An evaluation method of fragile states index based on climate shock: A case of Bangladesh

Fragile states index reflects a country's ability to maintain stability. The main objective of this study is to analyze how climate change influences fragile states index. Firstly, we aim to modify the fragile states index. We devise an index system of climate shocks (MCS), which measures not climate change but also governance capacity. Meanwhile, a three-class index system is formulated to measure fragility of states (MCFS). Afterwards, we utilize MCS to modify the initial index system based on multiplication model. Furthermore, the weights of MCS are obtained by Delphi method while the weights in the third level of MCFS are gotten by CRITIC Weighting Model. The weights in the second level of MCFS then are determined by Entropy Weighting Model and Group Making Method. Finally, the classification standard of measuring fragility of states is calculated through System Clustering Model. And then Bangladesh is chosen to show the variation tendency of fragility based on the data between 2000 and 2015. To further predict the fragility of Bangladesh, Cascaded Neural Network Model (CNN) is adopted to predict MCFS from 2016 to 2030. Eventually we determine and define tipping points into 2 types-amelioration tipping points and deterioration tipping points. The result show that Bangladesh reached the deterioration tipping points in 2016.