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Article

Effects and Mechanisms of Higher Education Development on Intelligent Productivity Advancement: An Empirical Analysis of Provincial Panel Data in China

by
Pan Liang
1,2,* and
Yuancao Chen
3,*
1
School of Public Administration, South China University of Technology, Guangzhou 510640, China
2
Office of the Party Committee and the President, Guangxi University, Nanning 530004, China
3
Finance Office, Guangxi University, Nanning 530004, China
*
Authors to whom correspondence should be addressed.
Sustainability 2024, 16(24), 11197; https://doi.org/10.3390/su162411197
Submission received: 1 November 2024 / Revised: 6 December 2024 / Accepted: 18 December 2024 / Published: 20 December 2024
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Abstract

:
In the digital economy era, artificial intelligence implementation has accelerated the intellectualization of productive forces, emphasizing the critical relationship between higher education and this transformation. As the primary conduit for developing advanced human capital, the mechanisms through which higher education adapts to and promotes emerging productive forces require systematic examination. This research establishes a theoretical framework demonstrating the synchronous relationship between higher education development and productive force intellectualization, proposing that higher education development provides essential momentum for this transformation. The framework validation employed panel data analysis from 31 Chinese provinces (2012–2022) using fixed-effects (FE) and mediation effect models. The FE model reveals a positive effect coefficient of 1.561 for higher education development on intelligent productive force enhancement (p < 0.01), indicating substantial promotion of productive force intellectualization without saturation effects. Mediation effect analysis confirms the significance of three mediating factors—labor, capital, and technology (p < 0.05)—validating the influence pathways through human capital, material support, and research innovation mechanisms. The research innovation mechanism demonstrates premier efficacy, while material support mechanisms indicate optimization potential. The human capital mechanism, despite its promise, exhibits implementation time lags. These findings suggest prioritizing intelligent technology talent development, enhancing research investment, and strengthening innovation capabilities to advance higher education’s role in productive force intellectualization.

1. Introduction

Productive forces, as fundamental drivers of sustainable social development, serve as critical indicators of historical progress. In 1996, Tapscott, in his work The Digital Economy: Promise and Peril in the Age of Networked Intelligence, introduced the concept of the “digital economy” [1], describing a novel labor paradigm distinct from mechanical production, characterized by transformed production relationships and tools. With advancing technology, information technology emerged as a transformative force, accelerating human civilization’s development. The 2016 G20 Hangzhou Summit’s Digital Economy Development and Cooperation Initiative formally recognized “data” as the fifth major production factor, alongside land, labor, capital, and technology [2,3]. Initially, digitalization leveraged modern communication networks and digital repositories to aggregate research elements via digital channels, optimizing behavioral patterns in living, working, learning, and decision-making [4]. However, the rapid evolution of technologies such as cloud computing, blockchain, and big data has embedded artificial intelligence into digital production processes, infusing them with intellectual characteristics and reshaping production and lifestyle paradigms. This technological convergence has catalyzed production development through data-driven methodologies [5], cross-sector integration [6], and innovation ecosystem construction [7]. In recent years, escalating labor costs have further accelerated the intellectualization of digital factors, demonstrating significant economic potential. In response, many nations have implemented policies to support the transformation of productive forces. For example, China has introduced key policy documents such as Made in China 2025 and the New Generation Artificial Intelligence Development Plan, which aim to integrate AI technologies across industries, foster intelligent economic systems, and drive the shift toward advanced production modes through “AI+” initiatives. This study conceptualizes these developments in digital production as the intellectualization trajectory of digital production factors [8,9].
Higher education’s role in the evolution of intelligent productive forces warrants critical examination. While scholarly discourse has established the relationship between higher education and digital transformation, the intellectualization of production, as an emerging paradigm, potentially influences human development patterns. As this transformation permeates education, its facilitative nature might affect higher education’s developmental trajectory, potentially moderating its catalytic impact on productive forces. Additionally, the rapid expansion and increasing accessibility of higher education necessitate careful analysis of its productivity enhancement mechanisms. China exemplifies this transformation. Following the 1999 higher education expansion initiative, China’s gross enrollment ratio in higher education achieved 60.2% by 2023, with doctoral enrollment reaching 153,300 [10], indicating significantly enhanced educational accessibility [11]. Simultaneously, China’s GDP attained 126.0582 trillion yuan in 2023 [12]. Within this economic context, fundamental questions emerge regarding higher education’s response to production intellectualization challenges, its influence on productive force transformation, and the underlying mechanisms. This research examines the interaction between higher education and intelligent productive forces through empirical analysis. Utilizing China as a case study, the investigation employs a difference-in-differences methodology on provincial panel data (2012–2022) to analyze higher education’s impact on productive force intellectualization, including operational mechanisms and regional–temporal heterogeneity. This analysis aims to provide empirical evidence for higher education policy development within the intellectual transformation framework.

2. Literature Review

Academic research examining the nexus between higher education and production–economic development has focused on two fundamental trajectories.
Academic inquiry into the relationship between higher education and production development has evolved significantly. Early investigations focused on education’s fundamental impact on production metrics. Miller analyzed the correlation between educational duration and total factor productivity (TFP) [13], while Pritchett examined education returns relative to TFP [14]. Empirical studies from Portugal (1960–2001) and former Soviet republics (1995–2008) established positive correlations between educational attainment and TFP growth [15]. As higher education expanded globally, research shifted toward comparative analyses of educational tiers’ differential impacts on productivity. Notably, Fleisher et al.’s examination of Chinese provincial TFP (1990–2003) revealed that secondary and tertiary education significantly enhanced productivity growth [16]. Complementary research across 19 OECD nations (1960–2000) demonstrated that tertiary education uniquely drove TFP advancement, while lower educational tiers showed minimal impact [17]. Other perspectives suggest that education levels generally correlate positively with productivity, emphasizing the importance of supporting higher education in developing countries [18]. A review of existing research indicates that most scholars affirm the positive correlation between average education levels and production efficiency.
Research on the relationship between higher education and productivity often explores the mechanisms and pathways through which higher education influences productivity. These studies cover several dimensions: firstly, from the perspective of human resources, scholars suggest that higher education is a driving force for new-quality productivity. The core mechanism involves a chain reaction of “talent cultivation → technological innovation → industry incubation”, ultimately fostering high value-added industries [19]. Other studies examine how the digital economy reshapes labor capabilities [20], emphasizing improvements in data thinking, digital ethics, and skills, which drive the creation of intelligent “robots”, forming a new “real + virtual” worker paradigm [21]. Secondly, regarding production tools, analyses show that digital innovations in higher education, achieved through virtual teaching, research, and data analysis, transform into social productivity. This shift changes production tools from manual to intelligent systems and materials from electricity-driven to data-driven formats, generating knowledge spillovers and catalyzing technology-intensive industries. Scholars highlight the importance of focusing on core and frontier technologies, using cutting-edge instruments to advance productivity [22]. Finally, from the production environment perspective, scholars propose the theory of open innovation ecosystems, with universities as core elements. This theory reveals how higher education empowers new-quality productivity development [23]. To maximize this role, some advocate for a flexible higher education ecosystem that adapts to social change [24], while others stress the integration of education, innovation, industry, and talent chains to foster productivity growth [25].
Research on the relationship between higher education and productivity often examines the mechanisms by which higher education influences productivity. In recent years, a significant trend has emerged in China, focusing on higher education’s role in shaping new-quality productive forces. Scholars acknowledge that with rapid advancements in information technology, productive forces have transformed, exhibiting new characteristics. The “new” aspect refers to innovative production factors and their combinations, while “quality” indicates robust industrial foundations and development momentum [26]. Consequently, researchers are keenly interested in how higher education empowers these forces, exploring their value, logic, and pathways [27,28]. They propose strategies for advancing new-quality productive force development, emphasizing key factors like resource allocation, technology, and talent [29,30,31]. The author argues that the intellectualization of productive forces is both a form of new-quality productive forces and an inevitable outcome of their evolution. Thus, when examining this intellectualization, valuable insights can be drawn from existing research.
A review of the existing literature reveals substantial findings on the relationship between higher education and productivity development, particularly regarding how higher education enhances productivity and offers valuable perspectives. However, despite extensive discussions on emerging production modes, the research specifically examining the relationship between intellectualization and higher education remains limited. This study focuses on exploring the interaction mechanisms between intellectualization and higher education, addressing these research gaps.

3. Theoretical Analysis and Research Hypothesis

First, a theoretical hypothesis on the impact of higher education on production intellectualization: From the perspective of productive forces, this study hypothesizes that production intellectualization and the development of higher education share isomorphic characteristics. Accordingly, advancements in higher education are posited to effectively promote the intellectualization of productive forces.
In production force theory, workers, means of production, and objects of labor represent the three fundamental elements of productive forces [32], whose organic coordination and synergistic interaction ensure effective productivity. Building on the background outlined in the introduction, this study posits that the widespread adoption of artificial intelligence, big data, and related technologies in production processes has injected significant momentum into productive forces through data-driven mechanisms. In this process, the three elements of productive forces are progressively acquiring distinct intellectual characteristics, a phenomenon we define as “productive force intellectualization”. Productive force intellectualization refers to the transformative reconstruction of productive forces driven by the deep integration of information technology—including chips, computers, the internet, big data, cloud-computing, virtual reality, and intelligent algorithms—with artificial intelligence. This integration is revolutionizing production modes and social relationships, emerging as a critical driver of development in the digital era and advancing productive forces toward more sophisticated evolutionary states [33]. In the context of intelligent production, production factors evolve into new forms: workers transition from primarily physical to intellectual labor, tools of production advance from conventional to intelligent machines, and objects of labor shift from physical materials to data, information, and knowledge. These transitions illustrate that intellectualization has comprehensively innovated traditional productive force elements, with intelligent technologies inducing fundamental qualitative changes in productive forces.
The development of higher education and the cultivation of new productive force elements are closely interconnected in both processes and outcomes. Universities drive advancements in emerging technologies, such as intelligent manufacturing and artificial intelligence, through cutting-edge research and interdisciplinary innovation. Higher education fosters high-caliber talent equipped with professional expertise, innovative thinking, and digital competencies, enabling them to meet the demands of intellectualized production environments. Additionally, university-established industry–academia collaboration platforms promote knowledge transfer and technological dissemination, accelerating the transformation of intelligent technological innovations into practical productive forces [34]. By aligning with the requirements of productive force intellectualization, higher education serves as a key driver of advanced productive forces and contributes to the overall enhancement of social productivity. This study argues that the development of higher education facilitates the integration of traditional productive force elements with AI-related factors, acting as a catalyst for their evolution into more advanced forms.
Therefore, we propose the following:
Hypothesis 1:
The development of higher education exerts a positive promoting effect on the advancement of intellectualized productive forces.
Second, a theoretical hypotheses on the mechanisms of higher education’s influence on production intellectualization: By integrating the characteristics of production intellectualization with the developmental patterns of higher education, this study hypothesizes that higher education generates spillover effects, enhancing intellectualized productive forces through its influence on three critical factors: labor, capital, and technology.
Capital enhancement mechanism: Theodore Schultz’s seminal 1961 contribution to human capital theory [35] expanded traditional production factors beyond land, labor, and physical capital to encompass human capital, establishing workers as the paramount and most dynamic components within productivity systems. Nelson and colleagues subsequently explored education’s mediating role in total factor productivity, advancing the theoretical proposition that a nation’s human capital capacity directly correlates with its ability to assimilate and implement frontier technologies, thereby accelerating technological convergence and enhancing aggregate productivity [36,37]. Higher education fundamentally functions as an investment and production system for advanced human capital, with its developmental scope determining societal talent capacity [38]. At the microeconomic level, higher education graduates entering the workforce catalyze the optimization of production relationships and enhance operational efficiency. At the macroeconomic level, higher education’s continuous generation of innovative talent and skilled professionals provides the economy with sophisticated human capital resources, simultaneously advancing individual labor productivity and aggregate social productivity. Through this human capital enhancement mechanism, universities facilitate the transformation of productive forces by generating the intellectual capital essential for intelligent production systems. Therefore, we propose the following:
Hypothesis 2:
Higher education facilitates productive force intellectualization through human capital accumulation mechanisms.
Material resource support mechanism: Research investment constitutes the infrastructural foundation for productive force development, serving as a critical determinant in production factor advancement and system resilience. Higher education institutions function as primary centers for productive force research and development through strategic research fund allocation. With sustained fiscal support, these institutions systematically invest in advanced scientific infrastructure, specialized laboratory facilities, elite researcher recruitment, and diverse innovation initiatives. This comprehensive investment framework accelerates development in frontier technologies—including artificial intelligence, big data analytics, and cloud-computing systems—while strengthening institutional capacity for cultivating innovation-oriented talent. China’s research investment trajectory exemplifies this mechanism: maintaining annual growth rates exceeding 10%, research funding surpassed the trillion-yuan threshold in 2012, reaching 2.21436 trillion yuan in R&D expenditure by 2019—a twofold increase from 2012 levels. University research funding demonstrated particularly robust growth, attaining 179.662 billion yuan in 2019 [39]. Through this material resource support mechanism, universities establish advanced scientific research centers and technological infrastructure, providing the essential physical foundation for productive force intellectualization. Therefore, we propose the following:
Hypothesis 3:
Higher education enhances productive force intellectualization through material input allocation mechanisms.
Research and innovation integration mechanism: Industry–academia–research collaboration represents a cornerstone of contemporary higher education research strategy. Higher education institutions, as primary catalysts for technological advancement, have evolved toward an integrated development framework that aligns innovation trajectories with industrial requirements while developing talent ecosystems that support these innovation pathways. The industry–academia–research integration paradigm enables universities to generate and commercialize research outcomes, facilitating seamless incorporation of technological innovations into production systems and societal applications [40,41]. Innovation capability serves as the fundamental driver of productive force intellectualization, and higher education’s strategic position within the innovation ecosystem generates spillover effects across production domains, catalyzing comprehensive production system advancement. Therefore, we propose the following:
Hypothesis 4:
Higher education accelerates productive force intellectualization through scientific innovation and knowledge creation mechanisms.
The theoretical framework and its constituent relationships are delineated in Figure 1, while Figure 2 illustrates the hypothesized causal pathways.

4. Variable and Model Selection

4.1. Variable Selection and Quantification

4.1.1. Dependent Variable

Proxy variables constitute a fundamental concept in regression analysis and are extensively utilized across research domains. According to established principles of proxy variable selection in auditing [42,43], these variables must effectively substitute for factors that are challenging to directly observe or measure, thereby enhancing the observability and quantifiability of dependent variables. In the era of intelligence, the integration of artificial intelligence technology has fundamentally altered the composition of production factors, with knowledge, information, and digital technology emerging as increasingly significant elements. Given these characteristics, this study employs specific proxy variables to measure intelligent productivity.
Robot density. The deployment of industrial robots has fundamentally transformed production processes, enhancing human–machine complementarity and accelerating the integration of industrial development with intelligent technologies. This transformation has reshaped industrial structures and production modes, driving industries toward advanced developmental stages. Industrial robots serve as technological instruments that exemplify the synthesis of production and intelligence. Their implementation indicates both the extent of robot adoption and the penetration of intelligent technologies in regional industrial production [44]. Following the methodologies of Cai Xiangjie (2024) [45] and Tian Gaoliang (2024) [46], this study uses robot installation density as a quantitative measure of intelligent productive forces [47]. The measurement incorporates industrial robot quantities across Chinese industrial sectors from 2012 to 2022, weighted by the ratio of regional to national industrial employment. The calculation is expressed as follows: Regional industrial robot installations × (Regional industrial employment ÷ National industrial employment).
Number of internet broadband access ports: Broadband infrastructure constitutes a fundamental component of digital economic architecture [48]. Within the digital economy, enterprises leverage access networks for digital presence establishment, e-commerce operations, and service delivery platforms. Infrastructure capacity and quality parameters directly influence digital economic trajectories [49]. The proliferation of high-speed internet connectivity facilitates seamless data transmission, cloud-computing implementation, and real-time information exchange, thereby enabling sophisticated digital business models and intelligent production systems. The density and quality of broadband infrastructure significantly impact the development of intelligent manufacturing capabilities and digital service innovation. Aligned with the established literature, broadband access density serves as a critical indicator of intelligent productive force development.
Number of artificial intelligence enterprises by province: This indicator reflects the market-driven level of intelligent productive forces. The geographical distribution and concentration of AI enterprises serve as crucial metrics for assessing regional innovation capacity and technological advancement. These enterprises contribute to the development of intelligent productive forces through research and development activities, technology commercialization, and knowledge spillover effects. Moreover, the presence of AI enterprises creates innovation clusters that attract talent, capital, and complementary businesses, fostering a conducive ecosystem for intelligent production development. This study uses the annual count of AI enterprises by province and region as a key sub-indicator to measure intelligent productive capacity.
Drawing upon these three sub-indicators, the entropy method is applied to compute the specific values of the dependent variable.

4.1.2. Independent Variable

The development of higher education represents a complex process encompassing multiple entities and mechanisms, categorized into “hard power” and “soft power” components. Hard power encompasses higher education’s overall capacity in human resources, financial resources, and physical infrastructure-essentially the quantifiable scale of development. These material foundations serve as fundamental elements measurable through specific metrics. Soft power comprises multidimensional capabilities, including cultural heritage [50] and research strength [51], reflecting higher education’s integrated impact in talent cultivation, scientific research, social service, and cultural innovation. Hard power development provides both the foundation for enhancing comprehensive institutional strength and a key focus for national and governmental support. Therefore, while maintaining explanatory variable accuracy, data indicator accessibility must be ensured. This study utilizes higher education scale as a direct measure of overall development. Figure 3 illustrates the relationship between hard and soft power development in higher education.
The academic literature delineates four principal categories of indicators for measuring higher education scale.
First, a student scale encompasses both regional higher education development and talent cultivation capacity while demonstrating higher education’s contribution to regional human capital accumulation. This study integrates student enrollment data and institutional quantities to provide comprehensive measurement metrics [52].
Second, a faculty scale represents a critical determinant of higher education quality, directly influencing both instructional effectiveness and talent development outcomes. This study employs student-faculty ratios as a quantitative measure of faculty resources [30,53].
Third, an investment scale reflects governmental support while demonstrating institutional capacity for autonomous development, establishing foundational conditions for enhancing university excellence. Educational funding determines operational viability and development trajectory, influencing teaching infrastructure, research capabilities, and talent acquisition potential. Sufficient funding sustains academic operations, facilitates disciplinary advancement and research innovation, and elevates educational quality. This study utilizes per-student public budget expenditure as the primary investment metric [54,55].
Fourth, physical infrastructure comprises essential operational foundations for academic and research activities, encompassing educational facilities, laboratories, libraries, sports complexes, and specialized research equipment. These comprehensive resources provide fundamental support for educational functions while enabling advanced scientific research capabilities. Superior infrastructure enhances pedagogical effectiveness, optimizes learning and research environments, and strengthens institutional competitiveness. This study employs fixed asset valuations as the measurement criterion for physical resources [56].
The entropy methodology calculates quantitative values for explanatory variables derived from these established indicators.

4.1.3. Variables for Mechanism Testing

When investigating the influence of higher education on the intellectualization of productivity, this study considers the inherent complexity and diversity of the mechanism by utilizing proxy variables for quantification. Based on theoretical assumptions, the impact of regional higher education development on intelligent productivity is primarily realized through three mechanisms: human capital, physical resource support, and research innovation. In line with proxy variable selection logic, this study employs three proxy variables: (1) the structure of the higher-education population, (2) the proportion of science and technology expenditure in public fiscal spending, and (3) the natural logarithm of the number of R&D projects in large-scale industrial enterprises to measure these mechanism variables, respectively.
Human capital: The structure of the higher-education population [57,58]. The impact of human capital on industrial upgrading is primarily achieved through improvements in the population’s educational structure, as the demand for high-quality human capital becomes increasingly urgent in the process of productive force intellectualization. To accurately measure the effect of higher education on human capital accumulation, this study adopts methodologies from Cai Xiangjie (2024) [45] and Jin Yingjun (2018) [59]. Specifically, the proportion of individuals with higher education or above within the total human capital is used as an indicator to assess China’s population educational structure. The calculation method is as follows: the ratio of individuals with university-level education or above to the total population aged 6 years and above.
Material input: The proportion of science and technology expenditure within public fiscal spending [60]. This indicator quantifies governmental and institutional commitment to technological advancement through investment intensity. Enhanced technological expenditure allocation signifies increased fiscal resources directed toward innovation infrastructure, encompassing research facility development and project-funding initiatives. Through strategic technology investment, higher education institutions cultivate superior research environments and innovation ecosystems, facilitating technological transfer and industrial intellectualization. Consequently, this metric demonstrates higher education’s infrastructural contribution to intelligent productivity advancement.
Scientific Innovation: The natural log-transformed number of R&D projects conducted by large-scale industrial enterprises(natural log-transformed) [61], which indicates innovation implementation intensity and research capability dimensions. Primarily, R&D project volume demonstrates corporate innovation investment and strategic commitment to technological advancement. Enhanced research activity signifies heightened organizational emphasis on technological innovation and strategic awareness of intelligent production imperatives. Additionally, R&D project density reflects the robustness of industry-academia partnerships between higher education institutions and enterprises. Through collaborative R&D initiatives, academic institutions facilitate the transformation of theoretical advances into practical applications, accelerating industrial production intellectualization. Thus, this metric quantifies the research innovation mechanism’s effectiveness.

4.1.4. Control Variable Selection

To mitigate estimation bias in the explanatory variables’ coefficients due to omitted variables, this study incorporates control variables at both micro-enterprise and macro-provincial levels:
Consumption level: assessed via annual per capita disposable income of residents by province (yuan).
Economic development level: evaluated through annual provincial GDP (100 million yuan).
Real estate development intensity: measured by annual real estate investment by province (100 million yuan).
Degree of opening-up: determined by the total import and export value of goods by business location and province (100 million yuan).
Industrial enterprise development scale: represented by the number of large and medium-sized industrial enterprises (units).
Transportation development level: measured by annual provincial freight volume in transportation and postal services (10,000 tons).
All indicators have been natural log-transformed to ensure consistency in analysis.

4.1.5. Descriptive Statistics of Variables

This study employs panel data from 31 provincial-level administrative regions in China (excluding Hong Kong, Macau, and Taiwan) for the period 2012–2022, comprising a total of 341 observations. The raw data for the relevant variables were obtained from multiple authoritative sources, including the China Statistical Yearbook, China Education Statistical Yearbook, China Science and Technology Statistical Yearbook, Statistical Report on Internet Development in China, International Federation of Robotics (IFR), China Research Data Services Platform (CNRDS), National Bureau of Statistics of China website, provincial statistical bureau websites, and the Tianyancha database. Missing data were handled using interpolation methods. Table 1 provides the descriptive statistics of the variables.

4.2. Model Selection

4.2.1. Benchmark Regression Model

Panel model analysis encompasses three approaches: the pooled (POOL) model, fixed effects (FE) model, and random effects (RE) model. To determine the most appropriate model, we performed model selection tests. The Hausman test results (χ2 = 75.07, p = 0.000) indicate that the FE model is preferred over the RE model. Consequently, this study adopts the fixed effects (FE) model to estimate the impact coefficients of regional higher education development on intelligent productive forces. The model (1) specification is as follows:
PAIit = α0 + α1Heduit + βXit + λi + γt + εit
In Equation (1), subscript i denotes provinces, and t indicates time periods. λi captures province-fixed effects, and γt represents time-fixed effects. The dependent variable PAIit measures the annual development level of intelligent productive forces in each province. Heduit is the explanatory variable, specifically the annual development level of higher education in each province. This study focuses on the coefficient Heduit of the explanatory variable α1. A significant positive coefficient would indicate that regional higher education development positively affects intelligent productive forces, while a negative coefficient would suggest an adverse effect. Xit represents a vector of control variables, and εit denotes the random error term.

4.2.2. Mechanism Analysis Model

This study hypothesizes that higher education impacts the advancement of productive forces and boosts productivity through three mechanisms: human resource development, material support, and scientific research innovation. To examine these mediating pathways—human capital, material input, and scientific innovation—this study employs Jiang Ting’s (2022) [62] model framework, as represented in Equation (2):
Mediatorit = b0 + K1Heduit + PXit + λi + γt + εit
In Equation (2), subscript i indicates provinces, and t indicates time periods. The model includes provincial fixed effects (λi) and time-fixed effects (γt). The mediating variable Mediatorit comprises three mechanisms in separate models: human capital (LnHcap), material input(LnHcon), and scientific innovation(LnStin). A significantly positive coefficient K1 would validate these mediating mechanisms in the impact of higher education development on intelligent productive forces. Xit denotes a vector of control variables, and εit represents the random error term.

5. Results

5.1. Benchmark Regression Results

This study uses Equation (1) to estimate the impact of higher education development on intelligent productive forces, with results detailed in Table 2. Column (1) displays estimates without control variables, time, or regional effects, showing an impact coefficient of 1.092, significant at the 1% level. This initial result indicates a positive effect of higher education on improving intelligent productive forces. Column (2) adds time and regional fixed effects, while Column (3) introduces control variables. Column (4) integrates both fixed effects and control variables, yielding a coefficient of 1.561, also significant at the 1% level. The empirical evidence substantiates higher education’s significant catalytic role in advancing intelligent productivity development. The complete regression results are shown in Table 2.

5.2. Robustness Tests

To validate the robustness of our base model estimates, we conduct three sensitivity analyses: alternative specifications, subsample tests, and endogeneity checks.

5.2.1. Alternative Estimation Model Test

To align with our research objectives, we supplement the baseline fixed effects (FE) model with ordinary least squares (OLS) and Tobit models for re-estimation. The OLS model serves as a fundamental regression method, suitable for analyzing linear relationships between dependent and independent variables. In contrast, the Tobit model is designed for cases involving censored dependent variables, where the dependent variable is restricted to zero or falls within a specific range. The inclusion of alternative models aims to account for potential variations in estimation results across different regression approaches, thereby enhancing the robustness and reliability of our findings.
Table 3 presents the results. Column (1) shows the OLS regression outcomes, while Column (2) displays the Tobit model results. Both tests reveal that the impact coefficient of the explanatory variable is 1.561, remaining statistically significant at the 1% level. These robustness checks confirm the validity and reliability of the baseline results in Table 2.
For detailed regression results, refer to Table 3.

5.2.2. Alternative Sample Estimation Test

Analysis of provincial panel data reveals significant development disparities across China’s eastern, central, and western regions. The fourth round of disciplinary evaluation illustrates this disparity: Of China’s 748 A-grade disciplines, Beijing leads with 225 disciplines, followed by Shanghai, collectively representing one-third of the nation’s premier disciplines [63]. Economically, Beijing and Shanghai rank first and second nationally in GDP, with 2022 per capita GDP exceeding 200,000 yuan in both regions—more than double the national average [64]. Given their exceptional performance in both higher education and economic development, including Beijing and Shanghai could potentially skew the baseline model estimates. Column (3) of Table 3 presents results from a re-estimation of the baseline model excluding these two regions. The persistence of significant baseline regression results after this exclusion validates the findings presented in Table 2.
The regression results are reported in Table 4.

5.2.3. Endogeneity Analysis

Given that explanatory and dependent variables may face endogeneity issues due to unobservable factors and reverse causality, this study employs the first-order lag of time series dummy variables as instrumental variables and applies two-stage least squares (2SLS) regression analysis to reassess the validity of baseline results and enhance the robustness of baseline model estimation. When the weak identification test is significant at the 1% statistical level, the null hypothesis is rejected, indicating that not all variables are exogenous and confirming that the first-order lag of time series dummy variables can serve as valid instrumental variables. The model estimation results demonstrate that even in the presence of endogeneity issues, higher education maintains a significant positive impact on intelligent productivity, thereby verifying the robustness of the main conclusions from the baseline regression.
For detailed regression results, refer to Table 5.

5.3. Summary of Benchmark Regression Results

Initial robustness analyses encompassing model substitution, sample diversification, and endogeneity examination substantiate the stability of regression coefficients regarding higher education’s influence on intelligent productivity, empirically validating hypothesis H1. Contextualized within H1’s theoretical framework, this investigation posits that the developmental synergy between higher education and intelligent productivity aligns with broader societal evolution patterns. As an emergent productive paradigm, intelligent productivity’s continuous advancement necessitates multifaceted catalysts, particularly scientific–technological progression. Higher education’s developmental trajectory exhibits isomorphic coupling with this evolutionary process, functioning inherently as a mechanism for generating momentum and infrastructural support for intelligent productivity enhancement. This empirical evidence thus substantiates higher education’s catalytic role in advancing intelligent productivity development.
Empirical analysis excluding the Shanghai and Beijing samples reveals consistent positive associations between higher education development and intelligent productivity advancement. The coefficient differentials remain minimal, with both maintaining statistical significance at the 1% level. This empirical evidence suggests that higher education expansion effectively catalyzes productivity evolution without exhibiting apparent threshold constraints within contemporary production environments. This phenomenon potentially stems from intelligent productivity’s nascent developmental phase, wherein production factors’ allocation and utilization patterns remain exploratory and substantially below saturation thresholds. Higher education expansion facilitates expanded investigative domains and enhanced possibilities for intelligent productivity advancement, fostering continuous co-evolutionary development through reciprocal catalysis and collaborative exploration. Consequently, the current developmental stage exhibits sustained bidirectional advancement between higher education and intelligent productivity.

6. Mechanism Analysis

6.1. Mechanism Analysis Results

Using the econometric model in Equation (2), this study estimates the mediating mechanisms by which higher education influences intelligent productive forces, with results in Table 4. Columns (1) to (3) show that all three mechanisms are statistically significant at the 5% level. These findings indicate that high-quality human resources, scientific and technological investments, and research project development positively impact intelligent production. This confirms that higher education promotes intelligent productivity forces through human capital accumulation, material resource support, and scientific research innovation. The complete regression results are shown in Table 6.

6.2. Summary of Mechanism Analysis Results

Mechanistic regression analysis yields the following empirical insights:
First, the research innovation mechanism significantly promotes intelligent production. The research innovation mechanism emerges as the predominant catalyst in advancing intelligent production through facilitated resource transfer between higher education and industrial sectors via research initiatives and trilateral collaboration. This mechanism’s efficacy derives from the direct integration of academic research activities with production processes, simultaneously providing advanced technological support and responsive solutions to industrial requirements. Universities establish collaborative platforms that bridge research innovation with industrial applications, enabling accelerated diffusion of innovative resources. Research teams conduct targeted investigations addressing enterprises’ technical challenges during intelligent transformation, delivering precise solutions. The research innovation mechanism thus engenders productive synergy through optimized factor allocation and seamless integration of academic intellectual capital with industrial requirements, demonstrating enhanced coupling between research innovation and productivity advancement.
Secondly, the physical resource support mechanism emerges as the secondary driver of effectiveness, functioning as both the foundational infrastructure for research innovation and the cornerstone of comprehensive educational advancement. Analysis from the effects analysis indicates that the developmental synergy between higher education and intelligent productivity has not yet encountered threshold constraints, suggesting current inadequacies in physical resource support mechanisms. Specific deficiencies manifest across multiple dimensions: First, universities’ infrastructure and research equipment modernization lags behind intelligent production advancement, resulting in technological obsolescence within laboratory facilities. Second, structural imbalances persist in educational funding allocation, with suboptimal resource distribution in critical domains and emerging fields, constraining innovative capacity. Third, institutional intelligent infrastructure requires substantial enhancement, particularly regarding digital transformation investments. Additionally, resource utilization efficiency exhibits suboptimal patterns, with certain facilities operating below maximum capacity, generating operational inefficiencies. While existing physical infrastructure has facilitated intelligent productivity advancement, this mechanism demands significant reinforcement compared to research innovation protocols.
Thirdly, the human resource mechanism demonstrates relatively weak influence, ranking last among the three mechanisms. While labor is fundamental to productivity development, this ranking reflects the operational nature of the human resource mechanism rather than its effectiveness. Cultivating high-quality labor through higher education is a long-term process, manifested in the following aspects: First, the talent cultivation cycle is extensive, taking several years from enrollment to graduation and the development of practical skills. This process requires gradual knowledge absorption, capability development, and quality improvement. Second, intelligent production demands increasingly diverse skills, including innovative thinking, digital literacy, and interdisciplinary capabilities. Cultivating these abilities involves continuous practice and refinement, representing a complex, long-term process. Third, the ongoing nature of knowledge renewal and skill enhancement necessitates lifelong learning mechanisms to adapt to rapid changes in intelligent production environments. Thus, the human resource mechanism exhibits a lag in its effects. Although its short-term influence is limited, its long-term impact will become more evident, significantly promoting intelligent productivity over time.

7. Conclusions and Recommendations

This research examines how higher education development influences the intellectualization of productive forces. While the existing literature has documented positive correlations between educational attainment and productivity, emphasizing higher education investment in developing nations, this study extends current understanding by empirically analyzing both qualitative and quantitative educational capabilities in the digital era. The findings establish a robust positive relationship between higher education development and productive force intellectualization. Higher education enhances productive forces through multiple mechanisms. The primary pathway—human capital development—coupled with technological investment and research capacity enhancement, has substantially improved workforce quality, modernized production systems, and optimized operational relationships. Additionally, it has generated positive spillover effects on production factors, advancing intelligent productive force development and significantly enhancing productivity.
Based on these empirical findings and their implications for productivity optimization in the intelligent era, the following recommendations are proposed:
First, enhancing the alignment between higher education and intelligent productive force development is crucial for maximizing education’s role in advancing intellectualization. This research demonstrates that within current production environments and developmental contexts, higher education expansion effectively advances productive forces toward advanced evolutionary stages, with no significant diminishing returns observed. Therefore, higher education, as a cornerstone of intelligent technological innovation, must align strategically with economic, technological, and industrial frameworks. This alignment requires implementing problem-oriented and demand-driven approaches, optimizing talent development systems, fostering integration between education and productive force development, and strengthening research translation and application capabilities.
Second, the fundamental role of human capital warrants particular emphasis. In the contemporary landscape, global talent hubs and innovation centers serve as critical conduits for intellectual capital concentration [65]. While material support and research innovation demonstrate immediate effects, human capital mechanisms exhibit temporal delays due to the inherent nature of human capital accumulation. As a primary driver of productive force enhancement, human capital development requires sustained temporal cycles to manifest its full impact. Therefore, developing human capital for intelligent productive forces necessitates a comprehensive, long-term strategy. This approach requires cultivating both research innovators and skilled technical specialists. Higher education must align with intellectualization demands through a strategically differentiated talent development framework. This framework should incorporate adaptive cultivation hierarchies and evidence-based development mechanisms to ensure optimal alignment between human capital supply and productive force requirements. Such strategic alignment enables higher education to fulfill its essential role in advancing information-based production systems.
Third, optimizing research funding allocation and management mechanisms is essential. Developing nations consistently demonstrate insufficient research investment [66], with China exemplifying this challenge. In China’s western regions, research funding remains particularly inadequate, exhibiting a decremental financing gradient from east to west [67]. Given the progressive intellectualization of productive forces, addressing funding distribution inefficiencies requires systematic attention to allocation frameworks and mechanism development. This optimization necessitates coordinated institutional responses. Governments should implement targeted research funding strategies while facilitating private sector investment in technological innovation. Educational institutions must establish funding distribution systems aligned with research innovation capabilities and intellectual development priorities, thereby maximizing resource utilization efficiency. These systematic interventions can effectively advance the intellectualization of productive forces. Such strategic initiatives will enhance resource allocation efficiency, mitigate regional disparities, and accelerate technological advancement in accordance with intelligent production requirements.
Fourth, enhancing the research innovation framework is imperative. Within the context of economic globalization and innovation-driven development, technological advancement has become critical, with university research translation serving as the primary catalyst for productive force development. This necessitates fostering synergistic integration among industrial, academic, and research sectors while leveraging research capabilities of elite institutions to establish an intellectualization-oriented development system anchored in higher education. Such a system should facilitate the development of emerging intelligent production factors and generate innovative intellectualization models that transform productive force paradigms, advancing production processes toward sophisticated evolutionary stages.
While this research establishes empirical correlations between higher education and industrial intellectualization, several limitations merit consideration. The influence of higher education on industrial intellectualization demonstrates regional and sectoral variations. Moreover, the three primary mechanisms—human capital accumulation, material resource support, and research innovation—exhibit varying impacts across different stages of industrial development and educational contexts. The specific effects and underlying mechanisms of these heterogeneous relationships remain unexplored, presenting opportunities for future research and analytical investigation.

Author Contributions

Writing—original draft &review & editing, P.L.; Writing—original draft, Y.C. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Guangxi Degree and Graduate Education Reform Project of the Department of Education of Guangxi Zhuang Autonomous Region, China: Research and Practice on the Integration Mechanism of Industry and Education in Graduate Education at Local Universities (JGY2023018).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data are contained within the article.

Conflicts of Interest

The authors declare no conflicts of interest.

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Sustainability 16 11197 g001
Figure 1. Operational mechanism schematic (source: author’s synthesis and analysis).
Figure 1. Operational mechanism schematic (source: author’s synthesis and analysis).
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Figure 2. Research hypothesis logic schematic (source: author’s synthesis and analysis).
Figure 2. Research hypothesis logic schematic (source: author’s synthesis and analysis).
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Figure 3. Higher education institutional capability matrix (source: author’s synthesis and analysis).
Figure 3. Higher education institutional capability matrix (source: author’s synthesis and analysis).
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Table 1. Descriptive statistics of variables.
Table 1. Descriptive statistics of variables.
Variable TypeVariable NameSample SizeMeanStandard DeviationMaximum ValueMinimum Value
Dependent VariableIntelligentization Level of Productive Forces3410.08470.11230.00051.00001
Independent Variable Higher Education Development Index3410.28990.17260.00050.9210
Control VariablesConsumption Level34110.08450.41579.055811.2849
Economic Development Level3419.81840.99866.565511.7715
Real Estate Development Intensity3417.75691.12471.94599.7680
Degree of Opening-Up34117.65481.719612.646020.9856
Industrial Enterprise Development Scale3416.85431.33512.48499.2784
Transportation Development Level34111.52831.06517.027312.9815
Mechanism VariablesHuman Capital3410.15380.07810.02390.5049
Material Input3412.14871.53580.30006.7600
Scientific Innovation3418.72681.61432.995711.9929
(Source: authors’ calculations based on compiled data).
Table 2. Base model estimation results.
Table 2. Base model estimation results.
Intelligentization Level of Productive Forces
(1)(2)(3)(4)
Baseline Model (No Fixed Effects or Controls)Time and Regional Fixed EffectsControl Variables OnlyFull Model (Time and Regional Fixed Effects with Controls)
Higher Education Development Index1.092 ***
(0.197)		1.533 ***
(0.464)		1.525 ***
(0.423)		1.561 ***
(0.504)
Consumption Level −0.086
(0.091)		−0.026
(0.156)
Economic Development Level −0.037
(0.078)		−0.046
(0.103)
Real Estate Development Intensity 0.003
(0.012)		0.006
(0.019)
Degree of Opening-Up 0.001
(0.021)		0.003
(0.021)
Industrial Enterprise Development Scale 0.045
(0.029)		0.041
(0.029)
Transportation Development Level 0.070 *
(0.036)		0.060
(0.041)
Intercept Term−0.232 ***
(0.057)		−0.307 ***
(0.107)		−0.266
(0.832)		−0.692
(1.107)
Time Fixed EffectsNOYESNOYES
Regional Fixed EffectsNOYESNOYES
R20.7580.8020.8050.812
N341341341341
Note: *** p < 0.01, * p < 0.1, robust standard errors in parentheses.
Table 3. Robustness analysis: alternative model specifications.
Table 3. Robustness analysis: alternative model specifications.
Intelligentization Level of Productive Forces
(1)(2)
(Alternative OLS Model Estimation)(Alternative Tobit Model Estimation)
Higher Education Development Index1.561 ***
(0.238)		1.561 ***
(0.071)
Constant Term−0.960
(0.840)		−0.960
(0.859)
Control VariablesYESYES
Time Fixed EffectsYESYES
Regional Fixed EffectsYESYES
Weak Identification Test——
Weak Identification Test——
R20.909—
N341341
Note: *** p < 0.01, robust standard errors in parentheses.
Table 4. Robustness analysis: sample sensitivity tests.
Table 4. Robustness analysis: sample sensitivity tests.
Intelligentization Level of Productive Forces
(1)
Exclusion of Beijing and Shanghai Samples
Higher Education Development Index1.919 ***
(0.271)
Constant Term−0.167
(1.225)
Control VariablesYES
Time Fixed EffectsYES
Regional Fixed EffectsYES
Weak Identification Test—
Weak Identification Test—
R20.902
N319
Note: *** p < 0.01, robust standard errors in parentheses.
Table 5. Robustness analysis: endogeneity tests.
Table 5. Robustness analysis: endogeneity tests.
Intelligentization Level of Productive Forces
(1)
(Addressing Endogeneity)
Higher Education Development Index1.636 ***
(0.263)
Constant Term−1.413 *
(0.857)
Control VariablesYES
Time Fixed EffectsYES
Regional Fixed EffectsYES
Weak Identification Test636.265 ***
Under-identification Test34.526 ***
R20.653
N310
Note: *** p < 0.01, * p < 0.1, robust standard errors in parentheses.
Table 6. Mechanism analysis.
Table 6. Mechanism analysis.
(1)(2)(3)
(Human Capital)(Material Input)(Scientific Innovation)
Higher Education Development Index0.091 **
(0.038)		2.823 **
(1.389)		7.138 **
(3.376)
Constant Term−0.408
(0.497)		−48.822 **
(20.875)		62.916
(52.226)
Control VariablesYESYESYES
Time Fixed EffectsYESYESYES
Regional Fixed EffectsYESYESYES
R20.8170.3850.158
N341341341
Note: ** p < 0.05, robust standard errors in parentheses.
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Liang, P.; Chen, Y. Effects and Mechanisms of Higher Education Development on Intelligent Productivity Advancement: An Empirical Analysis of Provincial Panel Data in China. Sustainability 2024, 16, 11197. https://doi.org/10.3390/su162411197

AMA Style

Liang P, Chen Y. Effects and Mechanisms of Higher Education Development on Intelligent Productivity Advancement: An Empirical Analysis of Provincial Panel Data in China. Sustainability. 2024; 16(24):11197. https://doi.org/10.3390/su162411197

Chicago/Turabian Style

Liang, Pan, and Yuancao Chen. 2024. "Effects and Mechanisms of Higher Education Development on Intelligent Productivity Advancement: An Empirical Analysis of Provincial Panel Data in China" Sustainability 16, no. 24: 11197. https://doi.org/10.3390/su162411197

APA Style

Liang, P., & Chen, Y. (2024). Effects and Mechanisms of Higher Education Development on Intelligent Productivity Advancement: An Empirical Analysis of Provincial Panel Data in China. Sustainability, 16(24), 11197. https://doi.org/10.3390/su162411197

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