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A novel experimental approach of inter- and intramodel comparisons is conducted with five power market models to give recommendations for modelers working on decarbonization pathways of Europe until 2050. The experiments investigate the impact of model type (optimization vs. simulation), planning horizon (intertemporal vs. myopic), temporal resolution (8760 vs. 384 h), and spatial resolution (28 countries vs. 12 mega-regions). The model type fundamentally determines the evolution of capacity expansion. Planning horizon (assumed foresight of firms) plays a minor role for scenarios with high carbon prices. For low carbon prices in turn, results from myopic models deviate considerably from those of intertemporal models. Lower temporal and spatial resolutions foster wind power via storage and via neglected transmission boundaries, respectively. Using simulation instead of optimization frameworks, a shorter planning horizon of firms, or lower temporal and spatial resolutions might be necessary to reduce the computational complexity. This paper delivers recommendations on how to limit the discrepancies in such cases. • Comparison of five power market models with harmonized input data and model features. • Evaluating the role of model type, planning horizon, spatial and temporal resolution. • Model type decisive for investment decisions. • Myopic modeling hardly changes variable renewable energy expansion but might favor conventional power plants. • Spatial and temporal resolution crucial for ease of variable renewable energy integration. [ABSTRACT FROM AUTHOR]

Scientific and regulatory issues around approval of follow-on protein products, referred to as biosimilars in Europe, have been a topic of great interest and debate recently. The central issue is our limited understanding of how the different quality attributes of a product have an impact on its safety and efficacy. Crucial gaps in our knowledge include a lack of standardization in the way in which data are collected, analyzed and reported, and limitations in the ability of non-clinical tools for predicting clinical safety and efficacy. Complexity of protein products with respect to the numerous quality attributes and complexity of the biotechnology processes and the raw materials add to the challenges. In this paper, recommendations are presented to help at least partially alleviate these challenges. [Copyright &y& Elsevier]