LCA of emerging technologies: addressing high uncertainty on inputs' variability when performing global sensitivity...

Area of Science:

• Environmental Science
• Chemical Engineering
• Systems Analysis

Background:

• Global sensitivity analysis (GSA) is crucial for understanding life cycle assessment (LCA) model structure and ensuring result credibility.
• GSA ranks input parameters by influence on output variability, vital for parameterized LCA models.
• Uncertainty in input parameter descriptions, especially for new technologies, can lead to misleading GSA outcomes and inaccurate rankings.

Purpose of the Study:

• To systematically assess the sensitivity of GSA results to the description of input parameter variability.
• To develop a methodology for analyzing the stability of input rankings in GSA when input uncertainties are high.
• To enhance the application of GSA in LCAs dealing with significant data uncertainties.

Main Methods:

• Developed a methodology to evaluate the impact of input variability descriptions on GSA outcomes.
• Assessed the stability of input parameter rankings derived from GSA.
• Applied the methodology to a case study modeling greenhouse gas emissions for enhanced geothermal systems (EGS).

Main Results:

• The study demonstrates that the description of input variability significantly influences GSA results and input rankings.
• The proposed methodology successfully identifies key LCA model inputs while accounting for description-related uncertainties.
• The case study on EGS illustrates the practical application of the method in identifying critical parameters for emission modeling.

Conclusions:

• A novel methodology is presented for assessing GSA result sensitivity to input variability in LCA.
• This approach improves the reliability of GSA for emerging technologies with high data uncertainty.
• The findings contribute to more robust and credible LCA by addressing the critical issue of input description sensitivity.