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Techno-economic and environmental characterization of industrial technologies for transparent bottom-up energy modeling

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  • Lerede, D.
  • Bustreo, C.
  • Gracceva, F.
  • Saccone, M.
  • Savoldi, L.

Abstract

In the context of strategical planning in support of the energy transition, industry plays a key role, producing alone more than 20% direct global CO2 emissions, thus the adoption of low-carbon technologies in this field is crucial to reach ambitious environmental goals. An important tool in building long-term environmental strategies is scenario analysis, often built through quantitative bottom-up models, among which the TIMES model generator. However, the quality of the results strongly depends on the level of detail of the dataset it can rely on. Therefore, the development of insightful environmental-friendly energy use trajectories is favored by the use of a sufficiently rich and accurately built technological repository. A review of the technological outlook is presented in this paper in five industrial subsectors (iron and steel, non-ferrous metals, non-metallic minerals, chemicals, pulp and paper). That review leads to the composition of a dataset (referred to as industrial demand technology database) including both currently existing and announced technologies to be used in a variety of macro-scale energy models, with particular reference to those belonging to the TIMES family. Techno-economic and environmental parameters are critically selected from the available data sources, in order to give a picture of the degree of sustainability that each industrial sector can achieve in the long term. This is a first-of-a-kind comprehensive review – including, among others, the characterization of very innovative technologies still in research phase – and as such, it could be very helpful to develop useful insights focusing on a low carbon, sustainable energy system development.

Suggested Citation

  • Lerede, D. & Bustreo, C. & Gracceva, F. & Saccone, M. & Savoldi, L., 2021. "Techno-economic and environmental characterization of industrial technologies for transparent bottom-up energy modeling," Renewable and Sustainable Energy Reviews, Elsevier, vol. 140(C).
    • Handle: RePEc:eee:rensus:v:140:y:2021:i:c:s1364032121000381
    DOI: 10.1016/j.rser.2021.110742
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    1. Moglianesi, Andrea & Keppo, Ilkka & Lerede, Daniele & Savoldi, Laura, 2023. "Role of technology learning in the decarbonization of the iron and steel sector: An energy system approach using a global-scale optimization model," Energy, Elsevier, vol. 274(C).
    2. Matteo Nicoli & Francesco Gracceva & Daniele Lerede & Laura Savoldi, 2022. "Can We Rely on Open-Source Energy System Optimization Models? The TEMOA-Italy Case Study," Energies, MDPI, vol. 15(18), pages 1-37, September.
    3. Sonja Sechi & Sara Giarola & Pierluigi Leone, 2022. "Taxonomy for Industrial Cluster Decarbonization: An Analysis for the Italian Hard-to-Abate Industry," Energies, MDPI, vol. 15(22), pages 1-31, November.
    4. Antonio Oliva & Francesco Gracceva & Daniele Lerede & Matteo Nicoli & Laura Savoldi, 2021. "Projection of Post-Pandemic Italian Industrial Production through Vector AutoRegressive Models," Energies, MDPI, vol. 14(17), pages 1-18, September.

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