“一带一路”沿线国家社会风险——基于BP神经网络的实证分析  

Social Risks of Countries Involved in the Belt and Road Initiative:An Empirical Analysis Based on BP Neural Network

作　　者：赵振宇[1] 刘宇帆 刘善存[2] 马海波 ZHAO Zhenyu;LIU Yufan;LIU Shancun;MA Haibo(School of Economics and Management,Ningxia University,Yinchuan Ningxia 750021,China;School of Economics and Management,Beihang University,Beijing 100083,China)

机构地区：[1]宁夏大学经济管理学院,宁夏银川750021 [2]北京航空航天大学经济管理学院,北京100083

出　　处：《北京航空航天大学学报（社会科学版）》2023年第4期105-114,共10页Journal of Beijing University of Aeronautics and Astronautics：Social Sciences edition Edition

基　　金：国家社会科学基金项目(19BJY183);宁夏高校优秀青年教师培育基金项目(NGY2018013)。

摘　　要：中国与“一带一路”沿线国家的区域合作正在不断深化,对沿线国家的投资力度也在不断增强。但中国在政治制度、社会发展、文化传承上与“一带一路”沿线国家并不相同,“一带一路”沿线国家的国别间社会风险敞口也不同。利用大数据分析技术中的机器学习方法,构建基于“一带一路”沿线国家社会风险的BP神经网络,相对准确地测度了“一带一路”沿线国家的社会风险水平。通过对数据相对完整的50个沿线国家2010—2017年的风险数据进行评估后发现:在进行测度的“一带一路”沿线国家中,大部分国家的社会风险属于偏高风险等级,占比为46.0%;仅有东欧地区部分国家以及东亚、东南亚及大洋洲区域内的少数国家的社会风险属于低风险等级;即使处于同一地理区域内的国家的社会风险等级也呈现出明显的国别差异。The regional cooperation between China and the countries involved in the Belt and Road Initiative(BRI)is deepening,and China is increasing its investment in these countries.However,China is different from these countries in terms of political system,social development and cultural heritage,and the social risk exposure of these countries is also different.With the help of the machine learning method of big data analysis techniques,the paper constructs a BP neural network based on social risk of the countries involved in the BRI,and measures the social risk level of these countries with relative accuracy.An assessment of the risk data of the 50 countries involved in the BRI from 2010 to 2017 shows that most of these countries are at high levels of social risk,accounting for 46%of all the countries evaluated,and only some countries in Eastern Europe and a few countries in East Asia,Southeast Asia and Oceania are at low levels of social risk.Even for countries in the same geographical region,their levels of social risk also show significant differences.

关 键 词：“一带一路”倡议 社会风险 机器学习 BP神经网络 国别间投资风险防范 

分 类 号：F752.0[经济管理—国际贸易] F214[经济管理—产业经济]