摘要
科学全面评价城市新型城镇化发展水平,对识别诊断城市发展中的问题,进而因城施策,采取不同措施具有重要意义。利用熵权—灰色关联度法评价京津冀城市群14个城市2008—2017年的新型城镇化水平。基于评价结果,利用自然断点法分析不同城市新型城镇化的驱动因素。研究发现:(1)京津冀城市群新型城镇化水平呈现逐年上升态势。(2)京津冀城市群新型城镇化水平空间差异明显,以京津两地为核心,向周边城市呈“核心—边缘”递减趋势,城市群内部发展差距逐年扩大。(3)京津冀城市群新型城镇化水平呈现等级化特征。(4)不同等级城市新型城镇化水平的驱动因素不同。(5)除北京以外,所有城市新型城镇化的制约因素均为创新发展。
Scientific and comprehensive evaluation of the development level of new urbanization in cities is of great significance for identifying and diagnosing problems in urban development,and then taking different measures according to the city.This paper uses the entropy weight-gray correlation method to evaluate the new urbanization level of 14 cities in the Beijing-Tianjin-Hebei urban agglomeration from 2008 to 2017.Based on the evaluation results,the natural breakpoint method is used to analyze the driving factors of new urbanization in different cities.The study found that:(1)The new urbanization level of the Beijing-Tianjin-Hebei urban agglomeration is increasing year by year.(2)The spatial difference of the new urbanization level of the Beijing-Tianjin-Hebei urban agglomeration is obvious.Taking Beijing and Tianjin as the core,it shows a“core-margin”decreasing trend towards the surrounding cities,and the development gap within the urban agglomeration is increasing year by year.(3)The new urbanization level of the Beijing-Tianjin-Hebei urban agglomeration presents hierarchical characteristics.(4)The drivers of the new urbanization level in cities of different ranks are different.(5)Except for Beijing,the constraints to new urbanization in all cities are innovative development.
作者
曾建丽
赵玉帛
李淑琪
ZENG Jianli;ZHAO Yubo;LI Shuqi(School of Economics and Management,Tianjin ChengJian University,Tianjin 300384,China;School of Economics and Management,Hebei University of Technology,Tianjin 300401,China)
出处
《生态经济》
北大核心
2021年第10期100-107,共8页
Ecological Economy
基金
天津市教委科研计划项目“创新价值链视阈下天津市人工智能产业创新效率评价及提升路径研究”(2019SK059)。
关键词
京津冀
城市群
新型城镇化
时空格局
驱动因素
熵权法
Beijing-Tianjin-Hebei
urban agglomeration
new urbanization
time-space pattern
driving factors
entropy weight