Abstract
Purpose
Uncertainty analyses in life cycle assessment (LCA) literature have focused primarily on the life cycle inventory (LCI) phase, but LCA experts generally agree that the life cycle impact assessment (LCIA) phase is likely to contribute even more to the overall uncertainty of an LCA result. The magnitude of perceived uncertainties in characterization relative to that in LCI, however, has not been examined in the literature. Here, we use the pedigree approach to gauge the perceived uncertainty in the characterization phase relative to the LCI phase. In addition, we evaluate the level of approval on the pedigree approach as a means to characterize uncertainty in LCA.
Methods
Applying the Numeral Unit Spread Assessment Pedigree (NUSAP) approach to environmental risk assessment literature, we extracted the criteria for evaluating the uncertainty in the characterization phase. We used expert elicitation to identify a pool of experts and conducted a survey, to which 47 LCA practitioners from 12 countries responded. In order to reduce personal biases in perceived geometric standard deviation (GSD) values, we used two reference questions on weight and life expectancy at birth for calibration.
Results
Nearly half (49%) of respondents expressed their approval to the pedigree matrix approach as a means of characterizing uncertainties in LCA, and responses were highly sensitive to the respondent’s familiarity with the pedigree matrix. For instance, respondents who are highly familiar with the pedigree matrix were more polarized, with 15% and 19% of them expressing either strong approval or strong disapproval, respectively. Respondents less familiar with the pedigree approach were generally more favorable to its use. Compared with LCI, variability in characterization factors was influenced more strongly by geographical correlation and reliability of the underlying model, which showed 11 to 16% larger average GSDs when compared with the comparable criteria for LCI. Conversely, temporal correlation criterion was a less significant factor in characterization than in LCI.
Conclusions and discussion
Overall, survey respondents viewed LCIA characterization as only marginally more uncertain than LCI, but with a wider variability in responses on characterization than LCI. This finding indicates the need for additional research to develop more thorough methods for characterizing uncertainties in life cycle impact assessment that are compatible with the uncertainty measures in LCI.
Similar content being viewed by others
References
Alpert M, Raiffa H (1982) A progress report on the training of probability assessors
Althaus H, Doka G, Dones R, et al (2007) Overview and methodology: data v2.0 (2007). Ecoinvent Cent Zurich CH
Amara RC, Lipinski AJ (1971) Some views on the use of expert judgment. Technol Forecast Soc Chang 3:279–289
Armstrong JS (2008) Methods to elicit forecasts from groups: Delphi and prediction markets compared
Ayyub BM (2000) Methods for expert opinion elicitation of probabilities and consequences for corps facilities. US Army Corps Eng IWR Rep No 00-R-10
Björklund AE (2002) Survey of approaches to improve reliability in lca. Int J Life Cycle Assess 7:64–72
Brown B, Cochran S, Dalkey N (1969) The Delphi method, II: structure of experiments. RAND Corporation, Santa Monica
Cellura M, Longo S, Mistretta M (2011) Sensitivity analysis to quantify uncertainty in life cycle assessment: the case study of an Italian tile. Renew Sust Energ Rev 15:4697–4705
CIA (2018) The world factbook life expectancy at birth. Central Intelligence Agency
Ciroth A, Muller S, Weidema B, Lesage P (2013) Empirically based uncertainty factors for the pedigree matrix in ecoinvent. Int J Life Cycle Assess 21:1338–1348
Clavreul J, Guyonnet D, Christensen TH (2012) Quantifying uncertainty in LCA-modelling of waste management systems. Waste Manag 32:2482–2495
Clavreul J, Guyonnet D, Tonini D, Christensen TH (2013) Stochastic and epistemic uncertainty propagation in LCA. Int J Life Cycle Assess 18:1393–1403
Clemen RT, Winkler RL (1985) Limits for the precision and value of information from dependent sources. Oper Res 33:427–442
Cooke RM (1991) Experts in uncertainty: opinion and subjective probability in science. Oxford University Press, Oxford
Cooke R, Goossens L (1990) The accident sequence precursor methodology for the European Post-Seveso era. Reliab Eng Syst Saf 27:117–130
Cooper JS, Noon M, Kahn E (2012) Parameterization in Life Cycle Assessment inventory data: review of current use and the representation of uncertainty. Int J Life Cycle Assess 17:689–695
Cucurachi S, van der Giesen CC, Heijungs R, de Snoo GR (2017) No matter–how?: dealing with matter-less stressors in LCA of wind energy systems. J Ind Ecol 21:70–81
Czembor CA, Vesk PA (2009) Incorporating between-expert uncertainty into state-and-transition simulation models for forest restoration. For Ecol Manag 259:165–175
de Franca Doria M, Boyd E, Tompkins EL, Adger WN (2009) Using expert elicitation to define successful adaptation to climate change. Environ Sci Pol 12:810–819
Edelen A, Ingwersen WW (2018) The creation, management, and use of data quality information for life cycle assessment. Int J Life Cycle Assess 23:759–772
Ferrell WR (1994) Discrete subjective probabilities and decision analysis: elicitation, calibration and combination
Finnveden G, Hauschild MZ, Ekvall T, Guinée J, Heijungs R, Hellweg S, Koehler A, Pennington D, Suh S (2009) Recent developments in life cycle assessment. J Environ Manag 91:1–21. https://doi.org/10.1016/j.jenvman.2009.06.018
Frey C (1998) Briefing paper part 1: introduction to uncertainty analysis. Dep Civ Eng N C State Univ
Frischknecht R, Rebitzer G (2005) The ecoinvent database system: a comprehensive web-based LCA database. J Clean Prod 13:1337–1343
Fryar CD, Gu Q, Ogden CL (2012) Anthropometric reference data for children and adults: United States, 2007-2010. Vital Health Stat 11:1–48
Funtowicz SO, Ravetz JR (1990) Uncertainty and quality in science for policy. Springer Science & Business Media
Gavankar S, Anderson S, Keller AA (2015) Critical components of uncertainty communication in life cycle assessments of emerging technologies: nanotechnology as a case study. J Ind Ecol 19:468–479
Geisler G, Hellweg S, Hungerbühler K (2005) Uncertainty analysis in life cycle assessment (LCA): case study on plant-protection products and implications for decision making (9 pp+ 3 pp). Int J Life Cycle Assess 10:184–192
Gregory JR, Noshadravan A, Olivetti EA, Kirchain RE (2016) A methodology for robust comparative life cycle assessments incorporating uncertainty. Environ Sci Technol 50:6397–6405
Groen EA, Heijungs R, Bokkers EAM, de Boer IJM (2014) Methods for uncertainty propagation in life cycle assessment. Environ Model Softw 62:316–325. https://doi.org/10.1016/j.envsoft.2014.10.006
Hauschild MZ, Goedkoop M, Guinée J, Heijungs R, Huijbregts M, Jolliet O, Margni M, de Schryver A, Humbert S, Laurent A, Sala S, Pant R (2013) Identifying best existing practice for characterization modeling in life cycle impact assessment. Int J Life Cycle Assess 18:683–697
Heijungs R (1996) Identification of key issues for further investigation in improving the reliability of life-cycle assessments. J Clean Prod 4:159–166
Heijungs R, Huijbregts MA (2004) A review of approaches to treat uncertainty in LCA. Orlando Fla Elsevier
Henriksson PJ, Rico A, Zhang W et al (2015) Comparison of Asian aquaculture products by use of statistically supported life cycle assessment. Environ Sci Technol 49:14176–14183
Hickey AM, Davis AM (2003) Elicitation technique selection: how do experts do it? In: Requirements engineering conference, 2003. Proceedings. 11th IEEE international. IEEE, pp 169–178
Huijbregts MA (1998a) Application of uncertainty and variability in LCA. Int J Life Cycle Assess 3:273–280
Huijbregts MA (1998b) Part II: dealing with parameter uncertainty and uncertainty due to choices in life cycle assessment. Int J Life Cycle Assess 3:343–351
Huijbregts MA, Gilijamse W, Ragas AM, 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 37:2600–2608
Hung M-L, Ma H (2009) Quantifying system uncertainty of life cycle assessment based on Monte Carlo simulation. Int J Life Cycle Assess 14:19–27
International Standard Organization (1997) ISO 14040: environmental management-Life cycle assessment-principles and framework
Jaworska JS, Bridges TS (2001) Uncertainty in environmental risk assessment. In: Linkov I, Palma-Oliveira J (eds) Assessment and management of environmental risks. Springer, Dordrecht, p 203–207
Knol AB, Slottje P, van der Sluijs JP, Lebret E (2010) The use of expert elicitation in environmental health impact assessment: a seven step procedure. Environ Health 9:19
Lloyd SM, Ries R (2007) Characterizing, propagating, and analyzing uncertainty in life-cycle assessment: a survey of quantitative approaches. J Ind Ecol 11:161–179
Martin TG, Burgman MA, Fidler F et al (2012) Eliciting expert knowledge in conservation science. Conserv Biol 26:29–38
Maurice B, Frischknecht R, Coelho-Schwirtz V, Hungerbühler K (2000) Uncertainty analysis in life cycle inventory. Application to the production of electricity with French coal power plants. J Clean Prod 8:95–108
McBride MF, Burgman MA (2012) What is expert knowledge, how is such knowledge gathered, and how do we use it to address questions in landscape ecology? In: Expert knowledge and its application in landscape ecology. Springer, New York, p 11–38. https://doi.org/10.1007/978-1-4614-1034-8_2
Meozzi PG, Iannucci C (2006) Facilitating the development of environmental information into knowledge: government agency perspective to improve policy decision-making. In: 4th International Conference on Politics and Information Systems, Technologies and Applications
Moore DA, Healy PJ (2008) The trouble with overconfidence. Psychol Rev 115:502–517
Morgan MG (2014) Use (and abuse) of expert elicitation in support of decision making for public policy. Proc Natl Acad Sci 111:7176–7184
Muller S, Lesage P, Ciroth A et al (2014) The application of the pedigree approach to the distributions foreseen in ecoinvent v3. Int J Life Cycle Assess 21:1327–1337. https://doi.org/10.1007/s11367-014-0759-5
Murphy AH, Daan H (1984) Impacts of feedback and experience on the quality of subjective probability forecasts. Comparison of results from the first and second years of the zierikzee experiment. Mon Weather Rev 112:413–423
Mutel C, Liao X, Patouillard L, Bare J, Fantke P, Frischknecht R, Hauschild M, Jolliet O, Maia de Souza D, Laurent A, Pfister S, Verones F (2019) Overview and recommendations for regionalized life cycle impact assessment. Int J Life Cycle Assess 24:856–865
Nijhof CO, Huijbregts MA, Golsteijn L, van Zelm R (2016) Spatial variability versus parameter uncertainty in freshwater fate and exposure factors of chemicals. Chemosphere 149:101–107
Noshadravan A, Wildnauer M, Gregory J, Kirchain R (2013) Comparative pavement life cycle assessment with parameter uncertainty. Transp Res Part Transp Environ 25:131–138
OpenLCA (2018) User Manual. GreenDelta, Germany
Owens JW (1997) Life-cycle assessment: constraints on moving from inventory to impact assessment. J Ind Ecol 1:37–49
Pfister S, Scherer L (2015) Uncertainty analysis of the environmental sustainability of biofuels. Energy Sustain Soc 5:30
Qin Y, Suh S (2017) What distribution function do life cycle inventories follow? Int J Life Cycle Assess 22:1138–1145
Ragas AM, Huijbregts MA, Henning-de Jong I, Leuven RS (2009) Uncertainty in environmental risk assessment: implications for risk-based management of river basins. Integr Environ Assess Manag 5:27–37
Reap J, Roman F, Duncan S, Bras B (2008) A survey of unresolved problems in life cycle assessment. Int J Life Cycle Assess 13:374–388
Rowe G, Wright G (1999) The Delphi technique as a forecasting tool: issues and analysis. Int J Forecast 15:353–375
Roy P-O, Azevedo LB, Margni M et al (2014) Characterization factors for terrestrial acidification at the global scale: a systematic analysis of spatial variability and uncertainty. Sci Total Environ 500:270–276
Rypdal K, Winiwarter W (2001) Uncertainties in greenhouse gas emission inventories—evaluation, comparability and implications. Environ Sci Pol 4:107–116
Scherer L, Pfister S (2016) Dealing with uncertainty in water scarcity footprints. Environ Res Lett 11:054008
Sills DL, Paramita V, Franke MJ et al (2012) Quantitative uncertainty analysis of life cycle assessment for algal biofuel production. Environ Sci Technol 47:687–694
SimaPro (2016) User Manual. PRé Consultants, Netherlands
Slottje P, van der Sluijs JP, Knol AB (2008) Expert elicitation: methodological suggestions for its use in environmental health impact assessments. National Institute for Public Health and the Environment
Sonnemann GW, Schuhmacher M, Castells F (2003) Uncertainty assessment by a Monte Carlo simulation in a life cycle inventory of electricity produced by a waste incinerator. J Clean Prod 11:279–292
Sugiyama H, Fukushima Y, Hirao M, Hellweg S, Hungerbühler K (2005) Using standard statistics to consider uncertainty in industry-based life cycle inventory databases (7 pp). Int J Life Cycle Assess 10:399–405
US Environmental Protection Agency (2005) Guidelines for carcinogen risk assessment. In: Risk Assessment Forum. US EPA, Washington
van den Berg NW, Huppes G, Lindeijer EW, et al (1999) Quality assessment for LCA. Leiden: CML, Leiden University. (CML Report 152)
Van Der Sluijs JP, Craye M, Funtowicz S et al (2005) Combining quantitative and qualitative measures of uncertainty in model-based environmental assessment: the NUSAP system. Risk Anal 25:481–492
von Pfingsten S, Broll DO, von der Assen N, Bardow A (2017) Second-order analytical uncertainty analysis in life cycle assessment. Environ Sci Technol 51:13199–13204
Weidema BP (1998) Multi-user test of the data quality matrix for product life cycle inventory data. Int J Life Cycle Assess 3:259–265
Weidema BP, Wesnaes MS (1996) Data quality management for life cycle inventories—an example of using data quality indicators. J Clean Prod 4:167–174
Weidema BP, Bauer C, Hischier R, et al (2013) Overview and methodology: data quality guideline for the ecoinvent database version 3. Swiss Centre for Life Cycle Inventories
Wernet G, Bauer C, Steubing B, Reinhard J, Moreno-Ruiz E, Weidema B (2016) The ecoinvent database version 3 (part I): overview and methodology. Int J Life Cycle Assess 21:1218–1230
Winkler RL, Murphy AH (1968) “Good” probability assessors. J Appl Meteorol 7:751–758
Yang Y, Tao M, Suh S (2018) Geographic variability of agriculture requires sector-specific uncertainty characterization. Int J Life Cycle Assess 23:1581–1589
Acknowledgments
We are thankful to all 47 respondents who participated in the survey. We thank Dr. Sarah Anderson, Dr. Mark Huijbregts, and Dr. Lucas Laughery for their valuable inputs to this research.
Funding
This work was supported by the Assistance Agreement No. 83557901 awarded by the US Environmental Protection Agency to University of California, Santa Barbara. It has not been formally reviewed by EPA. The views expressed in this document are solely those of the authors and do not necessarily reflect those of the Agency. EPA does not endorse any products or commercial services mentioned in this publication.
Author information
Authors and Affiliations
Corresponding author
Additional information
Communicated By Ralph K. Rosenbaum
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Electronic supplementary material
ESM 1
(DOCX 1596 kb)
Rights and permissions
About this article
Cite this article
Qin, Y., Cucurachi, S. & Suh, S. Perceived uncertainties of characterization in LCA: a survey. Int J Life Cycle Assess 25, 1846–1858 (2020). https://doi.org/10.1007/s11367-020-01787-9
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11367-020-01787-9