Data-Driven Modelling of Behavioural Cost–Carbon Trade-offs for Smart Environmental Decision Support

Data-Driven Modelling of Behavioural Cost–Carbon Trade-offs for Smart Environmental Decision Support | 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 Data-Driven Modelling of Behavioural Cost–Carbon Trade-offs for Smart Environmental Decision Support Lukman O. Kolawole, Adeyinka G. Ologun, Sandra C. Akaelu, Stephanie O. Akaelu, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8866333/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 Achieving meaningful carbon reduction in business contexts requires more than technological solutions; it demands an understanding of how individuals perceive and negotiate cost–carbon trade-offs. This study presents a data-driven empirical analysis of behavioural factors influencing engagement with carbon-reduction practices using primary survey data (n = 38). The research develops a quantitative framework that operationalises subjective perceptions into measurable constructs, including a Cost Sensitivity Index and a logistic behavioural engagement model. Results reveal that perceived cost is the dominant barrier to action and operates as a behavioural threshold: once cost becomes salient, the probability of engagement declines sharply. Behavioural segmentation further identifies distinct respondent profiles, including environmentally motivated actors, structurally constrained participants, and cost-sensitive sceptics, highlighting the limitations of uniform sustainability interventions. The study contributes a novel integration of behavioural analytics and decision-support modelling for environmental management. By transforming perceptual data into actionable modelling outputs, the findings support the design of intelligent, adaptive sustainability systems that better align technological capability with human decision-making. The research offers practical implications for policymakers and business leaders seeking to develop more effective, data-informed carbon-reduction strategies. Cost–Carbon Trade-offs Behavioural Survey Analytics Decision-Support Modelling Sustainability Engagement Smart Environmental 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-8866333","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":592621476,"identity":"8766f97f-4309-48eb-9874-70b4c3b3d126","order_by":0,"name":"Lukman O. Kolawole","email":"","orcid":"","institution":"Middlesex University","correspondingAuthor":false,"prefix":"","firstName":"Lukman","middleName":"O.","lastName":"Kolawole","suffix":""},{"id":592621477,"identity":"54d52ba3-d8ba-4005-bd44-38116e6e21d7","order_by":1,"name":"Adeyinka G. 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