What gets measured gets managed: A new method of measuring household food waste

Elimelech, Efrat; Ayalon, Ofira; Ert, Eyal

doi:10.1016/j.wasman.2018.03.031

Cited by 104 publications

(81 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…It is notable that the measurement of food waste in some cases has relied on self-reporting/perception rather than an actual waste audit quantifying the reduction. Several studies have noted the limitations of using self-reporting to measure food waste [28], which, in some cases, can lead individuals to significantly underestimate reported amounts by 40% [29].…”

Section: Introductionmentioning

confidence: 99%

Food Waste Reduction: A Test of Three Consumer Awareness Interventions

Soma

Maclaren

2020

Sustainability

View full text Add to dashboard Cite

Halving food waste by 2050 as per the Sustainable Development Goal 12.3 is key to securing a food system that is sustainable. One approach to reducing household food waste is through education campaigns. We recruited 501 households divided into three types of intervention groups and compared with a control group to better understand the efficacy of diverse education campaign approaches. Food waste interventions included a passive approach (handouts), a community engagement approach, and a gamification approach. We conducted waste audits, household surveys (pre- and post-intervention), and a focus group at the end of the campaign. The passive and gamification groups had similarly high levels of participation, while participation in the community group was very low. The passive group and the gamification group had higher self-reported awareness of food wasting after the campaign and lower food wastage than the control group. Waste audits found marginally significant differences between the game group and the control (p = 0.07) and no difference between the other campaign groups and the control group in edible food wasted. Frequent gamers were found to generate less edible food waste than infrequent gamers. We conclude that the evidence about the potential for gamification as an effective education change tool is promising and we recommend further study.

show abstract

Section: Introductionmentioning

confidence: 99%

Food Waste Reduction: A Test of Three Consumer Awareness Interventions

Soma

Maclaren

2020

Sustainability

View full text Add to dashboard Cite

show abstract

“…Classification was made according to 'avoidability' (avoidable and nonavoidable food waste), which was determined according to the theoretical prevention potential, as suggested by Lebersorger and Schneider [32]. Any preparation residues, e.g., vegetables pilling, bones, and other nonedible food parts were classified as nonavoidable food waste [for detailed sampling and classification procedures see Elimelech et al [57] (pp. [74][75]].…”

Section: Measured Food Wastagementioning

confidence: 99%

“…Analysis of grocery shopping receipts revealed that, on average, weekly food purchases amounted to 24.5 kg (SD = 14.6 kg) per household (Elimelech et al [57], adjusted for N = 169).…”

Section: The Amount Of Purchased Foodmentioning

confidence: 99%

“…According to the physical waste measurements, the average generation rate of avoidable food waste was 3.012 kg (SD = 2.392 kg) per household per week (Elimelech et al [57], adjusted for N = 169). This figure might represent an underestimation of measured food wastage, since liquid waste that is disposed via the sewer was not measured [79].…”

Section: Measured Food Wastagementioning

confidence: 99%

See 1 more Smart Citation

Exploring the Drivers behind Self-Reported and Measured Food Wastage

2019

View full text Add to dashboard Cite

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.

show abstract

“…Some studies consider it as a percentage of consumed calories, some as a proportion of the weight of food bought or of the weight of domestic waste, and others measure the quantity of food being wasted in financial terms [6]. At present, in most Sustainability 2019, 11, 3173 2 of 12 applications, food waste monitoring and measurements are carried out manually [7], which is labour intensive, time-consuming, and often exposed to inconsistency and human errors [8], which leads to a commonly reported need for automated and accurate data collection procedures and systems to reduce food waste [9].…”

Section: Introductionmentioning

confidence: 99%

Monitoring Potato Waste in Food Manufacturing Using Image Processing and Internet of Things Approach

et al. 2019

View full text Add to dashboard Cite

Approximately one-third of the food produced globally is spoiled or wasted in the food supply chain (FSC). Essentially, it is lost before it even reaches the end consumer. Conventional methods of food waste tracking relying on paper-based logs to collect and analyse the data are costly, laborious, and time-consuming. Hence, an automated and real-time system based on the Internet of Things (IoT) concepts is proposed to measure the overall amount of waste as well as the reasons for waste generation in real-time within the potato processing industry, by using modern image processing and load cell technologies. The images captured through a specially positioned camera are processed to identify the damaged, unusable potatoes, and a digital load cell is used to measure their weight. Subsequently, a deep learning architecture, specifically the Convolutional Neural Network (CNN), is utilised to determine a potential reason for the potato waste generation. An accuracy of 99.79% was achieved using a small set of samples during the training test. We were successful enough to achieve a training accuracy of 94.06%, a validation accuracy of 85%, and a test accuracy of 83.3% after parameter tuning. This still represents a significant improvement over manual monitoring and extraction of waste within a potato processing line. In addition, the real-time data generated by this system help actors in the production, transportation, and processing of potatoes to determine various causes of waste generation and aid in the implementation of corrective actions.

show abstract

What gets measured gets managed: A new method of measuring household food waste

Cited by 104 publications

References 44 publications

Food Waste Reduction: A Test of Three Consumer Awareness Interventions

Food Waste Reduction: A Test of Three Consumer Awareness Interventions

Exploring the Drivers behind Self-Reported and Measured Food Wastage

Monitoring Potato Waste in Food Manufacturing Using Image Processing and Internet of Things Approach

Contact Info

Product

Resources

About