Investigating the role of refining urban flood direct loss in enhancing the accuracy of indirect economic loss assessment

Abstract

Indirect Economic Loss (IEL) constitutes a significant component of urban flood loss, particularly in disasters affecting densely populated cities with a substantial concentration of assets and industrial supply chains. Information on the scope of disaster-induced damage is vital for assessing IEL, and its accuracy largely depends on the precision of information regarding direct loss. However, existing studies usually rely solely on administrative statistical data such as sectoral GDP or employed population. Few studies have focused on the impact of differences in the accuracy of direct information on IEL. Here, we improve the AMIL model and conduct a comparative study on the “7.6 Wuhan flood disaster” in 2016, measuring the role of refining the following three dimensions in enhancing the accuracy of IEL assessment results - direct loss data, industry subdivision number, and ripple region number -. The results indicate that (1) Using multiple sources of data, such as geographic and spatial data, to refine Direct Economic Loss (DEL) and the affected labor population can increase IEL by up to 10.49 %; (2) Subdividing industries with more severe loss to detailed sectors can improve IEL accuracy by up to 12.50 % compared to non-subdivision; (3) Refining direct loss statistics for sectors with higher proportions of intermediate input and output can balance statistical costs and precision requirements. This study aims to enhance the accuracy of IEL assessments resulting from disasters by proposing a more efficient system for direct loss statistics and providing theoretical and empirical support for such improvement.