How artificial intelligence and environmental regulation influence the efficiency of the circular economy in the context of sustainable development

How artificial intelligence and environmental regulation influence the efficiency of the circular economy in the context of sustainable development | 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 How artificial intelligence and environmental regulation influence the efficiency of the circular economy in the context of sustainable development Yanfeng Guan, Junding Yang, Tong Zhao, Yihong Shan, Yiling Zhu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8753617/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 13 You are reading this latest preprint version Abstract Enhancing urban circular economy efficiency (UCEE) is crucial for sustainable development, yet the roles of artificial intelligence and environmental regulation remain unclear. Using panel data for 30 Chinese provinces from 2013–2022, we measure UCEE with a Super-SBM model including undesirable outputs, track dynamics via the Global Malmquist–Luenberger index, and estimate spatial effects with a spatial Durbin model. Nationally, the average UCEE index rises from about 0.3 to above 0.7, but large gaps persist, with leading eastern provinces approaching 1.0 and some northeastern provinces remaining below 0.1. The GML index and its EC and TC components stay above 1, indicating sustained efficiency gains dominated by technological progress. Spatial results show that both artificial intelligence and environmental regulation significantly inhibit local UCEE, while artificial intelligence generates positive spillovers; their interaction produces a “1 + 1<2” crowding-out effect, underscoring the need for smarter, better-coordinated green governance. Biological sciences/Ecology Earth and environmental sciences/Ecology Earth and environmental sciences/Environmental sciences Earth and environmental sciences/Environmental social sciences Circular economy Artificial intelligence Environmental regulation Efficiency of technology progress Sustainable development Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 08 May, 2026 Reviewers agreed at journal 07 May, 2026 Reviews received at journal 06 May, 2026 Reviewers agreed at journal 06 May, 2026 Reviewers agreed at journal 06 May, 2026 Reviews received at journal 14 Apr, 2026 Reviewers agreed at journal 21 Mar, 2026 Reviewers agreed at journal 16 Mar, 2026 Reviewers invited by journal 16 Mar, 2026 Editor assigned by journal 16 Mar, 2026 Editor invited by journal 25 Feb, 2026 Submission checks completed at journal 19 Feb, 2026 First submitted to journal 19 Feb, 2026 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. 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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-8753617","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":607264974,"identity":"1bc27548-775a-4ffb-9a8f-3e938afdc96b","order_by":0,"name":"Yanfeng Guan","email":"","orcid":"","institution":"Nanjing Xiaozhuang University","correspondingAuthor":false,"prefix":"","firstName":"Yanfeng","middleName":"","lastName":"Guan","suffix":""},{"id":607264975,"identity":"7a4c2cf5-9d44-49a5-973e-36826cc9f394","order_by":1,"name":"Junding Yang","email":"","orcid":"","institution":"Nanjing Xiaozhuang 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Using panel data for 30 Chinese provinces from 2013\u0026ndash;2022, we measure UCEE with a Super-SBM model including undesirable outputs, track dynamics via the Global Malmquist\u0026ndash;Luenberger index, and estimate spatial effects with a spatial Durbin model. Nationally, the average UCEE index rises from about 0.3 to above 0.7, but large gaps persist, with leading eastern provinces approaching 1.0 and some northeastern provinces remaining below 0.1. The GML index and its EC and TC components stay above 1, indicating sustained efficiency gains dominated by technological progress. 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