Multi-Objective Optimization of the Environmental, Social, and Economic Benefits of Green Infrastructure: Enhancing the Decision-Making Process Using Pareto-Optimal Solutions

Green Infrastructure (GI) is known for its diverse benefits, spanning environmental, social, and economic benefits. However, existing research tends to focus narrowly on optimizing singular benefits rather than embracing a holistic optimization approach. To address this gap, this study conducted a multi-objective optimization, incorporating the capital cost and economic value of GIs into a multi-objective optimization algorithm (i.e., the NSGA-II algorithm). First, an Agent-based model (ABM) simulating the implementation of various green infrastructure (GI) solutions over different land parcels was developed. Second, a quantification of GI's environmental, social, and economic dimensions, coupled with their associated costs, was conducted. Third, a tailored multi-objective optimization algorithm was developed. The results not only offered guidance regarding the most efficient GI layout but also underscored the important role of strategic GI planning. Moreover, the findings provided insights into the overall costs and economic value of GI's benefits involved in the optimal GI layout. This research provides practitioners, decision-makers, and policymakers with a robust framework for achieving comprehensive optimization in GI planning. Although tested in Newark, NJ, US, this adaptable framework holds potential for broader applications across diverse urban landscapes and various types of GI.