Research on the State fragility Assessment under the Influence of Environmental Change

2nd International Symposium on Resource Exploration and Environmental Science

生态环境科学

Wu, Wenhan^1 ; Yang, Ying^2 ; Wang, Shuyao^2 ; Cheng, Yuxiang^2,3

School of Information Science and Engineering, Central South University, Changsha, China^1

School of Business, Central South University, Changsha, China^2

School of Economics and Management, University of Chinese, Academy of Sciences, Beijing, China^3

关键词: Environmental change;    Environmental factors;    Fuzzy c-means clustering method;    Influence of environmental changes;    Principal component analysis method;    Projection pursuit method;    Simulated annealing algorithms;    state fragility;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/170/3/032081/pdf
DOI  :  10.1088/1755-1315/170/3/032081

学科分类：环境科学（综合）

来源: IOP

【 摘 要 】

In the past few decades, the impact of environmental change on the polity, economy, safety, and community of a country has also been taken seriously in many countries. To measure the comprehensive impact of environmental change on the country, this article first uses the principal component analysis method to construct a set of comprehensive indexes. Then, OLS method has been applied to analyse the relationship between environmental factors and other indexes (polity, economy, community, and safety), to further describe the impact of environmental change on other factors. Additionally, this paper employs the projection pursuit method based on the simulated annealing algorithm to construct the national fragility index, which is regarded as a comprehensive impact index reflecting the influence of environmental change on a state. Thus, the overall effect of environmental change on the country (national fragility) is manifested by the above OLS-Projection Pursuit two-step conduction model. In order to assess the degree of the environmental change influence, this paper considers the national fragility indexes of 80 countries and adopts the fuzzy C-means clustering method to classify the countries into three categories: fragile states, vulnerable states and stable states. By using our model to empirically analyse the data of India and Yemen, it is found that India is a vulnerable country and Yemen is a fragile country, which is consistent with expectations and reflects the rationality of the model.