Carbon-Aware Sustainable Digital Shopping: A Federated, Behavior-Aware System for Real-Time Basket-Level Emissions Optimization in E-Commerce

Carbon-Aware Sustainable Digital Shopping: A Federated, Behavior-Aware System for Real-Time Basket-Level Emissions Optimization in E-Commerce | 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 Carbon-Aware Sustainable Digital Shopping: A Federated, Behavior-Aware System for Real-Time Basket-Level Emissions Optimization in E-Commerce Kapil Kumar Reddy Poreddy, Ajit Sahu, Sanjoy Mukherjee, Bhavan Basavaraju This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8792629/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 11 You are reading this latest preprint version Abstract Consumer food and retail purchases account for a large share of household greenhouse gas emissions. Despite growing interest in sustainability, most e-commerce platforms do not provide real-time carbon information at checkout, when consumers make their final purchasing decisions. This paper presents Carbon-Aware Checkout (CAC), a system that combines life-cycle assessment, machine learning, uncertainty modeling, optimization, and large language models through the Model Context Protocol (MCP). CAC delivers real-time basket-level carbon scores, behavior-adjusted emissions forecasts, cost-constrained low-carbon product swaps, and AI-generated explanations. CAC differs from earlier work by introducing six novel metrics designed for retail checkout involving (i) Carbon Opportunity Gap (COG); (ii) Behavior-Adjusted Emissions (BAE), which accounts for the likelihood that shoppers will accept suggested swaps; (iii) Risk-Adjusted Carbon Score (RACS), which includes uncertainty from life-cycle data; (iv) Basket Marginal Abatement Cost (MAC basket), which shows the cost per unit of emissions avoided; (v) Recurring Purchase Emissions (RPE), which estimates long-term impact; and (vi) Composite Carbon-Health Score (CHCS), which balances carbon reduction with nutritional quality. We built a dataset by merging the Instacart Online Grocery Shopping dataset (3.1 million orders, 50k produces) with product footprint data from Poore and Nemecek, SU-EATABLE LIFE, Open Food Facts, and Eco-Score across 43 food categories. Testing shows a 30.5% emissions reduction with an average price change of ± 1.9%. The system implements all 21 mathematical formulas from our framework and all six metrics. Simulated studies indicate that AI-generated explanations increase swap acceptance by 19 percentage points (36% vs. 17%, p < 0.01) compared to numerical labels alone. The MCP-based design creates auditable records that comply with U.S. Federal Trade Commission Green Guides and proposed EU Green Claims Directive requirements. CAC offers a practical approach to sustainable e-commerce that could enable meaningful decarbonization when deployed at large retailers. CCS CONCEPTS • Information systems → Recommender systems • Applied computing → Environmental sciences • Computing methodologies → Machine learning • Human-centered computing → Ubiquitous and mobile computing Carbon footprint sustainable e-commerce life-cycle assessment recommender systems large language models Model Context Protocol optimization uncertainty quantification FTC Green Guides environmental labeling behavioral economics choice architecture Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 24 Mar, 2026 Reviews received at journal 20 Mar, 2026 Reviews received at journal 20 Mar, 2026 Reviewers agreed at journal 19 Mar, 2026 Reviewers agreed at journal 19 Mar, 2026 Reviewers agreed at journal 27 Feb, 2026 Reviewers invited by journal 26 Feb, 2026 Editor invited by journal 24 Feb, 2026 Editor assigned by journal 22 Feb, 2026 Submission checks completed at journal 22 Feb, 2026 First submitted to journal 22 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. We do this by developing innovative software and high quality services for the global research community. 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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-8792629","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":600124129,"identity":"335e66a7-ea80-49dc-810a-a61ee2c02f76","order_by":0,"name":"Kapil Kumar Reddy Poreddy","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7klEQVRIiWNgGAWjYLACHjYGAwYGxsYDHwxI1NJwcAaJWhgYDvMQo9qcvffhgzdlh435Zzc3HLYpsJHjZ2A+9vELHi2WPceNDeecO2wmcedgw+EcgzRjyQa25NkyeLQY3Ehjk+ZtO2zDcCMRpOVw4oYDPMbMEvi1sP8GaZEHabEw+F+/nwgtbMxALWYGIC0MBgcSDBh4jBk/4PXLMWbJOefSjQ2BfjnYY5BsOOMwWzIzHh3AEGtj/PCmzNpw3u32hw9+/LGT529vPsz4A5/DIFQzAwPc+UArmPFFEFRLHZIWIMBryygYBaNgFIw4AACzxVALufD1hwAAAABJRU5ErkJggg==","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Kapil","middleName":"Kumar Reddy","lastName":"Poreddy","suffix":""},{"id":600124131,"identity":"44137cbe-2ccc-407e-950d-c6fbfc702cc2","order_by":1,"name":"Ajit Sahu","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Ajit","middleName":"","lastName":"Sahu","suffix":""},{"id":600124145,"identity":"e459385a-7209-41d4-aebc-ad79deaaff0f","order_by":2,"name":"Sanjoy Mukherjee","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Sanjoy","middleName":"","lastName":"Mukherjee","suffix":""},{"id":600124147,"identity":"e5b2ae8e-f12f-4585-8df9-8b2d57f441c3","order_by":3,"name":"Bhavan Basavaraju","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Bhavan","middleName":"","lastName":"Basavaraju","suffix":""}],"badges":[],"createdAt":"2026-02-05 05:53:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8792629/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8792629/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[],"financialInterests":"No competing interests reported.","formattedTitle":"Carbon-Aware Sustainable Digital Shopping: A Federated, Behavior-Aware System for Real-Time Basket-Level Emissions Optimization in E-Commerce","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":"discover-artificial-intelligence","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"diai","sideBox":"Learn more about [Discover Artificial Intelligence](https://www.springer.com/44163)","snPcode":"","submissionUrl":"","title":"Discover Artificial Intelligence","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Carbon footprint, sustainable e-commerce, life-cycle assessment, recommender systems, large language models, Model Context Protocol, optimization, uncertainty quantification, FTC Green Guides, environmental labeling, behavioral economics, choice architecture","lastPublishedDoi":"10.21203/rs.3.rs-8792629/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8792629/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eConsumer food and retail purchases account for a large share of household greenhouse gas emissions. Despite growing interest in sustainability, most e-commerce platforms do not provide real-time carbon information at checkout, when consumers make their final purchasing decisions. This paper presents Carbon-Aware Checkout (CAC), a system that combines life-cycle assessment, machine learning, uncertainty modeling, optimization, and large language models through the Model Context Protocol (MCP). CAC delivers real-time basket-level carbon scores, behavior-adjusted emissions forecasts, cost-constrained low-carbon product swaps, and AI-generated explanations. CAC differs from earlier work by introducing six novel metrics designed for retail checkout involving (i) Carbon Opportunity Gap (COG); (ii) Behavior-Adjusted Emissions (BAE), which accounts for the likelihood that shoppers will accept suggested swaps; (iii) Risk-Adjusted Carbon Score (RACS), which includes uncertainty from life-cycle data; (iv) Basket Marginal Abatement Cost (MAC basket), which shows the cost per unit of emissions avoided; (v) Recurring Purchase Emissions (RPE), which estimates long-term impact; and (vi) Composite Carbon-Health Score (CHCS), which balances carbon reduction with nutritional quality. We built a dataset by merging the Instacart Online Grocery Shopping dataset (3.1\u0026nbsp;million orders, 50k produces) with product footprint data from Poore and Nemecek, SU-EATABLE LIFE, Open Food Facts, and Eco-Score across 43 food categories. Testing shows a 30.5% emissions reduction with an average price change of ± 1.9%. 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