Characterizing, Propagating, and Analyzing Uncertainty in Life‐Cycle Assessment: A Survey of Quantitative Approaches

By S. Lloyd, R. Ries. 2007

607 citations recorded by the source corpus.

Subfields: Environmental Engineering, Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/20891d83dcb21a0946d60e1a4335d54a5a7240ef

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Environmental Engineering

1. Reinout Heijungs
2. Jeroen B. Guinée
3. André Bardow
4. N. Assen
5. Johannes Jung
6. T. Ekvall
7. Sangwon Suh
8. Thomas H. Christensen

In Management Science and Operations Research

1. N. Assen
2. Ben A. Wender
3. Thomas H. Christensen
4. Dominique Guyonnet
5. Thomas P. Seager
6. André Bardow
7. D. Tonini
8. Julie Clavreul

In Statistics and Probability

1. Thomas H. Christensen
2. Julie Clavreul
3. Jeroen B. Guinée
4. N. Assen
5. Ben A. Wender
6. Dominique Guyonnet
7. Thomas P. Seager
8. Reinout Heijungs

Citations in the corpus

• Stochastic and epistemic uncertainty propagation in LCA (2013, methodology): “The most commonly-used method to propagate probability distributions is the Monte Carlo analysis, as shown by Lloyd and Ries (2007) who reviewed 24 LCA studies that included uncertainty analysis.”
• Recent developments in Life Cycle Assessment. (2009, background): “In this section, we build on and extend several review papers that concentrate on uncertainty in LCA, notably Huijbregts et al. (2001), Björklund (2002), Heijungs and Huijbregts (2004), Geisler et al. (2005), and Lloyd and Ries (2007).”
• Using fuzzy numbers to propagate uncertainty in matrix-based LCI (2008, background): “All of these factors contribute to overall uncertainty, which then affects the reliability of any findings when used for decision-making during the interpretation phase (Huijbregts 1998a, b; Bjorklund 2002 ;R oss et al.2002; Lloyd and Ries 2007; Reap et al. 2008a, b). Hence, a lot of LCA research ”
• Sensitivity coefficient-based uncertainty analysis for multi-functionality in LCA (2014, methodology): “In contrast to the influence of allocation procedures, data uncertainties are mostly studied using Monte-Carlo (MC) simulations (Lloyd and Ries 2007).”
• Interpretation of comparative LCAs: external normalization and a method of mutual differences (2017, methodology): “Probability distributions at characterization used in the overlap area calculations, derive from an uncertainty analysis using the Pedigree Matrix coefficients available in Ecoinvent 3.01 (Lewandowska et al. 2004; Lloyd and Ries 2007; Muller et al. 2016).”