Due to increasing interest in sustainable development, today s engineer is often tasked with designing systems that are environmentally benign over their entire life cycles. Unfortunately, environmental assessments commonly suffer from significant uncertainty due to lack of information, particularly for time-distant life cycle aspects. Under severe uncertainty, traditional uncertainty formalisms require more information than is available. However, a recently devised formalism, information-gap decision theory (IGDT), requires no more information than a nominal estimate; error bounds on that estimate are unknown. The IGDT decision strategy, accordingly, favors the design that is robust to the most estimation error while still guaranteeing no worse than some good enough critical level of performance. In some cases, one can use IGDT to identify a preferable design option without needing more information or more complex uncertainty analysis. In this dissertation, IGDT is investigated and shown to enhance decision support for environmentally benign design and manufacturing (EBDM) problems. First, the applicability of the theory to EBDM problems is characterized. Conditions that warrant an info-gap analysis are reviewed, the insight it can reveal about design robustness is demonstrated, and practical limitations to its use are revealed. Second, a new mathematical technique is presented that expands capabilities for analyzing robustness to multiple info-gap uncertainties simultaneously. The technique elicits scaling factors more rigorously than before and allows one to imprecisely express their beliefs about info-gap scaling. Two examples problems affected by info-gaps are investigated: oil filter selection and remanufacturing process selection. It is shown that limited information about uncertainty can, in some cases, indeed enable one to identify a most preferable design without requiring more information.