基于灰色理论的广东省人口老龄化趋势预测及影响因素  

Prediction of Population Aging Trend and Influencing Factors in Guangdong Province Based on Grey Theory

作　　者：陈思源 王明辉 余跃楷 陈汛铧 CHEN Siyuan;WANG Minghui;YU Yuekai;CHEN Xunhua(School of Mathematics and Statistics,Shaoguan University,Shaoguan 512005,China)

机构地区：[1]韶关学院数学与统计学院,广东韶关512005

出　　处：《经济研究导刊》2024年第22期67-72,共6页Economic Research Guide

基　　金：韶关学院2023年度大学生创新创业训练计划省级项目“后疫情时代下医药板块股票运行趋势的分析与预测”(S202310576028);2020年度韶关学院校级自然科学类科研项目“半参数可加时间序列模型的稳健统计推断”(SY2020KJ15)。

摘　　要：中国人口老龄化现象持续加剧,对经济运行全领域、社会建设各环节、社会文化多方面乃至国家综合实力和国际竞争力都具有深远影响。因此,将广东省人口老龄化作为研究对象,基于GM(1,1)模型,探究2022—2035年老年人口数量及其占比的变化趋势。再运用灰色关联分析和主成分分析方法,分析影响老龄化的8个因素,找出主要影响因素。结果表明:未来14年65岁及以上老年人口数量及其占比将呈现逐年递增趋势,预计到2035年,65岁及以上老年人口数量将达到1584.7352万人,占比将达到9.971543035%;城镇化率、死亡率、出生率、卫生技术人员数、医疗卫生机构床位数和人均GDP是影响老龄化的六个重要因素;经济发展因素和人口结构因素是影响老龄化的两个主要成分。The aging of China's population continues to intensify,which has a profound impact on all areas of economic operation,all links of social construction,social and cultural aspects,as well as national comprehensive strength and international competitiveness.Therefore,taking the aging population of Guangdong Province as the research object,based on the GM(1,1)model,this study explores the changing trend of the number and proportion of elderly population from 2022 to 2035.Using grey relational analysis and principal component analysis methods,analyze the 8 factors that affect aging and identify the main influencing factors.The results indicate that the number and proportion of elderly people aged 65 and above will show an increasing trend year by year in the next 14 years.It is expected that by 2035,the number of elderly people aged 65 and above will reach 158473500,accounting for 9.971543035%;urbanization rate,mortality rate,birth rate,number of health technicians,number of beds in medical and health institutions,and per capita GDP are six important factors affecting aging;economic development factors and population structure factors are the two main components that affect aging.

关 键 词：人口老龄化 GM(1 1)模型 灰色关联分析 主成分分析 影响因素 

分 类 号：C921[社会学—人口学]