How Do Rural Households Achieve Poverty Alleviation? Identification and Characterization of Development Pathways Using Explainable Machine Learning

Exploring the dynamic mechanisms of household poverty alleviation is crucial for achieving sustainable poverty reduction and preventing relapse into poverty. However, existing research is often constrained by a static perspective, failing to integrate poverty states with transition processes, and lacking the methodological tools to decipher the nonlinear heterogeneity and spatial dependence inherent in household pathways. This study addresses three critical questions: How can we conceptualize and quantify the dynamic trajectories of household poverty alleviation? What are the key mechanisms that drive households from poverty to stable sustainability? And how do these pathways vary across different spatial contexts? Our analysis, based on an explainable machine learning framework applied to longitudinal data from 107,637 households, yields several key findings. First, household pathways are strongly predicted by their initial typology. Those with heavy burdens and limited labor capacity (SI−4) predominantly remained in unstable states (62.5%), while households with human capital advantages (SI−3, SI−6) achieved stable poverty alleviation directly at rates of 84.9% and 100%, respectively. Second, the transition from instability to stability follows discernible bridging mechanisms, where pathways reliant on skill upgrading prove more decisive for long-term stability than those dependent solely on short-term subsidies. Third, pathways are intrinsically shaped by spatial context, creating a geography of opportunity and risk—from policy compensation in mountainous areas, to resource-institutional synergy in agricultural plains, and labor-market stabilization in mining and peri-urban regions. In conclusion, sustainable poverty alleviation hinges on interventions precisely aligned with both initial household profiles and regional contexts. The central policy implication is to move beyond one-size-fits-all approaches by balancing protective safety nets with capacity-building investments, thereby creating equitable development pathways across diverse geographies.