Research on data-driven analysis model for synergistic development of regional innovation capacity and higher education in the context of digital economy
Pubblicato online: 17 mar 2025
Ricevuto: 11 ott 2024
Accettato: 27 gen 2025
DOI: https://doi.org/10.2478/amns-2025-0331
Parole chiave
© 2025 Xiaojun Wang et al., published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.
With the rise of knowledge-based economy, regional innovation has become a key factor in driving economic growth and competitiveness. Higher education institutions, as indispensable knowledge producers and talent trainers in the innovation system, provide the region with the necessary human resources and scientific and technological support [1-2]. The role of higher education institutions in the regional innovation system is multifaceted, which is not only a producer of knowledge, but also a catalyst and promoter of innovation activities [3-4]. Higher education institutions are the birthplace of cutting-edge scientific research and new technologies, and the incubation space for industrial innovation, and the knowledge and technologies generated in universities will directly affect the economic growth and structural transformation of the region [5-7]. Higher education institutions, through institutions such as science and technology parks, incubators and technology transfer offices, transform research results into products and services with practical application value, which not only accelerates the process of marketization of technology, but also attracts external investment and promotes regional economic development and employment growth [8-11]. Higher education institutions, as centers of knowledge networks, can promote cross-fertilization between different disciplines and professional fields, providing more diversified perspectives and innovative approaches to solving complex problems, as well as providing international perspectives and resources for regional innovation systems [12-15]. In summary, through education, research, technology transfer, and policy engagement, higher education provides a solid foundation and a steady stream of power for the construction of regional innovation highlands [16-17]. Universities need to understand and adapt to the needs of regional economic development more deeply, and combine talent cultivation with regional industrial upgrading through precise talent strategic layout and innovative talent center development strategies, so as to provide a constant flow of innovation power and intellectual support for regional innovation highlands [18-21].
This paper selects China’s 31 provinces as the research object, and analyzes the overall development situation through the mutual comparison between provinces and cities and the entropy situation of spatial agglomeration location, so as to have a clearer understanding of the spatial layout and the degree of spatial agglomeration of higher education and regional innovation capacity. Then the synergistic development of higher education and regional innovation capacity is measured by using a synergistic coupling degree calculation model based on synergism - the minimization of coupling degree model with coefficient of divergence, to explore the synergistic development status and interaction mechanism. Finally, it discusses the sustainable development ability of higher education and regional innovation ability by utilizing relevant evaluation methods.
As a new type of economy, the digital economy has different characteristics and advantages from the traditional economy. The human economy and society have gradually completed the evolutionary transformation from an industrial economy to a digital economy through technological innovation, industrial restructuring, and institutional transformation, etc. The digital economy is the next new phase for the world’s economic development. As a new form of productivity, digital economy has many characteristics different from traditional industries, and its influence is wide and far-reaching, including the regional innovation capacity and higher education studied in this paper, and the coordinated development of these two is also inextricably linked to the digital economy.
The continuous innovation and rapid development in the field of science and technology have had a wide and profound impact on human society and economic development. Science and technology innovation has become one of the important driving forces of modern economic development and social progress, providing strong support for the progress of human civilization. The significance of such technological innovation is not only evident in the development of the technology itself, but also in the significant economic benefits and social development changes it engenders.
The concept of higher education agglomeration originated from industrial agglomeration. The formation mechanism of industrial agglomeration is shown in Figure 1. Industrial agglomeration refers to a process in which the same industry is highly concentrated in a specific geographic area, and the industrial capital elements are constantly converging in the spatial scope, and its purpose is to strengthen the economic ties between enterprises, realize the sharing of enterprise resources, and reduce the production costs of enterprises, so as to realize the maximization of economic benefits. The purpose of higher education agglomeration is to promote the exchange and sharing of knowledge among colleges and universities, and to realize the production and transfer of innovative knowledge, which is also the essential difference between higher education agglomeration and industrial agglomeration. Higher education agglomeration is firstly embodied in colleges and universities themselves, which gather knowledge, scholars, students and other kinds of elements, but the most fundamental is to gather scientific knowledge, and another manifestation is that many colleges and universities are gathered in the same area.
Industrial agglomeration formation mechanism analysis framework
Based on this, this paper defines higher education agglomeration as a phenomenon in which a number of colleges and universities of different levels and types are concentrated and distributed in a particular region, with the high-level universities in the region as the core, and are interconnected with each other to form an organic whole with complementary advantages, resource sharing, and coexistence of competition and cooperation, so as to jointly support the regional innovation and development.
At present, domestic and foreign methods of measuring the level of agglomeration are relatively abundant, mainly concentration, location entropy index, agglomeration index, Herfindahl--Hirschman index, spatial Gini coefficient, E-G index, etc., each of which has its own characteristic advantages and shortcomings. In this paper, location entropy is finally chosen to measure the level of industrial agglomeration. Although location entropy also has certain limitations and cannot reflect the absolute scale of the industry, it can image the differences in the degree of industrial agglomeration in the region, and the data of location entropy index is easier to obtain, so it can measure the agglomeration level of the new generation of information technology industry in each province and its change trend.
Location entropy (LQ for short) method is based on the theory of comparative advantage and is used to measure the scale level and specialization degree of an industry in a certain region, and is an indicator used to measure the spatial distribution of factors in a certain region[. Through location entropy, we can analyze the status of the dominant specialized sectors in a region, so as to understand the relative degree of agglomeration of a certain industry in the entire country. The location entropy model is used to analyze the spatial distribution of industries, explore the status and role of each industry in the province, draw conclusions, and put forward relevant suggestions after analyzing it. By calculating the location entropy of industries in a certain region, the comparative advantage of industries can be derived, and the advantageous industries with a certain status in the region can be judged. Location entropy index is mainly used to measure the ratio of the proportion of a certain industry in a certain region to the proportion of the industry in the whole region, usually using the number of employed people, output value and other indicators to measure, the specific calculation method is as follows:
Where
The research on the synergistic development of higher education science and technology innovation capacity in the region should not only start from the individual innovation structure of higher education in the region, but also take into account the degree of synergy between the level of regional economic and scientific and technological development and the scientific and technological innovation of higher education in the region, i.e., the current situation of the structure of higher education science and technology innovation capacity in the region. This paper takes higher education S&T innovation capacity in the region as the research object, measures the synergistic development status through the coupling development degree between regional higher education S&T innovation capacity and regional economic and scientific and technological development level, and explores its interaction mechanism.
Coupling is that two or more subsystems influence each other’s reaction by interacting with each other, thus producing the effect of 1+1>2. In this paper, we use the coupling degree calculation model based on synergetics - the model of minimizing the coupling degree of the coefficient of divergence - to measure the synergy degree of regional higher education science and technology innovation capacity.
Using
Eq:
This paper adopts the model of minimizing the coupling degree by the coefficient of departure to explore the interaction mechanism between regional higher education scientific and technological innovation capacity and regional economic and scientific and technological development level, and the final measurement model of the coupling degree is derived through calculation:
Where:
The coupling degree
Where:
Regarding the evaluation standard of coupling development, this paper draws on the existing research results, and divides the coupling development status of regional higher education and regional economy (science and technology) into 3 categories and 8 subcategories according to the coupling development index, and then divides them into 3 types according to the development sequence of regional higher education scientific and technological innovation capacity and regional economy (science and technology) development index
The sample of this paper includes 31 provinces (cities and autonomous regions), of which Tibet, Hong Kong, Macao, and Taiwan data are missing. The higher education data selected in this paper are taken from the 2016 edition of the Higher Education Science and Technology Statistical Yearbook, the statistical data of the Bureau of Statistics, in order to try to ensure the accuracy of the data measurement, all the data are dimensionless, through the coefficient of variation to minimize the coupling degree model, the interaction mechanism between the regional higher education scientific and technological innovation capacity and the regional economic and scientific and technological development level of the regional economy and scientific and technological data are taken from China. Explore.
The purpose of this paper’s research is to link with the regional innovation capacity, and the required indicators are ultimately translated into the productivity of science and technology innovation in the context of the digital economy, so the industrial agglomeration measure is used to measure the degree of spatial agglomeration of higher education. The number of students enrolled in general higher education institutions at the undergraduate and tertiary levels and the number of regional students in 31 provinces in China (Hong Kong, China, Macao, China and Taiwan are partially missing) from 2006 to 2024 were selected to be analyzed, and the specific indexes and data were obtained from China Statistical Yearbook and China Education Statistical Yearbook of the respective years.
The entropy of spatial agglomeration location of higher education in China’s provinces from 2006 to 2024 was measured. The distribution of higher education spatial agglomeration in 2006, 2012, 2018 and 2024 is visualized, and the results are shown in Figure 2. It can be seen that the distribution of higher education spatial agglomeration in China has produced obvious changes during the 20 years, and the regions with high locational entropy in 2006 were mainly in Beijing and Tianjin, the Yangtze River Delta, Beijing, Shanghai, and Tianjin with locational entropy of 1.6-2.0 or more, while the regions with low locational entropy were mainly concentrated in the western regions of these regions such as the Tibet Autonomous Region, Qinghai Province, the Ningxia Hui Autonomous Region, and Yunnan Province, which are mostly are adjacent to each other, indicating that the regions with weaker educational development have also formed a certain trend of agglomeration, and this result indicates that there was a serious bifurcation situation in the level of higher education agglomeration in China in 2006, and the development of education was extremely unbalanced.
China provincial domain higher education space gathering
After several years of development, in 2012, the entropy of the national higher education spatial agglomeration location was below 2 in the rest of the provinces except for Beijing and Tianjin which were above 2. The areas with higher entropy of the higher education spatial agglomeration location were mainly in Beijing-Tianjin area, Yangtze River Delta area, Shaanxi Province and Hubei Province, which had some changes compared with that of 2006. Among them, the location entropy of Jiangxi Province, Shandong Province, and Hainan Province increases most obviously, with a rise of 0.36, 0.25, and 0.32 respectively. The rest of the regions have small changes. This result shows that there is a certain trend of equalization in the distribution of higher education resources in China, but the effect is only limited to a few regions, and most of the country has not significantly improved, and the gap between some regions is still relatively obvious.
By 2018, the spatial entropy of spatial agglomeration of higher education across the country was below 2, with higher regions mainly concentrated in Beijing, Tianjin, Shaanxi, and Hubei provinces. There are five regions with a location entropy greater than 1.2, namely Beijing, Tianjin, Hubei, Shanxi, and Zhejiang. There are 19 regions with location entropy between 0.7 and 1.2. There are 7 regions with location entropy values below 0.7. As can be seen from the figure, both light-colored and dark-colored regions are gradually decreasing, indicating that the spatial agglomeration of higher education is developing towards a balanced trend, but the spatial agglomeration of the northwest and southwest regions has not increased significantly, indicating that the level of development of higher education business is relatively backward.
In 2024, the location entropy of spatial agglomeration of higher education in the country, except for Tianjin, declined to below 1.8, and the regions with location entropy values above 1.2 declined from 8 in 2006, 6 in 2012 and 4 in 2018 to 3 in 2024. Districts with locational entropy values between 0.7 and 1.2 decreased from 10 in 2006, 16 in 2012, and 23 in 2024. Regions with location entropy values below 0.7 decreased from 13 in 2006, 9 in 2012, and 7 in 2018 to 5 in 2024. From this result, it can be seen that the degree of bifurcation in China’s higher education agglomeration is gradually shrinking, but there is still an imbalance in the development of higher education across the country.
According to the latest economic zone division criteria released by the China Bureau of Statistics, China is divided into four regions: eastern, central, western and northeastern. According to measuring the location entropy of the four regions from 2006 to 2024, the results are shown in Figure 3. From the time dimension, the location entropy of the northeast and east regions shows a decreasing trend, while the central region has a smaller change, and the western region has a certain growth trend. It is worth noting that around 2019 the western region has a significant upward trend, and the northeast and east regions have a significant decline, which may be due to the expansion of colleges and universities under the reform of the education system. The degree of spatial agglomeration of education in the four regions is still quite different, but the bifurcation is gradually weakening, and the leading edge of the northeastern and eastern regions is slowly narrowed by the central and western regions, indicating that the gap between the degree of spatial agglomeration and development of China’s higher education is further narrowing.
The higher education space gathers the location entropy
In order to explore the evolution law of the balance and adequacy index, this paper draws a radar chart as shown in Fig. 4 by analyzing the balance and adequacy index of the innovation capacity development of each province and region in four major years, namely, 2006, 2012, 2018, and 2024, to see the changes of China’s 31 provinces and regions from different points of time. The comprehensive balanced adequacy index partition can be seen that in 2006, the level of balanced and adequate development of innovation capacity of all provinces and regions was relatively balanced, mainly distributed in the interval of 20~30. 2012 and 2018 saw the decline of the balanced and adequate development index of innovation capacity in most of the provinces and regions, mainly distributed in the interval of 0~10. 2024 saw an increase in the balanced and adequate development index of innovation capacity in some provinces and regions, and the gap between provinces and regions became wider. , the gap between provincial regions becomes larger, and the main distribution range expands to within the interval of 0~30. In terms of the development of specific provinces: Beijing, Tianjin, Shanghai, Jiangsu and Guangdong will maintain a leading position in the process of development of the index of balanced and sufficient development of innovation capacity for a long time.
The innovative ability balanced the full development index
The coupling degree and coupling coordination degree of the two major subsystems of higher education resource carrying capacity level and innovation capacity of Chinese provinces and regions from 2006 to 2024 are measured by using the coupling degree and coupling coordination degree measurement formula.
The average values of the coupling degree and coupling coordination degree of the level of higher education resources carrying capacity and innovation capacity in China from 2006 to 2024 are 0.950 and 0.491 respectively, which are mutually reinforcing and strongly related, and the coupling degree is at a high level. However, the overall level of development is low, only 0.275, resulting in a coupling coordination level of only mildly dysfunctional decline.
The time trend is shown in Figure 5, the level of higher education resource carrying capacity and innovation capacity grows from -0.30469 and -0.34847 in 2006 to 0.46629 and 0.48086 in 2024, and the level of development of the two systems shows a yearly upward trend, while the degree of coupling and coordination of the two rises from 0.37272 in 2006 to 2024 to 0.60469, the coupling coordination level is transformed from moderate dysfunctional decline to basic coordinated development, and the degree of coupling coordination has improved, but the gap from high-quality coordinated development is still large. Over this period of time, the evolution of China’s overall coupling coordination degree has mainly experienced three grades: before 2014 in the moderate dysfunctional recession grade, from 2014 to 2020 in the mild dysfunctional recession, and in the year after 2020 in the basic coordinated development grade. the value of the coupling coordination degree in the years before 2014 is less than the overall average value of 0.425789, and after that, the overall development of the nation’s coupling coordination degree After that, the overall coupling coordination degree development of the country slows down, and the overall higher education resource carrying capacity level and innovation capacity of the country have been improved to a large extent in this period, which may be the reason for the overall coupling coordination degree to appear greater improvement.
Coupled coordination score
The spatial layout of the degree of coordinated development of each province and region is shown in Figure 6, and the overall spatial distribution of the degree of coupled coordination of each province and region in China shows a pattern of “low in the west and high in the east” and “low inland and high along the coast”. In 2012, the imbalance of the coupled and coordinated development of the two systems was more obvious. In 2012, the problem of unbalanced development of the coupling of the two systems was more obvious, and regions with good coordination or higher were basically distributed in the eastern coastal region. Inland areas are mainly at the basic coordination level or below, and the degree of coupling coordination gradually decreases as the geographical location extends into the interior. Although the provinces with good and high-quality coordination are still mainly concentrated in the eastern coastal region, most of the provinces in the inland region are in the excessive stage of upward development of the level of coupling coordination, and good coordination has begun to appear in the region. With the trade cooperation of “Belt and Road” and the deepening of regional coordinated development, more provinces in the inland region have entered the stage of good coordination. Combined with the comparative analysis of the three years of 2012, 2018 and 2024 in the figure, it can be seen that China’s higher education resource carrying capacity and innovation capacity of the system coupled with the degree of coordination of the development of a certain geographic characteristics and radiation capacity. The coupling and coordination development of the provinces close to the better-developed regions such as Beijing, Shanghai, Guangdong, Jiangsu and Zhejiang is more stable, at a higher level and with a faster overall growth, while the overall growth rate of the coupling and coordination degree in the inland areas is slower. This indicates that China is advancing towards coordinated regional development and has already started to see results.
Coordinate the development of spatial layout evolution
This paper refers to the grouping method of relevant evaluation indexes and consults relevant experts to grade the evaluation values obtained, and the results are shown in Table 1, in order to determine the comprehensive degree and stage of development of each provincial and regional area. The levels of China’s higher education resource carrying capacity level and the level of innovation capacity in 2006-2024 are in relatively low grades, and have fluctuated up and down in the range of the level of grade IV for a long time.
The evaluation of sustainable development ability indicators
| Classification | High education resource bearing capacity Innovative ability | Degree of development | Endurance | Effective development | Comment |
|---|---|---|---|---|---|
| I | [0.98,0.98] | [1.70,1.73] | [0.0,0.08] | [1.69,1.87] | Very high |
| II | [0.74,0.96] | [1.32,1.72] | [0.08,0.45] | [1.32,1.78] | High |
| III | [0.26,0.77] | [0.54,1.42] | [0.47,1.32] | [0.58,1.36] | General |
| IV | [0.06,0.28] | [0.12,0.52] | [1.30,1.75] | [0.11,0.54] | Low |
| V | [0.0,0.06] | [0.0,0.09] | [1.78,1.86] | [0.0,0.13] | Very low |
Based on the agglomeration measurement method and the coupled synergistic development measurement, this paper measures and analyzes the regional innovation capacity and the level of synergistic development between higher education systems in 31 provinces in China. The study proves that:
The level of higher education development among China’s 31 provinces and regions is still uneven, but this gap and spatial agglomeration are gradually decreasing. The balanced and adequate development of innovation capacity in coastal areas such as Beijing and Tianjin has maintained a leading position among China’s 31 provinces for a long time. China’s overall coupling coordination degree is divided into three stages, with a moderate dysfunctional recession grade before 2014. A mild dysfunctional recession grade from 2014-2020, and a basic coordinated development grade after 2020. Most of the regions have a low sustained development capacity grade. Therefore, the results of this paper can enhance the research related to regional coordinated development and provide decision-making support for the high-quality development of regional innovation and higher education.
This research was supported by the Shanghai Educational Science Research Project: Research on the Evaluation and Improvement Mechanism of Ideological and Political Construction of College Curriculum (No.C2022304).