AbstractNature‐based solutions (NbS, and related concepts like natural infrastructure, Ecosystem‐based Adaptation, and green infrastructure) are increasingly recognized as multi‐benefit strategies for addressing the critical sustainability challenges of the Anthropocene, including the climate emergency and biodiversity crisis. Mainstreaming NbS in professional practice requires strategic, landscape‐level planning integrating multiple sources of benefits and their synergies and trade‐offs. Levee setbacks (LS) are among the best‐studied riverine NbS with recognized benefits for flood risk management, drought resilience, water quality management, recreational opportunities, and ecological restoration for biodiversity. Although awareness of the multifarious benefits of LS as forms of Natural Capital is growing, implementation remains ad‐hoc and opportunistic. To address this critical implementation gap for one major example of NbS, we review and synthesize literature across diverse disciplines to provide an overview of the primary social, economic, and ecological mechanisms that affect the co‐benefit delivery of LS projects. Next, to make this information relevant to NbS practitioners, we link these mechanisms to spatial metrics that can be used to approximate the relative magnitude of project benefits and costs across these mechanisms. Finally, we highlight examples of key synergies and trade‐offs among benefits that should be considered for LS planning. This synthetic approach is intended to familiarize readers with the diverse potential benefits of LS, and provide an understanding of how to select and prioritize potential sites for further study and implementation. Synergies and trade‐offs among important benefit drivers abound, and social equity concerns will be paramount in ensuring the successful implementation of LS and other NbS in the future.This article is categorized under: Engineering Water > Sustainable Engineering of Water Engineering Water > Planning Water Water and Life > Nature of Freshwater Ecosystems
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