Standardized benchmarking of direct air capture technologies: techno-economic and environmental assessment, and multi-criteria decision analysis

Large-scale deployment of direct air capture (DAC) technologies is essential to achieving net-zero emissions by 2050, yet selecting suitable DAC approaches remains challenging due to their diverse operating principles, maturity levels, and inconsistent performance data. Here, we develop a standardized comparative framework to assess four DAC technologies (HT-Aq, TVSA, MSA, and pH-swing) under consistent climate conditions, process assumptions, capture capacities, energy prices, and carbon intensities. The framework couples physics-based process modelling, enabling technology-specific unit sizing, with techno-economic and life-cycle assessment and advanced multi-criteria decision analysis (MCDA). Key performance indicators, including energy and water demand, levelized CO2 cost, and global warming impact, are quantified and used to rank DAC technologies across sectoral applications. The results effectively demonstrated that no DAC technology is universally optimal across all examined scenarios. We found liquid-DAC approaches (HT-Aq and pH-swing) to exhibit limited SEC variations across climate conditions, although their water demand increases significantly under dry environments. In contrast, solid-sorbent DAC systems (TVSA and MSA) are highly sensitive to ambient conditions and achieve optimal SEC under relatively humid environments (50%). HT-Aq process is found to be the most cost-effective under large-scale applications (>10 ktCO2/year), but its cost-effectiveness diminishes when lifecycle CO2 emissions are considered. On the other hand, due to its high sensitivity to electricity prices, pH-swing DAC process is found to be the closest approach to achieving the target DAC cost (100 $/tCO2), provided that low-cost electricity (<20 $/MWh) is used and that hydrogen byproducts are sold at reasonable market prices (>4 $/kg). Overall, the results of this work provide guidelines for supporting decision-makers in identifying the most suitable DAC technologies under various operating conditions and targeted applications.