| Title |
Implementing Smart Urban Metabolism in the Stockholm Royal Seaport: Smart City SRS |
| ID_Doc |
39044 |
| Authors |
Shahrokni, H; Årman, L; Lazarevic, D; Nilsson, A; Brandt, N |
| Title |
Implementing Smart Urban Metabolism in the Stockholm Royal Seaport: Smart City SRS |
| Year |
2015 |
| Published |
Journal Of Industrial Ecology, 19, 5 |
| DOI |
10.1111/jiec.12308 |
| Abstract |
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. |
| Author Keywords |
augmented reality; big data; industrial ecology; smart cities; sustainable city; urban metabolism |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:000363267800020 |
| WoS Category |
Green & Sustainable Science & Technology; Engineering, Environmental; Environmental Sciences |
| Research Area |
Science & Technology - Other Topics; Engineering; Environmental Sciences & Ecology |
| PDF |
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