To build a sustainable chemical industry, it's essential to evaluate environmental impacts such as emissions and resource use through life cycle assessment (LCA). However, applying prospective LCA (pLCA) during the initial stages of process development remains a significant challenge, primarily due to the limited availability of detailed mass and energy balance data for both foreground and background systems. This study adopts an established and previously published framework for foreground system modelling, which originates at low technology readiness levels (TRLs) and offers a structured methodology to simulate industrial-scale production at higher TRLs using limited experimental data [1]. Notably, this framework does not rely on prior knowledge of large-scale process behaviour. Laboratory operations are typically composed of discrete, unconnected steps and involve equipment that differs substantially from that used in commercial-scale facilities. As such, directly extrapolating life cycle inventory (LCI) data from lab-scale to industrial-scale settings is generally inappropriate. Instead, the framework advocates for scaling up each individual process step independently by developing models that accurately represent realistic industrial plant configurations. To represent a prospective background system, this study utilizes future scenarios aligned with the Paris Agreement’s climate targets for global energy system development. These scenarios are derived from an integrated assessment model (IAM), which plays a crucial role in shaping the outcomes of prospective life cycle assessment studies. Each scenario offers projections for key milestone years 2030, 2040, 2050 and extending to 2100 [2]. The pLCA is conducted using Activity Browser (AB), an open-source tool designed to implement LCA under evolving future conditions [3]. The goal of this pLCA is to address the gap in understanding how laboratory-scale chemical processes can be effectively scaled up, with the aim of enhancing the sustainability of emerging processes and materials. This approach enables the generation of results that more accurately reflect industrial-scale production.