TY - JOUR A1 - Lang, Silvio A1 - Engelmann, Bastian A1 - Schiffler, Andreas A1 - Schmitt, Jan T1 - A simplified machine learning product carbon footprint evaluation tool T2 - Cleaner Environmental Systems N2 - 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. KW - Management, Monitoring, Policy and Law KW - Environmental Science (miscellaneous) KW - Renewable Energy, Sustainability and the Environment KW - Environmental Engineering Y1 - 2024 UR - https://opus4.kobv.de/opus4-fhws/frontdoor/index/index/docId/5629 SN - 2666-7894 VL - 13 PB - Elsevier BV ER -