A multi-objective decision support framework to prioritize tree planting locations in urban areas

With tree planting initiatives being undertaken in different cities, careful thought needs to be put into the placement of trees, the beneficiaries of ecosystem services from these trees, and the potential impacts of alternative tree planting schemes. Using a spatially explicit methodology within biophysical ecosystem service models, this research develops a multi-objective decision support framework to guide future greening initiatives towards prioritizing planting locations that maximize multiple objectives. In a case study application of the framework in the Bronx, NY, the analysis utilizes spatially distributed census block group data and linear programming, a mathematical optimization technique, to identify optimal and equitable planting locations considering increases in tree cover, monetary benefits from avoided runoff, PM_2.5 air pollutant removal and heat index reduction as well as tree planting costs and the equality and equity of urban tree ecosystem services. Using different optimization scenarios, the framework identifies optimal planting schemes by minimizing planting costs, maximizing increases in tree cover and ecosystem service benefits, and the equity of canopy cover and ecosystem services, arriving at a wide range of different planting recommendations. We conclude that multi-objective prioritization frameworks can identify optimal locations for greater total benefits from urban greening and that the proposed framework has the potential to inform decision making in different cities.