A resource flow typology of African cities

Global urbanisation trends predict a future in which the already overburdened cities of Africa and Asia will house the bulk of the two billion new people born by 2050. This second wave of urbanisation will increase resource demands in these cities and extend the expanse of slums already surrounding them. Given the global imperative of sustainable development, and the existing imbalance of resource access, effective urban planning is necessary to meet this second urbanisation wave, and build resilient, equitable cities. However, preliminary investigation suggests a lack of data-supported decision-making in cities of the global south, due either to limited collection of, or lack of access to, city-level data. This has led to many urban development programmes being implemented with minimal scientific backing to support the success of proposed policy or infrastructure innovations. This directly impacts a city’s ability to reach service delivery, economic growth, and human development goals, let alone protect ecosystem services upon which it relies. This is particularly true in African cities, in which governments are (necessarily) more focused on delivery of basic services than on a greening or efficiency agenda. This is further compounded by the need for African cities to prepare adequate public services for the increased population expected in the second urbanisation wave. A quantitative assessment of cities’ resource profiles can support policy makers in making informed decisions about infrastructure configurations in order to improve their resource management. To this end, methods to accurately estimate and analyse these data are necessary. The primary objective of this study was to establish a resource consumption typology for African cities. Due to limitations in the availability and form of secondary data, this study shifted focus to explore how best to form a typology from limited data. It made use of data for 53 African nations and scaling theories proffered to estimate city-level economic and resource data for 120 African cities. The resultant resource profiles were then normalised and clustered in a number of ways to produce two national typologies and four city typologies. Insights from these typologies both inform the method for categorising cities by socioeconomic or resource indicators as well as provide insights into the shape and magnitude of resource profile for multiple African cities. They also highlight the key drivers of resource consumption in these spaces. Future work involves validating the scaling method with locally acquired data so as to increase confidence in the city-level data, before settling on the preferred method for clustering cities.National Research Foundatio