Estimating the Impact of Socioeconomic Drivers on Land Degradation in Italy via Spatio‐Temporal Additive Expectile Regression

ABSTRACT Climate changes, soil degradation, and desertification increasingly threaten the entire territory of Italy due to the complex interplay between natural processes and anthropogenic forces. Motivated by this pressing issue, this paper investigates how land‐use and socioeconomic drivers have shaped desertification dynamics across Italian provinces between 1960 and 2020 using an additive expectile regression model for spatio‐temporal data. The effects of continuous covariates are modeled as the sum of a linear part and a nonlinear smooth function using penalized B‐splines, while tensor‐product B‐splines based on coordinate information are used to capture spatial dependencies. To promote sparsity in the model, estimation is carried out via component‐wise boosting combined with a stability selection approach, retaining only the most dominant factors. The results identify industrialization and tourism development activities as the primary amplifiers of desertification risk in vulnerable regions. Our analysis also uncovers strong spatial heterogeneity of desertification processes related to province characteristics, which is particularly acute in southern Italy and in the major islands.

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Publication Details

Journal
Environmetrics
Published
2026-02-25
DOI
https://doi.org/10.1002/env.70085
Primary Topic
Remote Sensing in Agriculture
Type
article
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article

Estimating the Impact of Socioeconomic Drivers on Land Degradation in Italy via Spatio‐Temporal Additive Expectile Regression

Luca Salvati, Luca Merlo, Beatrice Foroni, Ioannis Konaxis et al.
Environmetrics
Remote Sensing in Agriculture
article

Estimating the Impact of Socioeconomic Drivers on Land Degradation in Italy via Spatio‐Temporal Additive Expectile Regression

Luca Salvati, Luca Merlo, Beatrice Foroni, Ioannis Konaxis, Lea Petrella
article en

Abstract

ABSTRACT Climate changes, soil degradation, and desertification increasingly threaten the entire territory of Italy due to the complex interplay between natural processes and anthropogenic forces. Motivated by this pressing issue, this paper investigates how land‐use and socioeconomic drivers have shaped desertification dynamics across Italian provinces between 1960 and 2020 using an additive expectile regression model for spatio‐temporal data. The effects of continuous covariates are modeled as the sum of a linear part and a nonlinear smooth function using penalized B‐splines, while tensor‐product B‐splines based on coordinate information are used to capture spatial dependencies. To promote sparsity in the model, estimation is carried out via component‐wise boosting combined with a stability selection approach, retaining only the most dominant factors. The results identify industrialization and tourism development activities as the primary amplifiers of desertification risk in vulnerable regions. Our analysis also uncovers strong spatial heterogeneity of desertification processes related to province characteristics, which is particularly acute in southern Italy and in the major islands.

EnvironmetricsVol. 37(2)
University of Piraeus (GR), Link Campus University (IT), Sapienza University of Rome (IT)
Decent work and economic growth
Openalex Percentile: Top 39%
Remote Sensing in Agriculture
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