Titolo della tesi: Embracing uncertainty in urban design: an adaptive planning approach to increase urban resilience in Mediterranean compact cities. -An application at district level in the city of Rome-
Architecture and planning have the challenging task to design the built environment addressing the complex issues that nowadays are affecting the urban tissue. Climate change, societal and economic issues, and population growth are all factors that can influence whether a plan will be successful or not. Traditionally, the determinism inherent within the planning process leaves no room for failure, plans and projects are supposed to achieve the envisioned objectives no matter how the future actually unfolds. If the assumptions made in the development of a plan will be different, how can it be adapted to the changing conditions?
Scenario planning represents a valid resource for addressing uncertainties undermining the success of the chosen strategies. In this thesis, I present an interdisciplinary approach for including uncertainties in the planning process, using a case study of a flood-prone area in the X Municipality of Rome.
The case study area is diachronically analysed to understand how the urban tissue evolved over time, then this analysis is used to develop a land-use change model for exploring how the built environment could develop in the future. The exploration is up to 2050 and accounts for future uncertainties for land-use demands and the influences of new infrastructures that could be implemented in the area. 5000 computational “what if” experiments are performed and the resulting land-use patterns for 2050 are clustered according to the similarity of the land-use patterns. Next, the clusters are analysed to assess which clusters are ¬relevant. Scenario Discovery is then performed for each decision-relevant cluster to understand the combination of uncertain input parameters under which land-use pattern emerges. Lastly, possible adaptive policy pathways are structured to address the vulnerabilities revealed by the Scenario Discovery results.