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In order to achieve meaningful climate protection targets at the global scale, each country is called to set national energy policies aimed at reducing energy consumption and carbon emissions. By calculating the monthly heating energy demand of dwellings in the Netherlands, our case study country, we contrast the results with the corresponding aspired national targets. Considering different future population scenarios, renovation measures and temperature variations, we show that a near zero energy demand in 2050 could only be reached with very ambitious renovation measures. While the goal of reducing the energy demand of the building sector by 50% until 2030 compared to 1990 seems feasible for most provinces and months in the minimum scenario, it is impossible in our scenario with more pessimistic yet still realistic assumptions regarding future developments. Compared to the current value, the annual renovation rate per province would need to be at least doubled in order to reach the 2030 target independent of reasonable climatic and population changes in the future. Our findings also underline the importance of policy measures as the annual renovation rate is a key influencing factor regarding the reduction of the heating energy demand in dwellings. (C) 2015 Elsevier Ltd. All rights reserved.
Winter storms are the most costly natural hazard for European residential property. We compare four distinct storm damage functions with respect to their forecast accuracy and variability, with particular regard to the most severe winter storms. The analysis focuses on daily loss estimates under differing spatial aggregation, ranging from district to country level. We discuss the broad and heavily skewed distribution of insured losses posing difficulties for both the calibration and the evaluation of damage functions. From theoretical considerations, we provide a synthesis between the frequently discussed cubic wind-damage relationship and recent studies that report much steeper damage functions for European winter storms. The performance of the storm loss models is evaluated for two sources of wind gust data, direct observations by the German Weather Service and ERA-Interim reanalysis data. While the choice of gust data has little impact on the evaluation of German storm loss, spatially resolved coefficients of variation reveal dependence between model and data choice. The comparison shows that the probabilistic models by Heneka et al. (2006) and Prahl et al. (2012) both provide accurate loss predictions for moderate to extreme losses, with generally small coefficients of variation. We favour the latter model in terms of model applicability. Application of the versatile deterministic model by Klawa and Ulbrich (2003) should be restricted to extreme loss, for which it shows the least bias and errors comparable to the probabilistic model by Prahl et al. (2012).