长江流域资源与环境 >> 2022, Vol. 31 >> Issue (4): 770-780.doi: 10.11870/cjlyzyyhj202204005

• 区域可持续发展 • 上一篇    下一篇

知识密集型服务业空间集聚的动态演化及驱动因素

霍  鹏1,2   

  1. (1.郑州师范学院社会服务与发展学院,河南 郑州 450044;2.河南大学商学院,河南 开封 475004)
  • 出版日期:2022-04-20 发布日期:2022-04-21

Research on the Dynamic Evolution and Driving Factors of Spatial Agglomeration of Knowledge-intensive Services

HUO Peng1,2   

  1. (1. Social Service and Development School, Zhengzhou Normal University, Zhengzhou 450044, China;2.Business School, Henan University, Kaifeng 475004, China)
  • Online:2022-04-20 Published:2022-04-21

摘要: 知识密集型服务业是我国构建现代自主创新体系的重要组成部分。通过构建集聚综合测度模型,探索了我国地级及以上城市知识密集型服务业空间集聚的动态演化趋势,采用地理加权回归模型揭示了知识密集型服务业空间集聚的驱动因素。研究发现:(1)我国知识密集型服务业总体集聚程度较低,行业集聚不充分,地区集聚不均衡,各细分行业集聚态势差异明显,空间集聚逐渐向以东部沿海地区为中心,中、西部地区为外围的趋势发展;(2)知识密集型服务业集聚存在明显的空间正自相关性,邻近地区知识密集型服务业集聚具有较强相互带动作用;(3)城市化水平、研发强度、制度环境因素、知识存量总体上是驱动知识密集型服务业空间集聚的重要力量,基于空间异质性的分析,知识密集型服务业空间集聚驱动因素对不同地区影响差异较大,总体上呈现明显的阶梯分布特征,相邻省会或直辖市变量弹性系数空间上呈显著的连片分布特征,集聚驱动因素空间外溢效应明显。

Abstract: Knowledge intensive service industry is an important part of China’s modern independent innovation system. This paper explores the dynamic evolution trend of spatial agglomeration of knowledge-intensive services in prefecture-level and above cities in China by constructing a comprehensive measurement model, and reveals the driving factors of spatial agglomeration of knowledge-intensive services by using geographically weighted regression model. The results show that:(1) The overall degree of knowledge-intensive service industry agglomeration in China is low, the industrial agglomeration is not sufficient, the regional agglomeration is not balanced, and the agglomeration trend of different subsectors is obviously different. The spatial agglomeration trend is gradually developing towards the eastern coastal region as the center, and the central and western regions as the periphery; (2) The agglomeration of knowledge-intensive service industry has obvious positive spatial autocorrelation, and the agglomeration of knowledge-intensive service industry in the neighboring area has strong mutual driving effect; (3) the system of urbanization level, research and development strength, environmental factors, the stock of knowledge, on the whole, are an important force in driving knowledge intensive service industries agglomeration, based on the analysis of the spatial heterogeneity, knowledge-intensive service industry spatial agglomeration driving factors on the varied impact of different areas, on the whole present obvious ladder distribution characteristics, The spatial distribution characteristics of the elasticity coefficients of variables in adjacent provincial capitals or municipalities are significant, and the spatial spillover effect of agglomeration driving factors is obvious.

No related articles found!
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
[1] 李建豹, 黄贤金, 孟 浩, 周 艳, 徐国良, 吴常艳. “十二五”时期中国碳排放强度累积目标完成率分析[J]. 长江流域资源与环境, 2018, 27(08): 1655 .
[2] 熊鸿斌, 周凌燕. 基于PSR-灰靶模型的巢湖环湖防洪治理工程生态环境影响评价研究[J]. 长江流域资源与环境, 2018, 27(09): 1977 -1987 .
[3] 李嘉译, 匡鸿海, 谭 超, 王佩佩. 长江经济带城市扩张的时空特征与生态响应[J]. 长江流域资源与环境, 2018, 27(10): 2153 -2161 .
[4] 唐子珺, 陈龙, 覃军, 郑翔. 武汉市一次污染过程的局地流场和边界层结构的数值模拟[J]. 长江流域资源与环境, 2018, 27(11): 2540 -2547 .
[5] 王东香, 张一鸣, 王锐诚, 赵炳炎, 张志麒, 黄咸雨, . 神农架大九湖泥炭地孔隙水溶解有机碳特征及其影响因素[J]. 长江流域资源与环境, 2018, 27(11): 2568 -2577 .
[6] 王海力, 韩光中, 谢贤健. 基于DEA模型的西南地区耕地利用效率时空格局演变及影响因素分析[J]. 长江流域资源与环境, 2018, 27(12): 2784 -2795 .
[7] 谢五三, 吴 蓉, 丁小俊. 基于FloodArea模型的城市内涝灾害风险评估与预警[J]. 长江流域资源与环境, 2018, 27(12): 2848 -2855 .
[8] 刘晓阳 黄晓东 丁志伟. 长江经济带县域信息化水平的空间差异研究[J]. 长江流域资源与环境, , (): 0 .
[9] 汪聪聪, 王益澄, 马仁锋, 王静敏. 经济集聚对雾霾污染影响的空间计量研究——以长三角洲地区为例[J]. 长江流域资源与环境, 2019, 28(01): 1 -11 .
[10] 赵树成, 张展羽, 夏继红, 杨洁, 盛丽婷, 唐丹, 陈晓安, . 鄱阳湖滨岸土壤磷素吸附特征研究[J]. 长江流域资源与环境, 2019, 28(01): 166 -174 .