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Assessment and Examination of Emergency Management Capabilities in Chinese Rural Areas from a Machine Learning Perspective

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  • Jing Wang

    (College of Marxism, Xi’an University of Science and Technology, Xi’an 710054, China)

  • Elara Vansant

    (Telfer School of Management, University of Ottawa, Ottawa, ON K1N 6N5, Canada)

Abstract

The Chinese government’s rural rejuvenation program depends on improving the national Rural Emergency Management Capability (REMC). To increase the resilience of Chinese rural areas against external dangers, REMC and its driving elements must be effectively categorized and evaluated. This study examines the variations in REMC levels and driving factors across different cities and regions, revealing the spatial distribution patterns and underlying mechanisms. To improve REMC in Chinese rural areas, this research employs the Projection Pursuit Method to assess REMC in 280 cities from 2006 to 2020. Additionally, we identify 22 driving factors and use the Random Forest algorithm from machine learning to analyze their impact on REMC. The analysis is conducted at both national and city levels to compare the influence of various driving factors in different regions. The findings show that China’s REMC levels have improved over time, driven by economic growth and the formation of urban clusters. Notably, some underdeveloped regions demonstrate higher REMC levels than more developed areas. The four most significant driving factors identified are rural road density, rural Internet penetration, per capita investment in fixed assets, and the density of township health centers. At the city level, rural Internet penetration and the e-commerce turnover of agricultural products have particularly strong driving effects. Moreover, the importance of driving factors varies across regions due to local conditions. This study offers valuable insights for the Chinese government to enhance REMC through region-specific strategies tailored to local circumstances.

Suggested Citation

  • Jing Wang & Elara Vansant, 2025. "Assessment and Examination of Emergency Management Capabilities in Chinese Rural Areas from a Machine Learning Perspective," Sustainability, MDPI, vol. 17(3), pages 1-25, January.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:3:p:1001-:d:1577488
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    References listed on IDEAS

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    1. Zhuguang Lan & Ming Huang, 2018. "Safety assessment for seawall based on constrained maximum entropy projection pursuit model," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 91(3), pages 1165-1178, April.
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