Claudio Marciano (p), Andrea Pirni, Gabriella Tocchi, Annamaria Zaccaria

Dimitris Foutakis

The city of Genoa in Italy has been flooded at least 11 times since 1970. Messina and Turin have been affected 5 times. More or less severe episodes of extreme rainfall followed by flooding by rivers and torrents affect other major national metropolitan areas almost every year. In terms of victims and economic losses, the damage caused by these events is dramatic.
Global warming has increased the severity and frequency of these events, but the cause of these tragedies is not only the increase in cumulative rainfall, but also the legacy of urbanisation processes close to the banks of watercourses, poor maintenance of canals, lack of risk awareness among the population and other socio-economic and infrastructural factors.
Although important progress has been made in terms of preparedness, particularly with regard to weather forecasting, the socio-technical systems responsible for risk prevention and management have not always proved capable of learning from past mistakes. There is therefore an urgent need to reflect on what contextual factors and patterns of action enable such systems to be resilient or, on the contrary, to fail.
The purpose of this paper is to present the results of a comparative analysis of flood risk management in three Italian cities that have been affected by at least two severe floods in the last twenty years: Genoa, Messina and Turin. The research, carried out by an interdisciplinary team, produced two main results: the construction of a method for identifying cases, based on a composite index of environmental, meteorological and socio-economic data; and the identification of good and bad practices of risk learning, as well as factors that facilitate or hinder such learning.
The research was carried out in the framework of the RETURN Extended Partnership, funded by the Italian Government and the European Commission, and aimed at improving the management of natural, environmental and climate risks.

Daniel Piatkowski (p), John Osth, Chaoru Lu

Claudio Marciano

Cycling is a healthy and sustainable mode of transportation. But urban environments are not always healthy nor sustainable. This research examines the relationship between where cyclists choose to ride, the physical environmental characteristics, and air quality. We do this by combining data collected from bicycle-mounted sensors with Google Street View imagery. The bicycle sensors, called Sniffer Bikes, were attached to bicycles in Utrecht, The Netherlands, logging riders´ routes and sampling air quality along the routes. Street view imagery has rapidly ascended as an important data source for geospatial data collection and urban analytics, deriving insights and supporting informed decisions.

To establish a comprehensive dataset, Global Positioning System (GPS) points were generated at 100-meter intervals along a walk-bike infrastructure network shapefile. At each GPS point, four Google Street View images, capturing views at 90-degree angles, were automatically retrieved using a Google Application Programming Interface (API) key. Advanced computer vision techniques were employed to capture the detailed features of the built environment at each location. By fusing the air pollution data with the detailed built environment information, regression methods were then applied. These regression techniques allowed for a thorough examination of the relationships between air quality, route choice, and the urban environment.

This study presents a novel approach for evaluating the correlation between urban environmental quality and travel behavior utilizing street view images and machine learning techniques. Traditional methods of air quality assessment often rely on stationary sensors and complex models, which can be expensive and logistically challenging. The approach offers a scalable, cost-effective, and geographically comprehensive means of understanding complicated interactions between people and place. The findings from this work can enable urban planners and policy makers to create healthier urban environments that in turn foster healthier travel behavior in cities.

Angela Stefania Bergantino, Alessandro Gardelli, Mario Intini (p), Siqi Zheng

Daniel Piatkowski

Understanding how atmospheric conditions and environmental hazards affect human well-being can inform effective mitigation and adaptation strategies. Much focus has been on the psychological effects of extreme events, but little is known about the daily impacts of environmental factors on Citizens’ well-being (CWB) (Wang et al., 2020). One of the main factors limiting citizens' well-being in many European cities (but also worldwide) is the intensification of heat stress episodes (Baylis et al., 2018) and pollution (Zheng et al., 2019). Summer 2023 in Italy has registered a temperature peak expected to rise further in the following years (Sabelli, 2023). This excess heat can cause cardiovascular stress, heart stroke, and other major health issues. Researchers and policymakers have increasingly used indicators as measures of life satisfaction to complement traditional objective development and economic metrics (Chai et al., 2023). Literature on citizens’ well-being evaluations mainly considers survey data for a specific territory and period (i.e., Patrick et al., 2020). However, daily and specific territorial heterogeneities are not much considered.This paper uses Twitter data (Chai et al., 2023) for the period 2012-2023 to explain how the sentiment score, defined as the probability of a post being classified as a post with a positive mood at a daily level for all the Italian provinces, is affected by urban and daily atmospheric and environmental variables (temperature, precipitation, pollution, humidity, etc.). Italy is an interesting case study because of the heterogeneous territory and the natural disasters that cross the country daily (landslides, floods, earthquakes, and droughts). To explain how citizens from different provinces react to atmospheric and environmental variables, given the nature of the dependent variable (sentiment score), the methodology used is the beta regression model fixed effects. This approach can estimate nonlinear sentiment responses to atmospheric conditions and environmental hazards considering unobserved variation across space and time and spatial and seasonal differences. We used the variable “score” as a dependent variable and PM10, humidity, precipitation, and maximum temperature as independent variables.Results will be used to develop specific territorial policies necessary for local and national policymakers to improve both territorial sustainability and CWB.

Dimitris Foutakis (p), Elisavet Thoidou

Mario Intini

In recent years, climate adaptation strategies have been emphasized above all due to the pressing need to prevent climate related risks. These strategies are adopted and promoted by supranational and national level organizations aiming to mobilize climate action as well as help achieve global climate targets. This is especially the case of the EU climate policy that has adopted a new climate adaptation strategy since 2021 so that the EU "can adapt to the unavoidable impacts of climate change and become climate resilient by 2050". At the same time climate change impacts have significant territorial dimensions that raise the need for territorial policies and particularly spatial planning to be involved in climate adaptation policies. Noteworthy is that cities and regions all over Europe prepare their adaptation strategies regardless of whether they are mandatory. Thus, the question arises as to how climate adaptation strategies can be mainstreamed into spatial planning at different scales, from national to regional and local scale. In this paper it is suggested that spatial planning on the one hand shapes the prerequisites of adaptation e.g. through prevention and reduced exposure measures and on the other hand has a coordinating role that facilitates climate adaptation mainstreaming in various policies. This paper examines the above issues and draws evidence from the case of in Greece, where local (municipality) level spatial planning is undergoing a development process through the initiation of a considerable number of local spatial plans. Specifications for these plans make provision for elaboration of climate adaptation plans, which reveals the necessity of local level adaptation strategies to be elaborated and adopted.