Evidence map›Paper›PMID 34429412›Full record

ArticleNature communications2021

Global scenarios of resource and emission savings from material efficiency in residential buildings and cars.

Stefan Pauliuk, Niko Heeren, Peter Berrill, Tomer Fishman, Andrea Nistad, Qingshi Tu, Paul Wolfram, Edgar G Hertwich

Abstract read
In one paragraph

Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers.

0numbers the graph read from it
0cells of the map it votes in
41citing 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

41 citing papers in PubMed.

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  19. Decarbonizing the global steel industry in a resource-constrained future-a systems perspective.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2024
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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

8 authors.

Stefan PauliukIndustrial Ecology Group, Faculty of Environment and Natural Resources, University of Freiburg, Freiburg, Germany. stefan.pauliuk@indecol.uni-freiburg.de.ORCID 0000-0002-6869-1405
Niko HeerenCenter for Industrial Ecology, School of the Environment, Yale University, New Haven, CT, USA.ORCID 0000-0003-4967-6557
Peter BerrillCenter for Industrial Ecology, School of the Environment, Yale University, New Haven, CT, USA.
Tomer FishmanSchool of Sustainability, Interdisciplinary Center (IDC) Herzliya, Herzliya, Israel.ORCID 0000-0003-4405-2382
Andrea NistadIndustrial Ecology Program, Norwegian University of Science and Technology (NTNU), Trondheim, Norway.
Qingshi TuCenter for Industrial Ecology, School of the Environment, Yale University, New Haven, CT, USA.
Paul WolframCenter for Industrial Ecology, School of the Environment, Yale University, New Haven, CT, USA.ORCID 0000-0002-8470-427X
Edgar G HertwichCenter for Industrial Ecology, School of the Environment, Yale University, New Haven, CT, USA. edgar.hertwich@ntnu.no.ORCID 0000-0002-4934-3421

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Material production accounts for a quarter of global greenhouse gas (GHG) emissions. Resource-efficiency and circular-economy strategies, both industry and demand-focused, promise emission reductions through reducing material use, but detailed assessments of their GHG reduction potential are lacking. We present a global-scale analysis of material efficiency for passenger vehicles and residential buildings. We estimate future changes in material flows and energy use due to increased yields, light design, material substitution, extended service life, and increased service efficiency, reuse, and recycling. Together, these strategies can reduce cumulative global GHG emissions until 2050 by 20-52 Gt CO

Identifiers

PMID34429412
PMCPMC8385048

What OpenQuestion holds

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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.