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Key ecological function areas are not only recognized as the critical subject for national and global ecosystem protection, but also the crucial hinterland for maintaining the livelihood of local residents. Studies on livelihood resilience contribute to better understand the adaptation strategies and development needs of poor and highly vulnerable groups in key ecological function zones, which is crucial to promoting the long run sustainability. Based on the survey data of 575 households, this paper constructs the livelihood resilience evaluation index system from three dimensions of buffer capacity, self-organization ability and learning ability. The method of weighted sum is used to evaluate the livelihood resilience of farmers, and the influencing factors are analyzed by multiple regression model. The results show that the overall level of livelihood resilience of farmers is weak, and the self-organization ability of farmers is significantly higher than their buffering ability and learning ability. We found that ecological policy, age of head of household, diversity of livelihoods, family size, and environmental dependence are the key factors that affect the resilience of farmers' livelihoods. We argued that, new methods such as establishing a technical training system for farmers, promoting livelihood diversity, improving and implementing active ecological policies, and building long-term and effective risk warning, emergency response, and poverty alleviation mechanisms should be considered and implemented. [ABSTRACT FROM AUTHOR]

While various measures of mitigation and adaptation to climate change have been taken in recent years, many have gradually reached a consensus that building community resilience is of great significance when responding to climate change, especially urban flooding. There has been a dearth of research on community resilience to urban floods, especially among transient communities, and therefore there is a need to conduct further empirical studies to improve our understanding, and to identify appropriate interventions. Thus, this work combines two existing resilience assessment frameworks to address these issues in three different types of transient community, namely an urban village, commercial housing, and apartments, all located in Wuhan, China. An analytic hierarchy process–back propagation neural network (AHP-BP) model was developed to estimate the community resilience within these three transient communities. The effects of changes in the prioritization of key resilience indicators under different environmental, economic, and social factors was analyzed across the three communities. The results demonstrate that the ranking of the indicators reflects the connection between disaster resilience and the evaluation units of diverse transient communities. These aspects show the differences in the disaster resilience of different types of transient communities. The proposed method can help decision makers in identifying the areas that are lagging behind, and those that need to be prioritized when allocating limited and/or stretched resources. [ABSTRACT FROM AUTHOR]