Skip to main content

Research Repository

Advanced Search

Developing Frameworks to Understand Disaster Causation: From Forensic Disaster Investigation to Risk Root Cause Analysis

Fraser, Arabella; Paterson, Shona; Pelling, Mark

Authors

Arabella Fraser

Shona Paterson

Mark Pelling



Abstract

The Sendai Framework for Disaster Risk Reduction 2015–2030 calls for science to support policy move toward more holistic solutions to disaster risk. This paper outlines an original framework to promote inter-disciplinary research into disaster causation, identifying the basis for holistic solutions. The PEARL Risk Root Cause Analysis framework responds to limits identified in the established FORensic INvestigations of disasters (FORIN) approach to root cause analysis. The paper documents a systematic review of the FORIN approach as a starting point for the development of the PEARL framework. The proposed PEARL framework offers a broad and adaptable conceptual, methodological and practical approach. In particular, we demonstrate the centrality of governance, including the role of disaster risk management in risk creation, of bringing historical insights into contemporary and future scenarios planning and of integrating research methods. These core elements can assist in repositioning science to better support the goals of the Sendai Framework.

Citation

Fraser, A., Paterson, S., & Pelling, M. (2016). Developing Frameworks to Understand Disaster Causation: From Forensic Disaster Investigation to Risk Root Cause Analysis. Journal of Extreme Events, 03(02), Article 1650008. https://doi.org/10.1142/s2345737616500081

Journal Article Type Article
Acceptance Date Mar 1, 2016
Publication Date 2016-06
Deposit Date Nov 14, 2018
Journal Journal of Extreme Events
Print ISSN 2345-7376
Electronic ISSN 2382-6339
Publisher World Scientific
Peer Reviewed Peer Reviewed
Volume 03
Issue 02
Article Number 1650008
DOI https://doi.org/10.1142/s2345737616500081
Public URL https://nottingham-repository.worktribe.com/output/1249901
Publisher URL https://www.worldscientific.com/doi/abs/10.1142/S2345737616500081