A sound understanding of a city’s resource flows is essential for
sustainable management of these resources. Yet, current resource flow analyses are still of limited use for urban planning and design because they are performed on a scale level that does match the level at which practitioners operates; namely the block, neighbourhood or district scale. Moreover, these analyses do not relate consumption patterns to underlying spatial characteristics that are within the sphere of influence of planners and designers. This thesis topic addresses these knowledge gaps.
The aim is to investigate whether ‘Urban Metabolic Profiles’ (UMPs) can be formulated for Dutch cities; meaningful units on block, neighbourhood or district level (demarcations based on city characteristics) that have distinct pattern in terms of energy and/or water consumption. The goal is to identify spatial Urban Metabolic Profiles based on available geodata on e.g. buildings types, demographics, socio-economic and more descriptive information. Having these spatial clusters, these will be related to water- and energy consumption within cities.
Currently a research is done for the city of Amsterdam and will soon be published. This research shows the potential for deriving UMPs within Amsterdam and does a first attempt in identifying the underlying drivers. This thesis topic is intended as a follow-up, where ideally all Dutch cities will be analysed.
Urban metabolism, a metaphor of a city as a living organism or ecosystem, is widely used to describe and analyse urban resource flows. The notion of urban metabolism has inspired new ideas about how cities can be made sustainable. Nowadays this metabolism is mainly linear, where resources are used mostly once and then discharged to the environment. Transitions towards more circular urban metabolism are advocated to improve resource use efficiency and increase resilience of urban systems.
Research showed that urban planning and design interventions envisioned
in Amsterdam require information on a higher spatiotemporal resolution than the resolution of current urban metabolism analyses (Voskamp et al., 2018). These studies are
generally yearly analyses that use the administrative city boundaries as spatial unit of investigation. Hence, they do not show the wide spatial diversity and temporal variability that exist within cities, which is essential to formulate effective strategies towards more circular metabolism. Besides, formulating effective policies and plans for circularity requires not only insight in what a city’s metabolism looks like - where is how much resource consumption, and when? -, but also, why the metabolism looks the way it does. What are the underlying factors behind the resource flow patterns? Only few studies so far have used actual resource consumption data to investigate the correlation between consumption and city characteristics (e.g. Porse et al., 2016).
Continue the research that is performed on the city of Amsterdam (to be published in 2020)
- Create reproducible scripts/models for deriving UMPs
- Apply & validate findings of this research to all other large Dutch cities
- Potentially extend to other European cities
- Porse, E., Derenski, J., Gustafson, H., Elizabeth, Z., & Pincetl, S. (2016). Structural, geographic, and social factors in urban building energy use: Analysis of aggregated account-level consumption data in a megacity. Energy Policy, 96, 179–192.
- Voskamp, I. M., Spiller, M., Stremke, S., Bregt, A. K., Vreugdenhil, C., & Rijnaarts, H. H. M. (2018). Space-time information analysis for resource-conscious urban planning and design: A stakeholder based identification of urban metabolism data gaps. Resources, Conservation and Recycling, 128(January), 516–525.
- Voskamp, I. M., Sutton, N. B., Stremke, S., & Rijnaarts, H. H. M. (2020). A systematic review of factors influencing spatiotemporal variability in urban water and energy consumption. Journal of Cleaner Production, 256, 120310.
- Preferably scripting skills for reproducibility and scaling up of your work (Geoscripting)
Theme(s): Modelling & visualisation; Human – space interaction