Sensitivity and Scenario Analysis to Reduce the Carbon Footprint of Polypropylene Processing Using Primary Industrial Data

Life cycle assessment (LCA) studies of polypropylene (PP) processing commonly rely on generic secondary databases, while primary industrial inventories for plastic conversion processes remain scarce. This study addresses this gap by quantifying the cradle-to-gate carbon footprint of polypropylene processing using anonymised primary industrial data collected in 2024 from four European polypropylene processing facilities. Unlike previous studies relying mainly on generic secondary inventories, the proposed approach combines primary industrial data with sensitivity and scenario analyses to identify practical priorities for emission reduction. The baseline carbon footprint was estimated at 1.44 tCO2e per tonne of finished product, with material production and energy-intensive processing identified as the major emission hotspots. One-Factor-at-a-Time (OFAT) sensitivity analysis showed that polypropylene type, process efficiency, renewable electricity use, and process waste management were the most influential parameters, whereas water consumption and additive use had only a minor effect on overall emissions. Scenario analysis indicated that combining recycled polypropylene, improved process efficiency and renewable electricity reduced emissions by 45.8%, while reducing process waste and fully recycling production residues achieved a 42.2% reduction compared with the baseline. By integrating primary industrial inventory data with sensitivity and scenario analyses, this study provides a more representative assessment of real industrial polypropylene processing conditions than approaches based solely on generic databases and identifies practical priorities for industrial carbon mitigation.