Early-Stage Environmental Impact Forecasting of Chemicals with Machine Learning and Data Analytics Tools

Early-Stage Environmental Impact Forecasting of Chemicals with Machine Learning and Data Analytics Tools | 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 Early-Stage Environmental Impact Forecasting of Chemicals with Machine Learning and Data Analytics Tools Harriet Dufie Appiah, Matthew Conway, Jahnvi Patel, Marcella McMahon, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8175831/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 04 Apr, 2026 Read the published version in Clean Technologies and Environmental Policy → Version 1 posted 7 You are reading this latest preprint version Abstract Life Cycle Assessment (LCA) is a method used to evaluate the environmental impacts of materials and processes throughout their entire life cycle, from production to end-of-life. However, performing LCA at the early design stage of a chemical process is often challenging because Life Cycle Inventory (LCI) data for new or emerging chemicals are not readily available. To enable impact assessment under these data-limited conditions, this study employs Machine Learning (ML) and Scaling Index Regression models to estimate environmental impacts across all life cycle stages. Artificial Neural Network (ANN) and eXtreme Gradient Boosting (XGBoost) are employed to develop models to predict Life Cycle Impact Assessment (LCIA) endpoint metrics such as Human Health Impact (HHI), Ecosystem Quality Impact (EQI), Global Warming Potential (GWP), and Resource Utilization Impact (RUI) during the production phase of a chemical based on thermodynamic and molecular descriptor properties of the chemicals. Regression models are then applied to estimate the impact of technologies used during the use phase and end-of life phase by determining emission factors for the different technologies involved in the process. To demonstrate the accuracy of the proposed framework a case study is presented to validate the model’s performance. Life Cycle Assessment Machine Learning Environmental Impact Emissions Full Text Additional Declarations No competing interests reported. Supplementary Files SupportingMaterialMLRegLCIA.pdf Cite Share Download PDF Status: Published Journal Publication published 04 Apr, 2026 Read the published version in Clean Technologies and Environmental Policy → Version 1 posted Editorial decision: Revision requested 08 Feb, 2026 Reviews received at journal 14 Jan, 2026 Reviewers agreed at journal 02 Dec, 2025 Reviewers invited by journal 30 Nov, 2025 Editor assigned by journal 27 Nov, 2025 Submission checks completed at journal 22 Nov, 2025 First submitted to journal 21 Nov, 2025 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-8175831","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":554301307,"identity":"c79d2f87-52ef-4c00-b20a-130b2840d756","order_by":0,"name":"Harriet Dufie Appiah","email":"","orcid":"","institution":"Rowan University","correspondingAuthor":false,"prefix":"","firstName":"Harriet","middleName":"Dufie","lastName":"Appiah","suffix":""},{"id":554301308,"identity":"e8fae49d-43cc-4719-a693-2b50ee3dc604","order_by":1,"name":"Matthew Conway","email":"","orcid":"","institution":"Rowan University","correspondingAuthor":false,"prefix":"","firstName":"Matthew","middleName":"","lastName":"Conway","suffix":""},{"id":554301309,"identity":"728f64a1-9a48-4180-a778-dd8f53fc6b19","order_by":2,"name":"Jahnvi Patel","email":"","orcid":"","institution":"Rowan University","correspondingAuthor":false,"prefix":"","firstName":"Jahnvi","middleName":"","lastName":"Patel","suffix":""},{"id":554301310,"identity":"366ef27a-14ab-4177-bf4a-79df23dc5226","order_by":3,"name":"Marcella McMahon","email":"","orcid":"","institution":"Rowan University","correspondingAuthor":false,"prefix":"","firstName":"Marcella","middleName":"","lastName":"McMahon","suffix":""},{"id":554301311,"identity":"3a1a57fb-d101-47d3-818e-e8532fdfe985","order_by":4,"name":"Robert P. 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Tools","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"clean-technologies-and-environmental-policy","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ctep","sideBox":"Learn more about [Clean Technologies and Environmental Policy](https://www.springer.com/journal/10098)","snPcode":"10098","submissionUrl":"https://submission.nature.com/new-submission/10098/3","title":"Clean Technologies and Environmental Policy","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Life Cycle Assessment, Machine Learning, Environmental Impact, Emissions","lastPublishedDoi":"10.21203/rs.3.rs-8175831/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8175831/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLife Cycle Assessment (LCA) is a method used to evaluate the environmental impacts of materials and processes throughout their entire life cycle, from production to end-of-life. However, performing LCA at the early design stage of a chemical process is often challenging because Life Cycle Inventory (LCI) data for new or emerging chemicals are not readily available. To enable impact assessment under these data-limited conditions, this study employs Machine Learning (ML) and Scaling Index Regression models to estimate environmental impacts across all life cycle stages. Artificial Neural Network (ANN) and eXtreme Gradient Boosting (XGBoost) are employed to develop models to predict Life Cycle Impact Assessment (LCIA) endpoint metrics such as Human Health Impact (HHI), Ecosystem Quality Impact (EQI), Global Warming Potential (GWP), and Resource Utilization Impact (RUI) during the production phase of a chemical based on thermodynamic and molecular descriptor properties of the chemicals. Regression models are then applied to estimate the impact of technologies used during the use phase and end-of life phase by determining emission factors for the different technologies involved in the process. To demonstrate the accuracy of the proposed framework a case study is presented to validate the model\u0026rsquo;s performance.\u003c/p\u003e","manuscriptTitle":"Early-Stage Environmental Impact Forecasting of Chemicals with Machine Learning and Data Analytics Tools","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-03 11:20:29","doi":"10.21203/rs.3.rs-8175831/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-09T00:49:27+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-14T20:59:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"249329495629314306731631724131879830687","date":"2025-12-02T21:54:05+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-30T21:46:09+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-27T16:51:34+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-22T10:16:09+00:00","index":"","fulltext":""},{"type":"submitted","content":"Clean Technologies and Environmental Policy","date":"2025-11-21T17:49:22+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"clean-technologies-and-environmental-policy","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ctep","sideBox":"Learn more about [Clean Technologies and Environmental Policy](https://www.springer.com/journal/10098)","snPcode":"10098","submissionUrl":"https://submission.nature.com/new-submission/10098/3","title":"Clean Technologies and Environmental Policy","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"eab09076-ebba-41ca-b1a5-6febf25d42cf","owner":[],"postedDate":"December 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-04-07T16:02:23+00:00","versionOfRecord":{"articleIdentity":"rs-8175831","link":"https://doi.org/10.1007/s10098-026-03479-8","journal":{"identity":"clean-technologies-and-environmental-policy","isVorOnly":false,"title":"Clean Technologies and Environmental Policy"},"publishedOn":"2026-04-04 15:58:24","publishedOnDateReadable":"April 4th, 2026"},"versionCreatedAt":"2025-12-03 11:20:29","video":"","vorDoi":"10.1007/s10098-026-03479-8","vorDoiUrl":"https://doi.org/10.1007/s10098-026-03479-8","workflowStages":[]},"version":"v1","identity":"rs-8175831","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8175831","identity":"rs-8175831","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}