Simulation and prediction of rural population changes using agent-based modeling.

Rural population change is a critical element of the strategy for rural revitalization in China. Many studies emphasize large-scale macro-population trends, but a noticeable gap exists in micro-level simulations and predictions regarding rural population size and structure. This study employs an agent-based model(ABM), defining a population agent and its behavioral rules. By modeling individual-level birth, death, and migration behaviors, it generates agent-based outputs that aggregate to capture population dynamics and forecast rural demographic trends over the next 11 years. Using two representative villages as study areas, the results were validated by comparing them with actual population data and predictions made by the Leslie model. The findings demonstrate the following: 1) the agent-based modeling effectively captures the dynamics of births, deaths, and migrations at the micro level, elucidating the underlying determinants of rural population retention. 2) In economically disadvantaged villages, the total population, labor force, and proportion of adolescents have significantly declined. Notably, emigration is pronounced in villages without industrial advantages, regardless of substantial per capita arable land; the youth labor force constitutes less than 30%, while the aging population is as high as 45%. 3) Migration and birth rates are key factors influencing rural population trends. To mitigate future rural population aging, enhancing birth rates and fostering rural industrial development is essential to curb migration. These findings support evidence-based policies to stimulate birth rates, attract and retain younger populations, and enhance economic opportunities in rural areas. The micro-level analysis enables the design of more effective and context-specific rural revitalization programs, bridging the gap between micro-level behaviors and macro-level demographic patterns.