Clustering of Basic Educational Resources and Urban Resilience Development in the Central Region of China—An Empirical Study Based on POI Data

Abstract

This paper presents an urban resilience evaluation index system and evaluation on the clustering of educational resources based on the data of 80 prefecture-level cities in China’s central region in 2012, 2016, and 2020. The results reveal a rising trend of urban resilience in the central region of China, with the provincial capital cities exhibiting the highest levels of resilience. Educational resources are clustered in urban areas of provincial capital cities and other prefectural-level cities. Furthermore, clustering of educational resources has a significant impact on urban resilience. Policy factors play a significant role in moderating the relationship between educational resource clustering and urban resilience in large cities; however, this moderating role is not significant in small cities. These findings have significant implications for the optimal allocation of educational resources, promotion of urban resilience, and advancement of social equity.

1. Introduction

The relationship between educational resource spatial layout of and urban development has long been a focus of academic attention. Evidently, a reasonable layout of educational resources has a positive effect on urban development. Clustering of educational resources has a significant effect on regional economic growth [1]. The Outline of the Fourteenth Five-Year Plan and Vision 2035 for National Economic and Social Development of the People’s Republic of China proposes to establish a high-quality education system and construct a country with strong education by 2035, which has been set as an important national strategic goal. To attain this goal, it is necessary to achieve overall improvement in the quality of education. In particular, the central region of China is endowed with rich educational resources, but is confronted with the problem of uneven distribution of these resources. To some extent, the uneven distribution of educational resources between cities has negatively affected the balanced development of the regional economy and the overall competitiveness of the region. In recent years, the economic and social development of the central region have been confronted with a series of unprecedented challenges due to a multitude of factors, including the COVID-19 pandemic. In order to address these challenges, the central and local governments have introduced a series of policies and measures aimed at promoting high-quality development in this region. Among these policies, the Opinions of the Central Committee of the Communist Party of China and the State Council on Promoting the High-Quality Development of the Central Region in the New Era has provided definite direction for the development of the central region and guidance for optimizing the layout of educational resources. The six provinces in the central region of China have formulated their own high-quality development roadmap, which coincidentally propose to develop science and technology, activate innovation, and vitality, and fully utilize the science and educational resources. Improvement in education quality is indispensable for achieving these goals. Therefore, education will play an important role in the development of these provinces and cities.

Urban resilience has become a significant research topic in urban development at present. It is defined as the capacity of a city to adapt to and recover from various shocks, such as natural disasters, social pressures, and economic fluctuations [2]. This capacity is crucial for the sustainable and stable development of cities. The majority of previous studies have employed a tripartite approach to measuring urban resilience, including natural environment, economy, and society. However, these studies vary considerably in their specific focus. In the field of ecology and environment, urban resilience assessment has been conducted in relation to a multitude of different hazards. In contrast to traditional and deterministically oriented disaster mitigation strategies, climate change resilience lays great emphasis on a multitude of uncertain and unpredictable shocks and stresses that urban systems may face in the future. In terms of climate change resilience [3], research has been mainly focused on extreme weather events such as heavy rainfall flooding [4], earthquakes [5], and heat waves [6]. By integrating ecosystem services into disaster management in urban systems, the strength of natural resources can be better utilized to improve the socio-ecological resilience of cities [7]. In particular, ecosystem services can provide crucial support for urban systems to better cope with and adapt to the challenges posed by climate change [8]. Economically resilient cities are more capable of adapting, learning, and transforming in the face of risky crises, and quickly returning to their original trajectory, or even embarking on a better development path [9]. Economic resilience is a crucial factor in the quality development of a city. Improvement in economic resilience has a significant impact on the quality of urban development [10]. It plays a significant role in the high-quality development of cities by strengthening the competitiveness and sustainable development of cities, thereby becoming the key to regional ecological protection and high-quality development [11]. In terms of social development, urban resilience can help to enhance the competitiveness and sustainability of cities. In terms of social development, the urban resilience subsystem encompasses a range of factors, including basic environmental conditions, educational and cultural resources, public healthcare, social security, and other aspects [12]. Among these, the social function of education has long been apparent, and understanding of its social function has been gradually associated with many fields, including politics, economics, culture, population, environment, social stratification, and social changes. Furthermore, education plays pivotal roles in cultural selection, dissemination, integration, change, and innovation. Therefore, it can be argued that education is not only a function of a city but also an important component of urban resilience and a key contributor to the sustainability and resilience of cities [13].

The degree of education clustering refers to the concentration of educational resources in a city, mainly represented by the number and distribution of education-related resources such as schools, teachers, and students. In the process of urban development, higher education can significantly enhance urban economic resilience through scientific and technological innovations [14]. Currently, there has been a lack of research adopting the agglomeration or convergence method to study the distribution of educational resources in China. Furthermore, the majority of previous studies have only investigated a specific stage or type of educational resources, and there is a dearth of analysis on the effect of educational resource clustering on economic development. Traditionally, educational resource clustering was generally measured by the Lorenz curve, the coefficient of variation, and the Gini coefficient [15]. However, these methods only analyze the equity of educational resources from the macro scale of supply and demand, which can hardly reflect the real situation effectively. With regard to GIS and spatial analyses, there is a lack of quantitative methods to reveal the causes of uneven spatial distribution [16]. This study uses POI data to quantify the spatial attributes of educational resources (schools) and the spatial clustering characteristics of geographic location information. This method does not require a priori knowledge or specific assumptions about the distribution of data; rather, it directly examines the distribution characteristics of the data samples. The method has several advantages, including informativeness, accessibility, and convenience for analysis, providing a new and effective means for related research. Secondly, the empirical research object of this paper is six provinces in central China, including Shanxi, Anhui, Jiangxi, Henan, Hubei, and Hunan, involving 80 prefecture-level cities. The six provinces are located in China’s inland and act as a key “connecting point” between the east and west and between the south and north, and therefore are crucial for the national development strategy. This study explores the development of educational resource clustering and urban resilience, and examines the mechanisms by which policies regulate the impact of educational resource clustering on urban resilience.

2. Theoretical Analysis

The theoretical basis of educational resource agglomeration can be traced back to the idea of agglomeration economics, namely MAR externalities. It pointed out that spatial agglomeration of firms or institutions can generate knowledge spillovers and innovation diffusion effects. As a typical phenomenon of spatial clustering, the impact of educational resource clustering is similar to the knowledge spillover mechanism in regional economics. Glaeser et al. further confirmed the role of geographic concentration of knowledge in promoting regional innovation, emphasizing the importance of agglomeration effects in economic development [17]. In the field of education, Frenken et al. analyzed the agglomeration effects across various domains and pointed out that the clustering of educational resources not only brings internal knowledge spillovers but also provides long-term momentum for regional innovation [18]. This demonstrates that the agglomeration of educational institutions provides an efficient platform for the creation and dissemination of knowledge, driving technological innovation and human capital accumulation in cities.

From the perspective of human capital externalities, Lucas [19] proposed that education is key to enhancing human capital reserves, and the clustering of educational resources can enhance workers’ productivity by promoting skill development and knowledge dissemination. Concentrated educational resources support technological upgrading and economic development within the region, while also improving a city’s ability to recover from economic fluctuations and technological changes. The clustering of educational resources can also attract more high-skilled talents to cities, further enhancing urban innovation and resilience, creating a virtuous cycle.

Urban resilience refers to the ability of a city to effectively adapt to, withstand, and quickly recover from various shocks, such as economic crises, natural disasters, and social changes. This concept encompasses not only a city’s capacity to respond to sudden events in the short term but also its long-term adaptability, innovation capability, and sustainable development capacity. Urban resilience emphasizes the system’s integrity and interactivity, involving multiple dimensions, including economic, social, and environmental aspects.

Firstly, educational resource clustering directly enhances urban economic resilience by improving human capital reserves. Hu Yan et al. [20] suggested that urban economic resilience depends on the accumulation of human capital and the enhancement of innovation capabilities, while educational resources, as key elements of knowledge production and dissemination, can effectively promote technological advancement and industrial upgrading. High-density educational institutions not only provide the city with highly skilled labor but also promote knowledge diffusion and innovation transformation through collaboration with enterprises [21]. In the face of global economic uncertainty, cities with educational resource clustering exhibit stronger risk resistance and economic resilience [22]. This economic resilience mainly manifests in the fact that educational resource clustering provides a foundation for regional economic diversification, reducing the city’s dependence on a single industry and thus mitigating the impact of market fluctuations on the urban economy [23].

Secondly, the clustering of educational resources enhances urban social resilience by improving social capital and resident participation. Putnam [24] proposed that education is a crucial component of social capital, enhancing trust and cooperation between individuals, thus strengthening community cohesion and crisis response capacity. The clustering of educational resources not only improves residents’ knowledge levels but also enhances community participation through the cultural and social activities of educational institutions, forming a strong social network that increases social resilience [25]. Especially in the face of social crises or emergencies, the accumulation of social capital can help communities organize more quickly, forming effective mutual aid mechanisms, thereby improving cities’ ability to respond to social shocks.

Lastly, the role of educational resource clustering in enhancing urban environmental resilience should not be overlooked. Educational institutions are not only places for knowledge dissemination but also key promoters of green technologies and sustainable development concepts. Cohen and Winn [26] noted that universities and research institutions, through environmental courses, research projects, and social responsibility activities, can drive the innovation and application of green technologies, thereby improving cities’ environmental adaptability. The clustering of educational resources facilitates the formation of green industry clusters in cities, promoting the diffusion and practice of green technologies and enhancing cities’ ability to respond to environmental crises [27]. For instance, in the context of climate change, clustered educational institutions can provide local governments with technical support and policy recommendations on climate mitigation and adaptation, thereby improving urban environmental resilience.

In summary, the clustering of educational resources enhances urban resilience through promoting economic diversification, strengthening social capital, and driving green development from multiple dimensions. Therefore, the clustering of educational resources is not only a key factor in improving cities’ ability to respond to emergencies but also an important driver for sustainable urban development.

Hypothesis 1:

The clustering of educational resources positively affects urban resilience.

Governments, by formulating and implementing relevant policies, directly influence the allocation and quality of educational resources. The formulation of policies not only determines the spatial layout of educational resources but also ensures their reasonable distribution and full utilization. Effective educational policies can improve the level of talent cultivation, promote regional innovation, and enhance overall urban resilience [28]. Based on the relationship between educational resources and urban resilience, government policy plays the role of a “mediator”: it not only directly influences the supply of educational resources but also promotes their contribution to urban resilience by shaping the external environment.

Firstly, policy implementation significantly impacts education levels, innovation capabilities, and talent cultivation [29]. Through specialized educational development plans, governments can direct funding and infrastructure development to improve the accessibility and quality of educational resources [30]. The policy priorities (such as promoting higher education and vocational education development, increasing educational investment in underdeveloped regions) [31,32] determine the regional distribution and scale of investment in educational resources, thereby influencing regional economic development and resilience [33]. For example, policies that promote education equity not only narrow the educational gap between regions but also enhance urban economic resilience through the improvement of human capital.

Secondly, policies encourage greater investment by businesses and individuals in educational resource development through incentive mechanisms. Tax relief, subsidies, and preferential policies can motivate enterprises and non-governmental organizations to participate in the expansion and optimization of educational resources. At the same time, government policies tend to create an environment conducive to the interaction between educational resources and urban resilience. Through urban planning policies and infrastructure development, governments provide necessary physical space and support for the clustering of educational resources, such as building educational parks and innovation and entrepreneurship bases. Moreover, policies can attract more high-skilled talent to regions with clustered educational resources by providing affordable housing and public transportation, further promoting innovation diffusion and enhancing urban resilience [34]. The effective implementation of these policies creates a favorable institutional environment for the clustering and rational utilization of educational resources, contributing to sustainable urban development.

In conclusion, the role of policy in the relationship between educational resources and urban resilience is not only reflected in direct resource control but also in shaping a favorable external environment and promoting public–private sector collaboration. This indirectly influences the contribution of educational resources to urban resilience. Therefore, we propose:

Hypothesis 2:

Policy significantly moderates the relationship between educational resources and urban resilience.

3. Data Sources and Research Methodology

3.1. Data Sources

This study utilizes data from GaoDe and Baidu maps to obtain the POI data of science, education, and cultural services in the central region of China, which are used as the basis for screening and retaining the schools in terms of the data on science, education, and cultural services in this region. The data of urban resilience measurement were derived from the China Urban Statistical Yearbook, CECN Statistical Database, and statistical yearbooks of each province. Given that the data of a single year may not be sufficient to illustrate the problem, we employed the data from 2012, 2016, and 2020 for empirical research, and data from 2013, 2014, and 2018 for robustness testing.

3.2. Research Methodology

3.2.1. Method for Measuring Urban Resilience

The entropy weight method is designed to determine the relative importance of indicators in each subsystem and constituent elements based on the entropy value. This method solves the limitations of the subjective assignment approach, which can result in arbitrary weight assignments. The entropy weight method can objectively determine the weights of the evaluation indicators. This study applies the entropy weight method to measure the level of urban resilience of the central region in China. The calculation formula is as follows:

$$S_{ij}={\displaystyle {\sum }_{j=1}^m{W}_j}\times Z_{ij}\left(i=1,2,3\dots n\right)$$

(1)

In the formula, $Z_{ij}$ is the value of data after de-scaling, $W_j$ is the weight of $j$, $m$ is the number of indicators, $n$ is the total number of subjects, and $S_{ij}$ is the composite score of urban resilience quality of each city, which is distributed within $\left[0,1\right]$, with a higher value indicting a higher urban resilience level of the city [35].

3.2.2. Spatial Clustering Characteristics of Educational Resources

The DBSCAN clustering algorithm is a density-based clustering method, which was employed to effectively identify and classify the spatial clustering characteristics of schools in this study. DBSCAN describes the closeness of the sample set through a set of neighborhood parameters (ϵ, MinPts), which allows the algorithm to delineate clusters in regions with a sufficiently high density and to discover arbitrary shapes in the spatial database of noisy clusters. Subsequently, the kernel density analysis was employed to elucidate the spatial distribution of educational resources in this region. This method can accurately reflect the spatial distribution characteristics of educational resources and further reveal the spatial distribution pattern of schools in the region as a whole. The combination of the DBSCAN clustering algorithm and kernel density analysis allows for effective identification and classification of spatial clustering characteristics in schools. Furthermore, it enables deeper understanding of the hotspots of spatial distribution of educational resources and the overall characteristics, thereby providing a robust data foundation for informed decision-making.

3.2.3. Mechanisms for the Effect of Educational Resource Clustering on Urban Resilience

(1)

Selection of influencing factors. Based on the theoretical analysis, the core explanatory variable is the clustering degree of educational resources, which is calculated as the proportion of schools in each prefecture-level city to the total school number in the study area [36]. The control variables mainly comprise socio-economic development variables and regional background variables, including the number of full-time teachers in primary and secondary schools, the number of students in primary and secondary schools, the green space rate of the built-up area, the resident population at the end of the year, the registered unemployment rate, the number of employees in public administration, social security, and social organizations, and the total energy consumption (

Table 1. Description of variables.

Variables	Minimum	Maximum	Mean	Standard Deviation	Median
Explanatory variables
Urban resilience	0.050	0.798	0.140	0.104	0.111
Core explanatory variables
Level of concentration of educational resources	0.001	0.068	0.050	0.011	0.010
Control variables
Number of full-time teachers in primary and secondary schools	2306	68,395	10,803.487	8838.851	8593.500
Number of students in primary and secondary schools	2.010	3735.480	60.021	381.877	13.185
Green space ratio in built-up areas	21.060	53.963	38.509	5.396	38.390
Year-end resident population	73.000	1261.680	439.799	232.340	413.790
Urban registered unemployment rate	1.310	55,160	501.749	3827.443	3.295
Public administration social security and social organisation practitioners	10,077	181,121.620	52,609.185	26,769.794	50,250
Total energy consumption	10.130	939.056	103.323	123.893	65.254

).

(2)

Quantitative analysis. This paper employs the mediating effect model to investigate the internal mechanism for the effect of educational resource clustering on urban resilience. Given the pivotal role of policy factors in mediating the relationship between the degree of educational resource clustering and urban resilience, the mediating effect model is constructed to reveal this mechanism, thereby providing a scientific basis for relevant decision-making.

4. Spatial Distribution Characteristics of Educational Resources and Urban Resilience

4.1. Results of Urban Resilience Measurement

The urban resilience evaluation index system was employed to calculate the city resilience level, which was then visualized using ArcGIS (Figure 1). The analysis was conducted on 80 cities in the central region of China between 2012 and 2020, with a focus on temporal and spatial differentiation characteristics of the resilience.

In general, from 2012 and 2020, the highest value of the comprehensive index of urban resilience in the central region of China increased from 0.512 to 0.798, while the lowest value increased from 0.049 to 0.085 (Figure 1). These results demonstrate that the resilience of cities in the central region is on an upward trend, indicating that the cities can effectively respond to a range of risk shocks, including major natural disasters, social development crises, and even wars. This capability enables them to minimize all kinds of risk losses while maintaining a normal operation or returning to normalcy as soon as possible after a short “shutdown”.

In terms of spatial patterns, the most resilient cities are dominated by provincial capitals such as Wuhan, Changsha, Zhengzhou, Taiyuan, Hefei, and Nanchang. These provincial capital cities are typically located in relatively large geographic areas, surrounded by satellite cities or rural areas. Upon the occurrence of shocks, these cities can leverage the resources and human support in their neighborhoods to create a spatial shading effect, thereby better coping with the shock. Furthermore, there was no discernible change in inter-city disparity, despite the advantages of provincial capitals in terms of resources, facilities, and policies. Non-capital cities may also enhance their resilience through their own efforts and resource integration without further widening the inter-city differences in development.

4.2. Clustering of Educational Resources

The number of schools and the quality of education greatly affect the distribution of educational resources in China [23]. This paper defines “educational resource clustering” as a quantitative module of clustering to reflect the polarization of the scale of educational resources. The degree of educational resource clustering was calculated as the proportion of schools in the prefectural-level cities to the total school number in the study area.

This section will present a comprehensive analysis of educational resource clustering in prefecture-level cities in the central region of China. Through an in-depth examination of the data from 2012 to 2020, it can be observed that the overall clustering degree of educational resources exhibited a clear upward trend during this period, as illustrated in Figure 2. In particular, in 2012, the cities of Wuhan, Jingzhou, Huangshi, Zhengzhou, Xuchang, and Jiaozuo exhibited a high clustering degree of educational resources. However, by 2016, this phenomenon was mainly observed in Zhengzhou and Wuhan. By 2020, this trend was still evident, with Zhengzhou and Wuhan still being at a leading position in the clustering of educational resources.

It is noteworthy that between 2012 and 2020, the number of cities in the top two intervals of clustering degree of educational resources showed a significant decrease. This trend may indicate a shift in the pattern of competition and distribution of educational resources, with some cities gradually losing their competition advantages for educational resources, while other cities are attracting more resources through measures such as optimizing the allocation of resources and improving the education quality. This trend may involve a number of complex factors. Firstly, government policies have a significant impact on the distribution of educational resources. Different cities may show different trends in the clustering and balanced distribution of educational resources due to different policy support and resource input. Secondly, economic development is also a key factor affecting the distribution of educational resources. The booming of the economy and inflow of talent capital can facilitate the improvement in educational resources in a city, while lag in economic development and problems in the industrial structure can cause a shortage of and development obstacles in educational resources.

5. Analysis of the Action Mechanism

5.1. Baseline Regression Analysis

The OLS regression results in

Table 2. Results of basic regression analysis.

	(1)	(2)	(3)	(4)
Variable	Urban Resilience	Urban Resilience	Urban Resilience	Urban Resilience
Level of concentration of educational resources	7.355 *** (0.721)	4.300 *** (0.976)	0.619 (0.412)	7.820 *** (2.084)
Number of full-time teachers in primary and secondary schools		0.000 *** (0.000)	−0.000 * (0.000)	−0.000 (0.000)
Number of students in primary and secondary schools		−0.000 (0.000)	0.000 *** (0.000)	0.008 *** (0.002)
Green space ratio in built-up areas		0.002 *** (0.001)	0.002 *** (0.000)	0.004 (0.003)
Year-end resident population		−0.000 *** (0.000)	0.000 (0.000)	−0.000 (0.000)
Urban registered unemployment rate		−0.001 (0.003)	−0.000 (0.002)	−0.022 (0.022)
Public administration social security and social organisation practitioners		−0.000 (0.000)	−0.000 *** (0.000)	−0.000 ** (0.000)
Total energy consumption		0.000 *** (0.000)	0.000 *** (0.000)	0.000 (0.000)
Fixed time	YES	YES	YES	YES
R2	0.645	0.854	0.634	0.945
R2 _a	0.641	0.848	0.616	0.916

Note: Standard deviations are presented in parentheses; ***, **, and * indicate significance at the 1%, 5%, and 10% confidence levels, respectively; same below.

show a significant positive relationship between the concentration of educational resources and urban resilience. The central region, with relatively lagging economic development, is in a critical phase of economic transformation and upgrading. The agglomeration of educational resources in these cities provides richer educational opportunities, attracting talent, capital, and technology. This not only enhances the cities’ innovation capacity but also injects strong momentum into economic transformation, enabling central cities to exhibit greater resilience in the face of economic challenges. Moreover, the concentration of educational resources improves the overall quality and skill level of urban residents, strengthening the adaptability and competitiveness of the labor market. Thus, Hypothesis 1 is confirmed: The agglomeration of educational resources can significantly enhance urban resilience.

5.2. Robustness Tests

Firstly, the data replacement approach was employed because there are discrepancies in the methodology employed for data collection and processing between POI data and statistical data, which may result in disparate research outcomes. By replacing the data source, the impact of such discrepancies on the research findings can be evaluated, thus enabling an assessment of the robustness of the research results.

Table 3. Results of regression analysis for the robustness test.

	(1)	(2)	(3)	(4)
Variable	Urban Resilience (2013)	Urban Resilience (2014)	Urban Resilience (2018)	Score
Level of concentration of educational resources	2.286 * (1.82)	5.041 *** (2.98)	7.283 *** (3.34)	3.591 *** (3.92)
Control variable	YES	YES	YES	YES
Fixed time	YES	YES	YES	YES
R2	0.809	0.856	0.888	0.841

Note: Standard deviations are presented in parentheses; *** and * indicate significance at the 1% and 10% confidence levels, respectively; same below.

presents the calculation results of composite scores for educational resource clustering and urban resilience each year between 2013 and 2018. These scores were derived from both POI data and statistical data and then regressed. A comparison of the empirical results with the original data source reveals that the sign and coefficient values of the key explanatory variables in columns (1), (2), and (3) of Table 3 remain largely unchanged, despite replacement of the original data source, and there are only slight differences in the significance of some control variables.

Secondly, in order to further eliminate the influence of extreme values on the regression results, we employed quantile regression for robustness testing. In quantile regression, a detailed regression analysis was conducted based on the data of the explanatory variables and control variables at the quantile points from 1% to 99%. This method not only considers the overall distribution of data but also focuses on the characteristics of the data at different quantile points. The results of the quantile regression analysis indicate that the positive relationship between educational resource clustering and urban resilience remains significant at different quantile points, further corroborating the robustness of the findings and providing more reliable evidence to support the main conclusions of the paper.

5.3. Moderating Effects

To gain further insights into the moderating role of policy factors in the relationship between educational resource clustering and urban resilience, we incorporated the interaction term between policy factors and educational resource clustering into the regression model and conducted detailed analysis on both the full and subgroup samples. The definition of “policy factors” primarily refers to the regulatory capacity of local governments in the allocation of educational resources and the implementation of related policies. We selected the fiscal decentralization index as the variable to measure policy factors, aiming to reflect the local government’s autonomy in fiscal management and resource allocation. The fiscal decentralization index captures the extent of local governments’ control over fiscal resources, including the flexibility of education funding and the ability to respond to local needs through policy adjustments. In the empirical analysis, we used this index to explore its impact on the relationship between the agglomeration of educational resources and urban resilience. By introducing the fiscal decentralization index, we aimed to analyze the role of policy in the distribution of educational resources and its effect on enhancing urban resilience, providing more detailed policy recommendations.

Table 4. Test results of the impact mechanism.

	(1)	(3)	(2)	(4)
Variable	Full Sample	Large Cities	Other Cities	Large Cities
Clustering of educational resources	7.355 ** (0.721)	7.820 *** (2.084)	0.619 (0.412)	12.097 *** (1.764)
Policy factors				48.813 *
				(27.746)
Policy factors x clustering of educational resources				−1514.072 *** (405.874)
Control variable	YES	YES	YES	YES
Fixed time	YES	YES	YES	YES
R2	0.645	0.945	0.634	0.964

Note: Standard deviations are presented in parentheses; ***, **, and * indicate significance at the 1%, 5%, and 10% confidence levels, respectively.

presents the results of the regression analysis.

Firstly, the coefficient of the interaction term (policy factors × educational resource clustering) was not significant in the regression results of all full samples (column 1 of Table 4), indicating that policy factors have an insignificant moderating effect on the relationship between educational resource clustering and urban resilience in the overall sample. However, regression of the sample on subgroups generated some different results. In the large city sample group (column 2 of Table 4), the interaction term had a significantly positive coefficient, indicating that policy factors have a significant moderating effect on the relationship between educational resource clustering and urban resilience in large cities. In contrast, insignificant coefficients on the interaction terms were obtained for other city sample groups (columns 3 and 4 of Table 4), suggesting that policy factors have insignificant moderating effects in these cities. There may be two main reasons for this result. Firstly, the pertinence and implementation strength of policies may vary across different cities. For large cities, policymakers may be more focused on improving the overall competitiveness and development quality of the city, and therefore pay more attention to the regulation of educational resource clustering. In other cities, due to the different developmental stages and needs, policymakers may focus more on the regulation of some other aspects, such as infrastructure construction and industrial structure transformation, and therefore play a relatively weaker role in regulating the clustering of educational resources. Secondly, differences in the developmental stages and needs of cities may also lead to different regulatory effects of the policies. Large cities are typically at a more advanced developmental stage, with a more comprehensive industrial structure and infrastructure, but are also faced with more intense competition for talent and resource shortages. Therefore, the policies may be more focused on addressing these issues when regulating the relationship between urban resilience and educational resource clustering in large cities. Conversely, other cities, which may still be in the early developmental stages or are undergoing industrial structure transition, are more susceptible to the effect of educational resource clustering, and policies tend to have a weaker regulatory effect on them. Thus, Hypothesis 2 is confirmed: Policy factors significantly moderate the relationship between the concentration of educational resources and urban resilience, particularly in large cities.

Hence, to fully exert the moderating effect of policies on the relationship between educational resource clustering and urban resilience, the following measures may be recommended.

(1)

Policy formulation: First, policy support should be provided for the development of education. The clustering of educational resources can be promoted by guiding and encouraging the agglomeration of high-quality educational resources within cities through initiatives such as rational land-use planning, optimized spatial distribution, and more favorable policies. Fiscal policies can create favorable conditions for the agglomeration of educational resources, promote equalization of basic public education services, expand high-quality educational resources, and facilitate the construction of a high-quality education system. Secondly, it is necessary to create favorable conditions for the development of urban resilience. The resilience of cities should be enhanced in accordance with the concepts of “innovation, coordination, green, openness, and sharing” through the optimal allocation of resources and refined management of cities, which will effectively respond to various risks and shocks. Furthermore, rational layout and planning of cities should be promoted, which can enhance the economic resilience, social resilience, ecological resilience, and organizational resilience of cities. Finally, the construction of cities should be strengthened and their capacity to address risks should be promoted to improve the resilience of cities.

(2)

Policy implementation: Firstly, policies should promote the sharing of educational resources and achieve optimal and rational allocation. Policies can not only improve the utilization efficiency of educational resources but also strengthen the exchange and cooperation of regional education and promote the balance of education. Furthermore, policies can also strengthen the regulation on education and improve the education quality. Secondly, policies can enhance urban resilience and promote scientific and technological innovations and economic development by strengthening university–enterprise cooperation and promoting the integration of industry, academia, and research. Finally, policies can coordinate the relationship between education resource clustering and urban resilience. Education resource clustering can promote urban resilience, urban resilience can in turn promote the development of education, and the two can be coordinated and developed in unison. Therefore, policies play a pivotal role in enhancing both education quality and urban resilience.

(3)

Policy feedback: The establishment of an effective tracking and feedback mechanism to assess the effectiveness and impact of policy implementation is essential for timely adjustment and optimization of the policy. Furthermore, public participation and feedback from educational institutions can be utilized to identify the problems and shortcomings during the implementation of the policy, which can allow timely adjustment and improvement of the policy. Secondly, the public should be encouraged to participate in the discussion and decision-making process on educational resource allocation and urban resilience, which may be achieved through suggestions and feedback on the policy by public consultation and public hearings, among other avenues. This can not only improve the scientific and democratic nature of the policy but also strengthen the sense of public acceptance and support for the policy.

In conclusion, policies can play supporting, guiding, regulating, supervising, feedback, and adjusting roles in the interaction between educational resource clustering and urban resilience, which can help to enhance urban resilience, optimize the allocation of educational resources, and improve the quality of education. Concurrently, it is imperative to implement ongoing adjustment and optimization on policies in accordance with the prevailing circumstances to guarantee their suitability for the requirements of urban development.

6. Conclusions and Discussion

6.1. Conclusions

This paper employs a case study to examine the relationship between educational resource clustering and urban resilience of 80 prefecture-level cities in the central region of China over the period 2012–2020. These cities were selected for their representativeness of the region.

This study constructs an urban resilience evaluation index system and evaluates the educational resource clustering. It then empirically demonstrates the relationship between educational resource clustering and urban resilience.

The findings of the study are as follows. (1) The capacity of urban resilience in this region is on the rise, with the highest levels of resilience being observed in provincial capital cities. There is no significant change in the difference in development between cities. (2) Educational resources are highly clustered in the urban areas of provincial capital cities and other cities. (3) Educational resource clustering enhances urban resilience at the prefecture level. (4) For this region, policy factors have a significant moderating effect on the relationship between educational resource clustering and urban resilience in large cities, while this moderating effect is not significant for small cities.

6.2. Discussion

The findings of this study are instructive, as they reveal a strong relationship between educational resource clustering and urban resilience, and further indicate the moderating role of policy effects. The following discussion is triggered by the findings of this paper.

(1)

Educational balance and urban development: The clustering of educational resources may exacerbate the problem of uneven distribution of educational resources. In pursuing urban resilience, it is also necessary to pay attention to the balance of educational resources, ensure the fairness and accessibility of education, avoid the problem of educational inequity caused by the excessive clustering of resources, and effectively provide fairer and higher-quality education.

(2)

Policy regulation and regional balance: The results of this study show that the relationship between educational resource clustering and urban resilience can be promoted or hindered through the formulation of appropriate policies. When formulating policies, it is essential to consider the differences between cities and their development needs, which can allow for the tailoring of policies according to local conditions and the provision of policy preferences to those cities that truly require support.

(3)

Social equity and sustainable development: Educational resources and urban resilience are not only related to sustainable economic development but also associated with social equity, harmony, and stability. It is imperative that the government actively promote educational equity and ensures that all individuals have access to superior educational resources, which will lay a solid foundation for enhancing urban resilience and promoting sustainable urban development.

In conclusion, the findings of this study provide important implications for promoting education balance, education equity, urban resilience, and sustainable urban development by optimizing the allocation of educational resources.