A Decision Support Tool for Green Infrastructure Planning in the Face of Rapid Urbanization

Abstract

Multifunctional green infrastructure, a key component of compact sustainable cities, is challenged by the pressures associated with rapid urbanization. In this paper, we present a method that uses remote sensing, GIS modeling and stakeholder engagement to produce a decision support tool that communicates the availability and need for green infrastructure benefits. The case study presented is the City of Tshwane, South Africa, a Global South city facing rapid urbanization. We found that this method of mapping green infrastructure benefits can provide simultaneous oversight on multiple objectives for green infrastructure, including climate change adaptation, biodiversity, and equitable distribution of urban green space. We found that low-scoring benefit areas occur in dense urban areas where small-scale nature-based solutions or rehabilitation activities are required. Moderate benefit scores occurred in parts of the city that are vulnerable to urban expansion and densification activities, warranting the careful planning of green infrastructure provision, and that moderate-to-high-scoring areas can be protected as conservation areas. The results are discussed in terms of the role of decision support tools for urban planning practice. Composite indexes can provide important guidance to decision-makers involved in spatial planning and urban upgrading and expansion activities.

Urban Planning and Green Infrastructure Under a Changing Climate in Africa

“…The authors of the study (Mudzengi et al, 2021) discuss the existing methods used in urban planning, which entail addressing the fast expansion of informal settlements, engaging local people, and prioritizing urban sustainability and resilience. The article also addresses particular obstacles encountered in urban planning in Africa, such as the fast increase in urban population, the management of informal settlements, restricted financial resources, urban governance, and socio-economic disparities (Brom et al, 2023). Urban planning in Africa provides several socio-economic advantages, including enhanced quality of life, employment generation, encouragement of economic growth, greater access to essential services, and promotion of sustainability.…”

“…Urban planning in Africa faces major challenges related to rapid population growth and changing political and economic dynamics (Brom et al, 2023). This growth necessitates rapid adaptation of planning strategies, but many municipalities in Africa face financial constraints, making it difficult to mobilize long-term funding (Thorn et al, 2021).…”

Computer-Supported Smart Green-Blue Infrastructure Management

“…with the help of AI tools using image-based species recognition functionality on mobile applications [41]. The ML algorithms (artificial neural network, probabilistic neural network, convolutional neural network, or other combinations of technologies [5]) assist field inspectors during the GBI inventory process. Better accuracy has been obtained when trained models use object datasets and libraries located geographically.…”

“…Research over time in the field of GBI has identified several representative gaps, which a DSS should be able to address. For example, GBI and DDS has been identified in [5] as a solution to rapid urbanization. The proposed DSS architecture solutions for GBI may face institutional and governance challenges such as unclear responsibilities, limited resources, and conflicting policies.…”

Scenario-Based Green Infrastructure Installations for Building Urban Stormwater Resilience—A Case Study of Fengxi New City, China

“…The utilization of Geographic Information Systems (GIS) enables the identification of priority areas for strategically placing and leveraging GI [30,33]. This analytical approach assumes paramount importance in enabling urban planners to make informed decisions regarding GI, fostering sustainable urban development [34,35]. However, existing research with limited exploration into the evaluation of GI's suitability [31] is often confined to small-scale analyses and sparse investigations at the broader city or community scales [1].…”