Environmental protection tax and green total factor productivity in China’s livestock enterprises: causal inference based on double machine learning

Environmental protection tax and green total factor productivity in China’s livestock enterprises: causal inference based on double machine learning | 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 Environmental protection tax and green total factor productivity in China’s livestock enterprises: causal inference based on double machine learning Jing Li, Zhen Han, Xiangdong Hu, Mingli Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8152419/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 Livestock enterprises are the core actors in the livestock industry, and the Environmental Protection Tax (EPT) plays a critical role in driving their green transformation. Addressing the "environment–efficiency " trade-off and unlocking the policy dividends of EPT remain urgent research priorities. Using data from Chinese publicly listed livestock firms spanning 2007–2024, this study treats the EPT policy as a quasi-natural experiment and employs a double machine learning framework to investigate the impact and underlying mechanisms of EPT on livestock enterprises' green total factor productivity (GTFP). The results reveal that EPT significantly enhances GTFP. Mechanism analysis demonstrates that EPT promotes GTFP through two channels: green innovation and green mergers and acquisitions, while corporate digital transformation exerts a significant positive moderating effect on this relationship. Heterogeneity analysis further indicates that EPT's positive effect on GTFP is particularly pronounced for livestock and poultry farming firms, feed production enterprises, privately owned companies, and firms located in China’s western regions. Finally, economic consequence analysis confirms that EPT implementation reduces livestock enterprises’ market value and intensifies industry competition. Collectively, these findings not only clarify the pathways through which EPT improves GTFP in China’s livestock sector but also offer critical policy insights for optimizing environmental tax design and advancing sustainable livestock development. Earth and environmental sciences/Environmental sciences Earth and environmental sciences/Environmental social sciences Environmental protection tax Livestock enterprises Green total factor productivity Double machine learning 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. 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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-8152419","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":581498014,"identity":"bb00a242-929b-4ab8-9777-69b49ad561c8","order_by":0,"name":"Jing Li","email":"","orcid":"","institution":"Chinese Academy of Agricultural Sciences Institute of Agricultural Economics and Development","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Li","suffix":""},{"id":581498015,"identity":"a4087e43-bf66-49a4-835a-c72ff7200345","order_by":1,"name":"Zhen Han","email":"","orcid":"","institution":"Shandong University of Finance and Economics","correspondingAuthor":false,"prefix":"","firstName":"Zhen","middleName":"","lastName":"Han","suffix":""},{"id":581498016,"identity":"c856b0ca-32a7-4efc-9268-44f7654047f4","order_by":2,"name":"Xiangdong Hu","email":"","orcid":"","institution":"Chinese Academy of Agricultural Sciences Institute of Agricultural Economics and Development","correspondingAuthor":false,"prefix":"","firstName":"Xiangdong","middleName":"","lastName":"Hu","suffix":""},{"id":581498017,"identity":"8ef1b48b-c6e6-4e7c-8ac2-4e9eabce76c3","order_by":3,"name":"Mingli Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+klEQVRIiWNgGAWjYNACAwYGPmbmgw8+/LDh4edvIFILGzNbsuHMnjQZyRkHiLSIjYHHTJiD7bCNQUMCAfOPnz386kbBHbs2dh4zZgae8zwGDAcYP3zMwaPlTF6adY7Bs+Q2ZrayxwUWt3nMmRuYJWduw63F7ECOmXGOweFkNmbm7cYzeG7zWDYcYGPmxafl/BuYFgYzaR62czwGBxIIaLmRY/wYqMWOjZkFpOUAYS32N96YMQO1JEADOZlHcsbBZrx+kezPMf6c8+ewPT//YVBU2gEZzQc/fMSjBQjYJIBEYgNCgLEBh0o4YP4AciAhVaNgFIyCUTCCAQCw6VCIma2ukQAAAABJRU5ErkJggg==","orcid":"","institution":"Chinese Academy of Agricultural Sciences Institute of Agricultural Economics and Development","correspondingAuthor":true,"prefix":"","firstName":"Mingli","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2025-11-19 07:55:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8152419/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8152419/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109124067,"identity":"02f8f1da-4421-458a-a348-1a36a8cd7841","added_by":"auto","created_at":"2026-05-12 18:11:05","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":570847,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8152419/v1_covered_3e1f2e01-4870-46f6-bd31-628d0606efe4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Environmental protection tax and green total factor productivity in China’s livestock enterprises: causal inference based on double machine learning","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":"Environmental protection tax, Livestock enterprises, Green total factor productivity, Double machine learning","lastPublishedDoi":"10.21203/rs.3.rs-8152419/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8152419/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLivestock enterprises are the core actors in the livestock industry, and the Environmental Protection Tax (EPT) plays a critical role in driving their green transformation. Addressing the \"environment\u0026ndash;efficiency \" trade-off and unlocking the policy dividends of EPT remain urgent research priorities. Using data from Chinese publicly listed livestock firms spanning 2007\u0026ndash;2024, this study treats the EPT policy as a quasi-natural experiment and employs a double machine learning framework to investigate the impact and underlying mechanisms of EPT on livestock enterprises' green total factor productivity (GTFP). The results reveal that EPT significantly enhances GTFP. Mechanism analysis demonstrates that EPT promotes GTFP through two channels: green innovation and green mergers and acquisitions, while corporate digital transformation exerts a significant positive moderating effect on this relationship. Heterogeneity analysis further indicates that EPT's positive effect on GTFP is particularly pronounced for livestock and poultry farming firms, feed production enterprises, privately owned companies, and firms located in China\u0026rsquo;s western regions. Finally, economic consequence analysis confirms that EPT implementation reduces livestock enterprises\u0026rsquo; market value and intensifies industry competition. 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