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A simplified machine learning product carbon footprint evaluation tool

  • On the way to climate neutrality manufacturing companies need to assess the Carbon dioxide (CO2) emissions of their products as a basis for emission reduction measures. The evaluate this so-called Product Carbon Footprint (PCF) life cycle analysis as a comprehensive method is applicable, but means great effort and requires interdisciplinary knowledge. Nevertheless, assumptions must still be made to assess the entire supply chain. To lower these burdens and provide a digital tool to estimate the PCF with less input parameter and data, we make use of machine learning techniques and develop an editorial framework called MINDFUL. This contribution shows its realization by providing the software architecture, underlying CO2 factors, calculations and Machine Learning approach as well as the principles of its user experience. Our tool is validated within an industrial case study.

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Metadaten
Author:Silvio Lang, Bastian EngelmannORCiD, Andreas SchifflerORCiD, Jan SchmittORCiD
DOI:https://doi.org/10.1016/j.cesys.2024.100187
ISSN:2666-7894
Parent Title (English):Cleaner Environmental Systems
Publisher:Elsevier BV
Document Type:Article
Language:English
Year of Completion:2024
Year of publication:2024
Release Date:2024/05/14
Tag:Environmental Engineering; Environmental Science (miscellaneous); Management, Monitoring, Policy and Law; Renewable Energy, Sustainability and the Environment
Volume:13
Article Number:100187
Institutes and faculty:Institute / Institut Digital Engineering (IDEE)
Open access colour:Gold
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