Quantifying the Environmental Impact of Cosmetic Ingredients through Novel Algorithmic Life Cycle Assessments

Fairglow refines INCI emission factors, reducing proxy reliance and enhancing LCA precision for accurate environmental impact assessments.

Learning Points

• Fairglow analyzed hundreds of LCAs of cosmetic ingredients to develop a process-based algorithm for estimating EFs using limited life cycle data. The methodology varies slightly for synthetic and plant-based ingredients:
  Synthetic Ingredients : LCAs are calculated using average inputs for chemical reactions and processes (ex. catalysts, solvents, and their respective recycling rates).
  Plant-based Ingredients : LCAs incorporate data on raw material extraction, farming activities, and harvesting.
• The initial test of Fairglow’s algorithm was conducted on 130 INCIs and validated in collaboration with a large French research lab. EFs calculated by the algorithm were compared against the lab’s internal results and classified into three categories: Good (<20% deviation), Improvement Needed (20-50% deviation), and Significant Deviation (>50% deviation).
  The majority of calculated EFs fell within the “Good” range, demonstrating the algorithm’s accuracy and reliability. For cases where deviations occurred, the differences could be attributed to: transportation assumptions, process variability, and coproduction complexities/allocation assumptions.
• Fairglow’s innovative algorithm fulfills a crucial need for accurate, scalable LCA estimations for cosmetic ingredients absent from current EF databases. By bridging these data gaps, it provides the cosmetics industry with a reliable tool to assess and reduce product environmental impacts more effectively. Future developments will focus on refining the algorithm’s boundary conditions as adoption expands and incorporating novel processes and materials to enhance its versatility and accuracy.