City-level population projection for China under different pathways from 2010 to 2100

Cities play a fundamental role in policy decision-making processes, necessitating the availability of city-level population projections to better understand future population dynamics and facilitate research across various domains, including urban planning, shrinking cities, GHG emission projections...

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Bibliographic Details
Published in:Scientific data 2023-11, Vol.10 (1), p.809-809, Article 809
Main Authors: Zhang, Shangchen, Zhao, Mengzhen, Liu, Zhao, Yang, Fan, Lu, Bo, Zhao, Zhenping, Gu, Kuiying, Zhang, Shihui, Lei, Mingyu, Zhang, Chi, Wang, Can, Cai, Wenjia
Format: Article
Language:English
Subjects:
704/158/1745
704/844/2787
Cities
Data Descriptor
Decision making
Disaster risk
Fertility
Greenhouse gases
Health risks
Humanities and Social Sciences
multidisciplinary
Population
Population dynamics
Poverty
Public health
Risk assessment
Risk reduction
Science
Science (multidisciplinary)
Urban planning
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Summary:Cities play a fundamental role in policy decision-making processes, necessitating the availability of city-level population projections to better understand future population dynamics and facilitate research across various domains, including urban planning, shrinking cities, GHG emission projections, GDP projections, disaster risk mitigation, and public health risk assessment. However, the current absence of city-level population projections for China is a significant gap in knowledge. Moreover, aggregating grid-level projections to the city level introduces substantial errors of approximately 30%, leading to discrepancies with actual population trends. The unique circumstances of China, characterized by comprehensive poverty reduction, compulsory education policies, and carbon neutrality goals, render scenarios like SSP4(Shared Socioeconomic Pathways) and SSP5 less applicable. To address the aforementioned limitations, this study made three key enhancements, which significantly refines and augments our previous investigation. Firstly, we refined the model, incorporating granular demographic data at the city level. Secondly, we redesigned the migration module to consider both regional and city-level population attractiveness. Lastly, we explored diverse fertility and migration scenarios.
ISSN:2052-4463
2052-4463
DOI:10.1038/s41597-023-02735-6