The “food waste plug-in” – reference year 2012
Project description and outputs
XXXX XX, 2015
DIRECTORATE E: SECTORAL AND REGIONAL STATISTICS
UNIT E-2: ENVIRONMENTAL STATISTICS AND ACCOUNTS, SUSTAINABLE
DEVELOPMENT
This document was prepared by Marie Pairon and Marie Roberti de Winghe, ICEDD (Institut de Conseil et
d’Etudes en Développement Durable asbl) and edited for publication by Eurostat.
2
TABLE OF CONTENTS
1
INTRODUCTION ........................................................................................................................................ 5
1.1
OBJECTIVES ................................................................................................................................................. 5
1.2
FOOD WASTE: CONTEXT AND NEED FOR DATA .................................................................................................... 5
1.2.1
Rationale ......................................................................................................................................... 5
1.2.2
The need for Reliable data .............................................................................................................. 6
1.3
FOOD WASTE: EXISTING PROJECTS AND CURRENT DEFINITIONS............................................................................... 6
1.3.1
Definition from the waste framework directive .............................................................................. 6
1.3.2
Definition from the FUSIONS project ............................................................................................... 6
2
THE FOOD WASTE PLUG-IN PROJECT ........................................................................................................ 7
2.1
CONTEXT OF THE PROJECT .............................................................................................................................. 7
2.2
DATA COLLECTION ON WASTE CONTAINING FOOD WASTE ..................................................................................... 8
2.2.1
Data collection on waste generation .............................................................................................. 8
2.2.1.1
2.2.1.2
Detail collected by EWC-Stat ....................................................................................................................... 8
Detail collected by NACE activitY ............................................................................................................... 10
2.2.2
Data collection on waste treatment .............................................................................................. 10
2.3
DATA VALIDATION ...................................................................................................................................... 11
2.3.1
Short description of the tests......................................................................................................... 11
2.3.2
Validation results........................................................................................................................... 12
2.4
SUGGESTED INDICATOR ON FOOD WASTE GENERATION ...................................................................................... 13
2.4.1
Presentation of a possible indicator .............................................................................................. 13
2.4.1.1
2.4.1.1
2.4.1.2
2.4.1.3
Waste containing food waste in the disaggregated waste data collected ................................................ 13
Estimates of food waste in ‘mixed municipal waste’ ................................................................................. 16
Indicator calculation and Hypotheses ....................................................................................................... 17
indicator computation results ................................................................................................................... 17
2.4.2
Presentation of the existing indicator ........................................................................................... 22
2.4.3
Comparison between both indicators ........................................................................................... 22
2.5
TREATMENT OF WASTE CONTAINING FOOD WASTE ............................................................................................ 24
2.5.1
Indicator on estimated food waste treatment .............................................................................. 24
2.5.2
indicator computation results ....................................................................................................... 25
2.5.2.1
Mean indicator value ................................................................................................................................. 25
3
CONCLUSIONS ........................................................................................................................................ 28
4
REFERENCES ........................................................................................................................................... 29
5
ANNEX .................................................................................................................................................... 31
3
INDEX OF FIGURES
Figure 1 The FUSIONS technical framework defining the food supply chain and food waste ................................ 7
Figure 2 : Food waste plug-in: total waste generated – classification into waste mainly, partly and not
containing food waste........................................................................................................................................... 16
Figure 3 : Percentage of food waste in 20 03 01, by country ............................................................................... 17
Figure 4 : Food waste indicator repartition by generating sectors ....................................................................... 18
Figure 5 : Food waste indicator repartition by List of Waste category ................................................................. 19
Figure 6 : Food waste estimate (kg per inhabitant) generated by country and NACE, 2012 ................................ 20
Figure 7 : Food waste estimate (kg per inhabitant) generated by country and NACE, detail for the
manufacturing sector, 2012 .................................................................................................................................. 21
Figure 8 : Comparison between the former indicator (Bio intelligence study, 2006 data) and the new indicator
(Food waste plug in, 2012 data), by NACE and for the total. ................................................................................ 23
Figure 9 : Estimated food waste treatment operations (mean kg per inhabitant for 13 countries) in 2012. ....... 25
Figure 10 : Estimated food waste treated, by LoW code (mean kg per inhabitant for 13 countries) in 2012. ..... 26
Figure 11 : Estimated food waste treated (mean kg per inhabitant for 13 countries) by treatment operation and
LoW code in 2012.................................................................................................................................................. 27
Figure 12 : Food waste estimate treatment operations (kg per inhabitant) by country in 2012. ........................ 28
INDEX OF TABLES
Table 1 Relevant waste categories and economic activities in WStatR for calculating Food waste estimates ...... 8
Table 2 LoW-entries that may contain food waste ................................................................................................. 9
Table 3 NACE activities that may be relevant for the food waste generation estimate ....................................... 10
Table 4 : Choice of proxies for food waste comparison among countries – correlation results ........................... 11
Table 5 : LoW codes mainly, partly or not containing food wastes : suggested classification.............................. 14
Table 6 : Food waste indicator by NACE activity and country, 2012 (kg per inhabitant) ...................................... 21
4
1
INTRODUCTION
1.1
OBJECTIVES
The objectives of the present document are to:




1.2
Give an overview of the context in which food waste data is needed, describe the existing projects as
well as provide existing definitions on food waste (chapter 1 sections 1.2 and 1.3)
Describe the ‘food waste plug-in’ project: what were the aims of the project, what are the limitations
of the data collected (chapter 2 sections 2.1 and 2.2)
Describe the data received as well as the methodological problems encountered by the Member
States to provide the data (chapter 2 section 2.2.2)
Present an indicator on food waste estimate and compare it with existing data from the literature
(chapter 2 section 2.4).
FOOD WASTE: CONTEXT AND NEED FOR DATA
1.2.1 RATIONALE
Food waste and its related environmental, economic, and social implications are of increasing public concern.
According to FAO (2011) one third of all food produced for human consumption is lost or wasted globally,
which amounts to about 1.3 billion tonnes per year.
Food waste occurs at every stage of the food supply chain i.e. at harvesting, processing, retail, and
consumption level. While in developing countries over 40% of food losses occur after harvesting and during
processing, in industrialised countries, over 40% occur at retail and consumer level (FAO 2011). These losses
may be due to many reasons. For instance, part of food waste is caused by legislation, which is often put in
place to protect human health. Another part could be linked to consumer preferences and habits (European
Commission 2015a).
In 2010, a preparatory study for the European Commission estimated that in the EU alone, 89 million tonnes of
food or 179 kg per person were wasted every year (Bio Intelligence Service 2010, 11)This amount of food
waste was expected to rise to 126 million tonnes by 2020, if nothing is done (Bio Intelligence Service 2010,
105).
The European Commission is therefore considering seriously the issue of tackling food waste. The food sector
and food waste are among the key areas highlighted in the European Commission’s ‘Roadmap to a resource
efficient Europe’ of September 2011 (European Commission 2011).
Reducing the amount of food waste is also important if Member States are to meet targets on addressing
climate change and limiting greenhouse gas emissions as well as fulfilling obligations under the European
Landfill Directive to reduce biodegradable waste going to landfill (Bio Intelligence Service 2010).
In 2014, the Commission's Communication ‘Towards a circular economy (European Commission 2014a): a zero
waste programme for Europe’, and the related legislative proposal (European Commission 2014b, 13) to review
recycling and other waste targets, put forward objectives for food waste reduction along the whole food supply
chain in the EU. It included a proposal for Member States to develop national food waste prevention strategies
with the aim of reducing food waste by at least 30 percent by 2025. Sectors concerned by these strategies
include: manufacturing, retail/distribution, food service/hospitality and households. In 2015, the Commission
5
withdrew its legislative proposal on waste targets to replace it with a new, more ambitious proposal to
promote circular economy by the end of the year.
The lack of reliable data on food waste hinders the assessment of the environmental impacts of food waste,
the anticipated developments in food waste generation over time, and the setting of targeted policies for food
waste prevention.
1.2.2 THE NEED FOR RELIABL E DATA
This is also a major conclusion arising from the final report ‘preparatory study on food waste across EU27’
published in October 2010 by the European Commission. It stresses the importance and necessity of statistical
data and time series for all Member States to provide reliable data on food waste, thereby allowing for more
robust and reliable estimations and forecasting (Bio Intelligence Service 2010, 106).
The ‘food waste plug-in’ project described in this document was set up by Eurostat and the respective national
data providers to see if reliable data could be obtained on food waste or on waste containing food waste based
on the existing data collection according to the Waste Statistics Regulation (EC.2002).
1.3
FOOD WASTE: EXISTING PROJECTS AND CURRENT DEFINITIONS
1.3.1 DEFINITION FROM THE WASTE FRAMEWORK DIRECTIVE
There is no current definition for food waste in the European Waste Framework Directive (European
Commission 2008, 7). The Waste Framework Directive defines bio-waste, which includes food waste, as
follows: ‘bio-waste’ means biodegradable garden and park waste, food and kitchen waste from households,
restaurants, caterers and retail premises and comparable waste from food processing plants. It does not
include forestry or agricultural residues, manure, sewage sludge, or other biodegradable waste such as natural
textiles, paper or processed wood. It also excludes those by-products of food production that never become
waste (European Commission 2015c).
1.3.2 DEFINITION FROM THE FUSIONS PROJECT
FUSIONS1 is a project which is funded by the Seventh Framework Programme for Research (FP7). Its main
objective is to work towards achieving a more resource efficient Europe by significantly reducing food waste
(FUSIONS 2015). Amongst its objectives, the project first aimed at establishing a standard approach on system
boundaries and definitions of food waste. It suggested the following definition (Östergren et al. 2014, 23):
Food waste is any food, and inedible parts of food, removed2 from the food supply chain to be recovered or
disposed (including - composted, crops ploughed in/not harvested, anaerobic digestion, bio - energy production,
co-generation, incineration, disposal to sewer, landfill or discarded to sea).
It uses the general system of resource flows in the agri-food system as a framework for defining food waste
(see Figure 1).
1
Food Use for Social Innovation by Optimising Waste Prevention Strategies
The term ‘removed from’ encompasses other terminology such as ‘lost to’ or ‘diverted from’. It assumes that
any food being produced for human consumption, but which leaves the food supply chain, is ‘removed from’ it
regardless of the cause, point in the food supply chain or method by which it is removed.
2
6
More specifically, it means that:


Any food and inedible parts of food, removed from the food supply chain sent to destinations B3-B11
are termed ‘food waste’.
Any food and inedible parts of food, sent to animal-feed, bio-material processing or other industrial
uses (B1-B2) are termed ‘valorisation and conversion’ and are distinct from ‘food waste’.
Figure 1 The FUSIONS technical framework defining the food supply chain and food waste
The food waste plug-in was not based on any particular definition of food waste. The underlying idea was to
examine whether existing data can be used to estimate food waste generation regardless of a given definition.
The data and classifications used in this document are not congruent with the FUSIONS concepts. Firstly, waste
statistics consider only waste that is handed over to the waste management system. Secondly, the waste
treatment categories used in the FUSIONS framework an in waste statistics overlap only partially.
2
2.1
THE FOOD WASTE PLUG-IN PROJECT
CONTEXT OF THE PROJE CT
Eurostat has been working together with Member States to see how food waste data could be collected within
the data collection framework set by the Waste Statistics Regulation (WStatR) (EC 2002). The project was
therefore set up to answer the following question: “What can the WStatR data tell us about food waste
7
generation and treatment?” Seventeen countries agreed to provide disaggregated data for food containing
food waste on a voluntary basis, together with their reporting obligation on reference year 2012. They were all
able to provide these data for waste generation and 15 of them were also able to provide data for waste
treatment.
2.2
DATA COLLECTION ON WASTE CONTAINING FOOD WASTE
The idea underlying the project on the food waste plug-in was that the easiest way of collecting data with
reasonable effort is to collect them given the existing legal framework in the EU, i.e. the Waste Statistics
Regulation. Within this framework, data are collected on waste generation and waste treatment according to
every second year. Data on waste generation are broken down into 51 waste categories according to the EWCStat classification and into 19 economic activities according to NACE Rev. 2 and households. Data on waste
treatment are collected according to 6 treatment operations.
2.2.1 DATA COLLECTION ON WASTE GENERATION
In order to get more information on the EWC-Stat items that might contain food waste, the food waste plug-in
consisted of a disaggregation of some data by List of Waste code and by NACE activity.
The EWC-Stat and NACE categories collected in the Waste Statistics Regulation, which were considered
relevant for food waste data collection, and therefore needed disaggregation, are shown in Table 1.
Table 1 Relevant waste categories and economic activities in WStatR for calculating Food waste estimates
NACE ACTIVITIES
Item
EWC-STAT
31
09.1
Animal and
mixed food
waste
32
09.2
Vegetal wastes
34
10.1
Household and
similar wastes
51
TT
Total
A01-A03
Agriculture,
forestry and
fishing
C10 - C12
Manufacture
of food
products,
beverages,
tobacco
G - U excl.
G46.77
Service
activities
Households
Total
Data reported for the Waste Statistics Regulation
(WStatR) – waste generation
2.2.1.1 DETAIL COLLECTED BY EWC-STAT
As can be seen in Table 1 (in blue cells), the WStatR breakdown of the EWC-Stat allows the distinction of the
following waste types containing food waste:
-
09.1 “animal and mixed food waste”,
09.2 “vegetable waste”,
10.1 “household and similar waste”.
However, these waste categories include more waste than just food waste. The level of aggregation in WStatR
data does not allow to easily determine the food waste content of these collected items. Therefore, in order to
8
improve the accuracy of data collected that may consist of food waste, the so-called “food waste plug-in” was
developed. This plug-in breaks down the EWC-Stat data according to the underlying List of Waste (LoW)
categories. This is shown in Table 2 with the blue cells representing data that are already available from the
WStatR. The green cells indicate data that reporting countries were asked to complete in the food waste plugin, whenever these data were available.
Table 2 LoW-entries that may contain food waste
09.1 Animal and mixed food waste
02 01 02
02 02 01
02 02 02
02 02 03
02 05 01
02 03 02
02 06 02
19 08 09
20 01 08
20 01 25
animal-tissue waste
sludges from washing and cleaning
animal-tissue waste
materials unsuitable for consumption or processing
materials unsuitable for consumption or processing
wastes from preserving agents
wastes from preserving agents
grease and oil mixture from oil/water separation containing only edible oil and fats
biodegradable kitchen and canteen waste
edible oil and fat
09.2 Vegetal wastes
02 01 07
20 02 01
02 01 01
02 01 03
02 03 01
02 03 03
02 03 04
02 06 01
02 07 01
02 07 02
02 07 04
wastes from forestry
biodegradable waste
sludges from washing and cleaning
plant-tissue waste
sludges from washing, cleaning, peeling, centrifuging and separation
wastes from solvent extraction
materials unsuitable for consumption or processing
materials unsuitable for consumption or processing
wastes from washing, cleaning and mechanical reduction of raw materials
wastes from spirits distillation
materials unsuitable for consumption or processing
10.1 Household and similar wastes
20 03 01
20 03 02
20 03 07
20 03 99
20 03 03
mixed municipal waste
waste from markets
bulky waste
municipal wastes not otherwise specified
street-cleaning residues
This breakdown allows distinguishing between the categories that mainly contain food waste and the ones that
do not (or at least should not) contain it. For instance, in item 09.1 “animal and mixed food waste”, “animaltissue waste” (02 01 02) should mainly include food waste, whereas “sludges from washing and cleaning” (02
02 01) should not contain food waste, according to the definition given in section 1.3 that specifies that water
from washing and cleaning is excluded from the scope of the definition.
Another example would be item 09.2 “vegetal wastes” that includes “wastes from forestry” (02 01 07) which
should not contain food waste but that also includes “plant-tissue waste” (02 01 03) which should mainly
consist of food waste.
9
2.2.1.2 DETAIL COLLECTED BY NACE ACTIVITY
WStatR data are provided for 19 sectorial activities. In the scope of this project, only the NACE activities related
to the food supply chain were considered. The NACE activities were further split into sub-categories at division
or group level (see Table 3). The red cells represent data that were collected under the Waste Statistics
Regulation, while the “skin” coloured cells are a further disaggregation on which countries were asked to
report data. For instance, the breakdown allows to better understand the waste production from NACE
divisions 10, 11, and 12, especially from division 10 (manufacture of food products), which is further split into
its group level (3 digit) codes. It also allows a clearer insight into the wholesale, retail and food service sectors.
Table 3 NACE activities that may be relevant for the food waste generation estimate
A 01-03
Agriculture, forestry and fishing
C 10-12
Manufacture of food products ; beverages and tobacco
10
11
12
G – U excl.
G46.77
G
I
P
Q
Households
Manufacture of food products
10.1
Processing and preserving of meat and production of meat products
10.2
Processing and preserving of fish, crustaceans and molluscs
10.3
Processing and preserving of fruit and vegetables
10.4
Manufacture of vegetable and animal oils and fats
10.5
Manufacture of dairy products
10.6
Manufacture of grain mill products, starches and starch products
10.7
Manufacture of bakery and farinaceous products
10.8
Manufacture of other food products
10.9
Manufacture of prepared animal feeds
Manufacture of beverages
Manufacture of tobacco products
Service activities
46 Wholesale trade, except of motor vehicles and motorcycles
47 Retail trade, except of motor vehicles and motorcycles
55 Accommodation
56 Food and beverage service activities
Education
86 Health
TOTAL ALL NACE + HOUSEHOLDS
2.2.2 DATA COLLECTION ON WASTE TREATMENT
Data on waste treatment consisted of the treatment of disaggregated EWC-Stat waste categories in LoW codes
that might contain food waste (the same LoW codes than those collected for waste generation – see 2.2.1.1)
according to the 6 treatment operations covered by the WStatR.
These six treatment operations (with their database codes in parenthesis) are:





Deposit onto or into land (DSP_D)
Land treatment and release into water bodies (DSP_O)
Incineration / disposal (D10) (INC)
Recovery other than energy recovery – Backfilling (RCV_B)
Incineration / energy recovery (R1) (RCV_E)
10

2.3
Recovery other than energy recovery - Except backfilling (RCV_O)
DATA VALIDATION
Seventeen data sets were provided for waste generation and fifteen data sets for waste treatment. These data
sets were validated using several tests described below. Questions were then sent to countries on the basis of
the potential issues detected. Answers to the questions were received from all countries.
2.3.1 SHORT DESCRIPTION OF THE TESTS
Several tests were automatically performed to validate the data reported in the food waste plug-in. These tests
included intra-country checks as well as cross-countries comparisons.
Intra-country checks on waste generation and treatment included:
-
-
-
-
-
Overall completeness checks: These checks aimed at verifying the data completeness. Where data
greater than zero were not reported, some countries did not report items either as 0 (not occurring)
or missing (M flag). For some NACE activities some waste items at the LoW level were missing.
Overall checks of totals reported: This check was designed to make sure the totals reported under the
three waste aggregates ( EWC-Stat 10.1, 09.1 and 09.2) were equal to the sum of individual waste
items in LoW codes.
Comparison with Waste Statistics Regulation: This check aimed at making sure that data collected
pursuant to the Waste Statistics Regulation was coherent with data reported in the food waste plugin. As mentioned earlier, this should be the case for the NACE aggregates 01-03, C10-C12, the
households and the totals for all three EWC-Stat categories. For the service activities, the sum of
individual NACE cells 46, 47, 55, 56, P, 86 should be lower than the totals reported under the
aggregate of sections G – U excl. G46.77.
Comparison with the municipal waste data: The EWC-Stat waste item 10.1 (household and similar
wastes) was compared with the data reported by the country on municipal waste generation.
Municipal waste was expected to be higher than food waste 10.1 reported under the food waste
plugin. This should be true for both waste generation and for total waste treatment.
Comparison between waste generated and treated in the country: Specific tests aimed at comparing
data reported in the generation and treatment tables.
Cross-country comparisons mainly consisted of three tests for data on waste generation: box-and-whisker plots
of indicators, comparison of the most important waste types reported and a test to detect implausible zero
values.
Indicators were computed to ensure the comparability among countries by dividing the waste reported in each
NACE sector by proxies that were correlated to the amounts of waste generated.
Table 4 : Choice of proxies for food waste comparison among countries – correlation results
nace_r2
A
A
C101
C102
C103
C104
C105
C106
C107
Proxy
luse
crp_prod
mt_prod
fish_prod
luse
prodval_ind
cows_dr
luse
prodval_ind
Definition
Sum of utilised agriculture area and wooded area
Crop production
Meat production
Fish products production
Sum of utilised agriculture area and wooded area
Production value in industry
Number of dairy cows
Sum of utilised agriculture area and wooded area
Production value in industry
11
Correlation
0.7
0.88
0.82
0.57
0.88
0.93
0.86
0.92
0.96
P value
0.01
0
0
0.18
0
0
0
0
0
C108
C109
C11
C12
EP_HH
G46
G47
I55
I56
P
Q86
prodval_ind
lvstock
prodval_ind
prodval_ind
population
prodval_tr
prodval_tr
empl_acco
prodval_srvc
employment
employment
Production value in industry
Livestock
Production value in industry
Production value in industry
Population
Production value in trade activities
Production value in trade activities
Employment in accomodation
Production value in service activities
Employment
Employment
0.97
0.85
0.73
0.98
0.99
0.99
0.95
0.57
0.89
0.59
0.73
0
0
0.02
0
0
0
0
0.08
0.02
0.1
0.06
Once waste data and proxies were shown to be correlated, waste data were divided by the proxy data. This
analysis was carried out by NACE activity, as each NACE has a different possible proxy, but for the entire waste
items available (both aggregates and LoW items separately). Box-and-whisker plots were produced for each
NACE item and outliers were detected when the indicator values were not within 1.5 the interquartile range of
the lower or upper quartile.
2.3.2 VALIDATION RESULTS
France was only able to provide data on disaggregated NACE level for waste generation, but not at the LoW
level.
Similarly, the Netherlands and France provided data on waste treatment according to EWC-Stat categories
(10.1, 09.1, and 09.2) but not in LoW codes. Therefore, data from 16 countries were used to compute the
indicator on waste generation and data from 13 countries were used to compute the indicator on waste
treatment.
Some missing estimates were identified. The main reasons for missing values or missing estimates were:
-
-
Impossibility of disaggregating EWC-Stat codes in LoW for:
•
NACE A – agriculture, forestry and fishing (4 countries)
•
NACE C12 – manufacture of tobacco products (3 countries)
•
Services activities (all or part of) and separating these wastes from household waste
collected (5 countries)
•
Households (3 countries)
Difficulty to obtain data of LoW codes for:
•
‘waste from preserving agents’: W020602 (7 countries), W020302 (5 countries)
The comparability among countries was sometimes limited by the following findings:
-
Some countries mentioned specific items of food waste that could not be reported because they were
not in the EWC-Stat categories asked for in the food waste plug in.
•
e.g. Polish code 16 03 80 - Food products past their “use-by” date or unfit for consumption is
assigned to EWC-STAT 10.22 as waste code 16 03 06 : “organic wastes other than those
mentioned in 16 03 05” but is definitely food waste.
12
•
2.4
e.g. Belgian food waste that had become by-products (specific LoW codes starting with 99)
were mentioned as well.
-
Most countries have their own waste codes. The link between national codes and LoW is more
sensitive at the 6 digits level. For instance, Austria mentioned that their data collection is very
accurate since they made a specific survey for collecting the data for the food waste plug in. However,
several discrepancies were pointed out in the cross-country comparison. These were due to a problem
of assignment of Austrian codes to LoW codes at the 6 digits levels. For instance, one of the LoW
codes often pointed out in the analysis as potentially over-estimated was made of 27 individual
Austrian codes.
-
By-products versus wastes: some countries did not report data on specific wastes because they were
considered by-products whereas the same by-products are considered as wastes, and therefore
reported, in other countries. An example can be found in NACE 10.5 (manufacture of dairy products)
and more specifically on the way countries reported on whey. Whey is mainly produced in this NACE
activity during the cheese production process and is composed of 95% of water. Poland reported
significantly too high amounts of whey in this NACE under code 02 05 01 (685 675 t), whereas Finland
uses it for animal feeding and does therefore not report it.
-
Delimitation of economic sectors is sometimes not as clear as the NACE divisions. For instance, in
Slovenia, some enterprises in NACE G46 (wholesale trade) also process food as ancillary activity and
therefore produce some waste starting with 0203. or 0202., which is often not the case in other
countries.
-
Wet versus dry weight reporting for sludges. Some countries reported wastes in wet weight rather
than dry weight. For instance, Belgium reported wet weight of sludges from washing and cleaning
(LoW item 020301) in NACE 10.3 (processing and preserving of fruit and vegetables) and Poland
reported wet weight of sludges in NACE section A under code 020704. These represented high
quantities of waste that were reported in dry weight by other countries.
SUGGESTED INDICATOR ON FOOD WASTE GENERATION
2.4.1 PRESENTATION OF A POSSIBLE INDICATOR
2.4.1.1 WASTE CONTAINING FOOD WASTE IN THE DISAGGREGATED WASTE DATA
COLLECTED
As explained earlier, collecting data with the food waste plug-in was mainly done to see if information on the
food waste generated by the countries could be obtained using the legal framework of the WStatR.
Data collected using the plug-in are partly food waste, but also partly non-food waste or waste that should not
be considered as food waste, according to the definition of food waste presented above. It is safe to say that
data collected using the food waste plug-in are wastes containing food wastes, but do not represent only food
waste per se. This can easily be explained when looking at the List of Waste codes at 6 digits level, that were
not initially created to differentiate food waste from non-food waste.
Creating an indicator that is specifically dedicated to food waste therefore requires additional work on LoW
codes in order to detect which wastes are most probably mainly composed of food wastes, which wastes are
partly made of food wastes and which wastes are most probably not food wastes in the sense of the definition.
Table 5 presents the LoW codes together with their possible food waste content. A differentiation is made
13
according to three classes: waste that should not contain food waste according to the food waste definition
(‘no’ in the third column of Table 5), waste that partly contain food waste (‘partly’ in the third column of Table
5) and waste that mainly contain food waste (‘mainly’ in the third column of Table 5). Comments have been
added in the last column of the table to explain why a given waste has been assigned to one of the three
categories if it was not straightforward.
Table 5 : LoW codes mainly, partly or not containing food wastes: suggested classification
09.1 Animal and mixed food waste
09.11 Animal waste of food preparation and products
02 01 02
animal-tissue waste
Food waste?
no
agricultural waste generated
during the pre-harvest process
Water used in the food supply
chain, but not incorporated into
a product, is not considered as
part of ‘food and inedible parts
of food removed from the food
supply chain’ (e.g. water used to
flush food down the drain during
cleaning down)
02 02 01
sludges from washing and cleaning
no
02 02 02
animal-tissue waste
materials unsuitable for consumption or
processing
mainly
02 02 03
materials unsuitable for consumption or
processing
09.12 Mixed waste of food preparation and products
02 05 01
mainly
mainly
Food waste?
02 03 02
02 06 02
wastes from preserving agents
wastes from preserving agents
no
no
19 08 09
grease and oil mixture from oil/water
separation containing only edible oil and
fats
mainly
20 01 08
biodegradable kitchen and canteen waste
mainly
20 01 25
edible oil and fat
09.2 Vegetal wastes
09.21 Green wastes
Comment
Comment
mainly
Food waste?
02 01 07
wastes from forestry
no
20 02 01
biodegradable waste
no
09.22 Vegetal waste of food preparation and products
Food waste?
Comment
agricultural waste
garden and park waste / green
waste
Comment
02 01 01
sludges from washing and cleaning
no
agricultural waste
02 01 03
plant-tissue waste
partly
agricultural waste generated
during the harvesting and the pre
harvesting process
Water used in the food supply
chain, but not incorporated into
a product, is not considered as
part of ‘food and inedible parts
of food removed from the food
supply chain’ (e.g. water used to
flush food down the drain during
cleaning down)
02 03 01
sludges from washing, cleaning, peeling,
centrifuging and separation
partly
02 03 03
wastes from solvent extraction
no
14
02 03 04
02 06 01
02 07 01
materials unsuitable for consumption or
processing
materials unsuitable for consumption or
processing
wastes from washing, cleaning and
mechanical reduction of raw materials
02 07 02
wastes from spirits distillation
materials unsuitable for consumption or
02 07 04
processing
10.1 Household and similar wastes
10.11 Household wastes
mainly
mainly
partly
mainly
mainly
Food waste?
20 03 01
20 03 02
mixed municipal waste
waste from markets
partly
partly
20 03_OTH
bulky waste, municipal wastes not
otherwise specified, street-cleaning
residues
no
Comment
food waste amounts negligible
compared with bulky waste
Based on the information presented in Table 5, an indicator on food waste could theoretically be computed by
summing the LoW codes mainly containing food waste and the fractions of the LoW codes partly containing
food waste that are food waste.
The food waste fraction of the LoW codes partly containing food waste is however not easy to identify and
needs further investigation in practice. Figure 2 presents the total waste generated, as reported in the food
waste plug in, with a distinction between LoW codes mainly, partly or not containing food waste.
The most important waste partly containing food waste is, by far, mixed municipal waste (200301). The three
remaining wastes that partly contain food waste (020103 – plant-tissue waste- , 020301 – sludges from
washing, cleaning and peeling -, and 020701 – sludges from washing, cleaning and mechanical reduction) are
negligible in comparison. Having reliable estimates on the fraction of mixed municipal waste that is food waste
is therefore crucial, but it seems less important for the three remaining waste categories.
Two main wastes not containing food waste were reported: 200201 (biodegradable waste in waste from parks
and gardens), 2003_OTH (other waste in EWC-Stat 10.1 than LoW 200301 and 200302). These are not food
waste and should therefore be excluded from the food waste indicator computation.
Several wastes mainly containing food waste were reported, among which 020304 (materials unsuitable for
consumption), 200108 (biodegradable and kitchen and canteen waste), 020704 (materials unsuitable for
consumption or processing), 020202 (animal-tissue waste), 190809 (grease and oil mixture from oil/water
separation containing only edible oil and fats), 020501 (materials unsuitable for consumption or processing),
020702 (wastes from spirits distillation) are the most important ones. These wastes are mainly composed of
food wastes and should therefore be included as a whole in the food waste generation indicator.
15
Figure 2 : Food waste plug-in: total waste generated – classification into waste mainly, partly and not
containing food waste
2.4.1.1 ESTIMATES OF FOOD WASTE IN ‘MIXED MUNICIPAL WASTE’
Estimates of food waste in ‘mixed municipal waste’ vary according to regions or seasons and are very sensitive
to estimation methodologies. Countries participating to the food waste plug in exercise have been asked to
provide estimates of the fraction of food waste in mixed municipal waste (20 03 01) when national data or
studies existed. Ten countries were able to provide such estimates. A significant variation was observed for
these estimates among countries (Figure 3), ranging from 15.9% for Belgium to 62.2% for Malta.
16
Percentage of food waste in mixed municipal
waste
70
60
50
40
30
20
10
0
% FW in 200301
BE
SI
HR
LU
FI
AT
NL
SE
FR
MT
15.9
21.0
24.0
24.8
25.0
25.3
26.0
33.0
33.7
62.2
Figure 3 : Percentage of food waste in 20 03 01, by country
2.4.1.2 INDICATOR CALCULATION AND HYPOTHESES
According to the findings presented in paragraph 2.4.1.1, an indicator on estimated food waste generated
based on the food waste plug in data can be computed using the following formula:
𝑊𝑎𝑠𝑡𝑒 𝑚𝑎𝑖𝑛𝑙𝑦 𝑐𝑜𝑛𝑡𝑎𝑖𝑛𝑖𝑛𝑔 𝑓𝑜𝑜𝑑 𝑤𝑎𝑠𝑡𝑒 𝑔𝑒𝑛𝑒𝑟𝑎𝑡𝑒𝑑 + 𝐹𝑟𝑎𝑐𝑡𝑖𝑜𝑛 𝑜𝑓 𝑓𝑜𝑜𝑑 𝑤𝑎𝑠𝑡𝑒 𝑖𝑛 𝑚𝑖𝑥𝑒𝑑 𝑚𝑢𝑛𝑖𝑐𝑖𝑝𝑎𝑙 𝑤𝑎𝑠𝑡𝑒 𝑔𝑒𝑛𝑒𝑟𝑎𝑡𝑒𝑑
𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑖𝑛ℎ𝑎𝑏𝑖𝑡𝑎𝑛𝑡𝑠
The following hypotheses were taken for the indicator computation:
-
An average value of 25% of food waste in mixed municipal waste can be used for countries that did
not provide estimates;
-
Other waste codes partly containing food waste are negligible compared to mixed municipal wastes.
The share of food waste in other ‘partly’ containing food waste codes was therefore not assessed.
2.4.1.3 INDICATOR COMPUTATION RESULTS
An indicator was computed for each country. Results presented here are the mean of the values obtained for
the different reporting countries.
A mean number of 127 kg per inhabitant of food waste was estimated to have been produced in 2012. The
repartition among sectors and by waste is presented in Figure 4. Food waste estimates coming from
households represent 42% of the total food waste estimates from all sectors. The manufacturing sector (NACE
section C) accounted for 44% of the total (55 kg per capita), with NACE groups 10.4 (Manufacture of vegetable
and animal oils and fats) and 10.1 (Processing and preserving of meat and production of meat products)
representing almost half of the estimated food waste production in the manufacturing sector (10% of the total,
each or 13 kg per capita).
The fraction of food waste in mixed municipal waste represented 32% of the total food waste estimate (Figure
5). This again stresses the importance of getting accurate percentage of food waste in mixed municipal waste.
17
A
5%
Households
42%
G46-G47
5%
I55-I56
2%
P
1%
Q86
1%
C
44%
C105
4%
C101
10%
C108 A
C11
6% 5%
6%
C104
10%
C103
3%
G47
3%
G46
2% I56
2%
P
1%
Q86
1%
I55
1%
C109
0%
Other
6%
C107
1%
EP_HH
42%
Figure 4 : Food waste indicator repartition by generating sectors
18
C102
0%
C106
2%
C12
0%
020202
14%
200108
9% 020704
8%
020203
5%
190809
4%
020702
2%
020601
1%
Other
7%
020304
21%
200125
0%
020501
4%
200301
32%
Figure 5 : Food waste indicator repartition by List of Waste category
Figure 6 provides a detailed representation of the indicator by country and NACE activity. This is mainly
presented to illustrate the significant variety of reporting, especially in NACE sections A, C and G. France is not
presented in the graph as no detailed information by LoW codes had been provided by this country.
In NACE section A (agriculture, forestry and fishing), Poland reported huge amounts of 020202 (animal-tissue
wastes, 1 068 357 t) and 020704 (materials unsuitable for consumption, 330 568 t) which made up most of the
36 kg per inhabitant obtained for Poland in this sector. This value is much higher than that obtained in the
indicator computation for the other countries (mean without Poland = 0.32 kg per inhabitant). A question was
asked about this specific issue to Poland and the answer was the following: “Data concerning animal tissue
waste based on administrative data of Chief Inspectorate of Veterinary are all allocated to NACE A, because it is
not possible to disaggregate this stream within the different NACE sections”. This particularity is therefore due
to impossible allocation of some data to specific NACE activities rather than a real specificity of the agricultural
sector in Poland.
In NACE section G (wholesale trade and retail trade, except of motor vehicles and motorcycles), the indicator
seemed quite significant for Malta (G46: 13.2 kg per inhabitant and G47: 44.1 kg per inhabitant, compared to
mean values without Malta of 2.0 and 3.3 kg per inhabitant, respectively). This is mainly due to the relatively
high amounts reported for 200301 (mixed municipal waste, 8 780 t in G46 and 29 449 t in G47) and the fact
that Malta has reported a large fraction of mixed municipal waste as being food waste (62%).
19
Figure 6 : Food waste estimate (kg per inhabitant) generated by country and NACE, 2012
In NACE section C (Manufacturing), the Netherlands and Belgium seem to have quite significant values of food
waste estimates per inhabitant (324.6 and 102.3 kg per inhabitant, respectively, as compared with a mean
value of 20.1 kg per inhabitant for the other countries). Since the manufacturing sector represents such a high
share of the food waste indicator (37%), Figure 7 presents this sector in full detail (NACE C 10.1, 10.2, 10.3,
10.4, 10.5, 10.6, 10.7, 10.8, 10.9, 11 and 12).
It can easily be seen that the sectors generating the highest food waste estimate per inhabitant in the
Netherlands are C10.4 (Manufacture of vegetable and animal oils and fats - 160.8 kg per inhabitant) and C108
(Manufacture of other food products - 76.7 kg per inhabitant). Values are flagged confidential for these sectors,
so no information on the nature of the waste can be provided.
For Belgium, NACE groups 10.1 (Processing and preserving of meat and production of meat products – 60.9 kg
per inhabitant) and 10.3 (Processing and preserving of fruits and vegetables – 22.6 kg per inhabitant) mainly
account for the high indicator value in the manufacturing sector. In NACE 10.1, wastes 020202 (animal-tissue
wastes - 570 118 t) and 020203 (materials unsuitable for consumption - 102 643 t) significantly affect the
20
indicator. In NACE group 10.3, 020304 (materials unsuitable for consumption – 240 197 t) cause the relatively
high value of the indicator. One of the potential reasons for such a high value might be a reporting in wet
weight rather than dry weight.
Figure 7 : Food waste estimate (kg per inhabitant) generated by country and NACE, detail for the
manufacturing sector, 2012
Results by country and by sector are provided in Table 6. Values range from 24.7 kg per inhabitant to 422.3 kg
per inhabitant. Countries presenting the highest indicator values are the Netherlands (422 kg per inhabitant)
and Belgium (259 kg per inhabitant). These results illustrate the wide variety of situations in Europe, even
though differences might be partly due to methodological shortcomings in data collection as they can often
only be attributed to one or two waste streams in specific sectors.
These results should therefore be taken with caution as this data collection is a first exercise and clearly shows
that some countries might still need some time to improve the comparability of their statistics.
Table 6 : Food waste indicator by NACE activity and country, 2012 (kg per inhabitant)
21
Country
A
C
G
Households
I
P
Q
total
AT
0.5
16.5
8.1
72.8
18.6
3
1.7
121.2
BE
0
102.3
12.3
133.7
7.8
0.7
2.3
259.1
BG
0.2
18.1
0.8
65.2
4.3
2.7
0
91.3
CZ
0.6
5.1
5.4
50.4
2.7
0
1.4
65.6
DE
0.4
10.2
7.3
41.1
6.7
1.3
1.2
68.2
EE
0
0.9
3.7
35.3
0.9
0.1
0.4
41.3
FI
0
42.9
0
24
0
0
0
66.9
HR
0.1
1.5
1.4
54.2
1
0
0
58.2
LU
0
7.3
7.7
94.1
8.4
0.4
2
119.9
MT
0.2
10.6
57.3
130.8
19.3
0
0.7
218.9
NL
0
324.6
7.5
78
7
2.7
2.5
422.3
PL
36.4
73.3
7.9
56.3
0.1
0
0.1
174.1
RS
0
20.7
0.9
0
1.7
0.3
1.1
24.7
SE
0.2
55.1
1.3
26.5
4.6
2.7
0.4
90.8
SI
1.6
10.9
7.3
39.2
10.8
4.2
1.4
75.4
SK
0.1
8.5
1.3
54.9
0.1
0
0
64.9
2.4.2 PRESENTATION OF THE EXISTING INDICATOR
A previous report ‘preparatory study on food waste across EU27’ developed a food waste generation indicator
for 2006 partly based on Eurostat data and further complemented with other national studies and estimates
(Bio Intelligence Service 2010). The following hypotheses were made to compute the indicator estimate:
The relevant waste categories used from the Waste Statistics Regulation were EWC-Stat 9, excluding item 09.3
‘animal and vegetal wastes’.
The relevant sources of food waste were:
-
NACE Rev. 1.1 division DA - Manufacture of food products; beverages and tobacco, (this corresponds
to divisions 10 to12 of NACE Rev. 2)
Other economic activities
HH – Households
2.4.3 COMPARISON BETWEEN BOTH INDICATORS
As the former food waste indicator only considered NACE Rev. 2 sections C, G, I and households; data from
NACE sections A, Q and P have been removed in the new food waste indicator to ease the comparison. If these
are removed, the total amount of food waste estimated (in kg per inhabitant) using the new indicator is 117 kg
per inhabitant.
Results are compared sector by sector in the following paragraphs.
22
Figure 8 : Comparison between the former indicator (Bio intelligence study, 2006 data) and the new indicator
(Food waste plug in, 2012 data), by NACE and for the total.
The total amounts of food waste estimated in kg per inhabitant were much lower using the food waste plug in
data (hereafter called FWPI indicator) compared with the former food waste indicator (hereafter called BIOS
indicator). This can be attributed to lower amounts computed in all sectors, and more specifically in NACE C,
households and NACE I.
For the manufacturing sector, the BIOIS indicator was computed assuming that the Waste Statistics Regulation
data were plausible3 for the EU27 except for the UK, where a more recent national study was used. Obtaining
lower values for the FWPI indicator is therefore expected as data for the FWPI indicator are a subset of the
waste category EWC_09 (excluding EWC_093).
3
The plausibility of Eurostat data was checked based on the AWARENET study on food waste and by-products
and on data from a WRAP study on the manufacturing sector in the UK. The latter study gives a proportion of
food waste contained (17%) in the food waste and by-products aggregate reported in the AWARENET study.
For more details on the studies used and on the calculations, see: (Bio Intelligence Service 2010)
23
For the household sector, as methodologies for collecting and calculating household data seemed to vary so
widely among Member States, a minimum scenario was used by BIOIS to compare with both WStatR and
national studies’ data. This minimum scenario assumes that there is 8.375%4 of food waste in municipal waste.
Whenever national studies’ data were available, those were used because considered more accurate, with
more intensive research and more rigorous methodologies compared to WStatR data. WStatR data were only
used when no national research was identified. When WStatR or national studies’ data per inhabitant were
anomalously low, the minimum scenario, based on a food waste share of 8.375% of municipal waste, was taken
instead. Estimates for these countries should be considered as conservative in the BIOIS indicator.
For the other sectors, supplementary evidence from national studies was used in the BIOIS study,
distinguishing between the wholesale/retail sector and the food service/catering sector. This gives an idea of
the respective shares of these sectors. The following assumptions were made:
o
o
o
o
For the wholesale/retail sector an average of 8.89kg of retail food waste per inhabitant was
reached5.
Food service/catering sector: National data available came from both EU15 and EU12, and so an
average for both was calculated. The EU15 (27 kg per inhabitant) and the EU12 (12 kg per
inhabitant) were used to complete data for MS lacking other evidence, based on their
populations.
A total of 16,821,345 tonnes of food waste from other sectors was obtained with Eurostat data
compared with 16,696,541 tonnes with data obtained from the assumptions made above.
The figures obtained are approximate estimations representing the best available data. To note
that for the retail sector in particular, data were limited and methodologies of calculation vary
widely.
In general, when data from national studies are available, these tend to give higher food waste estimates per
inhabitant than those computed using the food waste plug in data. Similarly, when averages are used to
compute the BIOIS indicator, the numbers tend to be higher than those computed using the FWPI data.
A detailed comparison country by country is needed to get a full understanding of the differences between the
two studies. These comparisons are presented in Annex of this report.
2.5
TREATMENT OF WASTE CONTAINING FOOD WASTE
As mentioned earlier, data were also collected on waste treatment by LoW codes and 13 countries were able
to provide data on waste containing food waste treatment using the same detailed LoW codes than those used
in the generation table. France and the Netherlands only provided data by EWC-Stat codes 10.1, 09.1 and 09.2
and were therefore not included in the following analyses.
2.5.1 INDICATOR ON ESTIMATED FOOD WASTE TREATMENT
As seen in chapter 2.4, which described a new indicator on the estimated food wastes generated (in kg per
inhabitant), not all wastes included in the LoW codes collected include food waste. The same classification in
4
This share is an estimate calculated from the assumption that 33.5% of municipal waste is bio-waste (taken
from Bulgaria) and that there is 25% of food waste contained in bio-waste (Arcadis 2009 in (Bio Intelligence
Service 2010)).
5
Obtained using the British, Danish and Swedish data. For more details, see: (Bio Intelligence Service 2010, 58)
24
waste “mainly”, “partly” or “not” containing food waste as the one performed on the waste generation data
was applied to the waste treatment data.
Similarly to what has been done to build the indicator on estimated food waste generation, an indicator on
estimated food waste treated based on the food waste plug in data can be computed using the following
formula:
𝑊𝑎𝑠𝑡𝑒 𝑚𝑎𝑖𝑛𝑙𝑦 𝑐𝑜𝑛𝑡𝑎𝑖𝑛𝑖𝑛𝑔 𝑓𝑜𝑜𝑑 𝑤𝑎𝑠𝑡𝑒 𝑡𝑟𝑒𝑎𝑡𝑒𝑑 + 𝐹𝑟𝑎𝑐𝑡𝑖𝑜𝑛 𝑜𝑓 𝑓𝑜𝑜𝑑 𝑤𝑎𝑠𝑡𝑒 𝑖𝑛 𝑚𝑖𝑥𝑒𝑑 𝑚𝑢𝑛𝑖𝑐𝑖𝑝𝑎𝑙 𝑤𝑎𝑠𝑡𝑒 𝑡𝑟𝑒𝑎𝑡𝑒𝑑
𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑖𝑛ℎ𝑎𝑏𝑖𝑡𝑎𝑛𝑡𝑠
2.5.2 INDICATOR COMPUTATION RESULTS
2.5.2.1 MEAN INDICATOR VALUE
The mean estimated number of food waste treated in the 13 reporting countries, regardless of the treatment
method, is 79 kg per inhabitant. Food wastes reported are mainly recovered (40% in RCV_O – recovery other
than energy recovery and 21% in RCV_E - incineration with energy recovery, see Figure 9) and landfilled (27% in
DSP_D). Backfilling and other disposal are almost never used to manage food wastes in the surveyed countries.
DSP_O RCV_B
0%
0%
INC
12%
RCV_O
40%
RCV_E
21%
DSP_D
27%
Figure 9 : Estimated food waste treatment operations (mean kg per inhabitant for 13 countries) in 2012.
Figure 10 illustrates the share of the different wastes in the total estimate. A high share of mixed municipal
waste (200301) in the countries reporting can be observed. Codes starting by ‘20’, i.e. originating from
households, represented 70% of the total mean estimated food waste treated (48 kg per inhabitant for mixed
municipal waste – 200301- , 7 kg per inhabitant for biodegradable kitchen and canteen waste – 200108 -)
(Figure 10).
These high shares of codes starting with ‘20’ might be explained by the following hypotheses (or a combination
of them):

Food waste treatment reporting might be biased towards municipal wastes because they are subject
to careful monitoring in most countries;
25



Food wastes have high moisture content and might therefore be subject to significant weight loss
during pre-treatment that is not reported in WStatR. Even though waste should normally be reported
in dry weight, some countries still have trouble to report in dry weight, and tend to do it only for
wastes like sludge;
Wastes that are subject to pre-treatment might obtain a different code after their pre-treatment
(code starting with ‘19’ – wastes from waste management facilities) and therefore be lost to the food
waste indicator presented here;
Exports are not included in WStatR statistics and imports are included.
200108
9%
020704
9%
020501
2%
020202
7%
020304
5%
020601
1%
020702
1%
Other
9%
020203
5%
200301
61%
200125
0%
190809
0%
Figure 10 : Estimated food waste treated, by LoW code (mean kg per inhabitant for 13 countries) in 2012.
Error! Reference source not found. illustrates the different treatment methods for each waste included in the
ndicator. Mixed municipal wastes are mainly landfilled (44%), then incinerated with energy recovery (28%),
incinerated without energy recovery (18%), or recovered – other than energy recovery (10%). Other wastes
composing the indicator on food waste treatment are mainly recovered.
26
Figure 11 : Estimated food waste treated (mean kg per inhabitant for 13 countries) by treatment operation
and LoW code in 2012
Figure 12 presents the values of the indicator on food waste treatment by country. This graph shows the high
variability in food waste management across countries. Incineration without energy recovery (INC) is mainly
used in Germany and Luxembourg. Malta, Bulgaria, Croatia, the Czech Republic and Slovakia are countries
presenting an important share of food waste landfilling (DSP_D). Finally, Finland, Germany, Luxembourg,
Poland and Sweden show an important share of their food waste going into recovery (other than energy
recovery or backfilling – RCV_O). Again, this was the first data collection at this level of detail and some
methodological issues need to be resolved if one wants to improve the comparability across countries.
27
Figure 12 : Food waste estimate treatment operations (kg per inhabitant) by country in 2012.
3
CONCLUSIONS
This report presents the results of the first so-called ‘food waste plug-in’ exercise that was conducted in 2014 in
order to see if it was possible to collect reliable statistics on food waste using the existing framework of the
Waste Statistics Regulation.
Seventeen countries contributed to this exercise, which proved to be quite a success, even though in some
cases, dataset were incomplete. Therefore, only data from 16 countries could be used for the estimated food
waste generation indicator and data from 13 countries could be used to estimate the food waste treatment
indicator.
Data collected using the food waste plug-in are data on waste containing food waste, but do not exclusively
represent food waste. A classification into waste that contain different shares of food waste (mainly, partly,
none) had to be made to extract only the data consisting of food waste from the data collected and build a
food waste indicator.
This was quite easy for waste codes mainly or not consisting of food waste, which were respectively included or
excluded from the food waste indicator computation; but more difficult for waste codes partly containing food
wastes. In particular, mixed municipal wastes represented a high share of the total waste containing food
waste collected. Reliable and accurate estimates of the share of food waste in mixed municipal waste are
therefore needed to further refine the calculation of the indicator.
28
The indicator was computed both for waste generation and waste treatment, as well as country by country and
for the aggregate of all countries. The mean estimated food waste generated in all reporting countries in kg per
inhabitant is 127 kg and the mean estimated food waste treated, 79 kg per inhabitant.
The calculated food waste indicator proved to vary considerably across countries, which illustrates the difficulty
of collecting data at this level of disaggregation. Differences in countries’ reporting were mainly due to
methodological issues rather than real economic differences. Some data provided by several countries,
including Belgium, the Netherlands and Poland for generation and Serbia and Slovenia for treatment, should be
looked at more carefully when this exercise is repeated.
The food waste indicator on generation was lower than that suggested by a former study based on 2006 data
because of methodological reasons.
Important limitations accompany this work of quantification, resulting from the variable reliability of the food
waste plug-in data collected. Methodologies for collecting and calculating the food waste plug-in data
submitted differ between countries and several aspects tend to limit the comparability across countries.
Harmonising the data collection methodologies would be one way of ensuring an enhanced comparability in
the future.
4
REFERENCES
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Paris: for the European Commission.
EC. 2002. Regulation (EC) No 2150/2002 of the European Parliament and of the Council of 25 November 2002
on Waste Statistics.
European Commission. 2011. Communication from the Commission to the European Parliament, the Council,
the European Economic and Social Committee and the Committee of the Regions: “Roadmap to a
Resource Efficient Europe” COM(2011)571 Final, September 2011. Brussels.
http://ec.europa.eu/environment/resource_efficiency/pdf/com2011_571.pdf.
———. 2014a. Communication from the Commission to the European Parliament, the Council, the European
Economic and Social Committee and the Committee of the Regions: “Towards a Circular Economy: A Zero
Waste Programme for Europe” COM(2014)398 Final, July 2014. Brussels. http://eur-lex.europa.eu/legalcontent/EN/TXT/?uri=CELEX:52014DC0398.
———. 2014b. Proposal for a Directive of the European Parliament and of the Council Amending Directives
2008/98/EC on Waste, 94/62/EC on Packaging Ang Packaging Waste, 1999/31/EC on the Landfill of
Waste, 2000/53/EC on End-of-Life Vehicles, 2006/66/EC on Batteries and Accumulators and Waste
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FAO. 2011. Global Food Losses and Food Waste - Extent, Causes and Prevention. Rome.
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FUSIONS. 2015. “About FUSIONS.” http://www.eu-fusions.org/what-is-fusions.
Nellemann, C., M. MacDevette, T. Manders, B. Eickhout, B. Svihus, A.G. Prins, and B.P. Kaltenborn. 2009. The
Environmental Food Crisis: The Environment’s Role in Averting Future Food Crises. A UNEP rapid
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response assessment. Norway: UNEP, GRID-Arendal.
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