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Research Detail

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Guangyou Zhou
School of Finance, Shanghai University of Finance and Economics, Shanghai, 200433, China

Jieyu Zhu
School of Finance, Shanghai University of Finance and Economics, Shanghai, 200433, China

Sumei Luo
School of Economics and Resource Management, Beijing Normal University, Beijing, 100875, China dInstitute of Economics, Minzu University of China, Beijing, 100081, China

Zihao Wu
School of Economics and Resource Management, Beijing Normal University, Beijing, 100875, China

Yan Jiang
Institute of Economics, Minzu University of China, Beijing, 100081, China

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' indexes. 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 the fragility of states (MCFS).  Afterward, we utilize MCS to modify the initial index system based on the 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.

  Climate shocks, Index system of fragility of states, Cascaded neural network model
  In Bangladesh
  
  
  Comparative study
  Climate change

 The main objective of this study is to analyze how climate change influences fragile states index.

Assumption: Based on the particularity of the above analysis and research content, we make the following assumptions.

1. The influences caused by the movement of non-governmental organizations are ignored.

2. Allowing for authoritative sources, we assumed that PM2.5 would be reasonable for the degree of air pollution.

3. Recently, an increasing number of states are managed to control carbon emissions to mitigate the degree of global warming. Meanwhile, on account of its accessible data, we assume CO2 emissions to represent greenhouse gases.

4. We do not take non-schooling education such as home school education into account so that we could explain the basic educational needs of the country by the indicator of primary net attendance rate.

5. Divisiveness, diversity, and heterogeneity within and between states and cities are very real. To simplify the model, all of our deductions in this paper is based on the assumption of homogeneity.  

Data source and preprocessing: Each indicator data in the MCFS system is obtained from the official website of the world bank, which provides detailed data on each indicator for all countries and regions in the world. Starting in 2006, Foreign Policy magazine, in collaboration with the Fund for Peace, produced the fragile states index (FSI). The FSI provides data on 12 indicators for 179 countries around the world, as well as a ranking of countries' vulnerability.

Collecting sufficient data is the basis of developing a complete index system. We searched the database and select 26 indicators of a fragile state and climate shock firstly. The availability of data is an essential issue. Consequently, it is essential to ensure the authenticity of the data. In this paper, if the data of a country only misses one or two indicators, the cubic spline interpolation method will be used to fit the missing data.

Primary index system: Modified index system of climate shocks (MCS)-

Firstly, 4 objective indicators are selected to construct the initial index system of climate shocks (UCS). Secondly, with consideration of climate policies of different states, government index (GI) as an objective indicator is added to modify the initial index system, where: MCS=UCS×GI

In this case, for each country, based on Group Decision Making (GDM) method government index is quantified by the score according to the experts’ experience. The score is between 0 and 2, which is corresponding to the qualitative description: high intensity, moderate intensity, low intensity. Consequently, the modified index system is more accurate to reflect climate shocks,

Unmodified index system of fragile states (UCFS)

In this section, we refer to some case studies to analyze the factors of fragility. Different from the past references (Rice and Patrick, 2008) (Fate, 2004), we focus on indicators that reflect the basic condition of a country, because a fragile country is the ability of a country to provide basic essentials of its people. According to this principle, 21 indicators are selected to reflect four aspects of fragility, they are: economics, social welfare, security and politics.  A modified model of index system of fragile states (MCFS). In this section, we aim to reflect how climate shock exerts an influence on fragility. Then based on the two systems (MCS and UCFS) aforementioned, we need to formulate a model where UCFS is modified by MCS. Furthermore, referring to some studies (Rice and Patrick, 2008), we assign climate shock indirectly influence some third-class indicators of security and politics (SP), they are: IDP, International violence score, Host, Sum of all armed conflicts of bordering states, Crime index, Regulation of Participation, Institutionalized Democracy Score.

  Journal of Environmental Management, Volume 275, 1 December 2020, 111142
  
Funding Source:
1.   Budget:  
  

We construct the revised indicator system to reflect the impact of climate shock on the fragile states index, and use the Cascade Neural Network Model to predict the fragility of Bangladesh, which has better solvent the shortcoming of the current 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 the fragility of states (MCFS). Afterward, we utilize MCS to modify the initial index system. 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.

At the same time, 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.

  Journal
  


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