Propagation of Uncertainty in Risk Assessments: The Need to Distinguish Between Uncertainty Due to Lack of Knowledge and Uncertainty Due to Variability

Hoffman, F.O.; Hammonds, J.S.

doi:10.1111/j.1539-6924.1994.tb00281.x

Cited by 623 publications

(277 citation statements)

References 2 publications

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“…Uncertainty is of two different natures: while epistemic uncertainty relates to an incomplete state of knowledge 56 (Hoffman and Hammonds, 1994), stochastic uncertainty originates from the inherent variability of the natural world. 57…”

Section: Abstract 26 27mentioning

confidence: 99%

Quantifying uncertainty in LCA-modelling of waste management systems

Clavreul¹,

Guyonnet²,

Christensen³

2012

Waste Management

290

158

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26 27Uncertainty analysis in LCA studies has been subject to major progress over the last years. In the context of waste 28 management, various methods have been implemented but a systematic method for uncertainty analysis of waste-29 LCA studies is lacking. The objective of this paper is (1) to present the sources of uncertainty specifically inherent to 30 waste-LCA studies, (2) to select and apply several methods for uncertainty analysis and (3) to develop a general 31 framework for quantitative uncertainty assessment of LCA of waste management systems. The suggested method is a 32 sequence of four steps combining the selected methods: (Step 1) a sensitivity analysis evaluating the sensitivities of 33 the results with respect to the input uncertainties, (Step 2) an uncertainty propagation providing appropriate tools for 34 representing uncertainties and calculating the overall uncertainty of the model results, (Step 3) an uncertainty 35 contribution analysis quantifying the contribution of each parameter uncertainty to the final uncertainty and (Step 4) 36 as a new approach, a combined sensitivity analysis providing a visualization of the shift in the ranking of different 37 options due to variations of selected key parameters. This tiered approach optimizes the resources available to LCA 38 practitioners by only propagating the most influential uncertainties. Waste management has during the last decade been subject to a range of life cycle assessment (LCA; described in 48 ISO, 2006) studies e.g. Damgaard et al. (2011, Lazarevic et al. (2010) and Pires et al. 49 (2011). The purposes of these studies have been to help quantifying, for example, where in the waste management 50 system the environmental loads and savings are taking place, which technologies are preferable under specific 51 conditions, or the balance between material and energy recovery. LCA-models specifically focusing on waste 52 management systems are available; see Gentil et al. (2010) for a review of the models. 53As for any LCA study, results are subject to uncertainty due to the combined effects of data variability, 54 erroneous measurements, wrong estimations, unrepresentative or missing data and modelling assumptions. 55Uncertainty is of two different natures: while epistemic uncertainty relates to an incomplete state of knowledge 56 (Hoffman and Hammonds, 1994), stochastic uncertainty originates from the inherent variability of the natural world. 57Such uncertainty can be spatial (e.g. when the farming practice of land receiving compost varies spatially) or 58 temporal (e.g. when the performance of a process varies with time). These two different natures of the uncertainty are 59 usually treated together and referred to by the term "uncertainty". 60 They found that stochastic modelling was the most frequently-used method to propagate uncertainties in LCA. This 75 method propagates probability distributions using random sampling like the Monte Carlo analysis. However, they 76 noted that many of the studies using such modelling...

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Section: Abstract 26 27mentioning

confidence: 99%

Quantifying uncertainty in LCA-modelling of waste management systems

Clavreul¹,

Guyonnet²,

Christensen³

2012

Waste Management

290

158

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“…When simple analytical expressions for the probability distributions are available, variance propagation can be applied for propagating the uncertainties (Morgan andHenrion, 1990, Hoffman andHammonds, 1994). When analytical methods cannot be applied, the uncertainties can be propagated using Monte Carlo analysis, this is the approach used in the tool.…”

Section: Tiermentioning

confidence: 99%

The ERICA Tool

Brown

Alfonso

Ávila

et al. 2008

Journal of Environmental Radioactivity

408

271

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Article (refereed)Brown AbstractThe ERICA Tool is a computerised, flexible software system that has a structure based upon the ERICA Integrated Approach to assessing the radiological risk to biota. The Tool guides the user through the assessment process, recording information and decisions and allowing the necessary calculations to be performed to estimate risks to selected animals and plants. Tier 1 assessments are media concentration based and use pre-calculated environmental media concentration limits to estimate risk quotients. Tier 2 calculates dose rates but allows the user to examine and edit most of the parameters used in the calculation including concentration ratios, distribution coefficients, percentage dry weight soil or sediment, dose conversion coefficients, radiation weighting factors and occupancy factors. Tier 3 offers the same flexibility as Tier 2 but allows the option to run the assessment probabilistically if the underling parameter probability distribution functions are defined. Results from the Tool can be put into context using incorporated data on dose effects relationships and background dose rates.

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“…Epistemic uncertainty can be reduced whereas aleatory uncertainty is not reducible. As a consequence, many researchers argue that both types of uncertainty should be treated separately (e.g., Hoffman and Hammonds, 1994, Ferson and Ginzburg, 1996, Hora, 1996, Parry, 1996, Haimes, 1998, Cullen and Frey, 1999, Hall, 2003, Helton and Oberkamp, 2004, Merz and Thieken, 2005. The separation between these two kinds of uncertainty is particularly important in risk analyses, where aleatory uncertainty arises from the many possible failure scenarios that may occur, and epistemic uncertainty arises from a lack of knowledge with respect to the quantification of the frequency, evolution or consequences of failures.…”

Section: Introductionmentioning

confidence: 99%

Flood risk curves and uncertainty bounds

2009

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Although flood risk assessments are frequently associated with significant uncertainty, formal uncertainty analyses are the exception rather than the rule. We propose to separate two fundamentally different types of uncertainty in flood risk analyses: aleatory and epistemic uncertainty. Aleatory uncertainty refers to quantities that are inherently variable in time, space or populations of individuals or objects. Epistemic uncertainty results from incomplete knowledge and is related to our inability to understand, measure and describe the system under investigation. The separation between aleatory and epistemic uncertainty is exemplified for the flood risk analysis of the City of Cologne, Germany. This flood risk assessment consists of three modules, (1) flood frequency analysis, (2) inundation estimation, and (3) damage estimation. By the concept of parallel models, the epistemic uncertainty of each module is quantified. The epistemic uncertainty associated with the risk estimate is reduced by introducing additional information into the risk analysis. Finally, the contribution of different modules to the total uncertainty is quantified. The flood risk analysis results in a flood risk curve, representing aleatory uncertainty, and in associated uncertainty bounds, representing epistemic uncertainty. In this way, the separation reveals the uncertainty (epistemic) that can be reduced by more knowledge and the uncertainty (aleatory) that is not reducible.

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Propagation of Uncertainty in Risk Assessments: The Need to Distinguish Between Uncertainty Due to Lack of Knowledge and Uncertainty Due to Variability

Cited by 623 publications

References 2 publications

Quantifying uncertainty in LCA-modelling of waste management systems

Quantifying uncertainty in LCA-modelling of waste management systems

The ERICA Tool

Flood risk curves and uncertainty bounds

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