Evidence map›Paper›PMID 40796595›Full record

ArticleScientific reports2025

Advanced food waste quantification at municipal level to strengthen the assessment of prevention actions.

Begoña Untzizu Olano-Oteiza, Manuel Amador-Cervera, María Virginia Vargas-Viedma, Ainhoa Alonso-Vicario

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.

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

The trial behind it

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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.

Begoña Untzizu Olano-OteizaDeustoTech, Faculty of Engineering, University of Deusto, Av. Universidades, 24, Bilbao, 48007, Spain.
Manuel Amador-CerveraDeustoTech, Faculty of Engineering, University of Deusto, Av. Universidades, 24, Bilbao, 48007, Spain. manuel.amador@deusto.es.
María Virginia Vargas-ViedmaDeusto Research and Knowledge Transfer (DIT), University of Deusto, Av. Universidades, 24, Bilbao, 48007, Spain.
Ainhoa Alonso-VicarioDeustoTech, Faculty of Engineering, University of Deusto, Av. Universidades, 24, Bilbao, 48007, Spain.

Funding

Eusko Jaurlaritza Research group IT1677-22Horizon 2020 101000617Universidad de Deusto FPI grant
6 · The paper itself

Abstract

Food loss and waste (FLW) demands urgent attention: over 58 million tonnes wasted annually in the EU, while 828 million people face hunger worldwide. SDG 12.3 targets a 50% reduction in FLW by 2030, requiring rigorous quantification. Governments must quantify FLW, especially at the consumption stage, where most of FLW is generated. However, the limited granularity of existing data emphasises the need for improved quantification methods that enable reliable comparisons and set solid basis to measure the impact of future FLW prevention actions. This paper proposes a FLW quantification methodology at municipal and regional scales aligned with the existing urban waste management requirements. The methodology involved experts and stakeholders to define the optimal FLW quantification framework and measurement method. It was validated in 6 Basque municipalities with a common waste characterisation matrix to compare waste fractions, generator types and waste collection systems. A cartographic analysis, from regional to street level, demonstrates how data granularity shapes FLW pattern interpretation. As in the case of Zamudio, where data at container level permits detailed insights. This approach enables improved waste collection and the design of ad-hoc FLW prevention actions (like economic instruments) according to the generator profile and type of FLW.

Indexed as

FoodWaste ManagementCitiesFood Loss and WasteHumansSpainFood Loss and WasteCircular economyFood wasteFood waste quantificationSustainabilityWaste managementWaste prevention

Identifiers

PMID40796595
PMCPMC12343935

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.