Spatio-Temporal Evolution and Influencing Factors of Open Economy Development in the Yangtze River Delta Area

Abstract

Since economic globalization is unstable, it is difficult for the traditional open economic development model to meet the requirements of China’s development, and there is an urgent need for new ideas and models to be reoriented. Based on the analysis of the development mechanism of China’s open economy at this stage, we used the entropy method, Theil coefficient, Gini coefficient, and spatial Durbin model (SDM) to analyze the spatio-temporal pattern and influencing factors of the high-quality development level of the open economy in the Yangtze River Delta Area (YRDA). The results indicated that during the study period, the development level and development difference of open economy were on the rise, and the spatial difference in different regions was significant. The development of open economy was affected by many factors, and there was a spatial spillover effect. Based on the existing problems, at the stage of high-quality economic development, the YDRA should seize the opportunities brought by the new development pattern, improve government services, optimize innovation drive, and promote the development of open economy to a higher level. We believe that the results of this study can also provide relevant experience for the development of open economy in other regions of China.

1. Introduction

Under the background of economic globalization, the political, economic, and cultural ties between countries in the world have reached a close degree unprecedentedly, providing a good environment for developing the open economy [1]. Under the guidance of Adam Smith and David Ricardo’s theory of the international division of labor and theory of comparative advantage, countries give full play to their comparative advantages to actively participate in the international division of labor, promote the more effective allocation of resources among countries and regions, improve labor productivity, and create the driving force for economic development [2]. Since the reform and opening up policy was implemented, China has continuously deepened its opening up to the outside world. Then, China’s rapid development was maintained, and other countries simultaneously provided opportunities for joint action. However, with the impact of the international ideological trend of “Anti-globalization” and the competition in the international market becoming more and more fierce, the COVID-19 epidemic has led to an increase in the risk of the global financial crisis and significant changes in the world economic and trade order and pattern, which have brought significant challenges to the development of China’s opening up to the outside world [3].

The open economy has always been one of the essential issues paid attention to by academic circles. The current study on the development of the open economy is mainly focused on the connotation of the development of the open economy [4,5], evaluation of development level [6,7,8], influencing factors [9,10,11], and so on. Specifically, there are two main arguments about the connotation study. One is that an open economy is an export-oriented economy [12,13], and the other is that an open economy is a strategy in which export orientation and import substitution coexist [14]. However, China’s understanding of the connotation of the open economy is mainly affected by policies [15], which are integrated with China’s national conditions based on learning from relevant foreign theories. In the early stage, the measurement of the development level of an open economy was replaced by a single measurement of trade openness [16] or capital openness [17], that is, it was measured directly by the degree of dependence on foreign trade or the degree of convergence of relative prices. As De. Lombaerde [18] proposed that the measurement of the development level of the open economy should comprehensively consider many factors, such as trade development and system reform, the construction of the index system tends to be diversified gradually. The study methods mainly involve quantitative evaluation methods such as the DSGE model [19], entropy weight TOPSIS [20], grey correlation model [21], and dynamic factor analysis [22]. It can be seen that there is not a unified understanding of the evaluation index system and evaluation methods of the open economy, and most scholars choose the corresponding evaluation indicators and measurement methods based on the actual situation of the study object, which provides a good reference for the construction of open economy evaluation in this study. As for the study of influencing factors, scholars at home and abroad continue to enrich them from both theoretical and empirical study and analysis through the ideology of cross-sectional data and multiple regression [23,24], but the consideration of spatial factors has not been taken into account, and there is a lack of analysis on regional heterogeneity and spatial spillover effects.

Via academic study, the multi-dimensional perspective of open economy analysis was expanded [25,26,27], and a powerful way is provided for an in-depth understanding of the development pattern of the open economy and its driving forces. The existing problems in the relevant research also provide a new direction for the development of this study. Apart from the above issues, most existing research focuses on the development of the past open economy. But as China’s economy moves from a phase of rapid growth to a phase of high-quality development, and driven by the “Double-cycle” development strategy, China’s open economy development has been transformed from a traditional “Quantity-driven” model to an “Innovation-led” model of high-level development [28,29]. Under such conditions, what are the changes in the development mechanism of China’s open economy? What are the ways to promote the effective development of an open economy? These are the vital issues that we need to think about [30]. Moreover, because of the influence of the spatial variability of the research objects, the policy recommendations put forward by the previous research have problems of weak universality and insufficient pertinence, which cannot effectively reflect the overall characteristics of China’s open economic development. The YRDA is one of the core regions with the most active economic development, vital openness, and innovation ability in China. It is the most representative region in the development process of China’s open economy and plays a vital role in China’s opening-up strategy [31]. Therefore, starting from the new environment of China’s open economic development, based on the discussion of the development mechanism of China’s open economy at the present stage, this paper selects three provinces and one city in the YRDA as a case study to analyze the space-time pattern and influencing factors of its open economic development level, and then finds out the problems and proposes solutions. We hope that it can provide a reference basis for enhancing the competitiveness of the YRDA in the domestic and international cycle, promoting regional integration, speeding up the development of a higher-level open economy, and providing the relevant experience for the development of open economy in other regions of China.

The paper is arranged as follows. The second part discusses the mechanism of development of the open economy, the third part introduces the study regions and methods, and constructs the evaluation index system of comprehensive development level in combination with the meaning of open economy. The fourth part analyzes the empirical results. The fifth part discusses the results of the analysis, and the sixth part summarizes the results and points out the study’s shortcomings.

2. Development Mechanism of Open Economy

2.1. The Definition of Open Economy

The open economy is an economic model that allows the free cross-border movement of all factors and products, unlike a closed economy [32]. In an open economy, factors, goods, and services can move more freely across borders to achieve optimal allocation of resources and maximum economic efficiency [33]. The open economy mainly includes base, scale, structure, and benefit. It emphasizes linking the domestic economy with the whole international market, participating in the international division of labor as fully as possible, and giving play to the comparative advantage of the national economy in the International Division of Labor. In the trend of economic globalization, countries have lowered tariff barriers to promote the free flow of capital, making a significant development of the open economy [34].

2.2. Development Mechanism of Open Economy

In the past, China’s open economy mainly realized “High-speed” growth by expanding the scale of low-end factor input, blindly expanding production capacity, pursuing the volume of goods exports, and introducing foreign investment [35]. However, this extensive development model has not only caused a series of problems, such as environmental pollution, waste of resources, low efficiency, and so on, but it is also difficult for the traditional development model of the open economy to meet the new needs of China’s socio-economic transformation and upgrading and opening-up strategy and adapt to the new pattern of the global industrial division of labor [36]. The new development model of an open economy emphasizes a steady growth tone, starting with the interaction between supply and demand, interconnection, innovation-driven, industrial structure, regional coordination, and other aspects, and drives the Omni-directional upgrading of the scale of open economy, the foundation of open economy, potential of open economy, the quality and efficiency of open economy, to realize the virtuous circle of economic development and the promotion of international competitiveness [37]. Given this, this paper focuses on the four main focal points of the scale of open economy, the foundation of open economy, the potential of open economy, the quality and efficiency of open economy to analyze the development mechanism of the open economy through the mechanism transmission of domestic and international “Double cycle” new development pattern, scientific and technological innovation, and attracting foreign investment (Figure 1).

2.2.1. The Mechanism of Action of the New Development Pattern of “Double Circulation” on the Development of Open Economy

Under the new situation of a sharp decline in external demand caused by the global epidemic, speeding up the construction of a new development pattern of domestic and international “Double cycle” is an effective way to promote the high-level development of an open economy [38]. China takes the great domestic cycle as the main body, starting with the domestic market demand structure and product supply structure, and combines the strategy of expanding domestic demand with deepening supply-side structural reform to form a high-level dynamic balance of order pulling supply and supply, creating demand. This measure has effectively stimulated the economic vitality of the country’s interior and laid the foundation for the efficient development of an open economy. Meanwhile, China has seized the opportunity of the development of the international cycle, actively carried out foreign economic and trade cooperation, docked high-standard international economic and trade rules, and continued to participate in the international division of labor through the implementation of the “Belt and Road Initiative” strategy to strengthen connectivity and coordinated development with countries along the Road, and maximize the scale of opening up to the outside world.

2.2.2. The Mechanism of Action of Scientific and Technological Innovation on the Development of Open Economy

Under the invasion and attack of the wave of anti-globalization, if China wants to get rid of the dilemma that others control vital industries and technologies, the core is to persist in using scientific and technological innovation to promote the development of an open economy. On the one hand, scientific and technological innovation maximizes production efficiency through accelerating enterprise process iteration and improving the utilization efficiency of various resource elements and injects new vitality into the growth of the open economy. To this extent, scientific and technological innovation is an important driving force for promoting the transformation and upgrading of an open economy. On the other hand, persisting in taking scientific and technological innovation as the forerunner can provide new comparative advantages for market products, achieve Omni-directional quality improvement, help continuously tap the potential of the open economy, and promote the development of an open economy.

2.2.3. The Mechanism of Action of Investment Attraction on the Development of Open Economy

Investment attraction plays an important role in improving the quality and efficiency of an open economy [39]. Due to the obvious gradient differences in the level of economic development among regions in China, there is still a large gap in the quality and degree of openness between inland areas and the eastern coast, which to a certain extent, hinders the development of the open national economy. By using a reasonable way of investment attraction, the eastern region and even international superior resources will be transferred to inland areas through chambers of commerce and investment to win more development opportunities and promote a higher level of opening up under the premise of promoting coordinated regional development. In addition, the reasonable use of foreign capital can effectively make up for the capital and technological shortcomings of domestic industrial product, promote the transformation and upgrading of industrial structure, and then guide local enterprises to show strong competitiveness in the international market to provide support for the high-quality development of an open economy.

3. Materials and Methods

3.1. Selection of Study Area

According to the “Outline of the YRDA Integrated Development Plan” officially issued by the State Council in December 2019, the YRDA was planned to cover all four provinces and cities of Suzhou, Zhejiang, Anhui, and Shanghai, with a total of 41 cities. It is an alluvial plain formed before the Yangtze River enters the sea, located in the lower reaches of the Yangtze River in China, bordering the Yellow Sea and the East Sea and at the place where the rivers and the sea meet, with many coastal ports along the river. As of the end of 2019, it played a pivotal strategic role in the overall national modernization and all-round opening pattern as one of the most active, open, and innovative regions in China, with a population of 227 million and a regional area of 358,000 square kilometers (Figure 2).

3.2. Research Methods

3.2.1. Measurement of Open Economy Development Level

This study adopted the entropy method to measure the level of open economy development in the YRDA, which can reduce the artificial subjective influence in determining the weights and improve the objectivity of the attached consequences to obtain more scientific results. Moreover, it reflects the rate of change of sample data by the calculated information entropy, determines the weight according to the order of the information contained in each index, and then calculates the total score [40]. The formulas are as follows:

Standardization of positive indicators:

$${\mathrm{x}}_{\mathrm{ij}}^{\prime }=\left[\frac{{\mathrm{x}}_{\mathrm{ij}}-\min \left({\mathrm{x}}_{1\mathrm{j}},{\mathrm{x}}_{2\mathrm{j}},\cdots ,{\mathrm{x}}_{\mathrm{nj}}\right)}{\max \left({\mathrm{x}}_{1\mathrm{j}},{\mathrm{x}}_{2\mathrm{j}},\dots ,{\mathrm{x}}_{\mathrm{nj}}\right)-\min \left({\mathrm{x}}_{1\mathrm{j}},{\mathrm{x}}_{2\mathrm{j}},\dots ,{\mathrm{x}}_{\mathrm{nj}} \right)}\right]$$

(1)

Standardization of negative indicators:

$${\mathrm{x}}_{\mathrm{ij}}^{\prime }=\left[\frac{\max \left({\mathrm{x}}_{1\mathrm{j}},{\mathrm{x}}_{2\mathrm{j}},\dots ,{\mathrm{x}}_{\mathrm{nj}}\right)-{\mathrm{x}}_{\mathrm{ij}}}{\max \left({\mathrm{x}}_{1\mathrm{j}},{\mathrm{x}}_{2\mathrm{j}},\dots ,{\mathrm{x}}_{\mathrm{nj}}\right)-\min \left({\mathrm{x}}_{1\mathrm{j}},{\mathrm{x}}_{2\mathrm{j}},\dots ,{\mathrm{x}}_{\mathrm{nj}} \right)}\right]$$

(2)

where ${\mathrm{x}}_{\mathrm{ij}}^{\prime }$ refers to the value of the jth indicator in unit i and ${\mathrm{x}}_{\mathrm{ij}}^{\prime }$ is still denoted as ${\mathrm{x}}_{\mathrm{ij}}$ in this study.

Calculation of the ratio:

$${\mathrm{p}}_{\mathrm{ij}}=\frac{{\mathrm{x}}_{\mathrm{ij}}}{{\mathsf{\sum }}_{\mathrm{i}=1}^{\mathrm{n}}{\mathrm{x}}_{\mathrm{ij}}}$$

(3)

Calculation of the entropy:

$${\mathrm{e}}_{\mathrm{j}}=-{\mathrm{k}\mathsf{\sum }}_{\mathrm{i}=1}^{\mathrm{n}}{\mathrm{p}}_{\mathrm{ij}}\ln \left({\mathrm{p}}_{\mathrm{ij}}\right),\mathrm{k}>0,\mathrm{k}=\frac{1}{1\mathrm{n}{\left(\mathrm{n}\right)}^{\prime }}{\mathrm{e}}_{\mathrm{j}}\ge 0$$

(4)

Calculation of the variance coefficient:

$${\mathrm{g}}_{\mathrm{j}}=\frac{1-{\mathrm{e}}_{\mathrm{i}}}{\mathrm{m}-{\mathrm{E}}_{\mathrm{e}}} ,{ \mathrm{E}}_{\mathrm{e}}={\mathsf{\sum }}_{\mathrm{j}=1}^{\mathrm{m}}{\mathrm{e}}_{\mathrm{i}} , 0\le {\mathrm{g}}_{\mathrm{j}}\le 1 ,{ \mathsf{\sum }}_{\mathrm{j}=1}^{\mathrm{m}}{\mathrm{g}}_{\mathrm{i}}=1$$

(5)

Calculation of the weight:

$${\mathrm{w}}_{\mathrm{i}}=\frac{{\mathrm{g}}_{\mathrm{i}}}{{\mathsf{\sum }}_{\mathrm{j}=1}^{\mathrm{m}}{\mathrm{g}}_{\mathrm{i}}} (1\le \mathrm{j}\le \mathrm{m})$$

(6)

Calculation of the total score:

$${\mathrm{s}}_{\mathrm{j}}={\mathsf{\sum }}_{\mathrm{j}=1}^{\mathrm{m}}{\mathrm{w}}_{\mathrm{j}}{\ast \mathrm{p}}_{\mathrm{ij}} (\mathrm{i}=1,2,···,\mathrm{n})$$

(7)

3.2.2. Methods of Spatial-Temporal Variance Analysis

Range and standard deviation: Range and standard deviation are commonly used statistics to describe the degree of data dispersion. The range can reflect the fluctuation range of data, while standard deviation can represent the precision of data. The combination of the two can help us have a more comprehensive understanding of the changes in the level of open economic development in the YRDA. Therefore, in this study, the range ($\mathrm{R}$) and standard deviation (σ) were used to reflect the absolute differences in the level of open economy development in the YRDA. The formulas are as follows:

$$\begin{array}{c}\mathrm{R}={\mathrm{X}}_{\max }-{\mathrm{X}}_{\min }\\ {\sigma }_{\mathrm{t}}=\sqrt{\frac{1}{\mathrm{n}}{\sum }_{\mathrm{i}=1}^{\mathrm{n}}{\left({\mathrm{X}}_{\mathrm{i}}-\overline{\mathrm{x}}\right)}^2}\end{array}$$

(8)

where$\mathrm{R}$refers to the absolute differences in the level of development of the open economy;${ \mathrm{X}}_{\max }$is the maximum value;${ \mathrm{X}}_{\min }$is the minimum value; σ refers to the standard deviation of the level of open economy development in year $\mathrm{t}$; $\mathrm{n}$refers to the number of municipalities in the YRDA; $\overline{\mathrm{x}}$ refers to the average level of development;${ \mathrm{X}}_{\mathrm{i}}$ is the level of open economy development in the$\mathrm{i}$municipal area.

Coefficient of variation: Given the apparent differences in the population of each city in the YRDA, the coefficient of variation, as a dimensionless quantity, is more objective and practical when comparing data with different dimensions or mean values [41]. Therefore, this study selected the population-weighted coefficient of variation to measure the relative differences in open economic development among the municipalities in the YRDA, which is calculated as:

$$\mathrm{C}{\mathrm{V}}_{\mathrm{w}}=\sqrt{{\sum }_{\mathrm{i}=1}^{\mathrm{n}}{\left({\mathrm{X}}_{\mathrm{i}}-\overline{\mathrm{X}}\right)}^2\left({\mathrm{p}}_{\mathrm{i}}/\mathrm{p}\right)}/\overline{\mathrm{X}}$$

(9)

where $\mathrm{C}{\mathrm{V}}_{\mathrm{w}}$ refers to the coefficient of variation;${ \mathrm{p}}_{\mathrm{i}}$ refers to the population in municipality i; and $\mathrm{p}$ is the total population of the YRDA.

Gini coefficient: In view of the objective reality that there will be a particular gap in the development of each indicator system in an open economy, the Gini coefficient can give the quantitative limit of the difference degree of each indicator, thereby more intuitively reflecting the development gap between the indicators [42]. Therefore, the Gini coefficient was used to analyze the development differences of various indicator systems in the open economy of YRDA and their contributions to the difference in overall development level, which is calculated as:

$$\begin{array}{c}\mathrm{G}=\sum {\mathrm{G}}_{\mathrm{d}}^{*}{\mathrm{S}}_{\mathrm{d}}\\ {\mathrm{G}}_{\mathrm{d}}^{*}=1-\frac{1}{\mathrm{g}}\left(2{\sum }_1^{\mathrm{g}-1}{\mathrm{W}}_{\mathrm{g}}+1\right)\end{array}$$

(10)

where $\mathrm{G}$ refers to the total Gini coefficient of open economy development;${\mathrm{G}}_{\mathrm{d}}^{*}$ represents the sub-Gini coefficient of each indicator system;${\mathrm{S}}_{\mathrm{d}}$ represents the ratio of the development level score to the total score of the subsystem of the$\mathrm{d}$-indicator system in open economy development;$\mathrm{g}$ is the number of groups;${\mathrm{W}}_{\mathrm{g}}$refers to the ratio of the open economy development level score of group $\mathrm{g}$ to the total score of the study area; the percentage contribution of the d-indicator system subsystem to the overall Gini coefficient is ${\mathrm{G}}_{\mathrm{d}}^{*}\ast {\mathrm{S}}_{\mathrm{d}}/\mathrm{G}\times 100\%$.

Thiel coefficient: Theil coefficient can measure the proportion of the intra-group gap and the inter-group gap in the total gap and subdivide inter-regional gap and intra-regional gap, thus providing a basis for finding the main factors of regional gap change [43]. Therefore, the Thiel coefficient was used to measure the inter-regional and intra-regional differences in the level of open economic development in the YRDA, which is calculated as follows:

$$\begin{gathered} \mathrm{T}={\mathrm{T}}_{\mathrm{inter}-\mathrm{regional}}+{\mathrm{T}}_{\mathrm{intra}-\mathrm{regional} }= \\ \hspace{1em} {\sum }_{\mathrm{k}}\frac{{\mathrm{X}}_{\mathrm{k}}}{\mathrm{X}}\ln \left(\frac{{\mathrm{X}}_{\mathrm{k}}/\mathrm{X}}{{\mathrm{P}}_{\mathrm{k}}/\mathrm{P}}\right){\sum }_{\mathrm{k}}\frac{{\mathrm{X}}_{\mathrm{k}}}{\mathrm{X}}\left[{\sum }_{\mathrm{i}}\frac{{\mathrm{X}}_{\mathrm{ki}}}{{\mathrm{X}}_{\mathrm{k}}}\ln \left(\frac{{\mathrm{X}}_{\mathrm{ki}}/{\mathrm{X}}_{\mathrm{k}}}{{\mathrm{P}}_{\mathrm{ki}}/{\mathrm{P}}_{\mathrm{k}}}\right)\right] \end{gathered}$$

(11)

where $\mathrm{T}$ refers to the Theil coefficient of the open economy development level in the study area; $\mathrm{X}$represents the level of open economy development;${ \mathrm{X}}_{\mathrm{k}}$represents the level of open economy development in region $\mathrm{k}$;${ \mathrm{X}}_{\mathrm{ki}}$is the level of open economic development of study unit $\mathrm{i}$ in region $\mathrm{k}$;$\mathrm{P}$ refers to the total population of the study area;${ \mathrm{P}}_{\mathrm{k}}$ refers to the population of region $\mathrm{k}$;${ \mathrm{P}}_{\mathrm{ki}}$ is the population of study unit $\mathrm{i}$ in region $\mathrm{k} ;$ the proportion of intra-regional and inter-regional contributions are $\frac{{\mathrm{T}}_{\mathrm{inter}-\mathrm{regional}/\mathrm{intra}-\mathrm{regional}}}{{\mathrm{T}}_{ }}\ast 100\%$; the proportion of contribution from region $\mathrm{k}$ is $\frac{{\mathrm{T}}_{\mathrm{k} }}{{\mathrm{T}}_{\mathrm{intra}-\mathrm{regional} }}\ast 100\%$.

Spatial autocorrelation: ${\mathrm{Moran}}^{\prime }\mathrm{s} \mathrm{I}$ is usually used to judge whether there is a correlation between spatial entities within a certain range. Therefore, we used ${\mathrm{Moran}}^{\prime }\mathrm{s} \mathrm{I}$ to measure the spatial agglomeration characteristics of the open economy development level in the YRDA and estimated its statistical significance by calculating the z-score and p-value. Generally, it takes values in the range of −1 to 1, while larger absolute values represent larger spatial correlations [44]. The equation is as follows:

$${\mathrm{Moran}}^{\prime }\mathrm{s} \mathrm{I}=\frac{{\sum }_{\mathrm{i}=1}^{\mathrm{n}}{\sum }_{\mathrm{j}=1}^{\mathrm{n}}{\mathrm{w}}_{\mathrm{ij}}\left({\mathrm{X}}_{\mathrm{i}}- \overline{\mathrm{x}}\right)\left({\mathrm{X}}_{\mathrm{j}}- \overline{\mathrm{x}}\right)}{{\mathsf{\sigma }}^2{\sum }_{\mathrm{i}=1}^{\mathrm{n}}{\sum }_{\mathrm{j}=1}^{\mathrm{n}}{\mathrm{w}}_{\mathrm{ij}}}$$

(12)

where ${\mathsf{\sigma }}^2=\frac{1}{\mathrm{n}}{\sum }_{\mathrm{i}=1}^{\mathrm{n}}\left({\mathrm{X}}_{\mathrm{i}}- \overline{\mathrm{x}}\right);\overline{\mathrm{x}}=\frac{1}{\mathrm{n}}{\sum }_{\mathrm{i}=1}^{\mathrm{n}}{\mathrm{X}}_{\mathrm{i}};$ ${\mathrm{X}}_{\mathrm{i}}$and${ \mathrm{X}}_{\mathrm{j}}$ represent the level of open economy development in study unit $\mathrm{i}$ and study unit j;${ \mathrm{w}}_{\mathrm{ij}}$ is the spatial weight matrix.

The degree of its local spatial association and distribution pattern could be further measured with the help of ${\mathrm{I}}_{\mathrm{i}}$, which is calculated as:

$${\mathrm{I}}_{\mathrm{i}}={\mathrm{z}}_{\mathrm{i}}^{\prime }{\sum }_{\mathrm{j}}{\mathrm{w}}_{\mathrm{ij}}{\mathrm{z}}_{\mathrm{j}}^{\prime }$$

(13)

where ${\mathrm{z}}_{\mathrm{i}}^{\prime }$ and ${\mathrm{z}}_{\mathrm{j}}^{\prime }$ represent standardized observations, and other variables have the same meaning as before.

3.2.3. Method of Influencing Factors Analysis

Based on the reality of unbalanced spatial development in the YRDA, we need to consider the heterogeneity between different regions when investigating the influencing factors of open economic development, so the SDM was used to conduct the analysis [45]. The equation is as follows:

$${\mathrm{Y}}_{\mathrm{it}}=\mathrm{C}+\mathsf{\rho }\sum {\mathrm{w}}_{\mathrm{ij}}{\mathrm{Y}}_{\mathrm{it}}+{\mathrm{X}}_{\mathrm{it}}\mathsf{\beta }+\mathsf{\delta }\sum {\mathrm{w}}_{\mathrm{ij}}{\mathrm{X}}_{\mathrm{it}}+{\mathsf{\mu }}_{\mathrm{i}}+{\mathsf{\theta }}_{\mathrm{t}}+{\mathsf{ϵ}}_{\mathrm{it}}$$

(14)

where ${\mathrm{Y}}_{\mathrm{it}}$ represents the variable responding to the level of open economy development;$\mathrm{C}$ refers to the constant term;$\mathsf{\rho }\sum {\mathrm{w}}_{\mathrm{ij}}{\mathrm{Y}}_{\mathrm{it}}$ refers to the spatial lag term of the explained variable;$\mathsf{\rho }$ is the lag term coefficient;${ \mathrm{X}}_{\mathrm{it}}$ refers to the explanatory variable;$\mathsf{\delta }\sum {\mathrm{w}}_{\mathrm{ij}}{\mathrm{X}}_{\mathrm{it}}$ represents the spatial lag term of the explanatory variable;$\mathsf{\delta }$ represents its spatial lag term coefficient;${ \mathrm{w}}_{\mathrm{ij}}$ is the spatial weight matrix, and nested matrices were constructed for each interval with reference to Hao et al. [46] and Xue et al. [47] (

Table 1. Weight matrix description.

Spatial Weight Matrix	Meaning	Formula	Explanation
Adjacency Weight Matrix W1	The provinces are geographically adjacent to each other	${\mathrm{w}}_1=\{\begin{array}{c}0\\ 1\end{array}$	0 represents no connection between two regions, 1 represents the connection between two areas
Economic distance Weight Matrix W2	The economic gap between the provinces	${\mathrm{w}}_2=\frac{1}{\left|{\mathrm{P}}_{\mathrm{i}}-{\mathrm{P}}_{\mathrm{j}}\right|}$✕$\frac{1}{{\mathrm{d}}_{\mathrm{ij}}}$	${\mathrm{P}}_{\mathrm{i}}$$\mathrm{and} {\mathrm{P}}_{\mathrm{j}}$ represent the average of real GDP per capita between province I and province j over the sample period, dij is the geographical straight-line distance of each provincial capital city.
Nested Weight Matrix W3	The geographical proximity and economic gap between the provinces	${\mathrm{w}}_3={\mathrm{w}}_1^{*}{\mathrm{w}}_2$	The product of the adjacency matrix and the economic matrix

);$\mathsf{\lambda }$ is the lagging factor of the perturbation term;${\mathsf{\mu }}_{\mathrm{i}}$ represents the fixed effect of a region;${\mathsf{\theta }}_{\mathrm{t}}$ represents the fixed effect of time.

3.3. The Construction of the Indicator System and Selection of Variables

3.3.1. Indicator System Construction

At present, the evaluation index system of open economy development is being enriched and developed as the research progresses, which is mainly reflected in the development of domestic and foreign scholars from one or a few indicators to a comprehensive evaluation index system with richer and more diverse perspectives and distinctive indicators [48,49]. Based on the analysis of the development mechanism of the open economy in the second part, combined with the existing research results [20,21,50], based on the principles of scientific, systematic, operability, and comparability, the index system is constructed from the four levels of the foundation, scale, quality and efficiency, and potential of the development of the open economy, with 20 secondary indicators (

Table 2. Comprehensive evaluation index system of development level of open economy in YRDA.

First-Level Indicators	Secondary Indicators	The Meaning of Indicators	Unit
The foundation of open economy	GDP per capita	Regional GDP / total regional population	Million yuan
	The proportion of secondary and tertiary industries	Secondary industry output/regional GDP	%
	Human capital level	Number of people in scientific services, technical exploration / total regional population	%
	Fixed asset investment per capita	Regional fixed asset investment amount / total regional population	Million yuan
	Urbanization rate	Urban population / total regional population	%
The scale of open economy	Foreign trade volume	Total import and export	Billion
	Domestic trade volume	Total retail sales of social consumer goods	100 Million yuan
	Amount of foreign direct investment	Actual utilization of foreign investment	Billion
	Foreign trade dependence	Foreign trade in goods/regional GDP	%
	Foreign investment dependence	Actual foreign direct investment/regional GDP	%
The quality and efficiency of open economy	Trade economic contribution	(Total retail sales of social consumer goods + total exports-total imports)/regional GDP	%
	The net export contribution rate	Increase in net exports / increase in GDP	%
	Share of international tourism revenue	Tourism foreign exchange earnings / regional GDP	%
	Contribution of foreign investment	FDI / social fixed asset investment	%
	The proportion of foreign-invested enterprises	Number of foreign-invested enterprises/number of industrial enterprises	%
The potential of open economy	Fiscal expenditure to GDP ratio	Fiscal spending/regional GDP	%
	Share of expenditure on science and education	Expenditure on science and education / total regional financial expenditure	%
	Number of invention patents per 10,000 people	Number of granted invention patent applications/total regional population	Piece
	The proportion of total post and telecommunication business in GDP	Gross postal and telecommunications business/regional GDP	%
	Internet penetration	Number of Internet users	Door

).

(1)

The foundation of open economy

The foundation of open economy is an important support to realize the development of an open economy and also an indispensable factor in introducing foreign capital, expanding trade, and promoting economic growth [20]. Among the selected indicators, the GDP per capita index is an important indicator to measure the status of regional economic development. The higher the GDP per capita, the stronger the development foundation of the Open economy. The index of the proportion of the secondary and tertiary industries can reflect the degree of the regional industrial structure, and the index of the level of human capital is a sign of the strength of the scientific and technological strength of the regional economic development. The level of fixed asset investment per capita can reflect the ability of local people to invest, and the urbanization rate reflects the regional urbanization by country.

(2)

The scale of open economy

The scale of open economy is a vital aspect in evaluating the development of an open economy [51], which measures the foreign trade capacity of a region from the perspective of quantity and makes the accumulation of quantitative change for the qualitative change of an open economy. Among the selected indicators, the volume of foreign trade and the importance of domestic trade indicate the size of the region’s capacity for foreign and domestic trade. The degree of dependence on foreign trade and foreign investment is the index to reflect the degree of influence of trade activities on regional economic development and the degree of dependence on foreign capital constituted by gross domestic product, the size of the foreign direct investment reflects the region’s ability to attract foreign investment.

(3)

The quality and efficiency of open economy

The quality and efficiency of open economy is an important aspect of evaluating the effectiveness of open economy development, which measures the foreign trade of a region from a qualitative perspective [51]. Meanwhile, it emphasizes the importance and frontier of quality elements in the open economy development process, effectively improving the opening scale. Among the selected indicators, the contribution of trade economy and foreign investment refers to the impact of trade and foreign investment on the regional economy and fixed asset investment, respectively. The contribution rate of net exports reflects the degree of contribution of trade surplus to the economy and the degree of balance between imports and exports of a region’s foreign trade. The proportion of international tourism income reflects the share of foreign exchange tourism income in GDP. The greater the proportion, the greater the role of international tourism in the region’s development. The proportion of foreign-invested enterprises refers to the proportion of the number of foreign-invested enterprises in the total number of regional industrial enterprises. The greater the value, the more significant the contribution of foreign-funded enterprises to regional development.

(4)

The potential of open economy

The potential of open economy is an essential element in measuring the sustainable development of an open economy and an important indicator to promote the high-level development of an open economy [20]. Among the selected indicators, the ratio of target fiscal expenditure to GDP reflects the government’s budgetary support. The proportion of total postal and telecommunications business in GDP and the internet penetration rate are two indicators that reflect the degree of perfection of a region’s infrastructure. Financial support from the government and sound infrastructure can provide the impetus for the efficient development of the open economy. The share of expenditure on science and education and the number of invention patents owned by 10,000 people reflect the capacity of science and innovation in a region. Excellent innovation ability and a high level of human capital can effectively promote the development of the open regional economy.

3.3.2. Variables Selection

As the development of the open economy is affected by many factors, this paper, based on relevant research, starts from the six aspects of regional economic strength, industrial structure, social development, scientific and technological level, labor force factors and infrastructure, a total of 10 explanatory variables are selected to explore the influencing factors of Open economy development level in the YRDA [52,53,54,55]. The relevant variables are selected as follows.

(1)

Explained variable

The explained variable was selected as the values of the open economy development level of 41 municipalities in the YRDA from 2005 to 2019, obtained using the entropy value method and expressed as Y.

(2)

Explanatory variables

Regional economic strength is necessary to promote the efficient development of the open regional economy. The more muscular the regional financial stability, the higher the open economy level of development. We choose GDP per capita (X1), a measure of regional economic strength, and fiscal revenue per capita (X2), which represents the range and amount of services the government provides in economic activities, to reflect the regional financial strength.

The industrial structure can reflect the status quo of regional industrial development. Optimizing the industrial system is essential to promoting the development of the open regional economy. We measure the regional industrial structure by the proportion of secondary and tertiary industries (X3). The larger the ratio, the more dynamic the regional economic development is and the more attractive it will be to foreign capital and foreign enterprises.

A stable and harmonious social development is essential for the normal development of open economy activities. We use the urbanization rate (X4), closely related to economic growth, and the per capita retail sales of consumer goods (X5), the most direct reflection of the social consumption demand, to reflect the social development.

The advancement of science and technology can strengthen the division of labor and cooperation among the local open economy in the development process, thereby enhancing the potential of the open economy. Therefore, we use the human capital level (X6) and the ratio of science and technology expenditure to fiscal expenditure (X7) to measure the level of science and technology development.

Infrastructure such as transportation and communications is an indispensable material basis for developing a region’s open economy. The traffic condition directly affects the volume of trade and investment. Therefore, we select the size of freight volume (X8) to reflect the traffic situation and the post and telecommunications business volume (X9) to reflect the development of communications construction.

The development of an open economy cannot be separated from the labor factors (X10). In this paper, we choose the sum of employment in the secondary and tertiary industries to reflect the demand for labor.

3.3.3. Data Source and Statistical Description

Based on data updates, the relevant data used to measure the level of open economic development of 41 cities in the YRDA from 2005–2019 are mainly from the China Statistical Yearbook, Shanghai Statistical Yearbook, Jiangsu Statistical Yearbook, Anhui Statistical Yearbook, Zhejiang Statistical Yearbook, statistical yearbooks of each city and statistical bulletins on national economic and social development, with some data coming from the website of the Ministry of Commerce of the People’s Republic of China and the statistical bureaus of each urban area.

4. Result Analysis

4.1. The Temporal Evolution of the Development Level of the Open Economy

As seen from

Table 3. The development level of the Open Economy in YRDA from 2005 to 2019.

City	2005	2010	2015	2019	M	R	City	2005	2010	2015	2019	M	R
Shanghai	0.271	0.467	0.494	0.615	0.435	1	Taizhou	0.044	0.059	0.075	0.090	0.068	22
Suzhou	0.200	0.287	0.329	0.365	0.305	2	Lianyungang	0.054	0.062	0.070	0.075	0.066	23
Hangzhou	0.113	0.186	0.256	0.278	0.209	3	Tongling	0.052	0.069	0.076	0.069	0.066	24
Nanjing	0.124	0.153	0.212	0.263	0.183	4	Huaian	0.033	0.090	0.071	0.088	0.065	25
Ningbo	0.113	0.147	0.195	0.214	0.173	5	Taizhou	0.046	0.054	0.071	0.088	0.065	26
Wuxi	0.105	0.155	0.182	0.213	0.167	6	Lishui	0.035	0.042	0.077	0.066	0.065	27
Changzhou	0.076	0.119	0.149	0.174	0.131	7	Xuzhou	0.031	0.048	0.064	0.088	0.057	28
Jiaxing	0.083	0.101	0.132	0.164	0.122	8	Yancheng	0.029	0.048	0.056	0.077	0.056	29
Nantong	0.080	0.096	0.116	0.124	0.109	9	Bengbu	0.039	0.037	0.065	0.068	0.055	30
Zhenjiang	0.075	0.104	0.107	0.111	0.105	10	Xuancheng	0.030	0.036	0.060	0.068	0.052	31
Huzhou	0.069	0.089	0.107	0.132	0.099	11	Huainan	0.053	0.044	0.057	0.052	0.051	32
Hefei	0.067	0.078	0.103	0.153	0.096	12	Quzhou	0.029	0.036	0.060	0.068	0.046	33
Zhoushan	0.058	0.080	0.141	0.109	0.095	13	Huaibei	0.028	0.039	0.053	0.052	0.045	34
Shaoxing	0.061	0.078	0.117	0.115	0.094	14	Chuzhou	0.021	0.025	0.050	0.062	0.044	35
Jinhua	0.054	0.065	0.096	0.117	0.090	15	Suqian	0.020	0.027	0.062	0.061	0.041	36
Yangzhou	0.058	0.091	0.085	0.108	0.088	16	Lu’an	0.025	0.027	0.047	0.062	0.041	37
Huangshan	0.051	0.071	0.080	0.088	0.079	17	Bozhou	0.019	0.028	0.049	0.055	0.040	38
Wenzhou	0.047	0.055	0.088	0.111	0.077	18	Suzhou	0.021	0.027	0.047	0.068	0.039	39
Wuhu	0.053	0.067	0.090	0.104	0.077	19	Anqing	0.024	0.030	0.037	0.047	0.037	40
Ma’anshan	0.046	0.069	0.084	0.099	0.074	20	Fuyang	0.026	0.029	0.034	0.049	0.036	41
Chizhou	0.042	0.051	0.081	0.085	0.070	21

Notes: M is the mean value of the development level of the open economy in each region from 2005 to 2019; R is the ranking of the development level of open economy in various regions.

and Figure 3, the open economy in the YRDA has made significant progress in the past 15 years, and the development level of each city shows a fluctuating upward trend over time, but the absolute differences between cities are growing. Specifically, the variation trend of the range used to characterize the fundamental difference is the same as that of the standard deviation and can be divided into the rapid rising stage of 2005–2010, 2017–2019, and the slow rising stage of 2010–2017, while the change of weighted coefficient of variation is more complicated and goes through three stages: fluctuation rise in 2005–2010, fluctuation decline in 2010–2017 and continuous rise in 2017–2019. Overall, the overall differences in the development level of the open economy in the YRDA showed an expanding trend during the study period.

From the point of view of evaluating the four subsystems of the development level of the open economy, the foundation of open economy in the YRDA shows a trend of steady growth, the scale of open economy shows a trend of rising and falling repeatedly. The quality and efficiency of open economy first increase and then decrease. On the whole, the potential of open economy shows a fluctuating upward trend (

Table 4. The development level of the subsystems of the open economy in YRDA from 2005 to 2019.

Year	FO	SO	QEO	PO
2005	0.679	0.657	0.878	0.291
2006	0.720	0.771	0.928	0.308
2007	0.762	0.903	0.955	0.355
2008	0.811	0.987	0.935	0.378
2009	0.864	0.919	1.100	0.467
2010	0.955	1.069	0.817	0.625
2011	0.978	1.184	0.917	0.631
2012	1.050	1.262	0.918	0.735
2013	1.160	1.305	0.846	0.874
2014	1.234	1.324	0.806	0.876
2015	1.324	1.295	0.771	1.022
2016	1.311	1.297	0.756	1.035
2017	1.166	1.732	1.161	1.623
2018	1.401	1.460	0.683	1.318
2019	1.519	1.508	0.653	1.405

Notes: FO is the foundation of open economy; SO is the scale of open economy; QEO is the quality and efficiency of open economy; PO is the potential of open economy.

). It reflects that the YRDA pays excellent attention to the dimension of the foundation of open economy and potential of open economy, but limited to the bottleneck that open economic structure is unreasonable, it has not effectively realized the coordinated development of quality, benefit, and scale. The Gini coefficient was used to analyze the development differences of each subsystem further. As seen from

Table 5. Gini coefficient of development of open Economy in YRDA from 2005 to 2019.

Year	G	FG	SG	QEG	PG
2005	0.406	0.383	0.619	0.336	0.187
2006	0.407	0.379	0.601	0.336	0.203
2007	0.402	0.376	0.580	0.335	0.190
2008	0.413	0.376	0.577	0.346	0.229
2009	0.423	0.371	0.567	0.412	0.266
2010	0.435	0.374	0.569	0.369	0.384
2011	0.413	0.354	0.563	0.347	0.320
2012	0.405	0.356	0.537	0.341	0.330
2013	0.394	0.350	0.527	0.340	0.306
2014	0.392	0.351	0.533	0.342	0.280
2015	0.399	0.362	0.545	0.358	0.293
2016	0.392	0.345	0.540	0.359	0.291
2017	0.368	0.377	0.467	0.383	0.246
2018	0.388	0.341	0.536	0.371	0.283
2019	0.390	0.343	0.521	0.353	0.318

Notes: G is the Gini coefficient of open economy; FG is the Gini coefficient of FO; SG is the Gini coefficient of SO; QEG is the Gini coefficient of QEO; PG is the Gini coefficient of PO; FO, SO, QEO and PO have the same meaning as before.

and Figure 4, the overall Gini coefficient of the development level of the open economy in the YRDA from 2005 to 2019 shows a trend of first increasing and then decreasing, indicating that the regional difference shows a fluctuating trend over time. On the whole, the difference in the scale of open economy index subsystem is the main reason for the overall difference in the development level of the open economy in the YRDA, followed by the foundation of open economy index subsystem. Before 2015, the contribution of the open quality and benefits index subsystem to the overall difference is more significant than that of the potential of the open economy index subsystem, and on the contrary, after 2015.

4.2. The Spatial Evolution of the Development Level of the Open Economy

Using the natural breakpoint method in Arcgis, the development level of the open regional economy in each year is stratified, and the threshold is rounded. Finally, it is divided into four groups: (0, 0.4] is a low level, (0.4, 0.8] is a secondary level, (0.8, 1.2] is a good level, and more than 1.2 is a better level and spatial distribution map of the development level of open economy in typical years is drawn.

As seen in Figure 5, the spatial differences in the development level of open economy in various cities in the YRDA are noticeable. Still, they show an upward trend overall, and the development level of Huai’an and Taizhou fluctuates wildly, and the development is unstable. In 2019, there were only one low-level city, 15 medium-level cities, 14 good-level cities, and 11 superior-level cities in the YRDA. On the whole, the development level of open economy in the YRDA shows a distribution pattern of “prominent in the middle, high in the south, low in the north, high in the east and low in the west”. The level of development of the open economy of cities in the central region is generally higher than that of other cities. The development of Shanghai and provincial capitals such as Nanjing, Hangzhou, and Hefei has been at a high level, maybe because of political and policy advantages. These areas are rich in products, have convenient transportation, rapid economic development, a high level of talent, scientific and technological development, and have certain advantages in the high-quality development of an open economy. From the provincial point of view, the order of the development level of the open economy in various provinces did not change during the study period. Shanghai ranked first, followed by Jiangsu and Zhejiang, and Anhui had the lowest level of development.

Theil index is used to study the overall regional differences in the development level of the open economy in the YRDA. According to the geographical conditions, taking the Yangtze River as the boundary, the YRDA is divided into two regions: the north and the south. As seen from

Table 6. Theil coefficient and its decomposition of development level of open economy in YRDA from 2005 to 2019.

Year	T	TER	TRR	TS	TN	TEC	TRC	TSC	TNC
2005	0.1293	0.0671	0.0622	0.0500	0.0828	0.5189	0.4811	0.8047	1.3307
2006	0.1301	0.0689	0.0611	0.0516	0.0849	0.5300	0.4700	0.8438	1.3880
2007	0.1286	0.0670	0.0616	0.0549	0.078	0.5207	0.4793	0.8907	1.2661
2008	0.1364	0.0719	0.0646	0.0615	0.0813	0.5267	0.4733	0.9523	1.2596
2009	0.1474	0.0683	0.0790	0.0609	0.0752	0.4638	0.5362	0.7701	0.9515
2010	0.1435	0.0734	0.0702	0.0638	0.0821	0.5110	0.4890	0.9093	1.1694
2011	0.1318	0.0672	0.0646	0.0615	0.0723	0.5096	0.4904	0.9520	1.1195
2012	0.1309	0.0638	0.0670	0.0609	0.0665	0.4877	0.5123	0.9085	0.9921
2013	0.1281	0.0585	0.0696	0.0556	0.0612	0.4566	0.5434	0.7980	0.8792
2014	0.1237	0.0556	0.0681	0.0491	0.0615	0.4492	0.5508	0.7208	0.9023
2015	0.1202	0.0520	0.0682	0.0481	0.0556	0.4325	0.5675	0.7046	0.8146
2016	0.1074	0.0449	0.0625	0.0395	0.0500	0.4184	0.5816	0.6321	0.8010
2017	0.0827	0.0317	0.0510	0.0375	0.0262	0.3829	0.6171	0.7353	0.5127
2018	0.0971	0.0387	0.0584	0.0362	0.0412	0.3989	0.6011	0.6195	0.7055
2019	0.0944	0.0373	0.0571	0.0356	0.0389	0.3951	0.6049	0.6240	0.6812

Notes: T is the Thiel coefficient; TER is the inter-regional Thiel coefficient; TRR is the intra-regional Thiel coefficient; TS is the Thiel coefficient of the South; TN is the Thiel coefficient of the North; TEC is the contribution of the inter-regional Thiel coefficient; TRC is the contribution of the intra-regional Thiel coefficient; TSC is the contribution of the Southern Thiel coefficient; TNC is the contribution of the Northern Thiel coefficient.

, the overall difference in the development level of the open economy in the YRDA shows a trend of expanding at first and then decreasing. During the study period, the changing trend of the Theil index between regions is the same as that within areas, but the contribution rate of the Theil index within regions shows an upward trend, while that between areas is, on the contrary, indicating that differences within regions are the main realization form of regional differences. From 2005 to 2019, the Theil index of the north and the south fluctuated and decreased, indicating that the regional differences tended to narrow. Except that the contribution rate of the Theil index of the north was lower than that of the south in 2017, the contribution rate of the Theil index of the north was more significant than that of the south in other years. Thus, it can be seen that the north is the main contributor to the widening regional gap.

4.3. Evolution of Spatial Correlation Pattern

In order to explore the spatial correlation characteristics of the development level of open economy among municipal units in the YRDA, combined with Geoda software and spatial statistical analysis tools in ArcGIS, the global Moran’I (

Table 7. The global Moran’I index of development level of open economy in YRDA from 2005 to 2019.

Year	Moran’s I	p Value	Z Value	Year	Moran’s I	p Value	Z Value
2005	0.28	0.02	3.30	2013	0.25	0.03	2.93
2006	0.29	0.02	3.43	2014	0.20	0.05	2.52
2007	0.28	0.01	3.40	2015	0.21	0.04	2.58
2008	0.25	0.03	3.07	2016	0.19	0.07	2.40
2009	0.31	0.01	3.64	2017	0.23	0.02	2.84
2010	0.21	0.04	2.77	2018	0.22	0.05	2.71
2011	0.25	0.04	2.95	2019	0.19	0.05	2.55
2012	0.25	0.03	2.93

) of the development level of open economy among municipal units in the YRDA is calculated. Moran’s I of the development level of the open economy in the YRDA from 2005 to 2019 is all greater than zero, which indicates that its distribution has a positive correlation. During the study period, the P and Z values passed the significance test, meaning that each municipal unit’s spatial agglomeration effect was enhanced.

The global Moran’s I verify the enhancement of the spatial distribution agglomeration of the development level of the open economy in the YRDA. Then the local autocorrelation model is introduced to test and concretely analyze the problems existing in the spatial distribution of the development of the open economy in the YRDA. The LISA figures (Figure 6) in 2005, 2010, 2015, and 2019 are drawn.

As a whole, the local spatial autocorrelation of the development level of open economy in the YRDA is mainly dominated by the H-H and L-L districts, and spatial homogeneity is more prominent than spatial heterogeneity. In terms of spatial distribution, the spatial scope of the H-H area has an expanding trend and is mainly distributed in the middle and southeast of the Yangtze River Delta; the L-L region has evolved from agglomeration in the northwest of the YRDA to agglomeration in the west and north. The spatial distribution of the L-H and H-L regions is scattered, mainly in the south and middle. In space, the distribution pattern of “prominent in the middle, high in the south, low in the north, high in the east and low in the west” is formed.

4.4. Analysis of Influencing Factors

4.4.1. Model Selection

From the previous analysis, we know that the development level of the open economy in the YRDA has a certain spatial correlation, so we choose the SDM to analyze the influencing factors (Figure 7). Before the model selection, we conducted stationarity and multicollinearity tests on the collected panel data to avoid spurious regression. The results showed that the panel data was relatively stable. But we found that the variance inflation factors of two variables, GDP per capita (X1) and the post and telecommunications business volume (X9), were much larger than the critical value of 10, so they failed the test and were removed. Therefore, the remaining eight explanatory variables were selected as the influencing factors of the level of open economy development in the YRDA and used to construct a relevant analysis model. We take the logarithm of all indicators to eliminate the effect of heteroscedasticity. Then, we combine the three-step method proposed by Elhorst to select the specific form of the spatial panel measurement model [56].

First, the LM test is used to determine whether to reject the non-spatial model and accept the spatial lag model (SLM), the spatial error model (SEM) or the SDM. According to the p-value results of four statistics LM Spatial Lag, Robust LM Spatial Lag, LM Spatial Error and Robust LM Spatial Error that the null hypothesis that there is no SLM or SEM is rejected, so the Spatial econometric model should be chosen (

Table 8. Spatial econometric model test results.

Test of Spatial Econometric Model		Statistics
LM Test	LM_Spatial error	61.199 ***
	Robust LM_Spatial error	17.747 ***
	LM_Spatial lag	53.203 ***
	Robust LM_Spatial lag	9.751 ***
Hausman Test		77.470 ***
LR Test	LR Test for SLM	105.290 **
	LR Test for SEM	90.180 ***
Wald Test	Wald Test for SLM	115.110 *
	Wald Test for SEM	101.100 ***

Note: * p < 0.1, ** p < 0.05, *** p < 0.001.

). Secondly, the data are subject to the Hausman test to determine whether to select the fixed or random effect. The results show that the Hausman test rejects the null hypothesis at the significance level of 1%. Hence, the model using spatial fixed effects is more consistent with the model’s estimation effect than random effects. In addition, the model can be controlled according to individual fixed effect, time fixed effect and double fixed effect, and time fixed effect is selected as the best model by comparing goodness of fit R². Finally, the SDM is constructed on this basis, and whether the SDM can be simplified to SLM or SEM is tested by Wald and LR statistics. The test results of Wald and LR both passed the significance test and rejected the original hypothesis, which indicates that SDM cannot be simplified to SLM and SEM. Therefore, the SDM is the most appropriate choice.

4.4.2. Analysis of Model Results

Before carrying out the spatial econometric model analysis, we first took on the ordinary panel model regression and compared the two regression results. It can be seen from

Table 9. The regression results of the ordinary panel model and spatial Durbin model.

Explanatory Variable	OLS		SDM		The Lagged Item Result
	Coefficient	Standard Error	Coefficient	Standard Error	Variable	Coefficient	Standard Error
X2	0.1628 ***	0.0330	0.4119 ***	0.3277	W*X2	−0.2098 ***	0.0407
X3	1.0667 *	0.2183	−0.0884	0.1953	W*X3	0.1529	0.1721
X4	0.1134	0.0261	0.0707 ***	0.0212	W*X4	0.1448 ***	0.0246
X5	0.2115 **	0.0415	0.3043 ***	0.0376	W*X5	0.1458 ***	0.0405
X6	0.1325 ***	0.0200	0.0405 **	0.0173	W*X6	−0.0867 ***	0.0190
X7	−0.0704	0.0166	0.0090	0.0186	W*X7	−0.0396 **	0.0171
X8	0.0047	0.0196	0.0473 *	0.0184	W*X8	−0.0622 ***	0.0188
X10	0.1629 *	0.0184	0.1178 ***	0.0158	W*X10	−0.0492 ***	0.0178
ρ	——		0.1713 ***
Log-likelihood	——		147.1855
N	615		615
R2	0.8442		0.9425

Note: * p < 0.1, ** p < 0.05, *** p < 0.001.

that the regression coefficients of the ordinary panel model and SDM of per capita financial expenditure (X2), human capital level (X6), freight volume (X8), and labor factor (X10) are all positive and passed the significance test, which indicates that these four factors have a significant positive impact on the development of the open economy in this region, but the lag coefficients are all negative, which indicates that after the addition of spatial factors, the improvement of these factors can facilitate the efficient development of the overall open economy in this region and the YRDA, but will restrain the progress of the development level of the open economy in the neighboring areas. The ordinary panel model regression coefficient, spatial Durbin model regression coefficient, and lag coefficient of per capita retail sales of social consumer goods (X5) are all positive and have passed the significance test, which indicates that the improvement of this factor has a significant positive impact on the overall development level of open economy of the YRDA. Meanwhile, the development of this region has spillover effects on the neighboring cities. The regression coefficient of the ordinary panel model of the proportion of the secondary and tertiary industries (X3) is significantly positive. In contrast, the regression and lag coefficient of the SDM does not pass the significance test, which indicates that without considering the spatial factors, there is a significant positive correlation between this factor and the development level of the open economy in the YRDA. The regression coefficient of the ordinary panel model of the urbanization rate (X4) is positive but insignificant. In contrast, the regression and lag coefficients of the SDM are significantly positive, which indicates that it cannot only promote the efficient development of the overall open economy in the YRDA to a certain extent but also has a significant positive spatial spillover effect on the adjacent areas. The regression coefficient of the ordinary panel model of the proportion of science and technology expenditure to financial expenditure (X7) is negative. The regression coefficient of SDM is positive but does not pass the significance test. The lag coefficient is significantly negative, which indicates that under the action of spatial factors, this factor will positively impact the development level of open regional economy. Still, it has a competitive relationship with the neighboring regions.

4.4.3. Robustness Test

In order to conduct a robustness evaluation on the above estimation results, the range attenuation weight matrix applied by Zhou [57] and Liu [58] is used to test the robustness. After Hausman, LR, LM, and Wald tests, the SDM is finally selected for estimating parameters. See

Table 10. The regression results of the robustness test.

Explanatory Variable	SDM		The Lagged Item Result
	Coefficient	Standard Error	Variable	Coefficient	Standard Error
X2	0.4167 ***	0.0332	W*X2	−0.2044 ***	0.0413
X3	0.1888	0.1959	W*X3	0.2228	0.1805
X4	0.0729 ***	0.0210	W*X4	0.1374 ***	0.0232
X5	0.2789 ***	0.0388	W*X5	0.1510 ***	0.0420
X6	0.0355 **	0.0175	W*X6	−0.0918 ***	0.0194
X7	0.0030	0.0188	W*X7	−0.0286 *	0.0173
X8	0.0410 **	0.0184	W*X8	−0.0539 ***	0.0187
X10	0.1290 ***	0.0160	W*X10	−0.0682 ***	0.0179
ρ	0.1939 ***
Log-likelihood	149.8725
N	615
R2	0.9431

Note: * p < 0.1, ** p < 0.05, *** p < 0.001.

for the results. From the perspective of regression results, under the action of the range attenuation weight matrix, the proportion of the secondary and tertiary industries (X3) changed from negative to positive, and there were no fundamental changes in the significance of the other variables. By comparison, the analysis results based on the two kinds of weight matrices are the same, which indicates that the conclusion of the analysis part of influencing factors is robust and reliable.

5. Discussion

Currently, China’s economy has entered a double-cycle strategic model which takes the tremendous domestic cycle as the main body, coordinates the domestic and international double cycles, and plans new economic growth points [59]. The high-level development of an open economy must be based on the interaction between the domestic economy and the international market. It is necessary to adhere to and improve the basic economic system of socialism with Chinese characteristics, promote the modernization of the national governance system and governance capacity, highlight the vital role of the government in promoting the efficient development of an open economy, create a long-term, stable, predictable and high-quality environment under the rule of law, build a high-standard market system, and encourage all kinds of market entities to release economic vitality to cope with the more complex and severe global industrial chain and foreign trade environment, form a new model of innovation-driven open economy development, and promote and lead the sustainable development of the open global economy [60]. So, what can we learn from this study?

5.1. It Is Necessary to Promote Regional Coordinated Development with the New Development Pattern of Domestic and International “Double Cycles”

Coordinated development is an essential index for reflecting high-quality development [61]. Suppose the development gap between regions is too broad. In that case, a scientific and efficient division of labor and cooperation between relatively developed and underdeveloped areas will not be formed, which will hinder the construction of an open economy, let alone the efficient development of an open economy [62]. From the results of the spatio-temporal analysis in part 4, it can be seen that the development level of the open economy of each city in the YRDA shows a fluctuating and rising trend over time, but the absolute and relative differences are enlarged, and the development level is uneven. In space, it shows a pattern of “prominent in the middle, high in the south, low in the north, high in the east and low in the west”, and the regional differences among the subsystems also show a trend of increasing at first and then decreasing. The new development pattern can promote the circulation of various resource elements at home and abroad by optimizing the market layout to promote the region’s coordinated development and the open economy’s high-quality development [63].

Under the “Double cycle” pattern, as an essential hub of the double cycle system, the YRDA can achieve internally balanced and coordinated development better and faster through industrial chain transfer, upgrading, and reconstruction [64]. In addition, the degree and quality of coordinated development among different cities in the YRDA are also the key factors that determine the smoothness of the overall economic cycle under the new development pattern of the “Double cycle”. It is not only an essential basis for accelerating the high-level development of an open economy but also an important guarantee for social harmony, political stability, and sustainable economic development to promote coordinated regional development. On the one hand, we can encourage the logical flow and efficient agglomeration of various elements through the regional coordinated development strategy to help smooth the domestic cycle, and control the regional development gap within a reasonable range under the condition of taking the domestic cycle as the main body, give full play to the competitive advantages of different cities in the YDRA and form a high-coupling industrial chain network with the mutual division of labor. On the other hand, it is necessary to deepen foreign economic ties, make good use of two markets and two kinds of resources, promote the establishment of an Omni-directional, wide-range, and multi-level open financial system, and promote the benign interaction of domestic and international double cycles in the YRDA to move forward the middle and high end of the global value chain [65].

5.2. Government Services Provide Essential Support for the Development of the Open Economy

In China’s economic system reform, the relationship between the government and the market has always been an inevitable and essential issue [66]. With the deepening of reform and opening up, our government has constantly explored the orientation of its functions in practice, deepened the understanding of its functions, and continuously adjusted the focus of its functions [67]. In constructing an open economy, the government should focus on creating an excellent institutional environment and providing policy support for the high-level development of the open economy, providing public goods and services, and establishing an economic regulation and control system compatible with the development of the open economy [68]. Since the reform and opening up, the open economy in the YRDA has made remarkable achievements [69]. In 2020, the total foreign trade of the YRDA was USD 1.63938 trillion, and the amount of foreign investment utilized was USD 82.329 billion, accounting for 35.8% and 59% of the country’s total, respectively. It plays an essential leading role in driving the rapid development of China’s open economy. These achievements cannot be achieved without a series of supporting policies to encourage the development of an open economy. This conclusion is also confirmed by our study, as can be seen from the analysis results, government factors such as per capita financial expenditure, per capita retail sales of social consumer goods, the proportion of science and technology expenditure in financial spending, and the development of urbanization have a significant impact on the development level of the open economy in the YRDA, which to a certain extent reflects that it is crucial to formulate targeted and comprehensive supporting policies to encourage the development of an open economy. Therefore, it is necessary to constantly improve the social security system and residents’ income growth mechanism, establish a consumer rights protection system and a class action system, improve the financial redistribution adjustment mechanism, effectively expand domestic demand and promote domestic circular development; improve the functional design of government public services, innovate the supply mechanism of essential public services, improve the efficiency and quality of public services, and create a good market environment for the construction of an open economy [70]; optimize the mechanism for cultivating, introducing and retaining talents, provide talent guarantee for the development of open economy, improve financial support measures, and support foreign-funded enterprises to explore the international market; discount financing loans and achieve technology transformation and renovation.

5.3. An Innovation-Driven Engine also Needs to Be Built for the High-Level Development of the Open Economy

As China has entered a new stage of high-quality development, the open economy of the YRDA should not only maintain the forefront of the country but shall also be transformed from quantity to quality faster. Innovation is regarded as the key driving force to lead the development and the critical support to promote the efficient development of an open economy [71], because growth is driven by knowledge accumulation, technological progress, and the improvement of labor quality. Knowledge innovation, technological innovation, and management innovation can effectively enhance the core competitiveness of enterprises and then stimulate the quality and potential of development of the open economy. Although the YRDA is the region with the most economic vitality, the highest level of openness, and the most robust innovation ability in China, internationally, due to the rising global industrial technical barriers and the developed countries’ control or suppression of technology export to China, the stock of technology that can be introduced into China is becoming less and less, especially the critical core technology and “Containment “ technology have become the trump card created by developed countries to suppress China. Due to technological bottleneck, China’s scientific and technical foundation is relatively weak [72]. There is a relatively high import dependence on core key technologies and components, which may also lead to the low development level of the potential of the open economy subsystem of development of open economy in the YRDA and be the reason that the proportion of science and technology expenditure in financial expenditure has not played a full role in promoting it. Therefore, it is necessary to build a new open economic system that can promote innovation-driven development [73]; take scientific and technological innovation as the center, realize power transformation in the process of improving the quality and efficiency of the development of an open economy, and strengthen the combination of mobile Internet, cloud computing technology, big data, Internet of Things and international trade, to promote the development of the open economy by forming a new driving force of innovation; pay attention to the cultivation of innovative talents, improve the investment level of education and human capital, through the protection of intellectual property rights, maintain the interests of individual innovation and organizational innovation at the institutional and legal status, and finally realize the systematic innovation of economy and society.

6. Conclusions and Deficiencies

Under the guidance of the new development pattern of the domestic and international “Double cycle”, the development of the open economy is facing both good development opportunities and significant challenges. YRDA can only promote the development of the open economy better and faster by defining its own position and recognizing its shortcomings in future development. Based on analyzing the development mechanism of China’s open economy at the present stage, this paper reveals its spatio-temporal pattern and its existing problems by measuring the development level of the open economy in the YRDA and then uses the SDM to analyze its influencing factors to find ways to improve the quality of development given the existing problems to achieve its sustainable and healthy development. The study found that: (1) From 2005 to 2019, the development level and differences of open economy in the YRDA showed an upward trend, and the trend of differences among subsystems was complex. (2) From the spatial point of view, the development level of each region shows a spatial distribution pattern of “prominent in the middle, high in the south, low in the north, high in the east and low in the west”. The regional difference shows a trend of increasing at first and then decreasing. The effect of spatial agglomeration is constantly strengthening, and spatial homogeneity is more prominent than spatial heterogeneity. (3) The per capita financial expenditure, labor factor, urbanization rate, per capita retail sales of social consumer goods, human capital level, and freight volume play a prominent role in improving the development level of the open economy in this region. Urbanization rate and per capita retail sales of social consumer goods have a significant positive impact on the development of the YRDA and neighboring regions. The human capital level, the proportion of science and technology expenditure in financial expenditure, freight volume, and labor factors are the factors that hinder the development of the open economy in neighboring regions and regions as a whole. (4) In the stage of high-quality development, the YRDA should promote regional coordinated development with the new development pattern of domestic and international “Double cycle”, improve the quality of government services to provide a guarantee for high-level development and create an innovation-driven engine to promote open economy to high-quality development.

By studying the open economy’s development level in the YRDA and its influencing factors, this paper preliminarily discusses the essential characteristics of its development. However, it is still also necessary to further improve the relevant mechanism and theoretical analysis of the transformation of the open economy from scale and speed dominance to quality and efficiency dominance. Meanwhile, since there is a limitation of data acquisition and reserve, the data of the research sample can only be collected until 2019, but the follow-up research can expand it to 2022 to further explore the relevant impact of the COVID-19 on China’s open economy development. In addition, this paper discusses the role of the new development pattern of the domestic and international “Double cycle” in promoting the coordinated development of the open economy in the YRDA. On this basis, in the future, we can carry out an in-depth study on the high-quality development of the open economy from the perspective of the new development pattern of the double cycle to enrich the relevant study results.