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In developed countries, the largest share of food is wasted at the household level. Household food waste results from a complex interaction between economic factors, well-established routines, and social norms. To explain this interaction, we propose a simple model of waste behavior where the individual and social economic costs generated by wasting are counterbalanced by the security and status generated through acquiring excess food, thus causing a social dilemma. This trade-off is mediated by social capital, which measures the intensity with which each individual within a community evaluates the negative effects of waste. We test this model's hypotheses using a 2016 dataset of food behaviors and opinions of Italian households, which we merge with variables known to elicit the local level of social capital. We find individual food waste levels to be negatively related with social capital. Contrastingly, status concerns with respect to food and the lack of organizational abilities are both more prevalent in low social capital areas, and are related to increased food waste. This relationship is mediated by income. ; In developed countries, the largest share of food is wasted at the household level. Household food waste results from a complex interaction between economic factors, well-established routines, and social norms. To explain this interaction, we propose a simple model of waste behavior where the individual and social economic costs generated by wasting are counterbalanced by the security and status generated through acquiring excess food, thus causing a social dilemma. This trade-off is mediated by social capital, which measures the intensity with which each individual within a community evaluates the negative effects of waste. We test this model's hypotheses using a 2016 dataset of food behaviors and opinions of Italian households, which we merge with variables known to elicit the local level of social capital. We find individual food waste levels to be negatively related with social capital. Contrastingly, status concerns with respect to food and the lack of organizational abilities are both more prevalent in low social capital areas, and are related to increased food waste. This relationship is mediated by income.

Consumer food waste, like many environmental behaviours, takes place in private, and is not directly subject to social monitoring. Nevertheless, social interactions can affect private opinions and behaviours. This paper builds an agent-based model of interactions between consumers heterogeneous in their sociability, their initial opinions and behaviours related to food waste, and their willingness to consider different opinions, in order to assess how social interactions can affect private behaviours. Compared to existing models of opinion dynamics, we innovate by including a range of "cognitive dissonance" between stated opinions and actual behaviours that consumers are willing to accept before changing one of the two. We calibrate the model using questionnaire data on household food waste in Italy. We find that a limited degree of mixing between different socio-demographic groups, namely adult and young consumers, is enough to trigger change, but a certain openness of mind is required from more wasteful individuals. Equally, a small group of environmentally committed consumers can attract a sizeable share of the population towards low-waste behaviours if they show a certain variability of opinions and are willing to compromise with individuals in their close neighbourhood in terms of opinions. These findings can help design effective interventions to promote pro-environmental behaviours, taking advantage of the beneficial network effects while anticipating negative externalities.

In developed countries, the largest share of food waste is produced at household level. Most studies on consumers' food waste use models that identify covariates as significant when in fact they may not be, particularly where these models use many variables. Here, relying on EU-level Eurobarometer data from 2013, we use alternative analytical methods that avoid these problems (Bayesian Networks) to identify the impact of household characteristics and other variables on self-assessed food waste. Our analysis confirms that the country, the age of the respondent, the status (student/non-student), and a belief that the family wastes too much are related to the level of self-assessed food waste. But we found no evidence that waste behaviours differ between people living in urban and rural areas, and little support of a difference between genders. Households from lower-income EU countries (e.g. Portugal, Greece, Bulgaria, Cyprus and Latvia), as well as students and young adults tend to report higher levels of food waste. Hence, the adoption of an EU strategy based on the concept of subsidiarity, and of country-level policy measures targeting different age groups is suggested. Furthermore, our analysis shows that policy makers need to be wary of relying on analysis based on large datasets that do not control for false-positives, particularly when sample sizes are small. ; In developed countries, the largest share of food waste is produced at household level. Most studies on consumers' food waste use models that identify covariates as significant when in fact they may not be, particularly where these models use many variables. Here, using EU-level Eurobarometer data from 2013, we use alternative analytical methods that avoid these problems (Bayesian Networks) to identify the impact of household characteristics and other variables on self-assessed food waste. Our analysis confirmed that the country, the age of the respondent, the status (student/non-student), and a belief that the family wastes too much are related to the level of self-assessed food waste. But we found no evidence that waste behaviours differ between people living in urban and rural areas, and little support of a difference between genders. Households from lower-income EU countries (e.g. Portugal, Greece, Bulgaria, Cyprus and Latvia), as well as students and young adults tend to report higher levels of food waste. Hence, the adoption of an EU strategy based on the concept of subsidiarity, and of country-level policy measures targeting different age groups is suggested. Furthermore, our analysis shows that policy makers need to be wary of relying on analysis based on large datasets that do not control for false-positives, particularly when sample sizes are small.

Food waste from households contributes the greatest proportion to total food waste in developed countries. Therefore, food waste reduction requires an understanding of the socio-economic (contextual and behavioural) factors that lead to its generation within the household. Addressing such a complex subject calls for sound methodological approaches that until now have been conditioned by the large number of factors involved in waste generation, by the lack of a recognised definition, and by limited available data. This work contributes to food waste generation literature by using one of the largest available datasets that includes data on the objective amount of avoidable household food waste, along with information on a series of socio-economic factors. In order to address one aspect of the complexity of the problem, machine learning algorithms (random forests and boruta) for variable selection integrated with linear modelling, model selection and averaging are implemented. Model selection addresses model structural uncertainty, which is not routinely considered in assessments of food waste in literature. The main drivers of food waste in the home selected in the most parsimonious models include household size, the presence of fussy eaters, employment status, home ownership status, and the local authority. Results, regardless of which variable set the models are run on, point toward large households as being a key target element for food waste reduction interventions.

https://doi.org/10.4324/9780429462795 ; The generation of food waste at both the supplier and the consumer levels stems from a complex set of interacting behaviours. Computational and mathematical models provide various methods to simulate, diagnose and predict different aspects within the complex system of food waste generation and prevention. This chapter outlines four different modelling approaches that have been used previously to investigate food waste: discrete event simulation, which has been used to examine how the shelf life of milk and many actions taken around shopping and use of milk within a household influence food waste; machine learning and Bayesian networks, which have been used to provide insight into the determinants of household food waste; agent-based modelling, which has been used to provide insight into how innovation can reduce retail food waste; and mass balance estimation, which has been used to model and estimate food waste from data related to human metabolism and calories consumed.

The paper begins with a discussion of the concept of "shrinking", and its origins, outside the realm of rural development. Building on this, the paper shows the distribution of shrinking rural areas across Europe. Using both the project's literature review and findings from its eight case studies the socio-economic processes which drive demographic decline in rural areas are then described. A brief account of the evolution of EU interventions to alleviate the effects of shrinking, and some remarks about the current policy/governance landscape follow. We conclude by considering how a better understanding of the problem and process of shrinking may lead to more effective interventions, within the context of a refreshed long-term vision for Rural Europe. The latter needs to fully acknowledge the expanding repertoire of opportunities confronting rural areas as COVID-19 changes in working behaviour, and the geography of economic activity, accelerate, and fulfil, previously incremental shifts in technology and markets. ; published version ; peerReviewed