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 ()
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Alberto Costantiello: LUM - Università LUM Giuseppe Degennaro = University Giuseppe Degennaro
Carlo Drago: UNICUSANO - University Niccolò Cusano = Università Niccoló Cusano
Massimo Arnone: Unict - Università degli studi di Catania = University of Catania
Angelo Leogrande: LUM - Università LUM Giuseppe Degennaro = University Giuseppe Degennaro

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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. JEL CODES: O32 C23 Q56 R58 I23; Research Intensity; ESG Factors; Machine Learning; Panel Data Models; Italian Regions. JEL CODES: O32; C23; Q56; R58; I23 (search for similar items in EconPapers)
Date: 2025-03-30
Note: View the original document on HAL open archive server: https://hal.science/hal-05012010v1
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