Aversa. Extreme Climate Events, Demographic Change, Vulnerability and Urban Risk: a case study

The definition and measurement of risk remain subjects of ongoing debate within the scientific literature. The various disciplines concerned with risk provide distinct interpretations, both regarding its definition and the elements to be included in its assessment. As a result, the concept of risk is continuously evolving within each scholarly community. Significant discrepancies persist in the literature, as risk is fragmented across numerous disciplinary approaches. This research is situated within the fields of Risk Assessment and Risk Management, with particular attention to two major areas of study: Disaster Risk Reduction and Climate Change Adaptation. Today, the most severe and frequent threats faced by cities are closely linked to the overlap of multiple transformations. This paper explores the relationships between demographic change, vulnerability, and urban climate risks, emphasizing how the concept of vulnerability acts as a connecting element between the two aforementioned fields of study and serves as a driver for conceptual synthesis. The overall objective of the research is to assess the impact of these transformations on risk levels. Despite recent progress in adaptation efforts, current policies remain fragmented and inadequate to address potential risks effectively. Integrating scientific knowledge with local expertise and experience is therefore essential for developing effective and sustainable adaptation strategies (IPCC, 2022). Accordingly, this study produces a sub-municipal multi-hazard risk mapping based on prospective scenario analysis. The research seeks to address the following questions: a) How are risks associated with specific extreme events (including heatwaves and intense rainfall events) spatially distributed within an urban context? b) How can demographic changes evolving over time influence the spatial distribution of risk at the sub-municipal level? To achieve the research objectives, a case study approach was adopted, as it enables the implementation of risk assessment metrics based on granular data, which are essential for constructing micro-scale maps that define the level of risk associated with extreme phenomena. Statistical data related to the census sections of the city of Aversa were collected and organized to highlight spatial variations in the analyzed phenomena. The risk profile was developed through a quantitative estimation for each census area, based on the interaction of three main factors: hazard, exposure, and vulnerability. The analysis relies on computational models and advanced digital tools for simulation, primarily using Python. The risk model, calculated from the three components, provides a value for each census area, which is subsequently normalized and classified into quantiles. Identifying the most vulnerable urban areas is intended to support local policymakers in planning climate change adaptation strategies and strengthening the urban resilience of the city of Aversa.