The National Recovery and Resilience Plan (NRRP) represents a transformative opportunity to reduce territorial, gender and generational disparities in Italy. It plays an even more important role for inner areas, which make up about three-fifths of the entire national territory and require structural investment to improve infrastructure, social services and access to healthcare services. This study aims to analyse the distribution of funds by project type, and to develop a geostatistical analysis-based methodology to critically evaluate two key aspects: the ability of small municipalities to access resources, and the effectiveness of the funding programme in meeting the specific needs of inner areas. The developed methodology consists of several steps aimed at collecting, standardising, geo-spatialising and analysing data relating to NRRP funds. This methodology is then applied to a case study of the Abruzzo region (Italy), which is considered particularly interesting due to its physical, historical and socio-economic characteristics that make it particularly vulnerable to natural disasters. The developed methodology consists of several steps aimed at collecting, standardising, geo-spatialising and analysing data relating to NRRP funds. The results of the spatial autocorrelation and cluster analyses were then overlapped and compared with the internal areas defined by the National Strategy for Inner Areas (NSIA). The outcomes reveal how investments interact with existing spatial planning instruments and development strategies, underscoring the critical role of accessibility, infrastructure, and public services in fostering equitable and sustainable regional development. The analysis offers insights into addressing structural disparities and enhancing territorial cohesion, with implications for policy alignment across multiple levels of governance.

Deep Dive into the Recovery Fund: A (Real) Chance for Inner Areas? The Abruzzo Region Study Case, Italy

Angela Pilogallo
Primo
;
Lucia Saganeiti
Secondo
;
2025

Abstract

The National Recovery and Resilience Plan (NRRP) represents a transformative opportunity to reduce territorial, gender and generational disparities in Italy. It plays an even more important role for inner areas, which make up about three-fifths of the entire national territory and require structural investment to improve infrastructure, social services and access to healthcare services. This study aims to analyse the distribution of funds by project type, and to develop a geostatistical analysis-based methodology to critically evaluate two key aspects: the ability of small municipalities to access resources, and the effectiveness of the funding programme in meeting the specific needs of inner areas. The developed methodology consists of several steps aimed at collecting, standardising, geo-spatialising and analysing data relating to NRRP funds. This methodology is then applied to a case study of the Abruzzo region (Italy), which is considered particularly interesting due to its physical, historical and socio-economic characteristics that make it particularly vulnerable to natural disasters. The developed methodology consists of several steps aimed at collecting, standardising, geo-spatialising and analysing data relating to NRRP funds. The results of the spatial autocorrelation and cluster analyses were then overlapped and compared with the internal areas defined by the National Strategy for Inner Areas (NSIA). The outcomes reveal how investments interact with existing spatial planning instruments and development strategies, underscoring the critical role of accessibility, infrastructure, and public services in fostering equitable and sustainable regional development. The analysis offers insights into addressing structural disparities and enhancing territorial cohesion, with implications for policy alignment across multiple levels of governance.
2025
Istituto di Metodologie per l'Analisi Ambientale - IMAA
Territorial transformations, National Recovery and Resilience Plan (NRRP), inner areas, cohesion strategies, spatial statistics, cluster analysis
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/554373
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