Journal of Geographical Sciences

, Volume 25, Issue 12, pp 1507–1520

Spatial pattern and its evolution of Chinese provincial population: Methods and empirical study

  • Yu Deng
  • Shenghe Liu
  • Jianming Cai
  • Xi Lu
  • Chris P. Nielsen
Article

DOI: 10.1007/s11442-015-1248-x

Cite this article as:
Deng, Y., Liu, S., Cai, J. et al. J. Geogr. Sci. (2015) 25: 1507. doi:10.1007/s11442-015-1248-x

Abstract

China has been experiencing an unprecedented urbanization process. In 2011, China’s urban population reached 691 million with an urbanization rate of 51.27%. Urbanization level is expected to increase to 70% in China in 2030, reflecting the projection that nearly 300 million people would migrate from rural areas to urban areas over this period. At the same time, the total fertility rate of China’s population is declining due to the combined effect of economic growth, environmental carrying capacity, and modern social consciousness. The Chinese government has loosened its “one-child policy” gradually by allowing childbearing couples to have the second child as long as either of them is from a one-child family. In such rapidly developing country, the natural growth and spatial migration will consistently reshape spatial pattern of population. An accurate prediction of the future spatial pattern of population and its evolution trend are critical to key policy-making processes and spatial planning in China including urbanization, land use development, ecological conservation and environmental protection. In this paper, a top-down method is developed to project the spatial distribution of China’s future population with considerations of both natural population growth at provincial level and the provincial migration from 2010 to 2050. Building on this, the spatial pattern and evolution trend of Chinese provincial population are analyzed. The results suggested that the overall spatial pattern of Chinese population will be unlikely changed in next four decades, with the east area having the highest population density and followed by central area, northeast and west area. Four provinces in the east, Shanghai, Beijing, Tianjin and Jiangsu, will remain the top in terms of population density in China, and Xinjiang, Qinghai and Tibet will continue to have the lowest density of population. We introduced an index system to classify the Chinese provinces into three categories in terms of provincial population densities: Fast Changing Populated Region (FCPR), Low Changing Populated Region (LCPR) and Inactive Populated Region (IPR). In the FCPR, China’s population is projected to continue to concentrate in net immigration leading type (NILT) area where receives nearly 99% of new accumulated floating population. Population densities of Shanghai, Beijing, Zhejiang will peak in 2030, while the population density in Guangdong will keep increasing until 2035. Net emigration leading type (NELT) area will account for 75% of emigration population, including Henan, Anhui, Chongqing and Hubei. Natural growth will play a dominant role in natural growth leading type area, such as Liaoning and Shandong, because there will be few emigration population. Due to the large amount of moving-out labors and gradually declining fertility rates, population density of the LCPR region exhibits a downward trend, except for Fujian and Hainan. The majority of the western provinces will be likely to remain relatively low population density, with an average value of no more than 100 persons per km2.

Keywords

China provincial population urbanization migration spatial pattern natural growth 

Copyright information

© Institute of Geographic Science and Natural Resources Research (IGSNRR), Science China Press and Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Yu Deng
    • 1
    • 2
  • Shenghe Liu
    • 1
    • 2
  • Jianming Cai
    • 1
    • 2
  • Xi Lu
    • 3
  • Chris P. Nielsen
    • 3
  1. 1.Institute of Geographic Sciences and Natural Resources Research, CASBeijingChina
  2. 2.Key Laboratory of Regional Sustainable Development Modeling, CASBeijingChina
  3. 3.School of Engineering and Applied SciencesHarvard CambridgeUSA

Personalised recommendations