An Analysis of The Energy Burden of Low-Income Households in Aurora

Tanush Shastry et al.
Wed, 29 Apr 2026 15:50:00 PDT

This project examines the concept of energy burden, which is defined as the percentage of a household’s income that goes toward paying for energy costs. By analyzing how this energy

burden disproportionately impacts communities based on income, race, and geography, this project supports UNSDG #9: Industry, Innovation and Infrastructure. The problem is illustrated by the fact that, on average, an American family uses approximately 3% of its annual income to pay for energy. This is in contrast to a family that falls into the low-income brackets, which will use 8%-10%, which is as much as 15% of their total income, depending on their energy usage. This energy burden does not happen by chance, but is caused by decades of disinvestment in low-income housing, limited access to energy efficiency improvements, and utility billing structures that did not account for the financial constraints of the customers they provide service to. Citizens of these communities typically use less energy than those in communities with higher incomes, but they pay much more than their affluent counterparts. The transition to clean energy, which many consider a historic step towards a cleaner future, was not going to help the communities already impacted by the infrastructure built on the old energy system. In addition to economic losses, low-income households that paid too much towards energy bills had less available for other essential items such as food, health care, and education. Paying large amounts of money towards energy was one of many reasons that housing instability existed among low-income families, and was one of the reasons why wealthier families did not experience the same level of anxiety and tradeoffs as low-income families did. To understand why and how this energy burden occurs, we used a variety of federal and state datasets including the Department of Energy's LEAD Tool, the U.S. Census income and housing data, and Illinois utility records, to map out the geographic areas with the highest levels of energy burden and to provide an explanation for why those particular regions are affected in that way.