A dynamic approach for life cycle global warming impact assessment of machine tool considering time effect

Purpose Machine tools are the equipment used for the cutting and shaping of materials, like metals, which generate greenhouse gas (GHG) emissions across their life cycles due to material use and energy consumption. The life cycle emissions of machine tools are distributed over time and may vary with technology advancement. This paper aims to incorporate these temporal factors into the global warming impact (GWI) assessment of machine tools and further reveal their influences on the results.
Method Incorporating emission time into the GWI assessment of machine tools is based on the following dynamic life cycle assessment (LCA) framework. First, compute temporally differentiated GHGs of machine tools based on the activity-based modeling. And then, use time-dependent characterization factors (CFs), which are developed based on the radiative forcing concept, to assess their GWI. By using this framework, a dynamic life cycle GWI assessment of machine tool is conducted using two gear hobbing machines. Both the emission time and the potential changes of life cycle emissions due to the improvement of electricity mix and the variation of machine tool use modes are considered.
Results and discussion The results demonstrated that when the emission time was considered, both machines offered 3% of reduction of GWI, compared with their static results, respectively. Further reductions were found for the two machines, when the electricity improvement and the changes of the machine tool use modes were considered. All the differences between the static and the dynamic environmental impact results become smaller with the extension of the time horizons (THs) that accounted for the evaluation.
Conclusions and recommendations The conventional static LCA has the potential to overestimate the real GWI of machine tools. It is more important to account for the emission time in GWI assessment at shorter THs or for a longer lifetime of machine tools. This work offers a method to dynamically assess the real GWI of machine tools. The proposed method helps to make robust decision-making for environmentally friendly machine tool selection and support sustainable production.