Exploring N₂O Emissions at World Level: Advanced Econometric and Machine Learning Approaches in the ESG Context

Exploring N₂O Emissions at World Level: Advanced Econometric and Machine Learning Approaches in the ESG Context | 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 Exploring N₂O Emissions at World Level: Advanced Econometric and Machine Learning Approaches in the ESG Context Carlo Drago, Massimo Arnone, Angelo Leogrande This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6248127/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The paper examines nitrous oxide (N₂O) emissions from an Environmental, Social, and Governance (ESG) standpoint with a combination of econometric and machine learning specifications to uncover global trends and policy implications. Results show the overwhelming effect of ESG factors on emissions, with intricate interdependencies between economic growth, resource productivity, and environmental policy. Econometric specifications identify forest degradation, energy intensity, and income inequality as the most significant determinants of N₂O emissions, which are in need of policy attention. Machine learning enhances predictive power insofar as emission drivers and country-specific trends are identifiable. Through the integration of panel data techniques and state-of-the-art clustering algorithms, the paper generates a highly differentiated picture of emission trends, separating country groups by ESG performance. The findings of the study are that while developed nations have better energy efficiency and environmental governance, they remain significant contributors to N₂O emissions due to intensive industry and agriculture. Meanwhile, developing economies with energy intensity have structural impediments to emissions mitigation. The paper also identifies the contribution of regulatory quality in emission abatement in that the quality of governance is found to be linked with better environmental performance. ESG-based finance instruments, such as green bonds and impact investing, also promote sustainable economic transition. The findings have the further implications of additional arguments for mainstreaming sustainability in economic planning, developing ESG frameworks to underpin climate targets. JEL Codes: Q53, Q54, C23, C45, G32 Microeconomics Macroeconomics Environmental Economics Nitrous Oxide Emissions ESG Models Econometric Analysis Machine Learning Sustainability Policy Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted 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-6248127","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":430173894,"identity":"70499053-7473-4932-86f8-a75363c8ec10","order_by":0,"name":"Carlo Drago","email":"","orcid":"","institution":"Unicusano University","correspondingAuthor":false,"prefix":"","firstName":"Carlo","middleName":"","lastName":"Drago","suffix":""},{"id":430173895,"identity":"a924acd4-ac48-4a28-9e71-a05ee95fbc78","order_by":1,"name":"Massimo Arnone","email":"","orcid":"","institution":"University of Catania","correspondingAuthor":false,"prefix":"","firstName":"Massimo","middleName":"","lastName":"Arnone","suffix":""},{"id":430173896,"identity":"7e8d174a-ee68-436f-b2b9-64af8b1b9057","order_by":2,"name":"Angelo Leogrande","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9ElEQVRIiWNgGAWjYBACPijN2AAiH4AI9mYQWwKnFjYULQkJQILnIFgLTj1YtEgkgtm4tbA3H35dUXNHtp+B+ZlE4g+bfH7Jh21SNxgs6nBq4TmWZnnm2DPjmQ1sZhIJCWmWM2cntknn4HOYRI6ZYQPb4cQNB3jYgFoOGxjcJqRF/g1Qy7/DifthWuxvHiRkC4/xw8Y2oC0MMFskGAlo4UlLY2zsO2w84zCbsUVCWpqBxJnEZuscAwnJBhxa+NkPH/7Y8O2wbH9788MbH2xsDPjbDx+8nVNRx4/LFrDbwBQziqABHg1AtR/wSo+CUTAKRsEoAABg1U/Q4kxZZwAAAABJRU5ErkJggg==","orcid":"","institution":"Indipendent Researcher","correspondingAuthor":true,"prefix":"","firstName":"Angelo","middleName":"","lastName":"Leogrande","suffix":""}],"badges":[],"createdAt":"2025-03-18 00:29:37","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6248127/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6248127/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":78839621,"identity":"85712339-2cb7-4c39-9194-da869ff0e811","added_by":"auto","created_at":"2025-03-19 15:30:20","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":996760,"visible":true,"origin":"","legend":"","description":"","filename":"ExploringNOEmissionsatWorldLevel.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6248127/v1_covered_b629f7ff-e284-4fd0-bb8d-3c7022163424.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eExploring N₂O Emissions at World Level: Advanced Econometric and Machine Learning Approaches in the ESG Context\u003c/strong\u003e\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"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":"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":"Nitrous Oxide Emissions, ESG Models, Econometric Analysis, Machine Learning, Sustainability Policy","lastPublishedDoi":"10.21203/rs.3.rs-6248127/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6248127/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe paper examines nitrous oxide (N₂O) emissions from an Environmental, Social, and Governance (ESG) standpoint with a combination of econometric and machine learning specifications to uncover global trends and policy implications. 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