Model selection and averaging in the assessment of the drivers of household food waste to reduce the probability of false positives
Model selection and averaging in the assessment of the drivers of household food waste to reduce the probability of false positives
Matthew James Grainger 0 1
Lusine Aramyan 1
Simone Piras 1
Thomas Edward Quested 1
Simone Righi 1
Marco Setti 1
Matteo Vittuari 1
Gavin Bruce Stewart 0 1
0 School of Natural and Environmental Sciences, Newcastle University , Newcastle upon Tyne , United Kingdom , 2 Wageningen Economic Research, Wageningen , The Netherlands , 3 Department of Agricultural and Food Sciences, Alma Mater Studiorum - University of Bologna , Bologna , Italy , 4 Waste & Resources Action Programme (WRAP) , Second Floor, Blenheim Court, Banbury, Oxfordshire , United Kingdom
1 Editor: Andre M. N. Renzaho, Western Sydney University , AUSTRALIA
Food waste from households contributes the greatest proportion to total food waste in developed countries. Therefore, food waste reduction requires an understanding of the socioeconomic (contextual and behavioural) factors that lead to its generation within the household. Addressing such a complex subject calls for sound methodological approaches that until now have been conditioned by the large number of factors involved in waste generation, by the lack of a recognised definition, and by limited available data. This work contributes to food waste generation literature by using one of the largest available datasets that includes data on the objective amount of avoidable household food waste, along with information on a series of socio-economic factors. In order to address one aspect of the complexity of the problem, machine learning algorithms (random forests and boruta) for variable selection integrated with linear modelling, model selection and averaging are implemented. Model selection addresses model structural uncertainty, which is not routinely considered in assessments of food waste in literature. The main drivers of food waste in the home selected in the most parsimonious models include household size, the presence of fussy eaters, employment status, home ownership status, and the local authority. Results, regardless of which variable set the models are run on, point toward large households as being a key target element for food waste reduction interventions.
Data Availability Statement: All relevant data are
available from Open Science Framework at the
following URL: https://osf.io/rnkyc/.
Funding: This work was carried out under the
H2020 project REFRESH - Resource Efficient Food
and dRink for the Entire Supply cHain. REFRESH is
funded by the Horizon 2020 Framework
Programme of the European Union under grant
agreement no. 641933.
Food waste has been drawing increasing scholarly attention due to the sizeable proportions it
has assumed, and its socio-economic and moral implications ([
];after having been well below
100, the yearly number of scientific papers including the keyword ªfood wasteº reached 100 in
2007, then grew to 219 in 2011, and peaked 722 in 2016 see https://www.scopus.com/). Its
definition has been characterized by a lively debate that lead different national and
international organizations to identify different boundaries emphasizing diverse elements
characterizing the food waste issue [2±4]. In what follows, we define food waste as ªavoidable food wasteº,
i.e. any ªfood and drink thrown away that was, at some point prior to disposal, edibleº [
In developed countries, households are responsible for the relatively largest proportion of
all food wasted [1±3, 6]. Indeed, economic development and urbanisation result in the
adoption of lifestyles, working conditions and social dynamics typical of urban areas which, in turn,
increase food waste in the downstream segments of the value chain (retail, food services, and
]. Furthermore, developed countries differ from one another as for the food
waste generated and the policies adopted to address this challenge. Cross-country differences
in waste generating behaviours may also depend on habits embedded in the national culture
]. This holds although urbanisation and globalisation create increasingly homogeneous
dietary and food waste patterns worldwide .
Some authors have detected geographical differences in the individual behaviours towards
food waste among EU countries, due to factors such as per-capita income, and citizens'
perception of sustainability issues [7, 9±11]. Although developed countries present high per-capita
incomes, hunger in these countries is a reality: e.g., approximately 4% of US households, and
more than 5% of Australian households are experiencing hunger [
]. On the other hand, the
abundance of food results in high food waste levels [
]. For example, household food waste
account for 6.7 million tonnes of edible food or 33% of all food purchased in the UK, 6.3
million tonnes or 20% of food purchased in Australia, and more than 160 million tonnes in the
]. If redistributed to people in need, this food could help reduce hunger, while food
waste levels could also decrease.
Food waste generates negative environmental impacts and economic costs. It has been
estimated that nearly one third of the food mass, and one quarter of the food calories globally
produced are either lost or wasted, corresponding to 3.3 Gtonnes of CO2 equivalent [
]. In the
EU-28, annual food waste amounts to 180 kilograms per person, i.e. 25% of the food purchased
by households [
]. Hence, the valorisation of physiological and unavoidable waste and
residues as inputs for diverse productive processes, such as bioenergy or the production of
biobased products, might create socio-economic benefits and reduce environmental
repercussions. Indeed, most wasted foods are of primary interest to biofuel production [
Nevertheless, the social, economic and environmental viability of food waste as a source of biofuel
remains underdeveloped, thus requiring effective strategies to reduce food waste generation.
Overall, food waste is a broad topic that has been discussed from different angles in recent
literature [1±3, 6, 12, 13]. In order to contribute to the understanding of and the reduction of
food waste, various attempts have been made to identify and analyse the socio-demographic
factors influencing food waste behaviours (e.g. [7, 14±22]). This study focuses on the drivers of
food waste generation at household level. Indeed, a proper identification of them could help
design effective policies for food waste prevention and reduction.
The rest of the paper is organised as follows. The next section provides a background on
food waste drivers at household level, and addresses the issues of complexity related to the
empirical modelling of these drivers. A detailed description of the data and methodology can
be found in the subsequent section, followed by the results and their discussion.
Food waste drivers at household level
Individual and situational factors leading to the generation of household food waste include
household characteristics, shopping habits and location in relation to shops, eating/cooking
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behaviours, and awareness (e.g. the understanding of date labels on products, attitudes to
waste and recycling, consumer preference for perceived high-quality food, etc.).
Literature suggests that food waste is influenced by household characteristics with a major
factor represented by the composition of the family:
· in absolute terms, larger households waste more food than smaller households, but they are
also more efficient, wasting less food per person than smaller households; instead,
singleperson households tend to throw away more food on a per capita basis [6, 20±25];
· adults waste more in absolute terms than children, but households with children tend to
waste more than households without children, with food waste rates varying with children's
age [6, 20±25];
· the gender of the person mainly responsible for grocery shopping, and for food storing and
cooking might also have implications [
6, 7, 20, 24
· differences between older and younger people are not consistent, yet retired households
seem to waste less because they have more available time (compared to younger households
and households with children) and tend to be smaller [6, 20±23, 26, 27];
· income levels matter, but the relationship between individual income, food behaviours, and
household food waste [
10, 28, 29
As for shopping habits, the frequency of shopping [20, 30±32], the location of the stores
related to the frequency of the purchase, and the planning of the shopping [
other aspects of consumer behaviour related to food waste. On the one hand, consumers may
over-purchase if they need to shop infrequently [
]; on the other hand, frequent shopping
may induce unplanned and impulsive purchases, which tend to increase food waste [
planning shopping trips, absence of shopping lists, not planning meals, and not checking
stocks lead to the generation of food waste at household level [3, 7, 35±39].
Lack of awareness and/or knowledge is one of the most commonly identified drivers of
food waste at household level [2, 10, 21±23, 30, 32, 37, 40]. This includes consumers' confusion
with product labelling, as well as a lack of knowledge on how to use food efficientlyÐe.g.
making the most of leftovers, or cooking with available ingredients [
]. Consumers are rarely
aware of the difference between the labels ªuse byº and ªbest beforeº; hence, they are not using
them effectively when planning food usage and/or discard to avoid the risks associated to
food safety [
3, 37, 38, 41, 42
]. Not understanding and/or not abiding by food storage and use
instructions provided on food packages also leads to food waste . Finally, consumers may
not use packaging functionality, e.g. taking some products out of their packaging after getting
home, thus losing the protection of modified atmosphere packaging, or not using cool bags to
bring chilled food home [
33, 35, 43, 44
While food waste drivers have been discussed extensively in recent literature, their relative
importance and their interactions have received little attention. Literature suggests that food
waste drivers are multiple and interrelated, characterizing the problem as ªwide and
]. This framework is further complicated by the time and location gap ªbetween choices
made upstream (food purchasing and using decisions) and actions downstream (frequency of
household food waste)º, which prevents intentionality and commitment from working
Besides, since different authors propose different definitions of food waste, the
boundaries of the systems considered are also different (e.g., what is avoidable and non-avoidable
food waste) [
]. This lack of consistency in the notion of food waste may lead consumers to
resort to their subjective perception of what food waste is, when asked to assess related
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behaviours and quantities. Indeed, the adoption of different methodologies for data
collection (questionnaires, diaries, waste sorting analysis), or of poor or no measurements
hampers the lack of consistency in terms of quantification [
]. Due to the high costs of
measuring household food waste, most studies in the existing literature base their inference
on self-reported measures detected by means of questionnaires. Here, the use of real food
waste as a dependent variable helps overcome the problem of underestimation for social
desirability bias, and of misreporting due to other behavioural biases, thus reducing the risk
of incorrect inference.
Addressing complexity in food waste models
The high number of interconnected food waste drivers described above implies that traditional
modelling approaches may not be appropriate, or need specific adjustments. The approaches
to address multivariate problems have traditionally followed a procedure whereby data are
collected on several variables that may plausibly explain the response variable, and analysed to
find a single ªbestº model [
]. The model's structure is often defined a priori, and the estimate
from this model then forms the basis of inference. This approach ignores the potential for
other models to explain the data, and this model uncertainty increases the potential for
incorrect or misleading inference [
]. This is shown empirically for sociological models (OLE
regression) by Young [
], where statistical significance is overturned by minor and sensible
changes in model structure. Hence, there is a higher probability of false inferences (i.e. Type I
errors or false positives, and Type II errors or false negatives).
False positives (Type I) are often more costly than false negatives (Type II) because they
lead to wasted resources on further research and ineffective policy interventions [
probability of Type I errors can be increased by increasing the number of parameters modelled
but also by ªresearcher degrees of freedomº (sensu [
]). Unreported aspects of the research
can lead to increased risk of false positives through changes in the selection of dependent
variables or covariates, altering sample sizes and only reporting subsets of experimental conditions
Food waste drivers are multiple, interconnected and influenced by a number of diverse
factors related to the influence of the technological, institutional and social ªcontextsº where they
are situated [
]. Addressing such a complexity requires the inclusion of multiple explanatory
variables, increasing the risk of Type I and Type II errors. However, most assessments of food
waste use a regression framework with multiple explanatory variables without addressing
issues of model structural uncertainty, and rely on a single model specification, based either on
the extant literature or on the author's hypotheses, to make inferences from (e.g. [
9, 10, 32, 50,
]). Basically, while the set of variables gathered are bounded to be selected according to the
theory of the collectors, it is possible to avoid any further bias on the model construction due
to the artificial selection of variables and interaction terms to be included in the model itself.
This theory-based approach (using one single model) is blinkered to other possible
explanatory models (within the realms of the data collected). In presence of multiple potential
explanatory variables, model selection has long been championed as being more robust to Type I
Here, we adopt a novel empirical approach to identifying the drivers of food waste to
inform waste reduction policies. Our approaches for variable and model selection, differ from
the more common (and highly biased; [
]) stepwise selection based on the coefficients' level
of significance. With this approach, the aim is to identify the key drivers of household food
waste, whilst more accurately reflecting the uncertainty inherent in the analysis of
observational multidimensional data.
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Materials and methods
Data on UK consumers' demographics and behaviours collected by The Waste and Resources
Action Programme [
] are used in order to appraise the weight of ªavoidable food waste per
householdº using model selection and model averaging [
] to account for model uncertainty.
The dataset consists of face-to-face in-home interview responses (categorical data) on
socio-demographic aspects of households and behavioural responses to food waste, along with
data on the amount of waste collected from the kerbside. We undertook a complete case
analysis utilising only the households for whom all information was reported, which resulted in a
sample size of 1,770 (from 1,799) UK households. Household waste was collected from outside
each home (flats and houses with shared waste collections were not assessed) by ad hoc teams.
After collection, the waste of each household was weighed and sorted. All non-food items were
removed and weighed. Food items without packaging were sorted by food type and then
weighed. Food items with packaging were removed from the packaging, weighed separately,
and any details on the packaging (e.g. best before dates) were recorded (for more details, see
] and references within). Finally, food waste was standardised per household (i.e. food waste
per person was calculated) to account for the difference that a larger number of family
members could make to the amount of waste produced.
With 50 variables, the set of potential models was well over a quadrillion and, therefore,
variable reduction was first undertaken using the random forest algorithm [
]. The ªBorutaº
algorithm (in the package ªBorutaº, [
], in the R statistical environment [
]; all R code for
analyses is provided in S1 File) adds randomness to the variable set by creating shuffled copies
of all variables (ªshadow featuresº). It then runs a random forest classifier on the extended
dataset, and assesses the mean decrease in accuracy to evaluate the importance of each variable
(higher means are more important). At each iteration, ªBorutaº assesses if each variable has a
higher Z-score than the maximum Z-score of its shadow features. Variables with scores lower
than shadow features are deemed highly unimportant, and removed from the set. The
algorithm runs until all variables are confirmed or rejected (or it reaches a specified limit of runsÐ
here, we used 500 trees maximum).
Generalised Linear Models (GLMs) were applied to assess correlations between ªavoidable
household food wasteº and the socio-demographic and behavioural variables retained after
applying the ªBorutaº algorithm (Table 1).
All categorical variables were treated as factors in the analysis. The Akaike Information
Criterion corrected for small sample size (AICc) was used to determine a set of plausible models;
modelling averaging [
] was used to obtain estimates of the effect of predictors on ªavoidable
household food wasteº. Variables that were retained in the model selection procedure were
assessed for interaction. GLMs, model selection and model averaging were carried out using
the ªglmultiº package [
] in the R programme.
Exploratory sensitivity analysis
The variables summarizing the self-reported discard of different types of food have the
potential to introduce circularity, as they may predict overall food waste. Therefore, after running
the variable reduction and model-selection procedures, we removed them from the full model,
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Food and drink thrown away that was, at some point prior to
disposal, edible, e.g. milk, lettuce, fruit juice, meat (excluding
bones, skin, etc.)
The ownership status of the house, e.g. privately rented or
owned with mortgage
The employment status of the person responsible for the
majority of the household shopping and cooking
PLOS ONE | https://doi.org/10.1371/journal.pone.0192075
1st Quartile: 379
3rd Quartile: 2300
Mixed aged household
Under 65 years old only
65 years and above only
1,2,3,4,5,or 6 people
Owned with a mortgage
Family with at least one child under 18 years olds
Family with child(ren) all 18 years or over
Council/housing association rented
ABC1: Higher & intermediate managerial, administrative,
professional occupations or Supervisory, clerical & junior
managerial, administrative, professional occupations
C2: Skilled manual occupations
DE: Semi-skilled & unskilled manual occupations, Unemployed and
lowest grade occupations
All main meals are planned
Most main meals are planned
Few main meals are planned
Decide on the day
Don't buy the item
Don't eat this food
Wrapped / box / bag
Other / don't know
Don't buy / store
Use fridge (and possible other place to store)
Use fruit bowl/Use cupboard
Don't know / can't remember
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Proportion of occupants of the household classed by the
survey respondent to be fussy eaters
Quite a lot
A reasonable amount
A small amount
Don't eat it
I buy almost all my food in a main shop
I buy some food in a main shopping trip and some in `top-up' shops
I mostly buy food in smaller, `top-up' shops
I do a main shop more than once a week
I do a main shop about once a week
I do a main shop about once a fortnight
I do a main shop about once a month
I almost never do a main shop
Proportion (between 0 and 1)
and re-ran these two steps. Similarly, the local authority was considered as a non-designed
confounder (it was recorded but without any underlying justification). Again, we removed this
variable in the full model and re-ran the analysis. Finally, we re-ran the analysis with both
discard behaviours and local authority removed.
Model set reduction
The ªBorutaº algorithm consistently identified household size, home ownership status,
household composition, employment status and the presence of fussy eaters as significant drivers
of food waste in all sets of variables (Fig 1a±1d), including those reduced for exploratory
sensitivity analysis. Household size was always the most important variable in the variable set
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Fig 1. a) Variable importance (the loss of accuracy of classification) as determined by the ªBorutaº algorithm for the full variable set. Variables retained
for model selection (those with high or medium importance) are highlighted in green and yellow. Shadow feature minimum, mean and maximum are
highlighted in blue; b) with ªDiscard behavioursº removed from the variable set; c) with ªLocal authorityº removed from the variable set; d) with both
ªlocal authorityº and ªdiscard behavioursº removed from the variable set.
The key drivers of consumers food waste included in the full model (as determined by the
ªBorutaº algorithm, Fig 1a) were household size, local authority, household composition,
house type, home ownership status, employment status, the presence of fussy eaters, the
presence of children aged between 3 and 11, age of the respondent, social grouping, checking
cupboards for tinned food prior to shopping, and discard behaviours related to vegetables, cheese,
and food past its sell by date. This equated to a potential 16,384 models.
Of the 14 variables, seven were retained in the final model sets (the most parsimonious
models, ΔAICc <2; see Table 2).
The variables with the largest positive effect included the presence of fussy eaters, household
size, and one particular local authority (individual local authority identity was anonymized).
Variables with the largest negative effect included discard behaviours interacting with the
presence of fussy eaters, employment status interacting with the presence of fussy eaters, four
specific local authorities and home ownership status (owning a house outright).
Exploratory sensitivity analysis
The variables included in the model with discard behaviours removed (Fig 1b) were household
size, local authority, household composition, house type, home ownership status, the presence
of fussy eaters, and employment status. This equated to a potential 128 models. The final
model set included six of these variables: household size, local authority, home ownership
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Avoidable waste per Household ~ Discard Sellby+ Discard vegetables+ Fussy eaters + Household size + employment+ Local
Authority + Home ownership+ Discard Vegetables:Fussy eaters+ Fussy eaters:Employment
Avoidable waste per Household ~ Age+ Discard Sellby+ Discard vegetables+ Fussy eaters + Household size + employment+
Local Authority + Home ownership+ Discard Vegetables:Fussy eaters+ Fussy eaters:Employment
Avoidable waste per Household ~ Age+ Discard Sellby+ Discard vegetables+ Fussy eaters + Household size + employment+
Local Authority +Discard Vegetables:Fussy eaters+ Fussy eaters:Employment
Avoidable waste per Household ~ Age+ Discard vegetables+ Fussy eaters + Household size + employment+ Local
Authority + Home ownership+ Discard Vegetables:Fussy eaters+ Fussy eaters:Employment
Avoidable waste per Household ~ Sellby+ Discard vegetables+ Fussy eaters + Household size + employment+ Local
Authority + Home ownership+ Discard Vegetables:Fussy eaters+ Fussy eaters:Employment
df logLik AICc
47 -15481.2 31059.1
status, the presence of fussy eaters, respondent age, and employment status (S2 Table).
Variables with the largest positive effect included the presence of fussy eaters, employment
(working), household size (increasing with a larger number of occupants) and age (35±64). Variables
with the largest negative effect included interactions between fussy eaters and employment,
age (35±64), employment (not working), two specific local authorities, and home ownership
status (with a mortgage or owned outright).
Variables with the largest positive effect in the model with local authority removed (see Fig
1c for the variables retained) included household size (two, three, four or five people), while
variables with the largest negative effect included home ownership (owned outright and
owned with a mortgage) and employment (retired) (See S3 Table). The results of the models
with both local authority and discard behaviours excluded were very similar (See S4 Table).
The drivers of UK household food waste
The variables selected in the most parsimonious models always included household size, the
presence of fussy eaters, employment, home ownership status, and local authority. Household
size (i.e. the number of people in the household) appears to be a generally well-supported
explanatory variable [14, 16±18, 20±22, 59]. Levels of avoidable food waste per household
increased with increasing household size. Aschermann-Witzel et al. [
] suggest that
household size and composition (i.e. the age of household members) are the key demographic
drivers of food waste, because they relate to multiple behavioural factors, which typically differ
across household types. These include, for example, the purported advanced food skills of the
older generation (making use of leftovers, etc.), higher food security and safety concerns of
households with children, greater levels of fussiness in households with children, and lower
degrees of planning in young or single-person households. Our results support the idea that
fussiness in a household has a small but noteworthy effect on food waste generation.
Regardless of variable set, our results point toward families (i.e. large households) as being a
key target group for food waste reduction initiatives. Targeted initiatives (such as educational
campaigns and increased frequency and modalities of waste collection) in areas with a high
density of larger households need to be prioritised for study and intervention. Other evidence
 indicates that the reasons these households waste food are more likely to be due to
cooking or serving too much or fussy eating (rather than not using food before it goes off).
Survey respondents stating that they discard ªa reasonable amountº of vegetables was
related to higher levels of waste compared to other food categories. Discarding ªquite a lotº
had a similar mean value of the remaining food categories, but greater variation. Low levels of
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vegetables discarded by consumers logically lead to reduced avoidable food waste as vegetables
are the single largest food group contributing to household food waste in the UK [
there may be some discrepancy between stated and actual levels of discard due to a range of
]. Interventions aimed at preventing vegetable waste through, for example,
supporting the purchase of an appropriate amount, storing it optimally or providing recipes to help
use up leftovers may help further reduce food waste.
Local authority was not intended as a predictive variable in the original data collection,
as there were no socio-demographic assumptions underlying the sampling regime. The fact
that this descriptive variable (treated as a random variable in the model) is an important
explanatory variable highlights the large geographical variability in the food waste behaviours
observed. A combination of imprecision and high heterogeneity in the variables used to assess
consumer food waste may explain the difficulty in determining significant relationships. An
alternative explanation is that regional factors are important (but we could not determine any
evidence for this in our dataset). The location could be a proxy for socio-economic factors, as
well as factors related to the availability and the identity of retailers. Further investigation into
the drivers of these regional differences is warranted.
Developing an evidence-based approach to food waste
By using model selection to identify the most suitable structure of a model, researchers can
reduce the probability of spurious results. The danger of Type I errors is that they lead to
increased uncertainty in the effectiveness of interventions, because of both incorrectly
targeting consumers' behaviours and wrongly assigning significance to specific interventions.
Selective reporting, where only some of the variables measured are reported in the outcome,
further reduces the ability to synthesise across studies (e.g. through systematic review and
meta-analysis) an issue already highlighted as a constraint in consumer food waste research
Type II errors are reduced effectively by increasing the sample size; however, Type I errors
may still be highly probable where a large number of variables are used (i.e. ªp-hackingº),
and/or where many models are run but only those which confirm pre-conceived ideas or
theories are reported. To effectively reduce Type I error (one can never totally eliminate Type I or
Type II errors), researchers can take a number of potential approaches:
1. Careful selection of variables with a rationale for inclusion: a pre-published protocol can be
used to identify the variables that will be tested and processed to reduce the biases
undertaken by the researcher. This is a popular approach in meta-analysis and systematic review,
but can be applied more widely.
2. Provision of all analysis and data in the rawest possible form in an open online data
repository (e.g. Open Science Foundation, https://osf.io) to allow independent analysis (data
sharing is not always appropriate or possible, due to commercial sensitivities, etc.).
3. Transparent variable selection and model averaging, as well as reporting multiple model
results with a clear indication of the range of potential outcomes and the errors associated
with these (e.g. confidence limits, credible intervals, etc.) should be standard practice.
Our approach accounts for model structural uncertainty in a frequentist paradigm. Of
course, the issue of Type I errors becomes irrelevant when using Bayesian models, however
with frequentist statistics still dominating research in consumer science there is a need to
reduce the probability of spurious results in a robust manner. Stepwise approaches (which are
superficially similar to our approach) have largely been discredited in many fields (e.g. in
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medicine and ecology; [
]) because they increase (among other problems) the Type I
In addition to the problems of variable choice and Type I errors in models of consumer
research, there are problems with the typical approaches to complexity adopted in this field.
There is a well-developed body of complexity theory (e.g. [
]) which appears to be largely
ignored in favour of a generic mixed methods approach to data acquisition and regression
based modelling (e.g. factor analysis, structural equation modelling, mixed regression model,
etc.). The lack of a coherent framework is often justified with adoption of a single theoretical
perspective exacerbated by failure to consider model (structural) uncertainty. The tools to
undertake more structured and nuanced analysis exist (e.g. agent based models, network
analysis, systems dynamics; [
]) and should be routinely deployed in consumer research as they
are in other scientific disciplines.
The drivers of food waste are complex and interrelated, and may not lend themselves well to
traditional modelling approaches. This high complexity may be better analysed through other
statistical models or paradigmsÐsuch as Bayesian analysisÐin order to reduce the probability
of false positives. What is clear is that food waste policies must be developed using an
evidence-based approach, since traditional modelling paradigms are not sufficient to address this
complexity. This field of study can learn much from medicine and ecology, where data are
often similarly complex and uncertain [
]. Standard protocols for data collection and
definition would need to be agreed to allow meta-analysis. For data collection, protocols are
emerging, such as the FUSIONS Definitional Framework for Food Waste [
] and Food Waste
Quantification Manual [
] and the World Resources Institute Food Loss and Waste Standard
(http://flwprotocol.org/). With more rigorous evidence-based approaches, the drivers of food
waste can better be determined, and the effectiveness of any trialled intervention can be more
certain. This will lead to decreased cost and a more meaningful contribution to the
understanding of food waste.
Among the most important drivers identified is household size; however, the procedure of
model reduction and selection allows us to uncover a positive relationship between household
size and food waste, at odds with most of the previous literature on the issue [
14, 23, 26, 69
Other important drivers are the various dimensions of the household composition, for which
the results corroborate those of the literature. Interestingly, some of the drivers identified as
important by the literature, such as awareness of the food waste problem and shopping habits,
here are found as not important. This testifies the relevance of unbiased model selection of an
evidence-based approach to data analysis.
Finally, no evidence emerges on the behavioural characteristics of individuals at the point
of purchase (i.e. in the supermarket), and on how they may influence the food waste
generation. Any further research and, in particular, those focusing on large households, would need
to include this aspect.
S1 File. The complete R Code.
S1 Table. Model average coefficients for the full model including interaction terms. The
variables are sorted by z value.
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S2 Table. Selected models and averaged coefficients (sorted by z value) for the model with discard behaviour variables excluded.
S3 Table. Selected models and averaged coefficients (sorted by z value) for the model with local authority excluded.
S4 Table. Selected models and averaged coefficients (sorted by z value) for the model with local authority and discard behaviours excluded.
This work was carried out under the H2020 project REFRESHÐResource Efficient Food and
dRink for the Entire Supply cHain. REFRESH is funded by the Horizon 2020 Framework
Programme of the European Union under Grant Agreement no. 641933. The views reflected in
this article represent the professional views of the authors and do not necessarily reflect the
views of the European Commission or other REFRESH project partners. We thank Toine
Timmermans, Hilke Bos-Brouwers, Hans van Trijp, Erica van Herpen, Graham Moates and Keith
Waldron for comments on an earlier draft of this paper.
Conceptualization: Matthew James Grainger, Gavin Bruce Stewart.
Data curation: Thomas Edward Quested.
Formal analysis: Matthew James Grainger.
Funding acquisition: Marco Setti, Matteo Vittuari, Gavin Bruce Stewart.
Investigation: Matthew James Grainger.
Methodology: Matthew James Grainger, Gavin Bruce Stewart.
Project administration: Matteo Vittuari.
Supervision: Gavin Bruce Stewart.
Visualization: Matthew James Grainger.
Writing ± original draft: Matthew James Grainger, Gavin Bruce Stewart.
Writing ± review & editing: Matthew James Grainger, Lusine Aramyan, Simone Piras,
Thomas Edward Quested, Simone Righi, Marco Setti, Matteo Vittuari, Gavin Bruce
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