AI-Driven Discovery Reveals Critical Thresholds and Persistent Inequities in Urban Sustainability

AI-Driven Discovery Reveals Critical Thresholds and Persistent Inequities in Urban Sustainability | 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 AI-Driven Discovery Reveals Critical Thresholds and Persistent Inequities in Urban Sustainability Jiajia Wang, Zhihan Tao, Brian Deal This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7983944/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 Cities worldwide aim to simultaneously achieve environmental protection, social equity, and economic vitality. These seemingly simple goals, however, require evaluating sustainability trade-offs across hundreds of instances, projects and places – a cognitive limitation traditionally mistaken for physical impossibility. In this paper, we present a multi-objective AI framework that analyzes hundreds of census tracts and variable configurations across three competing objectives to achieve maximum performance (in all three) simultaneously (+ 90%). The solution sets of such a large and complex array have been computationally invisible to conventional analysis. Using the city of Chicago as our test case (801 census blocks, 59 configurations) we found 22 uniquely optimal solutions that cluster along century-old green space corridors established by Burnham's original 1909 Plan. Interestingly, these solutions are found in the wealthier north and northwest parts of city with none in the highly disinvested south and west. This pattern demonstrates how historic infrastructure decisions can create path dependencies that may be difficult to overcome. Our AI framework reveals critical thresholds that appear to enable solution success, including critical open space access and population densities. The work provides an evidence-based approach for climate-responsive urban design and planning decisions. The framework supports an emerging urban cognitive ecosystem approach that uses data and AI to create intelligent, adaptive ecosystems that can learn and proactively deliver services, transforming planning from pattern recognition to revealing systemic and historic inequities. Earth and environmental sciences/Environmental social sciences Scientific community and society/Geography Social science/Geography Physical sciences/Mathematics and computing Scientific community and society/Social sciences Full Text Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterials251103finalupload.docx 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-7983944","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":546511015,"identity":"44b8d67f-2c9d-49c2-ae91-e55348441a45","order_by":0,"name":"Jiajia 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Sustainability","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 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These seemingly simple goals, however, require evaluating sustainability trade-offs across hundreds of instances, projects and places \u0026ndash; a cognitive limitation traditionally mistaken for physical impossibility. In this paper, we present a multi-objective AI framework that analyzes hundreds of census tracts and variable configurations across three competing objectives to achieve maximum performance (in all three) simultaneously (+\u0026thinsp;90%). The solution sets of such a large and complex array have been computationally invisible to conventional analysis. Using the city of Chicago as our test case (801 census blocks, 59 configurations) we found 22 uniquely optimal solutions that cluster along century-old green space corridors established by Burnham's original 1909 Plan. Interestingly, these solutions are found in the wealthier north and northwest parts of city with none in the highly disinvested south and west. This pattern demonstrates how historic infrastructure decisions can create path dependencies that may be difficult to overcome. Our AI framework reveals critical thresholds that appear to enable solution success, including critical open space access and population densities. The work provides an evidence-based approach for climate-responsive urban design and planning decisions. 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