Abstract
The population has a significant influence on economic growth, energy consumption, and climate change. Many scholars and organizations have published projections for China's future population due to its substantial population amounts. However, these projections have not been evaluated or analyzed, which may lead confusion to extensional studies based on these datasets. This manuscript compares several China's projection datasets at multiscale and analyzes the impacting factors affecting projection accuracy. The results indicate that the slow of actual population growth rates from 2017 is earlier than most datasets projected. Therefore, the turning point of population decline probably comes rapidly before these datasets expected during 2024 and 2034. Furthermore, the projections do not reveal the population decline from 2010 in the Northeast provinces such as Jilin and Heilongjiang, and underrate the population increase in the southern provinces such as Guangdong and Chongqing. According to the results of regression models, the rate of population changes and the number of migrations people play a significant role in projection accuracy. These findings provide meaningful guidance for scholars to understand the uncertainty of those projection datasets. Moreover, for researchers performing population projections, our discoveries provide insights to increase the projection accuracy.
Highlights
The population has a significant influence on economic growth, energy consumption, and climate change
The Institute for Health Metrics and Evaluation (IHME), United Nations (UN), and Wittgenstein Centre Data Explorer (WCDE) are higher than the actual data since the 1970s, yet the WCDE coincides with the proper condition in most historical years
The projections of UN, IHME, WCDE, and Centre of Expertise on Population and Migration (CEPAM) are higher than the actual population
Summary
The population has a significant influence on economic growth, energy consumption, and climate change. To accurately grasp China’s future population growth tendency, the projections of NUIST (Nanjing University of Information Science and Technology) and THU (Tsinghua University) were created recently by Huang et al.[21] and Chen et al.[22], respectively These spatially explicit population datasets have been widely used to explore the influence of future population levels on global climate change[23–26], extreme weather disaster events[27–29], land-use change[24], and ecosystem service change[30]. They have been applied in many research fields, we know little about their projection accuracy and poorly understand the factors that affect their projection accuracy. It is necessary to evaluate their projection accuracy and determine their applicability in different regions
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