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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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Autores principales: Zhang, Shangchen, Zhao, Mengzhen, Liu, Zhao, Yang, Fan, Lu, Bo, Zhao, Zhenping, Gu, Kuiying, Zhang, Shihui, Lei, Mingyu, Zhang, Chi, Wang, Can, Cai, Wenjia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10656476/
https://www.ncbi.nlm.nih.gov/pubmed/37978198
http://dx.doi.org/10.1038/s41597-023-02735-6
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author Zhang, Shangchen
Zhao, Mengzhen
Liu, Zhao
Yang, Fan
Lu, Bo
Zhao, Zhenping
Gu, Kuiying
Zhang, Shihui
Lei, Mingyu
Zhang, Chi
Wang, Can
Cai, Wenjia
author_facet Zhang, Shangchen
Zhao, Mengzhen
Liu, Zhao
Yang, Fan
Lu, Bo
Zhao, Zhenping
Gu, Kuiying
Zhang, Shihui
Lei, Mingyu
Zhang, Chi
Wang, Can
Cai, Wenjia
author_sort Zhang, Shangchen
collection PubMed
description 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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spelling pubmed-106564762023-11-17 City-level population projection for China under different pathways from 2010 to 2100 Zhang, Shangchen Zhao, Mengzhen Liu, Zhao Yang, Fan Lu, Bo Zhao, Zhenping Gu, Kuiying Zhang, Shihui Lei, Mingyu Zhang, Chi Wang, Can Cai, Wenjia Sci Data Data Descriptor 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. Nature Publishing Group UK 2023-11-17 /pmc/articles/PMC10656476/ /pubmed/37978198 http://dx.doi.org/10.1038/s41597-023-02735-6 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Data Descriptor
Zhang, Shangchen
Zhao, Mengzhen
Liu, Zhao
Yang, Fan
Lu, Bo
Zhao, Zhenping
Gu, Kuiying
Zhang, Shihui
Lei, Mingyu
Zhang, Chi
Wang, Can
Cai, Wenjia
City-level population projection for China under different pathways from 2010 to 2100
title City-level population projection for China under different pathways from 2010 to 2100
title_full City-level population projection for China under different pathways from 2010 to 2100
title_fullStr City-level population projection for China under different pathways from 2010 to 2100
title_full_unstemmed City-level population projection for China under different pathways from 2010 to 2100
title_short City-level population projection for China under different pathways from 2010 to 2100
title_sort city-level population projection for china under different pathways from 2010 to 2100
topic Data Descriptor
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10656476/
https://www.ncbi.nlm.nih.gov/pubmed/37978198
http://dx.doi.org/10.1038/s41597-023-02735-6
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