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中国机器人产业的空间格局演变及影响因素研究

林婕 朱奥欣 路继业 张勋

统计与决策2025,Vol.41Issue(13):76-82,7.
统计与决策2025,Vol.41Issue(13):76-82,7.DOI:10.13546/j.cnki.tjyjc.2025.13.012

中国机器人产业的空间格局演变及影响因素研究

Research on the Spatial Pattern Evolution and Influencing Factors of China's Robot Industry

林婕 1朱奥欣 1路继业 1张勋2

作者信息

  • 1. 汕头大学 商学院,广东 汕头 515821
  • 2. 北京师范大学 统计学院,北京 100875
  • 折叠

摘要

Abstract

Driven by technological iteration breakthroughs and market demand,China's robot manufacturing industry has experienced explosive growth.Based on the data from Chinese robot enterprises,this paper uses the kernel density estimation method to examine the spatial pattern and evolution characteristics of China's robot industry,and then utilizes a geographically and temporally weighted regression(GTWR)model to explore the influencing factors.The results go as below:From 2001 to 2024,the number of robot enterprises in China has shown a trend of transitioning from slow growth to rapid growth.The spatial distribu-tion has obvious agglomeration characteristics.The early agglomeration centers are mainly located in the eastern regions such as the Beijing-Tianjin-Hebei area,the Pearl River Delta and the Yangtze River Delta,and then gradually expand to the core cities in the central and western regions.The GTWR analysis reveals that industrial support policies have the greatest impact on the loca-tion selection decisions of robot enterprises.The regional level of scientific and technological innovation,informatization and fi-nance has significant influences on the agglomeration of robot enterprises,while the wage level and globalization level have inhibi-tory effects on the location selection of enterprises.

关键词

机器人产业/空间格局/影响因素/核密度估计/时空地理加权回归

Key words

robot industry/spatial pattern/influencing factors/kernel density estimation/geographically and temporally weighted regression

分类

管理科学

引用本文复制引用

林婕,朱奥欣,路继业,张勋..中国机器人产业的空间格局演变及影响因素研究[J].统计与决策,2025,41(13):76-82,7.

基金项目

国家社会科学基金重点项目(23AZD028) (23AZD028)

广东省基础与应用基础研究基金自然科学基金面上项目(2022A1515010774) (2022A1515010774)

广东省基础与应用基础研究区域联合基金青年基金项目(2024A1515110058) (2024A1515110058)

广东省普通高校特色创新项目(2020WTSCX020) (2020WTSCX020)

统计与决策

OA北大核心

1002-6487

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