Evidence map›Paper›PMID 38598638›Full record

ArticleScience advances2024

Mixed diets can meet nutrient requirements with lower carbon footprints.

Yin Long, Liqiao Huang, Jie Su, Yoshikuni Yoshida, Kuishuang Feng, Alexandros Gasparatos

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 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
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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

6 authors.

Yin LongDepartment of Technology Management for Innovation, Graduate School of Engineering, University of Tokyo, Tokyo, Japan.ORCID 0000-0002-6761-9735
Liqiao HuangDepartment of Technology Management for Innovation, Graduate School of Engineering, University of Tokyo, Tokyo, Japan.ORCID 0000-0002-8117-8999
Jie SuInstitute for Future Initiatives (IFI), University of Tokyo, 7-3-1 Hongo, 113-8654 Tokyo, Japan.
Yoshikuni YoshidaDepartment of Technology Management for Innovation, Graduate School of Engineering, University of Tokyo, Tokyo, Japan.ORCID 0000-0002-8834-8671
Kuishuang FengDepartment of Geographical Sciences, University of Maryland, College Park, MD, USA.ORCID 0000-0001-5139-444X
Alexandros GasparatosInstitute for Future Initiatives (IFI), University of Tokyo, 7-3-1 Hongo, 113-8654 Tokyo, Japan.ORCID 0000-0001-9323-1366

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Achieving sustainable dietary change is essential for safeguarding human and environmental health. However, dietary recommendations based on broad food groups may not accurately reflect real-world realities because individuals select and consume dishes with multiple food items influenced by diverse context-specific factors. Therefore, here we explored the sustainability trade-offs of dietary choices at the dish level through an optimization modeling approach tested in Japan. We estimated the nutritional quality, price, and carbon footprint of major Japanese dishes and examined 16 dietary scenarios to identify options that meet the nutritional requirements and minimize carbon footprint. Overall, mixed diets contain more combinations of dishes that meet nutritional requirements with lower carbon footprints compared to more restrictive dietary scenarios. We argue that the approach developed here enables a better understanding of dietary trade-offs, complements existing methods, and helps identify sustainable diets by offering nuanced information at the national and sub-national levels.

Indexed as

Carbon FootprintDietFoodHumansNutrientsNutritional RequirementsNutrients

Identifiers

PMID38598638
PMCPMC11006225

What OpenQuestion holds

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LicenceCC BY-NC
Read underepoch 390

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.