An examination of daily CO2 emissions prediction through a comparative analysis of Machine learning, Deep learning, and Statistical models

An examination of daily CO2 emissions prediction through a comparative analysis of Machine learning, Deep learning, and Statistical models | 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 An examination of daily CO2 emissions prediction through a comparative analysis of Machine learning, Deep learning, and Statistical models Adewole Adetoro Ajala, Oluwatosin Lawrence Adeoye, Olawale Moshood Salami, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4648686/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 13 Jan, 2025 Read the published version in Environmental Science and Pollution Research → Version 1 posted 5 You are reading this latest preprint version Abstract Human-induced global warming, primarily attributed to the rise in atmospheric CO 2 , poses a substantial risk to the survival of humanity. While most research focuses on predicting annual CO 2 emissions, which are crucial for setting long-term emission mitigation targets, the precise prediction of daily CO 2 emissions is equally vital for setting short-term targets. This study examines the performance of 14 models in predicting daily CO 2 emissions data from 1/1/2022 to 30/9/2023 across the top four polluting regions (China, USA, India, and EU27&UK). The 14 models used in the study comprise four statistical models (ARMA, ARIMA, SARMA, and SARIMA), three machine learning models (Support Vector Machine - SVM, Random Forest - RF, and Gradient Boosting - GB), and seven deep learning models (Artificial Neural Network - ANN, Recurrent Neural Network variations such as Gated Recurrent Unit - GRU, Long Short-Term Memory - LSTM, Bidirectional-LSTM - BILSTM, and three hybrid combinations of CNN-RNN). Performance evaluation employs four metrics (R 2 , MAE, RMSE, MAPE). The results show that machine learning (ML) and deep learning (DL) models, with higher R 2 (0.714–0.932) and l ower RMSE (0.480 − 0.247) values, respectively, outperformed the statistical model, which had R 2 (-0.060–0.719) and RMSE (1.695 − 0.537) values, in predicting daily CO 2 emissions across all four regions. The performance of ML and DL models was further enhanced by differencing, a technique that improves accuracy by ensuring stationarity and creating additional features and patterns the model can learn from. Additionally, applying ensemble techniques such as bagging and voting improved the performance of ML models by about 9.6%, while hybrid combinations of CNN-RNN enhanced the performance of RNN models. In summary, the performance of both ML and DL models was relatively similar. However, due to the high computational requirements associated with DL models, the recommended models for daily CO 2 emission prediction are ML models using the ensemble technique of voting and bagging. This model can assist in accurately forecasting daily emissions, aiding authorities in setting targets for CO 2 emissions reduction. Daily CO2 Emissions Prediction and Forecast Machine learning model Deep learning model statistical model Full Text Cite Share Download PDF Status: Published Journal Publication published 13 Jan, 2025 Read the published version in Environmental Science and Pollution Research → Version 1 posted Reviewers agreed at journal 16 Nov, 2024 Reviewers invited by journal 16 Nov, 2024 Editor invited by journal 13 Nov, 2024 First submitted to journal 12 Nov, 2024 Editorial decision: Minor Revision 30 Aug, 2024 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-4648686","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":378925507,"identity":"0c19cc6f-e846-4f89-8144-482b28560166","order_by":0,"name":"Adewole Adetoro Ajala","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAklEQVRIie3RsUsDMRTH8V8I5JborRFK/RfecSCIqP9KSkEXCweCFBx6IOQW3fXPcBHHlDd0OZwd6+4QEUTBQa9uQtqOgvmOL3zICwFSqT8aoQJyKb1/qdHb/BnaFYSArcYNpjc1tFqHdAjUtiVvrEPyWs6rdzoAHi3x/j1/E/kUhDuKEuMVFZc0hLi2lkdtR1RphDuJ7+RBpElOLoz1PHIdwQ6EG0fFts9C8UkTKDOoeXdBsrelhLymUhNDawaLBdHdLfHFCtZV2aMZTOYwvXLHWkl9auxD/Pn9WXNXPI/Pccj5a/hwe/08a25DOBvGny+h6Pdk5UfK+fLzVCqV+vd9AaGPS194hZ38AAAAAElFTkSuQmCC","orcid":"https://orcid.org/0009-0001-6228-5430","institution":"University of Hull Faculty of Science and Engineering","correspondingAuthor":true,"prefix":"","firstName":"Adewole","middleName":"Adetoro","lastName":"Ajala","suffix":""},{"id":378925508,"identity":"c109c7c9-342b-47e4-aa43-0975fc1da629","order_by":1,"name":"Oluwatosin Lawrence Adeoye","email":"","orcid":"","institution":"University of Hull Faculty of Science and Engineering","correspondingAuthor":false,"prefix":"","firstName":"Oluwatosin","middleName":"Lawrence","lastName":"Adeoye","suffix":""},{"id":378925509,"identity":"47240c9a-869f-464d-bfe5-374fcb95e998","order_by":2,"name":"Olawale Moshood Salami","email":"","orcid":"","institution":"University of Hull Faculty of Science and Engineering","correspondingAuthor":false,"prefix":"","firstName":"Olawale","middleName":"Moshood","lastName":"Salami","suffix":""},{"id":378925510,"identity":"8b072e97-9f29-4f7d-b5bc-9a134ba787df","order_by":3,"name":"Yusuf Ayoola Jimoh","email":"","orcid":"","institution":"KWASU: Kwara State University","correspondingAuthor":false,"prefix":"","firstName":"Yusuf","middleName":"Ayoola","lastName":"Jimoh","suffix":""}],"badges":[],"createdAt":"2024-06-27 12:58:47","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4648686/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4648686/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11356-024-35764-8","type":"published","date":"2025-01-13T15:57:45+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":74285720,"identity":"830e606b-2911-44a8-81bf-e17c1e7b1f64","added_by":"auto","created_at":"2025-01-20 16:14:42","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1869106,"visible":true,"origin":"","legend":"","description":"","filename":"NewRevisedManuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4648686/v1_covered_fd66845b-683b-4271-ac5a-799fa00f20de.pdf"}],"financialInterests":"","formattedTitle":"An examination of daily CO2 emissions prediction through a comparative analysis of Machine learning, Deep learning, and Statistical models","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":"environmental-science-and-pollution-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"espr","sideBox":"Learn more about [Environmental Science and Pollution Research](https://www.springer.com/journal/11356)","snPcode":"11356","submissionUrl":"https://submission.nature.com/new-submission/11356/3","title":"Environmental Science and Pollution Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Daily CO2 Emissions, Prediction and Forecast, Machine learning model, Deep learning model, statistical model","lastPublishedDoi":"10.21203/rs.3.rs-4648686/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4648686/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHuman-induced global warming, primarily attributed to the rise in atmospheric CO\u003csub\u003e2\u003c/sub\u003e, poses a substantial risk to the survival of humanity. While most research focuses on predicting annual CO\u003csub\u003e2\u003c/sub\u003e emissions, which are crucial for setting long-term emission mitigation targets, the precise prediction of daily CO\u003csub\u003e2\u003c/sub\u003e emissions is equally vital for setting short-term targets. This study examines the performance of 14 models in predicting daily CO\u003csub\u003e2\u003c/sub\u003e emissions data from 1/1/2022 to 30/9/2023 across the top four polluting regions (China, USA, India, and EU27\u0026amp;UK). The 14 models used in the study comprise four statistical models (ARMA, ARIMA, SARMA, and SARIMA), three machine learning models (Support Vector Machine - SVM, Random Forest - RF, and Gradient Boosting - GB), and seven deep learning models (Artificial Neural Network - ANN, Recurrent Neural Network variations such as Gated Recurrent Unit - GRU, Long Short-Term Memory - LSTM, Bidirectional-LSTM - BILSTM, and three hybrid combinations of CNN-RNN). Performance evaluation employs four metrics (R\u003csup\u003e2\u003c/sup\u003e, MAE, RMSE, MAPE). The results show that machine learning (ML) and deep learning (DL) models, with higher R\u003csup\u003e2\u003c/sup\u003e (0.714\u0026ndash;0.932) and l ower RMSE (0.480\u0026thinsp;\u0026minus;\u0026thinsp;0.247) values, respectively, outperformed the statistical model, which had R\u003csup\u003e2\u003c/sup\u003e (-0.060\u0026ndash;0.719) and RMSE (1.695\u0026thinsp;\u0026minus;\u0026thinsp;0.537) values, in predicting daily CO\u003csub\u003e2\u003c/sub\u003e emissions across all four regions. The performance of ML and DL models was further enhanced by differencing, a technique that improves accuracy by ensuring stationarity and creating additional features and patterns the model can learn from. 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