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China has achieved the goal of building a moderately prosperous society in a well-rounded way by 2020. At this stage, effectively dealing with poverty and not returning to it has become the bottom-line task of rural revitalization. The purpose of this study is to construct a poverty-return early warning and evaluation system for X and Y counties in Guangxi. Based on the field survey data of 150 households from the questionnaire survey in X County and Y County of Guangxi Province, an early warning evaluation system for returning to poverty in the two counties of Guangxi Province is constructed. The AHP analytic hierarchy process is used to evaluate the early warning of returning to poverty for farmers. The BP neural network algorithm is used to verify the rationality of the method; the overall poverty relief situation in the two counties is stable and the living conditions are good. The early warning results are as follows: One household in X County has a severe early warning, six households have a slight early warning, and sixty-four households have no early warning; in Y County, six households had severe early warning, six households had mild early warning, and sixty-seven households had no early warning. For farmers, serious early warnings are mainly caused by the lack of labor force and low annual per capita net income, as well as the lack of the main means of livelihood and capacity. The characteristics of mild early warnings for farmers are mainly that the proportion of non-labor income is relatively high, and the farmers lack the ability and way of long-term development. Different suggestions are put forward for farmers with different early-warning levels, focusing on improving their viability and development ability. [ABSTRACT FROM AUTHOR]

Eliminating poverty is the primary goal of sustainable development. China has eliminated absolute poverty in 2020, yet there is a chance that it could happen again. The poor population is mostly concentrated in ecologically fragile areas. We need to take more inclusive and effective initiatives to prevent the population in ecologically fragile areas from returning to poverty. In this study, a decision tree and logistic regression model were used to assess the risk of returning to poverty in Karst ecologically fragile areas. The data comes from 303 households in four counties in Guizhou and Guangxi. There are 12 main influencing factors identified, with the percentage of workforce numbers and loans having interactive effects. The results show that: (1) Poor resilience of livelihood assets, external shocks, and the effects of some support measures will be visible after a long period, leading to "transient" poverty and return to poverty. (2) Ecological environment management in ecologically fragile areas is very important to solve the problem of returning to poverty. (3) Appropriate loans can reduce poverty, especially when loans are used to cultivate a new excellent labor force. At the same time, it is necessary to evaluate farmers' repayment ability reasonably and scientifically to reduce the risk of returning to poverty. The combination of ecological restoration and agricultural development is the key to solving ecological and social problems in Karst areas. Efforts should be made to improve the risk-resilience of farmers' livelihood assets and the efficiency of livelihood assets utilization by implementing targeted support measures. This research provides a new approach to studying the mechanism of poverty recurrence, which is of great practical significance for consolidating the results of poverty eradication and realizing rural vitalization. [ABSTRACT FROM AUTHOR]