Parameterization in Life Cycle Assessment inventory data: review of current use and the representation of uncertainty
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Parameterization refers to the practice of presenting Life Cycle Assessment (LCA) data using raw data and formulas instead of computed numbers in unit process datasets within databases. This paper reviews parameterization methods in the European Reference Life Cycle Data System (ELCD), ecoinvent v3, and the US Department of Agriculture's Digital Commons with the intent of providing a basis for continued methodological and coding advances.
Parameterized data are reviewed and categorized with respect to the type (raw data and formulas) and what is being represented (e.g., consumption and emission rates and factors, physical or thermodynamic properties, process efficiencies, etc.). Parameterization of engineering relationships and uncertainty distributions using Smirnov transforms (a.k.a. inverse transform sampling), and ensuring uncertain individual fractions (e.g., market shares) sum to the total value of interest are presented.
Seventeen categories of parameters (raw data and formulas) are identified. Thirteen ELCD unit process datasets use 975 parameters in 12 categories, with 124 as raw data points and 851 as formulas, and emission factors as the most common category of parameter. Five additional parameter categories are identified in the Digital Commons for the presentation and analysis of data with uncertainty information, through 146 parameters, of which 53 represent raw data and 93 are formulas with most being uncertainty parameters, percentages, and consumption parameters.
Parameterization is a powerful way to ensure transparency, usability, and transferability of LCI data. Its use is expected to increase in frequency, the categories of parameters used, and the types of computational methods employed.
About this Article
- Parameterization in Life Cycle Assessment inventory data: review of current use and the representation of uncertainty
The International Journal of Life Cycle Assessment
Volume 17, Issue 6 , pp 689-695
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