Investigating Spatial-Temporal Change of Emergency Logistics System Vulnerability—Taking China as an Example

Abstract

Emergency logistics is an important safeguard to ensure the safety of human beings, properties, and economic activities under major disasters and emergencies. This study proposed a vulnerability assessment framework for emergency logistics systems (ELS) from three dimensions: exposure, sensitivity, and adaptability, in which the principal component analysis and comprehensive index evaluation methods are adopted. Furthermore, a geographically weighted regression model (GWR) was established to explore the driving mechanism of vulnerability change of ELS. By taking provincial-level data in the mainland of China from 2010 to 2022, this empirical study found that ELS vulnerability showed strong fluctuation, in which the overall trend was upward during 2010–2018, but downward during 2019–2022. Meanwhile, ELS vulnerability was generally low in southeast China and high in northwest China, and strength of this spatial clustering showed a fluctuating upward trend. Four driving factors affected the spatiotemporal change of vulnerability—ecological threat level mainly positively drove the change, while natural environment protection level, logistics transportation capacity, and logistics fixed asset investment were negatively correlated. This study provides management implications for improving regional emergency logistics systems according to their spatiotemporal variation and local conditions.

Keywords

China / Emergency logistics system / Spatial-temporal changes / Vulnerability assessment / Vulnerability scoping diagram (VSD)

References

[1] Boori MS, Choudhary K, Paringer R, Kupriyanov A. Using RS/GIS for spatiotemporal ecological vulnerability analysis based on DPSIR framework in the Republic of Tatarstan, Russia. Ecological Informatics, 2022, 67: Article 101490.

[2] Chen Y. An analytical process of spatial autocorrelation functions based on Moran’s index. PLoS One, 2021, 16(4): Article e0249589.

[3] Chen H, Guo Y, Lin X, Qi X. Dynamic changes and improvement paths of China’s emergency logistics response capabilities under public emergencies – Research based on the entropy weight TOPSIS method. Frontiers in Public Health, 2024, 12: Article 1397747.

[4] Chen H, Lin X, Guo Y, Qi X. Heterogeneity and spatiotemporal evolution characteristics of regional emergency logistics response capacity: A case of China. Frontiers in Public Health, 2025, 13: Article 1461354.

[5] Di D, Wu Z, Wang H, Zhang F. Spatial pattern analysis on the functions of water resources economic-social-ecological complex system. Journal of Cleaner Production, 2022, 336: Article 130323.

[6] Ding Z, Xu X, Jiang S, Yan J, Han Y. Emergency logistics scheduling with multiple supply-demand points based on grey interval. Journal of Safety Science and Resilience, 2022, 3(2): 179-188.

[7] Ergün S, Usta P, Gök SZA, Weber GW. A game theoretical approach to emergency logistics planning in natural disasters. Annals of Operations Research, 2023, 324: 855-868.

[8] Fan L, Wu Z, Gong Z, Wang DZW. Emergency logistics planning through optimizing the multi-trip vehicle routing with time windows and limited trip duration. Expert Systems With Applications, 2026, 298(PA): Article 129465.

[9] Feng J-R, Gai W-M, Li J-Y, Xu M. Location selection of emergency supplies repositories for emergency logistics management: A variable weighted algorithm. Journal of Loss Prevention in the Process Industries, 2020, 63: Article 104032.

[10] Feng Z, Yang X, Li S. New insights of eco-environmental vulnerability in China’s Yellow River Basin: Spatio-temporal pattern and contributor identification. Ecological Indicators, 2024, 167: Article 112655.

[11] Fotheringham AS, Yue H, Li Z. Examining the influences of air quality in China’s cities using multi-scale geographically weighted regression. Transactions in GIS, 2019, 23(6): 1444-1464.

[12] Gong J, Jin T, Cao E, Wang S, Yan L. Is ecological vulnerability assessment based on the VSD model and AHP-entropy method useful for loessial forest landscape protection and adaptative management? A case study of Ziwuling Mountain region, China. Ecological Indicators, 2022, 143: Article 109379.

[13] Guo J, Liu H, Liu T, Song G, Guo B. The multi-objective shortest path problem with multimodal transportation for emergency logistics. Mathematics, 2024, 12(17): Article 2615.

[14] Hao, S., H. Shan, and J. Wang. 2022. Study on vulnerability evaluation of emergency logistics system based on DPSIR and fuzzy matter-element model. In Proceedings of the 2022 International Conference on Management Engineering, Software Engineering and Service Sciences (ICMSS), 7–9 January 2022, Wuhan, 146–152.

[15] Hu Y, Liu Q, Li S, Wu W. Robust emergency logistics network design for pandemic emergencies under demand uncertainty. Transportation Research Part E, 2025, 196: Article 103957.

[16] Jiang Y, Yuan Y. Emergency logistics in a large-scale disaster context: Achievements and challenges. International Journal of Environmental Research and Public Health, 2019, 16(5): Article 779.

[17] Johnson PM, Barbour W, Camp JV, Baroud H. Using machine learning to examine freight network spatial vulnerabilities to disasters: A new take on partial dependence plots. Transportation Research Interdisciplinary Perspectives, 2022, 14: Article 100617.

[18] Kundu T, Sheu J-B, Kuo H-T. Emergency logistics management—Review and propositions for future research. Transportation Research Part E: Logistics and Transportation Review, 2022, 164: Article 102789.

[19] Lei L, Yu S, Ke Y, Deng L, Kang Q. Evaluation model for emergency material suppliers in emergency logistics systems based on game theory-TOPSIS method. Systems, 2025, 13(6): Article 493.

[20] Li X, Tan S, Li R. Full-chain comprehensive assessment and multi-scenario simulation of geological disaster vulnerability based on the VSD framework: A case study of Yunnan Province in China. Ecological Indicators, 2025, 175: Article 113573.

[21] Li Y, Yan B, Qin Y, Shi W, Yan J. Analysis of the types of animal husbandry and planting that influence household biogas in rural China. Journal of Cleaner Production, 2022, 332: Article 130025.

[22] Liu Y, Li L, Tu Y, Mei Y. Fuzzy TOPSIS-EW method with multi-granularity linguistic assessment information for emergency logistics performance evaluation. Symmetry, 2020, 12(8): Article 1331.

[23] Liu B, Zhao X, Li Y. Review and prospect of studies on emergency management. Procedia Engineering, 2016, 145: 1501-1508.

[24] Lu H, Zheng J, Ou H, Liu Y, Li X. Vulnerability assessment and spatio-temporal difference analysis of inland fisheries flood disaster in China. Frontiers in Ecology and Evolution, 2022, 10: Article 899786.

[25] Nan G, Han L, Wang Y. Assessing vulnerability of coupled human-environment systems and its influence factors in Yan’an City, China. Human and Ecological Risk Assessment: An International Journal, 2022, 28(7): 783-801.

[26] Polsky C, Neff R, Yarnal B. Building comparable global change vulnerability assessments: The vulnerability scoping diagram. Global Environmental Change, 2007, 17(3–4): 472-485.

[27] Raj A, Sharma LK. Spatial E-PSR modelling for ecological sensitivity assessment for arid rangeland resilience and management. Ecological Modelling, 2023, 478: Article 110283.

[28] Romanowski C, Raj R, Schneider J, Mishra S, Shivshankar V, Ayengar S, Cueva F. Regional response to large-scale emergency events: Building on historical data. International Journal of Critical Infrastructure Protection, 2015, 11: 12-21.

[29] Shen L, Tao F, Shi Y, Qin R. Optimization of location-routing problem in emergency logistics considering carbon emissions. International Journal of Environmental Research and Public Health, 2019, 16(16): Article 2982.

[30] Sheu J-B. An emergency logistics distribution approach for quick response to urgent relief demand in disasters. Transportation Research Part E: Logistics and Transportation Review, 2007, 43(6): 687-709.

[31] Shi C, Miao X, Zhang L, Chiu Y-H, Zeng Q, Zhang C. Spatial patterns of industrial water efficiency and influencing factors – Based on dynamic two-stage DDF recycling model and geographically weighted regression model. Journal of Cleaner Production, 2022, 374: Article 134028.

[32] Shi Y, Yang J, Qian H, Hao S, Guo H. Optimal decision-making of post-disaster emergency material scheduling based on helicopter-truck-drone collaboration. Omega, 2024, 127: Article 103104.

[33] Singh S, Kumar R, Panchal R, Tiwari MK. Impact of COVID-19 on logistics systems and disruptions in food supply chain. International Journal of Production Research, 2021, 59(7): 1993-2008.

[34] Smit B, Wandel J. Adaptation, adaptive capacity and vulnerability. Global Environmental Change, 2006, 16(3): 282-292.

[35] Sun L, Yu Y, He J, Li C, Yu X, Zhang L, Lu Y, Malik I, Wistuba M. Vulnerability assessment of social-ecological systems in arid regions: A cross-efficiency modified DEA model with entropy weight aggregation. Ecological Indicators, 2025, 179: Article 114149.

[36] Tovia F. An emergency logistics response system for natural disasters. International Journal of Logistics Research and Applications, 2007, 10(3): 173-186.

[37] Wang X, Chen H, Wang K. Studies on emergency logistics operation model for unexpected events at Yangtze chemical industrial park. Procedia Engineering, 2012, 43: 353-358.

[38] Wang J, Li Y, Liu W, Gou A. Spatial and temporal evolution characteristics and factors of heat vulnerability in the Pearl River Delta urban agglomeration from 2001 to 2022. Heliyon, 2024, 10(13): Article e34116.

[39] Wang S, Liu H, Pu H, Yang H. Spatial disparity and hierarchical cluster analysis of final energy consumption in China. Energy, 2020, 197: Article 117195.

[40] Wang H, Ni Y, Sun L, Chen Y, Xu T, Chen X, Su W, Zhou Z. Hierarchical visualization of geographical areal data with spatial attribute association. Visual Informatics, 2021, 5(3): 82-91.

[41] Wang Y, Peng S, Xu M. Emergency logistics network design based on space-time resource configuration. Knowledge-Based Systems, 2021, 223: Article 107041.

[42] Wang W, Song Y, Huang L, Shi Y, Zhang C. Vulnerability assessment of disaster chains: A case study of rainstorm-landslide disaster chains in the Greater Bay Area. International Journal of Disaster Risk Reduction, 2025, 119: Article 105272.

[43] Wang Q, Zhang Q-P, Liu Y-Y, Tong L-J, Zhang Y-Z, Li X-Y, Li J-L. Characterizing the spatial distribution of typical natural disaster vulnerability in China from 2010 to 2017. Natural Hazards, 2020, 100: 3-15.

[44] Wu W, Li Y. The multi-compartment truck and trailer petrol station replenishment problem with domino hazard risks. Transportation Research Part B, 2024, 190: Article 103078.

[45] Wu W, Ma J, Liu R, Jin W. Multi-class hazmat distribution network design with inventory and superimposed risks. Transportation Research Part E, 2022, 161: Article 102693.

[46] Xu S, Cui Y, Yang C, Wei S, Dong W, Huang L, Liu C, Ren Z, Wang W. The fuzzy comprehensive evaluation (FCE) and the principal component analysis (PCA) model simulation and its applications in water quality assessment of Nansi Lake Basin, China. Environmental Engineering Research, 2021, 26(2): Article 200022.

[47] Xu W, Lu Y, Proverbs D. An evaluation of factors influencing the vulnerability of emergency logistics supply chains. International Journal of Logistics Research and Applications, 2023, 27(10): 1891-1924.

[48] Xu B, Xu L, Xu R, Luo L. Geographical analysis of CO₂ emissions in China’s manufacturing industry: A geographically weighted regression model. Journal of Cleaner Production, 2017, 166: 628-640.

[49] Yin Z, Yin Z, Ye J, Liu R. Optimization of emergency logistics delivery path based on guided local search algorithm. Journal of Computational Methods in Sciences and Engineering, 2024, 24(3): 1889-1902.

[50] Zarafshani, K., L. Sharafi, H. Azadi, and S. Van Passel. 2016. Vulnerability assessment models to drought: Toward a conceptual framework. Sustainability 8(6): Article 588.

[51] Zhang L, Lu J. Optimizing oil spill emergency logistics: A time-varying multi-resource collaborative scheduling model. Environmental Science and Pollution Research, 2024, 31(2): 2773-2801.

[52] Zhang Y, Ding Q, Liu J-B. Performance evaluation of emergency logistics capability for public health emergencies: Perspective of COVID-19. International Journal of Logistics Research and Applications, 2022, 25(12): 1509-1522.

[53] Zhang X, Jia W, He J. Spatial and temporal variation of ecological quality in northeastern China and analysis of influencing factors. Journal of Cleaner Production, 2023, 423: Article 138650.

[54] Zhang J, Long D, Li Y. A reliable emergency logistics network for COVID-19 considering the uncertain time-varying demands. Transportation Research Part E, 2023, 172: Article 103087.

[55] Zheng D, An Z, Yan C, Wu R. Spatial-temporal characteristics and influencing factors of food production efficiency based on WEF nexus in China. Journal of Cleaner Production, 2022, 330: Article 129921.

[56] Zhu X, Yao K, Guo A, Li T, Wu W, Gao X. Research on joint optimization of reserve and dispatching for multivariety emergency materials based on NSGA-II. Discrete Dynamics in Nature and Society, 2025, 2025(1): Article 9990362.

[57] Zhu C, Zhang X, Zhou M, He S, Gan M, Yang L, Wang K. Impacts of urbanization and landscape pattern on habitat quality using OLS and GWR models in Hangzhou, China. Ecological Indicators, 2020, 117: Article 106654.

[58] Zou T, Yoshino K. Environmental vulnerability evaluation using a spatial principal components approach in the Daxing’anling region, China. Ecological Indicators, 2017, 78: 405-415.

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