US federal resource allocations are inconsistent with concentrations of energy poverty

Recent data from the United States (US) Energy Information Administration reveals that nearly one in three households in the US report experiencing energy poverty, and this number is only expected to rise. Federal assistance programs exist, but allocations across states have been nearly static since 1984, while the distribution of energy poverty is dynamic in location and time. We produce a novel machine learning approach based on sociodemographic and geographical information to estimate energy burden in each US census tract for 2015 and 2020. Our analysis confirms that average household energy burdens increased, and the range of households suffering energy poverty broadened. We provide an optimized allocation structure to urge policy makers to revise the distribution of funds to better match assistance needs., We use machine learning to determine how various demographic and physical characteristics are correlated with household energy burdens across the US. Energy burden estimates allow us to identify where energy poverty may be concentrated at the census-tract level. Our analysis extends and improves upon the Low-income Energy Affordability Data (LEAD) tool, developed by the US Department of Energy’s National Renewable Energy Laboratory to estimate energy expenditures and burdens in several ways (28). The LEAD tool is designed to help local and state governments with decisions for addressing energy poverty; however, it is static in time and uses self-reported energy expenditures given only for one month of the year, which is not reported publicly. The reliance on one month implies that the estimation of annual values is not guaranteed to account for the seasonal variation in energy costs throughout the months. The sampling done by the survey must sufficiently cover all months of the year, an..., , # US federal resource allocations are inconsistent with concentrations of energy poverty

[https://doi.org/10.5061/dryad.9kd51c5rj](https://doi.org/10.5061/dryad.9kd51c5rj)

This dataset contains the necessary R scripts and data files to replicate this analysis' results. All analysis is completed in R, and an internet connection is required as the RECS input files are loaded directly from the US Energy Information Administration's webiste for the most up-to-date information.

## Description of the data and file structure

### “Analysis” Folder

The folder titled \"Analysis\" contains all of the results presented in this paper. The \"Coeffs\" subfolder conatins the .csv files of model coefficients for both 2015 and 2020.

* 2015_coeffs.csv
* 2020_coeffs.csv

The \"Figures\" subfolder contains all of the maps, graphs, and performance output from the R scripts. 

* Graphs: Histograms of tract average energy burdens for 2015, 2020, and the comparison of 2015 and 2020. Subfolder \"2020\" also conta...

美国能源信息署（United States Energy Information Administration, EIA）的最新数据显示，美国近三分之一的家庭报告称经历过能源贫困，且这一比例预计还将上升。目前虽有联邦援助计划，但自1984年以来各州的拨款规模几乎停滞不前，而能源贫困的分布在空间与时间维度上均呈动态变化。本研究提出一种基于社会人口学与地理信息的新颖机器学习方法，用于估算2015年与2020年美国各普查街区的能源负担。分析结果证实，美国家庭平均能源负担有所上升，陷入能源贫困的家庭范围也进一步扩大。我们还提出了优化后的拨款分配框架，以敦促政策制定者调整资金分配方式，从而更好地匹配援助需求。

本研究利用机器学习方法，探究美国各地不同人口统计与物理特征与家庭能源负担之间的相关性。通过能源负担估算，我们能够识别出普查街区尺度下能源贫困可能集中的区域。本研究对美国能源部下属国家可再生能源实验室（National Renewable Energy Laboratory, NREL）开发的低收入能源负担能力数据工具（Low-income Energy Affordability Data, LEAD）进行了拓展与优化——该工具原本可通过多种方式估算能源支出与能源负担（参考文献28）。LEAD工具旨在协助地方与州政府制定应对能源贫困的决策，但该工具在时间维度上呈静态，且仅使用每年单一个月的自我报告能源支出数据，此类数据并未公开披露。仅依赖单月数据意味着，年度估值无法保证覆盖全年各月的能源成本季节波动。因此，该调查的抽样必须充分覆盖全年所有月份，……

# 美国联邦资源分配与能源贫困集中区域不匹配

[https://doi.org/10.5061/dryad.9kd51c5rj](https://doi.org/10.5061/dryad.9kd51c5rj)

本数据集包含复现本研究分析结果所需的R脚本与数据文件。所有分析均通过R语言完成，且需要联网——因为住宅能源消费调查（Residential Energy Consumption Survey, RECS）的输入文件直接从美国能源信息署的网站获取，以确保使用最新数据。

## 数据与文件结构说明

### "Analysis" 文件夹
名为"Analysis"的文件夹包含本论文展示的全部研究结果。其中"Coeffs"子文件夹包含2015年与2020年的模型系数.csv文件：
* 2015_coeffs.csv
* 2020_coeffs.csv

"Figures"子文件夹包含所有由R脚本生成的地图、图表与模型性能输出结果：
* 图表：2015年、2020年普查街区平均能源负担的直方图，以及2015年与2020年的对比图。"2020"子文件夹同样包含……