摘要
随着我国新型城镇化进程的不断加快,物流业作为支撑经济发展的重要产业,在其中发挥着越来越重要的作用。文章以长三角重要中心城市常州为例,研究城市物流业与新型城镇化的互动关系,探讨两者关系对于城市发展的促进作用。文章通过收集2019—2023年常州市相关统计数据,构建包含15项评价指标的常州市物流发展和新型城镇化水平的评价指标体系,并采用多元线性回归模型探究二者的互动关系。结果表明:常州市新型物流和城镇化之间相互促进作用明显且由多因素共同驱动,其中邮政业务收入对常州新型城镇化水平促进作用较为显著,并就实证结果提出常州市物流业与新型城镇化协调发展的建议,进而为相关部门政策制定和实际操作提供理论依据和实证参考。
As China's new urbanization process continues to accelerate,the logistics industry,as an important industry supporting economic development,is playing an increasingly important role.This article takes Changzhou,an important central city in the Yangtze River Delta,as an example to study the interactive relationship between urban logistics industry and new urbanization,and explore the role of this relationship in promoting urban development.By collecting relevant statistical data of Changzhou from 2019 to 2023,the article constructs an evaluation index system for logistics development and new urbanization level in Changzhou,which includes 15 evaluation indicators,and uses a multiple linear regression model to explore the interactive relationship between the two.The results show that there is a significant mutual promotion effect between new logistics and urbanization in Changzhou,which is driven by multiple factors.Among them,postal business income has a significant promoting effect on the level of new urbanization in Changzhou.Based on the empirical results,suggestions for the coordinated development of logistics industry and new urbanization in Changzhou are proposed,providing theoretical basis and empirical reference for relevant departments to formulate policies and operate in practice.
作者
张鑫
孟晓怡
陈子杉
刘一帆
ZHANG Xin;MENG Xiaoyi;CHEN Zishan;LIU Yifan(Changzhou Vocational Institute of Textile and Garment,Changzhou 213164,China;Business School,Changzhou University,Changzhou 213159,China)
出处
《物流科技》
2026年第7期21-25,共5页
Logistics Sci Tech
基金
江苏省教育科学规划课题项目“数智化背景下新商科人才‘双创’能力培养模式与提升路径研究”(C/2023/02/19)
常州纺织服装职业技术学院2026年教师企业实践锻炼项目。
关键词
常州市
物流业
新型城镇化
互动关系
线性回归
Changzhou City
logistics industry
new urbanization
interactive relationship
linear regression
