中国物流业与数字经济融合水平时空分异及影响因素研究

基于熵权-TOPSIS和耦合协调度模型对2012—2021年中国30个省份(因数据缺失，未包括西藏和港澳台地区)物流业与数字经济融合水平进行测度，利用Dagum基尼系数及莫兰指数对物流业与数字经济融合水平的时空分异情况进行分析，并通过Tobit模型探究其影响因素。研究发现，我国物流业与数字经济融合水平存在明显区域差异，融合水平最高的是广东和北京，融合水平最低的是宁夏和青海；融合水平总体基尼系数在波动中上升，东部、东北和西部地区是融合水平时空分异的重要来源；我国物流业与数字经济融合水平在空间上存在明显的正向空间集聚效应和空间依赖性；融合水平的正向影响因素有产业结构、经济发展和基础设施等。据此，提出相关对策建议以促进我国物流业与数字经济深度融合。

Research on the Spatiotemporal Differentiation and Influencing Factors of the Integration Level of China's Logistics Industry and Digital Economy

Based on entropy-weighted TOPSIS and coupling coordination degree model, the level of integration between the logistics industry and digital economy in 30 provinces of China from 2012 to 2021 was measured. The spatial-temporal differentiation of the integration level between the logistics industry and digital economy was analyzed using Dagum Gini coefficient and Moran''s index, and the influencing factors were explored through the Tobit model. The study find that there are significant regional differences in the level of integration between the logistics industry and digital economy in China. Guangdong and Beijing have the highest level of integration, while Ningxia and Qinghai have the lowest level. The overall Gini coefficient of the integration level shows an upward trend with fluctuations, and the eastern, northeastern, and western regions are important sources of spatial-temporal differentiation in the integration level. There is a clear positive spatial clustering effect and spatial dependence in the level of integration between the logistics industry and digital economy in China. Positive influencing factors of the integration level include industrial structure, economic development, and infrastructure, among others. Based on these findings, relevant policy recommendations are proposed to promote deep integration between the logistics industry and digital economy in China.