As stormwater and its embedded nutrients continue to impede our nation's waterways, green infrastructures (GIs) have been increasingly applied in urban and suburban communities as a sustainable alternative to the combined sewer systems. Although GIs have been widely studied for their life cycle impacts and benefits, most of these studies adopt a static approach which is not transferrable to other environments on a spatial or temporal scale. This research utilizes a dynamic life cycle assessment (LCA) to evaluate seven different GIs through both an economic and environmental perspective by integrating the conventional LCA with a system dynamics model simulating the daily loading and removal of nutrients by the GIs. The model was calibrated by the measured annual nutrient removal efficiencies through field studies. Evaluated impacts include cumulative energy demand, global warming potential, marine and freshwater eutrophication potentials, and life cycle cost in terms of net present value across a life span of 30 years. The influence of geographical locations, land use types, system design sizes, and climate change scenarios on the GIs' performance was examined. It was found through this research that the system which, on average, performed best at reducing dissolved inorganic nitrogen (DIN) across its lifespan is the subsurface gravel wetland. This high capacity for reducing dissolved inorganic nitrogen is upheld across all other scenarios with it experiencing at least the second highest to highest life cycle DIN reductions. The subsurface gravel wetland also shows high resiliency (37% average deviation) as compared to other systems (107% average deviation for bio-retention systems). The gravel wetland showed similar performance capabilities within the phosphorous model with this system out ranking all others regardless of scenario. Similarly, within the total phosphorous scenarios the system which on average performed the best is the gravel wetland. It has the highest capacity and maximum removal percentage which allows this system to handle a large range of influent masses. It is also shown to be the most resilient against environmental changes with an average deviation of 12% as compared to other systems (20% average deviation for sand filters).