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2016
Conference Paper
Titel
Energy saving cost curves as a tool for policy development - case study of the German building stock
Abstract
The building sector within the EU accounts for about 40% of final energy use and one-third of greenhouse gas emissions. Buildings therefore should play an important role in meeting the EU climate targets. Using the example of Germany, the largest economy of the EU, this paper sets out the methodology for appraising the contribution that comprehensive building renovations, comprising both fabric insulation and heating system upgrades, can make towards decreasing energy use. A dynamic bottom-up simulation model, the Invert/EE-Lab model, evaluates the effects of three scenarios of economic and regulatory incentives for three different renovation packages oriented towards the standards defined by the German building code (EnEv) as well as the support programmes of the KfW development bank. Results are presented visually through Energy Saving Cost Curves which communicate the monetary costs (or savings) and the energy savings for 16 building categories that represent the entirety of the German building stock. The Energy Saving Cost Curves developed in this paper represent the investors' perspective to 2030. Under the Business As Usual scenario, the total cost effective energy savings potential amounts to 60 TWh/a, avoids 1.1 bne/a in energy costs, and comprises most of the non-residential building categories and the oldest residential buildings built before 1948. Increasing the level of subsidy in the High Subsidy scenario results in an almost doubling of cost-effective savings to 118 TWh/a while increasing energy cost savings to 1.9 bne/a. Energy Saving Cost Curves provide a means to compare the impact of different policy options from the perspective of the investor for different building categories, and can thereby feed directly into the design of renovation strategies -whether at national, regional or city level- under a wide variety of conditions and taking into consideration economic parameters ranging from subsidies and energy prices, to transaction costs, learning curves and discount rates.
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