Interaction Mechanism and Coupling Strategy of Higher Education and Innovation Capability in China Based on Interprovincial Panel Data from 2010 to 2022

Abstract

The sustainable development of higher education exhibits a strong and measurable association with the level of regional innovation capacity. Drawing on panel data covering 31 provincial-level administrative regions in China from 2010 to 2022, we construct evaluation frameworks for both higher education and regional innovation capacity using the entropy weight method. These are complemented by kernel density estimation, spatial autocorrelation analysis, Dagum Gini coefficient decomposition, and the Obstacle Degree Model. Together, these tools enable a comprehensive investigation into the spatiotemporal evolution and driving mechanisms of coupling coordination dynamics between the two systems. The results indicate the following: (1) Both higher education and regional innovation capacity indices exhibit steady growth, accompanied by a clear temporal gradient differentiation. (2) The coupling coordination degree shows an overall upward trend, with significant inter-regional disparities, notably “higher in the east and low in the west”. (3) The spatial distribution of the coupling coordination degree reveals positive spatial autocorrelation, with provinces exhibiting similar levels tending to form spatial clusters, most commonly of the low–low or high–high type. (4) The spatial heterogeneity is pronounced, with inter-regional differences driving overall imbalance. (5) Key obstacles hindering regional innovation include inadequate R&D investment, limited trade openness, and weak technological development. In higher education sectors, limitations stem from insufficient social service benefits and efficiency of flow. This study recommends promoting the synchronized advancement of higher education and regional innovation through region-specific development strategies, strengthening institutional infrastructure, and accurately identifying and addressing key barriers. These efforts are essential to fostering high-quality, coordinated regional development.

1. Introduction

In the era of the knowledge economy and globalization, innovation has become a key driver of national sustainable development, with regional innovation capability serving as a critical indicator of competitiveness [1]. Higher education enhances knowledge dissemination, industrial collaboration, and economic transformation, thereby directly boosting regional innovation [2,3,4]. As Ashby noted, “A university is the product of heredity and environment,” evolving from a “repository of knowledge” to a “knowledge hub,” and a vital engine for sustainable innovation [5]. Schumpeter’s regional innovation theory highlights the role of universities, enterprises, and research institutions in forming regional innovation systems [6], while the triple-helix model underscores the collaborative ecosystem of government, industry, and universities through market-driven interactions [7].

Within this framework, higher education, as a hub of knowledge and reservoir of human capital, is intrinsically linked to regional innovation capability. This relationship manifests through the intertwined symbiosis of the knowledge production–diffusion chain and the innovation value chain [8], as well as through human capital accumulation [9] and knowledge spillover effects [10], reshaping the spatial configuration of regional economies.

As the world’s largest emerging economy, China prioritizes higher education as the cornerstone of its innovation-driven development agenda. Key national policy documents, such as the Overall Plan for Promoting the Construction of World-class Universities and First-class Discipline and the Outline of National Innovation-driven Development Strategy, underscore the pivotal role of higher education in boosting regional innovation and economic growth [11,12].

Despite expanding higher education and increasing scientific output, regional capabilities in innovation capacity exhibit marked disparities due to varying economic structures, industrial foundations, and policy support. These imbalances are particularly pronounced across eastern, central, and western regions, resulting in complex spatiotemporal interactions between higher education and regional innovation. Analyzing these dynamics is crucial for formulating targeted regional strategies and fostering high-quality, coordinated growth [13].

Panel data has become a crucial methodological tool for analyzing the complex interplay between education and innovation, allowing for the examination of both cross-sectional heterogeneity and temporal dynamics. Most existing studies employ panel regression models to investigate the linear relationships between higher education and innovation capacity. For instance, Pinto demonstrated that university R&D investment significantly boosts regional patent output using EU NUTS-2 data [14]. However, many of these studies overlook bidirectional interaction and dynamic co-evolution. More recently, the coupling coordination model has gained attention for assessing the interactive development of higher education and regional innovation. Studies by Hu et al. [15] and Zhang et al. [16] used this model to examine the coordination between education investment and innovation performance across Chinese provinces, revealing notable regional imbalances.

This study challenges the conventional focus in educational economics on the direct link between higher education and regional economic metrics such as GDP, industrial structure, and employment. Instead, it emphasizes regional innovation capacity as the key driver in a knowledge-based economy. While existing studies tend to highlight macro-level correlations and often neglect the complex mechanisms and spatial dynamics of innovation, this study analyzes panel data from 31 Chinese provinces to develop a “higher education–regional innovation” framework grounded in triple-helix theory. We employ an innovative combination of the entropy weight TOPSIS method, the coupling coordination degree model, and obstacle factor identification to systematically examine the coupling relationship and spatiotemporal evolution of the two systems. This study contributes in three major areas: Theoretically, it deepens interdisciplinary research between higher education and regional innovation systems. Methodologically, it integrates multiple spatial analysis to assess uneven resource agglomeration and innovation spillovers, revealing regional convergence and divergence patterns. Practically, it offers region-specific policy recommendations to address the spatial imbalance in innovation capacity, particularly the “east is clearly superior to west” pattern. These findings support China’s “education–science and technology–talent” strategy and redefine the strategic role of higher education within regional innovation ecosystems.

2. Literature Review and Theoretical Analysis

2.1. Regional Development Context in China

In November 2018, the “Opinions on Establishing a More Effective New Mechanism for Regional Coordinated Development” highlighted that widening regional development disparities, marked differentiation, and flawed mechanisms have become central obstacles to China’s regional coordination strategy in the new era. Nationally, the eastern coastal regions, benefiting from geographic advantages, policy incentives, and foreign investment, have spearheaded industrialization and urbanization, creating growth hubs like the Yangtze River Delta and the Pearl River Delta. Conversely, the central and western regions lag behind, hindered by geographical barriers, inadequate infrastructure, and limited industrial diversity. Eastern provinces like Guangdong and Jiangsu significantly surpass western provinces such as Gansu and Qinghai in total GDP and per capita income, exhibiting smaller urban–rural disparities. Notably, substantial inequalities exist even within central and western provinces, exemplified by the stark contrast between Sichuan’s Chengdu Plain and its western mountainous regions. While central government fiscal transfers and regional strategies like the Western Development Strategy have somewhat mitigated these disparities, entrenched structural differences—such as the level of marketization and innovation capacity—persist. This imbalance undermines the national innovation system’s efficacy. The migration of talent from western and northeastern regions to the east has led to a problematic concentration of innovation resources, threatening long-term coordinated development. Although policies like the Western Development Strategy, the Rise of Central China, and the Revitalization of Northeast China aim to address these gaps, the uneven distribution of higher education resources remains a critical bottleneck. In 2022, data from the National Bureau of Statistics revealed that the per student general public budget for regular higher education institutions in Beijing (CNY 60,731.14) was 3.9 times higher than in Liaoning Province (CNY 15,726.25) and 4.2 times greater than in Henan Province (CNY 14,432.17). Additionally, the average R&D investment intensity in eastern provinces (3.2%) was 2.5 times that in western regions (1.3%) and 2 times that in northeastern regions (1.6%). This structural disparity underscores the urgent need for regional coordination policies.

2.2. Research Progress on Higher Education and Regional Innovation

This study conducted a systematic review of the domestic and international research landscape on higher education and regional innovation capacity using Cite Space 6.2.R4 for bibliometric analysis. The analysis was based on data from the Web of Science Core Collection spanning the years 2010 to 2023 [17]. Employing “regional innovation” and “higher education” as dual-core retrieval terms, 802 highly relevant publications were identified. A keyword co-occurrence network was constructed, comprising 297 nodes and 448 links (network density = 0.0102). The resulting visualization reveals a complex structure characterized by well-defined knowledge clusters and clear evolutionary pathways (Figure 1).

According to Price’s Law and time-series trend analysis, the research development can be categorized into three distinct stages: (1) From 2010 to 2015, the focus was on knowledge production, addressing foundational topics like university technology transfer and regional innovation policies. This stage emphasized a one-way interaction model between higher education and regional innovation, underscoring knowledge capital’s role in industrial advancement [18]. (2) The 2016 to 2020 period marked the system coupling stage, shifting attention toward non-linear processes such as knowledge spillover and industry–university–research collaboration, leading to the development of the triple-helix model of “university–industry–government” collaboration. (3) From 2021 to 2023, the spatial reconstruction stage emerged, focusing on the spatial reorganization of regional innovation networks, particularly in digital innovation ecosystems and transboundary knowledge flows, highlighting new trends in the global redistribution of innovation resources under the context of Globalization 4.0.

A bibliometric analysis of the existing literature revealed a persistent and dynamic relationship between higher education and regional innovation. This relationship is both shaped by and contributory to broader economic and social development trends [19,20,21]. Higher education plays a pivotal role in enhancing regional innovation by cultivating talent, generating and disseminating knowledge, and fostering collaboration among industry, academia, and research institutions. As the central knowledge hub within regional innovation systems, higher education institutions supply essential resources and intellectual support, contributing to human capital accumulation, fundamental research breakthroughs, and the diffusion of new technologies.

Strengthening regional innovation capabilities compels higher education institutions to adapt their discipline structures, talent development models, and research organization to align with market demands, integrated innovation factors, and optimized institutional environments. Empirical studies indicate that the concentration of higher education resources positively correlates with regional innovation density and the efficiency of research commercialization. However, this promotional effect exhibits significant spatial heterogeneity, with higher education exerting a more substantial impact on technological innovation in economically advanced regions with well-developed industrial infrastructures [22,23,24].

The establishment of a “university–enterprise–government” triple-helix collaborative innovation network enables higher education to efficiently integrate regional and external innovation resources, facilitate the flow of knowledge, enhance regional innovation ecosystems, and promote industrial upgrading and innovation-driven development strategies. At the same time, regional innovation capacity significantly influences the development of higher education. Empirical analysis reveals that a 1% increase in regional innovation density leads to a 0.6–0.8 percentage point rise in the proportion of applied disciplines in universities. Moreover, the elasticity coefficient between technology market turnover and university horizontal research funding is estimated at 0.43 [25].

Technological progress drives the evolution of academic disciplines, compelling traditional fields to integrate with emerging ones. This spatial concentration of innovative elements is redefining the distribution of higher education resources, fostering a symbiotic relationship between universities and industrial clusters, characterized as an “innovation pole–talent pool” dynamic. Rising demands for innovation outcomes are also accelerating reforms in university evaluation systems and facilitating the practical application of knowledge production models.

This interrelationship is characterized by significant spatial heterogeneity and temporal lag effects. In developed regions, innovation and education co-evolve through a “demand–pull–supply–response” mechanism. In contrast, underdeveloped regions frequently experience a negative cycle of “factor siphoning–development retardation.” Simultaneously, the ongoing enhancement of regional innovation ecosystems is catalyzing the transformation of higher education governance systems towards greater openness, market orientation, and internationalization through policy coordination, platform development, and the expansion of global cooperation networks.

2.3. Research on the Coupling and Coordination Mechanism

Coupling coordination theory has emerged as a critical analytical framework for examining complex multi-system interactions. Recent advancements in this field have significantly improved our understanding of the synergistic mechanisms linking higher education and regional innovation systems [26]. The theory enables a paradigm shift from static, single-system assessments to dynamic, multi-dimensional analyses across various spatial and temporal scales. Empirical studies increasingly demonstrate that the agglomeration of high-quality higher education resources generates substantial positive spillover effects on regional innovation capacity. For instance, Zhou et al. empirically demonstrated a significant positive correlation between the spatial concentration of universities’ intellectual capital and regional technological innovation capacity at the urban agglomeration scale [27]. Chen et al. further identified inefficiencies in resource allocation and institutional barriers as critical constraints, specifically, the impeded flow of factors in the industry–university–research nexus [28].

The eastern coastal regions of China have successfully established a beneficial cycle of “education–science and technology–industry” through the integration of university clusters and industrial innovation networks. Research into the driving mechanisms of this coupling has revealed three core pathways: the efficiency of policy intervention, the effectiveness of spatial coordination, and the multiplier effect of innovation resource inputs.

However, the current body of research still faces three major theoretical limitations. First, the long-term dynamics of institutional transitions and technological leapfrogging remain insufficiently explored. Second, there is a lack of empirical validation for cross-scalar transformations that occur across administrative levels. Third, there is still no well-established analytical framework for measuring institutional elasticity—the responsiveness of institutional environments to the flow of innovation factors.

To address these gaps, future studies should prioritize constructing a dynamic, multi-tiered analytical framework, targeting three core dimensions: Methodologically, it is essential to integrate complex system simulation with multi-source heterogeneous data analytics, thereby constructing multi-scale coupling models that operate across provincial, urban agglomeration, and national levels. In terms of data, the inclusion of novel sources such as technology transaction networks and patent citation landscapes will enhance the realism and policy relevance of simulation scenarios. Theoretically, advancing cross-national comparative research will help distill elastic mechanisms governing innovation resource allocation across varied institutional settings. Systematic advances in these areas will contribute to building a more robust theoretical foundation, ultimately helping to overcome institutional bottlenecks and optimize regional innovation systems.

3. Materials and Methodology

3.1. Study Area

In this study, panel data are constructed using “individuals” representing the 31 provinces, municipalities, and autonomous regions in China, offering a comprehensive analysis of regional disparities in higher education and innovation capabilities. The term “regions” refers to China’s four major geographical divisions, as classified according to the commonly accepted framework in the Physical Geography of China textbook. Among them, the eastern region includes 7 provinces and 3 municipalities directly under the central government, including Beijing, Tianjin, Hebei, Shandong, Jiangsu, Shanghai, Zhejiang, Fujian, Guangdong, and Hainan. The central region includes six provinces: Shanxi, Henan, Anhui, Hubei, Hunan, and Jiangxi. The western region includes six provinces, one municipality directly under the Central Government, and five autonomous regions, including Chongqing, Sichuan, Guangxi, Guizhou, Yunnan, Shaanxi, Gansu, Ningxia, Qinghai, Xinjiang, Tibet, and Inner Mongolia. The northeast region includes three provinces: Liaoning, Jilin, and Heilongjiang. In order to maintain the coupling between policy and data, combined with the availability and reliability of data, the time frame of this study was selected as 2010–2022, allowing for the examination of developmental dynamics and long-term trends.

3.2. Data Source and Index Selection

This study employs a three-stage methodology—“theory-driven, indicator screening, and empirical testing”—to develop a comprehensive evaluation system for higher education and regional innovation capacity (

Table 1. Spatiotemporal coupling relationship between the level of higher education and regional innovation ability in China.

Coupling System	Target Level	Standard Floor	Indicator Layer	Unit	Indicator Weights	Attributes
Modernization of the higher education system U1	Size of teaching	School size 0.0328	Number of higher education institutions	Quantity	0.0126	+
			Number of students enrolled in general higher education institutions	Ten thousand people	0.0173	+
		School conditions 0.0669	Expenditure on general higher education	Thousand yuan	0.0232	+
			Per capita expenditure on education	Yuan	0.0257	+
			Number of full-time teachers in ordinary schools	Ten thousand people	0.0161	+
			Student–teacher ratio in general colleges and universities	%	0.0065	+
			Floor space of general colleges and universities	M2	0.0163	+
	Quality of education	Talent cultivation 0.0923	Number of undergraduate students	Ten thousand people	0.0160	+
			Number of graduate students	Ten thousand people	0.0268	+
			Number of graduates from general higher education institutions	Ten thousand people	0.0187	+
			Number of scientists and engineers in higher education	Units	0.0161	+
		Social benefits 0.1219	Number of technological inventions and patents	Units	0.0443	+
			State-level Provincial University Science and Technology Parks	Units	0.0437	+
			Conversion rate of applied research and development results in higher education	%	0.0239	+
	Educational structure	Flow structure 0.1343	Number of educational research universities	Units	0.0126	+
			Value of fixed assets in higher education	CNY ten thousand	0.0203	+
			Number of research and development topics in higher education	Units	0.0225	+
			Researchers and developers in higher education	Units	0.0204	+
			Higher education research and development expenditures	CNY ten thousand	0.0388	+
Growing regional innovation capacity U2	Innovative foundations	Public foundation 0.0384	GDP per capita	CNY ten thousand per person	0.0246	+
			Per capita disposable income of urban and rural residents	CNY ten thousand per person	0.0267	+
			Library holdings per capita	Copies per person	0.0335	+
			Internet broadband access ports per capita	Units	0.0259	+
		Innovative resources 0.0923	Scientific Research and Development Organization	Units	0.0260	+
			Financial science and technology expenditures	CNY 100 million	0.0411	+
	Innovation capacity	Input capacity 0.0625	R&D staff ratio	%	0.0381	+
			Intensity of R&D expenditures	%	0.0259	+
			Patents granted per capita	Cases per 10,000 people	0.0553	+
		Output capacity 0.3043	Output value of high-tech industries	Billion	0.0422	+
			Number of high-tech enterprises	Units	0.0552	+
			Total investment by foreign-invested enterprises	USD one million	0.0556	+
			Technology market turnover	Billion	0.0878	+
	Innovation potential	Financial potential 0.0243	Growth rate of fiscal science and technology expenditures	%	0.0072	+
			Growth rate of R&D inputs	%	0.0051	+
		Manpower potential 0.0300	Growth rate of students enrolled in general higher education	%	0.0127	+
			Growth rate of the number of research institutions	%	0.0153	+

Note: “+” indicates a positive indicator.

) [29,30,31]. The “variables” refer to the evaluation indicators used to measure the performance of the higher education and regional innovation capacity subsystems. The higher education subsystem comprises 3 target levels, 5 criterion levels, and 19 indicator levels, whereas the regional innovation capacity subsystem includes 3 target levels, 6 criterion levels, and 17 indicator levels. Indicator weights are determined using the entropy weight method, and weights for each criterion level are also provided. Data are drawn from the China Statistical Yearbook, China Education Statistical Data, China Education Expenditure Statistical Yearbook, China Science and Technology Statistical Yearbook, China Population and Employment Statistical Yearbook, China Torch Program Statistical Yearbook, the National Bureau of Statistics, the Statistical Analysis Database of Scientific Research Achievements of Chinese Universities, and various provincial statistical yearbooks from 2010 to 2022. A robust data verification mechanism is implemented to ensure the continuity, completeness, and comparability of all collected datasets.

3.3. Research Methods

3.3.1. Entropy Weight Method

Given the inherent complexity and multi-dimensionality of the evaluation system for higher education and regional innovation, this study adopts the entropy weight method—a technique well-regarded for its robustness in handling multi-level, multi-indicator decision-making problems. This method offers high applicability and objectivity in multi-indicator contexts, effectively minimizing the influence of subjective biases on final outcomes [32,33]. The computation proceeds as follows:

Step one involves standardizing the raw data. Because indicators differ in scale and units, standardization or normalization is applied to eliminate dimensional inconsistencies, thereby ensuring data comparability and resolving heterogeneity among variables. Standardization is performed according to Equation (1).

$$X_{ij}={\displaystyle \frac{x_{ij}-\min (x_{ij})}{\max (x_{ij})-\min (x_{ij})}}$$

(1)

Here, x_ij denotes the original observed value of the indicator, as shown in the Supplementary Materials, while X_ij refers to its normalized counterpart.

Subsequently, the entropy values and corresponding weights of each indicator are computed using Equation (2).

$$\begin{array}{l}\left\{\begin{array}{l}{Y}_{\mathrm{ij}}={\displaystyle \frac{X_{ij}}{{\displaystyle {\sum }_{i=1}^m{X}_{ij}}}}\\ e_j=-\mathcal{l}n{\displaystyle \frac{1}{m}}{\displaystyle \sum _{i=1}^m{Y}_{ij}\mathcal{l}n{Y}_{ij}}\\ d_j=1-e_j\\ w_j={\displaystyle \frac{d_j}{{\displaystyle {\sum }_{j=1}^n{d}_j}}}\end{array}\right.\\ \end{array}$$

(2)

In this context, Y_ij represents the characteristic weight, e_j denotes the entropy value of the j-th indicator, d_j indicates its information utility, and w_j corresponds to its final computed weight.

Finally, a comprehensive evaluation function is established to quantify the integrated performance score of each subsystem, calculated via Equation (3). A higher composite score reflects a more advanced or favorable state of subsystem development.

$${\mathrm{U}}_{\mathrm{i}}={\displaystyle {\sum }_{\mathrm{j}=1}^m{w}_{\mathrm{j}}}{X}_{ij}$$

(3)

In light of the constraint relationship between higher education and regional innovation capacity, if U₁ < U₂, it signals a lag in higher education development; if U₁ > U₂, it reflects a lag in regional innovation; and when U₁ = U₂, it suggests that both systems are advancing in synchrony. In practice, exact equality (U₁ = U₂) does not occur. When |U₁ − U₂| < 0.05, it is considered to indicate synchronized development capacity, meaning that higher education and regional innovation capacity are advancing in parallel [34].

To further address concerns that entropy weighting may undervalue low-variance yet important indicators, we conducted three robustness checks: (1) Using panel data from 2010 to 2022, we recalculated the weights annually. The coefficient of variation for each indicator’s weight series remained below 0.18, and the average weight assigned to low-variance indicators was never below 0.034. (2) Replacing min-max normalization with Z-score standardization yielded correlations of 0.98 and 0.97 between the recalculated and benchmark U₁ and U₂ scores, respectively. (3) Imposing a lower-bound weight of 0.05 on low-variance indicators resulted in a Spearman rank correlation exceeding 0.95 between the revised and benchmark indices, and the proportion of provinces with |U₁ − U₂| < 0.05 changed by merely 2%. These results confirm the robustness of our primary findings.

3.3.2. Coupling Coordination Model

Analyzing coupling coordination relationships offers valuable insights into systemic imbalance and facilitates mutual alignment and harmonious development across systems and their constituent elements. In recent years, the coupling coordination degree has gained widespread application in the social sciences, emerging as a key analytical tool for examining inter-system interactions and intra-system dynamics [35,36]. Building upon theoretical insights and the prior literature on the interplay between higher education and regional innovation, this study develops a quantitative model to measure both the coupling degree and the coupling coordination index. The core formulation is presented as follows:

$$C=2\sqrt{{\displaystyle \frac{U_1\times U_2}{U_1+U_2}}}$$

(4)

$$\left\{\begin{array}{l}T=\alpha U_1\times \beta U_2\\ D=\sqrt{C\times T}\end{array}\right.$$

(5)

Here, U₁ and U₂ denote the composite development indices of higher education and regional innovation capacity, respectively. C is the coupling degree, and the value of C is in the range of [0, 1]. When C = 0, the coupling degree is the lowest, and there is no correlation among the elements, which are in a state of disordered development. When C = 1, the coupling degree reaches the maximum, and the elements achieve a benign resonance [37]. T is the comprehensive coordination index of the two subsystems. D is the coordinated coupling degree, and α and β are the weights of the two coupling systems. Following the research of Sun et al. (2023) [38], we assign equal weights to higher education and regional innovation development, setting α = β = 0.5, based on the premise that both systems play equally vital roles in the coupling and coordination process. The coordination degree is categorized into 3 major categories, 5 subcategories, and 15 grades (

Table 2. Criteria for classifying the type of coupling coordination degree.

Typology	Sub-Genre		Subclass	Type of Coupling Coordination	Characterization Between Subsystems and Elements
	D-Value	Sub-Genre
Type of disorder	(0–0.2]	Disproportionate	U1 − U2 > 0.1	Inconsistency—lagging innovation capacity	Interactions and effects are not significant
			U2 − U1 > 0.1	Inconsistency—higher education lagging behind
			−0.1 ≤ |U1 -U2 | ≤ 0.1	Disproportionate
	(0.2–0.4]	Sue for harmonization	U1 − U2 > 0.1	Barely coordinated—lagging innovation capacity	Negligible interactions and affective relationships
			U2 − U1 > 0.1	Barely coordinated—higher education lagging behind
			−0.1 ≤ |U1 − U2| ≤ 0.1	Sue for harmonization
Excess type	(0.4–0.6]	Basic coordination	U1 − U2 > 0.1	Basic harmonization—lagging innovation capacity	Certain relationships of interaction and influence
			U2 − U1 >0.1	Basic harmonization—higher education lag
			−0.1 ≤ |U1 − U2 | ≤ 0.1	Basic coordination
Type of coordination	(0.6–0.8]	Good coordination	U1 − U2 >0.1	Good coordination—lagging innovation capacity	Strong interaction and influencing relationships
			U2 − U1 >0.1	Well-coordinated—higher education lagging behind
			−0.1 ≤ |U1 − U2 | ≤ 0.1	Good coordination
	(0.8–1.0]	Senior coordination	U1 − U2 >0.1	Advanced coordination—lagging innovation capacity	Very strong interaction and influence relationships
			U2 − U1 >0.1	Advanced coordination—lag in higher education
			−0.1 ≤ |U1 − U2 | ≤ 0.1	Senior coordination

).

3.3.3. Dagum Gini Coefficient

This paper examines the link between higher education and regional innovation capabilities, aiming to quantify disparities across regions. Utilizing Dagum’s [39,40] decomposition method, the Dagum Gini coefficient is broken down into intra-regional differences (G_W), inter-regional differences (G_nb), and hyper-variable density (G_t), all conforming to Equation (6).

$$G=G_W+G_{nb}+G_t$$

(6)

The calculation formula of the Dagum Gini coefficient is as shown in (7).

$$\mathrm{G}={\displaystyle \frac{{\displaystyle \sum _{\mathrm{j}=1}^k{\displaystyle \sum _{h=1}^k{\displaystyle \sum _{i=1}^{n_j}{\displaystyle \sum _{r=1}^{n_h}\left|y_{ji}-y_{hr}\right|}}}}}{2n^2\overline{y}}}$$

(7)

Here, k is the total number of divided regions, j and h represent regions, n is the total number of provinces, i and r represent provinces, n_j and n_h represent the number of provinces in the j-th and h-th regions, respectively, y_ji and y_hr are the innovation capabilities of high-tech industries in the i-th province of region j and the r-th province of region h, respectively, and $\overline{\mathrm{y}}$ represents the mean value of the innovation capabilities of high-tech industries of all provinces.

$$G_w={\displaystyle \sum _{j=1}^k{G}_{jj}}{p}_j{s}_j$$

(8)

$$G_{nb}={\displaystyle \sum _{j=2}^k{\displaystyle \sum _{h=1}^{j-1}{G}_{jh}(p_j{s}_h+p_h{s}_j)}}{D}_{jh}$$

(9)

$$G_t={\displaystyle \sum _{j=2}^k{\displaystyle \sum _{h=1}^{j-1}{G}_{jh}(p_j{s}_h+p_h{s}_j)(1-D_{jh})}}$$

(10)

Here, $p_j={\displaystyle \raisebox{1ex}{$n_j$}\!\left/ \!\raisebox{-1ex}{$n$}\right.}$, $s_j={\displaystyle \raisebox{1ex}{$n_j\overline{y}$}\!\left/ \!\raisebox{-1ex}{$n{\overline{y}}_j$}\right.}$, j =1,2,…,k, $p_h={\displaystyle \raisebox{1ex}{$n_h$}\!\left/ \!\raisebox{-1ex}{$n$}\right.}$, $s_h={\displaystyle \raisebox{1ex}{$n_h\overline{y}$}\!\left/ \!\raisebox{-1ex}{$n{\overline{y}}_h$}\right.}$, h = 1,2,…,k−1; $D_{jh}=(d_{jh}-p_{jh})(d_{jh}+p_{jh})$ represents the relative influence of the higher education and innovation capabilities of regions j and h.

$$G_{jj}={\displaystyle \frac{{\displaystyle \sum _{i=1}^{n_j}{\displaystyle \sum _{r=1}^{n_j}\left|y_{ji}-y_{jr}\right|}}}{2n^2{\overline{y}}_j}}$$

(11)

$$G_{jh}={\displaystyle \frac{{\displaystyle \sum _{i=1}^{n_j}{\displaystyle \sum _{r=1}^{n_h}\left|y_{ji}-y_{hr}\right|}}}{n_j{n}_h({\overline{y}}_j+{\overline{y}}_h)}}$$

(12)

Here, G_jj represents the Dagum Gini coefficient within region j, and G_jh represents the Dagum Gini coefficient between regions j and h.

3.3.4. Kernel Density Estimation

Kernel density estimation, a non-parametric method for probability density estimation, is invaluable in spatial statistical analysis. By generating a smooth, continuous probability density function, it effectively reveals the static distribution of a subject at a specific time and captures the dynamic evolution of its spatial pattern. This approach mitigates the impact of local outliers through kernel function smoothing, thereby enhancing the analysis of spatial heterogeneity [41]. Its non-parametric nature enables precise identification of collaborative evolution trends across regions without assuming a predefined distribution. Furthermore, multi-dimensional kernel density estimation incorporates spatiotemporal data, presenting both the global distribution of and local variation in the subject, serving as a visual tool for complex spatial correlation analysis. The calculation formula is as follows:

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

(13)

Here, n is the number of observations, and h is the bandwidth, which is calculated using Silverman’s rule of thumb. The function k(•) is the kernel function, and x_i represents independently and identically distributed observations. The Gaussian function selected in this paper is as follows:

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

(14)

3.3.5. Moran’s Index

Moran’s index is a statistical tool for assessing spatial autocorrelation. This study employs Moran’s index to examine the spatial distribution characteristics of the “higher education–innovation development” system’s coupling and coordination degree. Moran’s index is categorized into global and local indices [42,43]. The global Moran’s index assesses the concentration or dispersion of attribute values among neighboring units within a region, ranging from −1 to 1. A global Moran’s I value of less than indicates negative spatial correlation between higher education and regional innovation development, while a value greater than suggests positive correlation. A value of signifies no correlation between the two subsystems. The local Moran’s index identifies spatial clustering of attribute values within local units and across the entire study area, reflecting spatial agglomeration, consistency, or random distribution in the coupling and coordination between subsystems. A positive local Moran’s index indicates “high–high” or “low–low” clustering between regions, while a negative index suggests “high–low” or “low–high” clustering. The calculation formula for the Moran’s index is as follows:

$$\mathrm{Globemoran}’\mathrm{s} \mathrm{I}={\displaystyle \frac{{\displaystyle {\sum }_{\mathrm{i}=1}^{\mathrm{n}}{(\mathrm{D}}_{\mathrm{i}}-\overline{\mathrm{D}} ){\displaystyle {\sum }_{\mathrm{j}=1}^{\mathrm{n}}{w}_{ij}(D_j-\overline{D})}}}{S_{ij}^2{\displaystyle {\sum }_{i=1}^n{\displaystyle {\sum }_{j=1}^n{w}_{ij}}}}}$$

(15)

$$\mathrm{Local} \mathrm{moran}’\mathrm{s} \mathrm{I}={\displaystyle \frac{(D_i-\overline{D}){\displaystyle {\sum }_{\mathrm{j}=1}^n{w}_{ij}(D_j-\overline{D})}}{S_{\mathrm{ij}}^2}}$$

(16)

Here, I is the Moran’s index, n is the number of spatial units, S_ij² is the variance in the coupling coordination degree, D_i and D_j are the coupling coordination degrees of spatial units i and j, respectively, w_ij is the spatial weight coefficient matrix, and $\overline{\mathrm{D}}$ is the average value of the coupling coordination degree.

3.3.6. Barrier Degree Modeling Constructs

To address the exploration of bottleneck factors limiting the modernization of higher education and regional innovation capacity across China’s 31 provinces, municipalities, and autonomous regions, and to foster high-quality development in each region, it is essential to identify the primary obstacles hindering the modernization of higher education and regional innovation capacity. This paper introduces an Obstacle Degree Model based on the evaluation of the coupling and coordination of higher education modernization and regional innovation capacity. The model is designed to diagnose and analyze the obstacle factors of higher education modernization and regional innovation capacity in provinces, cities, autonomous regions, and regions [44].

The Obstacle Degree Model primarily involves three measurement indicators: Factor Contribution Degree, Indicator Deviation Degree, and Obstacle Degree. The Factor Contribution Degree (F_j) represents the impact degree of each individual indicator on the overall goal, which is the weight Wj of the j-th indicator on the overall goal. The Indicator Deviation Degree (P_ij) is the difference between the standardized value of the individual indicator and 1, reflecting the gap between each individual indicator and the system goal. The Obstacle Degree (Oj) indicates the impact degree of the j-th indicator on the system; the larger the value, the greater the impact of the indicator on the system [45]. The specific calculation formulas are as follows:

$$\begin{array}{l}{F}_j=W_j\\ P_{ij}=1-X_{ij}^{\prime }\\ O_j={\displaystyle \frac{F_j\times P_{ij}}{{\displaystyle {\sum }_{j=1}^n(F_j\times P_{ij})}}}\times 100\%\end{array}$$

(17)

where X_ij’ is the standardized value of a single indicator calculated by the extreme value method and n is the number of single indicators.

4. Results

4.1. Analysis of Higher Education and Regional Innovation Development Level

According to Formulas (1)–(3), the comprehensive development index U₁ of the higher education system can be calculated. The spatial and temporal distribution differences are shown in Figure 2a [46]. It is clear from the figure that higher education has made significant progress at the national level, with the higher education development index jumping from 0.0647 in 2010 to 0.1253 in 2022, an 85.91% increase, marking China’s transition from elite to mass higher education. Regionally, a gradient pattern emerges: the eastern region leads consistently, the central region has shown significant progress since 2014, the northeast region’s growth rate of 51.30% lags behind the national average, and the western region remains in a developmental trough. This spatial differentiation can be attributed to three key mechanisms:

Firstly, policy guidance and institutional innovation propel overall development. Structural reforms in the educational supply system have led to the expansion of universities to 3074, increased full-time faculty to over 2.0749 million, and raised the gross enrollment rate to 60.2% [47], establishing a new paradigm that balances educational scale and quality.

Secondly, regional economic geography influences development disparities. The eastern region outpaces others in R&D investment and technology market turnover, creating a virtuous cycle of upgrading, talent demand, and educational innovation. In contrast, the western region struggles with below-average per capita educational fiscal expenditure and a lower density of higher education institutions, limiting its ability to attract and retain resources.

Thirdly, regional development drivers have been redefined. The central region has experienced a year-on-year increase in higher education investment, facilitated by industrial transfers from the east. Meanwhile, the northeastern region lags in aligning its higher education system with emerging industries, due to the lock-in effect of traditional industries.

These dynamics exhibits a “convergence club” characteristic: Despite historical disadvantages, the central and western regions achieved growth rates of 90.49% and 95.17%, respectively. Their catch-up mechanism is driven by the gradient transfer effect of new educational infrastructure under the “dual-circulation” strategy. However, continuous brain drain has significantly weakened higher education momentum in the northeastern region.

Overall, this pattern of unbalanced yet progressively coordinated development underscores the strategic effectiveness of prioritizing efficiency in higher education resource allocation while maintaining equity considerations at a specific historical stage [48].

Similarly, the comprehensive development index U₂ of the regional innovation capability system can be calculated according to Formulas (1) to (3), and its spatiotemporal distribution differences are shown in Figure 2b. Over the past 13 years, the national innovation capability index has significantly increased from 0.0409 to 0.1136, with an average annual growth of 4.98%, underscoring the success of the innovation-driven development strategy. A distinct “three-tier gradient” pattern characterizes the regional innovation landscape: the eastern region has maintained its leading position, the central region experienced substantial acceleration after 2016, and the western region, leveraging its late-mover advantage, surpassed the northeastern region in innovation index rankings by 2021. In contrast, the northeastern region has struggled to shift its growth drivers, recording the lowest average annual growth rate of 3.01% and showing a downward trend since 2016. The spatial evolution of regional innovation capabilities can be explained by three key mechanisms:

Firstly, the institutional development of the national innovation system has played a pivotal role. Increased R&D investment and enhanced innovation infrastructure have fostered an ecosystem characterized by “policy guidance–factor agglomeration–achievement transformation.”

Secondly, the regional allocation of innovation resources shows significant disparities. The eastern region, accounting for over 60% [49] of high-tech enterprises and 50% [50] of national patent authorizations, has created a virtuous cycle of market demand, technological R&D, and industrial upgrading. In contrast, the western region lags significantly behind the eastern and central regions, remaining at a relatively low level in terms of innovation resources.

Thirdly, the interplay between path dependence and institutional change is evident in Northeast China. The region’s longstanding reliance on traditional industries has led to an innovation lock-in, resulting in technology transfer efficiency significantly below the national average. However, a notable convergence effect is observed in the regional innovation system. The central and western regions have achieved annual catch-up growth of 4.26%, driven by institutional innovations such as improved technology transfer mechanisms and the optimization of local innovation environments.

Meanwhile, Northeast China, under the comprehensive revitalization strategy, is forging a new path for transforming old industrial bases by reconstructing the innovation chain linking science and education resources, industrial foundations, and institutional supply. This pattern of differential yet coordinated development underscores the dynamic adaptability of China’s regional innovation system within the framework of a unified national market [51].

4.2. Analysis of Results of Coupling Coordination Degree

Using Formulas (4) and (5), we calculated the coupling and coordination degrees between higher education and regional innovation capabilities across 31 provinces, municipalities, and autonomous regions in China from 2010 to 2022.

4.2.1. Analysis of Regional Coupling and Coordination Level

Figure 3 illustrates a consistent upward trend in the coupling and coordination between higher education and regional innovation capacity over time. Nationwide, excluding the northeast, the coupling coordination degrees exhibit an average annual growth rate of approximately 2%, indicating a steady enhancement in the synergy between these two systems.

This upward trend signifies that as higher education continues to modernize, its integration with regional innovation capabilities becomes stronger. This enhancement is evident not only in the quantitative growth rates but also in the qualitative improvement in interactions, which have become increasingly robust and effective.

At the regional level, the eastern region has consistently outperformed both the national average and other regions in terms of coupling coordination since 2010, maintaining an annual growth rate of 2.06%. Its coupling status has progressed from marginal coordination to a stable state of basic coordination. The central region follows closely, with a higher annual growth rate of 2.22%. Although its trend closely aligns with the national average, its pace of growth has been more rapid, and since 2021, it has notably surpassed the national benchmark.

The western region has remained at the lower end of the spectrum in terms of coupling coordination but has demonstrated steady progress, with an annual growth rate of 1.86%. It has transitioned from a state of dis-coordination to marginal coordination. In contrast, the northeastern region has shown the slowest growth, with an average annual increase of only 1.24%, and even experienced a regression between 2017 and 2018. Its coordination level has persistently hovered at a marginally coordinated stage.

4.2.2. Analysis of Coupling Coordination Level by Province

Data from

Table 3. The coupling and coordination degree between higher education and regional innovation ability in China from 2010 to 2022.

Region	2010		2016		2022
	D	Type of Coupling Coordination	D	Type of Coupling Coordination	D	Type of Coupling Coordination
Shanghai	0.3551	Barely coordinated	0.3996	Barely coordinated	0.6904	Good coordination—lagging higher education
Jiangsu	0.3630	Barely coordinated—lagging innovation ability	0.4413	Basic coordination—lagging innovation ability	0.8664	Advanced coordination
Zhejiang	0.3096	Barely coordinated	0.3682	Barely coordinated	0.4796	Basic coordination—lagging higher education
Anhui	0.2315	Barely coordinated	0.2816	Barely coordinated	0.3811	Barely coordinated
Jiangxi	0.1983	Disproportionate	0.2505	Barely coordinated	0.3243	Barely coordinated
Shandong	0.2872	Barely coordinated—lagging innovation ability	0.3431	Barely coordinated—lagging innovation ability	0.4597	Basic coordination
Fujian	0.2337	Barely coordinated	0.2789	Barely coordinated	0.3559	Barely coordinated
Beijing	0.3834	Barely coordinated	0.4672	Basic coordination	0.8807	Advanced coordination
Tianjin	0.2501	Barely coordinated	0.2987	Barely coordinated	0.3444	Barely coordinated—lagging higher education
Shanxi	0.1855	Disproportionate	0.2135	Barely coordinated	0.2629	Barely coordinated
Anhui	0.2158	Barely coordinated—lagging innovation ability	0.2668	Barely coordinated—lagging innovation ability	0.3413	Barely coordinated
Inner Mongolia	0.1695	Disproportionate	0.2012	Barely coordinated	0.2313	Barely coordinated
Henan	0.2330	Barely coordinated—lagging innovation ability	0.2792	Barely coordinated—lagging innovation ability	0.3627	Barely coordinated—lagging innovation ability
Hubei	0.2584	Barely coordinated—lagging innovation ability	0.3169	Barely coordinated—lagging innovation ability	0.4124	Basic coordination—lagging innovation ability
Hunan	0.2309	Barely coordinated—lagging innovation ability	0.2700	Barely coordinated—lagging innovation ability	0.3701	Barely coordinated
Guangdong	0.3306	Barely coordinated	0.4112	Basic coordination	0.6563	Good coordination—lagging higher education
Guangxi	0.1879	Disproportionate	0.2215	Barely coordinated	0.2853	Barely coordinated
Hainan	0.1379	Disproportionate	0.1656	Disproportionate	0.2558	Barely coordinated—lagging higher education
Chongqing	0.2176	Barely coordinated	0.2594	Barely coordinated	0.3195	Basic coordination
Sichuan	0.2450	Barely coordinated—lagging innovation ability	0.2927	Barely coordinated—lagging innovation ability	0.3913	Barely coordinated—lagging innovation ability
Guizhou	0.1518	Disproportionate	0.1980	Disproportionate	0.2213	Barely coordinated
Yunnan	0.1839	Disproportionate	0.2110	Barely coordinated	0.2370	Barely coordinated—lagging innovation ability
Tibet	0.0849	Disproportionate	0.1093	Disproportionate	0.1423	Disproportionate
Shaanxi	0.2442	Disproportionate—lagging innovation ability	0.2913	Barely coordinated—lagging innovation ability	0.3715	Barely coordinated—lagging innovation ability
Gansu	0.1708	Disproportionate	0.1875	Disproportionate	0.2307	Barely coordinated
Qinghai	0.1401	Disproportionate	0.1489	Disproportionate	0.1864	Disproportionate
Ningxia	0.1337	Disproportionate	0.1608	Disproportionate	0.2100	Barely coordinated
Xinjiang	0.1624	Disproportionate—lagging innovation ability	0.1903	Disproportionate	0.2535	Barely coordinated
Heilongjiang	0.2214	Barely coordinated	0.2471	Barely coordinated—lagging innovation ability	0.2895	Barely coordinated—lagging innovation ability
Jilin	0.2027	Barely coordinated	0.2380	Barely coordinated	0.2626	Barely coordinated
Liaoning	0.2628	Barely coordinated—lagging innovation ability	0.2979	Barely coordinated—lagging innovation ability	0.3389	Barely coordinated—lagging innovation ability

reveal a marked transformation in the coupling coordination between higher education and regional innovation capacity across Chinese provinces during the 13-year span from 2010 to 2022. The increasing number of cities achieving at least a basic level of coordination, coupled with the declining number categorized as uncoordinated, indicates a clear national trend toward improved synergy between higher education and regional innovation systems.

Cities with lower coupling coordination rankings are predominantly situated in the western and northeastern regions, which is likely attributable to a combination of geographic constraints and comparatively weaker economic development. These structural limitations have likely impeded the effective integration of higher education systems with regional innovation strategies in many western provinces.

Interestingly, Hainan, despite its location in the economically dynamic eastern region, underperforms in terms of both higher education and regional innovation capacity, falling behind several central and western provinces. This underperformance may stem from insufficient investment in higher education, suboptimal innovation ecosystems, and a weak alignment between academic institutions and regional economic priorities.

In contrast, Shaanxi and Sichuan, though situated in the western region, demonstrate stronger performance in coupling coordination than many provinces in the eastern and central regions. Their success can be largely attributed to proactive higher education reforms, substantial R&D investment, robust academia–industry collaborations, and the effective implementation of innovation-driven development strategies.

4.3. Temporal Trends in Coupling Coordination Levels

To assess the temporal evolution of the coupling coordination degree, we employed the kernel density estimation method [52]. Figure 4 illustrates the three-dimensional kernel density distribution of the coupling coordination degree between higher education and regional innovation capacity at both national and regional levels. From 2010 to 2022, the national kernel density curve shifted rightward, indicating a steady national increase, consistent with previous findings. Concurrently, the curve’s main peak decreased in height and broadened, signifying a growing disparity in coupling coordination levels among provinces. The emergence of a rightward tail in the national curve suggests that some provinces exhibited significantly higher coordination degrees than others. This aligns with earlier analyses identifying Beijing, Shanghai, and Guangdong in the eastern region as outliers with superior performance, thus contributing to the rightward skew.

Figure 4b–e illustrate regional variations in the evolution of coupling coordination between higher education and regional innovation capacity across eastern, central, western, and northeastern regions from 2010 to 2022. The colour represents the value of the coupling coordination degree, yellow represents the high coupling coordination degree, and purple represents the low coupling coordination degree. The three-dimensional kernel density plots for all four regions exhibit a rightward shift, confirming an overall increase in regional coordination levels but also indicating widening internal disparities.

The eastern and central regions exhibited a unimodal distribution over the period. In the eastern region, the peak of the distribution consistently declined, signaling a gradual diffusion of coordination benefits among provinces. In contrast, the central region’s peak fluctuated, reflecting variability in coordination performance over time.

The western and northeastern regions, however, displayed multimodal distribution patterns. In the western region, a secondary, lower peak appeared on the right of the main peak, suggesting the early stages of convergence toward a unimodal distribution. Conversely, in the northeastern region, the presence of two comparable peaks suggests persistent divergence. The sudden peak in the kernel density of Northeast China around 2020 resulted from one province significantly outperforming the other two in terms of coordination degree.

Overall, this analysis reveals a spatial agglomeration effect in the eastern region, where internal disparities are gradually narrowing. In contrast, the central and western regions demonstrate weakening polarization, suggesting incremental progress toward balance. However, the northeastern region exhibits intensifying polarization, indicating a worsening internal imbalance in the coordination between higher education and regional innovation systems.

4.4. Spatial Correlation Analysis of Coupling Coordination

Spatial correlation techniques were employed using Stata 15.1 and ArcGIS 10.7 to visualize the spatial patterns of higher education and regional innovation capacity in China from 2010 to 2022. As shown in

Table 4. Results of the global spatial autocorrelation test of higher education and regional innovation ability from 2010 to 2022.

Year	Global Moran’s I	Standardized Normal Statistic (Z(I))	p-Value
2010	0.385	3.583	0.0003
2011	0.389	3.623	0.0003
2012	0.407	3.769	0.0002
2013	0.380	3.538	0.0004
2014	0.400	3.713	0.0002
2015	0.394	3.662	0.0003
2016	0.410	3.797	0.0005
2017	0.396	3.682	0.0002
2018	0.376	3.505	0.0005
2019	0.395	3.669	0.0002
2020	0.415	3.832	0.0001
2021	0.416	3.837	0.0001
2022	0.417	3.839	0.0001

, the p-values associated with the global Moran’s I index remain well below the 5% significance threshold throughout the study period, suggesting statistically significant spatial clustering for both higher education and regional innovation capacity.

The global Moran’s I values remained significantly positive at the 1% level throughout the study period, increasing from 0.385 in 2010 to 0.417 in 2022. This upward trend highlights a strengthening positive spatial autocorrelation between higher education and regional innovation capacity in China. In other words, provinces with high levels of higher education and innovation capacity tend to be geographically clustered, while those with lower levels similarly group together. However, slight declines observed in 2013, 2015, and 2017 suggest temporary slowdowns in the narrowing of spatial disparities in the coupling between the two systems.

As illustrated in Figure 5, from 2010 to 2022, most provinces and municipalities in China were concentrated in the first and third quadrants, reflecting a clear spatial clustering pattern of “high–high” and “low–low” associations. This spatial agglomeration implies that provinces with either low or high innovation capacity tend to cluster geographically, underscoring the substantial positive spillover influence of modern higher education on regional innovation. In essence, the more advanced a region’s higher education system, the stronger its corresponding innovation capacity. These findings reinforce the mutually reinforcing relationship between education and socioeconomic development, aligning with core national strategies such as innovation-driven growth and the recognition of talent as a core strategic resource. Consequently, accelerating the modernization of higher education is essential to advancing regional innovation and supporting sustained national economic and social development.

From 2010 to 2020, only a few provinces were located in the second quadrant, and none were in the fourth quadrant. This absence suggests that no province exhibited a high level of higher education modernization alongside a low level of regional innovation capacity. This further validates the synergistic nature of the relationship between the two systems. Additionally, it highlights that this relationship is not strictly linear but is affected by elements such as the policy environment, market demand, and resource allocation. Thus, advancing the coordinated development of higher education and regional innovation requires a comprehensive approach, incorporating multifaceted considerations to inform effective policy and strategic design.

4.5. Spatial Disparities in Coupling Coordination and Their Underlying Drivers

4.5.1. Overall Spatial Disparities

The preceding sections have identified clear regional heterogeneity in the coupling coordination between higher education and regional innovation systems. To further quantify the overall, subgroup, and source-specific disparities, this study utilizes panel data from 31 Chinese provinces spanning 2010 to 2022 and applies an enhanced Dagum Gini coefficient decomposition model. This structure-oriented decomposition framework not only broadens the scope of regional coordination measurement but also offers novel methodological insights from spatial econometrics to uncover systemic barriers to synergy.

As shown in

Table 5. Decomposition table of Dagum Gini coefficient.

Year	Dagum Gini Coefficient				Contribution Rate (%)
	G	Gw	Gnb	Gt	Gwr	Gnbr	Gtr
2010	0.173	0.038	0.114	0.021	22.04%	65.80%	12.16%
2011	0.167	0.037	0.108	0.022	22.10%	64.83%	13.07%
2012	0.172	0.037	0.114	0.021	21.68%	66.34%	11.98%
2013	0.175	0.038	0.113	0.024	21.76%	64.51%	13.74%
2014	0.176	0.039	0.115	0.022	21.97%	65.44%	12.59%
2015	0.171	0.038	0.113	0.021	21.88%	65.72%	12.40%
2016	0.176	0.038	0.117	0.020	21.82%	66.67%	11.52%
2017	0.177	0.038	0.117	0.021	21.74%	66.280%	11.98%
2018	0.178	0.039	0.118	0.022	21.76%	65.86%	12.38%
2019	0.176	0.037	0.120	0.020	21.03%	67.90%	11.07%
2020	0.177	0.036	0.125	0.016	20.56%	70.66%	8.78%
2021	0.180	0.038	0.125	0.017	21.01%	69.64%	9.35%
2022	0.184	0.039	0.127	0.018	21.06%	69.27%	9.67%

, the overall Dagum Gini coefficient decreased from 0.173 in 2010 to 0.167 in 2011, then increased to 0.176 in 2014, declined again in 2015, rose to 0.178 in 2018, and has since shown a consistent upward trend starting in 2019. The average annual Dagum Gini coefficient stands at 0.176, indicating notable spatial heterogeneity in provincial-level coupling coordination, with disparities steadily widening over time.

The average annual Dagum Gini coefficients for the intra-regional, inter-regional, and trans-variation components were 0.038, 0.117, and 0.020, respectively. The corresponding average contribution rates were 21.57%, 66.84%, and 11.59%. These results suggest that inter-regional disparities are the dominant contributors to the overall variation. Both the intra-regional and trans-variation components exhibited declining trends, with average annual declines of 0.168% and 7.899%, respectively. In contrast, the contribution rate of inter-regional disparities steadily increased, with an average annual growth rate of 10.92%.

4.5.2. Analysis of Regional Spatial Disparities

Figure 6 presents the intra-regional Dagum Gini coefficient trends across China’s four major regions from 2010 to 2022. Based on annual averages, a clear gradient in disparity is observed: east (0.143) > west (0.137) > central (0.063) > northeast (0.055). The eastern region persistently exhibited the highest intra-regional disparity, with values fluctuating between 0.135 and 0.150—a range of 11.11%—primarily driven by significant interprovincial development imbalances. The western region followed, with an average of 0.137 and a modest annual growth rate of 0.57%. While the central region recorded the lowest average intra-regional Gini value (0.063), it experienced the fastest rate, rising from 0.058 to 0.073 over the period, with an average annual growth of 1.94%. In contrast, the northeastern region not only maintained the lowest disparity level (0.055) but also exhibited a downward trend, with an average annual decrease of 0.14%.

Figure 6 illustrates the trends in inter-regional disparities as measured by the Dagum Gini coefficient. The greatest disparity was observed between the eastern and western regions, with an average coefficient of 0.269. The second highest was between the eastern and northeastern regions, with an average value of 0.182. Notably, since 2016, this disparity has surpassed other pairings to become the second most unequal. Other notable averages include east–central (0.165), central–west (0.155), and west–northeast (0.141), with the smallest disparity found between the central and northeastern regions (0.074).

Regarding disparity trends, the gaps between the central–eastern and eastern–western regions showed relative stability with limited fluctuations. The northeast–west gap has generally narrowed. In contrast, other pairings have seen widening disparities, especially between the central and northeastern regions, which recorded the fastest increase at 61.54%, followed by eastern–northeastern (31.45%) and central–western (23.29%) pairings. The slowest widening occurred between the eastern and western regions, with an average annual growth rate of only 0.46%.

4.6. Analysis of Obstacle Factors

The Obstacle Degree Model provides insights into the primary barriers within the higher education and regional innovation subsystems across 31 Chinese provinces, municipalities, and autonomous regions for the years 2010, 2016, and 2022, as detailed in

Table 6. Barrier degree of each barrier factor in 31 provinces and cities in China, 2010–2022.

Systems	Dividing Factor	Degree of Obstruction/%
		2010	2016	2022
Higher Education Modernization	Number of higher education institutions	0.0087	0.0087	0.0092
	Number of students enrolled in general higher education institutions	0.0178	0.0175	0.0170
	Expenditure on general higher education	0.0282	0.0267	0.0237
	Per capita education expenditure	0.0312	0.0309	0.0322
	Number of full-time teachers in ordinary schools	0.0155	0.0150	0.0149
	Student–teacher ratio in general colleges and universities	0.0057	0.0070	0.0059
	Floor space of general colleges and universities	0.0147	0.0143	0.0135
	Number of undergraduate students	0.0154	0.0143	0.0141
	Number of graduate students	0.0327	0.0332	0.0325
	Number of graduates from general higher education institutions	0.0155	0.0143	0.0141
	Number of scientists and engineers in higher education	0.0196	0.0193	0.0191
	Number of technological inventions and patents	0.0585	0.0591	0.0538
	State-level Provincial University Science and Technology Parks	0.0502	0.0508	0.0551
	Conversion rate of applied research and development results in higher education	0.0245	0.0289	0.0335
	Number of educational research universities	0.0122	0.0093	0.0098
	Value of fixed assets in higher education	0.0233	0.0210	0.0211
	Number of research and development topics in higher education	0.0267	0.0253	0.0220
	Researchers and developers in higher education	0.0243	0.0240	0.0217
	Higher education research and development expenditures	0.0498	0.0498	0.0465
Regional Innovation Capacity	GDP per capita	0.0220	0.0220	0.0219
	Per capita disposable income of urban and rural residents	0.0245	0.0241	0.0251
	Library holdings per capita	0.0293	0.0305	0.0341
	Internet broadband access ports per capita	0.0239	0.0197	0.0168
	Scientific Research and Development Organization	0.0254	0.0253	0.0227
	Financial science and technology expenditures	0.0394	0.0388	0.0380
	R&D staff ratio	0.0345	0.0344	0.0358
	Intensity of R&D expenditures	0.0212	0.0211	0.0227
	Patents granted per capita	0.0527	0.0521	0.0489
	Output value of high-tech industries	0.0408	0.0410	0.0416
	Number of high-tech enterprises	0.0513	0.0541	0.0596
	Total investment by foreign-invested enterprises	0.0550	0.0577	0.0626
	Technology market turnover	0.0867	0.0890	0.0852
	Growth rate of fiscal science and technology expenditures	0.0042	0.0049	0.0055
	Growth rate of R&D inputs	0.0027	0.0033	0.0050
	Growth rate of students enrolled in general higher education	0.0049	0.0059	0.0055
	Growth rate of the number of research institutions	0.0070	0.0064	0.0092

. The results indicate minimal changes in the Obstacle Degrees over time. In the higher education systems, the key constraints lie in research input and output, with technological inventions and patents, university-affiliated science parks, and university R&D expenditures identified as the top three obstacles.

This finding suggest that while China’s higher education system is extensive and globally competitive in terms of scale and enrollment, the main hurdles lie not in quantity-related indicators such as the number of institutions, student enrollment, or student–teacher ratios but in the quality of education and the translation of research into practical applications. Addressing these obstacles by enhancing research quality, improving technology transfer efficiency, and strengthening university–industry collaboration will be essential for further development and more effective integration with regional innovation strategies.

For the regional innovation subsystem, the major bottlenecks are associated with investment intensity, foreign trade activity, and technological advancement. The leading impediments include the volume of technology market transactions, total foreign enterprise investment, and per capita patent authorizations. Since 2010, in response to intensifying global technological competition, both national and regional governments in China have implemented a range of innovation-driven policy initiatives. These increased R&D investment, the creation of supportive innovation ecosystems and innovation ecosystems, and the promotion of academia–industry collaboration. These policies have strengthened the institutional framework for innovation and improved the structural resilience of regional innovation systems.

Traditional indicators such as per capita GDP, number of research institutes, and total science and technology expenditures are no longer the dominant obstacles. Instead, emphasis has shifted toward expanding market demand, fostering dynamic innovation ecosystems, and facilitating knowledge diffusion, which have become critical for enhancing regional innovation performance.

Table 7. Level of barriers to higher education modernization systems by region in China, 2022.

Region	Scale of Education	School Conditions	Cultivation of Talent	Social Benefit	Mobile Structure
Eastern	0.0636	0.2075	0.1816	0.3058	0.2415
Central	0.0370	0.1991	0.1531	0.3400	0.2707
Western	0.0594	0.1893	0.1764	0.2978	0.2769
Northeastern	0.0570	0.2062	0.1686	0.2933	0.2749
Average	0.0562	0.1987	0.1728	0.3081	0.2641

, based on 2022 data, evaluates the barrier intensities of core dimensions within the higher education subsystem across various regions. The findings reveal that social benefits and flow structure are the primary bottlenecks impeding higher education development. Social benefits notably hinder progress in both eastern and western regions, underscoring the shared challenges faced by higher education institutions in converting knowledge innovation into regional economic and social growth drivers. This indicates that, irrespective of the eastern region’s economic advancement or the western region’s limited resources, universities must overcome significant barriers to effectively address regional innovation demands and facilitate the local transformation and industrialization of scientific and technological advancements. In terms of flow structure, the scale, structural configuration, and flow efficiency of innovative resources—such as talent, funding, projects, and high-end platforms—are critical constraints. The investment scale, concentration of high-level research platforms, and the volume and intensity of R&D activities significantly limit the system’s ability to achieve coupling and coordination [53].

The analysis of obstacle factors reveals a strong correlation with the regional distribution of the coupling coordination degree, underscoring the interrelation between higher education and regional innovation capabilities. In the central and eastern regions, despite abundant higher education resources and robust innovation capabilities, the conversion of social benefits (such as the transformation rate of scientific and technological achievements) does not fully align with their innovation input and output. A structural imbalance in resource flow (including allocation efficiency and the responsiveness of top-tier platforms to regional needs) emerges as a key bottleneck, hindering their coupling coordination from reaching a higher level. High coupling demands efficient “flow” and “benefit,” making any deficiency a significant obstacle. Conversely, the western and northeastern regions, while facing considerable social-benefit challenges, have a relatively low coupling coordination degree, indicating less intense system interactions compared to the eastern and central areas. Their obstacles are more related to building foundational capabilities and establishing initial achievement transformation mechanisms, with the “absolute pressure” of these obstacles being potentially less than in high-coupling regions [54].

The intensity of inter-regional barriers in talent cultivation and school conditions is relatively consistent and minimal. This suggests that as China’s economy and society evolve, and as the concept of talent cultivation progresses, regions face similar challenges in these areas. Consequently, a national strategy for overall coordination is necessary.

Within the school-running scale dimension, inter-regional barriers are the least intense among all standard layers, signifying that China’s higher education has reached a stage of high-quality development. Higher education infrastructure is no longer the primary factor influencing regional innovation and development, and the emphasis has shifted from quantity to quality.

As shown in

Table 8. Levels of barriers to innovation capacity systems in China’s regions, 2022.

Region	Public Foundation	Innovative Resources	Absorptive Capacity	Output Capacity	Financial Potential	Manpower Potential
Eastern	0.1678	0.1070	0.1727	0.4885	0.0251	0.0389
Central	0.2039	0.1027	0.2121	0.4425	0.0163	0.0225
Western	0.1787	0.1220	0.2134	0.4506	0.0155	0.0199
Northeastern	0.1739	0.1248	0.2064	0.4519	0.0193	0.0238
Average	0.1796	0.1137	0.1993	0.4614	0.0191	0.0269

, obstacle intensities across the dimensions of the regional innovation subsystem follow a descending order: output capacity > input capacity > public infrastructure > innovation resources > human potential > financial potential. The substantial disparities across these categories indicate that key barriers include the limited contribution of high-tech industries to GDP, low levels of foreign investment, and insufficient knowledge transfer between academia and industry. These challenges require urgent and targeted policy intervention.

5. Discussion

This study assesses the synergistic interaction between China’s higher education and regional innovation capacity using a coupling coordination model. By employing kernel density estimation and Gini coefficient decomposition, it examines the temporal dynamics and spatial disparities of coupling coordination development. Furthermore, the Obstacle Degree Model is utilized to identify key hindering factors affecting system integration.

Despite significant regional disparities in the development levels of higher education and innovation capacity, there is an observable trend toward collaborative advancement and gradual coordination across regions. This differentiated yet converging development pattern indicates that, under the unified national market framework, individual regions are progressively enhancing both their adaptive capacity for innovation and their internal drive for educational progress.

The overall coupling and coordination between higher education and regional innovation capabilities steadily improved over the study period. Nonetheless, regional imbalances remain pronounced, following a “strong east, weak west” trajectory. The eastern and central regions benefit from robust economic foundations, well-established higher education institutions, and supportive innovation ecosystems, leading to higher degrees of coupling and coordination. Conversely, the western and northeastern regions continue to struggle with relatively weaker economies, limited educational infrastructure, and less developed innovation environments. To address these challenges, it is crucial to implement region-specific development strategies and enhance inter-regional cooperation mechanisms, thereby promoting balanced and integrated development across the country.

From 2010 to 2022, the coupling and coordination between higher education and regional innovation exhibited marked regional heterogeneity and a stepwise progression, though this trend has gradually moderated. Gini coefficient analysis reveals that intra-regional disparities follow the order eastern > western > central > northeastern. Among inter-regional disparities, the gap between the eastern and western regions is the largest, while that between the central and northeastern regions is the smallest. Overall, the primary source of spatial variation in coupling coordination is regional disparities, with interprovincial imbalances being a critical bottleneck in enhancing system-wide synergy.

Obstacle Degree analysis further pinpoints the crucial factors influencing the integration of higher education and regional innovation systems. Using 2022 data, the standard layer of regional innovation capacity shows relatively uniform Obstacle Degrees across all regions. This convergence indicates that regions encounter comparable structural obstacles in specific innovation capacity elements. Consequently, enhancing regional innovation capacity is not solely a regional imperative but a national, systemic undertaking requiring coordinated policy responses and shared strategic efforts.

6. Conclusions and Suggestions

6.1. Conclusions

This study constructs an evaluation framework comprising 19 indicators for higher education and 17 for regional innovation capacity, based on panel data from 31 Chinese provinces from 2010 to 2022. Using the entropy weight method, coupling coordination degree model, Dagum Gini coefficient, kernel density estimation, Moran’s index, and Obstacle Degree Model, we analyze the interaction between the two systems. The main findings are as follows:

(1) The temporal evolution reveals gradient differentiation. The higher education index increased from 0.0674 in 2010 to 0.1253 in 2022, while the regional innovation capacity index rose from 0.0409 to 0.1136. Growth rates differ significantly across regions, with the eastern region maintaining a leading edge and the western region persistently lagging. Notably, the innovation capacity gap between east and west continues to widen.

(2) The evolution of the coupling coordination degree reveals significant multi-dimensional heterogeneity. The national coupling coordination degree rose from 0.2292 to 0.3454 over the study period, showing progress yet remaining in the “barely coordinated” stage. Eastern provinces (e.g., Jiangsu and Shanghai) exhibit strong and even advanced coordination, forming a multi-center innovation network, whereas western and northeastern provinces show fragmented and polarized development.

(3) The spatial configuration exhibited a diminishing agglomeration effect. Initially, provinces with similar coordination levels exhibited distinct spatial clustering—either “high–high” or “low–low” groupings. However, this agglomeration effect has gradually weakened, suggesting a more dispersed spatial pattern over time.

(4) Regional disparities are undergoing dynamic expansion. Coupling coordination levels marked spatial heterogeneity, with interprovincial disparities widening throughout the study period. Notably, these disparities are driven primarily by inter-regional rather than intra-regional imbalances.

(5) Obstacle analysis revealed that regional innovation capacity is most hindered by limited investment, insufficient foreign trade engagement, and underdeveloped technology infrastructure. In the higher education system, the main obstacles are closely related to the insufficient allocation and flow efficiency of higher education resources and the low conversion rate of social benefits.

In addition, the coupling coordination model identifies spatiotemporal associations rather than causal relationships. The possible mechanisms behind these correlations include the following: (1) policy intervention increases educational investment, enhancing regional innovation capacity; (2) market-driven forces allow regional innovation clusters to attract educational resources; and (3) specific regional policies drive concurrent advancements in higher education and innovation capacity. Future research should employ panel data and Granger causality tests to verify directionality.

6.2. Policy Suggestions

The coordinated development of higher education and regional innovation capacity serves as a critical pathway for driving economic transformation and sustainable growth. At its core, this approach aims to establish a virtuous innovation ecosystem that links education, technology, and industry. Based on the preceding analysis, the following policy recommendations are proposed:

(1) The gradient regional development strategy addresses spatial imbalances by establishing a regionally differentiated support system to tackle the “high in the east and low in the west” pattern of coupling coordination. The eastern region prioritizes the creation of international science and innovation hubs, fostering integration between higher education and industry through cutting-edge laboratory platforms. In contrast, the central and western regions can leverage national initiatives like the “Special Program for Jointly-built Universities and Enclave R&D Centers” to connect with eastern innovation resources. Additionally, the state increases R&D funding specifically for western provinces and encourages the cross-regional flow of educational resources, including faculty exchanges and mutual credit recognition schemes between universities.

(2) The spatial collaborative network enhances the agglomeration effect. Given the observed spatial agglomeration effects in coupling coordination, we recommend a multi-level, government-led collaboration framework to establish regional innovation corridors in key urban clusters (e.g., Chengdu–Chongqing, Yangtze River middle reaches), supported by shared tax benefits and access to national research infrastructure. For “low–low” provinces, the Western Higher Education Pairing Assistance Program can be reinforced by making joint achievements in research, discipline construction, talent training, and industrial college development measurable criteria for performance evaluation.

(3) Systemic barriers should be addressed to optimize collaboration pathways by enhancing innovation platforms and advancing supply-side reforms in higher education. First, a demand-oriented research paradigm should be adopted by reforming the evaluation and benefit distribution mechanisms. This will help close the gap between scientific research, technological development, and industrial application. Promoting the integration of industry and education—particularly through vocational education reform—will elevate the societal contribution and relevance of higher education. Second, targeted investments in the central and western regions should be increased to support the development of distinctive academic disciplines and establish regionally tailored innovation communities. This approach will stimulate balanced growth and harness regional strengths. International cooperation and digital transformation should also be deepened. By organizing international trade fairs and optimizing foreign investment incentives, the technological content and innovation capacity of trade can be significantly enhanced, reinforcing coordinated regional development.

(4) To sustain regional synergy, a long-term trinity mechanism should be institutionalized, focusing on monitoring, incentives, and collaboration. A dynamic adjustment in support policies, based on real-time assessments of interprovincial collaboration levels, will enhance policy responsiveness. At the provincial level, higher education and industrial innovation coordination committees should be established. These bodies should oversee the creation of an integrated data-sharing platform linking government, universities, and enterprises. The “government–industry–university–research–application” ecosystem must be strengthened to improve translational outcomes. Blockchain technology should be adopted to align patents, talent, and R&D resources with greater precision. The implementation of the Eastern–Western Science and Technology Cooperation Plan under the 14th Five-Year Plan should be advanced by prioritizing the “four co-“ projects: the co-use of facilities, co-cultivation of talent, co-creation of think tanks, and co-execution of joint research. Together, these measures will reinforce the policy framework for regional cooperation, enhance the innovation capacity of higher education, and promote inclusive and coordinated national development.

Although this study conducts a preliminary empirical analysis of the coupling and coordination between higher education and regional innovation capacity and puts forward relevant policy suggestions, several limitations remain. At the data level, the use of provincial panel data from 2010 to 2022 excludes the initial effects of the higher education expansion policy implemented in the early 2000s and overlooks recent developments following the launch of the national strategy for building educational power post 2022. At the spatial scale, the use of provincial-level data conceals intraprovincial disparities, and the classification into four geographic regions fails to capture cross-regional innovation linkages—for instance, the spillover effects of the Yangtze River Delta on Anhui Province—thereby weakening the interpretation of regional synergies. At the theoretical level, the analysis remains largely macro in scope; while it confirms a coupling relationship between higher education and regional innovation, it lacks micro-level case studies illustrating integrated development among government, industry, academia, research, and application, which are necessary to trace specific transmission mechanisms. Future research should aim to develop clearly testable hypotheses and validate them through empirical investigation, thereby enhancing the theoretical applicability and practical relevance of the findings.