# Analysis of the Impact of Climate Change on National Vulnerability Based on Fuzzy Comprehensive Evaluation

Authors: Jia-Ming Zhu; Yang Chen; Su Zhang
Journal: Discrete Dynamics in Nature and Society
 (2020)

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## Abstract

Climate change has become one of the major threats to global security. On the impact of climate change on national vulnerability, the researchers firstly select relevant factors and data and analyze the principal components to determine the weight of climate change on vulnerability so as to analyze the impact of climate change on national vulnerability. Secondly, based on the influencing factors, we obtain the national vulnerability score through the fuzzy comprehensive evaluation method, delineate the impact scope according to the score, and predict climate change by means of regression analysis and time series analysis. Finally, according to the characteristics of cities and continents, the model is revised, a PSR model and a cluster analysis model are established, and the prediction accuracy is improved.


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## Body

## 1. Introduction

Climate change is one of the most universal global threats to peace and security in the 21st century. The damage, loss, and impact caused by various natural disasters worldwide are becoming serious. The research on vulnerability, adaptability, and resilience has become the focus of attention in the fields of global change, disaster prevention and mitigation, and sustainable development. As a key element of international relations and domestic welfare, it covers all areas of security, construction, peace, and development. The impact of climate change has had a negative impact on vulnerable groups, while improving the response capacity of the government [1].This paper argues that climate change is a “threat multiplier” which interacts with existing pressures, such as social conflict, economic inequality, mass migration, or competition for resources. Then, further countermeasures will be proposed to eliminate these problems and the instability that may arise in violent conflicts [2].Vulnerability is used to describe the systems and components that are vulnerable to damage, lack antijamming capabilities, and restore their structure and function. The state is a highly socioeconomically complex integrated system. It interacts with the natural environment and the social environment. According to the definition of vulnerability, national vulnerability refers to the probability of turning risk into disaster when a country’s system is adversely affected by the outside world, but the system has the ability to resist and reduce risk and self-recovery.Vulnerability manifests itself in a variety of areas [3], mainly including physical vulnerability, economic vulnerability, social vulnerability, and political vulnerability. Physical vulnerability refers to the risk of anthropogenic impacts on the climate, and other factors such as natural disasters and human-induced pollution. Economic vulnerability refers to the perception of internal and external threats to the economy. Social vulnerability refers to the potential disaster factors, the degree of damage, and the coping capacity of a social group, organization, or country exposed to disaster impact. Political vulnerability refers to the territorial integrity, core values, and internal unity which are exposed to internal and external threats.In the study of vulnerability, early scholars mainly focus on the ecological environment and then extend their research to humanities and regional economy. This paper establishes a national vulnerability assessment model, analyzes the impact of climate change on national vulnerability, predicts the critical point of national vulnerability, and gives government intervention measures. The overall idea is shown in Figure1.Figure 1
Overall thinking process.The data in this paper come from Question E of the 2018 American College Students Mathematical Modeling Competition and the statistics of the World Bank.


## 2. Basic Assumptions

In order to solve the problem, we make the following assumptions:(i)
Assuming the sovereign government can exercise its executive power during its time in office to control the factors affecting climate change.(ii)
Assuming the selected indicator adequately reflects the country’s vulnerability and does not affect the simulation below.(iii)
Assuming that in the process of state intervention in climate change, the country will not be affected by economic and political crisis, extreme natural disasters, etc. It can achieve the desired state, when we control the variables.(iv)
No unexpected factors affecting our assessment during the study period.


## 3. How to Evaluate a Country’s Vulnerability and Measure the Impact of Climate Change

### 3.1. Analysis Approach

With the research on the impact of climate change in recent years, climate change has become one of the important factors affecting national vulnerability directly or indirectly. The main manifestations of climate change are temperature, precipitation, etc., which, on the one hand, directly affect the vulnerability through subtle changes. On the other hand, climate change is indirectly affected by glaciers and sea levels. Economical, political, and social factors, such as species of flora and fauna, food production, have further affected the vulnerability of the country [4].In order to measure national vulnerability reasonably and to analyze the direct and indirect impact of climate change on vulnerability, we firstly create an index system for measuring national vulnerability, and then a fuzzy comprehensive evaluation model is established based on the analysis of the principal components.


### 3.2. Fuzzy Comprehensive Evaluation Model Based on PCA Model

#### 3.2.1. Model Preparation

Considering the influencing factors of the state vulnerability, we should establish the hierarchical structure to evaluate the vulnerability. The decision-making problem is divided into three layers: target layer (T), criterion layer (C), and program layer (P), and each layer has several factors [5], as shown in Table 1.Table 1
Hierarchy table.
Level 1 indicatorLevel 2 indicatorLevel 3 indicatorMarkNational vulnerabilityEconomic factorsPer capita GNIX1Gini coefficientX2Political factorsMilitary expenditure as a share of GDPX3Public education expenditure as a share of GDPX4Social factorsPer capita carbon dioxide emissionsX5Population growth rateX6International migrant share of total populationX7Climatic factorsTemperatureX8PrecipitationX9


#### 3.2.2. Model Establishing and Solving

SupposeU=u1,u2,…,un is a group of n countries to be evaluated, V=v1,v2,…,vm is a set of evaluation factors, and each solution in U is measured by each factor in V, and we select the observation matrix X from the observation data of 16 countries in 2015 [6]:(1)X=272029906270⋯6393036.743.248.5⋯21.4563.9672.311.323⋯0.666⋮⋮⋮⋱⋮−0.7380.9760.075⋯1.549.In the formula,xij represents the indicator value of the j-th program on the i-th evaluation factor, and the vector xj=x1j,x2j,…,xmj denotes the evaluation vector of the j-th program on the m-th evaluation indicator.Suppose the ideal urban air quality indicator vector isu:(2)u=u10,u20,…,um0=63930,21.456,0.17,7.357,0.075,−0.824,17620,−0.91,−0.738,where(3)ui0=max1≤j≤nxij,whenxijis a benefit index,min1≤j≤nxij,whenxijis acostindex,xij−x0x0,whenxijis a neutral index.According to the established ideal scheme, the relative deviation fuzzy matrixR∼ is calculated by substituting the observed data from 16 countries:(4)R∼=r11r12⋯r131r21r22⋯r231⋮⋮⋱⋮r71r72⋯r731,where(5)Yij=xij−ui0max1≤j≤nxij−min1≤j≤nxij,i=1,2,…,9;j=1,2,…,16.So, we can get(6)R∼=0.9750.9890.97⋯00.5640.8041⋯00.6980.3940.212⋯0.091⋮⋮⋮⋱⋮00.1750.171⋯0.48.We use the principal component analysis method to calculate the weight of each evaluation indicator. When calculating, the number of principal components can be selected to make the contribution rate of cumulative variance of factors large enough (larger than 85% here) so as to protect the interpretation ability of the common factor to data [7].As can be seen from Table2, when the 5 principal components are selected, the cumulative variance contribution rate of the factor has reached 88.508% and meets the requirements, so the first 5 factors are selected for analysis. The column vector of the i factor in Table 3 is divided by the square root of the corresponding characteristic root, and the variation coefficient vector of principal components is obtained, which is shown in Table 4.Table 2
The principal component eigenvalues and cumulative contribution rate.
ComponentsTotalRate of changeCumulative (%)13.63840.42740.42721.88920.99161.41830.96810.75872.17540.8349.26481.44050.6367.06888.50860.4645.15693.66470.4054.50098.16480.0971.07499.23890.0690.762100.000Table 3
Comprehensive score sheet.
CountryComprehensive scoreRankAngola0.78011Zimbabwe0.75272Yemen0.72093Kenya0.71564Laos0.68235Malawi0.64196India0.60407Malaysia0.57488Indonesia0.47929Namibia0.475710Mongolia0.282111Azerbaijan0.259712Poland0.241913Croatia0.182914Slovenia0.119315Switzerland0.089016Table 4
Principal component coefficient matrix.
FactorsZ1Z2Z3Z4Z5X1−0.43230.16630.10690.2852−0.0776X20.46960.1602−0.10400.09810.3115X30.1390−0.45620.55600.16300.4992X40.24490.2840−0.02670.8202−0.1289X5−0.39850.0760−0.00330.26200.5745X60.4083−0.1013−0.00530.1540−0.2289X7−0.07320.38080.7851−0.0793−0.3003X80.42460.28340.1792−0.28030.2308X9−0.04690.6454−0.1393−0.18030.3244According to the proportion of the contribution rate of each principal component to the total contribution rate, the comprehensive coefficient of each factor is calculated and the weight vector of each evaluation index is obtained:(7)W=−0.1214,0.2750,0.0798,0.2515,−0.0911,0.1597,0.12,0.272,0.1218T.Then, we establish a comprehensive evaluation model [8] as follows:(8)Fj=∑i=17wirij.Based on this model, the higher the comprehensive score is, the stronger the national vulnerability has. We calculated data from 16 countries, and the ranking of comprehensive score and vulnerability is shown in Table4 and Figure 2.Figure 2
Distribution maps of comprehensive score.


### 3.3. Result Analysis

According to the score and ranking, the score is divided into three levels, respectively, marked as follows: fragile, vulnerable, and stable, as shown in Table5.Table 5
The rating level.
Comprehensive score0–0.40.4–0.70.7–1National vulnerabilityStableVulnerableFragileWhen a country has a combined score between 0 and 0.4 in a given year, the country is stable. When the score is between 0.4 and 0.7, the country is fragile. When score is between 0.7 and 1, it is fragile [9].Temperature and precipitation are the main manifestations of climate and also direct factors affecting national vulnerability. When temperatures and precipitation deviate from a reasonable range, national vulnerability increases. According to principle component analysis model, it can be found that there is a direct correlation between indicators affecting national vulnerability. As a result, temperature is also vulnerable to indirect impacts of national vulnerability, thus affecting other indicators [10].


## 3.1. Analysis Approach

With the research on the impact of climate change in recent years, climate change has become one of the important factors affecting national vulnerability directly or indirectly. The main manifestations of climate change are temperature, precipitation, etc., which, on the one hand, directly affect the vulnerability through subtle changes. On the other hand, climate change is indirectly affected by glaciers and sea levels. Economical, political, and social factors, such as species of flora and fauna, food production, have further affected the vulnerability of the country [4].In order to measure national vulnerability reasonably and to analyze the direct and indirect impact of climate change on vulnerability, we firstly create an index system for measuring national vulnerability, and then a fuzzy comprehensive evaluation model is established based on the analysis of the principal components.


## 3.2. Fuzzy Comprehensive Evaluation Model Based on PCA Model

### 3.2.1. Model Preparation

Considering the influencing factors of the state vulnerability, we should establish the hierarchical structure to evaluate the vulnerability. The decision-making problem is divided into three layers: target layer (T), criterion layer (C), and program layer (P), and each layer has several factors [5], as shown in Table 1.Table 1
Hierarchy table.
Level 1 indicatorLevel 2 indicatorLevel 3 indicatorMarkNational vulnerabilityEconomic factorsPer capita GNIX1Gini coefficientX2Political factorsMilitary expenditure as a share of GDPX3Public education expenditure as a share of GDPX4Social factorsPer capita carbon dioxide emissionsX5Population growth rateX6International migrant share of total populationX7Climatic factorsTemperatureX8PrecipitationX9


### 3.2.2. Model Establishing and Solving

SupposeU=u1,u2,…,un is a group of n countries to be evaluated, V=v1,v2,…,vm is a set of evaluation factors, and each solution in U is measured by each factor in V, and we select the observation matrix X from the observation data of 16 countries in 2015 [6]:(1)X=272029906270⋯6393036.743.248.5⋯21.4563.9672.311.323⋯0.666⋮⋮⋮⋱⋮−0.7380.9760.075⋯1.549.In the formula,xij represents the indicator value of the j-th program on the i-th evaluation factor, and the vector xj=x1j,x2j,…,xmj denotes the evaluation vector of the j-th program on the m-th evaluation indicator.Suppose the ideal urban air quality indicator vector isu:(2)u=u10,u20,…,um0=63930,21.456,0.17,7.357,0.075,−0.824,17620,−0.91,−0.738,where(3)ui0=max1≤j≤nxij,whenxijis a benefit index,min1≤j≤nxij,whenxijis acostindex,xij−x0x0,whenxijis a neutral index.According to the established ideal scheme, the relative deviation fuzzy matrixR∼ is calculated by substituting the observed data from 16 countries:(4)R∼=r11r12⋯r131r21r22⋯r231⋮⋮⋱⋮r71r72⋯r731,where(5)Yij=xij−ui0max1≤j≤nxij−min1≤j≤nxij,i=1,2,…,9;j=1,2,…,16.So, we can get(6)R∼=0.9750.9890.97⋯00.5640.8041⋯00.6980.3940.212⋯0.091⋮⋮⋮⋱⋮00.1750.171⋯0.48.We use the principal component analysis method to calculate the weight of each evaluation indicator. When calculating, the number of principal components can be selected to make the contribution rate of cumulative variance of factors large enough (larger than 85% here) so as to protect the interpretation ability of the common factor to data [7].As can be seen from Table2, when the 5 principal components are selected, the cumulative variance contribution rate of the factor has reached 88.508% and meets the requirements, so the first 5 factors are selected for analysis. The column vector of the i factor in Table 3 is divided by the square root of the corresponding characteristic root, and the variation coefficient vector of principal components is obtained, which is shown in Table 4.Table 2
The principal component eigenvalues and cumulative contribution rate.
ComponentsTotalRate of changeCumulative (%)13.63840.42740.42721.88920.99161.41830.96810.75872.17540.8349.26481.44050.6367.06888.50860.4645.15693.66470.4054.50098.16480.0971.07499.23890.0690.762100.000Table 3
Comprehensive score sheet.
CountryComprehensive scoreRankAngola0.78011Zimbabwe0.75272Yemen0.72093Kenya0.71564Laos0.68235Malawi0.64196India0.60407Malaysia0.57488Indonesia0.47929Namibia0.475710Mongolia0.282111Azerbaijan0.259712Poland0.241913Croatia0.182914Slovenia0.119315Switzerland0.089016Table 4
Principal component coefficient matrix.
FactorsZ1Z2Z3Z4Z5X1−0.43230.16630.10690.2852−0.0776X20.46960.1602−0.10400.09810.3115X30.1390−0.45620.55600.16300.4992X40.24490.2840−0.02670.8202−0.1289X5−0.39850.0760−0.00330.26200.5745X60.4083−0.1013−0.00530.1540−0.2289X7−0.07320.38080.7851−0.0793−0.3003X80.42460.28340.1792−0.28030.2308X9−0.04690.6454−0.1393−0.18030.3244According to the proportion of the contribution rate of each principal component to the total contribution rate, the comprehensive coefficient of each factor is calculated and the weight vector of each evaluation index is obtained:(7)W=−0.1214,0.2750,0.0798,0.2515,−0.0911,0.1597,0.12,0.272,0.1218T.Then, we establish a comprehensive evaluation model [8] as follows:(8)Fj=∑i=17wirij.Based on this model, the higher the comprehensive score is, the stronger the national vulnerability has. We calculated data from 16 countries, and the ranking of comprehensive score and vulnerability is shown in Table4 and Figure 2.Figure 2
Distribution maps of comprehensive score.


## 3.2.1. Model Preparation

Considering the influencing factors of the state vulnerability, we should establish the hierarchical structure to evaluate the vulnerability. The decision-making problem is divided into three layers: target layer (T), criterion layer (C), and program layer (P), and each layer has several factors [5], as shown in Table 1.Table 1
Hierarchy table.
Level 1 indicatorLevel 2 indicatorLevel 3 indicatorMarkNational vulnerabilityEconomic factorsPer capita GNIX1Gini coefficientX2Political factorsMilitary expenditure as a share of GDPX3Public education expenditure as a share of GDPX4Social factorsPer capita carbon dioxide emissionsX5Population growth rateX6International migrant share of total populationX7Climatic factorsTemperatureX8PrecipitationX9


## 3.2.2. Model Establishing and Solving

SupposeU=u1,u2,…,un is a group of n countries to be evaluated, V=v1,v2,…,vm is a set of evaluation factors, and each solution in U is measured by each factor in V, and we select the observation matrix X from the observation data of 16 countries in 2015 [6]:(1)X=272029906270⋯6393036.743.248.5⋯21.4563.9672.311.323⋯0.666⋮⋮⋮⋱⋮−0.7380.9760.075⋯1.549.In the formula,xij represents the indicator value of the j-th program on the i-th evaluation factor, and the vector xj=x1j,x2j,…,xmj denotes the evaluation vector of the j-th program on the m-th evaluation indicator.Suppose the ideal urban air quality indicator vector isu:(2)u=u10,u20,…,um0=63930,21.456,0.17,7.357,0.075,−0.824,17620,−0.91,−0.738,where(3)ui0=max1≤j≤nxij,whenxijis a benefit index,min1≤j≤nxij,whenxijis acostindex,xij−x0x0,whenxijis a neutral index.According to the established ideal scheme, the relative deviation fuzzy matrixR∼ is calculated by substituting the observed data from 16 countries:(4)R∼=r11r12⋯r131r21r22⋯r231⋮⋮⋱⋮r71r72⋯r731,where(5)Yij=xij−ui0max1≤j≤nxij−min1≤j≤nxij,i=1,2,…,9;j=1,2,…,16.So, we can get(6)R∼=0.9750.9890.97⋯00.5640.8041⋯00.6980.3940.212⋯0.091⋮⋮⋮⋱⋮00.1750.171⋯0.48.We use the principal component analysis method to calculate the weight of each evaluation indicator. When calculating, the number of principal components can be selected to make the contribution rate of cumulative variance of factors large enough (larger than 85% here) so as to protect the interpretation ability of the common factor to data [7].As can be seen from Table2, when the 5 principal components are selected, the cumulative variance contribution rate of the factor has reached 88.508% and meets the requirements, so the first 5 factors are selected for analysis. The column vector of the i factor in Table 3 is divided by the square root of the corresponding characteristic root, and the variation coefficient vector of principal components is obtained, which is shown in Table 4.Table 2
The principal component eigenvalues and cumulative contribution rate.
ComponentsTotalRate of changeCumulative (%)13.63840.42740.42721.88920.99161.41830.96810.75872.17540.8349.26481.44050.6367.06888.50860.4645.15693.66470.4054.50098.16480.0971.07499.23890.0690.762100.000Table 3
Comprehensive score sheet.
CountryComprehensive scoreRankAngola0.78011Zimbabwe0.75272Yemen0.72093Kenya0.71564Laos0.68235Malawi0.64196India0.60407Malaysia0.57488Indonesia0.47929Namibia0.475710Mongolia0.282111Azerbaijan0.259712Poland0.241913Croatia0.182914Slovenia0.119315Switzerland0.089016Table 4
Principal component coefficient matrix.
FactorsZ1Z2Z3Z4Z5X1−0.43230.16630.10690.2852−0.0776X20.46960.1602−0.10400.09810.3115X30.1390−0.45620.55600.16300.4992X40.24490.2840−0.02670.8202−0.1289X5−0.39850.0760−0.00330.26200.5745X60.4083−0.1013−0.00530.1540−0.2289X7−0.07320.38080.7851−0.0793−0.3003X80.42460.28340.1792−0.28030.2308X9−0.04690.6454−0.1393−0.18030.3244According to the proportion of the contribution rate of each principal component to the total contribution rate, the comprehensive coefficient of each factor is calculated and the weight vector of each evaluation index is obtained:(7)W=−0.1214,0.2750,0.0798,0.2515,−0.0911,0.1597,0.12,0.272,0.1218T.Then, we establish a comprehensive evaluation model [8] as follows:(8)Fj=∑i=17wirij.Based on this model, the higher the comprehensive score is, the stronger the national vulnerability has. We calculated data from 16 countries, and the ranking of comprehensive score and vulnerability is shown in Table4 and Figure 2.Figure 2
Distribution maps of comprehensive score.


## 3.3. Result Analysis

According to the score and ranking, the score is divided into three levels, respectively, marked as follows: fragile, vulnerable, and stable, as shown in Table5.Table 5
The rating level.
Comprehensive score0–0.40.4–0.70.7–1National vulnerabilityStableVulnerableFragileWhen a country has a combined score between 0 and 0.4 in a given year, the country is stable. When the score is between 0.4 and 0.7, the country is fragile. When score is between 0.7 and 1, it is fragile [9].Temperature and precipitation are the main manifestations of climate and also direct factors affecting national vulnerability. When temperatures and precipitation deviate from a reasonable range, national vulnerability increases. According to principle component analysis model, it can be found that there is a direct correlation between indicators affecting national vulnerability. As a result, temperature is also vulnerable to indirect impacts of national vulnerability, thus affecting other indicators [10].


## 4. Take the Top 10 Most Fragile States to Illustrate How Climate Change Has Increased Their Vulnerability

### 4.1. Problem Analysis

According to the model, the national vulnerability indicators include purchasing power index, gross national income, Gini coefficient, military expenditure, public education, carbon dioxide emissions, population growth rate, international migration, temperature, and precipitation. The weight vector isW.From the above description, we know that climate change affects national vulnerability through changes in temperature, precipitation, and carbon dioxide emissions. In the absence of climate change, other indicators can reduce national vulnerability by changing the data to change the relative error fuzzy matrix, thereby affecting national vulnerability scores. To determine the impact of climate change on national vulnerability, we use control variables to process data analyze the impact of the vulnerability of selected countries.Taking Sudan as an example, we analyzed the impact of other factors on national vulnerability when the initial temperature increased or decreased by 5%, 10%, and 15%. Similarly, we take Sudan to analyze the impact of other factors on the vulnerability of the country [11].


### 4.2. Problem Solving

#### 4.2.1. The Impact of Temperature on Vulnerability

Combined with the above problem, we get the national vulnerability score of Sudan under different temperature data, as shown in Table6.Table 6
Impact of temperature on vulnerability.
Temperature changesDropRaw dataRise15%10%5%5%10%15%Temperature25.4825.4826.9028.3129.7331.1429.73Comprehensive score0.50850.57950.65060.68610.72160.75990.7799The relationship between national vulnerability and temperature was fitted, and the trend of national vulnerability was observed. From Table6, we know that when the temperature deviates from the baseline temperature, as the temperature rises, the state fragility score increases, which means the state vulnerability increases.


#### 4.2.2. The Impact of Precipitation on Vulnerability

The national vulnerability score of Sudan under different precipitation can be found in Table7.Table 7
Impact of precipitation on vulnerability.
Precipitation changesDecreaseRaw dataIncrease30%20%10%10%20%30%Precipitation deviation−0.7145−0.6003−0.4861−0.429−0.3719−0.2577−0.1435Comprehensive score0.82160.77540.73920.68610.6430.57680.5206As can be seen from the result of simulation, Sudan’s national vulnerability score varies with the change of precipitation. We can see from Table7 that the score of national vulnerability increases with the increase of precipitation; that is, the national vulnerability increases [12].


#### 4.2.3. The Impact of Carbon Dioxide (CO2) Emissions on Vulnerability

The national vulnerability score of Sudan under different CO2 emissions is shown in Table 8.Table 8
Impact of CO2 emissions on vulnerability.
CO2 emission changesDecreaseRaw dataIncrease15%10%5%5%10%15%CO2 emissions0.26270.27820.29360.30910.32460.34000.3555Comprehensive score0.51210.57330.62180.68610.70840.74310.7956We fit national vulnerability and carbon dioxide emissions and observe the changing trends in state fragility as shown in Figure3.Figure 3
Impact of CO2 emissions on state fragility.As shown in Figure3, with the increase of CO2 emissions, national vulnerability score increases and the national vulnerability increases. Thus, under certain conditions, the reduction of CO2 emissions may reduce the vulnerability. Carbon dioxide emissions are largely influenced by human factors. In order to reduce the emission, the state should advocate low-carbon and environment-friendly production and life style.


## 4.1. Problem Analysis

According to the model, the national vulnerability indicators include purchasing power index, gross national income, Gini coefficient, military expenditure, public education, carbon dioxide emissions, population growth rate, international migration, temperature, and precipitation. The weight vector isW.From the above description, we know that climate change affects national vulnerability through changes in temperature, precipitation, and carbon dioxide emissions. In the absence of climate change, other indicators can reduce national vulnerability by changing the data to change the relative error fuzzy matrix, thereby affecting national vulnerability scores. To determine the impact of climate change on national vulnerability, we use control variables to process data analyze the impact of the vulnerability of selected countries.Taking Sudan as an example, we analyzed the impact of other factors on national vulnerability when the initial temperature increased or decreased by 5%, 10%, and 15%. Similarly, we take Sudan to analyze the impact of other factors on the vulnerability of the country [11].


## 4.2. Problem Solving

### 4.2.1. The Impact of Temperature on Vulnerability

Combined with the above problem, we get the national vulnerability score of Sudan under different temperature data, as shown in Table6.Table 6
Impact of temperature on vulnerability.
Temperature changesDropRaw dataRise15%10%5%5%10%15%Temperature25.4825.4826.9028.3129.7331.1429.73Comprehensive score0.50850.57950.65060.68610.72160.75990.7799The relationship between national vulnerability and temperature was fitted, and the trend of national vulnerability was observed. From Table6, we know that when the temperature deviates from the baseline temperature, as the temperature rises, the state fragility score increases, which means the state vulnerability increases.


### 4.2.2. The Impact of Precipitation on Vulnerability

The national vulnerability score of Sudan under different precipitation can be found in Table7.Table 7
Impact of precipitation on vulnerability.
Precipitation changesDecreaseRaw dataIncrease30%20%10%10%20%30%Precipitation deviation−0.7145−0.6003−0.4861−0.429−0.3719−0.2577−0.1435Comprehensive score0.82160.77540.73920.68610.6430.57680.5206As can be seen from the result of simulation, Sudan’s national vulnerability score varies with the change of precipitation. We can see from Table7 that the score of national vulnerability increases with the increase of precipitation; that is, the national vulnerability increases [12].


### 4.2.3. The Impact of Carbon Dioxide (CO2) Emissions on Vulnerability

The national vulnerability score of Sudan under different CO2 emissions is shown in Table 8.Table 8
Impact of CO2 emissions on vulnerability.
CO2 emission changesDecreaseRaw dataIncrease15%10%5%5%10%15%CO2 emissions0.26270.27820.29360.30910.32460.34000.3555Comprehensive score0.51210.57330.62180.68610.70840.74310.7956We fit national vulnerability and carbon dioxide emissions and observe the changing trends in state fragility as shown in Figure3.Figure 3
Impact of CO2 emissions on state fragility.As shown in Figure3, with the increase of CO2 emissions, national vulnerability score increases and the national vulnerability increases. Thus, under certain conditions, the reduction of CO2 emissions may reduce the vulnerability. Carbon dioxide emissions are largely influenced by human factors. In order to reduce the emission, the state should advocate low-carbon and environment-friendly production and life style.


## 4.2.1. The Impact of Temperature on Vulnerability

Combined with the above problem, we get the national vulnerability score of Sudan under different temperature data, as shown in Table6.Table 6
Impact of temperature on vulnerability.
Temperature changesDropRaw dataRise15%10%5%5%10%15%Temperature25.4825.4826.9028.3129.7331.1429.73Comprehensive score0.50850.57950.65060.68610.72160.75990.7799The relationship between national vulnerability and temperature was fitted, and the trend of national vulnerability was observed. From Table6, we know that when the temperature deviates from the baseline temperature, as the temperature rises, the state fragility score increases, which means the state vulnerability increases.


## 4.2.2. The Impact of Precipitation on Vulnerability

The national vulnerability score of Sudan under different precipitation can be found in Table7.Table 7
Impact of precipitation on vulnerability.
Precipitation changesDecreaseRaw dataIncrease30%20%10%10%20%30%Precipitation deviation−0.7145−0.6003−0.4861−0.429−0.3719−0.2577−0.1435Comprehensive score0.82160.77540.73920.68610.6430.57680.5206As can be seen from the result of simulation, Sudan’s national vulnerability score varies with the change of precipitation. We can see from Table7 that the score of national vulnerability increases with the increase of precipitation; that is, the national vulnerability increases [12].


## 4.2.3. The Impact of Carbon Dioxide (CO2) Emissions on Vulnerability

The national vulnerability score of Sudan under different CO2 emissions is shown in Table 8.Table 8
Impact of CO2 emissions on vulnerability.
CO2 emission changesDecreaseRaw dataIncrease15%10%5%5%10%15%CO2 emissions0.26270.27820.29360.30910.32460.34000.3555Comprehensive score0.51210.57330.62180.68610.70840.74310.7956We fit national vulnerability and carbon dioxide emissions and observe the changing trends in state fragility as shown in Figure3.Figure 3
Impact of CO2 emissions on state fragility.As shown in Figure3, with the increase of CO2 emissions, national vulnerability score increases and the national vulnerability increases. Thus, under certain conditions, the reduction of CO2 emissions may reduce the vulnerability. Carbon dioxide emissions are largely influenced by human factors. In order to reduce the emission, the state should advocate low-carbon and environment-friendly production and life style.


## 5. Identify a Tipping Point and Predict When to Reach It and Become More Fragile

### 5.1. Problem Analysis

#### 5.1.1. Evaluation of Non Top Ten Fragile Countries

We chose Mongolia, which is not one of the ten most vulnerable countries to analyze this problem. Based on the indicator data in 2015, Mongolia’s vulnerability score was calculated and the range of the score was determined, thereby determining its vulnerability. By means of control variables, we dynamically analyze the changes in national vulnerability under different temperature conditions and find the trend that national vulnerability scores change with temperature, so as to predict the contribution of climate change to national vulnerability [11–13].


#### 5.1.2. Prediction about Reaching Critical Value

From the previous analysis, we know that CO2 emissions have a significant impact on national vulnerability. With the increase of CO2 emissions, the score of national vulnerability increase and the national vulnerability also increase. So we choose CO2 emissions as an indicator. The comprehensive national vulnerability score of Mongolia under different CO2 concentrations is calculated by using the control variable method, the direct functional relationship between the national vulnerability score and CO2 emissions is obtained, and the tipping point of vulnerability is also identified. Based on the annual CO2 emission data of the World Bank of Mongolia, we use the time series model to predict concentrations of CO2 in the coming decades, when to reach the tipping point of concentrations, and when to reach the international year record and become more fragile.


### 5.2. Problem Solving of Evaluating Non Top Ten Fragile Countries

According to the indicator system model, the national vulnerability scores of Mongolia are 0.2821 and 0 < 0.2821 < 0.4, which can be judged that Mongolia is stable in 2015. We change the temperature indicator data to make the temperature change by 5%, 10%, and 15%, respectively, and obtain the national vulnerability score at different temperatures, as shown in Figure4.Figure 4
Impact of temperature on vulnerability.As can be seen from Figure4, the national vulnerability of Mongolia also increases with the rise of temperature. In addition, we can see from the figure that, when the temperature rises from 10% to 15%, the national vulnerability score rises from 0.3829 to 0.4113; that is, Mongolia is transitioning from a stable state to a fragile state. Therefore, the Mongolian government should pay attention to the factors affecting temperature rise and strengthen climate management to prevent instability in the country.


### 5.3. Linear Regression Model [14]

#### 5.3.1. Model Preparation

We fit national vulnerabilities and carbon dioxide emissions and observe the changing trends in national vulnerability, as shown in Figure5.Figure 5
Scatter plot.


#### 5.3.2. Model Establishment

We have known that the increase in CO2 emissions will lead to the increase in national vulnerability scores. Therefore, we establish a linear regression model between CO 2 emissions and national vulnerability scores as follows [15]:(9)Yi=b0+b1Xi+εi,i=1,2,3,…,n.In solving the normal equations, the least squares estimators of the two parameters are obtained as follows:(10)b^1=SCPSSX,b^0=Y¯−b^1X¯,where(11)SCP=∑i=1nXiYi=nX¯Y¯,SSX=∑i=1nXi2−nX¯2.


#### 5.3.3. Model Solving and Result Analysis

With the help of MATBLE regression, we obtain the regression model of CO2 emissions and national vulnerability score as follows:(12)y=23.4768x+0.5672.Based on the regression model, we calculate that the CO2 emissions of Mongolia are 9.95792 when the national vulnerability threshold is 0.4.


### 5.4. Time Series Prediction Model Based on Exponential Smoothing

#### 5.4.1. Model Establishment

An exponential smoothing formula is as follows:(13)St1=aYt+1−aSt−11,where St1 is the exponential smoothing value of t period, a is smoothing constant, 0<a<1, and Yt is current data:(14)St1=aYt+a1−aYt−1+1−a2Yt−2+⋯+a1−alYt−l+⋯+a1−at−1Yt−t−1+1−atS01=a∑k=0t−11−akYt−k+1−atS01.So, Yt,Yt−1,Yt−2,…,weights are a,a1−a,a1−a2,…, and the change in weights decreases exponentially.The smooth value of periodT is taken as the predicted value of period T + 1 [16]:(15)Y^t+1=St1=aYt+1−a.


#### 5.4.2. Model Solving and Result Analysis

We use the time series model to predict the emissions of carbon dioxide in the coming 11 years and get data shown in Table9.Table 9
Prediction data (metric tons/person).
Year201720182019202020212022Carbon dioxide emissions7.31727.62587.84018.19648.51488.9027Year20232024202520262027—Carbon dioxide emissions9.38169.69319.960210.185810.3374—From Table9, we can see that in 2025, carbon dioxide emissions of Mongolia will reach 9.9602 metric tons per capita; that is, Mongolia may reach the critical state of fragility [17].


## 5.1. Problem Analysis

### 5.1.1. Evaluation of Non Top Ten Fragile Countries

We chose Mongolia, which is not one of the ten most vulnerable countries to analyze this problem. Based on the indicator data in 2015, Mongolia’s vulnerability score was calculated and the range of the score was determined, thereby determining its vulnerability. By means of control variables, we dynamically analyze the changes in national vulnerability under different temperature conditions and find the trend that national vulnerability scores change with temperature, so as to predict the contribution of climate change to national vulnerability [11–13].


### 5.1.2. Prediction about Reaching Critical Value

From the previous analysis, we know that CO2 emissions have a significant impact on national vulnerability. With the increase of CO2 emissions, the score of national vulnerability increase and the national vulnerability also increase. So we choose CO2 emissions as an indicator. The comprehensive national vulnerability score of Mongolia under different CO2 concentrations is calculated by using the control variable method, the direct functional relationship between the national vulnerability score and CO2 emissions is obtained, and the tipping point of vulnerability is also identified. Based on the annual CO2 emission data of the World Bank of Mongolia, we use the time series model to predict concentrations of CO2 in the coming decades, when to reach the tipping point of concentrations, and when to reach the international year record and become more fragile.


## 5.1.1. Evaluation of Non Top Ten Fragile Countries

We chose Mongolia, which is not one of the ten most vulnerable countries to analyze this problem. Based on the indicator data in 2015, Mongolia’s vulnerability score was calculated and the range of the score was determined, thereby determining its vulnerability. By means of control variables, we dynamically analyze the changes in national vulnerability under different temperature conditions and find the trend that national vulnerability scores change with temperature, so as to predict the contribution of climate change to national vulnerability [11–13].


## 5.1.2. Prediction about Reaching Critical Value

From the previous analysis, we know that CO2 emissions have a significant impact on national vulnerability. With the increase of CO2 emissions, the score of national vulnerability increase and the national vulnerability also increase. So we choose CO2 emissions as an indicator. The comprehensive national vulnerability score of Mongolia under different CO2 concentrations is calculated by using the control variable method, the direct functional relationship between the national vulnerability score and CO2 emissions is obtained, and the tipping point of vulnerability is also identified. Based on the annual CO2 emission data of the World Bank of Mongolia, we use the time series model to predict concentrations of CO2 in the coming decades, when to reach the tipping point of concentrations, and when to reach the international year record and become more fragile.


## 5.2. Problem Solving of Evaluating Non Top Ten Fragile Countries

According to the indicator system model, the national vulnerability scores of Mongolia are 0.2821 and 0 < 0.2821 < 0.4, which can be judged that Mongolia is stable in 2015. We change the temperature indicator data to make the temperature change by 5%, 10%, and 15%, respectively, and obtain the national vulnerability score at different temperatures, as shown in Figure4.Figure 4
Impact of temperature on vulnerability.As can be seen from Figure4, the national vulnerability of Mongolia also increases with the rise of temperature. In addition, we can see from the figure that, when the temperature rises from 10% to 15%, the national vulnerability score rises from 0.3829 to 0.4113; that is, Mongolia is transitioning from a stable state to a fragile state. Therefore, the Mongolian government should pay attention to the factors affecting temperature rise and strengthen climate management to prevent instability in the country.


## 5.3. Linear Regression Model [14]

### 5.3.1. Model Preparation

We fit national vulnerabilities and carbon dioxide emissions and observe the changing trends in national vulnerability, as shown in Figure5.Figure 5
Scatter plot.


### 5.3.2. Model Establishment

We have known that the increase in CO2 emissions will lead to the increase in national vulnerability scores. Therefore, we establish a linear regression model between CO 2 emissions and national vulnerability scores as follows [15]:(9)Yi=b0+b1Xi+εi,i=1,2,3,…,n.In solving the normal equations, the least squares estimators of the two parameters are obtained as follows:(10)b^1=SCPSSX,b^0=Y¯−b^1X¯,where(11)SCP=∑i=1nXiYi=nX¯Y¯,SSX=∑i=1nXi2−nX¯2.


### 5.3.3. Model Solving and Result Analysis

With the help of MATBLE regression, we obtain the regression model of CO2 emissions and national vulnerability score as follows:(12)y=23.4768x+0.5672.Based on the regression model, we calculate that the CO2 emissions of Mongolia are 9.95792 when the national vulnerability threshold is 0.4.


## 5.3.1. Model Preparation

We fit national vulnerabilities and carbon dioxide emissions and observe the changing trends in national vulnerability, as shown in Figure5.Figure 5
Scatter plot.


## 5.3.2. Model Establishment

We have known that the increase in CO2 emissions will lead to the increase in national vulnerability scores. Therefore, we establish a linear regression model between CO 2 emissions and national vulnerability scores as follows [15]:(9)Yi=b0+b1Xi+εi,i=1,2,3,…,n.In solving the normal equations, the least squares estimators of the two parameters are obtained as follows:(10)b^1=SCPSSX,b^0=Y¯−b^1X¯,where(11)SCP=∑i=1nXiYi=nX¯Y¯,SSX=∑i=1nXi2−nX¯2.


## 5.3.3. Model Solving and Result Analysis

With the help of MATBLE regression, we obtain the regression model of CO2 emissions and national vulnerability score as follows:(12)y=23.4768x+0.5672.Based on the regression model, we calculate that the CO2 emissions of Mongolia are 9.95792 when the national vulnerability threshold is 0.4.


## 5.4. Time Series Prediction Model Based on Exponential Smoothing

### 5.4.1. Model Establishment

An exponential smoothing formula is as follows:(13)St1=aYt+1−aSt−11,where St1 is the exponential smoothing value of t period, a is smoothing constant, 0<a<1, and Yt is current data:(14)St1=aYt+a1−aYt−1+1−a2Yt−2+⋯+a1−alYt−l+⋯+a1−at−1Yt−t−1+1−atS01=a∑k=0t−11−akYt−k+1−atS01.So, Yt,Yt−1,Yt−2,…,weights are a,a1−a,a1−a2,…, and the change in weights decreases exponentially.The smooth value of periodT is taken as the predicted value of period T + 1 [16]:(15)Y^t+1=St1=aYt+1−a.


### 5.4.2. Model Solving and Result Analysis

We use the time series model to predict the emissions of carbon dioxide in the coming 11 years and get data shown in Table9.Table 9
Prediction data (metric tons/person).
Year201720182019202020212022Carbon dioxide emissions7.31727.62587.84018.19648.51488.9027Year20232024202520262027—Carbon dioxide emissions9.38169.69319.960210.185810.3374—From Table9, we can see that in 2025, carbon dioxide emissions of Mongolia will reach 9.9602 metric tons per capita; that is, Mongolia may reach the critical state of fragility [17].


## 5.4.1. Model Establishment

An exponential smoothing formula is as follows:(13)St1=aYt+1−aSt−11,where St1 is the exponential smoothing value of t period, a is smoothing constant, 0<a<1, and Yt is current data:(14)St1=aYt+a1−aYt−1+1−a2Yt−2+⋯+a1−alYt−l+⋯+a1−at−1Yt−t−1+1−atS01=a∑k=0t−11−akYt−k+1−atS01.So, Yt,Yt−1,Yt−2,…,weights are a,a1−a,a1−a2,…, and the change in weights decreases exponentially.The smooth value of periodT is taken as the predicted value of period T + 1 [16]:(15)Y^t+1=St1=aYt+1−a.


## 5.4.2. Model Solving and Result Analysis

We use the time series model to predict the emissions of carbon dioxide in the coming 11 years and get data shown in Table9.Table 9
Prediction data (metric tons/person).
Year201720182019202020212022Carbon dioxide emissions7.31727.62587.84018.19648.51488.9027Year20232024202520262027—Carbon dioxide emissions9.38169.69319.960210.185810.3374—From Table9, we can see that in 2025, carbon dioxide emissions of Mongolia will reach 9.9602 metric tons per capita; that is, Mongolia may reach the critical state of fragility [17].


## 6. Empirical Analysis of Mitigating Climate Vulnerability by Intervention Measures

### 6.1. Problem Analysis

According to the ranking of the national vulnerability indicator, we selected 177 representative countries. Combined with the fuzzy comprehensive evaluation model, we study national interventions to mitigate the risks posed by climate change, while other conditions remain the same [18]. We select representative three-level countries, including fragile countries (South Sudan and Somalia), vulnerable countries (Indonesia and China), and stable countries (the UK and USA). In the absence of extreme weather events, natural disasters, and economic and political crises, intervention in the climate environment will gradually improve the postintervention climate conditions [19].


### 6.2. Empirical Analysis of Intervention Measures

According to the indicator system model, carbon dioxide emissions are selected as the main factor for climate change intervention. After the intervention, CO2 emissions are 50% to 95% of the previous emissions. To facilitate the statistics, we take 5% as the step size [20]. In order to compare the countries more clearly, we standardize CO2 emissions in data processing and obtain the following scores, as shown in Table 10.Table 10
Score chart.
Carbon dioxide emissionsSouth SudanSomaliaChinaIndonesiaThe United StatesEngland0.10.69070.66670.49710.43130.19190.14190.20.72740.69740.52620.46620.22210.17210.30.76180.73180.55400.49400.24400.19400.40.78450.75450.57500.51500.26220.21220.50.80640.77640.58960.52960.27820.22820.60.82330.79330.60470.54470.29270.24270.70.83490.80490.61450.55450.30220.25220.80.84590.81590.62800.56800.31030.26030.90.85140.82140.64110.58110.31430.264310.85430.82430.64340.58340.31600.2659In the case of other indicators unchanged, we calculate the contribution rate of the country to CO2 emissions and obtain the weight vector of the evaluation indicator [21]. According to the comprehensive evaluation model, the calculation score is shown in Figures 6–8 [22].Figure 6
Score of fragile countries.Figure 7
Score of vulnerable countries.Figure 8
Score of stable countries.Through data processing, we find that for countries with different levels of vulnerability, intervention in carbon dioxide emissions can effectively reduce national vulnerability indicators and enhance national stability [23]. By changing CO2 emissions to the same extent, the country’s vulnerability score decreases slowly as the country becomes more stable [24].


## 6.1. Problem Analysis

According to the ranking of the national vulnerability indicator, we selected 177 representative countries. Combined with the fuzzy comprehensive evaluation model, we study national interventions to mitigate the risks posed by climate change, while other conditions remain the same [18]. We select representative three-level countries, including fragile countries (South Sudan and Somalia), vulnerable countries (Indonesia and China), and stable countries (the UK and USA). In the absence of extreme weather events, natural disasters, and economic and political crises, intervention in the climate environment will gradually improve the postintervention climate conditions [19].


## 6.2. Empirical Analysis of Intervention Measures

According to the indicator system model, carbon dioxide emissions are selected as the main factor for climate change intervention. After the intervention, CO2 emissions are 50% to 95% of the previous emissions. To facilitate the statistics, we take 5% as the step size [20]. In order to compare the countries more clearly, we standardize CO2 emissions in data processing and obtain the following scores, as shown in Table 10.Table 10
Score chart.
Carbon dioxide emissionsSouth SudanSomaliaChinaIndonesiaThe United StatesEngland0.10.69070.66670.49710.43130.19190.14190.20.72740.69740.52620.46620.22210.17210.30.76180.73180.55400.49400.24400.19400.40.78450.75450.57500.51500.26220.21220.50.80640.77640.58960.52960.27820.22820.60.82330.79330.60470.54470.29270.24270.70.83490.80490.61450.55450.30220.25220.80.84590.81590.62800.56800.31030.26030.90.85140.82140.64110.58110.31430.264310.85430.82430.64340.58340.31600.2659In the case of other indicators unchanged, we calculate the contribution rate of the country to CO2 emissions and obtain the weight vector of the evaluation indicator [21]. According to the comprehensive evaluation model, the calculation score is shown in Figures 6–8 [22].Figure 6
Score of fragile countries.Figure 7
Score of vulnerable countries.Figure 8
Score of stable countries.Through data processing, we find that for countries with different levels of vulnerability, intervention in carbon dioxide emissions can effectively reduce national vulnerability indicators and enhance national stability [23]. By changing CO2 emissions to the same extent, the country’s vulnerability score decreases slowly as the country becomes more stable [24].


## 7. Evaluation and Spread of the Model

### 7.1. Revision of the Model to Make It Work on Smaller “States” or Cities

Combining with the evaluation system based on the abovementioned national vulnerability evaluation model, as well as the specific characteristics of cities, we rank and classify the indicators according to the risk level, sensitivity, and adaptability [25]. We have established a selection database of urban vulnerability indicators to prepare for future indicator screening.


### 7.2. The PSR Model

Firstly, the index data are standardized, and the entropy method is used to determine the indicator weight, and then the comprehensive weight method is used to calculate the indicator of urban vulnerability. After that, we use factor analysis in SPSS to find out the main factors influencing factors [26].The framework of the PSR model (risk sensitivity and adaptability) is adopted to establish the indicator evaluation system of urban vulnerability. National risk indicators are selected in terms of climate change, global and external pressure factors, including annual average precipitation change, annual average temperature change, frequency of extreme climate events, urban population density, and situation of economic development [27]. Urban sensitivity indicators are selected from the factors to explain situation of urban natural and social environment, including water resources, relative humidity, vegetation ecosystem, political conflict, social equity, and primary industrial structure. Urban resilience refers to the city’s ability to respond to climate change, including GDP per capita, education level, hospital beds, science and technology expenditure, ecological construction, pollution control, and other factors [28].(1)
Building the original index data matrix: it is composed of city and evaluation indexes.There arem cities and n evaluation indicators that form the original indicator data matrix:(16)X=xijm×n0≤i≤m,0≤j≤n,where xij is the j-th indicator value of the i-th city. Firstly, standardize the original indicator data.The standardized calculation of positive indicators is as follows:(17)X=Xij−minXijmaxXi−minXi.The standardized calculation of negative indicators is as follows:(18)X=maxXi−XimaxXi−minXi.After processing, calculate the proportion of the indicator valuePij of the i-th city under the j-th indicator:(19)Pij=yij∑i=1myij.Then, calculate the entropy of thej-th indicator:(20)Eij=−k∑i=1mPijlnPij,k=1lnm.(2)
Calculating the weights of thej-th evaluation indicator Wj:wj=gj/∑gj.(3)
Calculating the difference coefficient of thej-th evaluation indicator: based on the above method, the weight of the evaluation indicators at all levels of the urban vulnerability evaluation index system in the context of climate change has been finally determined.


## 7.1. Revision of the Model to Make It Work on Smaller “States” or Cities

Combining with the evaluation system based on the abovementioned national vulnerability evaluation model, as well as the specific characteristics of cities, we rank and classify the indicators according to the risk level, sensitivity, and adaptability [25]. We have established a selection database of urban vulnerability indicators to prepare for future indicator screening.


## 7.2. The PSR Model

Firstly, the index data are standardized, and the entropy method is used to determine the indicator weight, and then the comprehensive weight method is used to calculate the indicator of urban vulnerability. After that, we use factor analysis in SPSS to find out the main factors influencing factors [26].The framework of the PSR model (risk sensitivity and adaptability) is adopted to establish the indicator evaluation system of urban vulnerability. National risk indicators are selected in terms of climate change, global and external pressure factors, including annual average precipitation change, annual average temperature change, frequency of extreme climate events, urban population density, and situation of economic development [27]. Urban sensitivity indicators are selected from the factors to explain situation of urban natural and social environment, including water resources, relative humidity, vegetation ecosystem, political conflict, social equity, and primary industrial structure. Urban resilience refers to the city’s ability to respond to climate change, including GDP per capita, education level, hospital beds, science and technology expenditure, ecological construction, pollution control, and other factors [28].(1)
Building the original index data matrix: it is composed of city and evaluation indexes.There arem cities and n evaluation indicators that form the original indicator data matrix:(16)X=xijm×n0≤i≤m,0≤j≤n,where xij is the j-th indicator value of the i-th city. Firstly, standardize the original indicator data.The standardized calculation of positive indicators is as follows:(17)X=Xij−minXijmaxXi−minXi.The standardized calculation of negative indicators is as follows:(18)X=maxXi−XimaxXi−minXi.After processing, calculate the proportion of the indicator valuePij of the i-th city under the j-th indicator:(19)Pij=yij∑i=1myij.Then, calculate the entropy of thej-th indicator:(20)Eij=−k∑i=1mPijlnPij,k=1lnm.(2)
Calculating the weights of thej-th evaluation indicator Wj:wj=gj/∑gj.(3)
Calculating the difference coefficient of thej-th evaluation indicator: based on the above method, the weight of the evaluation indicators at all levels of the urban vulnerability evaluation index system in the context of climate change has been finally determined.


## 8. Conclusion

In the irrational world political and economic order dominated by the Western developed countries, poverty and war occur in the underdeveloped countries, social vulnerability exists in developed countries, and human beings in the industrial age are faced with the vulnerability of the natural environment [29].The research on the new theory of fragile countries can break through the limitations of the past, improve the ambiguity of the concept and the subjectivity of the evaluation criteria, and gradually improve the conceptual framework and evaluation indicator system of the national instability studies [30–35].At the same time, the evaluation system defines the vulnerability and incentive mechanism for national vulnerability, puts forward effective control or response measures, enhances the country’s ability to respond to various adverse effects, and strengthens the application of national vulnerability in the early warning of security risks. More importantly, it provides a scientific basis for our country to formulate sustainable development strategy.



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*Source: 3527540-2020-08-13.xml*