美国联邦资源分配与能源贫困的集中不一致
Carlos Batlle1,2,3, Peter Heller1,4, Christopher Knittel1,5,6
1Center for Energy and Environmental Policy Research, Massachusetts Institute of Technology, Cambridge, MA, USA.
Science advances
|October 9, 2024
概括
能源贫困影响了美国三分之一的家庭. 机器学习优化了能源援助,将资金转移到负担较高的南部州,与静态的联邦拨款不同.
科学领域:
- 能源政策 能源政策
- 机器学习应用程序 机器学习应用程序
- 社会经济学 社会经济学
背景情况:
- 美国近三分之一的家庭面临能源贫困,预计这一数字将增加.
- 目前联邦能源援助计划的拨款是静态的,并不反映能源贫困的动态分布.
- 由于分配方法过时,现有计划可能无法有效地针对最需要的家庭.
研究的目的:
- 开发一种机器学习模型,用于在人口普查区一级估计家庭能源负担.
- 为了比较目前的低收入家庭能源援助计划 (LIHEAP) 拨款与基于估计能源负担的优化拨款.
- 确定能源援助需求的地理转移,并提出更公平的资金分配.
主要方法:
- 使用基于LASSO的机器学习方法.
- 整合了社会人口和地理数据,以估计2015年和2020年的能源负担.
- 模拟了优化的资金分配,以将能源负担降低到最高允许的门.
主要成果:
- 该研究发现,美国人口普查区的能源负担存在显著差异.
- 一个优化的分配策略显著地将资金从北方寒冷的州转移到南方温暖的州.
- 拟议的方法确保援助到达负担最大的家庭,减少他们的能源支出.
结论:
- 机器学习为评估能源贫困和优化援助分配提供了一种动态和准确的方法.
- 目前用于能源援助计划的静态分配模型不足以应对不断变化的能源贫困局面.
- 根据机器学习驱动的负担估计重新分配能源援助可以提高公平性和有效性,特别有利于南部州.
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