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Estimating China’s Population over 21st Century: Spatially Explicit Scenarios Consistent with the Shared Socioeconomic Pathways (SSPs)

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  • Jie Chen

    (Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences (CAS), Beijing 100101, China
    College of Resources and Environment, University of Chinese Academy of Sciences (UCAS), Beijing 100049, China)

  • Yujie Liu

    (Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences (CAS), Beijing 100101, China
    College of Resources and Environment, University of Chinese Academy of Sciences (UCAS), Beijing 100049, China)

  • Ermei Zhang

    (Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences (CAS), Beijing 100101, China
    College of Resources and Environment, University of Chinese Academy of Sciences (UCAS), Beijing 100049, China)

  • Tao Pan

    (Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences (CAS), Beijing 100101, China
    College of Resources and Environment, University of Chinese Academy of Sciences (UCAS), Beijing 100049, China
    Bureau of Development and Planning, Chinese Academy of Sciences (CAS), Beijing 100864, China)

  • Yanhua Liu

    (Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences (CAS), Beijing 100101, China
    College of Resources and Environment, University of Chinese Academy of Sciences (UCAS), Beijing 100049, China)

Abstract

Accurate and reliable subnational and spatially explicit population projections under shared socioeconomic pathways (SSPs) for China will be helpful for understanding long-term demographic changes and formulating targeted mitigation and adaptation policies under climate change. In this study, national and provincial populations for China by age, sex, and education level to 2100 under five SSPs were estimated using the population-development-environment model. These parameters include fertility, mortality, migration, and education and consider the most recent birth policy in China. To quantify these projections spatially, the gridded population was provided at 1 km × 1 km by spatial downscaling. Results show the national population is highest under SSP3, with 1.71 × 10 9 people in 2100. Guangdong, Henan, and Shandong are the most populous in SSP1, 2, 4, 5, while Guangxi is the most populous province in SSP3, reaching 1.54 × 10 8 people. The differences in education level among scenarios are obvious, especially in 2100 where education level for SSP1 and SSP5 is the highest. The spatial distribution of population varies across the country, with the majority of the population concentrated in southern and eastern China, especially in the coastal regions. Our results under different SSPs could provide a reference to project disaster risks, formulate relevant policies and guide sustainable development from a long-term perspective.

Suggested Citation

  • Jie Chen & Yujie Liu & Ermei Zhang & Tao Pan & Yanhua Liu, 2022. "Estimating China’s Population over 21st Century: Spatially Explicit Scenarios Consistent with the Shared Socioeconomic Pathways (SSPs)," Sustainability, MDPI, vol. 14(4), pages 1-17, February.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:4:p:2442-:d:754176
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    References listed on IDEAS

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    1. Shi, Changfeng & Zhi, Jiaqi & Yao, Xiao & Zhang, Hong & Yu, Yue & Zeng, Qingshun & Li, Luji & Zhang, Yuxi, 2023. "How can China achieve the 2030 carbon peak goal—a crossover analysis based on low-carbon economics and deep learning," Energy, Elsevier, vol. 269(C).

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