Machine Learning Informed Policy for Environmental Justice in Atlanta with Climate Justice Implications (Proposals Track)

Lelia Hampton (Massachusetts Institute of Technology)

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Climate Policy Behavioral and Social Science Climate Justice Unsupervised & Semi-Supervised Learning


Environmental hazards are not evenly distributed between the privileged and the protected classes in the U.S. Neighborhood zoning and planning of hazardous treatment, storage, and disposal facilities (TSDs) play a significant role in this sanctioned environmental racism. TSDs and toxic chemical releases into the air are accounted for by the U.S. Environmental Protection Agency’s (EPA) Toxic Release Inventories (TRIs) [2,4,7, 14]. TSDs and toxic chemical releases not only emit carbon dioxide and methane, which are the top two drivers of climate change, but also emit contaminants, such as arsenic, lead, and mercury into the water, air, and crops [12]. Studies on spatial disparities in TRIs and TSDs based on race/ethnicity and socioeconomic status (SES) in U.S. cities, such as Charleston, SC, San Joaquin Valley, CA, and West Oakland, CA showed that there are more TRIs and TSDs in non-white and low SES areas in those cities [2,4,7]. Environmental justice recognizes that the impacts of environmental burdens, such as socioeconomic and public health outcomes, are not equitably distributed, and in fact bear the heaviest burden on marginalized people, including communities of color and low-income communities [12]. In our case, environmental justice has a strong tie to climate justice since the TRIs release carbon dioxide and methane into the atmosphere.

Recorded Talk (direct link)