Evidence map›Paper›PMID 41120422›Full record

ArticleScientific reports2025

A new heuristic framework for estimating indirect (Scope 3) emissions of large organizations.

Viraj Sawant, Kaiyi Wang, Vicky Tong, Zachary Fendler, Bala Krishnamoorthy, Seema Gandhi, Deepak Rajagopal

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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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

7 authors.

Viraj SawantInstitute of Environment and Sustainability, University of California, Los Angeles, USA.
Kaiyi WangDepartment of Anesthesia and Perioperative Care, University of California, San Francisco, USA.
Vicky TongInstitute of Environment and Sustainability, University of California, Los Angeles, USA.
Zachary FendlerDepartment of Mathematics and Statistics, Washington State University, Vancouver, USA.
Bala KrishnamoorthyDepartment of Mathematics and Statistics, Washington State University, Vancouver, USA.
Seema GandhiDepartment of Anesthesia and Perioperative Care, University of California, San Francisco, USA.
Deepak RajagopalInstitute of Environment and Sustainability, University of California, Los Angeles, USA. rdeepak@ioes.ucla.edu.

Funding

Commonwealth Fund 22-23071
6 · The paper itself

Abstract

Large businesses and organizations are pledging voluntary actions for climate change mitigation. Alongside such pledges, government regulations that mandate climate-related disclosures for large corporations are also emerging. Both for compliance with such mandates and effective voluntary action, organizations need reliable information on their indirect emissions, which refers to emissions arising upstream and downstream to their own operations, and which, for many types of industries (such as education, finance, health care, hospitality and retail) can be several-times their own or direct emissions. However, the best approach to estimating indirect emissions requires conducting a life cycle assessment (LCA) for a large number (potentially thousands) of different products and services, which is costly. To overcome this, we present a heuristic approach that combines insights derived from LCA and data analytic techniques to identify a relatively small number of products that might account for a large share of indirect emissions, specifically Scope 3 emissions. We apply our approach to a dataset comprising over 25,000 products spanning 105 different product categories, purchased by a large tertiary-care hospital and show that this can help organizations prioritize actions aimed at reducing their indirect emissions.

Identifiers

PMID41120422
PMCPMC12541089

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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.