Global sensitivity analysis reduces data collection efforts in LCA: A comparison between two additive manufacturing...

Area of Science:

• Environmental Science
• Engineering

Background:

• Manufacturers increasingly need to account for environmental impacts in technology design.
• Life Cycle Assessment (LCA) is a key method for quantifying environmental impacts and supporting eco-design.
• LCA often faces challenges with data availability, necessitating efficient data collection strategies.

Purpose of the Study:

• To develop a framework for prioritizing data collection efforts in LCA.
• To address the trade-off between data cost and robustness in LCA.

Main Methods:

• Screening Life Cycle Inventory (LCI) analysis with uncertainty ranges for all input parameters.
• Monte Carlo analysis to propagate uncertainty through the LCA model.
• Global sensitivity analysis (Sobol' indices) to rank input parameters by their contribution to result variability.

Main Results:

• The framework enables an iterative process to prioritize data collection on the most sensitive parameters.
• A case study comparing cold spray and wire arc additive manufacturing demonstrated the framework's operationalization.
• Learnings emphasized the importance of defining uncertainty ranges and convergence criteria.

Conclusions:

• The developed framework provides a systematic approach to optimize data collection in LCA.
• Sensitivity analysis is crucial for identifying key parameters influencing environmental impact assessments.
• Further research is needed on defining uncertainty ranges and convergence criteria for LCA data collection.