- Data, Statistics and AI for Well-Being of People and Organisations
- A cura di Luigi Fabbris, Alberto Franci, Pietro Renzi
Regional research intensity and ESG indicators in Italy: Insights from panel data models and machine learning
- Alberto Costantiello
- Margaret Antonicelli
- Carlo Drago
- Massimo Arnone
- Angelo Leogrande
- © 2026 Author(s) |
- CC BY 4.0
- DOI: 10.36253/979-12-215-1062-1.28
This study investigates the relationship between Research Intensity (RI) and a range of Environmental, Social, and Governance (ESG) variables for Italian regions using machine learning algorithms and panel data models. The study seeks to identify the most predictive variables of research intensity from a range of cultural, environmental, socio-economic, and governance indicators. Support Vector Machine, Random Forest, k-Nearest Neighbors, and Neural Network algorithms are used to ascertain comparative predictive power. Feature importance analysis identifies education levels, in particular tertiary education qualifications, and technological infrastructure as most predictive of research intensity. Regional differences in research intensity are also investigated on the basis of political representation, healthcare accessibility, material consumption, and cultural investment variables. Results indicate that economically developed regions with sufficient research capacity are more research-intensive but can also face environmental sustainability and social inclusiveness issues. The study concludes that policy measures to enable education, technological innovation, environmental management, and governance improvement are required to spur research capacity in Italian regions. The study also provides insight into the use of research intensity in informing broader ESG objectives, including policy intervention for mitigating regional imbalances. Future studies should provide insight into the dynamic interaction effects of research intensity and ESG variables over time using more sophisticated machine learning techniques to further enhance predictive power.
- Keywords:
- Research Intensity,
- ESG Factors,
- Machine Learning,
- Panel Data Models,
- Italian Regions,
LUM Giuseppe Degennaro University, Italy - ORCID: 0000-0002-0004-9635
IULM University of Milan, Italy - ORCID: 0000-0001-8740-1058
Niccolò Cusano University, Italy - ORCID: 0000-0002-3920-0267
University of Catania, Italy - ORCID: 0000-0002-5396-4431
LUM Giuseppe Degennaro University, Italy - ORCID: 0000-0003-1381-4006
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Informazioni sul capitolo
Titolo del capitolo
Regional research intensity and ESG indicators in Italy: Insights from panel data models and machine learning
Autori
Alberto Costantiello, Margaret Antonicelli, Carlo Drago, Massimo Arnone, Angelo Leogrande
Lingua
Inglese
DOI
10.36253/979-12-215-1062-1.28
Opera sottoposta a peer review
Anno di pubblicazione
2026
Copyright
© 2026 Author(s)
Licenza d'uso
Licenza dei metadati
Informazioni bibliografiche
Titolo del libro
Data, Statistics and AI for Well-Being of People and Organisations
Curatori
Luigi Fabbris, Alberto Franci, Pietro Renzi
Opera sottoposta a peer review
Numero di pagine
420
Anno di pubblicazione
2026
Copyright
© 2026 Author(s)
Licenza d'uso
Licenza dei metadati
Editore
Firenze University Press
DOI
10.36253/979-12-215-1062-1
ISBN Print
979-12-215-1061-4
eISBN (pdf)
979-12-215-1062-1
eISBN (xml)
979-12-215-1063-8
Collana
Applied Statistics Series