How to Conduct a Proper Sensitivity Analysis in Life Cycle Assessment: Taking into Account Correlations within LCI Data and Interactions within the LCA Calculation Model

Wei, Wei; Larrey-Lassalle, Pyrène; Faure, Thierry; Dumoulin, Nicolas; Roux, Philippe; Mathias, Jean‐Denis

doi:10.1021/es502128k

Cited by 144 publications

(88 citation statements)

References 39 publications

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“…This is possible because SA helps in increasing the understanding of the relationships between input and output variables in a system or model and has been increasingly demonstrated in scientific arenas and recognized by international institutions (Campolongo et al 2011). SA methods can be generally classified into local and global methods (Campolongo et al 2011;Wei et al 2015). While local sensitivity measures, also called one-at-a-time (OAT) methods, assess how uncertainty in one factor affects the model output keeping the other factors fixed to a nominal value (Campolongo et al 2011), global sensitivity measures (GSM) assess the effects of uncertainty factors while being changed simultaneously.…”

Section: Uncertainty and Sensitivity Analysismentioning

confidence: 99%

“…While local sensitivity measures, also called one-at-a-time (OAT) methods, assess how uncertainty in one factor affects the model output keeping the other factors fixed to a nominal value (Campolongo et al 2011), global sensitivity measures (GSM) assess the effects of uncertainty factors while being changed simultaneously. The advantage of global sensitivity analysis is that it is able to take into account interactions amongst factors, whereas OAT methods cannot (Campolongo et al 2011;Wei et al 2015). As reported by Wei and co-workers (Wei et al 2015), in LCA, most case studies are done with OAT methods (Bala et al 2010;Heijungs et al 1994), whereas global methods are rarely applied (see Padey et al 2013;Wei et al 2015).…”

Section: Uncertainty and Sensitivity Analysismentioning

confidence: 99%

“…The advantage of global sensitivity analysis is that it is able to take into account interactions amongst factors, whereas OAT methods cannot (Campolongo et al 2011;Wei et al 2015). As reported by Wei and co-workers (Wei et al 2015), in LCA, most case studies are done with OAT methods (Bala et al 2010;Heijungs et al 1994), whereas global methods are rarely applied (see Padey et al 2013;Wei et al 2015).…”

Section: Uncertainty and Sensitivity Analysismentioning

confidence: 99%

“…The software allows importing the outputs of the model's calculation and supports the estimation of the Sobol's sensitivity indices. In this paper, the first order and total order sensitivity indices were calculated (see Saltelli et al 2010;Wei et al 2015). The first order index (S i ) for factor i-th provides the estimation of the variance explained by the i-th without considering its interactions with other factors, whereas the total order index (S Ti ) for factor i-th provide the estimation of the variance explained by factor i-th and its interaction with other factors.…”

Section: Step 4: Calculation Of the Sobol's Sensitivity Indicesmentioning

confidence: 99%

“…Hong et al 2010;Clavreul et al 2012Clavreul et al , 2013Imbeault-Tétreault et al 2013;Wei et al 2015). Moreover, review articles on uncertainty have been published by various authors (Heijungs and Huijbregts 2004;Heijungs and Lenzen 2014;Lloyd and Ries 2007).…”

Section: Introductionmentioning

confidence: 99%

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Uncertainty and sensitivity analysis of normalization factors to methodological assumptions

Benini

Sala

2015

Int J Life Cycle Assess

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Purpose Normalisation is an optional step of a life cycle assessment, supporting the interpretation of the results of the characterization in terms of relative environmental relevance of the impacts. Normalisations factors (NFs) are calculated as results of regional/global inventories of emission and resources characterized through impact assessment methods. Several methodological assumptions are needed for building the inventory, as presented in Sala et al. (Int J Life Cycle Assess 20:1568-1585, 2015. NFs for EU27 have been calculated for 2010 compliant with ILCD recommendations defining a methodological approach for sources selection and the use of proxy indicators. Qualitative and quantitative uncertainty evaluation is needed for assessing the robustness of final figures. The present work aims at quantifying the influence of key methodological choices on the variability of the normalisation factors. Materials and methods Five sources of uncertainty have been analyzed in this work: (F1) the selection of the sources of data; (F2) the classification of data as life cycle inventory (LCI) elementary flows; (F3) the classification of substances for characterization; (F4) the specification of the emission compartments and (F5) the use of spatially differentiated characterization factors. The sensitivity of the normalization factors to such uncertainties were assessed through a global sensitivity method, for the impact categories acidification (ACID), terrestrial eutrophication (ET), marine eutrophication (EM), photochemical ozone formation (POF), respiratory inorganics/particulate matter (RIPM) and water depletion (WD). Results and discussionThe results demonstrate the need of thorough uncertainty and sensitivity analysis for supporting the use of NFs. Uncertainties are high for the impact categories respiratory inorganics (RIPM) and water depletion (WD) and improvement of these categories is a priority. For RIPM this is explained by the high variability amongst the characterization factors for PM 2.5 and PM 10 , together with the contextual lack of information about the height of the emission source in the inventory. For WD this is explained by variability of the regionalized factors available within the ILCD. For ACID, ET and EM the uncertainty is relatively low and generally completely led by factors F1 and F2. However, regionalized characterization factors were not tested for ACID and ET, therefore the results might be underestimating the overall uncertainty. For what concerns POF, the main source of uncertainty-amongst those included in the analysis-is the selection of the data source. Overall, improvements in the spatial resolution of the inventory are needed in order to confine uncertainty. This would allow the use of characterization factors specific for emission source typology and geographical location. 20:1568-1585, 2015).

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Section: Uncertainty and Sensitivity Analysismentioning

confidence: 99%

Section: Uncertainty and Sensitivity Analysismentioning

confidence: 99%

Section: Uncertainty and Sensitivity Analysismentioning

confidence: 99%

Section: Step 4: Calculation Of the Sobol's Sensitivity Indicesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Uncertainty and sensitivity analysis of normalization factors to methodological assumptions

Benini

Sala

2015

Int J Life Cycle Assess

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Sustainable Point of View: Life Cycle Analysis for Green Extraction Technologies

Aslanbay Guler,

Tepe,

Imamoglu

2024

ChemBioEng Reviews

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Microalgae have emerged as a promising source of renewable energy and natural bioproducts since they show high biomass productivity, offer carbon dioxide fixation, and exhibit a rich content of compounds. Recent efforts have focused on green extraction technologies that utilize green solvents to further promote sustainability and minimize the environmental impact of the microalgal process. At this point, life cycle analysis (LCA) provides valuable insights into the environmental impacts of specific products and techniques. A comprehensive overview of the life cycle environmental and energy assessments conducted for the extraction of metabolites from microalgae is presented. Special attention is given to using green extraction technologies, i.e., supercritical fluid extraction, pressurized liquid extraction, microwave‐assisted extraction, ultrasound‐assisted extraction, and pulsed‐electric field extraction, and solvents to ensure sustainability. Additionally, the main principles, historical development, tools, and challenges of LCA are discussed. By addressing these aspects, the paper attracts attention to the environmental impacts associated with green extraction techniques for obtaining microalgal metabolites.

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A critical analysis of the integration of life cycle methods and quantitative methods for sustainability assessment

Cerchione,

Morelli,

Passaro

et al. 2024

Corp Soc Responsibility Env

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The utilization of Life Cycle Assessment (LCA) and its corresponding methodologies gained considerable attention within the realm of corporate social responsibility (CSR) and sustainability assessment. Nevertheless, a lack of extensive investigation on their integration along with quantitative procedures, for example, statistical techniques and artificial intelligence (AI), has emerged. The purpose of this literature review is to investigate the extent to which these methodologies have been connected so far in order to achieve objectives concerning the assessment of sustainability. The scope of the study was restricted to articles published in peer‐reviewed journals throughout the period from 1960 to 2022. The investigation was conducted by using a broad set of keywords, encompassing both life cycle methods, including Life Cycle Assessment, Environmental Life Cycle Costing and Emergy Accounting, and quantitative methods, such as mathematical methods, economic methods and building information modeling methods. A total of 144 articles addressing the combined use of life cycle methods and quantitative methodologies for the evaluation of sustainability were identified in the literature review. The greater part of these studies relied on the combined use of LCA with mathematical models, statistical methods and AI methods. As a result, the studies proven that the joint application of such methods can improve consistency of sustainability assessment and enhance CSR. Additionally, many articles suggested novel approaches, including a combined use of LCA and building information modeling as well as simulation methods. The combined use of life cycle methods alongside quantitative methods offers promise in enhancing sustainability assessment by offering more precise and consistent outcomes. However, it becomes imperative to carefully evaluate the hypotheses, accuracy of data, and uncertainty associated with each method within the integration process. Additional research needs to be conducted in order to establish standardized protocols for combining these methodologies as well as to identify the most suitable procedures for their integration with respect to specific objectives concerning sustainability assessment.

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How to Conduct a Proper Sensitivity Analysis in Life Cycle Assessment: Taking into Account Correlations within LCI Data and Interactions within the LCA Calculation Model

Cited by 144 publications

References 39 publications

Uncertainty and sensitivity analysis of normalization factors to methodological assumptions

Uncertainty and sensitivity analysis of normalization factors to methodological assumptions

Sustainable Point of View: Life Cycle Analysis for Green Extraction Technologies

A critical analysis of the integration of life cycle methods and quantitative methods for sustainability assessment

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