How Do Rural Households’ Livelihood Vulnerability Affect Their Resilience? A Spatiotemporal Empirical Analysis from a Multi-Risk Perspective

Yue Sun, Yanhui Wang (), Renhua Tan, Yuan Wan, Junwu Dong, Junhao Cai and Mengqin Yang
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Yue Sun: Key Laboratory of 3Dimensional Information Acquisition and Application, Ministry of Education, Capital Normal University, Beijing 100048, China
Yanhui Wang: Key Laboratory of 3Dimensional Information Acquisition and Application, Ministry of Education, Capital Normal University, Beijing 100048, China
Renhua Tan: China Urban Construction Design & Research Institute Co., Ltd., Beijing 100120, China
Yuan Wan: Key Laboratory of 3Dimensional Information Acquisition and Application, Ministry of Education, Capital Normal University, Beijing 100048, China
Junwu Dong: State Key Laboratory of Resources and Environmental Information Systems, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
Junhao Cai: Beijing Institute of Surveying and Mapping, Beijing 100038, China
Mengqin Yang: Key Laboratory of 3Dimensional Information Acquisition and Application, Ministry of Education, Capital Normal University, Beijing 100048, China

Sustainability, 2025, vol. 17, issue 17, 1-38

Abstract: Poor rural households still face vulnerability of the sustainable livelihood capacity caused by multiple risk disturbances even after they are lifted out of poverty, and become vulnerable poverty-eradicated households. However, quantifying the spatiotemporal heterogeneity of the impact of rural household livelihood vulnerability on resilience from a multi-risk perspective remains a challenge. This study integrates the theoretical connotations of livelihood vulnerability and resilience to develop a systematic analysis framework of sustainable livelihood-vulnerability-resilience for rural households from the perspective of multi-risk disturbance, and reveals the dynamic interaction process and mechanism of the three. On this basis, the VEP model for forward-looking and multi-risk perspectives, which embeds multiple risk factors as feature vectors, and the cloud-based fuzzy integrated evaluation method are employed to measure rural households’ livelihood vulnerability and resilience, respectively. Subsequently, based on clarifying the correlation between the two, we use the quantile regression method and factor contribution model to reveal the spatiotemporal impact mechanism of multi-level and multi-risk dominated vulnerability of rural households on resilience. These methods collectively enable us to quantify the spatiotemporal heterogeneity of vulnerability and resilience impacts from a risk perspective, taking a step forward and broadening the analytical perspective in the field of sustainable livelihoods research. The case study in Fugong County of China shows that, both rural households’ livelihood vulnerability and resilience exhibit spatiotemporal heterogeneity, and the negative correlation between the two gradually increases over time; as the level of livelihood vulnerability increases, the internal main contributing factors of livelihood resilience and their degree of contribution change accordingly; as the types of risks that dominate vulnerability change, the impact of vulnerability on the overall livelihood resilience and its internal dimensions also varies, where the change in resilience is greatest when the vulnerability is dominated by social risks, while the least change occurred when vulnerability is dominated by labor and income risks. This study provides a feasible methodological reference and a technical foundation for decision-making aimed at guiding rural households out of poverty sustainably and achieving sustainable livelihood. It can effectively enhance the predictive and post-event coping capacity of vulnerable rural households when subjected to multi-risk disturbances.

Keywords: livelihood vulnerability; livelihood resilience; risk; spatiotemporal evolution; sustainable development (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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