The impact of natural disasters on rural areas has recently increased, the question of how to effectively improve the livelihood resilience of rural residents has therefore become an essential issue. In this context, a livelihood resilience evaluation index system for rural residents was constructed in four dimensions: livelihood quality, livelihood promotion, livelihood provision, and disaster stress. A structural dynamics model was used to analyze the changing characteristics of the livelihood resilience of rural residents in China between the years 1980 and 2020, and a livelihood resilience trend from 2021 to 2030 was predicted based on the ARIMA model. The main factors influencing livelihood resilience were explored using ridge regression analysis. The results show that: (1) livelihood resilience of rural residents in China fluctuated significantly between 1980 and 2020, tending generally to increase; (2) livelihood resilience is positively correlated with livelihood quality, livelihood promotion, and livelihood provision, while it is negatively correlated with disaster stress; (3) livelihood quality, livelihood promotion, and livelihood supply will still increase between the years 2021 and 2030, while the increase of livelihood resilience will tend to slow down; and (4) six variables have a significant positive impact on livelihood resilience, and provide a basis for the subsequent enhancement of livelihood resilience.

The Yangtze River Delta region is the most economically active and open region in China. It is also the region with the most prominent contradictions between urban and rural development. Therefore, it is necessary to strengthen the resilience of the rural areas in this region so that they can develop further with resources and opportunities after sustaining shocks. This study used a weighted TOPSIS method to measure the rural resilience of 153 research units from 2000 to 2019 and then applied the ESDA method to measure the spatial agglomeration or heterogeneity characteristics. The results show that: (1) The rural resilience of this region is higher in the east and lower in the west; (2) rural resilience has obvious spatial agglomeration characteristics, which have undergone a process of shifting from strong to weak before becoming strong again; and (3) the hotspots of rural resilience gradually shifted from Jiangsu to Zhejiang, while the coldspots gradually shifted from Zhejiang to Anhui. Finally, the K-means clustering algorithm was applied to divide all research units into five types: natural capital-dominated areas, productive capital-dominated areas, human capital-dominated areas, social-financial capital-dominated areas and general development areas. Then, the strategies for resilience promotion were proposed accordingly.

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