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County-Level City Shrinkage in China: Representation, Cause, and Response

Author

Listed:
  • Dingpan Chen

    (School of Earth Sciences, Zhejiang University, Hangzhou 310058, China
    Center for Balance Architecture, Zhejiang University, Hangzhou 310063, China)

  • Yuzhe Wu

    (School of Public Affairs, Zhejiang University, Hangzhou 310058, China)

  • Zhou Lin

    (School of Earth Sciences, Zhejiang University, Hangzhou 310058, China)

  • Zhiyi Xu

    (School of Public Affairs, Zhejiang University, Hangzhou 310058, China)

Abstract

Shrinkage in China’s cities has gradually received increasing attention. Specifically, county-level shrinking cities are numerous and widely distributed. City shrinkage even appears throughout Zhejiang Province, which is highly developed and has stable economic growth. County-level shrinking cities resulting from regional competition can still maintain long-term and stable economic growth, but their economic growth lags behind the whole competitive region. We identified five county-level shrinking cities in Zhejiang Province, and characterized the shrinkage phenomenon in Changshan County using census, nighttime lighting (NTL), and Tencent Location Big Data. City shrinkage resulted from the comprehensive effects of declining traditional industries, backward per capita income, highly skilled labor force shortages, and population age structure changes. Given the shrinkage status of Changshan, we proposed countermeasures and suggestions including aspects of industrial development, urban planning, social governance, and care for the elderly.

Suggested Citation

  • Dingpan Chen & Yuzhe Wu & Zhou Lin & Zhiyi Xu, 2022. "County-Level City Shrinkage in China: Representation, Cause, and Response," Land, MDPI, vol. 11(10), pages 1-21, October.
    • Handle: RePEc:gam:jlands:v:11:y:2022:i:10:p:1845-:d:947298
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    References listed on IDEAS

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    Cited by:

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    2. Luo, Haizhi & Wang, Chenglong & Li, Cangbai & Meng, Xiangzhao & Yang, Xiaohu & Tan, Qian, 2024. "Multi-scale carbon emission characterization and prediction based on land use and interpretable machine learning model: A case study of the Yangtze River Delta Region, China," Applied Energy, Elsevier, vol. 360(C).

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