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Predicting the Future Chinese Population using Shared Socioeconomic Pathways, the Sixth National Population Census, and a PDE Model

Author

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  • Aijun Guo

    (School of Economics, Lanzhou University, Lanzhou 730000, China)

  • Xiaojiang Ding

    (School of Economics, Lanzhou University, Lanzhou 730000, China)

  • Fanglei Zhong

    (School of Economics, Lanzhou University, Lanzhou 730000, China)

  • Qingping Cheng

    (Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
    University of Chinese Academy of Sciences, Beijing 100049, China)

  • Chunlin Huang

    (Heihe Remote Sensing Experimental Research Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
    Key Laboratory of Remote Sensing of Gansu Province, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China)

Abstract

A precise multi-scenario prediction of future population, based on micro-scale census data and localized interpretation of global scenarios, is significant for understanding long-term demographic changes. However, the data used in previous research need to be further refined. Few studies have focused on predicting the sex ratio at birth, which is vitally important for estimating the future size and structure of the population. It is also important to interpret and set parameters for China’s future population development in line with the framework for global shared socioeconomic pathways. This paper, therefore, used the structural population data for provinces, prefectures, and counties from the Sixth National Population Census of China. It comprehensively considered the impact of China’s economic development level, specific population policies, and loss of an only child on key parameters, and localized the population change parameters for different scenarios. A population–development–environment model was used to explain the population change parameters. The population of 340 districts was refined, forecast, and aggregated to the national scale. The results show that the Chinese population is expected to first increase then decrease under the five paths from 2010 to 2050. The aging demographic structure is not reversed under any paths, and the increase or decrease in the urban and rural populations between adjacent node years is closely related to the fertility rate and urbanization speed. We suggest that measures should be taken to encourage childbearing, manage the aging population problem, and reduce the pressure on young and middle-aged people.

Suggested Citation

  • Aijun Guo & Xiaojiang Ding & Fanglei Zhong & Qingping Cheng & Chunlin Huang, 2019. "Predicting the Future Chinese Population using Shared Socioeconomic Pathways, the Sixth National Population Census, and a PDE Model," Sustainability, MDPI, vol. 11(13), pages 1-17, July.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:13:p:3686-:d:245776
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    References listed on IDEAS

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    Cited by:

    1. Aijun Guo & Rong Zhang & Xiaoyu Song & Fanglei Zhong & Daiwei Jiang & Yuan Song, 2021. "Predicting the Water Rebound Effect in China under the Shared Socioeconomic Pathways," IJERPH, MDPI, vol. 18(3), pages 1-24, February.
    2. Hsiao-Hsien Lin & Tzu-Yun Lin & Chun-Wei Hsu & Che-Hsiu Chen & Qi-Yuan Li & Po-Hsuan Wu, 2022. "Moderating Effects of Religious Tourism Activities on Environmental Risk, Leisure Satisfaction, Physical and Mental Health and Well-Being among the Elderly in the Context of COVID-19," IJERPH, MDPI, vol. 19(21), pages 1-17, November.
    3. Aijun Guo & Daiwei Jiang & Fanglei Zhong & Xiaojiang Ding & Xiaoyu Song & Qingping Cheng & Yongnian Zhang & Chunlin Huang, 2019. "Prediction of Technological Change under Shared Socioeconomic Pathways and Regional Differences: A Case Study of Irrigation Water Use Efficiency Changes in Chinese Provinces," Sustainability, MDPI, vol. 11(24), pages 1-19, December.
    4. Lixuan Chen & Tianyu Mu & Xiuting Li & Jichang Dong, 2022. "Population Prediction of Chinese Prefecture-Level Cities Based on Multiple Models," Sustainability, MDPI, vol. 14(8), pages 1-23, April.

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