华东师范大学学报(自然科学版) ›› 2022, Vol. 2022 ›› Issue (1): 109-121.doi: 10.3969/j.issn.1000-5641.2022.01.013

• 地理学 • 上一篇    

中国汽车零部件供应网络时空格局演化研究

范大龙(), 曹卫东*(), 王雪微   

  1. 安徽师范大学 地理与旅游学院, 安徽 芜湖 241002
  • 收稿日期:2020-08-06 出版日期:2022-01-25 发布日期:2022-01-18
  • 通讯作者: 曹卫东 E-mail:fandl001@ahnu.edu.cn;weidongwh@163.com
  • 作者简介:范大龙, 男, 硕士, 工程师, 研究方向为区域发展与城乡规划. E-mail: fandl001@ahnu.edu.cn
  • 基金资助:
    国家自然科学基金 (41901151, 41571124)

Research on the evolution of the spatiotemporal pattern of China’s automobile parts supply network

Dalong FAN(), Weidong CAO*(), Xuewei WANG   

  1. School of Geography and Tourism, Anhui Normal University, Wuhu Anhui 241002, China
  • Received:2020-08-06 Online:2022-01-25 Published:2022-01-18
  • Contact: Weidong CAO E-mail:fandl001@ahnu.edu.cn;weidongwh@163.com

摘要:

供应网络是产业要素空间流动的重要载体, 其空间组织与演化可较好地反映相关产业的空间集群特征. 应用统计分析、空间网络分析等方法, 从全国和集聚区层面对我国2009年、2014年和2019年汽车零部件进行供应网络刻画与分析. 结果表明: ①汽车零部件企业主要分布在我国东部地区, 其次分布在中西部地区, 且形成以长三角地区、京津冀地区、东北地区、珠三角地区、华中地区和成渝地区为核心的6个集聚区. ②汽车零部件供应网络密度不断增加, 2019年网络密度达到0.5017, 表现出强联结状态. 2009年长春市外向性最高, 2014年和2019年武汉市外向性最高. 上海市始终保持最高内向性, 且不断增强, 内向性前十位的城市中50%以上位于长三角地区, 其余城市位于京津冀、东北、成渝和华中等地区. 此外, 汽车零部件供应网络存在明显层级性特征, 一级链接2009年仅有上海—长春, 2014年为上海—长春、十堰—武汉, 2019年为上海—长春、上海—武汉. ③分别以整车厂和汽车零部件厂为节点分析6个集聚区供应网络结构时空特征, 其中东北地区为整—零同心内向型供应网络结构, 长三角地区和成渝地区为整—零同心外向型供应网络结构, 京津冀地区和珠三角地区为整—零异心外向型供应网络结构, 华中地区为整—零异心内向型供应网络结构.

关键词: 汽车零部件, 供应网络, 时空格局, 演化, 集聚区

Abstract:

A supply network is an important carrier for the spatial flow of industrial elements, and its structure and evolution can reflect the spatial clustering characteristics of related industries. In this study, we used thermal analysis, network analysis, and other methods to understand and analyze China’s automobile parts supply network at the national and regional levels in 2009, 2014, and 2019; in addition, we explored the evolution of spatiotemporal pattern characteristics of the network’s structure. The study found that: ① Automobile parts companies are primarily distributed across the eastern part of China, followed by the central and western regions. The companies form six clusters centered on the Yangtze River Delta region, Beijing-Tianjin-Hebei region, Northeast region, Pearl River Delta region, Central China region, and Chengdu-Chongqing region. ② The density of the network continues to increase. In 2019, the network density reached 0.5017, showing strong connectivity. Changchun had the highest extroversion in 2009, and Wuhan had the highest in 2014 and 2019. Shanghai has always maintained the highest introversion and continues to increase. More than 50% of the top ten cities are located in the Yangtze River Delta, and the remaining cities are located in the Beijing-Tianjin-Hebei, Northeast, Chengdu-Chongqing, and Central China regions. In addition, the automobile parts supply network has obvious hierarchical characteristics. The first-level links included Shanghai-Changchun in 2009, Shanghai-Changchun and Shiyan-Wuhan in 2014, and Shanghai-Changchun and Shanghai-Wuhan in 2019. ③ If we analyze the spatiotemporal characteristics of the supply network of China’s six major clusters with OEMs and automobile parts factories as nodes, we find that the Northeast region forms a vehicle-parts concentric inward supply network structure, the Yangtze River Delta and Chengdu-Chongqing regions form vehicle-parts concentric outbound supply network structures, the Beijing-Tianjin-Hebei and Pearl River Delta regions form vehicle-parts eccentric outbound supply network structures, and Central China forms a vehicle-parts eccentric inward supply network structure.

Key words: automobile parts, supply network, spatiotemporal pattern, evolution, agglomeration area

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