The solution of the MIP presented above identifies the optimal binary variables y m (the optimal design of the system that determines technology types, sizes, and locations). We then fix these optimal binary variables to obtain a linear programming (LP) problem; the solution of the LP is equivalent to assume that the technologies are already deployed and we thus seek to find the best management of a value chain (best allocation of products between stakeholders) to maximize total profit. From the solution of the LP we obtain the dual variable of the product valance constraints π n,p ; these values are key and are interpreted as the clearing price of product p at the location node n. The clearing price π n,p is the inherent value of the product p at geographical location n. As such, the model helps discover inherent value for all products in the value chain (e.g., recyclables, pyrolysis oil, plastic bales); these prices vary in space, because they implicitly capture transportation costs and regional availability of waste. These prices also provide the mechanism to compensate stakeholders involved in the value chain and help us understand how revenue is distributed among stakeholders. For instance, even if counties offer recyclables for free, plastic upcycling can generate significant value from waste, which could be used to compensate county residents for providing their waste (as well as technologies and transportation for providing their services). The prices obtained by the model are such that they cover the operating costs of all stakeholders; for more details on the economic properties of the supply chain model, please refer to the work of Tominac and Zavala .