System understanding shapes insights for eco-design: a comparison of four temporal perspectives

System understanding shapes insights for eco-design: a comparison of four temporal perspectives | 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 System understanding shapes insights for eco-design: a comparison of four temporal perspectives Thomas Arblaster, Jeroen Guinée, Carlos Felipe Blanco Rocha, Ivana Burzic, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9554797/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 Purpose: LCA that supports eco-design is inherently future-oriented. Various recommendations have been made to systematically address future change with prospective LCA. However, in a changing world, a steady-state approach to LCA can fall short. Time-explicit LCA enables certain departures from these conventional assumptions, but at an increased data demand. We test how different understandings of time contribute decision-relevant insight to explore the grounds on which increasing an assessment’s complexity is justified. Materials and methods: We define four temporal perspectives, differentiating steady-state prospective LCA into ‘imminent-future static’ and ‘advanced-future static’ perspectives, depending on the degree of future change considered. We furthermore differentiate time-explicit LCA into ‘mosaic’ and ‘metabolic’ approaches, with mosaic time-explicit LCA resembling a mosaic of snapshots arranged across the lifecycle of a single product, while metabolic time-explicit LCA defines a functional unit involving products produced and utilised differentially across a timespan. Each of these four approaches provides a perspective on the subject of analysis that can inform eco-design. Insights following from these perspectives are compared for a case study in automotive plastics: a novel, lightweight softwood-polypropylene compound to replace talc-polypropylene in the interior trims for passenger cars. Results and discussion: Mosaic time-explicit LCA and both static perspectives largely lead to similar conclusions: in our present case study, softwood-polypropylene requires comparatively less of the polypropylene matrix and enables lightweighting, thereby leading to a better environmental performance. Metabolic time-explicit LCA nuances this narrative by comparing a circular economy transition (enabled by mechanical recycling) to a joint transition of circularity and material substitution. Introducing the newly developed softwood-polypropylene requires more primary material, as closed-loop waste streams are not available yet. This temporarily leads to a worse environmental performance. This reveals trade-offs and opportunities for improvement beyond those following from the other temporal perspectives. Conclusions and recommendations: We illustrate that assessing a dynamic system can lead to meaningfully enriched insights when compared to a steady-state system. However, we relate this foremost to how the system is understood, rather than its computational structure. Time-explicit LCA is a useful tool to represent temporal differentiation and changes over time, but can also lead to (unintentionally) stripping down time-dependent aspects of the system. Conversely, practitioners can encode a steady-state model with data representing a dynamic understanding of the system. We therefore encourage the development of tools for time-explicit LCA, but also encourage further systematic reflection on time-dependent change in LCA, regardless of the computational structure used. Environmental Engineering Modelling Dynamic LCA Prospective LCA Temporal evolution Design for sustainability Circular economy Polymer composites Automotive industry Full Text Additional Declarations The authors declare no competing interests. Supplementary Files SI.pdf Supplementary Information 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. 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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-9554797","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":631108732,"identity":"164ea8ff-9536-47a9-ae62-4339a6845a32","order_by":0,"name":"Thomas 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Various recommendations have been made to systematically address future change with prospective LCA. However, in a changing world, a steady-state approach to LCA can fall short. Time-explicit LCA enables certain departures from these conventional assumptions, but at an increased data demand. We test how different understandings of time contribute decision-relevant insight to explore the grounds on which increasing an assessment’s complexity is justified.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMaterials and methods:\u003c/strong\u003e We define four temporal perspectives, differentiating steady-state prospective LCA into ‘imminent-future static’ and ‘advanced-future static’ perspectives, depending on the degree of future change considered. We furthermore differentiate time-explicit LCA into ‘mosaic’ and ‘metabolic’ approaches, with mosaic time-explicit LCA resembling a mosaic of snapshots arranged across the lifecycle of a single product, while metabolic time-explicit LCA defines a functional unit involving products produced and utilised differentially across a timespan. Each of these four approaches provides a perspective on the subject of analysis that can inform eco-design. 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This reveals trade-offs and opportunities for improvement beyond those following from the other temporal perspectives.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions and recommendations:\u003c/strong\u003e We illustrate that assessing a dynamic system can lead to meaningfully enriched insights when compared to a steady-state system. However, we relate this foremost to how the system is understood, rather than its computational structure. Time-explicit LCA is a useful tool to represent temporal differentiation and changes over time, but can also lead to (unintentionally) stripping down time-dependent aspects of the system. Conversely, practitioners can encode a steady-state model with data representing a dynamic understanding of the system. 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