物流技术

本研究基于2016～2024年粤港澳大湾区11个城市的面板数据，从物流经济、物流信息化、物流智能技术和物流效益4个层面构建指标体系，并采用熵权法动态确定指标权重，量化分析大湾区智能物流数字化发展不均衡的深层机制。结果显示：（1）2024年大湾区综合指数较2016年增长了35.36%，达到40.50，呈现“规模导向型”特征；其中物流经济指数贡献最大，达到12.72，而物流效益指数最低，仅有7.95。（2）区域差异显著，如广州得分是肇庆的7.5倍，主要受区位优势、人才资源、产业结构等的差异影响。（3）城市智能物流发展水平高低主要受到从业人员、信息基础设施、科研人员及货运周转率等因素影响。因此，建议从大湾区、城市、企业三层面出发，构建梯度式产业协同体系、强化基建投资与政策引导、深化技术融合与产学研合作三个方面予以改进。

Based on the panel data of 11 cities in the Guangdong-Hong Kong-Macao Greater Bay Area(GBA) from 2016 to 2024, this study constructs an indicator system from four dimensions: logistics economy,logistics informatization, intelligent logistics technology, and logistics efficiency. The entropy weight method is employed to dynamically determine the weights of these indicators, aiming to quantitatively analyze the underlying mechanisms of unbalanced digital development in intelligent logistics across the GBA. The results show that:(1) In 2024, the GBA's comprehensive index rose by 35.36% compared to 2016, reaching 40.50, and demonstrated a "scale-oriented" feature. Among the four dimensions, the logistics economy index contributed the most, reaching 12.72, while the logistics efficiency index was the lowest, at only 7.95.(2) There are significant regional differences, with Guangzhou scoring 7.5 times higher than Zhaoqing, mainly due to the differences in location advantages, human resources, and industrial structure.(3) The development level of urban intelligent logistics is mainly influenced by factors such as the number of practitioners, information infrastructure, research personnel, and freight turnover rate. Accordingly, policy recommendations are proposed at three levels:the Greater Bay Area, individual cities, and enterprises, including establishing a gradient industrial collaboration system, enhancing infrastructure investment and policy guidance, and deepening technology integration and industry-university-research collaboration.