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Current regulatory tools are not well suited to address freshwater salinization in urban areas, and the conditions under which bottom-up management is likely to emerge remain unclear. We hypothesize that Elinor Ostrom’s social-ecological systems (SESs) framework can be used to explore how current understanding of salinization might foster or impede its collective management. We focus on the Occoquan Reservoir, a critical urban water supply in Northern Virginia, USA, and use fuzzy cognitive maps (FCMs) to characterize stakeholder understanding of the SES that underpins salinization in the region. Hierarchical clustering of FCMs reveals four stakeholder groups with distinct views on the causes and consequences of salinization, and actions that could be taken to mitigate salinization, including technological, policy, and governance interventions and innovations. Similarities and differences across these four groups, and their degree of concordance with measured or modeled SES components, point to actions that could be taken to catalyze collective management of salinization in the region. 

Equity is core to sustainability, but current interventions to enhance sustainability often fall short in adequately addressing this linkage. Models are important tools for informing action, and their development and use present opportunities to center equity in process and outcomes. This Perspective highlights progress in integrating equity into systems modeling in sustainability science, as well as key challenges, tensions, and future directions. We present a conceptual framework for equity in systems modeling, focused on its distributional, procedural, and recognitional dimensions. We discuss examples of how modelers engage with these different dimensions throughout the modeling process and from across a range of modeling approaches and topics, including water resources, energy systems, air quality, and conservation. Synthesizing across these examples, we identify significant advances in enhancing procedural and recognitional equity by reframing models as tools to explore pluralism in worldviews and knowledge systems; enabling models to better represent distributional inequity through new computational techniques and data sources; investigating the dynamics that can drive inequities by linking different modeling approaches; and developing more nuanced metrics for assessing equity outcomes. We also identify important future directions, such as an increased focus on using models to identify pathways to transform underlying conditions that lead to inequities and move toward desired futures. By looking at examples across the diverse fields within sustainability science, we argue that there are valuable opportunities for mutual learning on how to use models more effectively as tools to support sustainable and equitable futures.