Evaluation of economic disruptions from the 2016 Kumamoto Earthquake using a refined adaptive regional input-output model

Abstract

The Adaptive Regional Input-Output (ARIO) model is popular for quantifying indirect economic losses, which stem from business and supply chain interruption. However, refining this model to study new contexts is challenging in its basic form due to low-resolution modeling of behavioral parameters and temporally static reconstruction rates. This paper presents a refined ARIO, or R-ARIO model that incorporates dynamic reconstruction rates, sector-level modeling of behavioral parameters, and explicit modeling of housing losses separately from productive capital losses. We perform a global variance-based sensitivity analysis to identify the most influential parameters on predicted indirect loss from the R-ARIO model. A case study application to the 2016 Kumamoto Earthquake Sequence isolates trends in housing and economic recovery, capturing temporal differences in reconstruction demand and uncertainty across economic indicators.