Decoding Urban Metabolism´s black box - Advancements in Urban Analytics to support circular material flow

Doktorsavhandling, 2024

The overexploitation of natural resources is causing life on Earth to operate beyond its safe limits. To overcome this challenge, shifting away from the current linear economy (take-make-dispose) is urgent. Despite international efforts to address this challenge, less than 10% of materials are being reused at the end of their life, and global resource consumption is expected to double by 2050. As a result, it is recognised that our economic systems are wasteful, and resources must be used more efficiently. Most global wealth consumption and production occurs in urban areas. Therefore, it is essential to employ adequate quantification methods to study resource throughput in these systems. Urban Metabolism (UM) offers a framework to examine how resources flow in and out of cities at different geographical scales. However, it needs to provide more details to describe the complexities that generate those flows of resources. As these flows are caused by specific processes that occur at a particular time and location by specific actors, these processes deserve to be investigated to improve the system’s resource efficiency. This thesis addresses the need to deliver resource-efficient city regions, recognising the need for adequate tools and methods for exposing urban processes and their interactions. Urban Analytics (UA) can contribute to revealing urban processes and their interactions by employing computational techniques such as data collection, processing, visualisation and modelling. The overarching aim of this work is to advance the development of digital models to support urban metabolism by demonstrating: (I) How can UA methods contribute to the analysis of urban material flows? and exploring various applications to understand and (II) How can the Urban Metabolism framework be enriched to support urban strategies to close material loops? This last inquiry also considers how spatial, temporal, and behavioural factors influence the metabolism of urban areas. This thesis takes an explorative approach to show the importance of (i) knowledge management to organise information about waste and resources, (ii) geographic information to integrate spatial characteristics into urban and regional analysis and (iii) simulations to describe urban processes and evaluate potential outcomes of different planning scenarios. The first research question is addressed by first, proposing a general framework to manage knowledge about resources and waste and second by  employing UA to incorporate spatial, temporal and behavioural aspects. Adequate knowledge management and data standards about city resources are crucial because they allow information sharing across urban systems, enabling a more holistic understanding of material flows. Moreover, it contributes to identifying information gaps and enables data processes to be reproduced and generalised. The general framework was validated by developing various applications. These applications required the use of UA and contributed to demonstrating the role that UA can play in enhancing UM. The second research question is answered by exploring models incorporating the spatial, temporal, and behavioural elements in three strategies that promote circular material flows in city regions. A simulation of residential waste sorting at the neighbourhood scale informs how different urban scenarios influence residents’ waste sorting behaviour. Then, a city scale simulation of the construction and demolition sector discusses different planning scenarios and their consequences for material circularity. The thesis highlights the importance of considering location when analysing industrial symbiosis for potential waste exchanges between firms at the regional scale. By considering distances between firms, a methodology was developed to identify possible partnerships, resulting in fewer potential exchanges. To conclude, this thesis highlights the importance of data models and simulation techniques in advancing the field of UM. The models presented here serve as preliminary steps to demonstrate the importance of using UA to incorporate spatial, temporal and behavioural aspects when studying the metabolism of urban areas.