Towards “Right Tree, Right Place” in urban environments: A systematic review of decision-support methods and tools for urban tree planting

Urban tree planting is widely recognized as a nature-based solution for promoting sustainable urban development and improving human well-being. The decision-making process in urban tree planting is shaped by diverse objectives and contextual constraints. However, current studies provide limited clarity on the differences, applicability, and data requirements of existing methods, making it difficult for urban designers to understand available decision-support approaches. To address this gap, we conducted a systematic review to (1) identify and analyze existing decision-support methods for urban tree planting, helping designers understand which approaches may be suitable under different tasks and data conditions, and (2) assess their limitations and propose directions for future research. A total of 39 studies were reviewed, and the decision-support methods identified in these studies were analyzed across three levels: study context, focus and scope, and decision-support implementation, covering nine attributes. We further examined the relations among decision-making methods, objectives, and input data across four components of the decision process: tree species selection, identification of feasible planting areas, prioritization of planting locations, and layout optimization. Tree-related data, rule-based filtering methods, and environmental benefit maximization emerged as the most frequently used input data, method, and objective. We found that only a small number of studies proposed site-specific decision-making frameworks, which may limit the practical applicability of tree planting strategies. We conclude that future research should further explore the definition of actionable spatial units for implementation and integrate site-specific analysis and long-term tree dynamics into decision-support approaches.