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Spatial Differences in China’s Population Aging and Influencing Factors: The Perspectives of Spatial Dependence and Spatial Heterogeneity

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  • Yuanyuan Wu

    (School of Geographical Sciences, Northeast Normal University, Changchun 130024, China)

  • Yuxiang Song

    (School of Geographical Sciences, Northeast Normal University, Changchun 130024, China)

  • Tingting Yu

    (School of Tourism Management, Sun Yat-sen University, Zhuhai 519000, China)

Abstract

Since China became an aging society in 2000, the regional inequality of population aging has been highlighted, and the phenomenon of “aging before getting rich” has gradually become a core issue in China’s coordinated socioeconomic development. This paper aims to comprehensively assess the spatial differences and driving forces of China’s population aging through two-stage nested Theil decomposition, ESDA, and spatial econometric models. Empirical results show that spatial differences in population aging were evident at different spatial scales, and the distribution gradually decreased from east to west, showing a positive spatial correlation of similar value aggregation. Moreover, China’s population aging was determined by the demographic, socioeconomic, and natural environment, and there are different leading factors in different regions. The demographic aspects played a decisive role and had a direct influence, while the socioeconomic and natural environment indirectly affected population aging through demographic factors and became the root cause of regional differences in population aging. These findings provide an empirical basis for establishing a cooperative mechanism and formulating a targeted response to the problem of population aging in various regions in China.

Suggested Citation

  • Yuanyuan Wu & Yuxiang Song & Tingting Yu, 2019. "Spatial Differences in China’s Population Aging and Influencing Factors: The Perspectives of Spatial Dependence and Spatial Heterogeneity," Sustainability, MDPI, vol. 11(21), pages 1-20, October.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:21:p:5959-:d:280424
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    3. Zou, Guojian & Lai, Ziliang & Li, Ye & Liu, Xinghua & Li, Wenxiang, 2022. "Exploring the nonlinear impact of air pollution on housing prices: A machine learning approach," Economics of Transportation, Elsevier, vol. 31(C).
    4. Qiongzhi Liu & Dapeng Zhao, 2023. "A Study of the Impact of Population Aging on Fiscal Sustainability in China," Sustainability, MDPI, vol. 15(6), pages 1-15, March.
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    6. Xiaodong Zhang & Haoying Han, 2023. "Spatiotemporal Dynamic Characteristics and Causes of China’s Population Aging from 2000 to 2020," Sustainability, MDPI, vol. 15(9), pages 1-19, April.
    7. Justyna Rój & Maciej Jankowiak, 2021. "Socioeconomic Determinants of Health and Their Unequal Distribution in Poland," IJERPH, MDPI, vol. 18(20), pages 1-20, October.
    8. Ke Zhang & Hao Sun & Xiangyu Li, 2022. "Aging Population Spatial Distribution Discrepancy and Impacting Factor," Sustainability, MDPI, vol. 14(15), pages 1-22, August.
    9. Wang, Shaobin, 2020. "Spatial patterns and social-economic influential factors of population aging: A global assessment from 1990 to 2010," Social Science & Medicine, Elsevier, vol. 253(C).

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