Multi-country CO2 emission forecasting based on background value optimized Nonlinear Grey Bernoulli and BP neural network combined model

Multi-country CO2 emission forecasting based on background value optimized Nonlinear Grey Bernoulli and BP neural network combined model | 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 Multi-country CO2 emission forecasting based on background value optimized Nonlinear Grey Bernoulli and BP neural network combined model Sixuan Wu, Xiangyan Zeng, Chunming Li, Haoze Cang, Qiancheng Tan, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2705450/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 17 Aug, 2023 Read the published version in Soft Computing → Version 1 posted 5 You are reading this latest preprint version Abstract Under the background of green low-carbon economy, it is of great significance to accurately estimate the future CO 2 emissions of countries with large CO 2 emissions for the development of the world green economy. A new Nonlinear Grey Bernoulli and BP neural network combined model (BP-ONGBM (1,1) model) has been proposed to study the CO 2 emissions of China, the United States, the European Union, India and Japan. Firstly, the Particle Swarm Optimization (PSO) algorithm is optimized by using the idea of Artificial Fish Swarm Algorithm (AFSA), and then the background value of ONGBM (1,1) model is dynamically optimized. Based on the linearization of the model, the time response function is derived. Then, the ONGBM (1,1) model is combined with the BP neural network model. The combination weight and the background value coefficient are determined by improved PSO algorithm. Finally, according to the observation data from 2010 to 2021 in the Emissions Database for Global Atmospheric Research 2022, the model is established to calculate the CO 2 emissions of the selected countries from 2022 to 2026, and compared with the prediction results provided by multiple competitive models. The empirical application shows that the newly proposed BP-ONGBM (1,1) model is significantly better than other competitive models. CO2 emission forecasting Nonlinear Grey Bernoulli model Particle Swarm Optimization BP neural network model Full Text Cite Share Download PDF Status: Published Journal Publication published 17 Aug, 2023 Read the published version in Soft Computing → Version 1 posted Reviewers agreed at journal 08 Apr, 2023 Reviewers invited by journal 08 Apr, 2023 Editor invited by journal 31 Mar, 2023 Editor assigned by journal 28 Mar, 2023 First submitted to journal 18 Mar, 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. 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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-2705450","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":190257697,"identity":"b53b5876-8e05-4eaf-b2dc-4b036cf2ca7d","order_by":0,"name":"Sixuan Wu","email":"","orcid":"","institution":"Guilin University of Electronic Technology","correspondingAuthor":false,"prefix":"","firstName":"Sixuan","middleName":"","lastName":"Wu","suffix":""},{"id":190257698,"identity":"8726f34a-aeff-4ece-8e29-bc5860208e4d","order_by":1,"name":"Xiangyan 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Finally, according to the observation data from 2010 to 2021 in the Emissions Database for Global Atmospheric Research 2022, the model is established to calculate the CO\u003csub\u003e2\u003c/sub\u003e emissions of the selected countries from 2022 to 2026, and compared with the prediction results provided by multiple competitive models. The empirical application shows that the newly proposed BP-ONGBM (1,1) model is significantly better than other competitive models.\u003c/p\u003e","manuscriptTitle":"Multi-country CO2 emission forecasting based on background value optimized Nonlinear Grey Bernoulli and BP neural network combined model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-04-11 13:32:06","doi":"10.21203/rs.3.rs-2705450/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2023-04-09T02:15:06+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2023-04-09T01:40:31+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Soft Computing","date":"2023-03-31T21:30:03+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-03-28T11:48:35+00:00","index":"","fulltext":""},{"type":"submitted","content":"Soft Computing","date":"2023-03-18T11:50:36+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"soft-computing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"soco","sideBox":"Learn more about [Soft Computing](https://www.springer.com/journal/500)","snPcode":"500","submissionUrl":"https://submission.nature.com/new-submission/500/3","title":"Soft Computing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"426fd188-24a3-4b14-80a5-073d46ed275a","owner":[],"postedDate":"April 11th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2023-10-16T22:12:02+00:00","versionOfRecord":{"articleIdentity":"rs-2705450","link":"https://doi.org/10.1007/s00500-023-09063-2","journal":{"identity":"soft-computing","isVorOnly":false,"title":"Soft Computing"},"publishedOn":"2023-08-17 21:59:32","publishedOnDateReadable":"August 17th, 2023"},"versionCreatedAt":"2023-04-11 13:32:06","video":"","vorDoi":"10.1007/s00500-023-09063-2","vorDoiUrl":"https://doi.org/10.1007/s00500-023-09063-2","workflowStages":[]},"version":"v1","identity":"rs-2705450","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2705450","identity":"rs-2705450","version":["v1"]},"buildId":"J0_U0BvcaRcwD8yVFaRlm","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}