The U.S. Food Manufacturing Industry and the Environmental Hazards of Toxic Emissions to Socially Vulnerable Populations
Loading...
Date
2017-12-09
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Abstract
This dissertation examines the relationship between social vulnerability and plant level emissions. It utilizes a mixed methods approach that includes: 1) the historical context of the food manufacturing industry, environmental regulations, and environmental activism; 2) geographic mapping of population characteristics surrounding food manufacturing plants; and 3) quantitative multilevel analyses of how the relationship between a manufacturing facility’s toxic emissions and the social vulnerability of the local population is mediated by community characteristics, organizational characteristics, and larger political-legal arrangements. This project extends organizational political economy theory of the environment to incorporate community characteristics and fills important gaps in the environmental justice literature.
This project had several findings. First, the research suggests that populations with higher levels of social vulnerability are more at risk for being affected by emissions from food manufacturing facilities. Second, organizational and political-economy factors have a direct impact not only on organizational behavior (i.e. amounts of emissions), but how organizational behavior relates to additional factors such as social vulnerability, facility density, and environmental regulatory climate. Third, the fewer opportunities organizations had to exploit their local populations, the less likely the emissions were to be higher or hazardous. Finally, this dissertation calls for further research refining the
use of a social vulnerability score with additional population characteristics as well as a longitudinal analysis of the mediating factors outlined in this project.
Description
Keywords
Environmental Justice, Organizational Political Economy of the Environment, Food Manufacturing, Toxic Emissions, Multilevel modeling