Missing Food, Missing Data? A Critical Review of Global Food Losses and Food Waste Data

Abstract

Food losses and food waste (FLW) have become a global concern in recent years and emerge as a priority in the global and national political agenda (e.g., with Target 12.3 in the new United Nations Sustainable Development Goals). A good understanding of the availability and quality of global FLW data is a prerequisite for tracking progress on reduction targets, analyzing environmental impacts, and exploring mitigation strategies for FLW. There has been a growing body of literature on FLW quantification in the past years; however, significant challenges remain, such as data inconsistency and a narrow temporal, geographical, and food supply chain coverage. In this paper, we examined 202 publications which reported FLW data of 84 countries and 52 individual years from 1933 to 2014. We found that most existing publications are conducted for a few industrialized countries (e.g., UK and U.S.) and over half of them are based only on secondary data, which signals high uncertainties in the existing global FLW database. Despite these uncertainties, existing data indicate that per-capita food waste in the household increases with an increase of per-capita GDP. We believe more consistent, in-depth, and primary-data-based studies, especially for emerging economies, are badly needed in order to better inform relevant policy on FLW reduction and environmental impacts mitigation.

Full text

Missing Food, Missing Data? A Critical Review of Global Food Losses
and Food Waste Data
Li Xue,
†,‡
Gang Liu,*
,§
Julian Parfitt,
∥
Xiaojie Liu,
†
Erica Van Herpen,
⊥
Åsa Stenmarck,
#
Clementine O’Connor,
@
Karin O
̈stergren,
∇
and Shengkui Cheng
†
†
Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, 100101 Beijing, China
‡
University of Chinese Academy of Sciences, 100049 Beijing, China
§
SDU Life Cycle Engineering, Department of Chemical Engineering, Biotechnology, and Environmental Technology, University of
Southern Denmark, 5230 Odense, Denmark
∥
Anthesis Group, Oxford OX4 1RE, United Kingdom
⊥
Marketing and Consumer Behavior Group, Wageningen University, Wageningen 6708 PB, The Netherlands
#
IVL Swedish Environmental Research Institute, 114 27 Stockholm, Sweden
@
World Resources Institute, Washington, DC 20002, United States
∇
RISE Bioscience and Materials, Agrifood and Bioscience, 223 70 Lund, Sweden
*
SSupporting Information
ABSTRACT: Food losses and food waste (FLW) have become a global concern in recent
years and emerge as a priority in the global and national political agenda (e.g., with Target
12.3 in the new United Nations Sustainable Development Goals). A good understanding
of the availability and quality of global FLW data is a prerequisite for tracking progress on
reduction targets, analyzing environmental impacts, and exploring mitigation strategies for
FLW. There has been a growing body of literature on FLW quantification in the past years;
however, significant challenges remain, such as data inconsistency and a narrow temporal,
geographical, and food supply chain coverage. In this paper, we examined 202 publications
which reported FLW data for 84 countries and 52 individual years from 1933 to 2014. We
found that most existing publications are conducted for a few industrialized countries (e.g.,
the United Kingdom and the United States), and over half of them are based only on
secondary data, which signals high uncertainties in the existing global FLW database.
Despite these uncertainties, existing data indicate that per-capita food waste in the
household increases with an increase of per-capita GDP. We believe that more consistent,
in-depth, and primary-data-based studies, especially for emerging economies, are badly needed to better inform relevant policy on
FLW reduction and environmental impacts mitigation.
1. INTRODUCTION
Food losses and food waste (FLW) occur throughout the food
chain from farm to fork. FLW has become a worldwide concern
in recent years and is widely identified as a key barrier to global
sustainability due to its adverse impacts on food security,
1
natural resources
2
(e.g., land, water, and energy), environment
3
(e.g., greenhouse gas emissions), and human health
4
(e.g., toxic
emissions from incineration). Consequently, reduction of FLW
emerges as a priority on the global and national political
agenda. For example, the United Nations have adopted a
specific target in the recently released Sustainable Development
Goals (SDG) to halve per-capita global food waste at the retail
and consumer levels and reduce food losses along production
and supply chains by 2030 (Target 12.3).
5
The European
Union
6
and the United States
7
have consequently adopted this
target, and the African Union’s 2014 Malabo Declaration also
includes a commitment “to halve the current levels of post-
harvest losses by the year 2025.”
8
In response to the increasing public concerns and political
attention on FLW, the past decades have seen a growing body
of literature on the quantification of FLW across the food
supply chains at global, regional, and national levels. For
example, the Food and Agriculture Organization (FAO) of the
United Nations estimated that roughly one-third of food
produced for human consumption (or 1.3 billion tons) was lost
or wasted globally.
9
The carbon and water footprint of this
significant amount of FLW were estimated to be 4.4 gigatons
(or 8% of the world’s total) of CO2equivalent
10
and 250 km3
of blue water,
2
respectively. It would also mean 1.4 billion
hectares (or 28% of the world’s total) of agriculture land use
and an economic cost of about 750 million U.S. dollars (USD),
Received: January 23, 2017
Revised: May 3, 2017
Accepted: May 11, 2017
Published: May 11, 2017
Critical Review
pubs.acs.org/est
© 2017 American Chemical Society 6618 DOI: 10.1021/acs.est.7b00401
Environ. Sci. Technol. 2017, 51, 6618−6633

which equals the GDP of Turkey.
2
Many other studies at the
regional or individual country levels have also highlighted a
similar large scale of FLW (though not always directly
comparable) and its profound impacts on food security,
environment, and economic development. For example, the
EU-28 generate approximately 100 million tons of FLW
annually in which households contribute the most (45%).
11
As
to its member states, the U.K. households alone wasted about
7.2 million tons of food in 2012, over 60% of which was
identified as avoidable.
12
The amount of food thrown away
from households in Finland, Denmark, Norway, and Sweden
account for 30%, 23%, 20%, and 10−20% of food bought,
respectively.
13
Roughly 1/3of the edible calories produced in
Switzerland is wasted, and the household is the largest
contributor.
14
Other industrialized countries show a similar
trend too. For example, the per-capita FLW in the United
States increased by about 50% from 1979 to 2003.
15
Over 4.2
million tons of FLW is disposed to landfill in Australia every
year, costing over 10.5 billion USD only in waste-disposal
charges.
16
About 27 billion USD of food is wasted throughout
the food supply chains in Canada annually, equivalent to 40%
of all food produced and 2% of Canada’s GDP.
17
A few national agencies and intergovernmental organizations
have been working on FLW quantification continuously over
the past decades. In particular, the FAO has released several
influential reports on FLW quantification on a global level.
2,9,18
The United States Department of Agriculture Economic
Research Service (USDA-ERS) has created the Loss-Adjusted
Food Availability Data Series since 1997, reporting over 200
commodities by three stages (farm to retail, retail, and
consumer) of losses in terms of quantities, values, and
calories.
19−23
The Waste and Resources Action Programme
(WRAP) has issued a range of reports on FLW in the supply
chain, household, and food service in the United Kingdom
since 2007.
12,24−30
More recently, stakeholders from academia, industry, and
governmental and nongovernmental organizations have started
to join efforts in research projects and working groups for FLW
quantification and method standardization. For example, the
European Commission funded projects “Food Use for Social
Innovation by Optimising Waste Prevention Strategies
(FUSIONS)”(2012−2016) and “Resource Efficient Food
and dRink for the Entire Supply cHain (REFRESH)”(2015−
2019) have issued a series of publications, covering various
aspects of FLW definition, quantification, and mitigation and
valorization strategies.
12,31−34
In June 2016, a partnership of
leading international organizations (e.g., World Resources
Institute, FAO, WRAP, United Nations Environment Pro-
gramme, and World Business Council for Sustainable Develop-
ment) launched a first-ever global standard to measure FLW.
35
Despite these growing efforts on the quantification of FLW
and standardization of methodologies, several researchers have
also raised concerns on the data deficiency and inconsistency
and called for better and more measurements on FLW.
36−38
In
summary, we argue that the existing global FLW data suffer
from the following major gaps.
•Their spatial coverage is narrow. Most existing studies on
FLW are conducted in industrialized countries. For
example, there are numerous publications quantifying
FLW in the United States
39−61
and Sweden;
62−76
on the
contrary, only a handful of studies illustrate FLW in low-
income countries, such as Nepal,
77
the Philippines,
78
Egypt,
79,80
and countries undergoing rapid dietary
transition, such as China, Brazil, and India.
81
•There is an unbalanced focus on the different stages
along the food supply chain. There are a large number of
studies on food waste at the retailing and consumer
levels
23,39,44,48,49,58,82−85
(mainly in industrialized coun-
tries, e.g., the United States), while there are fewer
studies addressing the immediate postharvest losses
(mainly in a few developing countries, e.g., India
86,87
and Vietnam
88
).
•Some available data are outdated but are still used. Due
to lack of more recent data, researchers have to fall back
on older data. For example, data of the 1980s and 1990s
from the same reference were used in two publications
(published in 2005 and 2010, respectively) as the current
postharvest FLW of fresh fruits and vegetables in Egypt
and Venezuela.
78,79
•There is inadequate first-hand data and a number of
studies have to rely on data derived from literature. For
example, many studies have frequently quoted data
reported in the 2011 FAO report,
53,89−93
which may not
be representative or accurate for some countries and
commodities
37
(e.g., household food waste data in Asia
and Africa do not have a single measured data point in
this report). Data in the African Postharvest Losses
Information System (APHLIS) have also been widely
used for postharvest FLW analyses elsewhere.
49,94−96
•The system boundary and methods as well as definition
of FLW used vary in different studies, which make
systematic comparison and verification of FLW data
between countries, stages, and commodities often
difficult. Thus, any extrapolation based on the existing
data and discussion on relationship between FLW and
related socioeconomic, environmental, and technological
aspects would also be uncertain.
A good and clear understanding of the availability and quality
of global FLW data is of particular importance. First, it is a
prerequisite of benchmark progress toward the global SDG
Target 12.3 and national FLW reduction targets and of
assessing the effectiveness of interventions. Second, it would
help to raise awareness, explore mitigation strategies, and
prioritize efforts on FLW prevention and reduction. Third,
better data would enable verification and comparison between
countries, food supply chains, and commodities and thus help
identify patterns and driving factors of FLW generation. Fourth,
it provides a necessary basis for further analysis of the social,
economic, and environmental impacts of FLW.
In this paper, we aim to provide a critical overview of all the
existing FLW data in the current literature. We will assess their
availability, quality, methods of measurement, and discuss their
patterns and implications for future work. A spreadsheet
database containing all the collected FLW data is supplemented
in the Supporting Information, which we believe provides a
fundamental physical database for further analyses on environ-
mental impacts and appropriate mitigation strategies of FLW.
We aim to answer the following questions in this review:
•What are the bibliometric characteristics of existing
literature on FLW quantification?
•What are the methods used for FLW measurement, and
what are their advantages and disadvantages?
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•What are the patterns of FLW generation among
countries, food supply chains, and commodities and
over time?
•What are the implications for further research in the
future?
2. MATERIALS AND METHODS
2.1. System Definition. Food Losses and Food Waste.
FLW occurs at each stage throughout the food supply chain.
Distinctions between the terms food losses and food waste,
edible and inedible food waste, and avoidable and nonavoidable
food waste are sometimes made in the literature. These
distinctions were not quantitatively considered in our
comparison due to lack of consistencies and transparencies in
the reviewed literature. For example, many studies differentiate
food loss and food waste according to the FAO,
97
which defines
food loss as “the decrease in quantity or quality of food”and
food waste as part of food loss that “has been left to spoil or
expire as a result of negligence by the actor (predominantly, but
not exclusively, the final consumer)”. Food waste is usually
connected to deliberate discarding or alternative (nonfood) use
of food (e.g., animal feed) that is safe and nutritious for human
consumption. The reviewed data do not allow us to distinguish
between food loss and waste; thus, in this paper, we use FLW
to refer to the combined amount of food loss and waste.
Food Supply Chain. FLW can be related to six main
processes as shown in Figure 1 (note that not all stages are
relevant to all products; for example, fresh vegetables may be
supplied directly to market). We further categorized FLW as
three types: farm losses and waste (during agricultural
production and harvesting), postharvest losses and waste
(during postharvest handling and storage, manufacturing,
distribution, and retailing), and consumer waste (both in-
household and out-of-home).
Food Commodity Groups. A total of 10 groups of food
commodities were defined according to the classification used
by the FAO and characteristics of the data in the literature: (1)
cereal and cereal products (e.g., wheat, maize, and rice); (2)
roots and tubers (e.g., potatoes, sweet potatoes, and cassava);
(3) oilseeds and pulses (e.g., peanuts, soybeans, and olives); (4)
fruits; (5) vegetables; (6) meat; (7) fish and seafood; (8) dairy
products; (9) eggs; and (10) others or not specified.
Geographical and Temporal Boundary. We included all of
the reported FLW data at the global, regional, and national
levels and from as early as possible until December 2015 in the
literature. The countries were grouped as medium/high-income
countries and low-income countries (see Table S2) based on
per-capita GDP and the grouping principle of FAO.
9
2.2. Literature Selection. To ensure a broad coverage of
literature containing FLW data, both Web of Science and
Google Scholar were used in the literature search. In addition,
we also explored the “grey literature”, i.e., reports prepared by
academic institutions, industrial associations, and governmental
and nongovernmental organizations, considering their signifi-
cant amount in recent years. “Food waste”or “food losses”
were used as keywords in the search of titles of publications,
and only articles published in English by December 2015 were
filtered (more details in section 1 of the Supporting
Information).
To further ensure the relevance of the selected publications,
we reviewed the abstracts, keywords, and method details of all
the publications to screen out articles that contained data (e.g.,
weight and monetary values) on FLW for at least one food
commodity, one food supply stage, and one region or country.
Finally, 202 publications form the body of literature that we
reviewed and examined in depth in this analysis.
2.3. Data Extraction and Treatment. The compiled FLW
data were measured by different metrics, e.g., by physical
weight, calorific value, or by monetary value. They were also
reported in several ways: (i) single values, (ii) values in a range,
or (iii) mean value or mean values with a variation. These
values were either in absolute terms or as percentages. All of
these differences were considered in our extraction of data from
the literature (details are shown in the Supporting Informa-
tion).
Whenever possible, comparison and trend analysis of data by
physical weight (in terms of both percentage and absolute
values) were conducted across countries and over time and by
food commodity. To facilitate the comparison, original data
were further processed as follows:
•If the original data points were reported in a range, the
arithmetic averages were first determined based on the
minimum and maximum values. Furthermore, global
median values were generated and used in the
comparison of per-capita farm FLW and postharvest
FLW among different food commodities because median
values are not strongly affected by extreme values
(compared to average values) and thus might be more-
representative in the comparison. Consumer waste was
usually reported as the weight of cooked food, which was
kept in the database and comparison.
•The values reported as the total amount of FLW in a
region or country were divided by their corresponding
population in the same year, for the convenience of
comparison on a per-capita level. When the year of
estimation was not specified, 2 years before the year of
Figure 1. Food supply chain for FLW used in this review. Note that
we put “waste”alongside “losses”for the farm and postharvest stages
because some of the losses in these stages are arguably “wasteful”and
avoidable, which makes it difficult to distinguish between loss and
waste.
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publication was assumed as a reference for population
and per-capita GDP. Population statistics and GDP data
(in current USD) were obtained from the World Bank.
•We introduced a food losses and food waste rate
(FLWR) for each food supply stage, which was defined
as the proportion of FLW at each stage of the food
supply chain to the amount of total food initially
produced (reference flow, corresponding to a fictive
output of 100% of the amount produced). FLWR was
calculated by considering the proportion of FLW across
each single stage (see Figure 1), as shown below:
∏
=−≥
−
=
rriFLWR (1 )( 2)
ii
i
j
j
1
1
where rirepresents the proportion of FLW at the stage to be
calculated (between 0 and 1), and rjrepresents the proportion
of FLW at the previous stages of the food supply chain. Note
that the FLWRs are additive, while the proportion of FLW at
each stage (r) are not additive because the mass flow is
successively decreasing. For the reference stage (i= 2) the r(i−1)
is set to 0. The proportion of FLW at individual stages, r, was
derived from the reviewed literature (either directly or by
dividing the quantity of FLW reported in the literature by total
production reported in the FAOSTAT).
3. RESULTS AND DISCUSSION
3.1. Bibliometric Analysis of Literature on FLW
Quantification. Type of Publications. The 202 reviewed
publications were composed of 5 types: peer-reviewed journal
articles (53.5%), reports (35.6%), PhD and master theses
(5.9%), conference proceedings (3.0%), and book chapters
(2.0%). The 108 journal articles were published in 69 different
journals, covering a wide range of disciplines, and about 45% of
them were published in 10 journals (in descending order in
terms of number of published articles), i.e., Waste Management
(15.7%), Waste Management & Research (7.4%), Resources,
Conservation and Recycling (5.6%), Food Policy (4.6%),
Journal of Cleaner Production (2.8%), Environmental Science
& Technology (1.9%), Journal of Industrial Ecology (1.9%),
Journal of Environmental Management (1.9%), Environmental
Science & Policy (1.9%), and Sustainability (1.9%).
Distribution of Countries and Year of Estimation. The
compiled FLW data covered 84 countries (reported 498 times
in total) and 52 individual years (reported 383 times in total)
from 1933 to 2014. This adds up to 2933 rows and 5898 data
points of FLW physical data in the compiled database (one row
represents the entire food supply chain of one food community
in one country or region; see the supplementary spreadsheet).
Figure 2 illustrates the geographical distribution of case
countries and the top 10 countries that have been studied. It
can be seen that most of the existing data were found for the
United Kingdom
24−28,51,60,85,98−118
and the United States,
39−61
both of which accounted for over 10% in terms of reported
times, respectively. Then countries in Northern and Western
Europe, i.e., Sweden,
62−76
Germany,
56,70,74,76,117,119−125
and
Finland
13,70,74,84,126−129
followed with a share of 5.4%, 4.4%,
and 3.2%, respectively. Figure 3a shows the temporal trend of
the year of estimation (see Figure S1 for the trend in terms of
year of publication). Reported FLW data were found as early as
Figure 2. Geographical distribution of case countries (with the name of top 10 countries) reported in the reviewed literature. The numbers are the
times that individual countries are reported.
Figure 3. (a) Temporal trend of reported FLW data in terms of year of estimation; (b) the number of publications covering different food-supply
stages and different development levels of countries.
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1933, and then the number stayed steady and low until 1995.
After 1995, the number went up considerably and over 60%
was seen in the past decade (38.1% from 2006 to 2010 and
25.1% from 2011 to 2014).
Data Coverage along the Food Supply Chain and across
Countries. Figure 3b illustrates the number of publications
covering different food supply stages and development levels of
countries (medium- and high-income countries versus low-
income countries). It can be seen that most of the studies have
included the retailing and consumption stages. Household was
covered in almost half (49%) of all the publications, followed
by the retailing stage (35%). However, only a small share
(18%−30%) of publications covered the stages between
agricultural production and distribution (agricultural produc-
tion:26.7%;postharvesthandling and storage: 18.8%;
manufacturing: 28.7%; and distribution: 21.8%).
The number of publications on FLW amount of medium-
and high-income countries was substantially higher than that of
low-income countries throughout the food supply chain except
for the postharvest handling and storage stage, for which the
number of publications was the same for both. Publications
covering the retailing and consumption stages were mostly
found for the medium- and high-income countries, with very
few data sources in developing and emerging countries. Low-
income countries showed a clear focus in the early and middle
food-supply stages (especially agricultural production and
postharvest handling and storage).
3.2. Overview, Advantages, and Disadvantages of
Different Methods Used for FLW Quantification. Table 1
summarizes methods that were used to quantify FLW in the
reviewed publications. They can be categorized as two groups:
(i) direct measurement or approximation based on first-hand
data and (ii) indirect measurement or calculation derived from
secondary data.
Direct measurement involves several ways to directly
quantify or estimate the actual amount of FLW:
•Weighing: Using weighing scales to measure the total
weight of FLW; usually used in restaurants, hospitals, and
schools; may or may not include compositional analysis
of FLW with each fraction being weighed.
•Garbage collection: Separating FLW from other
categories of residual waste containers to determine the
weight and proportion of FLW and from weight data
derived from separate FLW collections; may or may not
Table 1. Description of Advantages, Disadvantages, and Examples of Different Methods Used for FLW Quantification
method symbol time cost accuracy objectivity reliability
example of case
countries and regions
food supply
chain reference
direct measurement or approximation
based on first-hand data
weighing W ••• ••• ••• ••• ••• Portugal P6b Ferreira et
al.
148
Italy P6b Falasconi et
al.
157
garbage
collection
G••• ••• ••• ••• ••• Austria P6a Lebersorger
etal.
150
Sweden P6a Bernstad et
al.
71
surveys S •• •• •• •• •• Sweden P5 Gustavsson et
al.
65
U.K. P1, P2, P3,
P5
Mena et al.
114
diaries D ••• •• •• •• •• U.K. P6a Langley et
al.
108
Sweden P6a Sonesson et
al.
63
records R • • •• •• •• Sweden P5 Eriksson et
al.
73
Sweden P5 Scholz et al.
158
observation O •• • • • U.K. P6b Sonnino et
al.
110
Italy P6b Saccares et
al.
159
indirect measurement or calculation
derived from secondary data
modeling M •• • • •• • United States P6 Hall et al.
15
EU-27 P1, P2, P3,
P4, P5, P6
Khan et al.
135
food balance F • • •• ••• •• United States P6 Buzby et al.
23
global P1, P2, P3,
P4, P5, P6
Gustavsson et
al.
9
use of proxy
data
P• • •• ••• •• Austria P5 Lebersorger et
al.
56
Singapore P6a Grandhi et
al.
160
use of
literature
data
L• • •• ••• • global P1, P2, P3,
P4, P5, P6
Lipinski et
al.
90
Denmark P1, P3, P4,
P6
Halloran et
al.
161
a
Note: •••, high; ••, medium; •, low. Cost includes both economic cost and labor cost of conducting the research. P1: agricultural production and
harvesting; P2: postharvest handling and storage; P3: manufacturing; P4: distribution; P5: retailing; and P6: consumption (including P6a: household
and P6b: out of home).
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include compositional analysis of FLW. It can be
collected from the curb
130
or collected by households
at home and handed over to researchers.
99,131
•Surveys: Collecting information regarding people’s
perceptions or behaviors on FLW through questionnaires
that are answered by a large number of individuals and
face-to-face interview of key stakeholders in this field. In
these surveys, people can be asked to directly estimate
the amount of food waste in their household, e.g., in
number of portions,
107
or to estimate the percentage of
food items bought into the household that goes to
waste.
132
Visual tools have sometimes been used to help
people indicate the amount of food waste.
133
•Diaries: Gathering data via keeping a daily record on the
amount and types of FLW for a period of time;
commonly used for households and commercial kitchens.
Households are sometimes provided with weighing scales
to measure the weight of food waste.
128,134
•Records: Determining the amount of FLW via the
routinely collected information that is not initially used
for FLW record (e.g., warehouse record books, point of
sales data, data from food manufacture regulatory
sources); usually used for the retailing stage and food
manufacture (especially supermarkets and larger food
businesses).
•Observation: Using scales with several points to evaluate
food leftover by visual method or by counting the
number of items to assess the volume of FLW.
Indirect measurement includes methods derived from
existing data of various secondary sources:
•Modeling: Using mathematical models based on factors
that affect the generation of FLW to calculate the amount
of FLW.
•Food balance: Calculating FLW by using a food balance
sheet (e.g., from FAOSTAT) or human metabolism (e.g.,
relating body weight to the amount of food eaten) based
on inputs, outputs, and stocks along the food supply
chain.
•Use of proxy data: Inferring quantities of FLW by using
data from companies or statistical agencies (mostly used
for scaling data to produce aggregated FLW estimates).
•Use of literature data: Directly using data from literature
or calculating the amount of FLW based on the data
reported in other publications.
Figure 4 illustrates how these methods were used in each of
the 202 publications. The result shows that only a small share
(around 20%; blue colors in Figure 4) of the reviewed
publications has relied on direct measurement or approxima-
tion based on first-hand data. The remaining majority relied on
indirect measurement or calculation derived from secondary
data (red-yellow colors in Figure 4); over 40% of them were
based only on literature data, and about 1/3used a combination
of literature data with one or two other types of methods in the
quantification, for example, with modeling
15,135,136
or proxy
data
59,94,137−139
(indirect measurement) or with weighing or
surveys
115,119,140−146
(direct measurement). For the 138
publications that used literature data (Figure 5), their estimates
often relied on each other and pointed to a handful of
publications; over a quarter of them cited data from the top 10
cited publications, and the number of citations has increased
greatly since 2008. Such a high share of use of secondary data
may signal high uncertainties in the available global FLW
database, especially when the literature data are not
representative but used for a different country or a different
year than it was collected for originally.
The advantages and disadvantages of different methods were
evaluated based on different criteria (e.g., time, cost, and
accuracy) listed in Table 1.
•Weighing and garbage collection result in relatively
objective and accurate information on FLW. The two
methods may result in a total quantification of FLW (i.e.,
operational data), or they can yield far more granular
data at food product category level. However, these two
methods are more time-consuming and expensive than
other methods and usually can only be conducted when
space is available for sorting food. For example, to
characterize the plate waste in Portuguese hospitals each
year, Ferreira et al. weighed plate waste in almost 8000
meals during 8 weeks by individual items (soup, main
dish, fruit, and bread) in a case hospital.
148
Of course, the
accuracy of a waste composition analysis depends on
methodological decisions, and various sources of error
have been identified.
149,150
In particular, in-home food
waste that is disposed of by other means than curb side
collection (e.g., sink garbage disposals, home compost-
ing, and animal feed) is usually not observed.
151
•Surveys, diaries, records, and observation are other ways
of direct measurement and approximation of FLW data
and are relatively less time-consuming and expensive
comparing to direct weighing. However, they largely
depend on personal perceptions, the manner that raw
data was collected, and the subjectivity of observers,
which may reduce the accuracy of the data. For surveys,
for instance, potential biases in FLW estimates can occur
because this method relies on people’s memory, and
people may provide socially desirable answers. Keeping a
food waste diary can be a considerable task for
participants, and this is reflected in a tapering of
enthusiasm of participants
108
as well as difficulties in
recruitment and high dropout rates.
152
Moreover, the
accuracy of diaries has been questioned, as keeping a
diary can by itself lead to increased awareness and
Figure 4. Overview of the methods used in the 202 reviewed
publications. Each dot represents one publication, and the colors
indicate different methods used. L: use of literature data; P: use of
proxy data; F: food balance; M: modeling; G: garbage collection; W:
weighing; O: observation; D: diaries; R: records; and S: surveys. For
the convenience of visualization, we have aggregated similar methods,
i.e., L/P, W/O, and D/R, in groups (see Figure S2 for a more-
disaggregated version).
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behavioral change.
108,134,152
For observation, it requires
less time than weighing but varies in accuracy and
reliability. For example, Hanks and colleagues compared
three types of observation measurements (quarter-waste,
half-waste, and photograph) in a school cafeteria setting,
and they found that on-site visual methods outperformed
photographs in inter-rater and intermethod reliability.
153
•Indirect measurement or calculation derived from
secondary data is more widely used due to their low
cost and high feasibility. However, these methods usually
bear higher uncertainty. For example, results from
modeling are heavily affected by the choice of model
parameters and their relationship with the quantities of
FLW. The accuracy of the food balance method depends
primarily on the quality and comprehensiveness of the
food balance sheet data. The use of proxy data and
literature data is the easiest among all methods, but its
accuracy depends ultimately on the quality and
representativeness of the source data that is used.
Arguably, no direct or indirect measurements can be all-
satisfactory by themselves. The direct measurements, despite
the advantage, are commonly performed in a certain
community or city and a certain stage of the food supply
chains involving limited number of participants, resulting in an
inevitable issue of lack of representativeness (especially
problematic for big countries like China and the United
States). The indirect measurements, on the contrary, can
provide an overall picture for the whole country or region and
for different stages. One way to go forward could be an
integrated approach of coupling direct with indirect measure-
ments: statistics-based estimation of FLW at the national and
regional levels to determine the magnitude of the problem
(more for policy-making and strategy-setting) and first-hand
measurements at the ground level plus in-depth examination of
FLW drivers and affecting factors so as to design effective
intervention steps.
The choice of method has critical influences on the
determined amount of FLW, which sometimes leads to data
discrepancy in the reviewed publications. For example,
EUROSTAT reported that about 5.7 million tons of FLW
were generated from the manufacturing sector in Italy in
2006,
147
while another model-based study estimated 1.9 million
tons for the sector.
74
The reason for such a significant
difference is that the two publications were based on different
data sources and assumptions. The former included both FLW
and byproducts that were reused and recycled, while the latter
one depended on the loss share of the manufacturing stage and
methodologies reported by FAO.
9,154
As another example,
Zhou reported that the wasted amount of wheat, maize, and
vegetables were 4.2, 4.9 and, 4.3 million tons in the early 1980s
in China, respectively,
155
whereas Smil estimated the wasted
quantity of these three food types as 1.9, 2.0, and 10.9 million
tons, respectively.
156
This discrepancy can be explained by the
fact that the data source of the former publication was the
FAOSTAT food balance sheet, whereas the latter was based on
Figure 5. Citation network of the 138 publications that used literature data. Each dot represents a publication. The size of the dot indicates the
number of citations, and the arrow represents the direction of citation. The dots in white on the right denote publications outside the citation
network. The top 10 cited publications are 1, Kantor et al., 1997;
41
2a, WRAP, 2009;
26
2b, Gustavsson et al., 2011;
9
3a, WRAP, 2008;
25
3b, Monier
et al., 2010;
147
3c, Buzby and Hyman, 2012;
52
4a, Kader, 2005;
79
4b, Kranert et al., 2012;
121
5a, Buzby et al., 2009;
20
and 5b, Langley et al., 2010.
108
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various literature data and assumed cereal waste at 4% and
vegetable waste at 10%.
3.3. Statistical Analysis of FLW Data. Farm Losses and
Waste. In general, farm FLW in agricultural production in low-
income countries is higher than that in medium- and high-
income countries because the former countries usually have less
advanced technology and infrastructure in harvest processing.
For example, it was estimated that FLW during agricultural
production accounts for 13% of the total FLW along the whole
supply chain in Canada,
93
whereas this stage made up the
largest share (26%) of the overall FLW in South Africa.
162
There is not much information about FLW by commodity
groups in agricultural production and harvesting (Figure 6).
According to the compiled data (note that the data are from a
global panel for different countries and years, and the same
goes for the statistical analysis in the Postharvest Loss and
Waste and Farm Losses and Waste sections below), the median
of cereal farm FLW is the largest among all food categories, at a
level of approximately 16 kg/cap. It was estimated that
approximately 5−9% of grain was lost in China at this stage,
which is similar to that of Ghana,
95
Armenia, and Turkey.
163
Fruits and vegetables are the second largest in farm FLW, with
a median of 13 kg/cap. However, the magnitude of fruits and
vegetables losses and waste varies significantly between
developing and developed countries. For example, it was
estimated that 20−30% of total fruits and vegetables production
was lost at the agricultural stage in China,
37
while this share was
only 6−15% in Italy.
96
This big difference can be explained by
the use of more advanced and new technologies and
innovations in more developed countries (where farm FLW
is mainly in the form of outgrades). The farm FLW rates of
meat and fish and dairy products and eggs are relatively small.
Postharvest Losses and Waste. Figure 7 presents
postharvest FLW (during postharvest handling and storage,
manufacturing, distribution, and retailing) of the four most-
relevant food commodities in the literature along the supply
chain.
•The postharvest FLW of cereals and cereal products vary
greatly at different stages. The major FLW are found at
the postharvest handling and storage stage (over 18 kg/
cap) and in developing countries. For example, it was
reported that cereals had the highest postharvest FLW
out of all food commodities in South and Southeast Asia.
In particular, rice as the staple food in the Philippines had
a postharvest FLW rate of 10%.
18
The retailing stage
seconds this with a median value of over 10 kg/cap,
followed by the manufacturing and distribution stages
(approximately 5 kg/cap).
•Fruits and vegetables dominate postharvest FLW among
all food commodities. For example, it was estimated that
the manufacturing FLW of fruits and vegetables was over
33 kg/cap in South Africa,
93
which was much higher than
that of all other food groups or stages. FLW at
manufacturing stage in developed countries are relatively
low, e.g., only about 5 kg/cap in Denmark.
161
The
distribution stage shows a high FLW of approximate 17
kg/cap, which is about 4 and 6 kg/cap, respectively,
higher than the postharvest handling and storage and
manufacturing stages. The FLW at retailing stage is the
smallest, about 3 kg/cap.
•Meat and fish products contribute the least to
postharvest FLW. Their FLW at postharvest handling
and storage stage is very small, at about 0.3 kg/cap. The
FLW at manufacturing and retailing stages are similar,
both with a median value of about 1.3 kg/cap. One study
reported that the FLW rates of meat at postharvest
handling and storage, manufacturing, and distribution in
Turkey were 0.2%, 5%, and 0.5%, respectively.
164
•The median FLW of dairy products and eggs is observed
at approximately 6, 3, 0.2, and 3.4 kg per capita for the
four substages, respectively. A study found that the FLW
rates of milk at manufacturing and distribution stages in
Ukraine were 3−15% and 8−11%, respectively, due
mainly to poor cooling systems.
164
FLW at the retailing stage in the United States is a particular
focus in the literature. It was estimated that about 2.4 million
tons of food (excluding inedible parts) was lost at the retailing
stage in 1995,
41
but it has gone up to 19.5 million (including
part of inedible food) tons in 2010, representing 10% of the
available food supply in the United States
23
Cereal products,
vegetables, and fruits contribute the most to the retailing FLW,
roughly about 10.5, 8, and 6 kg per capita, respectively, while
meat and fish products contributes the least (details in Figure
S3 and Table S8). For example, some studies reported that the
retailing FLW of cereal products equaled to 12% of the U.S.
food supply.
48,49,52
It should be noted that retailing FLW in
industrialized countries, including the United States, is likely to
be dominated by supermarkets but not street markets and
nonsupermarkets (often found in less-developed countries). In
2005 and 2006, for example, the U.S. supermarket FLW for
fresh fruits, vegetables, and meat and seafood were, on average,
11.4%, 9.7%, and 4.5%, respectively.
20
These data are consistent
with estimates from other industrialized countries, indicating
that fresh products and bakery make up the largest share of
retailing FLW due to factors such as expired sell-by dates,
product damage and quality issues, and improper stock
rotation.
41
Consumer Food Waste. Household Food Waste. In
medium- and high-income countries, household food waste
makes up the largest share in the total FLW, mainly because of
poor purchase planning, cooking or serving too much,
overstocking, and misinterpretation of “best before”and “use
Figure 6. Per-capita farm FLW of different food commodities.
Detailed data are available in Table S3.
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by”dates.
13,165
In the EU, about 45 million tons or 45% of the
total FLW was found at the household level.
11
Food waste
arising from households represented 51% of total FLW
throughout the food supply chain in Canada
17
and 19% of
food and drink purchased by U.K. households, equivalent to
70% of U.K. postfarm-gate FLW (i.e., FLW during postharvest
stages and consumption).
30
Similar patterns can also be
observed in the households in the United States,
54
Germany,
121
Sweden,
71
and Australia.
166
Low-income countries, on the
contrary, show a relatively small share of food waste in
households due to limited disposable household income.
9,165
However, upon closer inspection, we see little primary data
available at household level in emerging and developing
countries, and household food waste, especially in cities, may
be much larger than anticipated. Without significant primary
research in these countries, generalizations should be made
cautiously.
Figure 8a presents a positive relationship between per-capita
GDP and household food waste per capita. When per-capita
GDP rises, the amount of per-capita food waste generated in
households also increases. This pattern agrees with observa-
tions in a few previous studies.
11,134,164,166,167
For example, it
was reported that in 2007, the food waste generated in
households in South Africa was only 7.3 kg/cap,
89
while U.K.
households generated 109.3 kg/cap,
169
though data robustness
for the South African estimate is expected to be limited
Figure 7. Per-capita postharvest FLW of cereals and cereal products, fruits and vegetables, meat and fish, and dairy products and eggs at different
stages. Detailed data are shown in Tables S4−S7.
Figure 8. Correlation between per-capita GDP and per-capita consumer food waste: (a) households (R2= 0.34, P< 0.05); and (b) food service
sector (R2= 0.01, P> 0.05). Data are in Tables S9 and S10. Note that an outlier in panel a is excluded for the convenience of visualization (see
Figure S4 for the original version). Panel b distinguishes restaurants (empty circles) and other food service sectors (e.g., canteens; filled circles), and
the circles with a cross enclosed are for restaurants in Japan.
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(extrapolated from Sub-Saharan estimates, which are less
wealthy and industrialized than South Africa).
However, it is interesting to observe that, when per-capita
GDP gets higher than a certain level (roughly 50 000 USD),
per-capita food waste generation tends to level off. This might
reflect the increasing awareness of the public, food waste
prevention campaigns, stricter regulation (e.g., clearer labeling
and longer shelf life), and effect of market mechanisms (e.g.,
increasing cost of food purchase and food waste disposal). For
example, campaigns such as “Zero Waste”and “Love Food
Hate Waste”have been taken against food waste in
Australia
168,170
and the United Kingdom.
26,171
This may also
relate to higher consumption of prepared meals and less
cooking from scratch (which may transfer food waste from
household kitchen to food manufacturing to some extent) in
more-affluent countries and the fact that waste generation data
are based on the management of waste (which is generally
much higher in more-affluent countries).
Out-of-Home Food Waste. A number of studies have
estimated how much food has been wasted away from home,
i.e., in the food service industry, which is defined as a sector
responsible for preparing or serving food outside home,
85,129
including, for example, restaurants,
62,126,129,145,172
can-
teens,
126,161
schools,
19,46,157,173−175
hospitals,
45,101,110,148,159
care centers,
42,129
military institutions,
82
transport hubs, and
in-flight catering.
80,176
The research on food waste in the food service sector has
mostly been conducted in industrialized countries. For example,
it was estimated that 0.92 million tons of food was wasted in
the food service outlets each year in the United Kingdom
(equivalent to 17% of all meals served), 75% of which was
avoidable.
85
In Germany, the food service sector accounted for
17% (the second largest source) of total FLW along the supply
chain.
121
In Finland, 0.075−0.085 million tons of food was
wasted in food service, which was the third largest contributor
of FLW (20%) following households (35%) and food industry
(27%).
126
It should be noted that China, as the largest
emerging economy in the world, was also experiencing a high
level of food waste in the catering and restaurant sector,
accounting for about 11−17% of all food served.
37
On the whole, food waste per capita at away-from-home
consumption is lower than that in households (Figure 8b). It is
assumed that with higher per-capita GDP and living standards,
people tend to consume more food outside the home, which
may consequently result in a larger amount of food waste due
to various reasons (e.g., oversized dishes and taste). Yet the
correlation between per-capita GDP and per-capita food waste
out-of-home appears insignificant. The reason may be that the
food service sector is varied and includes both the “for profit”
(e.g., restaurant) and “cost”(e.g., care center) parts, leading to a
mixed pattern of food waste generation. Interestingly,
restaurant food waste in Japan shows a declining pattern in
recent years (the circles with a cross in Figure 8b). This may be
partly explained by the impact of the implementation of the
Food Recycling Law (which is to reduce food waste generation
by introducing specific targets for industry sectors) in Japan in
May 2001, which contributed to a reduction of out-of-home
food waste from 3.1 million tons in 2007 to 1.92 million tons in
2012. Accordingly, food waste per capita decreased from 24.22
to 15.05 kg in Japan.
85
In Figure 9, we take cereals and the United States, China, and
South Africa as examples of industrialized, emerging, and
developing countries to illustrate how the FLWR at different
stages along the supply chain evolves at different development
levels of an economy.
•As the United States is a highly industrialized country,
there are few data on its FLWR of cereals at postharvest
stages (it can also be assumed to be low). The FLWR at
agricultural production, postharvest handling and storage,
manufacturing, and distribution stages in South Africa are
all higher than those in China. This reflects the fact that
with increasing awareness and growing economy, more-
Figure 9. FLWR of cereals along the supply chain in the United States, China, and South Africa. The vertical chart on the left represents per-capita
GDP in current USD in 2015 for these three countries (according to the World Bank). P1: agricultural production and harvesting; P2: postharvest
handling and storage; P3: manufacturing; P4: distribution; P5: retailing; and P6: consumption. N.A. means not available. The reference flow is
assumed to be a fictive output of 100% of the amount produced. Due to a lack of FLW percentage for each stage in South Africa, the average amount
of waste reported between 2007 and 2009 was divided by the average quantity of production during the same period to calculate the FLWR.
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advanced harvesting technologies and more-efficient
storage systems are applied in agricultural production,
and improved transportation with large volumes and
relatively low costs are largely used in China.
136
This also
implies a huge potential of improving the technologies
and infrastructure in less-developed countries as an
efficient way to reduce FLW.
•The consumer cereal waste also increases as a country
develops and increases its GDP. The FLWR of cereals at
consumption stage in the United States is the highest
(15.8%), followed by the retailing stage (12%). In China,
with rapid economic development and household
income increase, the FLWR of cereals at the con-
sumption stage has increased in recent years to 6.4%,
higher than that of all other stages. As a lower-income
country, South Africa shows a low FLWR of cereals at
consumer stage yet (1.1%). It should be noted that,
because the production and consumption structure of
cereals (in terms of rice, wheat, maize, other cereals, and
bakery products) varies in different countries, it can be a
factor behind these differences as well.
3.4. Data Gaps and Recommendations for Future
Study. Our review suggests that the quantification of FLW has
become a research hotspot in recent years, with over 60% of
FLW data reported for the recent decade. Whereas these
growing efforts provide an order-of-magnitude understanding
of the scale of global FLW and for a few countries (e.g., the
United States and the United Kingdom) and stages in the food
supply chain (e.g., household), the extent of FLW in many
other countries and stages remains poorly understood. The
existing data are also often based on secondary sources (over
half of the reviewed publications) and outdated or inconsistent
data sources (e.g., due to choice of method). Moreover, in line
with the First Principle of Food Waste proposed by Rathje,
134
the potential for waste is expected to increase with continuing
urbanization, increasing household income, and growing
demand for more perishable foods. Yet the FLW data gaps
and deficiencies are most-significant for those countries and
regions that have undergone the most-rapid shifts away from
starchy staples toward more varied and fresh diets (e.g., China
and India).
36
Therefore, the existing global FLW data should be
used and interpreted with care.
To address these data gaps, we highlight the following
directions for future study:
•First, the systems and methodologies for FLW
quantification should be standardized, as is already
highlighted in the literature. Important aspects to be
considered include: the definition of FLW (e.g.,
questions regarding avoidable versus unavoidable food
waste),
177
stages of the food supply chain (e.g., different
segments in distribution and consumption), destination
of FLW (e.g., donation, feed, energy use, or landfill),
classification of food commodities and conversion factors
(e.g., factor to convert cooked food items to raw food
materials), units of measurement (e.g., physical weight or
calories), and the methods of measurement (cf. section
3.2 above). This would enable the comparison of existing
data across countries, commodities, and food supply
chains, which would further help explore patterns and
driving factors of FLW generation. For example, the
European FUSIONS project released a food waste
quantification manual
32
in 2016; the first global Food
Loss and Waste Protocol
35
published in 2016 provides a
standard that can be used by any entity (e.g., a country, a
company, a city, or an individual store or food outlet)
and should be promoted more widely.
•Second, more data based on direct measurement are
badly needed. Our review shows that only around 20% of
the existing publications on FLW quantification are based
on first-hand data, and any quotation of unrepresentative
data from literature may lead to high uncertainties.
Despite the higher time, labor, and economic cost, more
field work and primary data collection should be
encouraged and would help verify existing data, improve
the accuracy and reliability of the data, and fill in the gaps
in countries where data are currently not available.
•Third, more attention should be paid to countries
outside the current focus area (the United States and
Europe), especially to big developing and emerging
economies (e.g., the BRICs: Brazil, Russia, India, China,
and South Africa). There is less information regarding
FLW in those countries, but the scale may be significant
(e.g., a preliminary study
37
shows that consumer food
waste in China is already higher than that of the total of
EU-27). These countries are also experiencing a rapid
shift in terms of dietary change, urbanization, and
household income increase, and thus, their FLW is
expected to grow in the coming years. The use of
outdated data may have led to an overestimation of
agricultural FLW and underestimation of consumer food
waste in developing countries.
37,78
Social and cultural
context are also very important for FLW quantification
and mitigation, which can only be addressed when more
data for specific countries and cultures are available.
•Fourth, more in-depth analyses on FLW at different food
supply stages should be conducted. Household food
waste is a clear current focus (covered in almost half of
the reviewed publications, though almost exclusively in
developed countries). Research should be expanded to
food supply chains with less data and poorer under-
standing, e.g., FLW in other segments during out-of-
home consumption (e.g., canteens and restaurants) and
postharvest and retailing in developing countries. A
more-detailed quantification at each stage would also
help a better understanding of the driving factors of FLW
at different stages.
•Fifth, consistent databases (global, regional, and na-
tional) using a common reporting framework on FLW
should be established, maintained, and made available to
the public, with joint efforts from all stakeholders along
the entire food chain. Such databases would provide a
baseline for monitoring the progress of FLW reduction,
which is important for tracking progress toward SDG
Target 12.3, and national political targets on FLW.
Governmental and nongovernmental organizations such
as UN Environment and FAO and national statistical
agencies should take a stronger leadership in this effort
(the data series reported by WRAP and USDA-ERS are
good examples). Companies should be encouraged to
report their FLW regularly (e.g., in their annual corporate
social responsibility report). In the long run, the
“measurable, reportable, and verifiable (MRV)”principle
that is widely acknowledged in greenhouse gas emissions
reduction targets may be appropriate for tracking FLW
reduction.
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•Last but not least, quantification of FLW is only a first
step; the aim of better data measurement and monitoring
is to help better-understand the social, economic, and
environmental impacts of FLW, identify hotspots where
actions should be prioritized, develop long-term
scenarios to inform relevant policy-making, understand
which policies and strategies have been most-effective at
achieving FLW reductions, and contribute overall to the
reduction of FLW and the sustainability of the food
system. Research focusing on these topics should
naturally be conducted in parallel.
■ASSOCIATED CONTENT
*
SSupporting Information
The Supporting Information is available free of charge on the
ACS Publications website at DOI: 10.1021/acs.est.7b00401.
Additional details on the literature selection. Figures
showing temporal trends, an overview of methods, per-
capita FLW, and experimental relationships. Tables
showing metadata used in the paper. (PDF)
A table showing compiled food losses and food waste
data reported in the reviewed publications (by physical
weight). (XLSX)
■AUTHOR INFORMATION
Corresponding Author
*Phone: 45-65509441; e-mail: gli@kbm.sdu.dk.
ORCID
Gang Liu: 0000-0002-7613-1985
Notes
The authors declare no competing financial interest.
■ACKNOWLEDGMENTS
This work is funded by National Natural Science Foundation of
China (key program, project no. 71233007), National Key
Research and Development Plan of China (project no.
2016YFE0113100), and the Danish Agency for Science,
Technology and Innovation (International Network Pro-
gramme, reference nos. 5132-00029B and 6144-00036). We
thank Yao Liu for research assistance.
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