Dynamic Resilience Assessment of Communities Surrounding Chemical Parks: A CREAM-Based Approach with Fuzzy-Grey- Bayesian Integration

Dynamic Resilience Assessment of Communities Surrounding Chemical Parks: A CREAM-Based Approach with Fuzzy-Grey- Bayesian Integration | 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 Dynamic Resilience Assessment of Communities Surrounding Chemical Parks: A CREAM-Based Approach with Fuzzy-Grey- Bayesian Integration Yucheng Ji, Xingci Cheng, Hongchen Li, Lei Ma, Cheng-ling Wang, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7043088/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 In order to explore the quantitative theory and methodology of the overall level of disaster prevention and resilience of the communities around chemical parks, we have carried out a study on the meaning and quantitative theoretical model of community resilience around chemical parks. Based on the special characteristics of chemical park risks and the limitations of the traditional safety management model in dynamic risk response, the concept of "community resilience" around chemical industrial parks is proposed, which covers the four-dimensional capabilities of community resistance, adaptation, recovery and learning. By constructing an assessment system with 6 primary indicators and 27 secondary indicators, including community participation and emergency response capability, combining fuzzy theory, Grey-DEMATEL and Bayesian network method, and optimizing the CREAM model, the problem of subjective scoring bias and multifactorial dynamic correlation was effectively solved. Taking the toxic substance leakage in a petrochemical economic development zone as a typical case, the effectiveness and practicability of the constructed assessment system and method are verified, which provide a quantitative basis for the improvement of emergency evacuation, risk prevention and control of the community, and provide a closed-loop solution of "assessment-warning-optimization" for the resilience governance of the community around the chemical industry park, which is of great practical value for the improvement of the governance system of the community in high-risk areas of the city. Earth and environmental sciences/Environmental sciences Earth and environmental sciences/Environmental social sciences Earth and environmental sciences/Natural hazards Chemical industrial park community resilience assessment quantitative theoretical model community emergency management 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-7043088","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":486780543,"identity":"e0a5a526-4463-409c-ab04-64795ff56966","order_by":0,"name":"Yucheng Ji","email":"","orcid":"","institution":"Ministry of Emergency Management Big Data Center，Beijing","correspondingAuthor":false,"prefix":"","firstName":"Yucheng","middleName":"","lastName":"Ji","suffix":""},{"id":486780544,"identity":"57903e12-aa60-4904-8053-c16948a460e2","order_by":1,"name":"Xingci Cheng","email":"","orcid":"","institution":"University of Science and Technology 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