Regional Research Intensity and ESG Indicators in Italy: Insights from Panel Data Models and Machine Learning

Alberto Costantiello, Carlo Drago, Massimo Arnone and Angelo Leogrande

MPRA Paper from University Library of Munich, Germany

Abstract: 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. (search for similar items in EconPapers)
JEL-codes: C23 I23 O32 Q56 R58 (search for similar items in EconPapers)
Date: 2024-03-30
References: Add references at CitEc
Citations:

Downloads: (external link)
https://mpra.ub.uni-muenchen.de/124185/1/MPRA_paper_124185.pdf original version (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:124185

Access Statistics for this paper

More papers in MPRA Paper from University Library of Munich, Germany Ludwigstraße 33, D-80539 Munich, Germany. Contact information at EDIRC.
Bibliographic data for series maintained by Joachim Winter ().