Health communities: climate change and national fragility
By LI Lin-yu1, Marhulan Hoibahar1, WANG Yan1
(1. College of Water Resources and Environment, Chang’an University, Xi’an 710054, China)
Abstract: Climate change is getting increasing attention in the world, especially for fragile countries. Climate change and national fragility in the same community are just as that often one prosperity, one loss. We constructed a two-tier index system from five aspects of climate change, environment, society, economy and politics. At the same time, considering the possible impact of extreme climate on climate change, we established a vulnerability index model CESEP-EC to calculate the vulnerability index of all countries in the world. In order to easily measure the vulnerability of a country, we use K-means cluster analysis to divide countries into three types: fragile, vulnerable and stable. The corresponding vulnerability index ranges are 0.442-0.588, 0.330-0.434 and 0.201-0.328, respectively. The rationality of the model is then verified by ranking with FSI vulnerable countries. Analyzing Somalia, a country ranked second in the Vulnerability Index, we quantified the impact of climate change on Somalia’s vulnerability. It turns out that when the effects of man-made climate extremes are removed, Somalia will become less vulnerable, and its vulnerability index ranking will drop from second to third. Finally, we modified the model to fit the continent or the city, increasing its stability and applicability.
Key words: Health Community, climate change, Vulnerability Index, CESEP-EC model
LI Lin-yu, Marhulan Hoibahar, WANG Yan. Health communities: climate change and national fragility. BioGreen - Biodiversity Conservation and Green Development. Vol. 1, April 2023. Total Issues 39. ISSN2749-9065
1 Introduction
Climate change will bring about a series of environmental crises, such as rising carbon emissions leading to the greenhouse effect, polluting gas emissions adding to the environmental burden, shrinking glaciers contributing to rising levels of growth, loss of animal and plant diversity, and the emergence of extreme weather such as tropical storms. They not only directly affect the environment, but also may affect the economic and social conditions of a country or region.
Some fragile countries, which are unable to provide basic survival materials for their citizens, are often helpless to cope with climate change due to lack of resources, lack of technology, and unbalanced economic and social development, which will make the climate change situation in the country more severe, and may even bring about undesirable phenomena such as violent conflicts and social fragmentation. Therefore, climate change has become a challenge that needs to be addressed globally [1,2]. However, when dealing with climate change, vulnerable countries may only focus on climate change and the environment, ignoring the contribution of social, economic and political factors in addressing the climate change crisis. Although these contributions may be indirect, they may still play a significant role in addressing climate change. On the other hand, when environmental burdens are combined with neglected social governance, which may lead to social crisis, efforts to mitigate climate change will be even more difficult [3]. At this point, it is of certain research significance to determine the vulnerability of a country, evaluate when a country is fragile, vulnerable and stable, measure the impact of climate change on vulnerability, and finally study how to build a healthy community of climate change and national fragility.
2 National fragility assessment
2.1 Assessment of the selection of indicators
We put forward the vulnerability index to evaluate the vulnerability of a country or region. The larger the vulnerability index is, the more vulnerable the country or region is; the smaller the vulnerability index is, the vice versa. After referring to a variety of factors, we determine the evaluation index from five fields, namely, climate change, environment, society, economy and politics. These five fields are very first-level indicators, and their subordinate indicators are second-level indicators, as shown in Figure 1. In addition, if the larger the value of an index is and the more vulnerable the country or region is, the higher the corresponding vulnerability index will be. Then we call the index a positive indicator, and vice versa. All secondary index data for each country involved in Figure 1 are from the World Bank website [4].
Figure 1 Vulnerability assessment indicator system
(1) Climate change
The following three secondary indicators are all positive indicators, and the higher the index value, the worse the climate situation in the region.
a. Nitric oxide emissions (thousands of metric tons of carbon dioxide equivalent)
Excessive nitric oxide emissions are one of the main causes of air pollution, so we chose carbon monoxide emissions as a reflection of climate change.
b. Carbon dioxide emissions (metric tons per capita)
Although carbon dioxide is not a polluting gas, its high levels in the air can be harmful to human health.
c. Methane emissions (kilotons of carbon dioxide equivalent)
Methane is a polluting gas. The more it is in the air, the worse the climate in the region. This indicator can reflect the level of air pollution.
(2) Environment
The following three secondary indicators are all negative indicators, and the larger the value, the better the long-term climate and ecological conditions of the region.
a. Forest area (as a percentage of land area)
Forests are good for cleaning the environment, and a larger area usually means a larger climate and ecology in the area.
b. Arable land (as a percentage of land area)
The more farmland there is, the more favorable the climate is and the more cultivated it is. It also reflects the fact that there are people living in the area for a long time.
c. Agricultural land (as a percentage of land area)
As average temperatures rise significantly, glaciers will undoubtedly melt, leading to sea level rise, and the intuitive result is a decrease in agricultural land, so we introduce agricultural land but a percentage of land area to describe its response to climate shocks.
(3) Society
a. Crude mortality rate (per thousand people)
It represents the estimated average number of deaths per thousand people in a year in the middle of the year. The larger the number, the more deaths in the region and the more instability in the society, which is a positive indicator.
b. Dependency ratio (as a percentage of the working-age population)
It reflects the proportion of children in a family that parents have to support. A larger dependency ratio indicates the greater economic pressure on the family, which is a positive indicator.
c. Secure Internet servers (per million people)
The more people who have access to secure Internet servers, the more secure and stable society becomes, which is a negative indicator.
(4) Economy
a. The total number of unemployed (the proportion of the total labor force)
It reflects whether the jobs in the region meet the needs of the society. The more the number of unemployed, the worse the resilience and stability of the society, which is a positive indicator.
b. The electricity rate (as a percentage of the population)
Electricity is a prerequisite for an area’s economic development, which is a negative indicator.
c. Per capita GDP (in current dollars)
It reflects the per capita economic level of a country or region, which is a negative indicator.
(5) Politics
The higher the value of the following two secondary indicators, the higher the political level and social stability of the region, both of which are negative indicators.
a. The proportion of women in national parliaments
The higher the number of women’s seats, the better the gender equality in the region and the democracy of politics.
b. Index of corporate information disclosure degree (0= less disclosure, 10= more disclosure)
The more information disclosed by companies indicates the higher degree of political credit in the region, reflecting the degree of political stability in the region.
2.2 The calculation of index weight and vulnerability index
2.2.1 Entropy weight method, CRITIC method and indicator weight
When evaluating vulnerability, we should not only consider the correlation between indicators, but also consider the information characteristics of the indicators themselves. CRITIC method is an objective weight method, which determines the weight by comparing the correlation and strength of indicators, and can reduce the influence of correlation of indicators on weight. This method has the advantage of making the analysis results objective and reasonable. It has been applied to the benefit evaluation of ecological civilization construction fields such as air quality evaluation [5], water rights distribution [6] and river and lake construction level [7]. However, although CRITIC method can reflect the difference and correlation between indicators, it ignores the information characteristics in the indicators, resulting in failure to reflect the discrete degree of indicators. Entropy weight method can just make up for this deficiency.
In this paper, entropy weight method and CRITIC method are used to calculate the weight of second-level and first-level indicators respectively, making the weight calculation more scientific and accurate. Subsequently, we can deduce the vulnerability index of five comprehensive evaluation indicators: climate change index, environmental index, social index, economic index and political index. The calculation formula is as follows:
2.2.2 The adjustment of vulnerability index by extreme climate
There is no doubt that the Earth’s climate is changing more dramatically, extreme weather events are occurring more frequently, and may lead to increased national vulnerability. Statistical analysis of extreme weather is becoming an integral part of climate research. When building a community of climate and national fragility, in order to reflect the impact of extreme climate on countries, we added an adjustment factor “extreme climate” in the model, which is referred to as “EC”. The calculation formula is as follows:
We calculated the frequency of extreme climate in different countries in the past 20 years, as shown in Figure 2. Where, EC is the adjustment factor, Total Frequency is the number of extreme climate events occurring in a country in the last 20 years, and max{Total Frequency} is the country with the most extreme climate events occurring in the last 20 years. According to the data of the Global Disaster Data Platform [8], it can be known that the country with the most extreme climate events is China, and the value of max{Total Frequency} is 524.
Figure 2 Top 10 countries and continental situation with the most extreme climate events in the last 20 years
Obviously, when the EC value is large, it indicates that extreme climate occurs more frequently, which will have a great impact on the final vulnerability score. When the value of EC is small, it indicates that the occurrence of extreme climate is less, which has less impact on the final vulnerability score. For China, the EC value would reach a maximum of 0.1, meaning that extreme climate would increase China’s vulnerability index by 10 percent. Finally, we can derive the national vulnerability index, which is calculated as follows:
Based on all the above analysis, we have named this model CESEP-EC, which is a national fragility assessment model that takes into account climate change, environmental, social, economic, political and extreme climate impacts. It can be said that CESEP-EC model is the quantitative embodiment of the health community of climate change and national fragility.
2.3.1 The calculated results of the vulnerability index
Based on the above methods, we calculated the vulnerability index of each country and ranked the top ten countries, as shown in Figure 3.
Figure 3 The top ten countries in the Vulnerability Index
2.3.2 Cluster analysis
For task 1, we need to identify whether the state of a country is fragile, injury-prone or stable. After calculating the vulnerability index by using the method above, we use the method of K-means cluster analysis to divide countries into the three required categories, as shown in Figure 4. According to the clustering results, a country is considered stable when its vulnerability index is between 0.201 and 0.328; when a country’s vulnerability index is 0.330-0.434, we consider it vulnerable; and we consider a country vulnerable when its vulnerability index is 0.442-0.588.
Figure 4 Clustering results: Fragile, injury-prone, stable
2.3.3 Model verification
After using the CESEP-EC model to assess the vulnerability of individual countries, we need to validate the results of the model assessment. We stratified 20 countries according to the three levels of vulnerability defined above, and then compared the assessment results of the CESEP-EC model with the national fragility ranking provided by the non-profit organization The Fund for Peace [9]. The results are shown in Figure 5.
Figure 5 Comparison of vulnerabilities: Results from the FSI and CESEP-EC models
It is clear from Figure 5 that the FSI national fragility ranking and the CESEP-EC model are very close to each other. Therefore, we believe that CESEP-EC model is reasonable for national fragility assessment results.
3.1 Climate conditions in Somalia
Somalia ranks second in the FSI Fragile States Index ranking, and in our model it ranks second with a vulnerability index of 0.5876, which is typical of fragile states, so we chose Somalia for further study.
Somalia is located on the Somali Peninsula of the African continent, as shown in Figure 6. It is one of the least developed countries in the world, with a weak industrial base, food shortages and frequent natural disasters. Much of Somalia has a subtropical and tropical desert climate.
Figure 6 shows the location of Somalia. Source: Baidu Map (pictures processed by translation software)
Somalia is typically characterized by a high temperature and dry environment all year round, as shown in Figure 7. Somalia’s average temperature over the last two decades is 26.5℃, which is a good indication of its high temperature. In addition, due to Somalia’s special geographical environment and long coastline, its extreme climate events are mainly flood and drought. In the past two decades, Somalia’s instability has caused it to suffer about 429,787,000 dollars in direct economic losses and countless indirect losses, which is undoubtedly a heavy blow to the fragile Somalia.
Figure 7 Types of extreme weather in Somalia. Source: Global disaster data platform: https://www.gddat.cn/newGlobalWeb/#/FeatureAnalysis.
3.2 How does climate change increase Somalia’s vulnerability
The impact of climate change on the vulnerability index can be seen in two ways: the frequency of extreme weather events and the relationship between climate and other primary indicators. We will discuss next how these two indirect influences can increase Somalia’s vulnerability index.
(1) Extreme climate impacts
They are more likely to occur when climate conditions worsen, and the economic losses, deaths and other consequences of extreme weather events make the crumbling Somalia even more dangerous.
The frequency of extreme weather events is the basis for the calculation of the adjustment factor EC. We calculate that 59 extreme weather events occurred in Somalia in the last two decades, with an EC value of 0.01126. To exclude the effects of extreme weather events, the following formula is used to calculate the vulnerability index for Somalia.
(2) The influence of climate factors on other factors
Climate factors also have an impact on the environment, society, economy and politics that cannot be ignored. When the climate becomes extremely bad, environmental and social indicators will no longer develop smoothly in accordance with the moderate climate, but quickly collapse. The influence of climate on environmental, social and other indicators cannot be expressed by ordinary fitting methods. Therefore, we choose to use SPSS PRO to calculate the correlation coefficient between climate and other indicators to show their degree of correlation, the results are shown in Figure 8, and point out which indicators may be closely related to climate change and which indicators will be greatly affected by climate change, thus indirectly changing the vulnerability index. For example, the correlation coefficient between carbon dioxide emissions and nitric oxide emissions from climate change and forest area is 0.986 and 0.626, respectively, indicating that climate change may indirectly affect national fragility assessment by affecting forest area.
Figure 8 Correlation coefficients among secondary indicators in Somalia.
4 Human intervention and model extension
The CESEP-EC model reveals that climate change may affect vulnerability assessments either directly or indirectly by affecting other factors such as economic factors. Next, we will look at climate change, environmental, social, economic and political perspectives, and propose some country-driven interventions to improve national resilience to vulnerability.
(1) Climate change
a. Appropriate reduction of energy extraction
On the premise of ensuring demand, reduce the extraction and refining of energy, so as to reduce emissions of polluting gases and maintain superior air quality.
b. Reusing resources
Energy consumption, such as thermal power generation, contributes to greenhouse gas emissions. Reusing resources is not only good for saving resources but also for mitigating climate change.
(2) Environment
Use public works to protect the environment. Strengthening infrastructure construction, such as DAMS and drainage systems, and improving the ability to cope with natural disasters such as floods and rainstorms can reduce economic losses and casualties, as well as protect the environment.
(3) Society
Increase social security. If most people have access to social security benefits when they are hit by climate disasters, societies and countries will be more stable and less vulnerable.
The services of community-level medical institutions need to be improved. When countries become vulnerable, they may suffer from violence, conflict, etc., and with good medical care, we can minimize the harm.
(4) The economy
Promote the development of small and medium-sized enterprises. The government needs to optimize the industrial structure, accelerate the promotion of green upgrading of traditional industries, and strengthen the country’s resilience in dealing with the economic crisis that may arise from climate change.
(5) Politics
Improve education and science policies. More people have access to schools and scientific knowledge, and the increase of people’ literacy will lead to harmonious coexistence among people.
Our model has already been validated for use at the national level. In order to extend its applicability, we further verify that it can be used in small cities relative to countries (a region, city, or province in a country) or large continents (a peninsula, continent, or continent with several countries together), and point out where modifications may be needed in the model.
A continent is often composed of several countries, but the social, economic and especially political conditions of different countries are very different, which makes it impossible to form a unified index system or evaluation criteria when measuring vulnerability, and makes it difficult to conduct a reasonable assessment of the vulnerability of the continent.
Therefore, in the vulnerability assessment of the mainland and other larger regions, indicators without national differences should be selected as far as possible in the global index, and the actual situation of each country should be considered in the determination of index weights. In this way, we can get a more objective model applicable to the mainland.
For cities, many indicators are also different from those at the national level. For example, nitric oxide is mainly derived from automobile exhaust emissions, and automobiles are mainly concentrated within the city limits. Therefore, the weight of nitric oxide index should be increased in the calculation and evaluation of urban vulnerability.
In addition, cities have a lot of data that is not available at the national level. For example, urban infrastructure such as drainage system largely determines urban vulnerability. Therefore, when measuring urban vulnerability, such indicators must be more detailed and more accurate in statistics.
We have established a national fragility assessment model, CESEP-EC, which is the quantitative embodiment of climate change and national fragility community. The vulnerability index calculated by the model can be used to reflect the vulnerability of a country. After comparing our model with the fragile states ranking published by the Fund for Peace, we will then conduct a cluster analysis of the vulnerability index, classifying national fragility into fragile, vulnerable and stable.
CESEP-EC model involves the impact of extreme weather, climate change, politics, economy, environment and society in the selection of evaluation indicators. Taking Somalia as a case study, it can explain the impact of climate on the National fragility from data and theory. Finally, we extend the application of the model to continents and cities, indicating areas for improvement.
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