4.7 Article

Quantitative identification and evolution trend simulation of shrinking cities at the county scale, China

Journal

SUSTAINABLE CITIES AND SOCIETY
Volume 65, Issue -, Pages -

Publisher

ELSEVIER
DOI: 10.1016/j.scs.2020.102611

Keywords

Generalized shrinking cities; Shrinking trajectories; Evolution trend; Spatial panel data

Funding

  1. Late Project of National Social Science Foundation in China [20FJYB035]
  2. Natural Science Foundation of Chongqing in China [cstc2020jcyj-jqX0004]
  3. Ministry of education of Humanities and Social Science project [20YJA790016]
  4. Science and Technology Research Program of Chongqing Municipal Education Commission [KJZD-K201800702]

Ask authors/readers for more resources

Urban shrinkage has become increasingly evident during rapid urbanization. Understanding the mechanisms behind shrinking cities is crucial for urban transformation and sustainable development. This study in China analyzes the intensity, distribution, and trajectories of shrinking cities, predicting future trends and identifying economic, institutional, and social factors influencing city shrinkage. The results show that most shrinking cities are in specific regions with varying degrees of shrinkage, with economic factors like total GDP having a significant impact on population changes.
Urban shrinkage became increasingly obvious during the process of rapid urbanization. It is important to understand the mechanism behind the formation and evolution of shrinking cities to realize urban transformation and sustainable development. This paper establishes a shrinking cities index to measure generalized urban shrinkage by using the population change rate at the county scale in China. First, on the spatial scale, the shrinking intensity and pattern distribution are analyzed. Second, on the temporal scale, the shrinking trajectories of shrinking cities are analyzed, and a trend is predicted for shrinking cities for 2020 and 2025 based on the gray model (GM (1,1)). Finally, economic, institutional, and social factors are selected to identify the major influences on the different shrinking trajectories using a spatial panel data model. The results show the following. (1) Between 2000 and 2015, shrinking cities in China were mainly distributed in four regions: the three northeastern provinces, the Shaanxi-Gansu-Ningxia region, the Sichuan-Chongqing region, and the middle and lower reaches of the Yangtze River. They thus appear to have connecting piece and axis distribution characteristics. (2) The dominant shrinking city types are slight and moderate shrinking, which together account for 80 %-90 % of all shrinking cities. These types have less severe or extreme shrinkage. Considering the different trajectories, temporary shrinking cities represent 75.26 % of all shrinking cities and predominantly occurred in 2010-2015. Episodic shrinking cities represent 19.74 % of all shrinking cities. Continuous shrinking cities represent 5.00 % of all shrinking cities and occurred in 2005-2015. (3) From 2015 to 2025, the number of shrinking cities is projected to be 272, with slight shrinkage accounting for 43.38 % of the total shrinking cities, and extreme shrinkage accounting for 4.78 %. Examining the spatial distribution of shrinking cities shows that they are mainly distributed in the three northeastern provinces, the Shaanxi-Gansu-Ningxia region, Xinjiang and the middle and lower reaches of the Yangtze River in the future. (4)Total GDP, GDP per capita, fiscal expenditures, employment, and built-up area have a significant impact on the population of shrinking cities. Among them, the total GDP has the largest impact, and the built-up area has the smallest impact. In addition, the total GDP, GDP per capita, fiscal revenue, and employment have a significant spatial spillover effect in temporary shrinking cities. This study provides empirical evidence to support Chinese policy makers in implementing urban sustainable development goals.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.7
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available