The spatial pattern of rural poverty and its influencing factors are unique in regions located in the “double zone”, overlaying the Loess Plateau landform and interprovincial border socioeconomic zone. Using Huining County, located in the interprovincial border area of the Loess Plateau, as a case study, this paper examines the spatial heterogeneity of rural poverty patterns and poverty-causing factors by using geographically weighted regression (GWR) modeling. The potential accessibility indicator is employed to identify the formative mechanism of rural poverty. The results show that rural poverty is significantly correlated with county-level accessibility, water resource accessibility, and town-level accessibility. County-level accessibility and town-level accessibility have significant border effects on rural poverty. The arid characteristics in certain areas of the Loess Plateau mean that the impact of water resource accessibility on the incidence of rural poverty is second only to that of county-level accessibility. Forestland resources have a positive correlation with the incidence of rural poverty in the region dominated by farming. Finally, targeted poverty reduction policies are proposed based on the results of the analysis of poverty-causing factors. The findings derived from this paper can help other developing countries in designing their own poverty reduction policies.

Accessibility plays an important role in alleviating rural poverty. Previous studies have explored the relationship between accessibility and rural poverty, but they offer limited evidence of the collective influence of multiscale transport accessibility (town-level, county-level, and prefecture-level accessibility) and its nonlinear effects on rural poverty. This study adopted the gradient-boosting decision tree model to explore the nonlinear association and threshold effects of multiscale transport accessibility on the rural poverty incidence (RPI). We selected Huining, a poverty-stricken county in China, as a case study. The results show that multiscale transport accessibility collectively has larger predictive power than other variables. Specifically, town-level accessibility (12.97%) plays a dominant role in predicting the RPI, followed by county-level accessibility (9.50%) and prefecture-level accessibility (7.38%). We further identified the nonlinear association and effective ranges of multiscale transport accessibility to guide poverty-alleviation policy. Our results help inform policy and planning on sustainable poverty reduction and rural vitalization.