Screening stochastic Life Cycle assessment inventory models |
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Authors: | Kelly G Canter Dale J Kennedy Douglas C Montgomery J Bert Keats W Matthew Carlyle |
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Institution: | (1) Arizona State University, 85287-5906 Tempe AZ., USA |
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Abstract: | A screening methodology is presented that utilizes the linear structure within the deterministic life cycle inventory (LCI)
model. The methodology ranks each input data element based upon the amount it contributes toward the final output. The identified
data elements along with their position in the deterministic model are then sorted into descending order based upon their
individual contributions. This enables practitioners and model users to identify those input data elements that contribute
the most in the inventory stage. Percentages of the top ranked data elements are then selected, and their corresponding data
quality index (DQI) value is upgraded in the stochastic LCI model. Monte Carlo computer simulations are obtained and used
to compare the output variance of the original stochastic model with modified stochastic model. The methodology is applied
to four real-world beverage delivery system LCA inventory models for verification. This research assists LCA practitioners
by streamlining the conversion process when converting a deterministic LCI model to a stochastic model form. Model users and
decision-makers can benefit from the reduction in output variance and the increase in ability to discriminate between product
system alternatives. |
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Keywords: | Data quality indicator (DQI) input data quality LCI life cycle inventory (LCI) monte carlo simulation ranking screening stochastic modeling streamlining |
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