2018 MCM/ICM Summary Sheet

Transcription

1 For office use only T1 T2 T3 T4 Team Control Number Problem Chosen E 2018 MCM/ICM Summary Sheet For office use only F1 F2 F3 F4 Influence of Climate Change on State Fragility Summary The existing models used to measure a country s fragility rarely consider the effects of climate change, while the effects of climate change are gradually discovered and confirmed. Therefore, in this paper, we develop a new model to measure the impact of climate change on a country s fragility. Besides, we use our model to further study the features, predictions and countermeasures of climate change s impact. First, we establish a unified model to analyze the fragility of a country from four aspects of economy, politics, society and environment. Specifically, to measure state fragility, we introduce the metric Country Fragility Index (CFI), which consists of four indexes including Environment Fragility Index (ENFI), Economy Fragility Index (ECFI), Politics Fragility Index (PFI) and Society Fragility Index (SFI). Moreover, we apply Entropy Weight Method (EWM) to determine the weights of sub-indicators. Based on our model and Correlation Analysis, we identify the direct and indirect influences of climate change on a country s fragility. Finally, we evaluate our model by comparing the Fragile State Index (FSI). The data shows that both FSI and CFI share similar rankings (e.g., Top 3 fragile countries and their CFI: South Sudan, 96.98, Somalia, 95.53, Central African Republic,94.61), which means that our model is reliable. Then, we choose Afghanistan as an objective country since its ranking improves greatly in the comparison of FSI and CFI, which means that climate change has a greater potential impact on it. Accordingly, we use our model to investigate the impact of climate change on its country s fragility while identifying ways of affecting and affected aspects. The results show that the country s fragility of Afghanistan relies on climate change, which can be quantified as 9.74%. Moreover, the country s primary industry, demographic pressure and natural disasters and several other aspects are exacerbated by climate change. Considering that Egypt is also very likely to be affected by the potential impact of climate change, we choose it as the next research target. Our results illustrate that the impact of climate change on Egypt is relatively small (3.48%) and mainly concentrates on primary industry and demographic pressure, etc. Meanwhile, based on the fact that the CFI value of obey normal distribution, we set a tipping point empirically. By the utility of Gray Forecasting, we can predict that Egypt will reach the tipping point years later and become more fragile due to climate change. Also, based on our research, we propose a series of interventions that can reduce the negative effects of climate change, including increasing the productive capability of agriculture and improving level of the country s natural disaster risk managements. Furthermore, the implementation costs of these interventions can be calculated. Finally, we modify our model so that it can be used to measure the fragility of cities and the continents and the impact of climate change on them. We evaluate our modified model by applying it to New York and Osaka, the results show that the resulting model can successfully handle the case of continent.

2 Team # Page 1 of 20 1 Introduction 1.1 Problem Background In recent years, when discussing today s global security, development and poverty issues, the assessment and measurement of a country s fragility has been a hot issue for a long time. In order to measure the fragility of different countries, academic institutions and international organizations from all over the world provide various standards and methods, among which most of the measuring standards and methods focus on a country s political, social and economical situations. For example, the Country Policy And Institutional Assessment (CPIA) provided by the World Bank focus on four major aspects: "economic management, structural policies, policies for social inclusion and equity, and public sector management and institutions"[1]. Some other evaluation method further consider the influence of national security. However, as climate change becomes increasingly significant, by no means we should ignore the influence climate change has on country s fragility. Therefore, a new national fragility assessment method that take climate change as one of the major influence factors is needed. In order to measure a country s fragility and more importantly, to study the extent of climate change s influence and to identify the way climate change influences country s fragility. 1.2 Restatement of the Problem In order to help develop a new method to measure a country s fragility under the influence of climate change and to use the model to further study the impact of climate change, we are required to build a model to measure a country s fragility with the consideration of climate change. The detailed tasks are listed as follows: task1: We are required to create a model to measure a country s fragility while take the impacts on climate change into consideration, separate countries studied into fragile, vulnerable and stable countries. Our model should also be able to identify the way climate change influences the country s fragility. task2: Apply our model to one of the most fragile countries of the world (with the top10 Fragile State Index) to measure the extent climate change increases this country s fragility. task3: To further study the impacts of climate change, we are required to apply our model to another relatively stable county, measure this country s fragility and identify ways climate change influences this country s fragility. Set a tipping point to predict when climate change will push this country to be more fragile. task4: Select some effective interventions government can take to reduce the negative consequences of climate change and measure the cost of these interventions. task5: Analyze our model and make further modifications to enable it to measure the fragility of smaller regions(like cities)and larger regions(like continents) and impacts of climate change on them. 1.3 Our Work Firstly, we determine the overall idea of our model establishment and applications. Taking environment fragility, economy fragility, politics fragility and society fragility into consideration. Then we establish our model to measure the fragility of a country by using index Country Fragility Index(CFI), the whole model further includes 4 more indices, 13 indicators and 29 sub-indicators. Meanwhile we define the standard to classify countries as fragile, vulnerable and stable. We also develop the way to identify the direct and indirect ways climate change influences country fragility and to quantify this influence. We apply our model to Afghanistan and Egypt to measure the fragility, impact of climate change of both countries. Identify the definitive influence factors of climate change for Egypt and predict the future

3 Team # Page 2 of 20 influence of climate change by setting tipping point. Decide when Egypt will become a fragile country due to climate change. Based on our model s results, we make our recommendations on the interventions governments can take to reduce the impact of climate change and further reduce the country s fragility. For the intervention we select, we also develop the way to calculate the cost of them. By modifying our model through introducing new sub-indicators and developing new methods to use our model, we make our model be capable of measuring the fragility and impact of climate change of cities and continents. 2 General Assumption and Justification Climate change simultaneously has both direct and indirect impacts on national stability, only the extent of two kinds of impacts varies between countries and years. The external environment of a country is relatively stable. As dramatic changes in the external environment have high contingency and uncertainty, and will cause unpredictable influence on our model s results, we assume there is no such changes happens to this country during the time when we apply our model to it. 3 Notation Description Table 1: Major notations description Notations CF I P F I SF I ECF I ENF I P F I ij SF I ij ECF I ij ENF I ij ε ij I T Descriptions Country Fragility Index Environment Fragile Index Economy Fragile Index Society Fragile Index Politics Fragile Index The jth sub-indicator of the ith indicator for P F I The jth sub-indicator of the ith indicator for SF I The jth sub-indicator of the ith indicator for ECF I The jth sub-indicator of the ith indicator for ENF I The weight of the jth sub-indicator of the ith indicator The influence level of climate change on country fragility The tipping point of the influence level of climate change on country fragility

4 Team # Page 3 of 20 4 Model Establishment 4.1 Overall Modeling Idea As have mentioned above, most of the current standards and methods to measure a country s fragility mainly focus on the economy, politics and society of a country. By referring to the definition of country fragility provided by literature[2], we choose to take economy fragility, politics fragility and society fragility into consideration. Meanwhile, as another core research goal is the impact of climate change, we take environment into consideration. Economically Fragile: fragile economy makes this country have less power to maintain it s stability, and the deterioration of economy will induced a lot of instability factors. Politically Fragile: fragile politics deteriorating political environment typically will cause the lack of government s power and will to perform it s basic functions and will lead to the country s management missing, result in fragility. Socially Fragile: a chaotic and conflict-prone social environment obviously will increase a country s fragility. Environmentally Fragile: is an integrated concept, contains the country s reserve of natural resources, natural disasters of this country and the severity of climate change. The overall idea of our model is shown in Figure 1: Figure 1: Overall modeling idea based on consideration of four aspects 4.2 Model Overview We develop an index incorporating a country s fragility, named Country Fragility Index (CFI). The value of CFI varies from 0 to 100. When the CFI is large, it means the country is more fragile. Meanwhile, CFI is characterized by the fragility politics, society environment, economy and environment. We further develop four indices to measure the fragility of these four aspects, namely, Politic Fragility Index (PFI), which reflects a country s government s governance. Society Fragility Index (SFI), which reflects the severity of social problems; Economy Fragility Index (ECFI), which reflects a country s degree of economic backwardness and Environment Fragility Index (ENFI), which reflects the disadvantages of a country s natural environment, especially the severity of climate change. The values of all four indices vary from 0 to 100, higher index value means higher fragility. To measure the fragility of a country s politics, we use a series of indicators to calculate these four indices PFI,SFI,ECFI and ENFI, the details about the selection of indicators are in the following sections.

5 Team # Page 4 of 20 The overall structure and process flow of building our model is shown in Figure 2: Figure 2: The process flow of the establishment of the CFI model 4.3 Metric of Country Fragility Index As Figure2 has shown, the overall index CFI can be measured by four indices. To be specific, CFI can be calculated by using PFI, SFI, ECFI, ENFI and the equation: CF I = ω 1 P F I + ω 2 SF I + ω 3 ECF I + ω 4 ENF I, as we consider the fragility of each aspect mentioned above has equal influence on a country s total fragility, we assign weights as (0.25, 0.25, 0.25, 0.25). 4.4 Metric of Environment Fragility Index Considering the impact of country s environment situation (especially the extent of climate change), we set index ENFI as an innovative metric to take part in the measurement of a country s fragility. In our model, ENFI measures a country s environmental disadvantages, high ENFI means a country has tough environment and is very likely to be going through severe climate change, which is bad for it s state stability Selection of Indicators To make a comprehensive evaluation on a country s environment, we first consider the severity of this country s climate change. If the climate change is severe in a country, more unstable factors will appear, the country will tend to be more unstable and even fragile. Therefore We use indicator Severe Climate Change as one of the indicators used to measure a country s environment disadvantages. We further consider the richness of a country s natural resources such as oil, mineral and biological resources. If a country suffers resource shortage, it is very likely that this country s environment is not conducive to the stability of the country. Therefore, we use indicator Resource Shortage as another indicator.

6 Team # Page 5 of 20 Furthermore, as severe natural disasters will greatly reduce a country s stability, we also include indicator Severe Natural Disasters. Severe Climate Change: climate change includes the changes in precipitation, changes in mean temperatures, changes in carbon dioxide emissions, changes in sea level height, etc. "With inadequate preparation, the result could be a significant drop in the human carrying capacity of the Earth s environment."(see [3, Page 1]). Therefore, we consider the severity of a country s climate change will increase the disadvantage of a country s environment. To quantify the severity of climate change, we use Climate Change Performance Index (CCPI), high CCPI means severe climate change. Resource Shortage: to measure the resource shortage of a country, we consider the overall natural resources consists of two aspects, renewable resources and nonrenewable resources, we use sub-indicators Renewable Resource Reserves and Nonrenewable resource reserves. Additionally, as resources such as oil and minerals can greatly influence a country s economy and society, we use sub-indicator Self- Sufficiency Rate of Energy and Mineral Resources. Severe Natural Disasters: to quantify the negative consequences brought by natural disasters, we choose Direct Economic Loss Caused by Natural Disasters and Casualties Caused by Natural Disasters Construction of ENFI Metric System ENFI consists of three indicators, Severe Climate Change (defined as ENF I 1 ), Resource Shortage (defined as ENF I 2 ) and Severe Natural Disasters (defined as ENF I 3 ). Each indicator s sub-indicators are defined as ENF I ij, which means the jth sub-indicator of the ith indicator. The overall structure of metric ENFI is shown in Table 2: Table 2: Metric system of ENFI ENFI Indicators Notations Sub-Indicators Notations Correlation Severe Climate Change ENF I 1 Climate Change Performance Index ENF I 11 Resource Shortage ENF I 2 Non-Renewable Resource Reserves ENF I 22 Renewable Resource Reserves ENF I 21 Self-Sufficiency Rate of Energy and Mineral Resources ENF I 23 Severe Natural Disasters ENF I 3 Direct Economic Loss Caused by Natural Disasters Casualties Caused by Natural Disasters ENF I 31 ENF I 32 In Table 2, Correlation means the correlation between sub-indicators and indicators, positive correlation is shown as and negative correlation will be shown as. The index ENFI can be calculated by using ENF I 1, ENF I 2, ENF I 3 : ENF I = 1 3 ENF I ENF I ENF I 3, meanwhile, using sub-indicators to calculate all three indicators: ENF I i = n j=1 ν ij SF I ij, where n is the number of the sub-indicators of indicator i, ν ij is the weight of corresponding sub-indictor, for the uncertainty relationships between sub-indicators and indicators, the weights of sub-indicators require further calculation. Meanwhile, as all indicators values vary from 0 to 100, we make normalization to values the sub-indicators before calculating. The process of getting the value of ν ij and normalization is shown in section 4.8.

7 Team # Page 6 of Metric of Politic Fragility Index Metric PFI is created to measure the state government s governance ability, high PFI means the government has low governance ability Selection of Indicators To measure the governance of a government, we first refer to the definition given by the World Bank: "Governance is epitomized by predictable, open, and enlightened policymaking (that is, transparent processes); a bureaucracy imbued with a professional ethos; an executive arm of government accountable for its actions; and a strong civil society participating in public affairs; and all behaving under the rule of law" (see [4, Page vii]). From this definition, we conclude that the primary indicators effect a government s governance ability are government transparency, administrative efficiency, level of democracy and legal integrity. Government Transparency government transparency measures the extent the government share the information of it s management process to the country s citizens. In this model, we choose to use Freedom of the Press and Corruption Perceptions Index to measure government transparency, for a transparent government will set less limitations on the country s public media, meanwhile, more transparency means more supervision and less corruption. Administrative Efficiency: administrative efficiency measures the quality of administrative work carried out by state government, we choose Infrastructure and Public Satisfaction Index to measure it, for an efficient government usually is capable of making sufficient infrastructure constructions and winning people s support. Level of Democracy: level of democracy measures the development degree of democratic politics of a country, we choose Democracy Index to measure it. Legal Integrity: legal integrity reflects the soundness of the legal system of a country, and further reflects a country s degree of legalization. In this model, we choose Crime Rate to measure a country s legal integrity Construction of PFI Metric System As have mentioned above, high PFI means low governance ability and thus high country fragility, so we use Low Government Transparency(defined as P F I 1 ), Low Administrative Efficiency (defined as P F I 2 ), Low Level of Democracy (defined as P F I 3 ) and Low Legal Integrity (defined as P F I 4 ). Meanwhile, each indicators contains several sub-indicators (defined as P F I ij ). The metric we build is shown in Table 3. Table 3: Metric system of PFI PFI Indicators Notations Sub-Indicators Notations Correlation Freedom of P F I Low government the Press 11 P F I Transparency 1 Corruption P F I Perceptions Index 12 Infrastructure P F I Low Administrative 21 P F I Efficiency 2 Public P F I Satisfaction Index 22 Low Level of Democracy P F I 3 Democracy Index P F I 31 Low Legal Integrity P F I 4 Crime Rate P F I 41 The index PFI be calculated by using P F I 1, P F I 2, P F I 3 and P F I 4 : P F I = 1 4 P F I P F I P F I P F I 4,

8 Team # Page 7 of 20 meanwhile, each indicator can be calculated by using it s sub-indictors: P F I i = 100 n θ ij P F I ij, j=1 where n is the number of the sub-indicators of indicator i, θ ij is the weight of sub-indictors. The method to obtain the value of θ ij and make sub-indicators normalization can be seen in section Metric of Society Fragility Index In this model, we set index SFI to quantify the fragile level of a country s society, if the society has a high SFI, it means the society is fragile and the country s fragility will be increased due to it Selection of Indicators In order to measure the fragility of a society, we choose to focus on the living standards of social members, for a stable society typically is capable of providing people with high quality lives, the living standard of people is used. Meanwhile, to measure a society s fragility based the consequence of instability, we consider the cause of a unstable society, we choose demographic pressure and regional conflicts as our indicators too. Living Standard: living standard reflects the life quality of people. First, we use Unemployment Rate it will directly influence people s daily life, has strong connections between living standards. As we consider that a stable society will have low inequality, we further use GINI Coefficient for it can successfully reflect the gap between rich and poor. Then, Social Welfare Level is also used because sufficient social welfare will greatly enhance people s life quality. Demographic Pressure: demographic pressure appears when population growth exceeds the capacity of natural resources and the capacity society productivity, is a consequence of social fragility. To measure it, we choose indicator Population Growth, because the over growth of population is the main cause of demographic pressure. We further consider the results of demographic pressure, we use Food and Water Scarcity and Epidemic Intensity of Main Infectious Diseases to reflect it. Regional Conflicts: an unstable society generally tends to have more conflicts, which includes conflicts between ethnic groups, conflicts between different religions and war. Therefore, we use Direct E- conomic Loss Caused by Religious and Ethnic Conflicts, Casualties Caused by Religious and Ethnic Conflicts to measure the severity of religious and ethnic conflicts and Direct Economic Loss Caused by War, Casualties Caused by War to measure the severity of war Construction of SFI Metric System Here we set the metric SFI, high SFI means high social fragility. Therefore, for the convenience of calculating and using SFI, we use Low Living Standard(defined as SF I 1 ), Demographic Pressure(defined as SF I 2 ), Regional Conflicts(defined as SF I 2 ). Each indicator can be further measured by a series of sub-indicators(defined as SF I ij ). The metric of SFI is in Table 4:

9 Team # Page 8 of 20 Table 4: Metric system of SFI SFI Indicators Notations Sub-Indicators Notations Correlation Epidemic Intensity of Main SF I Demographic Infectious Diseases 11 SF I Pressure 1 Food and Water Scarcity SF I 12 Population Growth SF I 13 Direct Economic Loss Caused SF I by Religious and Ethnic Conflicts 21 Regional SF I Conflicts 2 Casualties Caused by SF I Religious and Ethnic Conflicts 22 Direct Economic Loss Caused by War SF I 23 Casualties Caused by War SF I 24 Low Unemployment Rate SF I 31 living SF I 3 GINI Coefficient SF I 32 Standard Social Welfare Level SF I 33 The index SFI be calculated by using SF I 1, SF I 2, SF I 3 : P F I = 1 3 SF I SF I SF I 3, meanwhile, using sub-indicators to calculate all three indicators: n SF I i = 100 γ ij SF I ij, j=1 where n is the number of the sub-indicators of indicator i, γ ij is the weight of corresponding sub-indictor, the normalizing all sub-indicators and the calculating weights γ ij is shown in section Metric of Economy Fragility Index We set index ECFI to measure the economic backwardness of a country, typically, underdeveloped economy will cause the government of this country be less capable of providing people with their essential needs. As a country s ECFI increases, the country s economic backwardness increases and this country tends to be more fragile Selection of Indicators Generally, a country s economy consists of three major parts, primary industry, secondary industry and tertiary industry. To some extent, the development level of three major industries can determine the overall situation of a country s economy. Therefore, we select the indicators to measure a country s three major industries development. We use indicators Low Primary Industry Development, Low Secondary Industry Development and Low Tertiary Industry Development. To further quantify the development level of all three main industries, we select Industry GDP, Industry Growth Rate and Per capita Income of Industry Practitioners of all three industries as sub-indicators. For a flourishing and highly developed industry generally will create a great amount of product, is able to remain relatively high growth speed and the industry s practitioners will have high income Construction of ECFI Metric System We define the indicators Low Primary Industry Development as ECF I 1, Low Secondary Industry Development as ECF I 2 and Low Tertiary Industry Development as ECF I 3. Furthermore, the subindicators selected are defined as ECF I ij. The metric system of ECFI is shown in Table 5:

10 Team # Page 9 of 20 Table 5: Metric system of ECFI ECFI Indicators Notations Sub-Indicators Notations Correlation Low Primary Primary Industry GDP ECF I 11 Industry Development ECF I 1 Primary Industry Growth Rate ECF I 12 Low Secondary Industry Development Low Tertiary Industry Development Per Capita Income of Primary Industry Practitioners ECF I 13 ECF I 2 Secondary Industry Growth Rate ECF I 22 Secondary Industry GDP ECF I 21 Per Capita Income of Secondary Industry Practitioners ECF I 23 ECF I 3 Tertiary Industry Growth Rate ECF I 32 Tertiary Industry GDP ECF I 31 Per Capita Income of Tertiary Industry Practitioners ECF I 33 We consider all three indicators have the same importance. Then, using ECF I 1, ECF I 2 and ECF I 3 to calculate ECFI: ECF I = 1 3 ECF I ECF I ECF I 3, meanwhile, using sub-indicators ECF I ij to calculate all three indicators: ECF I i = 100 n α ij ECF I ij, j=1 where n is the number of the sub-indicators of indicator i, α ij is the weight of corresponding sub-indictor, The process of sub-indicators normalization and getting the value of α ij is shown in section Weights of Sub-Indicators Because of the uncertainty and complication exist in the relationship between sub-indicators and indicators of each Index, we choose to use the Entropy Weight Method (EWM) to calculate the weights of sub-indicators ε ij, mentioned as θi, αi, γi and νi in previous sections Application of Entropy Weight Method The basic idea of EWM is to determine the weight of each input index according to the size of the index variability. Typically, indices with higher variability can provide us with more valid information and should have larger influence, thus, such indices earn larger weights. The steps we take when make sun-indicator normalization and use EWM to identify weights are listed below: Step1. Normalization of sub-indicator. As have shown in Table2, Table3, Table4, Table5, different sub-indicators have different correlations(positive or negative) with corresponding indicators. For sub-indicators have positive correlations with indicators, we use maximum difference normalization: Y ij = x ij min(x i ) max(x i ) min(x i ), for sub-indicators have negative correlations with indicators, we use minimum difference normalization: Y ij = max(x i) x ij max(x i ) min(x i ), where X 1,..., Xk are k different sub-indicators, x i = {x 1, x 2,..., x n } is the ith value of index X, Y ij is the normalized values of x ij, n is the number of a sub-indicator s values.

11 Team # Page 10 of 20 Step2. Calculation of information entropy. A sub-indicator s information entropy is n E j = ln(n) 1 ln(p ij ), i=1 where p ij = Y ij n j=1 Y, ij if p ij = 0, then lim Yij 0 p ij ln(p ij ) = 0. Step3. Determine the weight of each sub-indicator. After get the information entropy of k sub-indicators E 1, E 2,..., E k, calculate the weight of each subindicator, W i = 1 E i k E i, (i = 1, 2,..., k), because the normalization ensures that all sub-indicators values range from 0 to 1, and the results of EWM(W i ) also range from 0 to 1, but all indicators and indices values vary from 0 to 100. Thus the weight of the ith sub-indicator for the corresponding indicator is defined as: ε i = 100W i. 4.9 Fragile, Vulnerable or Stable After counting the CFI of a series of countries and referring to the FSI of 179 countries[5], we find that the CFI values and FSI values change according to normal distribution. Meanwhile, as we use CFI to quantify the fragility of a country, higer CFI means the country is more fragile. Therefore, we can determine the boundary CFI(C 1 ) between fragile country and vulnerable country, the boundary CFI(C 2 ) between vulnerable country and stable country based on normal distribution. Figure 3: Classification of fragile, vulnerable or stable based on normal distribution The normal distribution of our CFI values is N(µ, σ 2 ), in our model, µ = 50, and σ is the standard deviation of CFI values, as in normal distribution there is P { X µ < σ} = , we define countries with CFI of top 15.87% as fragile countries, countries with CFI of middle 68.26% as vulnerable countries and countries with CFI of bottom 15.87% as stable countries. The exact value of boundary CFI C 1 and C 2 can be calculated by using: C 1 = C max, where C max is the highest CFI value. C 2 = C max,

12 Team # Page 11 of The Method to Measure the Influence of Climate Change Identification of Direct and Indirect Influence Factors The influences climate change has on state fragility can be divided into direct influences and indirect influences. As for direct influences factors, we set the direct influence factor is Severe Climate Change, which means users of this model can determine wether there is a significant direct influence by calculating the proportion this indicator take in the index CFI and see how big it is, large proportion means there is significant direct influence from climate change. In order to identify the indirect influence factors of climate change, we apply Correlation Analysis to sub-indicator Climate Change Performance Index and other sub-indicator of this model. When the result of Correlation Analysis shows that the relevance between one sub-indicator and Climate Change Performance Index is high, we can identify that sub-indicator as an indirect influence factor, which means that climate change influences the output of the model (the country s fragility) indirectly through influencing this sub-indicator The Influence Level of Climate Change To measure in what extent climate change influences the country s fragility, after using the model to get the CFI and have determined the indirect influence factors, we define the weight of the jth subindicator that has been determined as indirect influence factor of the ith indicator is β ij, and the weight of the direct influence factor (Climate Change Performance Index) is δ, we can get the influence level of climate change: I = 16 i=1 kin j=1 β ij + δ 16 i=1 k j=1 ε ij 100%, where k in is the number of sub-indicators that have been determined as indirect influence factor for the ith indicator and k is the number of sub-indicators for the ith indicator. I is the influence level of climate change on country fragility, varies from 0% to 100%, high value of I means climate change s influence takes a large part of the country s fragility Comparison of FSI and CFI To evaluate our model before putting it into practical use, we using our model to measure the fragility of countries with top20 Fragile State Index(FSI)[5] and get the result of the Country Fragility Index (CFI) of all those countries. We make comparison on these countries ranks of FSI and CFI and find that 11 of 20 countries rank remain the same, 8 of 20 countries ranks change by 1, and only 2 countries rank change by 2. The detailed results of our model and the change of these 20 countries ranks can be seen in Appendix Table A3. The result of the comparison reveals that the result of our model is largely accurate and credible, the changes of countries ranks can be explained as the influence of climate change and other factors that FSI doesn t consider. 5 Analysis of Climate Change s Influence on Country s Fragility 5.1 Fragile Country: Afghanistan Selection of the Focus Country According to the Fragile State Index[3], we select Afghanistan (ranks the 10th of the top 10 most fragile states) as the country we focus on. We select Afghanistan for it s distinctive features in all four aspects of

13 Team # Page 12 of 20 fragility we consider in our model. Besides, the geographic location and current economy development level of Afghanistan indicate that this country is very likely be significantly influenced by climate change Data Collection Environmental Data: We collect the data of Afghanistan s natural environment from a series of websites, among which we mainly collect the data of climate change in Afghanistan from Germanwatch[6], the data of Afghanistan s natural resource from OECD[7], the data of the natural disasters in Afghanistan from University of Richmond[8]. Economical, Political and Social Data: We collect Afghanistan s economical data mainly from the World Bank[9],the political data from the Economist Intelligence Unit[10] Afghanistan s social data mainly from Fund for Peace[11] Country s Fragility of Afghanistan After the processing of data and put the data into our model, we have get the result of our model, which has been shown in Appendix TabelA1.The CFI of Afghanistan is 90.07, by using the method mentioned in section4.7, Afghanistan is determined as a fragile country. CFI of Afghanistan consists of the value of 0.25ENFI (21.24), 0.25ECFI (22.33), 0.25SFI (22.97), 0.25PFI (23.53). The intuitive representation of proportions taken by ENFI, ECFI, PFI and SFI can be seen in Figure 4: Figure 4: Proportion of ENFI, ECFI, PFI and SFI in CFI From the result we conclude that the reason fragility of Afghanistan is synthesized, environmental, economical, social and political factors all have significant influence. Index ENFI of Afghanistan reaches 85, and takes the proportion of 21.3% in the value of CFI. Therefore, the environment, especially climate change has considerable influence on Afghanistan s country fragility and the detail of such influences requires further study. 5.2 Analysis of the Influence of Climate Change Measure the Influence of Climate Change In order to measure the influence climate change has on Afghanistan s country fragility, we first use the method mentioned in section4.8.1, we apply Correlation Analysis to the data of sub-indicator Climate Change Performance Index and the data of other sub-indicators, based on results of Correlation Analysis, we find a series of sub-indicators that have high relevance with Climate Change Performance Index. Therefore, except the indicator Severe Climate Change, four indicators that contain sub-indicators that have high relevance with Climate Change Performance Index will be influenced by climate change, all sub-indicators and indicators we found will be influenced by climate change have been shown in Table 6:

14 Team # Page 13 of 20 Table 6: Indicators and sub-indicators influenced by climate change Indicators influenced by climate change Severe Climate Change Severe Natural Disasters Low Primary Industry Development High Demographic Pressures Severe regional conflicts Sub-indicators influenced by climate change CCPI Direct Economic loss caused by Natural Disasters Casualties Caused by Natural Disasters Primary Industry GDP primary industry growth rate Per capita income of primary industry practitioners epidemic intensity main infectious diseases Food and Water Scarcity Direct Economic loss caused by Religious and Ethnic Conflicts Casualties caused by Religious and Ethnic Conflicts We identify sub-indicator Climate Change Performance Index as direct influence factor, and all other influenced sub-indicators as indirect influence factor. After identified the sub-indicators can be seen as indirect influence factors, we use the method mentioned in section4.8.2 and calculate I. We get the I of Afghanistan is 9.74%, which means that 9.74% of this country s fragility can be seen as the direct and indirect consequences of climate change. Figure 5: Measured influence of climate change Without Climate Change To remove the influence climate change set on the final result, we choose to use our model to recalculate the index CFI and remeasure the country s fragility after removing all the direct and indirect factors shown in Table6. After the recalculation and remeasurement, we get the new result of CFI and Afghanistan s country fragility without the effect of climate change. According to the new result, the CFI drops to and the value of ENFI, ECFI, SFI changes. To make it convenient to compare, the model s results with and without climate change s influence are shown in Figure 6: (a)results with climate change (b)results without climate change From the results we conclude that: Figure 6: Results comparison

15 Team # Page 14 of 20 As there is no sub-indicator of the index PFI is chosen as indirect influence factor, after removed the climate change s effects, the PFI and politic fragility remain the same. Because the index ECFI and SFI each contains several sub-indicators that are defined as indirect influence factors, both PFI and SFI have a certain degree of decline, to be specific, ECFI drops 5.0%, SFI drops 1.2%. Due to the fact that the primary industry s GDP only takes a small proportion of Afghanistan s total GDP and other factors like the severe conflicts(including war) of this country and recent natural disasters, it is reasonable that only a small part of the fragility of Afghanistan s economy and society is caused by climate change. So that the economy and society of Afghanistan will be less fragile. It is obvious that the index ENFI and environment fragility of Afghanistan has decreased greatly (30.2%) after removing the influence of climate change, from which we can draw the conclusion that the most significant improvement happens in the environmental fragility, and the environment of Afghanistan will be much less fragile. 6 The Way Climate Change Influences a Country s Fragility 6.1 The Country We Focus on: Egypt Selection of the Focus Country According to the Fragile State Index[5], we choose to focus on Egypt to study the way climate change increases a country s fragility and even push a relatively stable country to become a fragile country. As Egypt s FSI ranks the 36th among 179 countries, we consider Egypt as a relatively stable country but still has many potential unstable factors Data Collection Environmental Data: We collect the data of climate change in Egypt from Germanwatch[6] and the data of the recent natural disasters happen in Egypt from University of Richmond[8] and data of the natural resources of Egypt from OECD[7]. Economical, Political and Social Data: The economical data, political data and social data of Egypt are mainly collected from Egypt Statistics[12] and the Economist Intelligence Unit[13] Country s Fragility of Egypt After processed the data we collect, we use our model and get the quantified result of Egypt s country fragility. CFI of Egypt is 71.91, determined as a vulnerable country according to the classification method in section4.7. Meanwhile, ENFI of Egypt is 70.51, ECFI of Egypt is 67.67, SFI of Egypt is and PFI of Egypt is The result is shown in Table 7 Table 7: Model result of Egypt s country fragility ENF I ECF I SF I P F I ENF I 1 ENF I 2 ENF I 3 ECF I 1 ECF I 2 ECF I 3 SF I 1 SF I 2 SF I 3 P F I 1 P F I 2 P F I 3 P F I , From this result we can conclude that compared with Afghanistan, Egypt performs much better in country fragility. However, among the four indices, ENFI and PFI are high, which means the fragility of the natural environment and politics in Egypt has largely increased this country s fragility. The result of our model can be seen in Figure 7.

16 Team # Page 15 of 20 Figure 7: Result of the measurement of Egypt s country fragility 6.2 Current Influence of Climate Change Identification of Climate Change s Influence Factors By applying Correlation Analysis to Egypt s Climate Change Performance Index and other sub-indicators of Egypt, we identify the direct influence factor Climate Change Performance Index and a series of subindicators determined as indirect influence factors, our result of this identification is shown in Table 8: Table 8: Indicators and sub-indicators influenced by climate change Indicators influenced by climate change Severe Climate Change Severe Natural Disasters Low Primary Industry Development High Demographic Pressures Severe Regional Conflicts Low living standards Sub-indicators influenced by climate change CCPI Direct Economic Loss Caused by Natural Disasters Casualties Caused by Natural Disasters Primary Industry GDP Primary Industry Growth Rate Per Capita Income of Primary Industry Practitioners Epidemic Intensity Main Infectious Diseases Food and Water Scarcity Direct Economic Loss Caused by Religious and Ethnic Conflicts Casualties caused by Religious and Ethnic Conflicts Unemployment Rate GINI Coefficient Social welfare level As all the direct influence factors and indirect factors identified, we calculate the I of Egypt by using the method in section4.8.2 and get the I of Egypt is 3.48%, which means 3.48% of Egypt s CFI is caused by climate, and 3.48% of this country s fragility is due to the influence of climate change Identification of Definitive Influence Factors To make sure in what way climate change influences the country fragility of Egypt, by referring to the previous results of the identification of climate change s direct and indirect influence factors and the weight of each sub-indicator identified that has been identified as influence factor, we search definitive indicators of climate change among sub-indicators identified as influence factors, and finally select a series of definitive influence factors, which are shown in Table 9: We further compare the fragility of Egypt when take the decisive factors into consideration with the fragility of Egypt without the influence of decisive indicators, to intuitively express the influence of decisive factors. The comparison is shown in Appendix Figure A1.

17 Team # Page 16 of 20 Table 9: Sub-indicators determined as definitive factors Corresponding Indicators Severe Climate Change Severe Natural Disasters Low Primary Industry Development High Demographic Pressures Severe Regional Conflicts Sub-indicators identified as definitive influence factors CCPI Direct Economic Loss Caused by Natural Disasters Primary Industry GDP Primary Industry Growth Rate Epidemic Intensity Main Infectious Diseases Food and Water Scarcity Direct Economic Loss Caused by Religious and Ethnic Conflicts 6.3 Prediction of the Future Influence of Climate Change Tipping Point As mentioned above, the I value measures the level of the influence climate change has on country s fragility, and it varies from 0% to 100%. The I value of Afghanistan is 9.74%, the I value of Egypt is 3.48%. We determine the tipping point based on countries I values. The tipping point is a certain value of I, defined as T, also varies from 0% to 100%. Figure 8: Choosing of tipping point based on normal distribution After setting the value of tipping point, if there is I > T, then we can say the direct and indirect influence of climate change has significantly effected country s fragility and climate change has become the decisive factor that causes this country to become a fragile country. We consider that the I value of all the countries measured in our model also obeys normal distribution, thus, like section4.7, we set: T = I max, where I max is the maximum value of I among all countries Application of Grey Forecasting Gray Forecasting is a method of predicting systems with uncertainties. Here, we use Grey Forecasting to predict the future influence of climate change by predicting the future I values of a country. The forecasting process will require the I values of previous years. We can calculate the past years I values of a country by using our model and data of previous years, then use the I values of past years as the input of Grey Forecasting. The result of Grey Forecasting will be the future I values of this country. As for when the country will reach tipping point and when climate change will be the decisive factor and pushes the country to become fragile, we can compare the results of Grey Forecasting and the tipping

18 Team # Page 17 of 20 point T, the year when the predicted I > T will be the year this country be pushed to be a fragile country by climate change Prediction of the Future Influence of Climate Change on Egypt We apply the method of predicting the future influence of climate change and determining when the country will reach tipping point to Egypt. Here we assume the tipping point T is 7% and use the data of The prediction result is shown in Figure 9. Figure 9: The prediction result of Grey Forecasting From the result we conclude that after years, Egypt will reach the tipping point and will become a very fragile country mainly due to climate change. 7 Plan for Interventions 7.1 Interventions and Effects As the results of our model shows, although the indirect influence factors vary between different countries, there are several sub-indicators very likely to be identified as indirect influence factors, for these sub-indicators are often identified as the decisive influence factors of climate change. These sub-indicators are Primary Industry GDP, Primary Industry Growth Rate, Epidemic Intensity Main Infectious Diseases, Food and Water Scarcity, Direct Economic Loss Caused by Natural Disasters. Climate change are very likely to influence the country s fragility indirectly by influencing these sub-indicators, therefore, to mitigate the influence of climate change, efficient intervention focus on these sub-indicators is needed. Interventions in Primary Industry: Because Primary Industry GDP and Primary Industry Growth Rate are highly possible to be influenced by climate change, the government need to take some effective interventions in primary industry. The intervention include: In order to provide farmers and other primary practitioners necessary climate change warning and weather warning, build an efficient meteorological information system; increase the productive capability of agriculture.[14] Interventions in Food and Water Security: In order to ensure a country s water security, the government can increase capacity of infrastructures related to water supply, Develop a variety of ways to get clean water and reduce losses of water during the transportation and storage of water. As for food security, the government can expand the national grain storage capacity. Interventions in Reducing the Lost Caused by Natural Disasters: Many natural disasters like extreme weather, which will harm the agriculture of a country. The government can reduce the lost caused

19 Team # Page 18 of 20 by natural disasters by enhancing the country s ability to handle the negative consequences of extreme weather and improving level of the country s natural disaster risk managements. 7.2 The Cost of Interventions To predict the total cost of the intervention we select, we consider the labor costs of applying this intervention, and as the intervention s application will be a long term action, we also consider the inflation situation of the country. Meanwhile, the level of expected outcome of the intervention improves the cost of the intervention improves, thus we take the level of expected outcome as a factor too. Last but not least, we take the level of the toughness of an intervention application into consideration, the more tough it is to apply an intervention, the higher it s total cost will be. The total cost can be measured by using the equation: T otalcost = n K A b i d i, i=1 where n is the total number of interventions taken, K is the labor costs of applying the intervention, can be measured by the country s average salary level, A is the inflation factor of the country, b i is the expected outcome of the ith intervention, d i is the toughness of the ith intervention s application. 8 Applying Our Model to Cities and Continents 8.1 Applying to Cities Because the indicators of our current model is designed to measure a country s situation, indicators such as Democracy Index, self-sufficiency rate of energy and mineral resources are too rough to directly and accurately reflect the situation of a city. Therefore, we need to modify our model before further apply it to measure the fragility of cities. The modification we do is focus on adding more branches and knots to our current model to make it more capable of reflecting details. For example, we replace the indicator Severe Climate Change with annual precipitation trends, aridity trends, the trend of average temperature in different seasons. The overall modified model that can be applied to measure a city s fragility is shown in Appendix TableA2. To evaluate our modification, we select two cities, New York and Osaka to measure their CFI with our modified model. Then we compare the model s results with Global Power City Index (GPCI) provided by The Mori Memorial Foundation[15]. We collect data of two cities mainly from Japan Meteorological Agency[16], the World Bank[17] and Website TimeandDate[18]. The result is shown in Table 10: Table 10: Result of the modified model City CFI ENFI ECFI SFI PFI New York Osaka The comparison between GPCI and each city s CFI shows that cities with higer GPCI rank higher of CFI too. New York and Osaka both get low CFI due to these two cities are both highly developed, while the CFI of New York is lower for New York is even more developed than Osaka. Thus we can say our modification of our model is successful. 8.2 Applying to Continents When it comes to apply our current model to larger states, such as continents, which always will be consist of several countries, our model s indicators are sufficient to measure the environmental, econom-