Ivan Tengbjerg Herrmann

Analysis of the link between a definition of sustainability and the life cycle methodologies

PurposeIt has been claimed that in order to assess the sustainability of products, a combination of the results from a life cycle assessment (LCA), social life cycle assessment (SLCA) and life cycle costing (LCC) is needed. Despite the frequent reference to this claim in the literature, very little explicit analysis of the claim has been made. The purpose of this article is to analyse this claim.MethodsAn interpretation of the goals of sustainability, as outlined in the report Our Common Future (WCED 1987), which is the basis for most literature on sustainability assessment in the LCA community, is presented and detailed to a level enabling an analysis of the relation to the impact categories at midpoint level considered in life cycle (LC) methodologies.ResultsThe interpretation of the definition of sustainability as outlined in Our Common Future (WCED 1987) suggests that the assessment of a products sustainability is about addressing the extent to which product life cycles affect poverty levels among the current generation, as well as changes in the level of natural, human and produced and social capital available for the future population. It is shown that the extent to which product life cycles affect poverty to some extent is covered by impact categories included in existing SLCA approaches. It is also found that the extent to which product life cycles affect natural capital is well covered by LCA, and human capital is covered by both LCA and SLCA but in different ways. Produced capital is not to any large extent considered in any of the LC methodologies. Furthermore, because of the present level of knowledge about what creates and destroys social capital, it is difficult to assess how it relates to the LC methodologies. It is also found that the LCC is only relevant in the context of a life cycle sustainability assessment (LCSA) if focusing on the monetary gains or losses for the poor. Yet, this is an aspect which is already considered in several SLCA approaches.ConclusionsThe current consensus that LCSA can be performed through combining the results from an SLCA, LCA and LCC is only partially supported in this article: The LCSA should include both an LCA and an SLCA, which should be expanded to better cover how product life cycles affect poverty and produced capital. The LCC may be included if it has as a focus to asses income gains for the poor.

Enabling optimization in LCA: from “ad hoc” to “structural” LCA approach—based on a biodiesel well-to-wheel case study

PurposeApplied life cycle assessment (LCA) studies often lead to a comparison of rather few alternatives; we call this the “ad hoc LCA approach.” This can seem surprising since applied LCAs normally cover countless options for variations and derived potentials for improvements in a product life cycle. In this paper, we will suggest an alternative approach to the ad hoc approach, which more systematically addresses the many possible variations to identify the most promising. We call it the “structural LCA approach.” The goals of this paper are (1) to provide basic guidelines for the structural approach, including an easy expansion of the LCA space; (2) to show that the structural LCA approach can be used for different types of optimization in LCA; and (3) to improve the transparency of the LCA work.MethodsThe structural approach is based on the methodology “design of experiments” (Montgomery 2005). Through a biodiesel well-to-wheel study, we demonstrate a generic approach of applying explanatory variables and corresponding impact categories within the LCA methodology. Explanatory variables are product system variables that can influence the environmental impacts from the system. Furthermore, using the structural approach enables two different possibilities for optimization: (1) single-objective optimization (SO) based on response surface methodology (Montgomery 2005) and (2) multiobjective optimization (MO) by the hypervolume estimation taboo search (HETS) method. HETS enables MO for more than two or three objectives.Results and discussionUsing SO, the explanatory variable “use of residual straw from fields” is, by far, the explanatory variable that can contribute with the highest decrease of climate change potential. For the respiratory inorganics impact category, the most influencing explanatory variable is found to be the use of different alcohol types (bioethanol or petrochemical methanol) in biodiesel production. Using MO, we found the Pareto front based on 5 different life cycle pathways which are nondominated solutions out of 66 different analyzed solutions. Given that there is a fixed amount of resources available for the LCA practitioner, it becomes a prioritizing problem whether to apply the structural LCA approach or not. If the decision maker only has power to change a single explanatory variable, it might not be beneficial to apply the structural LCA approach. However, if the decision maker (such as decision makers at the societal level) has power to change more explanatory variables, then the structural LCA approach seems beneficial for quantifying and comparing the potentials for environmental improvement between the different explanatory variables in an LCA system and identifying the overall most promising product system configurations among the chosen PWs.ConclusionsThe implementation of the structural LCA approach and the derived use of SO and MO have been successfully achieved and demonstrated in the present paper. In addition, it is demonstrated that the structural LCA approach can lead to more transparent LCAs since the potentially most important explanatory variables which are used to model the LCAs are explicitly presented through the structural LCA approach. The suggested structural approach is a new approach to LCA and it seems to be a promising approach for searching or screening product systems for environmental optimization potentials. In the presented case, the design has been a rather simple full factorial design. More complicated problems or designs, such as fractional designs, nested designs, split plot designs, and/or unbalanced data, in the context of LCA could be investigated further using the structural approach.