China’s agricultural non-point source pollution control: Policy logic, spatiotemporal characteristics and trend prediction

China’s agricultural non-point source pollution control: Policy logic, spatiotemporal characteristics and trend 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 China’s agricultural non-point source pollution control: Policy logic, spatiotemporal characteristics and trend prediction lilin zou, yifan liang, yuanyuan yang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5491047/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 Agricultural non-point source pollution (ANPSP) control is essential for advancing sustainable, low-carbon agricultural development and accelerating the establishment of a robust agricultural economy. However, existing research has paid limited attention to the policy rationale underlying ANPSP mitigation efforts, with insufficient exploration of the spatiotemporal characteristics of various agricultural pollutants across provinces nationwide and their projected future trends. This paper comprehensively analyzed aforesaid aspects through theoretical deductions, quantitative assessments, and predictive trend modeling. The research findings revealed that the policy formulation logic behind China’s ANPSP control followed a framework of “legislating first, adjusting next, and implementing subsequently.” Across different stages, the implementation logic manifested distinct operational modes and determining factors, while deviations arose from inducements within political incentives, promotion-based incentives, and fiscal incentives. From 1978 to 2022, pollution loads of chemical oxygen demand, total nitrogen, and total phosphorus in China agricultural production sector increased overall, though their emission intensity gradually decreased; livestock production and fertilizer application persisted as the primary sources of ANPSP. The ANPSP status across regions in China demonstrated considerable stability, with significant spatial spillover effects wherein the pollution status of adjacent units influenced the state of adjacent units, though the direction and degree of these effects vary. Projections indicate distinct evolutionary trends for various agricultural pollutants. Consequently, direct policy recommendations include establishing a comprehensive ANPSP control policy framework, advancing regionally differentiated pollution control strategies, enhancing dynamic monitoring of ANPSP, and implementing coordinated control measures across regions. Social science/Environmental studies Social science/Social policy Agricultural non-point source pollution Policy implementation Pollution loads Fuzzy-conflict model Spatial Markov Chains China 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-5491047","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":392737769,"identity":"257054cd-312d-4361-ad3c-c592eb4d5ff5","order_by":0,"name":"lilin zou","email":"","orcid":"","institution":"Huaqiao University","correspondingAuthor":false,"prefix":"","firstName":"lilin","middleName":"","lastName":"zou","suffix":""},{"id":392737770,"identity":"3724db11-3a2f-4e35-9eab-b72b4ce372bc","order_by":1,"name":"yifan liang","email":"","orcid":"","institution":"University of Bristol","correspondingAuthor":false,"prefix":"","firstName":"yifan","middleName":"","lastName":"liang","suffix":""},{"id":392737771,"identity":"95719396-ba4f-4113-86c3-9024b7cdd66b","order_by":2,"name":"yuanyuan yang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9UlEQVRIiWNgGAWjYNCDDxDKgHgdjDNI1sLMQ4wWg+NnD7/mbbPLM7iR/Oyx7Z7D8gzszdskGGru4NZyJi/NmrctudjgRpq5cc6zw4YNPMfKJBiOPcOt5UCOmTFvG3PihhsJZtI5Bw4nMEjkmEkwNhzGreX8G5CWeqCW9G/SFiAt8m8IaLmRY/yYt+0wUEuOmTQD2BYe/Fokb7wxY5xz7njizDNvyiR7DqQbtvGkFVskHMOthe98jvGHN2XViX3H07dJ/DhgLc/PfnjjjQ81uLUoHGBgk+KBMCCADUQk4NTAwCDfwMD88QeEMQpGwSgYBaMAOwAAW5ZY+NZr0SwAAAAASUVORK5CYII=","orcid":"","institution":"Renmin University of China","correspondingAuthor":true,"prefix":"","firstName":"yuanyuan","middleName":"","lastName":"yang","suffix":""}],"badges":[],"createdAt":"2024-11-20 12:53:45","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5491047/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5491047/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":75357727,"identity":"201bb8d2-81b9-43f3-bfc7-f91620110baa","added_by":"auto","created_at":"2025-02-03 17:16:50","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1359445,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5491047/v1_covered_456109ef-8a33-475b-a938-f355fe102325.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"China’s agricultural non-point source pollution control: Policy logic, spatiotemporal characteristics and trend prediction","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":"Agricultural non-point source pollution, Policy implementation, Pollution loads, Fuzzy-conflict model, Spatial Markov Chains, China","lastPublishedDoi":"10.21203/rs.3.rs-5491047/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5491047/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAgricultural non-point source pollution (ANPSP) control is essential for advancing sustainable, low-carbon agricultural development and accelerating the establishment of a robust agricultural economy. However, existing research has paid limited attention to the policy rationale underlying ANPSP mitigation efforts, with insufficient exploration of the spatiotemporal characteristics of various agricultural pollutants across provinces nationwide and their projected future trends. This paper comprehensively analyzed aforesaid aspects through theoretical deductions, quantitative assessments, and predictive trend modeling. The research findings revealed that the policy formulation logic behind China\u0026rsquo;s ANPSP control followed a framework of \u0026ldquo;legislating first, adjusting next, and implementing subsequently.\u0026rdquo; Across different stages, the implementation logic manifested distinct operational modes and determining factors, while deviations arose from inducements within political incentives, promotion-based incentives, and fiscal incentives. From 1978 to 2022, pollution loads of chemical oxygen demand, total nitrogen, and total phosphorus in China agricultural production sector increased overall, though their emission intensity gradually decreased; livestock production and fertilizer application persisted as the primary sources of ANPSP. The ANPSP status across regions in China demonstrated considerable stability, with significant spatial spillover effects wherein the pollution status of adjacent units influenced the state of adjacent units, though the direction and degree of these effects vary. Projections indicate distinct evolutionary trends for various agricultural pollutants. 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