IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i8p4844-d796369.html
   My bibliography  Save this article

Population Prediction of Chinese Prefecture-Level Cities Based on Multiple Models

Author

Listed:
  • Lixuan Chen

    (School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190, China)

  • Tianyu Mu

    (School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190, China)

  • Xiuting Li

    (School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190, China
    Key Laboratory of Big Data Mining and Knowledge Management, Chinese Academy of Sciences, Beijing 100190, China)

  • Jichang Dong

    (School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190, China
    Key Laboratory of Big Data Mining and Knowledge Management, Chinese Academy of Sciences, Beijing 100190, China)

Abstract

In recent years, the population growth rate has been gradually declining in China. As the population problem becomes increasingly significant, the accurate prediction of population development trends has become a top priority, used to facilitate national scientific planning and effective decision making. Based on historical data spanning a period of 20 years (1999–2018), this article presents predictions of the populations of 210 prefecture-level cities using the Malthusian model, Unary linear regression model, Logistic model, and Gray prediction model. Furthermore, because the gray prediction model exhibited the highest degree of accuracy in formulating predictions, this study uses the model to predict and analyze future population development trends. The results reveal that the population gap between cities is gradually widening, and the total urban population shows a pattern of rising in middle-tier cities (second-tier cities and third-tier cities) and declining in high-tier cities (first-tier cities and new first-tier cities) and low-tier cities (fourth-tier cities and fifth-tier cities). From the viewpoint of geographical distribution, the population growth rate is basically balanced between the northern part and the southern part of China. In addition, the population growth of the high-tier cities is gradually slowing while the low-tier cities are experiencing a negative growth of population, but middle-tier cities are experiencing skyrocketing population growth. From the viewpoint of regional development, although the development of regional integration has been strengthened over the years, the radiative driving effect of large urban agglomerations and metropolitan areas is relatively limited.

Suggested Citation

  • Lixuan Chen & Tianyu Mu & Xiuting Li & Jichang Dong, 2022. "Population Prediction of Chinese Prefecture-Level Cities Based on Multiple Models," Sustainability, MDPI, vol. 14(8), pages 1-23, April.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:8:p:4844-:d:796369
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/8/4844/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/8/4844/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Xiao-Yong Yan & Wen-Xu Wang & Zi-You Gao & Ying-Cheng Lai, 2017. "Universal model of individual and population mobility on diverse spatial scales," Nature Communications, Nature, vol. 8(1), pages 1-9, December.
    2. Yigang Wei & Zhichao Wang & Huiwen Wang & Yan Li & Zhenyu Jiang, 2019. "Predicting population age structures of China, India, and Vietnam by 2030 based on compositional data," PLOS ONE, Public Library of Science, vol. 14(4), pages 1-42, April.
    3. Murphy, Michael J., 2021. "Use of counterfactual population projections for assessing the demographic determinants of population ageing," LSE Research Online Documents on Economics 106185, London School of Economics and Political Science, LSE Library.
    4. Liu, Erjian & Yan, Xiaoyong, 2019. "New parameter-free mobility model: Opportunity priority selection model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 526(C).
    5. Jacques†François Thisse, 2018. "Human Capital and Agglomeration Economies in Urban Development," The Developing Economies, Institute of Developing Economies, vol. 56(2), pages 117-139, June.
    6. Canfei He & Xiyan Mao, 2016. "Population dynamics and regional development in China," Cambridge Journal of Regions, Economy and Society, Cambridge Political Economy Society, vol. 9(3), pages 535-549.
    7. Michael Murphy, 2021. "Use of Counterfactual Population Projections for Assessing the Demographic Determinants of Population Ageing," European Journal of Population, Springer;European Association for Population Studies, vol. 37(1), pages 211-242, March.
    8. Aijun Guo & Xiaojiang Ding & Fanglei Zhong & Qingping Cheng & Chunlin Huang, 2019. "Predicting the Future Chinese Population using Shared Socioeconomic Pathways, the Sixth National Population Census, and a PDE Model," Sustainability, MDPI, vol. 11(13), pages 1-17, July.
    9. Пигнастый, Олег & Koжевников, Георгий, 2019. "Распределенная Динамическая Pde-Модель Программного Управления Загрузкой Технологического Оборудования Производственной Линии [Distributed dynamic PDE-model of a program control by utilization of t," MPRA Paper 93278, University Library of Munich, Germany, revised 02 Feb 2019.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jun Li & Songbai Song, 2023. "Urban Water Consumption Prediction Based on CPMBNIP," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(13), pages 5189-5213, October.
    2. Jingru Chen & Hengyuan Zeng & Qiang Gao, 2023. "Using the Sustainable Development Capacity of Key Counties to Guide Rural Revitalization in China," IJERPH, MDPI, vol. 20(5), pages 1-26, February.
    3. Yilin Zhao & Feng He & Ying Feng, 2022. "Research on the Current Situation of Employment Mobility and Retention Rate Predictions of “Double First-Class” University Graduates Based on the Random Forest and BP Neural Network Models," Sustainability, MDPI, vol. 14(14), pages 1-22, July.
    4. Haowei Sun & Jinghan Ma & Li Wang, 2023. "Changes in per capita wheat production in China in the context of climate change and population growth," Food Security: The Science, Sociology and Economics of Food Production and Access to Food, Springer;The International Society for Plant Pathology, vol. 15(3), pages 597-612, June.
    5. Xiaojun Guo & Rui Zhang & Houxue Shen & Yingjie Yang, 2022. "An Optimized Damping Grey Population Prediction Model and Its Application on China’s Population Structure Analysis," IJERPH, MDPI, vol. 19(20), pages 1-25, October.
    6. Sebal Oo & Makoto Tsukai, 2022. "Long-Term Impact of Interregional Migrants on Population Prediction," Sustainability, MDPI, vol. 14(11), pages 1-21, May.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jungsuk Kim & Cynthia Castillejos Petalcorin & Donghyun Park & Shu Tian, 2023. "Determinants of the Elderly Share of Population: A Cross-Country Empirical Analysis," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 165(3), pages 941-957, February.
    2. Aijun Guo & Daiwei Jiang & Fanglei Zhong & Xiaojiang Ding & Xiaoyu Song & Qingping Cheng & Yongnian Zhang & Chunlin Huang, 2019. "Prediction of Technological Change under Shared Socioeconomic Pathways and Regional Differences: A Case Study of Irrigation Water Use Efficiency Changes in Chinese Provinces," Sustainability, MDPI, vol. 11(24), pages 1-19, December.
    3. Yang, Yitao & Jia, Bin & Yan, Xiao-Yong & Chen, Yan & Song, Dongdong & Zhi, Danyue & Wang, Yiyun & Gao, Ziyou, 2023. "Estimating intercity heavy truck mobility flows using the deep gravity framework," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 179(C).
    4. Yu, Weijie & Wen, Haosong & Wang, Wei & Zhao, De & Hua, Xuedong, 2024. "On the calibration and improvement of human mobility models in intercity transportation system," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 653(C).
    5. Guizhen Ma & Erin Trouth Hofmann & Annie Laurie Hines & Ethan Sharygin & Jack A. Goldstone & Justin Gest, 2025. "Demographic and Economic Implications of Alternative U.S. Immigration Policies," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 44(5), pages 1-26, October.
    6. Gustavo De Santis & Giambattista Salinari, 2023. "What drives population ageing? A cointegration analysis," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(5), pages 1723-1741, December.
    7. Elisa Alòs & Maria Elvira Mancino & Tai-Ho Wang, 2019. "Volatility and volatility-linked derivatives: estimation, modeling, and pricing," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 42(2), pages 321-349, December.
    8. Elena-Corina Cipu, 2019. "Duality Results in Quasiinvex Variational Control Problems with Curvilinear Integral Functionals," Mathematics, MDPI, vol. 7(9), pages 1-9, September.
    9. Xinping Xiao & Xue Li, 2023. "A novel compositional data model for predicting the energy consumption structures of Europe, Japan, and China," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(10), pages 11673-11698, October.
    10. Hanno Gottschalk & Marco Reese, 2021. "An Analytical Study in Multi-physics and Multi-criteria Shape Optimization," Journal of Optimization Theory and Applications, Springer, vol. 189(2), pages 486-512, May.
    11. Karel Van Bockstal, 2020. "Existence of a Unique Weak Solution to a Nonlinear Non-Autonomous Time-Fractional Wave Equation (of Distributed-Order)," Mathematics, MDPI, vol. 8(8), pages 1-16, August.
    12. Yanghua Zhang & Bin Liu & Hu Zhao & Weipeng Lin & Liang Cheng & Xiaogang Wang, 2024. "Analyzing the spatiotemporal pattern of the decoupling degree between carbon metabolism and economic development in village and town units," PLOS ONE, Public Library of Science, vol. 19(4), pages 1-23, April.
    13. Savin Treanţă, 2019. "On Locally and Globally Optimal Solutions in Scalar Variational Control Problems," Mathematics, MDPI, vol. 7(9), pages 1-8, September.
    14. Darvishi, M.T. & Najafi, Mohammad & Wazwaz, Abdul-Majid, 2021. "Conformable space-time fractional nonlinear (1+1)-dimensional Schrödinger-type models and their traveling wave solutions," Chaos, Solitons & Fractals, Elsevier, vol. 150(C).
    15. Anne Margarian & Cécile Détang-Dessendre & Aleksandra Barczak & Corinne Tanguy, 2022. "Endogenous rural dynamics: an analysis of labour markets, human resource practices and firm performance," SN Business & Economics, Springer, vol. 2(8), pages 1-33, August.
    16. Ivan Francisco Yupanqui Tello & Alain Vande Wouwer & Daniel Coutinho, 2021. "A Concise Review of State Estimation Techniques for Partial Differential Equation Systems," Mathematics, MDPI, vol. 9(24), pages 1-15, December.
    17. Edilberto Tiago Almeida & Raul Mota Silveira Neto & Jaime Macedo Brito Bastos & Rubens Lopes Pereira Silva, 2021. "Location patterns of service activities in large metropolitan areas: the Case of São Paulo," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 67(2), pages 451-481, October.
    18. Christian Klein & Julien Riton & Nikola Stoilov, 2021. "Multi-domain spectral approach for the Hilbert transform on the real line," Partial Differential Equations and Applications, Springer, vol. 2(3), pages 1-19, June.
    19. Marco Cirant & Roberto Gianni & Paola Mannucci, 2020. "Short-Time Existence for a General Backward–Forward Parabolic System Arising from Mean-Field Games," Dynamic Games and Applications, Springer, vol. 10(1), pages 100-119, March.
    20. Aijun Guo & Rong Zhang & Xiaoyu Song & Fanglei Zhong & Daiwei Jiang & Yuan Song, 2021. "Predicting the Water Rebound Effect in China under the Shared Socioeconomic Pathways," IJERPH, MDPI, vol. 18(3), pages 1-24, February.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:14:y:2022:i:8:p:4844-:d:796369. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.