Pattern Recognition of Gold and Mercury Supply Chain in Global Trade Data

Pattern Recognition of Gold and Mercury Supply Chain in Global Trade Data | 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 Pattern Recognition of Gold and Mercury Supply Chain in Global Trade Data Muhammad Sukri Bin Ramli This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8525304/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 Despite the Minamata Convention’s targeted reductions in mercury consumption, global trade data exhibits a ‘Compliance Paradox’ where reported flows vanish while artisanal gold mining output remains stable. This research proposes a ‘Mineral Intelligence’ pipeline utilizing unsupervised machine learning to detect illicit mercury trafficking disguised as Electronic Waste (HS 8549). By applying Gaussian Mixture Models (GMM) and Isolation Forest algorithms to UN Comtrade data (2020–2024), we identify a systemic ‘Balloon Effect’: as elemental mercury bans took effect in 2022, illicit volumes were structurally displaced into ‘fake waste’ classifications. Forensic analysis reveals a statistically significant ‘Smuggler’s Signature’ within these flows, characterized by a price anomaly of $ 24– $ 80/kg (mirroring liquid mercury markets) and a Net-to-Gross weight ratio exceeding 90%, physically corresponding to standard 34.5 kg steel mercury flasks. Furthermore, Node2Vec and spectral embedding analysis exposes a ‘Decoupling Chasm’ (Manifold Distance: 2.06) that topologically separates financial gold hubs from mercury-intensive mining zones. Finally, Recursive LSTM forecasts predict a ‘burnout’ of the current HS 8549 smuggling vector (-618M kg/yr), warning of an imminent regime shift toward chemically masked commodities. Environmental Economics Mercury Trafficking Artisanal and Small-scale Gold Mining (ASGM) Compliance Paradox Unsupervised Machine Learning Trade Anomaly Detection Balloon Effect Electronic Waste Misclassification (HS 8549) Minamata Convention Supply Chain Forensics Commodity Substitution Decoupling Chasm Global Trade Networks Smuggler's Signature Recursive LSTM Environmental Crime Full Text Additional Declarations The authors declare no competing interests. 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-8525304","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":569860126,"identity":"13b058f5-ba64-43a0-bfaa-9edc3b4367ed","order_by":0,"name":"Muhammad Sukri Bin 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Data\u003c/strong\u003e\u003c/p\u003e","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":"Mercury Trafficking, Artisanal and Small-scale Gold Mining (ASGM), Compliance Paradox, Unsupervised Machine Learning, Trade Anomaly Detection, Balloon Effect, Electronic Waste Misclassification (HS 8549), Minamata Convention, Supply Chain Forensics, Commodity Substitution, Decoupling Chasm, Global Trade Networks, Smuggler's Signature, Recursive LSTM, Environmental Crime","lastPublishedDoi":"10.21203/rs.3.rs-8525304/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8525304/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDespite the Minamata Convention\u0026rsquo;s targeted reductions in mercury consumption, global trade data exhibits a \u0026lsquo;Compliance Paradox\u0026rsquo; where reported flows vanish while artisanal gold mining output remains stable. This research proposes a \u0026lsquo;Mineral Intelligence\u0026rsquo; pipeline utilizing unsupervised machine learning to detect illicit mercury trafficking disguised as Electronic Waste (HS 8549). By applying Gaussian Mixture Models (GMM) and Isolation Forest algorithms to UN Comtrade data (2020\u0026ndash;2024), we identify a systemic \u0026lsquo;Balloon Effect\u0026rsquo;: as elemental mercury bans took effect in 2022, illicit volumes were structurally displaced into \u0026lsquo;fake waste\u0026rsquo; classifications. Forensic analysis reveals a statistically significant \u0026lsquo;Smuggler\u0026rsquo;s Signature\u0026rsquo; within these flows, characterized by a price anomaly of \u003cspan\u003e$\u003c/span\u003e24\u0026ndash;\u003cspan\u003e$\u003c/span\u003e80/kg (mirroring liquid mercury markets) and a Net-to-Gross weight ratio exceeding 90%, physically corresponding to standard 34.5 kg steel mercury flasks. Furthermore, Node2Vec and spectral embedding analysis exposes a \u0026lsquo;Decoupling Chasm\u0026rsquo; (Manifold Distance: 2.06) that topologically separates financial gold hubs from mercury-intensive mining zones. Finally, Recursive LSTM forecasts predict a \u0026lsquo;burnout\u0026rsquo; of the current HS 8549 smuggling vector (-618M kg/yr), warning of an imminent regime shift toward chemically masked commodities.\u003c/p\u003e","manuscriptTitle":"Pattern Recognition of Gold and Mercury Supply Chain in Global Trade Data","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-07 08:04:39","doi":"10.21203/rs.3.rs-8525304/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":"57b3a209-bd5f-41e1-9381-82b9a35898ca","owner":[],"postedDate":"January 7th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":60635332,"name":"Environmental Economics"}],"tags":[],"updatedAt":"2026-01-07T08:04:40+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-07 08:04:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8525304","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8525304","identity":"rs-8525304","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}