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Spatial Pattern Evolution and Influencing Factors of Cold Storage in China

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Abstract

Cold storage is the vital infrastructure of cold chain logistics. In this study, we analyzed the spatial pattern evolution characteristics, spatial autocorrelation and influencing factors of cold storage in China by using kernel density estimation (KDE), spatial autocorrelation analysis (SAA), and spatial error model (SEM). Results showed that: 1) the spatial distribution of cold storage in China is unbalanced, and has evolved from ‘one core’ to ‘one core and many spots’, that is, ‘one core’ refers to the Bohai Rim region mainly including Beijing, Tianjin, Hebei, Shandong and Liaoning regions, and ‘many spots’ mainly include the high-density areas such as Shanghai, Fuzhou, Guangzhou, Zhengzhou, Hefei, Wuhan, Ürümqi. 2) The distribution of cold storage has significant global spatial autocorrelation and local spatial autocorrelation, and the ‘High-High’ cluster area is the most stable, mainly concentrated in the Bohai Rim; the ‘Low-Low’ cluster area is grouped in the southern China. 3) Economic developmenficit.

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References

  • Allen J, Browne M, Cherrett T et al., 2012. Investigating relationships between road freight transport, facility location, logistics management and urban form. Journal of Transport Geography, 24: 45–57. doi: https://doi.org/10.1016/j.jtrangeo.2012.06.010

    Article  Google Scholar 

  • Anselin L, 1993. The Moran Scatterplot as an ESDA Tool to Assess Local Instability. Morgantown, WV: Regional Research Institute, West Virginia University, 111–125.

    Google Scholar 

  • Anselin L, 1995. Local indicators of spatial association-LISA. Geographical Analysis, 27(2): 93–115. doi: https://doi.org/10.1111/J.1538-4632.1995.tb00338.x

    Article  Google Scholar 

  • Anselin L, Florax R, Rey S J, 2004. Advanced in Spatial Econometrics Methodology, Tools and Applications. Berlin: Springer Science & Business Media.

    Book  Google Scholar 

  • Banks M R, 1954. Capacity of Refrigerated Warehouses in the United States. Berkeley, CA: University of California Libraries.

    Google Scholar 

  • Boschma R, Frenken K, 2011. The emerging empirics of evolutionary economic geography. Journal of Economic Geography, 11(2): 295–307. doi: https://doi.org/10.1093/jeg/lbq053

    Article  Google Scholar 

  • Bowen J T, 2008. Moving places: the geography of warehousing in the US. Journal of Transport Geography, 16(6): 379–387. doi: https://doi.org/10.1016/j.jtrangeo.2008.03.001

    Article  Google Scholar 

  • Cidell J, 2010. Concentration and decentralization: the new geography of freight distribution in U.S. metropolitan areas. Journal of Transport Geography, 18(3): 363–371. doi: https://doi.org/10.1016/j.jtrangeo.2009.06.017

    Article  Google Scholar 

  • Cui Zhongfu, 2020. Review of China’s cold chain logistics in 2019 and Trend Outlook in 2020. China Logistics & Purchasing, (1): 23–24. (in Chinese)

  • Dablanc L, Ogilvie S, Goodchild A et al., 2014. Logistics sprawl: differential warehousing development patterns in Los Angeles, California, and Seattle, Washington. TRB, Transportation Research Record (TRR). Available at: https://doi.org/10.3141/2410-12

  • Devereux M P, Griffith R, Simpson H et al., 2004. The geographical distribution of production activity in the UK. Regional Science and Urban Economics, 34(5): 533–564. doi: https://doi.org/10.1016/S0166-0462(03)00073-5 p]Gatrell A C, 1979. Autocorrelation in spaces. Environment and Planning A: Economy and Space, 11(5): 507–516. doi: https://doi.org/10.1068/a110507

    Article  Google Scholar 

  • Grazia S, 2018. Trends in transportation and logistics. European Journal of Operational Research, 264(3): 830–836. doi: https://doi.org/10.1016/j.ejor.2016.08.032

    Article  Google Scholar 

  • Heitz A, Beziat A, 2016. The parcel industry in the spatial organization of logistics activities in the Paris Region: inherited spatial patterns and innovations in urban logistics systems. Transportation Research Procedia, 12: 812–824. doi: https://doi.org/10.1016/j.trpro.2016.02.034

    Article  Google Scholar 

  • Heitz A, Dablanc L, Olsson J et al., 2018. Spatial patterns of logistics facilities in Gothenburg, Sweden. Journal of Transport Geography. Available at: https://www.sciencedirect.com/science/article/abs/pii/S0966692317305380

  • Heitz A, Launay P, Beziat A et al., 2019. Heterogeneity of logistics facilities: an issue for a better understanding and planning of the location of logistics facilities. European Transport Research Review, 11(5): 1–20. doi: https://doi.org/10.1186/s12544-018-0341-5

    Google Scholar 

  • Heuvel F P, Frank P, Langen P W et al., 2013. Spatial concentration and location dynamics in logistics: the case of a Dutch province. Journal of Transport Geography, 28: 39–48. doi: https://doi.org/10.1016/j.jtrangeo.2012.10.001

    Article  Google Scholar 

  • Holl A, Mariotti I, 2018. The geography of logistics firm location: the role of accessibility. Networks and Spatial Economics, 18: 337–361. doi: https://doi.org/10.1007/s11067-017-9347-0

    Article  Google Scholar 

  • Jiang Tianying, Shi Yanan, 2015. The spatial pattern evolution and influencing factors of logistics enterprises in Ningbo. Economic Geography, 35(10): 130–138. (in Chinese)

    Google Scholar 

  • Jiang X, Zhang L, Xiong C et al., 2016. Transportation and regional economic development: analysis of spatial spillovers in China provincial regions. Networks Spatial Economics, 16: 769–790. doi: https://doi.org/10.1007/s11067-015-9298-2

    Article  Google Scholar 

  • LeSage J, Pace R K, 2009. Introduction to Spatial Econometrics. New York: CRC Press, Taylor & Francis Group.

    Book  Google Scholar 

  • Li G Q, Jin F J, Chen Y et al., 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: https://doi.org/10.1007/s11442-017-1411-7

    Article  Google Scholar 

  • Li Xuemei, Zhang Xiaolei, Du Hongru et al., 2012. Spatial effect of mineral resources exploitation on urbanization: a case study of Tarim River Basin, Xinjiang, China. Chinese Geographical Science, 22(5): 590–601. doi: https://doi.org/10.1007/s11769-012-0554-9

    Article  Google Scholar 

  • Liu Sijing, Li Guoqi, Jin Fengjun et al., 2017. Location choice behaviors and hierarchical differences of star warehouses in China. Progress in Geography, 36(7): 843–852. (in Chinese)

    Article  Google Scholar 

  • Liu Sijing, Li Guoqi, Jin Fengjun et al., 2018. Quantitative measurement and development evaluation of logistics clusters in China. Acta Geographica Sinica, 73(8): 1540–1555. (in Chinese)

    Google Scholar 

  • Mckinnon A, 2009. The present and future land requirements of logistical activities. Land Use Policy, 26: S293–S301. doi: https://doi.org/10.1016/j.landusepol.2009.08.014

    Article  Google Scholar 

  • Ministry of Natural Resources of China, 2009–2019. China Land and Resources Statistical Yearbook (2009–2018). Beijing: Geological Publishing House.

    Google Scholar 

  • National Statistics Bureau of China, 2009–2019. China City Statistical Yearbook (2009–2018). Beijing: China Statistical Press.

    Google Scholar 

  • National Bureau of Statistics of China, 2009–2015. China Statistical Yearbook for Regional Economy (2009–2014). Beijing: China Statistical Press.

    Google Scholar 

  • Qian Qinglan, Chen Yingbiao, Li Yan et al., 2011. Spatial distribution of logistics enterprises in Guangzhou and its influencing factors. Geographical Research, 30: 1254–1261. (in Chinese)

    Google Scholar 

  • Sakai T, Kawamura K, Hyodo T et al., 2019. Evaluation of the spatial pattern of logistics facilities using urban logistics land-use and traffic simulator. Journal of Transport Geography, 74: 145–160. doi: https://doi.org/10.1016/j.jtrangeo.2018.10.011

    Article  Google Scholar 

  • Silverman B W, 1986. Density Estimation for Statistics and Data Analysis. New York: Chapman and Hall.

    Book  Google Scholar 

  • Verhetsel A, Kessels R, Goos P et al., 2015. Location of logistics companies: a stated preference study to disentangle the impact of accessibility. Journal of Transport Geography, 42: 110–121. doi: https://doi.org/10.1016/j.jtrangeo.2014.12.002

    Article  Google Scholar 

  • Xue Bing, Xiao Xiao, Li Jingzhong et al., 2019. POI-based spatial correlation of the residences and retail industry in shenyang city. Scientia Geographica Sinica, 39(3): 442–449. (in Chinese)

    Google Scholar 

  • Ye Huaizhen, Li Guoqing, 2019. Modern Logistics. Beijing, China: Higher Education Press. (in Chinese)

    Google Scholar 

  • Yuan Q, Zhu J, 2019. Logistics sprawl in Chinese metropolises: evidence from Wuhan. Journal of Transport Geography, 74: 242–252. doi: https://doi.org/10.1016/j.jtrangeo.2018.11.019

    Article  Google Scholar 

  • Yuan Q, 2018. Environmental justice in warehousing location: State of the art. Journal of Planning Literature, 33(3): 287–298. doi: https://doi.org/10.1177/0885412217753841

    Article  Google Scholar 

  • Zhang Dapeng, Cao WeiDong, Yao Zhaozhao et al., 2018. Study on the distribution characteristics and evolution of logistics enterprises in Shanghai metropolitan area. Resources and Environment in the Yangtze Basin, 27(7): 1478–1489. (in Chinese)

    Google Scholar 

  • Zhang Xicai, Zhang Liyang, 2018. Evaluation of regional cold chain logistics development of agricultural products in China. Agricultural Sciences, 8(4): 225–236. (in Chinese)

    Google Scholar 

  • Zhang Xicai, 2019. Economic characteristics, difficulties and countermeasures of cold chain logistics of agricultural products in China. Modern Economic Research, (12): 100–105. (in Chinese)

  • Zhao H X, Liu S, Tian C Q et al., 2018. An overview of current status of cold chain in China. International Journal of Refrigeration, 88: 483–495. doi: https://doi.org/10.1016/j.ijrefrig.2018.02.024

    Article  Google Scholar 

  • Zhu Hui, Zhou Gengui, 2017. Spatial agglomeration evolution and influencing factors of logistics enterprises in international inland port: a case study of Yiwu city. Economic Geography, 37(2): 98–105. (in Chinese)

    Google Scholar 

Download references

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Correspondence to Jinfeng Li.

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Foundation item: Under the auspices of the National Social Science Fund of China (No.15BGL185, 19XJL004), General Project of Humanities and Social Sciences Research and Planning Fund of Ministry of Education (No. 19YJA790097), Social Science Fund of Fujian Province (No. FJ2017C080), A Key Discipline of Henan University of Animal Husbandry and Economy ‘Business Enterprise Management’ (No. MXK2016201)

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Li, J., Xu, H., Liu, W. et al. Spatial Pattern Evolution and Influencing Factors of Cold Storage in China. Chin. Geogr. Sci. 30, 505–515 (2020). https://doi.org/10.1007/s11769-020-1124-1

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  • DOI: https://doi.org/10.1007/s11769-020-1124-1

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