Optimization models are central to guiding climate policy and investment decisions. However, they typically rely on static life cycle assessment (LCA) data, overlooking how emissions could evolve over time. Recently introduced prospective LCAs (pLCA) assess the environmental impact of industrial systems under likely socio-economic and technological changes, providing the basis to perform more accurate life cycle optimizations of energy systems. This study introduces a framework integrating future emissions trajectories into the optimization of low-carbon energy systems that capitalizes on the premise pLCA tool. Using the US hydrogen supply chain as a test bed, we develop a regional, multi-period optimization model until 2050 and compare the outcomes using static versus prospective emissions data. Our results show that relying on static emissions can overestimate both system costs and emissions by up to USD 162 billion (17%) and 13.6 Gt CO 2 (81%) across scenarios. In contrast, models informed by pLCA enable cleaner, more geographically diverse technology deployment and more efficient allocation of public subsidies. These findings highlight the importance of integrating future emissions into energy systems modeling to develop more effective, resilient and sustainable decarbonization strategies. • Relying on static emissions overestimate costs by USD 162 billion and emissions by 14 Gt CO 2 . • Integrating future emissions into optimization reshapes technology choices and costs. • Solar, hydro, nuclear electrolysis, and bio-based hydrogen are preferred technologies. • Future emissions across 18 hydrogen technologies in US states until 2050 are calculated.
Charalambous et al. (Sat,) studied this question.