Methods for uncertainty propagation in life cycle assessment

Groen, E.A.; Heijungs, Reinout; Bokkers, E.A.M.; Boer, I.J.M. de

doi:10.1016/j.envsoft.2014.10.006

Cited by 117 publications

(77 citation statements)

References 37 publications

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“…The analytical approaches are based on the theory of error propagation (Ciroth et al 2004) and address with differential calculus how input uncertainties propagate into the output uncertainties through the LCA mathematical model (Groen et al 2014). Multiple analytical expressions based on a wide range of formulations and assumptions are present in the literature (Heijungs et al 2005;Heijungs 2010;Hong et al 2010;Clavreul et al 2012;Imbeault-Tétreault et al 2013).…”

Section: Introductionmentioning

confidence: 99%

A global approach for sparse representation of uncertainty in Life Cycle Assessments of waste management systems

Bisinella

Conradsen

Christensen

et al. 2016

Int J Life Cycle Assess

117

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Purpose Identification of key inputs and their effect on results from Life Cycle Assessment (LCA) models is fundamental.Because parameter importance varies greatly between cases due to the interaction of sensitivity and uncertainty, these features should never be defined a priori. However, exhaustive parametrical uncertainty analyses may potentially be complicated and demanding, both with analytical and sampling methods. Therefore, we propose a systematic method for selection of critical parameters based on a simplified analytical formulation that unifies the concepts of sensitivity and uncertainty in a Global Sensitivity Analysis (GSA) framework. MethodsThe proposed analytical method based on the calculation of Sensitivity Coefficients (SC) is evaluated against Monte Carlo sampling on traditional uncertainty assessment procedures, both for individual parameters and for full parameter sets. Three full-scale waste management scenarios are modelled with the dedicated waste LCA model EASETECH and a full range of ILCD recommended impact categories. Common uncertainty ranges of 10 % are used for all parameters, which we assume to be normally distributed. The applicability of the concepts of additivity of variances and GSA is tested on results from both uncertainty propagation methods. Then, we examine the differences in discernibility analyses results carried out with varying numbers of sampling points and parameters.Results and discussion The proposed analytical method complies with the Monte Carlo results for all scenarios and impact categories but offers substantially simpler mathematical formulation and shorter computation times. The coefficients of variation obtained with the analytical method and Monte Carlo differ only by 1 %, indicating that the analytical method provides a reliable representation of uncertainties and allows determination of whether a discernibility analysis is required.The additivity of variances and the GSA approach show that the uncertainty in results is determined by a limited set of important parameters. The results of the discernibility analysis based on these critical parameters vary only by 1 % from discernibility analyses based on the full set but require significantly fewer Monte Carlo runs. ConclusionsThe proposed method and GSA framework provide a fast and valuable approximation for uncertainty quantification. Uncertainty can be represented sparsely by contextually identifying important parameters in a systematic manner. The proposed method integrates with existing step-wise approaches for uncertainty analysis by introducing a global importance analysis before uncertainty propagation.

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Section: Introductionmentioning

confidence: 99%

A global approach for sparse representation of uncertainty in Life Cycle Assessments of waste management systems

Bisinella

Conradsen

Christensen

et al. 2016

Int J Life Cycle Assess

117

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“…Among the most widely used ones are sampling methods such as Monte Carlo (MC) simulations that rely on determining the probability distribution of the results by brute computing force progressively increasing in time (Heijungs and Huijbregts 2004). Other methods such as Latin hypercube simulation, which uses a more efficient random sampling, could be used for propagation too (Groen et al 2014). However, given the aim of the paper, we focused on the most widely used and intuitively easiest approach: MC.…”

Section: Allocation Methodsmentioning

confidence: 99%

“…For this, several methods exist and have been used in LCA (Groen et al 2014;Heijungs and Lenzen 2014). Among the most widely used ones are sampling methods such as Monte Carlo (MC) simulations that rely on determining the probability distribution of the results by brute computing force progressively increasing in time (Heijungs and Huijbregts 2004).…”

Section: Allocation Methodsmentioning

confidence: 99%

A pseudo-statistical approach to treat choice uncertainty: the example of partitioning allocation methods

Beltrán

Heijungs

Guinée

et al. 2015

Int J Life Cycle Assess

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Purpose Despite efforts to treat uncertainty due to methodological choices in life cycle assessment (LCA) such as standardization, one-at-a-time (OAT) sensitivity analysis, and analytical and statistical methods, no method exists that propagate this source of uncertainty for all relevant processes simultaneously with data uncertainty through LCA. This study aims to develop, implement, and test such a method, for the particular example of the choice of partitioning methods for allocation in LCA, to be used in LCA calculations and software. Methods Monte Carlo simulations were used jointly with the CMLCA software for propagating into distributions of LCA results, uncertainty due to the choice of allocation method together with uncertainty of unit process data. In this study, a methodological preference is assigned to each partitioning method, applicable to multi-functional processes in the system. The allocation methods are sampled per process according to these preferences. A case study on rapeseed oil focusing on three greenhouse gas (GHG) emissions and their global warming impacts is presented to illustrate the method developed. The results of the developed method are compared with those for the same case similarly quantifying uncertainty of unit process data but accompanied by separate scenarios for the different partitioning choices. Results and discussion The median of the inventory flows (emissions) for separate scenarios varies due to the partitioning choices and unit process data uncertainties. Inventory variations are reflected in the global warming results. Results for the approach of this study vary with the methodological preference assigned to the different allocation methods per multi-functional process and with the continuous distribution of unit process data. The method proved feasible and implementable. However, absolute uncertainties only further increased. Therefore, it should be further researched to reflect relative uncertainties, more relevant for comparative LCAs. Conclusions Propagation of uncertainties due to the choice of partitioning methods and to unit process data into LCA results is enabled by the proposed method, while capturing variability due to both sources. It is a practical proposal to tackle unresolved debates about partitioning choices increasing robustness and transparency of LCA results. Assigning a methodological preference to each allocation method of multifunctional processes in the system enables pseudo-statistical propagation of uncertainty due to allocation. Involving stakeholders in determining these methodological preferences allows for participatory approaches. Eventually, this method could be expanded to also cover other ways of dealing with allocation and to other methodological choices in LCA.

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“…This sampling-based method has been chosen because the results allow for a sensitivity contribution analysis based on probability theory to identify most contributing inputs. In particular, the nature of the results (family of distributions) using possibility theory (for instance, Fuzzy interval arithmetic) does not allow such analysis [21,22]. The Spearman rank correlation (ρ) between each uncertain input and results distribution is calculated.…”

Section: Methods To Prioritise the Regionalisation Effortmentioning

confidence: 99%

Ready-to-use and advanced methodologies to prioritise the regionalisation effort in LCA

Patouillard

Bulle²,

Margni

2016

Matériaux & Techniques

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-The spatial dimension is an important aspect in life cycle assessment (LCA). Indeed life cycle processes, and therefore also elementary flows, are most likely to be geographically scattered due to global supply chains. The environmental impacts related to an elementary flow can be different across the globe depending on the spatial variability of ecosystem sensitivity. Integrating spatial dimension seems to be a promising way to increase LCA results' reliability by reducing spatial uncertainties. LCA regionalisation refers to the integration of the spatial variability that really exists to improve result representativeness and reduce spatial uncertainties. As regionalisation requires additional effort for LCA practitioners, it is necessary to ensure the interest of such an effort and to prioritise it. This work proposes two methodologies to prioritise the regionalisation effort in LCA depending on the decision context and the type of study. They allow the selection of impacts, processes and regionalisation aspects that LCA practitioners need to focus on. The first one is a ready-to-use methodology and the second one involves more advanced tools based on spatial uncertainty analysis. The proposed methodologies are stepwise. The ready-to-use methodology involves tools that are already used by LCA practitioners and is based on impact contribution analysis. Regarding the advanced methodology, most relevant impact categories and processes to be further investigated for regionalisation are selected according to their impact contribution and their uncertainty level by using Monte Carlo simulation and regression analysis. The regionalisation effort is estimated depending on the confidence level required for the case study. The relevance and limits of each methodologies are investigated. Results underline the importance to take into account both impact and uncertainty contributions to select processes that need to be regionalised. The proposed methodologies highlight the need to focus on the goal and scope at the early stages of a study in order to clearly identify the intended audience and its requirements regarding study quality. A dialogue with the decision maker during the study would be beneficial. Those approaches can be relevant for other types of uncertainty but should be adapted.

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Methods for uncertainty propagation in life cycle assessment

Cited by 117 publications

References 37 publications

A global approach for sparse representation of uncertainty in Life Cycle Assessments of waste management systems

A global approach for sparse representation of uncertainty in Life Cycle Assessments of waste management systems

A pseudo-statistical approach to treat choice uncertainty: the example of partitioning allocation methods

Ready-to-use and advanced methodologies to prioritise the regionalisation effort in LCA

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