Food waste quantities, carbon footprint and nutrient loss in university students' households in Sweden

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Food waste quantities, carbon footprint and nutrient loss in
university students' households in Sweden
Christopher Malefors, Amanda Sjölund, Niina Sundin
PII: S2352-5509(25)00017-X
DOI: https://doi.org/10.1016/j.spc.2025.01.017
Reference: SPC 1918
To appear in:
Received date: 20 November 2024
Revised date: 21 January 2025
Accepted date: 24 January 2025
Please cite this article as: C. Malefors, A. Sjölund and N. Sundin, Food waste quantities,
carbon footprint and nutrient loss in university students' households in Sweden, (2024),
https://doi.org/10.1016/j.spc.2025.01.017
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Food waste quantities, carbon footprint and nutrient loss in
university students’ households in Sweden
Christopher Malefors*, a, Amanda Sjölunda, Niina Sundina
aDepartment of Energy and Technology, Swedish University of Agricultural Sciences, Box 7032, Uppsala SE-75007
Uppsala, Sweden
Abstract
Food waste in households poses a significant barrier to achieving sustainable food systems. This study
examines food waste generated by university student households in Sweden, focusing on its weight,
carbon footprint, and nutritional impacts. Using kitchen diaries, 109 students quantified their waste by
weight over two weeks. On average, 115 g/person/day of food was wasted, with 46 g/person/day
classified as avoidable or edible. Avoidable waste generated a carbon footprint of 1.3 kg CO₂e/kg food
waste and contained key nutrients, such as dietary fiber (4.7 g/MJ) and folate (56 µg/MJ). Notably, the
top 10% of waste items accounted for 47% of total waste and 62% of the carbon footprint. Reducing
waste from this fraction by half could achieve a 23.7% reduction in total waste. When scaled to the
national level, food waste from university students in Sweden is estimated to generate 9,950 tonnes of
CO₂e annually. The findings highlight the importance of targeting both high-carbon-impact and nutrient-
rich waste to align with environmental and public health objectives. Educational interventions and
automated waste tracking are recommended to foster sustainable consumption patterns.
Keywords: Sustainable food systems, Life Cycle Assessment, Nutrition
Abstract
Food waste in households poses a significant barrier to achieving sustainable food systems. This study
examines food waste generated by university student households in Sweden, focusing on its weight,
carbon footprint, and nutritional impacts. Using kitchen diaries, 109 students quantified their waste by
weight over two weeks. On average, 115 g/person/day of food was wasted, with 46 g/person/day
classified as avoidable or edible. Avoidable waste generated a carbon footprint of 1.3 kg CO₂e/kg food
waste and contained key nutrients, such as dietary fiber (4.7 g/MJ) and folate (56 µg/MJ). Notably, the
top 10% of waste items accounted for 47% of total waste and 62% of the carbon footprint. Reducing
waste from this fraction by half could achieve a 23.7% reduction in total waste. When scaled to the
national level, food waste from university students in Sweden is estimated to generate 9,950 tonnes of
CO₂e annually. The findings highlight the importance of targeting both high-carbon-impact and nutrient-
rich waste to align with environmental and public health objectives. Educational interventions and
automated waste tracking are recommended to foster sustainable consumption patterns.
* Corresponding Author: christopher.malefors@slu.se

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1 Introduction
For several decades, the global demand and consumption of goods has reached well beyond the supply
capacity of the planet. This imbalance has resulted in Earth systems being put under major stress, which
will intensify if actions are not taken (Richardson et al., 2023).While some challenges, such as limited
resource availability, are related to supply constraints, the majority stem from the Earth's limited
assimilatory capacity for waste and pollutants. These include issues like climate change, ozone
depletion, acidification, and nutrient pollution, which result from the inability of natural systems to
effectively process human-generated activities and waste flows (Rockström et al., 2009).
Globally, the food system constitutes a major contributor to the environmental problems caused by
humanity (Crippa et al., 2021; Foley et al., 2011). One of the three key strategies for keeping the global
food system within planetary boundaries is the reduction of food waste (Springmann et al., 2018; Willett
et al., 2019). Reducing food waste is also a target of the United Nations Sustainable Development Goals
(SDGs), which aim to halve food waste by 2030, and it has been emphasized as a critical component in
meeting the commitments of the Paris Agreement (United Nations, 2015; You et al., 2022).
An impediment to advancing the efforts required to reach the SDGs is the lack of comprehensive
assessments that consider factors that impact the path of sustainable development within the food
system in different ways (Fanzo et al., 2021; Wu et al., 2024). For example, not all types of food waste
have the same environmental impact, with food waste from animal-based products having a higher
environmental footprint compared to plant-based items like fruits and vegetables (Brancoli et al., 2017).
However, since the mass of fruit and vegetable waste tends to be greater (Jansson-Boyd et al., 2024),
accurate quantification in terms of mass but also in terms of waste components is crucial to assess the
overall environmental impact (Amicarelli and Bux, 2021). This raises a critical question: should waste
reduction strategies focus on mass, carbon footprint, or other environmental metrics? Alternatively,
should considerations such as nutritional value be given higher priority given its direct implications on
food security and human health? Research has shown that food waste often includes nutrient-dense
foods, such as fruits, vegetables, and animal-based products, which are critical for addressing nutrient
deficiencies in poor and wealthy nations alike (Global Panel, 2018; Spiker et al., 2017) However, despite
this recognition, studies focusing specifically on nutrient losses associated with food waste remain
limited. Thus, to answer the aforementioned questions, comprehensive assessments of food waste are
required where not only quantities are considered but also the composition of food waste allowing the
assessment of aspects such as nutrient losses and environmental footprints (Gatto and Chepeliev, 2024).
Current global estimates indicate that households generate the majority of food waste (United
Nations Environment Programme, 2021). Consequently, households play a major role in driving the
environmental impacts of food waste, including the generation of a substantial amount of greenhouse
gas emissions (Zhu et al., 2023). When it comes to household food waste, there is a significant gap in
the data needed to fully understand the underlying causes and patterns, particularly in high-income
countries as highlighted by Krah et al. (2024). These gaps include insufficient insights into socioeconomic
influences, consumer behaviors, and regional variations in waste management practices
Although previous estimates have suggested that there is a discrepancy between high- and low-
income countries in how much of the food waste comes from households (FAO, 2011), recent
assessments contradict this, suggesting that similar amounts of food get wasted across all countries,
regardless of income level (United Nations Environment Programme, 2021). However, despite there
being little difference between countries, the differences within a country and between different socio-
demographic groups can vary greatly. It has been found that within a country, the income level of a
household can influence the amount of food wasted. However, findings diverge on whether higher or

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lower income leads to greater food waste. Some studies suggest that lower income households waste
more (Bilska et al., 2024; Setti et al., 2016). Meanwhile, others have found an opposite correlation
(Abdelradi, 2018; Van Dooren et al., 2019). There have also been studies that have found no correlation
at all (Ananda et al., 2021; Williams et al., 2012). It has also been found that the number of household
members can have an impact on the amount of food wasted where single-person households tend to
generate more food waste per person than multiple-person households (Bilska et al., 2024; Parizeau et
al., 2015). The age of household members may be yet another factor influencing the food waste where
younger age groups appear to waste more than older age groups (Karunasena et al., 2021; Secondi et
al., 2015; Thyberg and Tonjes, 2016).
A specific type of household where members are of a (mostly) young age, have a lower than average
income, and live alone is university students. In addition to the socio-demographical indications that
students would waste more food than others, students also constitute a group of people that will form
future society, which makes them an important group to study. Gaining insights into their behaviour and
impact could help foster sustainable habits and routines early in life. Consequently, a fair share of
research has been conducted on food waste among students. However, a majority of those studies have
been carried out in environments such as school cafeterias, while little seems to be known about food
waste in the home environment (Zhang and Jian, 2024). A research gap therefore persists in
understanding the amount and composition of food waste generated by student households,
particularly in the context of a comprehensive sustainability assessment that incorporates multiple
factors relevant to food system sustainability.
The aim of this study is therefore to examine the food waste generated by university students in
Sweden in their homes and to assess the related carbon footprint and nutritional impact within this
demographic. The study also discusses the potential for reducing food waste, identifies areas for
improvements, and evaluates how these changes could influence both carbon footprint and nutritional
outcomes.
2 Material and methods
2.1 Area of study and description of data collection
This study examined food waste in the households of students enrolled in the master’s course “Food
Waste – Current Situation and Future Opportunities” at the Swedish University of Agricultural Sciences.
The course is part of the Sustainable Food Systems master’s program and has been offered in the
autumn semester since 2020.
As part of the course, students were asked to quantify their own food waste over two weeks, using
kitchen scales. The aim was to give them hands-on experience in food waste quantification and
demonstrate how this data can offer insights. Each student worked individually, compiling their findings
into a report. The method involved weighing their food waste and comparing it to a self-chosen
reference point, such as the amount of food wasted per person per day in the household, the
percentage of wasted cooked items, or similar metrics. The details of the assignment are outlined in the
supplementary material (S1). It should be noted that university students in Sweden typically live off-
campus, are responsible for their own meals, and do not dine in dedicated university cafeterias.
In total, 109 students completed the task across 5 course sessions. Although this might represent a
relatively small sample, all students submitted reports and detailed raw data files with weights and food
item information which are substantially better than using questionaries or other survey methods
(Merian et al., 2024). All student reports and raw data files submitted for the assignment were collected

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from the university’s official archive, which is publicly accessible under the Swedish Transparency Act,
and were compiled for analysis.
2.2 Student households and how they quantify food waste
To understand how students quantified their waste and what they focused on during the project, each
report was analysed based on the following criteria: the chosen reference point, whether they
categorized food waste as avoidable/unavoidable or edible/inedible, and whether they quantified solid
waste, liquid waste, or both. Additional information was documented, including the values recorded for
their main reference point (i.e., food waste g/person/day), the number of participants involved, the
duration of the quantification period, the number of days waste was recorded, and the aggregation level
of the study (i.e., if waste was recorded per day or per item thrown away). An overview of what the
students chose to focus on during their quantification period is provided in Table 1.
Table 1. Summary of student’s focus areas for food waste quantification during the assignment, including the number of
students who quantified the state of the food waste, the reference point used, avoidability/edibility classifications,
aggregation levels chosen, and the number of participants per household.
State of the food waste quantified
Students (n)
Solid waste only
94
Solid and liquid waste
13
Liquid waste only
1
Main reference point
Students (n)
g/person/day
103
g/household/day
2
g/serving/day
1
kg/person/year
1
l/person/week
1
Percentages
1
Waste classification (Avoidability/Edibility)
Students (n)
Unavoidable/Avoidable
49
Edible/Inedible
16
Edible only
2
Avoidable/possibly avoidable
1
Aggregation level
Students (n)
Per item
85
Aggregated per day
20
Per waste bag
2
Per meal
1
Number of household participants
Answers (n)
1
56
2
29
3
11
4
6
5
6
6
1
Most students (87%) submitted their raw data in the form of kitchen diaries. In total, the kitchen diaries
captured 3944 observations from 95 students. Of these, 193 observations were aggregated by day,
while the rest were recorded at the food item level. The data was standardized to include the following

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details: the date the food was discarded, the household name, whether the waste was solid or liquid,
the weight of the waste (in grams) and, when available, the type of food, whether the waste was
classified as avoidable/unavoidable or edible/inedible, the weight of the food before preparation or
cooking, and any additional comments from the student.
If students explicitly stated whether their waste was solid or liquid in their reports, their own
categorization was used. When this information was not provided, the state of the food waste was
inferred based on the food item name. Additionally, a thorough cleaning, standardization, and
categorization of food item names was conducted to improve data accuracy and ease of analysis. The
categorization of food items was guided by their names and by previous studies (Adelodun et al., 2021;
Bilska et al., 2024; Ilakovac et al., 2020; Khalid et al., 2019; Mayanti, 2024; Sigala et al., 2024; Van
Dooren et al., 2019).
Following the cleaning and categorization process, a total of 236 unique food items were identified
as discarded by the students, categorized into 15 distinct groups. Table 2 presents and overview of these
categories, the food items within each category, and their corresponding definitions.
Table 2. Summary of the waste categories, including the number of food items in each category and a definition of what
each category includes. The table is sorted based on the number of food items.
Waste category
Number of food items
Definition
Vegetables
1213
Fresh, cooked, or processed vegetables such as leafy greens, root
vegetables, potatoes and legumes
Fruits
912
Fresh, dried, or processed fruits like apples, bananas, berries, citrus, and
other similar items.
Beverages
690
Refers to any liquid consumables, including coffee, tea, juice, soft drinks,
alcoholic beverages, and other drinkable liquids. Solids such as coffee
grounds and tea leaves are also included in this category.
Eggs
275
Mostly eggshells.
Unknown
245
Category for food items that could not be identified or were ambiguously
labelled in the data (aggregated daily data).
Plate waste
109
Refers to any leftover food that was served but not consumed by the
household members, regardless of the type of food.
Dairy
108
Encompasses milk, cheese, yogurt, butter, and other dairy-based
products.
Bakery
104
Bread, pastries, cakes, cookies, and other baked goods made from flour.
Meat
89
Consists of all types of animal meat, including beef, pork, chicken, lamb,
and processed meats like sausages or cold cuts.
Grain-based
80
Refers to foods such as pasta, rice, cereals, and grains, along with
processed items like chips or tortillas.
Other
50
A catch-all category for food items that do not fit into any of the defined
categories above.
Nuts
24
Includes whole nuts (shelled or unshelled)
Sauce
20
Consists of any liquid flavourings such as ketchup, mayonnaise, salad
dressings, and cooking sauces.
Fish & seafood
17
Seafood such as fish, shrimp, crabs, clams, and other edible sea creatures.
Dessert/Sweets
8
Includes candies, chocolates, ice cream and sweet pastries.

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2.3 Amount of food waste in Student Households
To assess the levels of food waste in student households, the data from their written assignments was
compiled into descriptive statistics. This analysis included both students who quantified only solid waste
and those who quantified both solid and liquid waste. To ensure a fair comparison, only students who
used the reference point “food waste (g/person/day)” were included, while those who quantified only
edible food or included drinking water used in cooking as food waste were excluded. After applying
these criteria, nine households were excluded from the assessment. A 95% confidence interval for the
mean total food waste was calculated for households that quantified only solid waste and those that
included both solids and liquids. Boxplots were used to visually compare the differences between
households that quantified only solid waste and those that included both solid and liquid waste. These
descriptive statistics included days where no food was wasted.
The kitchen diaries were analyzed to determine the frequency and weight of food waste, as well as
the proportions classified as solid or liquid. The top 10% of food waste items by weight were examined
to determine their composition, the total weight they represented, and the number of households
contributing to this waste. A scenario was then developed in which this waste fraction was reduced by
half, in alignment with the goal set by SDG12.3.
Additionally, the diaries provided insights into how much waste was classified as
avoidable/unavoidable or edible/inedible, and how many items were not classified. When students
recorded the mass of food items, this data was used to calculate the food waste percentage (%) by
weight for different categories and individual items. Since some students recorded dry weights for food
items and wet weights for food waste, a filter was applied to exclude cases where the food waste
percentage exceeded 100%. After this filter was applied, the dataset contained 794 records of food
items and their corresponding waste weights from 28 households. To better understand the distribution
of food waste across categories, the five most wasted food items were ranked by weight and frequency
for each category: avoidable/edible, unavoidable/inedible, and unclassified. This analysis highlighted the
food items that contributed most to waste in each category, based on both food waste in grams per
person per day (g/person/day) and as a percentage of the total waste.
To analyse the amount of food waste that could have been avoided, the quantification data from the
students who included information on the state of the food (avoidable/unavoidable etc.) was assessed.
Anything, liquid or solid, that was categorised as either avoidable, possibly avoidable, edible or unclear
was included, although one item specified as “paper towel” was removed. In total, 42 households (74
participants) provided data with at least one item categorised according to the inclusion criteria. To
assess the average amount of avoidable/edible food that was wasted per person/day, the total number
of participants who contributed to this data was multiplied by the total number of quantification days
from those households (ndays=611), which also included days when no avoidable/edible food was
wasted. In this way, the number of total participant days could be derived, which could then be divided
by the total amount of avoidable/edible food waste.
2.4 Carbon footprint of food waste in student households
Data on the carbon footprint of the wasted food was obtained using the SAFAD tool (Swedish University
of Agricultural Sciences, 2024), with the Swedish market being a consideration. Since landfilling is
prohibited in Sweden and food waste is instead treated through anaerobic digestion or composting, the
downstream emissions of the waste management were considered negligible and therefore excluded
from the scope of the study. Only food waste that was categorized as avoidable/edible by the students
was assessed (nitems=672). If a food waste item could not be found in the SAFAD tool, the carbon

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footprint of the food determined as being most similar to the item was used. When the food waste item
had been categorised with a lower level of detail, such as fish, cheese, or pizza, where a specific carbon
footprint could not be found, an average was derived from other sub-categorised food items.
When an item had a different carbon footprint when cooked than as a raw commodity, student’s
comments were used to select the most appropriate alternative. For instance, when a comment stated
that it was leftovers, it was assumed that the item was cooked. Similarly, when it was unclear what the
food item referred to (for example, when it only was labelled meat), students’ comments were also used
to select an appropriate carbon footprint alternative in the SAFAD tool. Food items categorized as
mixed, and kitchen sink strainer were assumed to be composed of the other food waste categorised and
were therefore assigned the average carbon footprint of the other food items. The items classified as
vegetables were assigned the average carbon footprint of the other specified vegetable items. The food
waste that had been classified as plate waste by the students was assigned a carbon footprint of 1kg
CO2e per kg food, based on Sundin et al. (2024). For items classified as leftovers, the comments
sometimes specified what they were comprised of, so they were therefore assigned the corresponding
carbon footprint suggested in the SAFAD tool. The items without specification were assigned the same
carbon footprint as the plate waste items, from which an average for the leftover items could be drawn
to apply to the whole category.
To illustrate how each food category contributed to the carbon footprint, their relative contribution
(percentage) was compared against their relative contribution in terms of weight. Moreover, similar to
the analysis of the amount of food waste, the top 10% of food waste items in terms of carbon footprint
(n=68) were examined to determine their composition and contribution to the total carbon footprint.
From this 10%, the top 10 single items were also analysed separately to examine this top tier more in-
depth.
To set the results of the carbon footprint in a larger context, the results were scaled up to national
level. According to official Swedish statistics, around 450 000 students are enrolled at Swedish
universities (Statistics Sweden, 2023). This number of students was therefore multiplied with the
average carbon footprint per student per day as well as with 365 to get the annual carbon footprint
from university students in Sweden. To obtain the average carbon footprint per student per day, the
total value of carbon footprint was divided with the total number of person-days (1068).
2.5 Nutrient loss calculations
Nutrient calculations were performed using Nutrition Data (2024) software to estimate the nutrient
losses within the avoidable/edible fraction of food waste. The analysis included energy content,
macronutrients, micronutrients, and dietary fiber. These values were calculated for the entire data
collection period and then expressed as mean values per kilogram of avoidable/edible food waste and
per person per day. This was done by dividing the total nutrient values by the total amount of
avoidable/edible waste (48.4 kg) and by the total person-days (1068), respectively.
Additionally, the macronutrient content was expressed as energy percentage (E%) values, while the
micronutrient content was presented as nutrient density (per MJ). To calculate this, the mean nutrient
values per kilogram of avoidable/edible food waste were divided by the mean energy content per
kilogram of edible food waste (223 MJ). The number of wasted nutrient days (WND), representing the
days during which the avoidable/edible waste could meet the daily recommended intake (RI) for adults
on a group level, was also calculated. This was done by dividing the total micronutrient values by the RI
values for males and females aged between 25-50 with average physical activity levels, using the larger

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value when differences occurred, based on the Nordic Nutrition Recommendations, 2023 (Blomhoff et
al., 2023)
3 Results
The results show that, on average, the students in this study generated 115g of food waste per person
per day (95% CI: 103-128 g) when combining households that quantified both solid waste and solid with
liquid waste. Of this food waste, 46 g/person/day was considered to be avoidable/edible. This fraction
was found to generate a carbon footprint of 1.3kg CO2e per kg. The analysis also revealed substantial
micronutrient losses, as the avoidable/edible waste fraction was notably rich in dietary fiber (4.7 g/MJ),
vitamin A (134 RE/MJ), vitamin C (26 mg/MJ), and folate (56 µg/MJ), exceeding the recommended
nutrient density for dietary planning (Blomhoff et al., 2023).Household food waste in terms of weight
Households that included liquids in their quantification reported a median of 145 g/person/day, while
those that measured only solid waste reported a lower median of 101 g/person/day. On average,
households that measured both solid and liquid waste reported 158 g/person/day, compared to 110
g/person/day for those quantifying only solid waste. Figure 1A illustrates this comparison between
households that included liquids and those that focused solely on solid food waste.
The average weight of individual wasted items was 53g (95% CI: 50-56 g), with a median weight of
26g. Figure 1B shows the distribution of wasted item weights. Solid items made up 97% of all the
recorded items. Among the solid items (n=3528) that were classified as either avoidable/edible or
unavoidable/inedible, 39% (by weight) were considered avoidable/edible, while 61% (by weight) were
considered unavoidable/inedible by the students. This breakdown is shown in Figure 1C, which also
indicates the number of items that were not classified by the students. Figure 1D presents the same
information for liquid waste (n=114), where, notably, none of the liquids were classified as
unavoidable/inedible by the students.

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Figure 1. Summary of the distribution and classification of reported food waste. (A) shows the distribution of food waste
(g/person/day) for households that quantified both solid and liquid waste, compared to those that recorded solid waste
only. (B) displays a histogram of the distribution of individual food waste items (g) recorded by the students in their
kitchen diaries, capped at 500g. (C-D) shows the proportions of food waste categorized as ■ Avoidable/Edible, ■
Unavoidable/Inedible, or ■ Unclassified for solid waste (C) and liquid waste (D).
Figure 1B shows a long tail in the distribution, indicating that a small number of items account for a
significant portion of the total food waste by weight. Analyzing the top 10% of the wasted items (by
weight) revealed that they account for nearly half (47%) of the total food waste and that 58 households
contributed to this waste. Reducing this waste by half would lead to an overall reduction of food waste
by weight of 23.7%. Table 3 lists the key food items that dominate the top 10% across the categories of
avoidable/edible, unavoidable/inedible, and unclassified.
Table 3. Top 10 most wasted food items divided into Avoidable/Edible, Unavoidable/Inedible and Unclassified by the
students. For each item, the category, percentage of total food waste by weight, and the number of observations (n) are
provided.
Avoidable/Edible
Food item
Category
% of total weight
Observations (n)
Plate waste
Plate waste
2.9
11
Beans
Vegetable waste
1.7
5
Bread
Bakery waste
1.7
8

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Cucumber
Vegetable waste
1.7
4
Noodles
Grain-based waste
1.6
1
Potato
Vegetable waste
1.5
6
Soup
Plate waste
1.5
2
Aubergine
Vegetable waste
1.4
2
Rice
Grain-based waste
1.2
4
Soft drink
Beverage waste
1.1
1
Unavoidable/Inedible
Food item
Category
% of total weight
Observations (n)
Coffee
Beverage waste
6
35
Corn
Vegetable waste
3.6
12
Melon
Fruit waste
2.1
7
Elderberries
Fruit waste
2.0
1
Watermelon
Fruit waste
1.1
3
Chicken
Meat waste
1
5
Tomato
Vegetable waste
0.8
1
Banana
Fruit waste
0.7
5
Pumpkin
Vegetable waste
0.7
2
Dhal
Plate waste
0.6
1
Unclassified
Food item
Category
% of total weight
Observations (n)
Pineapple
Fruit waste
4.2
3
Coffee
Beverage waste
3.9
20
Watermelon
Fruit waste
3.8
13
Potato
Vegetable waste
3.1
12
Cauliflower
Vegetable waste
1.9
5
Chicken
Meat waste
1.8
7
Banana
Fruit waste
1.7
10
Mixed
Other
1.6
6
Lamb
Meat waste
1.5
1
Mushroom
Vegetable waste
1.3
2
The majority of food waste classified as avoidable/edible by the students came from plate waste, with
11 observations contributing 2.9% of the total recorded weight. In some cases, a single observation had
a significant impact on the overall waste, such as noodles and a single instance of soft drink waste. The
second most wasted item in this category was bread, which made up 1.7% of the total weight.
In the unavoidable/inedible category, coffee waste and corn were the largest contributors to the
total waste. Coffee waste primarily consisted of leftover coffee grounds from filter coffee, and in many
cases, it was recorded as wet weight. A few individual observations also had a significant impact on the
total waste, such as one instance of elderberry waste, which accounted for 2% of the total, along with
notable contributions from tomato and dhal waste.
In the unclassified category, pineapple was the largest contributor, accounting for 4.2% of the total
weight, followed by coffee at 3.9%. Coffee also appeared in the unavoidable/inedible category. Other
items, such as watermelon, chicken, and banana, also overlapped with the unavoidable/inedible
category.
An analysis of the food waste, in which the initial mass of the food item had also been recorded,
showed that 19% of total food (95% CI: 18-20%) was wasted. When categorizing the wasted items based
on both indicators, food waste (g/person/day) and food waste (%), plate waste emerged as the largest
contributor, with a median of 36 g/person/day or 28%, as illustrated in Figure 2. Most observations were

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based on the reference point of food waste (g/person/day), where different types of animal-based foods
ranked second and third, with median values of 26 g/person/day for meat waste and 32 g/person/day
for fish waste. Vegetables, fruits and beverages were the most commonly discarded food items, with
1197, 909, and 672 observations, respectively, showing widespread reported waste values. The
distribution of waste across the different is illustrated in Figure 2.
Figure 2. Boxplots illustrating two food waste indicators across different waste categories. (A) shows food waste in grams
per person per day, and (B) shows food waste as a percentage (%). Each boxplot represents the distribution of waste
values within each category.

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Examining the food items within each waste category for different reference points reveals that the
most commonly wasted item classified as avoidable/edible by the students was bread, based on the
food waste (g/person/day) reference point, and apples, based on the food waste (%) reference point.
Bread is also the second most wasted item in the food waste (%) reference point, with 17% of its total
weight being wasted across 4 observations. Apples rank as the third most wasted item by food waste
(g/person/day), while plate waste ranks fourth in the same category and third in the food waste (%)
reference point. Table 4 provides descriptive statistics on the five most commonly wasted items across
different reference points and categories of avoidability/edibility. It also shows that coffee and tea are
frequently discarded and considered by students to be unavoidable/inedible, followed by eggs, onions,
and bananas (peels and shells). In the unclassified category, several items overlap with the
unavoidable/inedible category, including tea, onions, coffee, bananas, and eggs.
Table 4. Descriptive statistics for the five most wasted food items, classified as avoidable/edible, unavoidable/inedible or
unclassified, based on two reference points: Food waste (g/person/day) and Food waste (%).
Avoidable/Edible
Reference point
Food item
Observations (n)
Min
Mean
SD
Median
Max
Food waste (g/person/day)
Bread
48
0
31
51
10
221
Carrot
33
0
12
12
8
48
Apple
32
2
15
18
10
97
Plate waste
29
2
51
41
46
141
Cucumber
26
1
32
77
6
386
Food waste (%)
Apple
7
4
22
16
23
50
Bread
4
1
17
16
17
33
Plate waste
4
26
57
30
59
84
Potato
4
3
16
10
17
25
Broccoli
3
6
14
14
7
30
Unavoidable/Inedible
Reference point
Food item
Observations (n)
Min
Mean
SD
Median
Max
Food waste (g/person/day)
Coffee
207
5
37
23
33
120
Tea
201
0
12
10
9
71
Egg
190
1
11
14
8
181
Banana
141
6
39
23
32
131
Onion
129
1
12
21
8
195
Food waste (%)
Egg
54
4
13
4
12
20
Onion
29
3
13
7
12
27
Apple
27
3
13
5
12
25
Banana
25
20
36
8
36
50
Carrot
18
1
10
9
6
28
Unclassified
Reference point
Food item
Observations (n)
Min
Mean
SD
Median
Max
Food waste (g/person/day)
Tea
123
1
11
9
9
46
Onion
114
0
16
23
8
139
Coffee
112
7
53
45
38
324
Banana
101
5
43
27
35
130
Egg
84
2
18
15
14
114
Food waste (%)
Banana
50
20
36
11
35
91

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Onion
48
2
15
15
11
76
Carrot
34
1
15
7
15
34
Apple
31
4
18
11
15
55
Egg
29
9
15
6
13
39
3.1 Food waste in terms of carbon footprint
The assessment of the carbon footprint of the 48.4kg food waste that was categorized as
avoidable/edible revealed a total emission of 64.7 kg CO2e. This means that for each kg of wasted
avoidable/edible food, there is an associated carbon footprint of approximately 1.3 kg CO2e. The
average carbon footprint per student per day was 0.06 kg CO2e. When scaling this up to national level,
including all students enrolled in Swedish universities, this equals 9,950 tonnes CO2e per year.
Although the category with the most wasted food in terms of weight was vegetables (38%), the
category with the highest carbon footprint was dairy waste (21%), which only accounted for 7% of the
weight. The category with the relatively largest contribution discrepancy between weight and carbon
footprint was meat, which contributed almost 7 times more to carbon footprint than to weight. This was
followed by the fish and dairy categories, which contributed approximately 5 and 3 times more to
carbon footprint than to weight, respectively. Beverage waste, however, was shown to contribute more
than three times less to carbon footprint compared to weight. The relative contribution of each food
waste category to the total weight and carbon footprint is illustrated in

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Figure 3.

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Figure 3. The relative contribution of each food category in terms of weight (left panel) and carbon footprint (right
panel), based on the food waste categorized as avoidable/edible.
Moreover, it was found that the top 10% of items that contributed most to the carbon footprint
accounted for 62% of the total carbon footprint. Of this 62%, the categories that contributed most were
dairy (32%), plate waste (20%), and meat (15%). Vegetable waste, despite being the second most
frequently occurring category, only accounted for 11% of the carbon footprint. The 10 individual food
items contributing most to the overall carbon footprint accounted for 24% in total and are listed in Table
5.
Table 5. Summary of the 10 individual items with the highest carbon footprint. For each item, the corresponding food
category, weight, carbon footprint, and contribution to the total carbon footprint (%) are provided. The food items
cheese in the table refers to semi-hard cheese made from whole milk.
Food item
Category
Weight (g)
Carbon footprint (kg CO2e)
% Of total carbon footprint
Cheese
Dairy waste
278
2.61
4.0
Meat
Meat waste
74
2.33
3.6
Cream
Dairy waste
450
1.69
2.6
Cheese
Dairy waste
170
1.60
2.5
Gyros plate
Plate waste
132
1.38
2.1
Hamburger
Plate waste
54
1.23
1.9

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Noodles
Grain based waste
1469
1.22
1.9
Cottage cheese
Dairy waste
324
1.19
1.8
Creme fraiche
Dairy waste
300
1.14
1.8
Cheese
Dairy waste
118
1.11
1.7
3.2 Nutrient losses
The assessment of nutrient losses in avoidable/edible food waste revealed an energy content of 5 MJ
per kg of waste, or 0.2 MJ per person per day. Per kg of waste, the protein, carbohydrate, and fat
content were 38g, 145g, and 35g, respectively (Table A1). Additionally, the avoidable/edible waste
fraction contained 14% of energy (E%) from protein, 57 E% from carbohydrates, and 29 E% from fat,
indicating a balanced macronutrient composition in line with dietary recommendations (Table A1).
Furthermore, the analysis showed significant nutrient losses, as the avoidable/edible waste was
particularly rich in micronutrients. Notably, the waste exceeded the recommended nutrient density for
dietary planning (Blomhoff et al., 2023) in terms of dietary fiber (4.7 g/MJ), vitamin A (134 RE/MJ),
vitamin C (26 mg/MJ), and folate (56 µg/MJ). The evaluation of the WND indicated that the total
avoidable/edible waste could have met the daily micronutrient needs of 9 to 76 adults, depending on
the specific micronutrient (Table S2). Specifically, the WND values were 30 for dietary fiber, 37 for
vitamin A, 52 for vitamin C, and 38 for folate.
4 Discussion
The results show that the students wasted 158 g/person/day, which is lower than the Swedish national
average of 203 g/person/day (Swedish Environmental Protection Agency, 2024). Both figures include the
quantification of both solid and liquid waste. When focusing solely on solid waste, students report an
average of 110 g/person/day, compared to 153 g/person/day reported by the Swedish Environmental
Protection Agency. It is important to note that the method students used to capture their food waste
differs from that used by the Swedish Environmental Protection Agency for solid waste. The agency
relies on data from the organic fraction collected by garbage trucks, which is then scaled to a national
level. However, for liquid food waste, the agency also used a kitchen diary approach (Åkerblom, 2021)
similar to the method employed by the students. Despite these differences in methodology, there was
considerable variation in the waste reported by the students. Some households in the study reported
waste amounts well above both the 153 g/person/day for solid waste and the 203 g/person/day
national average. However, the proportion of solid food waste classified as avoidable/edible by the
students aligns closely with official Swedish figures (23% compared to 27%) and is consistent with
findings from other studies. For example, Hanssen et al. (2016) reported that roughly 37% of food waste
in Norway is classified as edible and 30% is avoidable in Greece (Abeliotis et al., 2015). It is possible that
the students’ reported avoidable/edible waste is actually higher, as a significant portion of the items
they recorded were unclassified. Also, the amount of liquid food waste generated across all households
is unknown as only 14 students included this fraction in their quantification. Although there are
indications that liquid food waste may reach high levels, it is a fraction that is commonly overlooked,
highlighting a need to include liquids in food waste quantification (Malefors et al., 2024a; Van Dooren et
al., 2019).
Major limitations in most food waste studies that try to quantify food waste are bound to occur
when there is no set standard of how food waste should be quantified, resulting in difficulties to, for
example, compare different studies against each other (Baquero et al., 2023; Withanage et al., 2021;

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Xue et al., 2017). Diaries where households self-report on their food waste have been found to be more
reliable than, for example, questionnaires where food waste is estimated retrospectively based on
participants memory (Van Herpen et al., 2019). However, considering that the use of diaries means that
participants are constantly reminded of their food waste (assuming that they are reporting their waste
levels accurately), there is a risk of behavioural changes leading to less food waste being generated
during the course of the study (Merian et al., 2024). In this case, because the participants were students
in a course on food waste, some of them may have already been more conscious of their food waste,
reducing the likelihood of such behavioural changes.
While this study relies on a relatively small sample of student-reported data, this flexibility allowed
students to explore aspects of food waste they found personally relevant. Despite the limited sample
size, the data exhibits substantial variation between households. This variation, even among households
that focused on similar aspects of food waste using the same reference points, highlights the potential
value of studying food waste using a longitudinal approach. This type of variation between households
was also found by Aitken et al. (2024), who also concluded that even larger variations can be found
within households. Thus, even though the amount of food waste generated by the sample in this study
was lower than the general population and may therefore not be considered representative, the
patterns of the distribution and variation were similar to those found in previous studies. Tracking the
waste patterns of households or individuals over an extended period, potentially even over a lifetime or
in different age groups, could therefore provide deeper insights into long-term waste behaviour
patterns and trends.
Building on this variation, most students focused on quantifying solid food waste using g/person/day
as their reference point. This enabled them to classify food items into categories and facilitated
comparisons between them. Additionally, 28 students recorded the weight of food items, allowing the
calculation of food waste as a percentage of the total weight. Both reference points indicated that
vegetables, fruits, and beverages (such as coffee grounds and tea leaves) were the most commonly
discarded food items, consistent with findings from other studies (Eičaitė and Baležentis, 2024; Herzberg
et al., 2020; Torode et al., 2023). However, plate waste, animal-based food items, grains, and bakery
products had more waste per item and were more often classified as avoidable. Bread and plate waste,
regardless of the reference point used, were frequently discarded and categorized by students as
avoidable/edible.
While this study involved students quantifying their food waste over two-week periods on five
separate occasions, it is important to acknowledge that short-term measurements may not capture all
high-impact waste events. Longer-term quantification is needed to better understand waste patterns
across different seasons and to fully capture these extreme occurrences. The fact that this top 10% of
waste comes from 58 different households, rather than being concentrated in a small group, shows that
the problem is widespread. Many households occasionally waste large amounts of food, reinforcing the
need for longer measurement periods to accurately capture these patterns. Reducing waste from this
top 10% by half could lead to a 23% overall reduction in food waste. If these extreme waste events were
eliminated altogether, halving total food waste could be within reach. This highlights key leverage points
where efforts should be focused to achieve significant reductions. These findings suggest that targeting
these high-waste events may be a practical strategy, and similar approaches should be explored in
broader population groups, including different demographic segments. Additionally, strategies should
be developed to address both extreme waste events and daily food waste. Similar patterns have been
observed in other areas, such as studies showing that a small number of individuals are responsible for
the majority of global greenhouse gas emissions (Khalfan et al., 2023). In the food service sector, 20% of
waste events account for 60% of total waste, highlighting important intervention points (Malefors et al.,

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2024b). Automating the process of quantifying household food waste, rather than relying on kitchen
diaries, could be a promising method for obtaining more reliable data (Sjölund et al., 2025). This
approach would also enable long-term quantification and better capture seasonal variations in food
waste, particularly for animal-based products, which may be discarded more frequently at certain times
of the year. Indication to the potential of applying technology for long-term monitoring can be drawn
fromthe food service sector where similar solutions are already being implemented (Goossens et al.,
2022; Malefors et al., 2024b; Mui et al., 2022).
Beyond the importance of reducing food waste as a whole, reducing waste with a high carbon
footprint is especially important for environmental sustainability (Wu et al., 2024). This study found that
each kg of wasted food had an associated carbon footprint of 1.3 kg CO2e. This is lower than what was
found by Adelodun et al. (2021) and Silvennoinen et al. (2022) where each kg of food waste was found
to generate approximately 2.5 and 2.3 kg CO2e respectively. This difference can be explained by the
higher quantity of meat wasted in the other two studies, as well as in the methods applied to calculate
the carbon footprint. Moreover, how high the carbon footprint of the food waste per capita gets also
depends on the amount of food wasted. Since the average amount of food waste among the sample of
this study was found to be low compared to other studies (e.g. Antonelli et al. (2024) and Liu et al.
(2023)), the associated carbon footprint of the wasted food may not stand out as substantial as
compared to studies where higher quantities of food waste are recorded. However, addressing the
carbon footprint is still of importance since this provides information necessary to guide policy so that
actions can be prioritized. Therefore, if results of the carbon footprint are found to be trivial when
compared to other issues and aspects, informed decisions can be made to direct the effort to where the
gain shows highest potential.
However, when scaling up to national level, the results showed that food waste from university
students in Sweden contribute with a carbon footprint of 9,950 tonnes of CO2e. This is comparable to
the yearly amount of food waste and its associated carbon footprint generated in Swedish elementary
schools if assuming that 1 kg of food waste in schools generates 1 kg of CO2e (Sundin et al., 2024;
Swedish Environmental Protection Agency, 2024). Another parallel can also be drawn to consumption-
based emissions from the average Swedish person, which amounts to approximately 8 tonnes per year
(Swedish Environmental Protection Agency, 2023). This means that the carbon footprint from students’
food waste amounts to the emissions of about 1,250 persons (0.01% of total population). However, this
assumes that all students would have equal amounts of food waste and waste similar types of foods,
which is unlikely to be true, especially considering that the study sample consists of students showing
interest in the food waste issue by taking part in the course. It is therefore likely that the carbon
footprint of all students in Sweden is higher.
Furthermore, the results revealed a discrepancy between the relative contribution to weight and to
carbon footprint among the food waste categories. The highest contributor to the carbon footprint of
avoidable/edible food waste (23.7% of all waste) came from dairy waste, despite having lower relative
weight compared to, for example, vegetable, fruit, and grain-based waste. The category that showed the
largest contribution to the carbon footprint relative to its contribution to weight was meat. Similar
findings have been presented by Cakar et al. (2020), Qian et al. (2022), and Silvennoinen (2022),
indicating that even though more fruit and vegetables are wasted by weight, addressing waste of animal
origin could have a larger positive effect on reducing carbon emissions, even if this fraction is
comparably small if weight is used as a metric. Additionally, as with the distribution of the food waste
weight, it may be that a minority of the wasted items contributed to a majority of the carbon footprint.
Considering that 10% of the wasted items contributed 62% of the carbon footprint and, moreover, that

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only 10 single items accounted for 24% of the total carbon footprint, the leverage point of targeting the
top tier of the food waste is further emphasised.
However, the findings also highlighted significant nutrient losses in the avoidable/edible food waste
generated by university student households, particularly in terms of dietary fiber (4.7 g/MJ) and folate
(56 µg/MJ). These nutrients are already under-consumed by the Swedish population, with the average
daily intake of dietary fiber falling short of the updated Nordic Nutrition Recommendations (NNR 2023),
which now recommend 30-35 grams per day for adults (females-males). The current intake levels in
Sweden average around 20-24 grams per day, leaving a gap of 6-15 grams per person per day. The
recently increased recommendations for both dietary fiber and folate in the NNR 2023 emphasize their
crucial role in reducing the risk of chronic diseases such as cardiovascular disease, type 2 diabetes, and
certain cancers (Blomhoff et al., 2023)
A trade-off is, thus, evident in our study; although approximately half of the avoidable/edible waste
consisted of vegetables and fruit—foods that are typically rich in fibre and folate—these foods did not
represent the highest contributors to carbon footprint. However, their reduction is essential for
minimizing nutrient losses and addressing dietary gaps in food intake. The Wasted Nutrient Days (WND)
analysis further underscores this missed opportunity, revealing that the total waste could have met the
daily folate needs of 38 adults or the fibre needs of 35-30 adults (females-males). This suggests that
nutrient-dense foods like fruits and vegetables, which are relatively low in environmental impact, should
still be a priority for waste-reduction efforts to simultaneously improve public health outcomes. Future
food waste prevention strategies should therefore balance the dual objectives of reducing
environmental impacts and closing nutrient intake gaps, particularly for populations at risk of nutrient
deficiencies. Educational interventions aimed at improving food planning, storage, and consumption
behaviors could be effective in reducing waste, while enhancing nutrient intake among university
students and the general population.
Moving forward, a key focus could be on reducing extreme waste events or accidents. Considering
that nearly half (47%) of the avoidable/edible food waste came from only 10% of the observations, it
suggests that a few isolated instances—likely accidents or unusual events—are responsible for a
significant portion of the waste. However, it is also crucial to look beyond waste weight and consider
factors like carbon footprint and nutrient losses. The findings of the study suggest that targeting fruit
and vegetable waste has the greatest potential to reduce weight and nutrient losses, while targeting
animal-based waste would be most effective from a carbon footprint viewpoint. Nevertheless,
regardless of which fraction is targeted, it is imperative that action is taken to reduce food waste.
Considering the participants of this study constitute a group of consumers that will play a key role in
future society, focusing on them and the top 10% fraction simultaneously has the potential to provide
long-term benefits and contribute to a food system with less waste.
5 Conclusions
This study assessed food waste in student households using kitchen diaries and reports, revealing an
average of 115 g/person/day, with considerable variation between households. Solid food waste was
the most frequently quantified, and there is potential to improve methodologies for reporting liquid
waste, which remains an underreported fraction. Avoidable/edible food accounted for 23% of the total
waste, representing a key opportunity for targeted interventions.
Nearly half (47%) of the total waste came from 10% of the wasted items, suggesting an opportunity
to target these events to reduce overall food waste. Halving this fraction could result in a 23.7%
reduction, and eliminating the top 10% would bring us close to achieving the goal of halving food waste,

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in line with Sustainable Development Goal 12.3. A policy recommendation is therefore to develop
methods to determine which items these are likely to be and to develop interventions to target this
fraction. The relatively even distribution of these high-waste items across households indicates that this
approach could be effective across a broader, more socio-economically diverse sample.
From an environmental perspective the top 10% of wasted food items accounted for 62% of the total
carbon footprint, underscoring the importance of focusing on this fraction of waste. Animal-based food
waste, particularly dairy, was the largest contributor to carbon footprint, followed by plate waste and
meat waste. Although vegetable waste was the most frequently discarded, it only accounted for 20% of
the total carbon footprint, and only 11% of the top 10% of wasted items. This reveals an interesting
trade-off: although reducing animal-based waste is crucial for lowering environmental impacts, the
nutrient-rich nature of vegetable and fruit waste—especially in terms of dietary fiber and folate—
underscores the importance of minimizing these losses. Future food waste reduction strategies should
therefore adopt a dual focus, targeting both the environmental impact of animal-based waste and the
nutrient loss associated with plant-based waste.
To address these issues there are potential for various policy recommendations on different levels.
Educational or awareness campaigns regarding food waste should focus on both the environmental and
nutritional consequences of food waste which are also framed towards high-impact items. Leverage
digitalization and automation to develop more accurate and efficient waste tracking systems that
provide direct feedback and personalized suggestions. These tools can encourage behaviour change,
enable longer quantification periods, and support the inclusion of a more diverse sample of households.
Develop and support methods specifically designed to quantify liquid waste, addressing this
underreported fraction of food waste. By focusing on these areas, it is possible to make meaningful
progress in reducing food waste in households. Such efforts will contribute to achieving environmental
sustainability, improving nutritional outcomes, and supporting global food waste reduction goals.
CRediT authorship contribution statement
Christopher Malefors: Conceptualization, Methodology, Visualization, Data curation, Formal analysis,
Writing – Original draft, Writing – Review & Editing, Funding acquisition Amanda Sjölund: Visualization,
Formal analysis, Writing – Original draft, Writing – Review & Editing, Niina Sundin: Formal analysis,
Writing – Original draft, Writing – Review & Editing.
Acknowledgements
This work was supported by the Swedish Environmental Protection Agency, grant number 2022-00077

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Declaration of interests
The authors declare that they have no known competing financial interests or personal relationships
that could have appeared to influence the work reported in this paper.
Graphical abstract