Multi-objective optimization of plant community characteristics in urban green spaces

Abstract

Urban green spaces are crucial for improving the quality of life of city residents because they provide numerous health advantages and foster overall wellness. However, previous research has concentrated only on individual goals and overlooked the inherent contradictions that arise when many objectives are considered. This study devised a transdisciplinary framework to link the optimization of plant community characteristics with multiple objectives. In Beijing, we selected 22 urban green spaces and utilized the non-dominated sorting genetic algorithm III (NSGA-III) to create a Pareto-optimal model. This model allowed us to reveal the relationships and limitations between plant community characterization factors and three objectives: temperature comfort, landscape aesthetics, and construction costs. Consequently, 91 Pareto-optimal solutions were obtained. The results indicate that a tradeoff exists between the three goals of temperature comfort, landscape aesthetics, and building costs, and this tradeoff is influenced by the interplay between the objectives and plant community characterization factors. We condensed four methodologies for building plant communities, each with distinct objective orientations, while also considering the preferences of decision-makers. The results of this study offer a novel perspective for conducting research in multi-objective scenarios to promote environmental sustainability.