Great effort is invested in mapping the extent of household food waste and its main determinants. However, food waste valuation remains a challenging task. Valuation methods can be divided roughly into objective measurements that are based on physical waste surveys, and subjective self-assessments that are based on diaries or questionnaires. Self-assessment methods have been more popular than food waste measurement because they are less costly. The goal of this paper is to empirically test whether self-assessments can accurately reflect objective measurement. To answer this question, we implemented a hybrid valuation approach by integrating and comparing three methods: a self-assessment questionnaire, a physical waste survey, and a food expenditure survey. Self-assessments slightly underestimated measured food waste proportion (13.7% vs. 16.3%, respectively). The results also show a positive, yet, not very strong correlation between the measures and the self-assessments of unconsumed and partly consumed avoidable food waste in most food categories. Self-assessments of monetary losses were €42.07 per household per month on average, overestimating calculated losses of €25.74 on average. Our findings question the validity of self-assessments. The current paper demonstrates the questionable nature of the implicit assumption that self-assessment reflects the true level of food waste and suggests a rigorous method for exploring this relation.

Understanding households’ food waste drivers is crucial for forming a coherent policy to meet the sustainable development goals. However, current studies have documented mixed evidence regarding food waste determinants. Most studies have relied on self-reports, assuming they reflect actual behaviors. This study applies a structural equation model that evaluates both self-reported and measured food wastage, and how they are affected by different households’ attributes, attitudes, and behaviors. As such, it also provides a test for the underlying logic that self-reports are a proxy for actual food waste. Results show that measured food wastage is, at best, weakly correlated with self-reports. Moreover, drivers affecting self-reported and measured food wastage are not necessarily the same. Household size affects only measured food wastage. Source separation behavior negatively affects self-reported and measured food wastage, while environmental attitudes have a negative effect only on self-reports. Meal planning, unplanned shopping, and food purchased have no impact on self-reported and measured food wastage. The relation between self-reported and actual food waste and their drivers are even less understood than we thought. The distinction between self-reports and actual waste is crucial for follow-up research on this subject as well as assessing policy measures.

The spatial distribution of vegetative agricultural residuals (VAR) implies that any waste treatment system (WTS) designed to manage VAR is particularly sensitive to transportation costs. Additionally, a wide range of treatment technologies is potentially available for VAR treatment, but some of them lack a well-developed market for their output products. This study develops a method to design an economically feasible VAR treatment system, analyzing the profitability of the system as a function of logistics and uncertain market prices of the available treatment technologies' products. The design method includes an economic optimization model followed by a sensitivity analysis of the potential changes in the system’s profitability. The results show that the market price of the treatment technologies' products has a larger impact on the system’s profitability than transportation costs. Specifically, if biochar prices reach the level forecasted by experts, pyrolysis will become the dominant technology of the WTS. The research highlights the importance of the treatment technology selection and the location of treatment facilities in the design of an optimal WTS for VAR.

Setting up a sustainable agricultural vegetative waste-management system is a challenging investment task, particularly when markets for output products of waste-treatment technologies are not well established. We conduct an economic analysis of possible investments in treatment technologies of agricultural vegetative waste, while accounting for fluctuating output prices. Under a risk-neutral approach, we find the range of output-product prices within which each considered technology becomes most profitable, using average final prices as the exclusive factor. Under a risk-averse perspective, we rank the treatment technologies based on their computed certainty-equivalent profits as functions of the coefficient of variation of the technologies’ output prices. We find the ranking of treatment technologies based on average prices to be robust to output-price fluctuations provided that the coefficient of variation of the output prices is below about 0.4, that is, approximately twice as high as that of well-established recycled-material markets such as glass, paper and plastic. We discuss some policy implications that arise from our analysis regarding vegetative waste management and its associated risks.

The quantification of household food waste is an essential part of setting policies and waste reduction goals, but it is very difficult to estimate. Current methods include either direct measurements (physical waste surveys) or measurements based on self-reports (diaries, interviews, and questionnaires). The main limitation of the first method is that it cannot always trace the waste source, i.e., an individual household, whereas the second method lacks objectivity. This article presents a new measurement method that offers a solution to these challenges by measuring daily produced food waste at the household level. This method is based on four main principles: (1) capturing waste as it enters the stream, (2) collecting waste samples at the doorstep, (3) using the individual household as the sampling unit, and (4) collecting and sorting waste daily. We tested the feasibility of the new method with an empirical study of 192 households, measuring the actual amounts of food waste from households as well as its composition. Household food waste accounted for 45% of total waste (573 g/day per capita), of which 54% was identified as avoidable. Approximately two thirds of avoidable waste consisted of vegetables and fruit. These results are similar to previous findings from waste surveys, yet the new method showed a higher level of accuracy. The feasibility test suggests that the proposed method provides a practical tool for policy makers for setting policy based on reliable empirical data and monitoring the effectiveness of different policies over time.