A new resilience rating system for Countries and States

This research presents a quantitative method to assess resilience at the state level. The approach introduced in this work is an evolution of the risk assessment concept. Risk is mainly a function of vulnerability, hazard, and exposure; on the other hand, resilience focuses more on the internal characteristics of a system rather than its vulnerability. To tackle this difference, a new formulation has been introduced for the evaluation of resilience. In this formulation, resilience is a function of hazard, exposure, and intrinsic resilience. Generally, intrinsic resilience deals with the internal characteristics of a system, and it differs from the traditional resilience index that takes into account external factors in its assessment, such as the disaster intensity and the level of exposure. The paper also provides a method to compute the intrinsic resilience of countries. This method is based on the data provided by Hyogo Framework for Action (HFA), which is a work developed by the United Nations ( UN). HFA evaluates the inherent resilience of countries based on a number of equally weighted indicators. However, further analysis has shown that the contribution made by each of those indicators toward the intrinsic resilience is different. This discrepancy has necessitated weighting the indicators based on their individual contribution towards the intrinsic resilience. To do that, we introduce the Dependence Tree Analysis (DTA). DTA is a method that determines the correlation between a component and its sub-components (i.e., between intrinsic resilience and its indicators), enabling us to orderly allocate new weights to the indicators to obtain a more representative output for the intrinsic resilience. Finally, a case study composed of 37 states has been conducted in order to illustrate the methodology in all details. Both intrinsic resilience and resilience indexes for each of the states were assessed. This was followed by a comparative analysis in order to test the applicability of the methodology, and the results were in line with the predictions. (C) 2017 The Authors. Published by Elsevier Ltd.