The Role of Digitalization on Carbon Emissions: Spatial DDML Test and Neural Networks Prediction

The Role of Digitalization on Carbon Emissions: Spatial DDML Test and Neural Networks Prediction | 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 Article The Role of Digitalization on Carbon Emissions: Spatial DDML Test and Neural Networks Prediction Mao Wu, Fanrui Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5755594/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 Based on the Chinese provincial panel data from 2011 to 2022, this paper innovatively use the spatial double/debiased machine learning (DDML) model, planar and spatial mediating model to study the effect, mediating mechanisms of the digitalization on carbon emissions in both local and surrounding areas. The empirical studies show that digitalization significantly reduces carbon emissions in the local area. Digitalization reduces local carbon emissions by promoting the transformation of energy industrial structure and green technological innovation, reduces carbon emissions in the surrounding regions by promoting the improvement of energy utilization efficiency and green technological progress, improve the industrial intensification in local and surrounding areas thus reducing carbon emissions. Prediction by using the LSTM and neural network shows that for 30 provinces in China except Tibet in 2030, peak carbon dioxide emissions is achievable. For digitally developed regions, or where digitization is lagging behind but developing rapidly, digitization can help these provinces achieve peak carbon dioxide emissions with less emissions. For provinces where digitization is relatively undeveloped, digitization makes little difference in reducing carbon emissions in the process of achieving peak carbon dioxide emissions. For regions where digitization is lagging behind and developing slowly, due to the extensiveness of the industrial model in these provinces, digitization shows a rebound effect, making these regions put more energy demand into the produce, and thus carbon emissions will increase. Earth and environmental sciences/Environmental sciences/Environmental impact Earth and environmental sciences/Environmental social sciences Digitalization Carbon Emissions Spatial DDML Spatial Mediating Mechanism Neural Networks Full Text Additional Declarations No competing interests reported. Supplementary Files S1Appendix.docx MethodXfile.zip 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-5755594","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":400448134,"identity":"78ef7e77-452c-4326-a2d5-7972c7c94ebd","order_by":0,"name":"Mao 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