Interpreting long-term energy scenarios and the role of bioenergy in Germany

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• 作者：Nora Szarka^a ; ^{nora.szarka@dbfz.de} ; Marcus Eichhorn^b ; ^{marcus.eichhorn@ufz.de} ; Ronny Kittler^a ; ^{ronny.kittler@dbfz.de} ; Alberto Bezama^b ; ^{alberto.bezama@ufz.de} ; Daniela Thrä ; n^a ; ^b ; ^{daniela.thraen@dbfz.de} ; ^{daniela.thraen@ufz.de}
• 关键词：Bioenergy ; Energy scenarios ; Policy making ; Decision support
• 刊名：Renewable and Sustainable Energy Reviews
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：68
• 期：part_P2
• 页码：1222-1233
• 全文大小：1497 K
• 卷排序：68

文摘

Defining the long-term development of Germany׳s energy sector, has been the subject of a series of studies carried out by governmental, industrial and independent interest groups. These studies play a significant role in energy political debate for understanding the long-term role of bioenergy in the national energy system. However, a deep insight and critical assessment of these studies is necessary to increase their transparency and traceability for policy and research. This article aims to provide with information for better understanding energy scenarios and to interpret the expectations of the role that bioenergy can play in 2050.Firstly, 18 long-term energy scenarios were selected based on defined criteria, and analyzed in details in terms of their goals, methods, data used and obtained results. Furthermore, four specific bioenergy-related indicators were selected to carry out a quantitative analysis and interpretation across the selected studies. The results for the four indicators show a high uncertainty and a wide range of potential bioenergy development futures in Germany by 2050 – e.g. the sustainable domestic biomass potential ranges from 350 to 1700 PJ, the share of biomass in final energy consumption lies between 5 and 28% – principally due to the different key questions and methods and heterogeneous driving forces.The study provides with recommendations for energy scenario users for quality measures (e.g. traceability and transparency of methods and data) and contextualization of the results