[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127165-en":3,"doc-seo-127165-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},127165,3985741905716,"Rowan","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Machine Learning to support prospective Life Cycle Assessment of emerging chemical technologies - review","Rising demands for safer, more sustainable chemical innovation require improved environmental assessment methods for emerging chemical technologies, still in R&D and not yet fully observable in markets. Prospective Life Cycle Assessment (LCA) builds future-oriented models that capture how both intrinsic and extrinsic factors may evolve, but these models create substantial indeterminacies. Machine learning techniques can help address these uncertainties. This review critically discusses recent demonstrations of machine learning in prospective LCA and outlines promising directions for further research.","Machine Learning to support prospective Life Cycle Assessment of emerging chemical technologies  \nBlanco, C.F.; Pauliks, N.; Donati, F.; Engberg, N.; Weber, J.  \nCitation  \nBlanco, C. F., Pauliks, N., Donati, F., Engberg, N., & Weber, J. (2024) . Machine Learning to support prospective Life Cycle Assessment of emerging chemical technologies. Current Opinion In Green And Sustainable Chemistry, 50. doi:10.1016/j.cogsc.2024.100979  \nVersion: Publisher's Version  \nLicense:  Creative Commons CC BY 4.0 license  \nDownloaded from:  [https://hdl.handle.net/1887/4107086](https://hdl.handle.net/1887/4107086)  \nNote: To cite this publication please use the final published version (if applicable) .  \nAvailable online [at](at www.sciencedirect.com)[ www.sciencedirect.com](at www.sciencedirect.com)[ ](at www.sciencedirect.com)ScienceDirect  \nCurrent Opinion in  \nGreen and Sustainable Chemistry  \nMachine learning to support prospective life cycle assessment of emerging chemical technologies C. F. Blanco 1 ,2 , N. Pauliks 1 , F. Donati 1 , N. Engberg3 and J. Weber4  \nIncreasing calls for safer and more sustainable approaches to innovation in the chemical sector necessitate adapted methods for the environmental assessment of emerging chemical technologies. While these technologies are still in the research and development phase, gaining an early understanding of their potential implications is crucial for their eventual introduction into markets worldwide. Life Cycle Assessment (LCA) is a core tool which has been recently adapted for such purpose. Prospective LCA approaches aim to develop plausible futureoriented models which account for the evolution of factors both intrinsic and extrinsic to the technologies assessed. Such future-oriented models introduce many indeterminacies, which could, to some extent, be addressed by Machine Learning techniques. Recent demonstrations of such techniques in the context of prospective LCA, as well as promising avenues for further research, are critically discussed.  \nAddresses  \n1 Institute of Environmental Sciences (CML), Leiden University, the Netherlands  \n2 TNO, Circularity and Sustainability Impacts Department, Utrecht, the Netherlands  \n3 Delft University of Technology, Industrial Design Engineering, Delft, the Netherlands  \n4 Delft University of Technology, Department of Intelligent Systems, Delft, the Netherlands  \nCorresponding author: Blanco, C.F. ([c.f.blanco@cml.leidenuniv.nl](c.f.blanco@cml.leidenuniv.nl))  \nCurrent Opinion in Green and Sustainable Chemistry 2024, 50:100979  \nThis review comes from a themed issue on AI and Sustainability Edited by Jana Weber and Kevin Leonard  \nAvailable online 18 October 2024  \nFor complete overview of the section, please refer the article collection-AI and Sustainability [https://doi.org/10.1016/j.cogsc.2024.100979](https://doi.org/10.1016/j.cogsc.2024.100979)  \n2452-2236/© 2024 The Author(s) . Published by Elsevier B.V. This is an open access article under the CC BY license ([http://creativecommons](http://creativecommons). org/licenses/by/4.0/) .  \nIntroduction  \nThe call for a more responsible approach to innovation has spurred methodological advancements for the  \nGiven the role as Guest Editor, Jana Weber had no involvement in the peer review of the article and has no access to information regarding its peer-review. Full responsibility for the editorial process of this article was delegated to Kevin Leonard.  \nenvironmental assessment of novel materials and products. This is of particular importance for emerging chemical technologies, which are largely driving global oil consumption [1], belong to the hard-to-abate sectors [2] and whose products preponderate in most aspects of our lives [3] . Since the 1980’s, Life Cycle Assessment (LCA) has prevailed as the preferred tool for assessing product and service performance across diverse environmental criteria [4] . In assessing multiple criteria across entire product life cycles, LCA is unique in its ability ","cbCaioYEqMqM9fud","https://ap.wps.com/l/cbCaioYEqMqM9fud","pdf",434317,1,9,"English","en",105,"# Introduction\n## Prospective LCA and future-oriented modeling\n## Why uncertainty matters in emerging technologies\n## Role of machine learning","[{\"question\":\"What is prospective Life Cycle Assessment (LCA) and why is it needed?\",\"answer\":\"Prospective LCA develops future-oriented models to evaluate emerging technologies that are not yet fully measurable. It supports early environmental appraisal during research and development.\"},{\"question\":\"What makes prospective LCA particularly challenging?\",\"answer\":\"Prospective LCA must anticipate how internal and external factors evolve over long development horizons, creating high levels of uncertainty and multiple indeterminacies.\"},{\"question\":\"How can machine learning contribute to prospective LCA?\",\"answer\":\"Machine learning can help address some of the indeterminacies in future-oriented LCA models. The document reviews demonstrations and suggests further research directions for applying these techniques.\"}]","Machine Learning to support prospective Life Cycle Assessment of emerging chemical technologies - review | PDF",1785937295,23,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"machine-learning-to-support-prospective-life-cycle-assessment-of-emerging-chemical-technologies-review","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-to-support-prospective-life-cycle-assessment-of-emerging-chemical-technologies-review/127165/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-22","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What is prospective Life Cycle Assessment (LCA) and why is it needed?","Question",{"text":76,"@type":77},"Prospective LCA develops future-oriented models to evaluate emerging technologies that are not yet fully measurable. It supports early environmental appraisal during research and development.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What makes prospective LCA particularly challenging?",{"text":81,"@type":77},"Prospective LCA must anticipate how internal and external factors evolve over long development horizons, creating high levels of uncertainty and multiple indeterminacies.",{"name":83,"@type":74,"acceptedAnswer":84},"How can machine learning contribute to prospective LCA?",{"text":85,"@type":77},"Machine learning can help address some of the indeterminacies in future-oriented LCA models. 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