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The scope for better industry representation in long-term energy models: Modeling the cement industry

Author:
Kermeli, Katerina, Edelenbosch, Oreane Y., Crijns-Graus, Wina, van Ruijven, Bas J., Mima, Silvana, van Vuuren, Detlef P., Worrell, Ernst
Source:
Applied energy 2019 v.240 pp. 964-985
ISSN:
0306-2619
Subject:
carbon dioxide, cement, coal, cost effectiveness, energy, fly ash, greenhouse gas emissions, greenhouse gases, industry, metallurgy, models, pollution control, power plants, steel
Abstract:
Although the cement industry emits around 6% of global CO2 emissions, most global Integrated Assessment Models (IAMs) barely represent this industrial subsector or do not cover all important processes. This study, describes the state-of-the-art of cement modelling in IAMs, suggests possible improvements and discusses the impacts of these on energy and greenhouse gas emissions (GHG) in the IMAGE global IAM.It is found that two cement-sector specific GHG mitigation measures are often not explicitly accounted for in IAMs, namely: (i) retrofitting and (ii) reducing the clinker to cement ratio. For retrofitting, many measures are identified as cost-effective and when incorporating these in the IMAGE model overall energy use reduces between 2010 and 2035 by 9.8 and 11 EJ (4% and 5%) under the baseline and GHG mitigation scenarios, respectively. When incorporating the clinker to cement ratio by linking material availability to the activities in the steel industry and coal-fired power plants, the 2050 energy use reduces by 15% under the baseline scenario and increases by 9% under the GHG mitigation scenario as fewer coal-fired power plants are in operation. This is even more prominent in the long term. The 2100 energy use is 14% higher in the GHG mitigation scenario as even fewer coal-fired power plants are used drastically limiting the potential for clinker substitution with fly ash. These results highlight the importance of capturing cross-sectoral relationships between industries and of including sector specific mitigation measures in long-term energy models.
Agid:
6301664