Carbon Emission Quantification via Explainable Deep Learning Demand Forecasting in Retail Supply Chains

Carbon Emission Quantification via Explainable Deep Learning Demand Forecasting in Retail Supply Chains | 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 Carbon Emission Quantification via Explainable Deep Learning Demand Forecasting in Retail Supply Chains YuXuan Wu, Haowen Dai, Hengyi Zhang, Lin Kai This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9286812/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Supply chain carbon emissions account for over 80% of greenhouse gas output in most consumer industries, yet the operational link between demand forecasting accuracy and downstream environmental impact remains poorly quantified. Here we propose CGAN-MCDFN, an integrated framework coupling an explainable deep learning demand predictor with a simulation-based carbon emission model aligned with the Greenhouse Gas Protocol Scope 3 standard. The framework incorporates Conditional Generative Adversarial Network augmentation for sparse product demand, multi-channel encoding for heterogeneous signal fusion, and per-prediction SHAP-based explanations. Experiments on the M5 and Rossmann retail benchmarks yield rootmean- square-error improvements of 22.7% and 19.4% over the strongest baselines (both p < 0.001). Translating these accuracy gains through a carbon simulation calibrated with Ecoinvent 3.9 emission factors, we estimate a 32.5% scenario-level reduction in overstock-related CO 2 e emissions relative to an XGBoost baseline, with sensitivity analysis confirming that relative model rankings are preserved under ±30% emission-factor variation. SHAP analysis reveals that a carbon-index sustainability covariate ranks fourth among 47 predictors, and its importance increases at longer forecast horizons. We acknowledge that the carbon estimates are simulation-based and propose a three-stage validation roadmap for transitioning to auditable life-cycle-assessment data. Code and augmented datasets will be released upon acceptance. Earth and environmental sciences/Climate sciences Earth and environmental sciences/Environmental sciences Physical sciences/Mathematics and computing Full Text Additional Declarations No competing interests reported. Supplementary Files supplementaryinformation1.pdf Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 04 May, 2026 Reviewers agreed at journal 01 May, 2026 Reviewers invited by journal 17 Apr, 2026 Editor invited by journal 06 Apr, 2026 Editor assigned by journal 02 Apr, 2026 Submission checks completed at journal 02 Apr, 2026 First submitted to journal 01 Apr, 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. 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-9286812","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":624693881,"identity":"c4b24112-5a95-47f2-89b0-41a4c416c4b4","order_by":0,"name":"YuXuan Wu","email":"","orcid":"","institution":"Jianghan University","correspondingAuthor":false,"prefix":"","firstName":"YuXuan","middleName":"","lastName":"Wu","suffix":""},{"id":624693885,"identity":"94861017-8e67-4471-84f1-e1b283b8c36b","order_by":1,"name":"Haowen Dai","email":"","orcid":"","institution":"University of Nottingham Ningbo China","correspondingAuthor":false,"prefix":"","firstName":"Haowen","middleName":"","lastName":"Dai","suffix":""},{"id":624693889,"identity":"b2370ca1-3e61-493b-856a-393d9bc5c5d1","order_by":2,"name":"Hengyi Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/klEQVRIiWNgGAWjYDACCQYGxg8GbDz27c0HH3wAcYnRwixRwCdjwHMs2XAGsVoYeD7I2RhI5JgJ8xDjLv7ZzcceSBiY8ZhLJJgx27ZZ5DGwJ29g+FGxDbcld46lGxQYpPFY9jxIe5zbJlHMwPOsgLHnzG2cWkDukZAwOMbDcDzhuHHOGYnEBokcA2bGNnxa8r9J8Bj852E4kNgmbUGclhw2oBY2HoMTyWzSDBVEaJG4kWYmLQHUItlzjNmwp0KimA3ol4P4/MI/I/mZ5Ic/bPb87P0fH/wwqMvjZ0/e+OBHBW4tGCCBjSHB4ADx6kFagMiAJB2jYBSMglEw7AEA4B9P9tDFlE4AAAAASUVORK5CYII=","orcid":"","institution":"Beijing Jiaotong University","correspondingAuthor":true,"prefix":"","firstName":"Hengyi","middleName":"","lastName":"Zhang","suffix":""},{"id":624693892,"identity":"cb31df75-858a-4b49-8e2d-43167d9fa015","order_by":3,"name":"Lin Kai","email":"","orcid":"","institution":"GuangZhou university","correspondingAuthor":false,"prefix":"","firstName":"Lin","middleName":"","lastName":"Kai","suffix":""}],"badges":[],"createdAt":"2026-04-01 05:23:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9286812/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9286812/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107707182,"identity":"131b3e66-432b-417b-a9f9-7da97e31df98","added_by":"auto","created_at":"2026-04-24 09:19:44","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1166375,"visible":true,"origin":"","legend":"","description":"","filename":"TemplateforsubmissionstoScientificReports1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9286812/v1_covered_89c258c2-90eb-44dd-94e5-3f8449caa944.pdf"},{"id":107627072,"identity":"5e4543f7-925b-4415-92c2-25df9795706f","added_by":"auto","created_at":"2026-04-23 10:49:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":492476,"visible":true,"origin":"","legend":"","description":"","filename":"supplementaryinformation1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9286812/v1/f91314139980cad9d549f133.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Carbon Emission Quantification via Explainable Deep Learning Demand Forecasting in Retail Supply Chains","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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-9286812/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9286812/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSupply chain carbon emissions account for over 80% of greenhouse gas output in most consumer industries, yet the operational link between demand forecasting accuracy and downstream environmental impact remains poorly quantified. 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