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基于灰色模型的内蒙古人口预测研究

Study on Population Forecast in Inner Mongolia Based on Grey Model
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摘要 该项研究的目标是深入探索利用灰色系统理论中的灰色预测模型(GM(1,1))来预测内蒙古自治区未来的人口规模的实用性和精确度。根据内蒙古各城市历年统计数据建立了相应的数学模型。鉴于内蒙古地区特有的社会经济环境和人口增长趋势,传统的人口预测手段可能不能充分地考虑到这一地区的复杂性和不可预测性。同时,在实际工作中人们也常常会遇到由于缺乏相关统计数据而导致无法准确判断未来人口数量发展趋势的情况。因此,我们选择使用灰色模型来进行未来的预测分析,目的是为政策制定者和规划者提供一个更加高效的工具,以应对可能出现的未来人口变化的挑战。依据2010~2022年内蒙古的人口统计数据,采用灰色系统GM(1,1)模型来预测和分析2023~2050年间的人口数量的变化趋势。基于灰色模型的预测结果,研究给出了一系列政策建议,这些建议涵盖了调整生育政策、完善相关配套政策、加强和完善社会保障体系,以及尊重老年人群的职业选择倾向等方面。 The objective of this study is to explore the practicality and accuracy of using the grey prediction model (GM(1,1)) in grey system theory to predict the future population size of Inner Mongolia Autonomous Region. Corresponding mathematical models have been established based on statistical data from various cities in Inner Mongolia over the years. Given the unique socio-economic environment and population growth trend in Inner Mongolia, traditional population forecasting methods may not fully consider the complexity and unpredictability of the region. Meanwhile, in practical work, people often encounter situations where the lack of relevant statistical data makes it difficult to accurately determine the future trend of population development. Therefore, we choose to use grey models for future predictive analysis, with the aim of providing policymakers and planners with a more efficient tool to address potential challenges of future population changes. Based on the population statistics data of Inner Mongolia from 2010 to 2022, we use the grey system GM(1,1) model to predict and analyze the trend of population changes between 2023 and 2050. Based on the prediction results of the grey model, the study provides a series of policy recommendations, which include adjusting the birth policy, improving relevant supporting policies, strengthening and improving the social security system, and respecting the occupational choice tendencies of the elderly population.
作者 武文华
机构地区 内蒙古师范大学
出处 《社会科学前沿》 2024年第5期564-570,共7页 Advances in Social Sciences
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