Evidence map›Paper›PMID 35885405›Full record

ArticleFoods (Basel, Switzerland)2022

How to Reduce Food Waste Caused by Normative Illusion? A Study Based on Evolutionary Game Model Analysis.

Mengling Tian, Yangyang Zheng

Open access · goldAbstract read
In one paragraph

Article in Foods (Basel, Switzerland), 2022. 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
0.6field-weighted citation impact, top 31% of its field
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, 4 citations in OpenAlex.

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

2 authors at 1 institution in 1 country.

Mengling TianBusiness School, Wenzhou University, Wenzhou 325035, China.
Yangyang ZhengBusiness School, Wenzhou University, Wenzhou 325035, China.
Wenzhou University · CN

Funding

Yangyang zheng 21CJY049
6 · The paper itself

Abstract

Reducing food waste is a priority for all sectors of society as it threatens national food security and the sustainability of global agriculture. Many studies on food waste have focused on a single subject, and the psychological factors of consumer waste are often overlooked. Based on evolutionary game theory, this paper introduces consumers' normative illusion, constructs an evolutionary game model in which the government, caterers and consumers collaborate to reduce food waste, and simulates and analyses the behavioural strategies of the three stakeholders. The results show that: Firstly, food waste can be reduced under certain conditions by incentive-guided and punishment-inhibited policies. Moreover, incentive-guided policies can reduce government expenditures more than punishment-inhibited ones. Secondly, implementation of prior intervention, the resultant intervention and reducing the probability of consumers' aversion to the intervention of caterers can optimise the government's punishment-inhibited policy. Finally, under the punishment-inhibited policy, caterers can bear 60% of the prior intervention costs for food waste management. When caterers invest 40-60% of the prior intervention costs, both caterers and consumers can achieve the ideal state of cooperation; caterers can accept 40% of the resultant intervention cost for food waste management, and when the resultant intervention cost is less than 40%, consumers choose not to waste. Both caterers and consumers are involved in reducing food waste when the probability of consumer dissatisfaction with a caterer's intervention is reduced to less than 40%.

Indexed as

evolutionary gamefood wastenormative illusionstakeholders

Identifiers

PMID35885405
PMCPMC9323889
OpenAlexW4286489007

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.