Predicting of aging population density by a hybrid grey exponential smoothing model (HGESM): a case study from Sri Lanka

Abstract

Purpose

The global population has been experiencing an unprecedentedly rapid demographic transition as the populations have been growing older in many countries during the current decades. The purpose of this study is to introduce a Grey Exponential Smoothing model (GESM)-based mechanism for analyzing population aging.

Design/methodology/approach

To analyze the aging population of Sri Lanka, initially, three major indicators were considered, i.e. total population, aged population and proportion of the aged population to reflect the aging status of a country. Based on the latest development of computational intelligence with Grey techniques, this study aims to develop a new analytical model for the analysis of the challenge of disabled and frail older people in an aging society.

Findings

The results suggested that a well-defined exponential trend has been seen for the population ages 65 and above, a total of a million) during 1960–2022; especially, the aging population ages 65 and above has been rising rapidly since 2008. This will increase to 24.8% in 2040 and represents the third highest percentage of elderly citizens living in an Asian country. By 2041, one in every four Sri Lankans is expected to be elderly.

Originality/value

The study proposed a GESM-based mechanism for analyzing the population aging in Sri Lanka based on the data from 1960 to 2022 and forecast the aging demands in the next five years from 2024 to 2028.