引用本文:
【打印本页】   【HTML】   【下载PDF全文】   查看/发表评论  【EndNote】   【RefMan】   【BibTex】
←前一篇|后一篇→ 过刊浏览    高级检索
本文已被:浏览 39次   下载 26 本文二维码信息
码上扫一扫!
分享到: 微信 更多
国家中心城市基础设施承载力评价及障碍因素诊断
吴晨阳1, 王婉莹1,2
1.西安建筑科技大学 管理学院, 陕西 西安 710055;2.西安建筑科技大学华清学院, 陕西 西安 710043
摘要:
[目的] 定量分析国家中心城市2006—2017年基础设施承载力的具体情况,为促进国家中心城市建设良性健康发展提供科学依据。[方法] 选取2006—2017年国家中心城市基础设施承载力面板数据,从压力和状态两个层面构建城市基础设施承载力评价指标体系。运用熵权TOPSIS方法和障碍度模型对2006—2017年国家中心城市基础设施承载力和障碍因素进行评价和诊断,并基于基础设施承载力评价结果,定量测度基础设施承载力状况并诊断其障碍因子。[结果] 2006—2010年国家中心城市的基础设施承载力水平总体上大幅度提升,2011—2017年基础设施承载力的增长速度有所减缓,北京、上海、成都、郑州、西安的基础设施承载力大幅度提高,广州市的基础设施承载力改善幅度最小;随着国家中心城市的人口持续增加,基础设施的承载压力也持续增加,通过障碍度模型分析发现制约国家中心城市基础设施承载力的主要因素为人均绿地面积、人均家庭年生活用水量和城市移动电话普及率。[结论] 随着中国经济的发展,中心城市对人口的虹吸作用越加强烈,人均绿地面积、城市移动电话普及率以及人均家庭年生活用水量可能与城市的经济与人口规模产生脱钩现象,无法满足人们日常的基础设施需求。
关键词:  国家中心城市  基础设施承载力  熵权TOPSIS模型  障碍度模型
DOI:10.13961/j.cnki.stbctb.2020.03.038
分类号:F294.1;F299.27
基金项目:国家自然科学基金项目“绿色节能导向的旧工业建筑功能转型机理研究”(51678479)
Evaluation of Infrastructure Carrying Capacity of National Central Cities and Diagnosis of Its Obstacles
Wu Chenyang1, Wang Wanying1,2
1.School of Management, Xi'an University of Architecture and Technology, Xi'an, Shaanxi 710055, China;2.Xi'an University of Architecture and Technology Huaqing College, Xi'an, Shaanxi 710043, China
Abstract:
[Objective] This study quantitatively analyzed the specific situation of the infrastructure carrying capacity of Chinese national central cities from 2006 to 2017 to provide a scientific basis for promoting the development of national central cities.[Methods] The panel data of the infrastructure carrying capacity of national central cities from 2006 to 2017 were selected to construct an evaluation index system of the urban infrastructure carrying capacity from two levels:pressure and state. The entropy TOPSIS method and obstacle degree model were used to evaluate and diagnose the infrastructure carrying capacity and its obstacle factors for national central cities from 2006 to 2017. In addition, the status of the infrastructure carrying capacity was quantitatively measured and the obstacle factors were diagnosed based on the evaluation results of the infrastructure carrying capacity.[Results] From 2006 to 2010, the level of the infrastructure carrying capacity in national central cities generally increased significantly, and the growth rate of the infrastructure carrying capacity slowed down from 2011 to 2017. Specifically, the infrastructure carrying capacity in Beijing, Shanghai, Chengdu, Zhengzhou, and Xi'an cities increased significantly, whereas that in Guangzhou City was the smallest. The infrastructure carrying capacity also continued to increase as the population of the cities increased. The analysis using the obstacle degree model revealed that the main factors restricting the infrastructure carrying capacity in the national central cities were the per capita green space area, the per capita annual household water consumption, and the urban mobile phone penetration rate.[Conclusion] With the development of China's economy, the siphoning effect of central cities on the population is becoming increasingly intense. The per capita green space area, urban mobile phone penetration rate, and per capita annual household water consumption might be decoupled from a city's economy and population size, which would not be able to meet the daily infrastructure needs of the population.
Key words:  national central cities  infrastructure carrying capacity  entropy TOPSIS method  obstacle degree model