The aim of this study was to quantify EU consumer food waste, taking account of uncertainty. In particular, we have quantified not only the total amount of food waste but also the food waste per product group. We have also differentiated between total food waste and avoidable food waste. In addition, we have quantified the associated natural resources required for the production of this food waste, in terms of water and nitrogen. For this, we have used the WF and NF concepts. We chose these two footprints as they are well established and because detailed studies have been made on the WF and NF of EU food production and consumption [ 9 , 24 – 26 ]. Future research should include additional resources/footprints.

A number of studies have quantified particular natural resources (including energy [ 14 ]) associated with food waste, often by means of environmental footprints, the latter being an umbrella term for the different footprint concepts that have been developed during the last two decades [ 15 ]. Well-established footprint indicators include the water footprint (WF) [ 16 ], nitrogen footprint (NF) [ 11 , 17 , 18 ], land footprint (LF) [ 19 ] and carbon footprint (CF) [ 20 ]. The WF is an indicator of direct and indirect water use. The concept has been introduced into water-management science to show the importance of consumption patterns and the global dimensions of good water governance [ 16 ]. The NF comprises the sum of the nitrogen contained in food (based upon protein content) and the food production NF. The LF measures the appropriation of land as a resource. The CF measures the emission of greenhouse gases (GHGs) into the atmosphere. For example, Chapagain and James [ 21 ] calculated the WF and CF of household food and drink waste in the UK. The FAO [ 22 ] quantified the CF, WF and LF of global food wastage (i.e. both food loss and food waste). Kummu et al [ 23 ] quantified the WF as well as the cropland and fertiliser use associated with food wasted on a global level.

An essential limitation of previous quantification studies on consumer food waste for Europe—e.g. [ 8 ] or the EU—e.g. [ 10 , 11 ]—is that they only use one value (total and/or per product/product group), thereby not taking into account the uncertainty and lack of reliable data on food waste statistics. The FAO study [ 8 ] on food losses and waste only uses UK data for consumer food waste in Europe [ 12 ]. The study on food waste carried out on behalf of the European Commission [ 10 ] quantifies total consumer food waste for each EU Member State based on assembled statistical data, but gives no range on uncertainty and does not differentiate between product groups. A recent study on behalf the European Parliament [ 13 ] acknowledges that the estimations in the latter study are conservative, and probably underestimate.

According to the UN's Food and Agriculture Organization (FAO) [ 8 ], roughly one-third of food produced for human nutrition is either lost or wasted globally, which amounts to approximately 1.3 billion tonnes per year. Food is lost or wasted throughout the entire supply chain, from initial agricultural production down to final household consumption. In industrial countries, the fraction of food wasted by consumers of the total food lost and wasted is high. According to this FAO study [ 8 ], the food wasted per-capita by consumers in Europe and North America is about 95 to 115 kg yr −1 . Total per-capita food loss and waste along the entire supply chain in Europe and North America is 280–300 kg yr −1 . Thus, consumer food waste represents about one-third of this amount. A study on consumer food waste in the EU-27 [ 10 ] quantified total food waste per capita at 101 kg yr −1 (76 kg yr −1 for households and 25 kg yr −1 for the food service/catering sector).

Global food security will be one of mankind's main challenges this century. A key question is whether humanity has the natural resource base to feed itself equitably and sustainably both now and in the future. Currently, 842 million people are chronically undernourished [ 1 ], while 1.46 billion adults are overweight, of whom 502 million are obese [ 2 ]. Within the EU, 227 million (58% of all) adults are overweight, of whom 91 million (23% of all adults) are obese [ 3 , 4 ]. By 2050, a projected 9–10 billion people should be provided with a healthy diet in a sustainable way [ 5 ]. In creating a more sustainable food system, solutions need to combine demand-side and supply-side options [ 6 ]. Demand-side solutions should target dietary habits as well as food waste [ 7 – 9 ]. This paper focuses on food waste.

2.1. General

We have quantified consumer food waste for the EU-28 with the associated uncertainty. We have analysed average annual values for the period 1996–2005. During this period, the average population of the EU was 487 million. The methodology is presented below and an overview of the workflow of the methodology is given in figure 1, with explanations of the abbreviations used throughout the manuscript provided in table 1. Figure 1. Workflow of the methodology; the equations are listed in table 2. Download figure: Standard image High-resolution image Export PowerPoint slide Table 1. Abbreviations used in the letter. Abbreviation Definition EU European Union WF Water footprint WF prod Water footprint of production WF cons Water footprint of consumption WF cons,agr Agricultural water footprint of production WF cons,agr,gn ; WF cons,agr,bl Green; blue agricultural water footprint of consumption NF Nitrogen footprint N cons Nitrogen contained in food NF prod Food production NF FAO Food and Agriculture Organization of the United Nations FBS Food balance sheet FOOD_SUPPLY Food supply quantity (data) FOOD_CONS Food consumption quantity (data) CORR1 Correction factor 1, which accounts for product primary equivalent conversions CORR2 Correction factor 2, which accounts for food waste CORR2_TOT Correction factor 2, which accounts for total food waste CORR2_AV Correction factor 2, which accounts for avoidable food waste CORR2_PA Correction factor 2, which accounts for potentially avoidable food waste CORR2_NA Correction factor 2, which accounts for non-avoidable food waste WASTE_TOT Total food waste quantity WASTE_AV Avoidable food waste quantity WASTE-NA Non-avoidable food waste quantity DEFRA UK Department for Environment, Food & Rural Affairs Mt Million tonnes l/cap/d Litre per capita per day

2.2. Quantification of food consumption data (FOOD_CONS)

As a first step, average EU food consumption data have been quantified by taking food supply quantity data (FOOD_SUPPLY) of different product groups from the FAO Food balance sheets (FBS) [27]. These provide data on the amount of food that reaches the consumer in private households, as well as that in the non-household sector, i.e. catering establishments, boarding schools, hospitals, prisons, armed forces' bases and other communities. The data are given on an 'as purchased' basis, i.e. as the food leaves the retail shop or enters the household by other means. Quantities are provided on the basis of 'primary equivalents'. Within the FAO FBS, food data are standardized in that processed commodities are converted back to their 'primary equivalent'. This is for standardization (different countries report their data to the FAO), simplification and limitation of the number of commodities within the FBS. The latter implies that it is beneficial to reduce the amount of data, and therefore the number of commodities involved, to a level and size more suited to analytical purposes. E.g., instead of listing flour of wheat, bread or pasta separately in the FBS, they are quantified as wheat equivalent. Similarly, meat (reaching consumers in many forms, e.g. for chicken as—amongst others—a whole chicken, chicken filet, sausages or chicken nuggets) is quantified as carcass weight in the FBS. We have estimated food consumption at the point where it reaches the consumer, using a first correction factor (CORR1) which accounts for product primary equivalent conversion (equation (1)). To calculate total and avoidable wasted food quantities from FOOD_SUPPLY, two correction factors are applied. The first factor (CORR1) accounts for product primary equivalent conversions (equation (1)) and the second for food waste (CORR2). The specific values for CORR1 have been taken from [28]. For example, the CORR1 of wheat equivalent is 0.8, as only 80% of the original wheat weight remains after conversion to wheat flour. In addition to the first factor values listed in the latter publication, 7.4 milk equivalent has been taken for cheese, as obtained from [3], 7 milk equivalent for cream, and 1 milk equivalent for yoghurt. After applying CORR1, food consumption data (FOOD_CONS) have been quantified.

2.3. Quantification of food waste amounts

Total and avoidable quantities of wasted food from FOOD_SUPPLY have been obtained by applying a second correction factor for food waste (CORR2) on food consumption quantities (FOOD_CONS). This factor relates either to total food waste (CORR2_TOT) or to avoidable food waste (CORR2_AV), as we have differentiated between total (WASTE_TOT) and avoidable (WASTE_AV) food waste. The latter excludes non-avoidable food waste (WASTE_NA) such as meat bones, egg shells, fruit stones or the peel of certain fruit and vegetables. Avoidable food waste means food that at some point prior to disposal was edible. The relevant equations (2)–(6) are listed in table 2 below. Table 2. Equations used to compute waste amounts (equations (1)–(6)) and related resources (equations (7)–(9)). FOOD_SUPPLY × CORR1 = FOOD_CONS (1) with: FOOD_SUPPLY = Food supply quantity as given in FAO Food Balance Sheets (FBS). Food supply is food (in tonnes) reaching the consumer, i.e. in private households, as well as the food service/catering sector CORR1 = Correction factor 1, which accounts for product primary equivalent conversions FOOD_CONS = Food consumption. FOOD_CONS × CORR2_TOT = WASTE_TOT (2) FOOD_CONS × CORR2_AV = WASTE_AV (3) FOOD_CONS × CORR2_NA = WASTE_NA (4) With: CORR2 = Correction factor 2, which accounts for food waste CORR2_TOT = Correction factor 2, which accounts for total food waste CORR2_AV = Correction factor 2, which accounts for avoidable food waste CORR2_NA = Correction factor 2, which accounts for non-avoidable food waste WASTE_TOT = Amount of total consumer food waste WASTE_AV = Amount of avoidable consumer food waste WASTE_NA = Amount of non-avoidable consumer food waste WASTE_TOT = WASTE_AV + WASTE_NA (5) FOOD_CONS = INTAKE + WASTE_TOT (6) With: INTAKE = Amount of food actually consumed WASTE_AV CORR = (7) with WASTE_AV CORR = Corrected amount of avoidable consumer food waste to relate to the resource footprint (WF or NF) of WASTE_AV CORR2_NA = WF cons,agr of WASTE_AV = × WF cons,agr (8) with WF cons,agr = WF cons,agr of FOOD_CONS NF of WASTE_AV= × NF (9) With NF = NF of FOOD_CONS Based upon FOOD_CONS, total (WASTE_TOT, equation (2)) and avoidable food (WASTE_AV, equation (3)) waste quantities are thus calculated by means of CORR2_TOT and CORR2_AV. Non-avoidable food waste (WASTE_NA) is the difference between WASTE_TOT and WASTE_AV (equation (5)). CORR2_TOT and CORR2_AV values have been obtained for the product groups from/based upon six selected national studies, i.e. the UK [29],

the Netherlands [30],

Denmark [31],

Finland [32],

Germany [33],

Romania [34]. For other EU countries, either no data were found or they were unreliable. These values are shown in table 3 below. Table 3. Estimates of food waste fractions (CORR2 in %) of household food purchases (FOOD_CONS) by food group, based upon literature values. UK [29] The Netherlands, based upon [30] Denmark, based upon [31] Finland [32] Germany, based upon [33] Romania [34] Product group CORR2_AV CORR2_PA CORR2_NA CORR2_TOT CORR2_AV CORR2_NA CORR2_TOT CORR2_AV CORR2_NA CORR2_TOT CORR2_AV CORR2_AV CORR2_TOT CORR2_AV Cereals 26 3 0 29 12 0 12 12 1 13 13 11–15 >0 and <10 Potatoes 17 28 0 45 5 4 9 19 Vegetables 23 13 9 45 9 11 20 5 11 16 19 15–20 18–23 >0 and <10 Fruit 19 3 19 41 9 16 25 5 11 16 13 8–10 18–23 >0 and <10 Sugar Pulses, nuts and oil crops Crop oils 4 12a 1 17 Animal fats 4 12a 1 17 Stimulants Spices Alcoholic beverages 5 0 0 5 Meat 11 3 9 23 6 2 8 5 2 7 7 4–6 11–15 >0 and <10 Fish 11 3 9 23 2 7 9 5 2 7 7 4–6 11–15 >0 and <10 Eggs 5 0 12 17 2 6 8 7 Milk 7 0 0 7 5 0 5 17 3–4 7–9 >0 and <10 Yoghurt 13 0 0 13 5 0 5 17 3–4 7–9 >0 and <10 Cheese 10 0 0 10 3 1 4 17 3–4 7–9 >0 and <10 aIn ([29]) the potentially avoidable waste for oils and fats includes all oils and fats which have only been used once—however, this is not recommended in other countries, therefore a second use is discarded in this analysis and the potentially avoidable waste proportion reduced to 0%. Only estimations of household waste have been quantified in these studies. For the non-household (food service/catering) sector part of FOOD_CONS, the same CORR2 values have been taken. The most detailed assessment was done in the UK [29] for the year 2007 (a field study of over 2000 households), where a distinction is made between avoidable waste, potentially avoidable waste and unavoidable waste. Where purchased amounts of specific products or product groups are missing, DEFRA (the UK's Department for Environment, Food & Rural Affairs) statistics have been used. Apart from avoidable and non-avoidable food waste, the UK study also identifies potentially avoidable food waste. However, in our study we have added this fraction (CORR2_PA) to avoidable food waste (CORR2_AV) for the UK statistics, as other national studies do not account for potentially avoidable food waste. Table 3 shows that for some product groups detailed statistics are available for several countries, e.g. for cereals, vegetables, fruit, meat, milk and milk products. For other product groups, statistics are only available for selected countries (e.g. alcoholic beverages or crop oils) and for some product groups, no statistics are available (e.g. sugar or stimulants). The data compiled in table 3 show that for all product groups: (1) CORR2_TOT and CORR2_AV values are highest in the UK (except for milk/milk products where Finland has a higher value); and (2) CORR2_AV values are amongst the lowest in Romania. To account for any uncertainty in the estimations, we have assumed that Romania represents the minimum CORR2 values and the UK the maximum values for EU countries, and that all other values are between these two extremes. Schneider [35] has already pointed out that in Europe, the proportion of household expenditure on food is highest in Eastern Europe (e.g. 44.2% in Romania) and that this might be expected to have a significant impact on food waste behaviour. The UK has already been identified as the EU country with the highest per capita consumer food waste [10]. To compute the CORR2_TOT and CORR2_AV distributions for the EU, based upon the six selected national studies, we have made some assumptions. In particular, we have assumed that the correction factors (CORR2_TOT and CORR2_AV) per product group to calculate WASTE_TOT and WASTE_AV are distributed normally. Their means and standard deviations (as shown in table 4 below) were calculated from the available country data (table 3). To calculate these mean EU values from the six national values, these national values are weighted according to national populations. For example, the values for Germany are weighted at 43% and those of the Netherlands at 8%. For sugar, stimulants, spices and alcoholic beverages the associated correction factors were assumed to be uniformly distributed, as detailed data are missing. Table 4. Statistical normal distribution values (av = average; stdev = standard deviation; max = maximum; min = minimum) of total and avoidable food waste fractions (CORR2_TOT and CORR2_AV in %), based upon the literature values in table 3. For sugar, stimulants, spices and alcoholic beverages, correction factors are assumed to be uniformly distributed. Total waste Avoidable waste Product group av stdev max min av stdev max min Cereals 17.12 8.6 29 5 17.12 8.6 29 5 Potatoes 25.5 14.2 45 5 25.2 15 45 5 Sugar 10 5 10 5 Pulses 5 1.8 7 2 4.7 1.9 7 2 Crop oils 5 1.8 7 2 4.7 1.9 7 2 Vegetables 26.2 13.9 45 5 20.9 11.5 36 4 Fruit 25.5 12 41 5 12.6 6.8 22 4 Stimulants 10 5 10 5 Spices 10 5 10 5 Alcoholic beverages 7 3 7 3 Meat 14.5 6.6 23 4 7.7 4.6 14 2 Animal fats 5 1.8 7 2 5 1.8 7 2 Eggs 11.9 3.9 17 6 5.1 1.8 7 2 Milk (and yoghurt) 7 2.8 17 2 5.2 3 17 2 Cheese 7.9 3.2 17 2 6 3.9 17 2 Cream 5.2 3 17 2 5.2 3 17 2 Fish 14.5 6.5 23 4 7.4 4.8 14 2

2.4. Quantification of related water resources (the water footprint or WF)

In our study, we used the WF concept. For the purpose of water resources management (e.g. in the EU, as in our study), a geographical WF assessment is relevant. It is important to distinguish between the WF of production (WF prod ) and the WF of consumption (WF cons ) of a region [16]. The first refers to the total use of domestic water resources within the region (for producing goods and services for domestic consumption or export). The second refers to the use of domestic and foreign water resources behind all the goods and services consumed domestically. Dependent on the authors, a WF comprises either three (blue, green and grey) or two (blue and green) components. As defined in [36], green water is the soil water retained in the unsaturated zone, which is formed by precipitation and is available to plants; blue water refers to water in rivers, lakes, wetlands and aquifers. Irrigated agriculture receives blue water (from irrigation) as well as green water (from precipitation), while rain-fed agriculture only receives green water. Thus, the green WF is the rainwater consumed by crops. The grey WF is an indicator of the degree of water pollution [37]. However, many authors perceive this component critically [24, 38, 39]. To quantify the water resources related to these wasted food amounts, we have used the WF of consumption (WF cons ) concept [16, 37]. In particular, we have assessed the green and blue WF cons of agricultural products (WF cons,agr ) for wasted food. Therefore, the WF cons,agr refers to the use of domestic and foreign water resources for all agricultural goods that are consumed domestically. The green WF cons,agr (WF cons,agr,gn ) represents the consumptive rainwater use for the production of crops and animal products which EU citizens consume. Similarly, the blue WF cons,agr (WF cons,agr,bl ) represents the consumptive use of water from rivers, lakes and groundwater for this production. As a first step, the WF cons,agr of FOOD_CONS needs to be quantified. To assess the WF cons,agr , we have followed the methodology of the Global Water Footprint Standard developed by the Water Footprint Network [36]. National green and blue WF cons,agr data are accessed from [16, 40] and aggregated to product groups at the EU level. As a result, the blue and green WF cons,agr (WF cons,agr,gn and WF cons,agr,bl ) of FOOD_ CONS are obtained. As a next step, the WF cons,agr (WF cons,agr,gn ) and (WF cons,agr,bl ) of avoidable waste amounts (WASTE_AV) needs to be quantified. To do this, first the unavoidable fraction (CORR2_NA) must be added to WASTE_AV because the WF cons,agr of FOOD_CONS relates to the whole product. Thus we have calculated a new component WASTE_AV CORR by means of equation (7) (table 2). When both the WF cons,agr of FOOD_CONS and WASTE_AV CORR are calculated, the WF cons,agr,gn and WF cons,agr,bl for WASTE_AV are computed using equation (8) (table 2). To clarify, we present the example of vegetables. Using equation (1), we have calculated the FOOD_CONS value for vegetables as 120.8 kg/cap/yr (table 5). The related WF cons,agr,gn of this amount is 47 l/cap/d (table 5). With an average avoidable food waste fraction (CORR2_AV) of 20.9% (table 4), we have used equation (3) to calculate the average amount of avoidable consumer fruit waste (WASTE_AV) as 25.3 kg/cap/yr. Table 5. Average annual (1996–2005) FOOD_SUPPLY and FOOD_CONS values (in kg/cap/yr) of different product groups for the EU. The WF cons,agr,gn and WF cons,agr,bl (in l/cap/d) and the N cons and NF prod (in kg/cap/yr) are also shown. Product group FOOD_SUPPLY (kg/cap/yr) FOOD_CONS (kg/cap/yr) Green WF cons,agr (l/cap/d) Blue WF cons,agr (l/cap/d) N cons (kg/cap/yr) NF prod (kg/cap/yr) Cereals 123.7 93.3 337 21 1.7 1.9 Potatoes 81.9 81.9 33 6 0.2 0.3 Sugar 39.7 39.7 158 30 0.0 0.3 Pulses, nuts and oil crops 10.3 10.3 95 10 0.2 0.1 Crop oils 18.5 18.5 293 23 0.0 0.4 Vegetables 120.8 120.8 47 14 0.2 0.3 Fruit 98.8 98.8 115 30 0.1 0.3 Stimulants 7.5 7.5 322 2 0.1 0.0 Spices 0.5 0.5 9 1 0.0 0.0 Alcoholic beverages 109.3 109.3 84 6 0.1 1.2 Meat 88.5 59.4 1247 82 1.7 18.3 Animal fats 13.3 13.3 103 6 0.0 0.5 Eggs 12.4 12.4 68 4 0.2 0.6 Milk (excluding butter)a 234.1 (including cheese, cream and yoghurt) 95.3 (milk and yoghurt)—yoghurt 1 l milk for 1 kg 192 15 0.5 2.6 Cheesea 0 15.2 (112.5 milk eq.—7.4 l milk for 1 kg) 227 17 0.6 3.1 Creama 0 3.8 (26.3 milk eq.—7.0 l milk for 1 kg) 53 4 0.1 0.7 Fish and seafood 21.2 8.5 0 0 0.4 1.8 Total 980.5 788.5 3383 270 6.0 32.4 aFOOD_CONS values for cheese and cream are taken from FAOSTAT food supply statistics. Milk equivalent (milk eq.) values are computed from EUROSTAT-data. In order to calculate the WF cons,agr,gn of this avoidable food waste, first we added the unavoidable fraction of the amount of waste. For example, when an onion is wasted the avoidable waste quantity (WASTE_AV) is the onion without its skin. The WF cons,agr,gn of all onions consumed and wasted by EU consumers (FOOD_CONS of onions) relates to the whole product, including the skin. Therefore the skin must be added to the wasted onion to calculate its WF cons,agr,gn . For all vegetables, the corrected avoidable waste amount (WASTE_AV CORR )—which is only useful to calculate the related footprint—is computed by means of equation (7). WASTE_AV CORR = (equation (7)) = = 26.7 kg/cap/yr with CORR2_NA = = = 0.053 with WASTE_NA (equation (4)) = FOOD_CONS× CORR2_NA = 120.8 kg/cap/yr × 5.3% = 6.4 kg/cap/yr The WF cons,agr,gn of avoidable vegetable waste is then calculated using equation (8):

2.5. Quantification of related nitrogen resources (the nitrogen footprint or NF)