The Influence of Policy Investment on the Sustainable Development of Universities in Underdeveloped Regions: An Empirical Analysis of China’s Higher Education Landscape

Abstract

The regional disparity in higher education development is critical for the sustainable advancement of the national education system. To address this issue, the Chinese government has implemented targeted support policies for higher education in underdeveloped regions, with the “Ministry-Province Co-construction Policy” serving as a notable example. This study investigates the impact of such policies on the development of universities in China’s underdeveloped central and western regions. First, the study analyzes policy documents to identify six primary policy pathways through high-frequency word extraction, social semantic analysis, and path integration. These policy pathways are quantified using representative indicators, which constitute the independent variables of the study. Second, by employing the framework of modern university functions, the study develops a sustainable development indicator system for higher education institutions. The performance scores for the sustainable development of 14 universities, derived using the entropy method, serve as the dependent variables. The study subsequently measures the effects of individual and combined policy pathways through two equations. The results indicate that some pathways are more effective than others. While all combined policy pathways yield positive effects, an excessive number of combinations can lead to diminishing returns. Finally, the study elucidates the measurement results, emphasizing that effective pathways highlight the essential roles of faculty, research, and teaching. Conversely, less effective pathways stem from inadequate inputs or slow responses. Although combined policy pathways generally exert positive impacts, an overabundance of combinations can dilute these benefits. The study suggests that administrative support for higher education in underdeveloped regions is advantageous. It underscores the necessity of distinguishing between more and less effective input methods, concentrating on resource allocation, and ensuring universities’ autonomy in utilizing support resources to achieve sustainable development in higher education within these regions.

1. Introduction

In 2015, at the “United Nations Sustainable Development Summit”, the document titled “Transforming Our World: The 2030 Agenda for Sustainable Development” was adopted. This agenda delineates a global objective focused on providing inclusive and equitable quality education while promoting lifelong learning opportunities for all individuals. Additionally, it encompasses a specific target to guarantee that all persons have access to quality higher education [1]. Higher education serves a pivotal role as both an integral component of quality education goals for sustainable development and a fundamental pillar for achieving all sustainable development objectives [2]. It is imperative that higher education function effectively as a “driver of change” and an “engine” of progress. However, on a global scale, regional disparities in the development of higher education present a significant challenge. Nearly every country, particularly large nations, experiences varying degrees of imbalance in higher education across regions, coupled with difficulties in enhancing educational quality [3]. These factors profoundly impact a country’s capacity to achieve sustainable development through its higher education system.

As a developing nation, China faces significant disparities in higher education development across its regions. Historically, high-quality higher education resources have been concentrated in the expansive coastal and eastern areas along the Yangtze River, leaving the central and western regions at a disadvantage in terms of resource availability. This uneven distribution has resulted in a pronounced educational imbalance, with certain regions lagging behind in higher education development [4]. Previous research identifies two primary factors influencing universities’ sustainable development: internal factors and external factors, such as infrastructure and socio-economic conditions [5]. China has prioritized external support for higher education in its central and western regions since implementing the Western Development Strategy, aiming to balance regional higher education development [6]. National policies have strategically directed resources to underdeveloped areas, thereby improving public satisfaction with educational provisions [7]. Key support initiatives include the Pairing Assistance Program, the Central and Western Higher Education Revitalization Plan [8], and the Provincial-Ministerial Joint Construction of Universities. In 2018, to further enhance the quality of higher education institutions in these regions, the 30th National Education System Reform Leading Group approved the provincial-ministerial joint construction policy. This policy targets one university in each of the 13 central and western provinces and regions, as well as the Xinjiang Production and Construction Corps, which previously lacked directly affiliated universities [9].

Table 1. Fourteen Ministry-Province Co-constructed Universities in China.

Province	University Name	Region
Hebei Province	Hebei University	Eastern Region
Shanxi Province	Shanxi University	Eastern Region
Henan Province	Zhengzhou University	Central Region
Yunnan Province	Yunnan University	Western Region
Xinjiang Uygur Autonomous Region	Xinjiang University	Western Region
Jiangxi Province	Nanchang University	Central Region
Guizhou Province	Guizhou University	Western Region
Hainan Province	Hainan University	Eastern Region
Qinghai Province	Qinghai University	Western Region
Tibet Autonomous Region	Tibet University	Western Region
Inner Mongolia Autonomous Region	Inner Mongolia University	Western Region
Guangxi Zhuang Autonomous Region	Guangxi University	Western Region
Ningxia Hui Autonomous Region	Ningxia University	Western Region
Xinjiang Production and Construction Corps	Shihezi University	Western Region

presents a comprehensive list of the fourteen universities participating in the Ministry-Province Co-construction initiative.The policy provides comprehensive support in various areas, including information platforms, funding, research, staffing, teaching, enrollment, and partnerships. The government continues to increase funding, focusing on developing at least two academic clusters per university aligned with local key industries, establishing extensive research facilities and platforms, and fostering partnerships between 37 directly affiliated universities and the joint construction universities [10].

The joint university construction initiative between central and provincial governments has been operational for nearly six years. China has invested substantially in developing 14 co-constructed universities, yielding significant progress. This case study was selected to examine the issue of balancing regional educational development due to its representativeness and potential insights. The geographical distribution of these 14 universities reveals their concentration in China’s central and western regions(For a detailed illustration, please refer to Figure 1), areas typically lacking universities directly administered by the Ministry of Education—an indicator of underdeveloped higher education. The “joint construction” policy addresses several critical aspects of sustainable higher education development. It aims to bridge the gap between eastern and central-western regions in higher education development while aligning with national long-term interests, overall development strategies, and goals for educational modernization and establishing a robust higher education system. Furthermore, this policy balances central government objectives with the economic and social development needs of central and western regions. It responds to the growth aspirations of universities in these areas, promotes educational equity, and strives to deliver an education system that meets public expectations.

This study examines China’s policy practices to address the question of how countries can implement supportive policies to enhance the development of higher education institutions in underdeveloped regions, ultimately achieving sustainable higher education development nationwide. The research employs policy tool theory and university development performance theory as its theoretical framework. It establishes a regression model with government supportive policy investment as the independent variable and university construction and development performance as the dependent variable. This model aims to elucidate the relationship between policy investment and sustainable university development in underdeveloped regions. Furthermore, the study seeks to identify essential characteristics of effective supportive policies and implementation pathways through empirical analysis. By synthesizing universal patterns, this research aims to provide methodological foundations for addressing regional disparities in higher education development across various countries and regions.

2. Literature Review

Regarding the conceptualization and contextualization of sustainable development in higher education, many institutions have published dedicated manifestos addressing this topic [11,12,13]. Specialized conferences have been organized to explore and discuss the sustainable development of higher education, such as R. Lozano-Ros, Sustainable Development in Higher Education: Incorporation, Assessment and Reporting of Sustainable Development in Higher Education Institutions, Lund University, IIIEE, Lund (2003) [14]. Scholarly consensus suggests that while “sustainability” is frequently used in contemporary discourse, its precise meaning warrants clarification. Sustainability has been defined as “meeting the needs of the present without compromising the ability of future generations to meet their own needs” [15]. Higher education functions as a catalyst and foundation for society’s sustainable development by equipping future generations with the essential knowledge, values, and skills needed to address contemporary challenges. In this process, it plays a pivotal role in cultivating future leaders, thus driving societal progress and sustainability [16,17].

The development of universities in less-developed regions is a crucial aspect of sustainable higher education. The OECD has conducted a historical analysis of this issue, examining higher education within the context of regional policies and theoretical frameworks. They observe that the formalization of regional policies emerged after the World Wars, when spatial disparities were recognized as a significant concern. These inequalities were perceived as violations of implicit equality norms. In addressing this challenge, scientific approaches have proven invaluable. The application of statistics, economic modeling, and meticulously crafted measures has enabled governments to formulate rational and effective policies. This scientific foundation has been instrumental in guiding decision-making processes aimed at mitigating regional educational disparities. Numerous factors, both tangible and intangible, often impede sustainable development in higher education within underdeveloped regions. These obstacles encompass the quality of faculty, educational resources, and infrastructure; the extent of community engagement; funding availability; the provision and accessibility of educational services; and the level of educational equity [18]. Moreover, less tangible elements, such as organizational culture [19], significantly hinder this transition, ultimately obstructing the advancement of higher education in these areas.

The Chinese government’s role in addressing higher education challenges in underdeveloped regions has emerged as a significant focus within Chinese academia, particularly given the government’s active engagement in this sphere. Since the implementation of the Western Development Strategy, the government has initiated various support measures, yielding substantial outcomes. These efforts have facilitated the redistribution of high-quality higher education resources, with a notable shift towards China’s less developed central and western regions. Consequently, local universities in these areas have experienced marked improvements, demonstrating the efficacy of targeted governmental interventions in mitigating educational disparities [20]. Chinese scholars widely acknowledge the government’s pivotal role in ensuring the sustained, stable, and effective implementation of counterpart support policies. These academics emphasize that the government’s involvement is not only crucial but also requires expansion and intensification. This perspective underscores the belief that enhanced governmental active engagement is essential for the long-term success and continuous improvement of these support mechanisms addressing educational disparities [21]. The efficacy of governmental influence in counterpart support programs can be attributed to institutional factors. These initiatives suffer from inadequate institutionalization, evidenced by the absence of standardized mechanisms for policy implementation oversight and feedback collection. This institutional deficiency leads to suboptimal policy execution and misaligned governmental support. Consequently, a substantial discrepancy exists between intended policy outcomes and actual results, ultimately impeding the realization of program objectives. This situation underscores the critical need for robust institutional frameworks to ensure the effectiveness and sustainability of governmental efforts in educational support [22]. Some scholars attribute the underlying causes to limitations on university autonomy, identifying constraints in areas such as independent admissions, curriculum development, research funding allocation, international partnerships, institutional governance, and financial management. These restrictions, they argue, can undermine the efficacy of supportive policies. While acknowledging the importance of governmental support, these experts emphasize the necessity of expanding university autonomy. This expansion should encompass reinforcing legal frameworks, mitigating bureaucratic impediments, actively advocating for increased institutional independence, and enhancing oversight and evaluation systems. The ultimate objective is to transform resources into sustainable catalysts for long-term growth and development in higher education [23]. Diverse analytical approaches have been employed in examining this issue, with some researchers focusing on investment modalities, while others utilized a framework centered on policy instruments. These studies have scrutinized the evolution and innovative strategies of policy tools within the higher education policies of western regions. They have identified deficiencies in the existing policy instrument structure and proposed various optimization strategies. These include developing talent pools, implementing reciprocal support initiatives, facilitating resource sharing and integration, and leveraging regional characteristics to enhance policy efficacy [24].

A significant contingent of scholars argues that China’s efforts to promote sustainable higher education in disadvantaged regions represent a notable contribution to global higher education. While existing studies provide comprehensive analyses of national investments, they have overlooked a crucial aspect: the specific roles of various forms of government support in fostering sustainable development and the mechanisms for optimizing the impact of governmental investment to generate momentum for sustainability. To address this gap in the literature, this research focuses on the relationship between policy-level investments and their long-term outcomes, examining how policies can enhance the sustained development of higher education in underdeveloped sectors through strategic investment.

3. Theoretical Analysis and Research Hypothesis

3.1. The Theory of Policy Instruments and Policy Pathways

Educational policy, as a subset of public policy, inherently shares its fundamental characteristics [25]. Analyzing the evolution of higher education through the lens of educational policy has consistently proven to be an effective approach in international higher education research [26]. The theory of policy instruments is pivotal in elucidating the intricacies of policy formulation and implementation.

Policy tools are crucial because they play a vital role in policy implementation. Within policy activities, these tools are seen as a collection of various policy actions [27]. Technically, they consist of techniques used by the government to exercise authority, secure support or influence, and, in some cases, to impede social change [28]. From a strategic standpoint, policy tools are the methods employed by the government to achieve its policy objectives [29]. Mechanistically, they function as behavioral mechanisms that direct government actions towards achieving these objectives [30].

Consequently, there is a consensus that public policies achieve their objectives through the application of policy instruments, which serve as mechanisms for governmental influence on socio-economic activities. The selection of policy instruments has a direct impact on policy outcomes. Policy implementation is inherently a complex process involving the strategic choice of instruments, and the identification and deployment of effective policy instruments during the implementation phase are crucial for policy success [31]. The deployment of policy instruments, which involves the strategic allocation of policy resources, is a process whereby policy actors systematically organize and distribute various resources to achieve predetermined policy objectives.

The selection of policy instruments is directly influenced by a multitude of factors, including conceptual frameworks, institutional structures, vested interests, individual and international contexts [32], the nature of policy issues, environmental variables, and the characteristics of the target population [33]. The appropriateness of chosen policy instruments significantly impacts the extent to which intended policy objectives are realized [34].

A single policy typically employs multiple instruments, which interweave to form a complex network of tools [35]. While categorizing and classifying these instruments poses challenges for research, these processes also enhance research efficiency and precision. To better analyze the impact of diverse instruments on policy outcomes, this study adopts policy pathways as a measurement approach.

Policy pathways emerge from the collective actions of multiple actors within a specific policy subsystem [36]. A policy pathway represents the focal point of a policy, delineating the specific domains in which the policy exerts its influence. Typically, it necessitates the deployment of a series of instruments to achieve its intended outcomes. Policy pathways operate through the application of various specific policy tools; thus, the utilization of policy tools along each pathway constitutes the activation of that pathway. The cumulative utilization of these pathways, in turn, forms the overarching design of the policy [37]. Assessing policy pathways can elucidate the impact and efficacy of broad categories of policy instruments.

In line with the aforementioned theoretical perspectives, the study has formulated the following theoretical logical framework, illustrated in Figure 2:

This study, grounded in policy theory, hypothesizes that the heterogeneity of policy pathways may differentially impact the sustainable development of higher education institutions in underdeveloped areas.

3.2. Model Selection

This study primarily uses quantitative regression analysis to investigate the impact of policy tools on the sustainability of higher education institutions in underdeveloped regions. In this analysis, the policy trajectory is the independent variable, while the developmental outcomes of these institutions are the dependent variables.

To elucidate the influence of specific policy tools, a regression model was constructed to evaluate their effectiveness [38].

$$Y_{it}=B_0+B_1{Tool}_{it}+B{X}_{it}+{\lambda }_i+{\gamma }_t+{\epsilon }_{it}$$

(1)

In Equation (1), the symbol $i$ denotes higher education institutions jointly established by central and provincial governments, while $t$ signifies the year. The term ${\lambda }_i$ captures fixed effects at the provincial level, and ${\gamma }_t$ accounts for temporal fixed effects; ${\gamma }_t$ reflects the developmental status of these co-constructed institutions; ${Tool}_{it}$ pertains to instrumental variables associated with diverse foundational research policy elements; $X_{it}$ includes additional control variables; and ${\epsilon }_{it}$ is the random error component. The primary focus of this study is on estimating the coefficients. If the policy trajectory of the joint development policy between the central and provincial governments significantly and positively affects the growth of these institutions, the coefficients are expected to be positively significant.

Policy instruments typically do not operate in isolation; they are often part of a collective suite. When assembling policy portfolios, integrating these instruments can lead to either synergistic effects—mutually reinforcing enhancements—or diminishing effects, where counteractions occur [39,40].

To discern the influence of various combined pathways on the overall development of higher education institutions co-constructed by ministries and provincial governments, this research employs an econometric regression model for analysis [41].

$$Y_{it}=C_0+C_1{Combination}_{it}+C{X}_{it}+{\lambda }_i+{\gamma }_t+{\epsilon }_{it}$$

(2)

In Equation (2), the symbol $i$ denotes higher education institutions co-established by central and provincial governments, while $t$ signifies the year. The term ${\lambda }_i$ captures fixed effects at the provincial level, and ${\gamma }_t$ accounts for temporal fixed effects; $Y_{it}$ reflects the developmental status of these co-established institutions; ${Combination}_{it}$ represents the composite variables of policy pathway instruments under the co-establishment policy (including non-repeating interaction terms for two to six elements); $X_{it}$ includes additional control variables; and ${\epsilon }_{it}$ is the random error component. This paper focuses on the estimated coefficients. If the amalgamation of policy pathways significantly and positively influences the development of co-established institutions, the coefficients are expected to be positive. Furthermore, by assessing and comparing the magnitude and significance of the impact coefficients across different pathway combinations, the study seeks to identify the most effective combination of policy pathways.

The policy was formally implemented in February 2018, demarcating 2018 as the policy’s inaugural year. To elucidate the comparative effects of the policy’s implementation through quantitative analysis, this study incorporates data from a three-year pre-implementation period, specifically 2015, 2016, and 2017. This methodological approach facilitates a more comprehensive evaluation of the policy’s impact, enabling a robust pre- and post-implementation comparison.

3.3. Quantification of Independent Variables and Results

3.3.1. Methodology for the Selection of Independent Variables

Current scholarly work utilizes two primary methodologies to quantify policy investment. The first method measures investment based on actual input levels, while the second quantifies policy investment through an analysis of policy documents. These documents comprehensively reflect the policy’s objectives, the arrangement of policy instruments, and the emphasis placed on them, serving as direct indicators of governmental resource allocation and guiding the attitudes and actions of policy targets [42,43]. Consequently, the second approach effectively addresses the challenge of aligning specific policy initiatives with statistical data, which the first method may struggle to achieve, enabling a more holistic assessment of policy circumstances.

This study examined the policy framework co-established by the Ministry of Education and provincial governments, analyzing a comprehensive corpus of documents: 14 agreements between the Ministry of Education and local governments hosting the target universities, 3 collaboratively drafted work manuals, 14 provincial-level policy implementation plans, and 20 university-issued institutional documents detailing policy execution. Despite access limitations resulting in complete policy document chains for only Guangxi University and Nanchang University, the collected materials adequately represent the three key policy levels: the Ministry of Education, local governments, and universities. This distribution enables effective analysis of policy implementation pathways.

3.3.2. Policy Path Extraction

This study utilizes the ROSTC (Version 6.0) [44,45] tool to explore pathways of policy investment.

Step 1: High-frequency word screening. The initial phase involves screening high-frequency terms. The research uses ROSTCM for preprocessing raw documents, including tokenization and filtering, resulting in a compilation of high-frequency terms from the policy text, thereby establishing the university’s policy-related high-frequency lexicon (

Table 2. Frequency analysis of key terms in policy documents related to ministry-province co-construction policy.

High-Frequency Word	Frequency	High-Frequency Word	Frequency	High-Frequency Word	Frequency
University	849	Service	196	Communication	128
Construction	628	Cultivation	177	Ability	127
Development	496	Strengthen	175	Implementation	127
Discipline	481	Management	174	Scientific Research	121
Universities	406	Education	174	Science and Technology	121
Joint Construction	326	Country	161	Platform	119
Talent	322	Resources	161	Leadership	114
Ministry of Education	301	Promote	156	Center	112
Innovation	278	Establish	153	Cadre	111
Institution	273	Enhance	153	Society	111
School	262	Department of Education	152	Major	110
Cooperation	246	Project	144	System	104
Ministry and Province	236	Key	142	Task	103
First-class	234	School Management	137	Plan	103
Research	232	Features	137	Funding	102
Mechanism	215	Directly Affiliated	135	Evaluation	101
Level	211	Project	132	Strengthen	100
Reform	200	Science	130	Policy	100
Province	197	Technology	130

).

Step 2: Social and semantic analysis of high-frequency words. The subsequent phase encompasses socio-semantic analysis of the above high-frequency terms. Building on the extraction outcomes, the research further applies ROSTCM to analyze social and semantic networks within the preprocessed documents [46,47]. This analysis aims to elucidate the interconnections between terms, offering insights into the agents of action, the nature of verbs, and the associated objectives or processes. The study presents a visual schematic as follows (Figure 3):

The network analysis of high-frequency terms categorizes them into three groups(See

Table 3. Tabulation of high-frequency terms associated with policy implementation pathways.

High-Frequency Word	Frequency	High-Frequency Word	Frequency	High-Frequency Word	Frequency	High-Frequency Word	Frequency
Discipline	481	Talent	322	Management	174	Resources	161
Engineering	144	Teacher	128	Scientific Research	121	Platform	119
Joint Construction	326	Technology	130	Science and Technology	121	Leadership	114
Funding	102	Teaching	130	Research	232	Reform	200

for details). Group one represents policy actors, such as the state, the Ministry of Education, provincial ministries, autonomous regions, and educational institutions. Group two is action verbs, nouns, and adjectives that lack concrete significance, like “promote”, “serve”, and “enhance”. Their presence underscores the intended objectives of policy instrument utilization, yet they do not hold substantial meaning. Group three pertains to the policy pathways. By summarizing frequency charts and network diagrams, key terms related to the policy pathway can be identified.

Step 3: Policy pathway extraction. The third phase is the extraction of the policy pathway. Upon obtaining a meaningful list of high-frequency terms, the study integrates contextual usage within document analysis and consolidates similar concepts. By aggregating the frequency analysis for the joint policy between ministries and provinces, the principal policy pathways of this joint policy are delineated as follows (

Table 4. Extraction Summary of Policy Implementation Pathways in Ministry-Province Co-construction.

Selected Pathway	Reason for Selection
Funding Allocation	Integration of high-frequency words “Funding” and “Resources”
Faculty Development	Integration of high-frequency words “Talent” and “Teacher”
Teaching Strengthening	Integration of high-frequency word “Teaching”
Research Capability Enhancement	Integration of high-frequency words “Research”, “Platform”, “Technology”, “Science and Technology”, and “Research”
Advantageous Discipline Construction	Integration of “Discipline” and “Platform”
Management Reform	Integration of high-frequency words “Leadership”, “Reform”, “Management”, and “Joint Construction”

):

3.3.3. Selection of Policy Path Indicators and Quantification of Results

To operationalize policy pathways, it is essential to quantify them using clear and definitive metrics. This quantification relies on identifying indicators that accurately reflect the characteristics of each pathway, enabling precise evaluation of policy investment impacts. The selection of these indicators is guided by criteria such as data availability, fidelity of indicator representation, and breadth of data coverage. The research has carefully selected a suite of indicators, detailed as follows(

Table 5. Selection Criteria Matrix for Policy Implementation Pathways.

Policy Pathway	Indicator Type	Selected Indicator
Funding Allocation [48]	Single Indicator	Government Investment
Faculty Development [49,50,51]	Composite Indicator	Proportion of Teachers with Senior Titles		Number of High-End Talents
Pedagogical Enhancement [52,53]	Composite Indicator	Professor’s Lecture Rate		Student-Teacher Ratio
Research Capability Advancement [54,55,56]	Composite Indicator	Total Research Funding	Research Staff Scal		Number of Research Platforms
Advancement of Strategic Disciplines [57,58]	Composite Indicator	Proportion in China’s Best Discipline Ranking for Discipline Onone		Proportion in China’s Best Discipline Ranking for Discipline Two
Administrative Restructuring [59,60,61,62]	Composite Indicator	Adjustment of School Leadership		Introduction of Academic Backbones as School Leaders

):

Initially, the data undergo a two-step quantification process: Normalization: Standardization techniques are applied to address variations in scales and magnitudes, yielding normalized values for all relevant indicators. For the government investment indicator, which has a singular value, the standardized value is used directly. Weight Allocation: The entropy weighting method is employed to assign weights to composite indicators within policy pathways. These weights are then used to calculate scores for the composite policy pathways (detailed calculation methods are provided in Appendix A to maintain analytical consistency and avoid potential interference with the overall analysis of dependent variables).

A descriptive statistical analysis of the data across the six policy pathways is conducted, with the findings presented in

Table 6. Descriptive statistics of six policy implementation pathways.

Variable Name	Sample Size	Mean	Standard Deviation	Maximum Value	Minimum Value
Funding Allocation	112	2.441	0.441	3.444	1.435
Faculty Development	112	0.279	0.195	0.951	0.039
Pedagogical Enhancement	112	0.486	0.213	0.912	0.017
Research Capability Advancement	112	0.247	0.168	1.000	0.000
Advancement of Strategic disciplines	112	0.348	0.209	0.725	0.000
Administrative Restructuring	112	0.987	1.183	5.500	0.000

.

3.4. Quantification of the Dependent Variable and Results

3.4.1. Methodology and Principles for Selecting the Dependent Variable

The evolution of higher education is closely linked to the responsibilities of universities. Humboldt University, founded in 1810 in Germany, played a crucial role in establishing scientific research as a fundamental university function, alongside teaching, thus defining their dual foundational missions [63]. In the 1930s, the University of Wisconsin in the United States pioneered the inclusion of societal service as a university function, connecting faculty evaluation to societal contributions. This innovation shifted universities from the periphery to the center of society. Consequently, universities now embody dual roles: catalysts for social progress and agents of societal development. The notion that universities serve as the third pillar of society has become firmly established. The concept of “three key functions” is now a standard depiction of the role and objectives of higher education worldwide [64]. Pursuing sustainable development within higher education institutions requires cultivating a constructive and dynamic relationship with society, aligned with their core responsibilities [65,66]. This study aims to create an evaluative index system for assessing sustainable development in higher education, particularly in underdeveloped areas, while considering the evolving roles of modern universities. The system’s development will rely on the strategic selection of indicators based on sustainability principles, which will serve as the dependent variable of the research. A comprehensive set of indicators supporting sustainable university development was compiled, considering the unique characteristics of Chinese higher education institutions and drawing from both domestic and international ranking criteria [67,68,69,70,71,72]. An expert evaluation method was utilized to determine specific indicator compositions. The detailed indicator selection process is elucidated in Section Appendix B to ensure transparency and completeness. This section presents only the Framework of Sustainable Development Indicators for Ministry-Province Co-constructed Universities.

The role of scientific research within universities is to drive innovation and knowledge accumulation, addressing key societal and economic challenges [73]. Evaluating the effectiveness of these research initiatives requires examining key performance indicators, including the volume of scholarly publications, the level of financial research support, and the strength of academic disciplines. We selected indicators such as the number of national research projects, the count of articles in reputable international journals, the total entries in the Chinese Social Sciences Citation Index (CSSCI), the total number of master’s degree programs, and the specialized excellence of dominant fields to quantitatively assess the research capabilities of universities. The aim of talent development in university education is to nurture a diverse and accomplished cohort of graduates with critical thinking and creativity, essential for navigating globalization’s complexities [74]. Evaluating a university’s talent cultivation efforts involves assessing the entire development continuum. These indicators include total student enrollment, the graduate-to-undergraduate student ratio, post-graduation employment rates for undergraduates, national accolades for teaching excellence, and recognitions from scientific and technological innovation contests. Within the academic sphere, universities fulfill their social service mandate through collaborative engagements with societal stakeholders. This involves applying research findings, providing expert counsel, contributing to public policy formulation, and promoting community development [75,76]. The efficacy of a university’s social service efforts was evaluated across several dimensions, including technology transfer, societal impact, and the execution of social service initiatives. These include the influx of industry-sponsored research funding, the creation of social service platforms, the cumulative number of invention patents awarded, the total of utility model patents granted, and the quantity of design patents conferred.The logical framework of the indicator system is shown in Figure 4.

In accordance with the established index system, this study collected data from three primary sources. First, we utilized annual undergraduate and graduate teaching quality reports from 14 higher education institutions jointly established by central and provincial governments. Second, we supplemented this information with historical profiles and publicly available statistical data from these institutions. Finally, we gathered specific project data from official websites of the Ministry of Education, Ministry of Science and Technology, National Natural Science Foundation of China, National Office for Philosophy and Social Sciences Planning, and individual university homepages. This multi-faceted approach ensured data accessibility, accuracy, and reliability.The descriptive analysis of the indicator system data is shown in

Table 7. Descriptive statistics of performance indicators.

Variable Name	Sample Size	Mean	Standard Deviation	Maximum Value	Minimum Value	Forward or Reverse
Total Student Enrollment	112	36,510.09	29,773.68	301,257.00	9211.00	+
Graduate-to-Undergraduate Student Ratio	112	0.21	0.08	0.46	0.08	+
Total Faculty Headcount	112	465.29	486.22	2686.00	0.00	+
Employment Rate of Bachelor’s Degree Recipients	112	1941.43	777.97	5310.00	594.00	+
National Teaching Excellence Award Count	112	0.85	0.08	0.98	0.54	+
Science and Innovation Competition Award Count	112	0.46	0.65	3.00	0.00	+
National Research Project Count	112	31.61	26.41	214.00	0.00	+
International Peer-Reviewed Publication Count	112	123.39	84.42	419.00	17.00	+
CSSCI-Indexed Publication Count	112	6238.41	7191.36	43,250.00	235.00	+
Number of National Key Disciplines	112	174.33	113.24	444.00	11.00	+
Total Master’s Program Count (Academic and Professional)	112	0.18	0.05	0.29	0.07	+
Total Doctoral Program Count (Academic and Professional)	112	0.24	0.13	0.61	0.06	+
Corporate-Sponsored Research Funding	112	61.22	17.22	95.00	30.00	+
Number of Social Service Platforms	112	15.09	8.01	35.00	3.00	+
Granted Invention Patent Count	112	0.40	0.46	2.80	0.00	+
Granted Utility Model Patent Count	112	5.02	6.05	30.00	0.00	+
Granted Design Patent Count	112	316.51	325.28	1924.00	7.00	+
International Student Enrollment Count	112	223.63	368.73	2556.00	3.00	+
Percentage of International Research Collaborations	112	13.07	21.99	108.00	0.00	+

.

3.4.2. Quantification Results of Development Levels in Ministry-Province Co-Constructed Universities

This study employs a systematic approach to quantify the development levels of various universities, comprising the following key steps:

Data normalization: dimensionless processing is applied using a standardized method to address variations in dimensions and magnitudes, yielding normalized values for all relevant indicators.

Weight assignment: The entropy weight method is utilized to allocate weights to composite indicators within each policy pathway category. These weights are then used to calculate scores for the composite policy pathways (detailed calculation methods are provided in Appendix A to maintain analytical consistency and avoid potential interference with the overall analysis of dependent variables).

Table 8. Indicator Weights for Sustainable Development of Ministry-Province Co-constructed Universities.

Target Level	Criterion Level	Indicator Level	Weights of Index (%)
Sustainable Development of Ministry-Province Universities	Talent Development	Total Student Enrollment	5.29
		Graduate-to-Undergraduate Student Ratio	4.82
		Total Faculty Headcount	4.05
		Employment Rate of Bachelor’s Degree Recipients	6.21
		National Teaching Excellence Award Count	2.57
		Science and Innovation Competition Award Count	7.16
		National Research Project Count	6.54
	Scientific Research	International Peer-Reviewed Publication Count	6.71
		CSSCI-Indexed Publication Count	6.50
		Number of National Key Disciplines	3.47
		Total Postgraduate Program Count (Academic and Professional)	5.21
		Total Doctoral Program Count (Academic and Professional)	2.37
		Corporate-Sponsored Research Funding	3.43
		Number of Social Service Platforms	3.69
	Social Service	Granted Invention Patent Count	6.19
		Granted Utility Model Patent Count	8.11
		Granted Design Patent Count	5.70
		International Student Enrollment Count	5.04
		Percentage of International Research Collaborations	6.94
			100.00

presents the resulting weight distribution for the indicator system of ministry-province co-constructed universities.

Utilizing the derived weight distribution and normalized data, a comprehensive assessment of sustainable development levels was conducted for ministry-province co-constructed universities.

Table 9. Longitudinal Performance of Ministry-Province Co-constructed Universities.

University Performance Profile	Comprehensive Level		2015		2016		2017		2018		2019		2020		2021		2022
	Average Performance	Ranking	Average Performance	Ranking	Average Performance	Ranking	Average Performance	Ranking	Average Performance	Ranking	Average Performance	Ranking	Average Performance	Ranking	Average Performance	Ranking	Average Performance	Ranking
Guangxi University	44.08	5	50.15	3	50.07	3	47.04	3	48.26	3	35.17	5	40.26	4	44.46	4	37.26	4
Yunnan University	61.29	4	57.19	2	59.29	1	56.32	1	59.92	1	64.45	2	58.92	2	67.59	2	66.6	2
Zhengzhou University	34.91	1	38.88	4	43.65	4	28.5	5	27.93	6	39.24	4	32.31	6	33.48	5	35.25	5
Nanchang University	38.20	2	26.51	6	27.69	7	23.83	8	26.27	7	48.46	3	47.78	3	52.72	3	52.33	3
Guizhou University	65.63	3	57.24	1	54.89	2	55.13	2	58.77	2	72.43	1	70.52	1	79.34	1	76.7	1
Shanxi University	29.33	6	23.94	8	23.73	8	28.93	4	28.34	5	31.49	7	38.91	5	32.75	6	26.53	7
Hainan University	26.73	7	28	5	16.78	11	16.82	10	33.07	4	30.35	8	30.74	7	27.84	7	30.21	6
Hebei University	17.40	9	15.54	12	23.41	9	10.41	13	12.01	12	14.56	13	28.94	9	17.56	11	16.8	11
Ningxia University	14.11	11	14.69	13	13.19	13	11.71	12	12.48	11	15.67	12	15.81	12	15.06	12	14.29	13
Qinghai University	25.42	13	18.4	11	30.92	6	28.4	6	17.17	9	34.17	6	28.52	10	25.67	8	20.13	9
Inner Mongolia University	10.21	12	4.35	14	16.25	12	15.85	11	3.73	14	12.29	14	14.33	13	7.05	14	7.85	14
Tibet University	20.96	14	18.94	10	18.42	10	18.72	9	18.22	8	20.72	11	26.87	11	25.6	9	20.2	8
Xinjiang University	24.38	8	24.39	7	30.94	5	25.1	7	16.22	10	29.45	9	29.56	8	20.62	10	18.72	10
Shihezi University	11.71	10	19.23	9	3.99	14	4.78	14	6.11	13	21.85	10	12.66	14	10.6	13	14.49	12

presents the detailed evaluation results.

3.5. Selection of Control Variables

The development of higher education institutions is a complex process influenced by numerous interrelated factors that can positively or negatively impact institutional activities. We selected control variables based on two primary principles. First, variables with minimal variation between experimental and control groups were included to isolate changes within random disturbances, thereby reducing noise and improving estimate precision. Second, variables showing pre-treatment disparities between groups, unaffected by the treatment itself, were incorporated to eliminate attributable variances and better align with the parallel trends assumption. Following these principles, we selected the following control variables: provincial GDP where the co-established institutions are located, educational expenditure as a proportion of half the public budget, total societal employment, and the ratio of total import and export value at the business unit’s location (in thousands of US dollars).

4. Results

To facilitate the research, this section will denote the policy pathway instruments of funding allocation, faculty development, pedagogical enhancement, research capability advancement, advancement of strategic disciplines, and administrative restructuring as M1, M2, M3, M4, M5, and M6 respectively.

4.1. Illustration of Individual Pathway Impacts

The research incorporated multiple policy pathways into Equation (1), including funding allocation, faculty development, pedagogical enhancement, research capability advancement, and advancement of strategic disciplines.

Table 10. Impact of individual policy pathways.

Dependent Variables	Sustainable Development Levels of Ministry-Province Co-Constructed Universities
Independent Variables	M1	M2	M3	M4	M5	M6
Pathway Effects	0.053 **	0.587 ***	0.103 ***	0.299 ***	0.088 *	0.012 **
	(0.020)	(0.086)	(0.026)	(0.078)	(0.048)	(0.005)
Intercept	−2.959	2.399	−0.854	0.952	−2.201	−2.448
	(2.705)	(2.260)	(2.423)	(2.466)	(2.735)	(2.626)
Control Variables	YES	YES	YES	YES	YES	YES
Regional Fixed Effects	YES	YES	YES	YES	YES	YES
Time Fixed Effects	YES	YES	YES	YES	YES	YES
N	112	112	112	112	112	112
R2	0.941	0.959	0.943	0.945	0.936	0.938

Note: *** p < 0.01, ** p < 0.05, * p < 0.1, robust standard errors in parentheses.

illustrates the individual investment mechanisms for each policy pathway.

In the context of policy investment implementation, this study presents the following findings:

Firstly, regarding the validation of policy pathway effectiveness, the provincial-ministerial co-construction policy, through the deployment of various policy tools, establishes six policy pathways: financial allocation, faculty development, teaching enhancement, research capability improvement, and management reform. The impact coefficients of these pathways on policy outcomes are positive and significant, indicating that the inputs from the provincial-ministerial co-construction policy positively affect the universities involved.

Secondly, examining the pathways from the perspective of their coefficients, the impact coefficients for faculty development, research capability improvement, and teaching enhancement are 0.587, 0.299, and 0.103, respectively. These three policy tools demonstrate a more substantial impact and are considered advantageous policy pathways.

Thirdly, analyzing the remaining pathways, the impact coefficients for discipline development, financial allocation, and management reform are 0.088, 0.053, and 0.012, respectively. While these policy tools play a less significant role, the reasons for their limited impact require further analysis.

4.2. Illustration of Integrated Pathway Impacts

This investigation explores the combination of six policy pathways within the framework of universities co-built by ministries and provinces, resulting in 57 distinct combinatorial approaches. These include 15 combinations involving two types of policy instruments, 20 with three types, 15 with four types, 6 with five types, and 1 that incorporates all six types. By generating interaction terms among these diverse policy tools and integrating them into econometric model 2, the study estimates the impact coefficients for each category of policy instruments. The impact of combined policy pathways is shown in

Table 11. Impact of combined policy pathways.

Dependent Variables	Sustainable Development Levels of Ministry-Province Co-Constructed Universities
Independent Variables	Combined Policy Pathway
Combination Methods	Effect Coefficients	Combination Methods	Effect Coefficients	Combination Methods	Effect Coefficients	Combination Methods	Effect Coefficients
M1 × M2	0.1294 ***	M1 × M2 × M3	0.0990 ***	M2 × M5 × M6	0.0515 ***	M2 × M3 × M4 × M5	0.7638 ***
M1 × M3	0.0388 ***	M1 × M2 × M4	0.0990 ***	M3 × M4 × M5	0.5370 ***	M2 × M3 × M4 × M6	0.0487 ***
M1 × M4	0.0911 ***	M1 × M2 × M5	0.1428 ***	M3 × M4 × M6	0.0455 ***	M2 × M3 × M5 × M6	0.0865 ***
M1 × M5	0.0410 **	M1 × M2 × M6	0.0086 ***	M3 × M5 × M6	0.0528 ***	M2 × M4 × M5 × M6	0.0455 ***
M1 × M6	0.0048 **	M1 × M3 × M4	0.0911 ***	M4 × M5 × M6	0.0455 ***	M3 × M4 × M5 × M6	0.0810 ***
M2 × M3	0.3364 ***	M1 × M3 × M5	0.0646 ***	M1 × M2 × M3 × M4	0.1324 ***	M1 × M2 × M3 × M4 × M5	0.2194 ***
M2 × M4	0.3520 ***	M1 × M3 × M6	0.0080 ***	M1 × M2 × M3 × M5	0.1474 ***	M1 × M2 × M3 × M4 × M6	0.0141 ***
M2 × M5	0.5496 ***	M1 × M4 × M5	0.1204 ***	M1 × M2 × M3 × M6	0.0152 ***	M1 × M2 × M3 × M5 × M6	0.0258 ***
M2 × M6	0.0282 ***	M1 × M4 × M6	0.0074 ***	M1 × M2 × M4 × M5	0.1497 ***	M1 × M2 × M4 × M5 × M6	0.0132 ***
M3 × M4	0.2928 ***	M1 × M5 × M6	0.0106 ***	M1 × M2 × M4 × M6	0.0077 ***	M1 × M3 × M4 × M5 × M6	0.0230 ***
M3 × M5	0.2016 ***	M2 × M3 × M4	0.4497 ***	M1 × M2 × M5 × M6	0.0152 ***	M2 × M3 × M4 × M5 × M6	0.0849 ***
M3 × M6	0.0225 ***	M2 × M3 × M5	0.5372 ***	M1 × M3 × M4 × M5	0.1494 ***	M1 × M2 × M3 × M4 × M5 × M6	0.0244 ***
M4 × M5	0.4207 ***	M2 × M3 × M6	0.0497 ***	M1 × M3 × M4 × M6	0.0134 ***
M4 × M6	0.0249 ***	M2 × M4 × M5	0.5292 ***	M1 × M3 × M5 × M6	0.0163 ***
M5 × M6	0.0320 ***	M2 × M4 × M6	0.0262 ***	M1 × M4 × M5 × M6	0.0129 ***
Control Variables	YES
Regional Fixed Effects	YES
Time Fixed Effects	YES

Note: *** p < 0.01, ** p < 0.05.

.

Incorporating the execution of policy investments, this research posits the following:

Firstly, the combined impact of all policy tool configurations significantly promoted the development of higher education institutions co-established by provincial and ministerial governments. This finding suggests that the diverse amalgamations of policy tools within the co-construction policy are conducive to the holistic advancement of academic institutions. It also demonstrates that the comprehensive investment strategy of the co-construction policy is highly effective in expediting the development and construction of these institutions.

Secondly, reviewing the findings reveals that combinations of two policy tools generally exhibit a synergistic effect, while specific combinations of three tools have an overall positive impact. Conversely, combinations of four, five, or six policy tools tend to show a universally diminished effect. Among all policy tool combinations, the “M2M3M4*M5” amalgamation is identified as the most effective in enhancing institutional development.

5. Discussion

First, the significant positive impacts of faculty development, research capability enhancement, and teaching reinforcement underscore their foundational roles in higher education institution advancement. Substantial investments in faculty development have notably mitigated talent acquisition and retention challenges in China’s central and western universities, addressing the “peacock flying south” brain drain phenomenon. Research capability enhancement has generally improved co-built universities’ capacity to expand advanced platforms and nurture significant projects. Teaching reinforcement has corresponded with marked improvements in educational quality and environments [77].

Second, advantageous discipline development and financial resource allocation are categorized as underinvested pathways. Co-built universities have long faced financial support scarcity, indicating a substantial funding shortfall. The 14 co-built universities start from relatively weak academic foundations with constrained growth potential. These suboptimal effects primarily stem from the universities’ enduring “cumulative poverty and weakness”, suggesting inadequate current policy efforts. Maximizing performance from these pathways requires refining existing policy instruments with a focus on quantitative enhancement.

Third, the reformative management policy pathway is characterized by a “slow effect”. University decision-making and management involve complex processes, including mechanisms, procedures, and methodologies, alongside the need to mobilize, integrate, and allocate diverse resources such as educational, economic, political, technological, intellectual, informational, and financial assets. The study posits that management reform factors have relatively weak influence, reflecting the unique nature of reformative pathways and their progressive impact. Consequently, management reforms are not expected to yield immediate outcomes but should be gradually introduced to influence co-built universities’ development.

Fourth, the uniform positive effects observed across all pathway combinations reflect the pressing resource scarcity in co-built universities. Prolonged low levels of incentives, constraints, and safeguards for higher education in China’s central and western regions have not yet reached the threshold of marginal effectiveness, a key factor in the potential for increased tool investment to yield superior results.

Fifth, while robust policy tool investment is necessary to support co-built universities’ development, an overabundance is not inherently beneficial. Multiple policy tools may lead to resource contention, potentially neutralizing policy objectives and diminishing overall effectiveness. An excess of policy instruments could dilute government resources, preventing concentrated efforts to address critical issues and impairing policy outcomes.

Generally, higher education development typically exhibits a “siphon effect” [78], where regions with superior socioeconomic conditions attract disproportionate resources compared to less developed areas. This phenomenon perpetuates a cycle of advantage for resource-rich regions while exacerbating scarcity in disadvantaged areas. In this context, national administrative intervention in macro-level resource allocation becomes imperative. Through the strategic deployment of policy instruments, targeted pathways are established to channel resources towards underdeveloped regions, fostering university development in these areas. This approach serves to counteract the natural tendency of resource concentration in privileged regions, thereby promoting equitable and sustainable development of higher education across the nation.

6. Conclusions

Model testing has confirmed the impact of diverse investments on the development of higher education institutions. Based on these findings, we propose strategies to leverage policy investments for sustainable growth in the higher education sectors of less developed regions.

First, the strategic allocation of resources to support higher education in underdeveloped regions through administrative measures yields substantial benefits. China’s experience demonstrates that disparities in higher education development are an inevitable challenge in national growth trajectories. The state bears the responsibility of ensuring equitable access to high-quality education and must proactively intervene to address these imbalances. By leveraging its administrative authority, the government can implement macro-level coordination mechanisms to prioritize resource allocation to underdeveloped regions. This approach ensures that universities in these areas receive crucial support for their growth and development, thereby fostering a more balanced national higher education landscape.

Second, institutional frameworks should be established to sustain the efficacy of advantageous pathways. Faculty development, research capability enhancement, and teaching reinforcement have been identified as effective pathways with swift policy response and investment. To ensure long-term impact, mechanisms for resource allocation and utilization must be developed, particularly by refining ancillary policies that support these investments.

Third, identifying policy pathways with slower response rates is crucial. Underinvestment in certain areas may indicate lower efficiency, characteristic of investments with a gradual impact. Long-term commitment to discipline development and management reform is necessary, with attention to the subtle effects that emerge over time.

Fourth, ensuring the effectiveness of financial allocation in resource distribution is paramount. Financial constraints widely affect policy implementation. As a critical input, funding requires stable, continuous, and long-term commitment to mitigate conflicts arising from financial limitations during policy execution.

Fifth, safeguarding the autonomy of higher education institutions in resource utilization is vital. Governance in higher education is guided by academic principles, requiring the government to provide operational autonomy to optimize resource use. The Chinese government’s “decentralization, regulation, and service” reforms aim to delegate more autonomy to institutions. Empirical evidence from provincial and ministerial investments suggests that institutional autonomy is essential for effective academic governance. To support higher education in underdeveloped regions, the central government must further empower these institutions with substantial autonomy, enabling them to convert resources into comprehensive institutional strength.