es9b00084_si_002.xlsx (1.82 MB)
Uncertainty Implications of Hybrid Approach in LCA: Precision versus Accuracy
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posted on 2019-03-07, 00:00 authored by Jessica Perkins, Sangwon SuhThe
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.
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Hybrid Approachprocess dataMonte Carlo simulationuncertainty contributorsGHGprecision supplier-specific datalife cycle greenhouse gastruncation errorprocess LCALife Cycle Assessmentquality supplier-specific datadata collectionLCA resultinput-output dataprioritizing supplier-specific data collectionLCA resultsUncertainty Implications
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