Dissertation 16 December
Hossein Shahrokni presents his thesis "Smart Urban Metabolism - Toward a new understanding of causalities in cities”.
Faculty opponent is Adjunct assistant professor Anthony Townsend, NYU Rudin Center, USA.
The grading committé is composed by
- Associate professor Robin Teigland, Department of Marketing and Strategy, Handelshögskolan i Stockholm.
- Professor Peter Funk, Division of Intelligent Future Technologies, Mälardalens högskola.
- Professor Christer Åhlund, Luleå tekniska högskola, LTU Skellefteå.
Chairman: Fredrik Gröndahl
For half a century, urban metabolism has been used to provide insights to support transitions to sustainable urban development (SUD). Internet and Communication Technology (ICT) has recently been recognized as a potential technology enabler to advance this transition. This thesis explored the potential for an ICT-enabled urban metabolism framework aimed at improving resource efficiency in urban areas by supporting decision-making processes. Three research objectives were identified: i) investigation of how the urban metabolism framework, aided by ICT, could be utilized to support decision-making processes; ii) development of an ICT platform that manages real-time, high spatial and temporal resolution urban metabolism data and evaluation of its implementation; and iii) identification of the potential for efficiency improvements through the use of resulting high spatial and temporal resolution urban metabolism data. The work to achieve these objectives was based on literature reviews, single-case study research in Stockholm, software engineering research, and big data analytics of resulting data. The evolved framework, Smart Urban Metabolism (SUM), enabled by the emerging context of smart cities, operates at higher temporal (up to real-time), and spatial (up to household/individual) data resolution. A key finding was that the new framework overcomes some of the barriers identified for the conventional urban metabolism framework. The results confirm that there are hidden urban patterns that may be uncovered by analyzing structured big urban data. Some of those patterns may lead to the identification of appropriate intervention measures for SUD.