Spatiotemporal evolution, agglomeration heterogeneity, and poverty causing factors of poverty-stricken counties in China
File(s)
Accepted version
Author(s)
Wang, Yazhu
Duan, Xuejun
Zhang, Riqi
Zhou, Guangjin
Huan, Yizhong
Type
Journal Article
Abstract
Poverty remains a pervasive global challenge that hinders both social and economic advancement. This research aims to investigate rural poverty in China through a
spatiotemporal analysis of impoverished counties across various time periods. The examination identifies poverty clusters and risks by utilizing the DBSCAN spatial
clustering algorithm. Additionally, it employs the multi-scale geographically weighted regression model to assess the spatial characteristics of factors and mechanisms
leading to poverty. The study identifies 663, 832, and 52 state-level impoverished counties in China in 2001, 2016 and 2021, respectively. Moreover, the counties are
categorized into six types based on the principal factors causing poverty, namely: frontier terrain constraints, infrastructure limitations, geological disaster vulnerabilities,
minority aggregation industry constraints, underdeveloped old revolutionary areas, and ecological fragility. Among these factors, topographic relief and healthcare institutions
exhibit a negative correlation with poverty, while average slope and nighttime light index display a positive correlation. Furthermore, location conditions, farmland
production potential, vegetation index, residents' deposit balance, and local government revenue demonstrate a two-way influence on GDP per capita in impoverished counties. The ecological fragility in karst areas exacerbates poverty. This study provides valuable insights for local governments to implement effective and targeted poverty alleviation strategies.
spatiotemporal analysis of impoverished counties across various time periods. The examination identifies poverty clusters and risks by utilizing the DBSCAN spatial
clustering algorithm. Additionally, it employs the multi-scale geographically weighted regression model to assess the spatial characteristics of factors and mechanisms
leading to poverty. The study identifies 663, 832, and 52 state-level impoverished counties in China in 2001, 2016 and 2021, respectively. Moreover, the counties are
categorized into six types based on the principal factors causing poverty, namely: frontier terrain constraints, infrastructure limitations, geological disaster vulnerabilities,
minority aggregation industry constraints, underdeveloped old revolutionary areas, and ecological fragility. Among these factors, topographic relief and healthcare institutions
exhibit a negative correlation with poverty, while average slope and nighttime light index display a positive correlation. Furthermore, location conditions, farmland
production potential, vegetation index, residents' deposit balance, and local government revenue demonstrate a two-way influence on GDP per capita in impoverished counties. The ecological fragility in karst areas exacerbates poverty. This study provides valuable insights for local governments to implement effective and targeted poverty alleviation strategies.
Date Acceptance
2026-02-07
Citation
Ecosystem Health and Sustainability
ISSN
2096-4129
Publisher
American Association for the Advancement of Science (AAAS)
Journal / Book Title
Ecosystem Health and Sustainability
Copyright Statement
Copyright This paper is embargoed until publication. Once published the Version of Record (VoR) will be available on immediate open access.
License URL
Publication Status
Accepted