The Role of SETAC in the Development of LCA

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DOI: http://dx.doi.org/10.1065/lca2006.04.019

Background

Life cycle assessments have been performed using different methods before the name was coined since about 1970 in several countries of North America and Europe. It was the merit of SETAC to start a standardization process which culminated in the LCA-guidelines ('A code of practice') in 1993. It is the aim of this paper to trace back this and further LCA-related achievements by SETAC on the basis of documents and personal memories. It may be subjective in the selection and weighting of some events, but objectivity is strived for with regard to the whole and, in my view, singular development.

Results and Discussion

Starting 1990 with two workshops in Smuggler's Notch (Vermont) and Leuven (Belgium), SETAC and SETAC Europe organized several workshops during which important topics (framework, impact assessment, data quality, etc.) were treated and published in the form of reports which are still available. The main contribution by CML and its head, Helias Udo de Haes, was a practical method of impact assessment, transforming the formerly more technocratic LCA (energy, resources, waste) into an instrument of environmental assessment of product systems. In addition, important contributions to the allocation problem were made. Starting in 1993, ISO took over the leadership in standardization and SETAC started the famous working groups in North America and Europe, often dealing with the same topics in parallel. Due to the different cultures, the results were frequently complimentary rather than harmonic. The CML-method of LCIA, widely accepted in Europe, had to wait for about 10 years to be accepted at the other side of the Atlantic. It was helpful that SETAC – meanwhile a global organization – looked for a partner in order to implement LCA all over the world. This partner was found in the 'United Nations Environmental Programme' (UNEP) and the UNEP/SETAC Life Cycle Initiative was officially launched by Klaus Töpfer in Prague in April 2002. SETAC also assumed an important role in communicating LCA via publications: workshop and conference reports, the 'code of practice', working group results and LCA News Letters. The annual meetings offered forums for LCA scientists, practitioners and users, well prepared by the LCA Steering Committee (SETAC Europe) and the LCA Advisory Group (SETAC North America).

Recommendation

. The main recommendation to SETAC is to adhere to LCA as the main environmental assessment tool for products and to expand it to a true sustainability assessment tool by adding Life Cycle Costing (LCC) and a still to be invented 'Social Life Cycle Assessment'. SETAC is to remain the scientific arm within the UNEP/SETAC LC Initiative, without loosing its identity. Working groups should be global rather than regional in the future, as suggested by the SETAC Europe LCA Steering Committee at the 2004 World Congress in Portland, Oregon.

     

SETAC Europe Young Scientist LCA Award 2022 for Anna Furberg

The International Journal of Life Cycle Assessment -     

SETAC Europe Young Scientist LCA Award 2020 for N. Mirabella

The International Journal of Life Cycle Assessment -     

SETAC Europe Young Scientist LCA award 2019 for P. James Joyce

The International Journal of Life Cycle Assessment - At the 29th SETAC Europe annual meeting, May 26–30, 2019, Helsinki (Finland), P. James Joyce received the SETAC Europe Young Scientist LCA...     

Introducing the UNEP/SETAC methodological sheets for subcategories of social LCA

Purpose  

In May 2009, the Guidelines for Social Life Cycle Assessment of Products (the Guidelines) were launched at the occasion of the International Organization for Standardization (ISO) 26000 (Social Responsibility) meeting in Quebec City, Canada. Developed by a United Nations Environment Programme/Society of Environmental Toxicology and Chemistry (“UNEP/SETAC”) Life Cycle Initiative project group on Social Life Cycle Assessment (S-LCA), the Guidelines provide a framework to assess social impacts across product life cycles. A year later, the Methodological Sheets for the Subcategories of Social LCA (“the Methodological Sheets”) are being made available to support practitioners engaging in the field. The Methodological Sheets provide practical guidance for conducting S-LCA case studies by offering consistent, yet flexible assistance.     

Application of data quality assessment methods to an LCA of electricity generation

Background, Goal and Scope  For the life cycle assessment (LCA) tool to provide maximum benefit for decision makers, the uncertainty of its results should be reported. Several methods for assessing uncertainty have been developed, but despite recent efforts, there remains disagreement about their merits. Objectives  The objectives of the study were to review several assessment methods for estimating numerical and qualitative uncertainty of impact scores and recommend an appropriate uncertainty assessment scheme. The methods review has been conducted on the basis of an LCA case study regarding the comparison of the use of either brown or black coals in Australian electricity generation. Results and Discussion  Each assessment method indicated greater uncertainty in the impact scores calculated for black coal use than for brown coal use. Due to overlap of the uncertainty ranges in calculated impact scores neither of the coals could be regarded environmentally preferred. Conclusions  Both qualitative and quantitative methods were found to provide useful information about the uncertainty of calculated impact scores for the case study. Methods that combine qualitative and quantitative uncertainty provided no additional benefits, and obscured much of the information gained from using qualitative methods. Recommendation and Outlook  It is recommended that LCA results should include separate numerical (using Monte-Carlo simulation) and qualitative uncertainty assessments. When the ranges of calculated impact scores for compared options overlap, the normalised difference method is recommended.     

Data representativeness in LCA: A framework for the systematic assessment of data quality relative to technology characteristics

A shortcoming in current data quality assessment schemes is that the data quality information is not used systematically to identify the critical data in a life cycle inventory (LCI) model. In addition, existing criteria employed to evaluate representativeness lack relevance to the specific context of a study. A novel framework is proposed herein for the evaluation of the representativeness of LCI data, including an analysis of the importance of the data and a modification of quality criteria based on unit process characteristics. Temporal characteristics are analyzed by identifying the technology shift, because data generated before this time are considered outdated. Geographical and technological characteristics are analyzed by defining a “related area” and a “related technology,” which is done by identifying a number of relevant geographical and technical factors, and then comparing the collected data with these factors. The framework was illustrated in a case study on household waste incineration in Denmark. The results demonstrated the applicability of the method in practice, and they provided data quality criteria unique to waste incineration unit processes, for example, different time intervals to evaluate temporal representativeness. However, the proposed method is time demanding, and thus sector‐level characteristic analyses are feasible instead of the user having to do the analyses.     

Visual data analysis and decision support methods for non-deterministic LCA

Principal components analysis coupled with non-parametric bootstrapping is introduced with an example as a powerful tool to help visualise, analyse and decide on comparative or single non-deterministic LCI/LCIA results. Decision support is provided by adding non-parametric bootstrapping (NPB) to the GA1A plane, which is a special case of principle comments analysis (PCA) built around the Promethée multicriteria decision aid model. In addition to an easy to analyse visual presentation of otherwise complex and cluttered numerical results, PCA with NPB is equally at ease will all data uncertainty formats used to date in LCA.