Digital Green Innovation Pathway: Artificial Intelligence and Urban Green Innovation Levels - A Case Study of the Yangtze River Economic Belt in China.

Digital Green Innovation Pathway: Artificial Intelligence and Urban Green Innovation Levels - A Case Study of the Yangtze River Economic Belt in China. | 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 Digital Green Innovation Pathway: Artificial Intelligence and Urban Green Innovation Levels - A Case Study of the Yangtze River Economic Belt in China. He Xingxing, ruan junjie, bian caixing, sun yu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3736301/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract With the iterative evolution of artificial intelligence technology, its characteristics such as intelligence and digitization have gradually become one of the important new engines for China's green innovation development. Based on the theory of discontinuous innovation and using panel data from 2011–2020 for 100 prefecture-level and above cities in the Yangtze River Economic Belt, this paper analyzes the internal mechanism of AI's effect in enhancing green innovation from the perspective of industrial agglomeration. It constructs a logical framework for AI-empowered green innovation development. The research shows that AI effectively promotes the enhancement of green innovation levels in the Yangtze River Economic Belt, a conclusion that still holds after endogeneity and robustness tests. Industrial agglomeration is an important mechanism pathway for AI to effectively enhance the green innovation level in the Yangtze River Economic Belt. Heterogeneity analysis reveals that, from the perspective of city size, the larger the city, the better the effect of AI in empowering green innovation development in cities along the Yangtze River Economic Belt. Regarding the heterogeneity of the three major city clusters in the Yangtze River Economic Belt, AI's green innovation effect is manifested in the Chengdu-Chongqing and Middle Yangtze city clusters, while in the Yangtze River Delta city cluster, AI does not play a green innovation effect. Further analysis finds that digital inclusive finance and the degree of informatization have significant dual threshold effects in the process of AI promoting green innovation levels. When digital inclusive finance and the degree of informatization cross the first threshold value, the marginal benefit of AI-empowered green innovation further increases. However, when digital inclusive finance crosses the second threshold value, the marginal benefit of AI-empowered green innovation weakens. Extended analysis discovers that AI has a significant spatial spillover effect in promoting green innovation development in the Yangtze River Economic Belt. Focusing on the dual perspectives of industrial agglomeration and green innovation, this provides theoretical support and empirical evidence for promoting China's green, low-carbon, and circular development. Artificial Intelligence Green Innovation Level Threshold Effect Spatial Spillover Effect Yangtze River Economic Belt Full Text Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 08 Feb, 2024 Reviewers invited by journal 08 Feb, 2024 Editor assigned by journal 22 Jan, 2024 First submitted to journal 13 Jan, 2024 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-3736301","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":271973859,"identity":"f1523643-b81a-4e3a-b343-d4015f983374","order_by":0,"name":"He Xingxing","email":"","orcid":"","institution":"Guilin College of Electronic Industry: Guilin University of Electronic Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"He","middleName":"","lastName":"Xingxing","suffix":""},{"id":271973860,"identity":"8ecebd5c-25a0-46b9-aebe-debaaf0b4ea5","order_by":1,"name":"ruan junjie","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0klEQVRIiWNgGAWjYBACeWbG9g8fKiTkGNubDxCnxbCduY1xxhkLY+aeYwlEWnOevY2Zt6UisX1GjgFxOhibGdsezmyQSOydkfPxxhsGOzndBgJa2IF+Mfi4Q8J4Zs/bzZZzGJKNzQ4QtqVBcuYZCdmN7bnbpHkYDiRuI6SF4TBjgzRvmwTj/gM5z4jW0gbSotjYkcNGnBbDZsZmwxlnJIwZe44ZW84xIMIv8vzHHz74UFEHisqHN95U2MkR1IICJHiIjBpkLaTqGAWjYBSMghEBAOsqRqde3gZzAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0009-0009-0823-5764","institution":"Guilin College of Electronic Industry: Guilin University of Electronic Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"ruan","middleName":"","lastName":"junjie","suffix":""},{"id":271973861,"identity":"a94b50d6-b343-425c-89a9-e214f9773e51","order_by":2,"name":"bian caixing","email":"","orcid":"","institution":"Guilin College of Electronic Industry: Guilin University of Electronic Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"bian","middleName":"","lastName":"caixing","suffix":""},{"id":271973862,"identity":"a1b43372-6c26-4fc7-9dcd-7c45765d5b2a","order_by":3,"name":"sun yu","email":"","orcid":"","institution":"Guilin College of Electronic Industry: Guilin University of Electronic Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"sun","middleName":"","lastName":"yu","suffix":""}],"badges":[],"createdAt":"2023-12-11 01:55:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3736301/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3736301/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":51014613,"identity":"d395f11f-b03b-4c08-8e78-e62df6e50148","added_by":"auto","created_at":"2024-02-12 18:10:56","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":563095,"visible":true,"origin":"","legend":"","description":"","filename":"DigitalGreenInnovationPathway.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3736301/v1_covered_52b63875-102e-4e64-8c05-0b1cfd2b9c22.pdf"}],"financialInterests":"","formattedTitle":"Digital Green Innovation Pathway: Artificial Intelligence and Urban Green Innovation Levels - A Case Study of the Yangtze River Economic Belt in China.","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"environmental-science-and-pollution-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"espr","sideBox":"Learn more about [Environmental Science and Pollution Research](https://www.springer.com/journal/11356)","snPcode":"11356","submissionUrl":"https://submission.nature.com/new-submission/11356/3","title":"Environmental Science and Pollution Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Artificial Intelligence, Green Innovation Level, Threshold Effect, Spatial Spillover Effect, Yangtze River Economic Belt","lastPublishedDoi":"10.21203/rs.3.rs-3736301/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3736301/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWith the iterative evolution of artificial intelligence technology, its characteristics such as intelligence and digitization have gradually become one of the important new engines for China's green innovation development. Based on the theory of discontinuous innovation and using panel data from 2011\u0026ndash;2020 for 100 prefecture-level and above cities in the Yangtze River Economic Belt, this paper analyzes the internal mechanism of AI's effect in enhancing green innovation from the perspective of industrial agglomeration. It constructs a logical framework for AI-empowered green innovation development. The research shows that AI effectively promotes the enhancement of green innovation levels in the Yangtze River Economic Belt, a conclusion that still holds after endogeneity and robustness tests. Industrial agglomeration is an important mechanism pathway for AI to effectively enhance the green innovation level in the Yangtze River Economic Belt. Heterogeneity analysis reveals that, from the perspective of city size, the larger the city, the better the effect of AI in empowering green innovation development in cities along the Yangtze River Economic Belt. 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Focusing on the dual perspectives of industrial agglomeration and green innovation, this provides theoretical support and empirical evidence for promoting China's green, low-carbon, and circular development.\u003c/p\u003e","manuscriptTitle":"Digital Green Innovation Pathway: Artificial Intelligence and Urban Green Innovation Levels - A Case Study of the Yangtze River Economic Belt in China.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-12 18:02:43","doi":"10.21203/rs.3.rs-3736301/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2024-02-08T08:55:12+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-02-08T08:46:40+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-01-22T05:39:39+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Science and Pollution Research","date":"2024-01-14T00:25:16+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-science-and-pollution-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"espr","sideBox":"Learn more about [Environmental Science and Pollution Research](https://www.springer.com/journal/11356)","snPcode":"11356","submissionUrl":"https://submission.nature.com/new-submission/11356/3","title":"Environmental Science and Pollution Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"3a2ac569-8a5c-44e7-a7cc-2951fee47fc6","owner":[],"postedDate":"February 12th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-02-12T18:02:44+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-12 18:02:43","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3736301","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3736301","identity":"rs-3736301","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}