Reaching climate goals requires a rapid scale-up of clean energy technologies, which, in many cases, are still under development. Low-temperature CO₂ electrolysis (LT CO2E) is a promising pre-commercial technology (TRL 3 to 6) that can produce CO₂-based fuels and chemicals using electricity. To understand the future competitiveness of such novel technologies, techno-economic assessments (TEAs) are conducted using the best available knowledge at the time, ensuring that the highest-quality TEA information supports decision-making regarding future investments. As LT CO₂E advances, its techno-economic research must evolve toward more in-depth process designs, integrating the latest knowledge regarding the technology's development and any aspects essential to commercial implementation. To do so, it is important to understand the robustness and limitations of existing LT CO2E TEAs to identify areas for further improvement; for example, electricity and CO2 cost assumptions vary significantly between TEAs for syngas, accounting for 18–81% and up to 28% of the total operational expenditure, respectively. This review assessed the origins and justifications behind common assumptions used in TEAs of LT CO2E with three main findings: 1) the methodological justifications seem stuck in the past, relying on three key studies and mature electrolysis technologies from previous decades; 2) the latest advancements in electrolyzer modeling underscore the need to update existing LT CO₂E performance benchmarks, and 3) future LT CO₂E TEAs need to include pre-treatment of CO₂ and water, product separation steps, as well as heat integration, recycling, and waste valorization, to progress beyond the preliminary conceptual design phase.@en

The storage of renewable electricity in chemical bonds is a compelling technological option that combines flexibility with the synthesis of high energy-dense fuels and chemicals and may use CO2 as raw material. The electrochemical conversion of CO2 is not yet a mature technology. Both fields, electrochemical conversion and carbon dioxide utilisation (CDU), have their own trade-offs; CO2 electrochemical reduction (CO2ER) environmental and economic performance is highly context-dependent. The successful deployment of CO2 electrochemical conversion will depend not only on the further development and scaling of the technology but also on finding appropriate combinations of technologies, business models, and socioeconomic strategies. The current project aims to create critical knowledge on the sustainable implementation of CO2 electrochemical devices for a variety of contexts. The research approach presented in the current work will develop a multidisciplinary framework to assess the contributions and trade-offs of CO2 electrochemical systems, including centralised and decentralised configurations, which are evaluated under realistic conditions. This is a crucial step in understanding the role and contribution of CO2ER within the different CO2 mitigation options in place in the upcoming years. To achieve the project’s goal, we propose a multidisciplinary methodology that includes process systems engineering (PSE) and operations research (OR) tools, and humanistic and social sciences methodologies. Modelling and optimisation techniques, value-sensitive design, and identification of government and market-based governance interventions will help identifying potential areas of improvement and bottlenecks to successfully bring CO2ER to the market. The assessment will be performed at several levels: unit (reaction pathways), process (scheduling and operation, plant layout optimisation), supply chain (optimisation under deterministic and stochastic conditions), and system (social, governance and markets perspectives) of CO2ER. The project results will (i) propose optimal CO2ER-based plants and (ii) supply chains under different contexts; (iii) translate stakeholders’ sustainability value into design requirements for CO2ER; (iv) propose a list of government interventions and market mechanisms that will allow CO2ER market penetration, and (v) identify, quantify and mitigate the influence of the most relevant sources of uncertainty. @en