Recent advances in computing and data availability have allowed researchers to draw conclusions linking environmental factors to social outcomes. FIRE Sustainability Analytics uses quantitative methods to understand the socioeconomic consequences of climate change and environmental regulations.


The stream's current research goal is to assess the NOx Budget Program's impact on different socioeconomic groups. Research methods involve combining data atmospheric science simulations, and census surveys to assess the impact on different demographic communities.


“No group of people should bear a disproportionate share of the negative environmental consequences resulting from industrial, governmental, and commercial operations or policies” (EPA). Historically, this is not the case as people of color tend to live near high polluting sources. Unequal pollution exposure among different socioeconomic groups can be amended or exacerbated with emerging policies.


Developments in economic theory also show that market-based pollution regulations may reduce emissions in an economically efficient manner. Nevertheless, it can also increase the pollution exposure gap among specific demographic groups. We must utilize methods from multiple research fields, such as environmental economics and atmospheric science, to study the intended and unintended consequences of past policies to inform the future.


FIRE Sustainability Analytics works in a learning-by-doing, application-first environment where students implement authentic research methods to answer policy questions. Students immerse themselves in multidisciplinary research, including environmental science, applied economics, sociology, and atmospheric science. By using the computing language R, they learn to communicate and visualize their results; in tables, graphs, and maps.


More importantly, students learn to evaluate scientific research and environmental policies critically. They understand the nuances of policymaking. In the end, students have reported a more in-depth understanding of environmental issues, increased appreciation in data analytics, and improved ability to communicate scientific outcomes.