Quantifying uncertainty in food waste across an urban area: The role of industrial, commercial, and institutional sectors.

Industrial, commercial, and institutional (ICI) food waste is a large, poorly quantified component of municipal solid waste. Landfill disposal of food waste results in greenhouse gas emissions and lost resources. Here, we apply a spatial-probabilistic modelling approach for estimating sector-specific ICI food waste generation and demonstrate this technique for Montreal, Canada. We identified 14,508 ICI establishments from municipal food inspection records and classified them into 14 subsectors. Point locations were matched to building footprints to acquire areal estimates for each establishment. Values for other operational variables were taken from a set of 421 Canadian ICI facility audits. A Monte Carlo approach was used to estimate uncertainty (relative standard error) for each establishment, with results summarized by establishment type and subsector. Overall, the model showed that the ICI sectors in Montreal sent 215.4 ± 7.8 kt yr-1 of food waste to landfill, with 6% of the mapped grid cells accounting for over 50% of total disposal. Food service and retail contributed 83% of the total ICI food waste, while the food manufacturing subsector had the greatest uncertainty. Areas with a higher intensity of food waste generation had consistently lower uncertainty, suggesting that they could be strategic areas to target policy interventions. Our approach draws on readily accessible building footprint data and could be adapted for cities in other regions where food waste audits are available. By explicitly modelling sector-specific and geographic variability, our model can help pinpoint where urban food waste reduction and landfill diversion strategies could be most effective, supporting efforts toward a circular bioeconomy.