The Impact of Regional Policies on the Efficiency of Scientific and Technological Innovation in Universities: Evidence from China

Abstract

The efficiency of scientific and technological innovation in universities is strongly influenced by both institutional structures and policies. However, existing research predominantly emphasizes the role of internal factors—such as resource allocation, management efficiency, personnel systems within universities, and education-sector policies—on innovation efficiency. This focus often overlooks the significant impact of regional factors on innovation outcomes. This study compares and analyzes the scientific and technological innovation efficiency of universities, growth rates, sources of inefficiency, inter-regional disparities, and intra-regional differences between universities in three strategically important regions in China, namely the Yangtze River Delta, the Pearl River Delta, and the Beijing–Tianjin–Hebei region, based on their respective regional planning from 2007 to 2017. Additionally, it employs the Tobit model to explore the pathways to improve the scientific and technological innovation efficiency of universities within these three major strategic regions. This study finds that the implementation of targeted regional policies significantly enhances the efficiency of scientific and technological innovation in Chinese universities. Furthermore, it reveals that this positive impact also exhibits differences between universities and regions. The promotion effect of targeted regional policies on the efficiency of scientific and technological innovation in universities shows a high degree of similarity. In more developed cities, however, the scientific and technological innovation efficiency of universities tends to be lower. Both the Yangtze River Delta and the Pearl River Delta regions within the three major strategic areas are significantly affected by the negative impact of financial assets, while the Beijing–Tianjin–Hebei region, although affected negatively, is not significant. Moreover, this study uncovers that population density and fixed assets also serve as factors that can enhance the scientific and technological innovation efficiency of universities.

1. Introduction

The main driving force of regional economic development is technological progress and innovation [1], with the significant development of the economy and society, to promote the deep integration of scientific and technological innovation in universities and the economy; this is a realistic requirement for sustainable development. The formulation of China’s key regional orientation policies is rooted in the stage characteristics of China’s economic development [2]. In 2020, General Secretary Xi Jinping proposed the important strategic concept of dual circulation, emphasizing China’s need to fully leverage the advantages of its domestic large market and internal demand potential to better connect the domestic and foreign markets [3,4]. The further implementation of regional orientation policies is an important part of the national strategy under the context of dual circulation [5]. The “14th Five-Year Plan” highlights the need for China to comprehensively coordinate regional development [6], optimize the distribution of regional higher education resources, and promote the revitalization of education resources in central and western regions, indicating a more significant role for university development in regional development [7]. As an important force for basic research and original innovation in the field of high technology, universities provide a strong impetus for scientific and technological progress and sustainable socio-economic development [8].

The development of universities within regions is closely related to economic development [9]. University development needs to serve economic and social development and is closely linked with industrial development and various sectors [10,11]. Technological innovation in universities promotes sustainable economic development [12,13], and the efficiency of university scientific and technological innovation is closely related to university development [14,15]. Therefore, analyzing the relationship between key regional orientation policies and university scientific and technological innovation efficiency is crucial, which is the focus of this paper.

1.1. Related Work

In reviewing the existing literature, it is useful to compare China’s context with that of other countries with regional innovation policies focusing on universities’ roles. Studies on the impact of regional policies on university innovation efficiency in countries such as the United States, Germany, and Japan provide valuable comparative insights. For example, research on the European Union’s cohesion policy emphasizes the significance of regional policies in strengthening innovation through educational systems and collaboration [16]. Studies in Germany have examined the impact of Excellence Initiative funding on regional economic development and innovation, revealing that targeted support for research universities can foster regional growth and innovation clusters [17]. Similarly, U.S. research has highlighted the role of state funding and regional innovation clusters in enhancing university innovation output, particularly in sectors like technology and engineering.

Japanese studies also contribute to this discussion, demonstrating that industry–university collaborations in regions with specialized industrial clusters (e.g., robotics in Osaka) enhance university innovation productivity and regional development [18]. These international perspectives are relevant to China’s policy approach, as they reflect a common interest in leveraging university capabilities to boost regional innovation through targeted regional policies.

In addition to regional innovation policies, international sources on China’s regional development underscore the importance of strategic investments in university systems. Studies by organizations like the OECD analyze China’s dual circulation strategy and its impact on educational and technological innovation, stressing that fostering university-driven innovation is critical to meeting domestic and international economic demands [19]. World Bank studies on China’s innovation policy similarly highlight the importance of coordinated regional policy in supporting balanced development across regions, with an emphasis on closing regional disparities in educational resources [20].

Further examples of large-scale government studies and laws can be found in the United Kingdom’s Higher Education and Research Act (2017), which emphasizes the strategic importance of research universities in regional economic development, and South Korea’s Innovation Growth Policy, which promotes university–industry collaboration in specific technology sectors to enhance national and regional competitiveness [21]. Incorporating these global perspectives expands the context of this research and strengthens the relevance of the chosen methodology by positioning China’s policy environment in a broader, international framework.

The literature on the study of China’s key regional orientation policies in academia mostly focuses on the research of changes in regional ecological efficiency. For instance, researchers analyzed ecological total factor productivity in China, highlighting a growth pattern driven by technology dependence, although there has been an overall slowdown in labor, capital, and energy productivity [22]. Similarly, Liu et al. developed an index system to assess ecological efficiency in the Yangtze River Economic Belt, concluding that the region has relatively high ecological efficiency, with downstream areas performing better than upstream and midstream regions [23]. Additionally, Khan et al. evaluated environmental efficiency in 94 cities within the Yellow River Basin from 2007 to 2016 using a super-efficiency SBM model, finding that the region’s average environmental efficiency is in a stage of moderate-quality development [24]. Liu et al. compared ecological efficiency across the Yangtze and Yellow River Basins, concluding that ecological efficiency in these strategic regions follows a U-shaped trend, with the Yangtze River Economic Belt surpassing the Yellow River Basin in performance [25].

Some researchers further analyzed ecological efficiency across major economic regions in China under the “14th Five-Year Plan”, demonstrating that specific policies have significantly impacted regional differences in ecological efficiency [26]. Studies by Li and Liu found, based on a factor decomposition perspective, that China’s ecological total factor productivity exhibits a pattern of growth driven by technology dependence, with comprehensive slowdown in total factor labor, capital, and energy productivity [27]. Zhao, Sun and Tao and others calculated the ecological efficiency of the Yangtze River Economic Belt through a constructed index system, concluding that the average ecological efficiency of provinces in the Yangtze River Economic Belt is relatively high, with downstream ecological efficiency averaging higher than that of the upper and middle reaches [28]. Zeng, Liu, and Niu calculated the environmental efficiency of 94 cities in the Yellow River Basin from 2007 to 2016 using a super-efficiency SBM model, concluding that the average environmental efficiency level in the Yellow River Basin is in a stage of moderate-quality development [29]. Liu, Qiao, and Shi compared the regional differences and sources of ecological inefficiency between the Yangtze River Basin and the Yellow River Basin, concluding that the ecological environmental efficiency of the two major strategic regions exhibits a U-shaped change, with the ecological efficiency of the Yangtze River Economic Belt surpassing that of the Yellow River Economic Belt [30].

As shown above, the academic research on the impact of China’s key regional orientation policies on regional ecological efficiency has evolved from holistic evaluation to regional evaluation and then to regional comparison, but there is no research that specifically focuses on the impact of key regional orientation policies on university development. This paper conducts horizontal and vertical comparisons of multiple indicators of scientific and technological innovation efficiency of universities in the three major strategic regions of the Yangtze River Delta, Pearl River Delta, and Beijing–Tianjin–Hebei region, and uses the Tobit model to further examine the impact of various factors such as economic development on university scientific and technological innovation efficiency, exploring differentiated paths for improving university scientific and technological innovation efficiency in different strategic regions, filling the research gap on the impact of China’s key regional orientation policies on university development.

1.2. Contributions

To compare the differential impact of different key regional orientation policies on the scientific and technological innovation efficiency of universities in the region, this paper first compares and analyzes the overall scientific and technological innovation efficiency, scientific, and technological innovation efficiency of university growth rate, sources of financial inefficiency, and the overall financial gaps between universities in the three major strategic regions of the Yangtze River Delta, Pearl River Delta, and Beijing–Tianjin–Hebei region from 2007 to 2017. Secondly, in order to further investigate the differentiated impact paths of different regions on university scientific and technological innovation efficiency, this paper uses the Tobit model with university scientific and technological innovation efficiency as the dependent variable, and regional economic development level, population density, financial assets, foreign investment, industrial structure, fixed assets, government expenditure, and Internet levels as explanatory variables to examine the differentiated impact paths of regional factors on university scientific and technological innovation efficiency.

Firstly, from the perspective of the overall region, the scientific and technological innovation efficiency of universities in the three major strategic regions showed a slow upward trend from 2007 to 2017, with a generally high level and a continuous narrowing of the gap between regions. The overall growth rates of all three regions peaked in 2011, and have been declining since then, with the overall financial inefficiency mainly stemming from output inefficiency, and the overall gap within regions narrowing. Specifically, among the three major strategic regions, the Yangtze River Delta region has the highest scientific and technological innovation efficiency, followed by the Beijing–Tianjin–Hebei region, while the Pearl River Delta region is at a relatively lower level. However, the financial growth rate of universities in the Pearl River Delta region surpassed those of the Yangtze River Delta and Beijing–Tianjin–Hebei regions after 2014, becoming the region with the highest growth rate among the three major strategic regions. The overall scientific and technological innovation efficiency of universities gap among the three regions has been continuously narrowing. The Beijing–Tianjin–Hebei region has the highest overall level of financial inefficiency and the highest proportion of input inefficiency, while the Yangtze River Delta region has the highest proportion of output inefficiency, and the Pearl River Delta region has the lowest overall level of financial inefficiency. This paper measures the regional disparity of university scientific and technological innovation efficiency using the Gini coefficient of universities. The Gini coefficient of universities in the Yangtze River Delta region was relatively high in the early stage, showing a trend of continuous decline, while the Gini coefficients of universities in the Yangtze River Delta and Pearl River Delta regions showed relatively small changes in the measurement years, and after 2015, the overall gap between universities in the Yangtze River Delta region narrowed to the smallest among them.

Secondly, from the perspective of the impact of different factors on university scientific and technological innovation efficiency, except for the significant positive impact of public education expenditure on the scientific and technological innovation efficiency of universities in the three major regions, other factors exhibit significant regional differences. Specifically, the level of economic development has a significant positive impact on the Beijing–Tianjin–Hebei region and other regions, while the proportion of financial assets has a significant negative impact on the efficiency of universities in the Yangtze River Delta and Pearl River Delta regions. Other factors such as fixed asset investment and population density also affect the level of university scientific and technological innovation efficiency in specific regions. In addition, this paper analyzes the data of university scientific and technological innovation efficiency in various cities within the region and finds that the efficiency of scientific and technological innovation in universities in economically developed areas of the region is lower than that in relatively underdeveloped areas.

In conclusion, this paper analyzes the differential impact of key regional orientation policies on university scientific and technological innovation efficiency from the perspectives of index calculation and empirical analysis, making contributions to the understanding of the relationship between key regional orientation policies and university scientific and technological innovation efficiency for policy makers.

2. Analysis of Policy Development Context

This section aims to analyze the development context of China’s regional policies, providing a comprehensive overview of the strategic shifts that have shaped the nation’s regional economic growth and innovation strategy. It reveals how evolving regional policies have laid a foundation for university-led scientific and technological innovation. By examining each policy stage and its objectives, this section establishes the background against which the study assesses the impact of regional policies on innovation efficiency. It also sets the stage for a discussion on the alignment between national policy directions and localized innovation capabilities, deepening the understanding of policy-driven regional development. This section is divided into three subsections: Section 2.1 “Stage of Uneven Regional Development Strategy (1978 to 1999)” details the initial policy measures focused on promoting rapid economic growth in the eastern regions; Section 2.2 “Stage of Coordinated Regional Development Strategy (1999 to 2012)” discusses the shift towards a more balanced regional approach, emphasizing the need to reduce disparities and foster development in central and western regions; and Section 2.3 “New Exploration Stage of Regional Coordinated Development Strategy (2012 to Present)” covers recent strategies aimed at further integrating regional economies and supporting underdeveloped areas, including the Belt and Road Initiative and urban cluster developments like the Beijing–Tianjin–Hebei region and the Greater Bay Area.

China, with its abundant resources, large population, and vast territory, ranks as the world’s third-largest country by land area [31]. However, due to uneven population distribution, complex geographical conditions, and diverse cultural environments, China exhibits distinct regional characteristics [32]. Since the initiation of reforms and opening up, China has implemented various regional development strategies [33], primarily including four main pillars: the strategy of leading development in the eastern regions, the strategy of promoting the rise in central regions, the revitalization strategy for northeast China, and the new round of development strategy for the western regions. Additionally, major urban clusters have been developed, including the Beijing–Tianjin–Hebei region, the Yangtze River Delta, the Guangdong–Hong Kong–Macao Greater Bay Area, and the Chengdu–Chongqing area, along with initiatives such as the Belt and Road Initiative. Furthermore, to deepen regional coordinated development and construct an endogenous mechanism for regional coordination, China has formulated a series of regional policy supports to address key issues, considering both internal and external environments, to support the aforementioned regional strategic constructions.

With the passage of time, China’s regional development strategies have continued to evolve, gradually implementing targeted policies to adapt to the economic, social, and temporal demands [34,35,36]. The historical context of China’s key regional orientation policies can be divided into three stages, As shown in

Table 1. Progress in China’s regional development policies.

Stage	Time Period	Key Strategies and Policies	Objectives and Characteristics
Stage of Uneven Regional Development Strategy	1978–1999	- Establishment of Special Economic Zones (SEZs) in coastal cities (e.g., Shenzhen, Zhuhai, Xiamen, Shantou); - Opening of 14 coastal port cities to foreign investment.	- Prioritize economic development in eastern coastal regions to stimulate rapid growth; - Leverage geographical advantages for export-oriented growth.
Stage of Coordinated Regional Development Strategy	1999–2012	- Western Development Strategy; - Revitalization of Northeast China; Rise of Central China Plan.	- Address regional disparities by promoting development in central and western regions; - Implement infrastructure projects and ecological protection initiatives.
New Exploration Stage of Regional Coordinated Development Strategy	2012–Present	- Belt and Road Initiative (BRI); - Beijing–Tianjin–Hebei Coordinated Development; - Yangtze River Economic Belt Development. - Guangdong–Hong Kong–Macao Greater Bay Area; - Chengdu–Chongqing Economic Circle.	- Enhance regional integration and connectivity; - Promote high-quality development and innovation-driven growth; - Support underdeveloped areas through targeted policies.

, which highlights the progress of China’s regional development policies, it illustrates the shift from an initial focus on coastal areas to a more balanced and coordinated approach that encompasses areas of national significance.

2.1. Stage of Uneven Regional Development Strategy (1978 to 1999)

Since December 1978, when Comrade Deng Xiaoping stated the need to allow certain regions, enterprises, and workers and farmers to earn more income in economic policies, and in 1979 when Comrade Deng Xiaoping agreed to establish export processing zones in Hong Kong, Macao, Shenzhen, Zhuhai, and Shantou, and officially approved the establishment of Shenzhen, Zhuhai, Xiamen, and Shantou as four special economic zones in 1980, the central government has provided flexible policy support to these areas, including tax incentives for foreign investment and expanding the autonomy of coastal port cities. In 1988, Comrade Deng Xiaoping put forward the important concept of “two overall situations”. The “first overall situation” was to “accelerate the opening-up of coastal areas, develop them more rapidly, and consider the overall situation of the interior”. This indicated that the eastern regions should take advantage of their geographical location to develop first and demonstrate a positive effect, facilitating China’s overall economic strength and the progress of reform and opening up. At the early stage of reform and opening up, when China’s overall economic strength was inadequate, choosing the “uneven development strategy” of leading development in the east to promote rapid economic development was a wise decision. The “second overall situation” was that “when coastal areas develop to a certain stage, more efforts should be made to help the interior develop, and coastal areas should also consider the overall situation”. The ultimate goal of development is to focus on coordinated development at a higher level in the region, ultimately aiming for common prosperity.

2.2. Stage of Coordinated Regional Development Strategy (1999 to 2012)

With the deepening implementation of the strategy of leading development in the east, regional disparities gradually widened, internal demand became inadequate, and polarization emerged. The “first overall situation” that mainly relied on “uneven regional development” as the main measure required strategic transformation. The report of the 15th National Congress of the Communist Party of China in 1997 clearly stated the need to “gradually narrow the regional development gap” and “promote rational regional economic layout and coordinated development”. Therefore, a series of key regional orientation policies were planned to promote development in the central and western regions. This included the Western Development Strategy, which proposed to “strengthen infrastructure construction and resource development in the central and western regions, and increase efforts to guide foreign investment to the central and western regions”, while simultaneously implementing major infrastructure projects such as “transmitting electricity from the west to the east” and “transmitting gas from the west to the east” to achieve economic development in the western regions and implement projects to protect natural forests and control sand in wind and sand drought areas to enhance the ecological environment. The Revitalization of Northeast China Strategy proposed to “support the adjustment and transformation of old industrial bases in Northeast China and other old industrial bases”, providing targeted support in terms of infrastructure, national debt investment, reform of state-owned enterprises, scientific and technological talents, and social security. The strategy of promoting the rise of central regions emphasized that “accelerating the development of central regions is an important aspect of regional balanced development”, and introduced plans to promote the rise of central regions, promoting rapid development in the central regions through institutional means.

2.3. New Exploration Stage of Regional Coordinated Development Strategy (2012 to Present)

Although China has explored regional coordinated development and achieved some results, there are still significant gaps between regions and the requirements for a moderately prosperous society, uncoordinated regional development, the over-concentration of resources in developed areas, and the internal mechanism for coordinated development are yet to be constructed [37]. The Fifth Plenary Session of the 18th Central Committee of the Communist Party of China put forward overall requirements for coordinating regional development and conducted new explorations. This includes initiatives such as the Belt and Road Initiative, the coordinated development strategy of the Beijing–Tianjin–Hebei region, the development strategy of the Yangtze River Economic Belt, ecological protection and high-quality development of the Yellow River Basin, the Guangdong–Hong Kong–Macao Greater Bay Area strategy, support strategies for “old, young, remote, and poor” areas, the strategy of building the Chengdu–Chongqing area into a double-city economic circle, and the construction of free trade zones, forming a comprehensive picture of multidimensional regional key orientation policy implementation.

3. Materials and Methods

The main research objectives of each subsection are as follows. Section 3.1 “The Measurement of Efficiency of Scientific and Technological Innovation in Universities”: this section describes the methods for measuring scientific and technological innovation efficiency in universities, detailing the selection of suitable indicators and models to accurately capture this efficiency. Section 3.2 “Sample Regional Definition and Division”: this subsection defines the key regions of focus—namely, the Yangtze River Delta, Pearl River Delta, and Beijing–Tianjin–Hebei regions—to enable effective comparison and analysis. Section 3.3 “Data Source and Processing”: this section covers the collection and organization of economic, educational, and policy data related to university innovation, ensuring the reliability and validity of the research findings. Section 3.4 “Model Construction”: here, the rationale for adopting the Tobit model is explained, allowing for a comprehensive analysis of the factors influencing university innovation efficiency.

3.1. The Measurement of Efficiency of Scientific and Technological Innovation in Universities

Based on the input–output data of university scientific and technological activities, this study defines university scientific and technological innovation efficiency from an output-oriented perspective. With the deepening and improvement of key regional orientation policies, it is possible to enhance factor endowments and optimize factor allocation efficiency, thereby maximizing output levels. Therefore, we adopt a Variable Returns to Scale Data Envelopment Analysis (VRS-DEA) model based on the output orientation, which calculates university scientific and technological innovation efficiency under the assumption of variable returns to scale. Specifically, the calculation method of this model is as follows.

Suppose there are $d$ decision units ($d=1,\dots k$), where each decision unit has the same $i$ input items ($i=1,\dots n$) and the same $j$ output items ($j=1,\dots m$). Therefore, the input–output ratio $p_d$ for the $d$-th decision unit can be expressed as:

$$p_d=\sum _{j=1}^m{\omega }_j{y}_{jd}/\sum _{i=1}^n{q}_i{x}_{id} \left(d=1,\dots ,k\right)$$

where $x_{id}$ represents the amount of input for the $i$-th item in the $d$-th unit, and $y_{jd}$ represents the output quantity of the $j$-th item in the $d$-th unit. $q_i$ and ${\omega }_j$ denote the weight coefficients of the $i$-th input and the $j$-th output, respectively. According to the above equation, under the assumption of constant returns to scale, the performance evaluation optimization model of the $d$-th decision unit can be expressed by the following equation:

$${max}_{q,\omega }\left({\sum }_{j=1}^m{\omega }_j{y}_{jd}/{\sum }_{i=1}^n{q}_i{x}_{id}\right)$$

$$s.t. {\sum }_{j=1}^m{\omega }_j{y}_{jd}/{\sum }_{i=1}^n{q}_i{x}_{id}\le 1, { \omega }_j,q_i\ge 0$$

For the above equation, we can calculate the efficiency of decision units by solving the set $\left({\omega }_j,q_i\right)$. To precisely obtain the unique maximum solution for the decision units, we introduce the assumption $\sum _{i=1}^n{q}_i{x}_{id}=1$, thereby transforming the above equation into a multiplier form:

$${max}_{\eta ,q}\left(\sum _{j=1}^m{\eta }_j{y}_{jd}\right)$$

$$\sum _{i=1}^n{q}_i{x}_{id}=1$$

$$s.t. \sum _{j=1}^m{\eta }_j{y}_{jd}-\sum _{i=1}^n{q}_i{x}_{id}\le 0 (d=1,\dots ,k)$$

$${ \eta }_j,q_i\ge 0$$

According to the above equation, the dual variable for $\sum _{i=1}^n{q}_i{x}_{id}=1$ is $\left(-\lambda \right)$, and the dual variable for $\sum _{j=1}^m{\eta }_j{y}_{jd}-\sum _{i=1}^n{q}_i{x}_{id}\le 0$ is $\theta$. By adding the convexity constraint $\lambda {\mathrm{N}}_{n\times 1}=1$ to the above equation, we can adjust the CRS linear programming model to a VRS model. In this case, the dual programming form of the above equation can be expressed as:

$${min}_{\lambda ,\theta }\theta$$

$$\sum _{d=1}^k{\lambda }_d{x}_{id}\le \theta x_{id}$$

$$s.t. \sum _{d=1}^k{\lambda }_d{y}_{jd}\ge \sum _{j=1}^m{\eta }_j{y}_{jd}$$

$$\lambda N_{n\times 1}=1,{ \lambda }_d\ge 0$$

The above equation represents the expression of the VRS linear programming model, where $\theta$ represents the efficiency score of the decision unit, satisfying $\theta \le 1$. This indicates that we can obtain an ideal decision unit through efficiency, which can achieve output greater than or equal to the evaluated decision unit with fewer inputs, thereby indicating that the evaluated unit is operating within the technical frontier, i.e., exhibiting non-DEA efficiency. When $\theta =1$, it implies that the decision unit’s efficiency point lies on the technical efficiency frontier, thus indicating a technically efficient decision unit. Through the above equation, we can calculate the efficiency of university scientific and technological innovation. Furthermore, we aggregate the calculated efficiency of university scientific and technological innovation to the regional level to obtain the regional-level efficiency of university scientific and technological innovation.

3.2. Sample Regional Definition and Division

This paper utilizes the VRS-DEA model to measure the efficiency of university technology innovation and empirically tests the impact of economic development, foreign investment, industrial structure, and government expenditure variables on university technology innovation efficiency based on the Tobit model. Specifically, the sample data for this paper are based on the “985” and “211” Project universities, as well as regular colleges recorded in the “Compilation of Statistics on Science and Technology in Higher Education Institutions”. Financial indicators and corresponding scientific and technological data from university annual financial reports are also used for efficiency measurement and empirical analysis. The data on university technology inputs and outputs are sourced from the “Compilation of Statistics on Science and Technology in Higher Education Institutions” for the years 2007–2017, while the relevant Tobit data and examination variable data are obtained from the “Compilation of Statistics on Science and Technology in Higher Education Institutions” as well as from the “China Statistical Yearbook” and the “China Science and Technology Statistical Yearbook” published by the National Bureau of Statistics.

3.3. Data Source and Processing

On one hand, this study utilizes the selection of input indicators from five aspects and the selection of output indicators from five aspects (

Table 2. Input–output evaluation index system.

Primary Indicator	Indicator Function	Secondary Indicator
Scientific Input	X1	Proportion of Scientific Personnel to Total Employment;
	X2	Proportion of R&D Personnel to Scientific Personnel;
	X3	Ratio of Scientific Expenditure to GDP;
	X4	Per Capita Scientific Expenditure of Scientific Personnel (in 10,000 RMB);
	X5	Proportion of Local Financial Allocation for Science to Total Local Financial Expenditure.
Scientific Output	Y1	Proportion of Output Value of High-tech Industry Enterprises above Designated Size to Total Output Value of Secondary and Tertiary Industries;
	Y2	Proportion of Export Value of High-tech Industry Products to Total Area Export Value;
	Y3	Number of Patents Granted per 10,000 Scientific Personnel;
	Y4	Number of Domestic Chinese Journal Papers Published per 10,000 Scientific Personnel;
	Y5	Total Contract Amount of Technology Market Transactions per 10,000 Scientific Personnel (in 10,000 RMB).

). On the other hand, this study examines the impact of variables such as economic development, population density, financial assets, foreign investment, industrial structure, fixed assets, government expenditure, and Internet penetration on the efficiency and convergence of innovation in university science and technology. Of these, the level of economic development is represented by the logarithm of the per capita GDP of the region; population density is represented by the ratio of the total year-end resident population to the land area of each region; financial assets are represented by the ratio of the value added of the financial industry to the GDP of the region; foreign investment is represented by the logarithm of the total foreign investment in the region; industrial structure is represented by the ratio of the value added of the tertiary industry to the GDP of the region; fixed assets are represented by the logarithm of the total fixed asset investment in the region; government expenditure is represented by the logarithm of the total education expenditure in the fiscal expenditure of the region; and Internet penetration is represented by the Internet penetration rate in the region. The above data are all obtained from publicly available data such as the “China Statistical Yearbook”, “China Science and Technology Statistics Yearbook”, and the website of the Ministry of Science and Technology of China from 2007 to 2017.

The data used in this study were obtained from publicly available sources, such as the China Statistical Yearbook, the China Science and Technology Statistics Yearbook, and the Ministry of Science and Technology of China. As micro-level university data have not been disclosed since 2018, we restricted the sample period to 2007–2017.

3.4. Model Construction

In selecting explanatory and dependent variables for regression analysis, the Ordinary Least Squares (OLS) method is typically applied to continuous sample data. However, in practical applications, some continuous data are truncated and restricted to a limited range of values, which violates the basic assumptions of OLS estimation and can lead to inconsistent estimates. In this study, where we measure university scientific and technological innovation efficiency, the efficiency values fall within a range of 0 to 1. The Tobit model is well-suited for analyzing data constrained within such a specific interval, providing more accurate estimates. Thus, we employ the Tobit model to further examine the factors influencing the efficiency of scientific and technological innovation in universities. The econometric model is constructed as follows:

$${Efficiency}_{i,t}={\beta }_0+{\displaystyle \sum }_{i=0}^8{\beta iFactor}_{i,t}+{\epsilon }_{i,t}$$

(1)

$$Efficiency=\left\{\begin{array}{cc}0 ,\hfill & \mathrm{if} Efficiency*\le 0\hfill \\ Efficiency* ,\hfill & \mathrm{if} 0<Efficiency*<1\hfill \\ 1 ,\hfill & \mathrm{if} Efficiency*\ge 1\hfill \end{array}\right.$$

(2)

where i represents the region, and t represents the year. The dependent variable $Efficiencyi,t$ denotes the research efficiency of universities in region i during period t. The variable $Factori,t$ represents the eight key regionally oriented policies implemented in region i during period t. ${\epsilon }_{i,t}$ represents the random error term.

Based on the existing literature, we select efficiency-driving factors from the following eight aspects. (1) Economic development level, represented by the logarithm of per capita GDP of each region. (2) Population density, represented by the ratio of the total population at the end of the year to the land area of each region. (3) Financial assets, represented by the ratio of the value added of the financial industry to the GDP of each region. (4) Foreign investment, represented by the logarithm of the total foreign investment in each region. (5) Industrial structure, represented by the ratio of the value added of the tertiary industry to the GDP of each region. (6) Fixed assets, represented by the logarithm of the total fixed asset investment in each region. (7) Government expenditure, represented by the logarithm of the total education expenditure in the fiscal expenditure of each region. (8) Internet penetration rate, represented by the internet penetration rate of each region. The above indicators are calculated from the data published in the “China Statistical Yearbook” by the National Bureau of Statistics over the years.

In this study, the Variable Returns to Scale Data Envelopment Analysis (VRS-DEA) framework is employed to robustly measure and analyze the efficiency of scientific and technological innovation in universities (Figure 1). By accounting for variable returns to scale across universities and regions, this model captures how effectively universities transform resources into innovative outputs. Comparative analysis—both horizontal and longitudinal—of university efficiency in the Yangtze River Delta, Pearl River Delta, and Beijing–Tianjin–Hebei regions reveals the variations and impacts of regional policies, providing a foundation for policy optimization. Additionally, the Tobit model effectively addresses the issue of truncated data, as university innovation efficiency scores are bounded between 0 and 1, ensuring the precise estimation of influencing factors and thereby strengthening the reliability and scientific validity of the study’s conclusions.

4. Results

The main research objectives of each subsection are as follows. Section 4.1. “Efficiency Level Comparison”: this subsection compares the changes in STI efficiency in universities under the key region-oriented policies in terms of the level of financial and economic efficiency, the changes in STI efficiency in universities, and the growth rate of STI in universities. Section 4.2 “Comparison of Efficiency Distribution in Different Regions”: this subsection analyzes the impact of key region-oriented policies on the regional distribution of STI efficiency in universities. Section 4.3. “Efficiency Regional Disparity Comparison”: This subsection measures and analyzes the efficiency of university science and technology innovation in the three strategic regions of the Yangtze River Delta, the Pearl River Delta, and Beijing–Tianjin–Hebei. Section 4.4. “Comparison of sources of inefficiency”: the inefficiency values of HEIs’ finances in the three strategically important regions of the Yangtze River Delta, Pearl River Delta and Beijing–Tianjin–Hebei are calculated and compared. Section 4.5. “The Differentiated Enhancement Path of Scientific and Technological Innovation Efficiency in Chinese Universities”: based on the data analysis and comparison in the previous subsections and using the Tobit model, we analyze the similarities and differences in the drivers of scientific and technological innovation efficiency in universities in the Yangtze River Delta, Pearl River Delta, Beijing–Tianjin–Hebei and other regions, and explore the Differentiated Enhancement Path of Scientific and Technological Innovation Efficiency in Chinese Universities.

4.1. Efficiency Level Comparison

This paper conducts a comparative analysis of the changes in the efficiency of university science and technology innovation under key regional-oriented policies. Figure 2 and Figure 3, respectively, depict the temporal changes in the scientific and technological innovation efficiency of universities levels and growth rates of universities in the Yangtze River Delta, the Pearl River Delta, and the Beijing–Tianjin–Hebei region, portraying the impact of key regional-oriented policies on the efficiency of university science and technology innovation from three perspectives: overall efficiency level, trend changes, and growth rates.

From the overall level of scientific and technological innovation efficiency of universities, it can be seen that there is a highly positive correlation between the targeted policies in the Yangtze River Delta, the Pearl River Delta, and the Beijing–Tianjin–Hebei region and the efficiency of scientific and technological innovation at universities. Moreover, the overall scientific and technological innovation efficiency of universities in the Yangtze River Delta and the Beijing–Tianjin–Hebei region is higher than that in the Pearl River Delta region. Under the targeted policy orientation, the scientific and technological innovation efficiency of universities level of universities during the period 2007–2017 exceeded 0.8. The efficiency of scientific and technological innovation at universities in the Yangtze River Delta and the Beijing–Tianjin–Hebei region has been generally maintained at above 0.85, and even reached around 0.9 in 2015. In contrast, nearly half of the period saw the efficiency of scientific and technological innovation in the Pearl River Delta region below 0.85, significantly lower than that in the other two major targeted regions. This indicates that the development of scientific and technological innovation efficiency at universities in the Pearl River Delta region does not match its level of economic development. The reason may be that the basic innovation capabilities of universities in the Pearl River Delta region are insufficient, and they have not formed an innovative collaborative mechanism with enterprises, resulting in a significant gap in basic science and technology compared to the Yangtze River Delta and Beijing–Tianjin–Hebei regions.

In terms of the trend of changes in the efficiency of scientific and technological innovation at universities, from 2007 to 2009, the efficiency of scientific and technological innovation at universities in the Yangtze River Delta, Pearl River Delta, and Beijing–Tianjin–Hebei regions showed a downward trend. From 2009 to 2015, although the efficiency levels fluctuated within these three major targeted regions, overall, they still showed an upward trend. During the period from 2015 to 2017, although there was a slight decline in the scientific and technological innovation efficiency of universities of regional universities, it tended to stabilize. The reasons for this changing trend may be as follows. In the early stage, the targeted policies in these regions provided policy support and resource tilt to universities in the region, but did not pay attention to the level of resource utilization. In the middle stage, regional universities began to focus on the output efficiency of scientific and technological innovation, and effectively allocated scientific and technological innovation resources in a market-oriented manner. In the later stage, the fluctuation range of scientific and technological innovation efficiency of regional universities decreased, gradually forming a stable mechanism for scientific and technological innovation.

In terms of the growth rate of scientific and technological innovation efficiency at universities, from 2008 to 2010, the growth rate of scientific and technological innovation efficiency at universities in the Yangtze River Delta, Pearl River Delta, and Beijing–Tianjin–Hebei regions remained negative. After 2010, it showed an alternating trend of positive and negative changes. This indicates that although the targeted policies provided abundant material and financial resources and favorable policy environment support to universities in these regions, it neglected the effective utilization of resources in the early stage, resulting in a decrease in the scientific and technological innovation efficiency of universities. Later, in order to change the negative growth trend of scientific and technological innovation efficiency at universities, the targeted policies gradually explored an efficiency-oriented resource allocation mechanism when providing policy support to universities. Meanwhile, universities continuously sought more effective solutions in the use and distribution of scientific and technological innovation resources. Therefore, in the process of dynamic adjustment by the government and universities, the growth rate of scientific and technological innovation efficiency at universities also changed accordingly.

4.2. Comparison of Efficiency Distribution in Different Regions

This paper analyzes the impact of key regional-oriented policies on the distribution of innovation efficiency in universities. As shown in

Table 3. Comparison of scientific and technological innovation efficiency distribution in Chinese universities.

Regional Strategy	Province	Efficiency of Scientific and Technological Innovation in Universities
		2007	2009	2011	2013	2015	2017
The Yangtze River Delta	Shanghai Municipality	0.816657	0.738114	0.749031	0.755638	0.880986	0.822904
	Anhui Province	0.902139	0.872815	0.928546	0.935126	0.93683	0.895272
	Jiangsu Province	0.84431	0.83789	0.879357	0.925181	0.933235	0.893878
	Zhejiang Province	0.824348	0.736507	0.780488	0.77242	0.871402	0.866062
Pearl River Delta	Guangdong Province	0.878933	0.794668	0.810485	0.83661	0.851603	0.873032
Beijing−Tianjin−Hebei	Beijing Municipality	0.841974	0.821446	0.86156	0.861655	0.876917	0.835967
	Hebei Province	0.928884	0.861759	0.926168	0.946688	0.949033	0.941978
	Tianjin Municipality	0.82904	0.757872	0.784145	0.794622	0.793869	0.682503

, there are significant differences in the distribution of innovation efficiency in universities among the three major strategic regions, especially in the Yangtze River Delta and Beijing–Tianjin–Hebei regions. From 2007 to 2017, in the Yangtze River Delta region, the innovation efficiency of universities in Shanghai was generally lower than that of universities in Anhui, Jiangsu, and Zhejiang provinces. It can be seen that although Shanghai’s economic level and resource advantages are significantly higher than those of other provinces and cities in the region, its resource utilization level is relatively low, especially in terms of innovation efficiency in universities, which does not match its economic strength. In the Beijing–Tianjin–Hebei region, the innovation efficiency of universities in Hebei Province is much higher than that of universities in Beijing and Tianjin. Compared with Beijing and Tianjin, although Hebei is at a disadvantage in terms of economic level and resource allocation, limited resource inputs have stimulated its utilization rate of resources. The scarcity of resources has prompted Hebei to pay more attention to output levels. Therefore, the scientific and technological innovation efficiency of universities in Hebei Province has always been at a relatively high level compared to Beijing and Tianjin. It can be seen that there are significant differences in the distribution of innovation efficiency in universities in key regions, insufficient regional coordinated development, and the unreasonable allocation of higher education resources, especially the unbalanced characteristics between the resource advantages of core cities and output efficiency.

4.3. Efficiency Regional Disparity Comparison

To specifically analyze the efficiency of scientific and technological innovation in the three major strategic regions, namely the Yangtze River Delta, the Pearl River Delta, and the Beijing–Tianjin–Hebei region, we calculated the average efficiency of scientific and technological innovation in universities in cities within each region from 2007 to 2014, as shown in

Table 4. The intra-discrepancy of scientific and technological innovation efficiency among universities in the Yangtze River Delta of China.

City	University Science and Technology Innovation Efficiency	City	University Science and Technology Innovation Efficiency	City	University Science and Technology Innovation Efficiency
Xuancheng	0.992722	Ma’anshan	0.90407	Nanjing	0.86144
Haozhou	0.980561	Lianyungang	0.899726	Chuzhou	0.860945
Suqian	0.964768	Suzhou	0.899057	Lishui	0.857452
Wuxi	0.957821	Changzhou	0.891807	Shaoxing	0.846608
Chaohu	0.944537	Quzhou	0.88767	Taizhou	0.844385
Tongling	0.942695	Huaibei	0.887144	Huzhou	0.82982
Huangshan	0.942513	Yancheng	0.884202	Jiaxing	0.825518
Taizhou	0.931617	Wuhu	0.882352	Yangzhou	0.812579
Fuyang	0.931586	Bengbu	0.880673	Jinhua	0.803804
Suzhou	0.927417	Nantong	0.880248	Ningbo	0.796504
Chizhou	0.922842	Huai’an	0.879165	Hangzhou	0.782876
Hefei	0.919715	Xuzhou	0.869822	Shanghai	0.775228
Anqing	0.914142	Zhenjiang	0.867165	Zhoushan	0.767681
Huainan	0.90618	Lu’an	0.862939	Wenzhou	0.766189

,

Table 5. The intra-discrepancy of scientific and technological innovation efficiency among universities in the Pearl River Delta of China.

City	University Science and Technology Innovation Efficiency
Zhaoqing	0.923938
Zhongshan	0.919967
Zhuhai	0.905742
Huizhou	0.865488
Guangzhou	0.861521
Jiangmen	0.853897
Foshan	0.810543
Dongguan	0.791196
Shenzhen	0.737445

and

Table 6. The intra-discrepancy of scientific and technological innovation efficiency among universities in the Beijing–Tianjin–Hebei region.

City	University Science and Technology Innovation Efficiency
Cangzhou	0.984299
Xingtai	0.981368
Hengshui	0.976719
Anyang	0.960647
Zhangjiakou	0.943339
Chengde	0.929469
Tangshan	0.91638
Langfang	0.911631
Baoding	0.910758
Qinhuangdao	0.899299
Shijiazhuang	0.89376
Handan	0.892714
Beijing	0.84488
Tianjin	0.769509

.

From Table 4, it can be observed that the average efficiency of scientific and technological innovation in universities in various cities in the Yangtze River Delta region ranged from 0.766 to 0.993 between 2007 and 2017. The majority of cities had a scientific and technological innovation efficiency of universities value of above 0.85. Among them, Xuan Cheng City, Bozhou City, Suqian City, and Wuxi City ranked in the top four in the Yangtze River Delta, with their efficiency of scientific and technological innovation reaching 0.95. In contrast, Shanghai City, Zhoushan City, and Wenzhou City had the lowest efficiency of scientific and technological innovation in universities, with none of them reaching 0.78.

From Table 5, it can be seen that the average scientific and technological innovation efficiency values of cities in the Pearl River Delta region ranged from 0.737 to 0.924 between 2007 and 2017. Two-thirds of the cities had scientific and technological innovation efficiency of universities values above 0.85. Among them, Zhaoqing, Zhongshan, and Zhuhai ranked in the top three cities in the Pearl River Delta, with scientific and technological innovation efficiency reaching 0.90. However, Dongguan and Shenzhen had the lowest scientific and technological innovation efficiency among the cities, both failing to reach 0.8.

From Table 6, it can be seen that the average scientific and technological innovation efficiency values of universities in the Beijing–Tianjin–Hebei region ranged from 0.769 to 0.985 between 2007 and 2017. Except for Beijing and Tianjin, the scientific and technological innovation efficiency of universities values of all cities were above 0.85. Among them, Cangzhou, Xingtai, Hengshui, and Anyang ranked the top four in the Beijing–Tianjin–Hebei region, with a scientific and technological innovation efficiency of 0.95. However, Beijing and Tianjin had the lowest scientific and technological innovation efficiency, with Beijing at 0.84488 and Tianjin below 0.77.

From the data in Table 4, Table 5 and Table 6, we can observe that the high-tech innovation efficiency of economically developed cities in the respective strategic regions is relatively low. Examples include Hangzhou, Shanghai, and Wenzhou in the Yangtze River Delta region; Dongguan and Shenzhen in the Pearl River Delta region; and the two municipalities in the Beijing–Tianjin–Hebei region. The high-tech innovation efficiency of these cities is at the bottom of their respective regions.

Additionally, we also conducted a statistical analysis of the financial situation of universities in the three major strategic regions: the Yangtze River Delta, the Pearl River Delta, and the Beijing–Tianjin–Hebei region. We calculated the Gini coefficients of universities in these regions from 2007 to 2017, as shown in Figure 4. From the figure, we can see that the financial disparity among universities in each region varies, and their trends over time are also different.

On one hand, the financial disparity among universities in the Yangtze River Delta region was significantly higher than the other two regions before 2015. However, after 2015, the financial disparity among universities in the Yangtze River Delta region has narrowed and become the smallest among the three regions. The financial disparity among universities in the Pearl River Delta and Beijing–Tianjin–Hebei regions was relatively small before 2016, but after 2016, the financial disparity among universities in the Beijing–Tianjin–Hebei region has significantly widened, making it the region with the largest disparity in high-tech innovation efficiency among the three major strategic regions.

On the other hand, in terms of development trends, although the financial disparity among universities in the Yangtze River Delta region has experienced several significant fluctuations, it has been continuously narrowing overall. The financial disparity among universities in the Beijing–Tianjin–Hebei region has shown an increasing trend from the smallest in 2007 to the highest in 2017. The financial disparity among universities in the Pearl River Delta region has remained relatively stable and consistently at a lower level.

4.4. Comparison of Sources of Inefficiency

We calculated the inefficiency values of university finances in the three major strategic regions of the Yangtze River Delta, the Pearl River Delta, and the Beijing–Tianjin–Hebei region, and plotted their sources of inefficiency as shown in Figure 5. From Figure 5, it can be seen that the sources of inefficiency in university finances in the three major strategic regions are generally similar but with some differences. On one hand, the overall level of inefficiency values in university finances in these regions is generally below 10%, with the main source being output inefficiency. This indicates that the overall efficiency of utilizing scientific research funds in these regions is relatively good, but there still exists a small amount of resource wastage, mainly reflected in insufficient scientific research output. On the other hand, the overall level and composition of inefficiency values vary among the Yangtze River Delta, Pearl River Delta, and Beijing–Tianjin–Hebei regions. Firstly, the Beijing–Tianjin–Hebei region has the highest inefficiency values in university finances, followed by the Yangtze River Delta, and the Pearl River Delta has the lowest inefficiency values. This suggests that universities in the Pearl River Delta region have the highest efficiency in utilizing scientific research funds, while those in the Yangtze River Delta and Beijing–Tianjin–Hebei regions rank second and lowest, respectively. Secondly, the proportion of inefficiency in input and output varies among the three strategic regions, with the Beijing–Tianjin–Hebei region having a higher proportion of input inefficiency compared to other regions, while the Yangtze River Delta region has a higher proportion of output inefficiency compared to others.

4.5. The Differentiated Enhancement Path of Scientific and Technological Innovation Efficiency in Chinese Universities

Due to the fact that the level of scientific and technological innovation efficiency of universities in each region falls within the truncated interval of 0 to 1, this paper adopts the Tobit model. The model uses the scientific and technological innovation efficiency of universities in each region as the dependent variable and economic development level, population density, financial assets, and other factors as independent variables. It examines the effects and degrees of various influencing factors on the efficiency of scientific and technological innovation in universities, analyzes the similarities and differences in the driving factors of scientific and technological innovation efficiency of universities in the Yangtze River Delta, Pearl River Delta, Jing–Jin–Ji, and other regions, and explores the differentiated paths for enhancing the efficiency of scientific and technological innovation in universities.

Based on the existing literature, we selected efficiency-driving factors from the following eight aspects. (1) Economic development level, represented by the logarithm of per capita GDP of each region. (2) Population density, represented by the ratio of the total population at the end of the year to the land area of each region. (3) Financial assets, represented by the ratio of the value added of the financial industry to the GDP of each region. (4) Foreign investment, represented by the logarithm of the total foreign investment in each region. (5) Industrial structure, represented by the ratio of the value added of the tertiary industry to the GDP of each region. (6) Fixed assets, represented by the logarithm of the total fixed asset investment in each region. (7) Government expenditure, represented by the logarithm of the total education expenditure in the fiscal expenditure of each region. (8) Internet penetration rate, represented by the internet penetration rate of each region. The above indicators are calculated from the data published in the “China Statistical Yearbook” by the National Bureau of Statistics over the years.

Table 7. The differentiated enhancement path of scientific and technological innovation efficiency in Chinese universities.

	Yangtze River Delta Region	Pearl River Delta Region	Beijing–Tianjin–Hebei Region	Other Regions
Economic development	0.017	−0.025	0.062 **	0.043 ***
	(0.015)	(0.020)	(0.032)	(0.008)
Population density	0.004	0.202 **	0.018	−0.010 **
	(0.011)	(0.102)	(0.025)	(0.005)
Financial assets	−0.054 ***	−0.178 **	−0.003	0.000
	(0.017)	(0.090)	(0.026)	(0.008)
Foreign investment	−0.009	0.056	−0.011	0.000
	(0.007)	(0.044)	(0.010)	(0.002)
Industrial structure	0.039	−0.120	−0.114 **	−0.019
	(0.038)	(0.106)	(0.048)	(0.013)
Fixed assets	0.079 ***	0.062	−0.031	−0.016 **
	(0.017)	(0.055)	(0.024)	(0.006)
Government expenditure	0.026 *	0.159 ***	0.070 **	0.023 ***
	(0.015)	(0.052)	(0.030)	(0.007)
Internet level	−0.018	0.014	0.006	−0.013 ***
	(0.012)	(0.009)	(0.014)	(0.004)
Intercept	0.388 *	2.058 ***	1.384 ***	0.934 ***
	(0.199)	(0.528)	(0.289)	(0.083)

Note: *, **, *** in the table respectively indicates that the probability corresponding to the statistical values is significant at the levels of 5%, 1% and 1‰ respectively.

presents the regression results of the Tobit model. From the overall results, it can be observed that government expenditure on public education has a significant positive coefficient on the scientific and technological innovation efficiency of universities in all regions, highlighting the considerable impact of public education expenditure on university finances. A comparison of the coefficients reveals that the effect of government education expenditure is particularly significant in the Pearl River Delta region, which is related to the relatively fewer universities and the need for improvement in the strength of universities in this region. In contrast, the coefficient value of government expenditure in the economically developed but university-rich Yangtze River Delta region is 0.026, only one-sixth of that in the Pearl River Delta region (0.159). In contrast to the significant effect of government expenditure, foreign investment in the region has not demonstrated a driving effect on the efficiency of scientific and technological innovation in universities. Foreign investment is more closely related to regional economic development and does not directly impact universities.

From the differences in various regions, it can be seen that the disparities in economic environments have a significant impact on the driving factors of scientific and technological innovation efficiency in regional universities. On one hand, the level of economic development has a significant positive effect on the Jing–Jin–Ji region and other regions, but no significant effect on the Yangtze River Delta and Pearl River Delta regions. Since the beginning of the new century, the Yangtze River Delta and Pearl River Delta regions have been the areas with the fastest economic growth and the largest total volume. The per capita GDP is close to the level of developed countries, and the marginal effect of economic development is no longer significant. In contrast, the Jing–Jin–Ji region and other regions still have considerable room for economic development, and the growth of per capita GDP significantly improves the efficiency level. On the other hand, the proportion of financial assets significantly reduces the efficiency of universities in the Yangtze River Delta and Pearl River Delta regions but has no significant impact on the Jing–Jin–Ji region and other regions. The Yangtze River Delta and Pearl River Delta regions are also the most financially developed regions. In this environment, universities have more channels to obtain funds, including investment income, interest income, and donation income, besides government expenditure. The abundance of funding sources may reduce the efficiency of fund utilization.

Other specific factors also affect the efficiency of scientific and technological innovation in universities in specific regions. The total fixed asset investment only significantly improves the efficiency of scientific and technological innovation in universities in the Yangtze River Delta region. The Yangtze River Delta region is the most densely populated area of higher education institutions, gathering various types of colleges and universities. Fixed asset investment is conducive to meeting the high-value special investment needs of university scientific and technological innovation. Population density has a positive impact on the Pearl River Delta region. The Pearl River Delta region has the highest population density in the country, with a large number of permanent residents being migrants. The influx of high-quality migrant population is conducive to improving the efficiency level of the region. Population density has a significant negative impact on non-key areas, but the degree of influence is relatively weak. In terms of industrial structure, the proportion of the tertiary industry only has a significant negative impact on the Jing–Jin–Ji region. The proportion of the tertiary industry in the Jing–Jin–Ji region has been continuously increasing in recent years, accounting for more than 60%, but the rapid development of the tertiary industry has not led to an improvement in the efficiency of scientific and technological innovation in universities. The internet penetration rate has a negative impact on the efficiency of scientific and technological innovation in universities in non-key areas, but the degree of influence is relatively weak.

5. Discussion

Through a literature review, the article identifies a research gap regarding the impact of China’s regionally oriented policies on the efficiency of scientific and technological innovation in universities, thus clarifying the study’s purpose and significance. Moreover, the VRS-DEA model is employed to calculate the technological innovation efficiency of universities across different regions. Additionally, the study defines the sample regions—focusing on the Yangtze River Delta, Pearl River Delta, and Beijing–Tianjin–Hebei—and conducts a comparative analysis of the technological innovation efficiency of universities in these regions from 2007 to 2017. Finally, a Tobit model is constructed to examine the factors that may influence the technological innovation efficiency of universities. The research findings are as follows.

Firstly, regarding development levels and trends, there are both shared characteristics and distinct differences among these three strategic regions [38,39,40]. Between 2007 and 2017, the scientific and technological innovation efficiency of universities in all three regions showed fluctuations, hitting a low in 2009 and peaking in 2015. These variations reflect the influence of economic and policy shifts on university operational efficiency. Notably, the sharp decline from 2008 to 2009 and the drop from 2015 to 2016 highlight the significant impact of external factors, particularly the global economic crisis, on university finances. While the Pearl River Delta region typically has lower technological innovation efficiency than the Yangtze River Delta and Beijing–Tianjin–Hebei regions, its economic efficiency has risen faster since 2014, suggesting potential gains from policy or investment shifts. Conversely, the Yangtze River Delta maintains relatively high technological innovation efficiency with a steady trend, whereas the Beijing–Tianjin–Hebei region experiences greater fluctuations, reflecting different approaches and outcomes in policy implementation and resource allocation.

Secondly, in terms of efficiency sources, the three regions share notable similarities. From 2007 to 2017, universities in all three areas demonstrated inefficiencies in both input and output, with output inefficiency roughly double that of input inefficiency. This implies that despite substantial resource input, there remain considerable challenges in converting these resources into tangible research outcomes and innovation capacity. Barriers may include factors such as internal management, research capacity development, and limited collaboration with industry. As a result, enhancing output efficiency should be a key priority in future policymaking.

Thirdly, within each strategic region, universities in economically advanced cities often exhibit lower technological innovation efficiency [41]. For example, in the Yangtze River Delta, Xuzhou has the highest efficiency, Nanjing falls in the middle, and Shanghai ranks third-lowest. This pattern may relate to the over-concentration of university resources and low management efficiency in economically developed cities. Similarly, in the Pearl River Delta, Zhaoqing ranks highest and Shenzhen lowest in innovation efficiency, reflecting disparities in development stages and resource allocation among cities. In the Beijing–Tianjin–Hebei region, universities in Tianjin and Beijing have relatively low innovation efficiency, while Cangzhou stands out, indicating differing regional higher education strategies and support across cities. These differences provide critical insights for optimizing regional policies.

Fourthly, in terms of influencing factors, there are both commonalities and distinctions among the three regions. Technological innovation efficiency in all three regions is significantly influenced by government expenditure, underscoring the crucial role of public funding in fostering higher education and innovation [42]. In the Yangtze River Delta, financial and fixed assets strongly impact efficiency, highlighting the importance of investment quality and structure. In the Pearl River Delta, population density and financial assets play a more prominent role, emphasizing the value of human resource concentration in enhancing innovation. In the Beijing–Tianjin–Hebei region, economic development levels and industrial structure shifts directly affect technological innovation efficiency, underscoring the intrinsic link between economic foundations and innovation capacity. Future research could further explore these dynamic relationships to better understand how regional policies can effectively enhance university innovation efficiency [43].

6. Conclusions

6.1. Implications

Considering the influencing factors and regional differences, the following policy implications are proposed. Firstly, regions and universities should enhance their resilience to external shocks [44,45]. During the global financial crisis from 2007 to 2009, which impacted the Chinese economy and consequently the scientific and technological innovation efficiency of universities, there was a rapid decline in the efficiency and growth rate of technological innovation in universities during this period, gradually recovering after 2009. In the increasingly accelerated process of globalization, while economic and trade exchanges between China and the world bring benefits such as profits and employment, they also bring risks of being affected by economic and financial risks from other countries. In the irreversible global trend, while seizing opportunities and developing with the momentum, it is also necessary to continuously strengthen the domestic regulatory system, enhance independent innovation capabilities and reduce dependence on domestic and foreign enterprises and supply chains in order to enhance the country’s ability to respond to external shocks and further ensure the stability of technological innovation efficiency in universities.

Secondly, looking at the factors influencing the technological innovation efficiency of universities, both the Yangtze River Delta and Pearl River Delta regions in the three strategic regions are significantly negatively affected by financial assets, while the Beijing–Tianjin–Hebei region [46], although negatively affected, is not significant. Considering that from 2007 to 2017, only the Shanghai Stock Exchange and the Shenzhen Stock Exchange were present in the Yangtze River Delta and Pearl River Delta regions, respectively, the significant impact of financial assets on these regions can be partially explained. However, in November 2021, the Beijing Stock Exchange officially opened, expanding the capital market’s main radiation range from the original Pearl River Delta and Yangtze River Delta regions to include the Beijing–Tianjin–Hebei region. In this regard, financial assets may significantly negatively affect the scientific and technological innovation efficiency of universities in the Beijing–Tianjin–Hebei region. Drawing lessons from the experiences of the Yangtze River Delta and Pearl River Delta regions, the Beijing–Tianjin–Hebei region should be prepared in advance to minimize the significant negative impact of financial assets on the technological innovation efficiency of universities.

Lastly, compared with other regions, the technological innovation efficiency of universities in the three strategic planning regions no longer solely depends on economic development and government expenditure [47]. Population density and fixed assets have also become factors that can enhance scientific and technological innovation efficiency of universities [48,49]. However, while these factors contribute to further enhancing scientific and technological innovation efficiency of universities, financial assets and industrial structures may also negatively affect the technological innovation efficiency of universities in the three strategic regions [50]. However, overall, the positive factors within the three strategic regions outweigh the negative ones, highlighting the superiority of strategic regional planning. China’s strategic regional planning can distinguish between the advantages and disadvantages of target areas, match their characteristics for corresponding target construction, reverse disadvantages, leverage advantages, build strengths, and promote rapid and stable development, thereby seizing opportunities, realizing regional value enhancement, and ultimately achieving comprehensive development and promoting national development goals [51,52].

6.2. Research Limitation and Future Discussion

The primary limitation of this study lies in its focus on certain key factors within the region that affect university-driven innovation efficiency, without an in-depth exploration of the role of regional industrial policy. Industrial policy is a critical element influencing the regional innovation environment; by stimulating R&D demand in specific industries, it can indirectly enhance university-led innovation efficiency. Consequently, future research could concentrate on examining the mechanisms through which regional industrial policy affects university innovation efficiency, providing further insights into how such policies drive innovation activities within universities.

Additionally, another limitation of this study pertains to the depth and scope of micro-level data utilized. While this research incorporates certain micro-level data, future studies could delve deeper into collecting and leveraging more comprehensive, high-quality micro-level data, especially regarding policy reforms within universities. For instance, data on specialized collaborations between local governments and universities, the allocation of innovation funds, and incentives for technology transfer could further elucidate the interaction between regional industrial policies and internal university policies, helping to assess how these factors jointly influence university innovation efficiency. Such an approach would offer more precise empirical support for the interplay between regional policy and university reforms, ultimately contributing to the development of more effective policies to encourage university-based technological innovation.