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将工业危害与社会不平等联系起来:印度古吉拉特邦的环境不公正。

Linking Industrial Hazards and Social Inequalities: Environmental Injustice in Gujarat, India.

机构信息

Department of Sociology & Anthropology, University of Texas at El Paso, El Paso, TX 79968, USA.

出版信息

Int J Environ Res Public Health. 2018 Dec 25;16(1):42. doi: 10.3390/ijerph16010042.

Abstract

Industrial development in India has rarely been studied through the perspective of environmental justice (EJ) such that the association between industrial development and significant economic and social inequalities remains to be examined. Our article addresses this gap by focusing on Gujarat in western India, a leading industrial state that exemplifies the designation of India as an "emerging economy." We link the geographic concentration of industrial facilities classified as major accident hazard (MAH) units, further subdivided by size (large or medium/small) and ownership (public or private), to the socio-demographic composition of the population at the subdistrict (taluka) level. Generalized estimating equations (GEEs) are used to analyze statistical associations between MAH unit density and explanatory variables related to the economic and social status of the residential population at the subdistrict level. Our results indicate a significant relationship between presence of socially disadvantaged populations (Scheduled Castes and Scheduled Tribes) and density of all types of MAH units, except those associated with the public sector. Higher urbanization and lower home ownership are also found to be strong predictors of MAH unit density. Overall, our article represents an important step towards understanding the complexities of environmental inequalities stemming from Gujarat's industrial economy.

摘要

印度的工业发展很少从环境正义(EJ)的角度进行研究,因此工业发展与显著的经济和社会不平等之间的关联仍有待检验。我们的文章通过关注印度西部的古吉拉特邦来解决这一差距,古吉拉特邦是一个领先的工业邦,是印度作为“新兴经济体”的典范。我们将被归类为重大事故危险(MAH)单位的工业设施的地理集中,进一步按规模(大型或中型/小型)和所有权(公共或私人)细分,与分区(taluka)级别的人口的社会人口构成联系起来。广义估计方程(GEE)用于分析 MAH 单位密度与与分区级居住人口的经济和社会地位相关的解释变量之间的统计关联。我们的结果表明,社会弱势群体(在册种姓和在册部落)的存在与所有类型的 MAH 单位密度之间存在显著关系,公共部门的 MAH 单位除外。城市化程度较高和住房自有率较低也被发现是 MAH 单位密度的强有力预测因素。总的来说,我们的文章代表了朝着理解古吉拉特邦工业经济所带来的环境不平等复杂性迈出的重要一步。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d55a/6339083/cd4f8ded80d8/ijerph-16-00042-g001.jpg

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