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Smart Urban Metabolism: Toward a New Understanding of Causalities in Cities
KTH, School of Architecture and the Built Environment (ABE), Sustainable development, Environmental science and Engineering, Industrial Ecology.ORCID iD: 0000-0003-2621-4253
2015 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

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.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2015. , xiii, 73 p.
Series
TRITA-IM, ISSN 1402-7615 ; 2015:01
Keyword [en]
industrial ecology, urban metabolism, smart cities, big data, data science
National Category
Other Environmental Engineering Environmental Analysis and Construction Information Technology
Research subject
Industrial Ecology
Identifiers
URN: urn:nbn:se:kth:diva-176892ISBN: 978-91-7595-737-1 (print)OAI: oai:DiVA.org:kth-176892DiVA: diva2:868753
Public defence
2015-12-16, F3, Lindstedtsvägen 26, KTH, Stockholm, 14:00 (English)
Opponent
Supervisors
Projects
Smart City SRS
Funder
VINNOVA, 2012-01148
Note

QC 20151120

Available from: 2015-11-20 Created: 2015-11-11 Last updated: 2015-11-20Bibliographically approved
List of papers
1. Smart Urban Metabolism: Towards a Real-Time Understanding of the Energy and Material Flows of a City and Its Citizens
Open this publication in new window or tab >>Smart Urban Metabolism: Towards a Real-Time Understanding of the Energy and Material Flows of a City and Its Citizens
2015 (English)In: The Journal of urban technology, ISSN 1063-0732, E-ISSN 1466-1853, Vol. 22, no 1, 65-86 p.Article in journal (Refereed) Published
Abstract [en]

Urban metabolism is a concept employed to understand the flow of energy and materials through urban areas. However, applying this approach at the city level has been limited by the lack of data at this scale. This paper reviews the current application of the urban metabolism concept and proposes the concept of a “smart urban metabolism” (SUM). Through integrating ICT and smart-city technologies, the SUM model can provide real-time feedback on energy and material flows, from the level of the household to the urban district. This is highlighted through an example of its application in the Stockholm Royal Seaport, Sweden.

Keyword
ICT, material flow analysis, real-time, smart cities, urban metabolism
National Category
Civil Engineering
Identifiers
urn:nbn:se:kth:diva-164525 (URN)10.1080/10630732.2014.954899 (DOI)000353409600002 ()2-s2.0-84928698773 (Scopus ID)
Note

QC 20150417

Available from: 2015-04-17 Created: 2015-04-17 Last updated: 2017-12-04Bibliographically approved
2. Making sense of smart city sensors
Open this publication in new window or tab >>Making sense of smart city sensors
2013 (English)In: Urban and Regional Data Management, UDMS Annual 2013 - Proceedings of the Urban Data Management Society Symposium 2013, Taylor & Francis Group, 2013, 117-127 p.Conference paper, Published paper (Refereed)
Abstract [en]

The rapid emergence of smart cities and their sensor networks is being accompanied by an increasing demand for systems to interpret and use the vast amounts of new data they make available. This paper describes the key system design decisions that needed to be taken when developing a calculation engine for a pilot project entitled Smart City Stockholm Royal Seaport. The system design decisions confronting researchers ranged from dealing with data gaps and drawing system boundaries, to developing data structures and ontologies that allow for comparability among smart cities. Most of these decisions are currently being made on an ad hoc basis by system architects while the need for comparability and transparency, demands standardization. The success of standardization bodies and unifying organizations, such as City Protocol, is dependent on the smart city pilot projects being transparent with regards to these design decisions, so they ultimately can be used as a foundation for developing a common language for smart cities.

Place, publisher, year, edition, pages
Taylor & Francis Group, 2013
Keyword
Common languages, Design decisions, Drawing system, KeY systems, Pilot projects, Smart cities, Stockholm, System architects, Data structures, Design, Information management, Sensor networks, Standardization, Systems analysis, Electronic commerce
National Category
Civil Engineering
Identifiers
urn:nbn:se:kth:diva-125984 (URN)000342637800011 ()2-s2.0-84877656844 (Scopus ID)978-113800063-6 (ISBN)
Conference
Urban Data Management Society Symposium 2013, UDMS Annual 2013, 29 May 2013 through 31 May 2013, London
Note

QC 20130816

Available from: 2013-08-16 Created: 2013-08-16 Last updated: 2015-11-20Bibliographically approved
3. Implementing Smart Urban Metabolism in the Stockholm Royal Seaport: Smart City SRS
Open this publication in new window or tab >>Implementing Smart Urban Metabolism in the Stockholm Royal Seaport: Smart City SRS
Show others...
2015 (English)In: Journal of Industrial Ecology, ISSN 1088-1980, E-ISSN 1530-9290, Vol. 19, no 5, 917-929 p.Article in journal (Refereed) Published
Abstract [en]

For half a century, system scientists have relied on urban metabolism (UM) as a pragmatic framework to support the needed transition toward sustainable urban development. It has been suggested that information and communication technology (ICT) and, more specifically, smart cities can be leveraged in this transition. Given the recent advances in smart cities, smart urban metabolism (SUM) is considered a technology-enabled evolution of the UM framework, overcoming some of its current limitations. Most significantly, the SUM framework works at high temporal (up to real-time) and spatial (down to household/individual) resolutions. This article presents the first implementation of SUM in the Smart City Stockholm Royal Seaport R&D project; it further analyzes barriers and discusses the potential long-term implications of the findings. Four key performance indicators (KPIs) are generated in real time based on the integration of heterogeneous, real-time data sources. These are kilowatt-hours per square meter, carbon dioxide equivalents per capita, kilowatt-hours of primary energy per capita, and share of renewables percentage. These KPIs are fed back on three levels (household, building, and district) on four interfaces, developed for different audiences. The most challenging barrier identified was accessing and integrating siloed data from the different data owners (utilities, building owners, and so forth). It is hard to overcome unless a significant value is perceived. A number of long-term opportunities were described in the SUM context; among those, it is envisioned that SUM could enable a new understanding of the causalities that govern urbanism and allow citizens and city officials to receive feedback on the system consequences of their choices.

Place, publisher, year, edition, pages
John Wiley & Sons, 2015
Keyword
augmented reality, big data, industrial ecology, smart cities, sustainable city, urban metabolism
National Category
Environmental Engineering
Identifiers
urn:nbn:se:kth:diva-172653 (URN)10.1111/jiec.12308 (DOI)000363267800020 ()2-s2.0-84949537790 (Scopus ID)
Note

QC 20151113

Available from: 2015-08-27 Created: 2015-08-27 Last updated: 2017-12-04Bibliographically approved
4. Big meter data analysis of the energy efficiency potential in Stockholm's building stock
Open this publication in new window or tab >>Big meter data analysis of the energy efficiency potential in Stockholm's building stock
2014 (English)In: Energy and Buildings, ISSN 0378-7788, E-ISSN 1872-6178, Vol. 78, 153-164 p.Article in journal (Refereed) Published
Abstract [en]

The City of Stockholm is making substantial efforts towards meeting its climate change commitments including a GHG emission target of 3 tonnes per capita by 2020 and making its new eco-district Stockholm Royal Seaport a candidate of Clinton Climate Initiative's Climate Positive Program. Towards achieving these policies, this study evaluated the energy efficiency potential in the city, in collaboration with the district heating and electricity utility Fortum. Drawing on their vast billing meter data on the housing stock in Stockholm, a new understanding of energy use in the city emerged. Analysis of the energy efficiency potential of different building vintages revealed that the retrofitting potential of the building stock to current building codes would reduce heating energy use by one third. In terms of market segmentation, the greatest reduction potential in total energy was found to be for buildings constructed between 1946 and 1975. This is due to the large number of buildings constructed during that era and their poor energy performance. However, the least energy-efficient buildings were those built between 1926 and 1945 in contradiction to commonly held beliefs. These findings indicate the need for a shift in public policy towards the buildings with highest retrofitting potential.

Keyword
Retrofitting, Big Data, Climate action planning
National Category
Other Civil Engineering
Identifiers
urn:nbn:se:kth:diva-149188 (URN)10.1016/j.enbuild.2014.04.017 (DOI)000339133200018 ()2-s2.0-84900461549 (Scopus ID)
Note

QC 20140818

Available from: 2014-08-18 Created: 2014-08-18 Last updated: 2017-12-05Bibliographically approved
5. Big Data GIS Analytics Towards Efficient Waste Management in Stockholm
Open this publication in new window or tab >>Big Data GIS Analytics Towards Efficient Waste Management in Stockholm
2014 (English)In: Proceedings of the 2014 conference ICT for Sustainability / [ed] Höjer, Lago, Wangel, Stockholm, 2014, 140-147 p.Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents preliminary findings from a big data analysis and GIS to identify the efficiency of waste management and transportation in the City of Stockholm. The aim of this paper is to identify inefficiencies in waste collection routes in the city of Stockholm, and to suggest potential improvements. Based on a large data set consisting of roughly half a million entries of waste fractions, weights, and locations, a series of new waste generation maps was developed. This was the outcome of an extensive data curation process, followed by batch geocoding of the curated entries. Thereafter, the maps were generated that describe what waste fraction comes from where and how it is collected. Finally, a preliminary analysis of the route efficiency was conducted. Maps of selected vehicle routes were constructed in detail and the efficiencies of the routes for the first half of July 2013 were assessed using the efficiency index (kg waste/km). It is concluded that substantial inefficiencies were revealed, and a number of intervention measures are discussed to increase the efficiency of waste management, including a shared waste collection vehicle fleet.

Place, publisher, year, edition, pages
Stockholm: , 2014
Keyword
Big Data Analytics, GIS, Smart Cities, Transportation, Waste Management
National Category
Other Environmental Engineering
Identifiers
urn:nbn:se:kth:diva-149939 (URN)000346245500017 ()2-s2.0-84928029537 (Scopus ID)978-94-62520-22-6 (ISBN)
Conference
2nd International Conference on ICT for Sustainability (ICTS), Stockholm, SWEDEN, AUG 24-27, 2014
Note

QC 20150122

Available from: 2014-08-28 Created: 2014-08-28 Last updated: 2015-11-20Bibliographically approved

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