How to decide and visualize whether uncertainty or variability is dominating in life cycle assessment results: A systematic review

Michiels, Freya

doi:10.1016/j.envsoft.2020.104841

Cited by 49 publications

(19 citation statements)

References 45 publications

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“…While the midpoint approach has a stronger relation to the environmental flows and a relatively low uncertainty than the endpoint one, the latter is more uncertain than the former for what concerns the assessment of DALYs, but provides better information on the environmental relevance of environmental flows ( Huijbregts et al, 2016 ). This is a typical issue in LCA, where uncertainty can be mainly imputed to data and models, both at the level of LCI and LCIA ( Bamber et al, 2019 ; Igos et al, 2019 ; Lloyd & Ries, 2007 ; Michiels & Geeraerd, 2020 ; Schaubroeck et al, 2020 ). Despite their importance, no quantitative characterisation or analysis of uncertainty was performed in the present study because: at the level of LCI, statistical data to build the energy metabolism inventory were retrieved from official statistics databases and used as such, with no relevant manipulation to change their quality.…”

Section: Resultsmentioning

confidence: 99%

Decrease in life expectancy due to COVID-19 disease not offset by reduced environmental impacts associated with lockdowns in Italy

Rugani

Conticini

Frediani

et al. 2022

Environmental Pollution

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The consequence of the lockdowns implemented to address the COVID-19 pandemic on human health damage due to air pollution and other environmental issues must be better understood. This paper analyses the effect of reducing energy demands on the evolution of environmental impacts during the occurrence of 2020-lockdown periods in Italy, with a specific focus on life expectancy. An energy metabolism analysis is conducted based on the life cycle assessment of all monthly energy consumptions, by sector, category and province area in Italy between January 2015 to December 2020. Results show a general decrease (∼5% on average) of the midpoint impact categories (global warming, stratospheric ozone depletion, fine particulate matter formation, etc.) over the entire year 2020 as compared to past years. These avoided impacts, mainly due to reductions in fossil energy consumptions, are particularly remarkable during the first lockdown phase between March and May 2020 (∼21% on average). Regarding the endpoint damage on human health, ∼66 Disability Adjusted Life Years (DALYs) per 100,000 inhabitants are estimated to be saved. The analysis shows that the magnitude of the officially recorded casualties is substantially larger than the estimated gains in human lives due to the environmental impact reductions. Future research could therefore investigate the complex cause-effect relationships between the deaths occurred in 2020 imputed to COVID-19 disease and co-factors other than the SARS-CoV-2 virus.

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Section: Resultsmentioning

confidence: 99%

Decrease in life expectancy due to COVID-19 disease not offset by reduced environmental impacts associated with lockdowns in Italy

Rugani

Conticini

Frediani

et al. 2022

Environmental Pollution

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“…A discussion and quantification of uncertainties is therefore helpful to interpret the results. First, a distinction must be made between variability and uncertainty [37]. Variability comprises explicit practitioner choices with regard to time, e.g., setting the reference year to 2017 or 2020 or choosing between impact assessment methods with a timeframe of 20 or 100 years.…”

Section: Uncertaintiesmentioning

confidence: 99%

“…In Table 1, the different types of uncertainty are separated against each other, and each type is exemplified for the Life Cycle Inventory (LCI) phase and the LCIA phase, respectively. Mostly addressed in LCA studies is parameter uncertainty of the LCI data [37,38] using, e.g., a Monte Carlo analysis or the pedigree matrix approach, which is implemented in the ecoinvent database [28]. Benetto et al [39] proposed to use these results from the Monte Carlo Analysis in the fuzzy multi-criteria method NAIADE.…”

Section: Uncertaintiesmentioning

confidence: 99%

“…As the three methods of LCSA-LCA, S-LCA and LCC-use different approaches for impact assessment, the methods are discussed separately in the following subsections. For LCA, several publications have been published regarding uncertainty [37,38,58]. Therefore, the approach is mainly developed based on the LCA and later adapted for the S-LCA.…”

Section: Determination Of Thresholds In Life Cycle Sustainability Assessmentmentioning

confidence: 99%

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Setting Thresholds to Define Indifferences and Preferences in PROMETHEE for Life Cycle Sustainability Assessment of European Hydrogen Production

et al. 2021

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The Life Cycle Sustainability Assessment (LCSA) is a proven method for sustainability assessment. However, the interpretation phase of an LCSA is challenging because many different single results are obtained. Additionally, performing a Multi-Criteria Decision Analysis (MCDA) is one way—not only for LCSA—to gain clarity about how to interpret the results. One common form of MCDAs are outranking methods. For these type of methods it becomes of utmost importance to clarify when results become preferable. Thus, thresholds are commonly used to prevent decisions based on results that are actually indifferent between the analyzed options. In this paper, a new approach is presented to identify and quantify such thresholds for Preference Ranking Organization METHod for Enrichment Evaluation (PROMETHEE) based on uncertainty of Life Cycle Impact Assessment (LCIA) methods. Common thresholds and this new approach are discussed using a case study on finding a preferred location for sustainable industrial hydrogen production, comparing three locations in European countries. The single LCSA results indicated different preferences for the environmental, economic and social assessment. The application of PROMETHEE helped to find a clear solution. The comparison of the newly-specified thresholds based on LCIA uncertainty with default thresholds provided important insights of how to interpret the LCSA results regarding industrial hydrogen production.

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“…By contrast, recent literature on model-based decisionmaking under deep uncertainty (reviewed in Marchau et al 2019) presents methods such as scenario discovery, which is functionally similar to a factor mapping analysis but is better suited to study input interactions, for instance, by identifying the combinations of uncertain input values under which a design alternative would meet a performance threshold (Groves and Lempert 2007). Published applications of scenario discovery (reviewed in Kwakkel and Haasnoot 2019) commonly rely on the Patient Rule Induction Method (PRIM; Friedman and Fisher 1999).…”

Section: Introductionmentioning

confidence: 97%

Variance-based global sensitivity analysis and beyond in life cycle assessment: an application to geothermal heating networks

Jaxa-Rozen

Pratiwi

Trutnevyte

2021

Int J Life Cycle Assess

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Purpose Global sensitivity analysis increasingly replaces manual sensitivity analysis in life cycle assessment (LCA). Variance-based global sensitivity analysis identifies influential uncertain model input parameters by estimating so-called Sobol indices that represent each parameter’s contribution to the variance in model output. However, this technique can potentially be unreliable when analyzing non-normal model outputs, and it does not inform analysts about specific values of the model input or output that may be decision-relevant. We demonstrate three emerging methods that build on variance-based global sensitivity analysis and that can provide new insights on uncertainty in typical LCA applications that present non-normal output distributions, trade-offs between environmental impacts, and interactions between model inputs. Methods To identify influential model inputs, trade-offs, and decision-relevant interactions, we implement techniques for distribution-based global sensitivity analysis (PAWN technique), spectral clustering, and scenario discovery (patient rule induction method: PRIM). We choose these techniques because they are applicable with generic Monte Carlo sampling and common LCA software. We compare these techniques with variance-based Sobol indices, using a previously published LCA case study of geothermal heating networks. We assess eight environmental impacts under uncertainty for three design alternatives, spanning different geothermal production temperatures and heating network configurations. Results In the application case on geothermal heating networks, PAWN distribution-based sensitivity indices generally identify influential model parameters consistently with Sobol indices. However, some discrepancies highlight the potentially misleading interpretation of Sobol indices on the non-normal distributions obtained in our analysis, where variance may not meaningfully describe uncertainty. Spectral clustering highlights groups of model results that present different trade-offs between environmental impacts. Compared to second-order Sobol interaction indices, PRIM then provides more precise information regarding the combinations of input values associated with these different groups of calculated impacts. PAWN indices, spectral clustering, and PRIM have a computational advantage because they yield stable results at relatively small sample sizes (n = 12,000), unlike Sobol indices (n = 100,000 for second-order indices). Conclusions We recommend adding these new techniques to global sensitivity analysis in LCA as they give more precise as well as additional insights on uncertainty regardless of the distribution of the model outputs. PAWN distribution-based global sensitivity analysis provides a computationally efficient assessment of input sensitivities as compared to variance-based global sensitivity analysis. The combination of clustering and scenario discovery enables analysts to precisely identify combinations of input parameters or uncertainties associated with different outcomes of environmental impacts.

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How to decide and visualize whether uncertainty or variability is dominating in life cycle assessment results: A systematic review

Cited by 49 publications

References 45 publications

Decrease in life expectancy due to COVID-19 disease not offset by reduced environmental impacts associated with lockdowns in Italy

Decrease in life expectancy due to COVID-19 disease not offset by reduced environmental impacts associated with lockdowns in Italy

Setting Thresholds to Define Indifferences and Preferences in PROMETHEE for Life Cycle Sustainability Assessment of European Hydrogen Production

Variance-based global sensitivity analysis and beyond in life cycle assessment: an application to geothermal heating networks

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