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The Effect of Distance on Cargo Flows: a Case Study of Chinese Imports

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The Effect of Distance on Cargo Flows: a Case Study of Chinese Imports See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/317752891 The effect of distance on cargo flows: a case study of Chinese imports and their hinterland destinations Article in Maritime Economics & Logistics · May 2017 DOI: 10.1057/s41278-017-0079-3 CITATIONS READS 3 117 3 authors: Likun Wang Anne Goodchild Shanghai Maritime University University of Washington Seattle 3 PUBLICATIONS 7 CITATIONS 70 PUBLICATIONS 616 CITATIONS SEE PROFILE SEE PROFILE Yong Wang Chongqing Jiaotong University 43 PUBLICATIONS 355 CITATIONS SEE PROFILE Some of the authors of this publication are also working on these related projects: Integrated collaborative logistics network design and optimization View project Experimental and Numerical Investigations of Stribeck Curves View project All content following this page was uploaded by Yong Wang on 02 October 2017. The user has requested enhancement of the downloaded file. Marit Econ Logist DOI 10.1057/s41278-017-0079-3 ORIGINAL ARTICLE The effect of distance on cargo flows: a case study of Chinese imports and their hinterland destinations 1 2 3 Likun Wang • Anne Goodchild • Yong Wang Ó Macmillan Publishers Ltd 2017 Abstract With the rapid development of ports in China, competition for cargo is growing. The ability of a port to attract hinterland traffic is affected by many factors, including distance to the hinterland destinations. This paper studies the effects of distance on import cargo flows from a port to its hinterland. Two major findings are reported. Through a Spatial Concentration Analysis, this study shows that cargo imported through ports with relatively low throughput is primarily delivered to local areas, with the proportion of cargo delivered to local areas from larger ports being much smaller. The present study also shows (according to a gravity model, the Gompertz function and several other methods) that cargo flows from a large port to its hinterland increase with distance below a certain threshold, while cargo flows approach a stable state once they exceed this threshold. These results can be used to inform port managers and policy makers regarding the hinterland markets for ports of different sizes. Keywords Import cargo flow Á Hinterland Á Ports of China Á Distance decay Á Gompertz model & Likun Wang [email protected] 1 College of Transport and Communications, Shanghai Maritime University, Shanghai 201306, People’s Republic of China 2 Department of Civil and Environmental Engineering, University of Washington, 121E More Hall, Seattle, WA 98195, USA 3 School of Economics and Management, Chongqing Jiaotong University, Chongqing 400074, People’s Republic of China L. Wang et al. Introduction Over the past two decades, sea ports in China have developed quickly. For example, the number of productive berths of deep-sea ports increased from 967 to 5675 from 1990 to 2013, of which the number of million-ton berths increased from 284 to 1607 (China port yearbook). As importers for a particular destination can choose which port to use as an import location, this growth has presented challenges for ports in terms of attracting traffic to hinterlands. A hinterland in this study is defined as the land area surrounding an import destination and from which exports are collected. Its overseas equivalent, foreland, is defined as an area from which cargo originates (Ferrari et al. 2011). Distance decay is a well-established phenomenon of passenger transportation (Fotheringham 1981; Luoma et al. 1993). The concept has also been explored in reference to freight transportation, in which hinterland shippers or consignees typically select the closest ports to import/export cargo. Such selection has been attributed to cost minimiza- tion. The cost of hinterland transport accounts on average for approximately 40% of all container transportation costs (Notteboom and Winkelmans 2001). It has been shown that in the US (Levine et al. 2009; Jones et al. 2011) and France (Guerrero 2014), cargo volumes decrease with distance. This paper investigates whether the same phenomenon can be observed for imports in China, and it describes the nature of this relationship. Literature review Shippers have been shown to choose the least costly route from an origin to a destination (Luo and Grigalunas 2003), and inland transportation costs seem to affect port choice (Blonigen and Wilson 2006). Hinterland rail capacity is known to be more restrictive than port handling capacity for container flows destined for US markets (Fan et al. 2010), and assuming that shippers tend to minimize total logistics costs, hinterland rail disruptions change port selection outcomes (Jones et al. 2011). Large shippers in the US emphasize factors that affect the speed of delivery more than they emphasize freight charges, unlike smaller shippers, and inland transit times appear to affect port choice (Tiwari et al. 2003; Steven and Corsi 2012). Statistically, inland distances have been shown to significantly affect port choice (Malchow and Kanafani 2004); US domestic haulage cargo imported from Asia has been forecasted to increase with improvements to inland infrastructure (Leachman 2008); hinterland cargo flows typically exhibit distance decay in French and Ligurian ports (Guerrero 2014; Ferrari et al. 2011). From the above papers, inland transportation times, distances, and prices are known to be key factors influencing port choice, and these three elements are positively correlated. Transportation price data are typically more difficult to obtain than distance and time data, while time data are subject to more variability due to varying road and traffic conditions. This paper only discusses the relationship between cargo flows and inland distances for ports in China. The effect of distance on cargo flows: a case study of Chinese… Levine et al. (2009) forecast hinterland cargo flows using the gravity model and using data from the US Maritime Administration and US Department of Commerce. US PIERS can provide information on every bill of lading, allowing for the application of a discrete choice model to analyze the relationship between hinterland cargo flows and distance (Malchow and Kanafani 2004). Hinterland cargo flow data were also obtained from survey data drawn from various studies as is shown in Table 1. In China, aggregated cargo flows of all consignments between ports and delivery locations are available. Data sources and data processing The raw data on port-hinterland cargo flows obtained from the China Customs information center include values, weights, origins, and destinations of China imports sent from 295 import customs offices to 743 destination areas in 2012. Creating port and hinterland data For the raw data, the origins of port-hinterland cargo flows are given as customs offices, which are found at terminals or sub-ports (hereinafter referred to as terminals) that are open to foreign trade. Of the top 63 ports in China, each port has one or more terminals with 115 terminals in total. Figure 1 shows data consolidation structures from the customs level to the port level; m, h, and i are indices for customs offices, terminals, and ports, respectively. In this paper, we discuss port destination hinterlands and refer to hinterlands on two scales: destination provinces and destination areas, indexed as k and j, respectively. Cargo flow data The raw cargo flow data cover the inland section from customs office m to CI PI PR destination area j as indexed by vmj. Here, vij and vik represent cargo flows from port i to destination area j and cargo flows from port i to destination province k, I R P respectively. Dj , Dk , and Oi represent the gross import cargo volume of destination area j, the gross import cargo volume of destination province k, and the gross import Table 1 Sources of cargo flow data Data sources Relating papers Foreign trade statistics bureau Levine et al. (2009), Ferrari et al. (2011), Guerrero (2014) Port import export reporting Luo and Grigalunas (2003), Malchow and Kanafani (2004), service (PIERS) database Leachman (2008), Jones et al. (2011), Steven and Corsi (2012) Survey Tiwari et al. (2003), Tongzon and Heng (2005), De Langen (2007), Chang et al. (2008), Yuen et al. (2012) L. Wang et al. Fig. 1 Data consolidation from the customs level to the port Customs office m Terminal h Port i level m1 h1 m2 h2 i1 m3 m4 h3 cargo volume of port i, respectively (m[{all customs offices serving port i}, j[{all areas of province k}). X PI CI vij ¼ vmj ð1Þ m X PR PI vik ¼ vij ð2Þ j X I PI Dj ¼ vij ð3Þ i X R PR Dk ¼ vik ð4Þ i X P PI Oi ¼ vij ð5Þ j The import cargo flow is in this paper given as the amount or value in kilograms or dollars; Guerrero (2014) believes that weight and value have little influence on the destination distribution analysis of import cargo. Distance data Cargo sent between ports and destinations is primarily transported by road. Monios and Wang (2013) found that 90% of inland freight transportation involves road transportation. In China, 85% of the throughput of the Shanghai port is collected and distributed by road (Shanghai port yearbook); this proportion is 75% for the Shenzhen port (Shenzhen port yearbook). In this paper, the distance between a port and delivery location is calculated as the length of the shortest driving path by road, PI CI which is more reasonable than the Euclidean distance. We set dij and dmj as the distance from import port i to destination area j and as the distance from import customs office m to destination area j, respectively. Our analysis is conducted through
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