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Seit 2002 wird das Standardkosten-Modell (SKM) als Ansatz zur Messung von Bürokratiekosten in einer Vielzahl von OECD-Ländern, darunter Deutschland und Großbritannien, angewendet. Im Zentrum dieser Arbeit steht die Frage, warum im Regulierungsreform-Nachzüglerland Deutschland die Ausweitung des Ansatzes auf den Bereich Bürger seit Jahren auf der politischen Agenda steht und bereits erste Schritte zur Umsetzung unternommen wurden, während SKM Bürger im Regulierungsreform-Vorreiterland Großbritannien scheinbar nie auf der Agenda stand. In Anlehnung an einen von Kingdon entwickelten Agenda-Setting-Ansatz werden Unterschiede im Bereich der Problemwahrnehmung, in der Bewertung der Policy SKM sowie im politischen Entstehungsprozess untersucht. Hierbei zeigt sich, dass hinsichtlich der Wahrnehmung des Problems der Bürokratiebelastung signifikante Unterschiede zwischen Deutschland und Großbritannien bestehen, die sich vor allem auf die in Deutschland höhere Intensität der Problemwahrnehmung beziehen. Weitere Unterschiede bestehen bezüglich der Bewertung der Policy SKM, die in Deutschland eine höhere Medienaufmerksamkeit erhält und allgemein positiver bewertet wird. Auch der Entstehungsprozess des SKM, der in Deutschland wesentlich stärker politisiert war als in Großbritannien, trägt zur Erklärung der beobachteten Unterschiede im Agenda-Setting bei.
The increased emphasis on labour market activation in many European countries has led to new forms of governance in recent decades. Primarily through qualitative data and document analysis, this article compares the restructuring of labour market service delivery in the UK and Germany. The comparison suggests the emergence of complex governance arrangements that seek to balance public regulation and accountability with the creation of room for market competition. As a result, we can observe in both countries a greater use of markets, but also of rules. While in both countries the relationships between different providers of labour market services can best be described as a mixture of cooperation and competition, differences exist in terms of instruments and the comprehensiveness of coordination initiatives. The findings suggest that the distinctions between governance models may be more important in theory than in practice, although the combinations of theoretical forms vary in different circumstances.
Information on extreme precipitation for future climate is needed to assess the changes in the frequency and intensity of flooding. The primary source of information in climate change impact studies is climate model projections. However, due to the coarse resolution and biases of these models, they cannot be directly used in hydrological models. Hence, statistical downscaling is necessary to address climate change impacts at the catchment scale.
This study compares eight statistical downscaling methods (SDMs) often used in climate change impact studies. Four methods are based on change factors (CFs), three are bias correction (BC) methods, and one is a perfect prognosis method. The eight methods are used to downscale precipitation output from 15 regional climate models (RCMs) from the ENSEMBLES project for 11 catchments in Europe. The overall results point to an increase in extreme precipitation in most catchments in both winter and summer. For individual catchments, the downscaled time series tend to agree on the direction of the change but differ in the magnitude. Differences between the SDMs vary between the catchments and depend on the season analysed. Similarly, general conclusions cannot be drawn regarding the differences between CFs and BC methods. The performance of the BC methods during the control period also depends on the catchment, but in most cases they represent an improvement compared to RCM outputs. Analysis of the variance in the ensemble of RCMs and SDMs indicates that at least 30% and up to approximately half of the total variance is derived from the SDMs. This study illustrates the large variability in the expected changes in extreme precipitation and highlights the need for considering an ensemble of both SDMs and climate models. Recommendations are provided for the selection of the most suitable SDMs to include in the analysis.