Due to the ongoing climate crisis and the resulting targets of several countries, as well as the EU, manufacturing companies face the need to address and reduce their greenhouse gas (GHG) emissions. To calculate these emissions, the product carbon footprint (PCF) can be a helpful tool, both to come up with reduction measures internally as well as to communicate it externally as competitive advantage. However, the PCF is subject to uncertainty factors that hinder its use and need to be systematically assessed. For this reason, this paper first collects and clusters relevant uncertainty factors resulting from differences between accounting standards, but also from imprecisions within the standards. Subsequently, the PCF is determined in different scenarios of uncertainty factors, applied to an industrial case study. This is used to be able to put the deviation in the final results into perspective. Recommendations for action are finally derived from the analysis of the literature and the results of the use case.

To mitigate the ongoing progress of climate change, the European Commission announced in the European Green Deal to reduce greenhouse gas emissions by 55% until 2030 compared to the reference year 1990 and to achieve climate neutrality by 2050 [1]. In this context, the industry in particular faces environmental challenges due to its high energy demand. To achieve the objective of becoming climate-neutral, increasing the energy and resource efficiency in the industry is crucial, because a large proportion of the greenhouse gases released are emitted during the provision of energy. In the automotive industry, paint shops are among the most energy-intensive processes and have great potentials for efficiency measures. These potentials can be identified with the assistance of energy or CO₂ balancing methods. This publication presents a tool to analyse the energy efficiency potentials of automotive paint shops. The approach offers the possibility to parameterize different painting processes and their sub-processes. After defining the process requirements, a thermodynamic and process engineering simulation of the individual process steps enables the identification of potentials for energy and resource savings and CO₂ reduction in existing or planned painting processes. In a validation on a real reference scenario, the simulated CO₂ emissions of a paint shop were reduced by up to 24%.

Against the backdrop of advancing climate change, the pressure on industry as the second largest producer of greenhouse gas emissions worldwide is increasing. Climate neutrality and the effects on the climate of products or services are gaining more and more political and social attention. Thus, this paper is dedicated to the investigation of the holistic influence of machined components on greenhouse gas emissions. Optimisations can only be achieved through a profound understanding of the important factors in relation to climate neutrality of industrial production. To this end, a method is developed that allows the low-effort quantification of part-specific greenhouse gases emitted during the production on a CNC machine tool. Validating experiments are conducted in a real industrial environment. Finally, potential for improvement is outlined.

Angesichts der sich weiter zuspitzenden Klimakrise gilt es unnötige Verbräuche von Ressourcen in der Produktion zu vermeiden. Basierend auf den Lean-Verschwendungsarten werden in diesem Beitrag sogenannte Ressourcenverschwendungsarten erarbeitet, die typische Stellen der Verschwendung von Ressourcen in produzierenden Unternehmen klassifizieren. Diese können als Grundlage dienen, um geeignete Maßnahmen zur Eliminierung von Ressourcenverschwendung umzusetzen. With a view to recent developments in the climate crisis, it is important to avoid unnecessary resource consumption in production. Based on lean waste types, this article identifies so-called resource waste types that summarize typical points of resource waste in manufacturing companies. These types may serve as a basis for implementing suitable measures to eliminate resource waste.

Industry is responsible for approximately 25% of global greenhouse gas (GHG) emissions, contributing substantially to climate change. Sustainable manufacturing has therefore become a significant topic of discussion, resulting in a better understanding of key GHG emitters in milling processes. This paper investigates the influence of different compositions of the German electricity mix on the CO ₂ -eq. Emissions of a milled steel disc defined by the Product Carbon Footprint (PCF). Using more wind and solar energy reduces CO ₂ -eq. Emissions significantly in comparison to an energy mix almost without green energy types. The cradle-to-gate PCF of the investigated steel workpiece ranges from 8.44 to 11.91 kg CO ₂ -eq., corresponding to a 41.18% increase caused solely by alternations in the energy mix composition. Feed rate was selected as the parameter for the optimization of the PCF, suggesting maximizing the feed rate to shorten production times. Emissions directly attributable to the milling process can be reduced by 11.59% and 16.93%, respectively in the two scenarios. Expected increase in cost and varying surface quality due to increased tool wear were not part of this investigation. This approach demonstrates the potential for environmentally sustainable manufacturing strategies by integrating electricity mix data into manufacturing parameter decisions.