Decomposing uncertainty of greenhouse gas emission reduction costs in MSW management: A case study.

As climate change mitigation becomes increasingly crucial, transitions in municipal solid waste management systems provide one avenue for reducing greenhouse gas (GHG) emissions. However, the identification of the most cost-effective emission reduction pathways can be challenged by various uncertainties that accompany these transitions. This study examined robustness and cost-effectiveness of solutions for reducing GHG emissions from municipal solid waste management in a real-world case study of the Gölbaşı municipality, Türkiye. The methodology combined life cycle assessment and life cycle costing for environmental and economic evaluations followed by Simulation Decomposition, which employed global sensitivity analysis to identify the most important input parameters and mapped their relative impacts on to the probability distribution of the model output. Uncertainty ranges of GHG emission reduction costs overlapped for all emission reduction pathways. Based on uncertainty decomposition results, although two pathways involving door-to-door collection of biowaste showed potential for lower emission reduction costs when biowaste collection is maximized, the pathway of door-to-door biowaste collection for electricity production (A2) was deemed unsafe as it failed in achieving emission reductions in certain conditions. Meanwhile, door-to-door collection of packaging waste (A1) and the mechanical-biological treatment of mixed waste with the production of refuse-derived fuel (A4) may be considered more robust as they carry a lower risk of high GHG emission reduction costs. Thus, this research showcased the potential of advanced hybrid sensitivity-uncertainty approaches to identify robust solutions and provide more informed recommendations for decision makers, while proving a clear need for comprehensive uncertainty consideration in today's world.