Post-disaster recovery in industrial sectors: A Markov process analysis of multiple lifeline disruptions

Lifeline disruptions can represent a serious economic threat at various scales. However, firm-level empirical evidence regarding the consequences and reliability of lifeline services, and how lifeline service disruptions impact on economic losses after disasters, is still lacking. This study applies a temporal, non-homogeneous, Markov process approach that explicitly considers multiple lifeline service disruptions. This approach is used to quantitatively estimate the impacts of multiple lifeline disruptions on business production capacity losses. The model is illustrated based on the 2011 Great East Japan Earthquake case study with detailed information obtained from individual business recovery data. The result indicates that the restoration of lifeline systems during disruptions should consider each business service given that it significantly affects business production capacity recovery. For example, the case study results indicate that if electrical power, water and gas are disrupted, restoration of electrical power supply yields the highest influence on production capacity improvement, amounting to 15% per day on average, compared with 10% per day for gas restorations, and 8% per day for water restoration. The proposed model can provide information to business managers and policymakers on the optimum recovery strategy and on how to mitigate economic losses in disruption event cases.

Automated summary

This study uses a temporal, non-homogeneous, Markov process approach to examine the impacts of multiple lifeline service disruptions on business production capacity, with the 2011 Great East Japan Earthquake as a case study. The results suggest that when restoring lifeline systems during disruptions, each individual business service should be taken into consideration, as the restoration of electrical power supply has the most significant influence on improving production capacity for businesses.