Decision-Tree Framework for Scaling up Emerging Technologies in Prospective Life Cycle Assessment: Case Study of Microalgae Residue Gasification

Prospective life cycle assessment (pLCA) is increasingly used to evaluate emerging technologies; however, its reliability is often constrained by the absence of life cycle inventory data (LCI) at commercial scale. Many studies rely on simple extrapolation from laboratory- or pilot-scale data, which can misrepresent process performance and environmental impacts at commercial scale. This study develops a decision-tree framework for selecting appropriate LCI scale-up methods based on technology maturity and data availability, while highlighting associated limitations. The proposed framework is informed by a systematic review and evaluation of scale-up methods and is demonstrated through a case study of microalgae residue gasification. Process simulation in Aspen Plus is selected to generate LCI for a 50-ha microalgae farm producing 611 kWe. Environmental impacts, including climate change, acidification, particulate matter formation, and photochemical oxidant formation are evaluated and compared with estimates using simple scale-up assumptions from lab- and pilot-scale data. Simulation yields a GWP100 of 0.17 kg CO2-eq/kWh compared with 0.19 and 0.58 kg CO2-eq/kWh for extrapolations from pilot and lab, respectively. While simulation and pilot-scale estimates are consistent with the median reported for bioenergy systems in literature, lab-scale estimate is much higher and similar to GWP of natural gas system. Energy efficiency is sensitive to scaling approach, increasing from 2% for lab and 18% for pilot to 37% for simulation. These findings demonstrate that the choice of scale-up method can substantially influence environmental impacts and process performance. The proposed framework provides a transparent approach improving the reliability of environmental assessments of emerging technologies.