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    Location-Based Method to Identify Industrial Clusters in Beijing-Tianjin-Hebei Area in China

    Source: Journal of Urban Planning and Development:;2019:;Volume ( 145 ):;issue: 002
    Author:
    Jie Guo; Xin Lao; Tiyan Shen
    DOI: 10.1061/(ASCE)UP.1943-5444.0000497
    Publisher: American Society of Civil Engineers
    Abstract: The extant quantitative methodologies to identify industrial clusters usually lack the combination of both industrial linkages and enterprise proximity information. To rectify this situation, this paper employs the location-based method, which considers both industrial linkages in the input–output (IO) table and enterprises’ microlocations, to identify industrial clusters in the Beijing–Tianjin–Hebei area in China. Using data on industrial enterprises larger than a designated size in 2012, we choose 5 km as the distance threshold after robustness tests and identify 19 industrial clusters. The result indicates that Beijing is superior in both cluster number and cluster type for the entire region, and enterprises in Hebei and Beijing clusters have a larger economic scale than those in Tianjin. The corresponding policy implication is that local governments of the Beijing–Tianjin–Hebei area should pay more attention to industrial cooperation and coordination across the administrative boundaries, such as shifting manufacturing enterprises from Beijing to Hebei, to promote industrial clustering. The proposed method in this study has distinct advantages over other methods in terms of reflecting the essence of industrial clusters, providing scientific evidence for policy making, and opening up a new avenue for quantitative and referenced empirical studies on industrial clusters from the perspective of individual enterprises.
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      Location-Based Method to Identify Industrial Clusters in Beijing-Tianjin-Hebei Area in China

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4254338
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    contributor authorJie Guo; Xin Lao; Tiyan Shen
    date accessioned2019-03-10T11:49:50Z
    date available2019-03-10T11:49:50Z
    date issued2019
    identifier other%28ASCE%29UP.1943-5444.0000497.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4254338
    description abstractThe extant quantitative methodologies to identify industrial clusters usually lack the combination of both industrial linkages and enterprise proximity information. To rectify this situation, this paper employs the location-based method, which considers both industrial linkages in the input–output (IO) table and enterprises’ microlocations, to identify industrial clusters in the Beijing–Tianjin–Hebei area in China. Using data on industrial enterprises larger than a designated size in 2012, we choose 5 km as the distance threshold after robustness tests and identify 19 industrial clusters. The result indicates that Beijing is superior in both cluster number and cluster type for the entire region, and enterprises in Hebei and Beijing clusters have a larger economic scale than those in Tianjin. The corresponding policy implication is that local governments of the Beijing–Tianjin–Hebei area should pay more attention to industrial cooperation and coordination across the administrative boundaries, such as shifting manufacturing enterprises from Beijing to Hebei, to promote industrial clustering. The proposed method in this study has distinct advantages over other methods in terms of reflecting the essence of industrial clusters, providing scientific evidence for policy making, and opening up a new avenue for quantitative and referenced empirical studies on industrial clusters from the perspective of individual enterprises.
    publisherAmerican Society of Civil Engineers
    titleLocation-Based Method to Identify Industrial Clusters in Beijing-Tianjin-Hebei Area in China
    typeJournal Paper
    journal volume145
    journal issue2
    journal titleJournal of Urban Planning and Development
    identifier doi10.1061/(ASCE)UP.1943-5444.0000497
    page04019001
    treeJournal of Urban Planning and Development:;2019:;Volume ( 145 ):;issue: 002
    contenttypeFulltext
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