## 1 Introduction
The authors of [35] calculated an hourly AEF and MEF until 2050, using a linear optimization model as a data basis, which represents a trend scenario. The authors of [51] also used an energy model to calculate an hourly AEF and an annual MEF for Germany explicitly for the years 2020 and 2030. Both MEFs of the named authors are calculated by comparing two model runs with a changed demand. However, the shortcoming of this method is that it compares two different energy systems. The two optimization results are derived from different coverage of demand, storage use, and trading activities. As a result, the single hours of the energy systems are no longer comparable with each other, unless only one time step is changed at a time, while the other 8759 time steps in the model remain unchanged. In the case of necessarily hourly resolved optimization models, this results in 8760 comparable calculation runs for the determination of the MEF of a single year. The computational time needed for such a task is impractical. In [52], hourly AEF and MEF were also calculated for the year 2030 using an energy model. This was done using the merit order, which serves as an indicator for the marginal power plant. Thus, the MEF can be determined in hourly resolution. However, it is already described above that the power system does not respond to changes in demand strictly according to the merit order. A consideration of upstream emissions in Germany in this literature summary is solely provided by [18]. Only [53] applies a prospective MEF with the linear regression method in a highly simplified manner. The authors use historical gradients of the linear fit for groups of power plants with the same energy carrier use and extrapolate these to the composition of power production in future scenarios for Belgium. With the exception of [PERSON] and [PERSON] [35] with a similar, but significantly different, method and scenarios, no prospective emission factors of power consumption with high temporal resolution have yet been calculated for the future German power supply (Energiewende). Therefore, this research is the first to calculate prospective hourly average emission factors of electricity consumption (AEF) using normative scenarios, and the first to calculate a future annual marginal emission factor (MEF) using the linear regression method for Germany until 2050. For the calculation of direct MEFs, the explicit use of normative scenarios is recommended without implied large-scale influences on electricity demand [7]. The data we use is derived from two different normative scenarios for the development of power production in Germany, based on different CO\({}_{2}\) caps using an electricity market model. The emission factors are determined with and without upstream emissions of the energy carriers and other environmental impacts, which is a novelty for emission factors of the German power supply as well. With the developed AEF, electricity from import, export, and storage is also taken into account. These emission factors can be used to produce more detailed analyses of the future GHG emissions from the use of sector coupling technologies in Germany. The results will be made available for open-access use in life cycle assessment (LCA) and for integration in life cycle inventories (LCI). The remainder of the paper is organized as follows: Section 2 describes the electricity market model used, the scenarios developed, and the calculation methods applied for the hourly AEF and average MEF. Section 3 describes the hourly AEF statistically and explains its development depending on the scenarios used. The results of the MEF generated are also shown here and summarized together with the AEF. Section 4 compares the emission factors with the existing research and discusses the results.