How Do Local Economic Structures Influence the Variability of Land Sensitivity to Degradation in Italy?

This study examines the relationship between local economic structures and environmental sensitivity in Italy, focusing on a novel indicator that estimates the spatial variability of the Environmentally Sensitive Area Index (ESAI) over time. This approach captures within-region disparities in degradation processes, addressing a key gap in the existing literature. Using a dataset covering all Italian provinces from 1960 to 2010 and considering multiple socio-economic variables, the research evaluates their impacts on ESAI variability. In particular, this study adopts a spatial autoregressive model (SAR), which allows both direct and indirect effects of selected predictors to be captured. The findings offer insights for policymakers in designing strategies to mitigate the spread of land degradation hotspots and promote strategies that balance environmental conservation with socio-economic development to ensure resource sustainability.

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Publication Details

Journal
Sustainability
Published
2025-03-02
DOI
https://doi.org/10.3390/su17052149
Citations
2
Primary Topic
Land Use and Ecosystem Services
Type
article
Field-Weighted Citation Impact
2.21
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article

How Do Local Economic Structures Influence the Variability of Land Sensitivity to Degradation in Italy?

Luca Salvati, Rosalia Castellano, Gennaro Punzo, Emma Bruno
2 citations
Sustainability
Land Use and Ecosystem Services
2.21
article

How Do Local Economic Structures Influence the Variability of Land Sensitivity to Degradation in Italy?

Luca Salvati, Rosalia Castellano, Gennaro Punzo, Emma Bruno
article en
2 citations

Abstract

This study examines the relationship between local economic structures and environmental sensitivity in Italy, focusing on a novel indicator that estimates the spatial variability of the Environmentally Sensitive Area Index (ESAI) over time. This approach captures within-region disparities in degradation processes, addressing a key gap in the existing literature. Using a dataset covering all Italian provinces from 1960 to 2010 and considering multiple socio-economic variables, the research evaluates their impacts on ESAI variability. In particular, this study adopts a spatial autoregressive model (SAR), which allows both direct and indirect effects of selected predictors to be captured. The findings offer insights for policymakers in designing strategies to mitigate the spread of land degradation hotspots and promote strategies that balance environmental conservation with socio-economic development to ensure resource sustainability.

SustainabilityVol. 17(5)
Parthenope University of Naples (IT), Sapienza University of Rome (IT)
Openalex Percentile: Top 15%
Land Use and Ecosystem Services
2.21
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