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

Costantiello, Alberto and Drago, Carlo and Arnone, Massimo and Leogrande, Angelo (2024): Regional Research Intensity and ESG Indicators in Italy: Insights from Panel Data Models and Machine Learning.

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.

• Item Type: MPRA Paper
• Language: English
• Keywords: Research Intensity, ESG Factors, Machine Learning, Panel Data Models, Italian Regions.
• Subjects: C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C23 - Panel Data Models ; Spatio-temporal Models; I - Health, Education, and Welfare > I2 - Education and Research Institutions > I23 - Higher Education ; Research Institutions; O - Economic Development, Innovation, Technological Change, and Growth > O3 - Innovation ; Research and Development ; Technological Change ; Intellectual Property Rights > O32 - Management of Technological Innovation and R&D; Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q5 - Environmental Economics > Q56 - Environment and Development ; Environment and Trade ; Sustainability ; Environmental Accounts and Accounting ; Environmental Equity ; Population Growth; R - Urban, Rural, Regional, Real Estate, and Transportation Economics > R5 - Regional Government Analysis > R58 - Regional Development Planning and Policy

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