This article connects the science of sustainability theory with applied aspects of sustainability deployment. A suite of 35 sustainability indicators spanning 12 environmental and socioeconomic categories has been proposed for comparing the sustainability of bioenergy production systems across different feedstock types and locations. Information on sustainability indicators and associated measurements for the feedstock production and logistics portions of the biofuel supply chain was available from a recent demonstration‐scale switchgrass‐to‐ethanol production system located in East Tennessee. Knowledge pertaining to the available indicators was distributed within a hierarchical decision tree framework to generate an assessment of the overall sustainability of this no‐till switchgrass production system relative to two alternative business‐as‐usual scenarios of unmanaged pasture and tilled corn production. The relative contributions of the social, economic, and environmental information were determined for the overall trajectory of this bioenergy system's sustainability under each scenario. The results show that, within this East Tennessee context, switchgrass production is an attractive option for improving environmental and social sustainability trajectories without adverse economic impacts, which can lead to enhanced sustainability overall. Although external trade does not yet exist for this switchgrass commodity, our economic modeling indicates that switchgrass production can still be beneficial to the counties surrounding the biorefinery in terms of dollars earned and jobs created. The opportunity to use inactive equipment and laborers is a potential benefit captured indirectly by the sustainability evaluation framework. Given the early stage of cellulosic ethanol production, it is currently difficult to determine quantitative values for all 35 proposed sustainability indicators across the entire biofuel supply chain. This case study demonstrates that integration of qualitative sustainability indicator ratings may increase holistic understanding of a bioenergy system in the absence of complete information.