Accepted Pre-Print version.
Article reference JFPO1696
Journal : Food Policy
Corresponding author: Christian Reynolds
First author: Christian Reynolds
Received at Editorial Office: 13 Apr 2018
Article revised: 30 Dec 2018
Article accepted for publication: 23 Jan 2019
Please visit publisher for published version: https://www.elsevier.com/locate/issn/0306-9192
Released with a Creative Commons Attribution Non-Commercial No Derivatives License
Review: Consumption-stage food waste reduction interventions – what
works and how to design better interventions.
Christian Reynolds, Department of Geography, University of Sheffield, UK and
Waste & Resources Action Programme (WRAP), UK.
email@example.com ; firstname.lastname@example.org
Liam Goucher, Management School and Advanced Resource Efficiency Centre,
Faculty of Social Sciences, University of Sheffield, UK
Tom Quested, Waste & Resources Action Programme (WRAP), UK
Sarah Bromley, Waste & Resources Action Programme (WRAP), UK
Sam Gillick, Waste & Resources Action Programme (WRAP), UK
Victoria K. Wells, The York Management School, York University, UK
David Evans, Faculty of Social Sciences, University of Sheffield, UK.
Lenny Koh, Management School and Advanced Resource Efficiency Centre,
Faculty of Social Sciences, University of Sheffield, UK
Annika Carlsson Kanyama, Strategic Sustainability Studies, SEED, KTH Royal
Institute of Technology, Sweden
Cecilia Katzeff, Architecture and the Built Environment, KTH Royal Institute of
Åsa Svenfelt, Strategic Sustainability Studies, SEED, KTH Royal Institute of
Peter Jackson Department of Geography, University of Sheffield, UK.
Review: Consumption-stage food waste reduction interventions – what
works and how to do better.
Food waste prevention has become an issue of international concern, with
Sustainable Development Goal 12.3 aiming to halve per capita global food waste
at the retail and consumer levels by 2030. However there is no review that has
considered the effectiveness of interventions aimed at preventing food waste in
the consumption stages of the food system. This significant gap, if filled, could
help support those working to reduce food waste in the developed world,
providing knowledge of what interventions are specifically effective at
preventing food waste.
This paper fills this gap, identifying and summarizing food-waste prevention
interventions at the consumption/consumer stage of the supply chain via a rapid
review of global 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 were reported to reduce 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
approaches to reduce food waste and, except for a few studies, 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: (1) intervention
design; (2) monitoring and measurement; (3) moderation and mediation; (4)
reporting; (5) systemic effects.
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-
Within the last decade, food waste has become an issue of international concern
to policy makers, practitioners, and researchers across a range of academic
disciplines. Recent estimates suggest that globally one third of food never
reaches a human stomach (FAO, 2011), and global food waste is associated with
large amounts of greenhouse gas emissions (FAO, 2013). Growing political and
public consensus around the urgency of these challenges has provided the
impetus for governments, regions, cities, businesses, organisations, and citizens
to act. Measures have been taken to reduce the amount of food waste
generated in agriculture, aquaculture, fisheries, food processing and
manufacturing (upstream), and in supermarkets, restaurants, schools, hospitals,
and homes (consumption).
Many food waste reduction targets have been set, including Sustainable
Development Goal 12.3 which aims by 2030, to halve per capita global food
waste at the retail and consumer levels and reduce food losses along production
and supply chains, including post-harvest losses (Lipinski et al., 2017).
the key challenges facing many actors working in this area is deciding where and
how to focus their efforts most effectively to reduce food waste. For each area of
the food system (Horton, 2017), there are a number of potential strategies
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
(which are not mutually exclusive), with diverse examples including: improved
communication of forecasting between retailers and agricultural producers;
public information campaigns, programmes to increase skills in the home or
workplace; and changes in how food is packaged and sold. Within each of these
strategies, there are numerous decisions to be made by policy makers and
practitioners that could influence the effectiveness of interventions in preventing
food from being wasted.
The aforementioned where can also be geographic in focus: a local area, region,
country or globally. Recent quantification of global food waste highlights a split
between developed and developing countries. In developing countries, the vast
majority of food waste occurs in primary production and within the supply chain
– for example in sub-Saharan Africa where more than 90% of food waste occurs
prior to the consumption phase (FAO 2011). In contrast, in so called developed
countries, the largest single contribution is reported to come from the
consumption stage – with much of that at the household level, e.g. in Europe,
around 50% of wasted food is estimated to come from households (Stenmarck
et al., 2016). There is clearly a need for researchers, policy makers, and
practitioners to understand how to prevent food from being wasted across the
supply chain. For those working on the issue in developed countries, however,
understanding how to influence food waste within the consumption phase –
and, in particular, in households, where the majority of food is consumed and
wasted – is important to make a meaningful impact (Porpino et al., 2016). Due to
this, there is current policy focused on the household food waste reduction, yet
– as shown below – the evidence base for is lacking.
In order to enhance the understanding of how to influence food waste within
the consumption phase, this paper set out to identify and categorise food-waste
prevention interventions at the consumption/consumer stage. Growing
attention to food waste is reflected in an increase in the volume of academic and
literature on the topic. As a result, several bibliometric studies and meta-
analyses of prior literature and studies can be found. Our review of these
studies (Table 1) reports how and what each study revealed (Aschemann-Witzel
et al., 2016; Carlsson Kanyama et al., 2017; Chen et al., 2015; Hebrok and Boks,
2017; Porpino, 2016; Quested et al., 2013; Schanes et al., 2018; Thyberg et al.,
2015; Xue et al., 2017). It can be noted that none of these studies reviewed the
effectiveness of interventions aimed at preventing food waste in the
consumption stages of the supply chain
, although Schanes, Doberning, and
Gӧzet (2018) do call for this to be carried out as an avenue of future research.
Table 1 – a summary of the nine bibliometric studies and meta-analyses that review
food waste literature.
See attached file
Grey literature refers to non-peer reviewed literature such as reports, conference proceedings, doctoral
theses/dissertations, newsletters, technical notes, working papers, and white papers.
I.e. where food is consumed such as in the household, and in hospitality and food service sectors.
In the grey literature, there are many documents summarising a wide range of
food-waste-related issues. However, to the best of our knowledge, there is no
review of the effectiveness of downstream food-waste interventions.
intervention studies were reviewed by WRAP (see appendix F of Parry et al.,
2014). These were all from the grey literature and UK-based. Since then a
number of further studies have emerged, the most important of which are
mentioned in the discussion section below.
In summary, there is no peer-reviewed study that has considered the
effectiveness of interventions aimed at preventing food waste in the
consumption stages of the food system. This represents a significant gap, which,
if filled, could help support those working to reduce food waste in the developed
world, providing knowledge of what interventions are specifically effective at
preventing food waste. This paper fills this gap, reporting a rapid review of the
food-waste literature from 2006 to 2017 focussing on downstream food-waste
. Based on the findings, the paper then categorises the
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.
“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.
successful interventions and discusses the components of a successful food
waste reduction intervention.
The methodology for rapid reviews has emerged as a streamlined approach to
synthesizing evidence in a timely manner – rather than using a more in-depth
and time-consuming systematic review (Khangura et al., 2012; Tricco et al.,
2015). As discussed by Tricco et al., there is no set method for a rapid review;
however, there are several common approaches. For this study, a rapid review
was undertaken to provide fast and up-to-date information, responding to
demand from the policy and academic community (c.f. Lazell and Soma, 2014;
We used Google Scholar to identify relevant papers using combinations of the
following terms: ‘Food waste’, ‘household’, ‘quantification’, ‘behaviour change’,
‘consumer’, and ‘downstream’. The time period was restricted to January 2006
until January 2017. This was a result of discussion with expert advisors and
evidence from other bibliometric studies that food waste studies only began to
be published from 2006/7 onwards (Chen et al. (2015), Hebrok and Boks (2017),
Carlsson Kanyama, Katzeff, and Svenfelt (2017), and Schanes, Doberning, and
Gӧzet (2018). This search enabled the inclusion of online first/only preprints of
2017 journal articles. The search was restricted to English-language publications.
Each paper was then mined using the Google Scholar “citation” function to
explore the network of papers that have cited each paper. Each of these papers
was then captured and explored via the process described above. Figure 1
outlines our rapid review method, with 454 items narrowed down to 17 peer
reviewed journal articles focussing on downstream food-waste reduction
Though it is common in rapid reviews to use scoring criteria to sort and exclude
papers on the basis of method or data quality, no such scoring method was
used in this paper. This is due to the small number of studies found, and wishing
to provide the food waste community with as comprehensive as possible
assessment of recent intervention studies.
It should also be noted that the waste reduction percentages reported here have
been calculated from all studies that reported weights and changes to waste
generation. The waste reduction percentages are not directly comparable with
each other as they have differing functional units, i.e. per plate, per person
(participating or general population), per organisation (kitchen and front of
house), per total weight of waste, etc.), or differing time scales (for data
collection or experiment duration).
Figure 1 Outline of our rapid review methodology
3.1 Broad rapid review
The rapid review identified 292 downstream food waste articles that were
published in 39 journals between 2006 and 2017.
From 2006, the number of downstream food waste articles published yearly
increased rapidly as greater attention was given to the challenge of food waste,
with the largest spike in articles that quantify food waste (Figure 2) occurring in
2013 after the publication of reports highlighting the global issue (Institution of
Mechanical Engineers, 2013; Lipinski et al., 2013). Out of the articles surveyed,
only 17 (5%) feature applied downstream food waste reduction interventions.
The most popular methodologies (Figure 3) used in the rest of the downstream
food waste studies include surveys (n=80, 27%), reviews (n=77, 26%) and Life
Cycle Assessment (LCA) modelling (n=50, 14%). Journal articles featuring
qualitative, observational and ethnographic methods (following Evans (2014))
are consistently published throughout the time period (n=18, 5%).
48 countries or geographic areas were identified within in the broader
downstream food waste literature (Figure 4) with 8 articles not identifying their
geographic location, and 53 global studies. The next most studied areas were
the USA (n=42), the UK (n=34), Sweden (n=21) and Italy (n=20). China (n=13) is
the only developing country in the top 10 countries / regions studied. Our results
show that global studies emerge after 2010 – as data quality and accessibility
increases. Countries that had an early identification of food waste as a social
problem (including USA, UK and, Sweden) continue to publish prolifically.
3.2 Intervention studies
The seventeen journal articles focussing on downstream food-waste reduction
interventions were first categorised by the main intervention types that were
applied: information based, technological solutions, and policy/system/practice
change. Journal articles can be in more than one category if multiple
interventions were used (either applied separately or together). Table 2 provides
a detailed summary of each intervention and paper.
Table 2 – a summary of the 17 journal articles found with interventions that achieved
a food waste reduction
See attached file
The seventeen articles with applied interventions were found in sixteen journals
covering nutrition and health (5 journals), psychology and consumer behaviour
(5), environmental (3), human computer interactions (2), food (1) and economics
(1). The majority of these articles were published in relatively ‘low’ impact factor
journals (under impact factor 3)
Within the applied downstream food waste reduction interventions ten
countries feature, with the USA being the site for 6 articles, 3 in the UK (one of
which is a cross country comparison with Austria), and 2 in the Netherlands. The
geographic spread of these 17 articles is focused on the global north, with
Thailand the notable exception.
The areas of study for the seventeen applied downstream food waste reduction
interventions are focused on households and the community (n=6), hospitality
and hotels (n=5), and educational establishments (n=6). This is a much narrower
field of study than what is found across the rest of the downstream food waste
literature with 8 categories of intervention area identified in Figure 4.
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
Information-based interventions ((Cohen et al., 2014; Devaney and Davies, 2017;
Dyen and Sirieix, 2016; Jagau and Vyrastekova, 2017; Kallbekken and Sælen,
2013; Lim et al., 2017; Manomaivibool et al., 2016; Schmidt, 2016; Whitehair et
al., 2013; Young et al., 2017)) are where information was provided to change the
behaviour of the target group – i.e. households (Devaney and Davies, 2017),
hotel managers and diners, (Kallbekken and Sælen, 2013) and social media users
(Young et al., 2017). Various ‘delivery’ methods were used including information
campaigns (Manomaivibool et al., 2016; Schmidt, 2016) and cooking classes
(Dyen and Sirieix, 2016).
The success of these interventions varied. A student-focused education
campaign (Martins et al., 2016) resulted in a 33% waste reduction in main dishes,
while the Home Labs intervention (a collaborative experiment with
householders) led to an overall reduction in food waste generation of 28%
(Devaney and Davies, 2017). New hotel signage reduced food waste by 20%
(Kallbekken and Sælen, 2013). E-newsletter use resulted in 19% reduction in self-
reported food waste in the home (Young et al., 2017). Schmidt’s information
campaign resulted in a 12% perceived (self-reported) improvement in food
waste reduction in the home (Schmidt, 2016). Whitehair et al.’s information
prompt resulted in a measured 15% food waste reduction in a university
cafeteria, while portion advertising information also resulted in greater uptake
of smaller portions (up to 6% from 3.5%) (Jagau and Vyrastekova, 2017).
Technological solutions ((Devaney and Davies, 2017; Ganglbauer et al., 2013;
Lazell, 2016; Lim et al., 2017; Wansink and van Ittersum, 2013; Williamson et al.,
2016a; Young et al., 2017) involve the introduction or modification of
technologies and/or objects that seek to alter the behaviours around food
(waste). These included changes to plate or portion sizes (Williamson et al.,
2016b) or the introduction of fridge cameras or food sharing apps (Ganglbauer
et al., 2013). Only plate and portion size studies have quantified waste reduction.
The largest reported waste reduction (57%) was due to shifting to smaller plate
sizes, although in this study there was also a 31% decrease in the amount of
food consumed via the plate size shift (Wansink and van Ittersum, 2013).
studies have reported a 19% reduction in food waste due to reduction in plate
size (Kallbekken and Sælen, 2013), and a 51% reduction in food waste was
achieved by using permanent rather than disposable plates (Williamson et al.,
2016a). A 31% reduction in french fries waste was enabled by moving to smaller
portion sizes (Freedman and Brochado, 2010).
Policy/system/practice change (Cohen et al., 2014; Dyen and Sirieix, 2016;
Freedman and Brochado, 2010; Kallbekken and Sælen, 2013; Martins et al., 2016;
Schwartz et al., 2015) is where polices or systems are altered and the population
changes food waste behaviours (or practices). Two articles involved changing
school dietary guidelines, which resulted in a 28% (Schwartz et al., 2015) and
14.5% (Cohen et al., 2014) vegetable waste reduction, while changing how
schools and students were taught about food waste resulted in a 33% waste
reduction from main dishes (Martins et al., 2016). These results indicate that diet
reformulation and healthy eating can be part of food-waste reduction strategies.
In the seventeen journal articles with interventions, five relied on self-reported
(usually survey-based) measurements of food waste (a method that is relatively
low-cost but suffers from substantial biases (World Resources Institute, 2016)).
One paper did not disclose any waste weights, while another two estimated food
waste via visual analysis or pictures. The remaining nine used weight-based
waste measurement. It is a challenge to accurately quantify food waste
prevented, largely due to the costs of waste measurement (especially in the
home). The cost of waste measurement could explain why only 123 of the 292
journal articles (42%) identified by the broader rapid review include some
Note had observational measurement and weight base measurement of waste in
quantification of food waste generation/ diversion/ reduction. Due to this
reliance on self-reporting, only the accuracy of the three plate-change/size-
reduction interventions can be assessed with any certainty (Kallbekken and
Sælen, 2013; Wansink and van Ittersum, 2013; Williamson et al., 2016a). The
comparative measurement of these studies is also not directly comparable as
the methods of weight measurement and the unit of measurement vary (i.e. per
plate or aggregated total waste), and time intervals (study duration, number of
observations etc.) differ between each study as reported in Table 2.
Around a third of these studies (5 articles) do not integrate any theoretical
framework or disciplinary orientation into their experimental design. Those that
do are typically single theory in nature, and do not interact with the broader
food waste literature. Theoretical frameworks and disciplinary orientations in
the downstream intervention articles include Social Practice Theory; Behavioural
Economics (nudge-approaches such as visual prompts), Transformative
Consumer Research, pro-environmental behaviour change, behaviour change
determinants, and the integrative influence model of pro-environmental
Figure 2 Downstream food waste studies with quantified results per year, 2006-2017, n=130.
2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
# journal articles
Other Study with quantified results Applied intervention with quantified results
Figure 3 Methods used and numbers of downstream food waste studies published per year 2006-2017, n= 368.
# journal articles
Figure 4 Areas of study and numbers of downstream food waste studies published per year 2006-2017, n=304, (generalist review studies
Education (n=19) Hospital (n=2) Hospitality (n=10) Household (n=152) Retail (including
Whole Supply Chain
# of journal articles
Other Study Applied intervention
Figure 5 Geographic distribution of downstream food waste studies, the ten most prolific geographic areas, and all other countries. Note muli-
country studies classified as “global” for this graphic 2006-2017, n=324
# of journal articles
Other Study Applied intervention
4. Discussion of themes and policy implications
In light of the above results, in this section we provide an overview of the
methodologies, theoretical lenses and types of interventions employed in both
the academic and grey literatures, and then recommended a series of
recommendations – or principles – for organisations undertaking intervention
studies relating to food waste prevention related to the consumption stages of
the supply chain.
Although there has been a rapid increase in articles that quantify or investigate
downstream food waste since 2006, there have been only 17 peer-reviewed
journal articles that feature downstream interventions that resulted in a food
waste reduction. Of these, nearly 30% (5 articles) used self-reported methods to
measure food-waste reductions, while another two estimated food waste via
visual analysis or pictures. Due to the methods used, the results from these
studies should be interpreted with caution (as indeed many of their authors
note); in these cases, a claimed reduction in food waste should not be read as an
actual reduction. Furthermore, 16 of the 17 interventions occurred in developed
countries and most interventions have focused on small groups with time-
Part of this limited methodological development may be due to previous food
waste research having had limited cross-pollination between disciplines, both in
terms of substantive questions as well as in theoretical development. Many
researchers tend to rely on the theories they are comfortable with, resulting in a
“silo”-ing not only of theories that could be useful in explaining food waste, but
regrettably also a “silo”-ing of substantive findings related to actually reducing
such waste. Further research is required to map the literature (and food waste’s
theoretical developments further) to understand if this is the case.
4.2 Theoretical lenses
The absence of explicit reference to theory means that readers are left to infer
connections between cause and effect in food waste behaviours or that
connections are imputed without explicit justification. Nearly 30% (5 articles) of
the downstream intervention studies did not mention a theoretical framework.
Of those that did, this was often not a key part of the paper or research design.
This is an interesting finding: on the one hand, it could imply that those working
on food-waste interventions are not aware of theoretical frameworks developed
for interventions in other domains; on the other hand, it could imply – as
discussed by Quested et al. 2013 – that food-waste prevention in consumption
settings is very different from other areas of behaviour change (see also Evans et
al. (2017)) and that many of the theories developed elsewhere are of limited
value without further development. The lack of theoretical integration into food
waste intervention design may also imply that theoretically rich accounts of
household food waste (for example Waitt and Phillips (2016)) have yet to fully
consider the implications of their analysis for interventions. We suggest that
there is a need for greater integration of theory and previous research findings
into the design of interventions. We also suggest that there is need to discuss
how different theoretical frameworks, disciplinary perspectives and
methodological techniques could combine to contribute to the reduction of food
waste. Would it, for instance, be possible to combine a qualitative account of the
social practices that generate food waste with quantitative tools that model the
effects of different interventions?
4.3 Intervention types
Reduction methods such as improved information (Manomaivibool et al., 2016)
or changes to plate type and size (Lazell, 2016; Wansink and van Ittersum, 2013;
Williamson et al., 2016a), portion size (Freedman and Brochado, 2010), or menu
composition (Cohen et al., 2014; Martins et al., 2016; Schwartz et al., 2015), all
accept that their effectiveness may be due to greater consumption of the food,
or shifts in the types of foods consumed and wasted. That is, as has been
observed in other interventions studies, there may be unintended consequences
(Peattie et al., 2016) that need further investigation. If this unintended shift is
towards the overconsumption of unhealthy foods or at the expense of healthy
foods, this could lead to negative health outcomes. For this reason, attention
must be given to communicating and encouraging people to monitor portion
size rather than reducing food waste at the expense of public health. However
some of the reviewed studies, indicate that some interventions result in a
reduction in consumption alongside waste prevention (Kallbekken and Sælen,
2013; Wansink and van Ittersum, 2013
; Williamson et al., 2016a). Further
research is needed to understand which (healthy or unhealthy foods) are
involved in this consumption shift and waste reduction. Moreover, it could be
the case that many of the unintended consequences could be due to a lack of
understanding around causal mechanisms and supporting theoretical
frameworks. If this is the case, further engagement with theory-based
evaluations would be an obvious solution.
Cooking classes (Dyen and Sirieix, 2016), additional technologies such as fridge
cameras (Ganglbauer et al., 2013) or apps (Lazell, 2016; Lim et al., 2017), and
advertising and information campaigns (Young et al., 2017) were all reported as
being effective but with no accurate quantification provided. This is worrying as
all these methods are now being proposed by peer reviewed studies as options
to reduce food waste with no reproducible quantified evidence to assure
credibility or long-term effectiveness. Future research and resources are needed
to test these interventions with accurate measurement methods.
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,
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
For many organisations working on food-waste prevention, they would like to
affect change across relatively large populations (e.g. a country, city or state /
province / county). Therefore, to assess the appropriateness of interventions,
these organisations require information on their cost effectiveness, how easy
they are to scale up and whether they can be tailored to different ‘audiences’
within the population. However, this additional information is currently non-
existent in the literature.
In addition, many of interventions that feature advertising or an information
campaign did not provide enough detail to analyse and correlate the content
type, and tone (positive, negative, shocking etc.), with the effectiveness of the
campaign. This is an avenue for future research.
4.4 Links to other literature
As noted above, academic literature is not the only source of research and
evidence relating to downstream food waste. Although not a primary focus of
this review, the authors are aware of a small number of intervention studies in
the practitioner/policy-focused ‘grey’ literature. For example, during 2016, the UK
supermarket chain Sainsbury’s undertook a year-long trial using a range of
methods to prevent or reduce food waste in the home (Waste less, 2016). These
interventions were a mix of information (via Food Saver Champions), technology
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).
(fridge thermometers, smart fridges and cameras, apps etc.) and
policy/system/practice change (introducing tenant welcome packs, new food
waste events and school programmes). Some of these interventions included
actual measurement of food waste (via audits or Winnow/Leanpath systems
resulted in between 18%-24% food waste reductions. Other interventions relying
on self-reported measures, resulted in between 43% and 98% food waste
reductions for the homes that took part.
In the USA, a partnership called Food: Too Good To Waste reported the findings of
seventeen community-based social marketing (CBSM) campaigns aimed at
reducing wasted food from households (U.S. EPA Region 10, 2016). These
interventions were mainly information interventions, which introduced new
information and tools into households. Measurement of food waste was
conducted before and after the campaigns using a mixture of self-reported
audits (participants weighing their own waste) and photo diaries. The results
showed measured decreases between 10% and 66% in average household food
waste (7% to 48% per capita) for fifteen of the seventeen campaigns. The
successful interventions were between 4 and 6 weeks long, with samples of
between 12 to 53 households.
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
The EU project FUSIONS reported several waste prevention strategies focused
on social innovation (Bromley et al., 2016). Though most interventions involved
food redistribution, the Cr-EAT-ive intervention worked with school children
(n=480) and their parents (n=207) to reduce food waste in the home and
promote key food waste prevention behaviours. The results from 18 households
(of 29 households) that completed the kitchen diary activity managed to reduce
their food waste by nearly half – if scaled (with the intervention effects kept
constant) to a yearly quantity, this would equal a reduction of 80 kg per
household per year. However, it is not known how long the intervention effects
would last for, the longer term engagement/attrition rates of children and
households, and if some of this reduction was caused by the effect of
measurement itself (rather than the intervention).
During 2012/13, WRAP ran a food-waste prevention campaign aimed at London
households (WRAP, 2013a). These interventions were mainly information
interventions. This was evaluated via waste compositional analysis and reported
a 15% reduction in household food waste. However, as noted by the authors,
some of this reduction could have been the result of the research itself (i.e.
households being influenced by participating in a detailed survey).
Between 2007 and 2012, household food waste in the UK reduced by 15%
(WRAP, 2013b). However, it is not possible to isolate the effect of different
interventions that were running over this period. In addition, economic factors –
increasing food prices and falling incomes in real terms – are likely to have
contributed to this reduction (WRAP, 2014b).
These examples from the grey literature do not alter the main conclusions of
this review: that there is a lack of research surrounding interventions designed
to reduce the amount of food waste generated, and a lack of evidence of the
ease with which it is possible to scale up previous smaller interventions.
It is important for researchers, policy makers and practitioners working to
prevent food waste that this evidence gap is filled with research of suitable
quality. Below, we offer guidance and general principles that, if followed, will
improve the quality of this emerging field of study, and allow the effectiveness of
interventions to be compared and fully understood. Building on the
shortcomings of previous studies and improvement suggestions as outlined by
Porpino, (2016), we categorise these recommendations into 5 strands:
intervention design; monitoring and measurement; moderation and mediation;
reporting; and consideration of systemic effects. These recommendations are
based on our review of the literature and the authors’ prior knowledge and
experience regarding food waste intervention design and application.
4.5 Recommended principles for effective interventions
This section presents a series of recommendations – principles – for
organisations undertaking intervention studies relating to food waste prevention
related to the consumption stages of the supply chain. We then discuss
interventions with potential with reference to our results.
1 Design of intervention
We recommend that an initial decision should be made about whether the study
is focusing on an ‘applied’ intervention and/or one used to develop
understanding of the intervention process. This should be explicitly stated in the
methods and (experimental or intervention) design.
An applied intervention aims to reduce food waste across a given population or
sub-population (i.e. it is scalable, with a clear target audience). For the
interventions reviewed this was not always the case. For a communications-
based intervention, this would need to be similar to the type and tone of
material that could be used by a campaign group or similar organisation. If it
were a change to food packaging, for example, it would need to be a change that
could be adopted by a wide range of food retailers (e.g. it would have to ensure
food safety and other packaging attributes whilst still being cost-competitive). To
ensure that the ‘quality’ of such interventions is sufficient for the study,
researchers should consider partnering with appropriate organisations with
expertise in, for the above examples, developing communications materials or
packaging technology. Partnerships also ensure that work is not being carried
out in this area by organisations at cross purposes. In addition, applying
techniques such as logic mapping (based on theory of change – see The
Travistock Institute, 2010) can aid the design process to ensure that the
intervention has the best possible chance of meeting its stated aims (i.e.
preventing food waste in the home or other downstream settings). In addition,
logic mapping and theory of change can enable the research to investigate how
change occurs, as well as quantifying the degree of change. Much of this
research and methods development has already been carried out on general
behaviour intervention strategies within the field of environmental psychology,
see Steg and Vlek (2009), or Abrahamse et al. (2005).
In contrast to ‘applied’ interventions, some research of interventions is designed
to understand and evaluate how different elements of an applied intervention
work. For these interventions the criteria discussed above are not strictly
applicable. These types of studies may aim to understand which element of a
larger intervention is responsible for the change – e.g. it may compare a range of
campaign messages drawn from different disciplines and theories under
controlled conditions. In such cases, it is not necessary that this module is
scalable, although it would help future application of the research if the
intervention studies needed only small modification to be deployed on a larger
We also note that many studies use convenience sampling, which is likely to
result in a group of study participants who are not representative of the wider
population (or target populations within it). It will often include a sample with
higher than average levels of education and income (Schmidt, 2016). Therefore,
where possible, the design of the study should be considered to ensure that the
sample is as representative of the population of interest as possible, ideally
through random selection or, failing that, some form of quota sampling.
Previous discussion has indicated a lack of theory involved in the development
of interventions; we feel that this stage is a key part of the intervention design
process where theoretical understanding could be used to help develop more
2 Monitoring and measurement methods
Measurement of outcomes and impact of the interventions is challenging.
Objective measures of food waste – such as through waste compositional
analysis of household waste – are relatively expensive and are more easily
deployed in geographically clustered samples (World Resources Institute, 2016).
In addition, these methods only cover some of the routes by which wasted food
can leave the study area, and so food and drink exiting the study area via the
drain, or food that members of a household/school etc. waste in locations
outside of the study area are not covered by such measurement methods
(Reynolds et al., 2014). However, where there is an opportunity to deploy
methods involving direct measurement, it is beneficial as these are generally
more accurate and also minimise the amount of interaction with the household,
reducing the impact of the measurement itself on behaviour.
Most of the other methods rely on some form of self-reporting – e.g. diaries,
surveys, self-measurement of food-waste caddies, taking photographs. All of
these methods generally give lower estimates of food waste in the home
compared to methods involving direct measurement (e.g. waste compositional
analysis) when comparison is made for a given waste stream. For diaries – one
of the more accurate methods – around 40% less food waste is reported
compared to waste compositional analysis (Høj, 2012). More recent analysis has
shown that measuring food waste via caddies or photos gives similar results to
diaries (Van Herpen et al., 2016). This lower estimate is likely due to a range of
factors: people changing their behaviour as a result of keeping the diary (or
other method), some items not being reported, and people with – on average –
lower levels of waste completing the diary exercise (or similar measurement
Few studies discussed the problems presented by self-reported data. However,
issues relating to self-report are discussed more extensively in the
environmental (in particular recycling) and social marketing literature where self-
reported measures of perceptions and behaviours are often considered
unreliable (Prothero et al., 2011) and a gap is expected between self-reported
and actual behaviour (Barker et al., 1994; Chao and Lam, 2011; Huffman et al.,
2014). This should be discussed with reference to each intervention to
understand the scale of uncertainty present in the results.
This means that those monitoring interventions have some difficult decisions to
make: methods that are accurate may be unaffordable while methods that are
affordable may be subject to biases that can compromise the reliability of the
results. For instance, a communication-based intervention monitored using
diaries may increase the level of underreporting of waste in the diaries, which
could be erroneously interpreted as decreasing levels of food waste. This could
have substantial – and costly – implications for those deploying the (potentially
ineffective) food waste intervention in the future.
To address these issues, studies should try to obtain the requisite funding to be
able to measure food waste directly (e.g. by waste compositional analysis). This
may mean fewer studies, or studies comprising a panel of households, in which
food waste is regularly monitored (with the householders’ consent), creating the
possibility of longitudinal studies. To make such an approach cost effective, this
would likely require a consortium of partners, who could explore the emerging
data to answer multiple research questions.
For studies using self-reported methods, these should carefully consider the
design of the monitoring to ensure that reporting is as accurate as possible. The
smaller the gap between actual and measured behaviour arising, the less
measurement artefacts can influence the results and the ensuing conclusions.
Recent work calibrating these self-reported methods has been undertaken (Van
Herpen et al., 2016) and this type of information should be used in the
measurement design. Further advances in calibration, especially in the context
of intervention studies (i.e. is the level of underreporting stable during typical
interventions?) would also help to improve monitoring and measurement.
In some circumstances, effects relating to self-reported measurement methods
can be mitigated by the careful use of control groups. Where possible these
should be used, as levels of food waste may change over time, influenced by
food prices, income levels and other initiatives aimed at preventing food waste.
However, adding a control to the research will increase costs and there can be
practical difficulties in creating equivalent (e.g. matched) control groups,
especially where samples are geographically clustered.
This discussion raises wider questions about the most appropriate evaluation
approach and method, where different research designs may be fit for different
intervention purposes. For example, where the priority is to measure an impact
or effect, an experimental or quasi-experimental method should be considered,
while assessing multiple outcomes and causal mechanisms may require a non-
experimental research design (e.g. including qualitative methods). If the purpose
is to decrease food waste by X percent, then the level of food waste should be
measured over the course of the intervention (and beyond, to understand the
longevity of the effect). In some contexts however, the purpose is to achieve a
precursor to food-waste prevention (e.g. increased reflection on food waste, or
to improve cooking skills), which may eventually lead to decreased food waste.
In the latter cases, evaluation may want to focus on measuring the level of
reflection, cooking skills, etc. to assess the effectiveness of the intervention.
We acknowledge that research on food waste is an interdisciplinary field. This
can be a virtue, with many perspectives tackling this ‘wicked problem’. However,
it also means that different disciplines have different conventions and priorities,
e.g. over the experimental scale or duration, and measurement of uncertainty
vis-à-vis determining how much food is actually wasted. These differences
should be acknowledged in order that more accurate and consistent
measurement takes place.
3 Moderation and mediation
In addition to changes in the level of food waste, intervention studies may
benefit from measuring changes in other quantities. This may help understand
whether the intervention is effective, especially in situations where
measurement of food waste is imperfect. Additional dietary (purchase and
consumption) data can be collected and would provide greater certainty
regarding food waste generation statistics. Additional waste generation data
(beyond just food waste) could also be useful to help understand wider waste
generation issues and drivers.
Examples of other measurements may include ‘intermediate outcomes’:
depending on the intervention and how it operates, there may be intermediate
steps that would need to occur for the intervention to operate as envisioned (as
articulated in the intervention’s logic map – see stage 1). This is an approach
often used in social marketing where changes in behaviour that are difficult to
measure might instead track changes in knowledge, beliefs and/or perceptions
(Lee and Kotler, 2015). For instance, an educational campaign aimed at
increasing the level of meal planning prior to people going shopping could
monitor the change in people’s awareness of educational material and their
(self-reported) level of meal planning. These types of learning processes are
slower, and are more difficult to assess in the short term, but they might still be
successful and might achieve more long-term effects. Triangulation data is not
sufficient in itself to state whether an intervention was successful, but can
provide supporting evidence. Such analysis of moderating or mediating effects is
useful and often uncovers interesting insights that would not be highlighted if
this analysis were not conducted.
Observational analysis and measurement can provide insight into why the
intervention works. By observing the intervention in action, this allows insight
into the intervention itself, in addition to the effects of the intervention. This
expands upon the intervention proposals of Porpino et al. (2016) by not only
measuring the main objective, but also the intervention process, reflecting
recent studies that highlight the importance of both process and outcome
evaluation in interventions (Gregory-Smith et al., 2017).
In order to make any study replicable and repeatable, there should be sufficient
information provided about the intervention and the measurement methods to
be able to replicate both elements.
The reporting of food waste has become standardised with the publication of
the Food Loss and Waste Accounting and Reporting Standard (World Resources
Institute, 2016). This standard was designed for countries, businesses and other
organisations to quantify and report their food waste; it was not developed with
intervention studies in mind. However, many of the principles it describes are
useful in this context: studies should clearly describe the types of food waste
measured (e.g. just the wasted food (i.e. edible parts) or including the inedible
parts associated with food such as banana skins; the destinations included (e.g.
only material bound for landfill, or also food waste collected for composting); the
stages included (e.g. in a restaurant, only plate waste, or also kitchen waste).
A description of the details of how the quantification method (e.g. for waste
compositional analysis) was undertaken is crucial, alongside what the study
classified as food waste and which waste destinations were included. Details of
the sample sizes and how they were drawn should also be covered. Data
reporting should include the average weight, alongside appropriate measures of
the spread of the data (e.g. standard deviation, standard error, interquartile
ranges). Detailed waste composition data, where available, should also be
provided. Changes of food waste between time periods should be reported as
both weights and percentages, with significance and p values clearly stated. This
minimum level of comparable data was lacking in many of the papers reviewed,
with only 12 (70%) of the papers providing some statistics or statistical analysis,
2 (11%) providing waste composition analysis, and 5 (29%) providing results or
analysis of food waste reduction from multiple time periods post intervention.
To allow for the actual measurement of food waste rather than participants’
perceptions, several methods of disruptive thinking and scaling innovations
could be considered. One such innovation is smart bins (Lim et al., 2017). This
allows automatic recognition of food waste type and their weighting which can
help remove uncertainty in self-reporting of food waste. Such data from smart
bins (and also smart fridges and online shopping devices) could be shared with
local authorities, policy organisations, community groups and industry, enabling
planning and optimisation of food waste management locally. Smart bins are
already being used in the hospitality industry to track food waste (e.g. products
such as Winnow or Leanpath).
5 Considering systemic effects
None of the intervention studies in the review considered systemic effects.
Systemic effects, like the rebound effect (i.e. improved technology to reduced
environmental impacts may, due to behavior and other system effects, result in
no change, or increased environmental impacts. See Khazzoom (1987) or Sorrell
and Dimitropoulos (2008) for further discussion), are relevant and vital to
consider for measures that are saving money or time for the consumer. Several
of the measures presented above are not only measures that can lead to
reduced food waste, and thus reduced environmental impact, but also measures
that could lead to reduced costs, both for consumers and for other actors in the
food chain. Since less food needs to be wasted, less food needs to be bought.
Reduced costs can be an advantage from a private economic point of view, but it
can also in the worst case, lead to further negative environmental effects. The
money saved can be used for other types of consumption and perhaps
increased environmental impact. These type of system effects, are sometimes
called second order effects or rebound effects (Arvesen et al., 2011; Börjesson
Rivera et al., 2014). How consumers choose to spend the money saved
determines what the overall environmental impact will be. If the money or time
is used for something more environmentally friendly, then the effect will be
positive, and the environmental potential will be realised. But if instead the
money is used for activities with more environmental impact, such as a food
with higher environmental impact or, taking a trip with a fossil fuel driven car or
even a flight, then the environmental impact is negative. Sometimes the second
order effect exceeds the environmental benefits of the intervention, and the
situation becomes worse than it was from the outset (known as the Jevons
paradox (Alcott, 2005)). This means that measures for reduced food waste do
not always only produce the desired results with regard to environmental
impact, but also more unintended side effects.
This does not mean that measures to reduce food waste are ineffective, but that
second order effects need to be taken into account. Otherwise, there is a risk
that interventions might not be efficient in a systems perspective. Due to the
complexities involved in considering full systemic effects, the practicality of
detailed analysis must be weighed up for each intervention. The use of theory-
based interventions, with extended logic mapping (e.g. with systems mapping as
discussed above) will be useful in enabling this detailed analysis, as the
theoretical background and logic mapping may be able to acknowledge cross-
boundary input and outcomes (but not necessarily assist with measuring them).
Ideally, Intervention studies, where possible, should collect data to monitor
these second-order effects, in addition to monitoring the direct impact on food
waste. However, as this may involve recording household spending (on food as
well as other expenditure) and food consumption, it will greatly inflate the cost
of studies and may not be possible. Another option is to, at least, identify risks
for second order effects, look for ways to minimize negative second-order
effects and maximize any potential positive effects of this nature.
4.6 Policy implications
According to our review, in spite of the shortage of downstream intervention
studies, there are still several evaluated interventions that have good potential
for use in a wider context. These include so-called “low hanging fruits” which
might not have a huge impact but also do not imply high cost, high maintenance
or side effects, or interventions that have been assessed and have produced
good results. One example of the former kind is to encourage guests at
restaurants and in large-scale households to adjust the portions to how hungry
they are (Jagau and Vyrastekova, 2017), or to take smaller portions at a buffet
and come back if you want more (Kallbekken and Sælen, 2013). This kind of
measure is relatively simple and inexpensive and could be combined with other
measures, such as for example a lower price for a smaller portion. Examples of
the latter kind, assessed with good results but with an economic cost, are the
interventions with smaller plates (Kallbekken and Sælen, 2013; Wansink and van
A number of interventions use social media (e.g. Lim et al., 2017) and the
evaluated studies indicate that there is potential for this in particular as a way of
spreading knowledge and creating discussion and reflection. However, caution
must be taken as using social media to message the correct audience with
content that resonates has its own challenges due to audience segmentation.
Another intervention that is quite simple and can be done without major
investment in apps, is colour coding of shelving or sections in the refrigerator
(Farr-Wharton et al 2012). Similar initiatives have been tested in "Food: Too good
to waste" where the solution was even easier - with just a note in the fridge on
food to be eaten soon (U.S. EPA Region 10, 2016). More extensive campaigns
(e.g. U.S. EPA Region 10, 2016 and WRAP, 2013b) have also had good effects,
although it is difficult to estimate the impact of individual components of the
overall campaign. With a mix of complementary interventions and actors at local
level, this type of measure should have good potential given that the necessary
resources and commitment, which seems to have been the case in both the UK
and the United States.
This paper has summarised 17 applied food-waste prevention interventions at
the consumption/consumer stage of the supply chain via a rapid review of
academic literature from 2006-2017. This led to the identification of
interventions that could be deployed effectively at scale in the home (e.g. fridge
colour coding, product labelling, and information provision), and out of the
home (e.g. plate and portion size adjustment, changes to menus and nutritional
guidelines, and redesign of class room syllabus).
Our discussion has identified the weaknesses of the current literature; proposed
guidelines for the development of further food waste interventions, and set out
an agenda for further research:
Well-designed interventions covering a range of types (including longer
interventions and those exploring a raft of measurers),
Tested using carefully selected methods to understand the outcome of
the intervention and how it works (or not),
Adoption of higher sample sizes and representative sampling for
Replication studies in different countries
Consideration of systemic effects
Improved, more consistent reporting.
This is a novel and important addition to the researchers’, policymakers’ and
practitioners’ tool kit. Our review found that the majority of current
interventions achieve only a 5% to 20% reduction in food waste. To achieve
Sustainable Development Goal 12.3 by 2030, (halve per capita global food waste
at the retail and consumer levels) these interventions (and others) need to be
combined, refined, tested further at different scales and geographies, and
adopted on a global scale.
Christian Reynolds and Liam Goucher are supported from the HEFCE Catalyst-
funded N8 AgriFood Resilience Programme and matched funding from the N8
group of Universities. Christian Reynolds has additional funding from NERC to
support an Innovation Placement at the Waste and Resources Action
Programme (WRAP) (Grant Ref: NE/R007160/1). Annika Carlsson-Kanyama,
Cecilia Katzeff and Åsa Svenfelt has funding from the Swedish National Food
Agency and MISTRA. Thanks to Richard Swannell, Mark Boulet, and Amy
Woodham, for discussions about the review process and the identification of
additional papers. Thanks to the two anonymous reviewers for their helpful
suggestions in refining the papers structure and argument.
Abrahamse, W., Steg, L., Vlek, C., Rothengatter, T., 2005. A review of intervention
studies aimed at household energy conservation. J. Environ. Psychol. 25,
Alcott, B., 2005. Jevons’ paradox. Ecol. Econ. 54, 9–21.
Arvesen, A., Bright, R.M., Hertwich, E.G., 2011. Considering only first-order
effects? How simplifications lead to unrealistic technology optimism in
climate change mitigation. Energy Policy 39, 7448–7454.
Aschemann-Witzel, J., de Hooge, I.E., Rohm, H., Normann, A., Bossle, M.B.,
Grønhøj, A., Oostindjer, M., 2016. Key characteristics and success factors of
supply chain initiatives tackling consumer-related food waste – A multiple
case study. J. Clean. Prod. doi:10.1016/j.jclepro.2016.11.173
Barker, K., Fong, L., Grossman, S., Quin, C., Reid, R., 1994. Comparison of Self-
Reported Recycling Attitudes and Behaviors with Actual Behavior. Psychol.
Rep. 75, 571–577. doi:10.2466/pr0.1922.214.171.1241
Blatt, E., 2017. Strategic Plan for Preventing the Wasting of Food. Portland.
Börjesson Rivera, M., Håkansson, C., Svenfelt, Å., Finnveden, G., 2014. Including
second order effects in environmental assessments of ICT. Environ. Model.
Softw. 56, 105–115. doi:10.1016/j.envsoft.2014.02.005
Bromley, S., Rogers, D., Bajzelj, B., 2016. FUSIONS WP4 Evaluation report.
Carlsson Kanyama, A., Katzeff, C., Svenfelt, Å., 2017. Rädda Maten: Åtgärder För
Svinnminskande Beteendeförändringar Hos Konsument (Save The Food:
Measures Pleasant Between Changes To Consumer). Stockholm.
Chao, Y.L., Lam, S.P., 2011. Measuring responsible environmental behavior: Self-
reported and other-reported measures and their differences in testing a
behavioral model. Environ. Behav. 43, 53–71.
Chen, H., Jiang, W., Yang, Y., Yang, Y., Man, X., 2015. State of the art on food
waste research: a bibliometrics study from 1997 to 2014. J. Clean. Prod. 140,
Cohen, J.F.W., Richardson, S., Parker, E., Catalano, P.J., Rimm, E.B., 2014. Impact
of the new U.S. department of agriculture school meal standards on food
selection, consumption, and waste. Am. J. Prev. Med. 46, 388–394.
Devaney, L., Davies, A.R., 2017. Disrupting household food consumption through
experimental HomeLabs: Outcomes, connections, contexts. J. Consum. Cult.
17, 823–844. doi:10.1177/1469540516631153
Dyen, M., Sirieix, L., 2016. How does a local initiative contribute to social
inclusion and promote sustainable food practices? Focus on the example of
social cooking workshops. Int. J. Consum. Stud. 40, 685–694.
Etchells, P., Chambers, C., 2018. Mindless eating: is there something rotten
behind the research? Guard.
Evans, D., 2014. Food Waste: Home Consumption, Material Culture and Everyday
Life. Bloomsbury Academic, London.
Evans, D., Welch, D., Swaffield, J., 2017. Constructing and mobilizing ‘the
consumer’: Responsibility, consumption and the politics of sustainability.
Environ. Plan. A 49, 1396–1412. doi:10.1177/0308518X17694030
FAO, 2013. Food Wastage Footprint. Rome, Italy.
FAO, 2011. Global Food Losses and Food Waste - Extent, Causes and Prevention.
Freedman, M.R., Brochado, C., 2010. Reducing portion size reduces food intake
and plate waste. Obesity 18, 1864–1866. doi:10.1038/oby.2009.480
Ganglbauer, E., Fitzpatrick, G., Comber, R., 2013. Negotiating food waste: Using a
practice lens to inform design. ACM Trans. Comput. Interact. 20, 1–25.
Garrone, P., Melacini, M., Perego, A., 2014. Opening the black box of food waste
reduction. Food Policy 46, 129–139. doi:10.1016/j.foodpol.2014.03.014
Gregory-Smith, D., Wells, V.K., Manika, D., McElroy, D.J., 2017. An environmental
social marketing intervention in cultural heritage tourism: a realist
evaluation. J. Sustain. Tour. 25, 1042–1059.
Hebrok, M., Boks, C., 2017. Household food waste: Drivers and potential
intervention points for design – An extensive review. J. Clean. Prod.
Høj, S.B., 2012. Metrics and measurement methods for the monitoring and
evaluation of household food waste prevention interventions. Ehrenberg-
Bass Inst. Mark. Sci. University of South Australia, Adelaide.
Horton, P., 2017. We need radical change in how we produce and consume food.
Food Secur. doi:10.1007/s12571-017-0740-9
Huffman, A.H., Van Der Werff, B.R., Henning, J.B., Watrous-Rodriguez, K., 2014.
When do recycling attitudes predict recycling? An investigation of self-
reported versus observed behavior. J. Environ. Psychol. 38, 262–270.
Institution of Mechanical Engineers, 2013. Global food - Waste not, want not.
Jagau, H.L., Vyrastekova, J., 2017. Behavioral approach to food waste: an
experiment. Br. Food J. 119, 882–894. doi:10.1108/BFJ-05-2016-0213
Kallbekken, S., Sælen, H., 2013. ‘Nudging’ hotel guests to reduce food waste as a
win–win environmental measure. Econ. Lett. 119, 325–327.
Khangura, S., Konnyu, K., Cushman, R., Grimshaw, J., Moher, D., 2012. Evidence
summaries: the evolution of a rapid review approach. Syst. Rev. 1, 10.
Khazzoom, J., 1987. Energy savings resulting from the adoption of more efficient
appliances. Energy 29, 1–26.
Lazell, J., 2016. Consumer food waste behaviour in universities: Sharing as a
means of prevention. J. Consum. Behav. 15, 430–439. doi:10.1002/cb.1581
Lazell, J., Soma, T., 2014. THE INTERNATIONAL FOOD LOSS AND FOOD WASTE
STUDIES GROUP (discussion forum) [WWW Document]. URL
Lee, N.R., Kotler, P., 2015. Social Marketing: Changing Behaviors for Good. Sage
Lim, V., Funk, M., Marcenaro, L., Regazzoni, C., Rauterberg, M., 2017. Designing
for action: An evaluation of Social Recipes in reducing food waste. Int. J.
Hum. Comput. Stud. 100, 18–32. doi:10.1016/j.ijhcs.2016.12.005
Lipinski, B., Clowes, A., Goodwin, L., Hanson, C., Swannell, R., Mitchell, P., 2017.
SDG TARGET 12.3 on Food Loss and Waste: 2017 Progress Report Executive
Summary. Washington DC, Banbury.
Lipinski, B., Hanson, C., Lomax, J., Kitinoja, L., Waite, R., Searchinger, T., 2013.
Reducing Food Loss and Waste. World Resour. Inst. 1–40.
Manomaivibool, P., Chart-asa, C., Unroj, P., 2016. Measuring the Impacts of a
Save Food Campaign to Reduce Food Waste on Campus in Thailand. Appl.
Environ. Res. 38, 13–22.
Martins, L.M., Rodrigues, S.S., Cunha, L.M., Rocha, A., 2016. Strategies to reduce
plate waste in primary schools - Experimental evaluation. Public Health
Nutr. 19, 1517–1525. doi:10.1017/S1368980015002797
Moult, J.A., Allan, S.R., Hewitt, C.N., Berners-Lee, M., 2018. Greenhouse gas
emissions of food waste disposal options for UK retailers. Food Policy 77,
Peattie, K., Peattie, S., Newcombe, R., 2016. Unintended consequences in
demarketing antisocial behaviour: project Bernie. J. Mark. Manag. 32, 1588–
Porpino, G., 2016. Household Food Waste Behavior: Avenues for Future
Research. J. Assoc. Consum. Res. 1, 41–51. doi:10.1086/684528
Porpino, G., Wansink, B., Parente, J., 2016. Wasted Positive Intentions: The Role
of Affection and Abundance on Household Food Waste. J. Food Prod. Mark.
22, 733–751. doi:10.1080/10454446.2015.1121433
Prothero, A., Dobscha, S., Freund, J., Kilbourne, W.E., Luchs, M.G., Ozanne, L.K.,
Thogersen, J., 2011. Sustainable Consumption: Opportunities for Consumer
Research and Public Policy. J. PUBLIC POLICY Mark.
Qi, D., Roe, B.E., 2017. Foodservice Composting Crowds Out Consumer Food
Waste Reduction Behavior in a Dining Experiment. Am. J. Agric. Econ. 99,
Quested, T.E., Marsh, E., Stunell, D., Parry, A.D., 2013. Spaghetti soup: The
complex world of food waste behaviours. Resour. Conserv. Recycl. 79, 43–
Quested, T.E., Parry, A.D., Easteal, S., Swannell, R., 2011. Food and drink waste
from households in the UK. Nutr. Bull. 36, 460–467.
Romani, S., Grappi, S., Bagozzi, R.P., Barone, A.M., 2018. Domestic food practices:
A study of food management behaviors and the role of food preparation
planning in reducing waste. Appetite 121, 215–227.
Schanes, K., Doberning, K., Gӧzet, B., 2018. Food waste matters - A systematic
review of households food waste practices and their policy implications. J.
Clean. Prod. 182, 978–991. doi:10.1016/j.jclepro.2018.02.030
Schmidt, K., 2016. Explaining and promoting household food waste-prevention
by an environmental psychological based intervention study. Resour.
Conserv. Recycl. 111, 53–66. doi:10.1016/j.resconrec.2016.04.006
Schwartz, M.B., Henderson, K.E., Read, M., Danna, N., Ickovics, J.R., 2015. New
School Meal Regulations Increase Fruit Consumption and Do Not Increase
Total Plate Waste. Child. Obes. 11, 242–247. doi:10.1089/chi.2015.0019
Sorrell, S., Dimitropoulos, J., 2008. The rebound effect: Microeconomic
definitions, limitations and extensions. Ecol. Econ. 65, 636–649.
Steg, L., Vlek, C., 2009. Encouraging pro-environmental behaviour: An integrative
review and research agenda. J. Environ. Psychol. 29, 309–317.
Stenmarck, Å., Jensen, C., Quested, T., Moates, G., 2016. Estimates of European
food waste levels, IVL-report C 186. doi:10.13140/RG.2.1.4658.4721
Stöckli, S., Dorn, M., Liechti, S., 2018a. Normative prompts reduce consumer
food waste in restaurants. Waste Manag. 77, 532–536.
Stöckli, S., Niklaus, E., Dorn, M., 2018b. Call for testing interventions to prevent
consumer food waste. Resour. Conserv. Recycl. 136, 445–462.
The Travistock Institute, 2010. Logic Mapping: Hints and Tips. London.
Thyberg, K.L., Tonjes, D.J., Gurevitch, J., 2015. Quantification of Food Waste
Disposal in the United States: A Meta-Analysis. Environ. Sci. Technol. 49,
Tricco, A.C., Antony, J., Zarin, W., Strifler, L., Ghassemi, M., Ivory, J., Perrier, L.,
Hutton, B., Moher, D., Straus, S.E., 2015. A scoping review of rapid review
methods. BMC Med. 13, 224. doi:10.1186/s12916-015-0465-6
U.S. EPA Region 10, 2016. Food: Too Good To Waste - An Evaluation Report for
the Consumption Workgroup of the West Coast Climate and Materials
Management Forum. Seattle.
van der Zee, T., 2017. The Wansink Dossier: An Overview [WWW Document]. URL
Van Herpen, E., van der Lans, I., Nijenhuis-de Vries, M., Holthuysen, N., Kreme, S.,
2016. Best practice measurement of household level food waste.
Waitt, G., Phillips, C., 2016. Food waste and domestic refrigeration: a visceral and
material approach. Soc. Cult. Geogr. 17, 359–379.
Wansink, B., van Ittersum, K., 2013. Portion size me: Plate-size induced
consumption norms and win-win solutions for reducing food intake and
waste. J. Exp. Psychol. Appl. 19, 320–332. doi:10.1037/a0035053
Waste less, S. more, 2016. Inspiring food waste behaviour change - Year one
results and analysis.
Whitehair, K.J., Shanklin, C.W., Brannon, L.A., 2013. Written Messages Improve
Edible Food Waste Behaviors in a University Dining Facility. J. Acad. Nutr.
Diet. 113, 63–69. doi:10.1016/j.jand.2012.09.015
Williamson, S., Block, L.G., Keller, P.A., 2016a. Of Waste and Waists: The Effect of
Plate Material on Food Consumption and Waste. J. Assoc. Consum. Res. 1,
Williamson, S., Block, L.G., Keller, P.A., 2016b. Of Waste and Waists: The Effect of
Plate Material on Food Consumption and Waste. J. Assoc. Consum. Res. 1,
World Resources Institute, 2016. Food Loss and Waste Accounting and Reporting
Standard. Washington, DC, USA.
WRAP, 2014a. UK food waste – Historical changes and how amounts might be
influenced in the future. Banbury, UK.
WRAP, 2014b. Econometric modelling and household food waste. Fathom
Consulting,WRAP, Banbury, UK.
WRAP, 2013a. Household food waste prevention case study: West London Waste
Authority in partnership with Recycle for London.
WRAP, 2013b. Household Food and Drink Waste in the UK 2012, October.
Banbury, UK. doi:10.1111/j.1467-3010.2011.01924.x
Xue, L., Liu, G., Parfitt, J., Liu, X., Van Herpen, E., Stenmarck, Å., O’Connor, C.,
Östergren, K., Cheng, S., 2017. Missing Food, Missing Data? A Critical Review
of Global Food Losses and Food Waste Data. Environ. Sci. Technol. 51, 6618–
Young, W., Russell, S. V, Robinson, C.A., Barkemeyer, R., 2017. Can social media
be a tool for reducing consumers ’ food waste ? A behaviour change
experiment by a UK retailer. "Resources, Conserv. Recycl. 117, 195–203.
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.
2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
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).
Education (n=13) Hospital (n=2) Hospitality (n=10) Household (n=152) Retail (including dumpster
Whole Supply Chain (n=93)
2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
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.
USA (n=38) UK (n=34) Sweden (n=21) Italy (n=20) EU (n=16) China (n=13) Australia
Germany (n=9) Did Not
2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
Quested et al.,
cited, 12 WRAP
food waste in the
home, which has
work funded by
the Waste &
2006 to 2012
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
point that many
behavioural models, are
not designed for multiple,
complex behaviours such
as food waste.
Thyberg et al.,
the US MSW
1989 to 2013
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).
Chen et al.
of Web of
analysis of peer-
1997 to 2014
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,
extensive attention during
the past decade. FW
research output is
distributed unevenly over
all countries. The majority
of research is published by
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
Witzel et al
Review into case
1998 to 2015
Multiple success factors
were identified. There are
three main types of
consumer food waste
initiatives: information and
redistribution , and supply
(building upon prior
initiatives) are important to
the success of future
campaigns. Supply chain
change should ensure
growth in business
need to stress multiple
aims to get maximum
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
opportunities, both may be
possible together. Only 4
case studies targeted at
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
Journal of the
in the Food
Insights given for future
impactful research (i.e.
shopping habits, over
consumption, income, .
Provides future research
on previous studies. (Lack
of emotional study,
income, cultural factors,
marketing, survey analysis
quantification.) Need for
Xue et al.
of Web of
overview of all
the existing FLW
data in the
Sorting by Food
1933 to 2014
Food Loss and
Most existing publications
are conducted for a few
(e.g.,UK, USA). Over half
of publications are based
only on secondary data (
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
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
interventions, nor what has
been shown to be
successful in the literature.
Review, use of
Review what the
drivers of food
waste are, and
waste less food.
2000 to 2015
with the words
Reviews aspects of
consumer food waste
attitudes, beliefs and
values, quantifications and
waste prevention, and
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
waste with the
focus on private
1987 to 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
studies of their effects are
Nevertheless, the studies
of interventions where
evaluations exist, indicate
a significant effect
regarding the decrease of
food waste as well as
evidence on the
well as social
1980 to 2017
Food waste is a complex
and multi-faceted issue
that cannot be attributed to
single variables. Authors
call for a stronger
integration of different
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.
% of food
& Sælen (2013,
and 2 test
norms – in
Both reducing plate
size and providing
social cues was
waste in Hotels.
19.5% (p <
(p < 0.001)
The 52 hotel
2. Young et al
et al., 2017)
of a large
Online and social
methods can be as
that only the e-
10% (p = <
19% (p = <
9% (p = < 0.05),
10% (p = <
, two weeks
, and five
3. Schwartz et
(Schwartz et al.,
ent by mass
n of 4 food
d to school
Menu updates led
selection of items
(Fruit and Entrée)
and reduced plate
and Entrée’s having
reduction in waste).
Fruit: 3% (Not
28% (p = <
15% (p = <
0.05), Milk 5%
nt per year
each year in
or June. To
food left on
et al (2016,
Journal of the
son et al., 2016a)
S1 n=68, S2
t and at
People waste more
food when eating
on disposable plates
permanent plates, if
snack (S1) or a
buffet meal (S3A,
S3B and S3C). In
S3A the plates were
different on each
S3B the plates were
replaced half way
through the meal
(first 20 participants
plates) and S3C, the
sessions with and
plates were 4 weeks
plates had a
plates (p < .05).
8.4% (p <
plates had a
Disposable (p <
10.8%. (p <
S1: one of
"S3A and B:
of the buffet
a waste bin,
perceived ability to
household food, pre
and 4 weeks after
group 4 weeks
nts of self
ool et al (2016,
et al., 2016)
nt of plate
data via visual
analysis and photos.
photo diaries, table
information and a
Pictures of plates
and waste rather
baseline and during
provided analysis of
probability of types
of waste occurring.
decreased due to
types of food
7. Dyen, Sirieix
al Journal of
Food as an
tool. Food to
and on the
Food Waste was
the interviews and
it was claimed that
the cooking classes
helped people to
manage their food
and reduce waste.
and Davies, 2016)
n and eating
household types in
Ireland. 5 weeks of
week covered a
different FW topic.
Week 1 included
FW audit. Semi-
waste decreased in
reductions of up
to 5.25 kg in
Week 1 and
s for 3 days
of their next
the type of
E., Fitzpatrick, G.
and Comber, R.
, 5 had
tours of all
deployed to 5
households for 1
Journal of the
(Whitehair et al.,
Over 6 weeks (2
message, 2 weeks
message, 2 Weeks.
study). Data from
student surveys and
message resulted in
15% FW decrease.
messaging did not
result in further FW
Journal of Human
Studies) (Lim et
S2 (n=6), S3
s, and social
Can the use
(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
waste. No FW
baseline, so no
alone not enough to
However App with
smart bins “eco
other measures, FW
12. Jagau and
(2017 British Food
14 days of study (5
Measure % of plate
waste (not weight),
and number of
portion types. No
difference in food
waste pre and post
could be due to 1)
available and 2)
of meals where
% of food
13. Lazell (2016
in this study
Insufficient usage of
tool to justify an in-
depth reporting of
and Rocha (2016,
(Martins et al.,
es of food
to a control
of individual meals
and leftovers was
performed on three
week (T1) and 3
Intervention A (
children) was more
waste than the
and the medium
Intervention A, a
decrease in soup
effect was greater
at T1. than at T2.
The plate waste of
strongly at T1; this
effect was not
found at T2.
Intervention B did
not have a
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
Soups T1 −6.8
1.6) % T2 −5.5
(SE 1.9) %
T1 3.7 (SE 2·6)
%; T2 −5.4 (SE
as the ratio
again in first
et al., 2014)
If the new
The new school
resulted in no
changes in entrée
due to policy
The percentage of
were no significant
differences in the
or quantity of fruit
Pre 64.0 Post
24.9 Post 41.1
Pre 51.8 Post
total # of
Pre 62.4 Post
25.8 Post 40.3
Pre 59.1 Post
56.9 p-value 0.
2 days of
nt per year,
On average, all
consumed 81.6% of
the FF, regardless of
portion size. As
greater number of
portions was taken,
however even with
more portions, few
sted more than at
88g (44,727 ±
88g (23,282 ±
per diner (g)
88g (74.3 ±
2.2), 73g (71.4
± 2.4), 58g
(53.0 ± 2.5),
44g (52.2 ±
88g (6,168 ±
(5,098 ± 250),
58g (4,983 ±
(4,242 ± 90);
and van Ittersum,
(Wansink and van
Only study 2
nt. Study 1:
2: Plate size
waste at an
Study 1: For
anchored to 70% fill
level. The larger the
bowl, the more
Study 2: Diners who
selected the larger
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
more than those
with smaller plates.
Diners with larger
14.4% of all the
food they served
Study 3: overall
larger plates served
more food than
with smaller plates.
Smaller plates took
Study 2: Large
plate: cm2 of
plate: cm2 of
61.4 (p <.01).
(7.25 vs. 2.25
salad (6.25 vs.
beef (6.0 vs.
vs. 3.5 trays),
and fried fish
trays vs. 3.0
trays) soup (.75
vs. .75 trays),
tacos (1.25 vs.
Study 1 -
Study 2- 4
of 43 diners,
Study 3 - 2
lines at one