高级检索

欠发达县域城乡融合与乡村韧性耦合协调发展研究以湖南武陵山片区为例

Coupling and coordinated development of urban-rural integration and rural resilience in underdeveloped counties: a case study of the Wuling Mountain Area in Hunan Province

  • 摘要: 城乡融合与乡村韧性的耦合协调发展关系研究,有助于提升乡村抵御内外部风险能力并加快新型城乡关系构建。本文以湖南省武陵山片区37个县(市、区)为研究对象,基于2008—2023年县域面板数据,运用熵权法与耦合协调模型,实证分析欠发达地区城乡融合发展与乡村韧性之间的耦合机制。研究结果表明:1)城乡融合指数快速增长,空间上呈“东部高、中部低”格局;乡村韧性指数增速由快趋缓,呈“东高西低”分布。2)两系统耦合协调度逐年提升,多数地区从基本失调转为中级协调,部分进入高级协调,空间分布呈正相关并具有明显集聚性。3)大部分县域属于乡村韧性滞后型,其韧性不足是制约二者协调发展的主要因素,其中抵御与恢复能力对耦合协调度起阻滞作用,适应与调整能力在部分地区发挥正向作用。4)地理探测器分析显示,平均海拔、二、三产业增加值及城乡建设用地比是影响耦合协调度的关键因素,多因子交互作用强于单因子,其中平均海拔与二、三产业增加值的交互作用最显著。

     

    Abstract: Exploring the coupled and coordinated relationship between urban-rural integration and rural resilience helps enhance rural capacity to withstand internal and external risks and promote new urban-rural relations. This study examines 37 counties, cities, and districts in the Wuling Mountain Area of Hunan Province. Using county-level panel data from 2008 to 2023, and applying the entropy weighting method and coupling coordination model, it analyzes the coupling mechanism between urban-rural integrated development and rural resilience in underdeveloped regions. The results show that: 1) The urban-rural integration index increased rapidly, displaying a spatial pattern of high in the east and lower in the central region, while the rural resilience index grew at a slowing rate, with a distribution of high in the east and low in the west. 2) The coupling coordination degree improved steadily, with most regions shifting from basic imbalance to moderate coordination and some reaching high coordination; the spatial pattern exhibits positive correlation and significant clustering. 3) Most counties were rural resilience lagging types, and insufficient resilience is the main constraint on coordinated development. Resistance and recovery capacity exerts a restraining effect, whereas adaptation and adjustment capacity promotes coordination in some areas. 4) Geographical detector results indicate that average elevation, the added value of the secondary and tertiary industries, and the ratio of urban-rural construction land are key determinants. Multi-factor interactions show stronger explanatory power than single factors, with the interaction between average elevation and the added value of the secondary and tertiary industries being the strongest.

     

/

返回文章
返回