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Toxic Releases and Risk Disparity: A Spatiotemporal Model of Industrial Ecology and Social Empowerment

机译:有毒物质释放和风险差异:工业生态和社会赋权的时空模型

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Information-based regulations (IBRs) are founded on the theoretical premise that public participation in accomplishing policy goals is empowered by open access to information. Since its inception in 1988, the Toxics Release Inventory (TRI) has provided the framework and regulatory impetus for the compilation and distribution of data on toxic releases associated with industrial development, following the tenets of IBR. As TRI emissions are reputed to disproportionately affect low-income communities, we investigated how demographic characteristics are related to change in TRI emissions and toxicity risks between 1989 and 2002, and we sought to identify factors that predict these changes. We used local indicators of spatial association (LISA) maps and spatial regression techniques to study risk disparity in the Los Angeles urban area. We also surveyed 203 individuals in eight communities in the same region to measure the levels of awareness of TRI, attitudes towards air pollution, and general environmental risk. We discovered, through spatial lag models, that changes in gross and toxic emissions are related to community ethnic composition, poverty level, home ownership, and base 1989 emissions (R-square = 0.034–0.083). We generated a structural equation model to explain the determinants of social empowerment to act on the basis of environmental information. Hierarchical confirmatory factor analysis (HCFA) supports the theoretical model that individual empowerment is predicted by risk perception, worry, and awareness (Chi-square = 63.315, p = 0.022, df = 42). This study provides strong evidence that spatiotemporal changes in regional-scale environmental risks are influenced by individual-scale empowerment mediated by IBRs.
机译:基于信息的法规(IBR)建立在理论前提下,即公开获取信息可以增强公众参与实现政策目标的能力。自1988年成立以来,《有毒物质排放清单》(TRI)遵循IBR的宗旨,为汇编和分发与工业发展相关的有毒物质排放的数据提供了框架和监管动力。由于TRI排放被认为会对低收入社区造成不成比例的影响,因此我们调查了1989年至2002年之间的人口统计学特征与TRI排放的变化和毒性风险之间的关系,并力求找出可预测这些变化的因素。我们使用空间关联(LISA)地图的局部指标和空间回归技术来研究洛杉矶市区的风险差异。我们还调查了同一地区八个社区中的203个人,以衡量对TRI的认识水平,对空气污染的态度以及一般的环境风险。通过空间滞后模型,我们发现总排放量和有毒排放量的变化与社区种族构成,贫困水平,房屋所有权和1989年基准排放量有关(R平方= 0.034–0.083)。我们生成了一个结构方程模型来解释社会赋权的决定因素,以便根据环境信息采取行动。分层确认因子分析(HCFA)支持通过风险感知,担忧和意识来预测个人赋权的理论模型(卡方= 63.315,p = 0.022,df = 42)。这项研究提供了有力的证据,表明区域性环境风险的时空变化受到IBR介导的个人规模赋权的影响。

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