Garland Jasmine, Baker Kyri, Rajagopalan Balaji, Livneh Ben
The Department of Civil, Environmental, and Architectural Engineering at the University of Colorado Boulder, Boulder, CO, USA.
Renewable and Sustainable Energy Institute (RASEI), University of Colorado Boulder, Boulder, CO, USA.
iScience. 2025 Jun 2;28(6):112559. doi: 10.1016/j.isci.2025.112559. eCollection 2025 Jun 20.
The United States is one of the largest energy consumers per capita, requiring households to have adequate energy expenditures to keep up with modern demand regardless of financial cost. This paper investigates energy burden, defined as the ratio of household energy expenditures to household income. There is a lack of research on creating equitable policies for energy-burdened communities, including environmental justice indicators and community characteristics that could be used to predict and understand energy burden, along with socioeconomic status, building characteristics, and power outages, beneficial to policymakers, engineers, and advocates. Here, generalized additive models and random forests are explored for energy burden prediction using the original dataset and principal components, followed by a leave-one-column-out (LOCO) analysis to investigate indicator influence, with 25 identical indicators out of 42 appearing in the top 100 models. The generalized additive models generally outperform the random forests, with the best-performing model yielding a coefficient of determination of 0.92.
美国是人均能源消费量最大的国家之一，要求家庭有足够的能源支出以跟上现代需求，而不考虑财务成本。本文研究能源负担，定义为家庭能源支出与家庭收入的比率。目前缺乏针对能源负担较重社区制定公平政策的研究，包括可用于预测和理解能源负担的环境正义指标和社区特征，以及社会经济地位、建筑特征和停电情况，这对政策制定者、工程师和倡导者有益。在此，使用原始数据集和主成分探索广义相加模型和随机森林用于能源负担预测，随后进行留一列法（LOCO）分析以研究指标影响，42个指标中有25个相同指标出现在前100个模型中。广义相加模型通常优于随机森林，表现最佳的模型的决定系数为0.92。
