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Designing successful urban regeneration strategies through a behavioral decision aiding approach

Ferretti, V. and Grosso, R. (2019) Designing successful urban regeneration strategies through a behavioral decision aiding approach. Cities, 95. ISSN 0264-2751

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Identification Number: 10.1016/j.cities.2019.06.017


Finding a new use for abandoned buildings provides an opportunity for urban and rural regeneration as reconversion policies arrest decay processes and re-establish continuity in the territorial system, using existing buildings and thus limiting the consumption of new soil. This study combines quantitative stakeholders' analysis with Decision Aiding tools to design and evaluate alternative strategies for the regeneration of an abandoned military barrack in Italy. In particular, the paper details how the collaborative decision support process developed from the initial map of the local needs to the final assessment of five different regeneration projects. The main objective of the study is to develop a transferable framework able to support collaborative planning and decision-making processes related to urban regeneration transformations. The contribution brought by the study is twofold and refers to: (i) the development of an inclusive final recommendation based on the innovative integration of Multi Attribute Value Theory with quantitative stakeholders' analysis and (ii) the provision of a replicable working tool for policy makers and urban regeneration specialists. The study has thus an innovative value and may stimulate a cross-disciplinary use of behavioral decision analysis to support a value-based generation and evaluation of alternative solutions in complex multi-actor decision making settings.

Item Type: Article
Additional Information: © 2019 Elsevier Ltd.
Divisions: Management
Subjects: H Social Sciences > HN Social history and conditions. Social problems. Social reform
H Social Sciences > HD Industries. Land use. Labor > HD100 Land Use
Date Deposited: 21 Aug 2019 14:09
Last Modified: 20 Oct 2021 00:05

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