Spatiotemporal Pattern Identification and Driving Mechanism of Urban Shrinkage in the Yellow River Basin from 2000 to 2020

Wei Gao, Xinzheng Zhao (), Jianwei Li, Dekang Zhang, Yang Rui, Tongsheng Li and Min Lei
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Wei Gao: School of Urban and Environmental Science, Northwest University, Xi’an 710127, China
Xinzheng Zhao: School of Urban and Environmental Science, Northwest University, Xi’an 710127, China
Jianwei Li: School of Urban and Environmental Science, Northwest University, Xi’an 710127, China
Dekang Zhang: School of Urban and Environmental Science, Northwest University, Xi’an 710127, China
Yang Rui: School of Urban and Environmental Science, Northwest University, Xi’an 710127, China
Tongsheng Li: School of Urban and Environmental Science, Northwest University, Xi’an 710127, China
Min Lei: School of Urban and Environmental Science, Northwest University, Xi’an 710127, China

Land, 2022, vol. 11, issue 8, 1-19

Abstract: The regional differences in the Yellow River Basin have increased, and the aggravation of this unbalanced state has seriously restricted the high-quality development of the Yellow River Basin during the accelerated urbanisation that has taken place in recent years. In this regard, heterogeneity in the trends of evolution and the causes of population shrinkage in different regions of the Yellow River Basin can be adopted as targeted countermeasures. The present study uses data from the fifth, sixth, and seventh national censuses and takes the administrative units of different levels in the Yellow River Basin as the object, considering 72 prefecture-level cities within the autonomous prefectures and 595 county-level administrative units in nine provinces (autonomous regions). The population shrinkage coefficient, night light index, bivariate spatial autocorrelation, geographic detectors, and other methods were used, with the final objective of exploring the spatial–temporal distribution pattern and impact mechanism of urban shrinkage from 2000 to 2020. The results of the study show the following: (1) The shrinkage patterns in 2000–2010 (T1) and 2010–2020 (T2) were quite different. From T1 to T2, the shrinkage situation worsened, with the number of districts experiencing population shrinkage increasing from 175 to 373 and the number of districts experiencing continuous night light and shrinkage districts increasing from 146 to 163. (2) The phenomenon of urban shrinkage is spatially scale dependent, with the shrinkage of prefecture-level cities and county-level cities being characterised by both spatial differentiation and spatial nesting relationships. (3) There is a certain inconsistency in the representation of the shrinkage patterns of the nighttime lighting and population data. The nighttime lighting dimension can reflect the structural shrinkage characteristics of the city more accurately and sensitively, and the representation of population loss is lagging. (4) The main impact factors and the intensity of urban shrinkage are the aggravated aging level, the declining level of industrial greening and intensification under market-driven economic structure adjustments, and the decreased natural growth rate in the population structure and public service facilities.

Keywords: shrinking cities; urban shrinkage; nighttime light; spatiotemporal pattern; Yellow River Basin (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2022
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