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Abstract
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.
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Details
1 Tsinghua University, Ministry of Education Ecological Field Station for East Asian Migratory Birds, Institute for Global Change Studies, Department of Earth System Science, Beijing, China (GRID:grid.12527.33) (ISNI:0000 0001 0662 3178)
2 Beijing Institute of Technology, School of Management and Economics, Beijing, China (GRID:grid.43555.32) (ISNI:0000 0000 8841 6246)
3 Beijing Institute of Economics and Management, School of Linkong Economics and Management, Beijing, China (GRID:grid.43555.32)
4 Renmin University of China, Center for Population and Development Studies, Beijing, China (GRID:grid.24539.39) (ISNI:0000 0004 0368 8103)
5 National Climate Center, China Meteorological Administration, Beijing, China (GRID:grid.8658.3) (ISNI:0000 0001 2234 550X)
6 Chinese Center for Disease Control and Prevention, National Center for Chronic and Noncommunicable Disease Control and Prevention, Beijing, China (GRID:grid.198530.6) (ISNI:0000 0000 8803 2373)
7 Tsinghua University, Vanke School of Public Health, Beijing, China (GRID:grid.12527.33) (ISNI:0000 0001 0662 3178)
8 Tsinghua University, State Key Joint Laboratory of Environment Simulation and Pollution Control (SKLESPC), School of Environment, Beijing, China (GRID:grid.12527.33) (ISNI:0000 0001 0662 3178)




