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The Climate Change Impact of Retail Waste from Horticultural Products Jenny Gustavsson Degree project for Master of Science in Environmental Science Department of Plant and Environmental Sciences University of Gothenburg February 2010 SUMMARY Waste of food generates unnecessary greenhouse gas emissions, since wasting, to no use, increases the amount of food produced. The aim of this thesis was to examine how much retail waste increases the climate change impact of producing 18 different horticultural products, namely fruit, berries, vegetables and potted flowers. This study also examined the waste reducing effect of packaging. The results were included in Life Cycle Assessments (LCA), carried out at the Swedish Institute for Food and Biotechnology (SIK). The levels of waste were examined in cooperation with one of the leading Swedish retail companies. Nine retail stores, of two different sizes, contributed data on the annual waste of each product. The results concluded that retail waste of horticultural products made up 0.4 to 6% of store supplies. For the same products, retail waste increased the climate change impact of production by 0.001 to 0.054 kg CO2-equivalent emissions for each kg or plant produced. The results did not show that packaging reduce waste of horticultural products. The results did however indicate that small retail stores, compared to large, produce more waste in relative terms. Large amounts of horticultural products pass through the Swedish retail sector each year. The fairly low levels of waste do in the end cause considerable amounts of greenhouse gas emissions. SAMMANFATTNING Livsmedelssvinn genererar onödiga växthusgasutsläpp eftersom svinn, till ingen nytta, ökar mängden producerad mat. Syftet med detta examensarbete var att undersöka hur mycket detaljhandelssvinn ökar klimatpåverkan från att producera 18 olika trädgårdsprodukter, nämligen frukter, bär, grönsaker och krukväxter. Denna studie undersökte också huruvida paketering minskar svinnet. Resultaten inkluderades i livscykelanalyser (LCA), utförda vid Institutet för Livsmedel och Bioteknik (SIK). Nivåerna av svinnet undersöktes i samarbete med ett av Sveriges ledande detaljhandelsföretag. Nio butiker, av två olika storlekar, bidrog med uppgifter om det årliga svinnet av respektive produkt. Resultaten visade att detaljhandelssvinnet av trädgårdsprodukter utgjorde 0.4 till 6 % av butikslagren. För samma produkter ökade detaljhandelssvinnet klimatpåverkan från produktion med 0,001 till 0,054 kg CO2-ekvivalenter för varje producerat kg eller planta. Resultaten visade inte att paketering minskar svinnet av trädgårdsprodukter. Resultaten indikerade dock att små butiker, jämfört med stora, relativt sett producerar mer svinn. Stora mängder trädgårdsprodukter passerar varje år svensk detaljhandel. De relativt låga nivåerna svinn orsakar till slut stora mängder växthusgasutsläpp. 1 PREFACE This thesis was part of a Master of Science degree in Environmental Sciences at the University of Gothenburg. It was carried out between September 2009 and January 2010 at the Swedish Institute for Food and Biotechnology (SIK) in Gothenburg, Sweden. First of all, I would like to thank the staff members, at all participating retail stores, who made this study possible! Also, I would like to thank my supervisor, Jesper Stage, at the University of Gothenburg for clever observations and good knowledge in the academic field. I would also like to thank my supervisors at SIK, Jennifer Davis and Britta Florén, for encouraging me throughout the process of conducting this study. Last, but definitely not least, I would like to thank all staff members at the department of Sustainable Food Production at SIK, for making me feel truly welcome and for including me as a member of the group. Thank you all! Gothenburg January 18th, 2010 Jenny Gustavsson Master’s Student in Environmental Sciences at the University of Gothenburg For questions or comments, please contact: [email protected] 2 TABLE OF CONTENTS SUMMARY ............................................................................................................................................................ 1 SAMMANFATTNING........................................................................................................................................... 1 PREFACE ............................................................................................................................................................... 2 TABLE OF CONTENTS........................................................................................................................................ 3 INTRODUCTION .................................................................................................................................................. 4 1.1 1.2 1.3 1.4 1.5 1.6 2 BACKGROUND ...................................................................................................................................... 4 THE CLIMATE CHANGE IMPACT OF HORTICULTURAL PRODUCTS ......................................................... 5 WASTE OF HORTICULTURAL PRODUCTS ............................................................................................... 6 SIGNIFICANCE OF TOPIC ........................................................................................................................ 7 AIM AND RESEARCH QUESTIONS ........................................................................................................... 8 DELIMITATIONS .................................................................................................................................... 8 METHOD ...................................................................................................................................................... 9 2.1 LIFE CYCLE ASSESSMENTS ................................................................................................................... 9 2.2 WASTE SURVEY ...................................................................................................................................11 2.2.1 Collection of data ...........................................................................................................................11 2.2.2 Calculations ...................................................................................................................................13 3 RESULTS.....................................................................................................................................................14 3.1 3.2 3.3 3.4 3.5 RETAIL WASTE OF FRUIT, BERRIES AND VEGETABLES SOLD PIECEMEAL...............................................14 RETAIL WASTE OF POTTED FLOWERS ...................................................................................................16 RETAIL WASTE OF PACKAGED COMPARED TO PIECEMEAL SALES .........................................................17 RETAIL WASTE IN SMALL COMPARED TO LARGE RETAIL STORES..........................................................19 CLIMATE CHANGE IMPACT OF RETAIL WASTE FROM HORTICULTURAL PRODUCTS ................................20 4 DISCUSSION...............................................................................................................................................22 5 CONCLUSIONS ..........................................................................................................................................26 6 RECOMMENDATIONS..............................................................................................................................27 REFERENCES.......................................................................................................................................................28 APPENDIX A: Inventory form APPENDIX B: Waste data for piecemeal sales, small retail stores APPENDIX C: Waste data for piecemeal sales, large retail stores APPENDIX D: Waste data for packaged sales APPENDIX E: Mean waste and standard deviation APPENDIX F: Climate change impact 3 INTRODUCTION Environmental issues, including the question of climate change, have been frequently debated during recent years. The Intergovernmental Panel on Climate Change (IPCC) published a report, in 2007, which determined that man-made greenhouse gas emissions most likely increase temperature on earth. The panel also concluded that this temperature rise may seriously disturb naturally occurring ecological interactions (Swedish Environmental Protection Agency 2009a). The Swedish parliament has decided that the national environmental policy should be carried out in the light of 16 environmental quality objectives. One of these objectives, reduced climate change, highlights the issue of greenhouse gas emissions. The goal is to limit emissions to levels where human activity not harmfully affects the climate system (Swedish Environmental Protection Agency 2009b). In order to reach this goal it is most important to identify sources of greenhouse gas emissions, in order to reduce them. 1.1 Background This master’s thesis was part of an ongoing research project carried out at the Swedish Institute for Food and Biotechnology (SIK) in Gothenburg, Sweden. The SIK project (Davis et al. 2010) is financed by The Swedish Farmers´ Foundation for Agricultural Research (SLF) and will be finished in 2010. The project aims to examine the climate change impact of producing certain horticultural products sold in Swedish retail stores, namely fruit, berries, vegetables and potted flowers. By doing Life Cycle Assessments (LCA) the project intends to quantify the amount of carbon dioxide equivalent (CO2-eq) emissions caused by producing these food and flower products. The LCAs reach from the initial agricultural cultivation to the final retail distribution and mainly apply to Swedish production. The results will be published in a report, along with suggestions on how to reduce the climate change impact of production. Until the completion of this thesis the LCA results, within the SIK research project, applied to all included production steps, except for waste at the retail level. Retail waste is not a production activity per se, but it increases the overall climate change impact of production since waste results in larger quantities being produced. Prior to this thesis, no extensive waste survey had been conducted at the Swedish retail level. Hence, in order to complete the LCAs performed at SIK, the climate change impact of retail waste from horticultural products had to be examined. 4 1.2 The Climate Change Impact of Horticultural products Fruit, berries and vegetables There is no doubt that the production of food affects the environment. The effect is to a great extent caused by the animal holding and crop growing carried out on farms. Furthermore, the food is refined, transported, stored, and often cooked before being put on the table, which also has an effect on the environment (Ahlmén 2002). As a consequence of this, food production causes approximately 20-25% of the Swedish greenhouse gas emissions (Sonesson 2008), mainly methane and nitrous oxide (Ahlmén 2002). Leakage from various kinds of refrigerants, sometimes used during transportation and storage of food, as well as carbon dioxide emissions from the use of fossil fuel, also contribute (Carlsson-Kanyama 1998). The early steps of production dominate the overall climate change impact of food with an animal origin, mainly due to feed provision and manure management connected to animal holding on farms (Sonesson 2009). The situation is somewhat different for food with a vegetable origin. For these products, the early steps of production sometimes have less impact on the overall climate change impact. However, the production and application of nitrogen fertilisers both cause emissions of nitrous oxide and carbon dioxide. The use of diesel, when operating agricultural machinery, also generates carbon dioxide emissions (Sonesson et al. 2009). Growing food in heated greenhouses often dominates the climate change impact, provided that fossil fuels are used for heating (Hospido 2008). All these early steps of production certainly have an impact. However, for food with a vegetable origin the post production steps often also have an impact. These apply to for example processing, transportation, packaging and waste handling. The reason for this is not foremost that the post production activity generates more greenhouse gas emissions when applied to vegetable products, compared to animal products. Instead, it depends on the fact that the early steps of production are so different in magnitude between the two. Hence, the relative contribution of post production activity, to the overall climate change impact of production, is larger for food with a vegetable origin compared to food with an animal origin. The production of food uses a lot of resources and produces significant amounts of greenhouse gas emissions. The industry as a whole is, as mentioned above, one of the major contributors to Swedish climate change impact. Potted flowers Potted flowers are sold in Swedish retail stores, all seasons of the year. Many are produced in Sweden, which, due to the cold climate, assumes indoor cultivation in heated greenhouses. Little research has been made on the climate change impact of producing potted flowers. However, it is known to be mainly caused by the greenhouse cultivation, especially when fossil fuels are used for heating. Besides this, post production activity, such as transportation and packaging, also contributes (Bergstrand 2009). Many producers in the cultivation business have switched from using fossil fuel to renewable energy sources, to heat their greenhouses. If this trend continues, the post production steps could very well rise in relative significance for the overall climate change impact of producing potted flowers (Bergstrand 2009). 5 1.3 Waste of Horticultural Products Fruit, berries and vegetables Waste occurs in all steps of the food production chain. The most obvious reason for this is of course that fresh food is delicate by nature, and simply does not keep for very long. The lack of coordination among food producers and distributors, as well as inefficient purchase and meal planning among consumers, further increases waste of food (Sonesson et al. 2009). WRAP’s report “The Food We Waste” (Ventour 2008) revealed detailed information on the quantities of food wasted by UK households. According to the study, UK households threw away about one third of all purchased food. More importantly, 61% of this waste could have been avoided, if the food had been better stored or handled. The avoidable food waste alone causes 18 million tonnes of carbon dioxide emissions each year. The food products mostly wasted were salads (45%), bakeries (31%), fruit (26%) and vegetable products (19%) (Ventour 2008). One major reason for the production of household food waste is passed sell by dates. Lots of food is, due to these labels, thrown away despite being perfectly edible, since the condition of food not always is examined before discarded. Lack of household planning is another major reason. Too much food is purchased, which results in fresh food turning mouldy, smelly or bad tasting. Also, too much food is prepared for meals which results in plate leftovers (Ventour 2008). Efforts have been made during recent years to estimate the food waste produced by UK and American retail companies. Sainsbury’s established that fruit, vegetables and salads made up about 26% of food-related waste (Garnett 2006). In general, this proportion is appraised at 26% - 44% of food waste at the retail level (Alexander et al. 2008). After having examined several published reports, Kantor et al. (1997) came to the conclusion that 2% of fresh fruit and vegetables are wasted within the American retail sector. These studies were however not based on actual measurements of waste amounts, but on estimates. The production of retail food waste happens due to a number of different reasons. Lack of planning within retail stores may lead to over-ordering, oversupplying and over trimming the supplies. In addition to this, fruit and vegetables are sometimes treated under poor environmental conditions, which also increases waste at the retail and consumer level (Nunes et al. 2008). Seasonality is also an important factor when considering retail food waste since over ordering in connection to drastic changes in weather, or traditional holidays, can create additional food waste (Alexander 2008, Kantor 1997). To summarize, food is wasted throughout the production chain, partly because food is delicate by nature and therefore easily destroyed. Man-made factors such as lack of coordination and planning throughout the production and consumption chain do however also contribute. Potted flowers To the author’s knowledge, no research has been made on the retail waste of potted flowers. However, the magnitude of, and causes for, are reasonably similar to those for retail waste of fruit and vegetables. 6 1.4 Significance of topic WRAP’s report (Ventour 2008) forcefully highlights the issue of avoidable food waste, produced by consumer households. Due to increasing world population, urbanisation and standards of living, widespread concerns are held regarding diminishing global food availability and rising food costs. It is of great importance to enlighten food waste in all steps of the food production chain (Alexander et al. 2008). The retail sector is one such step, where mainly fresh food is wasted due to unpredictable demand (Sonesson 2008). Waste of fruit and vegetables at the retail level has significant environmental impact, since what is wasted is the final product. In other words, all environmentally burdensome production steps have already been carried out in order to grow, transport and store the food (Ahlmén 2002). Although these products are known to be highly wasted, new knowledge is needed to clarify which types of fruit and vegetables that are being thrown away. As mentioned above, for food with a vegetable origin the post production activity, including waste handling, sometimes has notable impact on the overall climate change impact of production (Sonesson et al. 2009). The share of greenhouse gas burdensome fruit, berries and vegetables sold in Nordic countries, like Sweden, seems to have increased with time. When walking through a local retail store today it seems obvious that retailers aim to provide consumers with all kinds of food, during all seasons of the year. The issue of seasonality seems to have lost its importance. In fact, research suggests that urban consumers expect to be able to buy fresh fruit and vegetables all year long (Hospido et al. 2009). In northern parts of Europe this assumes growing food in heated greenhouses, alternatively importing from countries farther south. Hence, these demands further increase the climate change impact of production (Hospido et al. 2009). In the same fashion, potted flowers are cultivated and sold in Sweden during all seasons of the year, which also assumes indoor cultivation in heated greenhouses. Two of the top three most sold potted flowers in Sweden during 2005 were Poinsettia and Kalanchoë. Poinsettia is in Sweden strongly associated with Christmas time and is therefore mainly cultivated during the cold winter month of December (Bergstrand 2009). This further exemplifies how stepping aside from seasonality puts extra strain on the energy use associated with producing horticultural products. Furthermore, the waste of fruit, berries and vegetables at the retail level is most likely dependent on packaging, which sometimes is considered environmentally burdensome due to increased amounts of material waste (Monkhouse et al. 2004). Packaging can however also be considered waste reducing, since it protects fragile fruit and vegetables (Sonesson et al. 2009). The production of food has, as mentioned above, significant climate change impact. Due to this the food industry is highly affected by one of Sweden’s 16 environmental quality objectives, reduced climate impact, which aims to reduce the harmful human induced impact on the climate system (Swedish Environmental Protection Agency 2009b). Therefore, it seems highly relevant to focus on climate change when estimating the environmental impact of retail waste from horticultural products. 7 1.5 Aim and research questions The aim of this study was to examine how much retail waste increases the climate change impact of producing fruit, berries, vegetables and potted flowers. The results apply for the Swedish retail level. The following questions were addressed: • • • How much is wasted of horticultural products sold at the Swedish retail level? Does packaging reduce waste of fruit and vegetables at the retail level? How large is the climate change impact of retail waste from horticultural products? 1.6 Delimitations The LCAs of the horticultural products included in this study mainly applied for Swedish production. This study only examined the levels of waste produced at the Swedish retail level and within stores located in urban areas. The levels of retail waste were only examined for fresh fruit, berries, vegetables and potted flowers. No other items of food or plants were considered. This study only included the environmental aspect of climate change. Although this thesis only highlights the specific issue of greenhouse gas emissions, there are other environmental aspects to be considered in association with food production. They were, however, not dealt with in this thesis. 8 2 METHOD 2.1 Life Cycle Assessments All fruit, berries, vegetables and potted flowers included in the SIK research project (Davis et al. 2010) are presented in table 1 and 2. The climate change impact of production (excluding retail waste), for each horticultural product, is also presented in table 1 and 2. The SIK research project applied for Swedish production, except for kiwi which had been imported. The functional unit of these results was either one kilogram or one plant, incoming to the retail store. TABLE 1. The fruit, berries and vegetables included in the SIK research project, as well as the climate change impact of production (excluding retail waste). Climate change impact of production Excluding Fruit, berries retail waste and vegetables (kg CO2-eq/kg) Apples 0.10 Broccoli 0.78 Cabbages 0.36 Carrots 0.24 Cauliflowers 0.36 Celery root 0.29 Cucumbers 1.15 Iceberg lettuce 0.35 Kiwis 1.36 Leeks 0.25 Onions 0.14 Parsnips 0.36 Pears 0.12 Rutabagas 0.18 Strawberries 0.48 Tomatoes 0.72 TABLE 2. The potted flowers included in the SIK research project, as well as the climate change impact of production (excluding retail waste). Climate change impact of production Excluding Potted flowers retail waste (kg CO2-eq/plant) Kalanchoës 0.44 Poinsettias 0.58 The results on climate change impact of production are preliminary, and might change slightly. For final results, please see “Emissions of Greenhouse Gases from Production of 18 Fruits, Vegetables and Flowers sold in Sweden” (Davis et al. 2010). 9 In brief, LCA is an ISO standardised method (ISO 14044:2006), which is used to assess the overall environmental impact of a product. Which specific environmental impact(s) that are going to be considered, is decided based on the aim of each assessment. Which parts of the life cycle that are going to be included, is also decided for each specific LCA (Baumann et al. 2004). What characterises the LCA method in general is that it studies the whole life cycle of a product, from the cradle to the grave. This is done in order to identify those parts of the life cycle which entail the largest environmental impact. These need to be considered most, when aiming to reduce the overall environmental impact of a product. The LCAs performed at SIK examined the climate change impact of producing certain horticultural products. The assessments included all climate change contributing production steps, from initial agricultural cultivation to final retail distribution. All included steps are illustrated in figure 1. Data on greenhouse gas emissions surrounding these steps was collected. The different greenhouse gas emissions were then weighted, and expressed as CO2eq emissions, in order to show their relative contribution to the overall climate change impact (Baumann et al. 2004). Input Agricultural cultivation Retail stage Distribution Wholesale Transportation Transportation Industrial processing Packaging FIGURE 1. A simplified flowchart of the LCAs of horticultural products, illustrating all climate change contributing activities included in the assessments. The climate change impact of a product equals the accumulated amount of greenhouse gas emissions caused by its production, commonly expressed as kg CO2-eq emissions from producing one functional unit of a certain product. 10 2.2 Waste Survey The waste survey within this thesis was carried out in cooperation with one of the leading Nordic retail companies (anonymous), which contributed data on waste and sales of the fruit, berries and vegetables included in this study. The participating retail company offers a variety of goods in the stores, although mostly different kinds of food articles. The company stores, which are of different sizes, have slightly different ranges of products in order to meet the needs of different customer groups. The sales of potted flowers, within the participating retail stores, were handled in accordance with the shop-in-shop concept. The waste survey was therefore also carried out in cooperation with one of the major flower distribution companies, connected to the Swedish retail sector. 2.2.1 Collection of data Retail waste of fruit, berries and vegetables The retail stores were initially contacted by phone. Firstly, some basic information on this thesis was given and secondly the staff was asked to participate in the waste survey. After having established collaboration with a certain store, written instructions were sent in an email to the person responsible for the fruit and vegetable section, alternatively to the owner, of the store. The form included specific instructions on which particular data that were of interest (Appendix A). The staff was asked to send the filled out form back to SIK, prior to a certain date. If the answer was delayed, a second (and in some cases a third) phone call was made during which the staff was reminded and encouraged to fulfil the task. In total, 76 retail stores were contacted. Among these, 36 were fairly small with a gross turnover of approximately 50 MSEK, and 40 were larger with a gross turnover of between 150 – 650 MSEK. However, not all retail stores initially contacted kept adequate documentation of the wasted amounts of fruit, berries and vegetables, considering the aim of this study. Therefore, the participating retail stores were foremost selected based on ability to deliver the data of interest. Hence, all participating retail stores had ongoing inventory control systems, keeping track of waste and sales. Among all the 76 retail stores initially contacted, 39% (30/76) kept documentation of the wasted amounts of fruit, berries and vegetables. Among the small retail stores 31% (11/36) did, and among the large retail stores 48% (19/40) did (figure 2). No. of stores Waste documentation within contacted retail stores 80 60 No 40 Yes 20 0 Small retail stores Large retail stores All retail stores FIGURE 2. The number of contacted retail stores which did and did not keep documentation of the wasted amounts of fruit, berries and vegetables. 11 In the end, nine retail stores contributed data to the waste survey, four small and five large. The reasons why staff chose not to participate were mainly lack of time and/or feeling uncomfortable with the potential attention this kind of waste survey could give rise to. Since waste of food is a somewhat delicate topic, the retail company, as well as the retail stores, was kept anonymous throughout this report. The collected data applied for the annual quantities of waste and sales for each type of fruit, berry and vegetable included in this study. The quantities were put in kilograms, numbers or liters, depending on how the quantities had first been registered within the retail store. The collected data applied for piecemeal sales of all fruit, berries and vegetables included in this study. In addition to this, the same data were also collected for apples, kiwis, onions and tomatoes when sold packaged. Since most retail stores supplied a variety of brands of each fruit, berry and/or vegetable, the staff was asked to choose one brand for each product. The quantities of waste had been documented within the different retail stores using approximately the same routine. The fruit and vegetable supply was inventoried on a daily, alternatively weekly, basis. All items of fruit, berries and vegetables that were not considered saleable were then discarded. Before being discarded, the waste was registered digitally by weight/number/volume and product. The data applied for the extended time period between October 1st 2008 and October 1st 2009. This span was chosen since several retail stores did not save their data longer than approximately 400 days. For one participating retail store, the data instead applied for year 2008. Retail waste of potted flowers The flower distribution company was contacted by email. Firstly, some basic information on this thesis was given and secondly the staff was asked to participate in the waste survey. The correspondence was held with the sales manager. The company stated the annual financial loss of store supply for each of the included potted flowers, sold within the participating retail company. The financial loss was made up of both physical waste and price lowering waste. The physical waste represented the waste of interest for this study, namely potted flowers thrown away since they for various reasons had not been sold. This happened for example because of low demand or poor quality. The price lowering waste on the other hand represented lost income, during for example clearance sales. The flower distribution company was not able to specify the physical waste separately, and therefore it had to be estimated. Staff appraised that the financial loss made up of approximately 70% physical waste and 30% price lowering waste. Hence, the physical waste of potted flowers was assumed to be 70% of the stated financial loss. The data applied for the extended time period between October 1st 2008 and September 30th 2009. 12 2.2.2 Calculations Waste proportion of store supply for fruit, berries and vegetables In this study, focus was set on the waste proportion of store supply (W%). Each participating retail store stated the annual quantities of waste (W) and sales for each fruit, berry and vegetable included in this survey. The sum of waste and sales equaled the store supply (SS). W W% = ____ SS Waste proportion of store supply for potted flowers In this study, focus was set on the waste proportion of store supply (W%). The participating flower distribution company stated the financial loss proportion of store supply (FL%), for each potted flower included in this survey. The waste proportion of store supply was estimated to make up 70% of the financial loss proportion. W% = FL% · 0.7 Climate change impact of production (including retail waste) The climate change impact of production (including retail waste) was estimated by multiplying the climate change impact of production (excluding retail waste) with the retail waste factor described below. W% applied for the waste proportion of store supply. Retail waste factor = 1 (1 – W%) Climate change impact of retail waste The climate change impact of retail waste equalled the difference between climate change impact of production including, and excluding, retail waste. 13 3 RESULTS 3.1 Retail waste of fruit, berries and vegetables sold piecemeal The annual quantities of waste and sales, for each type of piecemeal fruit, berry and vegetable are found in appendix B and C, along with each computed store supply and waste proportion of store supply. Not every store kept all types of products in supply. Figure 3 illustrates the annual mean waste proportion of store supply, for fruit, berries and vegetables sold piecemeal, based on the waste proportions stated by all participating retail stores (appendix E). The mean waste ranged from 0.4% to 6.3% of store supply. Mean waste proportion of store supply for piecemeal sales Waste 0% 1% 2% 3% 4% 5% 6% 7% Broccoli Strawberries Cauliflowers Celery root Rutabagas Kiwis Parsnips Pears Tomatoes Leeks Iceberg lettuce Carrots Apples Cucumbers Cabbages Onions FIGURE 3. The annual mean waste proportion of store supply for fruit, berries and vegetables sold piecemeal. As shown in figure 3, the fruit, berries and vegetables wasted the most in relative terms were broccoli (6.3%), strawberries (4.8%) and cauliflowers (4.7%). In contrast, the types wasted the least in relative terms were onions (0.4%), cabbages (0.7%) and cucumbers (0.9%). 14 Also, the overall waste proportion of store supply, of the included fruit, berries and vegetables, was computed for each participating retail store. The results showed that the overall waste ranged from 1.4% to 4.4% of store supply (appendix E). Figure 4 illustrates the dispersion of data on annual waste proportion of store supply, for each fruit, berry and vegetable sold piecemeal. Each dot represents the waste within one single retail store (appendix E). Figure 4 also shows the standard deviation (SD) within each group of data (appendix E). Dispersion of data on piecemeal sales SD Broccoli 0,05 Strawberries 0,07 Cauliflowers 0,02 Celery root 0,05 Rutabagas 0,03 Kiwis 0,04 Parsnips 0,02 Pears 0,01 Tomatoes 0,01 Leeks 0,02 Iceberg lettuce 0,01 Carrots 0,01 Apples 0,01 Cucumbers 0,01 Cabbages 0,00 Onions 0,00 0% 2% 4% 6% 8% 10% 12% 14% 16% 18% 20% Waste FIGURE 4. The dispersion of data on annual waste proportion of store supply for fruit, berries and vegetables sold piecemeal. For piecemeal sales, the overall dispersion of data ranged from 0% to 16.6%. The majority of waste levels were however between 0% and 6%. There were a number of major outliers, foremost among the data on strawberries (16.6%), celery root (15.7%), broccoli (14.6%), rutabagas (11.2%), kiwis (11.3%) and parsnips (8.0%). These were all single outliers, within each group, which were considerably separated from the rest of the data. These outliers all derived from three different retail stores (appendix E). The incidence of outliers, as well as generally scattered data, increased the standard deviation. 15 The span between smallest and largest observed waste proportion was rather wide for three of the four top wasted products, namely broccoli (2.1% - 14.6%), strawberries (0.5% - 16.6%) and celery root (1.3% - 15.7%). This was mainly due to one single outlier within each group of data. For other products, the span between smallest and largest observed waste proportion was rather small. This applied for cabbages (0.2% - 1.5%), cucumbers (0.3% - 1.8%) and onions (0% - 1.6%). 3.2 Retail waste of potted flowers Figure 5 shows the annual waste proportion of store supply for the two potted flowers included in this study. Poinsettia generated less physical waste (2.5%) than Kalanchoë, which generated 3.0%. The financial loss (FL %) was estimated to be 3.6% for Poinsettias and 4.3% for Kalanchoës. Waste Waste proportion of store supply for potted flowers 4% 3% 2% 1% 0% Poinsettias Kalanchoës FIGURE 5. The annual waste proportion of store supply for potted flowers. Figure 3 and 5 together show that the mean waste proportions of store supply were similar for potted flowers as for fruit, berries and vegetables. 16 3.3 Retail waste of packaged compared to piecemeal sales The annual quantities of waste and sales, for each type of packaged fruit and vegetable are found in appendix D, along with each computed store supply and waste proportion of store supply. Not every store kept all types of products in supply. The data on piecemeal sales were the same as in figure 3 (appendix B & C). Waste Figure 6 illustrates the annual mean waste proportion of store supply for apples, kiwis, onions and tomatoes when sold packaged, compared to when sold piecemeal (appendix E). M ean waste proportion of store supply for packaged compared to piecemeal sales 5% 4% 3% 2% 1% 0% Packaged Piecemeal Apples Kiwis Onions Tomatoes FIGURE 6. The annual mean waste proportion of store supply for apples, kiwis, onions and tomatoes when sold packaged, compared to when sold piecemeal. The mean waste proportion of store supply for apples, kiwis, onions and tomatoes ranged from 0.9% to 3.8% when sold packaged and from 0.4% to 3.8% when sold piecemeal. When sold packaged, apples, onions and tomatoes generated larger amounts of waste in relative terms (3.4%, 0.9%, 3.8%), compared to when sold piecemeal (1.1%, 0.4%, 2.2%). The opposite applied for kiwis, which in relative terms generated less waste when sold packaged (2.8%) compared to when sold piecemeal (3.8%). 17 Figure 7 illustrates the dispersion of data on annual waste proportion of store supply, for apples, kiwis, onions and tomatoes sold packaged. Each dot represents the waste within one single retail store (appendix E). Figure 7 also shows the standard deviation (SD) within each group of data (appendix E). Dispersion of data on packaged sales SD Apples 0,02 Kiwis 0,02 Onions 0,01 Tomatoes 0,05 0% 5% 10% 15% 20% 25% Waste FIGURE 7. The dispersion of data on annual waste proportion of store supply for fruit and vegetables sold packaged. The dispersion of data on packaged fruit and vegetables ranged from 0% to 16.1%. All waste levels except for one, tomatoes at 16.1%, were between 0% and 6% (appendix E). The outlier on packaged tomatoes, at 16.1%, increased the mean waste of this product significantly. Without this one outlier, the retail waste of packaged tomatoes was less than for piecemeal tomatoes (2.1% vs. 3.8%). The tomatoe outlier also increased the standard deviation within this group of data. 18 3.4 Retail waste in small compared to large retail stores Figure 8 shows the mean waste proportions of store supply within small and large retail stores, for piecemeal and packaged sales respectively. The mean waste proportions of store supply within small retail stores are found in appendix B and D. The mean waste proportions of store supply within large retail stores are found in appendix C and D. Not every store kept all types of products in supply. The small retail stores had in general produced more waste, as illustrated in figure 8. This applied for 13 of the 16 piecemeal fruit, berries and vegetables. It also applied for three of the four fruit and vegetables sold packaged. Mean waste proportion of store supply for piecemeal and packaged sales, in small and large retail stores Waste 0% 1% 2% 3% 4% 5% 6% 7% 8% Broccoli Strawberries Cauliflowers Celery root Rutabagas Kiwis Parsnips Pears Tomatoes Leeks Small Iceberg lettuce Large Carrots Apples Cucumbers Cabbages Onions Apples (packaged) Kiwis (packaged) Onions (packaged) Tomatoes (packaged) FIGURE 8. The mean waste proportion of store supply for piecemeal, and packaged, sales within small and large retail stores respectively. The fruit and vegetables, which differed most between small and large retail stores, were celery root (7.4% vs. 2.4%), strawberries (6.5% vs. 2.3%) and packaged tomatoes (7.3% vs. 1.7%), which all had generated more waste within the small retail stores (appendix E). 19 The only fruit and vegetables which on average had generated less waste within small retail stores, compared to large, were broccoli (5.1% vs. 7.4%), carrots (1.0% vs. 1.5%), piecemeal kiwis (3.1% vs. 4.5%) and packaged apples (3.3% vs. 3.5%) (appendix E). 3.5 Climate change impact of retail waste from horticultural products Figure 9 shows the climate change impact of retail waste in relation to the overall climate change impact of production (including retail waste) (appendix F), for the potted flowers and piecemeal fruit, berries and vegetables included in this study. The black part in each bar represents the climate change impact of retail waste. Climate change impact of retail waste in relation to climate change impact of production (including retail waste), for horticultural products kg CO2-eq 0,0 0,5 1,0 1,5 Kiwis Cucumbers Broccoli Tomatoes Strawberries Poinsettias Kalanchoës Cauliflowers Parsnips Cabbages Iceberg lettuce Celery root Leeks Carrots Rutabagas Onions Pears Apples FIGURE 9. The climate change impact (CO2-eq emissions/kg/plant) of retail waste (black) in relation to total climate change impact of production (grey), for all piecemeal horticultural products included in this study. The overall climate change impacts of production (including retail waste) ranged from 0.10 to 1.41 kg CO2-eq emissions for each produced kg/plant leaving the retail store. As shown in figure 9, the products generating the largest climate change impact were kiwis (1.41), cucumbers (1.16) and broccoli (0.83). In contrast, the products generating the least climate change impact of production were apples (0.10), pears (0.12) and onions (0.14) (appendix F). 20 Figure 10 shows the climate change impact of retail waste for the potted flowers and piecemeal fruit, berries and vegetables included in this study (appendix F). These results equal the dark parts of the bars in figure 9. Climate change impact of retail waste from horticultural products kg CO2-eq 0,000 0,020 0,040 0,060 Kiwis Broccoli Strawberries Cauliflowers Tomatoes Poinsettias Kalanchoës Parsnips Cucumbers Celery root Rutabagas Iceberg lettuce Leeks Carrots Pears Cabbages Apples Onions FIGURE 10. The climate change impact (CO2-eq emissions/kg/plant) of retail waste for all piecemeal horticultural products included in this study. The climate change impact of retail waste ranged from 0.001 to 0.054 kg CO2-eq emissions for each produced kg/plant leaving the retail store. Three different fruit and vegetables stood out from the rest of the horticultural products by causing significantly higher climate change impacts due to retail waste, namely kiwis (0.054), broccoli (0.052) and strawberries (0.029). Those horticultural products which generated the lowest climate change impact of retail waste were onions (0.001), apples (0.001) and cabbages (0.002). The results in figure 9 and 10 together illustrate how the climate change impact of retail waste mostly followed the magnitude of overall climate change impact of production. There were, however, a few exceptions. Cucumbers for example caused the second largest climate change impact of production (figure 9), but due to the low waste level only the tenth largest climate change impact of retail waste (figure 10). 21 4 DISCUSSION This study aimed to examine how much retail waste increases the climate change impact of producing horticultural products. The retail sector is a significant part of the food distribution chain, handling a considerable amount of fruit, berries, vegetables and potted flowers heading towards the consumer households. To date, little research has been made on the subject of retail waste in Sweden. Therefore, this study filled a gap in the LCAs on the climate change impact of producing horticultural products. For the fruit, berries and vegetables included in this study, the annual waste proportions of store supply stretched from 0.4% to 6.3%. Not surprisingly, fragile products, like broccoli (6.3%), strawberries (4.8%) and cauliflowers (4.7%), were in relative terms wasted the most. Furthermore, solid fruit and vegetables were found among those categories wasted the least, for example onions (0.4%), cabbages (0.7%) and carrots (1.3%). Hence, the results in this study support the reasonable assumption that there is a link between fragility and large amounts of waste. How do the results in this study compare to waste levels in other parts of the food distribution chain? WRAP’s report on UK household food waste presented strikingly different results when concluding that 45% of all purchased salad, 26% of all purchased fruit and 19% of all purchased vegetables are thrown away (Ventour 2008). Hence, compared to UK households, the Swedish retail sector seems to generate significantly lower amounts of fruit and vegetable waste. The results in this study did not confirm that packaging reduce waste of fruit and vegetables at the retail level. Packages fill a purpose of reducing waste by protecting fragile fruit and vegetables from bruising and turning bad (Sonesson et al. 2009). These are concerns associated with some of the most frequently packaged products, for example kiwis, which in this study were wasted less when packaged compared to when sold piecemeal. Apples and onions, on the other hand, were in this study generally wasted more when sold packaged, compared to when sold piecemeal. These types of solid fruit and vegetables presumably do fairly well, even without packaging. Even though packaging not always seems to reduce waste, it does most likely boost the financial profit of retail stores by increasing the amounts of fruit and vegetables being sold. Packages also simplify the administration of sales by being pre market with bar codes. The production of packages does however take resources in claim, and generates large amounts of material waste (Williams et al. 2007). Due to these environmental drawbacks, packages should from an environmental point of view only be used when serving the aim of reducing waste. Otherwise, the overall environmental burden is most likely larger than the gain. Packaging should preferably reduce waste at the consumer level, where wasting is most severe. In order to do so, the size of packages should be optimized to meet the needs of various customers (Williams et al. 2007). Packages should preferably also consist of materials with low environmental impact, and be filled in a way so that excessive transportation is minimised. If not, packaging instead moves food waste from the retail sector to the consumer households. According to the WRAP report, approximately 28% of all household food waste was in its original packaging. Among this, 13% consisted of vegetables, 9% of salads and 4% of fruit (Ventour 2008). Apparently, fruit and vegetables are frequently found among food products thrown away packaged. 22 The results in this study indicated that small retail stores, compared to large, produce more waste of fruit berries and vegetables in relative terms. In the same fashion, WRAP’s report on household food waste concluded that single households produce the most food waste per capita (Ventour 2008). Hence, it seems as if large entities are beneficial for the purpose of reducing food waste, both within the retail sector and the consumer households. What could be possible explanations to the seemingly lower levels of waste in large retail stores? Waste of fruit and vegetables within the retail sector can be tied to various problems. Some of these include over-ordering; over-trimming the supplies; insufficient shelf-life; passed sell-by dates and seasonal ordering (Alexander et al. 2008). In addition to this, all retail stores regardless of size seem to aim at keeping non-stop filled supplies, which partly explains why fruit, berries and vegetables turn bad. In order to reduce retail waste it seems important to limit the time period fresh products spend in the store before being sold. Large retail stores, compared to small, understandably have a more rapid flow of customers. Large retail stores also most likely have a wider range of customers, which demand a wider range of products. All retail stores, regardless of size, would most likely reduce waste if cutting down the quantities on display and if adjusting order sizes to the current demand, for each product respectively. Narrowing the overall range of products would probably also reduce waste. The dispersion of data for fruit, berries and vegetables within this waste survey was large. Also, several major outliers among the data on piecemeal sales (figure 4) derived from three specific retail stores. Hence, there seemed to be structural differences in the magnitude of waste produced within different retail stores. Alternatively, certain retail stores carried out a more thorough waste documentation. Most likely, personal skills of staff members had some influence on the wasted amounts within each retail store. The person responsible for the fruit and vegetable section of the store could do a more or less good job of keeping supplies at the most waste reducing levels. Also, staff members made different judgements on which food items that were going to be considered as waste. And finally, every single item of waste might not have been registered before being thrown away. The transparency of data on fruit, berries and vegetables within this study was low, since it solely represented second hand information. The participating retail stores had prior to this study registered the quantities of waste purely because of internal motives, and not to be part of a waste survey. This brings questions to the accuracy of data. It does however also strengthen its credibility by avoiding bias. All retail stores participating in this study already indirectly worked on reducing the amounts of waste produced. Well established inventory control systems enabled to identify excessive wasting, and therefore most likely had a waste reducing effect. Even though the foremost motive for keeping track of waste surely was to increase the financial profit, the waste reducing effect would be expected. This bearing was of course a flaw in the process of choosing which retail stores that were going to be part of the waste survey. It was, however, necessary since this study depended on the retail stores being able to distribute detailed data on the amounts of waste produced. 23 The waste proportions of store supply for potted flowers were rough assumptions based on the financial loss of sales. Since the flower distribution company had no access to detailed data on the actual waste of potted flowers, these assumptions had to be made. Each financial loss could however be considered fairly accurate, since it derived from financial bookkeeping. Considering the low levels, these estimations did not affect the final results a whole lot. The climate change impact of producing the fruit, berries, vegetables and potted flowers included in this study was estimated by doing LCAs. This method makes environmental issues concrete by embodying them in numbers, which of course enables an efficient visualisation of the problems (Baumann & Tillman 2004). However, so called “hard numbers” can be somewhat misleading by giving the impression that environmental issues are more measurable and concrete than they really are (Eriksson & Wiedersheim-Paul 2008). However, this thesis only included the results from already completed assessments, and the quality of this thesis should solely be judged based on the methods actually used within it. Besides this, the LCA method challenges the result interpreter by assuming a certain level of awareness, regarding for example the choice of system boundaries. These determine which production steps that are going to be included in the LCA, and will of course affect the outcome of the assessment. Each LCA is in fact a rather subjective examination of the environmental impact of a product. Which processes that are included, and which aspects that are considered, is decided by the people conducting the assessment. Therefore, it is important as an interpreter of LCA results to study the circumstances, before drawing any conclusions (Baumann & Tillman 2004). The climate change impact of producing the fruit, berries and vegetables included in this study ranged from 0.10 to 1.41 kg CO2-eq emissions for each kg produced (leaving the retail store). These figures are relatively low, especially compared to food products with an animal origin. Producing chicken, salmon, cod and pork usually causes a climate change impact of somewhere between 1 and 5 kg CO2-eq emissions for each kg produced (SIK 2009). The climate change impact of beef is considerably higher, often ten times that of fruit and vegetables (SIK 2009). It is however difficult to determine the general climate change impact of producing certain types of food, due to various production processes being used. However, producing fruit, berries and vegetables has a relatively low climate change impact. The climate change impact caused by retail waste of horticultural products is indeed low compared to that of other food products, mainly due to low climate change impacts of production. It does, however, also depend on low levels of waste at the retail level. The low levels of retail waste should however be interpreted in relation to the Swedish consumption of each product respectively. For example, during year 2006 the Swedish annual per capita consumption of tomatoes amounted to 10 kg (Board of Agriculture 2010). Assuming that two thirds of these tomatoes had passed through retail stores, the annual Swedish retail waste of tomatoes would have totalled approximately 1,400 tons during the same year (over 9 million inhabitants) (Statistics Sweden 2010). Retail waste of tomatoes thereby caused about 1,000 tons CO2-eq emissions, which equals the average annual emissions from 395 Swedish cars (Sonesson 2008). These are of course rough assumptions, but do however illustrate how the low levels of retail waste (0.4% - 6%) in the end generate significant amounts of greenhouse gas emissions. 24 Another point to be made is that the retail waste levels, as well as the climate change impact of production, were modestly estimated in this thesis. The retail stores participating in this study were, as mentioned before, probably wasting less than the average Swedish retail store. Also, the climate change impact of production in this thesis only applies to Swedish production (except for kiwis). The impacts of domestic products are often lower than those of imported products, due to the environmental burden of transportation. There are however exceptions, since the impacts also depend on the premises for cultivation. Still, the results in this thesis were most likely not exaggerated, but rather somewhat understated. How waste is handled after having left the retail store is a relevant question when considering the overall climate change impact of retail waste. The aspect of waste handling was however not taken into account when estimating the climate change impact of producing the horticultural products included in this study. In Sweden, no biodegradable waste is allowed to be disposed at landfills (Ministry of Environment 2001). This law avoids methane emissions since food waste at landfills is degraded under anaerobic conditions (Sonesson 2009). Also, at least 35% of all Swedish food waste produced by households, caterers, restaurants and retail stores should by 2010 be recovered by means of biological treatment, which applies for digestion and composting. This goal has been set up in accordance with the Swedish national environmental objective - A Good Built Environment (Swedish Environmental Protection Agency 2009c). During year 2008 about 20% of food waste produced within Sweden was reused in this way (Swedish Environmental Protection Agency 2009d). In addition to this, food waste is also burned in order to produce energy (Swedish Environmental Protection Agency 2009e). Important to remember is that the food wasted by conventional retail stores not always is below standards for human consumption. Fresh fruit and vegetables easily loose its freshness and spotless appearance. It may, however, still be perfectly edible. Therefore, certain retail stores sell surplus food to customers at reduced prices. It also happens that food is donated to charities for human consumption, or to farms and zoos as animal feed (Alexander et al. 2008). Reusing retail surplus food, when possible, certainly decreases its climate change impact. Retail waste of food entails unnecessary climate change impact. The products have through their production contributed greenhouse gas emissions, only to be wasted. Considering the future world population growth, food should be considered a scarce resource, and efforts should by all means be made to continuously minimize wasting throughout the food production chain. We need not only to focus on how to make food production more efficient. We also need to focus on how to fully make use of the food already produced. 25 5 CONCLUSIONS This study aimed to examine how much retail waste increases the climate change impact of horticultural products. The waste levels produced, of the fruit, berries, vegetables and potted flowers included in this study, were examined. The potential waste reducing effect of packaging was also investigated. Finally, the climate change impact of retail waste from horticultural products was determined. The results in this study concluded that: • The mean waste proportions of store supply for horticultural products sold piecemeal ranged from 0.4% to 6%, at the retail level. Mostly wasted in relative terms were fragile products, sensitive to damage. • Packaging does not seem to reduce waste of fruit and vegetables at the retail level. This part of the study did however only include four different products. • The levels of waste, in general, seemed larger within small retail stores, compared to large, for fruit, berries and vegetables. • All participating retail stores had ongoing inventory control systems. Hence, the levels of waste were low within retail stores aware of the amounts of waste produced. • The dispersion of waste levels stated by the participating retail stores was large, for fruit, berries and vegetables sold piecemeal. There were some minor structural differences, between the participating retail stores, in the amounts of waste produced. • The climate change impact of retail waste from horticultural products ranged from 0.001 to 0.054 kg CO2-eq emissions, for each produced kg or plant leaving the retail stores. Two factors determined the climate change impact of retail waste, namely the waste proportion of store supply and the climate change impact of production (excluding retail waste). 26 6 RECOMMENDATIONS There are several ways in which the climate change impact of retail waste from horticultural products could be reduced. Based on the results of this study, and conversations held with staff members at the participating retail stores, the following recommendations can be made: • Efforts should by all means be made to continuously reduce retail waste of horticultural products, considering the large amounts passing through the retail sector each year. Efforts should however also be focused towards the consumer level, where wasting is most severe. • In order to reduce waste of horticultural products, retail stores should preferably limit the quantities on display and only keep products in supply which have a steady customer base. Order sizes should be adjusted to the current demand for each product respectively. • Efforts should be made to introduce inventory control systems, such as waste documentation, throughout the entire retail sector. • Future surveys on the subject of retail waste should, compared to this study, include a larger number of participating retail stores. Future surveys should also preferably conduct their own inventories of waste. • Surplus food within the retail sector should, when possible, not be wasted but instead reused. This could for example be done by selling it to consumers at reduced prices, donating it to charities for human consumption or to farms as animal feed. 27 REFERENCES Ahlmén, K. (2002) Maten och miljön – Livscykelanalys av sju livsmedel. Stockholm: Sigill Kvalitetssystem AB Alexander, C. & Smaje, C (2008) Surplus retail food redistribution: An analysis of a third sector model. Resources, Conservation and Recycling, vol. 52, pp. 1290-1298 Baumann, H. & Tillman, A. (2004) The Hitch Hiker´s Guide to LCA. Lund: Studentlitteratur AB Bergstrand, M. (2009) Climate impact from the production of pot plants – life cycle assessment (LCA) of poinsettia produced in Sweden. 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(1996) Wasteful food consumption: trends in food and packaging waste. Scandinavian Journal of Management, vol. 12, No. 3, pp. 291-304 UNFCCC - United Nations Framework Convention on Climate Change (1994) Article 1 definitions, http://unfccc.int/essential_background/convention/background/items/2536.php, 091021 29 Ventour, L. (2008) The food we waste. Banbury: Wrap. Food waste report v2, ISBN: 184405-383-0 Verbruggen A. (2007) Glossary of Terms used in the Intergovernmental Panel on Climate Change Fourth Assessment Report, http://www.ipcc.ch/pdf/glossary/ar4-wg3.pdf, 091022 Williams, H., Wikström, F. & Löfgren, M. (2007) A life cycle perspective on environmental effects of customer focused packaging development. Journal of Cleaner Production, vol. 16, pp. 853-859 30 APPENDIX A: Inventory form (X = Replaces names, addresses and telephone numbers due to secrecy.) For the attention of staff members at the fruit and vegetable section of the store, I have been assigned by The Swedish Institute for Food and Biotechnology to conduct a survey concerning waste of fruit and vegetables within the Swedish retail sector. The study is carried out in cooperation with X. However, neither X nor any specific retail stores will be mentioned in the report. I now need help from a number of retail stores with collecting data on the annual waste of fruit and vegetables. To be able to compute the waste proportion of store supply, I also need data on the annual sales of each product. The data should apply for the extended time period: October 1st 2008 - October 1st 2009. I will also compare the waste of certain fruit and vegetables, concerning packaging. I will examine whether waste differs, for the same product, when sold packaged compare to when sold piecemeal. The chosen fruit and vegetables for this part of the survey are tomatoes, apples, kiwis and onions. So, the data I wish to receive from you is (all is included in the tables below): • Data on the annual waste (SEK) and sales (SEK) for the entire fruit and vegetable section of the store. • Data on the annual waste (kg) of: tomatoes, cucumbers, cauliflowers, broccoli, carrots, parsnips, iceberg lettuce, onions, leeks, rutabagas, celery root, cabbage, apples, pears, strawberries, kiwis and potted salads. • Data on the annual sales (kg) of: tomatoes, cucumbers, cauliflowers, broccoli, carrots, parsnips, iceberg lettuce, onions, leeks, rutabagas, celery root, cabbage, apples, pears, strawberries, kiwis and potted salads. • Data on the annual waste (number of packages) of tomatoes, apples, kiwis and onions sold packaged, in for example plastic packaging or net. • Data on the annual sales (number of packages) of tomatoes, apples, kiwis and onions sold packaged, in for example plastic packaging or net. 31 Fill out the tables below. Please leave empty if there is no data available. 1. The waste, October 1st 2008 - October 1st 2009, for the entire store supply of fruit and vegetables = SEK The sales, October 1st 2008 - October 1st 2009, for the entire store supply of fruit and vegetables = SEK 2. Choose one representative brand within each category! Piecemeal October 1st 2008 - October 1st 2009 Category Waste (kg) Sales (kg) Tomatoes Cucumbers Cauliflowers Broccoli Carrots Parsnips Iceberg lettuce Onions Leeks Rutabagas Celery root Cabbages Apples Pears Strawberries Kiwis 32 3. Choose one brand representative within each category Packaged October 1st 2008 - October 1st 2009 Category Waste (Number of packages) Sales (Number of packages) Tomatoes Apples Kiwis Onions I would be very grateful to receive the data by mail/email before X, the address is found below. If you have any questions, please contact me: Jenny Gustavsson, student SIK, Department of Sustainable Food Production X X X For questions concerning the cooperation with X, please contact: X X X X X Thank you for participating! Best regards, Jenny Gustavsson 33 APPENDIX B: Waste data for piecemeal sales, small retail stores TABLE A. The annual quantities of waste (W) and sales (S) stated in kilograms, as well as the computed store supply (SS) and waste proportion of store supply (W%). The data applied for the time period between October 1st 2008 and October 1st 2009. Annual waste (W), sales (S), store supply (SS) and waste proportion of store supply (W%) - (kilograms) Small retail stores Piecemeal A B¤ C D W S SS W% W S SS W% W S SS W% W S SS 222 15324 15546 1.4% 81 3800 3881 2.1% 185 12994 13179 1.4% 91 12321 12412 Apples 158 2167 2325 6.8% 55 790 845 6.5% 49 2318 2367 2.1% Broccoli 45 5651 5696 0.8% 6 1492 1498 0.4% 15 3026 3041 0.5% 64 4694 4758 Cabbages 55 7531 7586 0.7% 35 2169 2204 1.6% 53 9333 9386 Carrots 203 2695 2898 7.0% 48 960 1008 4.8% 78 1775 1853 4.2% 112 1524 1636 Cauliflowers 37 561 598 6.2% 4 220 224 1.8% 32 494 526 6.1% 66 355 421 Celery root 108 16757 16865 0.6% 63 5435 5498 1.1% 272 14712 14984 1.8% 249 13826 14075 Cucumbers 168 8600 8768 1.9% 96 2525 2621 3.7% 276 6686 6962 4.0% 309 8608 8917 Iceberg lettuce 105 6166 6271 1.7% 41 780 821 5.0% 34 1191 1225 2.8% Kiwis 61 3444 3505 1.7% 17 998 1015 1.7% 50 3051 3101 1.6% 137 2335 2472 Leeks 27 8459 8486 0.3% 9 3225 3234 0.3% 53 10339 10392 0.5% 114 6900 7014 Onions 17 877 894 1.9% 5 320 325 1.5% 89 1018 1107 8.0% 24 546 570 Parsnips 302 5949 6251 4.8% 22 860 882 2.5% 80 5159 5239 1.5% 151 4709 4860 Pears 54 1086 1140 4.7% 8 540 548 1.5% 43 892 935 4.6% 59 467 526 Rutabagas 150 6327 6477 2.3% 119# 600# 719 16.6% 15 2767 2782 Strawberries 235 10972 11207 2.1% 85 4500 4585 1.9% 269 9217 9486 2.8% 446 9069 9515 Tomatoes * Stated in numbers ¤Data applied for year 2008 # Stated in litres (The different entities of waste/sales did not affect the results of this study.) 34 W% 0.7% 1.3% 0.6% 6.8% 15.7% 1.8% 3.5% 5.5% 1.6% 4.2% 3.1% 11.2% 0.5% 4.7% APPENDIX C: Waste data for piecemeal sales, large retail stores TABLE B. The annual quantities of waste (W) and sales (S) stated in kilograms, as well as the computed store supply (SS) and waste proportion of store supply (W%). The data applied for the time period between October 1st 2008 and October 1st 2009. Piecemeal Apples Broccoli Cabbages Carrots Cauliflowers Celery root Cucumbers Iceberg lettuce Kiwis Leeks Onions Parsnips Pears Rutabagas Strawberries Tomatoes Annual waste (W), sales (S), store supply (SS) and waste proportion of store supply (W%) - (kilograms) Large retail stores E F G H W S SS W% W S SS W% W S SS W% W S SS W% 2650 1745721 1748371 0.2% 1235 39533 40768 3.0% 78 41583 41661 0.2% 154 24413 24567 0.6% 8754 9163 4.5% 138 4402 4540 3.0% 372 2172 2544 14.6% 409 85 37621 37706 0.2% 382 25781 26163 1.5% 103 22553 22656 0.5% 121 11450 11571 1.0% 134 5530 5664 2.4% 177 11767 11944 1.5% 317 16017 16334 1.9% 621 10466 11087 5.6% 588 12294 12882 4.6% 158 8205 8363 1.9% 120 8853 8973 1.3% 53 2427 2480 2.1% 169 2621 2790 6.1% 26 1934 1960 1.3% 25 1947 1972 1.3% 753 62678 63431 1.2% 510 90918 91428 0.6% 240 87000 87240 0.3% 318 46617 46935 0.7% 1150 510072 511222 0.2% 813 51647 52460 1.5% 230 47062 47292 0.5% 201 38436 38637 0.5% 210 1641 1851 11.3% 2728* 151717* 154445 1.8% 47 9180 9227 0.5% 340 12362 12702 2.7% 315 16100 16415 1.9% 55 11527 11582 0.5% 33 9837 9870 0.3% 134 45312 45446 0.3% 220 49750 49970 0.4% 64 36175 36239 0.2% 26 26212 26238 0.1% 167 4155 4322 3.9% 206 5456 5662 3.6% 100 4287 4387 2.3% 116 4166 4282 2.7% 839 646058 646897 0.1% 541 26671 27212 2.0% 345 20136 20481 1.7% 733 18051 18784 3.9% 107 6358 6465 1.7% 211 6590 6801 3.1% 507 6678 7185 7.1% 129 5340 5469 2.4% 38 1229 1267 3.0% 754 51013 51767 1.5% 1419 59229 60648 2.3% 463 48466 48929 0.9% 514 36380 36894 1.4% * Stated in numbers # Stated in litres (The different entities of waste/sales did not affect the results of this study.) 35 W 351 I S SS W% 84502 84853 0.4% 62 49 778 34 315 763 20510 8127 13027 2583 86737 59250 20572 8176 13805 2617 87052 60013 0.3% 0.6% 5.6% 1.3% 0.4% 1.3% 460 12 72 759 152 686# 1290 16151 31413 3677 21696 7340 41483# 60615 16611 31425 3749 22455 7492 42169 61905 2.8% 0.0% 1.9% 3.4% 2.0% 1.6% 2.1% APPENDIX D: Waste data for packaged sales TABLE C. The annual quantities of waste (W) and sales (S) stated in number of packages, as well as the computed store supply (SS) and waste proportion of store supply (W%). The data applied for the time period between October 1st 2008 and October 1st 2009. Annual waste (W), sales (S), store supply (SS) and waste proportion of store supply (W%) - (number of packages) Small retail stores Packaged A B¤ C D W S SS W% W S SS W% W S SS W% W S SS W% 2 166 168 1.2% 4 85 89 4.5% 87 2004 2091 4.2% Apples 466 6.0% 40 826 866 4.6% 8 1489 1497 0.5% 28 438 Kiwis 8 2431 2439 0.3% 18 435 453 4.0% 8 1011 1019 0.8% Onions 66 343 409 16.1% 22 656 678 3.2% 27 1069 1096 2.5% Tomatoes ¤ Data applied for year 2008 TABLE D. The annual quantities of waste (W) and sales (S) stated in number of packages, as well as the computed store supply (SS) and waste proportion of store supply (W%). The data applied for the time period between October 1st 2008 and October 1st 2009. Packaged W Apples Kiwis Onions Tomatoes 125 0 88 Annual waste (W), sales (S), store supply (SS) and waste proportion of store supply (W%) - (number of packages) Large retail stores E F G H I S SS W% W S SS W% W S SS W% W S SS W% W S SS 376 5832 6208 6.1% 158 6130 6288 2.5% 123 5647 5770 2.1% 91 2641 2732 5848 5973 2.1% 154 3411 3565 4.3% 73 7161 7234 1.0% 75 3209 3284 2.3% 170 8718 8888 5974 5974 0.0% 84 9115 9199 0.9% 2 4191 4193 0.0% 23 2889 2912 0.8% 146 20520 20666 7170 7258 1.2% 98 23918 24016 0.4% 294 11613 11907 2.5% 290 10513 10803 2.7% 168 8657 8825 (The different entities of waste/sales did not affect the results of this study.) 36 W% 3.3% 1.9% 0.7% 1.9% APPENDIX E: Mean waste and standard deviation TABLE E. The waste proportion of store supply stated by each participating retail store, as well as the standard deviation within each group of data. The mean waste proportion of store supply within stores A-I (all), A-D (small) and E-I (large) are also shown. The data applied for the time period between October 1st 2008 and October 1st 2009. Annual waste proportion of store supply (W%) for piecemeal and packaged sales, and the standard deviation (SD) within each group of data. Mean Mean Mean Retail Store Piecemeal SD A-I A-D E-I A B¤ C D E F G H I Apples Broccoli Cabbages Carrots Cauliflowers Celery root Cucumbers Iceberg lettuce Kiwis Leeks Onions Parsnips Pears Rutabagas Strawberries Tomatoes Overall Packaged Apples Kiwis Onions Tomatoes Overall 1.4% 6.8% 0.8% 0.7% 7.0% 6.2% 0.6% 1.9% 1.7% 1.7% 0.3% 1.9% 4.8% 4.7% 2.3% 2.1% 2.8% A 1.2% 6.0% 0.3% 16.1% 5.9% 2.1% 6.5% 0.4% 1.6% 4.8% 1.8% 1.1% 3.7% 5.0% 1.7% 0.3% 1.5% 2.5% 1.5% 1.4% 2.1% 0.5% 0.7% 0.2% 14.6% 0.2% 1.0% 5.6% 2.1% 1.2% 0.2% 11.3% 2.7% 0.3% 3.9% 0.1% 1.7% 3.0% 4.5% 1.3% 1.5% 0.6% 2.4% 4.2% 6.8% 4.6% 6.1% 15.7% 6.1% 1.8% 1.8% 0.6% 4.0% 3.5% 1.5% 2.8% 1.8% 1.6% 5.5% 1.9% 0.5% 1.6% 0.4% 8.0% 4.2% 3.6% 1.5% 3.1% 2.0% 4.6% 11.2% 3.1% 16.6% 0.5% 3.0% 1.9% 2.8% 4.7% 1.5% 2.3% 2.4% 3.9% 4.4% 3.1% 2.6% Retail store B¤ C D E F 4.5% 4.2% 6.1% 4.6% 0.5% 2.1% 4.3% 4.0% 0.8% 0.0% 0.9% 3.2% 2.5% 1.2% 0.4% 4.1% 2.0% 1.1% 2.9% ¤ Data applied for year 2008 0.2% 3.0% 0.5% 1.5% 1.9% 1.3% 0.3% 0.5% 0.5% 0.5% 0.2% 2.3% 1.7% 7.1% 0.6% 0.4% 0.01 0.05 0.3% 0.00 1.9% 0.6% 0.01 1.3% 5.6% 0.02 1.3% 1.3% 0.05 0.7% 0.4% 0.01 0.5% 1.3% 0.01 0.04 0.3% 2.8% 0.02 0.1% 0.0% 0.00 2.7% 1.9% 0.02 3.9% 3.4% 0.01 2.4% 2.0% 0.03 1.6% 0.07 0.9% 1.4% 2.1% 0.01 1.5% 1.4% 1.7% G 2.5% 1.0% 0.0% 2.5% 1.5% H 2.1% 2.3% 0.8% 2.7% 2.0% I 3.3% 1.9% 0.7% 1.9% 2.0% SD 0.02 0.02 0.01 0.05 1.1% 6.3% 0.7% 1.3% 4.7% 4.6% 0.9% 1.9% 3.8% 2.1% 0.4% 3.3% 2.6% 4.2% 4.8% 2.2% 1.4% 5.1% 0.8% 1.0% 5.7% 7.4% 1.3% 1.9% 3.1% 2.6% 0.7% 3.9% 3.0% 5.5% 6.5% 2.9% Mean Mean Mean A-I A-D E-I 3.4% 2.8% 0.9% 3.8% 3.3% 3.7% 1.7% 7.3% (The different entities of waste/sales did not affect the results of this study.) 37 0.9% 7.4% 0.6% 1.5% 3.8% 2.4% 0.6% 0.8% 4.5% 1.6% 0.2% 2.9% 2.2% 3.2% 2.3% 1.6% 3.5% 2.3% 0.5% 1.7% APPENDIX F: Climate change impact TABLE F. Waste proportion of store supply (W%), computed retail waste factor, climate change impact of production (both excluding and including retail waste) and the climate change impact of retail waste. The functional unit of production equalled one kg/plant produced. Horticultural product Kiwis Cucumbers Broccoli Tomatoes Strawberries Poinsettias Kalanchoës Cauliflowers Parsnips Cabbages Iceberg lettuce Celery root Leeks Carrots Rutabagas Onions Pears Apples Retail W% waste factor 0.04 0.01 0.06 0.02 0.05 0.03 0.03 0.05 0.03 0.01 0.02 0.05 0.02 0.01 0.04 0.00 0.03 0.01 1.04 1.01 1.07 1.02 1.05 1.03 1.03 1.05 1.03 1.01 1.02 1.05 1.02 1.01 1.04 1.00 1.03 1.01 Climate change impact of production (kg CO2-eq/kg/plant) Excluding retail waste 1.36 1.15 0.78 0.72 0.58 0.58 0.44 0.36 0.36 0.36 0.35 0.29 0.25 0.24 0.18 0.14 0.12 0.10 38 Including Impact of retail waste retail waste 1.41 1.16 0.83 0.74 0.61 0.59 0.45 0.38 0.37 0.36 0.36 0.30 0.26 0.24 0.19 0.14 0.12 0.10 0.054 0.011 0.052 0.016 0.029 0.015 0.014 0.018 0.012 0.002 0.007 0.014 0.005 0.003 0.008 0.001 0.003 0.001