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The use of systems models to identify food waste drivers

Lookup NU author(s): Dr Matthew Grainger, Dr Gavin Stewart

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND).


Abstract

In developed countries, the largest share of food waste is produced at household level. Most studies on consumers’ food waste use models that identify covariates as significant when in fact they may not be, particularly where these models use many variables. Here, using EU-level Eurobarometer data from 2013, we use alternative analytical methods that avoid these problems (Bayesian Networks) to identify the impact of household characteristics and other variables on self-assessed food waste. Our analysis confirmed that the country, the age of the respondent, the status (student/non-student), and a belief that the family wastes too much are related to the level of self-assessed food waste. But we found no evidence that waste behaviours differ between people living in urban and rural areas, and little support of a difference between genders. Households from lower-income EU countries (e.g. Portugal, Greece, Bulgaria, Cyprus and Latvia), as well as students and young adults tend to report higher levels of food waste. Hence, the adoption of an EU strategy based on the concept of subsidiarity, and of country-level policy measures targeting different age groups is suggested. Furthermore, our analysis shows that policy makers need to be wary of relying on analysis based on large datasets that do not control for false-positives, particularly when sample sizes are small.


Publication metadata

Author(s): Grainger MJ, Aramyan L, Logatchevac K, Piras S, Righi S, Settib M, Vittuari M, Stewart GB

Publication type: Article

Publication status: Published

Journal: Global Food Security

Year: 2018

Volume: 16

Pages: 1-8

Print publication date: 01/03/2018

Online publication date: 01/02/2018

Acceptance date: 29/12/2017

Date deposited: 02/02/2018

ISSN (print): 2211-9124

ISSN (electronic): 2211-9124

Publisher: Elsevier

URL: https://doi.org/10.1016/j.gfs.2017.12.005

DOI: 10.1016/j.gfs.2017.12.005


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