A Multi-Stage Nonlinear Relationship between Artificial Intelligence Development and Environmental Pollution

A Multi-Stage Nonlinear Relationship between Artificial Intelligence Development and Environmental Pollution | 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 A Multi-Stage Nonlinear Relationship between Artificial Intelligence Development and Environmental Pollution Feiyang Zhao, Mingxia Qi, Chaojie Ma This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8847646/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 As China advances its green and low carbon transition, the influence of artificial intelligence (AI) development on environmental pollution remains a critical yet underexplored issue; nevertheless, evidence on stage dependent effects beyond the conventional inverted U Environmental Kuznets Curve (EKC) remains limited. Using provincial panel data from 2011 to 2022, this study constructs an Environmental Pollution Index (EPI) using the global entropy weight method (EWM), incorporating indicators of solid waste generation, chemical oxygen demand emissions, sulfur dioxide emissions, and carbon dioxide emissions. Kernel density estimation and Dagum Gini coefficient decomposition are employed to characterize distributional dynamics and identify the sources of regional disparities. After benchmarking seven machine learning models within a unified framework, the Generalized Additive Model (GAM) is selected as the optimal model due to its superior predictive performance and interpretability, and is then used to uncover nonlinear associations, turning points, and sign reversals. The GAM results reveal distinct multistage patterns: AI enterprises follow a segmented extended U-shaped curve, shifting from pollution aggravation to stable mitigation beyond a threshold; AI-Industry per capita GDP exhibits a W-shaped pattern; Industrial robot density declines overall with an inflection; AI patent grants display threshold effects with stronger mitigation at higher levels; R&D personal ratio in AI industry approximates a U-shaped curve, turning from positive to negative near a threshold before flattening; and AI technology innovation expenditure increases almost linearly with a stagewise shift from mitigation to aggravation. Public concern about environmental pollution follows an N-shaped curve, Environmental governance investment and Urbanization rate exhibit bimodal M-shaped patterns, and Marketization index shows a U-shaped relationship with mitigation concentrated in the middle range. Although EPI declines on average, interprovincial disparities persist, and these multistage patterns refine EKC interpretations of scale and technique effects. Artificial Intelligence Environment Pollution Index Generalized Additive Model Nonlinear Relationships Environmental Kuznets Curve Full Text Additional Declarations No competing interests reported. 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-8847646","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":592976076,"identity":"cc2a3e38-59cc-4d58-a5cc-181a76f777f2","order_by":0,"name":"Feiyang Zhao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEElEQVRIie3PMUvDQBjG8TtOmuUO1ytB/ArXOoh0yAdxKgGn3uCWIQ0Hhdel7hWkfgVdgmNC4LIEskZcWgQnhY5xELxai1OSVfD+03Pw/oZDyGb7gzkKq+8hEMIvdWAWIaqV0OSXkBNUbAnuIj/DkJ6LYTu7iDMD/vE49U6dTLuXy+n54ZUhdRA3E5pC/7rIydn8wh/dxLlcZFjhefHcSDw+Bs5A90QyGVQs1lIZQjA0E3q8hv4naCrKN8HZrZZ3nYRjcBmEXFSToctUKO87CR3PRkeQCFG9+oOFTuSDIWnbX6iTr5/eIfJE6aerTRjJZZmlqzpoJqYDjlC2f+xG0nZvIhuEov0jaru02Wy2f9oXy35fMRrWKYMAAAAASUVORK5CYII=","orcid":"","institution":"Macao Polytechnic University","correspondingAuthor":true,"prefix":"","firstName":"Feiyang","middleName":"","lastName":"Zhao","suffix":""},{"id":592976077,"identity":"3c759ef9-5486-4a26-b7b4-f100a6b631d2","order_by":1,"name":"Mingxia Qi","email":"","orcid":"","institution":"Zhengzhou University of Economics and Business","correspondingAuthor":false,"prefix":"","firstName":"Mingxia","middleName":"","lastName":"Qi","suffix":""},{"id":592976078,"identity":"92f76ce6-9995-44d0-9a8e-6381106911fb","order_by":2,"name":"Chaojie Ma","email":"","orcid":"","institution":"Macao Polytechnic University","correspondingAuthor":false,"prefix":"","firstName":"Chaojie","middleName":"","lastName":"Ma","suffix":""}],"badges":[],"createdAt":"2026-02-11 06:08:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8847646/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8847646/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103446892,"identity":"ffc99e42-40e9-4372-9b92-3b6f149b678c","added_by":"auto","created_at":"2026-02-25 19:10:16","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1436371,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8847646/v1_covered_42cee0ae-e2af-4279-ab7e-bd0305033e14.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Multi-Stage Nonlinear Relationship between Artificial Intelligence Development and Environmental Pollution","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":"Artificial Intelligence, Environment Pollution Index, Generalized Additive Model, Nonlinear Relationships, Environmental Kuznets Curve","lastPublishedDoi":"10.21203/rs.3.rs-8847646/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8847646/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAs China advances its green and low carbon transition, the influence of artificial intelligence (AI) development on environmental pollution remains a critical yet underexplored issue; nevertheless, evidence on stage dependent effects beyond the conventional inverted U Environmental Kuznets Curve (EKC) remains limited. Using provincial panel data from 2011 to 2022, this study constructs an Environmental Pollution Index (EPI) using the global entropy weight method (EWM), incorporating indicators of solid waste generation, chemical oxygen demand emissions, sulfur dioxide emissions, and carbon dioxide emissions. Kernel density estimation and Dagum Gini coefficient decomposition are employed to characterize distributional dynamics and identify the sources of regional disparities. After benchmarking seven machine learning models within a unified framework, the Generalized Additive Model (GAM) is selected as the optimal model due to its superior predictive performance and interpretability, and is then used to uncover nonlinear associations, turning points, and sign reversals. The GAM results reveal distinct multistage patterns: AI enterprises follow a segmented extended U-shaped curve, shifting from pollution aggravation to stable mitigation beyond a threshold; AI-Industry per capita GDP exhibits a W-shaped pattern; Industrial robot density declines overall with an inflection; AI patent grants display threshold effects with stronger mitigation at higher levels; R\u0026amp;D personal ratio in AI industry approximates a U-shaped curve, turning from positive to negative near a threshold before flattening; and AI technology innovation expenditure increases almost linearly with a stagewise shift from mitigation to aggravation. Public concern about environmental pollution follows an N-shaped curve, Environmental governance investment and Urbanization rate exhibit bimodal M-shaped patterns, and Marketization index shows a U-shaped relationship with mitigation concentrated in the middle range. Although EPI declines on average, interprovincial disparities persist, and these multistage patterns refine EKC interpretations of scale and technique effects.\u003c/p\u003e","manuscriptTitle":"A Multi-Stage Nonlinear Relationship between Artificial Intelligence Development and Environmental Pollution","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-19 08:43:54","doi":"10.21203/rs.3.rs-8847646/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"c9c0e896-31b8-4a69-95ad-e9150f5876f1","owner":[],"postedDate":"February 19th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-25T19:10:00+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-19 08:43:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8847646","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8847646","identity":"rs-8847646","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}