How key-enabling technologies' regimes influence sociotechnical transitions: The impact of artificial intelligence on decarbonization in the steel industry. (10th October 2022)

• Record Type: Journal Article
• Authors: John, Nikhil; Wesseling, Joeri Hendrik; Worrell, Ernst; Hekkert, Marko
• Is Part Of: Journal of cleaner production. Volume 370(2022)
• Publication Date: 2022-10-10
• Subjects: Digitalization -- Energy-intensive processing industries -- Barriers to innovation -- Multi-regime interactions -- Sociotechnical systems -- Multi-level perspective; Factory and trade waste -- Management -- Periodicals; Manufactures -- Environmental aspects -- Periodicals; Déchets industriels -- Gestion -- Périodiques; Usines -- Aspect de l'environnement -- Périodiques; 628.5
• DOI: 10.1016/j.jclepro.2022.133624
• Languages: English
• ISSNs: 0959-6526

Abstract

Abstract: Key Enabling Technologies (KETs) are pervasive groups of technologies expected to enable innovation. They have been promoted as technologies with tremendous potential for boosting economic growth and sustainability in all sectors of society – a claim whose validity remains underexplored. Building on systems thinking and the Multi-Level Perspective, we develop a novel approach to assess the socio-technical impact of a KET regime on a transitioning sectoral regime. This approach is applied to the case of the Artificial Intelligence (AI) KET impacting the decarbonization of the energy-intensive steel industry. To assess AI's technical impact, we compiled an inventory of AI tools based on reviewing technical scientific articles. Our analysis shows that AI adds technological value to the full range of areas in the steel industry, like predicting process parameters; optimizing operations, scheduling, and electrical energy; and forecasting product demand, quality, and site emissions. Semi-structured interviews were the primary data source to assess AI's broader socio-institutional impact. The results indicate that AI may currently be reinforcing path dependencies of the steel industry, as AI tools are more focused on incremental improvement for existing technologies rather than novel low-carbon technologies. However, AI also offers capabilities to reduce barriers to sustainability innovation, like system integration challenges, flexibility challenges, demand-side barriers, and risk-related barriers. Finally, we reflect on the generalizability of our approach for studying other transitions, and we induce characteristics of the AI-Digital KET regimes. We find that through these characteristics, the AI-Digital KET regime alters existing and creates new system structures (actors, networks, and institutions) within the impacted sector. Highlights: Our novel approach analyzes Key Enabling Technologies' socio-technical impact on transitions. We assess the impact of the AI-KET on the steel industry's decarbonization transition. We find more AI use on established technologies than on novel low-carbon innovation. We identify new or altered system structures in steelmaking, introduced by the AI-KET regime. These structural changes can help reduce some barriers to innovation.