The challenge of air pollution continues to affect global public health and environmental quality. This paper explores the extent to which territorial servitization may contribute to reducing PM2.5 levels in Italian municipalities. Based on the application of machine learning techniques alongside traditional regression analyses, we show that a 1% growth in the concentration of knowledge-intensive business services reduces PM2.5 levels by percentages between 0.11% and 0.47%. We also clarify how a wide range of socioeconomic and meteorological variables govern air quality. Moreover, we estimate the municipal environmental efficiency through a data envelopment analysis approach, showing that better scores are achieved by territories with higher servitization and quality of institutions. Our findings, which are stable across different geographical macro-areas, suggest that both the economic structure and the capabilities of local administration may drive air pollution dynamics. Such results are essential for policymakers aiming to design effective interventions targeting local environmental sustainability.

The effect of territorial servitization on air quality: Empirical analysis of PM2.5 concentration in Italian municipalities

Scotti, Francesco
2025-01-01

Abstract

The challenge of air pollution continues to affect global public health and environmental quality. This paper explores the extent to which territorial servitization may contribute to reducing PM2.5 levels in Italian municipalities. Based on the application of machine learning techniques alongside traditional regression analyses, we show that a 1% growth in the concentration of knowledge-intensive business services reduces PM2.5 levels by percentages between 0.11% and 0.47%. We also clarify how a wide range of socioeconomic and meteorological variables govern air quality. Moreover, we estimate the municipal environmental efficiency through a data envelopment analysis approach, showing that better scores are achieved by territories with higher servitization and quality of institutions. Our findings, which are stable across different geographical macro-areas, suggest that both the economic structure and the capabilities of local administration may drive air pollution dynamics. Such results are essential for policymakers aiming to design effective interventions targeting local environmental sustainability.
2025
Air quality
Machine learning
PM2.5
Regression
Servitization
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1304106
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