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

Regional Research Intensity and ESG Indicators in Italy: Insights from Panel Data Models and Machine Learning | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Regional Research Intensity and ESG Indicators in Italy: Insights from Panel Data Models and Machine Learning Alberto Costantiello, Carlo Drago, Massimo Arnone, Angelo Leogrande This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6340262/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version 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. JEL CODES: O32, C23, Q56, R58, I23. Environmental Economics Microeconomics Macroeconomics Research Intensity ESG Factors Machine Learning Panel Data Models Italian Regions Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6340262","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":436010820,"identity":"c7d1cecc-b35b-41e9-80e4-1d18e4da1cc4","order_by":0,"name":"Alberto Costantiello","email":"","orcid":"","institution":"LUM University Giuseppe Degennaro","correspondingAuthor":false,"prefix":"","firstName":"Alberto","middleName":"","lastName":"Costantiello","suffix":""},{"id":436010821,"identity":"f99bd5db-3b9d-49dc-ad2b-96d0bd7546cf","order_by":1,"name":"Carlo Drago","email":"","orcid":"","institution":"Unicusano University","correspondingAuthor":false,"prefix":"","firstName":"Carlo","middleName":"","lastName":"Drago","suffix":""},{"id":436010822,"identity":"837edb57-d4a0-40d2-9ae4-c70dbb05fc20","order_by":2,"name":"Massimo Arnone","email":"","orcid":"","institution":"University of Catania","correspondingAuthor":false,"prefix":"","firstName":"Massimo","middleName":"","lastName":"Arnone","suffix":""},{"id":436010823,"identity":"fec107a0-ec0f-4ff7-b7d7-6d3701a82a9b","order_by":3,"name":"Angelo Leogrande","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9ElEQVRIiWNgGAWjYBACPijN2AAiH4AI9mYQWwKnFjYULQkJQILnIFgLTj1YtEgkgtm4tbA3H35dUXNHtp+B+ZlE4g+bfH7Jh21SNxgs6nBq4TmWZnnm2DPjmQ1sZhIJCWmWM2cntknn4HOYRI6ZYQPb4cQNB3jYgFoOGxjcJqRF/g1Qy7/DifthWuxvHiRkC4/xw8Y2oC0MMFskGAlo4UlLY2zsO2w84zCbsUVCWpqBxJnEZuscAwnJBhxa+NkPH/7Y8O2wbH9788MbH2xsDPjbDx+8nVNRx4/LFrDbwBQziqABHg1AtR/wSo+CUTAKRsEoAABg1U/Q4kxZZwAAAABJRU5ErkJggg==","orcid":"","institution":"LUM University Giuseppe Degennaro","correspondingAuthor":true,"prefix":"","firstName":"Angelo","middleName":"","lastName":"Leogrande","suffix":""}],"badges":[],"createdAt":"2025-03-30 21:21:57","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6340262/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6340262/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":79763380,"identity":"6850c665-d727-4749-8d0f-5283195c19ba","added_by":"auto","created_at":"2025-04-02 11:47:46","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":640805,"visible":true,"origin":"","legend":"","description":"","filename":"RegionalResearchIntensityandESGIndicatorsinItalyInsightsfromPanelDataModelsandMachineLearning.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6340262/v1_covered_3a459a32-4242-4b23-bf75-7e3d5c65aa8f.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eRegional Research Intensity and ESG Indicators in Italy: Insights from Panel Data Models and Machine Learning\u003c/strong\u003e\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Research Intensity, ESG Factors, Machine Learning, Panel Data Models, Italian Regions","lastPublishedDoi":"10.21203/rs.3.rs-6340262/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6340262/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis 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. 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