Abstract
Abstract This study examines the risk of returning to poverty among Chinese farmers following external shocks, integrating the dimensions of livelihood vulnerability and environmental constraints. Based on household survey data of 2536 formerly impoverished households in Henan Province, this study employs Monte Carlo simulations rooted in Geometric Brownian Motion to quantify four types of return-to-poverty risks: policy withdrawal (P), natural disasters (N), employment disruption (E), and market fluctuations (M). The results indicate that natural disasters pose the highest risk (affecting 14.35% of households), while spatial heterogeneity is evident: mountainous peri-urban areas exhibit elevated risks due to terrain constraints and resource misallocation, in contrast to distance-decay patterns observed in plains. Multilevel linear models identify key determinants: labor force proportion (β = −2.33, p < 0.01), per capita arable land area (β = −1.29, p < 0.01), and poverty alleviation duration (β = −0.18, p < 0.01) are negatively correlated with risk, whereas the village return-to-poverty risk rate and terrain undulation increase vulnerability. Policy implications emphasize the importance of geographically differentiated interventions, livelihood diversification, and cooperative reforms to consolidate the achievements of poverty alleviation gains.