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2010 年至 2100 年中国基于共享社会经济路径的省级和网格化人口预测。

Provincial and gridded population projection for China under shared socioeconomic pathways from 2010 to 2100.

机构信息

State Key Joint Laboratory of Environment Simulation and Pollution Control (SKLESPC), School of Environment, Tsinghua University, Beijing, 100084, China.

Ministry of Education Key Laboratory for Earth System Modeling, and Department of Earth System Science, Tsinghua University, Beijing, 100084, China.

出版信息

Sci Data. 2020 Mar 9;7(1):83. doi: 10.1038/s41597-020-0421-y.

Abstract

In response to a growing demand for subnational and spatially explicit data on China's future population, this study estimates China's provincial population from 2010 to 2100 by age (0-100+), sex (male and female) and educational levels (illiterate, primary school, junior-high school, senior-high school, college, bachelor's, and master's and above) under different shared socioeconomic pathways (SSPs). The provincial projection takes into account fertility promoting policies and population ceiling restrictions of megacities that have been implemented in China in recent years to reduce systematic biases in current studies. The predicted provincial population is allocated to spatially explicit population grids for each year at 30 arc-seconds resolution based on representative concentration pathway (RCP) urban grids and historical population grids. The provincial projection data were validated using population data in 2017 from China's Provincial Statistical Yearbook, and the accuracy of the population grids in 2015 was evaluated. These data have numerous potential uses and can serve as inputs in climate policy research with requirements for precise administrative or spatial population data in China.

摘要

为满足对中国未来人口的分省和空间明确数据的日益增长的需求,本研究根据不同的共享社会经济途径(SSP),按年龄(0-100+)、性别(男性和女性)和教育程度(文盲、小学、初中、高中、大学、学士及以上)对 2010 年至 2100 年的中国省级人口进行了估计。该省级预测考虑了近年来中国实施的促进生育政策和特大城市的人口上限限制,以减少当前研究中的系统偏差。根据代表性浓度途径(RCP)城市网格和历史人口网格,将预测的省级人口分配到每年分辨率为 30 弧秒的空间明确人口网格中。使用中国省级统计年鉴 2017 年的人口数据对省级预测数据进行了验证,并评估了 2015 年人口网格的准确性。这些数据具有许多潜在用途,可以作为在中国进行气候政策研究的输入,这些研究需要精确的行政或空间人口数据。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/009a/7062824/5a939340903a/41597_2020_421_Fig1_HTML.jpg

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