When correlation matters: a practical guide to dealing with uncertainty in the case of data disaggregation.
Correctly modeling the relationships between correlated, uncertain input data is crucial for producing accurate uncertainty estimates of model results. This requires both an uncertainty analysis that accounts for correlations and the appropriate communication of the results, so that other analysts c...
| Publicado en: | Journal of Industrial Ecology Vol. 30; no. 2; pp. 665 - 682 |
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| Autores principales: | , , |
| Formato: | Artículo |
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Springer Nature
Apr2026
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| 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=196447903&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 196447903 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: Apr2026 vid: 30 iid: 2 pid: 237 pub: Springer Nature artinfo: ui: 196447903 10.1007/s44498-026-00048-6 ppf: 665 ppct: 17 formats: tig: atl: When correlation matters: a practical guide to dealing with uncertainty in the case of data disaggregation. aug: au: Schulte, Simon Jakobs, Arthur Lupton, Rick affil: https://ror.org/04m5j1k67 Life Cycle Sustainability, Department of Sustainability and Planning, Aalborg University, 9000, Aalborg, Denmark https://ror.org/0245cg223 Industrial Ecology Freiburg, University of Freiburg, Freiburg, Germany https://ror.org/03eh3y714 Center for Energy and Environmental Sciences & Center for Nuclear Engineering and Sciences, Laboratory for Energy Systems Analysis, Technology Assessment Group, Paul Scherrer Institute, Villigen PSI, Switzerland https://ror.org/002h8g185 Centre for Sustainable Energy Systems, Institute of Sustainability and Climate Change, University of Bath, Bath, UK su: Electronic data processing Industrial ecology Statistical correlation Uncertainty (Information theory) Maximum entropy method Input-output analysis Distribution (Probability theory) sug: subj: Electronic data processing Industrial ecology Data Processing, Hosting, and Related Services Statistical correlation Uncertainty (Information theory) Maximum entropy method Input-output analysis Distribution (Probability theory) keyword: Dirichlet IO LCA Maximum entropy MFA Dirichlet IO LCA Maximum entropy MFA ab: Correctly modeling the relationships between correlated, uncertain input data is crucial for producing accurate uncertainty estimates of model results. This requires both an uncertainty analysis that accounts for correlations and the appropriate communication of the results, so that other analysts can correctly interpret the reported uncertainties. However, neither is common practice in industrial ecology modeling. A typical case for correlated results is the disaggregation of a total value into uncertain shares, for which we present a practical yet robust approach to model the uncertainty. Our approach is based on two standard and two generalized Dirichlet distributions, and it uses the maximum entropy principle to choose minimally biased distribution parameters in the absence of specific known values. We discuss how correlation should be communicated to preserve accurate uncertainty information and provide examples to quantify the difference it makes to the results when the correlation is simplified or completely neglected. The proposed procedure will improve the accuracy of uncertainty quantification in Material Flow Analysis (e.g. where allocation coefficients split flows to sectors), Input Output Analysis (e.g. where aggregated environmental impact data has to be disaggregated to detailed economic sectors), and some instances in Life Cycle Assessment (e.g. where market shares are uncertain). Last but not least, to lower the technical barrier to applying these approaches, we provide easy-to-use Python and R packages which automate the approach. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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