Do Sectoral AI Adoption Rates Reduce Carbon Intensity? Evidence from EU Industries, 2021–2023

Do Sectoral AI Adoption Rates Reduce Carbon Intensity? Evidence from EU Industries, 2021–2023 | 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 Article Do Sectoral AI Adoption Rates Reduce Carbon Intensity? Evidence from EU Industries, 2021–2023 Mintian He, Shuili Yang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7836528/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 We test whether higher sectoral adoption of artificial intelligence (AI) aligns with lower greenhouse-gas (GHG) intensity across EU country-by-industry cells. Using harmonised Eurostat sources—enterprise AI use (isoc_eb_ain2; share of firms ≥ 10 employees using ≥ 1 AI technology) and air-emissions intensities by NACE activity (env_ac_aeint_r2; kg CO₂e per chain-linked euro)—we assemble a 2021 and 2023 panel at Level-1 NACE sections and estimate two-way fixed-effects models with country×industry and year effects. On average, the AI–intensity association is statistically indistinguishable from zero over this short window. However, context-dependent patterns are consistent with a cybernetic feedback view: in energy/process-intensive manufacturing and selected information-intensive services with high baseline intensity, higher AI adoption correlates with economically meaningful reductions in GHG intensity. Results are robust to alternative outcomes (GVA-based intensity), functional forms, winsorisation, and difference-specifications. The findings imply that the returns of AI to decarbonisation are heterogeneous and likely stronger where energy flows are proximate and baseline intensity is high, underscoring the importance of sector-specific AI programmes and complementary investments in data pipelines, OT–IT integration, skills, and cleaner power mixes. Business and commerce/Business and management Social science/Business and management Earth and environmental sciences/Environmental sciences Earth and environmental sciences/Environmental social sciences Business and commerce/Information systems and information technology Artificial intelligence Greenhouse-gas intensity Eurostat NACE industries Fixed effects European Union Full Text Additional Declarations No competing interests reported. Supplementary Files TableS1variabledictionary.csv TableS2Adescriptivebyyear.csv TableS2Bdescriptivebysection.csv TableS3industrycoefficients.csv TableS4firstdifferencebygroups.csv TableS5mechanismprobes.csv SupplementaryPackage.zip FigureS1scatterAIvslnGHG2023.png FigureS2raincloudresidualsbyAIquintilewithinterciles1.png FigureS3tercileslopes.png 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-7836528","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":544129019,"identity":"39d2be33-a39c-4c1a-9eb2-38da8da8e9d9","order_by":0,"name":"Mintian 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Evidence from EU Industries, 2021–2023","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"Artificial intelligence, Greenhouse-gas intensity, Eurostat, NACE industries, Fixed effects, European Union","lastPublishedDoi":"10.21203/rs.3.rs-7836528/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7836528/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWe test whether higher sectoral adoption of artificial intelligence (AI) aligns with lower greenhouse-gas (GHG) intensity across EU country-by-industry cells. Using harmonised Eurostat sources\u0026mdash;enterprise AI use (isoc_eb_ain2; share of firms\u0026thinsp;\u0026ge;\u0026thinsp;10 employees using\u0026thinsp;\u0026ge;\u0026thinsp;1 AI technology) and air-emissions intensities by NACE activity (env_ac_aeint_r2; kg CO₂e per chain-linked euro)\u0026mdash;we assemble a 2021 and 2023 panel at Level-1 NACE sections and estimate two-way fixed-effects models with country\u0026times;industry and year effects. On average, the AI\u0026ndash;intensity association is statistically indistinguishable from zero over this short window. However, context-dependent patterns are consistent with a cybernetic feedback view: in energy/process-intensive manufacturing and selected information-intensive services with high baseline intensity, higher AI adoption correlates with economically meaningful reductions in GHG intensity. Results are robust to alternative outcomes (GVA-based intensity), functional forms, winsorisation, and difference-specifications. The findings imply that the returns of AI to decarbonisation are heterogeneous and likely stronger where energy flows are proximate and baseline intensity is high, underscoring the importance of sector-specific AI programmes and complementary investments in data pipelines, OT\u0026ndash;IT integration, skills, and cleaner power mixes.\u003c/p\u003e","manuscriptTitle":"Do Sectoral AI Adoption Rates Reduce Carbon Intensity? 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