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.

中文翻译：

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基于气候冲击的脆弱国家指数评估方法：以孟加拉国为例。

脆弱国家指数反映了一个国家维持稳定的能力。这项研究的主要目的是分析气候变化如何影响脆弱状态指数。首先，我们旨在修改脆弱状态指数。我们设计了一个气候冲击指标体系（MCS），它不仅衡量气候变化，而且衡量治理能力。同时，制定了三级指标体系来衡量状态的脆弱性（MCFS）。然后，我们利用MCS在乘法模型的基础上修改初始索引系统。此外，MCS的权重通过Delphi方法获得，而MCFS第三级的权重通过CRITIC加权模型获得。然后，通过熵权重模型和分组制作方法确定第二级MCFS中的权重。最后，通过系统聚类模型计算出状态脆弱性的分类标准。然后根据2000年至2015年的数据选择孟加拉国以显示脆弱性的变化趋势。为了进一步预测孟加拉国的脆弱性，采用了层叠神经网络模型（CNN）来预测2016年至2030年的MCFS。最终，我们确定和将引爆点定义为两种类型：改善引爆点和劣化引爆点。结果表明，孟加拉国在2016年达到了恶化的临界点。最终，我们将引爆点确定为两种类型，即改善引爆点和劣化引爆点。结果表明，孟加拉国在2016年达到了恶化的临界点。最终，我们将引爆点确定为两种类型，即改善引爆点和劣化引爆点。结果表明，孟加拉国在2016年达到了恶化的临界点。