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Article

Regional Transportation Integration and High-Quality Economic Development, Coupling Coordination Analysis, in the Yangtze River Delta, China

by
Na Li
1,
Yun Song
2,*,
Wen Xia
3 and
Shu-Ning Fu
1
1
Institute of Urban and Demographic Studies, Shanghai Academy of Social Sciences, Shanghai 200020, China
2
Rotterdam School of Management, Erasmus University Rotterdam, 3062 PA Rotterdam, The Netherlands
3
Shanghai E-House Real Estate Research Institute, Shanghai 200072, China
*
Author to whom correspondence should be addressed.
Systems 2023, 11(6), 279; https://doi.org/10.3390/systems11060279
Submission received: 14 April 2023 / Revised: 18 May 2023 / Accepted: 25 May 2023 / Published: 1 June 2023
(This article belongs to the Section Systems Practice in Social Science)
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Abstract

:
Regional transportation integration is a key aspect in promoting regional integration and high-quality economic development, as it can improve inter-regional connectivity, decrease transportation costs, and facilitate the flow of labor, capital, technology, and data. However, regional transportation integration is also difficult to quantitatively evaluate, causing difficulties in comprehensively understanding the specific transportation–economic relationships for different regions that are planned for integration. This article studied 41 cities in the Yangtze River Delta, which is known as the largest regional integration project in China. Two sets of index systems were created to quantitatively evaluate regional transportation integration and high-quality economic development. Coupling coordination degree analysis was then performed to investigate the relationship between the two systems. It was found that areas with a high degree of coupling coordination are located in the Z-shaped belt with the Shanghai–Nanjing–Hefei and Shanghai–Hangzhou–Ningbo urban agglomerations. Furthermore, specific developmental gaps between regional transportation integration and high-quality economic development were identified and mapped, showing areas with transportation development falling behind economic development and vice versa. Based on these findings, a number of policy suggestions are provided from the perspective of province and regional development. It is recommended to continue to invest in transportation development and integration in the well-coordinated Z-shaped region and areas with transportation development falling behind economic development, while it is not recommended to use transportation investment to solve economic problems for those under-developed regions that already have relatively advanced transportation than economic development.

1. Introduction

Regional integration, often referring to a combination of enterprises or economic groups established among geographically close or adjacent countries (regions) to obtain the effects of regional economic agglomeration and complementarity, is a crucial topic in regional studies. Regional integration aims to reduce transaction costs, promote the free flow of commodities and factors, and implement optimal resource allocation [1,2]. The concept of regional integration can have different focuses when applied in different situations. For example, when applied to transnational regional cooperation, regional integration focuses on cross-border trade cooperation. When applied to cross-regional cooperation within a country, regional integration focuses on the cross-regional flow of production factors [3,4]. In this situation, regional transportation integration is a very important aspect in regional integration as it provides the infrastructure support for the free flow of production factors and eventually promotes regional economic development [5,6]. Regional transportation integration emphasizes not only the effective connection of transportation facilities and different transportation modes, for example roads, highways, railways, etc. [7], but also the transportation cooperation between different regions, including trans-regional infrastructure planning [8], management and coordination [9,10], and the transportation market [11].
It has been proven in the current literature that transportation infrastructure development makes a direct contribution to economic development [12], and a new research interest has appeared examining the contribution of regional transportation integration in economic development. For example, Ottaviano, Tabuchi, and Thisse (2002) explained that the influence of transportation infrastructure on economic activities in urban agglomerations can shape the economic spatial layout within the urban agglomeration [13]. Jia and Qin (2015) further stated that every 1% promotion in expressway density can drive about 0.034% economic growth, and every 1% promotion in railway density can drive 0.002% economic growth in China [14]. Examining such economic benefits within a certain region from either regional transportation development or integration has drawn quite some interest in China. Studies on high-speed rail have shown that the upgrade from non-high-speed rail cities to high-speed rail cities can help optimize the redistribution of regional economic activities [15,16,17]. Furthermore, Wang and Ni (2016) revealed the direct effects of regional transportation integration, such as the shortening of the space–time distance between cities, in boosting regional economic growth [18]. Thus, the current literature has proven that transportation infrastructure development has a direct economic contribution within a certain region; however, the economic contribution of regional transportation integration is not as clear as infrastructure development, and this may due to the difficulty of the quantitative analysis of regional transportation integration.
Furthermore, only using economic growth, such as GDP growth, as the measurement of economic development is becoming insufficient, especially in emerging economies such as China, which emphasizes the need to reform its economic model from quantity growth to quality growth. For example, in 2017, the Chinese President Xi clearly stated that China’s economy must shift from a phase of rapid growth to a stage of high-quality development [19]. The central government has also proposed five major concepts for the high-quality economic development of China: innovation, coordination, green, openness, and sharing [20]. Some Chinese researchers [21,22] have built evaluation systems based on the five concepts, examining economic structure optimization, innovation-driven development, efficient resource allocation, market mechanism improvement, regional coordination, a sharing economy, etc. However, currently, there is limited discussion on the high-quality economic development under a regional integration perspective, especially regarding regional transportation integration. Thus, this paper aimed to examine the relationship between high-quality economic development and regional transportation integration, especially regarding the following issues:
(1)
Currently, the quantitative evaluation of regional transportation integration is limited. Most of the literature focuses on the physical development of regional transportation infrastructure, especially high-speed rail, such as the planning and construction of high-speed rail networks [23,24] and the measurement of inter-city accessibility and connectivity with the high-speed network [25,26,27,28]. On the other hand, the discussion of regional transportation integration on the level of transportation service, management, and institutions remains at the qualitative level. Thus, there is a need to comprehensively quantitatively evaluate regional transportation integration.
(2)
To study the relationship between regional transportation integration and high-quality economic development, it is necessary to build a theoretical framework for the understanding of the interactive mechanism between the two systems. For example, how do the two systems mutually promote each other, and what happens if there is a development gap between the two systems?
(3)
This study used coupling analysis to examine the relationship between the two systems of regional transportation integration and high-quality economic development. The existing literature has stated that regional transportation integration can promote economic development by optimizing the free flow of production factors and the allocation of resources [5,6]. Based on this knowledge, this paper aimed to further explore the possible interactive coordination and spatial characteristics of the two systems.
The Yangtze River Delta (YRD) in China was selected as the empirical case study for this research for the following reasons. The YRD not only has the largest and most-developed urban agglomeration in China, but also has become a focal point of China’s effort to promote regional integration, alongside two other major mega-city regions, the Pearl River Delta and the Beijing–Tianjin–Hebei region. The YRD has been designated by the central government as a pilot area for extensive investments in regional transportation infrastructure and also for testing comprehensive regional integration planning and policies. Hence, the YRD is one of the most-suitable cases in China for studying regional transportation integration and high-quality development.
This paper is divided into six sections. In Section 2, a theoretical framework regarding the interactive mechanism between regional transportation integration and high-quality economic development is proposed. Section 3 briefly demonstrates the research methods and data sources. Section 4 gives the results of the comprehensive evaluation of the two systems followed by coupling analysis. Section 5 discusses the implications of the main findings. Section 6 summarizes the main contributions, limitations, and suggestions for policy-making and future study.

2. Theoretical Framework: Interaction Mechanism between Regional Transportation Integration and High-Quality Economic Development

2.1. Regional Transportation Integration

Based on the existing literature, this study focused on both the physical integration of the transportation infrastructure and the non-physical integration of transportation planning, management, cooperation, and coordination. Regarding the physical integration of regional transportation, the current literature emphasizes the construction of an integrated regional system connecting various transportation modes such as highways, railways, water transportation, aviation, and pipelines [7,29]. On the other hand, there is an increasing emphasis on the inter-government planning, cooperation, and management of the cross-regional transportation connections [8,9,10,11]. One key research difficulty of the latter group is how to quantitatively measure or evaluate regional transportation integration. Thus, this study aimed to evaluate regional transportation integration from three aspects: structure and scale, construction and connectivity, and operation and management (Table 1). For structure and scale, we aimed to measure the overall physical hardware foundation of regional transportation infrastructure. For construction and connectivity, we aimed to measure the inter-city accessibility of various transportation modes. For operation and management, we aimed to evaluate the inter-regional transportation services, inter-government cooperation, and policies.

2.2. High-Quality Economic Development

As mentioned before, high-quality development is a new concept adopted by the Chinese central government to promote its economic development model from high-speed growth to high-quality growth. The existing literature has developed several aspects of high-quality economic development, including innovation-driven development [30,31], ecologically sustainable development [32], quality efficiency [33,34], people’s livelihood sharing [35], regional coordinated development [36], and social equity and justice [37,38]. These aspects coincide with Mlachila’s definition of high-quality economic development, which includes both economic growth and development in social dimensions, such as happiness, gender equality, and working decency [39]. Based on these developments, as well as the five concepts of development promoted by the Chinese central government, we propose a number of evaluation aspects of high-quality economic development: innovation vitality, coordinated development, green development, open economy, and sharing service.

2.3. Mutually Promoting Mechanism between Transportation–Economic Systems

Based on the existing knowledge of regional transportation integration and high-quality economic development, we assumed there exists a mutual promotion mechanism between these two systems (as depicted in Figure 1). The improvement in regional transportation integration, including the physical improvement in regional transport infrastructure and non-physical improvement in regional transportation management and cooperation, can increase regional connectivity, reducing travel times and costs, facilitating the free flow of labor, capital, technology, and data, and thus, contributing to regional agglomeration and linkage effects in production. For example, the premise of labor agglomeration is that labor is mobile and profit-seeking. Transportation accessibility expands the scope of labor’s search for employment opportunities, enabling workers to find suitable jobs in a wider range, and optimizing the regional allocation of labor. Furthermore, better regional transportation connectivity can increase trade and the free flow of capital by making it easier for enterprises to move materials and goods between regions, as well as contribute to the agglomeration of enterprises and capital in the central area [40]. This can lead to the optimization of resources’ allocation, opening up new markets, creating more efficient supply chains, and increasing investment. The increase of regional transportation connectivity contributes to innovation as businesses have better access to talents, research facilities, and other knowledge resources located in different regions. This can also lead to the agglomeration of data, technology, and knowledge in the “central city” [41]. In other words, regional transportation integration promotes the free flow of labor, capital, technology, and knowledge, which produces agglomeration and linkage effects, promotes business cooperation and production specialization, optimizes the allocation of production factors, and finally, contributes to regional high-quality economic development. In return, high-quality economic development can also promote regional transportation integration. First, as regions experience high-quality economic growth, there is often an increase of regional transportation demand. which can lead to new investments in transportation infrastructure. Second, high-quality economic development also increases the need for more collaboration between different regions, businesses, and governments, which can further help identify transportation infrastructure needs and coordinate investments.

3. Research Methods and Data Sources

3.1. Scope of the Study

The Yangtze River Delta (YRD) in China was selected as the research area, which includes 41 cities from Jiangsu, Zhejiang, and Anhui provinces (Figure 2). In November 2018, the central government made the integrated development of the YRD region a national strategy, releasing the Yangtze River Delta Regional Integrated Development Plan, which has greatly promoted regional transportation integration. In 2020, the operating mileage of railway reached 13,000 km, forming one of the world’s densest railway networks. The commuting time between the central city Shanghai and the farthest city in the YRD has been decreased to less than 370 min, enabling one-day commuting within the YRD region. Moreover, the YRD region has 17% of the country’s population in 4% of the country’s land area, creating 24% of the country’s total GDP in 2020 [42]. The YRD region is not only one of the wealthiest regions in China, but also a pilot area for regional transportation integration. Hence, the YRD region was selected as the empirical case (1) to quantitatively evaluate regional transportation integration and high-quality economic development and (2) to analyze the coupling coordination degree between these two systems.

3.2. Research Methods

3.2.1. Index System and Weight

To quantitatively evaluate regional transportation integration and high-quality economic development, two sets of index systems were created based on different aspects introduced in the previous section. Specifically, regional transportation integration was evaluated from three aspects: structure and scale, construction and connectivity, and operation and management. Structure and scale reflect the construction of transportation infrastructure in cities and regions, which is the physical foundation of regional transportation integration [43]. They are mainly reflected in the urban transportation mode, road network density, passenger and cargo flow, etc. Therefore, the highway mileage per unit area, urban passenger traffic, urban freight traffic, and the number of transportation modes were selected as indicators. Construction and connectivity reflect the degree of connectivity of cross-regional transportation infrastructure. Hence, the shortest intercity high-speed rail commuting time, passenger turnover, and the density of unfinished roads in urban border areas were selected as indicators. Operation and management reflect regional traffic management and cooperation. Accordingly, the opening of one traffic card available for different cities, the implementation of policies to promote regional transportation integration, and the operation and information sharing of “Internet + Traffic” were used as indicators. Finally, the analytic hierarchy process (AHP) method was used to determine the weights of each indicator. The AHP method is widely used to quantify the weights of decision criteria in which experts estimate the relative magnitudes of indicators by pairwise comparisons. We designed a specific questionnaire (see Appendix A) for pairwise comparison of the three aspects in regional transportation integration first and then with specific indicators within each aspect. With 10 effective respondents, we built a pairwise comparison matrix, and with a consistency check, we calculated the weights of each indicator (Table 2).
On the other hand, high-quality economic development was evaluated from five aspects based on the Five Concepts of Development: innovation vitality, coordinated development, green development, open economy, and sharing service [20]. For innovation vitality, research and development (R&D) investment and the number of invention patents were selected as indicators. For coordinated development, the tertiary industry percentage (service sector) and urbanization rate were selected as indicators. For green development, electric and water consumption per unit of industrial added value were selected as indicators. For open economy, foreign trade dependency (import and export trade/GDP) and level of utilizing foreign capital (foreign direct investment/GDP) were selected as indicators. For sharing service, the number of hospital beds per 10,000 people and education expenditure per capita were selected as indicators. Overall, 10 indicators were selected considering the data availability. The raw data were first standardized, and the weights were determined by the principal component analysis (PCA) method, as shown in Table 3. The PCA method is often used to reduce the dimensionality of large datasets with many dimensions through linearly transforming the data into a new coordinate system. PCA can be also used to assign weights to input variables and create innovative indices [44], especially in the case of datasets with many different dimensions, which are hard to evaluate by experts from one specific field. As an objective mathematical option for weight selection, the PCA method determines the weights by using the coefficients of principal components that explain the largest variation in the original indicators. The reason why the PCA method was used here instead of the AHP method in regional transportation integration was because the indicators of high-quality economic development cover many dimensions from different fields. If you are interested in the specific calculation process, please see Appendix B.

3.2.2. Coupling Coordination

Coupling was originally a concept in physics referring to the phenomenon wherein two or more systems interact with each other to achieve mutual coordination. The coupling degree estimates the degree of correlation between systems; at the same time, it reflects the interaction between systems. For example, the scores of both systems can be relatively low, but the coupling degree can be higher than that of a city with both high and low scoring systems. As a reflection of the correlation condition and the interaction between systems, the coupling coordination degree [45,46] can represent whether each system promotes each other at a high level or restricts each other at a low level. In this paper, it was calculated as:
C = 2 U 1 U 2 U 1 + U 2 2
C represents the coupling degree of the two systems, and U1 and U2 are the regional transportation integration index and the high-quality economic development index, respectively. Before calculating the coupling degree, it is necessary to standardize the two indices so that subsequent calculations can be compared. Therefore, the C value is between 0 and 1; the larger the value of C, the better the coupling effect is.
In a situation when the coupling degree of two systems with both low scores may be higher than that of two systems with high and low scores is possible, it is necessary to further calculate the coupling coordination degree to improve the accuracy of the measurement. The formula of coupling coordination degree measurement is:
D = C T , T = α U 1 + β U 2
In Equation (2), D represents the coupling coordination degree between the two systems; C is shown in Equation (1). T represents the comprehensive coordination index of the two systems, and α and β represent the weights of the indices from the two systems. In this study, the two systems were regarded as equally important; thus, α and β were both taken as 0.5. According to Equation (2), T and D also range from 0 to 1. The larger the value of D, the better the coupling coordination degree is (i.e., the development of the two systems reaches a certain cooperative state). The smaller the D value, the worse the coupling coordination degree is and the more uncoordinated the development of the two systems, as shown in Table 4.

3.3. Data Sources

With regard to the indicators of regional transportation integration, the data on highway mileage per unit area, annual passenger traffic, annual freight traffic, and passenger turnover were collected from the 2020 statistical yearbook of each city and from the official website of each local government. The data of smart card systems’ availability were collected from the official platform of China T-union. The data of Internet + Transportation operation and information sharing were collected from Alipay’s transportation sector. The data of the shortest commuting time for intercity high-speed rail were collected and calculated from the official website of China Railway. The data of policies promoting regional transportation integration were obtained from the statistical calculation of the policies or measures released in paper media recorded by the Wisesearch platform. The density of unfinished roads in the administrative border areas mainly refers to the ratio of the number of unconnected roads within a certain range on both sides of the city boundary to the length of the boundary between cities. The data were collected from the 2019 public version of the 1:250,000 national basic geographic database, the transportation thematic map, and the national prefecture-level city administrative division map based on the SuperMap Online website. The raw data were further processed with the Geographic Information System (GIS) vector data. On the other hand, the data regarding the 10 indicators of high-quality economic development were all collected from the 2020 Urban Statistical Yearbook.

4. Results of Comprehensive Evaluation and Coupling Coordination Degree Analysis

4.1. Evaluation of Regional Transportation Integration

According to the evaluation index system of regional transportation integration (Table 2), we calculated a score of regional transportation integration for all 41 cities in the YRD region. Using the automatic breakpoint method in ArcGIS, these scores were divided into five categories from low to high (see Figure 3). Here are a number of findings based on the results.
First, the level of regional transportation integration varies dramatically across cities in the YRD region. Shanghai had the highest score (0.701), which was three-times more than the city with the lowest score (Xuancheng: 0.204). Overall, central cities in the YRD region had the highest scores, such as Suzhou (Jiangsu), Hangzhou, Nanjing, Ningbo, and Hefei. They are either provincial capital cities or economic central cities in the region. Basically, there was a gradual decay of the scores from the central cities to the peripheral cities. For example, the surrounding cities near the central city of Shanghai, such as Suzhou (Jiangsu), Jiaxing, and Nantong, had relatively high scores. Similarly, the surrounding cities near the provincial capital city Hangzhou, such as Huzhou, Shaoxing, and Jinhua, also had relatively high scores. These regional central cities and their surrounding cities have developed into urban agglomerations in which they are closely connected for industrial production, business, and trade. On the other hand, peripheral cities, such as Yancheng, Huangshan, Lishui, and Huaibei, are geographically located far away from the central cities, and they had the lowest scores in regional transportation integration. However, it is worth noting that cities such as Yangzhou, Xuancheng, and Tongling are geographically located near the central cities, but they still had the lowest scores. It is very likely that these cities are not part of nor playing an insignificant part in the urban agglomerations in the YRD region. Second, based on the cities with high scores in regional transportation integration, a W-shaped belt (pink line in Figure 3) can be drawn on the map along cities Xuzhou–Hefei–Nanjing–Shanghai–Hangzhou–Ningbo (see Figure 3). This belt is relatively consistent with the high-speed railway network: Shanghai–Huangzhou line, Hnaghzou–Ningbo line, and Ningbo–Hefei line. This showed that the construction of high-speed rail network has played a crucial role in promoting regional transportation integration.

4.2. Evaluation of High-Quality Economic Development

According to the evaluation index system of high-quality economic development (Table 3), we also calculated a score of high-quality economic development for all 41 cities in the YRD region. These scores were also divided into five categories from low to high using the automatic breakpoint method in ArcGIS (see Figure 4). Here are a number of findings based on the results.
First, it is very clear that Shanghai (1.230) is the absolute center in terms of high-quality economic development in the YRD region, while a huge difference can be found not only among cities (Chizhou had the lowest score of 0.276), but also among provinces. For example, cities in Zhejiang province had relatively high scores overall, while cities in Anhui province had relatively low scores. The gaps of scores among cities were relatively small in these two provinces. On the other hand, cities in Jiangsu province varied dramatically, as Suzhou, Wuxi, and Nanjing had high scores, but other cities scored very low. Second, a gradual decreasing pattern can be found from the eastern coastal areas to the western inland areas. Overall, it can be divided into two groups: high-value agglomeration and low-value agglomeration. An inverse K-shape (purple lines in Figure 4) can be drawn according to the cities with high scores. The Shanghai–Nanjing–Hefei and Shanghai–Hangzhou–Ningbo agglomerations can be found as the leading areas for high-quality economic development. Again, this showed that Shanghai is the central city in the YRD region to which all high-value agglomerations have to connect.

4.3. Coupling Coordination Degree Analysis

Based on the results from two evaluation systems for regional transportation integration and high-quality economic development, a coupling coordination analysis was conducted using Equations (1) and (2). For each city, a C value and a D value were calculated and are presented in Table 5.
Overall, the two systems interact well in the YRD region, as more than 75% of the cities had a coupling degree (C value) above 0.8, among which Shanghai, Huzhou, Jiaxing, Suzhou (Jiangsu), and Ningbo had a coupling degree of 1. However, as the D value represents the coupling coordination degree, the results are presented as seven categories (well-coordinated; intermediately coordinated; poorly coordinated; barely coordinated; slightly uncoordinated; moderately uncoordinated; badly uncoordinated) based on the D value. Here are a number of findings.
The well-coordinated category (D > 0.8) has four cities: Shanghai, Suzhou (Jiangsu), Nanjing, and Hangzhou. The regional transportation integration and high-quality economic development systems are well coupled and coordinated, which means the two systems are promoting each other mutually. In the intermediately coordinated category (0.700 < D < 0.799), the two systems also rank high, but with small gaps, and there is also a mutual promoting effect between them. The category has three cities: Wuxi, Ningbo, and Hefei, among which Wuxi and Ningbo had higher rankings in high-quality economic development, while Hefei had a higher regional transportation integration ranking. The poorly coordinated category (0.600 < D < 0.699) has seven cities: Xuzhou, Changzhou, Jiaxing, Shaoxing, Jinhua, Wenzhou, and Nantong. Significant differences in the rankings of the two systems began to appear in these cities, and the mutual promoting effect is much weaker here. Among them, the cities that ranked higher in the regional transportation integration were Xuzhou, Changzhou, Jiaxing, and Shaoxing; the cities with a higher high-quality economic development ranking were Jinhua and Nantong. Wenzhou had the same ranking in the two systems. Lastly, the barely coordinated category (0.500 < D < 0.599) has four cities: Zhenjiang, Huzhou, Wuhu, and Huai’an. Huzhou and Wuhu are cities whose regional transportation integration drives high-quality economic development. Zhenjiang and Huai’an are cities whose high-quality economic development drives regional transportation integration. The barely coordinated category of cities presented quite weak correlations between the two systems.
In the slightly uncoordinated category (0.400 < D < 0.499), the overall ranking of the two systems was relatively low, and mutual inhibition began to emerge (e.g., Taizhou (Jiangsu), Bengbu, Taizhou (Zhejiang), Lianyungang, Yangzhou, Zhoushan, Maanshan, Suqian, Quzhou, and Yancheng). Among them, only two cities had a reduced coupling coordination degree due to their low level of high-quality economic development, such as Bengbu and Maanshan. Other cities suppressed their economic development due to their low level of regional transportation integration. In the moderately uncoordinated category (0.300 < D < 0.399), the overall ranking of the two systems was lower, the ranking gap between the two systems narrowed, and the mutual inhibition effect began to increase. Notable examples include Chuzhou, Huangshan, Suzhou (Anhui), Huainan, and Bozhou. Among them, the high-quality economic development of Chuzhou inhibits the development of regional transportation integration, while other cities showed the opposite. Lastly, in the badly uncoordinated category (D < 0.300), the ranking gap between the two systems was particularly obvious, showing clear mutual inhibition. They are Tongling, Lu’an, Huaibei, Lishui, Fuyang, Anqing, Xuancheng, and Chizhou. The cities whose high-quality economic development inhibits regional transportation integration include Lu’an, Fuyang, and Anqing. Moreover, the cities where regional transportation integration inhibits high-quality economic development include Tongling, Huaibei, and Lishui. Xuancheng and Chizhou had low levels of both factors, inhibiting each other’s development.
The results of the coupling coordination degree are visualized in Figure 5. Again, with Shanghai as the center, there is a gradual decrease from the eastern coastal areas to western inland areas. A Z-shaped belt (pink line in Figure 5) can be drawn according to high coupling coordination degree areas, forming a Shanghai–Nanjing–Hefei and Shanghai–Hangzhou–Ningbo agglomerations. These results can be correlated with the YRD integration planning as the Shanghai–Nanjing–Hefei and Shanghai–Hangzhou–Ningbo areas have the advantage of being located along the river and along the bay, where the expressway, high-speed rail, and water transportation are quite developed. Additionally, most of these cities are provincial, sub-provincial capitals, and economically developed cities, which have already formed into the earliest urban agglomerations in the YRD region.
In order to better understand the relationship and developmental gap between the two systems of regional transportation integration and high-quality economic development, the difference between the rankings of the two systems are mapped in Figure 6. The cities with high ranks in regional transportation integration, but low ranks in high-quality economic development are marked green, which means they have a better development level of regional transportation integration than high-quality economic development. On the other hand, cities with high ranks in high-quality economic development, but low ranks in regional transportation integration are marked white, which means they have a better development level of high-quality economic development than regional transportation integration. Only four cities, Shanghai, Hangzhou, Wenzhou, and Bozhou, have basically the same level of regional transportation integration and high-quality economic development, and they are marked yellow.

5. Discussion

5.1. Coupling Coordination Analysis and Developmental Gap Analysis

By comparing the results from the coupling coordination analysis with the developmental gap between the regional transportation integration and high-quality economic development, not only can we have a full picture of the situation of regional integration of the YRD region, but also we can have an in-depth discussion about the development problems of the different areas.
From the perspective of province development, the situation varies significantly and great unbalanced development can be found. Therefore, it would be beneficial to the provincial governments in promoting regional integration to understand their position, strengths, and challenges regarding regional transportation integration and high-quality economic development. First of all, Shanghai, as a provincial-level mega-city, is the absolute leading center in the YRD region with a well-coordinated high (transportation) and high (economy) development. On the other hand, the neighboring Jiangsu province has a very different situation. Overall, Jiangsu has a low (transportation) and high (economy) situation with distinctive spatial differences. The southern Jiangsu areas, which are closer to Shanghai, had a much higher coupling coordination degree compared to the northern areas, where the cities have slightly uncoordinated transportation-economy systems. From the developmental gap analysis, especially the northern Jiangsu areas have rather weak regional transportation integration, and this can hinder both economic growth and the realization of the “coastal integrated development belt” from the YRD region integration plan.
Zhejiang province overall has rather balanced and well-coordinated development in transportation–economic systems in the YRD region. Except for the periphery areas, such as Lishui and Quzhou, most areas in Zhejiang have mutual promotion effects between transportation and economic development. It is clear that Hangzhou and surrounding cities have formed a great urban agglomeration area, where regional transportation is highly integrated. Cities such as Jiaxing, Huzhou, and Shaoxing, which are located near the urban agglomeration central cities, had higher ranking in transportation than economic development. However, the Shanghai–Ningbo–Zhoushan coastal development belt is comparatively weak, which lags behind other coastal economic developed regions in China [48]. This is very likely due to the fact that the major coastal transportation channels between Shanghai–Ningbo–Zhoushan have not yet been connected, restricting the linkage between the Shanghai and Ningbo economic centers.
Lastly, Anhui province, also known as the least-developed area in the YRD region, has a low (transportation) and low (economy) development with a low coupling coordinated degree. Seventy-five percent of the cities in Anhui have barely coordinated, or worse, transportation and economic systems. It is fair to say that Hefei, the capital city of Anhui, has not yet developed into a central city of urban agglomeration; thus, it has limited influence on the surrounding cities, which had rater low scores in the coupling coordination degree. Additionally, most areas in Anhui had higher rankings in regional transportation integration than that of high-quality economic development, which means the problem of Anhui’s under-performance in economic development is unlikely due to the lack of transportation development.
From the perspective of regional development, the impact of regional transportation integration on the economic development of the under-developed regions is marginal and lagging. For example, the northern areas of the YRD region in Jiangsu and Anhui province, cities such as Nantong, Yancheng, and Lianyungang have already been connected to the high-speed network for the past few years, which has increased their regional transportation connectivity; however, the influence on high-quality economic development remains less visible. A similar situation can be also found in the Hefei–Xuzhou development belt, whose regional transportation integration has advanced rapidly, but its high-quality economic development has lagged behind. The agglomeration of Hefei, Xuzhou, Suzhou (Anhui), Bengbu, Huainan, and Chuzhou had fairly good scores in regional transportation integration, while having pretty low scores in high-quality economic development, especially Suzhou (Anhui), Chuzhou, and Huainan. It seems that, in the cities with a weak economic foundation, the improvement in regional transportation integration, especially the high-speed rail construction, has a rather limited contribution to high-quality economic development. For example, Xuzhou is a national-level transportation hub, the intersection of the Lanzhou–Lianyungang and Beijing–Shanghai high-speed rail lines. Despite having an important position in regional transportation, Xuzhou is not regarded as a destination for capital and labor flow. It is known in the literature that the construction of high-speed rail stations can bring huge development opportunities to a city in the first year, but the impact can fade away very quickly afterwards [49]. The local governments of the under-developed areas have high aspirations in fully making use of high-speed rail stations as a stimulus for urban development projects, known as high-speed rail new towns, which can bring great GDP growth, as well as local debt risks at the same time [50,51]. Moreover, high-speed rail stations are often located very far away from the city center, bringing great potential for urban development, but also high risks of over-development. For example, the high-speed rail station of Suzhou (Anhui) is located 20 km away from the city center, and the local government has planned a massive high-speed rail new town there almost as large as Suzhou (Anhui)’s original city area, which has been accused of building a “ghost town” [52]. Thus, for those cities with a weak economic foundation and an unclear position in the regional industrial and business supply chains, the economic benefit by connecting to the high-speed rail network is overestimated and the construction of high-speed rail stations can also bring high risks if local governments focus on urban development rather than business development [52].
In other words, the coupling coordination degree analysis has shown very clearly that the highly coordinated areas are indeed the developed urban agglomeration areas with Shanghai as the center, especially the Z-shaped belt with the Shanghai–Nanjing–Hefei and Shanghai–Hangzhou–Ningbo urban agglomerations. The developmental gap analysis further provided information about which system has lagged behind between regional transportation integration and high-quality economic development. Based on these findings, specific policy suggestions can be generated for different regions, provinces, and cities based on their own situations.

5.2. Policy Suggestions

Theoretically speaking, the higher the difference in the ranking of the transportation and economic development systems, the lower the coupling coordination degree of the two systems is, thus the influence on the general development of the area. Therefore, a general suggestion can be drawn to decrease the gap between the two systems of regional transportation integration and high-quality economic development. Practically speaking, this is straightforward for areas with higher ranking in economic development than regional transportation integration: increase investment and cooperation in regional transportation development and integration. For example, for the surrounding areas of the Shanghai–Hangzhou urban agglomeration in Jiangsu and Zhejiang provinces, inter-city and inter-province transportation should be strengthened to further facilitate high-quality economic development. Especially in cities such as Zhenjiang, Yangzhou, Taizhou, and Nantong, which have low scores on physical transportation infrastructure, the effect of increased transportation investment will be obvious.
However, for the areas with a higher ranking in regional transportation integration than high-quality economic development, such as most areas in Anhui province, the situation is a bit more complicated. To suggest the economically under-developed region strengthen economic development is meaningless, but it is clear that, for these regions, more investments in transportation facilities will have quite a limited impact on economic development, as there is already a development gap, where economic development is lagging behind transportation development. The economically under-developed regions and cities should focus on improving their local business environment and finding the right position in regional business and industrial supply chains. The strategy of using regional or national transportation projects such as high-speed rail for an opportunity to stimulate massive urban development investments presents a high financial risk and might be dangerous. In other words, their developmental problems are more structural and harder to solve by adjusting the transportation–economic relationship.
Lastly, apart from the coupling coordination analysis, the single indicator “density of unfinished roads near administrative borders” showed that provincial transportation policy barriers still exist as, overall, cities close to provincial borders have a much higher density of unfinished roads. For example, Xuancheng (Anhui) is the border city to Jiangsu and Zhejiang provinces; however, it has the highest density of unfinished roads near the administrative borders in the whole YRD region. It may also be due to the very weak position of Xuancheng in economic development, but connecting these unfinished roads requires relatively small efforts, while the increase of regional connectivity will be significant and instant. Thus, connecting unfinished roads near provincial borders can be suggested as a very specific policy recommendation for inter-provincial transportation cooperation.

6. Conclusions

Through the coupling coordination analysis of the two major systems of regional transportation integration and high-quality economic development in the YRD region, various developmental gaps can be found in different areas. Based on the discussion comparing coupling coordination analysis and the developmental gaps of the two systems, a number of findings can be concluded as follows.
First, the two systems of regional transportation integration and high-quality economic development have obvious mutual promotion effects in the highly coordinated regions. The study found that the areas with a high degree of coupling coordination degree are the Shanghai–Hangzhou–Ningbo urban agglomeration and the Yangtze River development belt (Shanghai–Nanjing–Hefei), which highly coincide with the core areas of the YRD regional integration plan. It was proven that successful urban agglomeration areas have mutually promoting transportation–economic systems, where a virtuous circle can be created: regional transportation integration promotes the flow of production factors and optimizes resources’ allocation to stimulate economic development; in return, high-quality economic development will increase regional transportation demands and investments in infrastructure and inter-government transportation cooperation, which further promote regional transportation integration.
Second, coupling coordination degree analysis with developmental gap comparison can help to identify the areas that have more investment in transportation development, which will have the optimal effect on promoting economic development. Our findings have shown that, apart from the Shanghai–Hangzhou–Ningbo urban agglomeration, also known as the core area of the YRD region, the surrounding areas in Jiangsu and Zhejiang provinces have high degree in coupling coordination and a development gap, where regional transportation integration is lagging behind economic development. This means that the transportation–economic relationship of these areas is highly corelated and their transportation development has lagged behind. Increased transportation investment, either in infrastructure development or transportation cooperation, will be the most-beneficial. For the rest of the areas with a similar developmental gap, such as northern Jiangsu and southern Zhejiang, the promotion effect from increased transportation development is less clear as the coupling coordination degree is lower in these regions.
Third, the study also identified the economic under-developed areas of the YRD region where further transportation investment has had a rather limited effect on promoting economic development. This finding is critical to the general development strategy of the under-developed regions, where increasing investment in transportation development is very often utilized as a universal solution to improve their economic development. Our findings, on the other hand, suggest that these regions, especially regarding most areas in Anhui province, have already had a leading development of regional transportation integration. In other words, a lack of transportation connectivity is not the major reason for the economic under-development. Surely, increased transportation investment will be somehow beneficial, but for them, the effect on promoting economic development is marginal and costly. Their economic development problem is more structural and beyond the transportation–economic relationship. What these areas really need to promote economic growth is to improve their local business environment and find suitable positions in regional business and industrial supply chains.
This study also has several limitations for which further research suggestions can be identified. First, this study quantitatively evaluated regional transportation integration with 10 indicators from three categories in regional transportation development and cooperation. Admittedly, many inter-government transportation cooperations cannot be quantitatively evaluated, especially the informal ones, and these cooperations often play an important role in regional transportation integration. Thus, our findings should be further tested qualitatively with a specific case study. Second, this study used cross-sectional data in the evaluation of regional transportation and high-quality economic development. The coupling coordination analysis over a longer period of time, in other words using panel data analysis, will be more comprehensive in discussing the changing spatial–temporal characteristics of the coupling coordination degree.

Author Contributions

Conceptualization, N.L. and W.X.; methodology, W.X.; software, W.X.; validation, N.L., W.X. and Y.S.; formal analysis, W.X. and Y.S.; resources, S.-N.F.; writing—original draft preparation, N.L.; writing—review and editing, Y.S.; visualization, W.X. and Y.S.; supervision, N.L.; project administration, N.L.; funding acquisition, N.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Social Science Foundation of China, Grant Number: NO.20BJY060.

Data Availability Statement

The data will be made available upon request.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A. Questionnaire on the Importance of Evaluation Indicators of Transportation Integration in the Yangtze River Delta Region

In order to understand the development of the transportation integration in the Yangtze River Delta region and better promote the development of the transportation integration in the Yangtze River Delta region to a deeper level, this paper establishes a comprehensive evaluation index system for the transportation integration in the Yangtze River Delta region. For the investigation of the importance of the evaluation indicators of the regional transportation integration, the expert scoring method is used to construct the judgment matrix and determine the weight of each indicator. Your point of view will be of great help to the final writing. I hope you can fill out this questionnaire. Thank you very much for your cooperation.
This paper attempts to select its evaluation indicators from three aspects: scale and structure, construction and connectivity, operation and management, and then select the following indicators according to the availability of data. They are highway mileage per unit area, urban passenger volume, urban freight volume, number of transportation modes, shortest intercity high-speed rail commuting time, passenger turnover, density of “dead end roads” at urban boundaries, whether to open a traffic card, and policies to promote transportation integration A total of 10 indicators including implementation, “Internet + transportation” operation and information sharing are used to comprehensively evaluate the development degree of transportation integration in the Yangtze River Delta region. Indicators are explained in the last section.
Table
Table A1. “Staaty” importance scale.
Table A1. “Staaty” importance scale.
Importance Scale Meaning
1ai is as important as aj
3ai is slightly more important than aj
5ai is more important than aj
7ai is very important than aj
9ai is definitely more important than aj
2, 4, 6, 8Represent values between 1–3, 3–5, 5–7, 7–9
ReciprocalIf the judgement value of ai and aj is aij, then the judgement value of aj and ai is 1/aij.
Please fill in the points in the blanks in the form below, and leave the gray shaded blanks blank.
Scoring example: if (indicator A (Construction and Connectivity))/(indicator B (Operation and Management)) = 3, it means that A is slightly more important than B (Construction and Connectivity are 3 times as important as Operation and Management);
If (Indicator A (Construction and Connectivity))/(Indicator B (Operation and Management)) = 1/5, it means that B is more important than A (the importance of Construction and Connectivity is 1/5 of that of Operation and Management, and Operation and Management is 5 times more important than Construction and Connectivity).
Table
Table A2. Comparison Matrix of Regional Transportation Integration.
Table A2. Comparison Matrix of Regional Transportation Integration.
Indicator BScale and StructureConstruction and ConnectivityOperation and Management
Indicator A
Scale and Structure1
Construction and Connectivity 1
Operation and Management 1
Table
Table A3. Comparison Matrix of Scale and Structure.
Table A3. Comparison Matrix of Scale and Structure.
Indicator BHighway Mileage per Unit AreaAnnual Passenger TrafficAnnual Freight TrafficNumber of Transportation Modes
Indicator A
Highway mileage per unit area1
Annual passenger traffic 1
Annual freight traffic 1
Number of transportation modes 1
Table
Table A4. Comparison Matrix of Construction and Connectivity.
Table A4. Comparison Matrix of Construction and Connectivity.
Indicator BIntercity High-Speed Rail Shortest Commuting TimeAnnual Passenger TurnoverDensity of Unfinished Roads in Administrative Border Areas
Indicator A
Intercity high-speed rail shortest commuting time 1
Annual Passenger turnover 1
Density of unfinished roads in administrative border areas 1
Table
Table A5. Comparison Matrix of Operation and Management.
Table A5. Comparison Matrix of Operation and Management.
Indicator BUnified Smart Card SystemNumber of Policies Promoting Regional Transportation Integration‘Internet + Transportation’ Operation and Information Sharing
Indicator A
Unified smart card system1
Number of policies promoting regional transportation integration 1
‘Internet + transportation’ operation and information sharing 1
Thank you so much for your input!

Appendix B. Calculation Process of Determining Weights of Indicators with PCA Method

First, the PCA test was performed in SPSS 20.0 for all indicators of high-quality economic development. Table A6 shows the principal components (PCs) that explain the cumulative variance. As we can see, the first component explained 50.773% of the variance, and the first three components explained 75.001% of the cumulative variance. Thus, the PCA test produced three PCs with our dataset.
Table
Table A6. Cumulative variance explained.
Table A6. Cumulative variance explained.
ComponentsInitial EigenvaluesExtracted Loads
TotalVariance (%)Cumulative (%)TotalVariance (%)Cumulative (%)
15.07750.77350.7735.07750.77350.773
21.36013.60364.3771.36013.60364.377
31.06210.62475.0011.06210.62475.001
40.6136.12981.130
50.5415.41486.544
60.4934.92691.470
70.3563.56295.031
80.2432.42897.460
90.1691.69199.151
100.0850.849100.000
Table A7 shows the principal component loadings (aij); this refers to the correlation coefficient between the i-th original index and the j-th principal component. If the absolute value of aij is larger, the relationship between the common factor Fj and the original index xi is closer.
Table
Table A7. Principal component loading matrix.
Table A7. Principal component loading matrix.
Components
123
Education expenditure per capita (CNY/person)0.9140.016−0.040
Urbanization rate (%)0.882−0.1000.270
Number of effective invention patents owned by ten thousand people (pieces/ten thousand people)0.810−0.217−0.244
Foreign trade dependence (%)0.763−0.087−0.164
Proportion of tertiary industry (%)0.7540.327−0.047
Intensity of utilizing foreign capital (%)0.7130.4140.119
Number of hospital beds per 10,000 people (pieces/10,000 people)0.683-0.3130.071
R&D input as a percentage of GDP (%)0.661−0.3390.500
Electricity consumption per unit of industrial added value (10,000 kWh/CNY 100 million)−0.0320.7580.529
Water consumption per unit of industrial added value (10,000 tons/CNY 100 million)0.4920.479−0.591
From the above loading table, the correlation coefficient of each index in different linear combinations can be obtained; the formula is as follows:
μ i j = a i j λ j
In the formula, μ i j represents the coefficient of the i-th index in the i-th principal component, a i j represents the coefficient of the j-th principal component in the i-th index, and λj is the characteristic root of the j-th principal component. According to the above formula, we can calculate the coefficient of each indicator under different principal components, as shown in Table A8 below.
Table
Table A8. Principal component coefficients.
Table A8. Principal component coefficients.
IndicatorsComponents
F1F2F3
R&D input as a percentage of GDP (%)0.293 −0.291 0.485
Number of effective invention patents owned by ten thousand people (pieces/ten thousand people)0.360 −0.186 −0.237
Proportion of tertiary industry (%)0.335 0.280 −0.046
Urbanization rate (%)0.391 −0.085 0.262
Electricity consumption per unit of industrial added value (10,000 kWh/CNY 100 million)−0.014 0.650 0.513
Water consumption per unit of industrial added value (10,000 tons/CNY 100 million)0.218 0.411 −0.574
Foreign trade dependence (%)0.339 −0.075 −0.160
Intensity of utilizing foreign capital (%)0.317 0.355 0.116
Number of hospital beds per 10,000 people (pieces/10,000 people)0.303 −0.268 0.069
Education expenditure per capita (CNY/person)0.406 0.014 −0.038
Finally, we can calculate the weight of each indicator with the formula below:
w i = ∑ j = 1 9 ( μ i j ∗ s j ) ∑ 1 9 s j
In the formula, wi is the weight before normalization, μ i j is the sparseness of the i-th indicator in the j-th principal component, and sj is the percentage of the variance of the j-th principal component. Then, after normalizing wi, the results are shown in Table A9.
Table
Table A9. Calculated weights of indicators.
Table A9. Calculated weights of indicators.
IndicatorsWeights (%)
R&D input as a percentage of GDP (%)9.76
Number of effective invention patents owned by ten thousand people (pieces/ten thousand people)8.02
Proportion of tertiary industry (%)12.34
Urbanization rate (%)13.05
Electricity consumption per unit of industrial added value (10,000 kWh/CNY 100 million)8.24
Water consumption per unit of industrial added value (10,000 tons/CNY 100 million)6.42
Foreign trade dependence (%)8.79
Intensity of utilizing foreign capital (%)13.43
Number of hospital beds per 10,000 people (pieces/10,000 people)7.58
Education expenditure per capita (CNY/person)12.37

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Systems 11 00279 g001 550
Figure 1. The mutual promoting virtuous circle between regional transportation integration and high-quality economic development.
Figure 1. The mutual promoting virtuous circle between regional transportation integration and high-quality economic development.
Systems 11 00279 g001
Systems 11 00279 g002 550
Figure 2. The location of the Yangtze River Delta in China and the Regional Integration Plan for the YRD region, source: revised from “Regional Integration Plan for the YRD region”.
Figure 2. The location of the Yangtze River Delta in China and the Regional Integration Plan for the YRD region, source: revised from “Regional Integration Plan for the YRD region”.
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Systems 11 00279 g003 550
Figure 3. Regional transportation integration scores of the YRD region.
Figure 3. Regional transportation integration scores of the YRD region.
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Systems 11 00279 g004 550
Figure 4. The high-quality economic development scores of the YRD region.
Figure 4. The high-quality economic development scores of the YRD region.
Systems 11 00279 g004
Systems 11 00279 g005 550
Figure 5. Coupling coordination degree of transportation–economic systems of the YRD region.
Figure 5. Coupling coordination degree of transportation–economic systems of the YRD region.
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Figure 6. Distribution of the developmental gaps between two systems ranking of the YRD region.
Figure 6. Distribution of the developmental gaps between two systems ranking of the YRD region.
Systems 11 00279 g006
Table
Table 1. Three aspects of regional transportation integration.
Table 1. Three aspects of regional transportation integration.
Structure and ScaleConstruction and ConnectionOperation and Management
MeasurementNetworkAccessibilityConvenience
ContentRoad network density; structural balancePlanning and construction; facility connectivityInformation sharing;
unified standard
Table
Table 2. The comprehensive evaluation index system and weight of indicators of regional transportation integration.
Table 2. The comprehensive evaluation index system and weight of indicators of regional transportation integration.
AspectsIndicatorsWeight
Regional transportation integration Structure and scaleHighway mileage per unit area (km/km2)0.0676
Annual passenger traffic (10,000 people)0.1352
Annual freight traffic (10,000 tons)0.1152
Number of transportation modes (types)0.0819
Construction and connectivityIntercity high-speed rail shortest commuting time (min)0.1771
Annual passenger turnover (10,000 people/km)0.0679
Density of unfinished roads in administrative border areas (km/km2)0.1549
Operation and managementUnified smart card system (Y/N)0.0340
Number of policies promoting regional transportation integration0.0886
“Internet + transportation” operation and information sharing (Y/N)0.0775
Table
Table 3. The evaluation index system and weight of indicators of high-quality economic development.
Table 3. The evaluation index system and weight of indicators of high-quality economic development.
AspectsSignIndicatorsWeight
High-quality economic developmentInnovation vitality+R&D input as a percentage of GDP (%)0.0976
+Number of effective invention patents owned by ten thousand people (pieces/ten thousand people)0.0802
Coordinated development+Proportion of tertiary industry (%)0.1234
+Urbanization rate (%)0.1305
Green ecology−Electricity consumption per unit of industrial added value (10,000 kWh/CNY 100 million)0.0824
−Water consumption per unit of industrial added value (10,000 tons/CNY 100 million)0.0642
Open development+Foreign trade dependence (%)0.0879
+Intensity of utilizing foreign capital (%)0.1343
Inclusive sharing+Number of hospital beds per 10,000 people (pieces/10,000 people)0.0758
+Education expenditure per capita (CNY/person)0.1237
Table
Table 4. Coupling coordination degree of the two systems.
Table 4. Coupling coordination degree of the two systems.
D ValueCoordination Level
0.90–1.00Well-coordinated
0.80–0.89
0.70–0.79Intermediately coordinated
0.60–0.69Poorly coordinated
0.50–0.59Barely coordinated
0.40–0.49Slightly uncoordinated
0.30–0.39Moderately uncoordinated
0.20–0.29Badly uncoordinated
0.10–0.19
0.00–0.09
Resource: modified from [47].
Table
Table 5. Coupling coordination degree value and level.
Table 5. Coupling coordination degree value and level.
CityCDLevelCityCDLevel
Shanghai1.0001.000Well-coordinatedTaizhou (Jiangsu)0.9490.498Slightly uncoordinated
Suzhou (Jiangsu)1.0000.849Well-coordinatedBengbu0.9730.492Slightly uncoordinated
Nanjing0.9970.829Well-coordinatedTaizhou (Zhejiang)0.9300.480Slightly uncoordinated
Hangzhou0.9990.821Well-coordinatedLianyungang0.9530.458Slightly uncoordinated
Wuxi0.9910.747Intermediately coordinatedYangzhou0.8380.455Slightly uncoordinated
Ningbo1.0000.712Intermediately coordinatedZhoushan0.6990.431Slightly uncoordinated
Hefei0.9940.702Intermediately coordinatedMaanshan0.9360.427Slightly uncoordinated
Xuzhou0.9800.672Poorly coordinatedSuqian0.9880.416Slightly uncoordinated
Changzhou0.9990.655Poorly coordinatedQuzhou0.9960.414Slightly uncoordinated
Jiaxing1.0000.636Poorly coordinatedYancheng0.8940.413Slightly uncoordinated
Shaoxing0.9990.628Poorly coordinatedChuzhou0.7430.368Moderately uncoordinated
Jinhua0.9940.615Poorly coordinatedHuangshan0.9980.347Moderately uncoordinated
Wenzhou0.9950.604Poorly coordinatedSuzhou (Anhui)0.7850.342Moderately uncoordinated
Nantong0.9770.602Poorly coordinatedHuainan0.6270.308Moderately uncoordinated
Zhenjiang0.9890.594Barely coordinatedBozhou0.9150.304Moderately uncoordinated
Huzhou1.0000.576Barely coordinatedTongling0.9700.296Badly uncoordinated
Wuhu0.9930.546Barely coordinatedLu’an0.7680.295Badly uncoordinated
Huai’an0.9970.517Barely coordinatedHuaibei0.9320.275Badly uncoordinated
Lishui0.4340.270Badly uncoordinated
Fuyang0.3960.233Badly uncoordinated
Anqing0.2650.208Badly uncoordinated
Xuancheng0.0000.000Badly uncoordinated
Chizhou0.0000.000Badly uncoordinated
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Li, N.; Song, Y.; Xia, W.; Fu, S.-N. Regional Transportation Integration and High-Quality Economic Development, Coupling Coordination Analysis, in the Yangtze River Delta, China. Systems 2023, 11, 279. https://doi.org/10.3390/systems11060279

AMA Style

Li N, Song Y, Xia W, Fu S-N. Regional Transportation Integration and High-Quality Economic Development, Coupling Coordination Analysis, in the Yangtze River Delta, China. Systems. 2023; 11(6):279. https://doi.org/10.3390/systems11060279

Chicago/Turabian Style

Li, Na, Yun Song, Wen Xia, and Shu-Ning Fu. 2023. "Regional Transportation Integration and High-Quality Economic Development, Coupling Coordination Analysis, in the Yangtze River Delta, China" Systems 11, no. 6: 279. https://doi.org/10.3390/systems11060279

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