{\mathrm{j}}{\mathrm{X}}_{\mathrm{j}}$$

(1)

${\mathrm{W}}_{\mathrm{i}}$ refers to the weights calculated by the entropy method [33,36], ${\mathrm{X}}_{\mathrm{i}}$ refers to the standardized values of indicators after dimensionless standardization, ${\mathrm{U}}_1$ refers to the evaluation index of modernization of higher education, and ${\mathrm{U}}_2$ refers to the evaluation index of high-quality economic development. ${\mathrm{U}}_1<{\mathrm{U}}_2$ refers to lagging behind in higher education investment. ${\mathrm{U}}_1>{\mathrm{U}}_2$ is lagging behind in economic development. ${\mathrm{U}}_1={\mathrm{U}}_2$ refers to synchronous development.

Due to the absence of cases where the absolute difference is exactly equal, provinces with |${\mathrm{U}}_1$ − ${\mathrm{U}}_2$| < 0.05 are considered to exhibit synchronous development, indicating synchronization between higher education and economic development [34,37].

3.2.2. Coupling-Coordination Degree Model

By utilizing ArcGIS software and employing both the global Moran’s I index and the local Getis–Ord Gi* coefficient, the degree of coupling and coordination between the variables of modernization of higher education and high-quality economic development can be verified.

$$\mathrm{C}={\displaystyle \frac{2\sqrt{U_1{U}_2}}{U_1+U_2}}$$

(2)

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

(3)

$$\mathrm{T}=\mathsf{\alpha }{U}_1+\mathsf{\beta }{U}_2$$

(4)

D represents the coupling coordination, while T denotes the comprehensive coordination index of higher education and economic development levels.

α and β are undetermined coefficients, with α + β = 1, representing the contribution of the two subsystems of higher education and economic development to the system coupling. As higher education and economic development are equally important, α = β = 0.5 is taken [38,39].The classification of coupling degree and coordination degree levels can be found in

Table 2. Classification Criteria for Coupling Degree and Coordination Degree.

Coupling-Coordination Degree (D Value)	Coupling Status	Coordination Level	Description of Characteristics between Subsystems and Elements
(0–0.1]	Low-level Coupling	Extremely Imbalanced	Insignificant Interaction and Impact Relationship/Basically Uncoordinated
(0.1–0.2]		Severely Imbalanced
(0.2–0.3]		Moderately Imbalanced	Insignificant Interaction and Impact Relationship/Barely Coordinated
(0.3–0.4]		Slightly Imbalanced
(0.4–0.5]	Antagonistic Period	On the Verge of Imbalance	Certain Interaction and Impact Relationship/Relatively Coordinated
(0.5–0.6]		Barely Coordinated
(0.6–0.7]	Adjustment Period	Primary Coordinated	Strong Interaction and Impact Relationship/Well Coordinated
(0.7–0.8]		Intermediate Coordinated
(0.8–0.9]	High-level Coupling	Well-Coordinated	Very Strong Interaction and Impact Relationship/Especially Coordinated
(0.9–1.0]		High-Quality Coordinated

.

This study establishes a coupling-coordination model and conducts spatial correlation analysis to evaluate the alignment between the modernization of Chinese higher education and high-quality economic development. As economic structure optimizations and reforms in both the economic and educational systems continue, the relationship between higher education and regional economies becomes increasingly intertwined. Regional economics drives progress in higher education, and higher education, in turn, supports economic growth. However, challenges, such as structural mismatches and insufficient measures, remain [38]. Therefore, comprehensive strategies are essential to enhance the coordination between higher education and regional economic development across dimensions of scale, strength, scope, depth, and effectiveness.

3.2.3. Obstacle Factors Analysis

Given the complexity of identifying factors constraining coordinated development through the coupling-coordination model, the introduction of an obstacle degree model becomes imperative. This model enables the calculation of the factors that hinder the coordinated development of the system [40].

$${\mathrm{Z}}_{\mathrm{i}\mathrm{j}} ={\displaystyle \frac{(1-{\mathrm{X}}_{\mathrm{i}\mathrm{j}})\times {\mathrm{W}}_{\mathrm{i}\mathrm{j}}\times 100\mathrm{\%}}{\sum (1-{\mathrm{X}}_{\mathrm{i}\mathrm{j}}^{\mathrm{\prime }})\times {\mathrm{W}}_{\mathrm{i}\mathrm{j}}}}{\mathrm{Z}}_{\mathrm{i}}=\sum {\mathrm{Z}}_{\mathrm{i}\mathrm{j}}$$

(5)

${\mathrm{X}}_{\mathrm{i}\mathrm{j}}^{\mathrm{\prime }}$ represents the standardized value of indicator j in system i, ${\mathrm{W}}_{\mathrm{i}\mathrm{j}}$ is the weight of indicator j in system i, ${\mathrm{Z}}_{\mathrm{i}\mathrm{j}}$ represents the barrier degree of individual indicators to the coupling coordination between higher education and economic development, and ${\mathrm{Z}}_{\mathrm{i}}$ represents the degree of obstacles for system i to the development of coupling coordination.

4. Results and Discussion

4.1. Comprehensive Evaluation Analysis

4.1.1. Higher Education Development Level

As shown in Figure 1, from 2012 to 2023, the modernization level of higher education in China’s 31 provinces and municipalities showed an overall upward trend [41], with the eastern region leading, followed by the central region, nationwide, northeast, and western regions. The northeast region exhibited the highest growth rate at 2.739%, followed by the nationwide at 0.912%, eastern region at 0.542%, central region at 0.357%, and western region at 0.271%.

Nationally, the comprehensive evaluation index for higher education modernization increased from 0.423 in 2012 to 0.467 in 2023, averaging an annual growth rate of 0.912%. The highest growth periods were in 2015–2016 and 2020–2021, with rates of 4.687% and 4.325% respectively, while the largest decrease occurred in 2018–2019, declining by 2.075%. In the eastern region (10 provinces and cities), the comprehensive evaluation index rose from 0.567 in 2012 to 0.599 in 2023, with an average annual growth rate of 0.542%. For the central region (six provinces), the index increased from 0.509 in 2012 to 0.528 in 2023, with an average annual growth rate of 0.357%. For the 12 provinces and cities in the western region, the comprehensive evaluation index of the modernization level of higher education development has increased from 0.265 in 2012 to 0.272 in 2023, with an average annual growth rate of 0.271%. In the three provinces of the northeastern region, the comprehensive evaluation index of higher education modernization increased significantly from 0.353 in 2012 to 0.468 in 2023, considerably exceeding the national average.

Using the natural break method in ArcGIS software, the comprehensive evaluation index of the modernization level of higher education development in 2012, 2017, and 2023 was categorized into five levels, namely low, relatively low, medium, relatively high, and high, and was visualized spatially (As shown in Figure 2).

Over the past 12 years, the comprehensive evaluation index for higher education modernization has consistently exceeded 0.5, indicating a high level maintained by Jiangsu, Guangdong, Beijing, and Shandong. In contrast, Jilin, Tianjin, and Shanxi started at a low level in 2012, remaining relatively low in 2017 and 2023. However, Liaoning showed progress, advancing from a low to a moderate level during the same period. The number of provinces with an index below 0.2 decreased from 11 in 2012 to 7 by 2023, all of which are located in the western region. Other provinces and cities generally showed stable evaluations over time.

4.1.2. High-Quality Economic Development

Figure 3 and Figure 4 illustrate that from 2012 to 2023, high-quality economic development in China’s 31 provinces and municipalities showed a gradual upward trend, with the eastern region consistently leading, followed by the national average, the central region, the northeast, and the western region [42]. The national average remained stable at 0.370, while the indices for the eastern, central, and western regions increased from 0.503, 0.351, and 0.277 in 2012 to 0.520, 0.395, and 0.298 in 2023, respectively. In contrast, the northeast region experienced a decline from 0.359 in 2012 to 0.268 in 2023.

The northeast region holds a pivotal role in China’s industrial, agricultural, and security sectors. Efforts have been made to revitalize the region, with a focus on innovation-driven development. In 2023, China introduced the “Outline of Building a Quality-Oriented Country,” which emphasizes the need to enhance the development environment, transition to new growth drivers, transform industries, and promote regional rejuvenation.

Over the past 12 years, China’s high-quality economic development has followed an “east high, west low” trend, similar to the spatial–temporal pattern observed in higher education modernization [43]. Notably, Jiangsu leads in economic development, followed by Zhejiang, Guangdong, Beijing, Shanghai, and Shandong, respectively, while Tibet ranks at the lower end of the spectrum in terms of economic development.

4.2. Measurement and Analysis of the Coupling Coordination

According to the calculation Formulas (2) and (4), the coupling coordination between higher education and economic development in China’s 31 provinces and regions from 2012 to 2023 has been assessed. The specific results are detailed in

Table 3. Empirical results of coupling-coordination degree between higher education and economic development in 31 Provinces, municipalities, and autonomous regions of China.

Year	Eastern Region	Central Region	Western Region	Northeastern Region	Average
2012	0.501	0.456	0.351	0.420	0.432
2013	0.502	0.455	0.356	0.426	0.435
2014	0.503	0.453	0.353	0.416	0.431
2015	0.509	0.460	0.357	0.425	0.438
2016	0.516	0.461	0.359	0.435	0.443
2017	0.518	0.467	0.362	0.429	0.444
2018	0.512	0.461	0.356	0.416	0.436
2019	0.509	0.463	0.356	0.409	0.434
2020	0.507	0.464	0.355	0.397	0.431
2021	0.515	0.456	0.356	0.417	0.436
2022	0.505	0.459	0.347	0.405	0.429
2023	0.513	0.475	0.363	0.418	0.442
Average	0.509	0.461	0.356	0.418	0.436

.

4.2.1. Temporal Pattern Analysis of Coupling-Coordination Degree

The temporal evolution of coupling coordination shows a “rise-fall-rise” trend, as shown in Table 3. Coordination increased from 2012 to 2017, declined from 2018 to 2020, and rose again from 2021 to 2023. The average coordination degree was 0.432 in 2012, peaked at 0.444 in 2017, and settled at 0.442 by 2023. This improvement reflects China’s economic growth, which has led to increased investment in higher education, thereby enhancing talent cultivation and scientific research outputs.

Regionally, the average coupling-coordination levels are as follows: east > central > national average > northeast > west (As shown in Figure 5). In the eastern region, Jiangsu, Guangdong, Beijing, Shandong, and Zhejiang consistently exhibit higher coordination degrees. Jiangsu and Guangdong have coordination degrees exceeding 0.6, indicating primary coordination, while Beijing, Shandong, and Zhejiang range from 0.5 to 0.6, indicating marginal coordination. These provinces have established strong interactions between higher education and economic development, achieving relatively high coupling-coordination levels over the years.

From

Table 4. Coupling-coordination degree of higher education investment and economic development in 2012.

Province	Coupling Degree C	Coupling-Coordination Degree D	Coupling-Coordination Level	U1 and U2	Coupling-Coordination Type
Guangdong	0.488	0.621	Primary Coordinated	U1 > U2	Economic Development Lagging Type
Jiangsu	0.493	0.613	Primary Coordinated	U1 > U2	Economic Development Lagging Type
Shandong	0.493	0.578	Barely Coordinated	U1 > U2	Economic Development Lagging Type
Beijing	0.486	0.561	Barely Coordinated	U1 > U2	Economic Development Lagging Type
Zhejiang	0.500	0.531	Barely Coordinated	U1 < U2	Higher Education Development Lagging Type
Henan	0.481	0.522	Barely Coordinated	U1 < U2	Higher Education Development Lagging Type
Shanghai	0.497	0.516	Barely Coordinated	U1 < U2	Higher Education Development Lagging Type
Sichuan	0.484	0.511	Barely Coordinated	U1 > U2	Economic Development Lagging Type
Hubei	0.486	0.497	On the Verge of Imbalance	U1 < U2	Higher Education Development Lagging Type
Hunan	0.493	0.476	On the Verge of Imbalance	U1 < U2	Higher Education Development Lagging Type
Hebei	0.495	0.472	On the Verge of Imbalance	U1 > U2	Economic Development Lagging Type
Liaoning	0.500	0.467	On the Verge of Imbalance	U1 > U2	Synchronous Development Type
Anhui	0.492	0.451	On the Verge of Imbalance	U1 < U2	Higher Education Development Lagging Type
Shaanxi	0.485	0.447	On the Verge of Imbalance	U1 > U2	Economic Development Lagging Type
Fujian	0.499	0.444	On the Verge of Imbalance	U1 < U2	Higher Education Development Lagging Type
Tianjin	0.472	0.418	On the Verge of Imbalance	U1 < U2	Higher Education Development Lagging Type
Jiangxi	0.495	0.416	On the Verge of Imbalance	U1 < U2	Higher Education Development Lagging Type
Chongqing	0.500	0.411	On the Verge of Imbalance	U1 < U2	Higher Education Development Lagging Type
Guangxi	0.498	0.409	On the Verge of Imbalance	U1 > U2	Economic Development Lagging Type
Heilongjiang	0.500	0.402	On the Verge of Imbalance	U1 > U2	Synchronous Development Type
Jilin	0.499	0.393	Slightly Imbalanced	U1 < U2	Synchronous Development Type
Yunnan	0.499	0.385	Slightly Imbalanced	U1 > U2	Synchronous Development Type
Shanxi	0.500	0.376	Slightly Imbalanced	U1 < U2	Synchronous Development Type
Inner Mongolia	0.460	0.359	Slightly Imbalanced	U1 < U2	Higher Education Development Lagging Type
Guizhou	0.499	0.351	Slightly Imbalanced	U1 > U2	Synchronous Development Type
Xinjiang	0.492	0.325	Slightly Imbalanced	U1 < U2	Higher Education Development Lagging Type
Gansu	0.500	0.316	Slightly Imbalanced	U1 < U2	Synchronous Development Type
Hainan	0.421	0.258	Moderately Imbalanced	U1 < U2	Higher Education Development Lagging Type
Tibet	0.473	0.243	Moderately Imbalanced	U1 < U2	Higher Education Development Lagging Type
Ningxia	0.383	0.229	Moderately Imbalanced	U1 < U2	Higher Education Development Lagging Type
Qinghai	0.379	0.221	Moderately Imbalanced	U1 < U2	Higher Education Development Lagging Type

,

Table 5. Coupling and coordination status of higher education investment and economic development in 2018.

Province	Coupling Degree C	Coupling-Coordination Degree D	Coupling-Coordination Level	U1 and U2	Coupling-Coordination Type
Jiangsu	0.493	0.637	Primary Coordinated	U1 < U2	Higher Education Development Lagging Type
Guangdong	0.49	0.627	Primary Coordinated	U1 > U2	Economic Development Lagging Type
Beijing	0.491	0.593	Barely Coordinated	U1 > U2	Economic Development Lagging Type
Shandong	0.491	0.581	Barely Coordinated	U1 > U2	Economic Development Lagging Type
Zhejiang	0.500	0.550	Barely Coordinated	U1 > U2	Synchronous Development Type
Shanghai	0.498	0.529	Barely Coordinated	U1 < U2	Higher Education Development Lagging Type
Henan	0.482	0.519	Barely Coordinated	U1 > U2	Economic Development Lagging Type
Hubei	0.486	0.515	Barely Coordinated	U1 > U2	Economic Development Lagging Type
Sichuan	0.486	0.514	Barely Coordinated	U1 > U2	Economic Development Lagging Type
Hunan	0.494	0.486	On the Verge of Imbalance	U1 > U2	Economic Development Lagging Type
Hebei	0.492	0.467	On the Verge of Imbalance	U1 > U2	Economic Development Lagging Type
Fujian	0.498	0.460	On the Verge of Imbalance	U1 < U2	Higher Education Development Lagging Type
Anhui	0.495	0.460	On the Verge of Imbalance	U1 > U2	Economic Development Lagging Type
Liaoning	0.484	0.453	On the Verge of Imbalance	U1 > U2	Economic Development Lagging Type
Shaanxi	0.484	0.451	On the Verge of Imbalance	U1 > U2	Economic Development Lagging Type
Jiangxi	0.498	0.422	On the Verge of Imbalance	U1 > U2	Economic Development Lagging Type
Tianjin	0.493	0.416	On the Verge of Imbalance	U1 < U2	Higher Education Development Lagging Type
Chongqing	0.500	0.415	On the Verge of Imbalance	U1 < U2	Synchronous Development Type
Heilongjiang	0.495	0.413	On the Verge of Imbalance	U1 > U2	Economic Development Lagging Type
Guangxi	0.499	0.402	On the Verge of Imbalance	U1 > U2	Synchronous Development Type
Yunnan	0.499	0.397	Slightly Imbalanced	U1 > U2	Synchronous Development Type
Jilin	0.496	0.383	Slightly Imbalanced	U1 > U2	Economic Development Lagging Type
Guizhou	0.499	0.375	Slightly Imbalanced	U1 < U2	Synchronous Development Type
Shanxi	0.496	0.365	Slightly Imbalanced	U1 > U2	Synchronous Development Type
Inner Mongolia	0.49	0.352	Slightly Imbalanced	U1 < U2	Higher Education Development Lagging Type
Xinjiang	0.489	0.335	Slightly Imbalanced	U1 > U2	Economic Development Lagging Type
Gansu	0.494	0.302	Slightly Imbalanced	U1 > U2	Economic Development Lagging Type
Tibet	0.443	0.261	Moderately Imbalanced	U1 < U2	Higher Education Development Lagging Type
Hainan	0.408	0.257	Moderately Imbalanced	U1 < U2	Higher Education Development Lagging Type
Ningxia	0.392	0.236	Moderately Imbalanced	U1 > U2	Economic Development Lagging Type
Qinghai	0.400	0.233	Moderately Imbalanced	U1 > U2	Economic Development Lagging Type

and

Table 6. Coupling and coordination status of higher education investment and economic development in 2023.

Province	Coupling Degree C	Coupling-Coordination Degree D	Coupling-Coordination Level	U1 and U2	Coupling-Coordination Type
Jiangsu	0.493	0.645	Primary Coordinated	U1 > U2	Economic Development Lagging Type
Guangdong	0.494	0.618	Primary Coordinated	U1 > U2	Economic Development Lagging Type
Beijing	0.487	0.591	Barely Coordinated	U1 > U2	Economic Development Lagging Type
Shandong	0.488	0.573	Barely Coordinated	U1 > U2	Economic Development Lagging Type
Zhejiang	0.499	0.560	Barely Coordinated	U1 < U2	Higher Education Development Lagging Type
Hubei	0.488	0.537	Barely Coordinated	U1 > U2	Economic Development Lagging Type
Shanghai	0.500	0.531	Barely Coordinated	U1 < U2	Synchronous Development Type
Sichuan	0.489	0.519	Barely Coordinated	U1 > U2	Economic Development Lagging Type
Henan	0.485	0.513	Barely Coordinated	U1 > U2	Economic Development Lagging Type
Hunan	0.497	0.501	Barely Coordinated	U1 > U2	Economic Development Lagging Type
Anhui	0.496	0.475	On the Verge of Imbalance	U1 > U2	Economic Development Lagging Type
Hebei	0.494	0.473	On the Verge of Imbalance	U1 > U2	Economic Development Lagging Type
Fujian	0.493	0.471	On the Verge of Imbalance	U1 < U2	Higher Education Development Lagging Type
Liaoning	0.476	0.468	On the Verge of Imbalance	U1 > U2	Economic Development Lagging Type
Shaanxi	0.485	0.468	On the Verge of Imbalance	U1 > U2	Economic Development Lagging Type
Jiangxi	0.500	0.434	On the Verge of Imbalance	U1 > U2	Synchronous Development Type
Heilongjiang	0.492	0.424	On the Verge of Imbalance	U1 > U2	Economic Development Lagging Type
Chongqing	0.500	0.414	On the Verge of Imbalance	U1 < U2	Synchronous Development Type
Tianjin	0.499	0.409	On the Verge of Imbalance	U1 < U2	Higher Education Development Lagging Type
Yunnan	0.499	0.404	On the Verge of Imbalance	U1 < U2	Synchronous Development Type
Guangxi	0.500	0.401	On the Verge of Imbalance	U1 > U2	Synchronous Development Type
Shanxi	0.499	0.388	Slightly Imbalanced	U1 > U2	Synchronous Development Type
Inner Mongolia	0.486	0.379	Slightly Imbalanced	U1 < U2	Higher Education Development Lagging Type
Jilin	0.470	0.361	Slightly Imbalanced	U1 > U2	Economic Development Lagging Type
Guizhou	0.498	0.342	Slightly Imbalanced	U1 < U2	Synchronous Development Type
Xinjiang	0.491	0.34	Slightly Imbalanced	U1 < U2	Higher Education Development Lagging Type
Gansu	0.499	0.338	Slightly Imbalanced	U1 < U2	Synchronous Development Type
Qinghai	0.472	0.263	Moderately Imbalanced	U1 < U2	Higher Education Development Lagging Type
Hainan	0.418	0.258	Moderately Imbalanced	U1 < U2	Higher Education Development Lagging Type
Ningxia	0.407	0.248	Moderately Imbalanced	U1 < U2	Higher Education Development Lagging Type
Tibet	0.429	0.243	Moderately Imbalanced	U1 < U2	Higher Education Development Lagging Type

, it can be seen that, after 12 years, the coupling-coordination degrees in Xinjiang, Gansu, Tibet, Hainan, Ningxia, and Qinghai have improved in absolute terms but remain low, placing these regions among the bottom six. Geographical challenges and relatively underdeveloped economies contribute to the poor coupling-coordination in these western provinces. Gansu shows synchronous development between higher education and economic growth, while Tibet consistently lags in higher education. Surprisingly, despite being in the eastern region, Hainan lags behind many western provinces in both higher education and economic development levels.

Based on the results in

Table 7. Coupling and coordination status of higher education and economic development in 31 provinces, municipalities, and autonomous regions in China in 2023.

Region	Primary Coordination	Barely Coordinated	Verge of Imbalance	Slightly Imbalanced	Moderately Imbalanced
eastern	Jiangsu, Guangdong	Beijing, Shandong, Zhejiang, Shanghai	Hebei, Fujian, Tianjin	-	Hainan
central	-	Hubei, Henan, Hunan	Anhui, Jiangxi	Shanxi	-
western	-	Sichuan	Shaanxi, Chongqing, Yunnan, Guangxi	Inner Mongolia, Guizhou, Xinjiang, Gansu	Qinghai, Ningxia, Tibet
northeast	-	-	Liaoning, Heilongjiang	Jilin	-

, Jiangsu and Guangdong have consistently achieved the highest level of coupling coordination from 2012 to 2023, maintaining a primary coordination status. The number of provinces and cities in a marginal coordination stage increased from six in 2012 to seven in 2018, and further to eight in 2023. Several provinces and cities are on the brink of imbalance, with 12 in 2012 and 11 in both 2018 and 2023. Provinces such as Shanxi, Inner Mongolia, Jilin, Guizhou, Xinjiang, and Gansu have experienced mild imbalances, while Guangxi has consistently oscillated between mild imbalance and being on the brink of imbalance. The lowest level of coupling coordination, classified as moderate imbalance, is observed in Qinghai, Hainan, Ningxia, and Tibet.

As China’s economy grows, the overall coupling coordination between higher education and economic development in the 31 provinces and cities is steadily increasing [44]. However, there is still significant room for advancement, particularly in the western and central regions. Most provinces and cities in the western region, with the exception of Sichuan, continue to face coordination challenges. Despite initiatives such as the Western Development Strategy designed to boost economic growth, the higher education infrastructure in these regions remains relatively weak. Challenges include insufficient human, financial, and material resources, which hinder economic development.

4.2.2. Global Spatial Correlation Analysis of Coupling Coordination

Using Stata 15.1 and ArcGIS 10.7 software, spatial correlation methods were employed to validate and visualize the levels of higher education modernization and economic development in China from 2012 to 2023.

Table 8. Results of the global spatial autocorrelation test for higher education and economic development levels from 2012 to 2023.

Global Moran’s I	Standardized Normal Statistic (Z(I))	p-Value
0.357	44.721	0.009
0.367	44.82	0.007
0.377	51.063	0.005
0.387	49.563	0.008
0.397	48.062	0.003
0.407	46.561	0.004
0.417	45.064	0.006
0.427	44.624	0.006
0.437	43.482	0.009
0.447	42.504	0.007
0.457	41.542	0.008
0.467	43.122	0.007

Note: E(I) and VAR(I) are both 0.

indicates that Moran’s I index p-values for each year were significantly below 5%, indicating significant regional agglomeration in both higher education modernization and economic development.

The global Moran’s I index, consistently positive at the 1% significance level throughout the period, increased from 0.357 in 2012 to 0.467 in 2023. This trend indicates a strengthening of spatial positive autocorrelation between higher education modernization and economic development in China. However, minor declines in 2018 and 2023 suggest a recent slowdown in the improvement of spatial unevenness in the overall coupling between these factors.

As shown in Figure 6, from 2012 to 2023, nine provinces and cities in China were located in the first and third quadrants, exhibiting spatial characteristics of “high-high” and “low-low” agglomeration. In 2012, there were four provinces in the eastern region, four in the central region, and one in the western region. By 2023, this composition shifted to six provinces in the eastern region, two in the central region, and one in the western region. This shift underscores the synergistic effect of the developed eastern region on the higher education modernization levels in the surrounding provinces, effectively demonstrating the spatial clustering effect of higher education development across many regions of China.

From 2012 to 2023, the number of provinces in the second quadrant (“low-high” agglomeration) decreased from 11 to 8. Shandong, Henan, and Tianjin transitioned from a “low-high” to a “high-high” agglomeration. Six western provinces remained in the second quadrant, indicating that their higher education development levels are lower compared to neighboring provinces. This disparity reflects ongoing resource outflows that hinder local higher education growth in these western regions.

Provinces in the third quadrant (“low-low” agglomeration) decreased from five to three, all in the central and western regions. Meanwhile, those in the fourth quadrant (“high-low” agglomeration) increased from nine to ten provinces and cities by 2023. These provinces exhibit higher education levels themselves but negatively impact neighboring areas, exacerbating higher education resource scarcity nearby.

4.3. Analysis of Obstacle Factors

Based on the obstacle-degree calculation formula, the obstacle degrees for the higher education and economic development systems in various regions of China for 2023 were computed. The obstacle degrees for the higher education system ranged from 24.514% to 70.234%, while those for the economic development system ranged from 23.311% to 54.142%. It is evident that the obstacle degree of the higher education system is significantly higher than that of the economic development system [45]. This indicates that the higher education system is the primary obstacle affecting the coupling coordination between higher education and economic development. The development of higher education profoundly influences the development trend of the regional economy through both “quality” and “quantity” aspects.

As shown in Figure 7, from a regional perspective, the top five provinces or cities with the highest obstacle degrees for higher education development are Qinghai (70.234%), Ningxia (69.987%), Hainan (68.755%), Tibet (68.561%), and Inner Mongolia (63.822%). The five provinces and municipalities with the lowest degree of obstacles to higher education development are Guangdong (24.514%), Jiangsu (28.184%), Beijing (31.405%), Shandong (33.060%), and Henan (37.212%).

The top five provinces or cities with the highest obstacle degrees for economic development are Jilin (54.142%), Tibet (49.608%), Hainan (49.592%), Qinghai (48.450%), and Guizhou (47.016%). On the other hand, the provinces or cities with the lowest obstacle degrees for economic development are Jiangsu (23.311%), Zhejiang (24.486%), Guangdong (27.767%), Fujian (28.911%), and Shandong (30.796%).

4.3.1. Obstacle Degree at the Criterion Level of the Higher Education System

The analysis of the criterion-level obstacle degree in the higher education system highlights substantial challenges within the subsystems of higher education development across various regions. This provincial-level examination reveals significant obstacles affecting the advancement of higher education in different areas.

In terms of educational scale, Tibet, Qinghai, and Ningxia emerge as the top three provinces with the highest obstacle degrees. This indicates that these regions face considerable barriers related to expanding the scale of higher education institutions.

Based on the results in

Table 9. Barrier levels of higher education systems in 31 provinces, municipalities, and autonomous regions in China in 2023.

Ranking	Educational Scale		Investment Scale		Talent Cultivation		Traffic Structure
1	Tibet	17.414	Ningxia	22.798	Tibet	17.717	Tibet	13.462
2	Qinghai	17.051	Qinghai	22.122	Hainan	17.684	Qinghai	13.419
3	Ningxia	16.393	Hainan	22.118	Ningxia	17.676	Ningxia	13.121
4	Hainan	16.002	Tianjin	20.501	Qinghai	17.643	Hainan	12.952
5	Inner Mongolia	13.614	Inner Mongolia	20.286	Henan	17.624	Xinjiang	12.770
6	Gansu	13.565	Gansu	19.983	Xinjiang	17.581	Inner Mongolia	12.558
7	Tianjin	13.308	Tibet	19.969	Guangxi	17.572	Gansu	12.299
8	Xinjiang	13.211	Jilin	19.935	Guizhou	17.545	Guizhou	12.052
9	Shanghai	13.203	Xinjiang	19.883	Hebei	17.484	Yunnan	11.665
10	Jilin	12.104	Shaanxi	18.951	Jiangxi	17.421	Shaanxi	11.665
11	Beijing	11.766	Heilongjiang	18.872	Inner Mongolia	17.364	Jiangxi	11.315
12	Guizhou	11.261	Guizhou	18.214	Shaanxi	17.208	Guangxi	11.032
13	Heilongjiang	11.132	Chongqing	17.879	Yunnan	17.088	Hebei	10.931
14	Chongqing	10.896	Yunnan	17.638	Gansu	16.943	Chongqing	10.655
15	Shaanxi	10.825	Liaoning	17.19	Fujian	16.929	Jilin	10.486
16	Yunnan	10.396	Fujian	17.069	Anhui	16.608	Henan	10.381
17	Fujian	10.071	Guangxi	16.803	Hunan	16.515	Heilongjiang	10.296
18	Shaanxi	9.081	Shanghai	16.275	Chongqing	16.429	Tianjin	10.276
19	Liaoning	8.878	Jiangxi	15.973	Heilongjiang	16.264	Fujian	9.874
20	Guangxi	8.779	Shaanxi	15.556	Jilin	16.179	Liaoning	9.756
21	Zhejiang	8.731	Anhui	15.341	Zhejiang	16.128	Anhui	8.995
22	Jiangxi	7.654	Hunan	14.461	Tianjin	16.122	Hunan	8.749
23	Anhui	7.128	Hubei	13.589	Shandong	15.987	Hubei	7.854
24	Hebei	5.892	Hebei	13.327	Sichuan	15.896	Shaanxi	7.615
25	Hunan	5.728	Beijing	13.237	Guangdong	15.646	Sichuan	7.244
26	Hubei	5.678	Zhejiang	12.208	Liaoning	15.061	Shandong	7.093
27	Sichuan	4.229	Sichuan	11.778	Shaanxi	14.819	Zhejiang	6.889
28	Jiangsu	2.485	Henan	8.717	Hubei	14.557	Shanghai	5.646
29	Shandong	1.876	Shandong	8.104	Jiangsu	13.227	Jiangsu	4.552
30	Guangdong	1.163	Jiangsu	7.921	Shanghai	12.682	Guangdong	4.402
31	Henan	0.492	Guangdong	3.303	Beijing	6.401	Beijing	0.000

, for the investment scale, Ningxia, Qinghai, and Hainan rank as the top three provinces with the highest obstacle degrees. This suggests challenges to mobilizing adequate financial resources to support higher education initiatives in these regions.

In terms of talent cultivation and flow structure, Tibet, Hainan, and Ningxia are identified as the top three provinces with the highest obstacle degrees. This indicates significant challenges related to talent retention, recruitment, and skill development in these regions.

Notably, Tibet, Qinghai, and Ningxia are predominantly located in the central and western regions of China. Addressing these obstacles is crucial for improving the quality and accessibility of higher education across different provinces. By focusing on targeted interventions and policies, stakeholders can work towards reducing disparities in higher education quality and promoting overall development and prosperity.

4.3.2. Obstacle Degree at the Criterion Level of the Economic System

From a provincial perspective, the analysis of the economic development subsystem reveals distinct challenges across different regions (

Table 10. Barrier levels of economic systems in 31 provinces, municipalities, and autonomous regions in China in 2023.

Ranking	Economic Output Scale		Shared Development		Collaborative Development		Output Structure		Traffic Structure
1	Jilin	15.351	Gansu	4.429	Gansu	17.385	Tibet	14.579	Beijing	13.513
2	Hainan	12.009	Heilongjiang	4.243	Qinghai	17.182	Qinghai	14.298	Shanghai	12.429
3	Shanghai	11.337	Guangxi	4.211	Shanxi	16.879	Ningxia	14.167	Tianjin	10.942
4	Tibet	10.649	Guizhou	4.205	Jilin	16.753	Tianjin	13.509	Hainan	10.298
5	Tianjin	10.312	Jilin	4.113	Xinjiang	16.542	Hainan	13.194	Guangdong	10.177
6	Beijing	9.917	Hebei	4.063	Guizhou	16.481	Gansu	12.787	Zhejiang	10.117
7	Guizhou	9.835	Tibet	4.019	Ningxia	16.199	Jilin	12.505	Tibet	9.941
8	Qinghai	8.558	Qinghai	3.949	Guangxi	15.829	Beijing	12.023	Chongqing	9.878
9	Liaoning	7.992	Yunnan	3.919	Yunnan	15.761	Shanxi	11.858	Shandong	9.838
10	Heilongjiang	7.440	Henan	3.907	Shaanxi	15.658	Shanghai	11.764	Sichuan	9.684
11	Chongqing	7.137	Hainan	3.77	Heilongjiang	15.633	Xinjiang	11.452	Jiangsu	9.661
12	Guangxi	6.729	Sichuan	3.734	Henan	15.485	Chongqing	11.085	Anhui	9.607
13	Xinjiang	6.526	Xinjiang	3.711	Tibet	14.957	Guizhou	10.992	Hubei	9.602
14	Sichuan	5.641	Liaoning	3.704	Hainan	14.690	Inner Mongolia	10.915	Hunan	9.586
15	Ningxia	5.622	Ningxia	3.673	Hebei	14.440	Heilongjiang	10.652	Jilin	9.565
16	Henan	5.138	Jiangxi	3.638	Liaoning	14.415	Liaoning	10.584	Liaoning	9.513
17	Anhui	5.041	Hunan	3.557	Inner Mongolia	13.926	Jiangxi	10.471	Gansu	9.504
18	Guangdong	4.754	Anhui	3.557	Jiangxi	13.766	Shaanxi	10.294	Guizhou	9.396
19	Inner Mongolia	4.634	Shanxi	3.555	Sichuan	13.679	Yunnan	9.374	Hebei	9.372
20	Hebei	4.627	Shaanxi	3.274	Anhui	13.263	Guangxi	9.344	Henan	9.348
21	Zhejiang	4.553	Shandong	3.179	Shandong	13.167	Anhui	8.652	Yunnan	9.326
22	Gansu	4.552	Chongqing	3.037	Chongqing	13.014	Fujian	8.412	Guangxi	9.264
23	Yunnan	4.257	Hubei	2.994	Hubei	12.776	Hebei	8.065	Jiangxi	9.239
24	Shanxi	4.206	Inner Mongolia	2.863	Hunan	12.585	Hunan	7.461	Fujian	9.218
25	Shaanxi	4.121	Guangdong	2.694	Tianjin	9.496	Zhejiang	7.364	Qinghai	8.948
26	Jiangxi	3.467	Zhejiang	2.189	Fujian	9.346	Hubei	6.773	Heilongjiang	8.903
27	Jiangsu	3.449	Tianjin	2.166	Guangdong	9.003	Sichuan	5.727	Ningxia	8.788
28	Hubei	3.372	Fujian	1.935	Jiangsu	7.204	Henan	5.492	Shaanxi	8.768
29	Hunan	3.214	Jiangsu	1.399	Beijing	2.376	Shandong	3.254	Xinjiang	8.765
30	Shandong	2.847	Shanghai	0.317	Zhejiang	2.104	Jiangsu	1.821	Shanxi	8.533
31	Fujian	2.715	Beijing	0.000	Shanghai	0.016	Guangdong	1.139	Inner Mongolia	8.279

). Jilin, Hainan, and Shanghai emerge as the top three provinces with the highest obstacle degrees, indicating significant barriers to expanding economic output in these areas. Provinces requiring particular attention for the subsystems of shared development, coordinated development, and output structure include Gansu, Heilongjiang, and Guangxi. Additionally, Gansu, Qinghai, and Shanxi, as well as Tibet, Qinghai, and Ningxia, demonstrate high obstacle degrees in these subsystems. These findings highlight the need to address challenges related to balanced development, coordination, and structural optimization in these regions.

Regarding the obstacle degree of flow structure, the provinces with the highest obstacle degrees are predominantly in the central and western regions. Specifically, Beijing, Tianjin, and Hainan rank as the top three provinces with the highest obstacle degrees in the economic development subsystem related to flow structure. This indicates significant barriers related to the flow of resources, capital, and labor in these regions, highlighting the need for targeted interventions to improve economic efficiency and optimize resource allocation.

4.4. Limitations and Future Research Directions

Despite the valuable insights provided by this study, there are several limitations and areas for improvement:

(1) Data constraints: the study relies on provincial panel data from 2012 to 2023 due to data availability issues. Prior to 2012, detailed provincial breakdowns for educational indicators were not available, limiting the study to this time frame. While the sample size generally meets the minimum requirements for data analysis, it remains relatively small, which may affect the robustness of the findings;

(2) Geographical scope: the research is based on provincial-level data. Future studies could benefit from incorporating data at the municipal or county level, which would allow for more precise and targeted empirical analyses;

(3) Comprehensive indicator selection: future research should consider a broader range of indicators, including ecological factors. High-quality economic development is a multifaceted concept that encompasses not only economic scale, quality, and structure but also social, ecological, and environmental dimensions.

Future work will involve expanding the data set and incorporating variables, such as ecology and the environment, in the selection of indicators. Additionally, we will use econometric methods, including spatial autocorrelation models and the Dagum Gini coefficient. These efforts aim to enrich existing research findings and provide empirical evidence and policy recommendations for the coordinated development of higher education and the economy in China.

5. Conclusions and Recommendation

Based on relevant theories and previous research, this study has developed a systematic, comprehensive, and scientific evaluation index system for assessing the coupling and coordination between higher education and economic development. The study utilized panel data from 31 provinces in China from 2012 to 2023 to explore spatiotemporal differentiation and identify obstacle factors constraining coupling and coordination. The key findings include:

(1) For higher education modernization, an index system was developed comprising 3 primary indicators, 4 secondary indicators, and 16 tertiary indicators. Likewise, a separate index system for high-quality economic development includes 3 primary indicators, 5 secondary indicators, and 13 tertiary indicators;

(2) The level of higher education modernization across China’s 31 provinces, municipalities, and autonomous regions exhibited a fluctuating upward trend. The ranking is as follows: east > central > national > northeast > west. In terms of growth rates, the figures are as follows: northeast (2.739%) > national (0.912%) > east (0.542%) > central (0.357%) > west (0.271%). Nationwide, the comprehensive evaluation index for higher education modernization increased from 0.423 in 2012 to 0.467 in 2023, reflecting an average annual growth rate of 0.912%. The periods with the highest growth rates were 2015–2016 and 2020–2021, with rates of 4.687% and 4.325%, respectively. Conversely, the largest decrease in the growth rate occurred during 2018–2019, with a reduction of 2.075%;

(3) The level of high-quality economic development in China’s 31 provinces, municipalities, and autonomous regions showed a slow, fluctuating upward trend, with the ranking as follows: east > national > central > northeast > west. Nationwide, the average level of high-quality economic development remained stable at 0.370. In the east, central, and west regions, the average levels of high-quality economic development increased from 0.503, 0.351, and 0.277 in 2012 to 0.520, 0.395, and 0.298 in 2023, respectively. In contrast, the northeast region’s average level decreased from 0.359 in 2012 to 0.268 in 2023;

(4) The temporal variation in the coupling and coordination between higher education and economic development in China displayed a fluctuating trend of “rising–falling–rising”. In 2012, the average degree of coupling and coordination was 0.432, which increased to 0.444 by 2017 and slightly decreased to 0.442 by 2023;

(5) The obstacle degree of higher education systems across regions ranges from 24.514% to 70.234%, while for economic development systems, it ranges from 23.311% to 54.142%. It is evident that the obstacle degree for higher education systems is significantly higher than that for economic development systems in each region. The provinces with the highest obstacle degrees in higher education development are Qinghai, Ningxia, Hainan, Tibet, and Inner Mongolia. For economic development, the provinces with the highest obstacle degrees are Jilin, Tibet, Hainan, Qinghai, and Guizhou.

Based on these research findings, the following recommendations are proposed to further enhance the high-quality development of higher education and economic development in China.

Efforts should be focused on improving the development of higher education in China, particularly in the central and western provinces, where significant disparities in educational and economic development persist. First, it is crucial to increase investment in higher education resources and direct policies to support the western and northeastern regions. Second, optimize the allocation of higher education resources by facilitating the transfer of resources from the eastern and central regions to the western and northeastern regions. This strategy aims to address the uneven distribution of quality higher education resources and ensure more equitable opportunities across the country.

Efforts should be directed towards narrowing the economic development gap between the eastern, central, and western regions. The eastern region, leveraging its leading position, should focus on sharing innovations, promoting resource spillovers, and engaging in joint scientific and technological advancements. This approach will enhance collaborative interactions among the regions, allowing the stronger areas to support and uplift the weaker ones in higher education and economic development.

The factors constraining higher education and economic development differ across provinces. Each province should focus on addressing its specific weaknesses in academic disciplines and regional development.