Innovation-led productivity lifts urban energy utilization efficiency via policy and governance

Innovation-led productivity lifts urban energy utilization efficiency via policy and governance | 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 Innovation-led productivity lifts urban energy utilization efficiency via policy and governance Jing Zhou, Zhongwu Zhang, Xiaodong Chang, Kunwei Zhang, Lifen Qin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8463830/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 13 You are reading this latest preprint version Abstract The global pursuit of sustainable urban development is often undermined by the classic rebound effect where gains in energy utilization efficiency are paradoxically offset by increased consumption. This challenge underscores the limitations of isolated technological fixes and calls for a more systemic framework. Under this background, China's national strategy of "New Quality Productive Forces" (NPRO) presents a novel paradigm, yet its causal impact on urban energy utilization efficiency (TE) remains empirically underexplored. This study addresses this critical gap using a panel of 280 Chinese cities from 2010–2022, employing Double Machine Learning (DML) for robust causal inference and spatial econometric models to quantify spillover effects. The findings are as follows: first, the spatial analysis uncovers a pattern of asynchronous evolution. NPRO generation exhibits strong geographic stickiness in eastern coastal hubs, whereas its TE benefits display a broad diffusion pattern inland, driven by positive spatial spillovers to economically interconnected neighboring regions. Second, causal evidence indicates that NPRO significantly enhances urban energy efficiency; a one-standard-deviation increase in the NPRO index is associated with a 0.644-standard-deviation improvement in TE. Third, positive effect is primarily channeled through two institutional mechanisms: the intensification of low-carbon policies and the enhancement of governance transparency. Finally, this positive impact is heterogeneous, proving most pronounced in cities that prioritize environmental protection, possess advanced industrial structures, are in the eastern region, or have a lower degree of government intervention. Ultimately, this study offers robust causal evidence that a systemic, state-led productivity framework can mitigate traditional efficiency paradoxes, providing crucial implications for designing spatially-aware energy transition policies in emerging economies. Earth and environmental sciences/Environmental social sciences Scientific community and society/Geography Social science/Geography New quality productive forces Energy utilization efficiency Causal inference Double machine learning Spatial spillover effect China Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 25 Mar, 2026 Reviews received at journal 24 Mar, 2026 Reviewers agreed at journal 24 Mar, 2026 Reviews received at journal 16 Feb, 2026 Reviewers agreed at journal 07 Feb, 2026 Reviewers agreed at journal 26 Jan, 2026 Reviewers agreed at journal 21 Jan, 2026 Reviewers agreed at journal 21 Jan, 2026 Reviewers invited by journal 21 Jan, 2026 Editor invited by journal 02 Jan, 2026 Editor assigned by journal 31 Dec, 2025 Submission checks completed at journal 31 Dec, 2025 First submitted to journal 27 Dec, 2025 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. 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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-8463830","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":578472276,"identity":"31365564-427d-49cb-8f46-be449785cdee","order_by":0,"name":"Jing Zhou","email":"","orcid":"","institution":"Shanxi Normal University","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Zhou","suffix":""},{"id":578472277,"identity":"eaa9819c-1f6d-4690-81dc-d3a2eb46da44","order_by":1,"name":"Zhongwu 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forces, Energy utilization efficiency, Causal inference, Double machine learning, Spatial spillover effect, China","lastPublishedDoi":"10.21203/rs.3.rs-8463830/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8463830/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe global pursuit of sustainable urban development is often undermined by the classic rebound effect where gains in energy utilization efficiency are paradoxically offset by increased consumption. This challenge underscores the limitations of isolated technological fixes and calls for a more systemic framework. Under this background, China's national strategy of \"New Quality Productive Forces\" (NPRO) presents a novel paradigm, yet its causal impact on urban energy utilization efficiency (TE) remains empirically underexplored. This study addresses this critical gap using a panel of 280 Chinese cities from 2010\u0026ndash;2022, employing Double Machine Learning (DML) for robust causal inference and spatial econometric models to quantify spillover effects. The findings are as follows: first, the spatial analysis uncovers a pattern of asynchronous evolution. NPRO generation exhibits strong geographic stickiness in eastern coastal hubs, whereas its TE benefits display a broad diffusion pattern inland, driven by positive spatial spillovers to economically interconnected neighboring regions. Second, causal evidence indicates that NPRO significantly enhances urban energy efficiency; a one-standard-deviation increase in the NPRO index is associated with a 0.644-standard-deviation improvement in TE. Third, positive effect is primarily channeled through two institutional mechanisms: the intensification of low-carbon policies and the enhancement of governance transparency. Finally, this positive impact is heterogeneous, proving most pronounced in cities that prioritize environmental protection, possess advanced industrial structures, are in the eastern region, or have a lower degree of government intervention. 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