The International Journal of Life Cycle Assessment

, Volume 23, Issue 9, pp 1888–1902 | Cite as

Assessing the individual and combined effects of uncertainty and variability sources in comparative LCA of pavements

  • Hessam AzariJafari
  • Ammar Yahia
  • Ben AmorEmail author



Several efforts have attempted to incorporate the sources of uncertainty and variability into the life cycle assessment (LCA) of pavements. However, no method has been proposed that can simultaneously consider data quality, methodological choices, and variability in inputs and outputs without the need for complementary software. This study aims to develop and implement a flexible method that can be used in the LCA software to assess the effects of these sources on the conclusions.


A Monte Carlo analysis was conducted and applied in a comparative LCA of pavements to assess the preferred scenario. The uncertainty of the results was first estimated by considering the data quality using the ecoinvent database. Moreover, the variabilities of the materials, construction methods, and repair stages of the pavement life cycle were included in the analysis by assigning continuous uniform probability distributions to each variable. Ultimately, the probability of methodological choices was modeled using uniform distributions and assigning a portion of the area of the distribution to each scenario. The individual and combined effects of these uncertainty and variability sources were assessed in a comparative LCA of asphalt and concrete pavements in a cold region such as Quebec (Canada).

Results and discussion

The results of the Monte Carlo analysis show that the allocation choices can change the environmentally preferred scenario in four midpoint categories. These categories are significantly dominated by the crude oil supply chain. The variability in construction materials and methods can change the preferred scenario in the damage categories, namely, human health and global warming. Additionally, parameter uncertainty has a significant effect on the conclusion of the preferred scenario in ecosystem quality. The worst qualitative scores are given to the geographical uncertainty of the elementary flow that primarily contributes to this category (i.e., zinc). The simultaneous effect of the uncertainty and variability sources prevents the decision-maker from reaching a less uncertain conclusion about ecosystem quality, human health, and global warming effects.


This study demonstrates that it is feasible to assess the cumulative effects of common uncertainty and variability sources using commercial LCA software, including Monte Carlo simulation. Based on the variability and uncertainty of the results, the identification of a certain conclusion is case specific at both the midpoint and endpoint levels. Increasing the quality of the inventory is one solution to decreasing the uncertainties related to human health, ecosystem quality, and global warming regarding pavement LCA. This improvement can be achieved by avoiding the adaptation of a similar process to match the considered process and using practical construction efficiencies and realistic construction materials. The effectiveness of these tasks must be assessed in future studies. It should be noted that these conclusions were determined regardless of the uncertainty in the characterization factors of the impact assessment method.


Asphalt pavement Concrete pavement Data quality Methodological choices Monte Carlo simulation Technological variability Uncertainty analysis 



The authors thank the anonymous dedicated reviewers for their helpful and constructive criticism and their support of the approach taken. The authors are also grateful to the Natural Sciences and Engineering Research Council of Canada (NSERC) for its financial support through its ICP program and Fonds de Recherche du Québec-Nature et Technologie (FRQNT) through its Merit Scholarship Program for International Students (V1).

Supplementary material

11367_2017_1400_MOESM1_ESM.docx (840 kb)
ESM 1 (DOCX 839 kb)


  1. Aktas CB, Bilec MM (2012) Impact of lifetime on US residential building LCA results. Int J Life Cycle Assess 17(3):337–349CrossRefGoogle Scholar
  2. Anastasiou EK, Liapis A, Papayianni I (2015) Comparative life cycle assessment of concrete road pavements using industrial by-products as alternative materials. Resour Conserv Recycl 101:1–8CrossRefGoogle Scholar
  3. AzariJafari H, Yahia A, Ben Amor M (2016) Life cycle assessment of pavements: reviewing research challenges and opportunities. J Clean Prod 112(Part 4):2187–2197CrossRefGoogle Scholar
  4. Biswas WK (2014) Carbon footprint and embodied energy assessment of a civil works program in a residential estate of Western Australia. Int J Life Cycle Assess 19(4):732–744CrossRefGoogle Scholar
  5. Ciroth A, Muller S, Weidema B, Lesage P (2016) Empirically based uncertainty factors for the pedigree matrix in ecoinvent. Int J Life Cycle Assess 21(9):1338–1348CrossRefGoogle Scholar
  6. CSA A3000 (2008) Cementitious materials compendium. CSA International, Canadian Standards Association, TorontoGoogle Scholar
  7. de Koning A, Schowanek D, Dewaele J, Weisbrod A, Guinée J (2010) Uncertainties in a carbon footprint model for detergents; quantifying the confidence in a comparative result. Int J Life Cycle Assess 15(1):79CrossRefGoogle Scholar
  8. ecoinvent (2015) Ecoinvent v.3.2 database. Swiss Centre for Life Cycle Inventories, ZurichGoogle Scholar
  9. Ferreira VJ, Sáez-De-Guinoa Vilaplana A, García-Armingol T, Aranda-Usón A, Lausín-González C, López-Sabirón AM, Ferreira G (2016) Evaluation of the steel slag incorporation as coarse aggregate for road construction: technical requirements and environmental impact assessment. J Clean Prod 130:175–186CrossRefGoogle Scholar
  10. Frischknecht R, Jungbluth N, Althaus H-J, 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(1):3–9CrossRefGoogle Scholar
  11. Gregory JR, Montalbo TM, Kirchain RE (2013) Analyzing uncertainty in a comparative life cycle assessment of hand drying systems. Int J Life Cycle Assess 18(8):1605–1617CrossRefGoogle Scholar
  12. Gregory JR, Noshadravan A, Olivetti EA, Kirchain RE (2016) A methodology for robust comparative life cycle assessments incorporating uncertainty. Environ Sci Technol 50(12):6397–6405CrossRefGoogle Scholar
  13. Henriksson PJ, Heijungs R, Dao HM, Phan LT, de Snoo GR, Guinée JB (2015) Product carbon footprints and their uncertainties in comparative decision contexts. PLoS One 10(3):e0121221CrossRefGoogle Scholar
  14. Hossain MU, Poon C, Lo IC, Cheng JP (2016) Evaluation of environmental friendliness of concrete paving eco-blocks using LCA approach. Int J Life Cycle Assess 21(1):70–84CrossRefGoogle Scholar
  15. Huang Y, Spray A, Parry T (2013) Sensitivity analysis of methodological choices in road pavement LCA. Int J Life Cycle Assess 18(1):93–101CrossRefGoogle Scholar
  16. Huijbregts MAJ (1998) Application of uncertainty and variability in LCA. Int J Life Cycle Assess 3(5):273CrossRefGoogle Scholar
  17. IPCC (2006) IPCC Guidelines for National Greenhouse Gas Inventories. August 20th 2016, from
  18. ISO (2006) ISO 14044, Environmental management - Life Cycle Assessment - Requirements and guidelines 2006:1–47Google Scholar
  19. Jolliet O, Margni M, Charles R, Humbert S, Payet J, Rebitzer G, Rosenbaum R (2003) IMPACT 2002+: a new life cycle impact assessment methodology. Int J Life Cycle Assess 8(6):324–330CrossRefGoogle Scholar
  20. Jolliet O, Soucy G, Shaked S, Saadé-Sbeih M, Crettaz P (2015) Goal and system definition. Environmental life cycle assessment. CRC Press, Boca Raton, pp 23–46Google Scholar
  21. Jullien A, Dauvergne M, Proust C (2015) Road LCA: the dedicated ECORCE tool and database. Int J Life Cycle Assess 14040:655–670CrossRefGoogle Scholar
  22. Kicak K, Ménard J-F (2009) Analyse comparative du cycle de vie des chaussées en béton de ciment et en béton bitumineux à des fins d’intégration de paramètres énergétiques et environnementaux au choix des types de chaussées. Ministère des Transports du Québec, QuébecGoogle Scholar
  23. Larrea-Gallegos G, Vázquez-Rowe I, Gallice G (2017) Life cycle assessment of the construction of an unpaved road in an undisturbed tropical rainforest area in the vicinity of Manu National Park, Peru. Int J Life Cycle Assess 22:1109–1124CrossRefGoogle Scholar
  24. Lautier A, Rosenbaum RK, Margni M, Bare J, Roy P-O, Deschênes L (2010) Development of normalization factors for Canada and the United States and comparison with European factors. Sci Total Environ 409(1):33–42CrossRefGoogle Scholar
  25. Lloyd SM, Ries R (2007) Characterizing, propagating, and analyzing uncertainty in life-cycle assessment: a survey of quantitative approaches. J Ind Ecol 11(1):161–179CrossRefGoogle Scholar
  26. Meil J (2006) A life cycle perspective on concrete and asphalt roadways: embodied primary energy and global warming potential. Athena Research Institute(September)
  27. Muller S, Lesage P, Ciroth A, Mutel C, Weidema B, Samson R (2016a) The application of the pedigree approach to the distributions foreseen in ecoinvent v3. Int J Life Cycle Assess 21:1327–1337CrossRefGoogle Scholar
  28. Muller S, Lesage P, Samson R (2016b) Giving a scientific basis for uncertainty factors used in global life cycle inventory databases: an algorithm to update factors using new information. Int J Life Cycle Assess 21(8):1185–1196CrossRefGoogle Scholar
  29. National Renewable Energy Laboratory (2011) U.S. Life Cycle Inventory Database. National Renewable Energy Laboratory, GoldenGoogle Scholar
  30. Nijhof COP, Huijbregts MAJ, Golsteijn L, van Zelm R (2016) Spatial variability versus parameter uncertainty in freshwater fate and exposure factors of chemicals. Chemosphere 149:101–107CrossRefGoogle Scholar
  31. Noshadravan A, Wildnauer M, Gregory J, Kirchain R (2013) Comparative pavement life cycle assessment with parameter uncertainty. Transp Res Part D: Transport Environ 25:131–138CrossRefGoogle Scholar
  32. Reza B, Sadiq R, Hewage K (2014) Emergy-based life cycle assessment (Em-LCA) for sustainability appraisal of infrastructure systems: a case study on paved roads. Clean Technol Environ 16(2):251–266CrossRefGoogle Scholar
  33. Rodríguez-Alloza AM, Malik A, Lenzen M, Gallego J (2015) Hybrid input–output life cycle assessment of warm mix asphalt mixtures. J Clean Prod 90:171–182CrossRefGoogle Scholar
  34. Sayagh S, Ventura A, Hoang T, François D, Jullien A (2010) Sensitivity of the LCA allocation procedure for BFS recycled into pavement structures. Resour Conserv Recycl 54(6):348–358CrossRefGoogle Scholar
  35. Stripple H (2001) Life cycle assessment of road: a pilot study for inventory analysis, 2nd revised edition, IVL Report B1210E. Swedish Environmental Research Institute, GothenburgGoogle Scholar
  36. Swiss Centre for Life Cycle Inventories (2011) EcoInvent. Swiss Centre for Life Cycle Inventories, DubendorfGoogle Scholar
  37. Trupia L, Parry T, Neves LC, Lo Presti D (2017) Rolling resistance contribution to a road pavement life cycle carbon footprint analysis. Int J Life Cycle Assess 22:972–986CrossRefGoogle Scholar
  38. Turk J, Mladenović A, Knez F, Bras V, Šajna A, Čopar A, Slanc K (2014) Tar-containing reclaimed asphalt—environmental and cost assessments for two treatment scenarios. J Clean Prod 81:201–210CrossRefGoogle Scholar
  39. Van Dam TJ, Harvey JT, Muench ST, Smith KD, Snyder MB, Al-Qadi IL, Ozer H, Meijer J, Ram PV, Roesler JR (2015) Towards sustainable pavement systems: a reference document. FHWA-HIF-15-002, vol 51. Federal Highway Administration, Washington, p 61801Google Scholar
  40. Vidal R, Moliner E, Martínez G, Rubio MC (2013) Life cycle assessment of hot mix asphalt and zeolite-based warm mix asphalt with reclaimed asphalt pavement. Resour Conserv Recycl 74:101–114CrossRefGoogle Scholar
  41. Wang T, Lee I-S, Kendall A, Harvey J, Lee E-B, Kim C (2012) Life cycle energy consumption and GHG emission from pavement rehabilitation with different rolling resistance. J Clean Prod 33:86–96CrossRefGoogle Scholar
  42. Weidema BP, Bauer C, Hischier R, Mutel C, Nemecek T, Reinhard J, Vadenbo C, Wernet G (2013) Overview and methodology: data quality guideline for the ecoinvent database version 3. Centre for Life Cycle Inventories, SwissGoogle Scholar
  43. Yang R, Kang S, Ozer H, Al-Qadi IL (2015) Environmental and economic analyses of recycled asphalt concrete mixtures based on material production and potential performance. Resour Conserv Recycl 104(Part A):141–151CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  1. 1.Interdisciplinary Research Laboratory on Sustainable Engineering and Ecodesign (LIRIDE), Department of Civil EngineeringUniversité de SherbrookeSherbrookeCanada
  2. 2.NSERC Research Chair on Development and Use of Fluid Concrete with Adapted Rheology, Department of Civil EngineeringUniversité de SherbrookeSherbrookeCanada

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