With roots in ancient civilizations, sustainability has gained public attention in recent decades with a series of landmark reports, events, and treaties. These efforts recognize the necessity to promote equitable and prosperous communities while simultaneously maintaining the ecosystems that support them. This need is particularly relevant for water technologies as they safeguard basic human rights (water and sanitation ) and are essential components of broader initiatives to re-envision other systems designed to meet societal needs (e.g., resource recovery, green chemistry, regenerative agriculture ). In line with this need, the field of sustainability science and sustainability engineering continue to evolve in support of the society's transition toward sustainability. To apply the concept of sustainability in the research, development, and deployment (RD&D) of technologies, implementable and systematic methodologies are necessary to quantitatively evaluate technologies as they mature. To this end, multiple sets of guiding principles (e.g., the Green Chemistry 39 and Green Engineering 21 Principles) and frameworks (e.g., the UN Inclusive Wealth framework 40 ) have been proposed. These principles and frameworks often represent aspirational goals for technologies, engineered systems, or national entities, and are not intended to provide structured guidance to inform the prioritization of RD&D for specific technologies. Similarly, methodologies such as Life Cycle Sustainability Assessment have been proposed to address the "whole-picture" of sustainability, but methodologies such as this often lack quantitative, transparent strategies to navigate tradeoffs across dimensions of sustainability and technology-specific indicators of engineering performance (e.g., contaminant removal). More critically, there is a growing recognition of the importance of uncertainty, especially for earlystage technologies associated with higher levels of uncertainty. Nonetheless, the results of sustainability analyses are often presented as single values. This "false precision" overlooks aleatory (due to randomness) and epistemic (due to the lack of knowledge) uncertainty that is Ultimately, these limitations undermine the accessibility and utility of sustainability analyses to inform decision-making for the RD&D of technologies, which is crucial in the society's pursuit of sustainability. In this paper, we review and synthesize published literature related to sustainability analyses to present a tutorial review on Quantitative Sustainable Design (QSD). QSD integrates concepts associated with sustainability science and engineering to expedite and support the RD&D of technologies (Figure ). Through the lens of QSD, we establish a shared lexicon as the foundation for interdisciplinary communication and to support methodological transparency and consistency (Table ). In presenting this methodology, we discuss published studies using the shared lexicon to put them in the context of QSD. Specifically, we begin by defining the problem space, which includes specifying the system of interest as well as relevant decision variables, technological parameters, and contextual parameters (Section 2). Next, we establish design and process algorithms to generate the system inventory (mass and energy flows that enter and leave the system). Sustainability indicators can then be quantified using techniques that span economic, environmental, human health, and social dimensions (Section 4). By compiling all algorithms used in system simulation and sustainability characterization, a system model can be created to quantify uncertainty and generate the desired types of insight (Section 5). Through the review of existing tools, we discuss the status of tools for sustainability analyses and how the different steps of QSD can be executed under uncertainty. With literature examples and representative figures, we further illustrate how QSD can be used (i) to characterize sustainability indicators under uncertainty, (ii) to identify sustainability drivers, (iii) to set RD&D targets, (iv) to understand uncertainty drivers, (v) to explore alternative scenarios (i.e., distinct combinations of decision variables, technological parameters, and contextual parameters), and (vi) to inform practical deployment. Ultimately, QSD can be used to elucidate the complex and intertwined connections among design decisions, technology characteristics, contextual factors, and sustainability indicators, thus enabling transparent and agile planning and design processes. Finally, we identify future research needs for the continued development and application of sustainability analyses for technology RD&D, with the goal of supporting society's pursuit of sustainable development.