Literature DB >> 32606492

A Decision Analysis Approach to Electronics Standard Development Informed by Life Cycle Assessment Using Influence Diagrams.

Therese Garvey1, David Meyer1, Michael Gonzalez1, Brian Dyson1, John F Carriger1.   

Abstract

Life cycle assessment (LCA) provides holistic information on systems including the trade-offs between environmental impacts and the drivers of such impacts. Coupling life cycle assessment with a decision analysis (DA) method can help ensure that a life cycle assessment is focused on pertinent decision performance measures. In this paper, a framework integrating life cycle assessment with a decision analysis method to enhance the application of life cycle assessment is presented with a real-world case study of developing a material inclusion criterion for sustainable electronics standards. The proposed DA-LCA framework is a five-step process that tracks the flow of information between the steps of decision analysis and life cycle assessment. The case study considered the level of post-consumer-recycled or biobased content in laptop enclosures. Elicitation with a mock stakeholder panel was used to structure a means-ends network and create a utility-based influence diagram to link changes in material inclusion to environmental objectives using life cycle impact scores. Unlike typical life cycle assessment, the decision analysis approach allows for explicit incorporation of non-environmental factors and better constrains product options. Using this approach, the optimum decision for a possible range of 0-30% material content is 5% or 10%, depending on weighting. The DA-LCA framework can provide a blueprint for placing life cycle assessment results in context for decision-makers.

Entities:  

Keywords:  Decision Analysis; Influence Diagram; Life Cycle Assessment; Product Standards; Sustainable Electronics

Year:  2020        PMID: 32606492      PMCID: PMC7326198          DOI: 10.1016/j.jclepro.2020.120036

Source DB:  PubMed          Journal:  J Clean Prod        ISSN: 0959-6526            Impact factor:   9.297


  6 in total

1.  Quantifying and reducing uncertainty in life cycle assessment using the Bayesian Monte Carlo method.

Authors:  Shih-Chi Lo; Hwong-Wen Ma; Shang-Lien Lo
Journal:  Sci Total Environ       Date:  2005-03-20       Impact factor: 7.963

2.  Reflections on the use of Bayesian belief networks for adaptive management.

Authors:  Hans Jørgen Henriksen; Heidi Christiansen Barlebo
Journal:  J Environ Manage       Date:  2007-06-29       Impact factor: 6.789

Review 3.  Reviewing Bayesian Networks potentials for climate change impacts assessment and management: A multi-risk perspective.

Authors:  Anna Sperotto; José-Luis Molina; Silvia Torresan; Andrea Critto; Antonio Marcomini
Journal:  J Environ Manage       Date:  2017-11-01       Impact factor: 6.789

4.  USEEIO: a New and Transparent United States Environmentally-Extended Input-Output Model.

Authors:  Yi Yang; Wesley W Ingwersen; Troy R Hawkins; Michael Srocka; David E Meyer
Journal:  J Clean Prod       Date:  2017-08       Impact factor: 9.297

5.  Representing causal knowledge in environmental policy interventions: Advantages and opportunities for qualitative influence diagram applications.

Authors:  John F Carriger; Brian E Dyson; William H Benson
Journal:  Integr Environ Assess Manag       Date:  2018-02-22       Impact factor: 2.992

6.  Benefits and risks of emerging technologies: integrating life cycle assessment and decision analysis to assess lumber treatment alternatives.

Authors:  Michael P Tsang; Matthew E Bates; Marcus Madison; Igor Linkov
Journal:  Environ Sci Technol       Date:  2014-09-11       Impact factor: 9.028

  6 in total

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