Detecting Carbon-Credit Laundering Through Integrated ESG and Transaction-Network Analysis

Detecting Carbon-Credit Laundering Through Integrated ESG and Transaction-Network Analysis | 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 Detecting Carbon-Credit Laundering Through Integrated ESG and Transaction-Network Analysis Karin Holm, Erik Sandström, Emma Lindgren, Jonas Bergström This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8566189/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 This paper investigates laundering risks in carbon-credit markets by integrating environmental, social, and governance (ESG) data with financial transaction records. A dataset covering 1,247 firms across three jurisdictions from 2018–2023 was assembled, combining carbon-registry information, credit-trading logs, ESG disclosures, and banking records. A hybrid detection model combining a graph neural network with a gradient-boosted classifier was trained using 1,350 labeled suspicious cases and 6,700 normal cases. The model achieved an AUC of 0.92 and a precision of 0.71 at 70% recall, outperforming rule-based systems by 17.3 percentage points. Entities flagged by the model frequently showed abnormal credit recycling within 30 days and emissions inconsistencies exceeding sector benchmarks by more than 25%. These findings indicate that integrated multi-source analysis can effectively identify carbon-credit laundering. Theoretical Computer Science Computer Architecture and Engineering Artificial Intelligence and Machine Learning Green money laundering Carbon credits ESG data Transaction networks Financial crime detection 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-8566189","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":572397831,"identity":"06717bfc-2910-418a-acb8-f9a4b74f3fa0","order_by":0,"name":"Karin 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