Does hybrid LCA with a complete system boundary yield adequate results for product promotion?




Hybrid life cycle assessment (LCA) with a complete system boundary is recognized as an advanced approach widely applied in comparative analysis with the goal of product promotion. Here, I evaluate the theoretical foundation, or assumptions, of hybrid LCA in this application and discuss alternative models. The goal of this article is partly to call attention to the restrictive assumptions involved in the models used in LCA and to instigate further research and effort to improve these models.


As with process-based LCA, hybrid LCA is a type of linear model when it is used to estimate changes. It relies on several restrictive assumptions such as fixed input/output coefficients and unlimited supply of inputs. Besides, hybrid LCA further rests on the assumption of economy-wide effect, i.e., a change of any magnitude in the output of any product would affect the entire economy. This may be another restrictive assumption, and to what extent it is reasonable depends on an array of factors, including the product being studied, its role in the economy, the magnitude of change, and the structure of the economy.

Results and discussion

Because of the restrictive assumptions, hybrid LCA may not necessarily yield adequate results for product promotion. This, however, does not mean that it entirely falls short, but that the assumptions need to be scrutinized and determined if they reasonably reflect the reality. If so, the results yielded by hybrid LCA may be adequate. But if not, the results fall short, and further research is needed.


For comparative analysis with the goal of product promotion, understanding how increases in the output of the product being studied would affect the economy is crucial. And this should form the basis of decision making. Alternative models to consider for large-scale changes include  computable general equilibrium models and rectangular choice of technology models, recognizing their limitations and assumptions as well. Alternatively, one may use simpler models such as process-based inventory but build scenarios to study how the impact of product promotion may ripple through the economy.


Hybrid LCA Life cycle assessment Process-based LCA Product promotion 

Supplementary material

11367_2016_1256_MOESM1_ESM.docx (21 kb)
ESM 1 (DOCX 20 kb)


  1. Anex R, Lifset R (2014) Life cycle assessment: different models for different purposes. J Ind Ecol 18:321–323CrossRefGoogle Scholar
  2. Duchin F, Levine SH (2011) Sectors may use multiple technologies simultaneously: the rectangular choice-of-technology model with binding factor constraints. Econ Syst Res 23:281–302CrossRefGoogle Scholar
  3. Fargione J, Hill J, Tilman D et al (2008) Land clearing and the biofuel carbon debt. Science 319:1235–1238CrossRefGoogle Scholar
  4. Finnveden G, Hauschild M, Ekvall T et al (2009) Recent developments in life cycle assessment. J Environ Manag 91:1–21CrossRefGoogle Scholar
  5. Heijungs R, Suh S (2002) The computational structure of life cycle assessment. Kluwer Academic Pub, DordrechtCrossRefGoogle Scholar
  6. Hertwich EG, Gibon T, Bouman EA et al (2015) Integrated life-cycle assessment of electricity-supply scenarios confirms global environmental benefit of low-carbon technologies. Proc Natl Acad Sci 112:6277–6282CrossRefGoogle Scholar
  7. Krugman P (1980) Scale economies, product differentiation, and the pattern of trade. Am Econ Rev 70:950–959Google Scholar
  8. Lenzen M (2001) Errors in conventional and input output—based life—cycle inventories. J Ind Ecol 4:127–148CrossRefGoogle Scholar
  9. Liska A, Yang H, Bremer V et al (2009) Improvements in life cycle energy efficiency and greenhouse gas emissions of corn ethanol. J Ind Ecol 13:58–74CrossRefGoogle Scholar
  10. Nakamura S, Nansai K (2016) Input–output and hybrid LCA. In: Finkbeiner M (ed) Special types of life cycle assessment. SpringerGoogle Scholar
  11. Rose A (1995) Input-output economics and computable general equilibrium models. Struct Change Econ Dyn 6:295–304CrossRefGoogle Scholar
  12. Sandén BA, Karlström M (2007) Positive and negative feedback in consequential life-cycle assessment. J Clean Prod 15:1469–1481CrossRefGoogle Scholar
  13. Searchinger TD, Heimlich R et al. (2008) Estimating greenhouse gas emissions from soy-based US biodiesel when factoring in emissions from land use change. Lifecycle Carbon Footpr Biofuels, pp 35–45Google Scholar
  14. Suh S, Huppes G (2005) Methods for life cycle inventory of a product. J Clean Prod 13:687–697CrossRefGoogle Scholar
  15. Suh S, Yang Y (2014) On the uncanny capabilities of consequential LCA. Int J Life Cycle Assess 19:1179–1184CrossRefGoogle Scholar
  16. Suh S, Lenzen M, Treloar G et al (2004) System boundary selection in life-cycle inventories using hybrid approaches. Env Sci Technol 38:657–664CrossRefGoogle Scholar
  17. Tyner W, Taheripour F (2008) Biofuels, policy options, and their implications: analyses using partial and general equilibrium approaches. J Agric Food Ind Organ 6:1–18Google Scholar
  18. Weber CL, Matthews HS (2008) Food-miles and the relative climate impacts of food choices in the United States. Environ Sci Technol 42:3508–3513CrossRefGoogle Scholar
  19. Wiedmann T, Minx J (2008) A definition of “carbon footprint.” In: Ecological economics research trends. Nova Science Publishers, Hauppauge NY, USA, pp 1–11Google Scholar
  20. Yang Y, Campbell JE (2016) Improving attributional life cycle assessment for decision support: the case of local food in sustainable design. J Clean Prod (in review)Google Scholar
  21. Yang Y (2016) Two sides of the same coin: consequential life cycle assessment based on the attributional framework. J Clean Prod 127:274–281Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  1. 1.CSRA IncFalls ChurchUSA

Personalised recommendations