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

オーロラ市における低所得世帯のエネルギー負担の分析 (AI 翻訳)

Tanush Shastry, Akhil Narayanan, Ajay Srinivasan

DigitalCommons-IMSA (Illinois Mathematics and Science Academy)📚 査読済 / ジャーナル2026-04-29#エネルギー転換Origin: US
原典: https://digitalcommons.imsa.edu/slx/2026/unsdg8/3

🤖 gxceed AI 要約

日本語

本研究は、低所得世帯におけるエネルギー負担(収入に占めるエネルギー費用の割合)を分析し、人種・地理的格差を明らかにした。連邦・州のデータを用いてオーロラ市内の地域別負担をマッピングし、低所得地域ほど高い負担に直面する構造的原因を探る。クリーンエネルギー移行がこうしたコミュニティに与える影響についても考察する。

English

This study analyzes energy burden (percent of income spent on energy) for low-income households in Aurora, revealing disparities by race and geography. Using federal and state datasets, it maps high-burden areas and identifies structural causes such as disinvestment and lack of efficiency upgrades. It also discusses implications for the clean energy transition.

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

📝 gxceed 編集解説 — Why this matters

日本のGX文脈において

日本ではエネルギー負担の議論が少ないが、低所得世帯を考慮した公正なエネルギー移行を考える上で示唆に富む。特に、家庭のエネルギー効率改善支援や料金制度改革に応用可能な分析枠組みを提供する。

In the global GX context

This paper contributes to global just transition discussions by quantifying energy burden and its social determinants. It underscores the risk that the clean energy transition may exacerbate existing inequalities without targeted policies, a key concern for modern climate disclosure and social equity frameworks.

👥 読者別の含意

🔬研究者:Provides a localized empirical method for measuring energy burden and linking it to income, race, and housing quality.

🏢実務担当者:Utilities and community organizations can use the mapping approach to identify high-burden areas and design targeted energy assistance programs.

🏛政策担当者:Highlights the need for energy equity metrics in transition planning and utility regulation to avoid regressive impacts.

📄 Abstract(原文)

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.

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