Sensitivity Analysis of Environmental Process Modeling in a Life Cycle Context: A Case Study of Hemp Crop Production

Andrianandraina,; Ventura, Anne; Kiessé, Tristan Senga; Cazacliu, Bogdan; Idir, Rachida; Werf, Hayo M.G. van der

doi:10.1111/jiec.12228

Cited by 41 publications

(34 citation statements)

References 21 publications

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“…As explained in the methodology section, we believe that this pseudo-statistical method is closer to the domain of uncertainty analysis, given that not only unit process data is propagated but also the methodological preference of allocation methods are propagated too by means of a statistical method (in this case Monte Carlo), to the LCA results. We are aware though, that for example, Andrianandraina et al (2015) account for the propagation of the uncertainty due to methodological preference of allocation methods to the LCA results as a way of sensitivity analysis, therefore placing Fig. 5 Left panels: LCI results for the main GHG emissions to air of 4000 MC simulations for the method introduced in this study using three different sets of methodological preferences for the allocation methods as defined in Table 3.…”

Section: Discussionmentioning

confidence: 99%

“…Besides the analytical method of Hanes et al (2015), there is the statistical approach of Andrianandraina et al (2015). They apply local and global sensitivity analyses to determine the influence of uncertainty in unit process data and methodological and modeling parameters to the total uncertainty in LCA results.…”

Section: Introductionmentioning

confidence: 99%

“…Particularly, the partitioning method is treated as a qualitative methodological parameter with a uniform discrete distribution and two possible values that correspond to economic and mass partitioning. Moreover, of the available literature, only Andrianandraina et al (2015) treat variability of LCA results due to unit process data uncertainty as well as due to the choice of allocation method.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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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“…2014; Andrianandraina et al. ). Because nonlinearities can only be suspected to be interactions (Andrianandraina et al.…”

Section: Introductionmentioning

confidence: 99%

Evaluating the Use of Global Sensitivity Analysis in Dynamic MFA

Džubur

Buchner

Laner

2016

J of Industrial Ecology

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Summary Dynamic material flow analysis (MFA) provides information about material usage over time and consequent changes in material stocks and flows. In order to understand the effect of limited data quality and model assumptions on MFA results, the use of sensitivity analysis methods in dynamic MFA studies has been on the increase. So far, sensitivity analysis in dynamic MFA has been conducted by means of a one‐at‐a‐time method, which tests parameter perturbations individually and observes the outcomes on output. In contrast to that, variance‐based global sensitivity analysis decomposes the variance of the model output into fractions caused by the uncertainty or variability of input parameters. The present study investigates interaction and time‐delay effects of uncertain parameters on the output of an archetypal input‐driven dynamic material flow model using variance‐based global sensitivity analysis. The results show that determining the main (first‐order) effects of parameter variations is often sufficient in dynamic MFA because substantial effects attributed to the simultaneous variation of several parameters (higher‐order effects) do not appear for classical setups of dynamic material flow models. For models with time‐varying parameters, time‐delay effects of parameter variation on model outputs need to be considered, potentially boosting the computational cost of global sensitivity analysis. Finally, the implications of exploring the sensitivities of model outputs with respect to parameter variations in the archetypical model are used to derive model‐ and goal‐specific recommendations on choosing appropriate sensitivity analysis methods in dynamic MFA.

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“…This is particularly important as many economies strive towards circularity, where LCA systems will encounter more often multifunctional processes (Mendoza Beltran et al 2015). Despite some other studies treating uncertainty sources such as methodological choices, modeling assumptions, and inventory data u n c e r t a i n t y, b y m e a n s o f d i ff e r e n t a p p r o a c h e s (Andrianandraina et al 2015;Gregory et al 2016), we are not aware of any study so far treating uncertainty due to the choice of allocation method for all multi-functional background processes.…”

Section: Comparative Lcas With Uncertainty Analysismentioning

confidence: 99%

Accounting for inventory data and methodological choice uncertainty in a comparative life cycle assessment: the case of integrated multi-trophic aquaculture in an offshore Mediterranean enterprise

Beltrán

Chiantore

Pecorino

et al. 2017

Int J Life Cycle Assess

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Purpose Integrated multi-trophic aquaculture (IMTA), growing different species in the same space, is a technology that may help manage the environmental impacts of coastal aquaculture. Nutrient discharges to seawater from monoculture aquaculture are conceptually minimized in IMTA, while expanding the farm economic base. In this study, we investigate the environmental trade-offs for a small-to-medium enterprise (SME) considering a shift from monoculture towards IMTA production of marine fish. Methods A comparative life cycle assessment (LCA), including uncertainty analysis, was implemented for an aquaculture SME in Italy. Quantification and simultaneous propagation of uncertainty of inventory data and uncertainty due to the choice of allocation method were combined with dependent sampling to account for relative uncertainties and statistical testing and interpretation to understand the uncertainty analysis results. Monte Carlo simulations were used as a propagation method. The environmental impacts per kilo of fish produced in monoculture and in IMTA were compared. Twelve impact categories were considered. The comparison is first made excluding uncertainty (deterministic LCA) and then accounting for uncertainties. Results and discussion Deterministic LCA results evidence marginal differences between the impacts of IMTA and monoculture fish production. IMTA performs better on all impacts studied. However, statistical testing and interpretation of the uncertainty analysis results showed that only mean impacts for climate change are significantly different for both productive systems, favoring IMTA. For the case study, technical variables such as scales of production of the species from different trophic levels, their integration (space and time), and the choice of species determine the trade-offs. Also, LCA methodological choices such as that for an allocation method and the treatment of relative uncertainties were determinant in the comparison of environmental trade-offs. Conclusions The case study showed that environmental trade-offs between monoculture and IMTA fish production depend on technical variables and methodological choices. The combination of statistical methods to quantify, propagate, and interpret uncertainty was successfully tested. This approach supports more robust environmental trade-off assessments between alternatives in LCAs with uncertainty analysis by adding information on the significance of results. It was difficult to establish whether IMTA does bring benefits given

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Sensitivity Analysis of Environmental Process Modeling in a Life Cycle Context: A Case Study of Hemp Crop Production

Cited by 41 publications

References 21 publications

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

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

Evaluating the Use of Global Sensitivity Analysis in Dynamic MFA

Accounting for inventory data and methodological choice uncertainty in a comparative life cycle assessment: the case of integrated multi-trophic aquaculture in an offshore Mediterranean enterprise

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