Evidence map›Paper›PMID 42181013›Full record

ArticlePNAS nexus2026

Tunable and computationally efficient framework for retrofitting: Budget allocation using income and spatial equity.

Aparna Kishore, Kaarthik Sundar, Deepjyoti Deka, Madhav V Marathe

Abstract read
In one paragraph

Article in PNAS nexus, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Aparna KishoreDepartment of Computer Science, University of Virginia, Charlottesville, VA 22904, USA.ORCID https://orcid.org/0000-0001-7447-4100
Kaarthik SundarComputational Intelligence & Modeling Group, Los Alamos National Laboratory, Los Alamos, NM 87545, USA.ORCID https://orcid.org/0000-0002-6928-449X
Deepjyoti DekaMIT Energy Initiative, Massachusetts Institute of Technology, Cambridge, MA 02142, USA.ORCID https://orcid.org/0000-0003-3928-3936
Madhav V MaratheDepartment of Computer Science, University of Virginia, Charlottesville, VA 22904, USA.ORCID https://orcid.org/0000-0003-1653-0658

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The residential sector accounts for roughly 35% of US electricity use. Retrofitting residential buildings has been proposed as an effective strategy to reduce energy costs and improve individuals' quality of life and health. Federal and state funding plays a crucial role in supporting these initiatives by providing financial assistance through grants, tax credits, and low-interest loans. Key challenges here include selecting which households to serve and determining assistance levels so that savings are not skewed toward particular regions or income brackets. We propose RAISE (Retrofitting Allocation using Income & Spatial Equity), a framework that allocates a state's retrofit budget to maximize total energy savings while ensuring fair distribution across counties and income groups. RAISE estimates prospective savings through hourly household consumption models-pre- and post-retrofit-that integrate demographics, weather data, solar irradiance, appliance inventories, and survey inputs. At the core of RAISE's budget allocation is an income-weighted

Indexed as

budget allocationIWϵ indexoptimizationretrofitting

Identifiers

PMID42181013
PMCPMC13196871

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.