地理学报 ›› 2021, Vol. 76 ›› Issue (2): 352-366.doi: 10.11821/dlxb202102008

• 人口与城市研究 • 上一篇    下一篇

北上广深城市人口预测及其资源配置

王勇1,2(), 解延京1, 刘荣1, 张昊1   

  1. 1.东北财经大学统计学院,大连 116025
    2.东北财经大学博士后科研流动站,大连 116025
  • 收稿日期:2020-03-09 修回日期:2020-09-30 出版日期:2021-02-25 发布日期:2021-04-25
  • 作者简介:王勇(1988-), 男, 山东临沂人, 副教授, 硕导, 研究方向为人口统计、城市经济。E-mail: ywang@dufe.edu.cn
  • 基金资助:
    国家社科基金青年项目(19CTJ008);“兴辽英才”计划青年拔尖人才(XLYC1907012);辽宁省经济社会发展研究课题(2020lslktyb-036);辽宁省教育厅项目(LN2019Q48)

Population prediction and resource allocation in megacities from the optimum population perspective: A case study of Beijing, Shanghai, Guangzhou and Shenzhen

WANG Yong1,2(), XIE Yanjing1, LIU Rong1, ZHANG Hao1   

  1. 1. School of Statistics, Dongbei University of Finance and Economics, Dalian 116025, Liaoning, China
    2. Postdoctoral Research Station, Dongbei University of Finance and Economics, Dalian 116025, Liaoning, China
  • Received:2020-03-09 Revised:2020-09-30 Published:2021-02-25 Online:2021-04-25
  • Supported by:
    National Social Science Foundation of China(19CTJ008);"Xingliao Talent" Program(XLYC1907012);Economic and Social Development of Liaoning Province(2020lslktyb-036);Research Project of Education Department of Liaoning Province(LN2019Q48)

摘要:

人口数量持续增长是困扰超大型城市发展的重要因素,适度人口为研究人口增长下的城市资源合理分配提供了较好的思路。本文以适度人口为切入点,利用可能—满意度模型测算北京、上海、广州和深圳4座超大型城市2035年适度人口规模,同时利用灰色BP神经网络模型预测各市2035年常住人口规模,并基于适度人口规模测算和常住人口规模预测结果对各市部分资源的配置、规划情况进行倒逼分析,提出人口增长下的资源配置方案。研究发现:① 在可能—满意度水平为0.6的条件下,北上广深2035年适度人口规模分别为2152.69万人、2309.68万人、1498.93万人和1352.19万人,均未超出政府规划红线;② 预测结果显示,北上广深2035年常住人口规模均将超出适度人口规模和政府规划红线,表现为适度人口规模<政府规划红线<常住人口规模;③ 从适度人口角度出发,在不影响经济社会发展与居住满意度前提下,为应对未来人口增长,2035年北京发电量和公园绿地面积需达到当前水平的2倍以上;上海发电量、公园绿地面积和公共交通营运车辆分别需达到当前水平的2.27倍、2.22倍和2.35倍;广州能源供应量和发电量需达到当前水平的3倍以上;深圳能源供应量、发电量和卫生机构床位数需达到当前水平的3倍以上。本文有利于为协调大型城市人口与资源之间的可持续发展提供科学依据。

关键词: 超大城市, 适度人口, 人口增长, 可能—满意度, 资源配置

Abstract:

Continuous population growth is an important factor affecting the development of megacities. Optimum population can advance the rational allocation of urban resources amidst the continuous growth. By taking the optimum population as a pointcut, this study first uses the possibility-satisfiability (P-S) model to calculate the optimum population size of Beijing, Shanghai, Guangzhou and Shenzhen in 2035, and then uses the grey back propagation neural network model to predict the resident population of all the four first-tier cities in 2035. Finally, it analyzes all the cities' allocation and planning of resources or infrastructure based on the results, and proposes optimum resource allocation to address an unexpected population growth. The results show that: (1) When the P-S degree is 0.6, the optimum population size of Beijing, Shanghai, Guangzhou and Shenzhen in 2035 is 21.5269 million, 23.0968 million, 14.9893 million and 13.5219 million, respectively, all below the red line of government planning. (2) The resident population of Beijing, Shanghai, Guangzhou and Shenzhen in 2035 will exceed the moderate population size and the red line of government planning, while the moderate population size will be lower than the red line of government planning, and the red line of government planning will be lower than that of the resident population. (3) From the perspective of optimum population, in order to cope with future population growth without affecting economic and social development and residential satisfaction, in 2035, the power generation and green areas of Beijing are expected to reach twice the current levels; power generation, green areas, and public transport vehicles of Shanghai will be 2.27, 2.22 and 2.35 times the current levels, respectively; the energy supply and power generation of Guangzhou are expected to reach three times the current levels; the energy supply, power generation, and the number of beds in health institutions of Shenzhen will be three times the current levels. This study provides a scientific basis for coordinating the sustainable development of population and urban resources in large cities.

Key words: megacity, optimum population, population growth, possibility-satisfiability, resource allocation