Data-driven selection of greenhouse gas mitigation measures in the plastics industry - Oetzel, Jannik; Ebersold, Felix; Hechelmann, Ron-Hendrik; Paris, Aaron (Energy reports, 2024)
The complexity of corporate decarbonisation strategies and a shortage of specialists in the field of energy consultants necessitate the development of data-based simulation tools. In this study, an ecological-economic simulation model is presented, which can be used to reduce information deficits and develop company-specific decarbonisation strategies. It uses the next-event time progression method. Data considered include those from a processed version of the publicly available Industrial Assessment Center database. Based on different cost and emission development forecasts, a decarbonisation strategy is developed with sector-specific filtering of the data and various other parameters. The Python-based simulation model calculates costs, savings, and investment times of measures within the framework of a decarbonisation strategy. The model is applied in a case study. The comparison of modelled and manually developed strategies shows a 2.1 % difference in total greenhouse gas emissions. 4 of the 13 identified measures in the simulation model are comparable with measures from the manually developed strategy. The similarity of the savings achieved as well as the overlap of measures indicate a successful applicability for industry companies. Furthermore, the identification of alternative measures can help consultants to develop decarbonization strategies. A decarbonization strategy can be devised for any company with minimal data such as industry type, energy requirements, and annual turnover. More detailed information allows more precise calculations, but industry averages are stored for all key values within the sector to ensure a tailored strategy can be developed with little data. With the model, each company of an implemented sector can receive a starting point for developing their own decarbonisation strategy.