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AbstractThis paper analyzes the dynamic behavior of day-ahead spot prices in the German electricity spot market due to positive structural shocks in wind and solar power. It uses a dynamic structural vector autoregressive model to estimate the related structural impulse response functions. The estimates suggest that wind power shocks have a more prolonged negative effect on spot prices than solar power shocks. These may be explained by significant autocorrelations of wind power for larger lags. The total negative merit order effect of a solar power shock, however, is larger. One reason might be that solar power shocks coincide with demand peaks. Past empirical results show differences in the total average negative merit order effects. The inherently dynamic nature of wind and solar power could explain these differences because the dynamics, which are ignored by past studies on the subject using static ordinary least squares estimations, could be transferred to the merit order effects.
Any integration of extra carbon dioxide removal (CDR) via terrestrial or marine sink enhancement into climate policies requires accounting for their effectiveness in reducing atmospheric carbon concentration and translating this information into the amount of carbon credits (to be used in official and voluntary emission trading schemes). Here, we assess accounting schemes in their appropriateness of assigning carbon credits. We discuss the role of temporary carbon storage and present the various ccounting methods for carbon credit assignment. We explain how we have implemented the methods numerically and analyse carbon assignments across the different accounting schemes, using stylized, model-based ocean sink enhancement experiments.
OceanCV provides computer vision algorithms and tools for underwater image analysis. This includes image processing, pattern recognition, machine learning and geometric algorithms but also functionality for navigation data processing, data provenance etc.
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