Heterogeneous land development opportunities induced by spatial regulation produce different advantages in areas, which undoubtedly differentiates farmers’ employment. The aim of this study was to quantitatively examine its impact. We selected Moshui Lake City Park (urban development planning area), Sino-French Eco-City (industrial development planning area), and Chenhu International Wetland (ecological protection planning area) as its principal research areas. These regions are all located in Wuhan city, Hubei province, China. After obtaining 907 valid responses from rural households, the Tobit model was adopted to identify the impact of land development opportunities on farmers’ nonfarm employment. The results show that, first, industrial development opportunity (IDO) and urban development opportunity (UDO) provide more job security than the reference group, which is ecological development opportunity (EDO), with the estimated coefficients of IDO and UDO being 0.325 and 0.944, respectively. However, a negative correlation was found between UDO and farmers’ employment selection and income. Second, heterogeneity analysis reveals that the promotion effect of land development opportunities on farmers’ employment is more significant for low- and middle-income, low-quantity, and high-quality households. Finally, further analysis shows that IDO can promote employment for all age groups, but UDO inhibits the elderly labor force from getting employed. These findings provide evidence-based insights which can enable the government to formulate land value-added distribution systems that promote balanced development between regions and stakeholders.

【目的】本文旨在揭示“监测户”生计韧性多维能力与返贫致贫风险消除的关联机制及差异化组态路径，为因人因户开展精准帮扶、推动稳定消除风险提供参考。【方法】基于湖南省怀化市2023年8个集中连片脱贫县139个乡镇18943户“监测户”的防返贫监测数据，综合运用熵权TOPSIS模型、Logit回归模型与模糊集定性比较分析（fsQCA），刻画“监测户”生计韧性水平与返贫致贫风险差异，探讨风险消除的影响机制及组态路径。【结果】①样本地区“监测户”返贫致贫风险消除率达79.78%，类型与空间异质性显著。边缘易致贫户风险消除率最高（94.36%），突发严重困难户最低（66.92%）；怀化市127个乡镇风险消除率超50%，其中69个近郊乡镇因交通便捷、政策倾斜及产业适配性强，消除率普遍超80%。②“监测户”返贫致贫风险消除受生计韧性多维能力协同驱动。风险已消除样本的生计韧性（0.313）高于未消除样本（0.282），核心差异在于技能习得转化能力，这是风险消除的“核心驱动因素”；外部资源整合能力依托政策帮扶能够实现资源禀赋向生计风险缓冲力转化，但基本资源积累能力因合作社“精英俘获”难以转化为实际收益。③不同类型“监测户”返贫致贫风险消除路径存在差异。脱贫不稳定户需技能培训与主动务工双重赋能激活内生动力；突发严重困难户依赖多元化紧急政策快速恢复“造血”能力；边缘易致贫户需通过生计资本补偿与产业转型破解资源和收益错配困局。【结论】生计韧性多维能力异质性是“监测户”返贫致贫风险消除差异的核心成因，地方政府应针对不同类型“监测户”实施差异化的帮扶措施。

Introduction: Against the background of post-poverty alleviation governance, livelihood capital acts as the core endogenous resource determining rural monitored households’ capacity to avert poverty relapse risks. Existing literature fails to fully differentiate three official categories of at-risk rural households, and lacks systematic evidence on heterogeneous mechanisms of multi-dimensional livelihood capital affecting poverty relapse prevention. This study fills such research gaps and provides empirical basis for classified targeted anti-poverty governance. Methods: Taking 19,326 monitored households across eight contiguous poverty-alleviation counties in Western Hunan as samples, including 5,896 poverty-relieved unstable households, 8,321 marginal vulnerable households, and 5,109 households with sudden severe hardships, this paper establishes a five-dimensional livelihood capital evaluation system covering natural, physical, human, social and financial capital. The entropy-weighted TOPSIS model is adopted to quantify livelihood capital endowment gaps, and four groups of binary Logistic regression models (full sample plus three sub-samples) are constructed to identify differentiated influencing factors of poverty relapse risk elimination. Results: The results show that 81.77% of all sampled households have eliminated relapse risks; marginal vulnerable households reach the highest risk elimination rate of 97.03%, while households with sudden severe hardships record the lowest rate at 69.90%. Households free of poverty-returning risks possess higher comprehensive livelihood capital scores, with financial capital as their dominant advantage. Livelihood capital exerts obvious heterogeneous effects across groups: financial capital dominates risk elimination in the full sample; human capital serves as the core driver for poverty-relieved unstable households; financial capital determines risk resistance for households with sudden severe hardships; social capital generates significantly positive effects on marginal vulnerable groups. Theoretically, this paper subdivides monitored households into three official types and enriches the empirical evidence of livelihood capital’s poverty reduction effects, expanding the application boundary of the sustainable livelihood framework in mountainous anti-poverty research. Practically, it provides a data-driven risk identification tool for regional dynamic poverty monitoring. Conclusion: Local governments should formulate differentiated assistance policies matching livelihood capital disadvantages of different household groups. Activating multi-dimensional livelihood capital endowments can effectively strengthen rural households’ risk resistance, consolidate long-term poverty alleviation achievements, and fundamentally curb repeated poverty among rural monitored households.