Abstract
Background, aim, and scope
Many studies evaluate the results of applying different life cycle impact assessment (LCIA) methods to the same life cycle inventory (LCI) data and demonstrate that the assessment results would be different with different LICA methods used. Although the importance of uncertainty is recognized, most studies focus on individual stages of LCA, such as LCI and normalization and weighting stages of LCIA. However, an important question has not been answered in previous studies: Which part of the LCA processes will lead to the primary uncertainty? The understanding of the uncertainty contributions of each of the LCA components will facilitate the improvement of the credibility of LCA.
Methodology
A methodology is proposed to systematically analyze the uncertainties involved in the entire procedure of LCA. The Monte Carlo simulation is used to analyze the uncertainties associated with LCI, LCIA, and the normalization and weighting processes. Five LCIA methods are considered in this study, i.e., Eco-indicator 99, EDIP, EPS, IMPACT 2002+, and LIME. The uncertainty of the environmental performance for individual impact categories (e.g., global warming, ecotoxicity, acidification, eutrophication, photochemical smog, human health) is also calculated and compared. The LCA of municipal solid waste management strategies in Taiwan is used as a case study to illustrate the proposed methodology.
Results
The primary uncertainty source in the case study is the LCI stage under a given LCIA method. In comparison with various LCIA methods, EDIP has the highest uncertainty and Eco-indicator 99 the lowest uncertainty. Setting aside the uncertainty caused by LCI, the weighting step has higher uncertainty than the normalization step when Eco-indicator 99 is used. Comparing the uncertainty of various impact categories, the lowest is global warming, followed by eutrophication. Ecotoxicity, human health, and photochemical smog have higher uncertainty.
Discussion
In this case study of municipal waste management, it is confirmed that different LCIA methods would generate different assessment results. In other words, selection of LCIA methods is an important source of uncertainty. In this study, the impacts of human health, ecotoxicity, and photochemical smog can vary a lot when the uncertainties of LCI and LCIA procedures are considered. For the purpose of reducing the errors of impact estimation because of geographic differences, it is important to determine whether and which modifications of assessment of impact categories based on local conditions are necessary.
Conclusions
This study develops a methodology of systematically evaluating the uncertainties involved in the entire LCA procedure to identify the contributions of different assessment stages to the overall uncertainty. Which modifications of the assessment of impact categories are needed can be determined based on the comparison of uncertainty of impact categories.
Recommendations and perspectives
Such an assessment of the system uncertainty of LCA will facilitate the improvement of LCA. If the main source of uncertainty is the LCI stage, the researchers should focus on the data quality of the LCI data. If the primary source of uncertainty is the LCIA stage, direct application of LCIA to non-LCIA software developing nations should be avoided.
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References
Andrae ASG, Moller P, Anderson J, Liu J (2004) Uncertainty estimation by Monte Carlo simulation applied to life cycle inventory of cordless phones and microscale metallization processes. IEEE T Electron Pack 27(4):233–245
Baumann H, Rydberg T (1994) Life cycle assessment: a comparison of three methods for impact analysis and evaluation. J Clean Prod 2(1):13–20
Bengtsson M, Steen B (2000) Weighting in LCA—approaches and applications. Environ Prog 19(2):101–109
Bjorklund AE (2002) Survey of approaches to improve reliability in LCA. Int J Life Cycle Assess 7(2):64–72
Bovea MD, Gallardo A (2006) The influence of impact assessment methods on materials selection for eco-design. Mater Des 27(3):209–215
Brent AC, Hietkamp S (2003) Comparative evaluation of life cycle impact assessment methods with a South African case study. Int J Life Cycle Assess 8(1):27–38
Dreyer LC, Niemann AL, Hauschild MZ (2003) Comparison of three different LCIA methods: EDIP97, CML2001 and Eco-indicator 99—does it matter which one you choose? Int J Life Cycle Assess 8(4):191–200
Frischknecht R, Jungbluth N, Althaus HJ, Doka G, Dones R, Heck T, Hellweg S, Hischier R, Nemecek T, Rebitzer G, Spielmann M (2005) The ecoinvent database: overview and methodological framework. Int J Life Cycle Assess 10(3):3–9
Geisler G, Hellweg S, Hungerbuhler K (2005) Uncertainty analysis in life cycle assessment (LCA): case study on plant-protection products and implications for decision making. Int J Life Cycle Assess 10(3):184–192
Goedkoop M, Spriensma R (1999) The eco-indicator 99: a damage-oriented method for life-cycle impact assessment. Pré Consultants, Amersfoort, The Netherlands
Hauschild M, Wenzel H (1998) Environmental assessment of products. Volume 2: Scientific background. Kluwer, Boston (ISBN 0-412-80810-2)
Huijbregts MAJ, Gilijamse W, Ragas AMJ, Reijnders L (2003) Evaluating uncertainty in environmental life-cycle assessment. A case study comparing two insulation options for a Dutch one-family dwelling. Environ Sci Technol 37(11):2600–2608
Huijbregts MAJ, Norris G, Bretz R, Ciroth A, Maurice B, von Bahr B, Weidema B, de Beaufort ASH (2001) Framework for modelling data uncertainty in life cycle inventories. Int J Life Cycle Assess 6(3):127–132
ISO 14040 (2006) Environmental management—life cycle assessment—principles and framework. International Organization of Standardization
ISO 14044 (2006) Environmental management—life cycle assessment—requirements and guidelines. International Organization of Standardization
Itsubo N, Sakagami M, Washida T, Kokubu K, Inaba A (2004) Weighting across safeguard subjects for LCIA through the application of conjoint analysis. Int J Life Cycle Assess 9(3):196–205
Raluy RG, Serra L, Uche J (2005) Life cycle assessment of desalination technologies integrated with renewable energies. Desalination 183(1–3):81–93
Raluy RG, Serra L, Uche J, Valero A (2004) Life-cycle assessment of desalination technologies integrated with energy production systems. Desalination 167(1–3):445–458
Ross S, Evans D, Webber M (2002) How LCA studies deal with uncertainty. Int J Life Cycle Assess 7(1):47–52
Seo S, Aramaki T, Hwang YW, Hanaki K (2004) Environmental impact of solid waste treatment methods in Korea. J Environ Eng-ASCE 130(1):81–89
Sugiyama H, Fukushima Y, Hirao M, Hellweg S, Hungerbuhler K (2005) Using standard statistics to consider uncertainty in industry-based life cycle inventory databases. Int J Life Cycle Assess 10(6):399–405
von Bahr B, Steen B (2004) Reducing epistemological uncertainty in life cycle inventory. J Clean Prod 12(4):369–388
Weidema BP, Wesnaes MS (1996) Data quality management for life cycle inventories—an example of using data quality indicators. J Clean Prod 4(3–4):167–174
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Hung, ML., Ma, Hw. Quantifying system uncertainty of life cycle assessment based on Monte Carlo simulation. Int J Life Cycle Assess 14, 19–27 (2009). https://doi.org/10.1007/s11367-008-0034-8
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DOI: https://doi.org/10.1007/s11367-008-0034-8