Empirical Research on Green Innovation Efficiency of Industrial Enterprises in the Yellow River Basin of China: A Two-stage Super-efficiency SBM Model Based on DEA Analysis

Empirical Research on Green Innovation Efficiency of Industrial Enterprises in the Yellow River Basin of China: A Two-stage Super-efficiency SBM Model Based on DEA Analysis | 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 Empirical Research on Green Innovation Efficiency of Industrial Enterprises in the Yellow River Basin of China: A Two-stage Super-efficiency SBM Model Based on DEA Analysis Zhen Guo, Hu Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8061125/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 Green technology innovation is a crucial engine for driving the high-quality economic development of the Yellow River Basin. Based on industrial carbon emissions and "three-waste" pollutants, this research constructs a two stage super efficiency SBM model. Combining with the Structural Equation Model, Malmquist index, and two-stage efficiency measurement method, a comprehensive analytical framework is established to systematically evaluate the green technology R&D and achievement transformation efficiency of industrial enterprises in various provinces of the Yellow River Basin in China. The research results show that there is significant heterogeneity in the green technology innovation efficiency among different provinces and regions in the Yellow River Basin. The innovation efficiency in the eastern coastal areas is significantly higher than that in the central and western regions. The effect of R & D activities on innovation efficiency is not always significantly positive, indicating that the improvement of innovation efficiency depends on a complex innovation ecosystem. The findings of this research will provide scientific support for China to implement differentiated support policies, promote coordinated regional development and overall innovation efficiency, and enhance the national strategy of ecological protection and high-quality development in the Yellow River Basin. Green technological innovation Efficiency evaluation SBM model Super-efficiency slack measurement-Data Envelopment Analysis (SBM-DEA) model 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-8061125","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":544404952,"identity":"b8259a08-3b2b-4715-9a1f-21d44208c38a","order_by":0,"name":"Zhen 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