Role of technology learning in the decarbonization of the iron and steel sector: a global approach using a TIMES energy model

The iron and steel sector, in addition to being one of the most impacting for greenhouse gases emissions on a global level (from 7 to 9% yearly), is one of the most difficult sectors to decarbonise, due to fierce competition and the absence of economically comparable alternatives to traditional processes, based on the use of fossil fuels. This paper therefore aims to analyze the impact that future investments in decarbonising technologies may have on their technological development, taking into account the uncertainty intrinsic to their correlation (called technology learning), and consequently, on the decarbonisation of the sector, combined with the presence of policies for the reduction of emissions on a global scale. Using an appropriate energy system model (EUROFusion TIMES Model), a learning model (Wright curve), and having supposed different levels of learning, a series of simulations were run. The results showed that a significant impact can be played by such phenomenon in the long term, as the decarbonization results particularly enhanced when the levels of learning are maximized for electrolysis and hydrogen-based processes, while CCS-based processes play only a marginal role in the short term. Therefore, long-term investments on the former technologies are recommended, while further, policy-centered studies should be performed to understand the impact the latter can have in the short term.