Predicting the Population Growth and Structure of China Based on Grey Fractional-Order Models

被引:6
|
作者
Guo, Xiaojun [1 ]
Zhang, Rui [1 ,2 ]
Xie, Naiming [2 ]
Jin, Jingliang [1 ,2 ]
机构
[1] Nantong Univ, Sch Sci, Nantong 226019, Peoples R China
[2] Nanjing Univ Aeronaut & Astronaut, Coll Econ & Management, Nanjing 211106, Peoples R China
基金
中国博士后科学基金;
关键词
RENEWABLE ENERGY-CONSUMPTION; GM(1,1); FUTURE;
D O I
10.1155/2021/7725125
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
Scientific prediction and accurate grasp of the future trend of population change are conducive to the formulation of different population policies at different stages, so as to alleviate the adverse effects of the aging population on society and provide scientific theoretical reference for controlling the population size and making policy. Considering that the population system is affected by many complex factors and the structural relationship among these factors is complex, it can be regarded as a typical dynamic grey system. In this paper, the fractional-order GM (1, 1) model and the fractional-order Verhulst model are established, respectively, based on the statistical data of China's population indices from 2015 to 2019 to forecast the population size and the change trend of population structure of China from 2015 to 2050 in the short-term and medium- to long-term. The forecast results show that China's population will grow in an inverse S shape from 2015 to 2050, when the total population will reach 1.43 billion. Moreover, during this period, the birth rate and natural growth rate of population will decrease year by year, and the proportion of aging population and the dependency ratio of population will increase year by year. Besides, the problem of aging population is going to become increasingly serious. The application of grey system method to population prediction can mine the complex information contained in the population number series. Meanwhile, the fractional-order accumulation can weaken the randomness of the original data series and reduce the influence of external disturbance factors, so it is a simple and effective population prediction method.
引用
收藏
页数:11
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