Reducing Plastic Packaging and Food Waste Through Product Innovation Simulation: Household Behavioural Insights Around Packaging, Single and Reuse Options, and Food: 2021-2022

Pickering, Jack (2022). Reducing Plastic Packaging and Food Waste Through Product Innovation Simulation: Household Behavioural Insights Around Packaging, Single and Reuse Options, and Food: 2021-2022. [Data Collection]. Colchester, Essex: UK Data Service. 10.5255/UKDA-SN-855838

THE PROBLEM Plastic packaging waste is a major issue that has recently entered public consciousness, with the British government committing to a 25-year plan that would phase out disposable packaging by 2042. Around 41% of plastic packaging is used for food, with the UK generating 1 million tonnes per year of packaging waste. Food packaging has had a 1844% increase in recycling since 2007, yet still only one third of food packaging is currently recycled [3]. Currently many consumers are boycotting plastic packaging. However, this is leading to a rise in food waste (and foodborne illness risk) due to decreased shelf life. Up to a third of the resources used to produce food could be saved by eliminating food waste [1]. In the UK, approximately 10 million tonnes of food are wasted every year, with the average family (i.e. a household containing children) spending £700 a year on food that is wasted. 31% of avoidable household food waste (1.3 million tonnes), is caused by a mismatch of packaging, pack, and portion size, and household food habits [2]. Plastic pollution and food waste can be reduced through product re-design and other household interventions. However, there is little evidence to determine the best solutions to reduce plastic pollution and food waste. The food industry and consumers have a variety of possible solutions, but no way of knowing the impacts and unintended consequences (without costly, time consuming trials and measurement). This is a major barrier to empowering the food system to enable the rapid reduction of plastic waste. THE VISION This project reduces plastic pollution (and food waste) by providing a decision support tool to trigger action in the food industry and by consumers. Evidence concerning plastic and food waste reduction (and trade-offs with cost, and environmental impacts) will be generated by updating the Household Simulation Model (HHSM). The HHSM was piloted by the University of Sheffield and WRAP (the Waste & Resources Action Programme) to model the impacts of food product innovation quickly, to enable manufacturers to select the best innovations and interventions, and to prioritise their development and deployment. This project will incorporate into the current HHSM, data on 1) plastic packaging options and composition (from Valpak/WRAP), 2) household behavioural insights around packaging (single and reuse options) and food (provided by UoS/WRAP), with specific fresh produce data (from Greenwich) 3) plastic in the supply chain and environmental impacts (via SCEnATi- a big data analytics tool of the food supply chain processes (provided by Sheffield). The updated HHSM will enable the quantification of plastic and food waste reduction, and the environmental and monetary trade-offs of various solutions. This will be done by developing an optimization engine and integrating it with the updated HHSM which will further the simulation optimization methodology with the findings from applying developed meta-heuristic algorithms to this problem. Possible solutions include offering consumers different pack sizes, or changing packaging type/shape/reusability/durability. The most successful solutions will be translated into consumer and industry guidance focusing on the top 30 foods linked to the highest waste and tradeoff potential. This will enable rapid product and food system redesign. This guidance will be open access, and deployed through WRAP and global industry networks, and open access web tools. WRAP is coordinating the voluntary agreements UK Plastics Pact and the Courtauld Commitment 2025 (focused on food waste and carbon reduction). This allows rapid scaling of the HHSM outputs throughout the UK. References: [1] Institution of Mechanical Engineers, "Global food - Waste not, want not" London, 2013 [2] Quested, T. E., et al. "Spaghetti soup: The complex world of food waste behaviours." RCR 79 (2013): 43-51. [3] Recoup 2018, UK Household Plastics Collection

Data description (abstract)

This dataset was produced as part of the project: Reducing plastic packaging and food waste through product innovation simulation, funded by the NERC (Ref: NE/V010654/1). The wider project was concerned with building a Discrete Event Simulation (DES) model that can simulate food and food packaging as it moves through a household, from purchase to disposal. This dataset is qualitative in nature and was created to inform the modelling process. Remote qualitative interviews were planned and conducted, and participants were also asked to fill out entries on a research diary covering 4 days within a 7 day period. The interviews were semi-structured, and questions relating to waste and food practices were planned around 4 main areas: general household background, daily routines, weekly routines and shopping habits, waste and disposal practices, and attitudes to potential innovation in food packaging. A fifth section was based on photo-elicitation, to gauge participant attitudes to ageing produce. The diaries provided to participants had a system of prompts and questions that aimed to capture the movement of particular food items through the home and practices related to this movement over four separate days. Image files could also be submitted by participants, either included in the text files or as email attachments to the researcher. Follow up interviews were planned, which would be conducted after the second diary entry was complete and returned, but time constraints meant that only one follow up interview was conducted. A screening questionnaire was used to recruit and select participants, which was disseminated through university research recruitment channels and through social media accounts set up for the project. Informed consent for this questionnaire was gained at that stage, but was also gained prior to the remote interview, for both the interview and the diary research. However, the questionnaire data was not analysed and is not included in the collection as it was for recruitment purposes only. The data collection is comprised of 28 interview transcripts, 25 completed research diaries and 91 image files (produced by research participants) as part of the research diaries. Where image files were included in text documents by participants, the image files have been extracted and saved as photographs separately. These are stored as .docx files and .tiff files accordingly. Each of these was anonymised according to the anonymization plan. As there are multiple forms of data for each participant, each participant that completed the questionnaire was given a unique identifier, with suffixes describing the type of data. These are described in the readme file for the data collection.

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