Quantification of Industrial Wastewater Discharge From the Major Cities in Sichuan Province (China) from 2003 to 2018

Quantification of Industrial Wastewater Discharge From the Major Cities in Sichuan Province (China) from 2003 to 2018 | 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 Quantification of Industrial Wastewater Discharge From the Major Cities in Sichuan Province (China) from 2003 to 2018 Hui Guo, Yawen Zhang, Zhen’an Yang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1025016/v2 This work is licensed under a CC BY 4.0 License Status: Under Review Version 2 posted 5 You are reading this latest preprint version Show more versions Abstract Wastewater discharge is produced as a side effect of socio-economic activities and exerts severe pressure on the environment, its characteristics depend on the rate of urbanization and industrialization. We used spatial autocorrelation, environmental Kuznets curve (EKC), and logarithmic mean Divisia index (LMDI) model to study the spatial characteristics and driving factors of industrial wastewater discharge in Sichuan province (2003–2018). We showed that the amount of industrial wastewater discharge in Sichuan province for the period was reduced from 116580 to 42064.96 million tons as observed from the Moran index ranging from -0.31 to 0.30. We identified five types of the EKC (monotonically decreasing, N, inverted N, U, and inverted U shape) in 18 major cities of Sichuan province. The technical effect (from -0.28 to -16.37) can reduce the discharge of industrial wastewater, while structure effect (0.05–3.83), economy effect (0.19–7.79) and population effect (from -0.08 to 0.46) can promote the industrial wastewater discharge. Our findings suggest that industrial wastewater discharge was reduced and showed a scattered distribution characteristic in Sichuan Province from 2003 to 2018. It is necessary to strengthen technical management measures to reduce industrial wastewater discharge in Sichuan province. Spatial autocorrelation Environmental Kuznets curve Logarithmic Mean Divisia Index driving factors technical effect structure effect economic development effect population effect Figures Figure 1 Figure 2 Figure 3 Full Text Tables Table 1 Global Moran’s I of industrial wastewater discharge at the Sichuan province in 2003–2018 Year Moran’s I z value E（ I ） SD P 2003 -0.115 -0.2219 -0.0588 0.1954 0.481 2004 -0.113 -0.2176 -0.0588 0.1847 0.500 2005 -0.113 -0.2222 -0.0588 0.1858 0.487 2006 -0.154 -0.3393 -0.0588 0.2314 0.435 2007 -0.224 -0.6021 -0.0588 0.2510 0.334 2008 -0.310 -0.8841 -0.0588 0.2776 0.204 2009 -0.134 -0.3169 -0.0588 0.2210 0.462 2010 -0.253 -0.6028 -0.0588 0.3127 0.323 2011 0.200 0.9308 -0.0588 0.2879 0.182 2012 0.161 0.7524 -0.0588 0.3002 0.236 2013 0.302 1.3110 -0.0588 0.2839 0.111 2014 0.108 0.5870 -0.0588 0.2970 0.261 2015 -0.128 -0.2900 -0.0588 0.2331 0.471 2016 -0.159 -0.4450 -0.0588 0.2260 0.408 2017 -0.137 -0.2429 -0.0588 0.2853 0.450 2018 -0.112 -0.1420 -0.0588 0.2904 0.480 E (I) is the value of mathematical expectation, SD is the standard deviation, P(I) is the significance level, Z represents the correlation between industrial wastewater and its location, and I is the Moran index. Table 2 Classification of EKC curve of Sichuan province and major cities Curve shape Monotonically decreasing U shape N shape Inverted U shape Inverted N shape Area Sichuan、CD、NJ、LS、 ZG LZ、GY、SN、MS PZH、YB、DY、NC GA DZ、YA、BZ、ZY、MY Supplementary Files SupportingInformation.docx Cite Share Download PDF Status: Under Review Version 2 posted Editorial decision: Minor Revision 05 Jan, 2022 Reviews received at journal 14 Dec, 2021 Reviewers invited by journal 14 Dec, 2021 Editor assigned by journal 24 Nov, 2021 First submitted to journal 17 Nov, 2021 You are reading this latest preprint version Show more versions 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-1025016","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[{"code":1,"date":"2021-11-02 14:25:25","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}}],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":73377594,"identity":"abdd0c95-7f1b-47ea-9902-df2da27b079b","order_by":0,"name":"Hui Guo","email":"","orcid":"","institution":"Shihezi University","correspondingAuthor":false,"prefix":"","firstName":"Hui","middleName":"","lastName":"Guo","suffix":""},{"id":73377595,"identity":"3db4ca2f-c2d5-4678-bfa5-415d6088d7fe","order_by":1,"name":"Yawen Zhang","email":"","orcid":"","institution":"China West Normal University","correspondingAuthor":false,"prefix":"","firstName":"Yawen","middleName":"","lastName":"Zhang","suffix":""},{"id":73377596,"identity":"e4b3ff5a-9838-43c3-a7ac-0c2593e572bc","order_by":2,"name":"Zhen’an Yang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxUlEQVRIiWNgGAWjYHACxsd/Kv7JsbE3HyBaC7MBz5kDxnw8xxKI1sImwdtyIHGeRI4CceoNzh9+ICHZcCe9jSGHgeFHxTYitBw4ZmBguONZbhvD2QOMPWduE6HlYINBQuIZ5tw2xr4EZsY2YrQcZv9w4GAbczobM48BkVqO8Rg2NrYdTmBjI1aL5BmeYmaGM2mGbTxsCQeJ8gvf+ePbfzNU2MjLz3988MGPCiK0KBxA4hzAoQgVyDcQpWwUjIJRMApGNAAAYqFADwyDxIYAAAAASUVORK5CYII=","orcid":"","institution":"Shihezi University","correspondingAuthor":true,"prefix":"","firstName":"Zhen’an","middleName":"","lastName":"Yang","suffix":""}],"badges":[],"createdAt":"2021-10-27 18:21:00","currentVersionCode":2,"declarations":"","doi":"10.21203/rs.3.rs-1025016/v2","doiUrl":"https://doi.org/10.21203/rs.3.rs-1025016/v2","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":16947267,"identity":"d5214dcd-09aa-4452-811a-975b55748f6a","added_by":"auto","created_at":"2022-01-03 20:58:36","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":77041,"visible":true,"origin":"","legend":"\u003cp\u003eStudy area of Sichuan Province, China.\u003c/p\u003e\u003cp\u003eCD (chengdu), DY(deyang), MY (mianyang), LS (leshan), MS (meishan), ZY (ziyang), SN (suining), YA (ya’an), ZG (zigong), LZ (luzhou), NJ (neijing), YB (yibin), GY (guangyuan), NC (nanchong), GA (guang’an), DZ (dazhou), BZ (bazhong), PZH (panzihua), LS (liangshanzhou), BA (a’bazhou) and GZ (ganzizhou). ECD (chengdu plain economic zone, including CD, DY, MY, LS, MS, ZY, SN and YA), ESS (southern sichuan economic zone, including ZG, LZ, NJ and YB), ENES (northeast sichuan economic zone, including GY, NC, GA, DZ and BZ), EXP (panxi economic zone, including PZH and LS) and ENWS (northwest sichuan ecological economic zone, including BA and GZ).\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1025016/v2/95d19b7b7e444a76a1657beb.jpg"},{"id":16947268,"identity":"6b6037df-daba-4cf6-b512-61bbe85a4abb","added_by":"auto","created_at":"2022-01-03 20:58:36","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":71073,"visible":true,"origin":"","legend":"\u003cp\u003eThe industrial wastewater discharge in 2003–2018 of Sichuan Province, China.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1025016/v2/823029bdaf7653d1ab86f54a.jpg"},{"id":16947484,"identity":"794f6bd8-c34f-4145-9efd-9590ad00c881","added_by":"auto","created_at":"2022-01-03 21:01:36","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":53066,"visible":true,"origin":"","legend":"\u003cp\u003eDecomposition analysis results of industrial wastewater discharge in 2003–2018 of Sichuan Province, China.\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1025016/v2/fce8b3e46c7ebb72e88a6f2e.jpg"},{"id":16947485,"identity":"9d2e3211-c01f-4c5f-8d3b-f2e0ef915779","added_by":"auto","created_at":"2022-01-03 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href='/article/rs-1025016/latest.pdf' target='_blank'\u003edownload as a PDF\u003c/a\u003e."},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1 Global Moran\u0026rsquo;s I of industrial wastewater discharge at the Sichuan province in 2003\u0026ndash;2018\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003eYear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.892156862745097%\"\u003e\n \u003cp\u003eMoran\u0026rsquo;s \u003cem\u003eI\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.441176470588236%\"\u003e\n \u003cp\u003ez value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003eE（\u003cem\u003eI\u003c/em\u003e）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e2003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.892156862745097%\"\u003e\n \u003cp\u003e-0.115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.441176470588236%\"\u003e\n \u003cp\u003e-0.2219\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e-0.0588\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e0.1954\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e0.481\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e2004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.892156862745097%\"\u003e\n \u003cp\u003e-0.113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.441176470588236%\"\u003e\n \u003cp\u003e-0.2176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e-0.0588\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e0.1847\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e0.500\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e2005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.892156862745097%\"\u003e\n \u003cp\u003e-0.113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.441176470588236%\"\u003e\n \u003cp\u003e-0.2222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e-0.0588\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e0.1858\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e0.487\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e2006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.892156862745097%\"\u003e\n \u003cp\u003e-0.154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.441176470588236%\"\u003e\n \u003cp\u003e-0.3393\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e-0.0588\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e0.2314\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e0.435\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e2007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.892156862745097%\"\u003e\n \u003cp\u003e-0.224\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.441176470588236%\"\u003e\n \u003cp\u003e-0.6021\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e-0.0588\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e0.2510\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e0.334\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e2008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.892156862745097%\"\u003e\n \u003cp\u003e-0.310\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.441176470588236%\"\u003e\n \u003cp\u003e-0.8841\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e-0.0588\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e0.2776\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e0.204\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e2009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.892156862745097%\"\u003e\n \u003cp\u003e-0.134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.441176470588236%\"\u003e\n \u003cp\u003e-0.3169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e-0.0588\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e0.2210\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e0.462\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e2010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.892156862745097%\"\u003e\n \u003cp\u003e-0.253\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.441176470588236%\"\u003e\n \u003cp\u003e-0.6028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e-0.0588\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e0.3127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e0.323\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e2011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.892156862745097%\"\u003e\n \u003cp\u003e0.200\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.441176470588236%\"\u003e\n \u003cp\u003e0.9308\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e-0.0588\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e0.2879\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e0.182\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e2012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.892156862745097%\"\u003e\n \u003cp\u003e0.161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.441176470588236%\"\u003e\n \u003cp\u003e0.7524\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e-0.0588\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e0.3002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e0.236\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e2013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.892156862745097%\"\u003e\n \u003cp\u003e0.302\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.441176470588236%\"\u003e\n \u003cp\u003e1.3110\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e-0.0588\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e0.2839\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e0.111\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.892156862745097%\"\u003e\n \u003cp\u003e0.108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.441176470588236%\"\u003e\n \u003cp\u003e0.5870\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e-0.0588\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e0.2970\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e0.261\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.892156862745097%\"\u003e\n \u003cp\u003e-0.128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.441176470588236%\"\u003e\n \u003cp\u003e-0.2900\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e-0.0588\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e0.2331\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e0.471\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.892156862745097%\"\u003e\n \u003cp\u003e-0.159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.441176470588236%\"\u003e\n \u003cp\u003e-0.4450\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e-0.0588\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e0.2260\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e0.408\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.892156862745097%\"\u003e\n \u003cp\u003e-0.137\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.441176470588236%\"\u003e\n \u003cp\u003e-0.2429\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e-0.0588\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e0.2853\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e0.450\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.892156862745097%\"\u003e\n \u003cp\u003e-0.112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.441176470588236%\"\u003e\n \u003cp\u003e-0.1420\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e-0.0588\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e0.2904\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e0.480\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eE (I) is the value of mathematical expectation, SD is the standard deviation, P(I) is the significance level, Z represents the correlation between industrial wastewater and its location, and I is the Moran index.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2 Classification of EKC curve of Sichuan province and major cities\u003c/strong\u003e\u003c/p\u003e\n\u003ctable\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.990512333965844%\"\u003e\n \u003cp\u003eCurve\u003c/p\u003e\n \u003cp\u003eshape\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.785578747628083%\"\u003e\n \u003cp\u003eMonotonically\u003c/p\u003e\n \u003cp\u003edecreasing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.129032258064516%\"\u003e\n \u003cp\u003eU shape\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.836812144212523%\"\u003e\n \u003cp\u003eN shape\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.749525616698293%\"\u003e\n \u003cp\u003eInverted U shape\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.50853889943074%\"\u003e\n \u003cp\u003eInverted N shape\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.990512333965844%\"\u003e\n \u003cp\u003eArea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.785578747628083%\"\u003e\n \u003cp\u003eSichuan、CD、NJ、LS、\u003c/p\u003e\n \u003cp\u003eZG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.129032258064516%\"\u003e\n \u003cp\u003eLZ、GY、SN、MS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.836812144212523%\"\u003e\n \u003cp\u003ePZH、YB、DY、NC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.749525616698293%\"\u003e\n \u003cp\u003eGA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.50853889943074%\"\u003e\n \u003cp\u003eDZ、YA、BZ、ZY、MY\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"environmental-science-and-pollution-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"espr","sideBox":"Learn more about [Environmental Science and Pollution Research](https://www.springer.com/journal/11356)","snPcode":"11356","submissionUrl":"https://submission.nature.com/new-submission/11356/3","title":"Environmental Science and Pollution Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Spatial autocorrelation, Environmental Kuznets curve, Logarithmic Mean Divisia Index, driving factors, technical effect, structure effect, economic development effect, population effect","lastPublishedDoi":"10.21203/rs.3.rs-1025016/v2","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1025016/v2","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWastewater discharge is produced as a side effect of socio-economic activities and exerts severe pressure on the environment, its characteristics depend on the rate of urbanization and industrialization. We used spatial autocorrelation, environmental Kuznets curve (EKC), and logarithmic mean Divisia index (LMDI) model to study the spatial characteristics and driving factors of industrial wastewater discharge in Sichuan province (2003–2018). We showed that the amount of industrial wastewater discharge in Sichuan province for the period was reduced from 116580 to 42064.96 million tons as observed from the Moran index ranging from -0.31 to 0.30. We identified five types of the EKC (monotonically decreasing, N, inverted N, U, and inverted U shape) in 18 major cities of Sichuan province. The technical effect (from -0.28 to -16.37) can reduce the discharge of industrial wastewater, while structure effect (0.05–3.83), economy effect (0.19–7.79) and population effect (from -0.08 to 0.46) can promote the industrial wastewater discharge. Our findings suggest that industrial wastewater discharge was reduced and showed a scattered distribution characteristic in Sichuan Province from 2003 to 2018. It is necessary to strengthen technical management measures to reduce industrial wastewater discharge in Sichuan province.\u003c/p\u003e","manuscriptTitle":"Quantification of Industrial Wastewater Discharge From the Major Cities in Sichuan Province (China) from 2003 to 2018","msid":"","msnumber":"","nonDraftVersions":[{"code":2,"date":"2022-01-03 20:58:34","doi":"10.21203/rs.3.rs-1025016/v2","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Minor Revision","date":"2022-01-05T08:37:44+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2021-12-14T21:52:51+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2021-12-14T20:09:20+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2021-11-24T05:39:08+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Science and Pollution Research","date":"2021-11-17T20:59:07+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-science-and-pollution-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"espr","sideBox":"Learn more about [Environmental Science and Pollution Research](https://www.springer.com/journal/11356)","snPcode":"11356","submissionUrl":"https://submission.nature.com/new-submission/11356/3","title":"Environmental Science and Pollution Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"c0d10260-923d-4049-a3f4-b6add88e4ed9","owner":[],"postedDate":"January 3rd, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2022-02-16T20:53:53+00:00","versionOfRecord":[],"versionCreatedAt":"2022-01-03 20:58:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v2","identity":"rs-1025016","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1025016","identity":"rs-1025016","version":["v2"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}