Assessing costs and environmental impacts of municipal food waste treatment options in Columbia, Missouri : a decision support tool integrating life cycle analysis and robust optimization
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Many municipal governments currently have goals in place to align with global efforts and policy to reduce greenhouse gas (GHGs) emissions and take advantage of waste as a resource for renewable energy and nutrients. To meet specified goals and targets, decisionmakers need data-driven analysis to understand both the costs and environmental impacts of their plans. This study develops a decision support tool applied in Columbia, Missouri, USA, with aims to model the economic and environmental tradeoffs in solid waste management decisions for the collection and treatment of food waste in the municipal solid waste stream while considering existing infrastructure and uncertainty in environmental impact data. The tool uses life cycle analysis environmental impact data from literature and cost data from case-studies to simulate both a FW collection route and the processing of FW through various potential and existing treatment options (anaerobic digestion, anaerobic co-digestion with sewage sludge, composting, landfilling, dry animal feed production, wet animal feed production). The model calculates the cost and greenhouse gas emissions of the transportation and treatment processes in each simulation. The tool can choose the best FW management scenario for the objective of minimizing cost or minimizing GHG emissions. Robust optimization incorporates uncertainty into the model by allowing environmental impacts for any FW treatment option to assume a maximum or minimum of a range of values from literature, representing the worst- and best-case values for environmental performance, respectively. Average case results indicate that a minimum cost scenario uses a combination of landfilling and composting FW that results in net positive GHG emissions. To minimize environmental impact, the average case results favor anaerobic digestion, a scenario which results in net negative GHG emissions. Compared to the minimum cost scenario, the transportation costs in the minimum impact scenario are similar, while the costs to treat the FW are nearly nine times higher. Robust results focus on variability in environmental impacts. In the model results, anaerobic digestion is favored when assuming its minimum environmental impact value but is outperformed by other options when anaerobic digestion assumes the maximum of its possible range. All considered options outperform landfilling, but the rankings among landfilling alternatives depend highly on assumptions regarding offsets estimated in life cycle assessment. Without any offsets, wet animal feed production is the best FW treatment solution. Environmental impact of transportation in this model is not influential. The results demonstrate the importance of model assumptions, uncertainty in life cycle GHG estimates, and consideration of existing infrastructure in determining the optimal scenarios.