地理科学 ›› 2021, Vol. 41 ›› Issue (1): 129-139.doi: 10.13249/j.cnki.sgs.2021.01.014

• • 上一篇    下一篇

中国主要城市技术创新影响环境污染的空间分异与机理

辛晓华1(), 吕拉昌1,2,3,*()   

  1. 1.首都师范大学资源环境与旅游学院,北京 100048
    2.首都师范大学管理学院,北京 100048
    3.北京城市创新与发展研究中心,北京 100048
  • 收稿日期:2020-01-18 修回日期:2020-05-16 出版日期:2021-01-25 发布日期:2021-03-04
  • 通讯作者: 吕拉昌 E-mail:18801240727@163.com;lachanglu@163.com
  • 作者简介:辛晓华(1997-),女,河北衡水人,博士研究生,主要从事创新地理、新经济与城市发展规划研究。E-mail: 18801240727@163.com
  • 基金资助:
    国家自然科学基金项目(41971201)

Spatial Differentiation and Mechanism of Technological Innovation Affecting Environmental Pollution in Major Chinese Cities

Xin Xiaohua1(), Lyu Lachang1,2,3,*()   

  1. 1. College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China
    2. College of Management, Capital Normal University, Beijing 100048, China
    3. Beijing Urban Innovation and Development Research Center, Beijing 100048, China
  • Received:2020-01-18 Revised:2020-05-16 Online:2021-01-25 Published:2021-03-04
  • Contact: Lyu Lachang E-mail:18801240727@163.com;lachanglu@163.com
  • Supported by:
    National Natural Science Foundation of China(41971201)

摘要:

基于中国285个地级及以上城市的数据,利用空间自相关模型,在全域和局域尺度分析技术创新与环境污染的空间关联,在此基础上,使用地理加权回归模型分析技术创新对环境污染影响程度的地区差异,并借助地理探测器分析其影响机理。研究结果表明:① 技术创新与环境污染均存在显著的空间集聚性,二者在全域尺度上存在正向的空间关联,在局域尺度上正向和负向关联并存,具有高创新-高污染、低创新-低污染、高创新-低污染、低创新-高污染4种集聚类型。② 所有城市的技术创新对环境污染均存在负向影响,影响程度呈现出由东向西渐次递增的空间分异格局,现有样本计算结果有条件地支持EKC曲线,技术创新能够促进经济与环境的良性发展。③ 环境污染是多种因素共同作用的结果,经济发展、产业结构、人力资本、外商直接投资、环境规制均能强化技术创新对环境污染的改善作用,技术创新对环境污染的影响更多的是通过技术进步优化产业结构,从而减少环境污染。

关键词: 技术创新, 环境污染, 空间分异性, 地理加权回归, 地理探测器

Abstract:

Technological innovation and environmental protection are two major themes of urban development at present. However, it is still controversial question that whether technological innovation can reduce environmental pollution and improve the ecological environment. This article holds that this debate is meaningless without considering spatial scale and factors. Therefore, it is necessary to study the spatial differentiation and mechanism of the impact of urban technological innovation on environmental pollution. In this study, based on the data of 285 cities (excluding those in autonomous prefectures, Hong Kong, Macau and Taiwan) at prefecture level and above in China, the spatial autocorrelation model is used to analyze the spatial correlation between technological innovation and environmental pollution at global and local scales, geographically weighted regression model is used to analyze the regional differences of the impact of technological innovation on environmental pollution, and Geodetector is used to analyze its influence mechanism. The results show that: 1) Both technological innovation and environmental pollution have significant spatial agglomeration, they have positive spatial correlation on the global scale, while positive and negative correlation coexist on the local scale. And there are 4 agglomeration types: high innovation-high pollution, low innovation-low pollution, high innovation-low pollution, low innovation-high pollution. 2) Technological innovation of all cities in the study area has a negative impact on environmental pollution, and the impact degree presents a spatial differentiation pattern with increasing impact degree from east to west. The calculation results of the existing samples conditionally verify the EKC curve, that is, technological innovation can promote the healthy development of economy and environment, and technological innovation in the eastern region can accelerate the arrival of EKC inflection point. 3) Environmental pollution is the result of many factors. Economic development, industrial structure, human capital, foreign direct investment and environmental regulation can all strengthen the effect of technological innovation on improving environmental pollution. The impact of technological innovation on environmental pollution is more to optimize industrial structure through technological progress, thereby reducing environmental pollution.

Key words: technological innovation, environmental pollution, spatial differentiation, geographically weighted regression, geographic detector

中图分类号: 

  • F124.3