地理研究 ›› 2011, Vol. 30 ›› Issue (5): 902-912.doi: 10.11821/yj2011050014

• 环境与生态 • 上一篇    下一篇

发达地区制造业集聚和水污染的空间关联——以无锡市区为例

高爽1,2, 魏也华3, 陈雯1, 赵海霞1   

  1. 1. 中国科学院南京地理与湖泊研究所, 南京 210008;
    2. 中国科学院研究生院, 北京 100039;
    3. 犹他大学地理系及公共与国际事务研究院, 美国 盐湖城 84112-9155
  • 收稿日期:2010-06-21 修回日期:2010-10-12 出版日期:2011-05-20 发布日期:2011-05-20
  • 作者简介:高爽(1984-), 女, 江苏连云港人, 博士生, 主要研究方向为区域发展与规划。E-mail: gaoshuang06@mails.gucas.ac.cn
  • 基金资助:

    国家自然科学基金项目(40771053、70703033); 美国福特基金(1085-1022)

Study on spacial-correlation between water pollution and industrial agglomeration in the developed region of China: A case study of Wuxi City

GAO Shuang1,2, WEI Ye-hua3, CHEN Wen1, ZHAO Hai-xia1   

  1. 1. Nanjing Institute of Geography and Limnology, CAS, Nanjing 210008, China;
    2. Graduate University of Chinese Academy of Sciences, Beijing 100039, China;
    3. Development of Geography and IPIA, University of Utah, Salt Lake City, Utah 84112-9155, USA
  • Received:2010-06-21 Revised:2010-10-12 Online:2011-05-20 Published:2011-05-20

摘要: 制造业集聚是城市发展的重要动力, 同时也可能对生态环境产生负效应。论文拟以无锡市区为例, 利用核密度函数(Kernel Density Distribution)对污染密集型制造业集聚程度进行评价, 按照河流自然综合特征划分的环境单元进行污染物分布格局评价, 在此基础上构建污染企业分布密度—COD排放量的双变量空间自相关模型, 探讨制造业与河道污染物分布格局的定量关系, 揭示制造业集聚和水污染的空间关联性。模型分析表明:无锡市区的污染密集型制造业呈现向郊区和环境非敏感区集聚的趋势, 污染强度以主要运河为轴线向两翼地区逐渐衰减, 二者空间格局的关联性存在行业差异性, 污染物分布与纺织、石油化工业以及冶金业集聚和扩散格局的空间关联性较为显著, 而与食品制造业和造纸印刷业的空间关联性则不显著。论文进一步根据产业集聚与污染格局的空间关联模式, 将研究区域划分为高集聚—高污染、低集聚—低污染、低集聚—高污染、高集聚—低污染四种类型区, 并提出相应的产业准入导向。本研究从空间效应角度为产业集聚与生态环境之间关联机理探讨提供新的视角, 也可以作为制造业布局调整的科学依据。

关键词: 产业集聚, 核密度函数, 水污染, 制造业, 空间自相关

Abstract: Agglomeration of the manufacturing industries is not only a fundamental driving force for urban development, but also may bring negative effects on regional environment. This study first estimates the degree of clustering of pollution-intensive manufacturing industries in Wuxi City by introducing the Kernel density distribution function, and then evaluates the pollution distribution pattern by dividing the study area into several environmental units according to the naturally integrated characteristics of the primary streams. We also quantitatively analyze the mechanism of the response of water environment quality to industrial distribution by utilizing the bi-variate spatial autocorrelation model. Results show that pollution-intensive manufacturing industries form clusters in suburban and non-sensitive areas. Besides, the density of pollution sources gradually decreases from the chief canals to the peripheral areas. Spatial autocorrelation analysis shows that spatial-relationship show differences according to industry categories: the agglomeration of textile, petrochemical and metallurgical industries prominently affects the spatial heterogeneity of water pollution distribution. However, the effects of food manufacturing and paper-making industry locations are not significant. Based on the spatial autocorrelation between industrial agglomeration and pollution distribution, we divide the study area into four types: high-agglomeration and high-pollution area, low-agglomeration and low-pollution area, low-agglomeration and high-pollution area, high-agglomeration and low-pollution area. Furthermore, we analyze the formation scheme and provide policy suggestions regarding industrial development. This paper provides a new perspective for the study of the interaction between industrial agglomeration and environment effects, which plays an important role in industrial allocation and sustainable urban development.

Key words: industrial agglomeration, Kernel Density Distribution function, water pollution, manufacturing industry, spatial autocorrelation