Analysis of the Dynamic Changes and Driving Factors of Energy and Carbon Flows in China from 2005 to 2021

Analysis of the Dynamic Changes and Driving Factors of Energy and Carbon Flows in China from 2005 to 2021 | 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 Analysis of the Dynamic Changes and Driving Factors of Energy and Carbon Flows in China from 2005 to 2021 Longwei Dai, Shaohua Wang, Shengxiang Ouyang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5300236/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 As global CO 2 emissions continue to rise, understanding regional carbon flows and the factors driving these increases is vital for shaping effective emission reduction policies and advancing low-carbon economies. This study analyzes China's CO 2 emissions from 2005 to 2021 using energy statistics and material flow analysis to construct carbon flow diagrams. Emissions were examined across energy supply, processing and conversion, and consumption sectors. The logarithmic mean divisia index (LMDI) method was used to decompose CO 2 emission growth into contributions from 10 driving factors. Key findings show that coal dominates China's energy supply, resulting in a uniform carbon structure. Electricity and heat production generate significant emissions in the processing sector, while residential consumption drives rising emissions across consumer sectors. The main positive drivers of CO 2 emission growth were per capita GDP (22.62%), vehicle numbers (1.27%), and household income (2.58%), while energy intensity in production (-9.60%) and residential sectors (-1.32%) were major negative drivers. This research provides empirical and theoretical support for China's "dual carbon strategy". Energy flow Carbon flow Carbon emissions LMDI Driving factors 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-5300236","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":376407498,"identity":"aeaf763a-7167-42f0-8d80-9b9088cdaf18","order_by":0,"name":"Longwei Dai","email":"","orcid":"","institution":"Hunan University of Arts and science","correspondingAuthor":false,"prefix":"","firstName":"Longwei","middleName":"","lastName":"Dai","suffix":""},{"id":376407499,"identity":"b16f921f-211f-41f4-ba0d-a9652b0f94dc","order_by":1,"name":"Shaohua Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxklEQVRIie3PMQrCMBiG4YRAphQdU7zEHwqZCh7EJUXoVJ07FCwodeyqt3B0tAR0iXTtGPACdRfU1UEaN4c88/cOH0Ke95cYtn0er2pCGuuaELEzKd5v6RxcEzoJNhofWibHTgG0Vw1BSUikkUSoiGfDSbdMbHikVGqUWnROF+VQIjsWgTCMvZML4FI7JK2RPKk4j9a44m7JKZO8qQCAEOqWTLssEqVRimtKQLl8CWsjbo/8qUZ1e7d9EQ8nn9Rvc8/zPO+bF04nQDTVhXpTAAAAAElFTkSuQmCC","orcid":"","institution":"Hunan University of Arts and science","correspondingAuthor":true,"prefix":"","firstName":"Shaohua","middleName":"","lastName":"Wang","suffix":""},{"id":376407500,"identity":"2a6894ac-6f0c-4a23-9b8e-55998b88d3ac","order_by":2,"name":"Shengxiang Ouyang","email":"","orcid":"","institution":"Changde Forestry Science Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Shengxiang","middleName":"","lastName":"Ouyang","suffix":""}],"badges":[],"createdAt":"2024-10-21 00:38:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5300236/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5300236/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":68865309,"identity":"ca4e48bf-f310-45df-ab25-e0038f32a350","added_by":"auto","created_at":"2024-11-13 01:16:51","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1087849,"visible":true,"origin":"","legend":"","description":"","filename":"241011AnalysisoftheDynamicChangesandDrivingFactorsofEnergyandCarbonFlowsinChinafrom2005to2021.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5300236/v1_covered_3d60a320-95c1-4d63-a305-3f1eb47a29d0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Analysis of the Dynamic Changes and Driving Factors of Energy and Carbon Flows in China from 2005 to 2021","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":"Energy flow, Carbon flow, Carbon emissions, LMDI, Driving factors","lastPublishedDoi":"10.21203/rs.3.rs-5300236/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5300236/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAs global CO\u003csub\u003e2\u003c/sub\u003e emissions continue to rise, understanding regional carbon flows and the factors driving these increases is vital for shaping effective emission reduction policies and advancing low-carbon economies. This study analyzes China's CO\u003csub\u003e2\u003c/sub\u003e emissions from 2005 to 2021 using energy statistics and material flow analysis to construct carbon flow diagrams. Emissions were examined across energy supply, processing and conversion, and consumption sectors. The logarithmic mean divisia index (LMDI) method was used to decompose CO\u003csub\u003e2\u003c/sub\u003e emission growth into contributions from 10 driving factors. Key findings show that coal dominates China's energy supply, resulting in a uniform carbon structure. Electricity and heat production generate significant emissions in the processing sector, while residential consumption drives rising emissions across consumer sectors. The main positive drivers of CO\u003csub\u003e2\u003c/sub\u003e emission growth were per capita GDP (22.62%), vehicle numbers (1.27%), and household income (2.58%), while energy intensity in production (-9.60%) and residential sectors (-1.32%) were major negative drivers. This research provides empirical and theoretical support for China's \"dual carbon strategy\".\u003c/p\u003e","manuscriptTitle":"Analysis of the Dynamic Changes and Driving Factors of Energy and Carbon Flows in China from 2005 to 2021","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-12 06:00:37","doi":"10.21203/rs.3.rs-5300236/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"943e5f0b-b74b-4d2c-8ee1-92d4dc7ce9d6","owner":[],"postedDate":"November 12th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-11-13T01:08:34+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-12 06:00:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5300236","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5300236","identity":"rs-5300236","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}