Spatially explicit downscaling and projection of population in mainland China

• Published in: The Science of the total environment 2024-09, Vol.941, p.173623, Article 173623
• Main Authors: Xu, Wenru, Zhou, Yuyu, Taubenböck, Hannes, Stokes, Eleanor C., Zhu, Zhengyuan, Lai, Feilin, Li, Xuecao, Zhao, Xia
• Format: Article
• Language: English
• Subjects: Gridded population; Population downscaling; Population projections; Shared Socioeconomic Pathways; Urban sprawl
• ISSN: 0048-9697; 1879-1026; 1879-1026
• DOI: 10.1016/j.scitotenv.2024.173623

Spatially explicit population data is critical to investigating human-nature interactions, identifying at-risk populations, and informing sustainable management and policy decisions. Most long-term global population data have three main limitations: 1) they were estimated with simple scaling or trend extrapolation methods which are not able to capture detailed population variation spatially and temporally; 2) the rate of urbanization and the spatial patterns of settlement changes were not fully considered; and 3) the spatial resolution is generally coarse. To address these limitations, we proposed a framework for large-scale spatially explicit downscaling of populations from census data and projecting future population distributions under different Shared Socio-economic Pathways (SSP) scenarios with the consideration of distinctive changes in urban extent. We downscaled urban and rural population separately and considered urban spatial sprawl in downscaling and projection. Treating urban and rural populations as distinct but interconnected entities, we constructed a random forest model to downscale historical populations and designed a gravity-based population potential model to project future population changes at the grid level. This work built a new capacity for understanding spatially explicit demographic change with a combination of temporal, spatial, and SSP scenario dimensions, paving the way for cross-disciplinary studies on long-term socio-environmental interactions. [Display omitted] •A framework was developed for spatially explicit downscaling and projecting population distributions.•The accuracy of the downscaled population was improved compared with other datasets.•The population projections align with the spatial patterns of urbanization.•The work provides a foundation for studies on long-term socio-environmental interactions.