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

Area of Science:

• Environmental Science
• Materials Science
• Industrial Ecology

Background:

• Life cycle assessment (LCA) of polypropylene (PP) processing often uses generic data.
• Primary industrial data for plastic conversion is limited, impacting assessment accuracy.

Purpose of the Study:

• To quantify the cradle-to-gate carbon footprint of PP processing using primary industrial data.
• To identify key emission hotspots and practical reduction priorities.

Main Methods:

• Collected anonymised primary industrial data from four European PP processing facilities in 2024.
• Combined primary data with One-Factor-at-a-Time (OFAT) sensitivity and scenario analyses.
• Calculated baseline carbon footprint and evaluated emission reduction strategies.

Main Results:

• Baseline carbon footprint estimated at 1.44 tCO2e/tonne of finished PP product.
• Material production and energy-intensive processing identified as major emission hotspots.
• Polypropylene type, process efficiency, renewable electricity, and waste management significantly influence emissions.

Conclusions:

• Primary industrial data provides a more representative assessment of PP processing emissions than generic databases.
• Recycled PP, improved efficiency, and renewable electricity can reduce emissions by up to 45.8%.
• Strategies like waste reduction and residue recycling offer substantial carbon mitigation potential (42.2% reduction).