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Review: Consumption-stage food waste reduction interventions – What works and how to design better interventions

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This paper identifies and summarises food-waste prevention interventions at the consumption/consumer stage of the supply chain via a rapid review of academic literature from 2006-2017. We identify 17 applied interventions that claim to have achieved food waste reductions. Of these, 13 quantified food waste reductions. Interventions that changed the size or type of plates were shown to be effective (up to 57% food waste reduction) in hospitality environments. Changing nutritional guidelines in schools also reduced vegetable waste by up to 28%, indicating that healthy diets can be part of food waste reduction strategies. Information campaigns were also shown to be effective with up to 28% food waste reduction in a small sample size intervention. Cooking classes, fridge cameras, food sharing apps, advertising and information sharing were all reported as being effective but with little or no robust evidence provided. This is worrying as all these methods are now being proposed as options approaches to reduced food waste, and except for a few studies, with there is no reproducible quantified evidence to assure credibility or success. To strengthen current results, a greater number of longitudinal and larger sample size intervention studies are required. To inform future intervention studies, this paper proposes a standardised guideline, which consists of 4 strands: (1) design of interventions; (2) monitoring and measurement; (3) moderation and mediation; and (4) reporting. Given the importance of food-waste reduction, the findings of this review highlight a significant evidence gap, meaning that it is difficult to make evidence-based decisions to prevent or reduce consumption-stage food waste in a cost-effective manner.
Content may be subject to copyright.
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Accepted Pre-Print version.
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Article reference JFPO1696
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Journal : Food Policy
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Corresponding author: Christian Reynolds
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First author: Christian Reynolds
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Received at Editorial Office: 13 Apr 2018
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Article revised: 30 Dec 2018
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Article accepted for publication: 23 Jan 2019
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Please visit publisher for published version: https://www.elsevier.com/locate/issn/0306-9192
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Released with a Creative Commons Attribution Non-Commercial No Derivatives License
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Review: Consumption-stage food waste reduction interventions what
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works and how to design better interventions.
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Christian Reynolds, Department of Geography, University of Sheffield, UK and
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Waste & Resources Action Programme (WRAP), UK.
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c.reynolds@sheffield.ac.uk ; christian.reynolds@wrap.org.uk
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Liam Goucher, Management School and Advanced Resource Efficiency Centre,
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Faculty of Social Sciences, University of Sheffield, UK
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Tom Quested, Waste & Resources Action Programme (WRAP), UK
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Sarah Bromley, Waste & Resources Action Programme (WRAP), UK
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Sam Gillick, Waste & Resources Action Programme (WRAP), UK
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Victoria K. Wells, The York Management School, York University, UK
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David Evans, Faculty of Social Sciences, University of Sheffield, UK.
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Lenny Koh, Management School and Advanced Resource Efficiency Centre,
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Faculty of Social Sciences, University of Sheffield, UK
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Annika Carlsson Kanyama, Strategic Sustainability Studies, SEED, KTH Royal
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Institute of Technology, Sweden
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Cecilia Katzeff, Architecture and the Built Environment, KTH Royal Institute of
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Technology, Sweden
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Åsa Svenfelt, Strategic Sustainability Studies, SEED, KTH Royal Institute of
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Technology, Sweden
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Peter Jackson Department of Geography, University of Sheffield, UK.
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Review: Consumption-stage food waste reduction interventions what
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works and how to do better.
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Abstract
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Food waste prevention has become an issue of international concern, with
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Sustainable Development Goal 12.3 aiming to halve per capita global food waste
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at the retail and consumer levels by 2030. However there is no review that has
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considered the effectiveness of interventions aimed at preventing food waste in
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the consumption stages of the food system. This significant gap, if filled, could
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help support those working to reduce food waste in the developed world,
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providing knowledge of what interventions are specifically effective at
42
preventing food waste.
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This paper fills this gap, identifying and summarizing food-waste prevention
44
interventions at the consumption/consumer stage of the supply chain via a rapid
45
review of global academic literature from 2006-2017.
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We identify 17 applied interventions that claim to have achieved food waste
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reductions. Of these, 13 quantified food waste reductions. Interventions that
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changed the size or type of plates were shown to be effective (up to 57% food
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waste reduction) in hospitality environments. Changing nutritional guidelines in
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schools were reported to reduce vegetable waste by up to 28%, indicating that
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healthy diets can be part of food waste reduction strategies. Information
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4
campaigns were also shown to be effective with up to 28% food waste reduction
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in a small sample size intervention.
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Cooking classes, fridge cameras, food sharing apps, advertising and information
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sharing were all reported as being effective but with little or no robust evidence
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provided. This is worrying as all these methods are now being proposed as
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approaches to reduce food waste and, except for a few studies, there is no
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reproducible quantified evidence to assure credibility or success. To strengthen
59
current results, a greater number of longitudinal and larger sample size
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intervention studies are required. To inform future intervention studies, this
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paper proposes a standardised guideline, which consists of: (1) intervention
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design; (2) monitoring and measurement; (3) moderation and mediation; (4)
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reporting; (5) systemic effects.
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Given the importance of food-waste reduction, the findings of this review
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highlight a significant evidence gap, meaning that it is difficult to make evidence-
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based decisions to prevent or reduce consumption-stage food waste in a cost-
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effective manner.
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Keywords
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Food waste
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Reduction
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Household
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Downstream
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Consumption
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Consumer
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1 Introduction
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Within the last decade, food waste has become an issue of international concern
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to policy makers, practitioners, and researchers across a range of academic
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disciplines. Recent estimates suggest that globally one third of food never
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reaches a human stomach (FAO, 2011), and global food waste is associated with
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large amounts of greenhouse gas emissions (FAO, 2013). Growing political and
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public consensus around the urgency of these challenges has provided the
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impetus for governments, regions, cities, businesses, organisations, and citizens
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to act. Measures have been taken to reduce the amount of food waste
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generated in agriculture, aquaculture, fisheries, food processing and
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manufacturing (upstream), and in supermarkets, restaurants, schools, hospitals,
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and homes (consumption).
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Many food waste reduction targets have been set, including Sustainable
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Development Goal 12.3 which aims by 2030, to halve per capita global food
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waste at the retail and consumer levels and reduce food losses along production
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and supply chains, including post-harvest losses (Lipinski et al., 2017).
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One of
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the key challenges facing many actors working in this area is deciding where and
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how to focus their efforts most effectively to reduce food waste. For each area of
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the food system (Horton, 2017), there are a number of potential strategies
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The Sustainable Development Goals are a collection of 17 global goals set by the United Nations General
Assembly in 2015. The SDGs cover social and economic development issues including poverty, hunger, health,
education, global warming, gender equality, water, sanitation, energy, urbanization, environment and social
justice.
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(which are not mutually exclusive), with diverse examples including: improved
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communication of forecasting between retailers and agricultural producers;
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public information campaigns, programmes to increase skills in the home or
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workplace; and changes in how food is packaged and sold. Within each of these
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strategies, there are numerous decisions to be made by policy makers and
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practitioners that could influence the effectiveness of interventions in preventing
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food from being wasted.
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The aforementioned where can also be geographic in focus: a local area, region,
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country or globally. Recent quantification of global food waste highlights a split
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between developed and developing countries. In developing countries, the vast
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majority of food waste occurs in primary production and within the supply chain
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for example in sub-Saharan Africa where more than 90% of food waste occurs
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prior to the consumption phase (FAO 2011). In contrast, in so called developed
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countries, the largest single contribution is reported to come from the
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consumption stage with much of that at the household level, e.g. in Europe,
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around 50% of wasted food is estimated to come from households (Stenmarck
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et al., 2016). There is clearly a need for researchers, policy makers, and
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practitioners to understand how to prevent food from being wasted across the
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supply chain. For those working on the issue in developed countries, however,
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understanding how to influence food waste within the consumption phase
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and, in particular, in households, where the majority of food is consumed and
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wasted is important to make a meaningful impact (Porpino et al., 2016). Due to
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this, there is current policy focused on the household food waste reduction, yet
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as shown below the evidence base for is lacking.
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In order to enhance the understanding of how to influence food waste within
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the consumption phase, this paper set out to identify and categorise food-waste
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prevention interventions at the consumption/consumer stage. Growing
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attention to food waste is reflected in an increase in the volume of academic and
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grey
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literature on the topic. As a result, several bibliometric studies and meta-
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analyses of prior literature and studies can be found. Our review of these
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studies (Table 1) reports how and what each study revealed (Aschemann-Witzel
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et al., 2016; Carlsson Kanyama et al., 2017; Chen et al., 2015; Hebrok and Boks,
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2017; Porpino, 2016; Quested et al., 2013; Schanes et al., 2018; Thyberg et al.,
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2015; Xue et al., 2017). It can be noted that none of these studies reviewed the
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effectiveness of interventions aimed at preventing food waste in the
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consumption stages of the supply chain
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, although Schanes, Doberning, and
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Gӧzet (2018) do call for this to be carried out as an avenue of future research.
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Table 1 a summary of the nine bibliometric studies and meta-analyses that review
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food waste literature.
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See attached file
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Grey literature refers to non-peer reviewed literature such as reports, conference proceedings, doctoral
theses/dissertations, newsletters, technical notes, working papers, and white papers.
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I.e. where food is consumed such as in the household, and in hospitality and food service sectors.
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In the grey literature, there are many documents summarising a wide range of
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food-waste-related issues. However, to the best of our knowledge, there is no
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review of the effectiveness of downstream food-waste interventions.
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Four
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intervention studies were reviewed by WRAP (see appendix F of Parry et al.,
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2014). These were all from the grey literature and UK-based. Since then a
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number of further studies have emerged, the most important of which are
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mentioned in the discussion section below.
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In summary, there is no peer-reviewed study that has considered the
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effectiveness of interventions aimed at preventing food waste in the
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consumption stages of the food system. This represents a significant gap, which,
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if filled, could help support those working to reduce food waste in the developed
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world, providing knowledge of what interventions are specifically effective at
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preventing food waste. This paper fills this gap, reporting a rapid review of the
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food-waste literature from 2006 to 2017 focussing on downstream food-waste
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reduction interventions
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. Based on the findings, the paper then categorises the
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4
While this manuscript was in final stages of peer review, a review of downstream food waste interventions
between 2012-2018 was published by Stöckli et al. (2018b). It identified the same papers as identified by this
manuscript (with addition of 2017-2018 peer reviewed papers:(Qi and Roe, 2017; Romani et al., 2018; Stöckli
et al., 2018a) ), and came to similar conclusions regarding the need for systematic evaluation of interventions
between. The additional novelty of our paper is 1) situating a broader range of peer reviewed intervention
papers (2006-2016) within the broader food waste literature (see figures 1-5), and 2) our in-depth discussion
and proposal of standardised guidelines for intervention development.
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“Downstream” being a wide definition, but meaning the consumer side of the food system. Downstream
interventions could include interventions in supermarkets, hospitality and food service sectors (including food
served in education and healthcare, government etc.), and household consumption.
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successful interventions and discusses the components of a successful food
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waste reduction intervention.
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2 Methods
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The methodology for rapid reviews has emerged as a streamlined approach to
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synthesizing evidence in a timely manner rather than using a more in-depth
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and time-consuming systematic review (Khangura et al., 2012; Tricco et al.,
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2015). As discussed by Tricco et al., there is no set method for a rapid review;
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however, there are several common approaches. For this study, a rapid review
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was undertaken to provide fast and up-to-date information, responding to
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demand from the policy and academic community (c.f. Lazell and Soma, 2014;
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Porpino, 2016).
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We used Google Scholar to identify relevant papers using combinations of the
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following terms: Food waste, household, quantification, behaviour change,
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consumer, and downstream. The time period was restricted to January 2006
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until January 2017. This was a result of discussion with expert advisors and
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evidence from other bibliometric studies that food waste studies only began to
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be published from 2006/7 onwards (Chen et al. (2015), Hebrok and Boks (2017),
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Carlsson Kanyama, Katzeff, and Svenfelt (2017), and Schanes, Doberning, and
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Gӧzet (2018). This search enabled the inclusion of online first/only preprints of
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2017 journal articles. The search was restricted to English-language publications.
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Each paper was then mined using the Google Scholar “citation” function to
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explore the network of papers that have cited each paper. Each of these papers
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was then captured and explored via the process described above. Figure 1
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outlines our rapid review method, with 454 items narrowed down to 17 peer
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reviewed journal articles focussing on downstream food-waste reduction
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interventions.
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Though it is common in rapid reviews to use scoring criteria to sort and exclude
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papers on the basis of method or data quality, no such scoring method was
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used in this paper. This is due to the small number of studies found, and wishing
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to provide the food waste community with as comprehensive as possible
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assessment of recent intervention studies.
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It should also be noted that the waste reduction percentages reported here have
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been calculated from all studies that reported weights and changes to waste
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generation. The waste reduction percentages are not directly comparable with
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each other as they have differing functional units, i.e. per plate, per person
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(participating or general population), per organisation (kitchen and front of
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house), per total weight of waste, etc.), or differing time scales (for data
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collection or experiment duration).
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Figure 1 Outline of our rapid review methodology
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3 Results
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3.1 Broad rapid review
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The rapid review identified 292 downstream food waste articles that were
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published in 39 journals between 2006 and 2017.
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From 2006, the number of downstream food waste articles published yearly
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increased rapidly as greater attention was given to the challenge of food waste,
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with the largest spike in articles that quantify food waste (Figure 2) occurring in
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2013 after the publication of reports highlighting the global issue (Institution of
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Mechanical Engineers, 2013; Lipinski et al., 2013). Out of the articles surveyed,
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only 17 (5%) feature applied downstream food waste reduction interventions.
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The most popular methodologies (Figure 3) used in the rest of the downstream
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food waste studies include surveys (n=80, 27%), reviews (n=77, 26%) and Life
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Cycle Assessment (LCA) modelling (n=50, 14%). Journal articles featuring
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qualitative, observational and ethnographic methods (following Evans (2014))
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are consistently published throughout the time period (n=18, 5%).
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48 countries or geographic areas were identified within in the broader
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downstream food waste literature (Figure 4) with 8 articles not identifying their
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geographic location, and 53 global studies. The next most studied areas were
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the USA (n=42), the UK (n=34), Sweden (n=21) and Italy (n=20). China (n=13) is
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the only developing country in the top 10 countries / regions studied. Our results
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show that global studies emerge after 2010 as data quality and accessibility
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increases. Countries that had an early identification of food waste as a social
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problem (including USA, UK and, Sweden) continue to publish prolifically.
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3.2 Intervention studies
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The seventeen journal articles focussing on downstream food-waste reduction
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interventions were first categorised by the main intervention types that were
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applied: information based, technological solutions, and policy/system/practice
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change. Journal articles can be in more than one category if multiple
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interventions were used (either applied separately or together). Table 2 provides
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a detailed summary of each intervention and paper.
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Table 2 a summary of the 17 journal articles found with interventions that achieved
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a food waste reduction
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See attached file
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The seventeen articles with applied interventions were found in sixteen journals
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covering nutrition and health (5 journals), psychology and consumer behaviour
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(5), environmental (3), human computer interactions (2), food (1) and economics
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(1). The majority of these articles were published in relatively ‘low’ impact factor
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journals (under impact factor 3)
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.
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Within the applied downstream food waste reduction interventions ten
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countries feature, with the USA being the site for 6 articles, 3 in the UK (one of
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which is a cross country comparison with Austria), and 2 in the Netherlands. The
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geographic spread of these 17 articles is focused on the global north, with
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Thailand the notable exception.
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The areas of study for the seventeen applied downstream food waste reduction
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interventions are focused on households and the community (n=6), hospitality
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and hotels (n=5), and educational establishments (n=6). This is a much narrower
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field of study than what is found across the rest of the downstream food waste
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literature with 8 categories of intervention area identified in Figure 4.
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This is also a representation of the cross-disciplinary and evolving nature of food waste research. In the social
sciences an Impact Factor of 3 would be quite high. However, in other fields, an Impact Factor of 3 could be
considered “low”.
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Information-based interventions ((Cohen et al., 2014; Devaney and Davies, 2017;
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Dyen and Sirieix, 2016; Jagau and Vyrastekova, 2017; Kallbekken and Sælen,
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2013; Lim et al., 2017; Manomaivibool et al., 2016; Schmidt, 2016; Whitehair et
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al., 2013; Young et al., 2017)) are where information was provided to change the
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behaviour of the target group i.e. households (Devaney and Davies, 2017),
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hotel managers and diners, (Kallbekken and Sælen, 2013) and social media users
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(Young et al., 2017). Various ‘delivery’ methods were used including information
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campaigns (Manomaivibool et al., 2016; Schmidt, 2016) and cooking classes
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(Dyen and Sirieix, 2016).
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The success of these interventions varied. A student-focused education
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campaign (Martins et al., 2016) resulted in a 33% waste reduction in main dishes,
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while the Home Labs intervention (a collaborative experiment with
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householders) led to an overall reduction in food waste generation of 28%
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(Devaney and Davies, 2017). New hotel signage reduced food waste by 20%
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(Kallbekken and Sælen, 2013). E-newsletter use resulted in 19% reduction in self-
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reported food waste in the home (Young et al., 2017). Schmidt’s information
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campaign resulted in a 12% perceived (self-reported) improvement in food
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waste reduction in the home (Schmidt, 2016). Whitehair et al.’s information
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prompt resulted in a measured 15% food waste reduction in a university
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cafeteria, while portion advertising information also resulted in greater uptake
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of smaller portions (up to 6% from 3.5%) (Jagau and Vyrastekova, 2017).
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Technological solutions ((Devaney and Davies, 2017; Ganglbauer et al., 2013;
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Lazell, 2016; Lim et al., 2017; Wansink and van Ittersum, 2013; Williamson et al.,
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2016a; Young et al., 2017) involve the introduction or modification of
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technologies and/or objects that seek to alter the behaviours around food
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(waste). These included changes to plate or portion sizes (Williamson et al.,
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2016b) or the introduction of fridge cameras or food sharing apps (Ganglbauer
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et al., 2013). Only plate and portion size studies have quantified waste reduction.
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The largest reported waste reduction (57%) was due to shifting to smaller plate
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sizes, although in this study there was also a 31% decrease in the amount of
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food consumed via the plate size shift (Wansink and van Ittersum, 2013).
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Other
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studies have reported a 19% reduction in food waste due to reduction in plate
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size (Kallbekken and Sælen, 2013), and a 51% reduction in food waste was
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achieved by using permanent rather than disposable plates (Williamson et al.,
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2016a). A 31% reduction in french fries waste was enabled by moving to smaller
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portion sizes (Freedman and Brochado, 2010).
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Policy/system/practice change (Cohen et al., 2014; Dyen and Sirieix, 2016;
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Freedman and Brochado, 2010; Kallbekken and Sælen, 2013; Martins et al., 2016;
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Schwartz et al., 2015) is where polices or systems are altered and the population
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changes food waste behaviours (or practices). Two articles involved changing
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school dietary guidelines, which resulted in a 28% (Schwartz et al., 2015) and
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14.5% (Cohen et al., 2014) vegetable waste reduction, while changing how
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schools and students were taught about food waste resulted in a 33% waste
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reduction from main dishes (Martins et al., 2016). These results indicate that diet
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reformulation and healthy eating can be part of food-waste reduction strategies.
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In the seventeen journal articles with interventions, five relied on self-reported
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(usually survey-based) measurements of food waste (a method that is relatively
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low-cost but suffers from substantial biases (World Resources Institute, 2016)).
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One paper did not disclose any waste weights, while another two estimated food
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waste via visual analysis or pictures. The remaining nine used weight-based
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waste measurement. It is a challenge to accurately quantify food waste
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prevented, largely due to the costs of waste measurement (especially in the
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home). The cost of waste measurement could explain why only 123 of the 292
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journal articles (42%) identified by the broader rapid review include some
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Note had observational measurement and weight base measurement of waste in
different experiments.
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quantification of food waste generation/ diversion/ reduction. Due to this
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reliance on self-reporting, only the accuracy of the three plate-change/size-
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reduction interventions can be assessed with any certainty (Kallbekken and
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Sælen, 2013; Wansink and van Ittersum, 2013; Williamson et al., 2016a). The
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comparative measurement of these studies is also not directly comparable as
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the methods of weight measurement and the unit of measurement vary (i.e. per
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plate or aggregated total waste), and time intervals (study duration, number of
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observations etc.) differ between each study as reported in Table 2.
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Around a third of these studies (5 articles) do not integrate any theoretical
305
framework or disciplinary orientation into their experimental design. Those that
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do are typically single theory in nature, and do not interact with the broader
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food waste literature. Theoretical frameworks and disciplinary orientations in
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the downstream intervention articles include Social Practice Theory; Behavioural
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Economics (nudge-approaches such as visual prompts), Transformative
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Consumer Research, pro-environmental behaviour change, behaviour change
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determinants, and the integrative influence model of pro-environmental
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behaviour.
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17
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Figure 2 Downstream food waste studies with quantified results per year, 2006-2017, n=130.
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0
5
10
15
20
25
30
35
40
45
2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
# journal articles
Other Study with quantified results Applied intervention with quantified results
18
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Figure 3 Methods used and numbers of downstream food waste studies published per year 2006-2017, n= 368.
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319
19
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Figure 4 Areas of study and numbers of downstream food waste studies published per year 2006-2017, n=304, (generalist review studies
321
excluded).
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323
0
20
40
60
80
100
120
140
160
Education (n=19) Hospital (n=2) Hospitality (n=10) Household (n=152) Retail (including
dumpster diving)
(n=27)
Whole Supply Chain
(n=93)
# of journal articles
Other Study Applied intervention
20
324
Figure 5 Geographic distribution of downstream food waste studies, the ten most prolific geographic areas, and all other countries. Note muli-
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country studies classified as “global” for this graphic 2006-2017, n=324
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0
10
20
30
40
50
60
70
80
90
# of journal articles
Other Study Applied intervention
21
4. Discussion of themes and policy implications
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In light of the above results, in this section we provide an overview of the
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methodologies, theoretical lenses and types of interventions employed in both
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the academic and grey literatures, and then recommended a series of
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recommendations or principles for organisations undertaking intervention
331
studies relating to food waste prevention related to the consumption stages of
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the supply chain.
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4.1 Methodologies
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Although there has been a rapid increase in articles that quantify or investigate
335
downstream food waste since 2006, there have been only 17 peer-reviewed
336
journal articles that feature downstream interventions that resulted in a food
337
waste reduction. Of these, nearly 30% (5 articles) used self-reported methods to
338
measure food-waste reductions, while another two estimated food waste via
339
visual analysis or pictures. Due to the methods used, the results from these
340
studies should be interpreted with caution (as indeed many of their authors
341
note); in these cases, a claimed reduction in food waste should not be read as an
342
actual reduction. Furthermore, 16 of the 17 interventions occurred in developed
343
countries and most interventions have focused on small groups with time-
344
limited evaluations.
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Part of this limited methodological development may be due to previous food
346
waste research having had limited cross-pollination between disciplines, both in
347
22
terms of substantive questions as well as in theoretical development. Many
348
researchers tend to rely on the theories they are comfortable with, resulting in a
349
“silo”-ing not only of theories that could be useful in explaining food waste, but
350
regrettably also a “silo”-ing of substantive findings related to actually reducing
351
such waste. Further research is required to map the literature (and food waste’s
352
theoretical developments further) to understand if this is the case.
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4.2 Theoretical lenses
354
The absence of explicit reference to theory means that readers are left to infer
355
connections between cause and effect in food waste behaviours or that
356
connections are imputed without explicit justification. Nearly 30% (5 articles) of
357
the downstream intervention studies did not mention a theoretical framework.
358
Of those that did, this was often not a key part of the paper or research design.
359
This is an interesting finding: on the one hand, it could imply that those working
360
on food-waste interventions are not aware of theoretical frameworks developed
361
for interventions in other domains; on the other hand, it could imply as
362
discussed by Quested et al. 2013 that food-waste prevention in consumption
363
settings is very different from other areas of behaviour change (see also Evans et
364
al. (2017)) and that many of the theories developed elsewhere are of limited
365
value without further development. The lack of theoretical integration into food
366
waste intervention design may also imply that theoretically rich accounts of
367
household food waste (for example Waitt and Phillips (2016)) have yet to fully
368
consider the implications of their analysis for interventions. We suggest that
369
23
there is a need for greater integration of theory and previous research findings
370
into the design of interventions. We also suggest that there is need to discuss
371
how different theoretical frameworks, disciplinary perspectives and
372
methodological techniques could combine to contribute to the reduction of food
373
waste. Would it, for instance, be possible to combine a qualitative account of the
374
social practices that generate food waste with quantitative tools that model the
375
effects of different interventions?
376
4.3 Intervention types
377
Reduction methods such as improved information (Manomaivibool et al., 2016)
378
or changes to plate type and size (Lazell, 2016; Wansink and van Ittersum, 2013;
379
Williamson et al., 2016a), portion size (Freedman and Brochado, 2010), or menu
380
composition (Cohen et al., 2014; Martins et al., 2016; Schwartz et al., 2015), all
381
accept that their effectiveness may be due to greater consumption of the food,
382
or shifts in the types of foods consumed and wasted. That is, as has been
383
observed in other interventions studies, there may be unintended consequences
384
(Peattie et al., 2016) that need further investigation. If this unintended shift is
385
towards the overconsumption of unhealthy foods or at the expense of healthy
386
foods, this could lead to negative health outcomes. For this reason, attention
387
must be given to communicating and encouraging people to monitor portion
388
size rather than reducing food waste at the expense of public health. However
389
some of the reviewed studies, indicate that some interventions result in a
390
reduction in consumption alongside waste prevention (Kallbekken and Sælen,
391
24
2013; Wansink and van Ittersum, 2013
8
; Williamson et al., 2016a). Further
392
research is needed to understand which (healthy or unhealthy foods) are
393
involved in this consumption shift and waste reduction. Moreover, it could be
394
the case that many of the unintended consequences could be due to a lack of
395
understanding around causal mechanisms and supporting theoretical
396
frameworks. If this is the case, further engagement with theory-based
397
evaluations would be an obvious solution.
398
Cooking classes (Dyen and Sirieix, 2016), additional technologies such as fridge
399
cameras (Ganglbauer et al., 2013) or apps (Lazell, 2016; Lim et al., 2017), and
400
advertising and information campaigns (Young et al., 2017) were all reported as
401
being effective but with no accurate quantification provided. This is worrying as
402
all these methods are now being proposed by peer reviewed studies as options
403
to reduce food waste with no reproducible quantified evidence to assure
404
credibility or long-term effectiveness. Future research and resources are needed
405
to test these interventions with accurate measurement methods.
9
406
8
The impact of Wansink and van Ittersum’s research may have been
affected by recent allegations of poor academic practices, with two other
publications by Wansink and van Ittersum having had corrections published
since the allegations were made (Etchells and Chambers, 2018; van der Zee,
2017).
9
It is worth noting that preventing food becoming wasted (e.g. via
preventing food waste at source, feeding to other people, etc.) may be more
effective than diverting food that has already been categorised as waste away
from landfill and incineration to other waste destinations higher up the food
waste hierarchy (e.g. composting, anaerobic digestion). This is because, for a
25
For many organisations working on food-waste prevention, they would like to
407
affect change across relatively large populations (e.g. a country, city or state /
408
province / county). Therefore, to assess the appropriateness of interventions,
409
these organisations require information on their cost effectiveness, how easy
410
they are to scale up and whether they can be tailored to different ‘audiences’
411
within the population. However, this additional information is currently non-
412
existent in the literature.
413
In addition, many of interventions that feature advertising or an information
414
campaign did not provide enough detail to analyse and correlate the content
415
type, and tone (positive, negative, shocking etc.), with the effectiveness of the
416
campaign. This is an avenue for future research.
417
4.4 Links to other literature
418
As noted above, academic literature is not the only source of research and
419
evidence relating to downstream food waste. Although not a primary focus of
420
this review, the authors are aware of a small number of intervention studies in
421
the practitioner/policy-focused ‘grey’ literature. For example, during 2016, the UK
422
supermarket chain Sainsbury’s undertook a year-long trial using a range of
423
methods to prevent or reduce food waste in the home (Waste less, 2016). These
424
interventions were a mix of information (via Food Saver Champions), technology
425
given weight of food waste, preventing it being wasted usually has a much larger
positive impact socially, environmentally and economically than diverting it
from (Blatt, 2017; Garrone et al., 2014; Moult et al., 2018; Quested et al., 2011).
26
(fridge thermometers, smart fridges and cameras, apps etc.) and
426
policy/system/practice change (introducing tenant welcome packs, new food
427
waste events and school programmes). Some of these interventions included
428
actual measurement of food waste (via audits or Winnow/Leanpath systems
10
)
429
resulted in between 18%-24% food waste reductions. Other interventions relying
430
on self-reported measures, resulted in between 43% and 98% food waste
431
reductions for the homes that took part.
432
In the USA, a partnership called Food: Too Good To Waste reported the findings of
433
seventeen community-based social marketing (CBSM) campaigns aimed at
434
reducing wasted food from households (U.S. EPA Region 10, 2016). These
435
interventions were mainly information interventions, which introduced new
436
information and tools into households. Measurement of food waste was
437
conducted before and after the campaigns using a mixture of self-reported
438
audits (participants weighing their own waste) and photo diaries. The results
439
showed measured decreases between 10% and 66% in average household food
440
waste (7% to 48% per capita) for fifteen of the seventeen campaigns. The
441
successful interventions were between 4 and 6 weeks long, with samples of
442
between 12 to 53 households.
443
10
Winnow and Leanpath offer in-kitchen ‘smart’ food waste weighing services for the hospitality sector.
Winnow was trailed in home as part of the Sainsbury’s intervention
27
The EU project FUSIONS reported several waste prevention strategies focused
444
on social innovation (Bromley et al., 2016). Though most interventions involved
445
food redistribution, the Cr-EAT-ive intervention worked with school children
446
(n=480) and their parents (n=207) to reduce food waste in the home and
447
promote key food waste prevention behaviours. The results from 18 households
448
(of 29 households) that completed the kitchen diary activity managed to reduce
449
their food waste by nearly half if scaled (with the intervention effects kept
450
constant) to a yearly quantity, this would equal a reduction of 80 kg per
451
household per year. However, it is not known how long the intervention effects
452
would last for, the longer term engagement/attrition rates of children and
453
households, and if some of this reduction was caused by the effect of
454
measurement itself (rather than the intervention).
455
During 2012/13, WRAP ran a food-waste prevention campaign aimed at London
456
households (WRAP, 2013a). These interventions were mainly information
457
interventions. This was evaluated via waste compositional analysis and reported
458
a 15% reduction in household food waste. However, as noted by the authors,
459
some of this reduction could have been the result of the research itself (i.e.
460
households being influenced by participating in a detailed survey).
461
Between 2007 and 2012, household food waste in the UK reduced by 15%
462
(WRAP, 2013b). However, it is not possible to isolate the effect of different
463
interventions that were running over this period. In addition, economic factors
464
28
increasing food prices and falling incomes in real terms are likely to have
465
contributed to this reduction (WRAP, 2014b).
466
These examples from the grey literature do not alter the main conclusions of
467
this review: that there is a lack of research surrounding interventions designed
468
to reduce the amount of food waste generated, and a lack of evidence of the
469
ease with which it is possible to scale up previous smaller interventions.
470
It is important for researchers, policy makers and practitioners working to
471
prevent food waste that this evidence gap is filled with research of suitable
472
quality. Below, we offer guidance and general principles that, if followed, will
473
improve the quality of this emerging field of study, and allow the effectiveness of
474
interventions to be compared and fully understood. Building on the
475
shortcomings of previous studies and improvement suggestions as outlined by
476
Porpino, (2016), we categorise these recommendations into 5 strands:
477
intervention design; monitoring and measurement; moderation and mediation;
478
reporting; and consideration of systemic effects. These recommendations are
479
based on our review of the literature and the authors’ prior knowledge and
480
experience regarding food waste intervention design and application.
481
4.5 Recommended principles for effective interventions
482
This section presents a series of recommendations principles for
483
organisations undertaking intervention studies relating to food waste prevention
484
29
related to the consumption stages of the supply chain. We then discuss
485
interventions with potential with reference to our results.
486
1 Design of intervention
487
We recommend that an initial decision should be made about whether the study
488
is focusing on an ‘applied’ intervention and/or one used to develop
489
understanding of the intervention process. This should be explicitly stated in the
490
methods and (experimental or intervention) design.
491
An applied intervention aims to reduce food waste across a given population or
492
sub-population (i.e. it is scalable, with a clear target audience). For the
493
interventions reviewed this was not always the case. For a communications-
494
based intervention, this would need to be similar to the type and tone of
495
material that could be used by a campaign group or similar organisation. If it
496
were a change to food packaging, for example, it would need to be a change that
497
could be adopted by a wide range of food retailers (e.g. it would have to ensure
498
food safety and other packaging attributes whilst still being cost-competitive). To
499
ensure that the ‘quality’ of such interventions is sufficient for the study,
500
researchers should consider partnering with appropriate organisations with
501
expertise in, for the above examples, developing communications materials or
502
packaging technology. Partnerships also ensure that work is not being carried
503
out in this area by organisations at cross purposes. In addition, applying
504
techniques such as logic mapping (based on theory of change see The
505
30
Travistock Institute, 2010) can aid the design process to ensure that the
506
intervention has the best possible chance of meeting its stated aims (i.e.
507
preventing food waste in the home or other downstream settings). In addition,
508
logic mapping and theory of change can enable the research to investigate how
509
change occurs, as well as quantifying the degree of change. Much of this
510
research and methods development has already been carried out on general
511
behaviour intervention strategies within the field of environmental psychology,
512
see Steg and Vlek (2009), or Abrahamse et al. (2005).
513
In contrast to ‘applied’ interventions, some research of interventions is designed
514
to understand and evaluate how different elements of an applied intervention
515
work. For these interventions the criteria discussed above are not strictly
516
applicable. These types of studies may aim to understand which element of a
517
larger intervention is responsible for the change e.g. it may compare a range of
518
campaign messages drawn from different disciplines and theories under
519
controlled conditions. In such cases, it is not necessary that this module is
520
scalable, although it would help future application of the research if the
521
intervention studies needed only small modification to be deployed on a larger
522
scale.
523
We also note that many studies use convenience sampling, which is likely to
524
result in a group of study participants who are not representative of the wider
525
population (or target populations within it). It will often include a sample with
526
31
higher than average levels of education and income (Schmidt, 2016). Therefore,
527
where possible, the design of the study should be considered to ensure that the
528
sample is as representative of the population of interest as possible, ideally
529
through random selection or, failing that, some form of quota sampling.
530
Previous discussion has indicated a lack of theory involved in the development
531
of interventions; we feel that this stage is a key part of the intervention design
532
process where theoretical understanding could be used to help develop more
533
effective interventions.
534
2 Monitoring and measurement methods
535
Measurement of outcomes and impact of the interventions is challenging.
536
Objective measures of food waste such as through waste compositional
537
analysis of household waste are relatively expensive and are more easily
538
deployed in geographically clustered samples (World Resources Institute, 2016).
539
In addition, these methods only cover some of the routes by which wasted food
540
can leave the study area, and so food and drink exiting the study area via the
541
drain, or food that members of a household/school etc. waste in locations
542
outside of the study area are not covered by such measurement methods
543
(Reynolds et al., 2014). However, where there is an opportunity to deploy
544
methods involving direct measurement, it is beneficial as these are generally
545
more accurate and also minimise the amount of interaction with the household,
546
reducing the impact of the measurement itself on behaviour.
547
32
Most of the other methods rely on some form of self-reporting e.g. diaries,
548
surveys, self-measurement of food-waste caddies, taking photographs. All of
549
these methods generally give lower estimates of food waste in the home
550
compared to methods involving direct measurement (e.g. waste compositional
551
analysis) when comparison is made for a given waste stream. For diaries one
552
of the more accurate methods around 40% less food waste is reported
553
compared to waste compositional analysis (Høj, 2012). More recent analysis has
554
shown that measuring food waste via caddies or photos gives similar results to
555
diaries (Van Herpen et al., 2016). This lower estimate is likely due to a range of
556
factors: people changing their behaviour as a result of keeping the diary (or
557
other method), some items not being reported, and people with on average
558
lower levels of waste completing the diary exercise (or similar measurement
559
method).
560
Few studies discussed the problems presented by self-reported data. However,
561
issues relating to self-report are discussed more extensively in the
562
environmental (in particular recycling) and social marketing literature where self-
563
reported measures of perceptions and behaviours are often considered
564
unreliable (Prothero et al., 2011) and a gap is expected between self-reported
565
and actual behaviour (Barker et al., 1994; Chao and Lam, 2011; Huffman et al.,
566
2014). This should be discussed with reference to each intervention to
567
understand the scale of uncertainty present in the results.
568
33
This means that those monitoring interventions have some difficult decisions to
569
make: methods that are accurate may be unaffordable while methods that are
570
affordable may be subject to biases that can compromise the reliability of the
571
results. For instance, a communication-based intervention monitored using
572
diaries may increase the level of underreporting of waste in the diaries, which
573
could be erroneously interpreted as decreasing levels of food waste. This could
574
have substantial and costly implications for those deploying the (potentially
575
ineffective) food waste intervention in the future.
576
To address these issues, studies should try to obtain the requisite funding to be
577
able to measure food waste directly (e.g. by waste compositional analysis). This
578
may mean fewer studies, or studies comprising a panel of households, in which
579
food waste is regularly monitored (with the householders’ consent), creating the
580
possibility of longitudinal studies. To make such an approach cost effective, this
581
would likely require a consortium of partners, who could explore the emerging
582
data to answer multiple research questions.
583
For studies using self-reported methods, these should carefully consider the
584
design of the monitoring to ensure that reporting is as accurate as possible. The
585
smaller the gap between actual and measured behaviour arising, the less
586
measurement artefacts can influence the results and the ensuing conclusions.
587
Recent work calibrating these self-reported methods has been undertaken (Van
588
Herpen et al., 2016) and this type of information should be used in the
589
34
measurement design. Further advances in calibration, especially in the context
590
of intervention studies (i.e. is the level of underreporting stable during typical
591
interventions?) would also help to improve monitoring and measurement.
592
In some circumstances, effects relating to self-reported measurement methods
593
can be mitigated by the careful use of control groups. Where possible these
594
should be used, as levels of food waste may change over time, influenced by
595
food prices, income levels and other initiatives aimed at preventing food waste.
596
However, adding a control to the research will increase costs and there can be
597
practical difficulties in creating equivalent (e.g. matched) control groups,
598
especially where samples are geographically clustered.
599
This discussion raises wider questions about the most appropriate evaluation
600
approach and method, where different research designs may be fit for different
601
intervention purposes. For example, where the priority is to measure an impact
602
or effect, an experimental or quasi-experimental method should be considered,
603
while assessing multiple outcomes and causal mechanisms may require a non-
604
experimental research design (e.g. including qualitative methods). If the purpose
605
is to decrease food waste by X percent, then the level of food waste should be
606
measured over the course of the intervention (and beyond, to understand the
607
longevity of the effect). In some contexts however, the purpose is to achieve a
608
precursor to food-waste prevention (e.g. increased reflection on food waste, or
609
to improve cooking skills), which may eventually lead to decreased food waste.
610
35
In the latter cases, evaluation may want to focus on measuring the level of
611
reflection, cooking skills, etc. to assess the effectiveness of the intervention.
612
We acknowledge that research on food waste is an interdisciplinary field. This
613
can be a virtue, with many perspectives tackling this ‘wicked problem’. However,
614
it also means that different disciplines have different conventions and priorities,
615
e.g. over the experimental scale or duration, and measurement of uncertainty
616
vis-à-vis determining how much food is actually wasted. These differences
617
should be acknowledged in order that more accurate and consistent
618
measurement takes place.
619
3 Moderation and mediation
620
In addition to changes in the level of food waste, intervention studies may
621
benefit from measuring changes in other quantities. This may help understand
622
whether the intervention is effective, especially in situations where
623
measurement of food waste is imperfect. Additional dietary (purchase and
624
consumption) data can be collected and would provide greater certainty
625
regarding food waste generation statistics. Additional waste generation data
626
(beyond just food waste) could also be useful to help understand wider waste
627
generation issues and drivers.
628
Examples of other measurements may include ‘intermediate outcomes’:
629
depending on the intervention and how it operates, there may be intermediate
630
steps that would need to occur for the intervention to operate as envisioned (as
631
36
articulated in the intervention’s logic map see stage 1). This is an approach
632
often used in social marketing where changes in behaviour that are difficult to
633
measure might instead track changes in knowledge, beliefs and/or perceptions
634
(Lee and Kotler, 2015). For instance, an educational campaign aimed at
635
increasing the level of meal planning prior to people going shopping could
636
monitor the change in people’s awareness of educational material and their
637
(self-reported) level of meal planning. These types of learning processes are
638
slower, and are more difficult to assess in the short term, but they might still be
639
successful and might achieve more long-term effects. Triangulation data is not
640
sufficient in itself to state whether an intervention was successful, but can
641
provide supporting evidence. Such analysis of moderating or mediating effects is
642
useful and often uncovers interesting insights that would not be highlighted if
643
this analysis were not conducted.
644
Observational analysis and measurement can provide insight into why the
645
intervention works. By observing the intervention in action, this allows insight
646
into the intervention itself, in addition to the effects of the intervention. This
647
expands upon the intervention proposals of Porpino et al. (2016) by not only
648
measuring the main objective, but also the intervention process, reflecting
649
recent studies that highlight the importance of both process and outcome
650
evaluation in interventions (Gregory-Smith et al., 2017).
651
37
4 Reporting
652
In order to make any study replicable and repeatable, there should be sufficient
653
information provided about the intervention and the measurement methods to
654
be able to replicate both elements.
655
The reporting of food waste has become standardised with the publication of
656
the Food Loss and Waste Accounting and Reporting Standard (World Resources
657
Institute, 2016). This standard was designed for countries, businesses and other
658
organisations to quantify and report their food waste; it was not developed with
659
intervention studies in mind. However, many of the principles it describes are
660
useful in this context: studies should clearly describe the types of food waste
661
measured (e.g. just the wasted food (i.e. edible parts) or including the inedible
662
parts associated with food such as banana skins; the destinations included (e.g.
663
only material bound for landfill, or also food waste collected for composting); the
664
stages included (e.g. in a restaurant, only plate waste, or also kitchen waste).
665
A description of the details of how the quantification method (e.g. for waste
666
compositional analysis) was undertaken is crucial, alongside what the study
667
classified as food waste and which waste destinations were included. Details of
668
the sample sizes and how they were drawn should also be covered. Data
669
reporting should include the average weight, alongside appropriate measures of
670
the spread of the data (e.g. standard deviation, standard error, interquartile
671
ranges). Detailed waste composition data, where available, should also be
672
38
provided. Changes of food waste between time periods should be reported as
673
both weights and percentages, with significance and p values clearly stated. This
674
minimum level of comparable data was lacking in many of the papers reviewed,
675
with only 12 (70%) of the papers providing some statistics or statistical analysis,
676
2 (11%) providing waste composition analysis, and 5 (29%) providing results or
677
analysis of food waste reduction from multiple time periods post intervention.
678
To allow for the actual measurement of food waste rather than participants’
679
perceptions, several methods of disruptive thinking and scaling innovations
680
could be considered. One such innovation is smart bins (Lim et al., 2017). This
681
allows automatic recognition of food waste type and their weighting which can
682
help remove uncertainty in self-reporting of food waste. Such data from smart
683
bins (and also smart fridges and online shopping devices) could be shared with
684
local authorities, policy organisations, community groups and industry, enabling
685
planning and optimisation of food waste management locally. Smart bins are
686
already being used in the hospitality industry to track food waste (e.g. products
687
such as Winnow or Leanpath).
688
689
5 Considering systemic effects
690
None of the intervention studies in the review considered systemic effects.
691
Systemic effects, like the rebound effect (i.e. improved technology to reduced
692
environmental impacts may, due to behavior and other system effects, result in
693
39
no change, or increased environmental impacts. See Khazzoom (1987) or Sorrell
694
and Dimitropoulos (2008) for further discussion), are relevant and vital to
695
consider for measures that are saving money or time for the consumer. Several
696
of the measures presented above are not only measures that can lead to
697
reduced food waste, and thus reduced environmental impact, but also measures
698
that could lead to reduced costs, both for consumers and for other actors in the
699
food chain. Since less food needs to be wasted, less food needs to be bought.
700
Reduced costs can be an advantage from a private economic point of view, but it
701
can also in the worst case, lead to further negative environmental effects. The
702
money saved can be used for other types of consumption and perhaps
703
increased environmental impact. These type of system effects, are sometimes
704
called second order effects or rebound effects (Arvesen et al., 2011; Börjesson
705
Rivera et al., 2014). How consumers choose to spend the money saved
706
determines what the overall environmental impact will be. If the money or time
707
is used for something more environmentally friendly, then the effect will be
708
positive, and the environmental potential will be realised. But if instead the
709
money is used for activities with more environmental impact, such as a food
710
with higher environmental impact or, taking a trip with a fossil fuel driven car or
711
even a flight, then the environmental impact is negative. Sometimes the second
712
order effect exceeds the environmental benefits of the intervention, and the
713
situation becomes worse than it was from the outset (known as the Jevons
714
40
paradox (Alcott, 2005)). This means that measures for reduced food waste do
715
not always only produce the desired results with regard to environmental
716
impact, but also more unintended side effects.
717
This does not mean that measures to reduce food waste are ineffective, but that
718
second order effects need to be taken into account. Otherwise, there is a risk
719
that interventions might not be efficient in a systems perspective. Due to the
720
complexities involved in considering full systemic effects, the practicality of
721
detailed analysis must be weighed up for each intervention. The use of theory-
722
based interventions, with extended logic mapping (e.g. with systems mapping as
723
discussed above) will be useful in enabling this detailed analysis, as the
724
theoretical background and logic mapping may be able to acknowledge cross-
725
boundary input and outcomes (but not necessarily assist with measuring them).
726
Ideally, Intervention studies, where possible, should collect data to monitor
727
these second-order effects, in addition to monitoring the direct impact on food
728
waste. However, as this may involve recording household spending (on food as
729
well as other expenditure) and food consumption, it will greatly inflate the cost
730
of studies and may not be possible. Another option is to, at least, identify risks
731
for second order effects, look for ways to minimize negative second-order
732
effects and maximize any potential positive effects of this nature.
733
41
4.6 Policy implications
734
According to our review, in spite of the shortage of downstream intervention
735
studies, there are still several evaluated interventions that have good potential
736
for use in a wider context. These include so-called low hanging fruits which
737
might not have a huge impact but also do not imply high cost, high maintenance
738
or side effects, or interventions that have been assessed and have produced
739
good results. One example of the former kind is to encourage guests at
740
restaurants and in large-scale households to adjust the portions to how hungry
741
they are (Jagau and Vyrastekova, 2017), or to take smaller portions at a buffet
742
and come back if you want more (Kallbekken and Sælen, 2013). This kind of
743
measure is relatively simple and inexpensive and could be combined with other
744
measures, such as for example a lower price for a smaller portion. Examples of
745
the latter kind, assessed with good results but with an economic cost, are the
746
interventions with smaller plates (Kallbekken and Sælen, 2013; Wansink and van
747
Ittersum, 2013).
748
A number of interventions use social media (e.g. Lim et al., 2017) and the
749
evaluated studies indicate that there is potential for this in particular as a way of
750
spreading knowledge and creating discussion and reflection. However, caution
751
must be taken as using social media to message the correct audience with
752
content that resonates has its own challenges due to audience segmentation.
753
Another intervention that is quite simple and can be done without major
754
investment in apps, is colour coding of shelving or sections in the refrigerator
755
42
(Farr-Wharton et al 2012). Similar initiatives have been tested in "Food: Too good
756
to waste" where the solution was even easier - with just a note in the fridge on
757
food to be eaten soon (U.S. EPA Region 10, 2016). More extensive campaigns
758
(e.g. U.S. EPA Region 10, 2016 and WRAP, 2013b) have also had good effects,
759
although it is difficult to estimate the impact of individual components of the
760
overall campaign. With a mix of complementary interventions and actors at local
761
level, this type of measure should have good potential given that the necessary
762
resources and commitment, which seems to have been the case in both the UK
763
and the United States.
764
5 Conclusion
765
This paper has summarised 17 applied food-waste prevention interventions at
766
the consumption/consumer stage of the supply chain via a rapid review of
767
academic literature from 2006-2017. This led to the identification of
768
interventions that could be deployed effectively at scale in the home (e.g. fridge
769
colour coding, product labelling, and information provision), and out of the
770
home (e.g. plate and portion size adjustment, changes to menus and nutritional
771
guidelines, and redesign of class room syllabus).
772
Our discussion has identified the weaknesses of the current literature; proposed
773
guidelines for the development of further food waste interventions, and set out
774
an agenda for further research:
775
43
Well-designed interventions covering a range of types (including longer
776
interventions and those exploring a raft of measurers),
777
Tested using carefully selected methods to understand the outcome of
778
the intervention and how it works (or not),
779
Adoption of higher sample sizes and representative sampling for
780
quantitative elements,
781
Replication studies in different countries
782
Consideration of systemic effects
783
Improved, more consistent reporting.
784
This is a novel and important addition to the researchers, policymakers and
785
practitioners tool kit. Our review found that the majority of current
786
interventions achieve only a 5% to 20% reduction in food waste. To achieve
787
Sustainable Development Goal 12.3 by 2030, (halve per capita global food waste
788
at the retail and consumer levels) these interventions (and others) need to be
789
combined, refined, tested further at different scales and geographies, and
790
adopted on a global scale.
791
792
44
Acknowledgements
793
Christian Reynolds and Liam Goucher are supported from the HEFCE Catalyst-
794
funded N8 AgriFood Resilience Programme and matched funding from the N8
795
group of Universities. Christian Reynolds has additional funding from NERC to
796
support an Innovation Placement at the Waste and Resources Action
797
Programme (WRAP) (Grant Ref: NE/R007160/1). Annika Carlsson-Kanyama,
798
Cecilia Katzeff and Åsa Svenfelt has funding from the Swedish National Food
799
Agency and MISTRA. Thanks to Richard Swannell, Mark Boulet, and Amy
800
Woodham, for discussions about the review process and the identification of
801
additional papers. Thanks to the two anonymous reviewers for their helpful
802
suggestions in refining the papers structure and argument.
803
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55
Online Appendix 1. Time series detail of Figures 3, 4, and 5.
Figure 3 Methods used and numbers of downstream food waste studies published per year 2006-2017, with time series
detail. n= 361.
0
5
10
15
20
25
30
35
40
2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
2006
2017
2006
2017
2006
2017
2006
2017
2006
2017
2006
2017
2006
2017
2006
2017
2006
2017
2006
2017
2006
2017
2006
2017
56
Figure 4, Areas of study and numbers of downstream food waste studies published per year 2006-2017, with time series
detail. n=297, (generalist review studies excluded).
0
10
20
30
40
50
60
Education (n=13) Hospital (n=2) Hospitality (n=10) Household (n=152) Retail (including dumpster
diving) (n=27)
Whole Supply Chain (n=93)
2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
2006
2017
2006
2017
2006
2017
2006
2017
2006
2017
2006
2017
57
Figure 5, Geographic distribution of downstream food waste studies, the ten most prolific geographic areas, and all other
countries, 2006-2017, with time series detail. n=317.
0
5
10
15
20
25
30
35
40
Global (n=53,
17%)
USA (n=38) UK (n=34) Sweden (n=21) Italy (n=20) EU (n=16) China (n=13) Australia
(n=12)
Denmark
(n=11)
Germany (n=9) Did Not
Identify (n=8)
All other
countries
(n=37)
2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
2006
2017
2006
2017
2006
2017
2006
2017
2006
2017
2006
2017
2006
2017
2006
2017
2006
2017
2006
2017
2006
2017
2006
2017
Table 1
58
Table 1
Paper
Sample
Analysis
methods
Aim
Measurement
Time intervals
Setting,
scope, search
words
Geography
Year
Results
Quested et al.,
(2013)
Resources,
Conservation
and Recycling
39 documents
cited, 12 WRAP
studies
research
synthesis, and
case study
Review of
insights about
food waste in the
home, which has
largely
emanated from
work funded by
the Waste &
Resources
Action
Programme
(WRAP)
2006 to 2012
Household
food waste
behaviours
UK
2013
Reviews
conceptualisations of food
waste, and the multiple
behaviours and practices
of food waste. Discussion
of how to integrate insights
into behavioural models
and the development of a
successful public-
engagement campaign.
Highlighted discussion
point that many
behavioural models, are
not designed for multiple,
complex behaviours such
as food waste.
Thyberg et al.,
(2015)
Environmental
Science &
Technology
62 waste
characterization
studies
Meta-analysis
and research
synthesis, use
of Google
search engine.
Quantification of
the US MSW
food waste
Determine if
specific factors
drive increased
disposal.
1989 to 2013
MSW, Food
waste, NOT
Food loss
USA
2015
The proportion of MSW
food waste increased with
time. The aggregate
proportion of food waste in
U.S. municipal solid waste
from 1995 to 2013 was
found to be 0.147 (95% CI
0.137−0.157) of total
disposed waste, which is
lower than that estimated
by U.S. Environmental
Protection Agency for the
same period (0.176).
Table 1
59
Chen et al.
(2015) Journal
of Cleaner
Production
2340 research
articles
Review and
bibliometric
analysis, use
of Web of
Science
database
Quantitative
analysis of peer-
reviewed articles
to summarize
food waste
publication,
identify the
research focuses
and hotspots,
identify the
trajectories of
research
(including
development of
theoretical and
practical
contributions and
future
challenges)
1997 to 2014
“Food waste*”
or “kitchen
waste*” or
“food residue*”
or “kitchen
residue*”
Global,
Engish
language
2015
The food waste literature
around biotechnology and
waste management was
larger than that around
waste reduction, with the
themes of clean energy,
treatment and valorization,
and management
innovation attracting
extensive attention during
the past decade. FW
research output is
distributed unevenly over
all countries. The majority
of research is published by
industrialized countries.
Discussion dominated by
methods for treating or
valorising food waste,
mainly in the upstream
stages of the supply chain
(reflecting the relative
amounts of research in this
area in the literature). The
literature on food-waste
prevention obscured.
Table 1
60
Aschemann-
Witzel et al
(2016) Journal
of Cleaner
Production
26 existing
initiatives
Case study
approach
Review into case
stuides to
understand how
to successfully
design future
interventons to
reduce
consumer-
related food
waste.
1998 to 2015
Case studies,
food waste
23 from
Europe,
one from
the US,
and two
from
Brasil.
2016
Multiple success factors
were identified. There are
three main types of
consumer food waste
initiatives: information and
capacity building,
redistribution , and supply
chain initiatives.
Collaboration and
knowledge sharing
(building upon prior
initiatives) are important to
the success of future
campaigns. Supply chain
change should ensure
growth in business
opportunities,
Redistribution initiatives
need to stress multiple
aims to get maximum
stakeholder engagement.
Information and capacity
building initiatives should
focus on the positive
aspect of valuing and
using the food (in a tasty
and fun/humorous way).
Focus tends to be on
either motivating conscious
choice and supporting
consumer abilities or
altering the choice context
towards providing
opportunities, both may be
possible together. Only 4
case studies targeted at
reducing downstream
consumer food waste. The
success of the
interventions was judged
by those involved in
delivering the intervention
and most had no estimate
of their actual impact on
levels of food waste.
Furthermore, these case
Table 1
61
Porpino (2016)
Journal of the
Association for
Consumer
Research
24 papers
Review.
Provide a
framework and
solutions for
conducting
future research
in the Food
Waste research
area
1975-2015
“wasted food”
consumer food
waste
Global
2016
Insights given for future
impactful research (i.e.
shopping habits, over
consumption, income, .
Provides future research
recommendations based
on previous studies. (Lack
of emotional study,
income, cultural factors,
marketing, survey analysis
and experiments,
quantification.) Need for
more ethnographic
observations,
measurements and
experiments.
Table 1
62
Xue et al.
(2017)
Environmental
Science &
Technology
202 publications
Review and
bibliometric
analysis, use
of Web of
Science and
Google
Scholar
A critical
overview of all
the existing FLW
data in the
current literature.
Sorting by Food
Supply Chain,
Food Commodity
Groups,
Geographical
and Temporal
Boundary.
1933 to 2014
Food Loss and
Waste
84
countries
(Global
scope)
2017
Most existing publications
are conducted for a few
industrialized countries
(e.g.,UK, USA). Over half
of publications are based
only on secondary data (
signalling high
uncertainties in the existing
global FLW database).
With these uncertainties,
existing data indicate that
per-capita food waste in
the household increases
with an increase of per-
capita GDP. Focused on
quantification and
measurement of levels and
types of food waste
mainly at the national level,
focussing on the sectors
with the most food waste.
Paper did not discuss
food-waste reduction
interventions, nor what has
been shown to be
successful in the literature.
Table 1
63
Hebrok and
Boks (2017)
Journal of
Cleaner
Production
112 scientific
sources
Review, use of
Oria and
Google
Scholar, with
additional
scoping of
reports from
ForMat,
WRAP, and
FUSIONS
Review what the
drivers of food
waste are, and
where can
designers
intervene in
order to
influence
consumers to
waste less food.
2000 to 2015
“Food waste”
in combination
with the words
“household”,
“packaging”,
“consumer”,
“behaviour”
and “design”.
Results
must be
written in
English,
the
resultant
were from
Western
Countries
2017
Reviews aspects of
consumer food waste
(consumer behaviour,
attitudes, beliefs and
values, quantifications and
compositional analyses,
waste prevention, and
design interventions).
Literature is more focused
on generating knowledge
about the problem than on
finding solutions. Little
knowledge of the actual or
potential effects on food
waste levels of design
interventions.
Carlsson
Kanyama,
Katzeff, and
Svenfelt
(2017), TRITA-
SEED-Rapport
2017:05
350 studies
Review/report,
english
language, use
of Google
Scholar and
Scopus.
Included peer
reviewed
publications,
conference
papers and
reports
Review of
interventions to
decrease
avoidable food
waste with the
focus on private
consumers
1987 to 2017
"food waste"
AND "behavior
change", "food
waste" AND
"intervention",
"food waste"
AND
"sustainable
consumption",
"food waste"
AND
"nudging".
Global,
Engish
language
2017
Studies reviewed use
various interventions E.g.
education and information;
apps, smaller plates.
Mostly, the evaluations of
the behaviour interventions
have only been carried out
using smaller groups of
people. Longitudinal
studies of their effects are
mostly missing.
Nevertheless, the studies
of interventions where
evaluations exist, indicate
a significant effect
regarding the decrease of
food waste as well as
raising households’
awareness and
encouraging their
reflection.
Table 1
64
Schanes,
Doberning, and
Gӧzet (2018)
Journal of
Cleaner
Production
60 articles
Systematic
literature
review, using
Web of
Science,
Scopus, and
GoogleScholar
Review and
analyse
evidence on the
factors impeding
or promoting
consumer food
waste. Discuss
the contributions
of psychology-
oriented
approaches as
well as social
practice theory.
1980 to 2017
“food waste”
AND
“consumer”,
and “food
waste” AND
“household”
Global,
Engish
language
2018
Food waste is a complex
and multi-faceted issue
that cannot be attributed to
single variables. Authors
call for a stronger
integration of different
disciplinary perspectives.
Current food waste
prevention strategies can
be designed around
determinants of waste
generation and household
practices. Discussion of
policy, business, and
retailer options for food
waste reduction, with
limited review of
effectiveness. Call for
review of effectivness to be
carried out as an avenue
of future research.
Table 2
65
Table 2
Paper
Sample
Setting
Waste
measureme
nt methods
Theory’s
used
Aim
Results
% of food
waste
reduction/
summary of
qualitative
findings
Intervention
category type
(Information,
Technology,
Policy/system/prac
tice change)
Measureme
nt Time
intervals
Yea
r
Geograph
y
1. Kallbekken
& Sælen (2013,
Economic Letters)
(Kallbekken and
Sælen, 2013)
52 hotels
(38 control
and 2 test
groups of
7).
Hospitali
ty
Hotels
reported
food waste
weights
(assumed
to be
gathered by
waste
audit)
No theories
discussed.
Using two
separate
non-
intrusive
‘nudges’ –
reducing
plate size
and
providing
social cues
based on
perceived
social
norms in
Hotels.
Both reducing plate
size and providing
social cues was
effective at
reducing food
waste in Hotels.
Plate size
reduction:
19.5% (p <
0.001),
Signage: 20.5%
(p < 0.001)
Information
Technology,
Policy/system/prac
tice change
"Study
duration:
2.5 months.
The 52 hotel
restaurants
recorded
and
reported
the amount
of food
waste daily
over the
whole
period."
201
3
Norway
Table 2
66
2. Young et al
(2017, Resources,
Conservation and
Recycling)(Young
et al., 2017)
4398
responded
to the
second
follow-up
survey
Househo
ld
Self-
reported
via online
survey of
participants
.
Drivers of
food waste,
social
influence
theory.
Using
traditional
and online
(social
media)
methods to
distribute
information
to
customers
of a large
UK retailer
to reduce
household
food waste
and disposal
frequency.
Online and social
media information
methods can be as
effective as
traditional methods
of information
dissemination. Note
that only the e-
newsletter
outperformed
exposure to
magazine.
No exposure:
10% (p = <
0.05), Exposure
to electronic
newsletter:
19% (p = <
0.05), Exposure
to Facebook
intervention:
9% (p = < 0.05),
Exposure to
magazine
(found online
and in-store)
10% (p = <
0.05).
Information
Online self
report, One
month
before
intervention
, two weeks
after
intervention
, and five
months
after
intervention
.
201
7
UK
Table 2
67
3. Schwartz et
al (2015,
Childhood
Obesity)
(Schwartz et al.,
2015)
12 schools,
3 years
(Annual
measureme
nt days)
400-500
students
per day
Educatio
n
Measurem
ent by mass
flow of
food from
kitchen to
plates to
bin. Waste
weighed.
No theories
discussed.
Examining
the
selection
and
consumptio
n of 4 food
items (Fruit,
Vegetable,
Entrée, and
Milk) before
(2012) and
after (2013
and 2014)
USDA
regulation
updates
were
implemente
d to school
lunches.
Menu updates led
to increased
selection of items
(Fruit and Entrée)
and reduced plate
waste (Vegetables
and Entrée’s having
significant
reduction in waste).
Fruit: 3% (Not
significant),
Vegetable:
28% (p = <
0.05), Entrée
15% (p = <
0.05), Milk 5%
(Not
significant).
Policy/system/prac
tice change
Over 3
years, one
measureme
nt per year
per school,
collected
each year in
April, May,
or June. To
calculate
average
weight of
serving,
three
servings of
all food
available
weighed
prior to
lunch
period,
Pictures of
food on
trays taken
before and
after
consumptio
n. Trays
collected
and
remaining
food left on
trays
weighed
and
recorded.
201
5
USA
Table 2
68
4. Williamson
et al (2016,
Journal of the
Association for
Consumer
Research)(William
son et al., 2016a)
Multiple
studies.
S1 n=68, S2
n=100, S3A
n=40, S3B
n=40, S3C
n=240
Educatio
n
Waste
weighed
(plate and
bin waste)
post
experiment
s.
Food choice
(physiologica
l and
psychological
explanations
) including
Sensory
Transference
Effects,
Psycholinguis
tic
Transference
Effects and
Automatic
Categorizatio
n Effects
Using
multiple
studies to
investigated
the
hypothesis
that plate
disposability
affects
amount of
food wasted
in lab
environmen
t and at
buffet
lunches.
People waste more
food when eating
on disposable plates
compared to
permanent plates, if
snack (S1) or a
buffet meal (S3A,
S3B and S3C). In
S3A the plates were
different on each
consecutive day,
S3B the plates were
replaced half way
through the meal
(first 20 participants
had permanent
plates) and S3C, the
sessions with and
without disposable
plates were 4 weeks
apart.
S1: Permanent
plates had a
51% reduction
in FW
compared to
Disposable
plates (p < .05).
S3A:
Disposable
plate waste:
15.5%,
Permanent
plate waste
8.4% (p <
.001).
S3B:
Permanent
plates had a
33% reduction
in FW
compared to
Disposable (p <
.01).
S3C:
Disposable
plate waste:
19.5%,
Permanent
plate waste
10.8%. (p <
.001)
Technology
S1: one of
measureme
nt event,
food
weighed
prior, waste
collected
after and
weighted.
"S3A and B:
Total weight
of the buffet
food was
measured in
the
kitchen
prior to
being
served"
"S3C: All
food
weighed
before
service, any
uneaten
food was
scraped into
a waste bin,
and
weighed. 2
days of
observation
s. Measure:
average
weights of
waste per
plate."
201
6
USA
Table 2
69
5. Schmidt
(2016, Resources,
Conservation and
Recycling)(Schmid
t, 2016)
N=217.
(experimen
tal N=108,
control
N=109).
Househo
ld
Self-
reported
level of
perceived
ability to
prevent
household
food waste
via survey
of
participants
.
Environment
al
psychological
theory
Use
environmen
tal
psychologic
al theory
(pro-
environmen
tal
behaviour)
to tailor
information
to specific
audiences
(households
).
Measured
perceived ability to
prevent
household food, pre
and 4 weeks after
intervention.
12% increase
in perceived
ability to
prevent
household
food in
Experimental
group 4 weeks
post
intervention (p
< 0.01).
Information
Baseline and
post
intervention
measureme
nts of self
reported
food waste
behaviours
201
6
Germany
Table 2
70
6. Manomaivib
ool et al (2016,
Applied
Environmental
Research)
(Manomaivibool
et al., 2016)
319
pictures
Educatio
n
Picture
measureme
nt of plate
waste
(fraction
left on
plate).
Theory of
planned
behaviour
psycho-social
factors that
cause the
generation
of food
waste.
Measuring
the impact
of an
awareness
campaign to
reduce food
waste on
campus.
Collect baseline
data via visual
analysis and photos.
The awareness
campaign included
photo diaries, table
information and a
social media
component.
Pictures of plates
and waste rather
than weights
collected at
baseline and during
intervention. This
provided analysis of
probability of types
of waste occurring.
Plate waste
decreased due to
intervention.
Probability of
types of food
waste
occurring, 2
categories
significant.
Rice and
Noodles:
before
campaign
probability=0.5
21, after
campaign
probability=0.3
31 (p<0.000).
Meat: before
campaign
probability=0.1
86, after
campaign
probability=0.0
88 (p<0.007).
Information
Visual
pictures
food waste
collected,
314 valid
pictures
taken at
baseline,
148 post
intervention
.
201
6
Thailand
Table 2
71
7. Dyen, Sirieix
(2016,Internation
al Journal of
Consumer
Studies)(Dyen and
Sirieix, 2016)
4
interviews,
3
observation
s
Educatio
n
Self-
reported
via
interview
of
participants
.
Food as an
educational
tool. Food to
create social
ties.
Observe
social
cooking
workshops
to
understand
the impact
they have
on the
adoption of
sustainable
food
practices,
and on the
social
inclusion of
participants
.
Interviews and
observations of
cooking classes
were conducted.
Food Waste was
discussed during
the interviews and
it was claimed that
the cooking classes
helped people to
manage their food
and reduce waste.
No statistics
presented.
Information ,
Policy/system/prac
tice change
Self
reported
waste
reduction
201
6
France
Table 2
72
8. Devaney,
Davies (2016,
Journal of
Consumer
Culture)(Devaney
and Davies, 2016)
5
Households
Househo
ld
Food waste
Audits
Social
practice lens
of food
waste
generation.
Transition
management
theory, living
laboratory
methodologi
es.
Using home
based
laboratory
intervention
s
(“HomeLabs
”) to
promote
resource
efficient
food
consumptio
n and eating
practices.
This
included
food waste
reduction.
Selecting 5
households that
represent common
household types in
Ireland. 5 weeks of
phased
intervention. Each
week covered a
different FW topic.
Week 1 included
FW audit. Semi-
structured
interviews
conducted during
intervention. Food
waste decreased in
all households,
(including
reductions of up
to 5.25 kg in
Household M).
Overall food
waste
generation
reduction of
28%
Information,
Technology
Week 1 and
Week 5
food waste
audit. Food
waste was
collected by
householder
s for 3 days
in advance
of their next
researcher
visit, with
participants
asked to
make a
record of
the type of
food wasted
and the
reason for
wasting it.
The
gathered
food waste
was then
weighed by
the
researcher.
201
6
Ireland
Table 2
73
9. Ganglbauer,
E., Fitzpatrick, G.
and Comber, R.
(2013, ACM
Transactions on
Computer-Human
Interaction)
(Ganglbauer et
al., 2013)
14
households
, 5 had
FridgeCams
for one
month
Househo
ld
Self-
reported
via
interview
of
participants
.
“theory of
practice”
lens
Using the
FridgeCam
technology
probe to
monitor and
intervene in
the food
waste
practices
(shopping)
and
generation
of 14
households
in Austria
and UK.
Interviews and
tours of all
households to
understand FW
behaviours.
FridgeCams
deployed to 5
households for 1
months, with
follow-up
interviews
indicating the
usefulness of
FridgeCams in
reducing and
preventing food
waste.
No statistics
presented.
Technology
Self
reported
waste
reduction
201
3
Multiple
country
(UK and
Austria)
10. Whitehair,
Shanklin and
Brannon (2013,
Journal of the
Academy of
Nutrition and
Dietetics)
(Whitehair et al.,
2013)
540
university
students,
19046 trays
of food.
Educatio
n
Weighing
of plate
waste.
Elaboration
Likelihood
Model of
Persuasion
Use Prompt
(“Eat
Over 6 weeks (2
weeks baseline,
deploy Prompt
message, 2 weeks
deploy Feedback
message, 2 Weeks.
study). Data from
student surveys and
tray waste
collected. Prompt
message resulted in
15% FW decrease.
Feedback
messaging did not
result in further FW
reduction.
15% FW
reduction from
baseline to
Prompt
Intervention.
(P<0.05)
Information
6-week data
collection
period.
Plate waste
individually
weighed.
201
3
USA
Table 2
74
11. Lim,Funk,
Marcenaro,
Regazzoni,
Rauterberg, (2017
International
Journal of Human
Computer
Studies) (Lim et
al., 2017)
S1 (n=27),
S2 (n=6), S3
(n=15)
Househo
ld
Weight
collected
by smart
bin. Self
reported
via
interview,
survey, and
focus group
of
participants
.
The Wizard
of Oz
approach,
Contento’s
(2010),
factors that
influence
food choices:
biological
predispositio
n, sensory-
affective
factors,
person-
related
determinant
s, and social
and
environment
al
determinant
s.
Can the use
of emerging
technology
(social
recipe apps,
food
logging, and
smart bins)
reduce
household
FW.
Using interviews
(S1), Focus groups
(S2), and Home
deployment (S3) to
test the usefulness
of social recipe
apps, food logging,
smart bins and food
sharing as ways for
reducing food
waste. No FW
baseline, so no
measured FW
reduction. App
alone not enough to
reduce FW.
However App with
smart bins “eco
feedback” and
other measures, FW
reduction possible.
No statistics
presented.
Technology,
Information
Self
reported
waste
reduction
201
7
Netherlan
ds
12. Jagau and
Vyrastekova,
(2017 British Food
Journal) (Jagau
and Vyrastekova,
2017)
2500 meals
Educatio
n
Visual
coding of
plate waste
(fraction
left on
plate).
Behavioural
insights and
nudges,
theory of
psychic
numbing
How
effective is
an in-
restaurant
information
campaign
advertising
the
availability
of smaller
portions
sizes.
14 days of study (5
pre), 9,
intervention).
Measure % of plate
waste (not weight),
and number of
portion types. No
difference in food
waste pre and post
intervention. This
could be due to 1)
smaller sizes
available and 2)
imprecise
measurement of
food waste.
Post
intervention
the proportion
of meals where
consumers
asked for
smaller
portions was
higher (6%)
than pre
intervention
3.5%
(p=0.0129).
Information
One week
baseline,
two weeks
intervention
. Measured
% of food
waste left
on plate
(not waste)
201
7
Netherlan
ds
Table 2
75
13. Lazell (2016
Journal of
Consumer
Behaviour) (Lazell,
2016)
None
stated
Educatio
n
None
stated
Human
computer
interaction
The
intervention
in this study
consisted of
a social
media
tool
(Twitter).
This tool
allowed
participants
to inform
others of
food that
would have
otherwise
been
wasted
within the
university.
Tool
advertised
via poster
and social
media.
Insufficient usage of
tool to justify an in-
depth reporting of
measurement/
findings
No statistics
presented.
Technology
Possible self
reported
waste
reduction
201
6
UK
Table 2
76
14. Martins,
Rodrigues, Cunha,
and Rocha (2016,
Public Health
Nutrition)
(Martins et al.,
2016)
151 fourth-
grade
children
from 3
Porto
primary
schools
who ate
lunch. 1742
lunches
during 14
days over
eight
different
menus
Educatio
n
Weighing
of
individual
meals and
leftovers
for all
meals
No theories
discussed.
How
effective
either
intervention
A, (designed
for children
and
focusing on
nutrition
education
and food
waste) or
intervention
B, (designed
for teachers
and focused
on the
causes and
consequenc
es of food
waste;) are
at reducing
plate waste
when
compared
to a control
group.
Physical weighing
of individual meals
and leftovers was
performed on three
non-consecutive
weeks
(baseline(T0), 1
week (T1) and 3
months (T2).
The study
results
demonstrated that
Intervention A (
designed for
children) was more
effective at
reducing plate
waste than the
intervention B
(focusing on
teachers). However,
food waste
reduction
decreased between
the short
and the medium
term only.
Intervention A, a
decrease in soup
waste was
observed. The
effect was greater
at T1. than at T2.
The plate waste of
identical main
dishes decreased
strongly at T1; this
effect was not
found at T2.
Intervention B did
not have a
Intervention A
% waste
Soups T1 −11.9
(SE 2.8) % T2
−5.8 (SE 4.4) %.
Main dishes T1
−33.9 (SE 4.8)
%; T2 −13.7 (SE
3.2) %;
Intervention B
% waste
Soups T1 −6.8
(SE
1.6) % T2 −5.5
(SE 1.9) %
Main dishes
T1 3.7 (SE 2·6)
%; T2 −5.4 (SE
2.4) %
Policy/system/prac
tice change
Five day
baseline,
with plates,
food and
plate waste
weight
collected for
each child.
Percentage
of plate
waste was
calculated
as the ratio
of edible
food
discarded
per edible
food served
to children.
Weighed
again in first
week and
then again
after 3
months.
201
6
Portugal
Table 2
77
15. Cohen,
Richardson,
Parker, Catalano,
and Rimm
(American Journal
of Preventive
Medicine) (Cohen
et al., 2014)
1030
Children,
5936
Meals.
Educatio
n
Weighing
of average
meals (10
weights)
and
individual
weighing of
all
leftovers. 2
days of
meal
measureme
nt pre
(2011) and
post (2012)
No theories
discussed.
If the new
school meal
standards
had an
effect on
the
consumptio
n, and
waste of
school
meals.
The new school
meal standards
resulted in no
changes in entrée
or vegetable
selection. Fruit
selection increased
significantly. Milk
selection Decreased
due to policy
change.
Changed.
The percentage of
foods consumed
increased for
entrees and
vegetables. There
were no significant
differences in the
percentage
or quantity of fruit
consumed.
Meals
consumed per
student (%)
Entrée Pre
72.3,Post 87.9
p-value
<0.0001; Milk
Pre 64.0 Post
53.9 p-value
<0.0001;
Vegetable Pre
24.9 Post 41.1
p-value
<0.0001; Fruit
Pre 51.8 Post
55.2 p-value
0.10.
Meals
consumed per
total # of
meals (%)
Entrée Pre
63.4,Post 73.6
p-value
<0.0001; Milk
Pre 62.4 Post
50.1 p-value
<0.0001;
Vegetable Pre
25.8 Post 40.3
p-value
<0.0001; Fruit
Pre 59.1 Post
56.9 p-value 0.
05.
Information ,
Policy/system/prac
tice change
2 days of
plate waste
measureme
nt per year,
post meal
trays
collected
and each
meal
components
waste
measured
separately.
201
4
USA
Table 2
78
16. Freedman
and Brochado
Obesity 2010
(Freedman and
Brochado, 2010)
1,475
students
Educatio
n
Weighing
of plate
waste.
No theories
discussed.
If the
reduction in
portion size
of French
Fries would
reduce
plate waste.
Portion
sizes tested
88g, 73g,
58g, 44g
On average, all
consumed 81.6% of
the FF, regardless of
portion size. As
portion size
decreased, a
greater number of
portions was taken,
however even with
more portions, few
diners
took/consumed/wa
sted more than at
baseline.
Total produced
(g)
88g (44,727 ±
6,328), 73g
(42,299 ±
3,299), 58g
(37,033 ±
3,767), 44g
(35,150 ±
3,350);
Total
consumed (g)
88g (23,282 ±
4,227), 73g
(24,158 ±
2,698), 58g
(18,295 ±
4,794), 44g
(17,846 ±
1,318);
Consumption
per diner (g)
88g (74.3 ±
2.2), 73g (71.4
± 2.4), 58g
(53.0 ± 2.5),
44g (52.2 ±
6.0);
Total wasted
(g)
88g (6,168 ±
265), 73g
(5,098 ± 250),
58g (4,983 ±
283), 44g
(4,242 ± 90);
Policy/system/prac
tice change
5 week
study (1
week
baseline),
weight of
food and
waste
measured
for each
bag.
201
0
USA
Table 2
79
17. Wansink,
and van Ittersum,
Journal of
Experimental
Psychology:
Applied, 2013.
(Wansink and van
Ittersum, 2013)
Study 1
n=219
Study 2
n=43, Study
3 n=237,
Study 4
n=135.
Hospitali
ty
Weighing
of plate
waste. (S2)
Pool and
Store
Theory. The
Delboeuf
illusion.
A multi
study paper
examining
how visual
norms
(plate size)
effect the
amount of
self-service
food taken.
Only study 2
had waste
measureme
nt. Study 1:
Assessed
norms of
portion size
and bowl
size. Study
2: Plate size
(small vs
large) and
waste at an
All-You-Can-
Eat Chinese
Buffet.
Study 3:
Plate size
(small vs
large) after
lecture on
plate size
and waste.
Study 4:
solving the
Delboeuf
illusion
(serving bias
towards
different
bowls)
Study 1: For
normal-sized
dinnerware,
portions are
anchored to 70% fill
level. The larger the
bowl, the more
people overfill.
Study 2: Diners who
selected the larger
plate served
themselves 52.0%
more total food
than those who
selected the smaller
plate. In addition to
larger plates serving
52.0% more food,
they also consumed
45.1% more, and
wasted 135.2%
more than those
with smaller plates.
Diners with larger
plates wasted
14.4% of all the
food they served
themselves,
compared with
7.9% (smaller
plates).
Study 3: overall
larger plates served
more food than
with smaller plates.
Smaller plates took
more tacos.
Study 2: Large
plate: cm2 of
food served
1216.9,
consumed
1072.5, wasted
144.4. Small
plate: cm2 of
food served
800.5,
consumed
739.1, wasted
61.4 (p <.01).
Study 3:
lettuce salad
(7.25 vs. 2.25
trays),
vegetable
salad (6.25 vs.
1.75 trays),
beef (6.0 vs.
3.75 trays),
enchiladas (6.5
vs. 3.5 trays),
and fried fish
(5.25
trays vs. 3.0
trays) soup (.75
vs. .75 trays),
tacos (1.25 vs.
2.25 trays).
Technology
Study 1 -
self
reported
size of
portion
Study 2- 4
restaurants,
visual
observation
of 43 diners,
with visual
estimation
of plate
waste.
Study 3 - 2
lines at one
lunch event
(209
individuals).
Food
weighed pre
service and
post service.
No waste
measureme
nt.
201
3
USA
Table 2
80
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