Operationalizing Credible AI-Assisted Carbon Footprinting: A Framework and Empirical Case Study

Operationalizing Credible AI-Assisted Carbon Footprinting: A Framework and Empirical Case Study | 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 Method Article Operationalizing Credible AI-Assisted Carbon Footprinting: A Framework and Empirical Case Study Shaena Ulissi, Andrew Dumit, P. James Joyce, Jacob Feintzeig, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8856470/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract The rapid scaling of corporate product-level emissions accounting has created a data scalability crisis where traditional manual verification methods cannot keep pace. While Large Language Models (LLMs) offer promising automation capabilities, their non-deterministic nature creates credibility challenges for auditors and practitioners accustomed to deterministic traceability. This paper addresses this gap through two contributions. First, we propose a hierarchical framework of credibility criteria for AI-assisted carbon footprinting (AI-CF), distinguishing between system-level defensibility (benchmarking, consistency, repeatability) and material-level transparency (match quality indicators, reasoning traces). These criteria are grounded in the ISO definition of data quality as fitness for use and were developed through iterative stakeholder elicitation with verification firms and corporate practitioners. Second, we operationalize this framework through empirical evaluation of two deployed AI systems: an Auto-Mapper achieving 91% defensible mapping rates on non-vague inputs (dropping to 60% for ambiguous inputs) against expert ground truth, and an Advanced Modeling System achieving median 33% error relative to 269 Environmental Product Declarations across 9 product categories. We demonstrate that AI output entropy correlates with input ambiguity, suggesting that non-determinism can serve as a diagnostic signal for data quality rather than solely a liability. The framework enables a shift toward system-level validation, where auditors verify the AI process rather than randomly sampling across all individual outputs. Artificial Intelligence Life Cycle Assessment Product Carbon Footprint Large Language Models Scope 3 Emissions Validation Frameworks Full Text Additional Declarations Competing interest reported. Employment: All authors are employees of Watershed Technology Inc., which develops and commercializes AI-assisted carbon footprinting systems. The systems evaluated in this paper are proprietary products of Watershed. Funding: No external funding was received for this research. The work was conducted as part of the authors’ employment at Watershed Technology Inc. Supplementary Files onlineresource1.pdf onlineresource2benchmarkepds.pdf Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 07 Apr, 2026 Reviews received at journal 04 Apr, 2026 Reviewers agreed at journal 10 Mar, 2026 Reviews received at journal 04 Mar, 2026 Reviewers agreed at journal 25 Feb, 2026 Reviewers agreed at journal 23 Feb, 2026 Reviewers invited by journal 23 Feb, 2026 Editor assigned by journal 16 Feb, 2026 Submission checks completed at journal 13 Feb, 2026 First submitted to journal 11 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. 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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-8856470","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Method Article","associatedPublications":[],"authors":[{"id":591023237,"identity":"0e7727a5-ed96-4951-ac91-e690633afd2a","order_by":0,"name":"Shaena Ulissi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEElEQVRIiWNgGAWjYDACCTBpA+EwNgARO5AkQksaAwMbTAszSEsCQS2HkbWABPBokZ/d/PBxQc35xP75zcc+/txhI9vfzNy64ecPGwb+9m6sGg3uHDM2nnHsduKMY2zJs3nPpBnPOMzYdrMnIY1B4szZDVi1SCSYSfOw3c5tOMZjzMzYdjhxA5C8wZNwGCiVi1WL/Iz07795/p3LnX+M/zPjz7b/YC03/yT8x6mF4UaOGTNv24HcDcd4mBmADLCW2zwJB3BqMbiRUyzN25dcv/FYmjFQbzLYL7dl0pJ5cPkF6LCNn3m+2RnLHT78GOgwO9n+9vZnN9/Y2Mnxt/didxhOwEOa8lEwCkbBKBgFyAAAeb9oAYR7P1MAAAAASUVORK5CYII=","orcid":"","institution":"Watershed Technology Inc.","correspondingAuthor":true,"prefix":"","firstName":"Shaena","middleName":"","lastName":"Ulissi","suffix":""},{"id":591023238,"identity":"03112a98-0fa6-41f7-873f-029c046790b6","order_by":1,"name":"Andrew Dumit","email":"","orcid":"","institution":"Watershed Technology Inc.","correspondingAuthor":false,"prefix":"","firstName":"Andrew","middleName":"","lastName":"Dumit","suffix":""},{"id":591023239,"identity":"ac7abea7-138b-41ac-9484-babf18191432","order_by":2,"name":"P. 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