food waste management system research paper

REVIEW article

Household food waste research: the current state of the art and a guided tour for further development.

• 1 Faculty of Economics and Business, University of Debrecen, College of Business and Economics, University of Johannesburg, Johannesburg, South Africa
• 2 Risk Management Directorate, National Food Chain Safety Office, Budapest, Hungary
• 3 John von Neumann University, Hungarian National Bank—Research Center, College of Business and Economics, University of Johannesburg, Johannesburg, South Africa
• 4 Department of Agricultural and Food Industrial Enterprise Management, Hungarian University of Agriculture and Life Science (MATE), Gödöllő, Hungary
• 5 Kebbi State University of Science and Technology, Aliero, Nigeria

Decreasing food waste is an important contribution to the practical achievement of Sustainable Development Goals of the United Nations. The last decades witnessed a dynamic expansion of food waste-related publications, parallel with this studies, systematic reviews and bibliometric analyses had been published on this topic. The novelty of the current publication is threefold: 1) it summarizes recent publications, and puts their results into development context; 2) applies the triangulation method by analyzing the food waste-based literature from the aspect of epistemological development, structural composition and scientometric mapping, 3) based on in-depth research of the literature and the determination of the most important ways of its development, the key steps of a modern waste research project as a function of research goals as well as available financial resources are outlined. The bibliometric research based on nearly three thousand resources has shown a considerable geographic disparity in food waste research: these topics are investigated mainly in developed and emerging countries. Bibliometric mapping highlights the importance of the application of qualitative methods for exploring motivational drivers and actual behaviour of households. A general workflow for food waste research is suggested by the authors based on a study carried out in developed countries. This method can be considered as a general, flexible framework, which could serve as a common platform for experts. The framework can be used independently from the of economic development level of the countries but it is especially useful for researchers in the global South because experiences gained by developed countries opens a favourable possibility to conceptualise, plan, realise and publish their food-waste related research.

The household food waste-related literature is increasing exponentially.

The topic is dominated by the authors from the most developed states.

The science mapping method helps to identify key research areas and their dynamics.

Nearing to end of questionnaire era: increasing of the importance of qualitative methods.

1 Introduction

In the recent decades increasing (household) food waste-related publications were published. The contribution of the present study for the available literature is threefold: 1) it summarises recent publications and puts the results into a development context; 2) the article applies the triangulation method by analysing the food waste-based literature from the aspect of epistemological development, structural composition and scientometric mapping, 3) based on in-depth research of the literature and the determination of the most important ways of its development the key steps of a modern waste research project are outlined as a function of research goals as well as available financial resources.

There is a close connection between food waste and the Sustainable Development Goals, declared by the United Nations ( Grosso and Falasconi, 2018 ). The most important direct relationship between the Sustainable Development Goals and food waste demonstrates the direct and indirect effect of food waste on the long-range sustainability goals of the UN ( Table 1 ; Figure 1 ).

TABLE 1 . Interactions between UN Sustainable Development Goals and food security.

FIGURE 1 . Direct and indirect effects of food waste reduction on the achievement of the Sustainable Development Goals of the UN.

UN SDG: Target 12—Sustainable Consumption and Production - requires not only the complete transformation of the use of natural resources, production technology, and consumer behaviour, but also the elimination of food waste at all stages of the food marketing chain ( Bringye et al., 2021 ).

The problem of household food waste has been the subject of growing scientific interest. More and more publications are available on this topic, also including review articles ( Cox et al., 2010 ; Lebersorger and Schneider, 2011 ; Bräutigam et al., 2014 ; Chen et al., 2017 ; Hebrok and Boks, 2017 ; Ingrao et al., 2018 ; Kibler et al., 2018 ; Schanes et al., 2018 ). The number of publications related to the factors influencing household food waste generation has also increased since 2000 ( De Hooge et al., 2017 ).

According to the most recent estimate, a considerable proportion–approximately 53%—of global food waste is generated by households in the EU ( FUSIONS, 2016 ). Based on the FAO’s estimation, consumers are primarily responsible for food waste generation in economically developed regions ( FAO, 2011 ). Thus, studies of consumer food waste were initially predominant in the highest income level countries, for example, in the United Kingdom and the United States ( Schneider, 2013 ). Principato et al. (2015) have made an important contribution to a better understanding of the topics studied by the construction of a “Household Wasteful Behavioural Framework”. Boulet et al. (2021) , in their review, determined the factors influencing food waste in households. Household waste composition has been also analysed by several studies ( Withanage et al., 2021 ).

There are significant differences between the results of studies using different methodologies. The estimated amount of household food waste was 76 kg per capita in Europe per year in the first years of the 21st century ( BIOIS, 2011 ). The experts of the FUSIONS (Food Use for Social Innovation by Optimising Waste Prevention Strategies) project—as a follow-up of the BIOIS study, based on a slightly different methodology − reported 92 kg per capita household food waste in the EU-28 ( FUSIONS, 2016 ). The calculated results published in this study are based on the EUROSTAT database of general animal and vegetable waste generation, officially reported by member states. In many cases, the authors of the above mentioned studies could also identify relevant national research reports, and used them to refine the statistically derived numbers. Nevertheless, due to the lack of research standards, the comparability and coherency of the national statistics are fairly limited ( Bräutigam et al., 2014 ). However, it is certain that mathematical estimations based on officially reported “bio-waste” statistics could not give a detailed picture of household food waste by country. Consequently, research using primary methodology (surveys, waste logbooks, in-depth interviews, waste composition analyses, etc.) is the only way to gain accurate information about food waste at the household level.

The novelty of the study is that it investigates the issue of household food waste by bibliometric analyses based on the latest results, focussing on the determination of different research directions, in this way giving practical assistance to research policy decision makers and researchers in the process of further investigations of this topic. On the basis of bibliometric analysis this study presents the major problems and stages of household-related food waste surveys in the form of a guided tour, review the most important, open-ended research questions, and key steps of research. The potential users of information, summarised in the current article can be (food waste) researchers and policy makers, especially in the developing world, who can set up their own research plans, adapting the experiences obtained in the developed states.

The structure of the current article is depicted in Figure 2 .

FIGURE 2 . Structure of the article.

2 Methodology

This study applies a combination of two leading academic literature databases: the Web of Science and Scopus. The mapping of the scientific literature was based on the Web of Science, because the coverage of this database is narrower compared to Scopus, allowing us to better focus on high-quality English publications ( Aghaei Chadegani et al., 2013 ). On the other hand, we have used the Scopus database for the qualitative literature survey, due to the wider spectrum of publications covered by Scopus ( Mongeon and Paul-Hus, 2016 ).

In the case of the bibliometric analysis the following search expression was applied:

The time span of research was 1975–2012 (31.12), but the first publication appeared in the database in 1993.

Bibliometric analysis was conducted with the Bibliometrix R package ( Derviş, 2019 ), following the general standards of bibliometric research ( Guler et al., 2016 ).

The epistemological background of the problem has been analyzed by CitNetExplorer software, which was developed specially for these purposes ( Van Eck and Waltman, 2014 ).

The clusters of different research concepts were analysed on the basis of the co-occurrence of words, by the VOS viewer tool ( Arifin et al., 2021 ).

In the seminal paper by Cobo et al. (2011) the authors suggested a highly innovative approach to positioning different research topics, based on keyword clustering and then the positioning of clusters based on their intellectual space, determined by the centrality and density of different topics. Centrality characterizes the frequency of citations to a given topic, density the intensity of citations between different publications in the same topic cluster. According to the theory of Cobo et al. (2011) , some topics are well developed and important. These can be characterised by a high degree of centrality and density. These are called motor themes. Some topics have intensive communication within the topics, but they are relatively poorly integrated into the larger research field. These topics are called specialised or peripheral topics, and can be characterised by high density and low centrality. The emerging or disappearing topics have low centrality and density. Themes which are situated in the lower-right quadrant are highly cited, but the intensity of communications within these topic clusters is relatively low. These themes are called general or basic themes.

We have applied this method to determine the most important themes of the research field. For a better understanding of the development of the topics, we have divided the set of publications into three parts, based on an algorithm for the determination of brake points in a time series, calculated by the generic algorithm of ( Doerr et al., 2017 ). On this basis we have divided the time interval into three periods: 1993–2016, 2017–2018, 2019–2021.

3.1 General Characteristics of the Bibliometric Database

The set of results consisted of 2,964 resources. Analysing the dynamics of the increasing number of publications related to household-food waste, it is obvious that this topic has gained in importance rather rapidly ( Figure 3 ). Between 1993 and 2016 only a few publications analysed this topic; later, the number increased sharply. The number of publications can be approximated by an exponential function. This highlights the increasing importance of this topic in academic research. At the same time, it underscores the rapidly growing public attention towards this problem. Although the food waste problem is quite serious in developing regions, the most important attention is paid to it in developed countries.

FIGURE 3 . Dynamics of annual publications related to household food waste and the approximation of this process by an exponential function (1993–2021).

The number of authors is relatively high: more than 9,000 researchers analysed this problem in the time interval under investigation. The food waste problem demands a concentrated effort of different specialists. This fact explains why the number of single authored papers is relatively low: no more than 0.5% of the total relevant papers have been written by a sole author. The number of authors per document is on average 3.23. The structure of the journals, which can be considered the most relevant sources shows similar tendencies, which can be seen in another field of science and technology: the Journal of Cleaner Production and Waste Management, as well as Resources Conservation and Recycling have important positions, but there are numerous new channels of food waste related communication: Sustainability, Foods and Energies.

The most important authors on food waste are those from developed countries. The number of publications on household food waste shows that this field is dominated by the United States and Western-European countries ( Table 2 ). Among developing and emerging countries, only China is worth mentioning. The level of international collaboration is moderate, with some exceptions (China, United Kingdom).

TABLE 2 . Key characteristic features of international publications on household food waste.

If we measure the influence of different countries based on the number of citations, it can be seen that developed countries dominate this field, too ( Table 3 ). The average number of article citations from the United Kingdom and Denmark, as well as Sweden, is extremely high. This fact highlights the level and trend-setting character of these publications.

TABLE 3 . Number of citations of articles from different countries.

3.2 Clustering of the Topics

3.2.1 clustering based on epistemiologic development.

The epistemological structure of household food waste research reveals its intellectual roots and the paths it has followed.

The total number of citation links in the corpus was 32,937. Based on their relationships, four groups could be identified. The first group is the largest. This group of publications consisted of 1777 publications. The second largest group had 970, the third 212, and the fourth 432 papers, respectively. The database included the non-matching cited references, and cited references with at least ten citations were included in the citation network. The publications were clustered on the basis of citation links. The resolution parameter was set to one, and the minimum cluster size to one hundred. In this way four clusters were identified. 434 publications did not belong to a cluster.

The first cluster consisted of publications focusing on general problems of food waste in the food chain and on consumer behaviour. The second cluster included studies on the utilization of food waste for energetic purposes. Minimizing food waste by changing consumer behaviour was the key topic of the third cluster. The fourth cluster consisted of publications focusing on non-energy related utilization of food waste.

In summary it can be concluded that the majority of publications analysed the general problems of food waste in households, a relatively smaller set of publications focused on specific aspects of consumer behaviour concerning waste, and two clusters analysed food waste from the perspective of by-product utilisation.

3.2.2 Clustering Based on Co-occurrences of Keywords

The analysis of the co-occurrence of different keywords shows that two clusters can be separated, one focussing on consumer behaviour and one on different, mainly technological questions ( Figure 4 ). The former cluster incorporates two main branches: one deals with behaviours, the other with determinants, namely barriers and attitudes. The cluster focussing on behaviour has some specific aspects in the field of quantification of wastes and analysis of consumer behaviour. The second large cluster also has two parts. One well separable part deals with the utilisation of food waste as biomass, with one part of this sub-cluster focusing on biogas-production and the other on waste-water utilisation.

FIGURE 4 . Dendrogram of topics.

The second sub-cluster is a rather complex one and includes different branches. One important branch focuses on the effect of food waste based on life-cycle analysis, system dynamics and emission reduction. Another branch analyses this problem from the perspective of sustainability and climate change. The largest sub-cluster is a relatively heterogonous one. One part studies the food technological aspects of decreasing food waste, while the other investigates the environmental aspects of food waste.

The structure of different research directions was analysed by the clustering of the co-occurrence of keywords. Results of this analysis are summarised in Figure 5 .

FIGURE 5 . The key topics of household waste research based on the co-occurrence of keywords.

The first cluster is dominated by consumer related behaviour focussing on the attitudes to and management of household food logistics. The most important keywords are management, sustainability and behaviour. Specific attention is given to the prevention of food waste and barriers to food waste reduction. The second cluster focusses on the utilisation of food waste, mainly by biogas production. Consequently, the most important keywords in this cluster are anaerobic fermentation, sewage-sludge mixture and co-digestion. The third cluster put an emphasis on the realisation of a circular economy by the recovery and re-use of food waste.

Life-cycle analysis and municipal waste-logistics problems as well as emissions reduction are at the centre of the fourth cluster, which embraces the different aspects of questions related to food waste from the viewpoint of regional management and city logistics systems. The fifth cluster evaluates the problem of healthy eating and nutrition. In this cluster food waste problems appear in relation with leftover food and irrational, unhealthy food consumer behaviour, followed by obesity and interventions aiming at the education of future generations. The sixth cluster focusses on global problems related to food waste with regard to the energy-food-nutrition triangle.

3.2.3 Dynamics of Research Topics

The relative position of different themes highlights the fact that in the first period (1993–2016) the most important topics with a high level of centrality and density were the global food shortage, the adverse consequences of food waste on the efficiency of the agro-food chain and the disposal of leftover food ( Figure 6 ). In this period food shortages were a relatively new phenomenon, therefore the simple description of the problem gained considerable attention. Other topics are related to technological solutions for the disposal of leftovers or energy technology.

FIGURE 6 . Relative position of different themes of the household food waste problem between 1993 and 2016.

Surveys of household behavior or the system analysis of the energy-water-climate triangle were relatively new, emerging technologies at this time. Interestingly, in the early period of food waste research leftovers in catering systems, the human energy balance and the relationship between healthy eating and the irrational use of food were relatively peripheral topics. Several authors analysed these problems without integrating them into the available knowledge on food waste.

The second period can be characterized by considerable changes in the relevant topics ( Figure 7 ). Food waste disposal, the place and role of municipalities and the different packaging systems were the basic topics, with a high level of citations. In the late 2010s these fields of knowledge formed the foundations of food waste research. The increasing understanding of the role of suboptimal food purchase as a factor in environmental burden promoted the introduction of new methods into household food waste research (e.g. the wide ranging application of video systems and in-depth interviews, as opposed to the traditional methods of paper and pencil surveys). These new, highly innovative approaches are based on the application of modern methods of info-communication systems. At the same time, the conversion of food waste to bioenergy remained a central topic. In this period “outsider” researchers into food waste started to appear, which can be explained by the emergence of mathematical methods (e.g., operational research and new technologies for the re-use of food waste). As a consequence of the widening interest towards the problem of food waste, some more specialized groups of researchers formed. Some of them began to apply more sophisticated statistical methods (above all structural modelling equations), alongside research into the further development of food technology and the optimization of portion sizes. At this time these fields of science were relatively separated, with an island-like phenomenon.

FIGURE 7 . Relative position of different themes relating to the household food waste problem between 2017 and 2018.

In the last few years ( Figure 8 ) there has been a considerable synthesizing work of the accumulated knowledge. Some techniques become routine. The PLS-SEM models got an increasing importance. Of course, the Covid-19 pandemics caused a new situation in food consumption patterns, which is a new topics in waste-research, too.

FIGURE 8 . Relative position of different themes on household food waste problem between 2019–2021.

3.3 Practical Problems of Food Waste Research—A Guided Tour

On base of the bibliometric analysis there is a possibility to outline the most important steps of preparation of a household food waste survey.

3.3.1 Current Problems of the Research

As we have seen, the generation of household food waste is a highly complex question. First of all it is useful to analyse its causes. On base of the literature, we have set up a general, conceptual model, depicted in Figure 9 .

FIGURE 9 . Conceptual model of causes of the household food waste.

On base of this model in the next paragraphs we outline the most important known and unknown parts of the different components.

3.3.1.1 Cognitive Components

Related to cognitive components, the observation made by researchers is that a significant proportion of consumers are unable to recognise the existence of the food waste-related problem itself ( Graham-Rowe et al., 2014 ; Withanage et al., 2021 ). The explanation for this is the lack of immediate and direct personal benefit if consumers reduce their waste generation ( Quested et al., 2013 ). Furthermore, they do not receive positive feedback from their residential environment i.e. the next door neighbour does not actually see their efforts to prevent food waste generation. Therefore, it is difficult to rationalize their food waste prevention behaviour.

According to Aschemann-Witzel et al. (2015) , certain positive social norms, such as the appearance in restaurants of so-called doggy-bags to take left-over food home, support conscious consumers in their attempts to avoid generating food waste.

Another interesting cognitive component that relates to food waste is the overestimation of food safety risks resulting from long-term food storage. In most of the cases, this perception is due to consumers’ lack of knowledge on food safety. The overestimation of risks is visible in their behaviour and practices, as well. Therefore, a lot of safe food is wasted for no conceivable reason. Research in this area goes back as far as the 1980’s.

In the United States, Van Garde and Woodburn (1987) proved that participants’ evaluation of food safety is incorrect, based on their experiment with 243 households. Lanfranchi et al. (2016) confirmed that concerns about food safety risks definitely also contribute to the production of food waste.

Similarly, the misinterpretation of the terms “Use-by date” and “Best before date” leads to food waste. The “Use-by date’” is a food safety indicator, showing the last day a perishable product (e.g., a dairy product) is safe to consume, while the “Best before date” is a quality indicator, reporting the date when a durable product (e.g,. canned food) may lose some of its quality ( DG SANTE, 2016 ). While foods cannot be legally sold after a “Best before” date, they can be consumed for a certain period as long as they are not damaged, although this ‘safe period’ is different for each item. Therefore, food experts face difficulties in producing a labelling system with relevant additional information for the different types of foodstuff ( Whitehead et al., 2013 ). Field-related research points out that consumers are unable to distinguish between the “Use-by date” and the “Best before date.” For them, both terms are related to food safety. This misinterpretation, rooted in a lack of knowledge, can lead to excessive waste generation ( Silvennoinen et al., 2014 ; Melbye et al., 2017 ).

In the United Kingdom, the WRAP (Waste & Resources Action Programme) investigated the use of resealable packages to protect food products and maintain their freshness, and found that consumers ignore using this function as they are unaware of the benefits that packaging can offer, which again may be explained by their lack of knowledge of food waste prevention. Unsealed food loses its freshness, appetising looks and quality. Therefore, especially in single-person households, it contributes to food waste, as a packaging unit is not consumed as fast as in large households ( WRAP, 2013 ).

3.3.1.2 Affective Components

In their study, Graham-Rowe et al. (2014) focus on mapping the connection between the affective attitude components and actual food consumption habits (motivational vs. impeding factors in reducing food waste). Their results demonstrate that consumers are generally aware of the negative consequences of food waste generation, and they are worried because of this. Maintaining a personal comfort zone, i.e. having an extra food supply stored, is one of the most important problems.

A Greek survey, involving 231 respondents, confirms the positive attitude of consumers in preventing food waste generation, which, however, does not materialise in action, as the majority of respondents shop on impulse and, in addition, they are ‘label ignorant’ in many ways ( Graham-Rowe et al., 2014 ). It is pointed out in a Belgian review study, that in our century people have no respect for food and consequently, they do not value it (Beaufort, 2014). Several studies observed the lack of consumers’ positive attitude in relation to food as a factor influencing food waste generation ( Evans, 2012 ; Grandhi and Appaiah Singh, 2016 ; Stancu et al., 2016 ). This can be the primary cause of consumers’ lack of a conscious handling of food. It was found in a Norwegian survey that people who are concerned about the environment also condemn food waste ( Melbye et al., 2017 ; Ingrao et al., 2018 ).

3.3.1.3 Conative Components

Research has identified consumers’ shopping habits, particularly their way of shopping, as a primary influencing factor. Evans (2012) and ( Principato et al., 2015 ) proved that list-based, conscious shopping can reduce food waste. The role of “ad hoc” or impulse shopping in food waste generation was also confirmed by a Finnish research team in the same year in a detailed and comprehensive analysis of 380 households ( Koivupuro et al., 2012 ). These results are consistent with some other quantitative consumer surveys ( Stefan et al., 2013 ; Jörissen et al., 2015 ). According to the research of Qi and Roe (2016) , purchasing bulk products also contributes to food waste generation. The importance of making a shopping list is emphasised by a recent survey involving 233 students.

Presumably, shopping habits and perception are related to the packaging of certain types of food items and their ‘fresh look’. This phenomenon is especially manifested in the case of bread and roll products, based on the results of a Czech primary research study ( Sulaiman et al., 2016 ).

In a shopping situation, in addition to the aesthetics of food presentation, the psychological effect of price reduction is also important. Experts note that when seeing the vast array of products on sale, consumers tend to misjudge their need for their food supply, which in the case of perishable foods may lead to waste ( Koivupuro et al., 2012 ; Aschemann-Witzel et al., 2015 ; Chan, 2022 ).

Another factor linked to shopping habits is packaging. A Swedish study points out that 20–25% of food waste can be related to packaging, particularly to the size and form of containers ( Williams et al., 2012 ).

Shopping and domestic food storage habits are directly related. Some studies have shown that inappropriate food storage practices lead to the decay of both perishable and durable food ( Chappell, 1954 ; Quested et al., 2013 ; Masson et al., 2017 ). In the case of perishable food, it is the unpleasant smell that draws the consumer’s attention to the process of decay, while durable food is usually detected during ‘spring cleaning’.

A subsequent, field-related study reached the same conclusion ( Jörissen et al., 2015 ). An Austrian-British study, based on in-depth (in-home-tour) interviews, revealed interesting consumer observations ( Ganglbauer et al., 2013 ). In this study, one of the respondents reported a conscious arrangement of food while striving for “transparency’ in the storage area. He uses jars and glass containers at home to store muesli, rice or flour, which makes it possible for continuous checking of supplies.

Cooking is another factor that can lead to food waste generation. In his sociological study, Evans (2012) emphasises that the differences in cooking practices between nations, in terms of basic ingredients and methods used, can be observed in families belonging to the same nation, as well. Cooking practices, as well as other beliefs and perceptions are transmitted from generation to generation. Therefore, the role of family traditions cannot be ignored because a “bad” practice acquired in childhood and “routinised” along the years can be changed with more difficulty than abandoning habits formed later at some stage in the individual’s life. On the contrary, in other cases, the problem itself is that the transmission of cooking practices does not take place within the family.

More important than recycling is the prevention of food waste through amount control. Graham-Rowe et al. (2014) used in-depth interviews (15 households) to identify the factors that impede food waste prevention. Their results reveal that the cause may be parents’ (most notably mothers’) endeavour to provide their families and visiting guests with good quality and plentiful food (preferring fresh food to canned food). With this attitude, parents would like to avoid unpleasant situations and possicarble family conflicts arising from shortage of supply. All this leads to surplus in cooking and eventually to food waste. This practice was confirmed by research on cooking habits conducted in the 2000’s ( Ganglbauer et al., 2013 ; Stefan et al., 2013 ; Jörissen et al., 2015 ).

National eating traditions and the eating habits act as a different parameter in research on food waste. While it often roots in national culture, according to a comparative study on the Scandinavian states, the eating traditions of Nordic people are considerably influenced by global trends Gjerris and Gaiani (2013) , and it is probably true for all developed countries. Besides the eating traditions, the eating habits are also important: Evans (2012) conducted an extensive investigation on the changing patterns of eating habits in families.

A question of practical nature relates to unconsumed food left after eating, known as “plate waste” in field-related research. In a study by Silvennoinen et al. (2014) based on factual assessment, irresponsible eating habits were also identified as a predictor of the quantity of food waste generation. These findings were equally confirmed by a primary study from the United Kingdom ( Van Garde and Woodburn, 1987 ). An interesting approach to the question of plate waste is that Australian consumers sometimes produce plate waste to avoid extra kilograms ( Hoek et al., 2017 ). According to some recent studies, practice of meal planning actually contributes to the minimization food wastage level in the households ( Quested et al., 2013 ; Stefan et al., 2013 ; Mallinson et al., 2016 ).

Overall, the lifestyle of the members of a household has a considerable impact on food waste generation, especially the lifestyle of the person responsible for shopping and cooking and the amount of time they can afford to plan the family menu ( Ghafoorifard et al., 2022 ). The fast pace of life as generator of food waste is present in many publications following the changes in lifestyle ( Ganglbauer et al., 2013 ; Jörissen et al., 2015 ; Ferro et al., 2022 ).

According to experts Aschemann-Witzel et al. (2015) , in case of urban dwellers, the connection between the suppliers of raw materials for food industry and consumers is practically non-existent ( Lakner and Baker, 2014 ; Barma and Modibbo, 2022 ). Therefore, consumers find it difficult to visualise the amount of work required for food production. Aschemann-Witzel et al. (2015) stressed that if consumers had food-production knowledge, they would learn to appreciate the value of foodstuff. This is confirmed by earlier findings. For example, an Austrian consumer reports that they have learned to value foodstuff more since they produce it. It is also typical that households composting biodegradable waste regularly throw out less food compared to consumers who disregard this opportunity ( Yepsen, 2009 ). In contrast, Tucker and Farrelly (2016) stated that the practice of composting usually reduces consumers’ motivation for food waste prevention.

Consequently, it may seem that active participation in food production can strengthen consumers’ food related perceptions and positive attitude.

3.3.1.4 Socio-Demographic Background

Researchers differ in their views on the impact of consumers’ socio-demographical background on the amount of food waste generation. For many of them, it is self-evident that the income of the household influences the amount of waste. However, some studies could even prove statistically the positive correlation between the income of the households in question and the amount of food waste they produced ( Schneider and Obersteiner, 2007 ).

According to Milanovic (2013) , this phenomenon can be explained by the fact that 3 or 4 decades ago income inequality around the world was not as substantial as it is nowadays, therefore, the role of income as a differentiating factor was less significant. Still, there are examples for uncorrelation between income and waste production ( Koivupuro et al., 2012 ; Williams et al., 2012 ). In addition, an interesting result in this field is that an Italian survey from 2016 examined this topic in depth and presented that mid-to-low income consumers waste more food than people with higher income in the case of products with lower price and quality ( Setti et al., 2016 ). Thus, it can be said that the predictive role of income in the amount of food waste generation is not exactly clear.

Results concerning the size and composition of households are contradictory, as well. It was proven earlier that larger households with several members produce more waste in total, but in terms of waste per capita single-person households lead a more wasteful lifestyle. It has been also observed that children under the age of 16 produce a disproportionately high amount of waste ( WRAP, 2014 ; Jörissen et al., 2015 ). The waste-avoidance attitude of the older generation originates in the post-war period when they had to learn to economise relying on scarce resources. A study on food waste thus refers to consumers above 60 as “the post-war generation” ( Schneider, 2008 ). However, Koivupuro et al. (2012) did not find any statistically provable correlation between the distribution of age in households and the amount of food waste they generated.

The gender of the person responsible for shopping can also influence the degree of wastage. It is claimed parents’ (most notably mothers’) excessive care to provide for their families often leads to overstocking supplies ( Graham-Rowe et al., 2014 ). Wassermann and Schneider (2005) found that in case of jobs requiring high education, the burden of responsibility and the desire to meet the requirements of the position need a considerable amount of time, which is taken away from household management. Therefore, high education can indirectly lead to waste generation.

Lebersorger and Schneider (2011) found relevant difference between them. Norway experts also proved that food wastage level is higher in the urban region than in the rural area ( Hanssen et al., 2016 ). In addition to the influence of urban or rural areas, ( Koivupuro et al., 2012 ; Ganglbauer et al., 2013 ) surveyed the type of residence (e.g. detached house, flat on a housing estate), but did not find any significant correlation in this respect, either. According to Ganglbauer et al. (2013) , residents in certain urban areas do shop more consciously and in the case of large households more food waste is generated due to the greater storage capacity at their disposal. This result may seem rational; however, it is qualitative in nature because it reflects the opinion of a reduced number of consumers only.

3.3.2 Practical Research Steps in Food Waste Research

The most important phases of the research workflow are outlined in Figure 10 .

FIGURE 10 . Workflow of an in-depth food waste research project.

The most commonly used method is the quantitative consumer survey in the field of household food waste research. Within this category, a relatively new, but frequently used research tool in this field is the application of online questionnaires and telephone surveys ( Jörissen et al., 2015 ; Qi and Roe, 2016 ). Classical quantitative consumer surveys provide an appropriate sample size, but respondents usually give general, socially accepted answers even in anonymous questionnaires. Therefore, conclusions will most likely be distorted in a positive direction ( Beretta et al., 2013 ).

Different kinds of combined methods are also a common practice in this research field within the examined period ( Hanssen et al., 2016 ). The methodology of using a waste logbook and qualitative interviews–which include in-depth, in-home-tour or focus group interviews–is less widespread ( Ganglbauer et al., 2013 ; Silvennoinen et al., 2014 ). The application of the self-report method resulting in a possible underestimation may prevent the exact analysis of the effect of social factors on food wastage ( Parizeau et al., 2015 ). In addition, in-depth interviews involving households are applied to explore the complexity of this context ( Ganglbauer et al., 2013 ; Graham-Rowe et al., 2014 ).

The wide range of assessment tools make the validation of the potentially influencing factors difficult and limit the identification of differences between nations ( Reynolds et al., 2014 ). Moreover, their reliability and comparability are very limited ( Lebersorger and Schneider, 2011 ). It is important to highlight that the World Resources Institute (WRI) and FUSIONS teams have already published reports on the harmonization of food waste measurements ( Tostivint et al., 2016 ; WRI, 2016 ).

4 Conclusion and Future Remarks

The food waste problem has become an important research topic, consequently a considerable increase in academic knowledge base in this field was observed. The rapidly augmenting number of tools of research and analysis, and the increasing multidisciplinary nature of this problem offers the possibility to better understand the causes and consequences of food waste. At the same time, there are considerable gaps in our knowledge in this field.

The majority of studies has been conducted in developed countries, but the food waste is a significant issue in developing countries, too. According to the opinion of Thi et al. (2015) the stochastic relationship between the level of economic development and the per capita food waste can be approximated by a parabolic function, that’s why the food waste in developing countries is relatively low, the number of people, living in the developing world makes this problem as an accoutre one, influencing the global food balance.

The food waste in emerging countries is an acute problem due to the rapidly increasing consumption of the elite in these countries driven by diverse motives, such as identity affirmation, self-expression, family-pride and hedonism as a self-esteem factor can be important generators of food waste ( Soma, 2018 ; Li and Wang, 2020 ). Researchers in developing regions, especially in the global South are able to identify these expanding tendencies in their own countries. Besides over-consumption of the elite some other problems can be also highlighted in these regions, such as under-developed food preservation technologies, challenges of the cold-chain and inadequate food hygiene knowledge, and their importance is increasing as a result of global warming. By using the ‘Workflow of an in-depth food waste research project’ not just target groups, motives and behavioural elements can be identified but also levers to change consumption patterns. Timely research results might support decision making in order to introduce food waste mitigating actions before the consumption (and food wastage-) habits of the elite affects general social norms. Thus risk mitigating actions might include targeted interventions, such as awareness campaigns, school programmes and local programmes (ideally embracing local communities).

The definition of household food waste should be further developed. The current approach is based on the assumption that food which is consumed is not waste. At the same time it should be considered that the overconsumption of food does not have any justification, and is even harmful, leading to obesity and accompanying diseases. That is why a more constructive dialogue should be created between dietitians, specialists, those focussing on sustainable diet development and food waste researchers ( Mortada et al., 2018 ; Tompa et al., 2020 ; Waitt and Rankin, 2022 ).

The food consumption of marginal communities in developed states is a rather specific problem. For example, we hardly know anything of the food consumption patterns of European Roma communities ( Dunajeva and Kostka (2022) ; however, this population, consisting of ca. ten million people, living mainly in Central and Eastern Europe is the “most vulnerable minority in Europe” ( Gómez et al. (2019) and there are considerable anecdotic evidences, that in case of this ethnic minority there is a non-rational food consumption leading to waste of food and water ( Halász, 2020 ).

The passing of time since the acknowledgement of importance of food waste problem and the introduction of the first educational programs offer an ever increasing possibility to measure these interventions on consumer behaviour and generation of food waste. On this base there is a possibility to choose the best practices and methods. It should be emphasized that the ‘Workflow of an in-depth food waste research project’ is more than just a help in the initial goal setting, but also provides a guided tour for the development path of a regional food waste prevention strategy when used as an iterative tool.

The Internet of things offers new possibilities for qualitative upgrading of consumer information ( Wen et al., 2018 ). It would be extremely important to have a general picture on consumer attitude towards these technologies from point of view of food waster decreasing. Different logistical and technological systems of household food waste re-utilisation should be further developed, based on their complex evaluation, taking into consideration the different environmental consequences. In design and performance evaluation of these systems the wide-range application of lifecycle analysis is a key approach ( Goodarzian et al., 2021 ; Hutchings et al., 2021 ).

The Covid-19 pandemics has accelerated considerably such changes, which could be observed soon before the lockdown and restrictions. It is especially important, and a relative lesser analysed, how these relatively new patterns of consumer behavior will change the food waste generated in households.

Minimizing food wastes by shortcutting the HORECA sector and the social care services offers a considerable possibility for the alleviation of the under-nutrition of socially segregated, often homeless people. The stakeholder attitude research towards these solutions could be an important step towards the understanding of the possibilities of and the barriers to a wide-range application of these seemingly simple methods.

The possibilities of development in different fields are determined by the A, B, C triangle ( Figure 11 ).

FIGURE 11 . The triple helix structure of factors influencing the development of food waste related policies.

The level of technology and its social acceptance (A), and info-communication technology and its acceptance, as well as the interaction of technology and info-communication will determine the theoretical sphere of action. In practice, the possibilities of integration and the effective co-working of technology and info-communication systems (X), the ethical limits of the acceptance of information technology (Y) and food science technology will determine the possibilities. This fact highlights the importance of a holistic attitude: e.g. info-communication technology offers considerable advantages for food planning and tracing, but there are considerable ethical concerns around the application of these technologies, related to the protection of personal data (Y). The technology offers new, functional products, but we do not have the necessary information on the actual health condition of consumers when planning systems (X), and the use of the data on consumers raises considerable ethical problems (Z).

Data Availability Statement

The raw data supporting the conclusion of this article will be made available by the authors, without undue reservation.

Author Contributions

DS and BS-B conceived the study and were responsible for the design and development of the data analysis. ZL was responsible for data collection and analysis. GK was responsible for data interpretation. JO, JP, GK, and DS wrote the first draft of the article. JO and ZL supervised and edited the paper.

The research was financed by the resources of the National Food Chain Safety Authority and has been supported by the Bolyai János Research Fellowship of the Hungarian Academy of Sciences and the Bolyai + Fellowship (ÚNKP-21–5) of the New National Excellence Program of the Ministry of Innovation and Technology.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Keywords: household food waste, sustainability, bibliometrics, big data, prevention, consumer science

Citation: Oláh J, Kasza G, Szabó-Bódi B, Szakos D, Popp J and Lakner Z (2022) Household Food Waste Research: The Current State of the Art and a Guided Tour for Further Development. Front. Environ. Sci. 10:916601. doi: 10.3389/fenvs.2022.916601

Received: 09 April 2022; Accepted: 10 May 2022; Published: 27 May 2022.

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Copyright © 2022 Oláh, Kasza, Szabó-Bódi, Szakos, Popp and Lakner. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Judit Oláh, [email protected] ; József Popp, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Sustainable Food Waste Management: A Review

• First Online: 05 January 2021

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• Karuna Singh 5  

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Food wastage and loss is the major problem faced globally and affects developing and developed countries equally. Lost and wasted food represents a missed opportunity to feed the growing world population. Food is wasted at all the levels of food supply chain. Food products are heterogenic in nature and types of food waste and its composition also varied so it is difficult to apply a waste hierarchy to food products. Therefore, the waste hierarchy must be assessed for each type of food waste, rather than for “food waste” as a whole. This chapter will discuss in detail various preventive measures, impact of food waste and along with this various technology that can be used for the treatment of food waste. Best way to prevent food waste is using sustainable food waste management approaches to reduce and prevent food waste.

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Singh, K. (2020). Sustainable Food Waste Management: A Review. In: Thakur, M., Modi, V.K., Khedkar, R., Singh, K. (eds) Sustainable Food Waste Management. Springer, Singapore. https://doi.org/10.1007/978-981-15-8967-6_1

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Using artificial intelligence to tackle food waste and enhance the circular economy: maximising resource efficiency and minimising environmental impact: a review.

1. Introduction

2. current state of food waste and the circular economy, 2.1. the circular economy concept and its potential for reducing waste and increasing resource efficiency, 2.2. the role of ai in addressing food waste and supporting the circular economy, 2.3. using ai to support circular economy initiatives, 2.3.1. use of ai to identify opportunities for waste reduction and recycling, 2.3.2. applications of artificial intelligence (ai) in waste management and recycling, 2.3.3. potential benefits of an ai-supported circular economy initiative, 3. using ai to monitor and optimise food production and supply chains, 3.1. using ai to analyse data on factors such as weather patterns, crop yield, and consumer demand to optimise pre- and post-harvest food production and supply chains, 3.2. examples of ai applications in agriculture, food processing, and transportation, 3.3. potential benefits of ai optimisation, including reduced food waste and increased resource efficiency, 3.4. examples of ai applications in food production.

• IBM Food Trust: This blockchain-based platform uses AI and other technologies to track food products from farm to table, enabling suppliers and retailers to identify the source of any safety or quality issues quickly. By providing end-to-end traceability, IBM Food Trust can help to reduce waste caused by recalls and increase consumer trust in the food supply chain [ 98 ].
• Blue River Technology: This company uses computer vision and machine learning algorithms to identify and selectively spray weeds in agricultural fields. By targeting only weeds, Blue River Technology can reduce the use of herbicides and increase crop yield, thus improving efficiency and sustainability in agriculture [ 99 ].
• Brightloom: This company uses AI and predictive analytics to optimise menu offerings and pricing for food retailers. By analysing data on sales and customer preferences, Brightloom can help retailers to reduce waste caused by overproduction and ensure that their offerings are aligned with customer demand [ 100 ].
• AgShift: This company uses computer vision and AI to automate the process of quality inspection for commodities such as grains, fruits, and vegetables. By analysing images and other data, AgShift can quickly and accurately identify defects, reducing waste caused by human error [ 101 ].
• ImpactVision: This company uses hyperspectral imaging and machine learning to analyse the composition of food products, enabling suppliers and retailers to ensure that their products meet quality standards. By identifying quality issues early, ImpactVision can help to reduce waste caused by recalls and improve overall efficiency in the supply chain [ 102 ].

4. AI-Powered Food Redistribution Systems

4.1. using ai to match food donors with food banks and other organisations that distribute food to people in need, 4.2. examples of ai-powered food redistribution systems, 4.3. connection between gis and ai, 5. discussion, 6. conclusions, author contributions, institutional review board statement, data availability statement, conflicts of interest.

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Onyeaka, H.; Tamasiga, P.; Nwauzoma, U.M.; Miri, T.; Juliet, U.C.; Nwaiwu, O.; Akinsemolu, A.A. Using Artificial Intelligence to Tackle Food Waste and Enhance the Circular Economy: Maximising Resource Efficiency and Minimising Environmental Impact: A Review. Sustainability 2023 , 15 , 10482. https://doi.org/10.3390/su151310482

Onyeaka H, Tamasiga P, Nwauzoma UM, Miri T, Juliet UC, Nwaiwu O, Akinsemolu AA. Using Artificial Intelligence to Tackle Food Waste and Enhance the Circular Economy: Maximising Resource Efficiency and Minimising Environmental Impact: A Review. Sustainability . 2023; 15(13):10482. https://doi.org/10.3390/su151310482

Onyeaka, Helen, Phemelo Tamasiga, Uju Mary Nwauzoma, Taghi Miri, Uche Chioma Juliet, Ogueri Nwaiwu, and Adenike A. Akinsemolu. 2023. "Using Artificial Intelligence to Tackle Food Waste and Enhance the Circular Economy: Maximising Resource Efficiency and Minimising Environmental Impact: A Review" Sustainability 15, no. 13: 10482. https://doi.org/10.3390/su151310482

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An intelligent food waste identification and analysis system based on convolution neural network

DOI: https://doi.org/10.1145/3674558.3674585 ICCTA 2024: 2024 10th International Conference on Computer Technology Applications , Vienna, Austria, May 2024

To achieve sustainable development, an efficient and time-effective management of waste, including household, industrial, and food waste, is crucial. This paper introduces an intelligent food waste (FW) identification and analysis system based on convolution neural network (CNN), significantly enhanced by the application of fine-tuning. This approach involves modifying the CNN model while retaining some original structures and pre-trained parameters, and training it using a new dataset. The implementation of this method has led to a remarkable increase in the accuracy of the CNN model from 94.7% to 97.6%, a reduction in the training time cost by approximately 82.3%, and a decrease in the number of parameters that need to be trained by about 96.3%. The proposed system, an Internet of Things (IoT) system, comprises a sensing layer, network layer, data storage layer, and application layer. It autonomously identifies the type of FW using CNN, collecting and analysing food waste data, including date, weight, type, and reason. The lightweight neural network MobileNetV2 was employed because of its low computing requirements. To further enhance accuracy and reduce resource costs, fine-tuning technology was applied using a new training dataset and a new output layer in the neural network. This paper presents a comparative analysis of the original and the improved CNN model, demonstrating the significant improvements achieved through the application of fine-tuning. These results suggest that fine-tuning can enhance the accuracy and training efficiency of CNN by saving time costs and parameters, thereby contributing to the application of the CNN and fine-tuning.

ACM Reference Format: Xiyuan Sun∗, Yanxi Li, Zhimei Ouyang, Jinyuan Ouyang, Guangjing Yang and Enchang Sun. 2024. An intelligent food waste identification and analysis system based on convolution neural network. In 2024 10th International Conference on Computer Technology Applications ICCTA 2024), May 15-17, 2024, Vienna, Austria . ACM, New York, NY, USA, 8 Pages. https://doi.org/10.1145/3674558.3674585

1 INTRODUCTION

A previous study showed that about 1.3 billion tonnes of food waste (FW) is generated yearly, equivalent to one-third of the total food produced, which is enough to save one-eighth of the world's population from hunger [ 1 ]. This significant amount of FW may indicate that it is crucial to have good management and reduction of FW in such a source-constrained world. There are some ways to reduce the FW. For example, a study in 2019 proposed an FW management system based on the Internet of Things (IoT), and the result showed that the FW was significantly reduced, which may indicate the capability of the IoT system to reduce FW [ 2 ]. Also, applying intelligent garbage systems may improve the accuracy and reduce the time cost of garbage management since some intelligent waste management systems or intelligent garbage bins have already been proposed by some studies, having a good result in promoting waste collection [ 3 ]. Thus, applying intelligent systems may also reduce the FW. However, more research is still needed in this field. This paper aims to apply convolution neural network (CNN) in FW identification and improve CNN by fine-tuning in terms of accuracy and training efficiency.

This paper proposes an intelligent FW identification and analysis system that leverages CNN and incorporates an IoT structure. The system has several key features: (1) It employs CNN to identify FW types automatically. (2) The application of fine-tuning enhances the identification accuracy and reduces training time costs. (3) The implementation of an IoT structure facilitates efficient data collection and storage. This paper will detail the construction of the four IoT layers of the system, which are the sensing layer, network layer, data storage layer, and application layer. It will also discuss the improvement of MobileNetV2 by fine-tuning [ 4 ]. The results demonstrate that fine-tuning has enhanced the accuracy of the CNN model from 94.78% to 97.6%, reduced the training time by approximately 82.3%, and decreased the number of parameters requiring training by about 96.3%. These findings indicate the capability of fine-tuning to improve the performance of CNN models in FW management.

2 STRUCTURE, COMPONENTS AND FUNCTIONS

2.1 the structure and iot layers of the system.

This study implements an IoT system structured into four layers: the sensing layer, the network layer, the data storage layer, and the application layer. This configuration may facilitate a highly efficient workflow and exhibit strong potential for effective food waste management [ 2 ]. The four layers function as follows:

• The sensing layer: This layer comprises a weighing device, a micro camera, and a computer. The computer is responsible for data collection and the operation of the convolutional neural network.
• The network layer: This layer involves a digital device that receives data from the weighing machine via Bluetooth. All collected data is then transmitted to a MySQL database through the network.
• The data storage layer: This layer has a MySQL database, which stores the data for subsequent analysis by the website in the application layer.
• The application layer: This layer includes a website that analyses the data stored in the database and presents the results to the user, providing a direct demonstration of the FW information.
• The structure is illustrated in Figure 1 .

The visualization of the IoT structure discussed above, no new information.

2.2 Working process and functions

Based on the previously discussed structure, this food waste identification and analysis system is capable of collecting and analysing food waste (FW) data from a specific location with low time cost and high accuracy in identifying the FW. The data includes date, weight, type, and reason. The reasons can be categorised as customer waste, kitchen waste, surplus waste, and equipment failure, while the categories of FW can be divided into seven classes: cereal, vegetables, meat, eggs, fungi, fruits, and snack food. The working process of the system and the corresponding function of each IoT layer are as follows:

• In the sensing layer: The system collects data. Firstly, the food types are automatically identified using the CNN operated on the computer and a wired micro camera. While the weight is obtained by a wireless weighing machine. Also, the date of the FW is generated by the computer within the system, while the reason is selected by the staff.
• In the network layer: The weighing machine transfers the data to the computer using Bluetooth connection. The computer packages the collected data and uploads them to a MySQL database.
• In the data storage layer: The MySQL database stores the FW data.
• In the application layer: The website within the application layer analyses the data in the database and displays the FW information using some diagrams.

2.3 Construction of the database

A MySQL database serving as the data storage layer, as previously discussed, was designed. It aims to be straightforward, easy to understand, and operate, thereby facilitating efficient data research and management. The database encapsulates several attributes of the FW:

• Id: Primary key of the food waste.
• Date: A data type storing the date of FW occurrence.
• Reason: Variable characters storing the cause of food waste.
• Food type: Stores the type of FW.
• Weight: Decimal numbers recording the quantity of FW.

Figure 2 shows some samples of collected data and the recorded attributes.

This figure illustrates the table of the mentioned database. Rows: different FW entities. Columns: the attributes mentioned above.

3 THE IMPROVED CONVOLUTION NEURAL NETWORK

3.1 mobilenetv2.

MobileNetV2 [ 4 ] is a lightweight CNN model that is an improved version of MobileNetV1 [ 5 ]. The model combines techniques such as lightweight network design, fast feature extraction, and dynamic network structure optimisation. Inverted Residual architectures and Linear bottlenecks were introduced, which can significantly reduce the computational and storage costs of models while maintaining high accuracy. A 1x1 convolutional layer, a 3x3 depth-wise separable convolutional layer, and a 1x1 convolutional layer with batch normalization were consistent. These techniques allow for low computing and storage costs while maintaining high accuracy. Thus, it is a suitable CNN model for such a FW identification system.

3.2 Fine-tuning

This paper employs fine-tuning, a process that modifies and trains a convolutional neural network (CNN). In the fine-tuning process, the top layers of the CNN will remain unchanged and not be trained, only the parameters in the bottom layers will be randomly initialised and trained. The detailed steps of fine-tuning are as follows:

• Obtain a pre-trained CNN model, referred to as the source model, which has been trained using a source
• Construct a new CNN model, referred to as the target model. This model duplicates the structure and top layer parameters of the source mode. The parameters of the bottom layers are initialised randomly. The output layer may need to be reconstructed to fit with the categories of the new training dataset (target dataset).
• Train the target model using the target dataset.

The fine-tuning method in this study only modifies the last layer of the source model. Figure 3 illustrates the fine-tuning method in this study.

The visualization of the fine-tuning method discussed above, no new information.

This method provides a feasible way to obtain a high-accuracy CNN with high training efficiency when a related trained reliable CNN is available. The number of parameters needed to be trained will be decreased, which means saving the time cost of training. These benefits of fine-tuning will be illustrated later in the training results.

3.3 Applying fine-tuning to improve the MobileNetV2

In this study, ImageNet [ 6 ] was used as the source dataset to pre-train MobileNetV2. The resultant CNN model has the capability of object identification. To further enhance its performance, the fine-tuning process was used. This involved copying all layers of the pre-trained MobileNetV2, excluding the final output layer, thereby eliminating the need for further training of these copied layers. The last layer of MobileNetV2 was then reconstructed and assigned with some initial weights. The fine-tuning process employed in this study is illustrated in Figure 4 .

This innovative approach, as discussed, has the potential to result in a high-accuracy CNN model while significantly reducing the time and parameters required for training since most layers of the original CNN model are replicated and do not need further training.

3.4 Training

The new dataset contains seven classes and sixty-three subclasses as the function needed. 18276 images are included overall. TensorFlow was used as the platform to implement the fine-tuning of MobileNetV2 and the training [ 7 ]. It is a free and open-source machine learning platform developed by Google. The fine-tuned CNN model and the one without the fine-tuned were both trained in 100 epochs.

4 RESULTS AND DISCUSSION

4.1 results.

This study provides a comparative analysis of the original and improved CNN model. It shows that the accuracy of the CNN was increased by 2.8%, the training time cost was reduced by approximately 82.3%, and the number of parameters that needed to be trained was decreased by about 96.3%. Figure 5 provides the accuracy curves for both situations. Figure 6 references another study. Table 1 below compares the time cost and parameter number of two conditions.

Initially, the accuracies of the original model and the fine-tuned model are 36.16% and 54.51% respectively. These figures was improved to 94.78% and 97.60% after 100 epochs of training.

The validation accuracy was about 76% initially. It converged to about 97% after 50 epochs of training.

As shown in Figure 5 , the CNN model without fine-tuning started with an initial accuracy of 36.16%, which grew to 94.78% after 100 epochs of training. In contrast, the fine-tuned CNN model exhibited an initial accuracy of 54.51%, which increased to 97.60% after the training. This comparison shows the enhancement in the final accuracy of the CNN model by 2.6%, elevating it from 94.78% to 97.60%, thereby highlighting the efficacy of fine-tuning in improving model performance.

Figure 6 presents the accuracy of the CNN model from a referenced study aimed at identifying damaged fruits to mitigate food waste [ 8 ]. This model, comprising four convolution layers, four pooling layers, and two fully connected layers, underwent training over 50 epochs. The results demonstrate that the final accuracy may reach approximately 97%, suggesting that the fine-tuned CNN model developed in this study may be comparable to similar systems.

The application of fine-tuning led to a significant reduction in the number of parameters requiring training. With fine-tuning, only 85,827 parameters needed to be trained, marking a substantial decrease of 96.3% compared to the situation without fine-tuning. Additionally, the time cost for training was notably reduced upon the implementation of fine-tuning. The training time for the fine-tuned neural network was 103,993 seconds, representing a time-saving of 82.3% compared to the non-fine-tuned situation. This further illustrates the efficiency of the fine-tuning approach in enhancing the training process of CNN models. The data are described in Table 1 .

The results above show that the method in this paper could improve the accuracy of CNN and enhance its training efficiency significantly in terms of saving time and reducing the number of parameters that need to be trained.

4.2 Discussion

As the results illustrated above, it can be concluded that the fine-tuning approach can make a remarkable improvement in the accuracy of the CNN model, a reduction in the training time costs, and a decrease in the number of parameters that need to be trained. The initial accuracy of the fine-tuned CNN model was higher than the one without the fine-tuned. This may be because some layers of the CNN model used in the fine-tuning process were pre-trained. These layers include the convolution layers and pooling layers of the original CNN model, which are used primarily for feature capture and extraction. Given the potential similarities in image features between the source dataset and the new dataset, the CNN model may already have some capability to identify the features of the input images, resulting in a higher initial accuracy compared to a randomly initialised CNN model. For the same reason, the final accuracy of the fine-tuning process was also higher after 100 epochs of training were done based on the pre-trained CNN model. Furthermore, since most of the model was copied, most parameters in the original CNN model were not trained further, which may comprise 95% of the original CNN model. This may lead to a decline in training time costs. Some future studies may needed to apply the attention mechanism in the convolution neural network [ 9 ], which may also enhance the accuracy of the convolution neural network.

5 CONCLUSION

Nowadays, the generation of a substantial amount of food waste (FW) necessitates a management approach that is both highly accurate and time-efficient. The significance of monitoring FW has been shown in some studies, highlighting the potential of intelligent waste management systems in waste collection and analysis. Thus, this study proposes an intelligent food waste Identification and analysis system based on convolution Neural network (CNN) and enhances the CNN through fine-tuning. This approach aims to provide a high-accuracy and low-time-cost solution for FW management. This method resulted in some improvements in the accuracy of the CNN model, a reduction in the parameters requiring training, and a significant saving in training time. The proposed system is capable of collecting FW data, including date, weight, type, and reason, which is then transmitted, stored in a database, analysed, and presented to the user.

This paper has illustrated the construction of the IoT layers of the system, including the sensing layer, network layer, data storage layer, and application layer. It also discusses the application of fine-tuning on MobileNetV2 and provides a comparative analysis of the original and improved CNN models. The results demonstrate that fine-tuning has enhanced the accuracy of the CNN model from 94.78% to 97.6%, reduced the training time by approximately 82.3%, and decreased the number of parameters requiring training by about 96.3%. These findings indicate the potential of fine-tuning in improving the accuracy of CNN models, enhancing training efficiency in terms of time-saving, and reducing the number of parameters requiring training. Future research may explore the application of attention mechanisms to improve the performance of convolution neural networks further and minimise resource costs.

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∗ Corresponding author.

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© 2024 Copyright held by the owner/author(s). ACM ISBN 979-8-4007-1638-6/24/05. DOI: https://doi.org/10.1145/3674558.3674585

Systematic literature review of food waste in educational institutions: setting the research agenda

International Journal of Contemporary Hospitality Management

ISSN : 0959-6119

Article publication date: 29 January 2021

Issue publication date: 6 May 2021

In the recent past, academic researchers have noted the quantity of food wasted in food service establishments in educational institutions. However, more granular inputs are required to counter the challenge posed. The purpose of this study is to undertake a review of the prior literature in the area to provide a platform for future research.

Design/methodology/approach

Towards this end, the authors used a robust search protocol to identify 88 congruent studies to review and critically synthesize. The research profiling of the selected studies revealed limited studies conducted on food service establishments in universities. The research is also less dispersed geographically, remaining largely focused on the USA. Thereafter, the authors performed content analysis to identify seven themes around which the findings of prior studies were organized.

The key themes of the reviewed studies are the drivers of food waste, quantitative assessment of food waste, assessment of the behavioural aspects of food waste, operational strategies for reducing food waste, interventions for inducing behavioural changes to mitigate food waste, food diversion and food waste disposal processes and barriers to the implementation of food waste reduction strategies.

Research limitations/implications

This study has key theoretical and practical implications. From the perspective of research, the study revealed various gaps in the extant findings and suggested potential areas that can be examined by academic researchers from the perspective of the hospitality sector. From the perspective of practice, the study recommended actionable strategies to help managers mitigate food waste.

Originality/value

The authors have made a novel contribution to the research on food waste reduction by identifying theme-based research gaps, suggesting potential research questions and proposing a framework based on the open-systems approach to set the future research agenda.

• Plate waste
• School cafeteria
• University cafeteria
• Out-of-home consumption
• Consumer behaviour
• Food waste cause

Kaur, P. , Dhir, A. , Talwar, S. and Alrasheedy, M. (2021), "Systematic literature review of food waste in educational institutions: setting the research agenda", International Journal of Contemporary Hospitality Management , Vol. 33 No. 4, pp. 1160-1193. https://doi.org/10.1108/IJCHM-07-2020-0672

Emerald Publishing Limited

Copyright © 2020, Puneet Kaur, Amandeep Dhir, Shalini Talwar and Melfi Alrasheedy.

Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode

1. Introduction

unavoidable food waste: expired or spoiled ingredients, food scraps such as meat scraps (e.g. end pieces of baked ham after slicing, meat pieces after trimming) and vegetable scraps (e.g. tomato ends, outer leaves of lettuce, potato peels, vegetable stems); and

avoidable food waste: meal scraps such as peeling or trimming waste arising from the less proficient handling of food items; overproduction for banquets, events and catering; poor ordering procedures; poor food rotation practices, causing food spoilage; and poor inventory systems, leading to food and plate waste such as unconsumed pasta ( Derqui and Fernandez, 2017 ).

Academics categorize food waste based on the stages of waste generation, such as pre- and post-consumer food waste ( Prescott et al. , 2019b ). Pre-consumer waste occurs at the production level, and post-consumption waste occurs at the consumer level. Scholars argue they associate different factors with food waste generation at these stages. Accordingly, various mitigation approaches perhaps can reduce such waste ( Papargyropoulou et al. , 2016 ). Furthermore, thorough diagnoses of food waste generated at various stages are crucial for ensuring the effective management of waste ( Dhir et al. , 2020 ).

Food waste is an important concern because it threatens the environment and sustainability. In fact, it is a serious concern in the hospitality and tourism domain (Okumus et al. , 2020). Close to 1.3 billion tonnes of edible food is wasted annually, leading to severe financial, environmental and health outcomes ( Gustavsson, 2011 ). Past research has identified several adverse outcomes of food waste, such as threats to food security ( Wang et al. , 2018 ), climate change and greenhouse gas emissions ( Kallbekken and Sælen, 2013 ; Katajajuuri et al. , 2014 ) and monetary loss (Hennchen, 2019). For instance, the annual emissions because of food waste in Finland constitute more than 1% of the country’s yearly greenhouse gas emissions ( Katajajuuri et al. , 2014 ). Similarly, scientists found the ecological impact of food waste in hotels, cafés and restaurants nearly twice the size of the arable land in Lhasa ( Wang et al. , 2018 ). Notably, sustainability has come under intense focus in the hospitality industry in the wake of the COVID-19 pandemic (Jones and Comfort, 2020). In addition, studies have underscored the nutritional loss associated with food waste. For instance, Blondin et al. (2017) revealed that, in the USA, fluid milk waste results in 27% and 41% losses, respectively, of the vitamin D and calcium required under school breakfast programme meals. Consequently, scholars argue that reducing food waste is critical from financial (e.g. food cost) and non-financial (e.g. sustainability) standpoints ( Okumus, 2019 ). In fact, research reports suggest that, by saving one-fourth of the food being wasted, we can feed 870 million hungry people ( Khadka, 2017 ). Similarly, the sustainable development goals of the United Nations (UN) have also emphasized responsible production and consumption, underscoring the importance of mitigating food waste ( Gustavsson, 2011 ).

Regarding food waste generation, prior studies have indicated that a large amount of food waste is generated at the consumption stage, which includes both out-of-home and at-home dining ( Martin-Rios et al. , 2018 ). Households represent at-home dining, whereas the food service sector represents out-of-home dining. The food service sector includes both non-commercial and commercial establishments ( Betz et al. , 2015 ), such as restaurants, hotels, health-care companies, educational institutions and staff catering.

An important subdomain where out-of-home dining takes place is food service establishments at educational institutions. In this context, prior studies have observed that school cafeterias are a major source of unconsumed food ( Smith and Cunningham-Sabo, 2014 ; Adams et al. , 2016 ). For instance, in the National School Lunch Program (NSLP) in the USA, more than 30% of the food served is wasted ( Byker Shanks et al. , 2017 ). In fact, food waste in educational settings is a significant issue ( Yui and Biltekoff, 2020 ). What is most worrying in this context is that, in spite of the acknowledgement of such a high quantity of waste generated, the authorities in educational institutions, food service managers in schools and university food service companies’ staff are not intent on reducing food waste ( Wilkie et al. , 2015 ). Furthermore, the academic research in this area is limited, with most studies in educational settings (particularly in the context of schools) skewed towards using food waste as a measure to estimate the amount of nutrients lost. Food waste does not hold a central place in the existing debate. Other studies have focused on aspects such as the composition of waste generated in the food service operations in schools (Hollingsworth et al. , 1995) and the monetary implications of various waste disposal strategies (Wie et al. , 2003).

the substantial volume of meals that educational institutions handle at a single location ( Wilkie et al. , 2015 ); and

the opportunity that such research presents for creating a culture of sustainability and for reinforcing the pro-environment habits of future consumers by making them ecologically aware of the food system and its importance ( Derqui et al. , 2018 ).

analyze the research profile of studies on food waste in food service establishments in educational institutions (RO1);

identify, comprehend and evaluate the thematic foci of the existing research on food waste in food service establishments in educational institutions (RO2);

critically assess emergent themes to highlight gaps in the extant literature and suggest potential research questions (RO3); and

develop a framework that multiple stakeholders can use as a reference to understand the contours of food waste in the food service establishments in educational institutions (RO4).

To achieve the ROs of the study, we used the systematic literature review (SLR) approach to identify, analyze and synthesize past studies in the area in consonance with recent studies ( Kushwah et al. , 2019 ; Dhir et al. , 2020 ; Ruparel et al. , 2020 ; Seth et al. , 2020 ). Towards this end, we conducted the following steps. First, we defined the extraction method of congruent studies concerning the conceptual boundary, database identification, keyword choice and actual search and shortlisting of relevant studies. We formulated a robust search protocol based on 18 keywords as well as comprehensive inclusion criteria (IC) and exclusion criteria (EC). We also conducted a peer review of shortlisted studies to finalize the total number of studies to be included in the review (88). Second, we conducted a research profiling of selected studies to present the summary statistics related to publication frequency, publication sources, geographical scope of each study, type of educational institution investigated and theoretical framework. Third, we performed a manual content analysis of the congruent studies to delineate the thematic foci of such studies. This helped us identify seven distinct themes. The emergent themes were critically analyzed to identify the gaps in the extant research and to suggest theme-based potential research questions and future research avenues. Fourth, we developed a framework (the food waste ecosystem) for presenting a systems view of food waste in the food service establishments in educational institutions by building on the key findings of the review that we conducted (i.e. research themes, research gaps and avenues of future research). Fifth, we discuss herein the theoretical and practical implications of the study, followed by the study limitations, which should be kept in mind while implementing the results of this study.

2. Research method

Step I. Planning the review: Setting the conceptual boundary and identifying the relevant keywords and databases to identify the congruent studies.

Step II. Specification of the study screening criteria: Defining the IC and EC.

Step III. Data extraction: Using multiple levels of screening to identify congruent studies.

Step IV. Data execution: Presenting the research profile and the thematic foci of the congruent studies uncovered through content analysis.

2.1 Planning the review

We proposed to review studies on food waste in food service establishments in educational institutions. These institutions include pre-schools, schools (primary, secondary and upper secondary), tertiary education centres, colleges and universities. Furthermore, we distinguished between food waste and food loss. Some prior studies used the terms “food loss” and “food waste” interchangeably ( Betz et al. , 2015 ). However, many scholars have treated them as two different concepts. They described food loss as food gone to waste in the initial stage of the value-added chain and food waste as food lost at the end of the food supply chain ( Parfitt et al. , 2010 ). Our understanding is that “food loss” pertains to food leaving the supply chain initially. “Food waste”, though, pertains to the food that is not consumed at the point of food consumption. Therefore, in this SLR study, we treated food waste and food loss as distinct concepts. Accordingly, we identified an initial set of keywords for use in searching the studies to be reviewed, as follows: pre-schools, schools, tertiary education centres, colleges and universities. We searched for these keywords on Google Scholar, and we analyzed the first 100 results to update the keywords list. Afterward, we examined leading journals from the areas of nutrition, food waste and hospitality to confirm if the list of keywords was exhaustive. We selected the final list of 18 keywords after consultation with three experts from the area of hospitality and food waste (two professors and one practitioner; Table 1 ). Finally, in consonance with Mariani et al. (2018) , we selected Scopus and Web of Science as the two academic databases from which to retrieve the relevant studies. These two are the most comprehensive databases of social science and hospitality academic studies, with extensive disciplinary coverage ( Mongeon and Paul-Hus, 2016 ).

2.2 Specification of study screening criteria

We specified ( Table 2 ) the IC and EC at this stage to screen the studies found using pre-specified keywords.

2.3 Data extraction

We converted the final set of keywords ( Table 1 ) into search strings using * and Boolean logic, as well as the connectors “OR” and “AND”. We then executed the search strings on both databases to search for the title, abstract and author keywords. The search was conducted from January 1 to March 28, 2020. In Scopus, we found 550 journal articles in English, with 420 articles in Web of Science. We used the pre-specified IC and EC to select studies congruent with the area at hand. First, we screened duplicated articles using Microsoft Excel spreadsheets. We identified articles with the same authors, title, volume, issue number and DOI. Subsequently, we removed 276 duplicated studies from the Web of Science list. After further screening of the joint pool of 694 studies, we excluded 350 studies from the pool.

For the next level of screening of the remaining 344 studies, three researchers with experience in food waste research reviewed the titles and abstracts of the retrieved studies based on the conceptual boundary and IC and EC. To ensure robust screening, the three researchers performed the task individually, after which they shared their shortlists with one another. The researchers discussed any variances in their respective shortlists to arrive at a consensus list that could be further analyzed. This process excluded 230 studies incongruent with the specific area and conceptual boundary of the current study. At the penultimate step of screening, 3 authors analyzed the full texts of the balance 114 articles to reconfirm their eligibility for inclusion in the review. By consensus, we removed 14 articles, as these dealt with issues not immediately relevant to the review, such as sustainability and food insecurity. In the final stage of the study screening process, two professors and a practitioner from the area of hospitality and food waste examined the 100 shortlisted studies and supplied feedback. Based on their observations, we eliminated 12 studies, making the final sample of 88 articles. Subsequent sections of this work will disclose the results of the research profiling and content analysis, which constituted the data execution process.

2.4 Data execution: research profiling

We present the research profile of the retrieved congruent studies concerning descriptive statistics, such as publication year, publication source, educational institution investigated, geographic scope of each study and theoretical framework. The year-wise publications ( Figure 1 ) indicate that there were few studies on food waste in the food service establishments in educational institutions until 2012, after which the studies increased, reaching a peak of 15 articles in 2019. Furthermore, the studies were published in a variety of journals in nutrition and waste management ( Figure 2 ). Figure 3 presents the number of studies that focused on each type of educational institution (e.g. school versus university). Figure 4(a) and (b) presents the countries where the studies were conducted for schools and universities, respectively. Interestingly, the reviewed studies drew upon seminal theories to propose a hypothesis and/or discuss findings ( Table 3 ).

3. Thematic foci

The studies included in the review examined food waste from different perspectives and investigated distinct aspects of it. To synthesize such diverse studies systematically, we attempted to identify the common themes within the studies. The key themes in the selected studies were identified through content analysis, in consonance with the recently published SLR literature ( Seth et al. , 2020 ). To ensure that emergent themes would present an unbiased view of the literature, we followed a three-step process. First, three researchers performed the open coding. Later, the deductive and inductive methods of axial coding identified relationships among the open codes. Second, to ensure consensus and inter-rater reliability, the three researchers discussed the identified codes and aligned their thought processes. As food waste is a universally understood phenomenon, there were no disagreements except in the sequencing and presentation of the themes. Third, two professors from the hospitality and food waste areas commented on the identified themes. Finally, seven themes synthesized the existing literature. These were the drivers of food waste; quantitative assessment of food waste; assessment of the behavioural aspects of food waste; operational strategies for reducing food waste at the pre- and post-consumer levels; strategies and interventions for inducing behavioural changes to mitigate food waste; food diversion and food waste disposal processes; and the barriers to the implementation of food waste reduction strategies. A mind map of the emergent themes and the related subthemes is showcased in Figure 5 .

3.1 Drivers of food waste

Two perspectives can assess food waste at food service establishments in educational institutions: pre- and post-consumer waste ( Prescott et al. , 2019a ). “Pre-consumer waste” is kitchen waste arising at the time of storage, preparation and production, whereas “post-consumer waste” consists of leftovers or plate waste ( Burton et al. , 2016 ; Bean et al. , 2018b ; Zhao and Manning, 2019b ). Scholars have also used the term “serving waste” or “display waste” (especially regarding buffet meals) to represent waste at the point of consumption ( Abdelaal et al. , 2019 ). Prior scholars examining food waste at the pre-school, elementary and middle school levels have discussed uneaten meals, representing post-consumer waste, to a large extent ( Smith and Cunningham-Sabo, 2014 ; Adams et al. , 2016 ; Zhao et al. , 2019 ). Most studies focused on food waste measurement as a tool to assess the nutritional aspects of leftovers from meals consumed in schools ( Getts et al. , 2017 ).

Pre-consumer waste : It is generated based on various functional, behavioural and contextual factors, as presented in Table 4 . A key driver of food waste in school food service establishments at this stage is production waste, which can also increase because of various regulatory requirements and contractual obligations. For instance, food safety guidelines may prevent food service establishments from re-using the extra amount of food prepared for a particular meal ( Derqui et al. , 2018 ). As such, serving an agreed-upon variety of food offerings as per a contract may force kitchen staff to prepare and serve food that ultimately may not be consumed ( Derqui et al. , 2018 ).

Post-consumer waste : The drivers of post-consumer waste comprise behavioural, contextual and demographic factors, as Table 4 presents. Within post-consumer waste, the key drivers of wasted, edible food at both the school and university levels are taking a portion size larger than required as per one’s age and satiation level ( Thorsen et al. , 2015 ; Huang et al. , 2017 ; Zhao and Manning, 2019a ); and the time allowed for eating (i.e. recess; Cohn et al. , 2013 ; Abe and Akamatsu, 2015 ). Students’ dietary habits ( Liu et al. , 2016 ) also influence the amount of food waste generated in the school dining halls. Other factors that contribute to food waste at the university food services were incorrectly labelled food items (which led to the choice of wrong food items), differences in appetite and diet-related choices ( Wu et al. , 2019 ; Yui and Biltekoff, 2020 ).

Low self-efficacy in finishing one’s meal if it does not taste good is a significant predictor of plate waste only among boys ( Abe and Akamatsu, 2015 ).

Male students tended to waste staple food less compared to females ( Wu et al. , 2019 ).

Male consumers were more likely to finish their meal compared to females ( Zhao and Manning, 2019b ).

Young consumers tend to waste more food than adults on average ( Ellison et al. , 2019 ).

Within the student groups, younger students wasted more food than older ones ( Dillon and Lane, 1989 ; Huang et al. , 2017 ; Niaki et al. , 2017 ).

Individuals with more disposable incomes waste more food ( Wu et al. , 2019 ).

Middle-income students generated more food waste compared to students with poorer backgrounds ( Dillon and Lane, 1989 ).

3.2 Quantitative assessment of food waste

the type of waste quantified;

the unit of measurement used; and

the method used for quantification.

The key concerns covered by each of these aspects are described below.

Type of waste: Some studies have measured all waste, edible or avoidable as well as inedible or unavoidable ( Langley et al. , 2010 ; Costello et al. , 2015 ). In comparison, many studies quantified only edible or avoidable food waste ( Whitehair et al. , 2013 ; Thorsen et al. , 2015 ). The items considered edible or avoidable food wastes are meat protein, soy protein, fruits, rice, potatoes, bread, pies, juice, beverages, milk, vegetables and salads ( Langley et al. , 2010 ; Thiagarajah and Getty, 2013 ; Blondin et al. , 2017 , 2018 ; Eriksson et al. , 2018b ). Conversely, the inedible or unavoidable food wastes are fruit or vegetable peels and spines, eggshells, bones and skins and seeds ( Langley et al. , 2010 ; Whitehair et al. , 2013 ; Derqui and Fernandez, 2017 ). The greatest amount of food waste is derived from vegetables, fruits, salads, main entrées and milk (Carmen et al. , 2014; Smith and Cunningham-Sabo, 2014 ; Blondin et al. , 2015 ; Silvennoinen et al. , 2015 ; Wu et al. , 2019 ).

Unit of measurement: In this regard, the reviewed studies collected wastes for quantification at different stages of food services. Accordingly, the serving waste, plate waste and production waste (prepared food left over after service) were quantified ( Gase et al. , 2014 ; Eriksson et al. , 2017 ; Boschini et al. , 2020 ). Hence, scientists measured the entire mass of food waste generated at every meal (Carmen et al. , 2014; Painter et al. , 2016 ); the aggregated discarded food at the pantry, kitchen, service station or plate level ( Derqui et al. , 2018 ); or the individually/aggregately weighed plate waste ( Chapman et al. , 2019 ). The most commonly used unit of food waste quantification is plate waste, which is the quantity/percentage of edible food served on a plate but left unconsumed ( Huang et al. , 2017 ). In schools, where the focus is nutrition, plate waste is the quantity of edible vegetables and fruits students did not consume during lunch ( Adams et al. , 2016 ; Capps et al. , 2016 ). In this context, studies have revealed that students waste 40% and 30%, respectively, of the fruits and vegetables they receive ( Templeton et al. , 2005 ; Carmen et al. , 2014). Most of the studies included in the review used plate waste as a unit of quantification of food waste ( Cohen et al. , 2013 ; Liz Martins et al. , 2016 ; Chapman et al. , 2017 ; Hudgens et al. , 2017 ).

Methods of quantification : There are multiple methods of quantifying and measuring plate waste, and one can observe method variations in the plate waste quantification approach that selected studies used, such as direct physical measurements and indirect visual observations ( Eriksson et al. , 2018b ). Plate waste can be weighed in grams per portion served ( Eriksson et al. , 2018a ) or as aggregate plate waste per meal ( Eriksson et al. , 2017 ). Although weighed plate waste is considered the gold standard for determining the quantity of plate waste, scientists have also applied visual assessment approaches such as the quarter-waste method, which is considered reliable ( Derqui and Fernandez, 2017 ; Getts et al. , 2017 ; Niaki et al. , 2017 ). In fact, the three visual waste measurement methods (photograph, half-waste and quarter-waste) have been found to be as accurate as the plate weighing method ( Hanks et al. , 2014 ). Visual methods are appealing, as they offer advantages such as convenience, time savings and ease of using a larger sample size to monitor plate waste ( Liz Martins et al. , 2014 ). Within visual methods, many studies have used photography ( Smith and Cunningham-Sabo, 2014 ; Yoder et al. , 2015 ; Bean et al. , 2018a ; Katare et al. , 2019 ; Prescott et al. , 2019a ; Serebrennikov et al. , 2020 ). Moreover, scholars have discussed the use of rubbish analysis to quantify food waste ( Dresler-Hawke et al. , 2009 ; Derqui and Fernandez, 2017 ).

Prior scholars have also tried to ascertain the efficacy of different methods of plate waste quantification. For instance, Bean et al. (2018a) compared a weighed and digital imagery-based assessment of plate waste and confirmed the accuracy of the digital imagery method in terms of plate waste estimation. However, Liz Martins et al. (2014) contended that the visual estimation method is not as accurate as the weighing method in assessing nonselective aggregated plate waste. Previous studies have used food waste audits to quantify the amount and type of food waste generated ( Wilkie et al. , 2015 ; Costello et al. , 2017 ; Derqui and Fernandez, 2017 ; Derqui et al. , 2018 ; Schupp et al. , 2018 ; Prescott et al. , 2019a ). Figure 6 depicts an overview of the stages of waste generation, the types of waste quantified and the key methods of quantification.

3.3 Assessment of the behavioural aspects of food waste

key methods;

type of data collected; and

variety of respondents.

Key methods : The methods used for assessing food waste include direct observation ( Marshall et al. , 2019 ), field notes ( Yui and Biltekoff, 2020 ), cross-sectional questionnaire ( Abe and Akamatsu, 2015 ), semi-structured interviews ( Zhao et al. , 2019 ), non-structured interviews ( Falasconi et al. , 2015 ), structured interviews ( Burton et al. , 2016 ), focus group discussion ( Blondin et al. , 2015 ), experiments ( Kim and Morawski, 2013 ) including randomized controlled experiments ( Katare et al. , 2019 ), quasi-experiments ( Visschers et al. , 2020 ), longitudinal studies ( Lagorio et al. , 2018 ; Marshall et al. , 2019 ) and pre- and post-test-based intervention studies ( Kowalewska and Kołłajtis-Dołowy, 2018 ; Kropp et al. , 2018 ; Lorenz-Walther et al. , 2019 ; Visschers et al. ,2020 ). Figure 7 presents a snapshot of the methods.

Type of data collected : Scientists use self-reporting questionnaires quite frequently to identify the key factors influencing food waste, the reason for plate waste and preferences ( Thorsen et al. , 2015 ; Liu et al. , 2016 ; Huang et al. , 2017 ; Kowalewska and Kołłajtis-Dołowy, 2018 ; Derqui et al. , 2020 ). In addition, questionnaires gathered eating behaviour-related information and food preferences ( Baik and Lee, 2009 ). Notably, prior scholars have made limited qualitative attempts to assess consumer behaviour concerning food waste generation. For instance, Jagau and Vyrastekova (2017) conducted a study to observe the differences between the intention to prevent food waste and the actual waste that consumers generated. Similarly, researchers examined staff and students’ insinuated intentions related to food waste ( Zhao and Manning, 2019b ). A few studies have also analyzed the changes in behaviour with regard to food waste and its reduction ( Whitehair et al. , 2013 ; Pinto et al. , 2018 ; Boulet et al. , 2019 ; Visschers et al. , 2020 ). Along the same lines, fewer studies have focused on the ethnic background of students or other demographic factors. For example, only two studies using a mixed-method approach have undertaken ethnographic investigations ( Lazell, 2016 ; Izumi et al. , 2020 ). Similarly, a limited number of researchers ( Nicklas et al. , 2013 ) have used a demographic questionnaire (e.g. age, ethnicity). Langley et al. (2010) acknowledged the effect of gender-based differences in food consumption and waste; they selected dining areas for the study based on gender composition.

Regarding the variety of respondents, qualitative studies have taken place with many stakeholders, such as kitchen managers, nutrition service directors and sustainability staff ( Prescott et al. , 2019b ), professionals engaged in food recovery ( Prescott et al. , 2019a ), stakeholders along the supply chain ( Liu et al. , 2016 ), school head teachers ( Derqui et al. , 2020 ), managers and staff in schools and catering firms ( Derqui et al. , 2018 ), key informants about stakeholder accountability ( Cohn et al. , 2013 ), food service managers, catering personnel, students ( Marais et al. , 2017 ), teachers ( Prescott et al. , 2019a ) and parents ( Baik and Lee, 2009 ).

3.4 Operational strategies for reducing food waste

strategies to reduce food waste at the pre-consumer level; and

strategies to reduce food waste at the post-consumer level.

This work will explore both strategies in what follows.

Pre-consumer level : The reviewed studies discussed several operational strategies to reduce waste at the pre-consumer level. The main objective of these strategies was to reduce food waste at the kitchen level. Waste at this level occurs largely because of overproduction, mishandling, staff inefficiency and the quality of food prepared. Accordingly, strategies largely target these issues ( Table 5 ). Post-consumer level : The operational strategies to reduce waste at the post-consumer level largely relate to avoiding serving food that would not be consumed. With plate waste being the focus of waste quantification, many previous scholars have discussed strategies to reduce plate waste. Most of the suggestions relate to the serving portion size based on age, going trayless and making better food choices, as Table 5 illustrates.

3.5 Interventions for inducing behavioural changes to mitigate food waste

communication; and

financial and economic incentives.

Education and communication have been suggested to be the most effective approaches for behaviour change ( Whitehair et al. , 2013 ).

Education : Past studies have recommended a holistic approach to decrease food waste, which involves multiple stakeholders in society, including parents and catering staff ( Marais et al. , 2017 ; Wu et al. , 2019 ; Izumi et al. , 2020 ). Studies also have indicated the need to identify and increase the engagement levels of families that have the lowest level of engagement in food waste reduction behaviour ( Boulet et al. , 2019 ). Students can receive education, as an intervention, through lectures on morals, sustainability and related environmental issues, or through a hands-on experience such as visiting landfill sites or segregating their plate waste themselves by putting the leftovers in separate bins ( Wu et al. , 2019 ). Curricula should integrate student engagement and social norms related to eating without waste into food-waste-related discussions, along with nutrition education ( Izumi et al. , 2020 ). Table 6 presents the key educational interventions introduced at the pre- and post-consumer levels. Besides discussing the interventions, some prior studies also tested their efficacy. For instance, Kowalewska and Kołłajtis-Dołowy (2018) revealed that students’ exposure to film was more effective in reducing food waste among students than giving an informational leaflet to parents or guardians. Similarly, Whitehair et al. (2013) reported that a to-the-point prompt-type message effectively reduced food waste by 15%.

Communication : Interaction among varied stakeholders is essential to reducing food waste ( Cohn et al. , 2013 ; Marais et al. , 2017 ; Derqui et al. , 2018 ). Clear and continuous communication among kitchen managers, kitchen staff, students and school authorities boosts the success of food waste reduction efforts ( Prescott et al. , 2019b ; Zhao and Manning, 2019b ).

Financial and economic incentives : These incentives encourage consumers to finish their meals ( Sarjahani et al. , 2009 ). However, there is a challenge here. Providing financial incentives to motivate food waste reduction behaviour among students is effective. However, a non-intended adverse outcome of such incentives for finishing the food on one’s plate could be overeating and obesity. Therefore, any intervention related to food waste in food service establishments in educational institutions should be integrated with healthy eating policies ( Katare et al. , 2019 ).

3.6 Food diversion and food waste disposal processes

The processes related to the diversion and disposal of the daily waste of food service establishments in educational institutions are important aspects of food waste reduction and control efforts. The primary objective at this stage of handling food waste should be to divert it from landfills through recycling ( Wilkie et al. , 2015 ). Such diversion processes are a way of reducing food waste, as they decrease the actual amount of scraps destined to be buried in landfills ( Prescott et al. , 2019a ). The reviewed studies discussed the following approaches to handling food waste: reuse (e.g. staff meals), recycling (e.g. composting) and disposal ( Derqui and Fernandez, 2017 ).

the redistribution of edible, non-perishable and perishable food by donating it to food banks, shelters and other food-insecure groups ( Burton et al. , 2016 ); and

the recovery of food waste through anaerobic digestion and composting, which are the processes of converting leftovers into useful end products, such as nutrient-rich soil amendments and bio-energy ( Sarjahani et al. , 2009 ; Wilkie et al. , 2015 ; Burton et al. , 2016 ; Wu et al. , 2019 ).

The key disposal method discussed by the past studies is the landfill. The approaches discussed by the extant studies range from pulping waste for landfilling to lunchroom food-sharing programmes and leftover lunch service in the form of redistributing leftovers ( Babich and Sylvia, 2010 ; Laakso, 2017 ; Prescott et al. , 2019a ).

Although a limited number of studies have discussed the food diversion and disposal processes in detail, most seem to agree on the donation of edible recovered food as a feasible option to redistribute waste. For instance, Deavin et al. (2018) revealed the popularity of a novel breakfast programme based on donated food to increase food security. Schupp et al. (2018) discussed a “backpack programme” where food-insecure students were to carry temperature-controlled leftovers home. Many other studies have discussed food donation to reduce food waste but emphasized that it is possible only through the collaborative efforts of food service establishments and the beneficiaries of such donations ( Hackman and Oldham, 1974 ; Sarjahani et al. , 2009 ; Blondin et al. , 2015 ; Marais et al. , 2017 ; Balzaretti et al. , 2020 ; Derqui et al. , 2020 ). The results of our study indicate that much of the generated food waste is landfilled, even though landfilling represents a missed opportunity to recover food and promote sustainable behaviour ( Prescott et al. , 2019b ). Finally, prior studies have contended that the sustainability initiatives of diversion, recovery and redistribution can be made successful and effective through proper waste sorting and waste audits by food service establishments ( Prescott et al. , 2019a ).

3.7 Barriers impeding the implementation of food waste reduction strategies

pre-consumer;

operational;

post-consumer;

food waste tracking; and

food diversion and recovery levels.

a lack of willpower and a negligent attitude;

the pressure to quickly finish one’s work; and

less experienced and incompetent personnel.

Prescott et al. (2019b) revealed that limited storage capacity for dry/cold storage also acted as a barrier to success in reducing food waste by impacting the inventory management plans of kitchen managers.

short lunch breaks and too few kitchen staff to allow the adoption of the batch cooking approach as a waste mitigation strategy ( Prescott et al. , 2019b );

the increased breakage of meal utensils and the need to wipe dining tables more frequently, which made it challenging to use the strategy of going trayless to reduce waste ( Thiagarajah and Getty, 2013 );

parents scolding their children for bringing home leftovers and providing bins at school, which presents an easy way to dispose of unconsumed food through the reuse of leftovers ( Boulet et al. , 2019 ); and

the timing of recess ( Chapman et al. , 2017 ).

Post-consumer level : The behavioural and perceptual aspects at the post-consumer level also help impede efforts to reduce food waste. In this context, Zhao et al. (2019) cited the differences in satiation level and social influences as key barriers. Consumers tended to throw away food that they disliked but found it unacceptable to waste the food that they liked. Similarly, Prescott et al. (2019b) argued that factors such as weather, changing tastes and preferences, and seasonal changes also acted as barriers to the success of the efforts to reduce food waste. Other barriers to food waste reduction also stemmed from consumers’ intention−behaviour gap (Lazell, 2). In addition, unsupportive school policy in terms of not allowing students to share food they did not want with others or take leftovers home also hampered food waste reduction efforts ( Zhao et al. , 2019 ).

the time devoted to weighing and keeping a record of food waste;

difficulties in weighing certain items, such as soups;

the ongoing training required for the weighing of waste because of employee turnover; and

spatial constraints.

food safety concerns and food quality standards, which impose limits on the donation of edible leftovers for human and animal consumption;

the prohibitive cost of transportation, heat treatment of waste for making it safe for animal consumption and setting up onsite composting units compared with the low cost of landfilling waste, making redistribution a financially unviable solution;

adverse publicity for the effectiveness of nutrition programmes, highlighted by the waste generated and where legal liability also acts as a disincentive; and

the lack of a clear understanding of the kinds of recovery activity the law permits.

4. Research gaps and potential research questions

We critically assessed the emergent themes to identify the gaps in the literature on food waste reduction measures. We mapped the identified gaps onto the seven themes to present theme-based gaps. We also suggested potential research questions that future researchers can address to close these gaps. The multiple gaps in the literature concerned the seven themes. Table 7 demonstrates potential research questions.

5. Framework development

Based on our content analysis, we identified the key themes on which the extant research on food services in educational institutions focused. The learning emerging through these themes has helped us develop a deeper understanding of the area. Our review has revealed that the entire food service–food waste debate represents a complex ecosystem consisting of different stakeholders and processes that interact but are driven by diverse priorities, as some of the reviewed studies also have argued ( Prescott et al. , 2019b ). Consequently, we have built on this learning to apply the systems approach.

a repeated input–process–output–feedback cycle; and

the influence of the external environment.

We adopted the systems approach to develop a framework that presents various aspects of food waste in the food service establishments in educational institutions as an open system that provides a holistic view of food waste in educational settings ( Figure 8 ). We call the framework developed by us the “food waste ecosystem (FWE)”. FWE consists of the following:

the internal and external environment;

transformative processes;

competing forces;

output; and

feedback loop.

FWE posits that food waste generation and mitigation in educational institutions depend on the interaction of various subsystems that are interdependent and integrated into an organized whole.

To begin with, the food waste system is conceptualized as an open system influenced not only by cues from the internal environment but also by cues and stimuli from the external environment. The internal environment represents the environment within the food service establishment in educational institutions and includes factors such as school policies and methods of food production. It impacts how transformative processes are executed. The external environment represents the environment outside the educational institution and includes factors such as government regulations, composting facilities and food banks.

Inputs are the first block in FWE. Inputs represent the first step in a systems model, and represent the decisions at the beginning of the process that finally result in waste generation. Typically, at this stage, they include decisions such as what is to be served per meal, the food service regime that mandated a particular type of meal to be served, dietary guidelines (particularly in the context of schools), the dining facility and the number of consumers. These decisions affect the amount and type of food prepared, the use of local produce, the storage facilities required, the beverages served, the use of temperature-controlled food items, the portion size, the method of service (self-serve, tray system or trayless system) and the ambiance of the dining area. The decisions at this stage set the tone for the extent to which food waste is generated in the next step in the systems model: the transformative process.

The four key transformative processes at this stage are food production, food service, food consumption and food diversion. Each of these processes presents a potential point of food waste generation. As discussed in the themes, food production is a part of the pre-consumer phase, where the kitchen staff’s role is important. Food service represents serving food for consumption. The food consumption stage is where consumers enter the picture. Food diversion is a process that takes place after the consumption phase is over.

These four activities are the subsystems of the transformative process that is a chaotic tradeoff of competing forces and conflicting priorities. FWE identifies seven broad competing forces based on the reviewed literature: functional issues, behavioural factors, demographic influences, contextual issues, interventions, waste tracking systems and supportive policies. For instance, the functional issues that can generate food waste are overproduction, a lack of trained staff, the mishandling of ingredients and the lack of awareness of the seriousness of food waste among the staff and consumers. Similarly, the size of the portion in staff-served meals, the amount of food added to serving dishes, meal presentation and spillage during handling can generate food waste. Functional issues associated with the donation of edible waste for human consumption, the treatment of waste for animal consumption, composting, anaerobic digestion or landfills also affect the amount of waste generated.

Regarding behavioural factors, the negligent attitude of a kitchen and service staff, the lack of willingness to prevent waste, food preferences, level of satiation, the influence of the social group and family, and the inherent intention–behaviour gap may lead to food waste. Demographic influences in terms of age, gender, household income and ethnic background also influence the amount of food consumed or left unconsumed, contributing to food waste. Contextual factors such as the quality and taste of meals, the unpleasant ambiance of the dining room, the extent of supervision (for younger consumers) and the eating duration can potentially increase food waste.

The four competing forces (functional, behavioural, demographic and contextual) represent the reasons behind the increased food waste in the food service establishments in educational institutions. However, interventions, robust waste tracking systems and supportive policies can reduce food waste. The challenge is that most of the interventions require some expense and effort in terms of time and money. For instance, offering financial incentives may reduce food waste, but for food service establishments, such food waste savings will make economic sense only if the money saved from less food going to waste is more than or at least equal to the financial incentive. Similarly, interventions such as education campaigns may cost money, and whether they are worthwhile will depend on the money saved from less food going to waste. One way of compensating for costs is for a government’s support policy to make the expenses incurred for food waste mitigation efforts tax-deductible. In addition, the initiatives for food diversion, such as food donations, have an associated legal liability that suitable policy guidelines can reduce.

The supportive policy of educational institutions can help by granting permission to take home leftovers, share food, provide better dining areas and make provisions for adequate eating time between academic commitments. In the case of the food tracking system, the immense effort required for sorting, weighing and training the staff to operate such a system represents a cost that must be offset by balancing the savings in food costs. In this way, the food waste ecosystem is an interdependent mass of competing forces that interact to increase or decrease the quantity of food generated, and the food waste mitigation decisions at the micro level are a trade-off between costs and benefits. The output of the transformative process is the quantity of waste generated. The amount and composition of the waste provide feedback, which can help revise decisions at the input level.

6. Conclusion, implications, limitations and future research areas

6.1 conclusion.

This study presents the status of food wastage in food service establishments in educational institutions, as reflected in the extant literature. To the best of the authors’ knowledge, there are no contemporary SLRs that have analyzed food wastage in the food service establishments in educational institutions as a separate vertical. The current study addresses this gap to offer insightful implications for theory and practice. First, it sets the conceptual boundary by including all food service establishments in schools and universities. We selected this subdomain because the focus of the studies has largely been school lunch, where researchers have mainly assessed food waste to compute nutritional loss. In comparison, studies focused on food waste as a central concern, and studies examining food waste in higher education are limited. This indicates a need to catalyze research in the area. Thereafter, the study rigorously follows the SLR method to identify, synthesize and critically evaluate the 88 studies on the topic to reveal their research profile and thematic foci. The seven themes we identified through content analysis are the drivers of food waste; quantitative assessment of food waste; assessment of behavioural aspects of food waste; operational strategies for reducing food waste; interventions for inducing behavioural changes to mitigate food waste; food diversion and food waste disposal processes; and barriers to the implementation of food waste reduction strategies. The review goes beyond presenting the state-of-the-art in the area to uncover the gaps in the extant investigations and to suggest potential research questions that could motivate future academic research from the hospitality perspective. In addition, we developed a framework based on the open-systems approach to depict the complexity of the area and the multiple factors that influence its decision-making.

For the novel contributions of this study, it is the first SLR to review food waste in food service establishments in educational institutions. To the best of the authors’ knowledge, no prior review study has systematically reviewed and evaluated the extant research on food waste in the education sector. The only other review study on food waste in the area was the review of the NSLP in the USA ( Byker Shanks et al. , 2017 ). This review focused on the methods of quantifying food waste and the respective results of each method in the NSLP context from 1978 to 2015. The current SLR goes beyond both quantification and NSLP. Another novel contribution of this study is that the gaps that we identified in the extant research are theme-oriented, paving the way for encouraging future academic research through tangible suggestions in the form of theme-based potential research questions. This study also presents a systems view of the dynamics of food waste in food service establishments in educational institutions by identifying the input decisions; the transformative processes; the influence of low-threshold interventions and barriers; and the output in terms of the quantity of food waste. Finally, the practical inferences offered by the study are actionable, useful, contextual and easily transferable across various food service establishments serving educational institutions.

6.2 Theoretical implications

SLR has four key theoretical implications. First, although several researchers have investigated food waste in food service establishments in educational institutions, most have skewed towards the nutritional implication of unconsumed food in the school lunch context, with the quantification of food waste merely serving as a basis to capture nutritional loss. The hospitality literature has yet to focus on the issue of food waste in institutional settings in spite of its strong implications for sustainability and direct association with food services, an inherent part of the hospitality sector. By presenting the key themes, we have provided a ready platform for hospitality researchers to expand the scope of their investigations to include food wastage in educational institutions.

Second, we identified theme-based gaps ( Table 7 ) in the extant research that need to be addressed through empirical investigations from a hospitality perspective. Besides identifying theme-based gaps, we also suggested potential research questions ( Table 7 ) in consonance with prior reviews ( Swani et al. , 2019 ), which can help set the future research agenda in the area. Furthermore, our study revealed that future studies need to focus on food waste as contributing to increased carbon footprints and food insecurity. Such studies will take the focus beyond the nutritional emphasis on ecological implications for the greater good.

Third, in addition to identifying the theme-based gaps and potential research questions, we conducted research profiling of the retrieved and screened literature to identify the scope of the future research concerning the need for theory-based examinations, geographies that need attention and the type of educational institutions that have remained neglected in food waste research. The need for theory-driven investigations, which are now quite deficient, is supported because “theory” alone can yield consistent conclusions from causal patterns in data ( Han,2015 ). The need to explore diverse geographies is justified, considering that food consumption and leaving food unconsumed may be rooted in culture ( Yoder et al. , 2015 ; Pinto et al. , 2018 ; Izumi et al. , 2020 ). The need to focus on hitherto under-explored subsectors in higher education is justified because more granular findings are required to help food service establishments, regulators and university authorities plan and execute sustainable food waste control strategies targeting a group that makes independent decisions. Finally, the FWE framework that we developed presents a systems approach to food waste management that provides researchers with a bird’s eye view of the key areas to investigate in a study examining food waste generation and mitigation in food service establishments in educational institutions.

6.3 Practical implications

SLR has six key practical implications. First, a systematic tracking system can help create awareness and motivate anti-food-waste behaviours at the pre-consumer level, as prior studies have discussed ( Burton et al. , 2016 ). Therefore, catering companies offering food services in educational institutions should implement software with a simple interface to capture food-waste-related data, forecast the number of meals, identify popular menu items and classify waste into edible and non-edible.

Second, the overemphasis on nutritional content and rigid food-serving guidelines can increase food waste, as school authorities may determine portion sizes accordingly. This could be counterproductive from both the nutritional and waste perspectives if the food served is not consumed. For instance, the larger portion sizes that the school determines may cause overnutrition and obesity ( Balzaretti et al. , 2020 ). Therefore, the dietary guidelines that the concerned authorities issue should be indicative so portion sizes are adjusted according to hunger level and personal preferences. Competitive foods that usually have higher fat and sugar contents ( Templeton et al. , 2005 ) can be removed or vended at other times to ensure that the served meals are consumed to satiate hunger.

Third, formal guidelines for quantifying food waste should be prepared and made available to the food service managers in the cafeterias. There also should be a board or display where the aggregate daily food waste at the pre- and post-consumer levels is displayed for everyone to see. This likely will increase food waste awareness and encourage kitchen staff and students to reduce food waste.

Fourth, as food waste is a critical issue, school and college authorities hiring catering services (including cooks and kitchen staff) can also adopt a more structured approach to discouraging food waste. For instance, an inefficiency index ( Falasconi et al. , 2015 ) can be calculated weekly as the percentage of food wasted at the pre-consumer and serving stages compared to the amount of food prepared. Such an index will highlight the deficiencies in the kitchen processes, the slackness of the staff and the inaccurate forecasting of the number of consumers.

Fifth, the proper sorting of food waste can reduce it in two ways: by increasing the chances of recovering edible leftovers for donation and by making concerned stakeholders aware of the waste they are generating. Therefore, regulators or administrative authorities at the educational institution level can make it compulsory for every dining hall to have separate bins with labels for the disposal of different types of waste, including liquid waste, according to Schupp et al. (2018) . Furthermore, consumers should be asked to throw their individual plate waste in the designated bins.

Finally, from a regulatory standpoint, the policy guidelines for food waste reduction should consider the cost of waste reduction processes and offer financial incentives such as tax rebates for initiatives to reduce waste through food diversion. The issue of the legal liability associated with donating food to non-profit organizations for charity is a great disincentive, preventing the giving away of food for charity. To overcome this impediment, donors can be freed of any such legal liability. This practice exists in countries such as Italy and the USA ( Derqui et al. , 2018 ). Furthermore, policymakers should promote an approach to menu design based on the inclusion of more low-carbon-emission food items and fewer high-carbon-emission food items. This is likely to provide food cost savings at the food service level and environmental cost savings at the societal level.

6.4 Limitations and future research areas

We conducted a deep analysis of the extant research on food waste in food service establishments in educational institutions to uncover key themes and gaps. This has made a significant contribution to theory and practice by presenting potential research questions and implementable practical suggestions. However, readers should evaluate the contributions of this study in the context of the following limitations. First, we used Scopus and Web of Science only to search congruent studies and did not juxtapose any other digital library or database. This could have resulted in the exclusion of studies not listed in these two databases. Second, we included articles published only in English and could have missed important regional findings in the local language. Third, like any other SLR study, we faced the challenge of executing extensive search and screening, complexities in synthesis and presentation of findings in a manner that would be palatable to a wide variety of readers. Accordingly, we could have missed information because of inadvertent human error. Fourth, although we followed a systematic approach to identify keywords for searching the congruent literature, the area of food waste is quite vast. We may have excluded keywords. However, we used a robust search and screening protocol to present rigorous analysis to serve as a reliable basis for guiding future research and practice. Future researchers can extend our work by including keywords such as “campus dining”, “food rescue”, “food scarcity on campus”, “food recycling”, “food waste tracking”, “meal plans”, “food supply chains” and “food clubs on campus”. Future work can advance this study by reviewing reports from governments and policies implemented to highlight the gaps between academic research and government initiatives or between evidenced-based and non-evidenced-based methods. In addition, researchers should examine food waste in schools/universities in developed and developing economies, because the extant literature primarily skews towards US-based educational institutions. In this regard, researchers can also focus on cross-cultural/national comparison to provide deeper and more generalizable insights. Food waste studies in educational institutions can also include employees who consume food in the school/university dining facility, as examined in the case of frontline employees working in various hospitality establishments (Luu, 2020). Furthermore, as the drivers and, ultimately, the remedial actions/strategies for handling the issue of food waste may differ between public and private educational institutions, future researchers can build on our findings by separately reviewing the sample of studies on public and private educational institutions. Finally, future studies can explore whether increasing organic food consumption ( Tandon et al. , 2020a , 2020b ; Tandon et al. , 2020c ) has impacted food waste behaviours in educational institutions.

Year-wise publications in food waste in food service establishments in educational institutions

Publications on food waste in the food service establishments in educational institutions, by journal

Food service establishments examined by the studies

Geographic scope of the studies

Thematic foci of studies on food waste in educational institutions

Methods of food waste quantification

Methods of data collection

Systems approach to food waste mitigation: The food waste ecosystem (FWE) framework

Keywords for the literature search

Study inclusion and exclusion criteria

Theoretical framework used in food waste in food service establishments in educational institutions

Drivers of food waste in food service establishments

Operational strategies for food waste reduction

Interventions for food waste reduction

Theme-based gaps and related potential research questions

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Acknowledgements

The authors acknowledge the Deanship of Scientific Research at King Faisal University for the financial support under Nasher Track (Grant No. 186300).

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Waste Management Practices for Food and Agricultural By-Products: Case Studies of Public Markets in Zamboanga City

34 Pages Posted: 27 Jul 2023

Emerissa Jane Tendero

International Register of Certificated Auditors (IRCA-Philippines)

Date Written: July 3, 2023

This research examines waste management practices pertaining to food and agricultural by-products in public markets within Zamboanga City, Philippines. The study adopts a comprehensive approach to assess current strategies employed by local authorities and market vendors to manage waste generated from these sectors. This research investigates the effectiveness of waste management systems in addressing the environmental and health concerns associated with food and agricultural by-product waste. The findings reveal that waste management practices in public markets in Zamboanga City are characterized by a mix of strengths and shortcomings. While the study identifies commendable efforts in waste segregation and recycling, significant gaps persist in terms of collection, disposal, and overall waste reduction strategies. Despite the existence of regulations and policies, the lack of awareness, limited infrastructure, and inadequate coordination among stakeholders hinder the proper implementation of waste management practices. The research highlights the pressing need for improved institutional support, enhanced education and awareness programs, and investment in infrastructure for efficient waste management systems. Addressing these challenges will contribute to the reduction of environmental pollution, minimize health risks, and foster sustainable development in Zamboanga City's public market ecosystem. This study provides valuable insights for policymakers, local authorities, and market vendors, offering recommendations to enhance waste management practices and promote a healthier and cleaner environment.

Keywords: waste management, food and agricultural by-products, public markets, Zamboanga City, Philippines, environmental sustainability, health implications

Suggested Citation: Suggested Citation

Emerissa Jane Tendero (Contact Author)

International register of certificated auditors (irca-philippines) ( email ).

CPADS, Normal Road Baliwasan Zamboanga City, Zamboanga del Sur 7000 Philippines 09207256273 (Phone)

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