Prioritizing urban green spaces in resource constrained scenarios

Urban Green Space management requires a multi-dimensional, evidence-based approach to effectively balance social, environmental, and economic objectives. City administrators currently lack a data-driven framework for allocating resources during constraint scenarios, leading to subjective decisions. Existing literature lacks objective solutions for managing city-scale green spaces, each with its distinct characteristics. Another challenge is handling varied spatial scales required for urban applications. This study proposes a novel goal programming-based model for urban green space management wherein multiple benefit objectives, such as conserving sequestered carbon in trees and enhancing quality and accessibility of parks, as well as handling demand constraints on available resources like water and personnel, are included. The proposed method was demonstrated in two cities with diverse conditions, Berlin and Melbourne, and evaluated on various benefit metrics, such as allocated green space units, resources consumed, and goals achieved. The model was analyzed with resource allocation decisions and goals at different spatial scales. The highest benefit achievement and resource allocation were observed when resources were allocated at the sub-district scale with a city-level target. Alternatively, setting targets at the district level provided a more even resource distribution; however, at the cost of reduced overall benefits. Results show that the proposed method increased the total benefits gained while effectively balancing conflicting goals and constraints. Additionally, it allows incorporating the city’s preferences and priorities, offering a scalable solution for informed decision-making in varied urban applications. Depending on data availability, this approach can be scaled to other cities, including additional benefits and resource constraints as required.