ArticlePloS one2022
Spatiotemporal evolution and driving mechanism of regional shrinkage at the county scale: The three provinces in northeastern China.
Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Conditional Agglomeration in China's Northeast Rust Belt: Density, Structural Orientation, and Ownership-Mixing Entropy.Entropy (Basel, Switzerland) · 2026Article
- Analyzing Spatio-Temporal Dynamics of Grassland Resilience and Influencing Factors in the West Songnen Plain, China, for Eco-Restoration.Plants (Basel, Switzerland) · 2024Article
- Article
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5 authors.
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Abstract
The three northeast provinces are typical areas of regional shrinkage in China. A scientific understanding of their shrinkage and driving mechanism is conducive to the transformation and development of traditional industrial bases in China. This study analyzed the spatiotemporal evolution and driving mechanism of regional shrinkage at the county scale in the three provinces. The main findings are as follows: (1) 40.86% of counties in the three provinces shrank, forming three concentrated shrinking regions. However, comprehensively shrinking regions were narrowed and lessened with the introduction of the Northeast Area Revitalization Plan. (2) The population-related shrinking regions accounted for more than 90% and continued to expand. Such shrinkage was higher in the north than in the south. The degree of economy-related shrinkage was the most serious, and the hotspots were mainly concentrated in Liaoning Province. The scope of space-related shrinkage was most minor, and such shrinkage was relatively mild. (3) When it came to influencing factors, the shrinkage index was positively correlated with the proportion of the secondary industry, the output value of agriculture, forestry, animal husbandry and fishery, the number of industrial enterprises above the designated size, fiscal expenditure, and the balance of resident deposits, and negatively correlated with the altitude, the proportion of the tertiary industry, and population aging. Geographically weighted regression (GWR) and ordinary least squares (OLS) produced similar regression results. The spatial pattern of influencing factors was consistent with the hotspot areas of population-related shrinkage or economy-related shrinkage, with significant spatial differences.
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