Abstract

For any given territory, disaster risk is a function of hazard, exposure and vulnerability. The conceptual frameworks for these dimensions are largely debated in the scientific studies, focusing on spatial and temporal references and on system perspective of risk assessment. Despite broad in their scope, the analytical frameworks proposed to analyse policy programmes to reduce risk generally miss that risk indicators should be grounded on geographical and spatial features of the neighbouring territories and not only on communities' behaviour, and their resilience, as the paradigmatic solution in front of system events in areas prone to natural multi-hazard. The definition of community and of neighbourhood that are relevant for risk assessment cannot be simply defined in terms of absolute size of population or economic activity of individual local units under analysis or of the formal aggregation provided by jurisdictional agreements on specific functions. The paper presents an empirical analysis on spatially-lagged data in Italy, which was massively hit by adverse natural events in the last decade. It suggests to focus on social and material vulnerability, by using the comprehensive indicator at municipality level elaborated by the Italian Institute of Statistics. At the municipality-level, the analysis shows a positive correlation between multi-hazards, on the one hand, and spatially-lagged exposure and vulnerability, on the other. Thus, conditions in neighbouring municipalities would matter in prioritizing policy interventions aiming at mitigating hazard impacts, reducing vulnerability and enhancing communities' resilience. The definition of significant neighbouring spaces opens to a conceptualization of vulnerability grounded on a relational perspective in the creation of collective goods. This could enhance more effective Disaster Risk Reduction programmes.

Highlights

  • In the last decade, Italy experienced a series of major natural disasters3 that have produced severe socioeconomic damages

  • Moving from empirical case studies to the definition of the analytical framework to be adopted for policy interventions, the definition of significant neighbouring spaces opens to a conceptualization of vulnerability grounded on a relational perspective, orienting Disaster Risk Reduction programmes towards the public and private interventions that would be more effective in enhancing communities' resilience by supporting local performance regimes

  • Grounded on the results by Pagliacci and Russo (2019a), who return an empirical analysis at municipality level of the territorial distribution of multi-hazards, exposure and vulnerability in Italy7, this paper considers the additional role played by neighbouring territories, in terms of their exposure and socio-material vulnerability

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Summary

Introduction

Experienced a series of major natural disasters that have produced severe socioeconomic damages. Moving from empirical case studies to the definition of the analytical framework to be adopted for policy interventions, the definition of significant neighbouring spaces opens to a conceptualization of vulnerability grounded on a relational perspective, orienting Disaster Risk Reduction programmes towards the public and private interventions that would be more effective in enhancing communities' resilience by supporting local performance regimes In such framework, an array of analyses of specific dimensions of natural hazards and vulnerabilities has been proposed, with increasing attention devoted to the role of resilient communities as a leverage for policy programmes aiming at mitigating the impact of multi-hazard areas. The issue of scale – at the core of the definition of indicators of vulnerability – is complemented by the classification returned by the National Strategy for Inner Areas, as a background for the analysis of the geographical and spatial relationships encompassing neighbouring territories as well

Italy: a country prone to adverse natural events
Territorial heterogeneity and spatial effects: a tale of two cities
Data and methodology
Modelling the neighbouring space
Results: the neighbouring space
Discussion and policy implications
Findings
F Ultra-peripheral
Conclusions

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