Multiscale Water Use Driving Effects and Dynamic Water Demand Research Based on the Kaya-LMDI-STIRPAT Model

Multiscale Water Use Driving Effects and Dynamic Water Demand Research Based on the Kaya-LMDI-STIRPAT Model | 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 Multiscale Water Use Driving Effects and Dynamic Water Demand Research Based on the Kaya-LMDI-STIRPAT Model Ting Wang, Wenrui Wang, Bin Liu, Jinxia Sha, Jinjun You, Dasheng Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4526854/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 A better understanding of the driving effects of socio-economic water use is essential to obtain accurate water demand prediction and to implement water resources management. In this study, six driving effects, including scale effect, structure effect, economic level effect, policy rationality effect, water price policy effect, and scientific and technological level effect, are considered. A Kaya-LMDI model is constructed to identify the driving effects of domestic, industrial and agricultural water use, and then a STIRPAT model is established for water demand production. Annual precipitation is introduced into the model for predicting agricultural water demand. The model is applied to Hebei province, China. The main conclusions are summarized as follows: ① The economic level effect plays a role in promoting the socio-economic water use in all prefecture-level cities of Hebei province; The water price policy effect plays a role in inhibiting the domestic and agricultural water use, while the scientific and technological level effect plays a role in inhibiting the industrial water use. ② The water use is mainly inhibited by the effects of policy rationality and water price policy before 2015 but mainly by the effects of water price policy and scientific and technological level after 2015; ③ There is a clear spatial difference in the driving effects of the socio-economic water use among the prefecture-level cities, and the economic level effect plays a major role in promoting the socio-economic water use in all prefecture-level cities; the water price policy effect plays an inhibitory role in nine cities; while the policy rationality effect plays an inhibitory role in two cities; ④ The water demand prediction results suggest that the water demand of Hebei province in 2030 is 22.01 billion m 3 in normal years (P = 50%) and 24.33 billion m 3 in medium-dry years(P = 75%), which are consistent with the red line set by the government. This study may contribute to optimizing the economic structure and provides guidance for water use management. Earth and environmental sciences/Hydrology Earth and environmental sciences/Environmental social sciences/Sustainability Earth and environmental sciences/Environmental sciences/Environmental impact Physical sciences/Mathematics and computing/Computational science 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. 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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-4526854","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":315272984,"identity":"a3a9b775-7dfd-4760-a7af-b90b98984d34","order_by":0,"name":"Ting 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In this study, six driving effects, including scale effect, structure effect, economic level effect, policy rationality effect, water price policy effect, and scientific and technological level effect, are considered. A Kaya-LMDI model is constructed to identify the driving effects of domestic, industrial and agricultural water use, and then a STIRPAT model is established for water demand production. Annual precipitation is introduced into the model for predicting agricultural water demand. The model is applied to Hebei province, China. The main conclusions are summarized as follows: ① The economic level effect plays a role in promoting the socio-economic water use in all prefecture-level cities of Hebei province; The water price policy effect plays a role in inhibiting the domestic and agricultural water use, while the scientific and technological level effect plays a role in inhibiting the industrial water use. ② The water use is mainly inhibited by the effects of policy rationality and water price policy before 2015 but mainly by the effects of water price policy and scientific and technological level after 2015; ③ There is a clear spatial difference in the driving effects of the socio-economic water use among the prefecture-level cities, and the economic level effect plays a major role in promoting the socio-economic water use in all prefecture-level cities; the water price policy effect plays an inhibitory role in nine cities; while the policy rationality effect plays an inhibitory role in two cities; ④ The water demand prediction results suggest that the water demand of Hebei province in 2030 is 22.01\u0026nbsp;billion m\u003csup\u003e3\u003c/sup\u003e in normal years (P\u0026thinsp;=\u0026thinsp;50%) and 24.33\u0026nbsp;billion m\u003csup\u003e3\u003c/sup\u003e in medium-dry years(P\u0026thinsp;=\u0026thinsp;75%), which are consistent with the red line set by the government. This study may contribute to optimizing the economic structure and provides guidance for water use management.\u003c/p\u003e","manuscriptTitle":"Multiscale Water Use Driving Effects and Dynamic Water Demand Research Based on the Kaya-LMDI-STIRPAT Model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-20 04:25:53","doi":"10.21203/rs.3.rs-4526854/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"b2cfd1c5-7604-48ae-9a24-4870d1c83e85","owner":[],"postedDate":"June 20th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":33336231,"name":"Earth and environmental sciences/Hydrology"},{"id":33336232,"name":"Earth and environmental sciences/Environmental social sciences/Sustainability"},{"id":33336233,"name":"Earth and environmental sciences/Environmental sciences/Environmental impact"},{"id":33336234,"name":"Physical sciences/Mathematics and computing/Computational science"}],"tags":[],"updatedAt":"2025-05-26T17:23:25+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-20 04:25:53","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4526854","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4526854","identity":"rs-4526854","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}