Forecast data of provincial carbon emissions in China from 2025 to 2035: based on ARIMA-BP model

Forecast data of provincial carbon emissions in China from 2025 to 2035: based on ARIMA-BP 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 Short Report Forecast data of provincial carbon emissions in China from 2025 to 2035: based on ARIMA-BP model Sanglin Zhao, Yuli Su, Mohammad Zubair This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8470361/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 Background: China is an important contributor to global carbon emissions. Accurately estimating carbon emissions is crucial for reducing carbon emissions in accordance with the United Nations Sustainable Development Goals and China's carbon neutrality strategy. Method: This study selected energy consumption data from Chinese provinces from 2000 to 2021, calculated carbon emissions using the carbon emission factor method, and then predicted carbon emissions data for 2022-2035 using the ARIMA-BP model. Result: Using the ARIMA-BP model, this study predicted the provincial carbon emissions in China from 2025 to 2035 based on data from 2000 to 2024. The results showed that during this period, the carbon emissions of the three major industries all increased, with the secondary industry accounting for the highest proportion and the largest increase (690%); The emissions of each province are showing an upward trend; The energy consumption intensity shows a trend of "secondary industry>tertiary industry>primary industry" and has all decreased, with the secondary industry experiencing the most significant decline. The model prediction is reliable, providing data support and policy basis for China's carbon reduction. Discussion: This dataset can be used to describe the spatiotemporal evolution trend of carbon emissions in China. This study can provide policy basis and wisdom for China's carbon reduction efforts. Carbon emissions Dual carbon targets Spatiotemporal evolution trend Carbon reduction Full Text Additional Declarations No competing interests reported. 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-8470361","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Short Report","associatedPublications":[],"authors":[{"id":587287779,"identity":"2b074bf9-2ea8-4e02-b1c7-a8ccd7121910","order_by":0,"name":"Sanglin Zhao","email":"","orcid":"","institution":"Hunan University of Finance and Economics","correspondingAuthor":false,"prefix":"","firstName":"Sanglin","middleName":"","lastName":"Zhao","suffix":""},{"id":587287781,"identity":"f7c0c576-3e58-422f-a571-6e538b9345a9","order_by":1,"name":"Yuli Su","email":"","orcid":"","institution":"Hunan University of Finance and Economics","correspondingAuthor":false,"prefix":"","firstName":"Yuli","middleName":"","lastName":"Su","suffix":""},{"id":587287782,"identity":"85ab8e23-3f98-492b-a2a1-4d4d77e97477","order_by":2,"name":"Mohammad Zubair","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4UlEQVRIiWNgGAWjYFACHgaGBAYJOzb2BgYGxgbitVgk8/McgGphI0YLA0MF48wZCURqkZ+Re/DDwz0SzAY33xg++LmDIY9fnoDrGGfkJUskPJPgM7idY2zYe4ahWLKNgC3MEjkGEgkHgLbczjGT4G1jSNxwjIAWNokc4x9ALYwbbp4x//kXqGU/IS08EkDDQVpmzuAxYwbbQsj7Ejzv0iyAWoCBnFYsLdsmkTjjWAJ+LfLtuYdv/jhQB4zKwxs/vm2zSexvPkDAGgTgMADZSrRyEGB/QJLyUTAKRsEoGDkAACO7QZnoUg5NAAAAAElFTkSuQmCC","orcid":"","institution":"Kabul University","correspondingAuthor":true,"prefix":"","firstName":"Mohammad","middleName":"","lastName":"Zubair","suffix":""}],"badges":[],"createdAt":"2025-12-29 07:38:37","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8470361/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8470361/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104783042,"identity":"58a06e96-6eeb-47a8-9bfb-cbc88a2e1fa9","added_by":"auto","created_at":"2026-03-17 07:58:08","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":604296,"visible":true,"origin":"","legend":"","description":"","filename":"2026112re.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8470361/v1_covered_1a078304-681b-40ef-96f4-4bab7cfb1264.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Forecast data of provincial carbon emissions in China from 2025 to 2035: based on ARIMA-BP model","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"Carbon emissions, Dual carbon targets, Spatiotemporal evolution trend, Carbon reduction","lastPublishedDoi":"10.21203/rs.3.rs-8470361/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8470361/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground: China is an important contributor to global carbon emissions. Accurately estimating carbon emissions is crucial for reducing carbon emissions in accordance with the United Nations Sustainable Development Goals and China's carbon neutrality strategy.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMethod: This study selected energy consumption data from Chinese provinces from 2000 to 2021, calculated carbon emissions using the carbon emission factor method, and then predicted carbon emissions data for 2022-2035 using the ARIMA-BP model.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eResult: Using the ARIMA-BP model, this study predicted the provincial carbon emissions in China from 2025 to 2035 based on data from 2000 to 2024. 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