Improving the integrated hybrid LCA in the upstream scope 3 emissions inventory analysis

INPUT-OUTPUT AND HYBRID LCA

Abstract

Purpose

The protocols of carbon footprints generally define three scopes for different greenhouse gas (GHG) emissions levels. The most important carbon footprint emissions source comes from upstream indirect emissions of scope 3 for products that do not consume energy during their use phase. Upstream scope 3 GHG inventory can usually be analyzed through input–output or hybrid LCA analysis. The economic input–output life cycle analysis (EIO-LCA) and the hybrid LCA model have been widely used for this purpose. However, a cutoff error exists in the hybrid model, and the lack of a truncation criterion between process and IO inventory may lead to a high level of uncertainty in the hybrid model. This study attempts to improve the problem of cutoff uncertainty in hybrid LCA and proposes a method to minimize the cutoff uncertainty.

Methods

The way to improve the cutoff uncertainty could follow two steps. First, through the IO inventory analysis of EIO-LCA, we can define the emissions by various tiers of product components. The IO inventory indicator can provide a definitive criterion for the process inventory of the hybrid model. Second, we connect the process- and IO-LCI according to the IO inventory result. The advantage of the process inventory is that it provides detailed manufacturing information on the target while the IO encompasses a complete system boundary. For improvements, the process inventory can catch the most important process of the GHG emissions, and the IO inventory could compensate for the remainder of the incomplete system inventory.

Results and discussion

In this case study, the printed circuit board production process is used to evaluate the efficiency of the improved method. The threshold M was set to 70 in this case study, and the IO inventory provides the remaining 30 %. For the integrated hybrid model, the tier 3 process inventory takes only 64 % while the incorporation of the proposed method can include 92 % of the total emissions, which shows the cutoff uncertainty can be reduced through the improvement.

Conclusions

This study provides a clear guideline for process and IO cutoff criteria, which can help the truncation uncertainty. When higher precision is required, process LCI will need to play an important role, and thus, a higher M value should be set. In this situation, the emissions from IO-LCI would be smaller than the emissions from the process LCI. The appropriate solution would attain a comfortable balance between data accuracy and time and labor consumption.

Keywords

Carbon footprint EIO-LCA Integrated hybrid LCA LCA uncertainty Print circuit board Scope 3 

Supplementary material

11367_2012_469_MOESM1_ESM.docx (18 kb)
ESM 1(DOCX 17 kb)

References

  1. Acquaye AA, Wiedmann T, Feng K, Crawford RH, Barrett J, Kuylenstierna J, Duffy AP, Koh SCL, McQueen-Mason S (2011) Identification of ‘carbon hot-spots’ and quantification of GHG intensities in the biodiesel supply chain using hybrid LCA and structural path analysis. Environ Sci Technol 45(6):2471–2478CrossRefGoogle Scholar
  2. Andrae A, Andersen O (2010) Life cycle assessments of consumer electronics—are they consistent? Int J Life Cycle Assess 15(8):827–836CrossRefGoogle Scholar
  3. Baboulet O, Lenzen M (2010) Evaluating the environmental performance of a university. J Cleaner Prod 18(12):1134–1141CrossRefGoogle Scholar
  4. WRI and WBCSD (2011) Corporate value chain (scope 3) accounting and reporting standard—supplement to the GHG Protocol Corporate Accounting and Reporting Standard, USAGoogle Scholar
  5. Hendrickson C, Horvath A, Joshi S, Lave L (1998) Peer reviewed: economic input–output models for environmental life-cycle assessment. Environ Sci Technol 32(7):184A–191ACrossRefGoogle Scholar
  6. Huang YA, Weber CL, Matthews HS (2009) Categorization of Scope 3 emissions for streamlined enterprise carbon footprinting. Environ Sci Technol 43(22):8509–8515CrossRefGoogle Scholar
  7. Junnila SI (2006) Empirical comparison of process and economic input-output life cycle assessment in service industries. Environ Sci Technol 40(22):7070–7076CrossRefGoogle Scholar
  8. Lave LB, Cobas-Flores E, Hendrickson CT, McMichael FC (1995) Using input-output analysis to estimate economy-wide discharges. Environ Sci Technol 29(9):420A–426AGoogle Scholar
  9. Lenzen M (2000) Errors in conventional and input-output–based life-cycle inventories. J Ind Ecol 4(4):127–148CrossRefGoogle Scholar
  10. Lenzen M (2007) Structural path analysis of ecosystem networks. Ecol Model 200(3–4):334–342CrossRefGoogle Scholar
  11. Lenzen M, Crawford R (2009) The path exchange method for hybrid LCA. Environ Sci Technol 43(21):8251–8256CrossRefGoogle Scholar
  12. Majeau-Bettez G, Strømman AH, Hertwich EG (2011) Evaluation of process- and input–output-based life cycle inventory data with regard to truncation and aggregation issues. Environ Sci Technol 45(23):10170–10177CrossRefGoogle Scholar
  13. Matthews HS, Hendrickson CT, Weber CL (2008) The importance of carbon footprint estimation boundaries. Environ Sci Technol 42(16):5839–5842CrossRefGoogle Scholar
  14. Rowley H, Lundie S, Peters G (2009) A hybrid life cycle assessment model for comparison with conventional methodologies in Australia. Int J Life Cycle Assess 14(6):508–516CrossRefGoogle Scholar
  15. Sinden G (2009) The contribution of PAS 2050 to the evolution of international greenhouse gas emission standards. Int J Life Cycle Assess 14(3):195–203CrossRefGoogle Scholar
  16. Suh S (2004) Functions, commodities and environmental impacts in an ecological-economic model. Ecol Econ 48(4):451–467CrossRefGoogle Scholar
  17. Suh S (2006) Reply: Downstream cut-offs in integrated hybrid life-cycle assessment. Ecol Econ 59(1):7–12CrossRefGoogle Scholar
  18. Suh S, Huppes G (2005) Methods for life cycle inventory of a product. J Cleaner Prod 13(7):687–697CrossRefGoogle Scholar
  19. Suh S, Kagawa S (2005) Industrial ecology and input-output economics: an introduction. Econ Syst Res 17(4):349–364CrossRefGoogle Scholar
  20. Suh S, Lenzen M, Treloar GJ, Hondo H, Horvath A, Huppes G, Jolliet O, Klann U, Krewitt W, Moriguchi Y, Munksgaard J, Norris G (2003) System boundary selection in life-cycle inventories using hybrid approaches. Environ Sci Technol 38(3):657–664CrossRefGoogle Scholar
  21. Treloar GJ (1997) Extracting embodied energy paths from input–output tables: towards an input–output-based hybrid energy analysis method. Econ Syst Res 9(4):375–391CrossRefGoogle Scholar
  22. Treloar GJ, Love PED, Faniran OO, Iyer-Raniga U (2000) A hybrid life cycle assessment method for construction. Constr Manag Econ 18(1):5–9CrossRefGoogle Scholar
  23. Williams ED, Weber CL, Hawkins TR (2009) Hybrid framework for managing uncertainty in life cycle inventories. J Ind Ecol 13(6):928–944CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2012

Authors and Affiliations

  1. 1.Graduate Institute of Environmental EngineeringNational Taiwan UniversityTaipeiTaiwan

Personalised recommendations