Geospatial analysis of food waste generation at the consumer-level in high-income regions, 2000-2023 - A scoping review.

Food waste (FW) is a growing problem globally, with around 13% of food lost from harvest to retail, and another 17% wasted by retailers, households, and the food service sector. In high-income countries, consumer-level FW, primarily originates from private households and the food service sector, forming the largest waste stream in the food supply chain. Despite extensive research on FW, there is still a lack of knowledge about its geographic distribution, sources, spatial locations, and volume, impeding effective waste management strategies. Geospatial analysis of FW examines geographic variations in FW generation with respect to consumer demographic, behaviour, and socioeconomic factors. To date, a comprehensive review of the application of geospatial analysis to consumer-level food waste generation is not available in the scientific literature. This study aims to identify existing studies employing spatial analysis of FW generation at the consumer-level, encompassing households, commercial (non-industrial) and the food service sector within high-income region. Using the PCC (Population, Concept, Context) review protocol, 14 relevant studies were identified, delineated into three spatial scales: local (29%, n = 4), regional (42%, n = 6), and national (29%, n = 4). Methodologies included spatial autocorrelation (n = 2), spatial cluster analysis (n = 2), and geospatial analysis integrating non-spatial data with spatial datasets (n = 8) at various geographic levels. This review evaluated the advantages and disadvantages of these spatial techniques and their effectiveness in analysing FW generation. Only one study focused on household FW incorporating socioeconomic and demographic characteristics of consumers. The low number of identified studies highlights a significant knowledge gap in the assessment of spatial associations between FW generation and associated drivers/predictors. Improved FW data and indices are essential for analysing FW patterns across different demographics or regions, identifying hotspots, and mapping FW data using geographic information system (GIS) at multiple scales, thus permitting increasingly bespoke waste reduction interventions and strategies based on spatial insights.