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Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling
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Simulation and prediction of rural population changes using agent-based modeling.

Shanshan Huang1,2, Yao Huang1,2, Shitai Bao1,2

  • 1College of Resources and Environment, South China Agricultural University, Guangzhou, China.

Plos One
|June 23, 2025
PubMed
Summary
This summary is machine-generated.

Agent-based modeling simulates rural population dynamics, revealing that low birth rates and emigration significantly impact population decline and aging. Enhancing birth rates and rural industry is crucial for revitalization.

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Area of Science:

  • Demography
  • Rural Sociology
  • Computational Social Science

Background:

  • Rural population change is vital for China's revitalization strategies.
  • Existing research often overlooks micro-level population dynamics, focusing on macro trends.

Purpose of the Study:

  • To develop and validate an agent-based model (ABM) for simulating and predicting rural population size and structure.
  • To identify key factors influencing rural population retention and demographic trends.

Main Methods:

  • An agent-based model (ABM) was developed, defining individual agents and their birth, death, and migration behaviors.
  • The model was applied to two representative villages and validated against actual data and the Leslie model.

Main Results:

  • The ABM accurately captured micro-level population dynamics, explaining retention factors.
  • Economically disadvantaged villages showed significant declines in total population, labor force, and adolescents.
  • Emigration was high in non-industrialized villages, leading to a youth labor force under 30% and an aging population over 45%.

Conclusions:

  • Migration and birth rates are critical drivers of rural population trends.
  • Policies should focus on increasing birth rates and fostering industrial development to reduce emigration and combat aging.
  • Micro-level insights from ABM can inform targeted rural revitalization programs.