Implementation of smart and sustainable energy projects in urban areas encounters different barriers. These barriers range from common financial shortage to specific constraints, which depend on local socio-economic, environmental and political characteristics of each city. In spite of various experiences of European cities in smart and sustainable energy projects, the transfer of lessons learnt on how to manage barriers in new projects is inefficient. The main aim of this paper is to apply a case-based learning methodology to predict barriers to a given smart and sustainable energy project. To achieve this aim, a learning methodology is proposed and applied to the case study of the city of Bolzano, within SINFONIA project. SINFONIA is a European Commission Seventh Framework Programme (FP7) project for integration of smart and sustainable energy solutions at urban district level. The proposed methodology operates in two main steps: first, identifying and selecting the most similar European smart and sustainable energy cases to the target-case (Bolzano within SINFONIA); second, investigating barriers to implementation of selected cases. The results show that the barriers fragmented ownership of properties, limited access to capital and cost disincentives, and perception of interventions as complicated and expensive, with negative social or environmental impacts are highly probable to occur in Bolzano within SINFONIA. The proposed methodology is applicable and replicable for urban planners and decision-makers in different territorial levels to facilitate and accelerate the implementation of smart and sustainable energy projects.

A case-based learning methodology to predict barriers to implementation of smart and sustainable urban energy projects / Mosannenzadeh, F.; Bisello, A.; Diamantini, C.; Stellin, G.; Vettorato, D.. - In: CITIES. - ISSN 0264-2751. - 60:(2017), pp. 28-36. [10.1016/j.cities.2016.07.007]

A case-based learning methodology to predict barriers to implementation of smart and sustainable urban energy projects

Mosannenzadeh F.;Diamantini C.;Stellin G.;Vettorato D.
2017

Abstract

Implementation of smart and sustainable energy projects in urban areas encounters different barriers. These barriers range from common financial shortage to specific constraints, which depend on local socio-economic, environmental and political characteristics of each city. In spite of various experiences of European cities in smart and sustainable energy projects, the transfer of lessons learnt on how to manage barriers in new projects is inefficient. The main aim of this paper is to apply a case-based learning methodology to predict barriers to a given smart and sustainable energy project. To achieve this aim, a learning methodology is proposed and applied to the case study of the city of Bolzano, within SINFONIA project. SINFONIA is a European Commission Seventh Framework Programme (FP7) project for integration of smart and sustainable energy solutions at urban district level. The proposed methodology operates in two main steps: first, identifying and selecting the most similar European smart and sustainable energy cases to the target-case (Bolzano within SINFONIA); second, investigating barriers to implementation of selected cases. The results show that the barriers fragmented ownership of properties, limited access to capital and cost disincentives, and perception of interventions as complicated and expensive, with negative social or environmental impacts are highly probable to occur in Bolzano within SINFONIA. The proposed methodology is applicable and replicable for urban planners and decision-makers in different territorial levels to facilitate and accelerate the implementation of smart and sustainable energy projects.
Mosannenzadeh, F.; Bisello, A.; Diamantini, C.; Stellin, G.; Vettorato, D.
A case-based learning methodology to predict barriers to implementation of smart and sustainable urban energy projects / Mosannenzadeh, F.; Bisello, A.; Diamantini, C.; Stellin, G.; Vettorato, D.. - In: CITIES. - ISSN 0264-2751. - 60:(2017), pp. 28-36. [10.1016/j.cities.2016.07.007]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/261124
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