Yi Yang Cai
ABSTRACT
Based on the official statistics, combined with the results of the seventh national census in 2020, this research uses Leslie model to predict the medium and long term population in Beijing. This paper carefully discusses the aging trend and future influence in Beijing, and predicts the future development trend of dependency ratio. The results show that the degree of aging in Beijing will increase over the next 30 years, with the old age dependency ratio reaching almost 1.2 in 2050, when the city will face social problems such as an accelerated rate of aging, a large old age dependency ratio and an excessive burden on the elderly. At the same time, the juvenile dependency ratio is close to 0.1, which also indicates a lack of intrinsic motivation to slow down the aging trend. The social labour supply and population regeneration will face great difficulties, requiring timely adjustment of population policies and the adoption of effective strategies for active aging and encouraging childbirth.
- Wu TY, Chen R. 2018. Study on Social Software Interface Subtracting Design Used by the Ageing Population—Taking Facebook as an Example.International Journal of Computer Theory and Engineering. 10(6): 212-215. https://doi.org/10.7763/IJCTE.2018.V10.1228.Google Scholar
Cross Ref
- Satcharoen K. 2019. Mobile Phone Background Design for Older Adults: A Case Study of Line. International Journal of Computer Theory and Engineering. 11(2): 31-34. https://doi.org/10.7763/IJCTE.2019.V11.1237.Google ScholarCross Ref
- Mo L, Wei Y, Mo L, 2020. Quantitative Study of Coordination Between Population Aging and Economic Development in China from 1980 to 2050.J. China's Demographic Dilemma and Potential Solutions: Population Aging and Population Control. https://doi.org/10.1007/978-981-10-1491-8_1.Google ScholarCross Ref
- Luo Y, Su B, Zheng X. 2021. Trends and challenges for population and health during population aging—China, 2015–2050.J. China CDC Weekly,3(28): 593.https://doi.org/10.46234/ccdcw2021.158.Google ScholarCross Ref
- E. F. Fang, C. Xie, J. A. Schenkel, C. Wu, Q. Long, H. Cui, Y. Aman, J. Frank, J. Liao, and H. Zou. 2020. A research agenda for aging in China inthe 21st century: Focusing on basic and translational research, long-term care, policy and social networks.aging research reviews.64.https://doi.org/10.1016/j.arr.2020.101174.Google ScholarCross Ref
- H. Zhang, H. Zhang and J. Zhang.2015.Demographic age structure and economic development: Evidence from Chinese provinces.Journal of Comparative Economics.43.170-185.https://doi.org/10.1016/j.jce.2014.07.002.Google ScholarCross Ref
- Wang P, Zhan H J, Liu J, 2022.Does the one‐child generation want more than one child at their fertility age?.J.Family Relations, 71(2): 494-512.https://doi.org/10.1111/fare.12620.Google ScholarCross Ref
- Bureau of Statistics of the People's Republic of China. 2020.China Statistical Yearbook.M.Beijing: China Statistics Press.Retrieved February 20, 2023 from http://www.stats.gov.cn/tjsj/pcsj/.Google Scholar
- L. Huang, J. He, A. Li, and C. Jingtao. 2022. "Population forecasting analysis based on Grey Model under Three-child Policy," in Proceedings of the 7th International Conference on Intelligent Information Processing, pp. 1-6.https://doi.org/10.1145/3570236.3570298.Google ScholarDigital Library
- B. Kuhn and T. Neusius. 2022. "Will China's three-child policy defuse the demographic time bomb?" wifin Working Paper.Google Scholar
- J. Wu. 2022. "Prediction of Birth rate in China under three-child Policy based on neural network," in 2022 7th International Conference on Intelligent Computing and Signal Processing (ICSP), pp. 1652-1655.https://doi.org/10.1109/ICSP54964.2022.9778548.Google ScholarCross Ref
- O. Dadras, S. SeyedAlinaghi, A. Karimi, A. Shamsabadi, K. Qaderi, M. Ramezani, S. P. Mirghaderi, S. Mahdiabadi, F. Vahedi, and S. Saeidi.2022. "COVID‐19 mortality and its predictors in the elderly: A systematic review," Health science reports, vol. 5, p. e657. https://doi.org/10.1002/hsr2.657.Google ScholarCross Ref
- Yadav KPS, Saini BS. 2019. Study of the Aging Effects on HRV Measures in Healthy Subjects. International Journal of Computer Theory and Engineering. 4(3). https://doi.org/10.7763/IJCTE.2012.V4.480.Google ScholarCross Ref
- Kengpol A, Klunngien J, Tuammee S. 2018. Development of a Decision Support Framework for Health Beverage Flavouring for the Ageing Society Using Artificial Neural Network. International Journal of Computer Theory and Engineering.10(6). https://doi.org/10.7763/IJCTE.2018.V10.1225.Google ScholarCross Ref
Index Terms
- Predictive analysis of Beijing municipal population aging based on Leslie model
Recommendations
Making the Web Accessible to the Aging Population
CHI EA '23: Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing SystemsThere is evidence that nearly half of the children born in industrialized economies will live beyond 100 years old. While aging populations are not occurring at the same pace in every nation, nearly all nations are experiencing longer lifespans. There ...
Attitudes Towards Aging and the Acceptance of ICT for Aging in Place
Human Aspects of IT for the Aged Population. Acceptance, Communication and ParticipationAbstractFacing the demographic developments in the Western world, Information and Communication Technologies (ICT) designed to support older people can represent a promising approach for the overloaded health care systems. Most older adults prefer to age ...
Sustainable Aging in Aix-Marseille-Provence Metropolis: Assessment Indicators and Interactive Visualizations for Policy Making
Computational Science and Its Applications – ICCSA 2023 WorkshopsAbstractAs the world’s population continues to age and urbanization accelerates, the focus of urban sustainability has evolved towards promoting the needs and well-being of older adults in addition to environmental concerns. Urban environments have a ...
Comments