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.