Prospective life cycle assessment of cellulose nanomaterials production: Evaluation of the environmental learning potential through learning rate screening for advanced biorefineries

Biowaste-derived nanocellulose fibers and crystals are considered promising alternatives to conventional fossil-based materials in packaging and electronics applications. As first-of-a-kind biorefineries evolve, they present opportunities for cost and environmental impact mitigation due to learning and scaling effects. Incorporating technological progress-driven environmental benefits into prospective life cycle assessments (pLCAs) remains constrained due to the absence of learning rates and challenges in capturing the dynamics of technological innovation. This study introduces a screening algorithm to accelerate the technical quantification of environmental learning rates for early-stage sugar beet pulp-to-cellulose nanomaterials (CNMs) conversion. 10 explorative n^th-of-a-kind sugar beet pulp valorization plants are developed and scaled using process simulations and bench-scale empirical data, factoring technological improvement through cost minimization and energy utilization streamlining. The iterative algorithm identifies ideal configurations and scales for achieving targeted learning outcomes. Explorative screening reveals an economic learning rate of 9.0 % for the bleaching-mechanical treatment-based CNM production, translating into environmental learning rates ranging from 3.1 to 6.6 %. Advancements in environmental learning rates are combined with Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs) to project CNMs development trajectories by 2075. The pLCA shows a 24.5 % reduction in climate change impacts by 2075 compared to 2025's first-of-a-kind configuration, leading to a final value of 78 kg CO_2-eq kg_CNM⁻¹ for the SSP1-Base, Optimistic-Market Scenario. Overall, sugar beet pulp valorization to CNMs offers an eco-evolving solution for reducing biomass waste generation through efficient processing, whereas learning rate screening facilitates a time-efficient structural plan for process optimization that can be applied to diverse early-stage technologies.