Uncertainty Implications of Hybrid Approach in LCA: Precision versus Accuracy

The hybrid approach in Life Cycle Assessment (LCA) that uses both input-output and process data has been discussed in the context of mitigating truncation error and burdens of data collection. However, the implication of introducing input-output data on the overall uncertainty of an LCA result has been debated. In this study, we selected an existing process LCA, performed a Monte Carlo simulation after hybridizing each truncated flow at a time, and analyzed the dispersion and position of the distribution in the results. The results showed that hybridization effectively moved the mean of the life cycle greenhouse gas (GHG) emissions 38% higher while maintaining the standard deviation within the 0.62-0.78 range (relative standard deviation, 3-4%). We identified key activities contributing to the overall uncertainty and simulated the potential effect of collecting higher quality supplier-specific data for those activities on the overall uncertainty. The results showed that replacing as few as 10 of the largest uncertainty contributors with high precision supplier-specific data substantially narrowed the distribution. Our results suggest that hybridizing truncated inputs improves accuracy of LCA results without compromising their precision, and prioritizing supplier specific data collection can further enhance precision in a cost-effective manner.