Evidence map›Paper›PMID 39383235›Full record

ArticleScience advances2024

US federal resource allocations are inconsistent with concentrations of energy poverty.

Carlos Batlle, Peter Heller, Christopher Knittel, Tim Schittekatte

Abstract read
In one paragraph

Article in Science advances, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Carlos BatlleCenter for Energy and Environmental Policy Research, Massachusetts Institute of Technology, Cambridge, MA, USA.ORCID 0000-0001-8451-5295
Peter HellerCenter for Energy and Environmental Policy Research, Massachusetts Institute of Technology, Cambridge, MA, USA.ORCID 0000-0002-8064-5671
Christopher KnittelCenter for Energy and Environmental Policy Research, Massachusetts Institute of Technology, Cambridge, MA, USA.ORCID 0000-0002-7654-8641
Tim SchittekatteFlorence School of Regulation, European Union Institute, Florence, Italy.ORCID 0000-0002-7196-0231

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Recent data from the US Energy Information Administration reveals that nearly one in three households in the United States 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 implement a LASSO-based machine learning approach using sociodemographic and geographical information to estimate energy burden in each US census tract for 2015 and 2020. We then compare the allocation to states from the Low Income Home Energy Assistance Program to an optimized allocation. We allocate funds to the most burdened households, providing them with enough assistance to reduce their energy expenditures so that their household energy burden is equal to a new maximum allowable energy burden. This markedly shifts funds from the northern cold-weather states to the southern warm-weather states.

Identifiers

PMID39383235
PMCPMC11463276

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.