Presenter: Katelyn Tenney
Authors: Katelyn Tenney
Faculty Advisor: Brandon Koford
Institution: Weber State University
Air pollution is a serious health and environmental issue. Salt Lake City has particularly bad air quality, especially during the winter, due to seasonal inversions. One key contributor to poor air quality is carbon monoxide (CO), which is produced largely by the transportation sector. According to economic theory, an increase in fuel prices could cause a decrease in fuel demand and consequently vehicle emissions, eventually leading to a decrease in air pollution. This project aims to test that theory by using time-series data to determine the effect of fuel prices on air quality in Salt Lake City (SLC). To do this, I will be using two models: one is the autoregressive distributed lag (ARDL) model in order to account for the differences between short-term and long-term estimates, and the other is a Log-Log multiple regression model with SLC CO level as the dependent variable and Utah retail gasoline price as the main independent variable. The other explanatory control variables used are temperature, precipitation, wind, and GDP per capita, along with auxiliary dummy variables. I hypothesize that an increase in fuel prices will cause a decrease in CO levels, holding all else constant. One key application of these models will be to predict what effect a carbon tax, such as the one proposed by the Clean the Darn Air campaign, would have on air pollution in SLC. Overall, this project could lead to actionable intelligence for both policymakers and public health officials in Utah.