Global sensitivity analysis of correlated uncertainties in life cycle assessment.
Recent advances in research have made global sensitivity analysis of very large and highly linear life cycle assessment systems feasible. In this paper, we build on these developments to include sensitivity analysis of correlated parameters and nonlinear models. We augment numerical uncertainty prop...
| Publicado en: | Journal of Industrial Ecology Vol. 29; no. 4; pp. 1090 - 1105 |
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| Autores principales: | , , |
| Formato: | Artículo |
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Wiley-Blackwell
Aug2025
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=187163952&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 187163952 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 10881980 FL1 jtl: Journal of Industrial Ecology issn: 10881980 maglogo: Y pubinfo: dt: Aug2025 vid: 29 iid: 4 pid: 480 pub: Wiley-Blackwell artinfo: ui: 187163952 10.1111/jiec.70036 ppf: 1090 ppct: 15 formats: tig: atl: Global sensitivity analysis of correlated uncertainties in life cycle assessment. aug: au: Kim, Aleksandra Mutel, Christopher Hellweg, Stefanie affil: Laboratory for Energy Systems Analysis, Paul Scherrer Institute, Villigen, Switzerland Department of Civil, Environmental and Geomatic Engineering, ETH Zurich, Zurich, Switzerland su: Sensitivity analysis Product life cycle assessment Monte Carlo method Uncertainty (Information theory) Applied sciences Beta distribution Carbon cycle sug: subj: Sensitivity analysis Product life cycle assessment Monte Carlo method Uncertainty (Information theory) Applied sciences Beta distribution Carbon cycle keyword: correlated sampling ENTSO‐E time‐series electricity data global sensitivity analysis industrial ecology parameterized life cycle inventory Shapley values correlated sampling ENTSO‐E time‐series electricity data global sensitivity analysis industrial ecology parameterized life cycle inventory Shapley values ab: Recent advances in research have made global sensitivity analysis of very large and highly linear life cycle assessment systems feasible. In this paper, we build on these developments to include sensitivity analysis of correlated parameters and nonlinear models. We augment numerical uncertainty propagation with Monte Carlo simulations (i) to include propagation of uncertainty from uncertain variables in parameterized inventory datasets; (ii) to account for correlations between process inputs and outputs and in particular incorporate the carbon balance of combustion activities; (iii) to employ published time‐series data instead of static values for electricity generation market mixes in Europe; (iv) to ensure that inputs which are supposed to reach a fixed total (e.g., the percentage contributions of power sources to an electricity mix) actually do so consistently by using the Dirichlet distribution. We then iterate on existing global sensitivity analysis protocols for high‐dimensional systems to improve their computational performance. To correctly calculate sensitivity rankings for correlated inputs, we use SHapley Additive exPlanations as feature importance metrics with gradient boosted trees. Our results for a case study of climate change impacts of an average Swiss household confirm that neglecting correlations limits the validity of uncertainty and sensitivity analysis. Our methodology and correlated sampling modules are given as open source code. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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