Pathways to Carbon Peak: Scenario-Based Forecasting for the Middle Yangtze River Urban Agglomeration Using Gaussian Process Regression

Pathways to Carbon Peak: Scenario-Based Forecasting for the Middle Yangtze River Urban Agglomeration Using Gaussian Process Regression | 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 Pathways to Carbon Peak: Scenario-Based Forecasting for the Middle Yangtze River Urban Agglomeration Using Gaussian Process Regression Xinbao Chen, Junqi Lei, Xinyi Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8107479/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 29 Jan, 2026 Read the published version in Applied Spatial Analysis and Policy → Version 1 posted You are reading this latest preprint version Abstract As a pivotal region within the Yangtze River Economic Belt, the Middle Yangtze River Urban Agglomeration is characterized not only by dense economic activity and population concentration but also as a significant source of carbon emissions. This study investigates the carbon emission characteristics of the region and forecasts future emission trajectories to support the achievement of China’s 2030 carbon peak target. A Gaussian Process Regression (GPR) model was employed, utilizing data from 31 prefecture-level cities spanning from 2000 to 2021, with rigorous accuracy validation. Comparative analyses against Artificial Neural Networks (ANN), Least Squares Support Vector Machines (LSSVM), and ARIMA models demonstrate the superior predictive performance of the proposed GPR approach. The results indicate that the timing of carbon peaking varies across different economic growth and energy consumption scenarios. Under low-growth conditions with either slow or rapid energy reduction, the region is projected to peak be-tween 2025 and 2028. Conversely, under high-growth scenarios with slow or rapid energy reduction, achieving the 2030 carbon peak target appears unlikely. To effectively advance emission reduction, policy strategies should be tailored to local contexts, focusing on industrial restructuring, energy system optimization, and technological innovation, while avoiding undue compromise of economic development. Through such measures, the Middle Yangtze River Urban Agglomeration can pursue carbon peaking alongside sustainable economic growth. Middle Yangtze River Urban Agglomeration Carbon emission dynamics Gaussian Process Regression (GPR) Scenario-based forecasting Carbon peak targe Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 29 Jan, 2026 Read the published version in Applied Spatial Analysis and Policy → 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-8107479","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":550542058,"identity":"79837e93-a5c6-4de2-afa1-b0661136e0c8","order_by":0,"name":"Xinbao Chen","email":"","orcid":"","institution":"Hunan University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Xinbao","middleName":"","lastName":"Chen","suffix":""},{"id":550542059,"identity":"3b1f5bcd-54b4-4a45-a243-52c2c4406601","order_by":1,"name":"Junqi 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