Quantitative measurement, spatial–temporal characteristics, and multi-dimensional dynamic evolution analysis of energy poverty in China

Quantitative measurement, spatial–temporal characteristics, and multi-dimensional dynamic evolution analysis of energy poverty in China | 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 Quantitative measurement, spatial–temporal characteristics, and multi-dimensional dynamic evolution analysis of energy poverty in China zhigang zhu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8200862/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 To better understand the evolution of energy poverty in China, this study uses a combined dynamic evaluation to method accurately measure and analyze the level and temporal characteristics of energy poverty in each dimension, examining the spatial pattern, displacement, and deformation of energy poverty from a spatial–temporal perspective. Subsequently, a spatial kernel density model is introduced to explore its dynamic evolution in greater depth. The research findings demonstrate the following: 1. China experienced a linear decline in overall energy poverty from 2000 to 2020, but the regional gap widened. 2. The northwest and northeast regions consistently remained in the high-value region, while the southeast coastal and central provinces achieved substantial poverty reduction, leading to the high-value region shrinking to cover only Heilongjiang. 3. The distribution center of energy poverty in China gradually shifted towards inland areas, with significant displacement and deformation in the early stage, before stabilizing in the later stage. 4. Without the influence of spatial factors, energy poverty reduction is highly sustainable. Under spatial conditions, the spatial spillover effect between provinces is insufficient. After incorporating the time condition, the correlation between local provinces and provinces with lower energy poverty weakens, leading to the intensification of the polarization effect. energy poverty quantitative evaluation spatial–temporal characteristics multi-dimensional dynamic evolution 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. 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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-8200862","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":587327863,"identity":"a6f201d7-55cc-4132-b6a1-7ce871e7afa5","order_by":0,"name":"zhigang zhu","email":"data:image/png;base64,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","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"zhigang","middleName":"","lastName":"zhu","suffix":""}],"badges":[],"createdAt":"2025-11-25 08:38:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8200862/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8200862/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102297871,"identity":"be77be26-756a-414f-b8db-3b305b6c5886","added_by":"auto","created_at":"2026-02-10 10:29:29","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":896968,"visible":true,"origin":"","legend":"","description":"","filename":"paper.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8200862/v1_covered_45700c30-25cf-4faa-be05-06f0a1925046.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Quantitative measurement, spatial–temporal characteristics, and multi-dimensional dynamic evolution analysis of energy poverty in China","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 poverty, quantitative evaluation, spatial–temporal characteristics, multi-dimensional dynamic evolution","lastPublishedDoi":"10.21203/rs.3.rs-8200862/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8200862/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTo better understand the evolution of energy poverty in China, this study uses a combined dynamic evaluation to method accurately measure and analyze the level and temporal characteristics of energy poverty in each dimension, examining the spatial pattern, displacement, and deformation of energy poverty from a spatial\u0026ndash;temporal perspective. 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