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Article

Managing Food Leftovers in Polish Households in Terms of the Food Waste Hierarchy

by
Marzena Tomaszewska
1,
Beata Bilska
1,* and
Danuta Kołożyn-Krajewska
2
1
Institute of Human Nutrition Sciences, Warsaw University of Life Sciences (SGGW), Nowoursynowska 159C, 02-776 Warsaw, Poland
2
Faculty of Science & Technology, Jan Dlugosz University in Czestochowa, Armii Krajowej 13/15, 42-200 Częstochowa, Poland
*
Author to whom correspondence should be addressed.
Sustainability 2025, 17(23), 10552; https://doi.org/10.3390/su172310552
Submission received: 10 October 2025 / Revised: 18 November 2025 / Accepted: 20 November 2025 / Published: 25 November 2025
(This article belongs to the Special Issue Consumer Behavior, Food Waste and Sustainable Food Systems)
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Abstract

Food leftovers are a key component of household food waste. A nationwide survey of 1115 Polish adults conducted in 2019 examined how such leftovers are managed, considering socio-demographic and economic factors. It also explored the impact of selected food management practices on the throwing away of unconsumed meal leftovers. The data obtained indicated that one-quarter of the respondents always and usually use unconsumed meals to prepare other dishes. The following positions were taken by: giving to animals (23.68%), disposal in a waste container (15.97%), freezing (15.78%) and ex aequo giving to family or friends and composting (8.07%). Place of residence strongly influenced behaviours. Rural residents were much more likely than city dwellers to feed animals unused ready meals and compost them. On the other hand, city dwellers and young consumers more often stated that they would dispose of such products in a waste container. The constructed regression model indicated that only the frequency of purchasing ready-made chilled and frozen meals and the importance of storage conditions, in the respondents’ opinion, significantly impacted throwing away leftovers in a waste container. In summary, Polish respondents do not follow the food waste hierarchy. For this reason, it is important to develop and implement various educational programmes and campaigns.

1. Introduction

When analysing issues related to food waste (FW), the scientific community’s most significant interest is household FW, which is also reflected in the number of scientific publications. Even though consumers perceive FW as a significant environmental, economic and social problem, they are most responsible for this state of affairs. According to UNEP estimates, in 2022, households worldwide wasted about 631 million tonnes of food, accounting for 60% of the total 1.05 billion tonnes of food wasted globally. Per capita, this equates to 79 kg of food per year—enough to provide 1.3 meals per day for all people suffering from hunger. An analysis of Eurostat data reveals significant variation in household food waste levels across 30 countries, ranging from approximately 30 kg to 125 kg per person per year [1]. According to a study by Bilska et al. [2], one person in Poland wasted an average of 62.6 kg of edible food (98.2 kg of edible and inedible food) per year. Food leftovers are considered one of the main components of household FW [3,4,5]. Leftovers are food that is produced during one meal and then becomes surplus (not served) or leftover from that meal (served but not consumed) [6]. The portion of the served food that is discarded is referred to as plate waste [7]. A study conducted among consumers indicated that the most frequently wasted category of food includes home-cooked or ordered meals [8,9,10]. In Polish households, the average amount of edible food waste per day was 350.53 g, of which 68.51 g were from home-cooked meals and 17.46 g were from meals delivered from outside. Combined, these categories accounted for 24.53% of total household FW [2].
A tool that can be used to assess, compare and plan food waste reduction strategies because it shows the actions that bring the greatest environmental, social and economic benefits is the food waste hierarchy. Preventing FW in households is undoubtedly an important issue, as it is the most desirable option in the food waste hierarchy [11,12]. However, even with minimisation strategies, unconsumed food must still be managed appropriately [13]. As noted by Nguyen et al. [14], some disposal practices are more environmentally, socially, and economically sustainable than others. Few available studies have addressed the management of unconsumed meals by consumers, and such an analysis can provide practical knowledge on factors that can promote sustainable practices in households. Activities such as freezing, safe storage, and using leftovers to prepare other meals can help reduce the amount of FW [15].
Having the right skills and knowledge for reusing leftovers is crucial [16,17] as this helps prevent them from being wasted, even after freezing or refrigeration [18]. Stored leftovers can be forgotten; their health safety can be wrongly assessed [19], especially by health-conscious consumers who may perceive them as devoid of nutritional value, which may result in their disposal [20,21]. Therefore, consumers should be aware of the benefits of minimising FW by reusing leftovers, which reduces the time and effort involved in shopping and cooking new meals, saving money and leading to environmental protection satisfaction [22]. Talwar et al. [23] showed a positive relationship between intentions against FW and routine procedures for reusing leftovers. Avoiding over-purchasing and consuming leftovers have the most significant impact on reducing FW [24,25,26,27]. The research results from Ananda et al. [28] indicate that social norms influence the planning, storage, preparation, and use of food leftovers. On the other hand, subjective norms and motivations for not wasting food are most effective with food disposal routines (e.g., feeding leftovers to animals).
Although consumers express a willingness to reduce food waste, they often find it difficult to turn this intention into action because of competing motives in food selection and purchasing [29]. The Theory of Planned Behaviour model explains intentional actions through behavioural intention, shaped by attitude, subjective norms and perceived behavioural control [30]. The stronger the behavioural intention, the more frequent the food waste reduction, for example, through portion control [31,32]. Conversely, strong behavioural control and appropriate competencies favour food waste reduction intentions and practices [33]. The results of a study, obtained by Habib et al. [34], confirmed that perceived consumer values positively influence their attitude towards reducing food waste. Furthermore, variables such as age and gender are significantly associated with social norms.
Aloysius et al. [35] believe it is worth promoting the reuse of leftovers to reduce household FW. The second most desirable option (after reduction) is to give unconsumed but safe food to others followed by redirecting it to animal feeding [36]. The third group of solutions presents several options for recycling, such as anaerobic digestion, composting, etc. The least preferred and sustainable option is disposing of FW in landfills [14]. According to Manika et al. [37], there is an urgent need to understand the factors related to FW production and disposal to reduce waste and improve disposal practices to enable better management. To reduce the level of leftovers, households should plan meals appropriately for the upcoming week, check the food supplies in the cupboards and refrigerator before shopping, cook only the required amounts, adjust portion sizes to each family member, and try new recipes to make meals attractive and tasty [35,38]. To the best of the authors’ knowledge, only a few previous studies review the current understanding of consumer behaviour towards leftover FW in households. There is an obvious need for research to identify the main factors that motivate and enable, or prevent, household FW minimisation behaviour and the proper disposal of FW [29]. The study described in this article fills the aforementioned research gap and provides information on the behaviour of consumers regarding uneaten meals. In our study, we included ready-made and home-cooked meals, which are the basis of the nutrition of Polish consumers. The work aimed to (1) present the ways of managing food leftovers in Polish households, (2) examine the impact of household socio-demographic and economic characteristics on the analysed means of management, and (3) examine the impact of selected explanatory variables, including those related to behaviour, on disposing of uneaten leftovers of meals in a waste container (the least desirable option in the food waste hierarchy). Our study is the first to analyse this problem on a representative sample of Polish respondents over the age of 18. Although the study was conducted in 2019, i.e., before the COVID-19 pandemic, it provides important knowledge that can be used for comparison with results obtained in later periods.

2. Materials and Methods

The study was part of a more extensive study aimed at examining the behaviour of Polish consumers in terms of FW. A professional market research agency conducted participant recruitment and data collection, respecting the ESOMAR (the European Society for Opinion and Marketing Research) code. ESOMAR is an international organisation responsible for raising research implementation quality and ethical standards. In addition, the professional market research agency conducting the research declared the implementation of: standards of the Code of Conduct in the Field of Market and Social Research; certification by the standards and code of the PKJPA (Interviewer Work Quality Control Programme); and compliance with the GDPR and personal data protection policy. The above standards require, among other things, receipt of informed consent from participants and assurance that they may refuse or withdraw from the study at any stage. Therefore, before data collection began, all participants were informed about the purpose of the study and their rights, and they provided informed consent to participate. A professional market research agency handled the formal aspects of the process, including obtaining informed consent. All collected data were processed anonymously and coded in a non-identifiable format.
The survey was conducted among a group of 1115 adult consumers. The selection of the sampling from the address survey of Statistics Poland fulfilled the condition of representativeness of the general population of Polish people older than 18 years in terms of gender, age, and size of place of residence. In the first phase of sample selection, territorial stratification was carried out, considering 16 voivodeships and six locality size classes. Figure 1 presents the regional distribution of respondents included in the nationwide study, illustrating the representation of participants from different parts of the country. For the localities identified in the previous stage, addresses were randomly selected from the TERYT register, i.e., the National Official Register of the Territorial Division of the Country, kept by Statistics Poland. In the final phase, demographic features (gender and age) were adjusted for each locality size class. The selection of respondents was carried out using the random route method, i.e., a predefined address path with a randomly selected starting point. The interviewer first visited the starting address, and if it was not possible to conduct the interview at that household, they proceeded to the next designated household along the route. This sampling approach ensured the representativeness of the study, and the structure of the sample in terms of gender, age, place of residence, and region did not differ significantly from that of the general population. The detailed sociodemographic characteristics of the respondents are presented in Table 1.

2.1. Data Collection and Questionnaire

Data were collected using a structured questionnaire survey distributed via computer-assisted personal interviewing (CAPI) from February to March 2019. The participants completed two questionnaire sections. The first one gathered information about consumer behaviour regarding using non-consumed meals. Respondents reported the frequency with which they used six methods of handling leftovers (uneaten meals not provided to household members; plate waste excluded) using a scale ranging from ‘always’ to ‘never’ (Figure 2). In addition, the survey participants answered questions regarding, among others, the frequency of shopping and preparing meals, the importance of selected product features, and the perception of the FW problem (Figure 2). The questions used in this study were part of a broader, previously developed questionnaire. The second section collected the sociodemographic characteristics of the respondents (Table 1).

2.2. Data Analysis

Microsoft Excel 2016 and Statistica 13 were used to analyse how leftovers are managed in Polish households and identify factors associated with throwing them away in waste containers. Firstly, the methods of managing uneaten meals, along with the indication of the frequency of their use, were evaluated through descriptive statistics. Descriptive statistics such as mean scores (M(X)), median (Mdn), mode and standard deviation (SD) were calculated to interpret the results. The mode, which is the value that appears most frequently in a data set, was also presented as a percentage share (% mode).
To determine the impact of factors such as gender, age, place of origin, level of education, number of adults and children in the household, budget for food and employment status on the response scores, the one-way analysis of variance test (one-way ANOVA) was applied. Analysis of variance is a technique used to compare whether the means of at least two samples differ significantly (using the F-distribution) [39]. The analysis of variance was complemented by the post hoc analysis performed using the LSD (least significant difference) test. This test made the indication of homogeneous arithmetic mean groups possible.
In the case of the most undesirable method of managing uneaten meals, i.e., disposing of them in a waste container, we used partial eta squared (η2P). η2P is an alternative measure of association for a sample that describes the proportion of total variation explained by a predictor variable, after excluding (partialling out) variance from other predictor variables from the total non-error variance in the denominator. Partial eta squared indicates how much of an effect the independent variable(s) had on the dependent variable.
Lastly, we utilised logistic regression to evaluate factors most closely related to one of the methods analysed for managing unconsumed ready meals, i.e., throwing them away in a waste container (explained variable). To correct the imbalance in the size of individual groups of respondents and enable the generalisation of regression results to the population level, as well as to ensure stable estimation of model parameters for particular results, weights were calculated.
In the regression analysis, the explanatory variables were grouped into five areas, i.e., (I) shopping habits, (II) the importance of selected features of purchased products, (III) the basis of current food handling, (IV) meal preparation, and (V) perception of FW as a problem. The analyses used a logit model (qualitative model), in which the dependent variable (Y) was “throwing away leftovers in a waste container”. The Y variable in the logit model took the following values: Y = 1: the so-called “rather throw away” group (R_Yes)-‘always’, ‘usually’, ‘sometimes’ answers (n = 444; 39.82%); Y = 0: the so-called rather not throwing away group (R_No)-‘rarely’, ‘never’, ‘hard to say’ (n = 671; 60.18%) answers. This dichotomisation was applied to clearly distinguish respondents who actually engage in discarding uneaten meals into a waste container from those for whom this behaviour does not constitute a stable or meaningful pattern.
When building the logistic model, after data specification, an initial selection of variables for the model was performed. For this purpose, a simple univariate analysis was performed using the Pearson chi-square test (X2) to identify the variables least moderately related to the response variable [40]. According to the recommendations of Hosmer and Lemeshow [41], variables for which the p-value in individual analyses did not exceed 0.25 were retained for further analysis. The results of the logistic model estimation were interpreted based on the odds ratio (OR) for each independent variable, which expressed the change in the odds of the occurrence of a given value (Y = 1) when the independent variable increases by 1 unit (ceteris paribus).

3. Results

3.1. Methods of Managing Food Leftovers in Polish Households

According to the data presented in Table 2, the most common method for managing leftovers was using them in other dishes, with 25% of respondents indicating that they do this “always” or “usually” do this. The next most common methods of managing uneaten meals were using them as animal feed, throwing them away in a waste container, and freezing them. The least prevalent practices were giving meals to family or friends and composting, with each method being reported as “always” or “usually” in only 8.07% of responses. Further analysis focused specifically on the practice of discarding uneaten meals in a waste container. According to the descriptive statistics (Table 2), nearly one-third of respondents selected “rarely” as their answer for this disposal method.

3.2. The Influence of Selected Respondent and Household Characteristics on Food Leftover Management Practices

Among the analysed sociodemographic and economic characteristics, the respondent’s place of residence had the most significant influence on the management of uneaten meals. It was a determining factor in all six analysed leftover management methods (Table 3).
The LSD test indicated that rural residents were significantly more likely (p < 0.05) to feed animals with uneaten ready meals and to compost them compared to urban residents, regardless of the size of the urban area. Conversely, urban residents (homogeneous group) were more inclined to dispose of these products in waste containers than rural residents (Table 4).
Further analysis of other methods for managing unused meals showed that rural residents and those living in small to medium-sized towns with populations of up to 200,000 (also a homogeneous group) were more likely to freeze unused meals than residents of larger cities with populations exceeding 200,000. Additionally, residents of medium-sized towns (populations between 100,000 and 200,000) were much more likely to give unused meals to family or friends than rural residents, and particularly compared to residents of the largest cities. Based on the analysis of the remaining sociodemographic and economic characteristics, it was found that food leftovers are more often used to prepare other meals and frozen by men compared to women. The age group of consumers that significantly more often indicated freezing unconsumed meals turned out to be respondents aged 35–44, but also the oldest aged over 60 (homogeneous group). Consumers over 45, including those over 60, were much less likely to declare throwing unused meals away in a container compared to young consumers aged 25–34. The level of education was important in the case of two of the six analysed disposal methods. Consumers with a lower level of education were more likely to use uneaten meals to feed animals. However, they were less likely to indicate, especially in comparison with those with secondary education, throwing them away in a waste container. Households with one adult person, significantly less often than the other groups, indicated freezing or feeding animals with uneaten meals. Households without children also disposed of such products in a waste container less often, compared primarily to households with one child.
On the other hand, it was noticed that in households with two or more children, uneaten meals were more often utilised to feed animals. In households allocating a significant part of the budget to food (100–61%), it was more common to state that they gave uneaten meals to family/friends or composted them. It was observed that the decrease in food expenditure made it less common to state that uneaten meals were thrown away in a waste container. This method of managing uneaten meals was also more often indicated by professionally active respondents.
To determine the magnitude of the influence of the respondents’ socio-demographic characteristics on the frequency of discarding uneaten meals into a waste container, the effect size (η2P) was taken into account in the analysis (Table 5). The strength of the calculated effects for the examined characteristics is quite low; the partial eta squared values range from 0.000674 to 0.039974, indicating that approximately 0.067% to 3.997% of the explained variance is accounted for by the analysed independent variables.
The analysis of effect size (η2P) indicates that the strongest factor differentiating the examined behaviour was place of residence (η2P = 0.039974). Age, food budget, and number of children showed small effects (η2P ≈ 0.01). In contrast, variables such as education level and employment status demonstrated very weak effects on the outcome. The number of adults in the household and gender did not have a significant impact on the frequency of discarding uneaten meals into a waste container (η2P = 0.003808 and 0.000674, respectively), indicating that differences related to household size (in terms of the number of adults) as well as differences between women and men were negligible.

3.3. Characteristics of Explanatory Variables and Their Impact on the Practice of Throwing Away Food Leftovers

According to Figure 2, the further analysis determined the influence of 13 explanatory (independent) variables, grouped into areas such as: (I) selected purchasing habits, (II) the importance of selected characteristics of purchased products, (III) the basis of current food handling practices, (IV) selected practices related to meal preparation, and (V) the perception of food waste as a problem, on the disposal of uneaten meals into waste containers (dependent variable). The Supplementary Materials (Table S1) present the respondents’ answers for all explanatory variables. The analysis examined whether the collected data indicated a strong association between each explanatory variable and the disposal of food leftovers in a waste container. The study utilises multi-way tables and the Chi-square test, with the results also available online in Supplementary Table S1. Table 6 presents selected explanatory variables that were significantly associated (p < 0.05) with throwing away unused meals in a waste container. Of the 13 explanatory variables analysed, almost half (6) were significantly associated (p < 0.05) with this behaviour.
In group I of explanatory variables related to purchases, a highly significant connection was found between the frequency of purchasing ready-made chilled meals (p < 0.00), as well as the frequency of purchasing frozen foods, including ready-made meals (p < 0.00) and throwing away food leftovers. The analysis of the response structure within the compared groups revealed that among consumers who reported discarding unfinished meals (R_Yes), the largest share consisted of individuals who purchased chilled ready-to-eat meals approximately 1–2 times per month (37.1%). In contrast, within the group of consumers who do not discard leftovers (R_No), those who reported never purchasing such products predominated (34.9%). These findings suggest that a lower frequency of purchasing chilled ready-to-eat meals is associated with a reduced likelihood of discarding unfinished meals. Confirmation of these observations is the median value, which is 4.00 (on average 1–2 times a month) in the R_Yes group and 5.00 (less than once a month) in the R_No group. A similar relationship was found between purchasing frozen food and throwing away ready-made meals in a waste container.
Shopping or preparing a shopping list turned out to be activities that had no significant effect (p > 0.05) on throwing away uneaten meals (Supplementary Table S1). In the second group of explanatory variables related to the perception of selected features of food products, a highly significant relationship was found between the importance of price (p < 0.00), as well as the storage conditions of purchased products (p < 0.00) and throwing away uneaten meals. A larger proportion of respondents in the group that does not dispose of uneaten meals in a waste container (R_No), compared to those who do dispose of them (R_Yes), indicated that the price of purchased products and storage conditions are “definitely important”—by 8.9% and 11%, respectively (Table 6).
In group IV of explanatory variables related to meal preparation, both the frequency of meal preparation and the care for hand hygiene during this process were significant (p < 0.05) in throwing away uneaten meals (Table 6). A smaller percentage of respondents in the R_Yes group, compared to R_No, declared that they prepared meals at home with a frequency of ‘always’. Similarly, a smaller percentage of consumers in the R_Yes group declared that they ‘always’ washed their hands before preparing meals.
In accordance with the methodology, we retained only those explanatory variables whose p-values in the individual analyses did not exceed 0.25 for the construction of the logistic model. Nine of the 13 variables met this condition and were included in the analysis: (1) frequency of preparing a shopping list, (2) frequency of purchase of chilled ready meals, (3) frequency of purchase of frozen food, including ready meals, (4) importance of the price of purchased products, (5) importance of storage conditions, (6) education level as the basis of current food handling, (7) frequency of preparing meals, (8) frequency of washing hands before preparing meals, and (9) food waste as a social problem. The logit model partially confirmed the results of the previous analysis. The constructed regression model (Table 7) indicated that only the frequency of purchasing ready-made chilled meals or frozen foods, including ready-made meals, as well as the significance of storage conditions in the opinion of respondents, have a significant impact on throwing away unused meals in a waste container. Although it should be emphasised, as shown in Table 8, that only about 35% of the recognised indications Y = 1 were correctly classified in the model.
The value of the calculated OR for most variables was close to 1, meaning that the risk of throwing away ready-made unused meals is equal in the compared groups (R_No and R_Yes). The unit odds ratio calculated for the frequency of purchases of ready-made chilled meals and the frequency of purchases of frozen foods, including ready-made meals, indicates that reducing the frequency of purchases of the aforementioned assortment by one range reduces the risk of throwing away unused meals in a waste container by 0.748 and 0.811 times, respectively, i.e., by about 26% and 19%. These observations are confirmed by the negative value of the coefficient (β1), which means that reducing the frequency of purchases (scale direction: from 1-daily to 6-never) of chilled/frozen ready-made meals reduces the probability of throwing away uneaten meals in a waste container. The model coefficients calculated for the respondents’ perception of the importance of the storage conditions of purchased products (OR = 1.327; β1 = 0.283) indicate that for each decrease in importance by one range (scale direction: from 1–definitely important to 6–definitely not important), the odds of throwing away uneaten meals increase by 33%.

4. Discussion

According to Redlingshöfer et al. [42], a key challenge for the sustainable development of food systems is how to use food that is not consumed. Popular guidelines in OECD countries are the food waste hierarchy and the 3R approach of “reduce, reuse, recycle”. We aimed to determine how Polish respondents deal with leftovers from unused food. The data obtained indicated that one-quarter of the respondents taking part in the survey most often utilised unused meals to prepare other dishes. The following positions were: giving to animals, disposal in a waste container, freezing, and ex aequo giving to family or friends and composting. The European hierarchy prioritises preventing FW, followed by redistribution of food and use as animal feed [42]. Therefore, it should be stated that Polish respondents did not follow these recommendations because the option of disposing of such food in a container should be at the end of the hierarchy. To change the behaviour of Polish households, alongside educational campaigns, a system of incentives should be introduced to encourage households to utilise uneaten food instead of throwing it away. These incentives can be economic in nature, translating into savings for households. Pursuant to the Polish Act on Maintaining Cleanliness and Order in Municipalities, the municipal council partially exempts property owners who compost biowaste from municipal waste management fees [43]. The results of the study showed that the financial savings resulting from biowaste composting were essential to 37% of surveyed households [44]. Therefore, it can be assumed that economic incentives, combined with appropriate information and training, can bring about real and positive changes.
A positive observation is that a relatively large group of respondents declared that they freeze leftovers, but it should be emphasised that the way in which they were ultimately utilised was not examined. According to Schanes et al. [45], consumers feel guilty about throwing away food but do not hesitate to throw away spoiled food. For this reason, they may postpone disposing of leftovers until they spoil [46]. As shown by Schanes et al. [45] and Teng et al. [21], consumers tend to avoid reusing leftovers due to the loss of quality and freshness. For this reason, they may also be reluctant to share leftovers with others [47]. Among the household characteristics analysed, the respondents’ residence had the most significant impact on managing leftovers. Rural residents were much more likely than urban residents to feed unconsumed ready meals to animals and compost them.
According to Nath et al. [48], using leftovers to feed farm animals is a standard practice in rural areas. Utilising unconsumed food as animal feed can create a circular economy and sustainable development [48,49]. On the other hand, urban residents and young consumers were more likely to dispose of such products in a waste container. According to a study by van Geffen et al. [50], older consumers waste less food. This observation was confirmed by Janssens et al. [51], Jörissen et al. [25], and Stancu et al. [52]. The study by Bilska et al. [2] showed that in households where meals were most often prepared by young consumers aged 18–24, the share of food products and semi-finished products and purchased ready meals in the total mass of wasted food was higher than in the other household groups. However, Koivupuro et al. [53] and Parizeau et al. [26] showed unclear differences in the discussed scope between older and younger consumers. According to Bravi et al. [54], young consumers have lower food-management skills due to lower skills and experience reusing leftovers compared to older consumers.
On the other hand, according to Wang et al. [55], age has a significant impact on knowledge and perception of risk associated with processing leftovers. It seems interesting to know the sources of knowledge about food handling among young consumers. According to Fischer et al. [56] and Kymäläinen et al. [57], Generation Z’s attitude towards food comes from the family. However, this age group’s daily, rather spontaneous life makes it difficult to properly manage food [57]. As our results showed, the source of knowledge about food does not determine Polish consumers’ disposal of uneaten meals.
The analysis found that unconsumed ready meals are more often utilised to prepare other dishes and frozen by men than by women. This observation is in line with the opinion of Koivupuro et al. [53], who found that women may tend to dispose of more leftovers than men in an attempt to provide their families with healthy and fresh meals. On the other hand, Wang et al. [55] found that women have better knowledge about processing leftovers than men and are more likely to store them in refrigerators [58,59]. According to Vittuari et al. [60], the role of gender in FW is not clearly defined. The research of Secondi et al. [61] indicated that women waste less food than men. Similarly, Bilska et al. [62] found that women’s behaviour contributes less to FW than the behaviour of men. However, Visschers et al. [27] showed that women tend to be more wasteful than men. Another factor that has a significant positive effect on knowledge and risk perception in processing leftovers is education level [55,58,63], especially when deciding on the safest way to store them. Our study showed that consumers with a lower level of education were more likely to feed leftovers to animals and less likely to dispose of them in a waste container compared to respondents with secondary and higher education. Our result differs from those of Miafodzyeva and Brandt [64], who found a significant positive association between education and recycling behaviour.
An interesting observation is that throwing away unused meals in a waste container was declared less often with a decrease in food expenditure. In contrast, Soma [65] showed that higher-income families are reluctant to consume leftovers due to health concerns. Household composition also influenced FW management behaviour. Households without children were less likely to dispose of such products in a waste container, compared primarily to households with one child. The finding is also in line with Muresan et al. [63] and Osaili et al. [66]. The constructed regression model indicated that only the frequency of purchasing ready-made chilled and frozen meals, including ready-made meals, and the importance of storage conditions in the opinion of respondents significantly impacted throwing away unused meals in a waste container. This may be due to the low quality of purchased dishes, inappropriate taste, lack of acceptance of certain ingredients or spices that the consumer is not used to. On the other hand, the ease of obtaining a dish (purchase, not self-preparation) may also encourage easier throwing away of uneaten food. It has been shown that reducing the frequency of purchasing chilled/frozen ready meals reduces the likelihood of throwing away unused meals in a waste container. Østergaard and Hanssen [67] examined the waste of fresh bread and found that consumers who waste more bread buy it more often and in larger quantities. Williams et al. [5] noted lower FW in consumers who shop for groceries frequently. Based on their research, Di Talia et al. [68] proved that a low frequency of food shopping increases the tendency to waste food in households. However, Jörissen et al. [25] showed that low shopping frequency is associated with high and low FW levels. The model indices calculated for the respondents’ perception of the importance of the storage conditions of purchased products indicate that for each decrease in importance by one interval, the chance of throwing away unused meals increases. According to Stöckli et al. [69], storing food at home is a critical point in food management. As shown by the study by Tomaszewska et al. [70], 70% of Polish respondents considered the storage conditions recommended by the manufacturer on the label important. According to researchers [71,72], when consumers stored food correctly (e.g., in appropriate containers, ensuring the right temperature in refrigerators and freezers), they reduced the risk of FW.
It is essential to note that our research was conducted before the COVID-19 pandemic. The lockdown and isolation period altered consumers’ purchasing and cooking habits, potentially leading to shifts in food waste levels [73,74]. The results of Laila et al. [73] indicated that, compared to pre-COVID-19, total household food waste did not change, avoidable food waste decreased, and unavoidable food waste increased among a sample of families with school-aged children. Several changes to the family’s routines due to the COVID-19 pandemic may contribute to the observed reduction in avoidable food waste.
A key research question as to whether the observed behavioural changes have persisted after returning to pre-pandemic conditions remains. Our findings may serve as a starting point for further comparative analyses.
A limitation of this study is that it uses the method of interviews with respondents who may not have declared their actual behaviour towards the discussed problem. The risk that respondents completing the questionnaire may have hidden important information, e.g., because they were ashamed that they wasted leftovers, should be considered. Future studies should be based on other methods, e.g., current diaries. Future research on FW should include consumer motivations and consider, for example, lifestyle, pro-environmental attitudes, and meal and portion planning.

5. Conclusions

Adhering to the food hierarchy in households supports the development of a circular economy by minimising waste and rationally managing resources. Waste prevention, reuse, and recycling practices maintain the value of materials in circulation, reduce greenhouse gas emissions, and lessen environmental burdens. Consequently, households play a crucial role in implementing the principles of a circular food system and achieving SDG 12.3. Polish respondents did not follow the food waste hierarchy because disposing of unused meals in a waste container was placed third. Only 25% of respondents used leftovers to prepare other dishes and used them to feed animals. Composting and giving them to others were placed at the bottom of the hierarchy. The results of our research indicated that the respondents’ disposal of unused leftovers in a waste container was influenced by such features as age, place of origin, education level, number of children, budget for food, and employment status. A worrying observation is that many young people declared throwing away this type of product in a waste container. This suggests that these people will also behave in this way later in their lives. For this reason, it is important to develop and implement various educational programmes (starting with school education) and wide-ranging campaigns to increase awareness of the problem of food waste, including leftovers, and to show how to counteract this phenomenon. It is impossible to eliminate food waste, but consumers should be educated on the most beneficial ways of disposing of it, per the food waste hierarchy.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/su172310552/s1, Table S1: Respondents’ responses regarding the analysed explanatory variables.

Author Contributions

Conceptualization, M.T. and B.B.; methodology, M.T. and B.B.; software, M.T.; validation, M.T. and B.B.; formal analysis, M.T. and B.B.; investigation, M.T. and B.B.; data curation, M.T.; writing—original draft preparation, M.T. and B.B., writing—review and editing, B.B., M.T. and D.K.-K.; visualisation, M.T.; supervision, D.K.-K. All authors have read and agreed to the published version of the manuscript.

Funding

This publication has been developed under the contract with the National Centre for Research and Development No. Gospostrateg1/385753/1/NCBR/2018 for carrying out and funding of a project implemented as part of the “The social and economic development of Poland in the conditions of globalizing markets—GOSPOSTRATEG” program called “Developing a system for monitoring wasted food and an effective program to rationalize losses and reduce food wastage” (acronym PROM).

Institutional Review Board Statement

Ethical review and approval were waived for this study due to the study was conducted in accordance with the standards set by the European Society for Opinion and Marketing Research (ESOMAR; https://esomar.org/), International Code on Market, Opinion, and Social Research, and the General Data Protection Regulation of the European Union (GDPR, Regulation 2016/679). The study was also conducted in accordance with the ethical principles outlined in the Declaration of Helsinki. Before collecting survey data, participants provided informed consent. They were informed of the nature of their involvement and their rights. They were required to confirm their understanding of the study’s overall objective and their agreement to participate in the survey before responding to the questionnaire. The collected data were processed anonymously and encoded in a non-identifiable format.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The original contributions presented in this study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Regional distribution of respondents participating in the nationwide survey.
Figure 1. Regional distribution of respondents participating in the nationwide survey.
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Figure 2. The conceptual research model adopted in the work.
Figure 2. The conceptual research model adopted in the work.
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Table 1. Sociodemographic characteristics of consumer groups, %.
Table 1. Sociodemographic characteristics of consumer groups, %.
CharacteristicsGroup%
GenderWomen51.1
Men48.9
Age18–248.3
25–3419.0
35–4418.0
45–5927.4
over 60 years27.4
InhabitancyVillages38.2
Cities up to 50,00024.8
Cities 50,001–100,0007.4
Cities 100,001–200,0009.1
Cities 200,001–500,0009.0
Cities over 500,00011.6
Education levelPrimary8.4
Basic vocational31.9
Secondary42.0
Higher17.7
No. of people over 18 years in household117.2
261.2
316.3
4 or more5.3
No. of children in household074.6
116.3
27.6
3 or more1.4
Portion of the household budget for food expenditureLarge (100–61%)12.3
Average (60–40%)50.2
Small (39–0%)27.7
Hard to say9.8
Employment status of the personWorking66.4
Not working (unemployed homemaker, including maternity/parental leave, pensioner/disability, student33.6
Table 2. Reported methods for handling food leftovers among respondents (n = 1115).
Table 2. Reported methods for handling food leftovers among respondents (n = 1115).
MethodsAnswersNo., % of RespondentsDescriptive Statistics
n%M(X)Min/
Max		SDMode%
Mode		Mdn
other dishesalways (1)494.393.111.00/5.001.013.0043.393.00
usually (2)23220.81
sometimes (3)46641.79
rarely (4)20718.57
never (5)12010.76
hard to say413.68excl *
family and friendsalways (1)90.814.021.00/5.001.025.0043.184.00
usually (2)817.26
sometimes (3)25422.78
rarely (4)26423.68
never (5)46241.43
hard to say454.04excl *
freezealways (1)282.513.521.00/5.001.073.0036.253.00
usually (2)14813.27
sometimes (3)38934.89
rarely (4)25522.87
never (5)25322.69
hard to say423.77excl *
animalsalways (1)1008.973.431.00/5.001.305.0028.963.00
usually (2)16414.71
sometimes (3)29626.55
rarely (4)20318.21
never (5)31127.89
hard to say413.68excl *
compostalways (1)201.794.301.00/5.001.055.0062.385.00
usually (2)706.28
sometimes (3)14212.74
rarely (4)16915.16
never (5)66559.64
hard to say494.39excl *
containeralways (1)474.223.651.00/5.001.134.0031.704.00
usually (2)13111.75
sometimes (3)26623.86
rarely (4)34230.67
never (5)29326.28
hard to say363.23excl *
* Answers ‘hard to say’ are excluded.
Table 3. Influence of selected respondent and household characteristics on the management of uneaten meals (one-way ANOVA results).
Table 3. Influence of selected respondent and household characteristics on the management of uneaten meals (one-way ANOVA results).
Methodsp-Value
GenderAgeInhabitancyEducation
Level		No. of AdultsNo. of ChildrenBudget for FoodEmployment Status
other dishes0.006 *0.0630.018 *0.6090.0620.3850.6950.144
family and friends0.8210.7220.003 *0.2300.5940.9910.000 *0.806
freeze0.034 *0.002 *0.000 *0.2330.019 *0.1740.1200.707
animals0.4400.1530.000 *0.040 *0.000 *0.001 *0.1970.451
compost0.5700.9830.000 *0.6360.4290.7860.000 *0.837
container0.3300.003 *0.000 *0.010 *0.2270.005 *0.006 *0.000 *
* p < 0.05.
Table 4. Frequency of using different methods of managing uneaten ready meals in Polish households (mean value) *. Post hoc analysis was performed using the least significant difference (LSD) test. Significance level = 0.05 **.
Table 4. Frequency of using different methods of managing uneaten ready meals in Polish households (mean value) *. Post hoc analysis was performed using the least significant difference (LSD) test. Significance level = 0.05 **.
CharacteristicsGroupMethods
Other Dishes
n = 1074		Family and Friends
n = 1070		Freeze
n = 1073		Animals
n = 1074		Compost
n = 1066		Container
n = 1079
genderwomen3.19 a4.043.60 a3.404.303.62
men3.03 b4.003.45 b3.464.313.68
age18–243.273.933.67 ab3.574.363.69 ab
25–343.204.083.67 a3.554.313.42 b
35–443.044.073.29 c3.464.343.61 ab
45–593.144.013.59 ab3.274.273.69 a
over 60 years3.003.973.45 bc3.454.293.80 a
inhabitancyvillages3.17 a4.00 b3.51 b2.96 c3.98 c3.93 a
cities up to 50,0002.99 b3.97 bc3.41 b3.69 b4.44 b3.48 b
cities 50,001–100,0002.90 b4.06 ab3.28 b3.59 b4.50 ab3.42 b
cities 100,001–200,0003.29 a3.73 c3.45 b3.51 b4.31 b3.61 b
cities 200,001–500,0003.18 ab4.08 ab3.85 a3.71 b4.78 a3.42 b
cities over 500,0003.12 ab4.33 a3.76 a4.05 a4.58 ab3.45 b
education
level		primary3.173.963.613.16 c4.303.88 a
basic vocational3.143.993.503.34 bc4.283.74 a
secondary3.104.083.573.48 ab4.323.54 b
higher3.053.943.393.60 a4.323.65 ab
no. of adults13.223.983.70 a3.73 a4.333.60
23.144.053.51 b3.46 b4.323.64
32.963.933.43 b3.15 c4.293.67
4 or more2.904.033.28 b3.02 c4.043.93
no. of children03.134.023.563.42 b4.293.71 a
13.114.033.453.66 a4.363.42 b
23.964.013.313.18 bc4.293.51 ab
3 or more2.944.003.382.56 c4.193.88 ab
budget for foodlarge (100–61%)3.143.70 c3.323.193.80 b3.49 b
average (60–40%)3.114.14 a3.513.484.37 a3.59 b
small (39–0%)3.063.93 b3.553.494.37 a3.84 a
hard to say3.194.04 ab3.723.324.37 a3.67 ab
Employment status of the personWorking3.134.013.523.464.293.59 b
Not working3.064.043.523.374.323.77 a
* Direction on the scale: 1—always; 2—usually; 3—sometimes; 4—rarely; 5—never. ** Groups that share the same letter in the column are not significantly different from each other at the adopted significance level.
Table 5. Values of the calculated effect sizes (η2P) for independent variables.
Table 5. Values of the calculated effect sizes (η2P) for independent variables.
The Independent VariablesdfFpη2PPower ObservedRank
gender10.730.3944090.0006740.1362408
age43.5960.006396 *0.0132140.8747592
inhabitancy58.9360.000000 *0.0399740.9999151
education level33.4360.016452 *0.0094980.7733245
no. of adults31.3700.2505670.0038080.3663587
no. of children34.0530.007057 *0.0111850.8440484
budget for food34.2990.005030 *0.0118540.8664943
employment status15.640.017738 *0.0052090.6600646
* p < 0.05.
Table 6. Explanatory variables significantly associated (p < 0.05) with unused meal disposal (the columns present the percentage distribution of responses within each group).
Table 6. Explanatory variables significantly associated (p < 0.05) with unused meal disposal (the columns present the percentage distribution of responses within each group).
Explanatory VariableR_Yes (1) aR_No (0) bX2p
%M(X)Mdn%M(X)Mdn
Frequency of purchase of chilled ready meals (n = 1057) **
every day (1)0.04.514.000.34.935.0050.050.00
every other day on average (2)1.90.5
1–2 times a week on average (3)13.07.4
1–2 times a month on average (4)37.124.6
less than once a month (5)28.432.3
never (6)19.634.9
Frequency of purchase of frozen food, including ready meals (n = 1059) **
every day (1)0.24.424.000.24.785.0035.850.00
every other day on average (2)1.40.5
1–2 times a week on average (3)13.87.7
1–2 times a month on average (4)38.429.2
less than once a month (5)32.737.4
never (6)13.525.0
Importance of the price of purchased products (n = 944) *
definitely important (1)51.01.581.0059.91.511.0013.180.01
rather important (2)40.932.8
neither important nor unimportant (3)6.74.5
rather unimportant (4)1.42.1
definitely unimportant (5)0.00.7
Importance of storage conditions of purchased products (n = 942) *
definitely important (1)31.62.022.0043.51.802.0015.9570.00
rather important (2)45.939.0
neither important nor unimportant (3)14.012.2
rather unimportant (4)5.74.0
definitely unimportant (5)2.91.3
Frequency of preparing meals (n = 1108) **
always (1)27.72.371.0032.52.521.0027.740.00
usually (2)30.919.2
sometimes (3)21.120.4
rarely (4)16.920.0
never (5)3.47.9
Frequency of washing hands before preparing meals (n = 1036) **
always (1)57.31.631.0068.31.511.0017.790.00
usually (2)27.417.7
sometimes (3)11.19.3
rarely (4)3.34.3
never (5)0.90.4
* excluded respondents who do not make purchases or indicated the answer “I don’t know/hard to say”; ** excluded respondents who indicated the answer “I don’t know/hard to say”. a R_Yes: rather throw away group—answers ‘always’, ‘usually’, ‘sometimes’; b R_No: rather not throwing away group—answers ‘rarely’, ‘never’, ‘hard to say’.
Table 7. Influence of selected explanatory variables on disposing of uneaten meals in a waste container-model regression results.
Table 7. Influence of selected explanatory variables on disposing of uneaten meals in a waste container-model regression results.
Explanatory VariableModel Parameters
Coefficient β1Std. Errort-Valuep-Value−95% CI+95% CIWald’s Chi-SquareOR
Frequency of preparing a shopping list0.1010.0671.5130.131−0.0300.2322.2891.106
Frequency of purchase of chilled ready meals−0.2910.096−3.0180.003 *−0.480−0.1029.1110.748
Frequency of purchase of frozen food, including ready meals−0.2090.103−2.0310.043 *−0.411−0.0074.1270.811
Importance of the price of purchased products−0.1080.109−0.9870.324−0.3220.1070.9750.898
Importance of storage conditions0.2830.0873.2470.001 *0.1120.45410.5451.327
School as the basis of current food handling0.5400.2971.8190.069−0.0431.1233.3091.16
Frequency of preparing meals−0.0640.067−0.9430.346−0.1960.0690.8900.938
Frequency of washing hands before preparing meals−0.0280.093−0.3040.761−0.2100.1540.0920.972
Food waste is a social problem0.0650.0850.7620.446−0.1020.2310.5811.067
* p < 0.05 OR-odds ratio.
Table 8. Table of the variable “disposing of uneaten meals” relevance.
Table 8. Table of the variable “disposing of uneaten meals” relevance.
ActualPredictedShare of Correctly Predicted Cases
Y = 1Y = 0
Y = 112524234.05994
Y = 08842982.97872
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Tomaszewska, M.; Bilska, B.; Kołożyn-Krajewska, D. Managing Food Leftovers in Polish Households in Terms of the Food Waste Hierarchy. Sustainability 2025, 17, 10552. https://doi.org/10.3390/su172310552

AMA Style

Tomaszewska M, Bilska B, Kołożyn-Krajewska D. Managing Food Leftovers in Polish Households in Terms of the Food Waste Hierarchy. Sustainability. 2025; 17(23):10552. https://doi.org/10.3390/su172310552

Chicago/Turabian Style

Tomaszewska, Marzena, Beata Bilska, and Danuta Kołożyn-Krajewska. 2025. "Managing Food Leftovers in Polish Households in Terms of the Food Waste Hierarchy" Sustainability 17, no. 23: 10552. https://doi.org/10.3390/su172310552

APA Style

Tomaszewska, M., Bilska, B., & Kołożyn-Krajewska, D. (2025). Managing Food Leftovers in Polish Households in Terms of the Food Waste Hierarchy. Sustainability, 17(23), 10552. https://doi.org/10.3390/su172310552

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