
==== Front
Heliyon
Heliyon
Heliyon
2405-8440
Elsevier

S2405-8440(24)12252-9
10.1016/j.heliyon.2024.e36221
e36221
Research Article
Impact of urban-rural development and its industrial elements on regional economic growth: An analysis based on provincial panel data in China
Zhao Yulin a
Li Junke ljk2006ljk@163.com
b∗
Liu Kai bc
Shang Chaowang a
a Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, 430079, China
b School of Information Engineering, Suqian University, Suqian, Jiangsu, 223800, China
c School of Computer and Information, Qiannan Normal University for Nationalities, Duyun, 558000, China
∗ Corresponding author. School of Information Engineering, Suqian University, Suqian, Jiangsu, 223800, China. ljk2006ljk@163.com
13 8 2024
30 8 2024
13 8 2024
10 16 e3622115 6 2024
6 8 2024
12 8 2024
© 2024 The Authors
2024
https://creativecommons.org/licenses/by-nc/4.0/ This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Urban-rural development is an important driving force for regional economic growth. The existing researches have studied this issue from various perspectives, but they ignore the impact of big data on the economy. In the post pandemic era, big data, as an emerging production factor, has a significant indicative effect in promoting urban-rural economic recovery and fostering new business forms. Therefore, fully considering the factor of big data can help reveal its impact mechanism on urban-rural economic growth in the post-epidemic period. Based on the data of 30 provinces and cities in China, this paper introduced big data on the basis of traditional models and constructed a multi-dimensional factor indicator system. At the same time, the panel regression model was established by using unit root test, Hausman test and precision test. Through benchmark regression and heterogeneity analysis, the impact of urban-rural development factors on economic growth was discussed. The results showed that the panel model passed all tests, and its regression error was stable below 5 %. Transportation, technology, and the three major industries can all promote positive economic growth, with a significance of 1 %. The three industries' contribution to economic growth ranks the third, second and first industries in order. In addition, the good ecological environment contributes to the benign economic growth during the study period. A 1 % increase in forest cover would drive economic growth by 0.215 %. But the impact of public's attention on the overall economy was an indirect effect manifested through its physical industries.The regional heterogeneity indicated that each element had different effects on economic development in eastern, central and western regions. Based on its results, this paper proposed suggestions for each region. In addition, this study found that the Internet attention reflected by big data did not directly drive economic growth, but affected economic growth through indirect channels such as information flow and resource allocation of real industries. This study provided data support for the existing theoretical review, and provided policy reference for the rational planning and industrial layout of China's regional economy.

Keywords

Urban-rural development factors
Regional economic growth
Impact effect
Panel regression model
Regional heterogeneity
==== Body
pmc1 Introduction

Urban-rural development is one of the strategies for economic development. As the world's second-largest economy, China's urban-rural development imbalance has long been one of the major challenges in its economic and social development. How to promote urban-rural integrated development has become a major issue at present. The current environment is in the post pandemic period, and the world economy is restarting and recovering. How to handle the relationship between urban-rural development and economic growth well in this environment is an important issue that needs to be solved urgently. China is in a stage of rapid urbanization. Urban-rural development is not only related to the optimization and upgrading of economic structure, but also directly affects people's life quality and social equity. However, there are still significant differences in factor allocation for urban-rural development at present. Rural areas are significantly inferior to urban areas in terms of infrastructure, education, healthcare and public services. This has led to uneven allocation of rural resources and lagging economic development, especially in the tertiary industry [1]. In addition, the imbalance of ecological environmental protection and information construction has further exacerbated the urban-rural gap. In response to these challenges, the Chinese government has adopted a series of policy measures to promote integrated urban-rural development. For example, "Key Tasks for New Urbanization and Urban Rural Integration Development in 2022″ and "National New Urbanization Plan (2021–2035)" [2,3]. Through policy guidance, its relevant measures promote the balanced allocation of urban and rural factors and narrow the gap. For example, by improving the level of rural public services, promoting industries upgrading and economy diversification [4]; By strengthening ecological environment protection, implementing green and low-carbon development strategies, improving urban-rural economies and promoting integration [5]; Utilizing modern information technologies such as big data and cloud computing to improve the urban-rural governance level and enhance the economic vitality. These policies are not only important means to narrow the urban-rural gap, but also a key driving force for promoting coordinated regional economic development.

The epidemic has had a major impact on the global economy, and China is no exception. In the post pandemic period, cities need to accelerate infrastructure construction to improve urban operational efficiency and competitiveness. Rural areas need to vigorously develop emerging industries such as modern agriculture and rural tourism to improve farmers' income and living standards [6]. In addition, strengthening information exchange and resource sharing between urban and rural areas can help break down barriers and facilitate the free flow of factors. During this critical period, how to effectively use urban-rural development factors to promote economic recovery is an important strategy to achieve high-quality economic development. In addition, thanks to the technology development, big data has become an important force in promoting economic and social development. It not only helps to accurately identify the gap and shortcomings in urban-rural development, but also provides more scientific and effective solutions. Through big data analysis to monitor traffic flow, assess scientific and technological level, manage energy consumption, track industrial development, and pay attention to social hotspots and livelihood needs, it can provide strong support for government decision-making [7,8]. Therefore, this study aims to explore the impact of urban-rural development elements on regional economic growth in the post-epidemic period under the background of big data support.

Regional economic growth is influenced by various factors of urban-rural development. Improving urban-rural infrastructure can facilitate the movement of goods and people, improve market efficiency and economic interaction within and outside the region. Through analysis of 277 urban and rural panel data, Ren et al. [9] found that the high-speed rail opening has driven local GDP growth by 1.96 %. Meanwhile, strengthening urban-rural network infrastructure construction (such as increasing broadband access rate and Internet penetration rate) can significantly reduce residents' income inequality [10]. Industrial development can not only drive a large number of jobs, but also increase agricultural and industrial output and fiscal revenue, promoting economic structural upgrading. Zeng et al. [11] stated that industrial agglomeration has a significant positive impact on economic growth. The higher industrial agglomeration level, the more obvious the economic effect of urban agglomeration. However, excessive energy consumption may lead to environmental problems, reduce the ecological carrying capacity, and inhibit sustainable economic development. Cathrine et al. [12] found that tourist arrivals and CO2 emissions have a negative impact on GDP. They suggested using renewable energy to reduce carbon emissions. Empirical results based on Autoregressive Distributed Lag (ARDL) perfectly respond to this view. It states that geothermal energy, as a clean and sustainable energy source, contributes to economic growth and environmental friendliness of developed countries in the long run [13]. Meanwhile, Neifar et al. [14] found that in the long run, there is a significant positive correlation between economic growth and environmental quality. Therefore, it is necessary to strengthen the governance of urban-rural human settlements and emphasize environmental investment [15]. In summary, regional economic growth is restricted by multiple factors.

Given the multiple impacts of the above factors on regional economy, this paper holds that the overall factor has a different impact on the economy than individual factors. This effect is complex and depends on the combined effect of various factors. This paper discussed the compound effect of urban-rural development factors on economic growth by combining big data and traditional data. It provided reference for promoting the benign interaction between urban-rural development and regional economy in the post epidemic period. The research questions focused on in this paper mainly include.(1) The overall factors of urban-rural development and their impact on regional economic growth;

(2) The impact effects of specific elements (material, industry, ecology, and network) in benchmark regression;

(3) The impact effects of each elements on regional heterogeneity (eastern, central, western);

The three research questions have enhanced understanding of the relationship between urban-rural development and regional economic growth at both the overall and local levels. This is highly significant for leveraging big data to drive urban-rural development and sustainable regional economic growth. Question (1) focuses on the comprehensive effects of the overall factors, and reveals the complex mechanisms of interactions among multiple factors from a global perspective. It helps decision-makers in utilizing big data platforms to more comprehensively assess the synergistic effects of various elements and optimize resource allocation. Question (2) can better understand the independent contributions and interrelationships of each element, and construct a more accurate economic growth model based on big data. It provides data basis for the formulation of special development policies and facilitates precise investment in various elements. Question (3) deepens the diversity and complexity understanding of regional economic development by revealing its heterogeneity. Differentiated analysis based on big data not only provides scientific guidance for regional differentiated development, but also contributes to inter-regional economic cooperation and resource sharing. In order to solve the above research question, this paper adopted Chinese traditional data and network big data to construct a panel regression model through unit root test, Hausman test, error test, etc. The model analyzed the relationship between urban-rural development factors and economic growth from many angles, and put forward suggestions for the sustainable development of regional economy.

The post-pandemic period is a critical period for economic recovery. This study focuses on identifying the key factors that constrain regional economic growth in the post pandemic period through quantitative analysis of provincial panel data, so as to provide scientific basis for regional economic planning. Based on the era of network economy, this paper introduced network elements into the traditional economic growth model to reveal the impact of network attention reflected by big data on economic growth, and how to use big data and network resources to promote economy. This provides new perspectives and ideas for academic research in related disciplines such as urban-rural economy and regional economy. Secondly, this study provides empirical support for the construction of multi-dimensional economic growth model by introducing the heterogeneity analysis of multi-dimensional factors.

In addition, this study provides new insights for policy-making in regional economic planning and development. Its contribution is manifold. First, this study reveals the intrinsic mechanism of how urban-rural development promotes economic growth, and identifies key issues and weak links in regional economic development. This not only enhances a deeper understanding of the operating laws of regional economy, but also helps to improve the pertinence and effectiveness of policy formulation, implementation, and evaluation. Second, the study clarified the mechanism of the various factors on regional economy by quantifying different factor indicators. This provides a basis for implementing industrial structure layout, strengthening policy guidance for ecological environment protection, and enhancing regional network connectivity in the regional economy. Finally, this study provides valuable insights for local governments to formulate differentiated development policies through heterogeneity analysis. They can optimize productivity, develop local characteristic industries, and build complementary advantages of regional economy according to actual conditions. This nuanced understanding of the whole and region enhances the applicability and relevance of the findings to policy makers and researchers.

2 Methodology

This paper selects appropriate methods for research based on the principles of model construction. The specific process is shown in Fig. 1. This study is divided into four main approaches. The content and methods of each operation are described below.Fig. 1 Research process and methods.

Fig. 1

Model definition: Select the model applicable to this study for description, and select appropriate criteria for model judgment.

Variable test: Test the rationality of the selected variables. The process is divided into a correlation test and unit root test.

Model construction: Build panel regression model.

Accuracy test: Verify the reliability of the constructed model.

2.1 Model definition

2.1.1 Model selection

To verify the influence of various factors in urban-rural development on the economy, The dependent variable of this study is regional economy, and the core explanatory variable is the quantitative indicators of various factors in urban-rural development. Since this paper takes 30 Provinces and regions in China as the research object, there are two dimensions of time and individual. Therefore, an econometric panel model is constructed [16]:(1) yit=α0+α1X1it+α2X2it+……+αnXnit+βCit+ui+vt+εit

In Equation (1), i represents province and city; t is the year; y represents the explained variable, i.e., the GDP of the regional economy; X represents the core explanatory variables, which are the specific quantitative indicators of material factors(traffic volume, scientific and technological level, total energy consumption), industrial factors(primary industry, secondary industry, tertiary industry), ecological factors(forest coverage, PM2.5 concentration), and network factors(traffic concern, air quality concern, industrial development concern). C represents a series of control variables; an is the regression coefficient of core explanatory variables, reflecting the impact of various factors on the regional economy. β is the regression coefficient of the corresponding control variable; a0 is the constant term; u stands for individual effect; v stands for time effect; ε is the random error term.

Due to the different dimensions of each indicator, errors may occur during calculation. Therefore, this paper deals with Equation (1) to reduce the error. By calculating the logarithm of the dependent variable, independent variable, and control variable, a new Equation is obtained as follows:(2) lnyit=α0+α1lnX1it+α2lnX2it+……+αnlnXnit+βlnCit+ui+vt+εit

This paper conducts modeling analysis based on Equation (2). On the one hand, it is to reduce the calculation error, on the other hand, it is to reduce the influence of multicollinearity and improve the accuracy of econometric model estimation.

2.1.2 Model judgment

There are three types of panel data measurement models: mixed pool model, fixed effect FE model, and random effect RE model. For which model to choose for regression analysis, it is necessary to determine according to the model's rules. The checking rules of the three models are:

The F test is used to compare the FE model and POOL model. If the P-value is less than 0.05, the FE model is better; otherwise, the POOL model is selected.

The BP test is used for comparison of the RE and POOL models. If P-value is less than 0.05, the RE model is better; otherwise, the POOL model is selected.

The Hausman test is used for comparison of the FE model and RE model. If the P-value is less than 0.05, the FE model is better; otherwise, the RE model is selected.

Finally, according to the comprehensive comparison test results, the optimal model is selected for subsequent modeling analysis.

2.2 Correlation calculation

Pearson correlation coefficient is a statistic used to reflect the degree of linear correlation between two variables, represented by PX,Y. The value range of PX,Y is between - 1 and 1. The larger PX,Y is, the higher the correlation between variables is. PX,Y is calculated as follows:(3) PX,Y=cov(X,Y)σxσy=∑i=1n(Xi−X‾)(Yi−Y‾)(n−1)σxσy

In Equation (3), cov(X,Y) represents the covariance, σxσv represent the standard deviation of the two variables, n is the sample size, and XiYi is the true value of the two variables. The judgment rule of PX,Y is: extremely strong correlation: 0.8 <PX,Y < 1; Strong correlation: 0.6 <PX,Y < 0.8; Weak correlation: 0.4 <PX,Y < 0.6; Very weak correlation: 0 <PX,Y < 0.2.

2.3 Unit root calculation

In the analysis involving time series, a series stationarity test is required. The unit root is a primary quantitative test method in the stationarity test. It contains the most classical test methods: DF, ADF, and PP tests. Referring to Ye's practice [17], this study uses ADF for the unit root test. ADF test model has three forms, which are as follows:(4-a) yt=ρyt−1+ut

(4-b) yt=c+ρyt−1+ut

(4-c) yt=c+γt+ρyt−1+ut

Equation (4-a) is the unit root without intercept and trend terms. Equation (4-b) is the model with intercept term; Equation (4-c) is the model with a linear trend. Its rule is that if the hypothesis is not rejected, the time series is considered a unit root process with drift; that is, yt is a non-stationary time series with a unit root. If the hypothesis is rejected, the time series is considered a stationary process with a linear trend. In other words, yt in Equation (4-a) and (4-b) are stationary time series, and yt in Equation (4-c) is stationary trend series.

2.4 Accuracy calculation

After the model is established, the model's accuracy should be checked to ensure its validity and rationality. Referring to the practices of existing scholars, this paper uses two methods to test the model accuracy to ensure that the model accuracy is within the range of controllable error.(1) Relative error

The relative error is to judge the error between the actual value and the predicted value. The specific calculation method is:(5) δ=|lnGactual−lnGpredict|lnGactual×100%

In Equation (5), δ represents the error between the actual value and the predicted value; lnGactual represents the true value of the measured indicator, while lnGpredict represents the expected value of the measured indicator.(2) Root mean square error

Root mean square error (RMSE) is generally used to measure the actual and predicted values deviation. The smaller RMSE is, the smaller the model error is. The calculation method is as follows:(6) RMSE=1m∑i=1m(ypredict(i)−y(i))2

In Equation (6), m is the prediction times, ypredict(i) is the predicted value of the measured index, and y(i) is the real value of the measured index.

2.5 Variable definitions

This paper takes various elements of urban-rural development as the research object. Based on the existing research [18], this paper selects four elements of urban-rural development (material, industry, ecology and network). It discusses the relationship between China's regional economic growth and its factors by constructing explained variables, core explanatory variables, and control variables to make a reference for the sustainable development of China's urban-rural economy.(1) Explained variable

Referring to the research literature on economic growth, the "national economy” indicator has been widely used in various studies [[19], [20], [21]]. Generally speaking, GDP is a vital reference index to measure regional economic development. In this paper, the GDP of China's provinces and cities (lnGdp) is used to measure the development of regional economy.(2) Core explanatory variable

Material elements are the components of material entities. This paper refers to the practice of existing scholars [22], and selects urban-rural transportation, science and technology, and energy consumption to measure the material factor index. Its quantitative indicators are: the number of civilian and private vehicles (lnVehicles); Number of employees in R&D institutions (lnTech); Total energy consumption (lnEnergy).

Industrial elements are an essential engine of regional economic development. The paper selects "primary industry” (lnPriInd), "secondary industry” (lnSecInd), and "tertiary industry” (lnTerInd) as quantitative indicators.

Ecology elements are the guarantee of building urban-rural space vitality. Referring to the research ideas of existing scholars [23,24], this paper measures the indicators of urban-rural ecological development from two aspects of ecology and environment. Given data availability, the paper selects forest coverage (lnForest) and PM2.5 (lnPM2.5) as its quantitative indicators.

Network elements are the way to realize big data decision-making and accurate governance. The paper uses the attention reflected by big data to measure the public's concern about urban-rural development. Specifically selected indicators are public attention to online car-hailing (lnOnlineCar), air quality (lnOnlineAir), and industrial development (lnOnlineInd).(3) Control variable

Referring to the research of Min et al. and Zhang et al. [25,26]on high-quality economic development, this paper selects the following variables that may affect the regional economy: ① Urbanization level (lnUrban). The improvement of urbanization level is conducive to narrowing the gap between urban and rural areas and driving the development of tertiary industry focusing on tourism and cultural leisure. This paper uses the proportion of the urban population to express. ② Level of opening up(lnOpen). The digestion ability of opening up is an important way to give play to opening up and promote economic growth [27]. The total amount of imports and export is used in this paper. ③ Foreign direct investment(lnFdi). FDI has increased foreign exchange reserves and enhanced economic vitality. This paper uses the actual amount of foreign direct investment to measure.

3 Experimental results

The panel data model carries double-dimensional information of data. Horizontally, it reflects the knowledge of different individuals simultaneously; vertically, it reflects the trend of the same individual changing over time. The panel data model can make the results more secure and reliable, including the characteristics of time series and cross-sectional data. As the data analyzed in this paper are short-term dynamic panel data of 30 provinces and cities from 2014 to 2019, the variables need to be tested before modeling to ensure the scientificity and rationality. In this study, a correlation test is conducted on variables to avoid the occurrence of the "pseudo-regression” phenomenon. Then unit root test is performed on variables to ensure the stability of each variable. Stationary variable regression is practical. Finally, the final panel regression model is constructed after passing all the tests.

3.1 Data processing

Considering the continuity and availability of data, this paper selects 30 provinces and cities in China as the research samples (Tibet, Hong Kong, Macao, and Taiwan are not studied in this paper due to the lack of some data). The statistical data required in this paper (except energy consumption and PM2.5 concentration) are from China Statistical Yearbook [28]. Energy consumption comes from China Energy Statistical Yearbook [29], and PM2.5 comes from the online air quality monitoring platform [30]. The PM2.5 value in each region is weighted and averaged from standardized data. The network attention of online car-hailing, air quality, and industrial development comes from Baidu Index [31]. To ensure the stability of variables, all variables are logarithmically processed. The descriptive statistics of variables are shown in Table 1.Table 1 Descriptive statistics of variables.

Table 1Variables	Code	Min	Max	Mean	Standard deviation	
Explained variable	lnGdp	7.742	11.587	9.916	0.839	
Core explanatory variable	lnVehicles	4.812	8.395	6.845	0.779	
lnTech	7.054	13.373	10.657	1.381	
lnEnergy	7.507	10.631	9.452	0.640	
lnPriInd	4.643	8.540	7.280	1.065	
lnSecInd	6.775	10.698	9.012	0.905	
lnTerInd	6.749	10.998	9.192	0.872	
lnForest	1.445	4.202	3.317	0.713	
lnPM2.5	2.833	4.771	3.858	0.367	
lnOnlineCar	0	5.999	3.494	2.286	
lnOnlineAir	3.296	7.613	5.225	0.762	
lnOnlineInd	1.386	4.963	4.172	0.647	
control variable	lnUrban	3.689	4.495	4.065	0.181	
lnOpen	1.686	9.291	6.206	1.545	
lnFdi	3.434	9.880	6.794	1.322	

3.2 Correlation test results

Spurious regression means that there is no causal relationship between independent and dependent variables. But for some reason, regression analysis shows a statistical correlation between them. To avoid such problems, this paper uses Python to perform correlation tests between each independent variable and the dependent variable based on Equation (3). This test ensures causality of the selected variables and determines that the sample data meet the statistical significance. If the independent variable and dependent variable pass a 1 % significant correlation test, it is said that there is a causal relationship between these two variables. This also indicates that the selection of sample data is reasonable. Fig. 2, Fig. 3, Fig. 4, Fig. 5 show the correlation test results between independent and dependent variables. Fig. 2 is the representative variable of material elements; Fig. 3 shows the industrial elements; Fig. 4 shows the ecology elements; Fig. 5 shows the network elements.Fig. 2 Variable relationship of material elements.

Fig. 2

Fig. 3 Variable relationship of industrial elements.

Fig. 3

Fig. 4 Variable relationship of ecology elements.

Fig. 4

Fig. 5 Variable relationship of network elements.

Fig. 5

Specifically, Fig. 2 shows the relationship between lnVehicles and lnGdp, lnTech and lnGdp, and lnEnergy and lnGdp in material elements. Fig. 3 shows the relationship between lnPriInd and lnGdp, lnSecInd and lnGdp, and lnTerInd and lnGdp in the industrial elements. Fig. 4 shows the relationship between lnForest and lnGdp, lnPM2.5 and lnGdp in ecology elements. Fig. 5 shows the relationship between lnOnlineCar and lnGdp, lnOnlineAir and lnGdp, lnOnlineInd and lnGdp in network elements. Take the variable in Fig. 2 as an example, and the same for other variables. The correlation coefficient between lnVehicles and lnGdp in Fig. 2 is 0.927, with a P-value of 0.000. This indicates that the selected variables lnVehicles and lnGdp have passed the 1 % significant correlation test, and the two variables have statistical significance.

A comprehensive comparison of other variables in Fig. 2, Fig. 3, Fig. 4, Fig. 5 shows that lnTech, lnEnergy, lnPriInd, lnSecInd, lnTerInd, lnOnlineAir, and lnOnlineInd are strongly correlated with lnGdp, and have passed the correlation test. Variables lnForest, lnPM2.5, and lnOnlineCar had a low correlation with lnGdp, but they also passed the 1 % correlation significant test. It shows that the selected independent and dependent variables have a causal relationship and conform to the statistical significance of regression analysis.

3.3 Unit root test results

Although panel data alleviates the non-stationarity of data, each variable may also have problems such as trend and intercept, which are non-stationary data with a unit root. Therefore, the panel data model needs to check whether there is a unit root in each variable before regression. According to Equation (4-a), (4-b), and (4-c), this study uses EViews10 software to conduct each variable's unit root ADF test. The principle of the ADF test is: if the variable sequence is stable, there is no unit root; Otherwise, there is a unit root. Therefore, the ADF test assumes that the original series has unit-roots. Suppose the significance test statistics obtained after the test are less than three confidence degrees (10 %, 5 %, and 1 %). In that case, the probability of rejecting the original hypothesis is 90 %, 95 %, and 99 %; that is, the sequence of selected variables belongs to a stationary series. Otherwise, it belongs to a non-stationary sequence. For the non-stationary series, it is necessary to do different processing and check the difference sequence successively until the sequence is stable. Table 2 shows each variable's unit root test results, and the corresponding test P-value is in parentheses.Table 2 Results of unit root test.

Table 2variable	zero-order	first-order	variable	zero-order	first-order	
lnGdp	−3.169(0.024)	−14.095(0.000)	lnPM2.5	−5.926(0.007)	−10.639(0.000)	
lnVehicles	−4.276(0.001)	−14.100(0.000)	lnOnlineCar	−2.158(0.223)	−4.88(0.000)	
lnTech	−2.766(0.065)	−13.175(0.000)	lnOnlineAir	−3.642(0.006)	−12.737(0.000)	
lnEnergy	−2.178(0.215)	−8.514(0.000)	lnOnlineInd	−3.697(0.005)	−9.729(0.000)	
lnPriInd	−3.691(0.005)	−13.451(0.000)	lnUrban	−3.739(0.004)	−14.452(0.000)	
lnSecInd	−3.253(0.019)	−13.861(0.000)	lnOpen	−2.591(0.097)	−14.062(0.000)	
lnTerInd	−3.434(0.011)	−14.468(0.000)	lnFdi	−1.862(0.350)	−5.504(0.000)	
lnForest	−2.543(0.107)	−13.283(0.000)				

It can be seen from Table 2 that the P-values of lnTech, lnEnergy, lnForest, lnOnlineCar, lnOpen, and lnFdi are all greater than the significance level of 5 %. P-values of lnEnergy, lnForest, lnOnlineCar, and lnFdi are all greater than the significance level of 10 %. So the original hypothesis was accepted, the variable was a non-stationary sequence. The difference sequence needs to be further tested. From the P-value of the first-order difference of each variable in Tables 2 and it can be seen that the values are all less than 1 % of the significance level. Therefore, the hypothesis is rejected with 99 % confidence, and the variable belongs to the first-order single integer sequence. Stationary series is helpful for model construction so that the panel regression model can be further constructed.

3.4 Analysis of benchmark regression results

(1) Benchmark regression analysis

To measure the impact of various factors of urban-rural development on the economy, this paper substitutes independent variables, dependent variables, and control variables into the model successively according to Equation (2). The results are shown in Table 3. Firstly, the optimal model is judged and selected according to the F, BP, and Hausman tests. To minimize the interference of other factors on the model results, the individual and time effects are controlled for all regressions. Secondly, this study investigates the impact of material elements, industrial elements, ecology elements, network elements, and overall elements on the economy. Among them, Model (a) reports the regression results of transportation, science and technology, and energy consumption on the economy. Model(b) reports the regression results of primary, secondary, and tertiary industries to the economy. Model(c) reports the regression results of forest coverage and PM2.5 on the economy. Model(d) reports the regression results of network elements, including public's attention on ride-hailing, air quality, and industrial development. Model(e) reports the regression results of overall factors on the economy. All models have passed the significance test, and the regression results are reliable.Table 3 Benchmark regression results.

Table 3Item	Model (a)	Model (b)	Model (c)	Model (d)	Model (e)	
lnVehicles	0.437**(9.72)				−0.014(-0.72)	
lnTech	0.194**(7.99)				−0.009(-1.58)	
lnEnergy	0.078 (1.14)				−0.018(-0.76)	
lnPriInd		0.023**(3.11)			0.096** (6.60)	
lnSecInd		0.397**(37.05)			0.440**(37.75)	
lnTerInd		0.536**(42.70)			0.516**(31.80)	
lnForest			0.215* (2.52)		0.095**(3.84)	
lnPM2.5			−0.047(-0.78)		−0.004(-0.47)	
lnOnlineCar				0.013**(2.75)	0.002* (2.55)	
lnOnlineAir				−0.072(-1.55)	−0.014*(-2.01)	
lnOnlineInd				0.050 (0.87)	−0.001(-0.06)	
lnUrban	−0.117 (−0.66)	−0.162**(-3.73)	0.822**(2.71)	1.934**(5.78)	−0.413**(-5.91)	
lnOpen	0.084** (3.64)	0.012** (2.74)	0.168**(4.83)	0.119**(3.32)	−0.004(-0.76)	
lnFdi	0.083** (3.09)	0.009 (1.63)	0.181**(4.77)	0.053 (1.73)	0.016** (3.12)	
Constant	3.511** (4.07)	1.766**(10.88)	3.772**(3.00)	1.080 (0.91)	2.233**(8.15)	
R2	0.967	0.998	0.624	0.726	0.964	
Type	RE	RE	RE	FE	FE	
Sample	180	180	180	180	180	
Note:*p<0.05 **p<0.01, t values are in parentheses.

The results in Table 3 show that all variables and constant terms have passed the significance test, and the fitting effect is good. According to the coefficients in Table 3, the panel regression model of the relationship between economic growth and urban-rural development elements can be obtained:

Material elements:(7) lnGdppredict=0.437lnVehicles+0.194lnTech+0.078lnEnergy−0.117lnUrban+0.084lnOpen+0.083lnFdi+3.511

Industrial elements:(8) lnGdppredict=0.023lnPriInd+0.397lnSecInd+0.536lnTerInd−0.162lnUrban+0.012lnOpen+0.009lnFdi+1.766

Ecology elements:(9) lnGdppredict=0.215lnForest−0.047lnPM2.5+0.822lnUrban+0.168lnOpen+0.181lnFdi+3.772

Network elements:(10) lnGdppredict=0.013lnOnlineCar−0.072lnOnlineAir+0.05lnOnlineInd+1.934lnUrban+0.119lnOpen+0.053lnFdi+1.080

(2) Accuracy test

Firstly, select some regions in 2019 and substitute their variable values into Equations 7–10 to obtain the GDP predicted values of each factor model. Secondly, taking the region as the unit, the GDP predicted value of each factor model and its actual value are substituted into Equation (5) successively to obtain the relative error. Finally, the accuracy test is conducted, and the test results are shown in Table 4. As can be seen from Table 4, the relative errors of each factor model are controlled below 2 %, 0.5 %, 3 %, and 3.5 %, respectively. The errors of the benchmark regression results are minor and within the expected range. Therefore, the models of benchmark regression are reasonable.Table 4 Accuracy test of the benchmark regression model.

Table 4Region	Actual value	material elements	industrial elements	ecology elements	network elements	
PV	Relative error%	PV	Relatived error%	PV	Relative error%	PV	Relative error%	
Jiangxi	10.117	10.128	0.111	10.138	0.210	10.118	0.010	9.964	1.509	
Fujian	10.655	10.464	1.788	10.660	0.045	10.695	0.376	10.465	1.781	
Shanxi	9.743	9.935	1.973	9.786	0.450	9.676	0.686	9.872	1.329	
Inner Mongolia	9.753	9.700	0.549	9.741	0.124	9.682	0.732	9.975	2.277	
Shaanxi	10.158	10.086	0.708	10.172	0.143	10.080	0.769	9.973	1.822	
Gansu	9.073	9.129	0.618	9.099	0.286	8.992	0.893	9.289	2.379	
Guangxi	9.964	9.962	0.011	9.951	0.123	10.057	0.940	9.765	1.994	
Zhejiang	11.041	11.250	1.900	11.063	0.206	10.926	1.042	10.661	3.439	
Chongqing	10.069	9.974	0.950	10.093	0.236	10.265	1.939	10.312	2.410	
Guizhou	9.727	9.619	1.110	9.722	0.055	9.452	2.829	9.391	3.454	

The analysis of each factor model of benchmark regression is as follows: The results of model (a) show that the regression coefficients for transportation (lnVehicles) and technology (lnTech) are 0.437 and 0.194, respectively, showing positive significance at the 1 % level. This indicated that both transportation and technology can significantly promote economic development, and the transportation's contribution to the economy was greater. Improving transport infrastructure can reduce transaction costs and enhance the efficiency of economic activities. This further verifies Maru's results [32]. Technological innovation and progress can enhance total factor productivity and achieve sustained economic growth. This is consistent with endogenous growth theory [33]. Energy consumption (lnEnergy) also has a positive impact on the economy, but its impact is not significant. This is consistent with the findings of Zhu et al. [34]. They found that energy consumption did increase GDP. But the Chinese government has taken energy-saving and emission reduction measures to reduce unit energy consumption, in order to balance the environmental problems caused by economic growth and energy emissions.

The results of model (b) show that the regression coefficients of the three industries are 0.023, 0.397 and 0.536, respectively, with a significance of 1 %. The secondary industry (lnSecInd) and the tertiary industry (lnTerInd) have the most significant impact on economic growth, while the primary industry (lnPriInd) has a smaller but equally important impact. The industrial structure tends to be "three, two, one”, which is consistent with Chong et al.'s result [35]. China's economy has undergone a transformation from agriculture to industry, and then to service-led in the past few decades. With the development of high and new technologies such as the Internet and big data, the influence of the tertiary industry on the national economy has gradually expanded. This further supports Zhao's research findings [36]. He stated that the agglomeration of the tertiary industry (service industry) is not only an inevitable result of economic growth, but also a prerequisite for sustained economic growth. However, Yang [37] believed that although the industrial structure is constantly optimizing and upgrading, the stable development of the primary industry (agriculture) has always been an important guarantee for economic growth.

The results of model (c) show that the regression coefficient of forest coverage (lnForest) is 0.215, with a significance of 5 %. A 1 % increase in forest cover would drive economic growth by 0.215 %. PM2.5 (lnPM2.5) has a negative impact on the economy, but not significant. The consistency between ecological and economic advantages during the study period indicates that a good ecological environment can promote economic development. This supports the results of Neifar et al. [14]. But this result differs from Xin et al.'s findings [38]. Their panel regression model revealed a "positive N-shaped” curve between ecological environment and economic development, with two inflection points. The negative impact of PM2.5 (lnPM2.5) further confirms the findings of Muhammad et al. [39,40]. Environmental degradation has a significant negative impact on economic growth, and active regulation should be taken to address environmental degradation.

The results of model (d) show that public attention to ride-hailing (lnOnlineCar) can promote economic growth, with a significance of 1 %. The effects of the other two factors were relatively weak and insignificant. In recent years, the market size of online car hailing in China has gradually expanded. Ride-hailing has not only changed the way of travel, but also provided more job opportunities. Sun et al. [41] verified this view. He noted that ride-hailing has become a mainstream form of transportation, boosting the economy and urbanization. Therefore, the impact of Internet attention on the economy is manifested through its real industry, and its effect is indirect.

Model (e) is a regression of the overall factors. It can be seen from the results of model (e) that the effects of industrial elements, ecology elements, and network elements are similar to those of models (b), (c), and (d), but the material elements are not significant. The reason may be that transportation, science, and technology belong to the tertiary industry, while energy belongs to the secondary industry [42]. Industry has a significant influence on the economy, which to some extent covers the industry influence it contains. Therefore, the material elements dominated by transportation, science and technology, and energy are not significant.

Among the control variables, the level of opening-up and foreign investment positively impacts the economy. At the same time, urbanization shows a negative direction in model (b) (e) and a positive direction in the model (c) (d). This further responds to Wang et al.'s finding [43] that the relationship between urbanization and economic growth has a threshold effect. The improvement of the urbanization level is not conducive to advancing the economic growth rate, and there is a threshold value. Import and export trade, reflected in the level of opening-up, is an essential part of China's economic structure. Import trade expands domestic demand, and export trade drives employment, thus promoting benign economic growth. Foreign investment contributes to the accumulation of material capital, which has an important impact on economic growth. Therefore, foreign investment indirectly affects economic growth [44].

3.5 Heterogeneity analysis

(1) Heterogeneity regression analysis

China has a vast territory, and each region is at a different stage of industrialization. Regional cities differ significantly in transportation, industrial planning, ecological quality, and economic level and show significant differences. Therefore, the impact of urban-rural development elements on the economy may be different in different regions. In order to analyze economic impacts' heterogeneity of urban-rural development factors in different regions, this paper divides the sample into three regions, east, middle and west for heterogeneity analysis.Table 5 lists the results of the heterogeneity analysis.Table 5 Heterogeneity regression results.

Table 5Item	east	central	west	
lnVehicles	−0.018* (−2.771)	0.013 (0.369)	0.032 (1.397)	
lnTech	−0.029(-0.730)	−0.009 (−0.963)	0.005 (0.956)	
lnEnergy	0.151* (2.503)	−0.059 (−1.677)	−0.021 (−1.293)	
lnPriInd	−0.029 (−1.310)	0.170** (6.902)	0.107** (5.980)	
lnSecInd	0.435** (22.064)	0.436** (23.557)	0.428** (30.073)	
lnTerInd	0.576** (20.996)	0.443** (20.041)	0.453** (31.862)	
lnForest	0.015 (0.537)	0.068 (0.981)	−0.038 (−1.380)	
lnPM2.5	−0.005 (−0.283)	−0.001 (−0.008)	0.004 (0.496)	
lnOnlineCar	0.001 (0.667)	0.001 (0.061)	−0.001 (−0.472)	
lnOnlineAir	−0.018 (−1.311)	0.007 (0.429)	−0.016** (−3.539)	
lnOnlineInd	−0.067** (−3.406)	0.029 (1.471)	−0.001 (−0.092)	
Constant	2.049** (3.703)	1.675** (3.033)	1.210** (3.695)	
control variable	YES	YES	YES	
Type	YES	YES	YES	
Sample	66	60	54	
R2	0.977	0.968	0.979	
Note:*p<0.05 **p<0.01, t values are in parentheses.

The results in Table 5 show that all variables and constant terms have passed the significance test, and the fitting effect is good. According to the results in Table 5, panel regression models of eastern, central, and western regions are obtained:

Eastern region:(11) lnGdppredict=−0.018lnVehicles−0.029lnTech+0.151lnEnergy+……−0.067lnOnlineInd−0.468lnUrban+0.006lnOpen+0.006lnFdi+2.049

Central region:(12) lnGdppredict=0.013lnVehicles−0.009lnTech−0.059lnEnergy+……+0.029lnOnlineInd−0.251lnUrban−0.004lnOpen+0.021lnFdi+1.675

Western Region:(13) lnGdppredict=0.032lnVehicles+0.005lnTech−0.021lnEnergy+……−0.001lnOnlineInd−0.015lnUrban+0.005lnOpen+0.003lnFdi+1.210

(2) Accuracy test

Select the variable values of some region in eastern, central, and Western China in 2019 and substitute them into Equation 11–13 to obtain the GDP prediction value in each region. Secondly, by substituting the predicted and actual values into Equation (5), each region's relative error and average relative error can be obtained, as shown in Table 6. The regression error of the heterogeneous model is stable within 1 %, and its average relative error is about 0.5 %. Meanwhile, according to Equation (6), the root means the square error of all regions in 2019 is calculated. Its RMSE = 0.0879, with a small error, fully indicates that the prediction accuracy of this model is high and the model is reasonable.Table 6 Accuracy test of heterogeneity regression.

Table 6Region	Province	Actual value	Predicted value	Relative error %	Mean relative error %	
east	Fujian	10.655	10.689	0.320	0.440	
Shanghai	10.549	10.493	0.539	
Zhejiang	11.041	11.097	0.510	
Tianjin	9.554	9.517	0.393	
central	Hubei	10.733	10.760	0.257	0.187	
Anhui	10.522	10.537	0.141	
Jilin	9.370	9.380	0.107	
Henan	10.902	10.928	0.243	
west	Ningxia	8.229	8.245	0.190	0.150	
Sichuan	10.750	10.742	0.073	
Guizhou	9.727	9.708	0.197	
Gansu	9.073	9.086	0.139	

In the eastern region, the secondary industry, tertiary industry, and energy consumption show positive significance. Its regression coefficients are 0.435, 0.576, and 0.151, respectively, and the tertiary industry's contribution is greater than that of the secondary industry.This differs from Wang et al.'s result [45]. They found that the secondary industry contributed the most to the GDP growth rate in eastern China, followed by the tertiary industry. The reason may be caused by the difference of the economic development stage concerned by the research. China's economy has undergone rapid industrial transformation and structural adjustment in the past few decades. The economy has gradually shifted from investment driven to domestic demand driven, and the tertiary industry's contribution rate has gradually exceeded that of the secondary industry. But the overall effect of the industry is similar to Wang et al.'s results [45]. The eastern region has advantages in the development of secondary and tertiary industries. Energy is a driver of industrial development and vital to the region's economy. Every 1 % increase in energy consumption in the eastern region will boost economic growth by 0.151 %. The regression coefficients of traffic volume and industrial development attention are −0.018 and −0.067, respectively, indicating that network attention has not promoted local economic growth. This also confirms Zhu et al.'s results [46]. The increase of traffic demand will lead to an increase of transit time and management cost, affecting economic benefits.

In the central region, the three major industries' impact effects are 0.170, 0.436, and 0.443, all showing a positive significance of 1 %. From a horizontal comparison, the primary and secondary industries have the greatest impact on the economy of the central region, indicating that the central region largely benefits from the primary and secondary industries. Wan et al. [47] indicated that the formation of an interconnected regional carbon inclusive mechanism in the central region is the general trend of developing the primary and secondary industries. However, the economic impact of the tertiary industry (service industry) on the central and western regions is lower than that of the eastern region, which is different from the results of Chen et al. [48]. Their heterogeneity found that the opening up of the service industry has a greater impact on the enterprise economy in the central and western regions than in the eastern regions. Upon investigation, it was found that the central and western regions benefited from the comprehensive effect of policy promotion, low wage base and employment growth driven by the service industry in the early stage of opening up. This paper's results mainly benefit from the advantages of the eastern region in infrastructure, labor market and investment environment in recent years.

In the western region, the three major industries' regression coefficients are 0.107, 0.428, and 0.453, respectively, showing a positive significance of 1 %. The influence effect of energy is −0.021, which has a negative effect on economic growth. The western region is an important energy base in China, but the efficiency of energy development and utilization is low, and there is a waste of resources. This finding is consistent with Wang's results [49], who found that energy dependence has a depressing effect on the economy. Secondly, the Internet attention's regression coefficients are −0.001, −0.016, and −0.001, respectively. It manifests as negative and has minimal impact. It indicates that Internet attention has no promoting effect on the economy of the western region. Public attention has not directly transformed into a driving force for economic growth. This may be related to many factors such as regional industrial structure, policies, actual investment, and lagging development measures. While network elements mainly affect economic growth through indirect channels such as information flow, resource allocation, and public expectations.

4 Discussion

4.1 A more nuanced discussion of the results

This study elaborated the influence of urban-rural development factors on regional economic growth through panel model. The results are multifaceted and complex.

Among the material elements, transportation and technology have a significant role in promoting economic growth. Improvements in transport networks and infrastructure can not only enhance market connectivity, but also promote regional economic integration and industrial agglomeration, injecting new vitality into economic development. Technological progress can promote the birth and development of emerging industries (such as biotechnology, new energy, etc.), and may become a new engine of economic growth in the future. Although the direct effect of energy consumption on economic growth is not significant, the policy orientation and environmental impact behind it cannot be ignored. Advocating the implementation of energy conservation and emission reduction, the use of clean energy and other policies will not only help to ease the energy shortage, but also promote the development of green economy.

Among the industrial elements, the secondary and tertiary industries are the main driving forces of economic growth. They mainly benefit from the acceleration of industrialization process and the rise of manufacturing, as well as the vigorous development of the Internet economy represented by the service industry. The trend of "three, two, one” industrial structure has enhanced the economy's ability to resist risks and achieve sustainable development. The primary industry's direct contribution to economic growth is relatively small. But as a fundamental industry of national economy, it is of great significance in ensuring national food security, promoting rural economy, and increasing farmers' income. Therefore, it is necessary to attach great importance to the stable development of the primary industry. Simultaneously utilizing scientific and technological innovation to enhance agricultural production efficiency and achieve agricultural modernization and sustainable development.

Among ecological elements, the increase of forest coverage is conducive to economic growth, while PM2.5 has a potential inhibitory effect on the economy. Specifically, a good ecological environment will affect residents' willingness to travel, thereby driving the development of transportation and tourism. At the same time, increasing tourist satisfaction, stimulating tourist consumption, and increasing economic income. In addition, the improvement of the ecological environment is conducive to attracting talent and investment, indirectly promoting economic growth. However, severe environmental pollution such as smog increases industries' operating costs. It is not conducive to residents' travel and reduces industrial revenue. Therefore, strengthening environmental supervision and governance is an important way to achieve coordinated development of economy and environment.

Among the network elements, the public's attention to ride hailing has significantly promoted economic growth. This reflects the important role of the digital economy and sharing economy in promoting the real economy's development. As an important part of the Internet economy, online car-hailing not only drives the development of relevant industrial chains (such as intelligent transportation, mobile payment, etc.), but also promotes flexible employment and enterprise innovation. Therefore, paying attention to the development trends and hot areas of the internet economy, actively guiding and supporting related industries' development, is of great significance for promoting economic growth.

These findings emphasize the multi-dimensional integration's role of different elements in economic growth. It provides empirical evidence for regional economic planning and policy-making in the post pandemic era.

4.2 Impact on policy formulation and implementation

The east is located in the coastal area, with urban cluster such as the Yangtze River Delta, Pearl River Delta, and Beijing-Tianjin-Hebei region, and a large distribution of labor-intensive industries. The positive effects of its secondary and tertiary industries on economic growth are evident, and the industrial structure has gradually shifted from traditional manufacturing to service industry. Therefore, policy-making should pay more attention to improving the service quality and innovation capability of the tertiary industry. Meanwhile, considering the positive impact of energy consumption on the economy, policies should focus on optimizing the energy consumption structure and promoting the use of green energy to reduce the negative impact on the environment. However, industry's further development in the eastern region may be limited by land resources and high costs, as well as urban congestion, environmental pollution and other problems. To address these challenges, the government can take measures such as optimizing land use policies and encouraging the development of high-tech service industries. Simultaneously strengthen urban planning and optimize transportation networks [50]; Promote green consumption and improve environmental quality.

The economic development of the central region benefits from the primary and secondary industries. Policy formulation should emphasize continuing to strengthen investment in agricultural science and technology, promote industrial technology upgrading, and improve production efficiency. At the same time, the government should attach importance to the development potential of the service industry, and gradually guide the tertiary industry's development in emerging service areas such as information technology services and intelligent logistics. The main challenge in the central region is how to further enhance the innovation and competitiveness of industry. In order to cope with this challenge, the central region can strengthen industry-academia-research cooperation and enhance independent innovation capabilities; Promote the transformation and upgrading of traditional industries, develop smart manufacturing and high-end manufacturing, and enhance product added value. At the same time, it is necessary to strengthen the introduction and cultivation of talents in emerging fields, and improve the quality of the labor force.

The three major industries in the western region have a significant positive effect on economic growth, but the negative impact of energy consumption on economic growth is prominent. Policy formulation should focus on promoting the transformation of energy structure, developing clean and renewable energy, and reducing dependence on traditional energy. In addition, given the minimal impact of online attention, policies should focus on strengthening network infrastructure and improving the public's information level. The main challenges in the western region are the infrastructure construction and energy efficiency's improvement. To address these challenges, the western region should increase infrastructure investment, improve transportation, network and other conditions, and enhance information capabilities; Strengthen technological transformation and upgrading, improve energy development efficiency, and realize low-carbon development [51]; Develop characteristic industries, attract foreign investment, and enhance regional competitiveness.

4.3 Implications for broader national economic strategy

These findings provide valuable insights for regional economic development as well as broader national economic strategies. This study indicates that regional economic growth is influenced by a combination of multiple factors of urban-rural development. Therefore, various measures can be taken at the national economic strategy level. First, optimize the layout of the transportation network and increase investment in scientific and technological research and development. At the same time, actively encourage digital technology innovation, promote the transformation and application of scientific and technological achievements, and lead high-quality economic development with technological innovation. Second, vigorously develop modern service industries and high-tech industries, and promote the transformation of industrial structure towards high-end, intelligent and green directions. At the same time, government should strengthen the construction of ecological civilization, improve environmental quality, and provide a solid ecological guarantee for sustainable economic development. Third, actively foster and develop new formats and models such as digital economy and sharing economy, promote the deep integration of information technology and the real economy, and stimulate new vitality for economic growth.

5 Conclusions

In the context of post-epidemic period, it is of great significance to explore the impact of urban-rural development elements on the economy for promoting the sustainable development of regional economy. Based on the data of 30 provinces, municipalities, and districts in China, this paper used panel model to empirically analyze the impact of urban-rural development factors on regional economy. The study found that the variables selected in the model passed the pseudo-regression test, unit root test, and model accuracy test. The error of the benchmark regression model is stable below 5 %, and the heterogeneous regression error is steady within 1 %. The model error is small, and the constructed model is reasonable. On the whole, China's economy is closely related to material, industrial, ecology, and network elements in its urban-rural development, but the influence degree of each factor is different. Specifically, transportation, science and technology, energy, and the three major industries in material and industrial elements can promote positive economic development, and the influence of the three industries on economic growth is ranked as three, two, and one industry. In addition, the good ecological environment contributes to the benign economic growth during the study period, while the impact of network attention on the overall economy is an indirect effect through its real industry. In terms of regional heterogeneity, the secondary industry, tertiary industry, and energy contributed more to the economic development in eastern China. The primary industry and the secondary industry have a more significant advantage in the economic development of the central region. Energy and Internet attention have a negative impact on the economy of the western region. Therefore, the western region should break down industry barriers and geographical restrictions, strengthen infrastructure construction, and give full play to the role of technology and network to drive the economy.

This study provided a theoretical reference for the sustainable development of the regional economy, but some deficiencies in this study still need to be further improved. Firstly, limited by data availability, this study selected short-term panel data for research measurement. The short-term effect may be different from the long-term effect, so the subsequent research can be further extended in the data cycle to improve long-term panel data. Secondly, this study enhanced the index system based on existing research, but it still can not fully cover the comprehensive measurement of urban-rural development. In future research, the index system will continue to be improved to make the measurement results more scientific and reasonable. Finally, this study was based on the data from provinces and cities. Due to the limited space, it has not been extended to the level of secondary cities, so the follow-up research can be further extended to improve the theoretical study.

Data availability statement

Data will be made available on request.

CRediT authorship contribution statement

Yulin Zhao: Writing – original draft, Validation, Software, Methodology, Investigation, Conceptualization. Junke Li: Writing – review & editing, Resources, Project administration, Funding acquisition, Conceptualization. Kai Liu: Supervision, Investigation, Formal analysis. Chaowang Shang: Visualization, Data curation.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

This work was supported in part by the 10.13039/501100001809 National Natural Science Foundation of China [Grant 62262055 ]; Nature Science Foundation of educational department under Grant No. [2022]100 ; Qing Lan Project of Jiangsu Province, Suqian Talent Xiongying Project (Grant No.2023-0035 ); the High Level Talent Foundation of Suqian University (Grant No. 2024XRC011 ). We also acknowledge the reviewers who provided helpful suggestions which have improved the manuscript.
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