Citation

Gong S, Ye Y, Gao X, Chen L, Wang T. Int. J. Disaster Risk Reduct. 2023; 95: e103862.

Copyright

(Copyright © 2023, Elsevier Publishing)

DOI

10.1016/j.ijdrr.2023.103862

PMID

unavailable

Abstract

Critical infrastructures (CIs), such as electric power, transportation, and energy systems, offer essential goods and services to maintain societal health and well-being. Any interruptions in their functioning will result in catastrophic consequences. Besides, due to their growing interdependencies, a local failure can spread across CI networks, exacerbating the vulnerability of the whole system. This study aims to investigate the interdependency patterns of CIs from a comprehensive and holistic perspective. The interdependency natures, including cascading paths, interdependency types, and degrees were analyzed through processing data from 12 typical cascading disaster cases covering various disaster categories. Furthermore, the criticality of the recovery time for CIs was also discussed. A comparative analysis with an existing database was conducted to validate the applicability of the data used in this research. The results reveal that first, almost all cascading effects across CIs propagate within the first and second-order ranges, accounting for 80.4% and 17.9%, respectively. In a few cases, positive feedback loop events with a self-reinforcing effect may also occur. Second, physical interdependency is the most common type among CIs, followed by geographic, and more than 79% of interaction behaviors among CIs are highly interdependent. The same CI pairs can have different interdependency types and degrees depending on specific disruption scenarios and temporal or spatial locations of the CI components being affected. Third, CI failure durations are the key factor in determining interdependency impacts. Maintaining moderate redundancy in CIs and improving their recovery performances can significantly mitigate these impacts. This study extends existing research and enriches the knowledge system of CI interdependencies, its findings can help inform decision-makers on improving the preparedness level of CIs and mitigating interdependency impacts.

Language: en

Keywords

Cascading disasters; Critical infrastructures; Interdependencies; Multi-case analysis; Recovery; Redundancy