The climate crisis is important to many people. This can even be observed in the shopping behavior of humans. When people’s shopping behavior changes, this also involves companies, as competitive advantages can be gained. Consequently, companies also see an economic reason to invest in environmental friendly products. However, to enable the production of these products or environmental friendly services, good planning on the part of the company’s management is necessary. Although, the planning of carbon emissions, especially those caused by cloud usage, is difficult and causes a high effort. To fix this issue, carbon emissions have to be forecasted. In this thesis, I present a model which allows estimating carbon emissions as well as energy consumption based on expected cloud usage. The implementation of this model is flexible and can be configured for various cloud services if enough data about the hardware hosting the cloud service is provided. Within a plausibility test, the forecasted emissions caused by the energy consumption of the cloud provider were similar to the emissions of an established tool for measuring and reporting carbon emissions from cloud computing. In addition, I investigate how missing data and fluctuating energy mixes affect the estimation of emissions and estimated energy consumption. The results show a high impact of missing data as well as unstable, fluctuating energy mixes. This suggests, that the energy consumption as well as the carbon footprint estimate forecast should consider the most recent data available. In addition, the amount of missing data should be kept as small as possible.