Sharing Cities used as example to assess energy performance of smart cities


Francesco Causone from Politecnico di Milano has written, in collaboration with other researchers, a scientific paper on the assessment of smart cities’ energy performance. The work uses information and data from Sharing Cities 3 lighthouse cities: London, Lisbon and Milan.


The massive urbanization process registered since 1950s and projected to continue for the coming decades is posing a crucial issue for the management of existing cities and the planning of future ones. Smart cities are often envisioned as ideal urban environments where the different dimensions of a city, such as economy, education, energy, environment, finance, etc., are managed in an effective and proactive way. Nevertheless, in order to reach this remarkable and challenging objective, analysis tools are required to create scenarios that are able to inform policy makers’ decisions. Focusing on energy, this paper proposes an analysis method, based on exergy, to support smart city planning. It may help the decision makers to assess the energy-smartness of different scenarios, and to address urban energy policies. Possibilities and limitations of the analysis method are discussed via the application to the cities of London, Milan, and Lisbon that committed to become smart cities.

Practical application: The paper summarizes a study on the possibilities and limitations of adopting an assessment technique, based on exergy, in order to evaluate the energy-smartness of policies in existing and future smart cities. As highlighted in the paper, building’s energy uses have a huge share of many cities’ energy breakdown. Thus, professionals in the building industry will be interested in the paper not only because it refers to smart cities, but because the built environment plays a pivotal role in them. Professionals may also refer to this study to perform a similar analysis in other urban environments to support decision makers.

The publications is available here: (you will need an account to access it)


Politecnico di Milano, Francesco Causone (



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This project has received funding from the European Union's Horizon 2020 research and innovation programme under Grant Agreement N°691895


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