Quantitative measurement and development evaluation of logistics clusters in China

doi:10.1007/s11442-018-1566-x

Abstract

Abstract:

The logistics clusters are the result of concentration, scale and specialization of logistics activities, and their quantitative measurement and development evaluation provide an important foundation for improving the land use efficiency and achieving economies of scale. Taking 289 cities at prefecture-level and above as research objects, this paper collected macro-statistical data of transport, postal and warehousing industry during 2000-2014, business registration data of more than 290 thousand logistics enterprises, and 170 thousand logistics points of interest (POI). With the integration of multi-index and multi-source data, the evolution process and spatial pattern of logistics clusters in China were explored with the methods of Location Quotient (LQ), Horizontal Cluster Location Quotient (HCLQ), Logistics Employment Density (LED) and modified Logistics Establishments’ Participation (LEP). The development levels, types and modes of different logistics clusters were quantified. Several important findings are derived from the study. (1) The logistics clusters are mainly located on the east side of the Hu Huanyong Line, and the accumulative pattern evolves from group to block structure, featuring wide coverage and high concentration. The evolution of logistics clusters has two stages of rapid convergence and stable change, resulting in gradual increase in the development level and efficiency of logistics clusters and in emergence of spillover effect. (2) 21 mature logistics clusters are distributed in the core and sub-cities of the main metropolitan areas of 16 provincial-level administrative divisions, conforming to the government logistics and transport planning. 43 emerging logistics clusters are distributed in 21 provincial administrative divisions, and different types of cities have huge disparities which highlight the differentiation of the market behaviors and government planning among them. (3) The logistics clusters present differentiated development modes with the change of scales. In urban agglomerations scale, the nested “center-periphery” structures with “main nucleus-secondary cores-general nodes” are clarified. The polar nuclear development, networked and balanced development, single core and multipoint, multi-core multipoint hub-spoke development patterns are formed in different provincial administrative divisions.

Key words: logistics cluster, agglomeration, spatial pattern, comprehensive integration, multi-scale

Sijing LIU, Guoqi LI, Fengjun JIN. Quantitative measurement and development evaluation of logistics clusters in China[J].Journal of Geographical Sciences, 2018, 28(12): 1825-1844.

Figures/Tables 10

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Figure 7

Figure 8

Figure 9

Table 1

References 38

[1]	Baranowski S, Busko E, Shishlo Set al., 2015. Formation mechanism of logistics cluster in Belarus. Agriculture and Agricultural Science Procedia, (7): 12-20.
[2]	Birtchnell T, Savitzky S, Urry J, 2015. Cargomobilities: Moving Materials in a Global Age. New York: Routledge. doi: 10.1080/00253359.2016.1135637
[3]	Cao X S, Xue D S, Yan X P, 2005. A study on the urban accessibility of national trunk highway system in China.Acta Geographica Sinica, 60(6): 903-910. (in Chinese)
[4]	Chhetri P, Butcher T, Corbitt B, 2014. Characterising spatial logistics employment clusters.International Journal of Physical Distribution & Logistics Management, 44(3): 221-241. doi: 10.1108/IJPDLM-03-2012-0086
[5]	Chung T W, 2016. A study on logistics cluster competitiveness among Asia main countries using the porter’s diamond model.The Asian Journal of Shipping and Logistics, 32(4): 257-264. doi: 10.1016/j.ajsl.2016.12.010
[6]	Cidell J, 2010. Concentration and decentralization: The new geography of freight distribution in the U.S.Journal of Transport Geography, 18(3): 363-371. doi: 10.1016/j.jtrangeo.2009.06.017
[7]	Cui Y Y, Song B L, 2017. Logistics agglomeration and its impacts in China.Transportation Research Procedia, 25: 3875-3885. doi: 10.1016/j.trpro.2017.05.302
[8]	Deng M, 2014. The relation between transport infrastructure and employment density in Chinese cities: Endogenous relation and spatial spillovers.Economic Management, (1): 163-174. (in Chinese)
[9]	Ding J X, Jin F J, Wang C J et al.Wang C J , 2011. Evaluation, optimization and simulation of the spatial layout of transport hubs in China.Acta Geographica Sinica, 66(4): 504-514. (in Chinese)
[10]	Ducret R, Lemarié B, Roset A, 2016. Cluster analysis and spatial modeling for urban freight. Identifying homogeneous urban zones based on urban form and logistics characteristics.Transportation Research Procedia, 12: 301-313. doi: 10.1016/j.trpro.2016.02.067
[11]	Hai F, Jin X P, Jia X H, 2016. An analysis on connotation and features of logistics cluster.China Soft Science, (8): 137-148. (in Chinese)
[12]	Han Z L, Shang Y Y, Guo J Ket al., 2017. Comprehensive assessment of inland spatial accessibility of the northeast seaports.Advances in Earth Science, 32(5): 502-512. (in Chinese)
[13]	He C F, 2017. Geographical Research of Transitional Economy in China. Beijing: Economic Science Press. (in Chinese)
[14]	Hu Y, Li H C, 2015. The estimate of transport hub level and its spatial spillover effects: A spatial panel data study on China’s prefecture-level city.China Industrial Economics, (5): 32-45. (in Chinese)
[15]	Hylton P J, Ross C L, 2017. Agglomeration economies’ influence on logistics clusters’ growth and competitiveness.Regional Studies, (4): 1-12. doi: 10.1080/00343404.2017.1327708
[16]	Jin F J, Wang C J, Li X W, 2008. Discrimination method and its application analysis of regional transport superiority.Acta Geographica Sinica, 63(8): 787-798. (in Chinese)
[17]	Kumar I, Zhalnin A, Kim A et al.Kim A , 2017. Transportation and logistics cluster competitive advantages in the U.S. regions: A cross-sectional and spatio-temporal analysis.Research in Transportation Economics, 61: 25-36. doi: 10.1016/j.retrec.2016.07.028
[18]	Li G Q, Jin F J, Chen Y et al.Chen Y , 2017. Location characteristics and differentiation mechanism of logistics nodes and logistics enterprises based on points of interest (POI): A case study of Beijing.Journal of Geographical Sciences, 27(7): 879-896. doi: 10.1007/s11442-017-1411-7
[19]	Li J F, Ding S Y, 2015. The evaluation and optimization of the logistics land use and construction standards: The case of Guangzhou.Urban Planning Forum, (6): 38-45. (in Chinese)
[20]	Li X J, 2017. New Aproaches to Eonomic Gography:A Chinese Perspective. Beijing: Science Press. (in Chinese)
[21]	Li Z, 2013. Cooperation patterns and mechanism among logistics clusters based on asset specificity [D]. Wuhan: Wuhan University. (in Chinese)
[22]	Pan Y J, 2016. Logistics Spatial Patterns and their Formation Mechanism of Wholesale Markets in Megalopolises. Beijing: The Commercial Press. (in Chinese)
[23]	Prause G, 2014. Sustainable development of logistics clusters in green transport corridors.Journal of Security and Sustainability Issues, 4(1): 59-68. doi: 10.9770/jssi.2014.4.1(5)
[24]	Qin X, Zhen F, 2017. Combination between big data and small data: New methods of urban studies in the information era.Scientia Geographica Sinica, 37(3): 321-330. (in Chinese)
[25]	Qiu L, Fang C L, 2013. Comprehensive assessment on the spatial agglomeration of producer services in Beijing.Geographical Research, 32(1): 99-110. (in Chinese) doi: 10.11821/yj2013010011
[26]	Rivera L, Sheffi Y, Knoppen D, 2016. Logistics clusters: The impact of further agglomeration, training and firm size on collaboration and value added services.International Journal of Production Economics, 179(9): 285-294. doi: 10.1016/j.ijpe.2016.05.018
[27]	Rivera L, Sheffi Y, Welsch R, 2014. Logistics agglomeration in the US.Transportation Research Part A: Policy and Practice, 59(11): 222-238. doi: 10.1016/j.tra.2013.11.009
[28]	Rodrigue J P, Dablanc L, Giuliano G, 2017. The freight landscape: Convergence and divergence in urban freight distribution.Journal of Transport and Land Use, 10(1): 557-572.
[29]	Rolkoa K, Friedrich H, 2017. Locations of logistics service providers in Germany: The basis for a new freight transport generation model.Transportation Research Procedia, 25: 1061-1074. doi: 10.1016/j.trpro.2017.05.479
[30]	Sheffi Y, 2012. Logistics Clusters:Delivering Value and Driving Growth. Cambridge: MIT Press.
[31]	Tang J R, Zhang X H, 2017. Spatio-temporal evolution, driving forces and spillover effects of logistics industry development: On spatial panel data analysis of Chinese provincial panel data.Finance and Trade Research, (5): 11-21. (in Chinese)
	Tang J R, Zhang X H, 2017. Spatio-temporal evolution, driving forces and spillover effects of logistics industry development: On spatial panel data analysis of Chinese provincial panel data.Finance and Trade Research, (5): 11-21. (in Chinese)
[32]	Wang C J, 2014. Space Network Mode and Organization Mechanism of Logistics Enterprises. Beijing: Science Press. (in Chinese)
[33]	Wang S J, 2010. Review of researches on logistics cluster.Journal of Wuhan University of Technology (Information & Management Engineering), 32(2): 337-340. (in Chinese)
[34]	Wen H X, 2005. Strategy of logistics industry in China based on cluster theory [D]. Wuhan: Wuhan University. (in Chinese)
[35]	Wu W, Cao Y H, Cao W Det al., 2011. The pattern of transportation superiority in Yangtze River Delta.Geographical Research, 30(12): 2199-2208. (in Chinese) doi: 10.1007/s11769-011-0450-8
[36]	Xu Z, Gao X L, 2016. A novel method for identifying the boundary of urban built-up areas with POI data.Acta Geographica Sinica, 71(6): 928-939. (in Chinese) doi: 10.11821/dlxb201606003
[37]	Zhao D Z, Zhang C Q, Sun D K, 2012. The analysis of agglomeration for logistics industry cluster.Journal of Beijing Institute of Technology (Social Sciences Edition), 14(6): 71-76. (in Chinese)
[38]	Zong H M, Cai B J, Ye J H, 2017. The construction and application of a coupling index system between the development of logistics and new urbanization.Journal of Southwest University (Natural Science), 39(6): 100-106. (in Chinese)

Category	Subcategory	Standards	Results
Mature logistics cluster	I	First level cities with one of the indexes for each of the two types satisfied	Shanghai, Beijing, Guangzhou, Shenzhen, Chengdu, Xi’an
	II	First level cities with type two indexes satisfied	Tianjin, Chongqing, Wuhan, Nanjing, Zhengzhou, Suzhou, Ningbo, Hangzhou, Qingdao
	III	Second level cities with LEP or one of the indexes for each of the two types satisfied and up cross-strata	Jinan, Kunming, Harbin, Dalian, Xiamen, Dongguan, Shenyang
Emerging logistics cluster	I	Other level first and second level cities	Changsha, Urumqi, Taiyuan, Foshan, Xuzhou, Qiqihar, Zhoushan, Haikou, Zhuhai, Lianyungang, Jinhua, Wuxi, Weifang, Baoding, Xiangtan
	II	Third level cities with one of the indexes for each of the two types or type two indexes satisfied	Yingkou, Shijiazhuang, Guiyang, Zhongshan, Hefei, Changchun, Fuzhou, Nanning
	III	Other third level cities, and fourth and fifth level cities with all indexes of both types satisfied	Fangchenggang, Qinhuangdao, Xiaogan, Jiaozuo, Shantou, Yichun, Ganzhou, Shangrao, Nanchang, Yantai, Changzhou, Quanzhou, Taizhou, Nantong, Yancheng, Tangshan, Hohhot, Fuyang, Bengbu, Liaocheng