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Carbon Capture, Storage and Utilization (CCS/CCU) is critical for achieving net-zero emissions. Although the recent surge in CCS/CCU projects announcement, there is a clear gap between announced capacity (around 400 Mt CO₂ per year) and the Net Zero Emissions (NZE) scenario deployment target (around 1 Gt per year) by 2030. This review examines breakthroughs and advancements across both established and emerging CCS/CCU systems with different Technology Readiness Levels (TRLs) in various industrial sectors, emphasizing the necessity of prospective assessments for their acceleration and scalability. It examines the development and application of prospective Life Cycle Assessment (pLCA) and prospective Techno-Economic Assessment (pTEA), highlighting their limitations and importance of their outcomes in decision-making processes. Differences between the evolving dynamics of the technological systems (foreground) and the evolution of the overall socioeconomic system (background) are discussed. Incorporating scenario data from Integrated Assessment Models (IAMs) into pLCA and pTEA reveals an iterative relationship that significantly influences the outcome of both the environmental assessments and the economics of large-scale production of the CCS/CCU systems under study. This, in turn, could reshape investment strategies towards advanced technologies, necessitating their consideration within the evolving structure of IAMs. It is concluded that the inherent limitations of CCS/CCU technologies at an early stage of development require quantitative uncertainty analysis and demand robustness, interdisciplinary collaboration, policy intervention, and data transparency. The rigorous evaluative frameworks are key for developing economic, environmental and climate policies and enable well-informed decisions across rapidly evolving sectors. A framework is proposed in this review, outlining a multistep process that includes a series of databases and open-source tools to interface pTEA and pLCA with enhanced IAMs for CCS/CCU, demonstrating its potential to improve decision-making and policy development.
Wang et al. (Fri,) studied this question.