[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124222-en":3,"doc-seo-124222-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124222,1099514067438,"River Wang","https://ap-avatar.wpscdn.com/avatar/100002539ee87300030?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780474512215547542",8,"Research & Report","Regional Research Intensity and ESG Indicators in Italy - Insights from Panel Data Models and Machine Learning - Research report","This study analyzes the relationship between Research Intensity (RI) and Environmental, Social, and Governance (ESG) indicators across Italian regions using machine learning algorithms combined with panel data models. It aims to determine which cultural, environmental, socio-economic, and governance variables most strongly predict research intensity, and it evaluates comparative predictive performance across Support Vector Machine, Random Forest, k-Nearest Neighbors, and Neural Network methods. Feature importance highlights education levels, especially tertiary qualifications, and technological infrastructure as key drivers. Findings suggest economically developed regions with adequate research capacity show higher RI, yet may also face sustainability and social inclusion challenges. Policy implications focus on education, innovation, environmental management, and governance improvements to reduce regional imbalances through RI-informed ESG objectives, with future work targeting dynamic interaction effects over time using advanced techniques.","Munich Personal RePEc Archive  \nRegional Research Intensity and ESG Indicators in Italy: Insights from Panel Data Models and Machine Learning  \nCostantiello, Alberto and Drago, Carlo and Arnone, Massimo and Leogrande, Angelo  \nLUM UNIVERSITY GIUSEPPE DEGENNARO, UNICUSANO UNIVERSITY, UNIVERSITY OF CATANIA, LUM UNIVERSITY GIUSEPPE DEGENNARO  \n30 March 2024  \nOnline at [https://mpra. ub. uni-muenchen. de/124185/](https://mpra. ub. uni-muenchen. de/124185/)  \n[MPRA Paper No. 124185](MPRA Paper No. 124185) , [posted 31 Mar 2025 08:20 UTC](posted 31 Mar 2025 08:20 UTC)  \nRegional Research Intensity and ESG Indicators in Italy: Insights from Panel Data Models and Machine Learning  \nAlberto Costantiello  \nLUM University Giuseppe Degennaro, Casamassima, [Costantiello@lum.it](Costantiello@lum.it)  \nCarlo Drago  \nUnicusano University, Rome, [carlo.drago@unicusano.it](carlo.drago@unicusano.it)  \nMassimo Arnone  \nUniversity of Catania, Catania, [massimo.arnone@unict.it](massimo.arnone@unict.it)  \nAngelo Leogrande  \nLUM University Giuseppe Degennaro, [leogrande.cultore@lum.it](leogrande.cultore@lum.it)  \nThis 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.  \nKeywords: Research Intensity, ESG Factors, Machine Learning, Panel Data Models, Italian Regions.  \nJEL CODES: O32, C23, Q56, R58, I23 .  \n1) Introduction  \nThe examination of research intensity (RI) in the context of Environmental, Social, and Governance (ESG) models is a novel and promising field of inquiry that can help expand the current stock of knowledge on regional development in Italy. While the theoretical and empirical literature has  \nexhaustively dealt with the role of research intensity in stimulating economic growth, innovation, and technological advancement, the relationship between research intensity and ESG variables remains largely under-explored. This research tries to fill this gap in knowledge by examining the interdependence between research intensity and various indicators of ESG at the Italian regional level through the application of high-performance machine learning algorithms and panel data models. Research intensity, classically measured as the share of GDP invested in intra-muros R&D activities, is a pertinent indicator of the scientific research, technological development, and innovation commitment of a region. High research intensity is typically associated with str","cbCaiqhiIGV7tBP1","https://ap.wps.com/l/cbCaiqhiIGV7tBP1","pdf",1141160,1,43,"English","en",105,"# Introduction\n## Research design and objectives\n## Regional heterogeneity in Italy\n## ESG dimensions and variables","[{\"question\":\"What does the study analyze regarding Italian regions?\",\"answer\":\"The study examines how Research Intensity (RI) relates to Environmental, Social, and Governance (ESG) variables across Italian regions, aiming to uncover the most predictive factors of RI.\"},{\"question\":\"Which machine learning methods are used to estimate predictive power?\",\"answer\":\"Support Vector Machine, Random Forest, k-Nearest Neighbors, and Neural Network models are applied to compare their ability to predict research intensity.\"},{\"question\":\"What variables are identified as most predictive of research intensity?\",\"answer\":\"Feature importance analysis indicates that education levels—especially tertiary education qualifications—and technological infrastructure are among the most predictive variables.\"}]","Regional Research Intensity and ESG Indicators in Italy - Insights from Panel Data Models and Machine Learning - Research report | PDF",1785821093,108,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"regional-research-intensity-and-esg-indicators-in-italy-insights-from-panel-data-models-and-machine-learning-research-report","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/regional-research-intensity-and-esg-indicators-in-italy-insights-from-panel-data-models-and-machine-learning-research-report/124222/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What does the study analyze regarding Italian regions?","Question",{"text":75,"@type":76},"The study examines how Research Intensity (RI) relates to Environmental, Social, and Governance (ESG) variables across Italian regions, aiming to uncover the most predictive factors of RI.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning methods are used to estimate predictive power?",{"text":80,"@type":76},"Support Vector Machine, Random Forest, k-Nearest Neighbors, and Neural Network models are applied to compare their ability to predict research intensity.",{"name":82,"@type":73,"acceptedAnswer":83},"What variables are identified as most predictive of research intensity?",{"text":84,"@type":76},"Feature importance analysis indicates that education levels—especially tertiary education qualifications—and technological infrastructure are among the most predictive variables.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]