The optimization model of the national upcycling problem comprises 4,010 geographical locations that are available as candidates for technology placement; a couple of sizes (capacities) for each technology type can be selected (their capital costs capture economies of scale). In total, the MIP problem contains 796,756 continuous variables, 8,020 binary variables, and 587,405 constraints; the large number of variables (which capture potential technology selection and placement decisions) make the problem challenging to solve. These sites were assigned to county nodes in order of descending population (meaning that the largest counties received technology sites first), in order to facilitate the placement of facilities near population hubs. This procedure limits the number of binary variables needed to formulate the problem. To further reduce model size, we connected only adjacent counties with arcs to allow product flow. This arrangement uses the minimum number of arcs to fully connect all county nodes, while eliminating trivial arcs that would add significantly to model size but minimally to model solution accuracy (such as connections between two small and distant counties).