Socio-Economic Statistics Research, 2026, 7(1); doi: 10.38007/SESR.2026.070108.
Yuxian Ou, ShanShan Zhu
School of Logistics Management and Engineering, Zhuhai College of Science and Technology, Zhuhai, 519000, Guangdong, China
Low-altitude logistics with emerging technologies like drones and eVTOL aircraft is a key enabler of the new quality productive forces in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA).However,issues with institutional variation in the GBA, complicated possibilities management and the variations in industrial actors make it the case horrible for this sector. This article uses the Interpretive Structural Modelling (ISM) framework for systematically analyzing the collaborative operational mechanisms. A list of 15 key influencing factors to the development of technologies were identified based on the expert scoring and Literature survey covering policy support to technological innovation. This research uses a hierarchical modeling approach to formulate an adjacency matrix and then a reachability matrix to plot the hierarchy of these factors. There is a 4-level structure: Root Environmental Drivers, Deep-level Technical Support, Organizational Transmission and Surface-level Coordination Outcomes. In addition, there are three main pathways for transmitting capabilities: Institutional Environment-Driven Pathway, Technological Capability Transmission Pathway and Market Demand and Social Feedback Pathway. This study offers some theoretical inspiration for decision makers and industry players in order to maximize synergy and sustainable development of the low altitude logistics industry chain in an integrated regional framework.
Low-altitude Logistics; Guangdong-Hong Kong-Macao Greater Bay Area; Industrial Chain Collaboration; Interpretive Structural Modelling (ISM); Drone Logistics
Yuxian Ou, ShanShan Zhu. The Mechanisms of Synergistic Impact within the Low-Altitude Logistics Industry Chain in the Guangdong-Hong Kong-Macao Greater Bay Area. Socio-Economic Statistics Research (2026), Vol. 7, Issue 1: 67-76. https://doi.org/10.38007/SESR.2026.070108.
[1] Ren Xinhui, Wang Liu, Wang Jiaxue. A Study on Fully Automated Drone Airport Site Selection Based on Partitioning Optimisation [J]. Operations Research and Management, 2023, 32(06): 20–26.
[2] Zhang Qiqian, Xu Weiwei, Zhang Honghai, Zou Yiyuan, Chen Yutong. Path Planning for Complex Low-Altitude Logistics UAVs [J]. Journal of Beihang University, 2020, 46(7): 1275–1286.
[3] He, X., He, F., Li, L., Zhang, L., & Xiao, G. A Route Network Planning Method for Urban Air Delivery[EB/OL]. arXiv preprint arXiv:2206.03085, 2022.
[4] Chen, Y., Chen, M., Chen, Z., Cheng, L., Yang, Y., & Li, H. Delivery Path Planning of Heterogeneous Robot System under Road Network Constraints [J]. Computers and Electrical Engineering, 2021, 92:107197.
[5] Xie Hua, Han Si, Yin Jianan, et al. A Method for Collaborative Simulation and Optimised Allocation of Urban Low-Altitude Drone Flight Plans [J]. Chinese Journal of Aeronautics, 2024, 45(19): 263–285.
[6] Zhou, et al. Multi-Agent Deep Reinforcement Learning for Low-Altitude Drone Logistics Distribution [J]. Journal of Intelligent & Robotic Systems, 2024.
[7] Luo C, Zhou X, Lev B. Core, Shapley Value, Nucleolus and Nash Bargaining Solution: A Survey of Recent Developments and Applications in Operations Management [J]. Omega, 2022, 110: 102638.
[8] Huang, C., Fang, S., Wu, H., Wang, Y., & Yang, Y. (2024). Low-Altitude Intelligent Transportation: System Architecture, Infrastructure, and Key Technologies. Journal of Industrial Information Integration, 42, 100694.
[9] Yang, J., & Xu, H. (2026). A Comprehensive Review of Building the Resilience of Low-Altitude Logistics: Key Issues, Challenges, and Strategies. Sustainability, 18(1), 461.
[10] Zhang, H., Tian, T., Feng, O., Wu, S., Zhong, G., & Huang, Y. (2023). Research on Demand-Based Scheduling Scheme of Urban Low-Altitude Logistics UAVs. Applied Sciences, 13(9), 5370.