Optimizing nature-based solutions by combining social equity, hydro-environmental performance, and economic costs through a novel Gini coefficient

A robust multi-functional framework for widespread planning of nature-based solutions (NBS) must incorporate components of social equity and hydro-environmental performance in a cost-effective manner. NBS systems address stormwater mitigation by increasing on-site infiltration and evaporation through enhanced greenspace while also improving various components of societal well-being, such as physical health (e.g., heart disease, diabetes), mental health (e.g., post-traumatic stress disorder, depression), and social cohesion. However, current optimization tools for NBS systems rely on stormwater quantity abatement and, to a lesser extent, economic costs and environmental pollutant mitigation. Therefore, the objective of this study is to explore how NBS planning may be improved to maximize hydrological, environmental, and social co-benefits in an unequivocal and equitable manner. Here, a novel equity-based indexing framework is proposed to better understand how we might optimize social and physical functionalities of NBS systems as a function of transdisciplinary characteristics. Specifically, this study explores the spatial tradeoffs associated with NBS allocation by first optimizing a local watershed-scale model according to traditional metrics of stormwater efficacy (e.g., cost efficiency, hydrological runoff reduction, and pollutant load reduction) using SWMM modeling. The statistical dispersion of social health is then identified using the Area Deprivation Index (ADI), which is a high-resolution spatial account of socioeconomic disadvantages that have been linked to adverse health outcomes, according to United States census properties. As NBSs have been shown to mitigate various adverse health conditions through increased urban greening, this improved understanding of geospatial health characteristics may be leveraged to inform an explicit representation of social wellness within NBS planning frameworks. This study presents and demonstrates a novel framework for integrating hydro-environmental modeling, economic efficiency, and social health deprivation using a dimensionless Gini coefficient, which is intended to spur the positive connection of social and physical influences within robust NBS planning. Hydro-environmental risk (according to hydro-dynamic modeling) and social disparity (according to ADI distribution) are combined within a common measurement unit to capture variation across spatial domains and to optimize fair distribution across the study area. A comparison between traditional SWMM-based optimization and the proposed Gini-based framework reveals how the spatial allocation of NBSs within the watershed may be structured to address significantly more areas of social health deprivation while achieving similar hydro-environmental performance and cost-efficiency. The results of a case study for NBS planning in the White Oak Bayou watershed in Houston, Texas, USA revealed runoff volume reductions of 3.45% and 3.38%, pollutant load reductions of 11.15% and 11.28%, and ADI mitigation metrics of 16.84% and 35.32% for the SWMM-based and the Gini-based approaches, respectively, according to similar cost expenditures. As such, the proposed framework enables an analytical approach for balancing the spatial tradeoffs of overlapping human-water goals in NBS planning while maintaining hydro-environmental robustness and economic efficiency.