Identifying Regional Differences in Correlations Between Transportation Infrastructure and Carbon Emissions with Data Analysis and Machine Learning

Identifying Regional Differences in Correlations Between Transportation Infrastructure and Carbon Emissions with Data Analysis and Machine Learning | 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 Identifying Regional Differences in Correlations Between Transportation Infrastructure and Carbon Emissions with Data Analysis and Machine Learning Nolan Baglio This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5214755/v2 This work is licensed under a CC BY 4.0 License Status: Posted Version 2 posted You are reading this latest preprint version Show more versions Abstract Transportation supports global connections in the world. Urban planning requires much thought about transportation to ensure connectivity and simplicity within a city. However, what should also be considered is the environmental impact of their decisions. The research aims to understand how different types of transportation infrastructure impact carbon emissions. Each country was categorized by region, and the study looked into six different regions. The ideal features for predicting the carbon emissions per capita were determined using random forest regression, a machine-learning model that creates many decision trees to make predictions. The carbon emissions per capita with the percentage of roads paved, the feature deemed most valuable to predicting carbon emissions per capita by the random forest regressor, in each region was compared. With linear regression, a relationship was determined in each region to understand the variance from the line and the slope of the line and to know how the data progresses as the values increase. The results showed that economic and governmental factors were often at the root of environmental factors, and transportation infrastructure reflects economic strength. This was understood from both correlations between carbon emission changes and financial events, as well as from policy reviews of countries that were anomalies in the data, showing that they had differing policies regarding transportation. In this research, transportation infrastructure was proven essential to environmental factors. The study also indicates that the jobs of urban planners and road workers play a significant role in how a country approaches its carbon-neutral goals. Artificial Intelligence and Machine Learning machine learning linear regression random forest regression carbon emissions transportation infrastructure geography Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 2 posted You are reading this latest preprint version Show more versions 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-5214755","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":363904170,"identity":"11dd409e-43d3-4789-869b-c8d9fbbbfb96","order_by":0,"name":"Nolan Baglio","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6ElEQVRIie3PPQrCMBTA8YQHr0uqq1K9gyAoIrRXUQJOLuIkDlYKmbyAFwldS8EuPYDQxY8TiAiKi4ni2mYUzJ8QXiA/2hBis/1kEBIaElIDQg/qyGrVhH4IAoGOJmhO1GrocyXpO+v1dRb7vnBgt7hN/RYSOJ72JWSwSSJvm3MuACdFW3L1Y9jtTktIZz8OPVcAR2C9oilBEYZeBYmerli9ybwpV0ZEqK+kvib0IlMDkidi6ObZCAG5R2XG1FDxlixKCzdeBvV6mlweUg1OdDyXkW/jUG3A9AgG13WB3ujd8LbNZrP9Vy/P50CBLK3jHAAAAABJRU5ErkJggg==","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Nolan","middleName":"","lastName":"Baglio","suffix":""}],"badges":[],"createdAt":"2024-10-07 01:12:43","currentVersionCode":2,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-5214755/v2","doiUrl":"https://doi.org/10.21203/rs.3.rs-5214755/v2","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":76604362,"identity":"b7cfc184-0a23-44fb-9987-6171366d46cc","added_by":"auto","created_at":"2025-02-18 23:01:05","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":830484,"visible":true,"origin":"","legend":"","description":"","filename":"IdentifyingRegionalDifferencesinCorrelationsBetweenTransportationInfrastructureandCarbonEmissionswithDataAnalysisandMachineLearning.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5214755/v2_covered_88b57401-c8e1-4518-a18c-51baca1f86fb.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eIdentifying Regional Differences in Correlations Between Transportation Infrastructure and Carbon Emissions with Data Analysis and Machine Learning\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Polygence Research Program","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"machine learning, linear regression, random forest regression, carbon emissions, transportation, infrastructure, geography","lastPublishedDoi":"10.21203/rs.3.rs-5214755/v2","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5214755/v2","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTransportation supports global connections in the world. 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