Uncertainty Propagation and Input Sensitivity in Life Cycle Assessment: An Application to Phase Change Materials

Global and local sensitivity analyses are essential for
identifying
key parameters in life cycle assessment models. However, due to limited
information on parameter uncertainty, they are often overlooked. This
paper’s objective is to address this gap by proposing a methodological
framework for defining input sensitivity, for midpoint and end point
indicators, and a quantitative approach for determining input uncertainties.
Applied to a case study on xylitol production as a phase change material,
the methodology uses Monte Carlo for uncertainty propagation and Python’s
SALib to calculate Sobol indices. Results show a 2% relative error
in midpoint indicators, aligning with pedigree matrix methods. While
accuracy depends on choosing the appropriate distribution function,
both global and local sensitivity analyses showed consistent outcomes.
This structured, user-friendly approach offers decision-makers a simplified
yet effective way to prioritize inputs, either by verifying multiple
indicators individually or focusing on damage-oriented indicators.
Future studies could refine database coefficients and explore their
influence on overall uncertainty, as well as the nonlinearity of the
model if the parameters are correlated, offering opportunities to
enhance accuracy.