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Cited by 415

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Critical infrastructure, panarchies and the vulnerability paths of cascading disasters

Open access
Pescaroli, Gianluca, Alexander, David·2016·Natural Hazards

Abstract

Cascading effects and cascading disasters are emerging fields of scientific research. The widespread diffusion of functional networks increases the complexity of interdependent systems and their vulnerability to large-scale disruptions. Although in recent years studies of interconnections and chain effects have improved significantly, cascading phenomena are often associated with the “toppling domino metaphor”, or with high-impact, low-probability events. This paper aimed to support a paradigm shift in the state of the art by proposing a new theoretical approach to cascading events in terms of their root causes and lack of predictability. By means of interdisciplinary theory building, we demonstrate how cascades reflect the ways in which panarchies collapse. We suggest that the vulnerability of critical infrastructure may orientate the progress of events in relation to society’s feedback loops, rather than merely being an effect of natural triggers. Our conclusions point to a paradigm shift in the preparedness phase that could include escalation points and social nodes, but that also reveals a brand new field of research for disaster scholars.

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Metadata

Authors
Pescaroli, G., & Alexander, D.
Venue
Natural Hazards
Published
2016-02-12
Volume
82
Issue
1
Pages
175-192
ISSN
  • 0921-030X (ISSN-L)
  • 1573-0840
OpenAlex
W2261084531
Cited by
415
References
3
FWCI
36.70
Type
journal article
Language
en
Open access
hybrid

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CoChat. (2026). Citation check: Critical infrastructure, panarchies and the vulnerability paths of cascading disasters. https://app.cochat.ai/papers/10.1007/s11069-016-2186-3
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Pescaroli, G., & Alexander, D. (2016). Critical infrastructure, panarchies and the vulnerability paths of cascading disasters. Natural Hazards. https://doi.org/10.1007/s11069-016-2186-3

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.