Cost–benefit analysis to appraise technical mitigation options for earthquake-induced liquefaction disaster events

Abstract

Purpose Recent earthquake-induced liquefaction events and associated losses have increased researchers’ interest into liquefaction risk reduction interventions. To the best of the authors’ knowledge, there was no scholarly literature related to an economic appraisal of these risk reduction interventions. The purpose of this paper is to investigate the issues in applying cost–benefit analysis (CBA) principles to the evaluation of technical mitigations to reduce earthquake-induced liquefaction risk. Design/methodology/approach CBA has been substantially used for risk mitigation option appraisal for a number of hazard threats. Previous literature in the form of systematic reviews, individual research and case studies, together with liquefaction risk and loss modelling literature, was used to develop a theoretical model of CBA for earthquake-induced liquefaction mitigation interventions. The model was tested using a scenario in a two-day workshop. Findings Because liquefaction risk reduction techniques are relatively new, there is limited damage modelling and cost data available for use within CBAs. As such end users need to make significant assumptions when linking the results of technical investigations of damage to built-asset performance and probabilistic loss modelling resulting in many potential interventions being not cost-effective for low-impact disasters. This study questions whether a probabilistic approach should really be applied to localised rapid onset events like liquefaction, arguing that a deterministic approach for localised knowledge and context would be a better base for the cost-effectiveness mitigation interventions. Originality/value This paper makes an original contribution to literature through a critical review of CBA approaches applied to disaster mitigation interventions. Further, this paper identifies challenges and limitations of applying probabilistic based CBA models to localised rapid onset disaster events where human losses are minimal and historic data is sparse; challenging researchers to develop new deterministic based approaches that use localised knowledge and context to evaluate the cost-effectiveness of mitigation interventions.