Air and rail connectivity patterns of major city clusters in China

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Abstract

This study proposes a series of statistical and regression approaches to investigate city-cluster transport connectivity patterns. Three major city clusters in China are selected for this study, namely, the Beijing-Tianjin-Hebei (BTH), the Yangtze River Delta (YRD), and the Pearl River Delta (PRD) economic zones. Our analyses examine three aspects of city-cluster connectivity patterns: (i) connectivity distribution among cities within the city cluster; (ii) dependence of the non-center city on the center city; and (iii) the impact of improved intra-city-cluster rail connectivity on air-connectivity distribution. First, it is found that the BTH has the most concentrated connectivity among the three city clusters, with Beijing clearly dominating the other cities in every kind of connectivity. The connectivity distributions are more balanced in the YRD and PRD. Second, the calculated “survival connectivity” and regression analyses suggest that non-center cities heavily rely on the center city as the hub to develop their network connectivity. Such a “hub-and-spoke system” helps a non-center city improve total connectivity but reduces its direct connectivity to other cities outside the city cluster. Finally, our regression analysis shows that improved intra-city-cluster rail connectivity further deteriorates the air-connectivity disparity within the city cluster, which occurs because the center city benefits more from the upgraded air-HSR (high speed rail) intermodal connectivity because it can attract air passengers from neighboring non-center cities. Relevant policy implications and suggestions are also discussed in the paper. We believe the proposed statistical and regression approaches can be easily applied to examine city-cluster connectivity in other contexts and countries.

Introduction

China has been urbanizing rapidly in the past four decades. In 1978, when China first adopted the ‘Open Door’ Policy, only 171 million people (17.9% of the population) lived in urban areas, compared to 848 million (60.6% of the population) in 2019. From 1978 to 2019, the average annual GDP growth rate in China increased more than 10% because of the astonishing economic growth caused by urbanization. Currently, the Chinese government expects to boost growth by bringing urbanization to another level. Such commitment to stepping up the pace of urbanization has been revealed by important government plans, such as the 13th Five-Year Plan for Economic and Social Development (for 2016–2020) and the National New Urbanization Plan (for 2014–2020). A major strategy of this new phase of urbanization is to emphasize the development of city clusters instead of individual cities. In particular, nineteen city clusters have been identified (Fig. 1), among which eleven already have development plans, while eight are still pending approval from the central government. The Chinese government is taking a holistic view and plans to link the clusters to form three ‘vertical’ (the Baotou-Kunming Railway Corridor, the Harbin-Beijing-Guangzhou Railway Corridor, and the Coastal Corridor) and two ‘horizontal’ corridors (the Yangtze River Corridor and the Land Bridge Corridor). The big picture is critical, as the Chinese city clusters will be components of the Belt and Road Initiative (BRI) through these corridors. The Chinese government has designated three of the nineteen city clusters to become world-class by 2020. Each of these three clusters, i.e., the Beijing-Tianjin-Hebei (BTH), the Yangtze River Delta (YRD), and the Pearl River Delta (PRD), has a GDP higher than Spain’s; together, they are expected to generate 45% of China’s total GDP by 2025.

In this regard, well-connected infrastructure and transport facilities are necessary for the successful development of these city clusters. Assessing the pattern of transportation connectivity, within a city cluster can offer valuable insights about the current status of the cluster and point out potential directions for further development. However, there have been few studies on how connectivity develops within and across city clusters. Especially because China is ambitious to develop urbanization in the form of city clusters, it is unclear how transport connectivity and its composition can contribute to distinct features, and the roles of each individual major city cluster in China are also unclear.

Moreover, it should be noted that a majority of the existing research focuses on the connectivity of a single transport mode or particular cities. Nevertheless, in China, there are several modes that interact with one another. Specifically, air and rail are the two major transport modes for intercity travel. Many studies have demonstrated that high-speed rail (HSR) and aviation are potential substitutes.1 Thus, it is essential to take both modes into account when examining the connectivity of a city cluster. The measurement of connectivity based on a single mode may not provide accurate information for the decision makers when planning the distinct connectivity patterns within and across city clusters to fit the overall development target. To provide insights for the further urbanization of China, as well as shed light on the urban developments of other countries, this study considers multimodal transport connectivity (air and rail) within China’s three main city clusters, i.e., the BTH, YRD, and PRD.

This paper first calculates the connectivity index of each sampled city in the three city clusters using the method developed by Zhu et al. (2019). Such connectivity indices are calculated for different categories, namely, air connectivity (international vs. domestic), rail connectivity, intermodal connectivity, direct and indirect connectivity, and aggregated connectivity. Then, a series of statistical analyses are employed to examine the city-cluster connectivity patterns, focusing on three aspects. First, this study examines the connectivity distribution (i.e., the degree of disparity) among individual cities within each city cluster. Using the HHI and Gini index, the concentration of each kind of connectivity is measured. This analysis is useful to clearly demonstrate how the center city (i.e., Beijing, Shanghai, and Guangzhou) dominates the connectivity in their respective city clusters. A more dominating center city in terms of transport connectivity reflects more unbalanced transport development in the city cluster, which could further affect the economic activity distributions within the city cluster (Lao et al., 2016). Second, our analysis measures the dependence of the non-center cities on their center city to develop network connectivity (i.e., the formation of a hub-and-spoke transport network). The “survival connectivity” of each non-center city is calculated as the residual connectivity after removing its center city from the non-center city’s transport network. A non-center city is concluded to rely heavily on center-city connectivity if its survival connectivity is significantly lower than its original connectivity. In addition, a regression analysis is conducted to further quantify how a non-center city’s connectivity with its center city can affect its connectivity to other cities. Finally, an analysis is conducted to shed light on the impact of improved rail connectivity within the city cluster on the air-connectivity distribution among the center and non-center cities. A regression model is specified to empirically examine whether better intra-city-cluster rail connectivity would alleviate or deteriorate air-connectivity disparity between center and non-center cities. This issue is particularly relevant for Chinese city clusters, as high-speed rail (HSR) greatly enhances intra-city-cluster connectivity between nearby cities, forming a more effective multiple airport system (MAS). Thus, airports in the same city cluster are more closely connected with each other, but their air connectivity could be differently affected. In summary, this paper proposes a relevant research framework to explore the detailed connectivity patterns for the major Chinese city clusters and then makes relevant suggestions for transport network development.

The contributions of this paper are twofold. First, it contributes to the existing literature as one of the first empirical studies to explicitly investigate multimodal connectivity from the perspective of city clusters. In particular, this study proposes a series of statistical and regression approaches to examine specific aspects of connectivity within and across city clusters. With necessary modifications, a similar research framework could also be applied to analyze transport connectivity for other city clusters in China and beyond. Second, our empirical results also offer a holistic comparison of China’s three main city clusters, pointing out the advantages and shortcomings of each of these city clusters. Moreover, our study provides fresh empirical evidence on air-HSR intermodal connectivity development at the city-cluster level. These results have policy implications for the further sustainable development of the three city clusters and provide useful lessons for the other city clusters that are under development in China and other countries.

The rest of the paper is organized as follows. Section 2 first reviews the relevant literature, which helps identify the research gap and demonstrate the contributions of this paper. This section also presents some background information on the three major city clusters in China. Section 3 illustrates the method and data used for the connectivity calculation and presents some descriptive statistics of the calculated connectivity indices. Section 4 is the main part of the paper, which analyzes the detailed connectivity patterns at the city-cluster level. Finally, the conclusion can be found in Section 5.

Section snippets

Literature review and background

This section first reviews the literature related to this study to better identify our research contributions. Then, this section introduces some basic background information about the three major city clusters in China, together with China’s overall transport development in recent decades.

Connectivity calculation

This section specifies the method and data employed to calculate the connectivity index for all the cities included in our sample (i.e., Section 3.1). Section 3.2 presents some overall connectivity patterns for the three city clusters.

City cluster connectivity analysis

The discussions in Section 3 present some preliminary connectivity comparisons across the three major city clusters. This section is dedicated to exploring more detailed connectivity patterns at the city-cluster level. More rigorous statistical methods and regression analyses are employed. First, this section analyzes the connectivity distribution (i.e., the disparity of connectivity) among different cities within each city cluster (i.e., Section 4.1). Second, this section examines the

Conclusion

This paper first calculates the connectivity index of cities in three major city clusters in China, namely, the BTH, YRD and PRD. Then, a series of statistical analyses are employed to investigate the city-cluster connectivity patterns. This is one of the first studies to examine connectivity within and across city clusters, since previous relevant studies concentrated on city-level or particular transport mode connectivity. Specifically, our study focuses on three essential aspects of the

Acknowledgments

The authors contributed equally to this manuscript. We appreciate three anonymous reviewers for their valuable comments. Financial support from the Social Science and Humanities Research Council of Canada (SSHRC) (project nos. 435-2017-0728, 435-2017-0735, and 430-2019-00725) and the National Natural Science Foundation of China (Grant: 71803131) are gratefully acknowledged.

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