Predicting Carbon Dioxide Emissions in the United States of America Using Machine Learning Algorithms

Predicting Carbon Dioxide Emissions in the United States of America Using Machine Learning Algorithms | 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 Predicting Carbon Dioxide Emissions in the United States of America Using Machine Learning Algorithms Bosah Philip Chukwunonso, Ibrahim Al-wesabi, Li Shixiang, Khalil AlSharabi, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3728503/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 29 Apr, 2024 Read the published version in Environmental Science and Pollution Research → Version 1 posted 5 You are reading this latest preprint version Abstract In the past few decades, several nations have experienced air pollution and environmental difficulties because of carbon dioxide (CO 2 ) emissions. One of the most crucial methods for regulating and maximizing CO 2 emission reductions is precise forecasting. Four machine learning algorithms with high forecasting precision and low data requirements were developed in this study to estimate CO 2 emissions in the United States (US). Data from a dataset covering the years 1973/01 to 2022/07 that included information on different energy sources that had an impact on CO 2 emissions were examined. Then, four algorithms performed the CO 2 emissions forecast from the layer recurrent neural network with 10 nodes (L-RNN), a feed-forward neural network with 10 nodes (FFNN), a convolutional neural network with two layers with 10 and 5 filters (CNN1), and convolutional neural network with two layers and with 50 and 25 filters (CNN2) models. Each algorithm's forecast accuracy was assessed using eight indicators. The three preprocessing techniques used are (1) Without any processing techniques, (2) Processed using max-min normalization technique, and (3) Processed using max-min normalization technique and decomposed by variation mode decomposition (VMD) technique with 7 intrinsic mode functions and 1000 iterations. The latter with L-RNN algorithm gave a high accuracy between the forecasting and actual values. The results of CO 2 emissions from 2011/05 to 2022/07 have been forecasted, and the L-RNN algorithm had the highest forecast accuracy. The research makes significant literary contributions. One of the first studies to focus on predicting CO 2 emissions in the USA using a combination of three preprocessing approaches and four machine-learning algorithms, predicting the number of overall CO 2 emissions while also accounting for a broader range of inputs. CO2 emissions machine-learning algorithms max-min normalization technique and variation mode decomposition (VMD) technique Full Text Cite Share Download PDF Status: Published Journal Publication published 29 Apr, 2024 Read the published version in Environmental Science and Pollution Research → Version 1 posted Reviewers agreed at journal 16 Jan, 2024 Reviewers invited by journal 07 Jan, 2024 Editor invited by journal 04 Jan, 2024 Editor assigned by journal 20 Dec, 2023 First submitted to journal 13 Dec, 2023 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-3728503","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":265782799,"identity":"270dd668-bced-45d0-be58-249620ae6486","order_by":0,"name":"Bosah Philip Chukwunonso","email":"","orcid":"","institution":"China University of Geosciences","correspondingAuthor":false,"prefix":"","firstName":"Bosah","middleName":"Philip","lastName":"Chukwunonso","suffix":""},{"id":265782800,"identity":"432a8bea-f6e9-4b4c-87d6-e72443774948","order_by":1,"name":"Ibrahim Al-wesabi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4UlEQVRIiWNgGAWjYBAC+RkMDMwMBgwMbAwMjA+AAjx8hLSwSSC0MBuAtLARpwXBBusloEW69+HngoJtiX3sh49Vfs2xk2FjYH746AY+LTLHjaVnGNw2ZuNJS7stuy0Z6DA2Y+McvA5LY5DmMbgtx8aQY3ZbchszUAsPmzQBLcy/gVp42PjfmBVLbqsnSgsbxBaJHDPGj9sOE6FF5hibNQ/ILxLPkqUZtx3nYWMm4Bf52W3Mt3n+3E6c35988OPPbdX2/OzNDx/j04ICmHnAJLHKQYDxBymqR8EoGAWjYMQAALVvOjAWPlrIAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0003-1848-427X","institution":"Wuhan China Geology University: China University of Geosciences","correspondingAuthor":true,"prefix":"","firstName":"Ibrahim","middleName":"","lastName":"Al-wesabi","suffix":""},{"id":265782801,"identity":"7634d96b-8e9c-4fef-b082-3166aa0dd956","order_by":2,"name":"Li Shixiang","email":"","orcid":"","institution":"China University of Geosciences","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Shixiang","suffix":""},{"id":265782802,"identity":"8b09e91e-a77b-4854-a823-1ec9421efdcb","order_by":3,"name":"Khalil AlSharabi","email":"","orcid":"","institution":"King Saud University","correspondingAuthor":false,"prefix":"","firstName":"Khalil","middleName":"","lastName":"AlSharabi","suffix":""},{"id":265782803,"identity":"1a94da08-94ac-4a01-acca-fe97319735b5","order_by":4,"name":"Abdullrahman A. 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