Smart energy city (SEC) development is a component of the urban development initiative smart city, which has been a popular response to the global energy challenge in Europe during the past two decades. SEC development aims to increase the sustainability of urban energy systems and services. Since 2011, SEC development has been supported by the European Commission as part of the Strategic Energy Technology plan (SET-Plan) and through the European Union Programmes for Research and Technological Development (specifically FP7 and Horizon 2020). This, along with the promising vision of SEC development and considerable financial support by the private sector, has encouraged numerous European cities to initiate SEC projects. Successful implementation of these projects at the urban scale is crucial to achievement of urban energy objectives and sustainability of future urban development.
The here presented thesis aims to support urban decision-makers towards successful implementation of urban scale smart energy city development in Europe. The study includes three stages. The first stage is dedicated to conceptual analysis. Within this stage, I conceptualized smart city through a keyword analysis of existing literature on the concept. Then, within the context of the smart city concept, I defined SEC development through literature review and expert knowledge elicitation. The second stage is dedicated to empirical investigation. Using the definition of SEC development, I distinguished and investigated 43 previously implemented SEC projects to identify common barriers that hinder successful implementation of SEC development. In addition, I proposed a new multi-dimensional methodology that allows a simultaneous prioritization of barriers against their probability, the level of impact, scale, origin, and relationship with other barriers. The third stage of the thesis is dedicated to learning methodologies that allow efficient transfer of knowledge from the past SEC experiences to the new SEC developments. I introduced the application of two learning methodologies that support decision-makers to predict barriers to the implementation of a new SEC project: case-based learning and decision tree learning. The former predicts barriers based on internal similarities between the new SEC project and the past projects. The latter uses the past projects and creates a predictive model for each barrier based on internal and external project characteristics. These models are later used to predict barriers to a new SEC project. Both methodologies were tested in a new SEC project, named SINFONIA. The conceptual analysis revealed that application of information and communication technologies, the collaboration of multiple stakeholders, integration of multiple urban domains, and sustainability evaluation are the constant characteristics (i.e. principles) of smart city and SEC development. It resulted in, to the best of my knowledge, the first multi-dimensional and comprehensive definition of SEC development, revealing its principles, objectives, domains of intervention, stakeholders, time and spatial dimensions. Furthermore, a list of smart energy solutions in each SEC domain of intervention was provided. The empirical investigation of the past SEC projects resulted in the identification of 35 common barriers to the implementation of SEC development, categorized in policy, administrative, legal, financial, market, environmental, technical, social, and information and awareness dimensions. The barrier prioritization showed that barriers related to collaborative planning, external funding of the project, providing skilled personnel, and fragmented ownership should be the key action priorities for SEC project coordinators. Application of case-based learning methodology resulted in identifying five past SEC projects that were the most similar to the SINFONIA project in terms of project internal characteristics. Investigating the barriers to the similar projects revealed that fragmented ownership is the most probable barrier to implementation of SINFONIA project. Application of the decision trees methodology resulted in generation of 20 barrier models, four of which showed a very good performance in prediction of barriers: lack of values and interest in energy optimization measures, time-consuming requirements by European Commission concerning reporting and accountancy, economic crisis, and local unfavorable regulations for innovative technologies. None of these four barriers were predicted to occur in the SINFONIA project. The application of this method in the SINFONIA showed a higher predicting power when a barrier was absent.
The findings of the here presented thesis contribute to successful implementation of SEC development by supporting decision-makers in different phases of SEC projects. The results of the conceptual analysis contribute to a common understanding and foster the dialogue on the concept among various SEC stakeholders, particularly decision-makers and urban planners. The results of the empirical investigation lead to a better comprehension and evaluation of the barriers to the implementation of SEC projects in order to efficiently allocate resources to mitigate barriers. The proposed learning methodologies proved to be promising in helping decision-makers to identify similar projects to a new SEC development and to predict barriers to the implementation of new SEC projects.
The thesis concludes that SEC is an outstanding urban development that can make a valuable contribution to the sustainability of urban energy systems. The specific characteristics of SEC development pose new challenges to the future smart and sustainable urban planning. Nevertheless, SEC development brings about unprecedented opportunities for integration and application of advanced quantitative techniques with current urban planning methods. This allows efficient knowledge transfer in not only intra-urban but also inter-urban levels in order to provide a collaborative, integrated and constructive movement towards successful implementation of SEC projects and sustainability of future urban development.
| Identifer | oai:union.ndltd.org:unitn.it/oai:iris.unitn.it:11572/368407 |
| Date | January 2016 |
| Creators | Mosannenzadeh, Farnaz |
| Contributors | Mosannenzadeh, Farnaz, Diamantini, Corrado, Vettorato, Daniele |
| Publisher | Università degli studi di Trento, place:TRENTO |
| Source Sets | Università di Trento |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/doctoralThesis |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | firstpage:1, lastpage:159, numberofpages:159 |
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