| 环境公正视角下广州市污染企业分布与区域人口社会特征的时空关系研究 |
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| 引用本文： | 沈静,王少谷,周楚平.环境公正视角下广州市污染企业分布与区域人口社会特征的时空关系研究[J].地理研究,2022,41(1):46-62. |
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| 作者姓名： | 沈静 王少谷 周楚平 |
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| 作者单位： | 1. 中山大学地理科学与规划学院,广州 5100062. 湖南省建筑设计院集团有限公司,长沙 410006 |
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| 基金项目： | 国家自然科学基金面上项目（41871111）； |
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| 摘 要： | 环境公正研究关注人民生活质量的空间公正问题。从环境经济地理学的角度,本研究以广州市为研究对象,采用1995年、2004年、2013 年的工业企业数据和1990年、2000 年、2010 年的人口普查数据,运用空间可视化工具和多尺度地理加权回归模型等方法,分析了广州市污染企业的分布及其与区域人口社会特征关系的时空变化,然后探讨转型背景下中国城市污染企业分布的环境公正问题。研究发现,广州市污染企业空间分布经历由中心城区集聚向近郊区和边界地带集聚的过程,在城市产业空间重构与社会空间分异的共同作用下,污染企业空间分布的环境不公正问题由最初表现不明显转变为全市范围的阶层差异与企业污染负担之间关系,具有不同于西方环境不公正现象的特征和成因。
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| 关 键 词： | 污染企业 环境公正 人口社会特征 多尺度地理加权回归 广州市 |
| 收稿时间： | 2021-06-23 |
Spatio-temporal relations between distribution of polluting enterprises and regional sociodemographic characteristics in Guangzhou from the perspective of environmental justice |
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| SHEN Jing,WANG Shaogu,ZHOU Chuping.Spatio-temporal relations between distribution of polluting enterprises and regional sociodemographic characteristics in Guangzhou from the perspective of environmental justice[J].Geographical Research,2022,41(1):46-62. |
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| Authors: | SHEN Jing WANG Shaogu ZHOU Chuping |
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| Institution: | 1. School of Geography and Planning, Sun Yat-sen University, Guangzhou 510006, China2. Hunan Architectural Design Institute Group Co., Ltd, Changsha 410006, China |
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| Abstract: | Environmental justice studies focus on the spatial justice of people's life quality. With the rapid development of urbanization and industrialization, industrial pollution has led to intensifying environmental degradation. Due to the spatial distribution of polluting enterprises, the levels of environmental degradation vary among urban settlements in the city. Recently, this variance has become a major environmental justice concern in China. Based on this understanding, it is imperative to understand the relationship between distribution of polluting enterprises and the social characteristics of the regional population from the spatial and temporal perspective. Cities with many industrial enterprises can lead to differential distributions in residential settlements. Guangzhou, a typical city, is selected as the empirical case. Based on a collection of enterprise census of 1995, 2004 and 2013 and the population census data of 1990, 2000 and 2010, this study analyzed the distribution of polluting enterprises and its spatial changes using ArcGIS visualization tool, and further examined the spatio-temporal changes of relationship between polluting enterprises and sociodemographic characteristics of regional population in Guangzhou by geographic weighted regression model. We find that the clustering of polluting enterprises has transferred from the central city to the suburban and border areas. The regression results reveal that old people and immigrants had become the main groups greatly affected by enterprise pollution in the 1990s, whereas differences among social classes including social stability, educational level and income level are related to the spatial distribution of polluting enterprises since 2000. This change of spatial relationship reflects the reconfiguration of urban industries and socio-spatial differentiation in modern Guangzhou. Further analysis discovers that the spatially influenced environmental injustice of pollution evolves from insignificance to a significantly city-wide relationship between different social classes and the pollution burden of enterprises. This evolution is different from the characteristics and mechanisms in a Western context. Compared to their Western counterparts, the public environmental resources are endowed with commodity attributes and the fundamental driving force to redistribute spatial environmental benefits through profit capture under the Chinese social market system. Based on our findings, this research further calls attention to seriously considering the environmental rights of low-income classes and emphasizing the need of public participation when making relevant policies. |
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| Keywords: | polluting enterprises environmental justice sociodemographic characteristics multiscale geographically weighted regression Guangzhou |
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