Evidence map›Paper›PMID 42799482›Full record

ArticleCleaner environmental systems2026

Estimating facility-level greenhouse gas emissions in U.S. hospitals with machine learning imputation of incomplete self-reported data.

Hao Yin, Sandrah P Eckel, Mehak Kaur, Gary Cohen, Lara Sutherland, Makaya Tome, Baoqi Eileen Chen, Arash Motamed, Najmedin Meshkati, Howard Hu and 2 more

Abstract read
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In one paragraph

Article in Cleaner environmental systems, 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

12 authors.

Hao YinDepartment of Economics, University of Southern California, Los Angeles, CA, 90089, USA.
Sandrah P EckelDepartment of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90033, USA.
Mehak KaurDepartment of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90033, USA.
Gary CohenHealth Care Without Harm, Boston, MA, 20190, USA.
Lara SutherlandHealth Care Without Harm, Boston, MA, 20190, USA.
Makaya TomeDornsife College of Letters, Arts and Sciences, University of Southern California, Los Angeles, CA, 90007, USA.
Baoqi Eileen ChenDornsife College of Letters, Arts and Sciences, University of Southern California, Los Angeles, CA, 90007, USA.
Arash MotamedDepartment of Anesthesiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90033, USA.
Najmedin MeshkatiViterbi School of Engineering, University of Southern California, Los Angeles, CA, 90089, USA.
Howard HuDepartment of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90033, USA.
Rob McConnellDepartment of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90033, USA.
Bhavna SharmaDepartment of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90033, USA.ORCID 0000-0003-0516-6125

Funding

Translational Research Support CoreP30ES007048 · NIEHS · UNIVERSITY OF SOUTHERN CALIFORNIA · PI ROB S MCCONNELL · 1996 to 2026
$46.4M
NIEHS NIH HHS P30 ES007048
6 · The paper itself

Abstract

The U.S. healthcare industry has the largest per-capita carbon footprint compared with those of other countries. Despite growing commitments and efforts to decarbonization hospitals face challenges in reducing emissions in part because hospital-level estimates of greenhouse gas (GHG) emissions are limited or unavailable. This study estimates hospital-level GHG emissions from 38 sources across seven operational sectors for 283 hospitals, representing approximately 4% of U.S. hospitals in 2020, that voluntarily participated in the Practice Greenhealth Environmental Excellence Award program. Using our previously developed and validated machine learning-based imputation approach based on gradient boosting machines, this study addresses incomplete self-reported operational and activity data to enable large-scale facility-level emissions accounting. Total GHG emissions from these hospitals were estimated at 9.3 megatonnes (Mt) carbon dioxide (CO

Indexed as

Climate changeGreenhouse gas emissionsHospitalsMachine LearningSustainability

Identifiers

PMID42799482
PMCPMC13614697

What OpenQuestion holds

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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.