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

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.

Automated summary

This paper reviews the technological outlook in five industrial subsectors and builds a dataset containing technologies currently in use or announced for use in various macro-scale energy models, particularly focusing on the TIMES family. The dataset aims to showcase the level of sustainability each industrial sector can achieve in the long term.