[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126049-en":3,"doc-seo-126049-105":31,"detail-sidebar-cat-0-en-105":93},{"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126049,962084928904,"Asher","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","A Method Based on Machine Learning Techniques for the Development of a Parametric Environmental Impact Model for Industrial Electric Vehicles","Designers must address environmental impacts early across the entire product life cycle, enabling informed eco-design choices. This paper presents an eco-design methodology to support different classes of industrial electric vehicles by developing a predictive parametric environmental impact model. Machine learning models use key vehicle design features—such as vehicle mass and lifetime distance traveled—as independent inputs to estimate emissions. The Climate Change indicator is modeled using training data from an automatic analytical environmental impact estimation tool, validated through an application to a tourist shuttle.","UNIVERSITÀ POLITECNICA DELLE MARCHE  \nRepository ISTITUZIONALE  \nA Method Based on Machine Learning Techniques for the Development of a Parametric Environmental Impact Model for Industrial Electric Vehicles  \nThis is the peer reviewd version of the followng article:  \nOriginal  \nA Method Based on Machine Learning Techniques for the Development of a Parametric Environmental Impact Model for Industrial Electric Vehicles / Manuguerra, Luca; Cappelletti, Federica; Rossi, Marta; Germani, Michele. - (2024), pp. 83-90. ( 3rd International Conference of the Italian Association of Design Methods and Tools for Industrial Engineering, ADM 2023 Florence, Italy 6-8 September 2023)[10.1007/978-3-031-58094-9_ 10] .  \nAvailability:  \nThis version is available at: 11566/345247 since: 2025-07-10T14:40:43Z  \nPublisher:  \nSpringer Science and Business Media Deutschland GmbH  \nPublished  \nDOI:10.1007/978-3-031-58094-9_ 10  \nTerms of use:  \nThe terms and conditions for the reuse of this version of the manuscript are specified in the publishing policy. The use of copyrighted works requires the consent of the rights’ holder (author or publisher) . Works made available under a Creative Commons license or a Publisher's custom-made license can be used according to the terms and conditions contained therein. See editor’s website for further information and terms and conditions.  \nThis item was downloaded from IRIS Università Politecnica delle Marche ([https://iris.univpm.it](https://iris.univpm.it)) . When citing, please refer to the published version.  \nPublisher copyright:  \nSpringer (conference paper) - Postprint/Author's accepted Manuscript  \nThis version of the conference paper has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use [https://www.springernature.com/gp/open](https://www.springernature.com/gp/open)research/policies/accepted-manuscript-terms, but is not the Version of Record and does not reflect postacceptance improvements, or any corrections. The Version of Record is available online at: 10.1007/978- 3-031-58094-9_ 10.  \n(Article begins on next page)  \n09 March 2026  \nA method based on machine learning techniques for the development of a parametric environmental impact model for industrial electric vehicles  \nLuca Manuguerra 1[0000-0003-0832-3886], Federica Cappelletti 1[0000-0001-5592-3150], Marta Rossi2[0000-0001-9287-8109] and Michele Germani 1[0000-0003-1988-8620]  \n1 Università Politecnica delle Marche, Via Brecce Bianche 12, 60131, Ancona, Italy  \n2 Università eCampus, Via Isimbardi, 10-22060 Novedrate  \n[l.manuguerra@pm.univpm.it](l.manuguerra@pm.univpm.it)  \nAbstract. Designers need to be aware early in the design phase of the environmental impact of their choices over the entire product life cycle. This paper proposes an eco-design method to support designers of different categories of electric vehicles, such as self-driving vehicles, cars, shuttles and buses. The methodology developed aims to realize a model for predicting the environmental impact of industrial electric vehicles. The proposed approach exploits machine learning methods to develop models with the design features ofa generic electric vehicle, such as vehicle mass and distance traveled during its entire lifetime as independent parameters, to estimate the emissions of new products. The environmental impact indicator for this study is Climate Change, the dependent parameter chosen for the impact model. Machine learning algorithms were trained on training data retrieved from an automatic environmental impact estimation software tool based on an analytical approach. All stages of the product life cycle have been considered in the construction of the database, and the model provides quantitative results that consider the consumption of material and energy resources. Finally, the model is tested by estimating the environmental impact of a tourist shuttle.  \nKeywords: Machine Learning, Data Science, Sust","cbCaig3qKf8xSRhO","https://ap.wps.com/l/cbCaig3qKf8xSRhO","pdf",497793,6,1,9,"English","en",105,"# Abstract\n# 1 Introduction\n## Life cycle assessment and design context\n## Prior studies on electric vehicles and LCA","[{\"question\":\"What problem does the proposed eco-design method address?\",\"answer\":\"It supports designers in evaluating environmental impacts early in the design phase across the full product life cycle.\"},{\"question\":\"Which inputs and impact indicator are used in the parametric model?\",\"answer\":\"The model uses vehicle design features such as mass and lifetime distance traveled, and predicts the Climate Change impact indicator.\"},{\"question\":\"How is the machine learning model trained and validated?\",\"answer\":\"It is trained on data produced by an automatic analytical environmental impact estimation tool covering the full product life cycle, and is tested by estimating impacts for a tourist shuttle.\"}]","A Method Based on Machine Learning Techniques for the Development of a Parametric Environmental Impact Model for Industrial Electric Vehicles | 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problem does the proposed eco-design method address?","Question",{"text":77,"@type":78},"It supports designers in evaluating environmental impacts early in the design phase across the full product life cycle.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"Which inputs and impact indicator are used in the parametric model?",{"text":82,"@type":78},"The model uses vehicle design features such as mass and lifetime distance traveled, and predicts the Climate Change impact indicator.",{"name":84,"@type":75,"acceptedAnswer":85},"How is the machine learning model trained and validated?",{"text":86,"@type":78},"It is trained on data produced by an automatic analytical environmental impact estimation tool covering the full product life cycle, and is tested by estimating impacts for a tourist 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