Optimal Selection of Decarbonization Measures in Manufacturing Using Mixed-Integer Programming

Scholars have highlighted the importance of decarbonizing manufacturing industries for several years already. Industry accounts for about 20% of the EU’s greenhouse gas emissions. In order to meet the targets set in the Paris Agreement, industry must reduce emissions to almost zero by 2050. A wide range of measures can be taken to achieve climate neutrality consisting of three categories: reducing greenhouse gases by adapting business models, substituting products or offsetting the emitted greenhouse gases. Companies have to determine the optimal set of measures taking into account their individual situation as well as available resources. From this, a complex optimization problem arises and the proposed decision model offers significant sup-port for the selection of decarbonization measures. By using the decision model, companies can achieve the greatest possible emissions reduction with a minimal set of resources according to their target system, thus taking into account net present value, benefits, and risks. This paper introduces a novel modeling of measures that incorporates relevant evaluation criteria. The arising decision model is solved by using Mixed-Integer Programming. The presented approach was validated in a case study with an industrial corporation.