ABSTRACT:
This study adopted a more macroscopic perspective to focus on the issue of rural poverty in China. By selecting indicators reflecting various levels of poverty recurrence, considering risk factors across multiple dimensions, and employing advanced methods, we constructed a novel China Rural Poverty Recurrence Risk Index. We utilized a Markov switching model to delve into the mechanisms of poverty recurrence. Building upon this foundation, we developed an advanced poverty recurrence risk early warning system using a convolutional neural network-long short-term memory (CNN-LSTM) model. The system is optimized through mechanism-based predictions to better capture the dynamic changes in poverty recurrence. Empirical results demonstrated that the integrated dynamic monitoring and early warning system have significantly effective outcomes in addressing the recurrence of rural poverty.
Key words:
poverty recurrence risk; multidimensional poverty; Markov switching model; CNN-LSTM hybrid network; early warning system