Community-Based Modeling and Sustainable Management of Urban Food Waste: A Planning Framework and Case Study in Chicago

Food waste is the largest single component in the municipal solid waste (MSW) disposal stream in the U.S.; only 5% of discarded food is recovered or reused (U.S. EPA, 2015). Current practices of food waste management (FWM) not... [ view full abstract ]

Food waste is the largest single component in the municipal solid waste (MSW) disposal stream in the U.S.; only 5% of discarded food is recovered or reused (U.S. EPA, 2015). Current practices of food waste management (FWM) not only undervalue all the energy, nutrients, and water that are embedded in the discarded food, but also require additional resources for waste hauling, processing, and final disposal. There are missing linkages between FW generators (“supply”) and potential users (“demand”), which directly result in economic inefficiencies (e.g., foregone resources, wasteful infrastructure, transportation and/or disposal costs), environmental pollutions (e.g., GHG emissions and wastewater), and social inequities. Limited (and decentralized) sources of re-usable food and time-sensitivity of edible food contribute to the logistical challenges of FW reuse and recovery (Staley, 2015). More importantly, community-wide FW generation volume is not generally known (U.S. EPA, 2015) to foster FWM partnerships that match the “supply” and “demand” locally.

This interdisciplinary study aims to promote community-based sustainable planning programs in the case of FWM, which represents complex challenges particularly in urban areas. In a case study in the City of Chicago, we demonstrate the potential opportunities of connecting urban FW with local reuse and recovery options. Numerically, we estimated FW generation volume at nearly 14,000 locations in Chicago employing a Material Flow Analysis (Leigh et al., 2007; Griffin et al., 2008) that couples generator type (e.g., residences, restaurants, food retails, and institutions) with generation rate (e.g., lbs per person, per employment, or per square footage) (Ai, 2015; Ai and Zheng, 2015). Next, we examine its spatial distribution and adopt the Affinity Propagation method (Frey and Dueck, 2007) to identify the clusters of food discards. Lastly, we resort to the Network Analysis in ArcGIS to spatially match discarded food clusters to the highest end uses in proximity. By overlaying FW clusters and demographic data, we also identify the communities that are under-served in terms of either FW collection for donation/recycling (e.g., high income communities), or edible food donation (e.g., low income communities and food banks).

Results from this study are anticipated to provide critical references for local FWM, including the efficient location of FW collection/drop-off centers, FWM zone delineation that reflects the heterogeneity of urban communities, and zonal sum of FW volume by potential use types. Given that FW bans from landfills have been adopted by an increasing number of cities and states, we anticipate that our empirical study will provide timely support for local and regional initiatives of FW planning with harmonized goals of efficiency, effectiveness, and equity.