Uncertainty and sensitivity analysis of normalization factors to methodological assumptions

Benini, Lorenzo; Sala, Serenella

doi:10.1007/s11367-015-1013-5

Cited by 36 publications

(6 citation statements)

References 37 publications

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“…In the LCIA phase, epistemic uncertainty relates to the selection of a particular method, the normative aspects embedded within LCIA models (Qin et al., 2020), such as in terms of accounting at mid‐ and end‐points or different impact assessment methods, and impact weighting (Igos et al., 2019). Forcing incommensurable environmental impacts—let alone social aspects—into a single indicator is challenging (Benini & Sala, 2016), to the extent that only a few studies addressed epistemic uncertainty in the LCIA phase (Avadí et al., 2020; Benetto et al., 2006b; Milani et al., 2011; Petrakopoulou & Tsatsaronis, 2014).…”

Section: Resultsmentioning

confidence: 99%

“…This approach merges experts’ beliefs with quantitative data to obtain potential ranges for parameters. The use of fuzzy logic has been proposed throughout the phases of LCA, including at the level of inventory (Ardente et al., 2004; Heijungs & Tan, 2010; Sabará, 2021; Tan, 2008; Tan et al., 2002); impact assessment (Benetto et al., 2006a, 2006b; Potting et al., 2006); and interpretation (Benetto et al., 2008).…”

Section: Resultsmentioning

confidence: 99%

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A critical perspective on uncertainty appraisal and sensitivity analysis in life cycle assessment

Piano

Benini

2022

J of Industrial Ecology

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In this study, we review approaches for uncertainty appraisal in the life cycle assessment literature. We cover the acknowledgment of stochastic and epistemic uncertainty in uncertainty and sensitivity analysis and knowledge quality assessment, respectively. Consistent with previous works, our findings indicate that uncertainty is only appraised in a few studies on life cycle assessment. Most of these contributions cover only one of the phases of life cycle assessment, mainly the life cycle inventory phase. Less attention has been devoted to the phases of goal and scope definition and life cycle impact assessment. Additionally, in most studies, uncertainty analysis and sensitivity analysis have been applied independently, as it is wrongly assumed they cover different uncertainty spaces. We also identify the scope for improvement in the appraisal of epistemic uncertainty and the correct definition of the probability distribution of the uncertain factors. We conclude by highlighting studies in which sensible practices have been adopted, identifying open challenges, and suggesting possible ways forward.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

A critical perspective on uncertainty appraisal and sensitivity analysis in life cycle assessment

Piano

Benini

2022

J of Industrial Ecology

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“…ISO 140044 does not have an established methodology. However, according to the literature of past sensitivity studies ,,− and the ILCD handbook, the proposed sensitivity analysis has been performed. Sensitivity analysis is carried out to check whether some changes in the processes or the LCA method used affect the environmental impact assessment or not.…”

Section: Methodsmentioning

confidence: 99%

Environmental and Economic Viability of Chitosan Production in Guayas-Ecuador: A Robust Investment and Life Cycle Analysis

2021

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Ecuador is a country where shrimp production is one of its primary industries. It generates annually about 72,000 tons of wastes in the form of shrimp shells. Therefore, using this waste as a raw material resource to produce chitosan, a biopolymer, is established. An environmental and economic performance study is carried out as a possible investment report, where a conceptual design of the process is defined and a financial viability report is obtained. An environmental impact report establishes the degree of harm to the environment. The economic viability study considered costs related to capital and operation to process 5000 tons of shrimp shells each year. On the other hand, a life cycle assessment was performed to obtain the environmental impact for 1 kg of chitosan produce, where a cradle-to-gate approach was established. Results showed that this new industry has a net present value of 10.38 million USD, a rate of return of 67.31%, and a payback period of 3.13 years. Additionally, it was calculated that the environmental impact with a higher normalized value was the human noncarcinogenic toxicity. It is concluded that the production of chitosan in Guayas-Ecuador is economically viable and cost-competitive in the market, and it represents an industrial activity with no considerable environmental impacts.

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“…We used different databases, as differences in LCA results can arise from differences in the methodological approaches used for LCI modeling (Hischier and Achachlouei ), other than variability due to differences in life cycle impact assessment (LCIA) modeling (Alvarenga et al. ; Benini and Sala ; Pizzol et al. ).…”

Section: Methodsmentioning

confidence: 99%

Structured Under‐Specification of Life Cycle Impact Assessment Data for Building Assemblies

Gregory

Ghattas

Kirchain

2018

J of Industrial Ecology

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SummaryThe existence of uncertainties and variations in data represents a remaining challenge for life cycle assessment (LCA). Moreover, a full analysis may be complex, time-consuming, and implemented mainly when a product design is already defined. Structured under-specification, a method developed to streamline LCA, is here proposed to support the residential building design process, by quantifying environmental impact when specific information on the system under analysis cannot be available. By means of structured classifications of materials and building assemblies, it is possible to use surrogate data during the life cycle inventory phase and thus to obtain environmental impact and associated uncertainty. The bill of materials of a building assembly can be specified using minimal detail during the design process. The low-fidelity characterization of a building assembly and the uncertainty associated with these low levels of fidelity are systematically quantified through structured under-specification using a structured classification of materials. The analyst is able to use this classification to quantify uncertainty in results at each level of specificity. Concerning building assemblies, an average decrease of uncertainty of 25% is observed at each additional level of specificity within the data structure. This approach was used to compare different exterior wall options during the early design process. Almost 50% of the comparisons can be statistically differentiated at even the lowest level of specificity. This data structure is the foundation of a streamlined approach that can be applied not only when a complete bill of materials is available, but also when fewer details are known. Keywords:buildings design process industrial ecology streamlined life cycle assessment structured under-specification uncertainty analysis Supporting information is linked to this article on the JIE website

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Uncertainty and sensitivity analysis of normalization factors to methodological assumptions

Cited by 36 publications

References 37 publications

A critical perspective on uncertainty appraisal and sensitivity analysis in life cycle assessment

A critical perspective on uncertainty appraisal and sensitivity analysis in life cycle assessment

Environmental and Economic Viability of Chitosan Production in Guayas-Ecuador: A Robust Investment and Life Cycle Analysis

Structured Under‐Specification of Life Cycle Impact Assessment Data for Building Assemblies

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