Skip to main content
SpringerLink
Log in

Quantitative approaches in life cycle assessment—part 2—multivariate correlation and regression analysis

  • UNCERTAINTIES IN LCA
  • Published:
The International Journal of Life Cycle Assessment Aims and scope Submit manuscript

Abstract

Purpose

This study examines the use of inferential statistics, specifically multivariate correlation and regression, as a means of interpreting LCA data. It is believed that these methods provide additional context in understanding data and results, and may serve as a way to present the uncertain results that are inherent to LCA.

Methods

Nine building envelope combinations were analyzed according to five service life models (N = 45). Three environmental indicators were used: global warming potential, atmospheric ecotoxicity, and atmospheric acidification from the Tool for the Reduction and Assessment of Chemical and Other Environmental Impacts assessment method. Multivariate correlation was performed using nine variables, including cumulative life cycle impact, major replacement, major replacement (frequency), minor replacement, major repairs, minor repairs, inspections 1 and 2, and total transportation (N = 45, 405 data points). The same data set was used for the regression analysis, although the variables were limited to major replacement, minor replacement, major repair, and minor repair (N = 45, 225 data points). SPSS software was used for all statistical calculations.

Results and discussion

Multivariate correlation analysis showed strong, statistically significant correlations between cumulative life cycle impact and major replacement across all environmental indicators. Similarly, the regression analysis showed strong R 2 values between cumulative life cycle impact and major replacement, such that the influence of all other variables was considerably diminished.

Conclusions

The use of inferential statistics provides useful information with respect to the strength and statistical significance of correlations between variables as in multivariate correlation, and allows for predictive capacity of impact, as demonstrated through regression analysis. Further studies should be conducted to confirm the added value of these analytical tools.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Atkas CB, Bilec MM (2012) Service life prediction of residential interior finishes for life cycle assessment. Int J Life Cycle Assess 17(3):362–371

    Article  Google Scholar 

  • Bare JC, Norris G, Pennington DW, McKone T (2003) TRACI: the tool for the reduction and assessment of chemical and other environmental impacts. J Ind Ecol 6:49–78

    Article  Google Scholar 

  • Berger M, Finkenbeiner M (2011) Correlation analysis of life cycle impact assessment indicators measuring resource use. Int J Life Cycle Assess 16:74–81

    Article  Google Scholar 

  • Bjorkland AE (2002) Survey of approaches to improve reliability in LCA. Int J Life Cycle Assess 2:64–72

    Article  Google Scholar 

  • Bojaca CR, Schrevens E (2010) Parameter uncertainty in LCA: stochastic sampling under correlation. Int J Life Cycle Assess 15:238–246

    Article  CAS  Google Scholar 

  • Ciroth A, Srocka M (2008) How to obtain a precise and representative estimate for parameters in LCA—a case study for the functional unit. Int J Life Cycle Assess 13(3):265–277

    Article  Google Scholar 

  • Ciroth A, Fleischer G, Steinbach J (2004) Uncertainty calculation in life cycle assessment—a combined method of simulation and approximation. Int J Life Cycle Assess 9(4):216–226

    Article  Google Scholar 

  • Dell‘Isola AJ, Stephen JK (2003) Life Cycle Costing for Facilities: Economic Analysis for Owners and Professionals in Planning, Programming, and Real Estate Development: Designing, Specifying, and Construction: Maintenance, Operations, and Procurement. Reed Construction Data, Kingston, MA

  • Fillie C, Lane S, Parham A, Sullivan J, Wahl J (2004) Analysis for gate-to-market effects for building materials in the Orlando, FL, Region. Report prepared for the Athena Sustainable Materials Institute (unpublished). August 2004

  • Fitch P, Smith Cooper J (2005) Life cycle modeling for adaptive and variant design part 2: case study. Res Eng Des 15:229–241

    Article  Google Scholar 

  • Grant A, Ries R (2013) Impact of building service life models on life cycle assessment. Build Res Inf 41(2):168–186

    Article  Google Scholar 

  • Grant A, Ries R, Kibert C (2014) Life cycle assessment and service life prediction: a case study of building envelope materials. J Ind Ecol 18(2):187–200

    Article  CAS  Google Scholar 

  • Heijungs R (2010) Sensitivity coefficients for matrix-based LCA. Int J Life Cycle Assess 15:511–520

    Article  Google Scholar 

  • Heijungs R, Frischknecht R (2005) Representing statistical distributions for uncertain parameters in LCA: relationships between mathematical forms, their representation in EcoSpold, and their representation in CMLCA. Int J Life Cycle Assess 10(4):248–254

    Article  Google Scholar 

  • Heijungs R, Tan RR (2010) Rigorous proof of fuzzy error propagation with matrix-based LCI. Int J Life Cycle Assess 15:1014–1019

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Hung ML, Ma HW (2009) Quantifying system uncertainty of life cycle assessment based on Monte Carlo simulation. Int J Life Cycle Assess 14:19–27

    Article  Google Scholar 

  • IBM Corp (2010) Released 2012. IBM SPSS statistics for windows, version 21.0. IBM Corp, Armonk

  • International Organization for Standardization (2006) Environmental management—life cycle assessment—principles and framework. ISO 14040:2006(E)

  • Kota S, Chakrabarti A (2010) A method for estimating the degree of uncertainty with respect to life cycle assessment during design. J Mech Des 132(9):0910007

    Article  Google Scholar 

  • Kucukvar M, Noori M, Egilmez G, Tatari O (2014) Stochastic decision modeling for sustainable pavement designs. Int J Life Cycle Assess 19:1185–1199

    Article  CAS  Google Scholar 

  • Lloyd SM, Ries R (2007) Characterizing, propagating, and analyzing uncertainty in life-cycle assessment—a survey of quantitative approaches. J Ind Ecol 11(1):161–179

    Article  Google Scholar 

  • Martinez E, Jimenez E, Blanco J, Sanz F (2010) LCA sensitivity analysis of a multi-megawatt wind turbine. Appl Energy 87:2293–2303

    Article  Google Scholar 

  • Meyers LS, Gamst G, Guarino AJ (2006) Applied multivariate research. Sage Publications, Inc., Thousand Oaks

    Google Scholar 

  • Moreau V, Bage G, Marcotte D, Samson R (2012) Estimating material and energy flows in life cycle inventory with statistical models. J Ind Ecol 16(3):399–406

    Article  Google Scholar 

  • Morgan MG, Henrion M (1990) Uncertainty: a guide to dealing with uncertainty in quantitative risk and policy analysis. Cambridge University Press, Cambridge

    Book  Google Scholar 

  • Morrison Hershfield in collaboration with the Athena Sustainable Materials Institute (2002) Maintenance, Repair and Replacement Effects for Building Envelope Materials. Prepared by Morrison Hershfield for Athena Sustainable Materials Institute, Merrickville, Ontario, Canada, January 2002

  • Neely ES, Neathammer RD, Stirn JR, Winkler RP (1991) Maintenance task data base for buildings: architectural systems. United States Army Corps of Engineers. USACERL Special Report P-91/23

  • Noori M, Tatari O, Nam B, Golestani B, Greene J (2014) A stochastic optimization approach for the selection of reflective cracking mitigation techniques. Transport Res 69:367–378

    Google Scholar 

  • Noori M, Kucukvar M, Tatari O (2015) A macro-level decision analysis of wind power as a solution for sustainable energy in the USA. Int J Sustain Energy 34(10):629–644

    Article  Google Scholar 

  • Reap J, Roman F, Duncan S, Bras B (2008) A survey of unresolved problems in life cycle assessment part 2: impact assessment and interpretation. Int J Life Cycle Assess 13:374–388

    Article  Google Scholar 

  • RS Means (1996) Cost planning and Estimating for Facility Maintenance. Rs means, Kingston, Ma

  • Smith Cooper J, Kahn E (2012) Commentary on issues in data quality analysis in life cycle assessment. Int J Life Cycle Assess 17:499–503

    Article  Google Scholar 

  • Smith Cooper J, Noon M, Kahn E (2012) Parameterization in life cycle assessment inventory data: a review of current use and the representation of uncertainty. Int J Life Cycle Assess 17:689–695

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Sustainable Buildings Industry Council (SBIC) (2005) Energy-10 software. The Building Performance Group. (1999). ―BPG Building Fabric Component Life Manual. E & FN Spon. London, England

  • USEPA (United States Environmental Protection Agency) (1989) Exposure factors handbook. Report EPA/600/8-89/043. US EPA, Washington, DC

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Instructional, Workforce and Applied Technology, University of West Florida, Pensacola, FL, USA

    Aneurin Grant

  2. M.E. Rinker School of Building Construction, University of Florida, Gainesville, FL, USA

    Robert Ries

  3. Research and Advanced Studies, University of West Florida, Pensacola, FL, USA

    Carla Thompson

Authors
  1. Aneurin Grant
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Robert Ries
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Carla Thompson
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Aneurin Grant.

Additional information

Responsible editor: Adisa Azapagic

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Grant, A., Ries, R. & Thompson, C. Quantitative approaches in life cycle assessment—part 2—multivariate correlation and regression analysis. Int J Life Cycle Assess 21, 912–919 (2016). https://doi.org/10.1007/s11367-015-0948-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11367-015-0948-x

Keywords

Search

Navigation