Determinants of Building-Sector CO₂ Emissions in the EU: A Combined Econometric and Machine Learning Approach

Determinants of Building-Sector CO₂ Emissions in the EU: A Combined Econometric and Machine Learning Approach | 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 Determinants of Building-Sector CO₂ Emissions in the EU: A Combined Econometric and Machine Learning Approach Marco Mele, Alberto Costantiello, Fabio Anobile, Angelo Leogrande This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8344438/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 paper evaluates the structural, environmental, and climatic factors influencing carbon dioxide emissions from the building sector (CBE) in 27 European Union member states from 2005 to 2023. This analysis uses panel data from the World Bank and four econometric models—Random Effects, Fixed Effects, Dynamic Panel GMM, and Weighted Least Squares—coupled with machine learning and clustering to provide a robust analysis of emissions. The econometric models show that all models support a negative relationship between agriculture, forestry, and fishing value added (AFFV) and forest area (FRST), suggesting that a robust rural economy and substantial natural carbon sinks are accompanied by lower emissions in the building sector. On the other hand, water stress (WSTR), PM2.5 pollution, heating and cooling degree days, and nitrous oxide emissions (N2OP) are found to significantly, yet positively, affect CBE. Tests of diagnostic analyses support Fixed Effects and Weighted Least Squares models, whereas results from GMM models are limited by instrument validity violations. In machine learning analysis, K-Nearest Neighbors (KNN) models are found to be most diagnostic, with all performance metrics being improved, establishing a prominent role for coal electricity, water stress, agricultural intensities, and climatic factors. Subsequently, a solution with 10 clusters, selected using Bayesian Information Criteria and silhouettes, identified a set of environmental and economic characteristics based on differences between low- and high-emission groups. High-emitting groups result from agricultural intensification, pollution, and low energy efficiency, while low-emitting groups are associated with renewable energy, low pollution, and a favorable climate. This analysis, hence, presents a multifaceted assessment of building sector emissions, with climatic, structural, and energy transition patterns as driving factors for meeting decarbonization targets for the European Union. JEL Codes: C33; Q54; Q41; Q56; C38 Building-sector carbon emissions Panel data econometrics Machine learning prediction Environmental and climatic drivers Cluster analysis 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-8344438","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":559310151,"identity":"c898725a-c536-44cc-8e48-7130a24c9177","order_by":0,"name":"Marco Mele","email":"","orcid":"","institution":"Unicusano University","correspondingAuthor":false,"prefix":"","firstName":"Marco","middleName":"","lastName":"Mele","suffix":""},{"id":559310152,"identity":"941cee88-351b-4be5-b165-a86fba4b3e29","order_by":1,"name":"Alberto Costantiello","email":"","orcid":"","institution":"LUM University Giuseppe Degennaro","correspondingAuthor":false,"prefix":"","firstName":"Alberto","middleName":"","lastName":"Costantiello","suffix":""},{"id":559310153,"identity":"cb8a0be8-7b24-4d30-9ebc-4b6c634df46d","order_by":2,"name":"Fabio Anobile","email":"","orcid":"","institution":"LUM University Giuseppe Degennaro","correspondingAuthor":false,"prefix":"","firstName":"Fabio","middleName":"","lastName":"Anobile","suffix":""},{"id":559310154,"identity":"73be6a48-ebbf-4797-bac0-b6a1f29009b6","order_by":3,"name":"Angelo Leogrande","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7ElEQVRIiWNgGAWjYBACA/nDBz//MDhcZ9/A/Eya8Z9NvX17D5s0D0NdfQMuLRJsydI8FY+TDRjYzKQZDNISN/CcYZOewyDBg9MWCR4zZp4znxM3MPCIAbUcTtwgkcMm/YeQFt42sBY2iBbJN2zSOfi0yPeYMf9su524H6ql3l46h4AWoC3WD4FaYLbUGBCjRdrw33m4ljQDyTOgECOgRbLtONz7yUARQlqAgczbdjzZgJnN2BgSyDnMxjwGuLXYzwdGJW8bMCrbmx8+/mFgk7i//fzDxzwVdTi1IAAzqu2ENYyCUTAKRsEowA0AAPZQRqtcYAUAAAAASUVORK5CYII=","orcid":"","institution":"LUM University Giuseppe Degennaro","correspondingAuthor":true,"prefix":"","firstName":"Angelo","middleName":"","lastName":"Leogrande","suffix":""}],"badges":[],"createdAt":"2025-12-12 10:03:47","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-8344438/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8344438/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":98435701,"identity":"2d94cb11-27f8-414c-8202-322ab9334e3f","added_by":"auto","created_at":"2025-12-17 16:54:13","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":640894,"visible":true,"origin":"","legend":"","description":"","filename":"12122025DeterminantsofBuildingSectorCOEmissionsintheEUACombinedEconometricandMachineLearningApproach.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8344438/v1_covered_2a699573-8c46-4fb4-8592-d3620ef30ae7.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eDeterminants of Building-Sector CO₂ Emissions in the EU: A Combined Econometric and Machine Learning Approach\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":"Building-sector carbon emissions, Panel data econometrics, Machine learning prediction, Environmental and climatic drivers, Cluster analysis","lastPublishedDoi":"10.21203/rs.3.rs-8344438/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8344438/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis paper evaluates the structural, environmental, and climatic factors influencing carbon dioxide emissions from the building sector (CBE) in 27 European Union member states from 2005 to 2023. 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