A Methodology for Robust Comparative Life Cycle Assessments Incorporating Uncertainty

Area of Science:

• Environmental Science
• Engineering
• Sustainability Studies

Background:

• Comparative life cycle assessment (LCA) often struggles to definitively identify environmentally preferable alternatives due to inherent uncertainties.
• Existing methods may not adequately address the full spectrum of scenario and input data variability.

Purpose of the Study:

• To develop a robust methodology for comparative LCA that explicitly leverages uncertainty.
• To identify definitively environmentally preferable choices among alternatives and the significance of differences under uncertainty.
• To pinpoint influential parameters and resolvable scenarios where one alternative has a clearly lower environmental impact.

Main Methods:

• Employing an aggregated probabilistic scenario-aware analysis to evaluate a broad range of possible scenarios.
• Utilizing decision-tree partitioning algorithms to group meaningful scenarios.
• Conducting sensitivity analysis to identify influential parameters when alternatives are not resolvable.
• Introducing novel definitions and approaches for characterizing uncertainty quantities in LCA.

Main Results:

• The methodology successfully identifies scenarios with definitively preferable alternatives and quantifies the significance of differences.
• Influential parameters driving the comparison outcomes were identified through sensitivity analysis.
• The case study on pavements demonstrated the practical application and effectiveness of the proposed method.

Conclusions:

• Leveraging uncertainty in LCA provides a more robust framework for environmental decision-making.
• The proposed methodology enhances the ability to identify clear environmental preferences and understand the impact of parameter uncertainty.
• This approach can lead to more reliable and actionable environmental impact assessments, particularly in complex systems like pavement design.