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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. Lutz, Wolfgang & Butz, William P. & KC, Samir (ed.), 2014. "World Population and Human Capital in the Twenty-First Century," OUP Catalogue, Oxford University Press, number 9780198703167.
    2. Seung-Ki Min & Xuebin Zhang & Francis W. Zwiers & Gabriele C. Hegerl, 2011. "Human contribution to more-intense precipitation extremes," Nature, Nature, vol. 470(7334), pages 378-381, February.
    3. Delavane Diaz & Frances Moore, 2017. "Quantifying the economic risks of climate change," Nature Climate Change, Nature, vol. 7(11), pages 774-782, November.
    4. James Raymer & Guy J Abel & Andrei Rogers, 2012. "Does Specification Matter? Experiments with Simple Multiregional Probabilistic Population Projections," Environment and Planning A, , vol. 44(11), pages 2664-2686, November.
    5. Guangqing Chi, 2009. "Can knowledge improve population forecasts at subcounty levels?," Demography, Springer;Population Association of America (PAA), vol. 46(2), pages 405-427, May.
    6. Detlef Vuuren & Elmar Kriegler & Brian O’Neill & Kristie Ebi & Keywan Riahi & Timothy Carter & Jae Edmonds & Stephane Hallegatte & Tom Kram & Ritu Mathur & Harald Winkler, 2014. "A new scenario framework for Climate Change Research: scenario matrix architecture," Climatic Change, Springer, vol. 122(3), pages 373-386, February.
    7. Brian O’Neill & Elmar Kriegler & Keywan Riahi & Kristie Ebi & Stephane Hallegatte & Timothy Carter & Ritu Mathur & Detlef Vuuren, 2014. "A new scenario framework for climate change research: the concept of shared socioeconomic pathways," Climatic Change, Springer, vol. 122(3), pages 387-400, February.
    8. Guy Abel, 2013. "Estimating global migration flow tables using place of birth data," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 28(18), pages 505-546.
    9. Shang, Juan & Li, Pengfei & Li, Ling & Chen, Yong, 2018. "The relationship between population growth and capital allocation in urbanization," Technological Forecasting and Social Change, Elsevier, vol. 135(C), pages 249-256.
    10. Elmar Kriegler & Jae Edmonds & Stéphane Hallegatte & Kristie Ebi & Tom Kram & Keywan Riahi & Harald Winkler & Detlef Vuuren, 2014. "A new scenario framework for climate change research: the concept of shared climate policy assumptions," Climatic Change, Springer, vol. 122(3), pages 401-414, February.
    11. Daisuke Murakami & Yoshiki Yamagata, 2019. "Estimation of Gridded Population and GDP Scenarios with Spatially Explicit Statistical Downscaling," Sustainability, MDPI, vol. 11(7), pages 1-18, April.
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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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