LI He, Kevin LO, ZHANG Pingyu. Population Shrinkage in Resource-dependent Cities in China: Processes, Patterns and Drivers[J]. Chinese Geographical Science, 2020, 30(1): 1-15. doi: 10.1007/s11769-019-1077-4
Citation: LI He, Kevin LO, ZHANG Pingyu. Population Shrinkage in Resource-dependent Cities in China: Processes, Patterns and Drivers[J]. Chinese Geographical Science, 2020, 30(1): 1-15. doi: 10.1007/s11769-019-1077-4

Population Shrinkage in Resource-dependent Cities in China: Processes, Patterns and Drivers

doi: 10.1007/s11769-019-1077-4
Funds:

Under the auspices of Key Research Program of the Chinese Academy of Sciences (No. ZDRW-ZS-2017-4-3-4), National Science and Technology Basic Project of the Ministry of Science and Technology of China (No. 2017FY101303-1), National Natural Science Foundation of China (No. 41001097, 41571152), Liaoning Revitalization Talents Program (No. XLYC1807249)

  • Received Date: 2018-11-10
  • Rev Recd Date: 2019-03-08
  • Resource-dependent cities (RCs) are a vulnerable group of urban settlements that often face population shrinkage; however, population changes in RCs in China are not well understood. This study offers new insight into this matter through a robust analysis that features a longer time scale, a larger sample of RCs, and a finer unit of analysis. It finds new evidence that problems of population shrinkage in RCs are more serious than previous literature has suggested. Approximately 30% of the studied units have experienced either long-term or short-term population shrinkage, and many more are experiencing a slowing down of population growth. Problems are especially common among three types of RCs:the resource-depleted RCs, the forestry-based RCs, and RCs in Northeast China. These results underscore transition policy inadequacies in addressing population loss, and call for a more comprehensive and diversified population policy that tackles the multifaceted factors that contribute to population shrinkage, including lack of industrial support, maladjustment to market oriented reformation, poor urban environment and natural population decline.
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Population Shrinkage in Resource-dependent Cities in China: Processes, Patterns and Drivers

doi: 10.1007/s11769-019-1077-4
Funds:

Under the auspices of Key Research Program of the Chinese Academy of Sciences (No. ZDRW-ZS-2017-4-3-4), National Science and Technology Basic Project of the Ministry of Science and Technology of China (No. 2017FY101303-1), National Natural Science Foundation of China (No. 41001097, 41571152), Liaoning Revitalization Talents Program (No. XLYC1807249)

Abstract: Resource-dependent cities (RCs) are a vulnerable group of urban settlements that often face population shrinkage; however, population changes in RCs in China are not well understood. This study offers new insight into this matter through a robust analysis that features a longer time scale, a larger sample of RCs, and a finer unit of analysis. It finds new evidence that problems of population shrinkage in RCs are more serious than previous literature has suggested. Approximately 30% of the studied units have experienced either long-term or short-term population shrinkage, and many more are experiencing a slowing down of population growth. Problems are especially common among three types of RCs:the resource-depleted RCs, the forestry-based RCs, and RCs in Northeast China. These results underscore transition policy inadequacies in addressing population loss, and call for a more comprehensive and diversified population policy that tackles the multifaceted factors that contribute to population shrinkage, including lack of industrial support, maladjustment to market oriented reformation, poor urban environment and natural population decline.

LI He, Kevin LO, ZHANG Pingyu. Population Shrinkage in Resource-dependent Cities in China: Processes, Patterns and Drivers[J]. Chinese Geographical Science, 2020, 30(1): 1-15. doi: 10.1007/s11769-019-1077-4
Citation: LI He, Kevin LO, ZHANG Pingyu. Population Shrinkage in Resource-dependent Cities in China: Processes, Patterns and Drivers[J]. Chinese Geographical Science, 2020, 30(1): 1-15. doi: 10.1007/s11769-019-1077-4
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