Prediction of population aging trend and analysis of influencing factors based on grey fractional-order and grey relational models: a case study of Jiangsu Province, China

Abstract

Background

With the rapid development of society, China is facing an increasingly serious problem of population aging. This trend poses new challenges to the labor force structure, public medical care construction and elderly care services, forcing the government to make a series of policy adjustments. Jiangsu Province, as a region with prominent aging problems in China, has a particularly significant aging phenomenon. Against the backdrop of the Chinese government's active response to the challenges of aging, this study conducts an in-depth analysis of the aging trend and its influencing factors in Jiangsu Province.

Methods

Based on the statistical data of the total population and the aging population in Jiangsu Province from 2011 to 2023, this study employs the grey fractional-order prediction model (FGM(1,1)) to forecast the trend of the aging population and the aging coefficient in Jiangsu Province over the next decade. Additionally, grey relational analysis (GRA) based on panel data was conducted to thoroughly examine the relevant influencing factors of population aging in Jiangsu Province. The analysis identified key factors such as general public budget expenditure, health technicians, urbanization rate, and education level as being highly correlated with population aging.

Results

The results of trend prediction indicate that the elderly population in Jiangsu Province is projected to continue increasing over the next decade, with the degree of aging becoming more pronounced. Additionally, GRA based on panel data reveals that factors such as general public budget expenditures and the number of health technicians significantly influence the aging process. This suggests that public financial investment and the quantity and quality of health technicians play crucial roles in shaping the aging trend.

Conclusions

In conjunction with the analysis results from FGM(1,1) model and GRA of panel data, this study enhances the comprehensive understanding of the aging issue in Jiangsu Province. The insights derived herein offer crucial data support and a scientific foundation for both Jiangsu Province and the Chinese government to develop policies addressing population aging. Considering the anticipated future trends in aging, it is recommended that the government revise fertility policies to optimize population structure, increase investment in public finance and medical security, and promote the development of elderly care systems. These measures aim to mitigate the challenges posed by aging and achieve sustainable economic and social development.