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Multicriteria Spatial Decision Support Systems for Future Urban Energy Retrofitting Scenarios

Author

Listed:
  • Patrizia Lombardi

    (InterUniversity Department of Regional and Urban Studies and Planning (DIST), Polytechnic University of Turin, 39 Viale Mattioli, 10125 Turin, Italy)

  • Francesca Abastante

    (InterUniversity Department of Regional and Urban Studies and Planning (DIST), Polytechnic University of Turin, 39 Viale Mattioli, 10125 Turin, Italy)

  • Sara Torabi Moghadam

    (InterUniversity Department of Regional and Urban Studies and Planning (DIST), Polytechnic University of Turin, 39 Viale Mattioli, 10125 Turin, Italy)

  • Jacopo Toniolo

    (InterUniversity Department of Regional and Urban Studies and Planning (DIST), Polytechnic University of Turin, 39 Viale Mattioli, 10125 Turin, Italy)

Abstract

Nowadays, there is an increasing concern about sustainable urban energy development taking into account national priorities of each city. Many cities have started to define future strategies and plans to reduce energy consumption and greenhouse gas emissions. Urban energy scenarios involve the consideration of a wide range of conflicting criteria, both socio-economic and environmental ones. Moreover, decision-makers (DMs) require proper tools that can support their choices in a context of multiple stakeholders and a long-term perspective. In this context, Multicriteria Spatial Decision Support Systems (MC-SDSS) are often used in order to define and analyze urban scenarios since they support the comparison of different solutions, based on a combination of multiple factors. The main problem, in relation to urban energy retrofitting scenarios, is the lack of appropriate knowledge and evaluation criteria. The latter are crucial for delivering and assessing urban energy scenarios through a MC-SDSS tool. The main goal of this paper is to analyze and test two different methods for the definition and ranking of the evaluation criteria. More specifically, the paper presents an on-going research study related to the development of a MC-SDSS tool able to identify and evaluate alternative energy urban scenarios in a long-term period perspective. This study refers to two Smart City and Communities research projects, namely: DIMMER (District Information Modeling and Management for Energy Reduction) and EEB (Zero Energy Buildings in Smart Urban Districts).

Suggested Citation

  • Patrizia Lombardi & Francesca Abastante & Sara Torabi Moghadam & Jacopo Toniolo, 2017. "Multicriteria Spatial Decision Support Systems for Future Urban Energy Retrofitting Scenarios," Sustainability, MDPI, vol. 9(7), pages 1-13, July.
    • Handle: RePEc:gam:jsusta:v:9:y:2017:i:7:p:1252-:d:105011
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    References listed on IDEAS

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    1. Chalal, Moulay Larbi & Benachir, Medjdoub & White, Michael & Shrahily, Raid, 2016. "Energy planning and forecasting approaches for supporting physical improvement strategies in the building sector: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 64(C), pages 761-776.
    2. Caputo, Paola & Costa, Gaia & Ferrari, Simone, 2013. "A supporting method for defining energy strategies in the building sector at urban scale," Energy Policy, Elsevier, vol. 55(C), pages 261-270.
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    5. Jhon Ricardo Escorcia Hernández & Sara Torabi Moghadam & Patrizia Lombardi, 2023. "Sustainability Assessment in Social Housing Environments: An Inclusive Indicators Selection in Colombian Post-Pandemic Cities," Sustainability, MDPI, vol. 15(3), pages 1-24, February.
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    7. Cinzia Colapinto & Raja Jayaraman & Fouad Ben Abdelaziz & Davide La Torre, 2020. "Environmental sustainability and multifaceted development: multi-criteria decision models with applications," Annals of Operations Research, Springer, vol. 293(2), pages 405-432, October.
    8. Becchio, Cristina & Bottero, Marta Carla & Corgnati, Stefano Paolo & Dell’Anna, Federico, 2018. "Decision making for sustainable urban energy planning: an integrated evaluation framework of alternative solutions for a NZED (Net Zero-Energy District) in Turin," Land Use Policy, Elsevier, vol. 78(C), pages 803-817.
    9. Arturas Kaklauskas & Gintautas Dzemyda & Laura Tupenaite & Ihar Voitau & Olga Kurasova & Jurga Naimaviciene & Yauheni Rassokha & Loreta Kanapeckiene, 2018. "Artificial Neural Network-Based Decision Support System for Development of an Energy-Efficient Built Environment," Energies, MDPI, vol. 11(8), pages 1-20, August.
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