The imperative of a widespread, climate-neutral industrial transition necessitates adopting sustainable-by-design e-ammonia production practices. However, as is the case with early-stage technologies, its full potential in decarbonization and substituting conventional infrastructure at higher manufacturing readiness levels remains unknown. While learning and scaling effects offer insights into future potentials through historical observations, a collection of learning-by-doing, learning-by-searching and scaling data is absent for emerging green transition-related technologies. This study addresses the knowledge gap by building on economic learning theory and combining it with process virtualization to develop an explorative and normative framework for (i) synthesizing environmental learning rates for first-of-a-kind (FOAK) technologies and (ii) using them in prospective life cycle assessment. We consecutively develop and scale 12 e-ammonia processes designing green hydrogen production, ammonia synthesis, and air separation units using ASPEN Plus® V11 software to construct environmental learning curves (R2> 0.95). The quantified environmental learning effects, harmonized with shared socioeconomic pathways, show the technology's comprehensive potential to evolve into an eco-efficient nth-of-a-kind production line following a 2.5 doubling of experience by 2050. The cumulative environmental progress is driven by a short technology doubling time and moderate to high 3.1-23.4% environmental learning and scaling rates. Prospective projections that involve learning and scaling effects in the foreground system markedly outperform scenarios that consider environmental progress solely in background life cycle inventories. Therefore, future-oriented sustainability assessments need to account for advancements in both foreground and background inventories simultaneously to support and guide eco-friendly technological developments effectively.