FOOD WASTE INDEXINDEXREPORT 2021
REPORT
2021

UNEP FOOD WASTE INDEX REPORT 2021

© 2021 United Nations Environment ProgrammeISBN No: 978-92-807-3851-3
Job No: DTI/2349/PA
This publication may be reproduced in whole or in part and in any form for educationalor non-profit services without special permission from the copyright holder, provided
acknowledgement of the source is made. United Nations Environment Programme would
appreciate receiving a copy of any publication that uses this publication as a source.
No use of this publication may be made for resale or any other commercial purpose
whatsoever without prior permission in writing from the United Nations Environment
Programme. Applications for such permission, with a statement of the purpose and extent
of the reproduction, should be addressed to the Director, Communication Division, United
Nations Environment Programme, P. O. Box 30552, Nairobi 00100, Kenya.
Disclaimers
The designations employed and the presentation of the material in this publication do
not imply the expression of any opinion whatsoever on the part of the Secretariat of theUnited Nations concerning the legal status of any country, territory or city or area or of its
authorities, or concerning the delimitation of its frontiers or boundaries.
Some illustrations or graphics appearing in this publication may have been adapted fromcontent published by third parties. This may have been done to illustrate and communicate
the authors’ own interpretations of the key messages emerging from illustrations orgraphics produced by third parties. In such cases, material in this publication do not imply
the expression of any opinion whatsoever on the part of United Nations Environment
Programme concerning the source materials used as a basis for such graphics orillustrations.
Mention of a commercial company or product in this document does not imply endorsementby the United Nations Environment Programme or the authors.
The use of information from this document for publicity or advertising is not permitted.
Trademark names and symbols are used in an editorial fashion with no intention on
infringement of trademark or copyright laws.
The views expressed in this publication are those of the authors and do not necessarilyreflect the views of the United Nations Environment Programme.
We regret any errors or omissions that may have been unwittingly made.

Suggested citation
United Nations Environment Programme (2021). Food Waste Index Report 2021. Nairobi.

2

ACKNOWLEDGEMENTS

ACKNOWLEDGEMENTS
Authors: Hamish Forbes (WRAP), Tom Quested (WRAP), Clementine O’Connor (United
Nations Environment Programme)Peer reviewers (modelling): Carola Fabi & Sharon Mayienga (FAO), Gang Liu (Universityof Southern Denmark), Julian Parfitt (Anthesis Group), Gustavo Porpino (Embrapa),
Felicitas Schneider (Thünen-Institut).
Other contributors and reviewers: Martina Otto (UNEP), Dany Ghafari (UNEP), Ludgarde
Coppens (UNEP), James Lomax (UNEP), Jean-Pierre Sfeir (UNEP), Richard Swannell(WRAP), Billy Harris (WRAP), Sam Gillick-Daniels (WRAP), Andrew Parry (WRAP).

3

UNEP FOOD WASTE INDEX REPORT 2021

FOREWORD:
UNEP FOOD WASTEINDEX REPORT
If food loss and waste were a country, it would be the third biggest source of greenhousegas emissions. Food waste also burdens waste management systems, exacerbates
food insecurity, making it a major contributor to the three planetary crises of climatechange, nature and biodiversity loss, and pollution and waste. This is why Sustainable
Development Goal 12.3 aims to halve food waste and reduce food loss by 2030.
In support of this vital target, UNEP’s first Food Waste Index report provides insights into
the scale of food waste and a methodology that enables countries to measure baselines
and track progress in meeting the SDG target. The report estimates that food waste fromhouseholds, retail establishments and the food service industry totals 931 million tonnes
each year. Nearly 570 million tonnes of this waste occurs at the household level. The
report also reveals that the global average of 74 kg per capita of food wasted each year isremarkably similar from lower-middle income to high-income countries, suggesting thatmost countries have room to improve.
It is fitting that this report is being launched just after the fifth United Nations Environment
Assembly, which at its previous meeting urged Member States to establish mechanisms
for measuring food loss and waste, and requested support in providing technicalassistance that would allow countries to make measure and make progress. The report isa first step in meeting this request. In April, we will establish regional food waste working
groups in Africa, Asia Pacific, Latin America and the Caribbean, and West Asia to support
member states in developing food baseline baselines and strategies – bringing the report
to life. The report is also part of UNEP’s offer to the UN Food Systems Summit process,with the aim of encouraging the widespread adoption of a Target-Measure-Act approach
to food waste reduction.
Some countries and private sector actors have already taken the SDG12.3 commitment
to heart. There is growing evidence of success in reducing food waste – though not at
the scale needed to achieve the target. Much more can be done. We need, for example,
to address the role of consumer behaviour, in all cultural contexts, in achieving thetarget. Let us all shop carefully, cook creatively and make wasting food anywhere socially
unacceptable while we strive to provide healthy, sustainable diets to all.

Inger Andersen

Executive Director
United Nations Environment Programme,
March 2021

4

TABLE OF CONTENTSTABLE OF CONTENTSACKNOWLEDGEMENTS.................. 33

LIST OF TABLES.............................. 6

INDEX LEVELS 2 AND 3:
MEASURING FOOD WASTE
AT THE NATIONAL LEVEL............ 73

3.1 OVERVIEW...................................................... 73

LIST OF FIGURES............................ 6

3.2 STEPS FOR MEASUREMENT.........................74

LIST OF BOXES............................... 6

3.3 DATA SOURCES, AVAILABILITY AND
PRODUCTION................................................. 84

EXECUTIVE SUMMARY.................. 7
KEY FINDINGS................................ 8
1

INTRODUCTION............................ 20

1.1

THE FOOD WASTE INDEXAND SUSTAINABLE DEVELOPMENT
GOAL 12.3....................................................... 21

2

INDEX LEVEL 1: EXISTINGDATA AND EXTRAPOLATIONTO OTHER COUNTRIES................. 23

3.4 SPECIFICS FOR LEVEL 3............................... 86
3.5 METHODOLOGICAL CHALLENGES AND
LIMITATIONS.................................................. 88
3.6 BENEFITS OF MEASUREMENT AND
EXAMPLES..................................................... 90

4

DISCUSSION AND/OR
RECOMMENDATIONS.................... 92
BIBLIOGRAPHY............................. 94

2.1 LEVEL 1 ESTIMATES OF FOOD WASTE:
WHAT AND WHY?.......................................... 23
2.2 SUMMARY OF THE METHODOLOGY........... 25
2.3 RESULTS: DATA COVERAGE......................... 27
2.4 REGION-SPECIFIC DATA................................ 32
2.5 FOOD WASTE AMOUNTS: MEASURED
ESTIMATES AND EXTRAPOLATIONS.......... 54

5

UNEP FOOD WASTE INDEX REPORT 2021

LIST OF TABLES
Table 1: Average food waste (kg/capita/year) by WorldBank income classification, averaging medium
and high confidence estimates for countries.. 8

Table 21: Average food waste (kg/capita/year) by WorldBank income classification, averaging medium
and high confidence estimates for countries.55

Table 2: Number of countries with measured data
points, by sector and World Bank income
classification..................................................... 11

Table 22: Average food waste (kg/capita/year) byregion, averaging medium and high confidence
estimates for countries.................................... 58

Table 3: Countries with high confidence food waste
estimates, by sector......................................... 13

Table 23: Household food waste estimates (frommeasured data points or extrapolation) for
each country...............................................60 - 69

Table 4: Appropriate methods of measurement fordifferent sectors............................................... 15

Table 24: Estimates of global food waste by sector..... 70

Table 5: Coverage of relevant food waste data points
globally, by sector............................................. 27

Table 25: Appropriate methods of measurement fordifferent sectors............................................... 78

Table 6: Number of countries with measured data
points, by World Bank income classification.. 27

Table 26: Worked example of Food Waste Indices forhousehold food waste for two hypotheticalcountries............................................................ 83

Table 7: Number of countries with measured data
points, by region (UNEP classification).......... 28
Table 8: Share of population residing in countries withexisting estimates, by region........................... 29

LIST OF FIGURES

Table 9: Share of global population residing in countries
with existing estimates.................................... 30

Figure 1: Food Waste Index Inventory Scope, using Food
Loss and Waste Standard................................ 14

Table 10: Number of data points, by geographical scope.
31

Figure 2: Food Waste Indices for two hypotheticalcountries............................................................ 17

Table 11: Data points relating to households fromAfrican studies.................................................. 34

Figure 3: Scope of the Food Waste Index (Levels 2 and
3) using the Food Loss and Waste Accounting
and Reporting Standard................................... 22

Table 12: Findings of studies providing results, byincome group.................................................... 36
Table 13: Data points relating to households fromstudies in Latin America and the Caribbean.. 39
Table 14: Data points relating to households fromstudies in Asia and the Pacific......................... 41

Figure 5: Food Waste Indices for two hypotheticalcountries............................................................ 83

Table 15: Data points relating to households fromstudies in West Asia.........................................44

LIST OF BOXES

Table 16: Data points from studies in North America... 47

Box 1: Within-country variation....................................... 35

Table 17: Data coverage in Europe, by sector and subregion................................................................. 49

Box 2: Retail challenges................................................... 39

Table 18: Data points relating to households fromstudies in Europe.............................................. 51

Box 4: Food service challenges.......................................48

Table 19: Data points relating to food service fromstudies in Europe.............................................. 52
Table 20: Data points relating to retail from studies in
Europe................................................................53

6

Figure 4: Scatter plot of food waste (household) against
GDP of country.................................................. 56

Box 3: Edible and inedible parts...................................... 45

EXECUTIVE SUMMARY
Food waste reduction offers multi-faceted wins for people and planet, improving foodsecurity, addressing climate change, saving money and reducing pressures on land, water,
biodiversity and waste management systems. Yet this potential has until now been woefully
under-exploited.
This potential may have been overlooked because the true scale of food waste and its impacts
have not been well understood. Global estimates of food waste have relied on extrapolation ofdata from a small number of countries, often using old data. Few governments have robust
data on food waste to make the case to act and prioritize their efforts.
Sustainable Development Goal 12.3 (SDG 12.3) captures a commitment to halve food waste
at the retail and consumer level and to reduce food loss across supply chains. This Food
Waste Index Report aims to advance progress on SDG 12.3 in two ways:• Firstly, it presents the most comprehensive food waste data collection, analysis andmodelling to date, generating a new estimate of global food waste. Country-level foodwaste estimates have been calculated, and while confidence intervals for estimates varyby region and by sector, they offer new insight into the scale of the problem and into thesubstantial prevention potential in low-, middle- and high-income countries.

• Secondly, this report publishes a methodology for countries to measure food waste, athousehold, food service and retail level, in order to track national progress towards 2030
and to report on SDG 12.3. Countries using this methodology will generate strong evidence
to guide a national strategy on food waste prevention, food waste estimates that are
sufficiently sensitive to pick up changes in food waste over two- or four-year intervals, andthat enables meaningful comparisons among countries globally.

In complement to the Food Loss Index, developed by the Food and Agriculture Organizationof the United Nations (FAO), the Food Waste Index covers the later stages of food’s journey –food waste – occurring at household, food service and retail level.
7

UNEP FOOD WASTE INDEX REPORT 2021

KEY FINDINGSThis report estimatesthat around 931 million
tonnes of food waste was
generated in 2019, 61 percent of which came fromhouseholds, 26 per centfrom food service and 13
per cent from retail. Thissuggests that 17 per cent of
total global food production
may be wasted (11 per centin households, 5 per cent infood service and 2 per centin retail)1.

Household per capita
food waste generation isfound to be broadly similar
across country income
groups, suggesting that
action on food waste
is equally relevant inhigh, upper‑middle andlower‑middle income
countries. This diverges
from earlier narratives
concentrating consumer foodwaste in developed countries,
and food production, storage
and transportation losses indeveloping countries.

Previous estimatesof consumer foodwaste significantly
underestimated its scale.
While data doesn’t permit
a robust comparison
across time, food waste at
consumer level (household
and food service) appears
to be more than twice theprevious FAO estimate(Gustavsson et al., 2011).

There is insufficient data on
the edible fraction of foodwaste to allow comparative
analysis across country
income groups, but even
if inedible parts (bones,
pits, eggshells, etc.)
predominate in lowerincome countries, there issufficient total food waste
in these areas for circularapproaches or other foodwaste diversion strategies
to be important.

Table 1: Average food waste (kg/capita/year) by World Bank income classification, averaging

medium and high confidence estimates for countries

Income group

Average food waste (kg/capita/year)
Household

Food service

RetailHigh-income countries

79

26

13

Upper middle-income countries

76

Insufficient data

Lower middle-income countries

91

Insufficient data

Low-income countries

Insufficient data

1 The apparent discrepancy between the sum of the percentages for each sector
and the total is due to rounding.

8

EXECUTIVE SUMMARY

Global food waste data availability is currently low, and measurement approaches have been highlyvariable. The report identifies 17 countries with high-quality data compatible with SDG 12.3.1(b) reportingin at least one sector: Australia, Austria, Canada, China, Denmark, Estonia, Germany, Ghana, Italy, Malta,
the Netherlands, New Zealand, Norway, the Kingdom of Saudi Arabia, Sweden, the United Kingdom andthe United States. 42 countries have data assigned a medium confidence2 level in this report for at least
one sector, meaning that small updates in methodology, geographical coverage or sample size would
allow these countries to create an SDG 12.3-compatible estimation. This report provides a frameworkthat supports countries in transitioning to a common global measurement approach that allows forconsistent reporting under SDG 12.3.

Scope and definition of food waste
For the purposes of the Food Waste Index, “food waste” is defined as food (see below) and theassociated inedible parts removed from the human food supply chain in the following sectors:
Retail, Food service , Households“Removed from the human food supply chain” means one of the following end destinations: landfill;controlled combustion; sewer; litter/discards/refuse; co/anaerobic digestion; compost / aerobic
digestion; or land application.
Food is defined as any substance – whether processed, semi-processed or raw – that is intended forhuman consumption. “Food” includes drink, and any substance that has been used in the manufacture,
preparation or treatment of food. Therefore, food waste includes both:• “edible parts”: i.e., the parts of food that were intended for human consumption, and• “inedible parts”: components associated with a food that are not intended to be consumed by humans.
Examples of inedible parts associated with food could include bones, rinds and pits/stones.

SDG 12.3 Indicators
SDG 12.33 covers food and inedible parts that exit the supply chain and thus are lost or wasted, andis tracked through two indicators:• Indicator 12.3.1(a), the Food Loss Index, measures losses for key commodities in a country acrossthe supply chain, up to but not including retail. FAO is its custodian.
• Indicator 12.3.1(b), the Food Waste Index, measures food waste at retail and consumer level(households and food service). The United Nations Environment Programme (UNEP) is its
custodian. In contrast to the Food Loss Index, the Food Waste Index measures total food waste(rather than loss or waste associated with specific commodities).
The Food Waste Index also allows countries to measure and report on food loss generated in
manufacturing processes, which would not be captured under key commodity losses by the Food
Loss Index.

2 The confidence rating is not a judgement on the quality of the study undertaken.
It is an assessment – based on the reviewers’ understanding of the study – of
how robust the estimate of food waste is for tracking food waste in the given
country. In many cases, this was not an aim of the original study.
3 SDG 12 https://sdgs.un.org/goals/goal12

9

UNEP FOOD WASTE INDEX REPORT 2021

D THE FOOD WASTE INDEX MEASUREMENT APPROACH
The Food Waste Index has a three-level methodology, increasing in accuracy and usefulness of data, but also
increasing in the resources required to undertake them:

Level 1 uses modelling to estimate food waste, forMember States that have not yet undertaken their own
measurement. Level 1 involves extrapolating data fromother countries to estimate food waste in each sector
for a given country. The estimates for these countries
are approximate: they are sufficient to provide insight
into the scale of the problem and to make a case foraction, but inadequate to track changes in food waste
over time. They are intended as a short-term support
while governments develop capacity for national
measurement (consistent with Level 2). Calculated
Level 1 estimates are presented in this report for all
countries.

Level 3 provides additional information to inform
policy and other interventions designed to reduce foodwaste generation. This includes the disaggregationof data by destination, edible/inedible parts, gender;
reporting of manufacturing food waste not covered by
the Food Loss Index (for example, where more than
one commodity is combined to produce complex foodproducts); and additional destinations such as sewer,home composting and (non-waste) animal feed.

Level 2 is the recommended approach. It involves
measurement of food waste in countries. The natureof the measurement will vary according to sector andcircumstances. It will be either undertaken by national
governments or derived from other national studies
undertaken in line with the framework described below.
Level 2 generates primary data on actual food waste
generation and fulfils the requirement for tracking
food waste at a national level, in line with the SDG 12.3
target.

LEVEL 1LEVEL 2• Modelling andextrapolation• Direct measurementof food waste• Additional informationand disaggregation• Provides approximate
estimate• Sufficiently accurate
for tracking• Supports developmentof food waste
prevention strategy• Not suitable for tracking
purposes

10

LEVEL 3

EXECUTIVE SUMMARY

D LEVEL 1: MODELLING APPROACH AND OUTCOMES
While the availability of food waste data remains limited, there have been a growing numberof national estimates of food waste from countries around the world in recent years.
A new global food waste baseline was developed using both existing data points fromstudies measuring food waste inside a Member State (where available) and extrapolations
based on the estimates observed in other countries (where no estimate was identified for
the Member State).
Adjust somedata forconsistency

Assignconfidence
rating toestimate

Extrapolate
for countries
without data
Searchand collate
existing
data

Filter data on
scope andapplicability tocurrent study‘s
needs

More relevant data was uncovered than expected, with 152 food waste data points identified in 54 countries.
While the majority of studies come from high-income countries, especially in the food service and retail sectors(78 per cent of countries with a food service estimate and 87 per cent of countries with a retail estimate are
high-income countries), data at the household level has a more even distribution across income groups, with 54
per cent of the 52 countries that have existing estimates being high-income countries, 23 per cent being upper
middle-income countries and 19 per cent being lower middle-income countries. Only two low-income countries
have household food waste estimates, representing 4 per cent of the global estimates.

Table 2: Number of countries with measured data points, by sector andWorld Bank income classification

World Bank income groupHousehold

Food service

RetailHigh-income countries

28

18

20

Upper middle-income countries

12

3

2

Lower middle-income countries

10

2

1

Low-income countries

2

0

0

Total

52

23

23

11

UNEP FOOD WASTE INDEX REPORT 2021

There is an uneven distribution of data between
regions as well as country income groups. Key data
gaps exist at the household level in low-income
countries, small island states, Central Asia andNorthern Africa, and at the food service and retail levels
more broadly (in low-income, lower middle-income
and upper middle-income countries). Extrapolation
to provide an approximate estimate of food waste incountries without data will have greater uncertainty
for these areas (see confidence classifications below).
Multiple studies were found at the household level in all
European regions, the Americas, most Asian regions,
Australia and New Zealand, and Sub-Saharan Africa.
Therefore, for most regions, estimating household
food waste in countries without data can be based on
extrapolation of nearby countries.
Food waste data points that were used in this study, for
an individual country and for extrapolation purposes,
were classified as:
1) High confidence estimates: likely to be suitable
for tracking national levels of food waste – i.e.,developed using a robust methodology, coveringa substantial part of the country and with noadjustment of the data required to align it withthe current studies’ purposes; or2) Medium confidence estimates: measured
using methodologies that may be suitable fordetecting larger changes in food waste, e.g., data
points from cities used to represent a country,
data points requiring adjustment to align withthe current studies’ purposes.

12

Extrapolations based on good regional data havebeen marked as low confidence estimates, andextrapolations where regional data was limited areconsidered very low confidence estimates.
Rich sub-national data has greatly improved
household food waste estimates. Household foodwaste data is more widely available than previously
expected, in particular due to the inclusion of a large
number of studies conducted at the city or municipality
level. Sub-national studies outnumber nationwide
studies at household level. In most cases, thesestudies were not focused specifically on food waste
but rather were related to general waste planning in an
area, and total waste was collected from households
and disaggregated to include a food-specific estimate.
Food waste is a significant waste material that local
authorities collect, and robust food waste data canguide both prevention and circular food strategies,
alleviating pressure on local waste management
systems.
Fourteen countries have household food waste
data compatible with the Food Waste Index. These
countries have measured food waste in a way that is
broadly consistent with the methods suggested in thisreport. As a result, the estimates are likely to be suitable
for tracking changes over time and for reporting
progress on the food waste component of SDG 12.3.
Nine countries have measured food waste in the foodservice sector and ten countries in the retail sector inthis way. 42 countries have medium confidence datain one or more sectors where the scope or parameters
could be expanded to align with SDG 12.3 reporting.

EXECUTIVE SUMMARY

Table 3: Countries with high confidence food waste estimates, by sector
Household

Food service

RetailAustralia

Australia

Australia

Austria

Austria

Austria

CanadaChina

Denmark

Denmark

Denmark

Germany

Germany

Estonia

Italy

Ghana

Germany

New Zealand

Malta

SwedenSaudi Arabia

Netherlands

United Kingdom

SwedenNew ZealandUnited States

United Kingdom

NorwayUnited States

Saudi Arabia
SwedenUnited Kingdom
United States

The household food waste estimate is the most robust
among the three sectors, based on nearly 100 data
points across a range of countries representing 75
per cent of the world’s population. In contrast, theestimates for the retail and food service sectors are
based on around 30 data points for each, with the
majority coming from high-income countries. Countries
with measured data points represented 32 per centof the world’s population for food service and 14 percent for retail. In addition, many of the food service
estimates are incomplete, not covering the range ofsettings outside the home in which food is served andconsumed.
Much more measurement is needed to spur action,
following the ‘Target – Measure – Act’ approachpromoted by high level food loss and waste coalition
Champions 12.3. Even though household data coverage
is good, the estimates in many countries come fromsmall, limited samples or required adjustment forcomparability. Only 9 per cent of the global population
lives in a country with a high confidence household foodwaste estimate, and rates are similarly low for retail (8per cent), but higher for food service (25 per cent). To
improve food waste responses, more countries needto measure food waste, using accurate methods on
significant sample sizes.

Similarly, further research to quantify the environmental,
economic and social impacts and to understand thecauses of this food waste is needed. Some countries
now have data and research on the types of food thatare wasted and why; increasing this understanding
to a wider range of countries would allow stronger
strategies and programmes of work to minimize wasteof valuable food resources in these sectors. Increased
reporting of food waste in the coming years will makeit possible to track progress over time and bettersupport Member States in delivering SDG 12.3 and aworld in which less food is wasted. The measurement
methodology presented in this report offers a common
approach to data collection.

13

UNEP FOOD WASTE INDEX REPORT 2021

D LEVELS 2 AND 3:

NATIONAL FOOD WASTE MEASUREMENT METHODOLOGY

Modelling and extrapolation are a first step, but direct
measurement of food waste is what is ultimately
needed for a country to track its food waste over time,
and to enable policymakers to make key strategic
decisions about how to prevent food waste.
Level 2 and Level 3 of the Food Waste Index provide
the framework for countries to measure and reportfood waste, in a way that is sensitive enough to trackprogress towards the SDG 12.3 target. Levels 2 and3 use data from measurements of food waste in therelevant country and time frame, rather than proxy data
(Level 1).

The Level 2 approach requires a reporting country to:• Define a scope – i.e. select the sector(s) they aregoing to report• Select suitable methods to measure food waste• Conduct studies using the chosen method(s)• Report food waste for the Food Waste Index• Repeat studies regularly using a consistentmethodology.

Figure 1: Food Waste Index Inventory Scope, using Food Loss and Waste Standard

RELATEDISSUESTIMEFRAME

12 months

MATERIAL TYPE

DESTINATIONBOUNDARY

Food

Animal feed

Inedible parts

Biomaterial/
processing

Food category =All food andbeverages

Co/anaerobic
digestion

Compost/aerobic
Controlled
combustion
Land application
Landfill
Not harvested
Refuse/discards
Sewer

14

Lifecycle stage =1. Retail2. Food service
3. Household

Geography =Entire country

Measurement
conducted
sector-bysector, on foodwaste from allcommodities
Manufacturing
food losses canbe reported
where multiple
commodities
combinedduringprocessing
Packagingexcluded fromestimates

EXECUTIVE SUMMARY

Table 4: Appropriate methods of measurement for different sectors
SectorMethods of measurement

Manufacturing(if included)Massbalance
RetailFood service

Directmeasurement(for foodonly waste
streams)

Waste
composition
analysis (forwaste streams
in which foodis mixed with
non-food)

Volumetricassessment

Household

Level 3 comprises supplementary indicators relating
to food waste. These are:
• Disaggregation of total food waste reported in theLevel 2 indicators by destination.
• Inclusion of additional destinations not included in
Level 2: sewer, home composting and food ‘surplus’(i.e. food redistributed for consumption by people,
used for animal feed or used for bio-based materials/ biochemical processing).
• Disaggregation of total food waste by edible parts(intended for human consumption) and theirassociated inedible parts (e.g., banana skins, bones,
eggshells) and by gender.

Counting/
scanning
Diaries (formaterial
going down
sewer, homecompostedor fed toanimals)

The disaggregation by edible and inedible parts isvaluable to policymakers in guiding policy interventions
to make the best possible use of food resources,
supporting a circular food system and the applicationof the waste hierarchy. In order to simplify Level 2
processes for the vast majority of countries that will be
developing a food waste baseline for the first time, andto improve data comparability in a context of diverse,
culturally sensitive interpretations of inedibility (e.g.
chicken feet, many skins and peels), this disaggregation
is proposed as a Level 3 advanced reporting option.

• Reporting of manufacturing food waste where it
is not covered by the Food Loss Index, e.g. wheremore than one commodity is combined to produce
processed/complex food products.

15

UNEP FOOD WASTE INDEX REPORT 2021

Level 3 comprises supplementary indicators relating to food waste. These are:
• Disaggregation of total food waste reported in the Level 2 indicators by destination.
• Inclusion of additional destinations not included in Level 2: sewer, home composting
and food ‘surplus’ (i.e. food redistributed for consumption by people, used for animal
feed or used for bio-based materials / biochemical processing).
• Disaggregation of total food waste by edible parts (intended for human consumption)and their associated inedible parts (e.g., banana skins, bones, eggshells) and by gender.
• Reporting of manufacturing food waste where it is not covered by the Food Loss Index,
e.g. where more than one commodity is combined to produce processed/complex foodproducts.
The disaggregation by edible and inedible parts is valuable to policymakers in guiding policy
interventions to make the best possible use of food resources, supporting a circular foodsystem and the application of the waste hierarchy. In order to simplify Level 2 processes
for the vast majority of countries that will be developing a food waste baseline for the
first time, and to improve data comparability in a context of diverse, culturally sensitive
interpretations of inedibility (e.g. chicken feet, many skins and peels), this disaggregation
is proposed as a Level 3 advanced reporting option.

D HOW TO REPORT ON SDG 12.3
Food waste data in relation to SDG 12.3 will be collected using the United Nations Statistics
Division (UNSD) / UNEP Questionnaire on Environment Statistics (Waste Section). The
questionnaire is sent out every two years to National Statistical Offices and Ministries
of Environment, which will nominate a single food waste focal point in the country tocoordinate data collection and reporting. The data will be made publicly available in the
SDG Global Database and in UNEP’s Food Waste Index Report, which will be publishedat regular intervals up to 2030. The next questionnaire will be sent to Member States in
September 2022, and results will be reported to the SDG Global Database by February
2023.
Countries do not need to conduct new measurements every two years, or to measure
every sector simultaneously. Measuring each sector at least once every four years is
recommended.
Recognizing the efforts and leadership of countries that already measure and report onfood waste, with variances in scope or methodology, (gradual) alignment with the Food
Waste Index approach is appreciated. This enables consistency between nations andsupports a common interpretation of SDG 12.3.

16

EXECUTIVE SUMMARYD HOW THE FOOD WASTE INDEX IS CALCULATEDFor each sector within a country, the level of food waste will be expressed as an index relative
to the level of food waste in the baseline year. A value of:• 100 would indicate the same level of food waste in that sector as the baseline year; and• 50 would indicate that food waste in that sector had halved since the baseline year, consistent
with the target of SDG 12.3
The indices for each sector will not be combined into a single Food Waste Index. This will allow
the granular data for individual sectors to be more easily communicated; it will also alleviate
issues if a country is unable to report all sectors in one reporting cycle.

Example: Food Waste Indices for two hypothetical countries
Figure 2 provides a worked example of the household Food Waste Index for two hypotheticalcountries. In both cases, the baseline year is 2022. Country 1 has 87 kg/capita/year ofhousehold food waste in 2022 and – as this is the first year of measurement – this is definedas 100 in the Food Waste Index. By 2030, this has reduced to 60 kg/capita/year: a value of
69 in the Food Waste Index. This represents a reduction of 31 per cent: good progress, but
insufficient to meet the 50 per cent reduction for SDG 12.3(b), represented by the blue dotted
line.
Country 2 has a baseline value of 84 kg/capita/year, which is defined as 100 in the Food Waste
Index for this country. By 2030, this country has achieved SDG 12.3(b) for this sector, with foodwaste less than half the baseline level (41 kg/capita/year). Therefore, the final Food Waste
Index value for Country 2 is a value less than 50.

Figure 2: Food Waste Indices for two hypothetical countries

17

UNEP FOOD WASTE INDEX REPORT 2021

D WHAT HAPPENS NEXT?
This report, aimed primarily at national governments, is a stepping stone to the delivery of SDG 12.3.
The following are some key next steps in that journey:• Use the Target Measure Act approach developed byChampions 12.3 to organize action, use ‘Enhancing
NDCs for Food Systems’ to raise ambition in national
climate strategies by integrating food loss andwaste, and strengthen food security and cut costs
to households by integrating food waste prevention
in COVID-19 recovery policy approaches.
• Co-create and adopt game-changing solutions tofood waste through the UN Food Systems Summit.
Member States and a wide range of other actors areheartily encouraged to join Action Track 2’s Food
Waste workstream, and to prioritize, lead and engage
in game-changers that will transform the scale ofglobal action and set us on a path to deliver SDG 12.3.
• Participate in a Regional Food Waste Working Group.
UNEP will convene Regional Food Waste Working
Groups in Asia Pacific, West Asia, Africa, and Latin
America and the Caribbean, to be launched in 2021,
together with technical partner WRAP. These Working
Groups will provide capacity building and training
to participating Member States in measuring foodwaste, developing a national baseline and designingnational strategies for food waste prevention.

18

They are intended to bring to life the methodology
presented here, creating opportunities for Member
States to ask technical questions, work throughproblems, and learn from and support their peersin the development of food waste measurement
processes.
• Report on food waste in 2022, using the methodology
in this report via the UNSD/UNEP Questionnaire on
Environment Statistics (Waste Section).
A few countries are making good progress on SDG
12.3, but most countries are just getting started. It isan ambitious target, with important impacts, on hunger,
economies, climate, nature, and pollution. This report
and these next steps will equip member states andother entities to manage what they measure.

EXECUTIVE SUMMARY

D DEFINITIONS
Food: Any substance – whether processed, semiprocessed or raw – that is intended for humanconsumption. “Food” includes drink, and any substancethat has been used in the manufacture, preparation ortreatment of food. “Food” also includes material thathas spoiled and is therefore no longer fit for humanconsumption. It does not include cosmetics, tobacco
or substances used only as drugs. It does not include
processing agents used along the food supply chain,for example water to clean or cook raw materials infactories or at home.
Food loss: Food losses are all the crop and livestock
human-edible commodity quantities that, directly
or indirectly, completely exit the post-harvest/
slaughter production/supply chain by being discarded,
incinerated or otherwise, and do not re-enter in any
other utilization (such as animal feed, industrial use,etc.), up to, and excluding, the retail level. Losses that
occur during storage, transport and processing, also ofimported quantities, are therefore all included. Losses
include the commodity as a whole with its non-edible
parts.
Food surplus: For the purposes of the Food Waste
Index, food surplus refers to food that is redistributedfor consumption by people, used for animal feed orused for bio-based materials / biochemical processing.
Food waste: For the purposes of the Food Waste
Index, “food waste” is defined as food (see definition,
including drink) and associated inedible parts removed
from the human food supply chain in the following
sectors: manufacturing of food products (under certain
circumstances); food/grocery retail; food service; andhouseholds. “Removed from the human food supply
chain” means one of the following end destinations:landfill, controlled combustion, sewer, litter/discards/
refuse, co/anaerobic digestion, compost / aerobic
digestion or land application.

Inedible (or non-edible) parts: Components associatedwith a food that, in a particular food supply chain, are
not intended to be consumed by humans. Examples
of inedible parts associated with food could include
bones, rinds and pits/stones. “Inedible parts” donot include packaging. What is considered inedible
varies among users (e.g., chicken feet are consumed
in some food supply chains but not others), changes
over time, and is influenced by a range of variables
including culture, socio-economic factors, availability,
price, technological advances, international trade andgeography. See also “edible parts”.
Edible parts of food waste: “Food” (see definition,
including drink) that is removed from the humanfood supply chain (i.e., to end up at the following
destinations: landfill, controlled combustion, sewer,
co/anaerobic digestion, compost / aerobic digestionor land application). See also “inedible parts”.
Municipal solid waste (MSW): Includes waste
originating from households, commerce and trade,small businesses, office buildings and institutions(schools, hospitals, government buildings). It also
includes bulky waste (e.g., old furniture, mattresses)
and waste from selected municipal services, forexample waste from park and garden maintenance,
waste from street cleaning services (street sweepings,
the content of litter containers, market cleansing
waste), if managed as waste. Further information onmunicipal solid waste is defined in the SDG indicatormethodology for SDG 11.6.1.

19

UNEP FOOD WASTE INDEX REPORT 2021

01

INTRODUCTION
This repor t sheds new light on themagnitude of food waste, and on theprevalence of household food waste on
all continents, irrespective of country
income levels. By throwing away 17 percent of food available at retail, food service
and consumer level, the impacts of foodsystems on climate, nature and pollution
are generated needlessly. An estimated
8-10 per cent of global greenhouse gas
emissions are associated with food that is
not consumed (Mbow et al., 2019, p. 200) –and yet none of the Nationally Determined
Contributions to the Paris Agreement
mention food waste (and only 11 mention
food loss) (Schulte et al., 2020).
As environmental impacts accrue acrossthe life cycle of food products, foodwaste at the consumer level presents thehighest burden. The Food and Agriculture
Organization of the United Nations (FAO)
estimates that 690 million people were
hungry in 2019, a number that is expected
to rise sharply during and post-COVID-19.
With a staggering 3 billion people thatcannot afford a healthy diet (FAO, 2020),
the message of this report is clear: citizens
need help to reduce food waste at home.

20

The scale of the food waste challenge has
not been fully understood. In 2011, a reportby the FAO estimated that around onethird of food produced globally was lost or
wasted, a total of 1.3 billion tonnes each year
(Gustavsson et al., 2011, p. 56); however, theauthors acknowledged a lack of household
food waste data outside of Europe andNorth America (Gustavsson et al., 2013).
Differences in definitions of food loss andwaste and diverse quantification methods
used have added to data ambiguity. Xueet al. (2017) noted that there are still large
gaps in national estimates of food loss
and waste. Of the estimates they analysed,
many did not involve new measurement
but relied on proxy data often from othercountries or that is outdated.
The lack of data is not just an issue at a
global level: most countries do not haverobust data on food waste. How muchfood is wasted or lost? In which sectors(parts of the supply chain) is the mostwaste being created? What types of foodhave the largest impact? Without thisinformation, governments, businesses andother organizations struggle to make a caseto take action, and lack the information toprioritize their efforts. To track progress
towards the Sustainable Development Goalon food loss and waste (SDG 12.3), betterdata is needed. Fortunately, this situation
can change.

01 Introduction

1.1 THE FOOD WASTE INDEX AND SUSTAINABLE DEVELOPMENT
GOAL 12.3
SDG 12.3 focuses on food and its inedible parts that exit the supply chain and thus are lost or wasted. This is
split into two indicators:• Indicator 12.3.1(a), the Food Loss Index, measures losses for key commodities in a country across the supply
chain, up to and not including retail. The FAO is its custodian. This indicator is not discussed in detail in thispaper, except to describe its boundary with the Food Waste Index.
• Indicator 12.3.1(b), the Food Waste Index, measures food and inedible parts wasted at the retail and consumer
levels (household and food service). UNEP is its custodian. In contrast to the Food Loss Index, the Food Waste
Index measures total food waste (rather than specific commodities).
For this reason, the three sectors covered by the Food Waste Index are:• Food retail• Households• Food service.
The Food Waste Index also allows countries to report on
manufacturing food loss not captured by the Food Loss
Index (for example where more than one commodity is
combined to produce complex food products). This is
an optional supplementary reporting area, a ‘Level 3’
methodology as explained below. Wholesale remains
under the Food Loss Index and therefore should not bereported under the Food Waste Index.
The Food Waste Index has a three-level methodology,
increasing in accuracy and usefulness of data, butalso increasing in the resources required to undertakethem:
Level 1 uses modelling to estimate food waste, forMember States that have not yet undertaken theirown measurement. Level 1 involves extrapolating
data from other countries to estimate food waste ineach sector for a given country. The estimates forthese countries are approximate: sufficient to provide
insight into the scale of the problem and make acase for action, but inadequate to track changes infood waste over time. They are intended as a shortterm support while governments develop capacity fornational measurement (consistent with Level 2). UNEP
has calculated Level 1 estimates on behalf of countries,
and they are presented in this report.
Level 2 is the recommended approach for countries.
It involves measurement of food waste. The nature of
the measurement will vary according to sector andcircumstance. It will be either undertaken by national
governments or derived from other national studies
undertaken in line with the framework described below.

Level 2 generates primary data on actual food waste
generation and fulfils the requirement for tracking
food waste at a national level, in line with the SDG 12.3
target.
Level 3 provides additional information to inform
policy and other interventions designed to reduce foodwaste generation. This includes the disaggregationof data by destination, edible/inedible parts, gender;
reporting of manufacturing food loss not coveredby the Food Loss Index (e.g. where more than one
commodity is combined to produce complex foodproducts); and additional destinations such as sewer,home composting and (non-waste) animal feed.
This three-level approach endeavours to balance
fitness for purpose with feasibility of implementationin as many UN Member States as possible. The
approach is designed to enable a country to measure
food waste, and to assess the impact of interventions,
ideally coinciding with a national strategy on foodwaste reduction.
The scope of the Food Waste Index is illustrated in
Figure 4. It includes both food waste and associatedinedible parts that end up in one of the destinations
listed. It includes both the recommended (Level 2)
and the additional (Level 3) destinations. Animal feed
and bioprocessed materials are not classified as foodwaste, as the material is deemed not to have been
removed from the human food supply chain. Definitionsof the destinations can be found in Appendix 5.

21

UNEP FOOD WASTE INDEX REPORT 2021

Figure 3: Scope of the Food Waste Index (Levels 2 and 3) using the Food Loss and Waste

Accounting and Reporting Standard

The coverage and quality of existing food waste datafor countries around the world is collated and presented
in section 2. This highlights where there is a growing
amount of evidence on the amounts of food waste. It
also identifies the data gaps around the world.
The methodology described in section 3 provides
guidance on how countries can measure their foodwaste (one part of SDG 12.3). The guidance provides
clear advice on what to measure, supporting the useof consistent terminology to describe what is being
measured. It also provides clear instructions on how
to measure and report food waste.

22

By implementing this guidance, a greater number ofcountries can measure food waste in a consistent way.
This allows them to track food waste over time and to
make meaningful comparisons with other countries. It
also provides some of the data to support change – tohelp countries demonstrate the current impact of foodwaste, and information that prioritizes their efforts totackle food waste.

02 Index level 1: existing data and extrapolation to other countries

02
LEVEL 1:02 INDEX
EXISTING DATA AND

EXTRAPOLATION TO
OTHER COUNTRIES

2.1 LEVEL 1 ESTIMATES OF FOOD WASTE
While the availability of food waste data remains limited, there have been a growing number of national estimatesof food waste from countries around the world in recent years. This section identifies these estimates, assesses
whether the measurement methodologies being used are suitable for tracking food waste over time as part ofSDG 12.3, and evaluates the coverage of food waste estimates globally.
This section:
1) Assesses the availability of national food waste estimates in the following three sectors:a) Householdsb) Food servicec) Retail.
2) Calculates approximate estimates of food waste for each sector for countries without an estimate (for2019), alongside an indication of uncertainty (Level 1 estimates).
3) Develops an approximate estimate for food waste globally.

23

UNEP FOOD WASTE INDEX REPORT 2021

A Level 1 estimate has been calculated for all Member
States of the United Nations and will be used for thosestates that have not measured food waste (i.e., in linewith the Level 2 or Level 3 frameworks, as describedin section 3). Level 1 estimates are derived either from:• existing data points from studies carried out inside a
Member State (where available) or• extrapolations based upon the estimates observed inother countries, where no estimate is available froma given Member State.
Most Level 1 estimates are not sufficiently accurate fortracking changes over time and reporting progress on
SDG 12.3. They are indicative estimates, which provide
a sense of scale of the issue. They support a country’s
case for action to tackle food waste and to prioritize
different sectors, while the government works towards
more accurate measurement (consistent with Level 2
or Level 3).

24This section contains:• An overview of the methodology used (section 2.2),
with full detail given in the Appendix (section 1)• The coverage of food waste data globally (section2.3), with information on the sector and on theincome level of a country and region. Informationis also provided on the level of confidence in data
points obtained.
• Deep dives into data coverage for each UNEP regionalgroup: Africa, Latin America and the Caribbean, Asiaand the Pacific, West Asia, North America and Europe(section 2.4).
• Estimates of individual countries, whether these aredata points from existing studies or extrapolations
from other countries’ data (section 2.5).
• Global estimates of food waste in the three sectors(section 2.5 ).

02 Index level 1: existing data and extrapolation to other countries

2.2 SUMMARY OF THE METHODOLOGY
There are five stages to the method used to obtain Level 1 estimates of food waste:
Adjust somedata forconsistency

Extrapolate
for countries
without data
Searchand collate
existing
data

Assignconfidence
rating toestimate

Filter data on
scope andapplicability tocurrent study‘sneeds

A summary of the methodology is given below. Full details of the methodology are provided
in the appendix (Appendix 1).

Search and collate existing data: An online literature review was performed to collectrecent estimates of food waste across the world. Existing meta-analyses and online
databases, both academic and non-academic, were used to search for possibly relevantpublished estimates of food waste at a sectoral level (household, food service, retail),with boundaries comparable to the definitions of the Food Waste Index. Studies carriedout both at the national level and at the sub-national level were included. The researchand practitioner communities were engaged to identify further useful studies. Estimatesof food waste were extracted from relevant studies.
Filter data: Only studies that involved direct measurement of food waste or using datafrom other studies that involved direct measurement were considered. This is in line withthe aim to track levels of food waste over time, which requires reasonably accurate data
while avoiding methodologies with substantial biases. Other food waste measurement
methodologies may be useful for other applications and policy questions, but direct
measurement was considered the most accurate and robust for estimating waste
amounts. As a result, studies that formed estimates based on proxy data or waste factors
not derived from direct measurement were not included.
Adjust some data: Some data points were adjusted to make them comparable with the
majority of studies. Household food waste studies that relied on measurements fromfood diaries were adjusted to account for underestimation known to occur with the diarymethodology. Studies that presented only the edible share of food waste were adjusted;
the inedible share was estimated based on data from other studies that included thisdisaggregation.
25

UNEP FOOD WASTE INDEX REPORT 2021

Extrapolate for countries without data: All estimates were normalized to give the amountof food waste per capita per year. The adjusted, normalized (per capita) estimates wereused for the calculation of regional, income group and global averages. With these figures,it was possible to extrapolate estimates to countries with no relevant study. A hierarchicalapproach was taken, which prioritized data from the country. In the absence of this, datafrom nearby countries and those of a similar income level 4 were used. If neither wereavailable, global data were used.
For the purposes of national and global estimates, these per capita waste estimates werescaled by 2019 population data by country, forming Level 1 estimates of food waste in
2019.
Assign confidence rating: Each Level 1 estimate was assigned a confidence rating. Thisrating indicates the degree to which the estimate is suitable for tracking national foodwaste over time.
High confidence indicates that the estimate is highly likely to be suitable for tracking.
Medium confidence estimates have the possibility for identifying larger trends in food waste
but may miss smaller changes. The distinction between high and medium confidence isbased on methodological details, such as geographic coverage, sample size and whether
the figure required adjustment.
Estimates with low and very low confidence ratings are based on extrapolation from othercountries; they are therefore not suitable for assessing trends in the country in question.
Nevertheless, they provide approximate information that may be useful to inform foodwaste prevention strategies.
It cannot be stressed enough that the confidence rating is not a judgement on the qualityof the study undertaken. It is an assessment – based on the reviewers’ understanding of
the study – of how robust the estimate of food waste is for tracking food waste in the given
country. In many cases, this was not an aim of the original study. Hence many good studies
will be classified at a medium confidence level (or even excluded from consideration
altogether) because the aims of the paper did not include national food waste tracking.
Additional resources: Based on the above methodology, a database of food waste
estimates was created and is available to download as supplementary information to thisreport. This is not an exhaustive list of studies that were considered, and, in the cases ofhigh confidence estimates, only the latest data are included (see Appendix 1.1 and 1.2).

26

4 ‘Income groups’ refer to World Bank classification, for the 2021 fiscal year. There are four categories: Lowincome countries (LIC), defined as those with Gross National Income (GNI) per capita of $1,035 or less; lower
middle-income economies (LMC), with GNI per capita between $1,036 and $4,045; upper middle-income
economies (UMC) with a GNI per capita between $4,046 and $12,535; high-income economies (HIC), thosewith GNI per capita of $12,536 or more

02 Index level 1: existing data and extrapolation to other countries

2.3 RESULTS: DATA COVERAGE
This section describes the extent and coverage of studies containing relevant estimatesof food waste. Information is presented by sector, by the income group of the country andby region.
A total of 152 data points5 were used in this analysis (see Table 5). A greater number ofdata points were found during the search process, but many were not used as either they
did not fit the filtering criteria (e.g., the sample size was too small) or there were multiple
data points from a single country and older / less relevant estimates were not used.

Table 5: Coverage of relevant food waste data points globally, by sector
Household

Food service

RetailTotal

Number of data points

91

32

29

152

Number of countries

52

23

23

54

A full list of the data points can be found in the appendix (Appendix 2). This describes the countries in which thestudies were conducted, methodological details and the confidence level assigned to each data point.
Subsequent discussion will focus on the number of countries with measured data points. Table 6 presents
estimates based on countries’ World Bank income groupings. For all sectors, the majority of studies come fromhigh-income countries. This is particularly pronounced for the food service and retail sectors, where respectively
78 per cent and 87 per cent of studies come from high-income countries.
The household sector has a more even distribution across income groups, with 54 per cent of the 52 countries
that have estimates being high-income countries, 23 per cent being upper middle-income countries and 19 percent being lower middle-income countries. Only two low-income countries have household food waste estimates,
representing 4 per cent of the global estimates.

Table 6: Number of countries with measured data points, by World Bank income classification
Household

Food service

RetailTotal no. of countries
in classification

High-income countries

28

18

20

81

Upper middle-income countries

12

3

2

55

Lower middle-income countries

10

2

1

50

Low-income countries

2

0

0

29

Total

52

23

23

215

World Bank income group

5 ‘Data point’ refers to an individual estimate in a study. Some countries have multiple data points due tostudies from different time periods or different sub-national areas.

27

UNEP FOOD WASTE INDEX REPORT 2021

Table 7 presents the same data points according to regional distribution6. As with income group, there is an uneven
distribution of studies among regions. Areas with higher coverage include Europe (especially northern, westernand southern Europe), North America, and Australia and New Zealand.
In contrast, a number of areas of the world have no available estimates, namely Northern Africa, Central Asia,
Melanesia, Micronesia and Polynesia. Extrapolations made for countries in these regions are therefore highly
uncertain. In addition, estimates for Latin America and the Caribbean all come from mainland Latin America,
with none from Caribbean islands. As a result, it can be said that food waste in small island states is a particulararea of uncertainty.

Table 7: Number of countries with measured data points, by region (UNEP classification)
Household

Food service

RetailNorthern Africa

0

0

0

Sub-Saharan Africa

8

1

1

Latin America and the Caribbean

4

0

0

North America

2

1

1

Central Asia

0

0

0

Eastern Asia

2

2

1

South-eastern Asia

3

1

1

Southern Asia

4

1

0

West Asia

6

1

2

Eastern Europe

3

0

1

Northern Europe

7

7

5

Southern Europe

5

2

3

Western Europe

6

6

6

Australia and New Zealand

2

1

2

Melanesia000Micronesia

0

0

0

Polynesia

0

0

0

Total

52

23

23

For the household sector, multiple studies were found in all sub-regions in Europe, the Americas, Australia and
New Zealand, and Sub-Saharan Africa, and in most sub-regions in Asia. Therefore, for most regions, estimatinghousehold food waste in countries without data can be based on extrapolation of nearby countries.
The regional distribution is more heavily pronounced for non-household sectors, with studies concentrated inEurope. In both the retail and food service sectors, 15 out of the 23 countries with estimates (65 per cent) are inEurope.

28

6 For the purposes of this report, the regional disaggregation used was the sub-regions as per UNSD
classification.

02 Index level 1: existing data and extrapolation to other countries

These differences in regional and economic coverageof food waste studies have implications:• Data gap: Much less is known about the amounts offood waste in certain areas:-- For the household sector: countries in Central
Asia and Northern Africa, and small island states;
additionally, low-income countries as a whole;-- For the food service and retail sectors: lowincome countries, lower middle-income countries
and upper middle-income countries.
• Inferring food waste levels from other countries’
data: Using extrapolation to provide an approximate
estimate of food waste in countries without data will
have greater uncertainty for these areas, leading toestimates being classified as very low confidence(Appendix 1.2.3).

The proportion of countries with an estimate isrelatively low, approximately one-quarter of countries
for household (Table 7). However, the estimatesfound are generally concentrated in more populous
countries. As a result, when expressed as a share of
global population residing in a country with a foodwaste estimate, data coverage is quite extensive: forhouseholds, it is three-quarters of the global population(Table 8). This demonstrates the importance of large
regional actors measuring their waste: food waste
estimates from Brazil, China and India have a large
impact on the regional coverage, as assessed bypopulation.

Table 8: Share of population residing in countries with existing estimates, by region
Household

Food service

RetailNorthern Africa

0%

0%

0%

Sub-Saharan Africa

51%

5%

5%

Latin America and the Caribbean

60%

0%

0%

North America

100%

90%

90%

Central Asia

0%

0%

0%

Eastern Asia

95%

95%

8%

South-eastern Asia

60%

5%

5%

Southern Asia

92%

8%

0%

West Asia

34%

3%

16%

Eastern Europe

66%

0%

50%

Northern Europe

95%

95%

85%

Southern Europe

79%

7%

48%

Western Europe

96%

91%

96%

Australia and New Zealand

100%

84%

100%

Melanesia

0%

0%

0%

Micronesia

0%

0%

0%

Polynesia

0%

0%

0%

Total

75%

32%

14%

29

UNEP FOOD WASTE INDEX REPORT 2021

When interpreting Table 8, it should be noted that, fora country to be considered to have an estimate, there
merely needs to be one study meeting the requirements
for inclusion (Appendix 1.1.1). In many cases, a large
country has a single, geographically focused study(e.g., focusing on a city) that has been included but
may not provide an estimate sufficiently accurate forthe country to allow tracking of food waste over time.
The tables above include data points used in thisstudy for an individual country and for extrapolation
purposes. These data points were subdivided into:• High confidence estimates: likely to be suitable fortracking national levels of food waste – i.e., developed
using a robust methodology, covering a substantial
part of the country and with no adjustment of thedata required to align it with the current studies’
purposes; and• Medium confidence estimates: measured using
methodologies that may be suitable for detecting
larger changes in food waste, e.g., data points
from cities used to represent a country, data points
requiring adjustment to align with the current studies’
purposes.

Splitting the global coverage of data points by these
confidence levels is instructive (see Table 9). For thehousehold sector, 75 per cent of the world’s population
lives in a country with either a high or medium
confidence estimate and therefore have some form ofdata point used for Level 1 modelling. However, only 9
per cent of the global population lives in a country witha high confidence estimate for the household sector(probably accurate enough for tracking purposes).
The percentage of the global population covered bya high confidence estimate is similarly low for retail (8per cent) but higher for food service (25 per cent). The
relatively high share of population with high confidence
food service estimates is driven by the inclusion ofChina.
To track progress on SDG 12.3, there is a need not only
to start measuring food waste in countries without
any estimate, but also to increase the accuracy andregularity of data being collected in many othercountries that have already measured food waste atleast once and in some way (i.e., those with medium
confidence).

Table 9: Share of global population residing in countries with existing

estimates

Percentage of global population with…
Sector… at least one data point used in current
study (medium or high confidence)… a high confidence
estimate

Household75%

9%Food service

32%

25%

Retail14%8%Some key narratives can be drawn from this preliminary exploration of the available data.
Firstly, household food waste data are more widely available than previously identified. This is particularly due to theinclusion of sub-national studies. A large number of the studies included here were conducted at a city or municipality
level (see Table 10). In the household sector, sub-national studies outnumber nationwide studies. In most cases, thesestudies were not focused on food waste; instead, they were largely studies relating to more general waste planning inan area, and total waste was collected from households and disaggregated to include a food-specific estimate. While
many studies of this nature only disaggregate “organic” waste (and were consequently not used in the current analysis),a substantial number included here present specific “food” or “kitchen waste” categories in their disaggregation. Moredetermined searches for these types of sub-national waste composition papers could return even more estimates offood waste than those gathered here.

30

02 Index level 1: existing data and extrapolation to other countries

Table 10: Number of data points, by geographical scope of study
Household

Food service

RetailNationwide

38

24

27

Municipality and sub-national region

53

8

2

A second narrative is the sharp divide between highincome countries and other income countries (uppermiddle, lower-middle and low-income countries) whenit comes to existing food service and retail estimates.
Estimates for these sectors are concentrated in highincome nations; China is the exception to this with
sufficiently robust food service estimates to inform acountry-level aggregation (Zhang et al., 2020). There
are numerous possible reasons for this: researchfunding and availability, the degree to which the sector
is formalized and the availability of data to scale audits.
The strategic importance of food waste in middle- andlow-income countries may also be a factor: a global
narrative of the past decade has been that food waste
is a rich-country problem. These challenges are
explored in more detail in Box 4 for food service andBox 2 for retail.
The challenge of data for scaling is particularly
important: many of the municipality-level waste
audits that captured household data and are included
here also carried out audits of retailers or markets.
These data were usually presented in terms of waste
per restaurant, per shop, per member of staff or per
meal. Where household data can be easily scaled bypopulation figures, scaling such food service and retailestimates requires extensive additional data such as
the number of food service institutions in a country,
number of staff members, market share of retailers,
etc., which was beyond the scope of this project. The
gathering and sharing of such data that would allow
for better scaling of food service and retail audits andaddress this information gap.

A third narrative relates to the importance of tackling
data gaps. At present, a number of sub-regions have noidentified estimates of their food waste for any sector.
Without any knowledge of the scale of the problem, it
becomes difficult to prescribe action. Northern Africa,
Central Asia and the Pacific islands of Melanesia,
Micronesia and Polynesia have substantial data
gaps. In addition, while a number of middle-income
countries were discovered to have estimates, lowincome countries are heavily underrepresented. It is
in low-income countries that food security concerns
are particularly pronounced; and understanding thescale of and addressing the causes of food waste is
crucial for tackling hunger, improving food security anddelivering on SDG 2 (“zero hunger”).
Finally, this research underlines the very broad absenceof national data collection processes and systems for
tracking food waste. Data have been collated from awide variety of sources, often ad hoc academic studies
and seldom from national waste statistics. Where
national waste statistics are available, the methodology
for data collection is not always clear, reducing
confidence in comparability between countries. Foodwaste is not something that has been habitually
measured by most countries, although the increase inpublished data points across time (Appendix 1, Table1). suggests a recent surge in attention to the issue.
Clear, consistent measurement in line with Levels 2 and
3 of the Food Waste Index across a broader range ofcountries is required.

31

UNEP FOOD WASTE INDEX REPORT 2021

2.4 REGION-SPECIFIC DATAWorld regions are considered in more depth to offer an insight into the types of available data and studies forming
estimates. These are grouped by the UNEP regional classification, as a basis for the Regional Food Waste Working
Groups that are to be launched by UNEP in four regions in 2021.

AFRICAIn Africa, a total of 14 data points from 12
studies were included, measuring food waste
in 8 countries. All of these studies covered
the household sector, with one study also
providing estimates for food service andretail waste.
For the African region, two sub-regions were
examined further. All of the identified studies
came from Sub-Saharan African countries.
Northern Africa has no identified food waste
estimates usable for Level 1 purposes(as per the methodology); this presents a
substantial data gap. All extrapolations madefor Northern Africa (section 2.5) are therefore
very low confidence extrapolations.

32

Two countries have multiple studies informing
the household estimate: South Africa and
Kenya. The remaining six countries are eachbased on a single source paper. The majorityof these papers were estimates at the subnational level, conducted in a particular,
often urban, area. The focus on urban areas
and extrapolation of food waste estimatesfrom urban areas for rural population is alimitation of this approach (discussed further
in Box 1). Only two studies were conducted
at a nationwide level, accounting for thewhole population: one in South Africa, which
combines literature-derived waste generation
rates to national statistics (Nahman et al.,2012), and one in Ghana (Miezah et al., 2015).

02 Index level 1: existing data and extrapolation to other countries

The study by Miezah et al. (2015) in Ghana is
notable as being the only African household
estimate judged as high confidence forthe purposes of the Food Waste Index.
The researchers sampled more than 1,000
households across three socioeconomic
groups in ten different districts across Ghana
for a period of three to five weeks. The waste
was sorted and disaggregated into 23 subfractions of waste, including a food-specificcategory. This thorough undertaking offers
Ghana a strong evidence base for action onfood waste and other forms of household
waste. One notable finding was the similarity
in food waste generation across all income
groups: the averages of low-, medium- andhigh-income daily food waste generation were80, 86 and 86 kg/capita/year, respectively.
Within-country variation is discussed further
in Box 1.

A summary of the African household data
is presented in Table 11. South Africa isparticularly notable for the substantial
range observed. From a methodologicalperspective, only one paper required
adjustment, due to being a diar ymethodology (Chakona & Shackleton,
2017). The other three South African data
points involved compositional analyses: one
applied literature data to national income
group statistics (Nahman et al., 2012), one
involved measurement of random samples
from waste collection trucks in residential
areas (Oelofse et al., 2018), and the third wasa direct audit of 123 households across three
weeks (Ramukhwatho, 2016).

33

UNEP FOOD WASTE INDEX REPORT 2021

Each study has limitations and may be impacted by bias,
but there is no obvious explanation for the wide variation
observed. South Africa has substantial domestic income
inequality, which may contribute to varied results basedon the socioeconomic profile of participants included or
excluded in each study (see Box 1 for further discussionof within-country heterogeneity). The experience here
encourages caution against putting too much weight ona single data point, as other countries may experiencesuch variation with more studies conducted.

Notably, the household per capita food waste estimatesobserved here are much higher than expected, given
previous assumptions that household food waste is only
a high-income country issue. The estimates are broadly
similar to the rates observed elsewhere, including incountries in Europe and North America. This is caveatedby the fact that the Food Waste Index covers edible andinedible parts of food; there is insufficient data to say withconfidence how this composition varies among regions.
Edible and inedible parts are discussed further in Box 3.

Table 11: Data points relating to households from African studies
Reference

Study area

Food waste
estimate (kg/
capita)Ethiopia(Assefa, 2017)

Laga Tafo Laga Dadi town, Oromia

92

Ghana

(Miezah et al., 2015)

Nationwide

84(JICA, 2010)

Nairobi

100(Takeuchi, 2019)

Nairobi99

Nigeria(Orhorhoro et al., 2017)

Sapele189

Rwanda(Mucyo, 2013)

Kigali164(Chakona & Shackleton,
2017)

Richards Bay, Dundee andHarrismith

18(Nahman et al., 2012)

Johannesburg and Ekurhuleni

8-12(Oelofse et al., 2018)

Johannesburg

12(Ramukhwatho, 2016)

Nationwide

134(Oberlin, 2013)

Kinondoni municipality, Dar es
Salaam119(Edema et al., 2012)

Ndola

78

Country name

Kenya

South Africa

United Republic of
Tanzania
ZambiaOnly one study identified covered non-household
sectors. The Japan International Cooperation Agency(JICA) study in Nairobi (JICA, 2010) audited waste from
90 food service and retail institutions for one week.
In the retail sector, the researchers disaggregatedbetween shops and markets and found that shops
generated far less food waste than markets. Adjusting
the Nairobi total retail food waste (91,252 kg/day) to
yearly per capita figures, shops and markets generated
respectively 1.3 kg and 9.6 kg. This lends some support
to the suggestion that outdoor markets, especiallyin urban settings within low- and middle-income
countries, could be an important under-studied datagap (see Box 2).

34

For food service, the JICA study in Nairobi also
estimated the combined waste from restaurants, hotels
and public facilities as 31.1 kg/capita/year. While one
data point should not be read into too deeply, this result
is slightly above the average food service waste fromall countries identified here (28 kg/capita), suggestingthat more work should be done to identify the possible
scale of African food service waste.

02 Index level 1: existing data and extrapolation to other countries

Box 1: Within-country variation

As has been highlighted by the experiences of South Africa and China (see section 2.4),
determining single estimates for countries with high heterogeneity may risk masking
highly varied experiences and levels of food waste generation. In these countries, multiple
studies in different areas have demonstrated that food waste may vary substantially.
In particular, China and South Africa are two of the only middle-income countries withstudies explicitly targeting smaller towns or rural areas (see (Li et al., 2021) for Chinaand (Chakona & Shackleton, 2017) for South Africa). Alongside these, the study from
Gujranwala, Pakistan (JICA, 2015) sheds some light on the urban-rural divide with 60 kg/
capita/year observed in the rural sample compared to 88 kg/capita/year in the urban
samples. The small sample sizes, particularly in the rural area, mean this variation cannot
be taken to represent urban-rural waste differences with any confidence. How food waste
differs in rural areas – particularly in middle- and lower-income countries – remains asubstantial data gap, and more research is needed.
With the surprisingly high levels of household food waste identified here, it may bereasonable to question to what extent samples were representative of the diversity of a
population. Indeed, in few cases was a sample described as being explicitly representativeof the national population, and it is likely that the very poorest were inadequately accounted
when sampling. Nevertheless, in many studies the authors explicitly mentioned samplingfrom a range of income groups, and in nine studies from low- and middle-income countries,
sufficient information about the results was available to observe the differences in foodwaste generation between income groups (see Table 12).

35

UNEP FOOD WASTE INDEX REPORT 2021

Table 12: Findings of studies providing results, by income groupHousehold
Reference

Country

Area

Northern Africa

Methodological notes

Sub-Saharan Africa(JICA, 2015)

North America

India(Yasir &Abudi, 2009)

Iraq

DehradunCentralAsia(Oberlin,
2013)

93

2

144 households sampled in total.
0
Unclear sample length.

65 households sampled in total. 2
Nassiriya
Waste collected over a period of 3seven
South-eastern Asia
months.

Southern Asia

Iraq

WestAl-Kut
AsiaCitySouth
Africa63

UnitedRepublic of
Dar esTanzania Melanesia
Salaam(JICA, 2010)

Kenya(Takeuchi,
2019)

Kenya

(Miezah etal., 2015)

Ghana

primarily high population density0
informal settlements. Three days’
waste collected.

74

0

111 1

68

7
2

0

1
0

161

Nairobi

90 households sampled in total.52
One
week’s waste collected.

40

Nationwide

1 014 households sampled in total
across 10 districts nationwide,sampled for a period of 3-5 weeks.

80

23

0 90
1169
2166
1

30

5
3

45

6
142

0
0

0 118

0

6
98

High

1

1
168

0
27

0

1

1

78

Total

0

Medium2

150 households sampled in total. One
0week’s waste collected.

Nairobi
Polynesia

1

155 1

3

Western Europe 75 households sampled in total, 6
only
Kinondoniinmiddleandlowincomesettlements,
Australia
and New Zealand2
municipality,

0

1

4

80 households sampled in total. One
week’s waste collected each month6
for seven months.

Income group waste rates
Northern Europe derived from secondary literature7
Nationwide
observations, combined with national
Southern Europe
5
waste statistics.

Micronesia

36

Low

Eastern Asia

Eastern Europe(Nahman etal., 2012)8

60 urban households sampled in total.
America andOne
theweek’s
Caribbean
4 ofPakistan LatinGujranwala
waste collected in each
three seasons.

(Grover &Singh, 2014)(Sulaymon
et al., 2010)

0

Food
waste
by income group
Food
service
Retail(kg/capita/year)

2
0
0

114
176
86

0151
23

125
86

02 Index level 1: existing data and extrapolation to other countries

This data present a mixed image, which is to be
expected. In places where a trend is observed, generally
high-income households have higher food waste than
low-income households. However, in some places(Nassiriya, (Yasir & Abudi, 2009) Ghana (Miezah et al.,2015)) no meaningful trend is distinguishable, and inothers (Nairobi (Takeuchi, 2019), Gujranwala (JICA,
2015)) a straightforward trend is less clear.
Some important caveats are necessary: firstly, while
all of these studies used three income groups (“low”,“medium” and “high”, these are not necessarily
comparable across studies. In each case, this was
defined contextually. It was primarily determined by
the area rather than by household income level, andin few cases did the studies make clear exactly how
this was determined (e.g., author’s judgement, an
existing municipal classification, based on income
data, based on housing types, etc.). As a result, “highincome” areas in Nairobi and Al-Kut will not necessarily
be comparable.
Secondly, due to the total sample sizes being quite
small, when divided into three the samples for specificincome groups become often very small, roughly 20-50
households. Due to these very small samples, in mostcases the differences cannot be said to be statisticallysignificant, and the variation observed may reflectnatural scatter in samples.

One notable exception to the small samples is that in
Ghana, where over 1,000 households were sampled in
total. Interestingly, this is the example with the least
variation between income groups.
As a result of these caveats, too much should not beread into this information; more, better measurement isrequired to say with any confidence how income groups
and food waste relate. It is likely that this relationshipwill vary substantially between countries and places
based on a range of national factors. Nonetheless,an important observation is that even in the lowincome samples presented here, food waste can besubstantial. As with other conclusions of this report, it
must be borne in mind that total food waste, including
inedible parts, is being measured here (see Box 3 fordiscussion). As a result, the suggestion is not that
low-income populations, possibly in food insecurity,
are wasting calories that they could have consumed;
there is insufficient evidence to either support or refutethat claim. What is clear, however, is that food waste inlow- and middle-income countries, and in low-income
populations within those countries, merits far more
research.

37

UNEP FOOD WASTE INDEX REPORT 2021

LATIN AMERICA
AND THECARIBBEAN
In Latin America and the Caribbean, sevendata points were identified in four countries.
All of these countries were in mainland
Latin America (South and Central America),with no estimates from Caribbean islands.
This represents a major data gap. Fourof the identified data points come from asingle study in Belize, the only country tohave multiple estimates. All of the usable
estimates relate to the household sector,
meaning that food service and retailwaste in Latin America and the Caribbean
remains a substantial data gap. All studies
were classified as medium confidence.
Two papers were conduc ted on anationwide level, in Brazil and Mexico. The
Brazilian study (Araujo et al., 2018) is a diarystudy, and therefore required adjustment
to account for diary underestimation.
(The figure in Table 13 is the adjusted
figure.) The Mexican figure is takenfrom a report (Kemper et al., 2019) thatcombined waste compositional analyses
measured directly at households acrossthree states and five municipalities, scaled
using national urban solid waste figures.
The authors signal that food waste may
be exaggerated due to small businesses
and illegal dumping by larger businesses
in the household municipal waste, but thescale of this contamination is not known.

38

The limitations with each nationwide study
mean they are both classified as medium
confidence.
T h e B e lize s tu d y (I n te r - A m e r i c a n
Development Bank, 2011) conducted waste
compositional analyses in four areas, withsamples between 130-183 households
in each area, sampled for the duration ofeight days. As the paper presents four
different areas independently, a specificscaling exercise accounting for national
demographics would be required to forma high confidence estimate. Instead, theaverage is taken of these four areas and iscoded as medium confidence.
The study in Bogota, Colombia (JICA,
2013) was conducted by JICA, sampling
more than 3,000 households over a 24hour period. The disaggregation of foodinto prepared and unprepared offerssome insight into edible waste, but thisis not directly comparable to studies that
disaggregate the edible or avoidable waste(see Box 3). In this study, JICA also audited
waste from “small commercial producers”
and “institutional, small producers.” These
were considered for retail and food service
estimates; however, the focus on small
producers was incomparable with otherstudies and would have led to a downward
bias in results; they were therefore notincluded.

02 Index level 1: existing data and extrapolation to other countries

Table 13: Data points relating to households from studies in Latin America

and the Caribbean

Study area

Food waste estimate(kg/capita)

Belize City

34

Caye Caulker

45

San Ignacio /Santa Elena95

San Pedro

36(Araujo et al., 2018)

Nationwide

60

Colombia(JICA, 2013)

Bogota70

Mexico(Kemper et al., 2019)

Nationwide

94

Country name

BelizeBrazilReference(Inter-American
Development Bank,
2011)

Box 2: Retail challenges
Retail has challenges similar to those experienced in the food service sector: ascommercially sensitive information, retailers may be unwilling to share waste data
even when they have it and may be resistant to researchers carrying out audits. While
supermarkets are increasingly publishing their food waste data publicly, a sufficientnumber of retailers in a given country would need to do so to enable accurate scaling tonational estimates.
A particular challenge in retail relates to the role of non-supermarket retailers, especiallyinformal ones such as street markets. Depending on the national circumstances,supermarkets may not play a central role, as wholesale markets may supply direct toconsumers; the importance of specialist retailers such as bakers or butchers will vary
based on the national context, and outdoor farmers markets can be particularly important.
In Mexico, for example, up to an estimated 50 per cent of food retail is informal (Kemper
et al., 2019).
As this activity is often informal and ad hoc, gathering accurate estimates on food wastefrom street markets in particular can be difficult. Many outdoor markets will straddle
definitions, offering both fresh produce (retail) and prepared, ready-to-eat “street food”(food service). Very few estimates considered here contain farmers markets, and none
are known to consider street food. The scale of waste in these sub-sectors is therefore asubstantial data gap, one that could be of particular importance in some countries.

39

UNEP FOOD WASTE INDEX REPORT 2021

ASIA ANDTHE PACIFIC
The Asia and the Pacific region, as containing multiple
sub-regions (Australasia, Southern Asia, Southeast
Asia, Eastern Asia, Central Asia), has a large numberof data points. Across all sectors, 45 data points wereidentified in 11 countries. The majority (32) of thesedata points were in the household sector, with 9 in thefood service sector and 4 in retail.
High-income and high-population countries are themost represented: countries in the Organisation for
Economic Co-operation and Development (OECD) –Australia, New Zealand and Japan – all have nationwide
estimates across multiple sectors.
Southeast Asia has household estimates identified
in sub-national territories in Indonesia, Malaysia andViet Nam. Only Malaysia has estimates in this region
for food service and retail; these estimates come froma secondary reference of a governmental source that
could not be found and further verified, leading to a
medium confidence classification. The other household
estimates in Southeast Asia all come from specificmunicipalities or areas, with direct measurement andcomposition of household waste from samples of atleast 100 households.
In South Asia, multiple small-scale studies acrossBangladesh, India, Pakistan and Sri Lanka wereidentified. JICA was responsible for studies in
Bangladesh, Pakistan and Sri Lanka.

40

Through the JICA study in Dhaka, Bangladesh (JICA,
2005), the only food service estimate in South Asia
can be derived. This is, however, a paper published in
2005, making it at the very beginning of the publication
time frame considered. Its relevance to current waste
levels is highly uncertain. Both household estimatesfor Bangladesh come from studies in Chittagong, bothstudies having small samples of households measured
for an unclear duration.
Three Indian studies were identified, two of which werecarried out in Dehradun. In all three, the sample size or
length is either small or unclear. The Pakistan estimate
comes from a JICA study in Gujranwala that had a small
sample size but long duration, repeating the week-longaudit across three seasons. This study is one of the
few that audited both urban and rural households,
these have been separated into two data points forcomparison. The samples informing the Sri Lanka
estimates are not known and combine generation datafrom households with compositional data from waste
sites, reducing their accuracy. However, samples weretaken from 10 different localities, making it one of themost geographically dispersed estimates in the region.
No estimates were identified in Central Asia or in the
Pacific island regions of Melanesia, Micronesia and
Polynesia. These areas remain substantial data gaps.

02 Index level 1: existing data and extrapolation to other countries

Table 14: Data points relating to households from studies in Asia and the PacificCountry name

Reference

Study area

Food waste estimate(kg/capita)

Australia(Arcadis, 2019)

Nationwide

102(Salam et al., 2012)

Chittagong

74(Sujauddin et al., 2008)

Chittagong

57(Gao et al., 2013)

Beijing

26(Gu et al., 2015)

Suzhou67(Li et al., 2021)

Shandong21(Lo & Woon, 2016)

Hong Kong101(Qu et al., 2009)

Beijing59(Song et al., 2015)

Nationwide

23(Zhang et al., 2020)

Urban China Total

150(Grover & Singh, 2014)

Dehradun

73(Ramakrishna, 2016)

Rajam, Andhra Pradesh

58(Suthar & Singh, 2015)

Dehradun

20

Indonesia(Dhokhikah et al., 2015)

Surabaya

77

Japan

(Food Industry Policy Office, 2017)

Nationwide

64(Jereme et al., 2013)

Nationwide

112(Watanabe, 2012)

Bandar Baru Bangi

71

New Zealand(Sunshine Yates Consulting, 2018)

Nationwide

61

Pakistan(JICA, 2015)

Gujranwala (urban)

88

Gujranwala (rural)

60

Jaffna118

Nuwara Eliya

95

Kataragama

95

Thamankaduwa

79

Katunayake

78

Moratuwa

75

Kesbewa75

Dehiwala Mt Lavinia

75

Kurunegala

47

Trincomalee

21(Thanh et al., 2010)

Mekong Delta

85(Vetter-Gindele et al., 2019)

Da Nang67

Bangladesh

China

India

Malaysia

Sri Lanka

Viet Nam(JICA, 2016)41

UNEP FOOD WASTE INDEX REPORT 2021

China is particularly noteworthy: as a very large country
and population with substantial internal variation, it isunsurprising that a wide range of food waste estimates
are identified within its borders. Due to its rapid income
growth and urbanisation, it is likely to be particularly
sensitive to the exact location of the estimates and how
recently the studies were conducted, highlighting theimportance of regular, accurate measurement.
For household waste, seven data points were identified,with a range of methods and scopes. Two of these
took a national approach: one (Song et al., 2015) uses
information from the China Health Nutrition Survey
diary dataset, grouped from samples across theyears 2004, 2006 and 2009, therefore only narrowly
being included in this review due to being a relatively
old sample. A second (Zhang et al., 2020) combines
localized studies of municipal solid waste with thehousehold generation rate in urban areas in orderto form a nationwide estimate for urban China. Evenaccounting for diary bias, the more than five-folddifference (23-150 kg/capita/year) in these estimatesis striking.
Of the sub-national estimates, a substantial variation
is observed both in results and methodology. Studies
in Beijing (Gao et al., 2013) and Hong Kong (Lo &Woon, 2016) come from statistics with unclear
methodological origins, making confidence in themlimited. Compositional analysis of waste collected
directly from households were conducted in Suzhou(Gu et al., 2015) and Beijing(Qu et al., 2009), withmeasured waste observed as 67 and 59 kg/capita/
year respectively.

42

One recent study is notable for targeting households invillages in Shandong in order to improve the estimate
for rural China (Li et al., 2021). This study used diarymethodology and only measured edible waste; evenafter accounting for methodological biases it returns
the smallest waste estimate in China (21 kg/capita/year). This low result – and the stark contrast to the
150 kg/capita observed in urban China – suggests thatthe rural-urban divide and within-country heterogeneity
could be particularly important, an observation that
may be relevant for other rapidly developing countries
around the world.
Appropriate and representative sampling is required
to develop a clearer understanding of how food waste
may vary within a country. On the recommendationof a food waste researcher with specialist knowledgeof China, all of the identified household estimates are
considered in forming China’s estimate, until a Level 2
baseline is published.
China is unique in Asia for its wealth of food service
estimates. One recent paper (Zhang et al., 2020)
aggregated 47 studies of food service waste froma range of sources to create regional estimates onfood waste, normalised to a 2019 baseline. This wastreated as an authoritative estimate and therefore
assigned high confidence, but the regional estimates
were considered as separate data points for forming
an estimate.

02 Index level 1: existing data and extrapolation to other countries

WEST ASIA
In West Asia, 14 data points were found across 11
studies in 6 countries (see Table 15). Eleven of thesedata points were household estimates, with two retailand one food service estimates identified. Other thanthe household estimate for Saudi Arabia, all data points
are classified as medium confidence.
In the household sector, there is more information thanhas previously been identified: nationwide studies havebeen undertaken in Israel, Saudi Arabia and Bahrain.
In Israel and Bahrain, the accessible publishedmethodological detail is insufficiently clear to makea judgement about its robustness: the Israeli study
refers to a “bottom-up” value chain model, and theoriginal Bahraini waste composition report could not
be identified, only a summary of its results. These are
therefore classified as medium confidence but could
be revised in the future with more methodologicalinformation.

The baseline study of the Kingdom of Saudi Arabia
is based on information from waste compositional
analysis. The repor t does not clearly present
the household sample size but does present the
information that 21,730 consumption-stage foodstuff
samples across 35 cities or governates were taken.
From this it was inferred that a sufficiently large sampleof households was taken to offer high confidence in the
result, although clearer methodological documentation
would increase the confidence in this claim.
Sub-national studies on household food waste wereidentified in Georgia, Iraq, Israel and Lebanon. These
were varied in their methodologies: the estimate forGeorgia comes from a sample of residual waste 7
from a residential area; those from Lebanon and
Baghdad, Iraq, come from food waste diaries (whichhave therefore been adjusted to account for bias, seesection Appendix 1.2.2.3), and additional studies in
Israel and Iraq come from direct measurement throughweighing of household waste. In all cases, the sample
size was under 100 households.

7 For the purposes of this report, residual waste is defined as waste contained within waste streams that are
not recycled or reused, usually destined for landfill or incineration.

43

UNEP FOOD WASTE INDEX REPORT 2021

Iraq is notable for having five identified sub-national
studies. Whilst independently they are limited in theirrobustness, together they give insight into the possible
range of household food waste, from 75 to 163 kg/
capita/year. What is notable across the region as awhole is the convergence of household food waste
estimates: in Israel, Lebanon and Saudi Arabia, values
around 105 kg/capita/year were observed. Moretransparent, robust measurement is needed to increase
confidence in the results, but the preliminary picture
is of substantial household food waste in the region,similar to levels in Western Europe and North America.

Israel and Saudi Arabia are the two countries for which
non-household studies were identified: in Israel, thefood rescue organisation Leket produced a whole“value chain model” that is built from a “bottom-up”
approach with estimates at both the retail and foodservice levels. The exact methodology and data
sources are not sufficiently clear in the documentation
identified, so this estimate is considered medium
confidence. Saudi Arabia’s baseline similarly
estimates retail food waste as part of a baseline study
conducted by the Saudi Grains Organisation (SAGO).
The published documentation does not provide all
relevant methodological detail, but extensive direct
measurement was undertaken over a range of
geographical locations and stores.

Table 15: Data points relating to households from studies in West Asia
Country name

Reference

Study area

Food waste estimate(kg/capita)

Bahrain(Alayam, 2018)

Nationwide

132

Georgia(Denafas et al., 2014)

Kutaisi

101(Al-Maliky & ElKhayat, 2012)

Baghdad75(Al-Rawi & Al-Tayyar, 2013)

Mosul

85

(Al-Mas’udi & Al-Haydari, 2015)

Karbala142(Sulaymon et al., 2010)

Al-Kut City

138

(Yasir & Abudi, 2009)

Nassiriya

163(Elimelech et al., 2018)

Haifa

94

(Leket Israel, 2019)

Nationwide

105

Lebanon(Chalak et al., 2019)

Beirut105

Saudi Arabia(SAGO, 2019)

Nationwide

105

Iraq

Israel44

02 Index level 1: existing data and extrapolation to other countries

Box 3: Edible and inedible parts

The Food Waste Index considers total food waste, meaning both the edible and inedible
share of food items. A country having high household food waste does not, therefore,
necessarily mean that a high amount of edible food that is suitable for human consumption
is being wasted. This is particularly the case for low- and middle-income countries, where
no estimates of the extent of edible food waste were identified. It could be the case thatthe large household food waste estimates are a consequence of home cooking, which
may generate more unavoidable food scraps. Certainly, one of the studies in Sub-SaharanAfrica (Mucyo, 2013, in Rwanda) described, but did not quantify, that household waste “isgenerally composed of leftover food and fruit, inedible parts of vegetables, and peels offruits, potato and cooking bananas with large quantities of the latter and vegetable wastes”(emphasis added), suggesting that inedible waste was prominent.
Two other studies may help illuminate this issue: one from Malaysia (Watanabe, 2012),
and one from Colombia (JICA, 2013). In a 2010 household waste composition analysis in
Bandar Baru Bangi, Malaysia, food waste was split into three categories: “Unused food”,“General kitchen waste” and “big fruit peels.” The latter category is particularly relevant
to Southeast Asia, where large fruits such as durian or jackfruit will lead to substantial
inedible waste. “General kitchen waste” is a bit ambiguous and accounted for 58 per centof the household food waste. This does not allow for any conclusions to be drawn. Whatis more notable is that “Unused food” accounted for some 18 per cent of total food waste
in those households, a substantial minimum share of food waste that was edible.
Similarly, a waste compositional analysis carried out in Bogota by JICA disaggregated foodinto “prepared” and “not prepared.” We can say with some confidence that the “prepared”
food represents food intended to be eaten and leftovers that could have been avoided;
this amounted to 14 per cent of total food waste in those households. The “not prepared”
category would likely include a mixture of inedible cooking scraps and unprepared fooduneaten and allowed to go bad, so it is not possible to say with confidence the true extent
of edible food waste, but here again the evidence of a minimum bound suggests it is far
from negligible.
Understanding the split between edible and inedible food waste is not a requirement
for reporting on SDG 12.3 using the Food Waste Index, and SDG 12.3 is a target to halve
total consumer food waste, including inedible parts. Understanding the composition andcauses of food waste does, however, help design better policy interventions, so it should
be considered by countries where feasible. In both cases, understanding the nature of the
problem will help in designing solutions: whether reducing edible food waste or finding
sustainable, circular food systems solutions to unavoidable waste.

45

UNEP FOOD WASTE INDEX REPORT 2021

NORTH AMERICA
North America has a total of four data
points from two studies in two countries.
In Canada, an estimate for household was
identified. In the United States, all threesectors had identified estimates from asingle source paper (U.S. Environmental
Protection Agency, 2020a). Both of thesestudies generated national-level figures by
aggregating other, localized studies andscaling by national data.
The Canadian household paper(Environment and Climate Change Canada,2019) is a recent analysis that aggregates
56 waste compositional analyses ofhousehold waste to form a national
average. The studies included involved a
mixture of curbside analysis and analyses
conducted at sorting facilities. The shareof food waste was multiplied by the total
residential waste to form an estimate.

46

The U.S. estimates all come from a recent
paper by the Environmental Protection
Agency (U.S. EPA 2020a) that was
published to improve upon methodology
and calculate sector-specific generationof surplus food and understand waste
management pathways, aligning thismeasurement with the Food Loss andWaste Accounting and Reporting Standard.
These measurements are also in line with
the Food Waste Index. For each sector,
studies were identified that directly
measured food waste and provided a waste
generation factor, such as food waste perhousehold, per employee or per U.S. dollar
revenue. These generation factors havebeen scaled by up-to-date information toform an estimate of total U.S. waste. To
compare these published figures with other
Level 1 studies in this report, the weight
was adjusted (U.S. tons to metric tonnes)
and normalized to provide a per capita
estimate. The U.S. and Canadian estimates
are displayed in Table 16.

02 Index level 1: existing data and extrapolation to other countries

Table 16: Data points from studies in North America
Reference

SectorFood waste estimate(kg/capita)

Canada(Environment andClimate Change
Canada, 2019)

Household79

United Statesof America(U.S. Environmental
Protection Agency,
2020a)

Household

59

Food service

64

Retail16

Country name

The U.S. estimate for household waste
is notable for how low it is: some pastestimates, such as Buzby et al. (2014), andcommon assumptions have held that U.S.
household waste is very high, whereas
these data suggest it is below the global
average. There are some possible reasonsfor this divergence:
Past U.S. estimates have differed inmethodology and in scope. For example,
Buzby et al. (2014) estimated food waste(or, using their terminology, food loss) inthe consumption stages of the supply
chain (including both household and foodservice) by comparing the amount of foodgoing into this stage (Loss-Adjusted Food
Availability data) with the amount that isconsumed. While this and similar methods
have many strengths, they are not designedwith tracking food waste over time.
Therefore, they do not meet the criteria set
out for inclusion in the current study:• There is no direct measurement of foodwaste, rather it is inferred via a massbalance approach.

Secondly, the high food service waste isimportant to factor in to understand U.S.
food waste. While the country’s household
waste is below the average, its foodservice waste is the highest of any highconfidence food service estimates. Thishigh waste generation may reflect that
the U.S. study covers the entirety of thefood service sector more thoroughly thanother studies (see Box 4 for a discussionof some of the challenges in measuring
food service sector food waste). It may
also reflect a higher proportion of meals
being consumed out of the home, which
would be expected to both increase foodservice waste and decrease household
waste, all other things being equal. While
U.S. household waste alone is below theaverage, U.S. household and food service
combined waste (123 kg/capita/year) is
comparable to or exceeds other countries
of similar levels of economic development
for which estimates exist, such as Australia(124 kg/capita/year) and the United
Kingdom (94 kg/capita/year).

• The household and food service sectors
are effectively combined.
• Inedible parts of food waste are notmeasured.

47

UNEP FOOD WASTE INDEX REPORT 2021

Thirdly, due to the detailed information on waste destinations, it was possible to appropriately scale the U.S. data
and remove non-waste management destinations for surplus food. Food donated to humans and fed to animals
was removed from retail (35 per cent of the total waste reported in the study), and donation was removed fromthe hospitality parts of food service (14 per cent of waste from those sources). In addition, as detailed in the
appendix (Appendix 1.2.2.7), in order to improve comparability with studies of residual waste in Level 1, foodwaste disposed in the sink/sewer was removed. Sewer waste is included in Level 3 of the Food Waste Index.
In-sink disposal is an important waste route in the United States and if included, would increase U.S. household
food waste by around 10 kg/capita/year.

Box 4: Food service challenges
Food service is a particularly problematic sector for measurement. Many studies exist
providing robust measurement of a single establishment or sub-sector of establishments,
such as hotels or university canteens. However, the food service sector involves a very
wide range of different sub-sectors, meaning that studies with a limited scope may bedifficult to accurately scale and extrapolate to form a nationwide estimate. As a result, theoverall level of confidence in food service estimates is reduced.
Measuring food service waste presents challenges. As commercially sensitive waste,many authors identified resistance or hostility to conducting a waste audit. Secondly,
scaling information from audits or surveys to a national estimate requires robust national
data on metrics such as the number of restaurants, number of meals consumed in outof-home settings such as schools or workplace canteens, and so on. For many countries,
it is unclear if such data exist. As a result, a number of robust studies that presented foodwaste in this manner (per meal, per restaurant, etc.) were not usable for the purposes of
forming national estimates. Scaling by population allows for readily available data to be
used, but may overlook nuances related to national specificities such as the share of meals
consumed out of the home. Appropriate scaling data would allow more food waste factors
from existing studies to be used for the purposes of forming Level 1 estimates and could
also form the basis of Level 2 estimates.
A further challenge relates to the heterogeneity of the sector. Section 3.2 demonstrates
the wide range of establishments that could be considered under food service. Measuring
waste in all of these locations presents practical challenges, and the relative importanceof each sub-sector will vary significantly based on national context. (Hotels, for example,
will be much more prominent in countries where tourism plays a larger part in grossdomestic product (GDP).) This can lead to an inconsistency in scopes: for example, sports
stadia appear to be included only in the UK (WRAP, 2020b) and U.S. (U.S. Environmental
Protection Agency, 2020a) baselines. Balancing an accurate estimate of food service
waste with practical resource limitations remains a challenge.

48

02 Index level 1: existing data and extrapolation to other countries

EUROPE

Europe is the most well-documented region, with a large number of data points across all sectors. A number ofpublished meta-analyses of European data points exist, notably BIO Intelligence Service (2010) , Stenmarck et al. (2016)
and, more recently, Caldeira et al. (2019). These formed the basis of the European data search. As a result, a large
share of Europe’s data points were from secondary literature that detailed the methodology of the studies sufficiently
to have confidence in the methodological criteria being met.
Within Europe’s four sub-regions there is an uneven spread of data points, with Northern and Western Europe having
both the highest number of data points and the highest number of countries represented across all sectors. Eastern
Europe has the least coverage, with no estimates for retail (see Table 17).

Table 17: Data coverage in Europe, by sector and sub-region
Data points

Number of countries

Household

RetailFood
service

Household

RetailFood
service

Northern Europe

9

9

9

7

7

5

Western Europe

7

8

8

6

6

6

Southern Europe

7

3

3

5

2

3

Eastern Europe

3

1

0

3

0

1

Subregion

49

UNEP FOOD WASTE INDEX REPORT 2021

The data point tables (sections 2.5, Appendix 2.2 and2.3) show more detail of the methodology. On a broad
level, what is notable is that a large number of countries
have estimates for all sectors, often conducted orcompiled as part of a single study in order to generate afood waste baseline. In a number of countries, such as
Norway or Slovenia, there are repeated data gathering
efforts for tracking food waste over time.
Between countries, however, there is a substantial
variation in methodology and assigned confidence
level. A large number of European studies wereconsidered medium confidence for this paper due tomeasuring only edible waste, using food waste diarymethodology, or both. This is a contrast to otherregions (such as Africa or Asia and the Pacific) where
many studies were marked medium confidence due
to studying at a sub-national or municipal level. Only
Belgium (Flemish Food Supply Chain Platform for Food
Loss, 2017) and Poland (Steinhoff-Wrześniewska,2015) were studies of specific sub-national areas.

50

In a number of cases, multiple medium confidence
estimates for a single sector have been identified andcombined in forming that country’s estimate.
With regard to food waste measurement, during
2019, the European Commission adopted a common
definition for food waste and accepted methodologies
for European Union (EU) countries to measure theirfood waste across the supply chain (European
Commission, 2020). These advances are in the context
of the revised Waste Framework directive, which callson EU countries to reduce food waste at each stage ofthe food supply chain, monitor food waste levels andreport back regarding progress made.
The measurement methods specified by the EU arevery similar to those specified in the Food Waste
Index (section 3.2), allowing EU countries to meet
reporting requirements of the European Commission
and SDG 12.3 simultaneously. Furthermore, increased
consistency in measurement within EU countries
should increase the degree to which levels of foodwaste can be compared between countries.

02 Index level 1: existing data and extrapolation to other countries

Table 18: Data points relating to households from studies in Europe
Country Name

Referencekg / capita
food waste
estimate

Confidence
level

Austria(Environment Agency Austria, 2017)

39

High

Belgium(Flemish Food Supply Chain Platform for Food
Loss, 2017)50

Medium(Danish Environmental Protection Agency, 2018)

79

High

(Edjabou et al., 2016)

83

High

(Moora, Evelin, et al., 2015)78

Medium(Katajajuuri et al., 2014)67

Medium(Stenmarck et al., 2016)64

MediumFrance(ADEME, 2016)85

MediumGermany(Schmidt et al., 2019)

75

High

Greece(Abeliotis et al., 2015)

142

MediumHungary(Kasza et al., 2020)94

MediumIreland(Stenmarck et al., 2016)

55

MediumItaly(Giordano et al., 2019)67

Medium(Luxembourg Environment Ministry, 2020)89

Medium(Caldeira et al., 2019)91

MediumMalta(Caldeira et al., 2019)

129

High

Netherlands(The Netherlands Nutrition Centre Foundation,
2019)

50

High

Norway(Hanssen et al., 2016)

79

High

Poland(Steinhoff-Wrześniewska, 2015)

56

MediumRussian Federation(Tiarcenter, 2019)33

Medium(Republic of Slovenia Statistical Office, 2020)

36

Medium(Republic of Slovenia Statistical Office, 2019)33

Medium77

Medium78

MediumDenmark
Estonia
Finland

Luxembourg

Slovenia
Spain(Caldeira et al., 2019)

Sweden(Swedish Environmental Protection Agency, 2014)

81

High

United Kingdom ofGreat Britain andNorthern Ireland(WRAP, 2020b)

77

High

51

UNEP FOOD WASTE INDEX REPORT 2021

Table 19: Data points relating to food service from studies in Europe
Referencekg / capita
food waste
estimate

Confidence
level(Caldeira et al., 2019)

31

High

(Environment Agency Austria, 2017)

26

High

Belgium(Flemish Food Supply Chain Platform for Food
Loss, 2017)20

MediumDenmark(Danish Environmental Protection Agency, 2014)

21

High

Estonia(Moora, Piirsalu, et al., 2015)

17

High

(Katajajuuri et al., 2014)23

Medium(Stenmarck et al., 2016)24

Medium(BIO Intelligence Service, 2010)17

Medium(ADEME, 2016)32

MediumGermany(Schmidt et al., 2019)

21

High

Ireland(Stenmarck et al., 2016)

56

MediumLuxembourg(Luxembourg Environment Ministry, 2020)21

MediumNorway(Stensgård et al., 2019)5

Medium

Serbia(Bogdanović, et al., 2019)6Medium(Republic of Slovenia Statistical Office, 2020)

20

Medium(Republic of Slovenia Statistical Office, 2019)20

Medium

20

High

21

High

Country Name

Austria

FinlandFranceSlovenia
Sweden(Swedish Environmental Protection Agency, 2014)

Switzerland(Beretta et al., 2013)40

MediumUnited Kingdom ofGreat Britain andNorthern Ireland(WRAP, 2020b)

17

High

52

02 Index level 1: existing data and extrapolation to other countries

Table 20: Data points relating to retail from studies in Europe
kg / capita
food waste
estimate

Confidence
levelCountry Name

Reference

Austria(Environment Agency Austria, 2017)

9

High

Belgium(Flemish Food Supply Chain Platform for Food
Loss, 2017)10

MediumDenmark(Danish Environmental Protection Agency, 2014)

30

High

(Moora, Piirsalu, et al., 2015)

5

Medium(Caldeira et al., 2019)5

MediumFrance(ADEME, 2016)26

MediumGermany(Schmidt et al., 2019)

6

High

Greece(Stenmarck et al., 2016)7

MediumItaly(Cicatiello et al., 2019)

4

High

(Luxembourg Environment Ministry, 2020)9Medium(Stenmarck et al., 2016)

4

Medium(Caldeira et al., 2019)9Medium(Stenmarck et al., 2016)11

Medium(Stensgård et al., 2019)14

Medium(Stensgård & Hanssen, 2016)14

Medium13

Medium14

MediumEstonia

Luxembourg
Netherlands

Norway(Caldeira et al., 2019)
Russian Federation(Tiarcenter, 2019)14

MediumSlovenia(Republic of Slovenia Statistical Office, 2020)7

MediumSweden(Swedish Environmental Protection Agency,
2020)

10

High

United Kingdom ofGreat Britain andNorthern Ireland(WRAP, 2020b)

4

High

53

UNEP FOOD WASTE INDEX REPORT 2021

2.5 FOOD WASTE AMOUNTS: MEASURED ESTIMATES AND
EXTRAPOLATIONS
Alongside assessing the coverage and quality ofnational food waste data points, a second aim of thecurrent study was to make country-level estimates for
those countries without robust data. These estimates
were developed using the data collected and presented
in section 2.3, employing a hierarchical approach:• For countries with one or more data points ofsufficient accuracy, these were used to form an
estimate for the country in question;• For countries without any data points of sufficientaccuracy, estimates from other countries in theregion and/or similar income classification were used
if available;• Otherwise, global averages were employed.
‘Sufficient accuracy’ in this context was considered
medium confidence data or higher. In other words, alldata that met the criteria set out in section 2.2. Data
points not meeting this criterion were excluded.
The extrapolation was carried out on the basis of theavailable data. For household, extrapolations combine
the average food waste from a country’s region andfrom the country’s World Bank income grouping.

54

Due to a sample much more concentrated inhigh-income countries, this method could not be
repeated for food service or retail. In these sectors,
extrapolations for high-income countries without theirown observed estimate are based on the average waste
for that sector in the high-income country group. For
upper middle-income countries, lower middle-income
countries and low-income countries, the global average
is used, as there are insufficient data to use group or
region-specific data. As a result, the confidence inthese results is lower; without further robust estimates,
it cannot be said with confidence whether the
extrapolations overestimate or underestimate the true
extent of food waste. Full details of this extrapolation
methodology are presented in Appendix 1.3).
Every estimate was given a confidence rating to reflect
the differences in methodology of data points andextrapolations. High and medium confidence were
only applied to countries in which data points wereidentified and correspond to the confidence rating of
those estimates. All extrapolations to countries without
estimates were rated low and very low confidence. The
low or very low classification depends on the numberof estimates in the same regional and income group
informing the extrapolation. The details of confidenceclassification are elaborated in the appendix (Appendix
1.3.3).

02 Index level 1: existing data and extrapolation to other countries

D FOOD WASTE ESTIMATES BY COUNTRY INCOME
LEVEL

Table 19 presents the average food waste, per capita, per year, in each of the World Bank income classifications
for each sector.

Table 21: Average food waste (kg/capita/year) by World Bank income classification, averaging

medium and high confidence estimates for countries

Income group

Average food waste (kg/capita/year)
Household

Food service

RetailHigh-income countries

79

26

13

Upper middle-income countries

76

Insufficient data

Lower middle-income countries

91

Insufficient data

Low-income countries

For extrapolation purposes at the household level,
low-income countries used an average from the two
low-income countries with data points in addition toestimates from lower middle-income countries. This
combined average of lower middle-income countries
and low-income countries was 97 kg/capita/year(see Appendix 1.3.1). for more on the calculation
methodology. This surprisingly high estimate makes
a strong case for more household food waste datacollection in low-income countries, to improve ourunderstanding of the scale of the problem and its
possible causes.

Insufficient data

For food service and retail, there were sufficient data
points to make averages for high-income countries. For
the other income groups, global averages were used(see Appendix 1.3.2).
For food waste in households, there are sufficient data
for a meaningful average for three of the four income
groups. For these three groups, the averages weresimilar, ranging from 76 to 91 kg/capita/year. Given
the number of studies and methodological differences
between studies, there is no evidence of markedly
different levels of per capita food waste between thesethree groups.

Key finding:
Levels of household food waste (the total of edible and inedible parts) are similar forhigh‑income, upper middle-income and lower middle-income countries.

55

UNEP FOOD WASTE INDEX REPORT 2021

Further analysis was undertaken to correlate household food waste and GDP per capita (see Figure 5). Thereis negligible correlation between these two factors. However, lower-GDP countries did exhibit a wider range ofvariation in their food waste estimates compared to countries with higher GDP. This could be due to a genuine
wider variation of food waste in lower-income countries, or an artefact of the studies measuring food waste (e.g.,
smaller sample sizes leading to more variability in the measured value).

Figure 4: Scatter plot of food waste (household) against GDP of country56

02 Index level 1: existing data and extrapolation to other countries

Recent analysis of similar data by Dou and Toth (2020)
also revealed no clear relationship between the levelof household food waste per capita and the GDP of acountry.
This convergence in household food waste data is anotably different finding to the narrative of the previous
decade that food waste is not an issue in low- andmiddle-income countries. While Table 1 shows thatan insufficient number of estimates were identified
for the low-income country group, in the case of
middle-income countries, there is a sufficient amountof evidence to conclude that there are substantial
amounts of food waste, similar to high-income
countries.

Recent research by van den Bos Verma et al. (2020)
used a modelling approach, based on an energybalance, to estimate the amount of food wasted in
the consumption stage (focusing on the parts offood generally eaten – i.e., the edible parts). Thissuggested that low-income countries produced lessedible food waste than high-income countries did.
The disaggregation of food waste data into edible
and inedible parts would provide useful information
for policymakers, in terms of the balance of policies
focusing on food waste prevention and on circular usesof less commonly eaten and inedible parts.

It is important to note that SDG 12.3 and the Food
Waste Index track total food waste, i.e., food and its
associated inedible parts. It is possible that inedible
parts play a larger role in household food waste in
middle-income countries, if, for example, more meals
are consumed at home and cooked from scratch.
While there are a sufficient number of estimates ofthe split between edible and inedible fractions offood waste in high-income countries, there are too
few studies in middle-income countries to make acomparison.

Key recommendation:
Increase measurement efforts globally todisaggregate estimates of food waste into
edible and inedible parts.

57

UNEP FOOD WASTE INDEX REPORT 2021

D FOOD WASTE ESTIMATES BY REGION
For the purposes of forming Level 1 estimates, income-group averages were combined with regional averages.
These regional averages can be viewed in Table 20. This is presented alongside the number of countries informing
the estimate to help demonstrate the level of robustness.
Comparisons should not be made between regions due to the small number of studies for most regions andmethodological differences between studies. However, it is notable that the average level of household foodwaste is substantial for all regions studied.

Key finding:In all countries where household food waste data was identified, per capita arisings
were substantial.

Table 22: Average food waste (kg/capita/year) by region, averaging medium

and high confidence estimates for countries
Average household
food waste(kg/capita/year)

No. of countries
with estimates
informing average

Northern Africa

n/a0

Sub-Saharan Africa

1088

Latin America and the Caribbean

69

4

Northern America

69

2

Central Asian/a0

Eastern Asia

64

2South-eastern Asia

82

3Southern Asia

66

4

Western Asia

110

6Eastern Europe

61

3

Northern Europe

74

7Southern Europe

905Western Europe

65

6Australia and New Zealand81

2Melanesian/a0Micronesia

n/a0Polynesian/a0

Due to the scarcity of data in food service and retail, the averages are not presented.
Section 2.4 contains discussion on the data availability by region.
58

02 Index level 1: existing data and extrapolation to other countries

D TABLE OF HOUSEHOLD ESTIMATES
Table 21 provides estimates of household food waste
for each country in the world. Similar data for foodservice and retail can be found in Appendix 3.2 and3.3 respectively.
To briefly reiterate the methodology: the best available
food waste data were collected, adjusted to accountfor biases and improve comparability, and grouped
into confidence ratings. Where available, the averageof these data points was applied for a country.

Where not available, an extrapolation was madebased on the average food waste observed in thatcountry’s region and income grouping. The high andmedium confidence refer only to estimates from thedata collected. All extrapolations are low or very
low confidence, based on the number of estimates
informing the extrapolation. This methodology isdetailed in Appendix 1.

59

UNEP FOOD WASTE INDEX REPORT 2021

Table 23

Household food waste estimates (from measured data points or extrapolation) for each country
Region Northern Africa
M49
code1

Country

Household food waste
estimate (kg/capita/year)

Household food waste
estimate (tonnes/year)

Confidence inestimate

12

Algeria91

3 918 529

Very low confidence

818

Egypt

91

9 136 941

Very low confidence

434

Libya

76

513 146

Very low confidence

504

Morocco91

3 319 524

Very low confidence

729

Sudan

97

4 162 396

Very low confidence

788

Tunisia91

1 064 407

Very low confidence

732

Western Sahara**

No estimate

Region Sub-Saharan Africa
M49
code1

Country

Household food waste
estimate (kg/capita/year)

Household food waste
estimate (tonnes/year)

Confidence inestimate

24

Angola100

3 169 523

Low confidence

204

Benin

100

1 175 297

Low confidence

72

Botswana

92

211 802

Low confidence

854

Burkina Faso

103

2 086 893

Low confidence

108

Burundi103

1 184 127

Low confidence

132

Cabo Verde100

54 765Low confidence

120

Cameroon100

2 577 064

Low confidence

140

Central African Republic

103

487 305

Low confidence

148

Chad

103

1 637 656

Low confidence

174

Comoros

100

84 742

Low confidence

178

Congo

100

535 851

Low confidence384

Côte d’Ivoire

100

2 561 140

Low confidence

180

Dem. Rep. of the Congo

103

8 912 903

Low confidence

262

Djibouti

100

96 962

Low confidence

226

Equatorial Guinea

92

124 670

Low confidence

232

Eritrea

103

359 132

Low confidence

748

Eswatini

100

114 341

Low confidence

231

Ethiopia92

10 327 236

Medium confidence

266

Gabon

92

199 748

Low confidence60

02 Index level 1: existing data and extrapolation to other countries

M49
code1

Country

Household food waste
estimate (kg/capita/year)

Household food waste
estimate (tonnes/year)

Confidence inestimate

270

Gambia103

241 095

Low confidence

288

Ghana

84

2 555 332

High confidence

324

Guinea103

1 311 530

Low confidence

624

Guinea-Bissau

103

197 266

Low confidence

404

Kenya

99

5 217 367

Medium confidence

426

Lesotho100

211 661

Low confidence

430

Liberia103

507 043

Low confidence

450

Madagascar

103

2 769 594

Low confidence

454

Malawi103

1 913 062

Low confidence

466

Mali

103

2 018 765

Low confidence

478

Mauritania

100

450 720

Low confidence

480

Mauritius

93

118 632

Low confidence

175

Mayotte**No estimate

508

Mozambique

103

3 118 416

Low confidence

516

Namibia92

229 344

Low confidence

562

Niger

103

2 393 877

Low confidence

566

Nigeria189

37 941 470

Medium confidence

638

Réunion**No estimate

646

Rwanda164

2 075 405

Medium confidence

654

Saint Helena**

No estimate

678

Sao Tome and Principe

100

21 422

Low confidence

686

Senegal100

1 622 980

Low confidence

690

Seychelles

93

9 128

Low confidence

694

Sierra Leone103

802 371

Low confidence

706

Somalia103

1 585 898

Low confidence

710

South Africa

40

2 329 228

Medium confidence

728

South Sudan

103

1 136 015

Low confidence

768

Togo

103

830 017

Low confidence

800

Uganda103

4 546 237

Low confidence

834

United Rep. of Tanzania

119

6 907 649

Low confidence

894

Zambia78

1 391 729

Medium confidence

716

Zimbabwe100

1 458 564

Low confidence

61

UNEP FOOD WASTE INDEX REPORT 2021

Table 23

Household food waste estimates (from measured data points or extrapolation) for each country
Region Latin America and the Caribbean
M49
code1

Country

Household food waste
estimate (kg/capita/year)

Household food waste
estimate (tonnes/year)

Confidence inestimate

660

Anguilla**

No estimate

28

Antigua and Barbuda

74

7 178

Very low confidence

32

Argentina

72

3 243 563

Very low confidence

533

Aruba

74

7 858

Very low confidence

44

Bahamas74

28 794

Very low confidence

52

Barbados

74

21 217

Very low confidence

84

Belize53

20 564

Medium confidence

68

Bolivia (Plurin. State of)80

922 012

Very low confidence

535

Bonaire, St. Eustatius &
Saba

*

*

No estimate

76

Brazil60

12 578 308

Medium confidence

92

British Virgin Islands

74

2 218

Very low confidence

136

Cayman Islands

74

4 798

Very low confidence

152

Chile

74

1 401 043

Very low confidence

170

Colombia

70

3 545 499

Medium confidence

188

Costa Rica

72

365 609

Very low confidence

192

Cuba

72

820 910

Very low confidence

531

Curaçao74

12 079Very low confidence

212

Dominica

72

5 201

Very low confidence214

Dominican Republic

72

777 849

Very low confidence

218

Ecuador72

1 258 415

Very low confidence222

El Salvador

80

516 828

Very low confidence

238

Falkland Islands(Malvinas)**

No estimate

254

French Guiana**

No estimate

308

Grenada72

8 112

Very low confidence

312

Guadeloupe**

No estimate

320

Guatemala72

1 273 466

Very low confidence

328

Guyana72

56 700

Very low confidence

332

Haiti

83

936 940

Very low confidence

340

Honduras

80

780 504

Very low confidence

388

Jamaica

72

213 552

Very low confidence

474

Martinique**

No estimate62

02 Index level 1: existing data and extrapolation to other countries

M49
code1

Country

Household food waste
estimate (kg/capita/year)

Household food waste
estimate (tonnes/year)

Confidence inestimate

484

Mexico94

11 979 364

Medium confidence

500

Montserrat**

No estimate

558

Nicaragua

80524 188

Very low confidence

591

Panama74

313 919

Very low confidence

600

Paraguay72

510 256

Very low confidence

604

Peru

72

2 354 806

Very low confidence

630

Puerto Rico

74

216 854

Very low confidence

652

Saint Barthélemy

**No estimate

659

Saint Kitts and Nevis

74

3 903

Very low confidence

662

Saint Lucia

72

13 241

Very low confidence

663

Saint Martin (French part)

74

2 809

Very low confidence

670

Saint Vincent &Grenadines

72

8 011

Very low confidence

534

Sint Maarten (Dutch part)

74

3 134

Very low confidence

740

Suriname72

42 112

Very low confidence

780

Trinidad and Tobago

74

103 127

Very low confidence

796

Turks and Caicos Islands

74

2 824

Very low confidence

850

United States Virgin
Islands

74

7 733

Very low confidence

858

Uruguay74

255 909

Very low confidence

862

Venezuela (Boliv. Rep. of)72

2 065 461

Very low confidence

Region Northern America
M49
code1

Country

Household food waste
estimate (kg/capita/year)

Household food waste
estimate (tonnes/year)

Confidence inestimate

60

Bermuda74

4 606

Very low confidence

124

Canada79

2 938 321

High confidence

304

Greenland

74

4 178

Very low confidence

666Saint Pierre and Miquelon**

No estimate

840

United States of America

59

19 359 951

High confidence

63

UNEP FOOD WASTE INDEX REPORT 2021

Table 23

Household food waste estimates (from measured data points or extrapolation) for each country
Region Central Asia
M49
code1

Country

Household food waste
estimate (kg/capita/year)

Household food waste
estimate (tonnes/year)

Confidence inestimate

398

Kazakhstan

76

1 404 584

Very low confidence

417

Kyrgyzstan

91

583 951

Very low confidence

762

Tajikistan

97

906 209

Very low confidence

795

Turkmenistan

76

449 895

Very low confidence

860

Uzbekistan

91

3 001 868

Very low confidence

Region Eastern Asia
M49
code1

Country

Household food waste
estimate (kg/capita/year)

Household food waste
estimate (tonnes/year)

Confidence inestimate

156

China

64

91 646 213

Medium confidence

344

China, Hong Kong SAR

71

531 023

Very low confidence

446

China, Macao SAR

71

45 731

Very low confidence

408

Dem. People’s Rep. Korea

81

2 070 528

Very low confidence

392

Japan

64

8 159 891

Medium confidence

496

Mongolia78

250 173

Very low confidence

410

Republic of Korea

71

3 658 024

Very low confidence

Region South-eastern Asia
M49
code1

Country

Household food waste
estimate (kg/capita/year)

Household food waste
estimate (tonnes/year)

Confidence inestimate

96

Brunei Darussalam

80

34 742

Very low confidence

116

Cambodia86

1 423 397

Very low confidence

360

Indonesia77

20 938 252

Medium confidence

418

Lao People’s Dem. Rep.

86

618 994

Very low confidence

458

Malaysia

91

2 921 577

Medium confidence

104

Myanmar86

4 666 125

Very low confidence

608

Philippines

86

9 334 477

Very low confidence

702

Singapore80

465 385

Very low confidence

764

Thailand79

5 478 532

Very low confidence

626

Timor-Leste86

111 643

Very low confidence

704

Viet Nam

76

7 346 717

Medium confidence6402 Index level 1: existing data and extrapolation to other countries

Region Southern Asia
M49
code1

Country

Household food waste
estimate (kg/capita/year)

Household food waste
estimate (tonnes/year)

Confidence inestimate

4

Afghanistan

82

3 109 153

Very low confidence

50

Bangladesh

65

10 618 233

Medium confidence

64

Bhutan79

60 000

Very low confidence

356

India

50

68 760 163

Medium confidence

364

Iran (Islamic Republic of)71

5 884 842

Very low confidence

462

Maldives

71

37 688

Very low confidence

524

Nepal

79

2 249 412

Very low confidence

586

Pakistan74

15 947 645

Medium confidence

144

Sri Lanka

76

1 617 738

Medium confidence

Region Western Asia
M49
code1

Country

Household food waste
estimate (kg/capita/year)

Household food waste
estimate (tonnes/year)

Confidence inestimate

51

Armenia93

275 195

Low confidence

31

Azerbaijan

93

934 872

Low confidence

48

Bahrain132

216 161

Medium confidence

196

Cyprus95

113 312Low confidence

268

Georgia101

403 573

Medium confidence

368

Iraq

120

4 734 434

Medium confidence

376

Israel100

848 395

Medium confidence

400

Jordan93939 897

Low confidence

414

Kuwait95

397 727

Low confidence

422

Lebanon105

717 491

Medium confidence

512

Oman

95

470 322

Low confidence

634

Qatar

95

267 739

Low confidence

682

Saudi Arabia

105

3 594 080

High confidence

275

State of Palestine101

501 602

Low confidence

760

Syrian Arab Republic

104

1 771 842

Low confidence

792

Turkey93

7 762 575

Low confidence

784

United Arab Emirates

95

923 675

Low confidence

887

Yemen

104

3 026 946

Very low confidence

65

UNEP FOOD WASTE INDEX REPORT 2021

Table 23

Household food waste estimates (from measured data points or extrapolation) for each country
Region Eastern Europe
M49
code1

Country

Household food waste
estimate (kg/capita/year)

Household food waste
estimate (tonnes/year)

Confidence inestimate

112

Belarus68

646 356

Very low confidence

100

Bulgaria

68

478 667

Very low confidence

203

Czechia

70

746 894

Very low confidence

348

Hungary94

908 669

Medium confidence

616

Poland56

2 119 455

Medium confidence

498

Republic of Moldova

76

307 419

Very low confidence

642

Romania70

1 353 077

Very low confidence

643

Russian Federation

33

4 868 564

Medium confidence703

Slovakia

70

381 301

Very low confidence

804

Ukraine76

3 344 904

Very low confidence

Region Northern Europe
M49
code1

Country

Household food waste
estimate (kg/capita/year)

Household food waste
estimate (tonnes/year)

Confidence inestimate

208

Denmark81

469 449

High confidence

233

Estonia78

102 743

Medium confidence

234

Faroe Islands

76

3 710

Low confidence

246

Finland65

361 937

Medium confidence352

Iceland76

25 829Low confidence

372

Ireland55

267 073

Medium confidence

833

Isle of Man

76

6 446

Low confidence

428

Latvia76

145 273

Low confidence

440

Lithuania

76

210 255

Low confidence

578

Norway79423 857

High confidence

752

Sweden81

812 948

High confidence

826

United Kingdom

77

5 199 825High confidence66

02 Index level 1: existing data and extrapolation to other countries

Region Southern Europe
M49
code1

Country

Household food waste
estimate (kg/capita/year)

Household food waste
estimate (tonnes/year)

Confidence inestimate

8

Albania83

238 492

Low confidence

20

Andorra84

6 498

Low confidence

70

Bosnia and Herzegovina

83

273 269

Low confidence

191

Croatia

84

348 091

Low confidence292

Gibraltar

84

2 840

Low confidence

300

Greece142

1 483 996

Medium confidence

336

Holy See**

No estimate

380

Italy

67

4 059 806

Medium confidence

470

Malta

129

56 812High confidence

499

Montenegro

83

51 988Low confidence

807

North Macedonia

83

172 480

Low confidence

620

Portugal

84

861 838

Low confidence

674

San Marino

84

2 857

Low confidence

688

Serbia83

726 196

Low confidence

705

Slovenia

34

71 107

Medium confidence

724

Spain

77

3 613 954

Medium confidence

Region Western Europe
M49
code1

Country

Household food waste
estimate (kg/capita/year)

Household food waste
estimate (tonnes/year)

Confidence inestimate

40

Austria39

349 249

High confidence

56

Belgium

50

576 036

Medium confidence

250

France85

5 522 358

Medium confidence

276

Germany75

6 263 775High confidence

438

Liechtenstein

72

2 725

Low confidence

442

Luxembourg

90

55 126

Medium confidence

492

Monaco72

2 796

Low confidence

528

Netherlands

50

854 855

High confidence

756

Switzerland

72

616 037

Low confidence

67

UNEP FOOD WASTE INDEX REPORT 2021

Table 23

Household food waste estimates (from measured data points or extrapolation) for each country
Region Australia and New Zealand
M49
code1

Country

Household food waste
estimate (kg/capita/year)

Household food waste
estimate (tonnes/year)

Confidence inestimate

36

Australia

102

2 563 110High confidence

554

New Zealand61

291 769

High confidence

Region Melanesia
M49
code1

Country

Household food waste
estimate (kg/capita/year)

Household food waste
estimate (tonnes/year)

Confidence inestimate

242

Fiji

76

67 385

Very low confidence540

New Caledonia

79

22 256

Very low confidence

598

Papua New Guinea

91

798 767

Very low confidence

90

Solomon Islands

91

60 963

Very low confidence

548

Vanuatu91

27 296

Very low confidence

Region Micronesia
M49
code1

Country

Household food waste
estimate (kg/capita/year)

Household food waste
estimate (tonnes/year)

Confidence inestimate

316

Guam

79

13 167

Very low confidence

296

Kiribati

91

10 704

Very low confidence

584

Marshall Islands

76

4 452

Very low confidence

583

Micronesia (Fed. States of)91

10 358

Very low confidence

520

Nauru

79

850

Very low confidence

580

Northern Mariana Islands

79

4 502

Very low confidence

585

Palau

79

1 417

Very low confidence

M49
code1

Country

Household food waste
estimate (kg/capita/year)

Household food waste
estimate (tonnes/year)

Confidence inestimate

16

American Samoa

76

4 187

Very low confidence

184

Cook Islands**

No estimate

258

French Polynesia

79

21 981

Very low confidence

570

Niue

*

*

No estimate

882

Samoa

76

14 923

Very low confidence

772

Tokelau**

No estimate

776

Tonga

76

7 912

Very low confidence

798

Tuvalu76

878

Very low confidence

876

Wallis and Futuna Islands**

No estimate

Region Polynesia

68

02 Index level 1: existing data and extrapolation to other countries

M49
code1

Country

Household food waste
estimate (kg/capita/year)

Household food waste
estimate (tonnes/year)

Confidence inestimate

830

Channel Islands**

No estimate

158

Other non-specified areas**

No estimate

Note: Territories with (*) have no estimates generated. These territories are included in the UN Statistics Division list of countries but are
not included in the World Bank’s income classification grouping.

1

UNSD Standard Country or Area Codes for Statistical Use.

69

UNEP FOOD WASTE INDEX REPORT 2021

D GLOBAL ESTIMATES
Given that food waste has been estimated for every country in the world (section 2.5 and Appendix 3), these can be
added together to obtain global estimates of food waste for 2019. This combines data for countries with existing
estimates, and estimates based on extrapolations (for countries without primary data).
This suggests a total of around 931 million tonnes of food waste across these three sectors: 61 per cent fromhouseholds, 26 per cent from food service and 13 per cent from retail (see Table 22).

Table 24: Estimates of global food waste by sector
Global average food waste(kg/capita/year)*2019 total (million tonnes)

Household

74

569

Food service

32

244

Retail

15

118

Total

121

931

* The global averages presented in this table differ slightly
from those presented earlier in section 2.5. Being built up
country-by-country, the averages in the current table are
population weighted, while those presented earlier are
simple, unweighted averages.
The estimate for the household sector is the mostrobust, based on almost 100 data points across a rangeof countries around the world representing 75 per centof the world’s population. In contrast, the estimates forthe retail and food service sectors are based on smaller
sets of data: around 30 data points for each, with the
majority coming from high-income countries. Countries
with measured data points represented 32 per cent of
the world’s population for food service and 14 per centfor retail.

70

In addition, many of the food service estimates areincomplete, not covering the range of settings outside
the home in which food is served and consumed.
In all cases, however, confidence should not be
overstated. Even though household coverage isgood, the estimates from many countries come fromsmall, limited samples or required adjustment forcomparability. The confidence in the global household
estimate should thus be considered medium-low.
For food service and retail, the confidence in thisestimate is very low, for the reasons discussed above.

02 Index level 1: existing data and extrapolation to other countries

Key finding:
To improve the food waste evidence base – both globally and at a country level – morecountries need to measure food waste across the supply chain and in households,
using accurate methods on substantial sample sizes.

The results above can be compared to foodavailable at the consumption stage of the
supply chain to estimate the proportion ofthis that becomes waste during retail andconsumption 9. In 2018, the most recent
year with data available, 5.3 billion tonnesof food was available globally. Comparing
this with the food waste figures (Table 22)
suggests that 17 per cent of food available
was wasted in the three sectors includedin this paper: 11 per cent in household, 5
per cent in food service and 2 per cent inretail10.
The FAO State of Agriculture report (2019)
estimates that around 14 per cent of global
food production is lost during supply chain
stages up to, but not including, retail. On thesurface, this percentage could be added
to that calculated for the present paper.
However, this approach is not advisedfor two main reasons: firstly, due to thedifferences in scope. The estimate of foodlosses is on a commodity-by-commodity
basis and includes all quantity losses for
multiple utilizations: food, feed, seed andother. This is expressed as a share of all
agricultural production, not just food.

9

By contrast, the Food Waste Index looks
at consumer food waste, i.e. of final foodproducts, not including non-food uses suchas animal feed or seed. Secondly, there aredifferences in the definition between thetwo estimates: one (the Food Waste Index)
includes inedible parts, whereas the other(the loss estimate in FAO (2019)) does not.
Due to these different baselines for thedifferent SDG 12.3 indicators, the two
estimates cannot be combined and shouldbe treated separately. FAO and UNEP arecollaborating to find ways to combine thetwo indicators in the future.
Similarly, further research to quantify
the environmental, economic and social
impacts of this food waste would be
important. Many countries now havedata and research on the types of foodthat are wasted and why. Increasing thisunderstanding to a wider range of countries
would allow stronger strategies andprogrammes of work to minimize waste ofvaluable food resources in these sectors.

Data from the FAO Food Balance Sheets for 2018. Food availability refers to ‘food’ in the balance sheets:
the total amount of the commodity available as human food. It excludes crops or animal products used forindustrial applications or animal feed. It also excludes food losses occurring before the retail stage of the
supply chain. As it covers food reaching the consumer level, it is therefore a good comparator for the foodwaste described in the Food Waste Index (FWI), for which retail and consumption stages are the focus.
Future work could also allow a comparison with the amount of food produced globally.

10 The apparent discrepancy between the sum of the percentages for each sector and the total is due torounding.

71

UNEP FOOD WASTE INDEX REPORT 2021

There are numerous differences between this estimate
and the FAO’s 2011 study (Gustavsson et al., 2011).
Unlike the FWI, the previous FAO estimate combines
loss and waste; relies on waste generation rates
applied to food available for each sector and often
based on assumptions at household level; and only
measures edible parts of food. Figures from that report
are not directly comparable with those presented in the
FWI. These differences mean it is not possible to do
a comparison over time in an attempt to deduce foodwaste trends.
However, in the technical report appendix to FAO(2013), which builds upon the 2011 data, adjustments
are made which do allow a degree of comparability.
This technical report estimates 2007 food loss andwaste including inedible parts. When disaggregatedby the stage in which waste is generated, it is possible
to compare the ‘Distribution’ – which includes retail –and ‘Consumption’ stages with the sectors consideredin this report (retail, food service and household). Taken
as approximate values from a report figure (FAO, 2013,
fig. 29), global ‘Distribution’ waste is estimated to beapproximately 195 million tonnes and ‘Consumption’waste approximately 340 million tonnes.
There are two possible ways to make comparisonsfrom these figures. Firstly, using the combined‘Distribution’ and ‘Consumption’ figures compared tothe total estimate of the FWI: in this case, the 2007
estimate was of 535 million tonnes. The 931 million
tonnes estimated here would therefore be 1.74 times
higher than previous estimates.

72

However, it may not be appropriate to compare whatis grouped in FAO (2013) as ‘Distribution’ with ‘retail’as defined in the FWI: the former includes transport
to markets and supermarkets. Supply chain logistics
and distribution losses are counted under the FLI.
It may therefore be more appropriate to compare‘Consumption’ as defined in the FAO study with thefinal consumption stages presented here: household
and food service. When doing this, the approximate
value of 340 million tonnes in FAO (2013) is comparedto 813 million tonnes in the FWI. This would make the
FWI estimate of waste at the consumption stage some
2.4 times higher than previous estimates.
Given the substantial differences in methodology
and distance between the two datapoints (2007 and2019), these comparisons should not be understood aspresenting any indication of the change in food waste
over time. What they do show, however, is that previous
assumptions regarding consumer food waste likely
significantly underestimated its scale. The evidence
informing the FWI suggests consumer food waste is amagnitude of two times higher than previous estimates,
reinforcing the key conclusion that much more needs
to be done to support consumer food waste reduction
if SDG 12.3 is to be realized.

03 Index levels 2 and 3: measuring food waste at the national level

03
INDEX LEVELS 2 AND 3:
MEASURING FOOD WASTE
AT THE NATIONAL LEVEL
3.1 OVERVIEWLEVEL 1LEVEL 2LEVEL 3• Modelling andextrapolation• Directmeasurement offood waste• Additional
information anddisaggregation• Sufficiently
accurate fortracking• Supports
developmentof food waste
prevention
strategy• Provides
approximate
estimate• Not suitable fortracking purposes

73

UNEP FOOD WASTE INDEX REPORT 2021

Level 1 estimates provide an indication of the scale offood waste in a country and are therefore useful formaking the case for action. However, modelling andextrapolation are insufficiently accurate for a country
to track their food waste over time, and rarely provide alevel of detail sufficient to enable policymakers to makekey strategic decisions about how to prevent foodwaste in that country. Therefore, direct measurementof food waste is required.
Level 2 and Level 3 of the Food Waste Index provide
the framework for countries to measure and reportfood waste, allowing progress to be tracked in linewith the SDG 12.3 target. Levels 2 and 3 use data frommeasurements of food waste in the relevant country
and time frame, rather than proxy data (Level 1). Thissection of the report gives guidance on accepted
methodologies for use within Level 2.

The Level 2 approach requires a reporting country to(see section 3.2):• Define a scope – i.e., select the sector(s) they aregoing to report;• Select suitable methods to measure food waste
within the above;• Conduct studies using the chosen method(s);• Report food waste for the Food Waste Index;• Repeat studies regularly using a consistentmethodology.

3.2 STEPS FOR MEASUREMENT

D DEFINE THE SCOPE
For a country to effectively quantify food waste, it needs clarity on what is measured, andover what time period. This section covers:• The sectors to include• The destinations to cover and disaggregation between destinations• Disaggregation between edible and inedible parts• The time period to cover.

D Sectors to include
The Level 2 framework covers food waste generated in the following sectors:• Retail• Food service• Household.
Manufacturing not covered in the Food Loss Index can be measured using Level 3 guidance (section 3.4).
These sectors are defined according to the International Standard Industrial Classification of all Economic
Activities (ISIC), revision 4 (UN, 2008) to comprise the following sub-sectors:

74

03 Index levels 2 and 3: measuring food waste at the national level

RETAIL

FOOD SERVICE

ISIC 47-11

Retail sale in non‑specialized
stores with food, beverages or
tobacco predominating

ISIC 47-2

Retail sale of food, beverages andtobacco in specialized stores

involves food waste generated in settings where food is consumed in
substantial quantities outside of the home. This could include all of theclassifications below. However, for practical purposes, measurement
can focus on those where the majority of meals are eaten within acountry, for example ISIC 56 and ISIC 85.

ISIC 47-81

Retail sale via stalls and markets
of food, beverages and tobacco
products(Retail excludes ISIC 46-30, Wholesale of food,beverages and tobacco – this is covered under
the Food Loss Index.)

HOUSEHOLDS• Households are not defined under the ISIC
system (except where people are employedby households or households produce goods
and services). The definition of household of
the UN Statistics Division is included below.
For the purposes of the Food Waste Index, ahousehold is any type of dwelling not coveredby the other sectors (e.g., hotels, student
residences).
• A household is classified as either: (a)a one-person household, defined as anarrangement in which one person makes
provision for his or her own food or otheressentials for living without combining withany other person to form part of a multiperson household or (b) a multi-personhousehold, defined as a group of two or more
persons living together who make common
provision for food or other essentials forliving (UNSD, 2020).

ISIC 49-11

Passenger rail transport, interurban

ISIC 49-21

Urban and suburban passenger land transport

ISIC 50-11

Sea and coastal passenger water transport (from foodprepared and consumed on voyages and cruises)

ISIC 50-21

Inland passenger water transport (from food prepared andconsumed on voyages and cruises)

ISIC 51-10

Passenger air transport (from food prepared andconsumed on flights)

ISIC 52-23

Service activities incidental to air transport (specifically
airports)

ISIC 55-10

Short term accommodation activities, especially in settings
where food is prepared and consumed, e.g., hotels,
guesthouses, and bed and breakfasts

ISIC 55-90

Other accommodation, especially in settings where foodis prepared and consumed, including student residences,school dormitories and workers hostels

ISIC 56

Food and beverage service activities, including restaurants,
cafeterias, fast-food restaurants, delivery and takeout eating places, mobile food carts, food preparationin market stalls, events catering, operation of foodconcessions at sports and similar facilities, operationof canteens or cafeterias (e.g., for factories, offices,
hospitals or schools) on a concession basis, bars, taverns,
cocktail lounges, discotheques (with beverage serving
predominant), beer parlors and pubs, coffee shops, fruit
juice bars and mobile beverage vendors

ISIC 84-22

Defence activities (specifically canteens and other places
for preparation and consumption of food associated withthe armed services)

ISIC 84-23

Public order and safety activities (specifically canteens
and other places for preparation and consumption of foodassociated with prisons)

ISIC 85

Education (specifically canteens and other places for
preparation and consumption of food associated witheducational settings)

MANUFACTURING(for Level 3): where manufacturing is includedin the Food Waste Index, it should include:
ISIC 10

Manufacture of food products

ISIC 11

Manufacture of beverages

75

UNEP FOOD WASTE INDEX REPORT 2021

In situations where food waste is already being
measured and that information is shared withgovernments, the resources required to collate andreport this information can be relatively modest.
However, where these measurements are notbeing undertaken or the information is not shared,
undertaking new food waste measurement studies
for all of the above sectors will require a higher level ofresources. Therefore, the indicators are structured sothat countries can begin by measuring food waste in
one of the relevant sectors (determined according tonational priorities) and work to include more sectors
over time.
The Level 1 modelling conducted for this report (seesection 2) suggests that household food waste is thelargest source of food waste in most countries. For thisreason, it is recommended that household food waste
is measured as a first step. When resources allow, themeasurement and reporting of retail and food service
food waste will also provide countries with important
information to inform policy development and support
the achievement of SDG 12.3.

Studies for each of the sectors do not need to be
conducted at the same time. They can be staggered
between years to avoid spikes in resource levels.

Destinations to cover
The Food Waste Index attempts to quantify the foodwaste generated from each of the above sectors.
For the purposes of the Food Waste Index, foodwaste is defined as edible parts (i.e., wasted food)and associated inedible parts going directly to thefollowing destinations (see also Appendix 5 for further
information):• Landfill (including licenced and unlicensed landfilled)• Controlled combustion• Litter discards/refuse• Compost / aerobic digestion11• Land application• Co/anaerobic digestion• Sewer12.

11 For households, food waste composted at home can be omitted from Level 2 due to its low prevalence inmost countries where it has been measured. For example, estimates for the European Union suggested
home composting accounted for 8 per cent of total household food waste. Other forms of composting fromhouseholds (i.e., industrial composting of food collected from households) should be included. Household
home composting can be included under Level 3.

76

12 For Level 2, it is not essential to measure food waste going to the sewer. This is because it requires
additional resources to measure, and – for some sectors – can represent a small proportion of total foodwaste. However, it is included under Level 3, and countries are encouraged to measure it where possible.
As an example, the amount of food discarded to sewer was 23 per cent of household food and drink waste
in the United Kingdom in 2015 (WRAP, 2018); the amount will vary between countries depending on culture,
foods eaten and the prevalence of waste disposal units that discharge to the sewer.

03 Index levels 2 and 3: measuring food waste at the national level

Only relevant destinations need be included. For example, in some countries, food waste from households
will not go to land application or controlled combustion. In such cases, only the destinations where the foodwaste goes should be quantified.

Disaggregation between edible and inedible parts
The total amount of food waste does not need to be disaggregated into edible and inedible parts for Level
2 reporting. However, as it is useful for understanding the national situation, it is included under Level 3
and discussed in section 3.4.

Time period
The estimates for the Food Waste Index should cover a one-year period (preferably January to December).
These estimates should ideally be produced with data obtained throughout the year to account for variations
in food waste generation by season. More information on when data will need to be reported can be foundunder section 3.2.3.

DSELECTION OF METHODS
The measurement methods chosen should generate up-to-date data that are sufficiently accurate to allow
tracking of food waste over time.
A range of methods are available for measuring food waste, each with its own advantages and disadvantages.
These are well documented in the Food Loss and Waste Accounting and Reporting Standard (Hanson etal., 2016a, 2016b).
For each sector, a method (or multiple methods) should be chosen to obtain food waste estimates that are
sufficiently accurate for tracking over time. Other information could also be obtained at the same time to helpa country in reducing food waste (e.g., obtaining information on the types of food that are most frequently
thrown away and the principal causes can support the development of a food waste prevention strategy).
Table 23 provides appropriate methods for different sectors – countries can use these methods, acombination of them, or any other method equivalent in terms of relevance, representativeness and reliability.
More detail on the most appropriate methods for each sector is found in Appendix 4.

77

UNEP FOOD WASTE INDEX REPORT 2021

Table 23 provides appropriate methods for different sectors – countries can use thesemethods, a combination of them, or any other method equivalent in terms of relevance,
representativeness and reliability. More detail on the most appropriate methods for eachsector is found in Appendix 4.

Table 25: Appropriate methods of measurement for different sectors
SectorMethods of measurement

Manufacturing(if included)Massbalance
RetailFood service

HouseholdDirectmeasurement(for foodonly waste
streams)

Waste
composition
analysis (forwaste streams
in which foodis mixed with
non-food)

Volumetricassessment

Counting/
scanning
Diaries (formaterial
going down
sewer, homecompostedor fed toanimals)

In addition, questionnaires, interviews and forms can be used to collate existing
information, but are not sufficiently accurate for obtaining primary data in these sectors(see section 3.3).

78

03 Index levels 2 and 3: measuring food waste at the national levelAn overview of the methods presented in Table 23
are given below, with more detail in the appendix(Appendix 4):• Direct measurement: using a measuring device todetermine the mass of food wasted. This could
involve weighbridges for collection vehicles orsimple scales in a household setting.
• Waste composition analysis: physically separating
food waste from other material to determine its
mass and composition. This can be the mostaccurate way to gain deeper understanding
into the differences in material type (edible andinedible parts) and types or categories of foodwasted. Thus, even in a separate food waste
stream, this method has some utility to achieve anarrower scope or provide greater detail.
• Volumetric assessment: assessing the physicalspace occupied by the food waste and using
the result to determine the mass. In a situation
where the entire quantity of food waste is likely tohave the same composition, for example a waste
stream from commodity processing, the densityof that waste is likely to be consistent. Therefore,a value for mass can be determined by applying
the density of the waste to the volume it occupies,
potentially something like a residue collection vat
in the above example.

• Counting/scanning: assessing the number of
discrete food items that have been discarded andusing the result to determine the mass. This could
include scanner data or simply counting bags ofwaste.
• Diaries: a log in which quantities of food waste
are recorded on a case-by-case basis as they
are becoming waste. This can involve weighing
or estimation/approximation by the person fillingin the log. For example, in a household setting,the diary keeper could log three tortillas or “ahandful” of ugali. The average mass of items forsuch reported measures would need to be used
to convert the measure into grams. Diaries are notparticularly accurate (see Quested et al., 2020)
and therefore are not recommended for situations
in which one of the above quantification methods
is available (such as food waste present in solid
waste streams). However, in some situations –e.g., food waste from households being homecomposted or going to the sewer – they are theonly tested method available.

• Mass balance: inferring the amount of food waste(either in total or for one particular destination) byidentifying all food-related inputs and all outputs(except for the one being quantified) for a siteor sector. The food waste can be calculated bysubtracting the outputs from the inputs, adjusting
for any changes within the site/sector (e.g.,
evaporation; dry foods being boiled and absorbing
water). Works best in situations requiring minimal
adjustment. An example is the estimation of foodwaste in retail in the United States by the U.S.
Department of Agriculture (Buzby et al., 2009).

79

UNEP FOOD WASTE INDEX REPORT 2021

The inclusion of more than one sector within thescope’s boundaries may necessitate multiple studies
and different methods for each sector chosen.
The methods above are appropriate for a governmentfunded study but could also be used by individual
businesses to quantify their own waste, ready forcollating using methods in section 3.3. For example,
some countries have voluntary agreements where
major retailers are required to measure their own foodwaste. By applying appropriate methods (those listed
above) in a robust manner, the data from these retailers
can be collated and used to inform a national estimate.
In some situations, more than one method may benecessary to quantify food waste. For example, somebusinesses may have food waste in two solid waste
streams: one sorted (i.e., food only) and one mixed
residual. For the first, direct weighing or volumetricassessment would be appropriate; for the mixed
waste stream, waste compositional analysis would be
required.
In general, the amount of food waste within a sector
shall be established by measuring food waste
generated by a sample of businesses or households.
These results will require scaling to obtain an estimate
for the entirety of that sector in the country. (Seesection 3.3 on data for scaling.) Therefore, the sample
needs to be adequate in size and representativenessto allow the estimates to be sufficiently accurate fortracking over time.
Relevant measurements may already be taking place
in a country for other purposes:
SDG indicator 11.6.1: This indicator tracks the
proportion of municipal solid waste (MSW) collected
and managed in controlled facilities out of the total
municipal waste generated, by cities. To provide thedata for this indicator, the amount and compositionof urban solid waste can be tracked by compositional
analysis of a range of sectors, including households,
food retail and food service. This data will include theamount of food waste, which can also be repurposed
for the Food Waste Index.

80

Consideration needs to be given to the difference in
geographic bounds between the two indicators: 11.6.1
covers cities while 12.3.1(b) (the Food Waste Index) isfor the entirety of a country. This difference could beovercome by including additional studies for rural areas
or extending city-based studies to surrounding areas.
Using MSW data as a basis of reporting for 12.3.1(b)may allow two SDG indicators to be reported with
one set of fieldwork in certain situations. However, if
a substantial amount of food waste is not collected
as part of the MSW collection system, then additional
measurement is required.
Food waste agreements: Some countries have anagreement (or legislation) that requires businesses
to share data on the amount of food waste theygenerate. If the agreement allows, these data can beused for tracking purposes where the coverage of therelevant sector is high, or the businesses covered are
representative of the wider sector. Examples include
the Courtauld Commitment in the United Kingdom(WRAP, 2018) and United Against Food Waste in the
Netherlands (United Against Food Waste, 2018).

03 Index levels 2 and 3: measuring food waste at the national level

DPREPARING FOOD WASTE DATA FOR REPORTING
The reporting of the Food Waste Index from each UN
Member State comprises the following elements foreach sector:
1. The total mass of food waste generated for that
sector in a 12-month period (in metric tonnes) –including edible and inedible parts and covering
the destinations in scope;
2. The mass of food waste (as above) per person
per year (in kilograms).
For both indicators, the mass is the fresh mass: i.e., theweight of waste at measurement (rather than the dryweight of the material).
Normalization should be undertaken per capita, as the
SDG target is based on reduction in food waste on a
per capita basis. Per capita data allows meaningfulcomparison over time, even in situations where thepopulation of a country is changing rapidly. It also
allows comparisons between countries.

Although not necessary for the Food Waste Index,
determining the level of food waste as a percentageof food entering each sector would also provide a
useful comparison metric, as this takes into accountadditional differences between countries/trends overtime, such as the proportion of food eaten in and outof the home within a country.
Estimates for the Food Waste Index will be requested
every two years, in line with data requests from the UN
Statistics Division. For countries measuring for the firsttime, the baseline year will be 2021 (or the first year forwhich they have measurement if they are not able toreport in 2021). For countries with measurements offood waste that pre-date 2021, they can report foodwaste in every year for which they have data. UNEP will
look for ways to capture relevant historic data, as thiswill be useful for understanding food waste prevention
and could be used in illustrative case studies.

There are four indicators under 12.3.1(b). These are defined for each sector as defined below:

where FW denotes food waste.

The indices for each sector will not be combined into a single Food Waste Index. This will allow the granular datafor individual sectors to be more easily communicated; it will also alleviate issues if a country is unable to report
all sectors in one reporting cycle.

81

UNEP FOOD WASTE INDEX REPORT 2021

The indices for each sector will not be combined into a single Food Waste Index. This will allow the granular datafor individual sectors to be more easily communicated; it will also alleviate issues if a country is unable to report
all sectors in one reporting cycle.

Example: Food Waste Indices for two hypothetical countries
Table and Figure 6 provide a worked example of the household Food Waste Index for two hypothetical countries. In
both cases, the baseline year is 2022. Country 1 has 87 kg/capita/year of household food waste in 2022 and – asthis is the first year of measurement – this is defined as 100 in the Food Waste Index. By 2030, this has reduced
to 60 kg/capita/year: a value of 69 in the Food Waste Index. This represents a reduction of 31 per cent: good
progress, but insufficient to meet the 50 per cent reduction for SDG 12.3(b), represented by the blue dotted line.
Country 2 has a baseline value of 84 kg/capita/year, which is defined as 100 in the Food Waste Index for thiscountry. By 2030, this country has achieved SDG 12.3(b) for this sector, with food waste less than half the baseline
level (41 kg/capita/year). Therefore, the final Food Waste Index value for Country 2 is a value less than 50.

82

03 Index levels 2 and 3: measuring food waste at the national level

Table 26: Data points relating to households from studies in West Asia
Country 1

Country 2

Year

Household food waste(kg/capita/yr)

Index

Household food waste(kg/capita/yr)

Index

2022

87

100

84

100

2024

76

87

80

95

2026

75

86

69

82

2028

65

75

50

60

2030

60

69

41

49

Figure 5: Food Waste Indices for two hypothetical countries

More details on the practicalities of UN Member States reporting to UNEP are given in the following sub-section.

83

UNEP FOOD WASTE INDEX REPORT 2021

HOW COUNTRIES SHOULD REPORT FOOD WASTE UNDER SDG 12.3
Food waste data in relation to SDG 12.3 will be collected using the UN Statistics Division-UNEP Questionnaire on
Environment Statistics: Waste Section. The questionnaire is sent out every two years to National Statistical Offices
and Ministries of Environment, which will nominate a single food waste focal point in the country to coordinate
data collection and reporting. The data will be made publicly available in the SDG Global Database and in UNEP’s
Food Waste Index Report, which will be published at regular intervals up to 2030. The next questionnaire will be sent
to member states in September 2022, and results will be reported to the SDG Global Database by February 2023.
Countries do not need to conduct new measurements every two years, or measure every sector simultaneously.
Measuring each sector at least once every four years is recommended.

3.3 DATA SOURCES, AVAILABILITY AND PRODUCTION
This section provides:• Sources of existing food waste data;• Methods for collecting existing data – if data of sufficient coverage and quality are already being collected,
these methods allow the data to be collated;• Data sources for scaling the above two types of data from a sample to the whole country.
These sections apply to indicators under both Level 2 and Level 3.

SOURCES OF EXISTING DATA
Some of the data to estimate the quantity of food waste
may already exist, having been generated for reasons
other than quantifying food waste. The following
should be investigated as potential data sources forbuilding a national estimate of food waste:• Food service: (formal) company records, stock
keeping, purchase and sales records, waste
m a n a g e m e n t r e c o r d s/r e c e i p t s; (i nfo r m a l )government surveys, academic surveys and studies;(both) studies focusing on MSW.

• Manufacturing (for Level 3 reporting): factory
records, stock keeping, purchase and sales ledgers,
waste management records/receipts (where charged
by volume).

• Households: data on waste collected (government
statistics or from waste management companies),
academic studies on generation and composition,
studies focusing on MSW.

• Retail: (formal) company records, stock keeping,purchase and sales records, waste management
records/receipts; (informal) government surveys,
academic surveys and studies; (both) studies
focusing on municipal solid waste (MSW), e.g., thoseused for other waste-related SDG indicators, such as
12.5.1 and 11.6.1.

84

03 Index levels 2 and 3: measuring food waste at the national level

METHODS FOR COLLATING EXISTINGDATAAs mentioned in the previous section, some countries
may already have data from which a food waste
estimate can be derived. It is generally more costeffective for a country to use existing data – assuming
they are of appropriate quality and coverage – than toundertake new measurement. This section describes
methods for collating existing data.
S tructured questionnaires or forms to gather
information from a large number of individuals or
entities. A survey is most appropriate when thecommissioner of such a method is confident in theability of the respondent to accurately provide the data
requested. This means that the respondent has alreadymeasured food waste in a robust way, rather thanasking for a recollection or opinion. This stipulation
invalidates surveying as a method for household foodwaste quantification, as respondents are unlikely tohave measured their food waste and remember it
accurately at time of asking. Use of surveys is more
appropriate for formal retailers, companies providing
out-of-home meals (restaurants, etc.) and food
manufacturers that are already measuring their foodwaste (i.e., using methods in section ‎0).

DATA SOURCES FOR SCALING DATA
Data collected often need scaling to obtain a national
estimate. For example, a government register ofcompanies may contain data on the number, size andtype of business (e.g., restaurant versus street vendor)
to scale other data (e.g., food waste per business) with.
This section contains examples of data that facilitate
this scaling.
• Manufacturing (for Level 3): company registration
data, factory records / stock keeping of amount offood processed, purchase and sales ledgers.
• Retail: (formal) company registration data, purchaseand sales records; (informal) government surveys,
academic surveys and studies.
• Food service: (formal) company registration dataof restaurants, hospitals, schools, etc.; company
records of amount of food purchased/sold; (informal)
government surveys, academic surveys and studies.
• Households: household income and expenditure
surveys on purchases, census data for population,
number and type of household, waste collection
company data (total amount of waste collected).

85

UNEP FOOD WASTE INDEX REPORT 2021

3.4 SPECIFICS FOR LEVEL 3
LEVEL 1LEVEL 2• Modelling andextrapolation• Direct measurementof food waste• Additional informationand disaggregation• Provides approximate
estimate• Sufficiently accurate
for tracking• Supports developmentof food waste
prevention strategy• Not suitable for tracking
purposes

Level 3 comprises supplementary indicators relating
to food waste. These are:
• Disaggregation of total food waste reported in theLevel 2 indicators by destination. This would include
any of the following that are used for food waste
management from the sector in question within acountry:-- Co-digestion / anaerobic digestion-- Composting / aerobic process-- Controlled combustion-- Land application-- Landfill-- Refuse/discards/litter.
• Inclusion of destinations not included in Level
2: sewer, home composted and food “surplus”
destinations: i.e., redistributed for consumption bypeople, used for animal feed or used for bio-basedmaterials / biochemical processing.
• Disaggregation of total food waste by edible parts(intended for human consumption) and theirassociated inedible parts (e.g., banana skins, bones,
eggshells).
• Reporting of manufacturing food waste where it
is not covered by the Food Loss Index: e.g., wheremore than one commodity is combined to produce
processed/complex food products.

86

LEVEL 3The disaggregation by edible and inedible parts (e.g.,
animal bones, eggshells, fruit pits) distinguishes
between items/parts of food that are considered
edible (and therefore have the potential to have been
consumed if better managed within the supply chain orwithin the home) and parts considered inedible, where
there are few opportunities for preventing the item
becoming food waste. In the case of inedible parts,
attention can be given to incentivize the destination
with the greatest environmental, social and economic
benefits (and fewest costs): for example, if fresh eggs
are purchased, then eggshells will need to be discarded,
and the challenge becomes finding a way of obtaining
the most value from them in a sustainable way.

03 Index levels 2 and 3: measuring food waste at the national level

Guidance is given on how to quantify and report edible
and inedible parts separately in the Food Loss andWaste Accounting and Reporting Standard (Hanson etal., 2016a), specifically sections 6.4 and 8.2. However,the Food Loss and Waste Standard does not give clear
guidance on how to distinguish these two categories forfood waste in the home. However, Nicholes et al. (2019)provide a methodology for making this classificationthat could be applied in different countries taking into
account cultural differences.
In addition to the disaggregation of data within Level
2 and Level 3 indicators, there are several other splitsthat can be very useful to countries developing foodwaste prevention strategies:• The types of food thrown away provides useful insightfor prioritizing solutions to prevent waste. Data canbe obtained for high-level categories (e.g., fruit,
vegetables, bakery) or for more detailed categories(e.g., apples, melons, bananas). Either level of detailcan be achieved via waste compositional analysis,
but the cost will increase with greater detail. In
retail and food service settings, the use of scanningsystems and/or smart bins can also provide this levelof detail.
• Disaggregation may also be useful based on
geography, e.g., obtaining data for individual states,
provinces, cities or other areas within a country. This
will help understand where efforts to reduce foodwaste need to be focused.

• For similar purposes, countries may wish toundertake studies to understand the differencesin the amounts and types of food waste between
groups of businesses or types of households/
people. For household food waste, this may include
understanding the variation by age, gender, income
levels, region, household composition, employment
status, etc.
For both businesses and households, qualitative
research (e.g., including interviews and observation)
can be useful in understanding how the factors such as
gender and income can influence how food is obtained,
managed, consumed and wasted in different culturaland geographical contexts.
These additional indicators provide information thatcreates a fuller picture of the food system within acountry, with specific focus on food waste and foodsurplus. This information will help authorities determine
the best way to prevent food waste or divert foodwaste/surplus to a destination with higher economic,
environmental and/or social value. This can feed intoa national food waste prevention strategy.
As with Level 2 indicators, Level 3 indicators should be
expressed in total and per capita. However, unlike Level
2 indicators, it is not necessary to track these using an
index – this may hinder clear interpretation of the data.

87

UNEP FOOD WASTE INDEX REPORT 2021

3.5 METHODOLOGICAL CHALLENGES AND LIMITATIONSThere are several challenges relating
to food waste measurement and theinterpretation of the resulting data.
As the measurement of food waste
be c omes more es tablishe d wi thincountries, Level 2 and Level 3 data should
allow comparison over time for individual
countries. With care, Level 2 and Level
3 data should also allow comparison
between countries. Similarly, Level 2 andLevel 3 data can be aggregated to regional
level, where country coverage is good.
Level 1 data should not be compared with
Level 2 and 3 data, as methodologies differ
substantively. Furthermore, Level 1 data are
not designed to (and should not be usedto) compare countries or track a country’s
food waste over time. This is because
data often come from other countries orcome from more than a few years ago.
Therefore, although Level 1 estimates
are a rough estimate of how much foodwaste is generated by a country for a given
sector (e.g., for informing a business caseto tackle the issue), they are not accurateenough for comparison purposes.

88

It is good practice for countries to estimate
the uncertainty associated with their foodwaste data and report it alongside theestimate. This can then be used to ensurethat comparisons are only made where thedegree of uncertainty is sufficiently small
to allow it. Therefore, countries should
estimate the total uncertainty in their Level
2 and 3 food waste estimates. Uncertainties
will come from random errors (e.g.,sampling errors) which are relatively easy
to estimate, and from systematic errors(e.g., using a measurement method thatsystematically over- or under-estimates
food waste, or sampling only for a portionof the year), which are harder to estimate.

03 Index levels 2 and 3: measuring food waste at the national level

Other relevant challenges to consider:• Variations in waste over time can havea significant impact on estimatedquantities of waste when short studies(e.g., a week) are used to represent a
longer time period (a year), either due to:-- The specific time of year when astudy takes place which may affectthe waste produced, which can leadto a systematic error. Solutions
include sampling throughout the year;
or, if a study has been undertaken at
one time of year, adjusting to accountfor known variations, for example,
see chapter 11 of WRAP’s household
food waste report (WRAP, 2013).
-- Natural scatter over time in amountsof waste generated by single entities(e.g., households or restaurants),
which contributes to the random
error associated with sampling.
This can be overcome by designing
food waste measurement to include
a sufficient number of entities andmeasuring over a sufficient length oftime.

These points are discussed further insection 8.1 of the Food Loss and Waste
Accounting and Repor ting S tandard(Hanson et al., 2016a).
• Different methods of quantification
can also be used for other related
purposes, for example, identifying thegreatest opportunities for reductions
within a sector or a country. Taking
households as an example, it is difficult
to obtain reasons for discarding food(and therefore the opportunities for
influencing citizen behaviour) without
the use of diaries or ethnography. Thesemethods provide information on thecauses of food waste but do not produce
accurate food waste data.
• At a national level, countries may
have to rely on other entities (e.g.,businesses generating food waste, waste
management companies, municipalities)
to measure their own waste and reportto the government, which would then becollated and analysed to estimate thetotal amount. How the data is collectedby each entity may vary. For example, agovernment may collate food waste datafrom grocery retailers; the government isreliant on these retailers measuring theirfood waste in a sufficiently accurate
way for the national estimate to berobust. Establishing clear guidance forthese entities can support this effort,
for example, WR AP’s guidance forsignatories to the Courtauld Commitment(WRAP, 2020a).
89

UNEP FOOD WASTE INDEX REPORT 2021

3.6 BENEFITS OF MEASUREMENT AND EXAMPLES
Accurate food waste measurement requires
resources and time to obtain. However,
sufficiently accurate data collection ispossible and provides the basis to build
the case for tackling food waste, to provide
an understanding of the nature of foodwaste in a country, to inform a national
food waste strategy based on food waste
hotspots and to track food waste over time.
Delivering reductions in food waste can be
an important avenue for stakeholders tosave money, improve food security, reduce
environmental impacts and add value tocircular economy processes.
To support food waste reduction, collecting
additional information is important. For
instance, recording the reasons why foodis thrown away can provide additional
insight and help develop solutions to tackle
food waste. Understanding the types offood thrown away in different settings
has similar benefits; this can be achieved
through detailed waste compositional
analysis, use of smart-bin technology,
diaries and/or systems that scan food as
it becomes waste for a retailer. In addition,observations, interviews and surveys canalso increase understanding of why food iswasted and what can be done to minimize
the amount wasted. In most situations, thisadditional information is not required forthe purpose of tracking food waste overtime but is important in developing aneffective strategy to achieve SDG 12.3.
There are several examples of data being
collected for tracking purposes from a
range of countries around the world. A
selection of these have been presented
below, by sector. These examples are notexhaustive. Neither should their inclusion
be taken to indicate that all elements of thestudy are consistent with the Food Waste
Index. However, they provide information
for the reader on how other countries aremeasuring food waste and how practicalproblems have been overcome.

90

RETAIL
Although the majority of studies come fromhigh-income countries, there is an example
of a study from a lower middle-income
country.
A couple of principal methods have beenused to estimate food waste in this sector,
including:• Use of industr y data by Australia(Arcadis, 2019), Austria (Environment
Agency Austria, 2017), Japan (Andrew
Parry et al., 2015; Food Industry Policy
Office, 2017), the United Kingdom (WRAP,
2020b) and Flanders (part of Belgium)(Flemish Food Supply Chain Platform for
Food Loss, 2017); and• Waste compositional analysis / waste
audit by Denmark (Danish Environmental
Protection Agency, 2014), Kenya (JICA,
2010) and New Zealand (Goodman-Smithet al., 2020).

FOOD SERVICE SETTINGS
The food service sector comprises anumber of sub-sectors: for example,
restaurants, hotels, education
establishments, prisons, etc. Few existingstudies collect data from all sub-sectors,
and therefore many estimates rely on proxymeasures for at least some of the subsectors.
Examples of studies include:• Waste compositional analysis / waste
audit by Austria (Environment Agency
Austria, 2017), Estonia (Moora, Evelin,
et al., 2015), Kenya (JICA, 2010) and theUnited Kingdom (WRAP, 2020b);• Direct weighing: In China, Wang et al.
(2017) measured restaurant waste infour cities by weighing the food waste
generated by a set number of tables;• Data from industry: In an Australia study(Arcadis, 2019), data were collated via
surveys for a range of sectors, and theauthors acknowledged a wide confidence
interval around the estimate due to small
sample sizes.

03 Index levels 2 and 3: measuring food waste at the national level

HOUSEHOLD
There are more national estimates offood waste from households than anyof the other sectors covered by the Food
Waste Index. Furthermore, the studies
are distributed among continents andcountries of different income levels to agreater extent than the other sectors.
Waste compositional analyses (WCAs) thathave been performed can be split into twogroups:• General/multi-material WCAs in which
the study is quantifying the amounts ofdifferent materials from households (orin municipal waste more widely). These
are often designed to support changes
to recycling infrastructure, rather thanbeing performed to understand foodwaste. Sometimes with a national focus,
many of these studies focus on a cityor region within a country. Examples
include: China (Gu et al., 2015), Ghana(Miezah et al., 2015), Rwanda (Mucyo,2013), Sweden (Swedish Environmental
Protection Agency, 2014) and Viet Nam(Thanh et al., 2010).

In addition, studies have taken data fromwaste compositional analysis and added
in data for other destinations (e.g., sewer,home composting) from other methods.
Examples include the Netherlands (vanDooren et al., 2019), the United Kingdom(WRAP, 2020b), the United States (U.S.
Environmental Protection Agency, 2020b)
and Flanders (Belgium) (Flemish Food
Supply Chain Platform for Food Loss,
2017).
Many studies also have been performed
using methods that do not give accurate
estimates of the amount of food wastefrom households: namely, diaries andquestionnaires. As discussed in themethodology, these are not suitedto tracking levels of food waste overtime; however, they can be useful inunderstanding why food is thrown away
in homes and finding out about related
behaviours, practices, knowledge, skills,
etc. They can also cover destinations –such as home composting – for whichobtaining data from more accurate
methods is difficult.

• Food waste-specific WCAs designed
to understand food waste generation,
often not quantifying other materials in
the waste streams. Examples include
Australia (Arcadis, 2019), Denmark(Edjabou et al., 2016), New Zealand(Sunshine Yates Consulting, 2018) andSouth Africa (Oelofse et al., 2018).

91

UNEP FOOD WASTE INDEX REPORT 2021

04
DISCUSSION AND
RECOMMENDATIONSThe evidence presented in this Food Waste
Index Report has demonstrated that foodwaste at the consumer level is everybody’s
problem. In all countries for which data
was available, food waste, particularly
at the household level, was substantial.
Food waste is a waste of resources, timeand money. Food waste means all of theenvironmental impacts of food production
without any of the benefits of people being
fed. With widespread food insecurity formany hundreds of millions around theglobe, addressing food waste is a criticalissue to creating low-impact, healthy andresilient food systems.

92

Earlier narratives of global food waste
suggested that consumer food waste took
place largely in developed countries, while
production, storage and transportation
losses were concentrated in developing
countries. However, this report has foundthat household food waste per capita
is similar across high-income, upper
middle-income and lower-middle income
countries, with insufficient data to makeconclusions on low-income countries. The
global estimates in the Food Waste Index
suggest global consumer food waste
could be approximately twice the size ofprevious estimates. This demonstrates that
action on consumer food waste is needed
worldwide.

04 Discussion and recommendations

Accurate, traceable and comparable
measurement is a key starting point fornational food waste strategies and policies
to deliver the 50% reduction in consumer
food waste targeted in SDG 12.3. At
present, 17 countries have high-quality datacompatible with SDG 12.3.1(b) reporting
for at least one sector, with a further
42 countries with some measurement
estimate which, with some small updates,
could create an SDG 12.3- compatible
estimation.

While this repor t improves ourunderstanding of global food waste,
there remain a number of data gaps.
One significant gap is the proportion of
inedible parts. Food waste as measured in
the Food Waste Index includes both fooddestined for human consumption and its
associated edible parts. Understanding
how food waste in a particular sector isdisaggregated between its edible andinedible parts will help stakeholders bothin understanding the problem and indesigning the solutions. At present, little is
known about this disaggregation in middleand low-income countries. The Food Waste
Index Report provides strong evidence
that makes the case for action globally.
Measurement of food waste at retail, foodservice and household level using theframework and methods established in thisreport will strengthen estimates in mostcountries, informing the development of
national food waste prevention strategies.

UNEP FOOD WASTE INDEX REPORT 2021

BIBLIOGRAPHY
Abeliotis, K., Lasaridi, K., Costarelli, V., & Chroni, C.
(2015). The implications of food waste generation
on climate change: The case of Greece. Sustainable
Production and Consumption, 3, 8–14. https://doi.
org/10.1016/j.spc.2015.06.006
ADEME. (2016). Pertes et gaspillages alimentaires:
L’état des lieux et leur gestion par étapes de la chaîne
alimentaire (p. 165). ADEME. https://www.ademe.fr/
etat-lieux-masses-gaspillages-alimentaires-gestiondifferentes-etapes-chaine-alimentaire
Alayam. (2018). Minister of works: 195 thousand tonsof food waste annually. Alayam. https://www.alayam.
com/online/local/737712/News.html
Al-Maliky, S. J. B., & ElKhayat, Z. Q. (2012). Kitchen
Food Waste Inventory for Residential Areas in
Baghdad City. Modern Applied Science, 6(8), p45.
https://doi.org/10.5539/mas.v6n8p45
Al-Mas’udi, R. M., & Al-Haydari, M. A. S. (2015). Spatial
Analysis of Residential Waste Solid in the City of
Karbala. Journal of Kerbala University, 13(2), 132–154.

Beretta, C., Stoessel, F., Baier, U., & Hellweg, S. (2013).
Quantifying food losses and the potential for reduction
in Switzerland. Waste Management, 33(3), 764–773.
https://doi.org/10.1016/j.wasman.2012.11.007
BIO Intelligence Service. (2010). Preparatory study
on food waste across EU 27. European Commission.
http://ec.europa.eu/environment/eussd/pdf/bio_
foodwaste_report.pdf
Bogdanović, M., Bobić, D., Danon, M., & Suzić, M.
(2019). Circular Economy Impact Assessment: Foodwaste in HORECA sector. Deutsche Gesellschaft fürInternationale Zusammenarbeit (GIZ). https://www.
giz.de/en/downloads/CE%20impact%20assessment_
HORECA.pdf
Buzby, J., Wells, H. F., Axtman, B., & Mickey, J.
(2009). Supermarket Loss Estimates for Fresh Fruit,
Vegetables, Meat, Poultry and Seafood and Their
Use in the ERS Loss-Adjusted Food Availability Data
(No. 44; Economic Information Bulletin, p. 26). U.S.
Department of Agriculture. https://www.ers.usda.gov/
webdocs/publications/44306/10895_eib44.pdf

Andrew Parry, Paul Beazard, & Koki Okawa. (2015).
Preventing Food Waste: Case Studies of Japan andthe United Kingdom (OECD Food, Agriculture andFisheries Papers No. 76; OECD Food, Agriculture
and Fisheries Papers, Vol. 76). https://doi.
org/10.1787/5js4w29cf0f7-en

Buzby, J., Wells, H., & Hyman, J. (2014). The Estimated
Amount, Value, and Calories of Postharvest Food
Losses at the Retail and Consumer Levels in the
United States (Economic Research Service No. 121;
Economic Information Bulletin). U.S. Department ofAgriculture. https://www.researchgate.net/profile/
Jean_Buzby/publication/285230768_The_Estimated_
Amount_Value_and_Calories_of_Postharvest_Food_
Losses_at_the_Retail_and_Consumer_Levels_in_the_
United_States/links/5a8f16220f7e9ba4296974ad/
The-Estimated-Amount-Value-and-Calories-ofPostharvest-Food-Losses-at-the-Retail-andConsumer-Levels-in-the-United-States.pdf

Araujo, G. P. de, Lourenço, C. E., Araújo, C. M. L. de, &Bastos, A. (2018). Intercâmbio Brasil-União Europeia
sobre desperdício de alimentos: Relatório final (p. 40).
https://www.embrapa.br/busca-de-publicacoes/-/publicacao/1105525/intercambio-brasil-uniaoeuropeia-sobre-desperdicio-de-alimentos-relatorio-final

Caldeira, C., Barco Cobalea, H., Serenella, S., De
Laurentiis, V., European Commission, & Joint
Research Centre. (2019). Review of studies on foodwaste accounting at Member State level. https://
op.europa.eu/publication/manifestation_identifier/
PUB_KJNA29828ENN

Arcadis. (2019). National Food Waste Baseline:
Final assessment report. Arcadis. https://www.
environment.gov.au/system/files/pages/25e36a8c3a9c-487c-a9cb-66ec15ba61d0/files/national-foodwaste-baseline-final-assessment.pdf

Chakona, G., & Shackleton, C. M. (2017). Local
setting influences the quantity of household foodwaste in mid-sized South African towns. PLOS ONE,12(12), e0189407. https://doi.org/10.1371/journal.
pone.0189407

Assefa, M. (2017). Solid Waste Generation Rate andCharacterization Study for Laga Tafo Laga Dadi
Town, Oromia, Ethiopia. International Journal ofEnvironmental Protection and Policy, 5(6), 84. https://doi.org/10.11648/j.ijepp.20170506.11

Chalak, A., Abiad, M. G., Diab, M., & Nasreddine,L. (2019). The Determinants of Household Food
Waste Generation and its Associated Caloric andNutrient Losses: The Case of Lebanon. PLOS ONE,14(12), e0225789. https://doi.org/10.1371/journal.
pone.0225789

Al-Rawi, S. M., & Al-Tayyar, T. A. (2013). A Study on
Solid Waste Composition And Characteristics ofMosul City/Iraq. Journal of University of Zakho, 1(2),496–507.

94

BIBLIOGRAPHY

Cicatiello, C., Franco, S., & Falasconi, L. (2019).
Gli sprechi alimentari nella grande distribuzione
organizzata in Italia. Quantificazione e analisi dei
prodotti alimentari smaltiti nei supermercati e
ipermercati. REDUCE. https://www.sprecozero.it/wpcontent/uploads/2020/07/Report-AR4-GDO.pdf
Danish Environmental Protection Agency.
(2014). Kortlægning af madaffald iservicesektoren: Detail handel, restauranterog storkøkkener. https://www2.mst.dk/Udgiv/
publikationer/2014/07/978-87-93178-75-5.pdf
Danish Environmental Protection Agency.
(2018). Kortlægning af sammenstaetningen af
dagrenovation og kildesorteret oranisk affald
fra husholdninger. https://www2.mst.dk/Udgiv/
publikationer/2018/03/978-87-93614-78-9.pdf
Denafas, G., Ruzgas, T., Martuzevičius, D., Shmarin,
S., Hoffmann, M., Mykhaylenko, V., Ogorodnik, S.,
Romanov, M., Neguliaeva, E., Chusov, A., Turkadze, T.,
Bochoidze, I., & Ludwig, C. (2014). Seasonal variationof municipal solid waste generation and composition
in four East European cities. Resources, Conservation
and Recycling, 89, 22–30. https://doi.org/10.1016/j.
resconrec.2014.06.001
Dhokhikah, Y., Trihadiningrum, Y., & Sunaryo, S.
(2015). Community participation in household solid
waste reduction in Surabaya, Indonesia. Resources,
Conservation and Recycling, 102, 153–162. https://doi.
org/10.1016/j.resconrec.2015.06.013
Dou, Z., & Toth, J. D. (2020). Global primary dataon consumer food waste: Rate and characteristics– A review. Resources, Conservation andRecycling, 105332. https://doi.org/10.1016/j.
resconrec.2020.105332
Edema, M. O., Sichamba, V., & Ntengwe, F. W. (2012).
Solid waste management—Case study of Ndola,Zambia. International Journal of Plant, Animal andEnvironmental Sciences, 2(3). https://www.academia.
edu/30874341/SOLID_WASTE_MANAGEMENT_CASE_STUDY_OF_NDOLA_ZAMBIA

Environment Agency Austria. (2017). Food Waste
Statistics Austria. https://ec.europa.eu/food/sites/
food/files/safety/docs/fw_eu-platform_20170925_
sub-fwm_pres-02b.pdf
Environment and Climate Change Canada. (2019).
National Waste Characterization Report: The
Composition of Canadian Residual Municipal
Solid Waste. http://publications.gc.ca/collections/collection_2020/eccc/en14/En14-405-2020-eng.pdf
European Commission. (2020). Food waste
measurement. Food Safety - European Commission.
https://ec.europa.eu/food/safety/food_waste/eu_
actions/food-waste-measurement_en
FAO. (2013). Food wastage footprint: Impacts
on natural resources: technical report. Food andAgriculture Organization of the United Nations. http://
www.fao.org/3/ar429e/ar429e.pdf
FAO. (2019). The State of Food and Agriculture Report:
Moving Forward on Food Loss and Waste Reduction.
Food and Agriculture Organization of the United
Nations. http://www.fao.org/3/ca6030en/ca6030en.pdf
FAO. (2020). The State of Food Security and Nutritionin the World. Food and Agriculture Organization of theUnited Nations. https://doi.org/10.4060/ca9692en
Flemish Food Supply Chain Platform for Food Loss.
(2017). Food Waste and Food Losses: Prevention andValorisation. https://www.voedselverlies.be/sites/
default/files/atoms/files/Monitor_EN_final.pdf
Food Industry Policy Office. (2017, August). Reducing
Food Loss and Waste & Promoting Recycling.
https://www.maff.go.jp/e/policies/env/attach/pdf/
frecycle-3.pdf
Gao, L., Cheng, S., Cao, X., Zhang, D., Xiaojie Liu, Qin,
Q., & Liu, Y. (2013). An Overview of the Resources andEnvironmental Issues from Wasted Food in Urban
Catering Across China. Journal of Resources andEcology, 4(4), 337–343. https://doi.org/10.5814/j.
issn.1674-764x.2013.04.006

Edjabou, M. E., Petersen, C., Scheutz, C., &Astrup, T. F. (2016). Food waste from Danish
households: Generation and composition. Waste
Management, 52, 256–268. https://doi.org/10.1016/j.
wasman.2016.03.032

Giordano, C., Alboni, F., & Falasconi, L. (2019).
Quantities, Determinants, and Awareness ofHouseholds’ Food Waste in Italy: A Comparison
between Diary and Questionnaires Quantities’.
Sustainability, 11(12), 3381. https://doi.org/10.3390/
su11123381

Elimelech, E., Ayalon, O., & Ert, E. (2018). What getsmeasured gets managed: A new method of measuring
household food waste. Waste Management, 76,
68–81. https://doi.org/10.1016/j.wasman.2018.03.031

Goodman-Smith, F., Mirosa, M., & Skeaff, S. (2020).
A mixed-methods study of retail food waste in
New Zealand. Food Policy, 92, 101845. https://doi.
org/10.1016/j.foodpol.2020.101845

95

UNEP FOOD WASTE INDEX REPORT 2021

Grover, P., & Singh, P. (2014). An Analytical Studyof Effect of Family Income and Size on Per Capita
Household Solid Waste Generation in Developing
Countries. Review of Arts and Humanities, 3(1),127–143.
Gu, B., Wang, H., Chen, Z., Jiang, S., Zhu, W., Liu,
M., Chen, Y., Wu, Y., He, S., Cheng, R., Yang, J., &Bi, J. (2015). Characterization, quantification andmanagement of household solid waste: A casestudy in China. Resources, Conservation andRecycling, 98, 67–75. https://doi.org/10.1016/j.
resconrec.2015.03.001
Gustavsson, J., Cederberg, C., & Sonesson, U. (2011).
Global food losses and food waste: Extent, causes
and prevention (p. 38). FAO. http://www.fao.org/3/ai2697e.pdf
Gustavsson, J., Cederberg, C., & Sonesson, U.
(2013). The methodology of the FAO study: “Global
Food Losses and Food Waste—Extent, causes andprevention”- FAO, 2011. The Swedish Institute for Foodand Biotechnology (SIK). http://www.diva-portal.org/
smash/get/diva2:944159/FULLTEXT01.pdf
Hanson, C., Lipinski, B., Robertson, K., Dias, D., Gavilan,
I., Gréverath, P., Ritter, S., Fonseca, J., van Otterdijk,
R., Timmermans, T., Lomax, J., O’Connor, C., Dawe, A.,
Swannell, R., Berger, V., Reddy, M., Somogyi, D., Tran,
B., Leach, B., & Quested, T. (2016a). Food Loss andWaste Accounting and Reporting Standard, Version
1.0 (p. 160). Food Loss + Waste Protocol. https://
flwprotocol.org/wp-content/uploads/2017/05/FLW_
Standard_final_2016.pdf
Hanson, C., Lipinski, B., Robertson, K., Dias, D., Gavilan,
I., Gréverath, P., Ritter, S., Fonseca, J., van Otterdijk,
R., Timmermans, T., Lomax, J., O’Connor, C., Dawe, A.,
Swannell, R., Berger, V., Reddy, M., Somogyi, D., Tran,
B., Leach, B., & Quested, T. (2016b). Guidance on FLW
Quantification Methods. Supplement to the Food Lossand Waste (FLW) Accounting and Reporting Standard,
Version 1.0 (p. 90). Food Loss + Waste Protocol.
Hanssen, O. J., Syversen, F., & Stø, E. (2016). Edible
food waste from Norwegian households—Detailed
food waste composition analysis among households
in two different regions in Norway. Resources,
Conservation and Recycling, 109, 146–154. https://doi.
org/10.1016/j.resconrec.2016.03.010

96Inter-American Development Bank. (2011). Waste
Generation and Composition Study for the Western
Corridor, Belize C.A. (2056/)C-BL). http://belizeswama.
com/wp-content/uploads/2018/12/WasteGeneration-Composition-Study-for-Western-CorridorBelize-C.A.-2056-OC-BL1.pdf
Jereme, I., Chamhuri, S., Talib, B. A., & Begum, R. A.
(2013). Household food composition and disposal
behaviour in Malaysia. The Social Sciences, 8(6), 553–
539. https://doi.org/10.3923/sscience.2013.533.539
JICA. (2005). The Study on the Solid Waste
Management in Dhaka City (Volume 2). Japan
International Cooperation Agency. https://
openjicareport.jica.go.jp/pdf/11785243.pdf
JICA. (2010). Preparatory Survey for Integrated
Solid Waste Management in Nairobi City in the
Republic of Kenya (Volume 1). Japan International
Cooperation Agency. https://openjicareport.jica.go.jp/
pdf/12005443.pdf
JICA. (2013). Project on Master Plan Study for
Integrated Solid Waste Management in Bogota, D.C.
(Volume 2). Japan International Cooperation Agency.
https://openjicareport.jica.go.jp/pdf/12126843.pdf
JICA. (2015). Project for Integrated Solid Waste
Management Master Plan in Gujranwala (Volume3). Japan International Cooperation Agency. https://
openjicareport.jica.go.jp/pdf/12246336_01.pdf
JICA. (2016). Data Collection Survey on Solid Waste
Management in Democratic Socialist Republic of
Sri Lanka. Japan International Cooperation Agency.
https://openjicareport.jica.go.jp/pdf/12250213.pdf
Kasza, G., Dorkó, A., Kunszabó, A., & Szakos, D. (2020).
Quantification of Household Food Waste in Hungary:
A Replication Study Using the FUSIONS Methodology.
Sustainability, 12(8), 3069. https://doi.org/10.3390/
su12083069
Katajajuuri, J.-M., Silvennoinen, K., Hartikainen, H.,
Heikkilä, L., & Reinikainen, A. (2014). Food waste in the
Finnish food chain. Journal of Cleaner Production, 73,
322–329. https://doi.org/10.1016/j.jclepro.2013.12.057
Kemper, K., Voegele, J., Hickey, V., Ahuja, P. S., Poveda,
R., Edmeades, S., Kneller, C., Swannell, R., Gillick,
S., Corallo, A., Aguilar, G., Alencastro, S., Felix, E., &Sebastian, A. (2019). Mexico Conceptual Framework
for a National Strategy on Food Loss and Waste(p. 68). https://beta.wrap.org.uk/resources/report/
conceptual-framework-national-strategy-food-lossand-waste-mexico

BIBLIOGRAPHY

Leket Israel. (2019). Food Waste and Rescue in Israel:
The Economic, Social and Environmental Impact.
https://www.leket.org/en/food-waste-and-rescuereport/
Li, Y., Wang, L., Liu, G., & Cheng, S. (2021). Rural
household food waste characteristics and driving
factors in China. Resources, Conservation andRecycling, 164, 105209. https://doi.org/10.1016/j.
resconrec.2020.105209
Lo, I. M. C., & Woon, K. S. (2016). Food waste
collection and recycling for value-added products:
Potential applications and challenges in Hong Kong.
Environmental Science and Pollution Research, 23(8),7081–7091. https://doi.org/10.1007/s11356-015-4235-y
Luxembourg Environment Ministry. (2020). Génération,
traitement et prévention des déchets alimentaires.
http://environnement.public.lu/fr/offall-ressourcen/
types-de-dechets/Biodechets/Gaspillage_alimentaire/
Etudes_et_resultats.html
Mbow, C., Rosenzweig, C., Barioni, L. G., Benton, T. G.,
Herrero, M., Krishnapillai, M., Liwenga, E., Pradhan, P.,
Rivera-Ferre, M. G., Sapkota, T., Tubiello, F. N., & Xu, Y.
(2019). Chapter 5. Food security. In Climate Change
and Land: An IPCC special report on climate change,
desertification, land degradation, sustainable land
management, food security, and greenhouse gas
fluxes in terrestrial ecosystems. Intergovernmental
Panel on Climate Change. https://www.ipcc.ch/site/
assets/uploads/sites/4/2021/02/08_Chapter-5_3.pdf
Miezah, K., Obiri-Danso, K., Kádár, Z., Fei-Baffoe,
B., & Mensah, M. Y. (2015). Municipal solid waste
characterization and quantification as a measure
towards effective waste management in Ghana. Waste
Management, 46, 15–27. https://doi.org/10.1016/j.
wasman.2015.09.009
Moora, H., Evelin, U.-P., & Õunapuu, K.
(2015). Toidujäätmete ja toidukao teke Eesti
kodumajapidamistes ja toitlustusasutustes.
Stockholm Environment Institute. https://www.sei.
org/wp-content/uploads/2017/12/sei-2015-reportfood-waste-and-food-loss-in-estonian-householdsand-catering-institutions-sei-tallinn1.pdf
Moora, H., Piirsalu, E., & Viilvere, T. (2015). Analysis offood waste in the Estonian food trade sector and thefood industry Summary (p. 2). Stockholm Environment
Institute. https://www.sei.org/wp-content/
uploads/2017/12/summary-food-waste-in-estonianfood-trade-sector-and-industry-sei-tallinn-2016.pdf

Mucyo, S. (2013). Analysis of Key Requirements for
Effective Implementation of Biogas Technology for
Municipal Solid Waste Management in Sub-SaharanAfrica. A Case Study of Kigali City, Rwanda [PhD,
Abertay University]. https://rke.abertay.ac.uk/en/
studentTheses/analysis-of-key-requirements-foreffective-implementation-of-biog
Nahman, A., de Lange, W., Oelofse, S., & Godfrey, L.
(2012). The costs of household food waste in South
Africa. Waste Management, 32(11), 2147–2153.
https://doi.org/10.1016/j.wasman.2012.04.012
Nicholes, M. J., Quested, T. E., Reynolds, C., Gillick,S., & Parry, A. D. (2019). Surely you don’t eat parsnip
skins? Categorising the edibility of food waste.
Resources, Conservation and Recycling, 147, 179–188.
https://doi.org/10.1016/j.resconrec.2019.03.004
Oberlin, A. S. (2013). Characterization of Household
Waste in Kinondoni Municipality, Dar Es Salaam.
Academic Journal of Interdisciplinary Studies. https://doi.org/10.5901/ajis.2013.v2n13p35
Oelofse, S., Muswema, A., & Ramukhwatho, F. (2018).
Household food waste disposal in South Africa: A casestudy of Johannesburg and Ekurhuleni. South African
Journal of Science, 114(5/6). https://doi.org/10.17159/
sajs.2018/20170284
Orhorhoro, E. K., Ebunilo, P. O., & Sadjere, G. E. (2017).
Determination and Quantification of Household Solid
Waste Generation for Planning Suitable Sustainable
Waste Management in Nigeria. International Journal of
Emerging Engineering Research and Technology, 5(8),10.
Qu, X., Li, Z., Xie, X., Sui, Y., Yang, L., & Chen, Y.
(2009). Survey of composition and generation
rate of household wastes in Beijing, China. Waste
Management, 29(10), 2618–2624. https://doi.
org/10.1016/j.wasman.2009.05.014
Quested, T. E., Palmer, G., Moreno, L. C., McDermott,C., & Schumacher, K. (2020). Comparing diaries andwaste compositional analysis for measuring foodwaste in the home. Journal of Cleaner Production, 262,
121263. https://doi.org/10.1016/j.jclepro.2020.121263
Ramakrishna, V. (2016). Municipal Solid Waste
Quantification, Characterization and Management inRajam. The International Journal of Engineering andScience, 5(2), 8.

97

UNEP FOOD WASTE INDEX REPORT 2021

Ramukhwatho, F. R. (2016). An Assessment of theHousehold Food Wastage in a Developing Country:
A Case Study of Five Areas in the City of Tshwane
Metropolitan Municipality, Guateng Province, South
Africa [University of South Africa]. https://www.
semanticscholar.org/paper/An-assessment-of-thehousehold-food-wastage-in-a-a-Ramukhwatho/6a1e0
1c9218dc0dd7b7cd6566fbcec3eda81bda0
Republic of Slovenia Statistical Office. (2019, October
22). Food Waste, Slovenia, 2018. https://www.stat.si/
StatWeb/en/news/Index/8433
Republic of Slovenia Statistical Office. (2020,
November 26). Food waste and waste indicators,
Slovenia, 2019. https://www.stat.si/StatWeb/en/
News/Index/9230
SAGO. (2019). Saudi FLW Baseline: Food Loss &Waste Index in Kingdom of Saudi Arabia. Saudi Grains
Organization. https://www.sago.gov.sa/Content/Files/
Baseline_230719.pdf
Salam, M. A., Hossain, L., Das, S. R., Wahab, R., &Hossain, M. K. (2012). Generation and Assessing theComposition of Household Solid Waste in Commercial
Capital City of Bangladesh. 1, 12.
Schmidt, T., Schneider, F., Leverenz, D., & Hafner, G.
(2019). Food waste in Germany – Baseline 2015 –Summary (No. 71). Thünen Institute. https://www.
researchgate.net/publication/339473130_Food_waste_in_Germany_-Baseline_2015_-_Summary
Schulte, I., Bakhtary, H., Siantidis, S., Haupt, F.,
Fleckenstein, M., & O’Connor, C. (2020). Enhancing
NDCs for Food Systems: Recommendations for
Decision-Makers. WWF Germany & WWF Food
Practice. https://www.climatefocus.com/sites/
default/files/200909_WWF_NDC_Food_final_low.pdf
Song, G., Li, M., Semakula, H. M., & Zhang, S. (2015).
Food consumption and waste and the embedded
carbon, water and ecological footprints of households
in China. Science of The Total Environment, 529, 191–
197. https://doi.org/10.1016/j.scitotenv.2015.05.068
Steinhoff-Wrześniewska, A. (2015). The Pilot Studyof Characteristics of Household Waste Generated
in Suburban Parts of Rural Areas. Journal of
Ecological Engineering, 16, 92–100. https://doi.
org/10.12911/22998993/1862

Stenmarck, Å., Jensen, C., Quested, T., Moates, G.,
Buksti, M., Cseh, B., Juul, S., Parry, A., Politano, A.,
Redlingshofer, B., Scherhaufer, S., Silvennoinen, K.,
Soethoudt, H., Zübert, C., & Östergren, K. (2016).
Estimates of European food waste levels. http://
edepot.wur.nl/378674
Stensgård, A. E., & Hanssen, O. J. (2016). Food Waste
in Norway 2010-2015 (OR.17.16). Matvett AS/ForMat
Project. https://norsus.no/wp-content/uploads/
or1716-format-sluttrapport-english.pdf
Stensgård, A. E., Prestrud, K., Hanssen, O. J., &Callewaert, P. (2019). Food Waste in Norway: Reporton Key Figures, 2015-2018 (OR.32.19). Matvett AS.
https://www.matvett.no/uploads/documents/
OR.32.19-Edible-food-waste-in-Norway-Report-on-keyfigures-2015-2018.pdf
Sujauddin, M., Huda, S. M. S., & Hoque, A. T. M.
R. (2008). Household solid waste characteristics
and management in Chittagong, Bangladesh.
Waste Management, 28(9), 1688–1695. https://doi.
org/10.1016/j.wasman.2007.06.013
Sulaymon, D. A. H., Ibraheem, D. J. A., & Graimed,
B. H. (2010). Household Behavior on Solid Waste
Management a Case of Al-Kut City. Engineering andTechnology Journal, 28(24), 11.
Sunshine Yates Consulting. (2018). New Zealand
Food Waste Audits. Prepared for WasteMINZ.
https://lovefoodhatewaste.co.nz/wp-content/
uploads/2019/02/Final-New-Zealand-Food-WasteAudits-2018.pdf
Suthar, S., & Singh, P. (2015). Household solid waste
generation and composition in different family
size and socio-economic groups: A case study.
Sustainable Cities and Society, 14, 56–63. https://doi.
org/10.1016/j.scs.2014.07.004
Swedish Environmental Protection Agency.
(2014). Food waste volumes in Sweden 2012.
http://www.naturvardsverket.se/Documents/
publikationer6400/978-91-620-8695-4.pdf
Swedish Environmental Protection Agency. (2020).
Matavfall I Sverige: Uppkomst och benhandling 2018.
https://www.naturvardsverket.se/Documents/publfiler/8800/978-91-620-8861-3.pdf?pid=26710
Takeuchi, N. (2019, September 18). Linkages with SDG
11.6.1 on MSW and composition analysis [Unpublished
UN Habitat presentation].
Thanh, N. P., Matsui, Y., & Fujiwara, T. (2010).
Household solid waste generation and characteristic
in a Mekong Delta city, Vietnam. Journal ofEnvironmental Management, 91(11), 2307–2321.
https://doi.org/10.1016/j.jenvman.2010.06.016

98BIBLIOGRAPHYThe Netherlands Nutrition Centre Foundation.
(2019). Synthesis report on food waste in Dutchhouseholds in 2019. https://www.voedingscentrum.
nl/Assets/Uploads/voedingscentrum/Documents/
Professionals/Pers/Persmappen/Verspilling%202019/
VC_Synthesis%20report%20on%20food%20waste%20
in%20Dutch%20households%202019.pdf
Tiarcenter. (2019). Foodsharing in Russia. https://
tiarcenter.com/wp-content/uploads/2019/11/ENG_
Foodsharing-in-Russia_2019.pdf
UN (Ed.). (2008). International Standard industrial
classification of all economic activities (ISIC) (Rev.
4). United Nations. https://unstats.un.org/unsd/
publication/seriesM/seriesm_4rev4e.pdf
United Against Food Waste. (2018). Infographic on
Taskforce on Circular Economy in Food. https://
champions123.org/sites/default/files/2020-09/
infographic-taskforce-circular-economy-in-food.pdf
UNSD. (2020). Demographic and Social Statistics—
Households and families—Standards and Methods.
https://unstats.un.org/unsd/demographic-social/sconcerns/family/#docs
U.S. Environmental Protection Agency. (2020a). 2018
Wasted Food Report (p. 42). https://www.epa.gov/
sites/production/files/2020-11/documents/2018_
wasted_food_report-11-9-20_final_.pdf
U.S. Environmental Protection Agency. (2020b). Food:
Material-Specific Data [Overviews and Factsheets].
US EPA. https://www.epa.gov/facts-and-figuresabout-materials-waste-and-recycling/food-materialspecific-data
van Dooren, C., Janmaat, O., Snoek, J., & Schrijnen,
M. (2019). Measuring food waste in Dutchhouseholds: A synthesis of three studies. Waste
Management, 94, 153–164. https://doi.org/10.1016/j.
wasman.2019.05.025
Verma, M. van den B., de Vreede, L., Achterbosch, T.,& Rutten, M. M. (2020). Consumers discard a lot more
food than widely believed: Estimates of global foodwaste using an energy gap approach and affluence
elasticity of food waste. PLOS ONE, 15(2), e0228369.
https://doi.org/10.1371/journal.pone.0228369
Vetter-Gindele, J., Braun, A., Warth, G., Bui, T. T.
Q., Bachofer, F., & Eltrop, L. (2019). Assessment ofHousehold Solid Waste Generation and Composition
by Building Type in Da Nang, Vietnam. Resources, 8(4),171. https://doi.org/10.3390/resources8040171

Wang, L., Liu, G., Liu, X., Liu, Y., Gao, J., Zhou, B., Gao,S., & Cheng, S. (2017). The weight of unfinished plate:
A survey based characterization of restaurant foodwaste in Chinese cities. Waste Management, 66, 3–12.
https://doi.org/10.1016/j.wasman.2017.04.007
Watanabe, K. (2012). The 3R Potential of Household
Waste in Bangi, Malaysia. In Understanding
Confluences and Contestations, Continuities
and Changes: Towards Transforming Society
and Empowering People (pp. 116–126). https://www.researchgate.net/publication/280642994_
The_3R_Potential_of_Household_Waste_
in_Bangi_Malaysia?enrichId=rgreq7baab3bfdb1ebd5045b09cfee5cc62f4-XXX&enrichS
ource=Y292ZXJQYWdlOzI4MDY0Mjk5NDtBUzoyNT
g2ODczMzU5MjM3MTJAMTQzODY4NzI3NTUyOQ%3D%3D&el=1_x_2&_esc=publicationCoverPdf
WRAP. (2013). Annex Report (v2) Methods usedfor Household Food and Drink Waste in the UK2012 (p. 103). https://wrap.org.uk/sites/files/wrap/
Methods%20Annex%20Report%20v2.pdf
WRAP. (2018). Courtauld Commitment 2025 Food
Waste Baseline for 2015 (p. 34). https://www.wrap.org.
uk/sites/files/wrap/Courtauld%20Commitment%20
2025%20-%20baseline%20report%20for%202015.pdf
WRAP. (2020a). Food Waste Reduction Roadmap &Toolkit. https://wrap.org.uk/sites/files/wrap/foodwaste-reduction-roadmap-toolkit.pdf
WRAP. (2020b). UK Progress against Courtauld
2025 targets and UN Sustainable Development Goal
12.3 (p. 54). https://wrap.org.uk/sites/files/wrap/
Progress_against_Courtauld_2025_targets_and_UN_
SDG_123.pdf
Xue, L., Liu, G., Parfitt, J., Liu, X., Van Herpen, E.,
Stenmarck, Å., O’Connor, C., Östergren, K., & Cheng,
S. (2017). Missing Food, Missing Data? A Critical
Review of Global Food Losses and Food Waste Data.
Environmental Science & Technology, 51(12), 6618–
6633. https://doi.org/10.1021/acs.est.7b00401
Yasir, R. A., & Abudi, Z. N. (2009). Characteristics andCompositions of Solid Waste in Nassiriya City. AlQadisiya Journal for Engineering Sciences, 2, 13.
Zhang, H., Liu, G., Xue, L., Zuo, J., Chen, T.,
Vuppaladadiyam, A., & Duan, H. (2020). Anaerobic
digestion based waste-to-energy technologies canhalve the climate impact of China’s fast-growing foodwaste by 2040. Journal of Cleaner Production, 277,
123490. https://doi.org/10.1016/j.jclepro.2020.123490

99

FOOD WASTE INDEXREPORT 2021

United Nations Avenue, Gigiri
P.O. Box 30552, 00100 Nairobi, Kenya
Tel. +254 20 762 1234
unep-publications@un.org
www.unep.org