Evidence map›Paper›PMID 40588410›Full record

ArticleEnvironmental science & technology2025

Reducing Food Loss and Associated Greenhouse Gas Emissions Using a Dynamic Shelf Life Approach.

Junzhang Wu, Yifeng Zou, Gang Liu, Li Xue, Zhimin Shi, Andrea Fedele, Alessandro Manzardo

Abstract read
In one paragraph

Article in Environmental science & technology, 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
–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

7 authors.

Junzhang WuCESQA (Quality and Environmental Research Center), Department of Civil, Environmental and Architectural Engineering, University of Padova, Via Marzolo 9, 35131 Padova, Italy.ORCID 0000-0002-1700-5129
Yifeng ZouSchool of Management, Guangzhou University, Guangzhou 510006, China.
Gang LiuCollege of Urban and Environmental Sciences, Peking University, Beijing 100871, China.
Li XueCollege of Economics and Management, China Agricultural University, Beijing 100083, China.
Zhimin ShiState Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China.ORCID 0000-0002-8385-1008
Andrea FedeleCESQA (Quality and Environmental Research Center), Department of Civil, Environmental and Architectural Engineering, University of Padova, Via Marzolo 9, 35131 Padova, Italy.
Alessandro ManzardoCESQA (Quality and Environmental Research Center), Department of Civil, Environmental and Architectural Engineering, University of Padova, Via Marzolo 9, 35131 Padova, Italy.ORCID 0000-0003-0245-6944

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The integration of IoT sensors with dynamic shelf life (DSL) systems unlocks real-time visibility into perishable goods, yet the full life-cycle trade-offs of such technologies remain underexplored. This study develops a process-based life cycle framework, incorporating a kinetic quality-degradation model and Monte Carlo simulations, to evaluate both avoided food loss and waste and sensor-embedded climate burdens across China's fresh food chains. Results show this IoT-DSL regime extends average shelf life by 8.1-13.8% in fruits, dairy, and vegetables, although gains fall two- to 5-fold for animal and aquatic products at lower quality thresholds, while nontechnical interventions deliver only 3.2-6.5% waste reductions. Large-scale IoT-DSL deployment could avert 17.32 ± 3.65 Mt yr

Indexed as

Greenhouse GasesChinaFood Loss and WasteMonte Carlo MethodFood Loss and WasteGreenhouse Gasesexpiry datefood loss and wastegreenhouse gas (GHG)Internet of Things (IoT)life cycle assessment (LCA)sensor

Identifiers

PMID40588410
PMCPMC12269072

What OpenQuestion holds

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LicenceCC BY
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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.