Andreas Jørgensen

Life Cycle Sustainability Dashboard

One method to assess the sustainability performance of products is life cycle sustainability assessment (LCSA), which assesses product performance considering the environmental, economic, and social dimensions of the life cycle. The results of LCSA can be used to compare different products or to support decision making toward sustainable production and consumption. In both cases, LCSA results could be too disaggregated and consequently too difficult to understand and interpret by decision makers. As non‐experts are usually the target audience of experts and scientists, and are also involved in decision‐making processes, the necessity for a straightforward but comprehensive presentation of LCSA results is becoming strategically important. The implementation of the dashboard of sustainability proposed in this article offers a possible solution. An outstanding characteristic of the dashboard of sustainability is the communicability of the results by means of a graphical representation (a cartogram), characterized by a suitable chromatic scale and ranking score. The integration of LCSA and the dashboard of sustainability into a so‐called Life Cycle Sustainability Dashboard (LCSD) is described here. The first application of LCSD to a group of hard floor coverings is presented to show the applicability and limitations of the methodology.

Social LCA—a way ahead?

Roughly since 2004, there has been an ever increasing interest in developing and using social life cycle assessment (SLCA). A quick overview of the SLCA publications indexed by SCOPUS shows a small but stable increase from 2004 (Fig. 1). With regard to scientific publications, the International Journal of Life Cycle Assessment has been the leading journal covering this field with around 85 % of the above publications, to a large extent probably due to the very early explicit interest from the journal and the announcement of a dedicated subject editor, David Hunkeler, whose role was later taken over by Tom Swarr. Thanks to these persons, the authors who have contributed and the timely interest from the journal, it seems that a new field of research has been established which is growing year by year—a development I will do my best to sustain during my time as subject editor on the SLCA area on the International Journal of Life Cycle Assessment. Despite this activity, it seems fair to state, as it has been so many times before, that SLCA is still in its infancy. But the ‘infant’ is now close to a decade, so how come it has not matured more? My claim is that SLCAwill continue to be considered in its infancy until it has more profoundly ‘proven to work’. But what does it mean that the SLCAworks? I would guess that for many a solid ‘proof’ would depend on at least two aspects: First of all that the methodology actually does ‘what it is supposed to do’ and secondly, that what SLCA does has to be unique or at least that the SLCA has to ‘do what it is supposed to do’ better than other comparable tools. If SLCA does not do what it is supposed to do, then at least the motivation for developing it needs to be reconsidered, and if what SLCA does is not unique in some way, there seems to be little point in developing a new tool for doing the same as some other tool is already doing. But what is SLCA supposed to do? From a first glance, it seems that SLCA is to deliver decision support relating to the social impacts of products (or services, systems or technologies—here termed products), to be used either for comparing products or identifying hotspots (Benoit and Mazijn 2009). In this regard, it may seem that SLCA already delivers. For example, case studies have already been conducted relating to, e.g. cut roses (Franze and Ciroth 2011), tomatoes (Evans et al. 2009) or laptop computers (Ekener-Petersen and Finnveden 2012). These studies do, indeed, provide an assessment of social impact of products which could be used for decision support. The question is, however, what decisions the decision support provided through SLCA is to support? The only obvious answer seems to be decisions leading to more beneficial social conditions throughout the product life cycles. If the use of SLCA in decision making does not lead to an improvement of these social impacts, then it seems as a rather pointless affair to develop, carry out and consider the SLCA result in a decision context. Thus, SLCA is to deliver decision support which improves the social impacts in the product life cycles when considered in a decision context. However, this idea of developing decision support in improving social conditions is far from new. Take, for Int J Life Cycle Assess (2013) 18:296–299 DOI 10.1007/s11367-012-0517-5

Addressing the effect of social life cycle assessments

PurposeIn the recently published ‘Guidelines for social life cycle assessment of products’, it is stated that the ultimate objective of developing the social life cycle assessment (SLCA) is to promote improvements of social conditions for the stakeholders in the life cycle. This article addresses how the SLCA should be developed so that its use promotes these improvements.MethodsHypotheses of how the use of SLCA can promote improvement of social conditions in the life cycle are formulated, after which theories and empirical findings from relevant fields of research are used to address the validity of these hypotheses.ResultsThree in some cases potentially overlapping SLCA approaches are presented, assumed to create a beneficial effect in the life cycle in different ways. However, empirical and theoretical findings show that the beneficial effects proposed to arise from the use of each of these three approaches may all be problematic. Some of these problems may be mitigated through methodological modifications.ConclusionsGiven the significant problems in relation to creating an effect through the use of the SLCAs, and given the significant practical problems in applying the SLCAs, it is questioned whether the development of SLCA is a fruitful approach for improving social conditions in the product life cycle.

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.

Workshop on life cycle sustainability assessment: the state of the art and research needs—November 26, 2012, Copenhagen, Denmark

The interest in life cycle sustainability assessment (LCSA) is currently booming in the LCA community, culminating lately in the forthcoming special issue from the International Journal of Life Cycle Assessment fully devoted to the topic. In the available literature, LCSA has mainly been conceptualised as a combination of LCA, social life cycle assessment (SLCA) and environmental life cycle costing (LCC). With the publication of guidelines for performing SLCA (Andrews et al. 2009), the code of practice for LCC (Swarr et al. 2011) and the existing standards for LCA, this could indicate that the question of how to perform an LCSA has been solved. However, the scientific publications give evidence of the need of further discussing the topic, both at conceptual and methodological level. The topic of LCSAwas at the core of a workshop organised on 26th November 2012 in the framework of the SETAC Europe 18th LCA Case Study Symposium in Copenhagen, with a dual aim: (1) to discuss the different schools of thoughts on LCSA and (2) to outline a research agenda framework for enabling/improving LCSA. The workshop was structured as four sessions (presentations) followed by a discussion part among participants which resulted in the identification of several research areas considered important for the successful future development of LCSA methodology and applications. The presentations provided insights on different approaches to LCSA both at conceptual and methodological level. A short summary of the main conclusions of the presentations and the main research topics proposed during the discussions is explained in the following sections.

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.

Spaceborne laser bathymetry

We want to measure water depth and chlorophyll content on coastal areas from satellite. This should be done using a green laser, which partly is transmitted on the sea surface and partly reflected. We want thereby to detect two return signals by help from a avalanche photodiode. From these two signals, we can determine the depth down to around 70 metres. We think our project should participate in “gron dyst” because it would be very helpful information for a lot of marine biologist, and not least help ships to navigate through shallow water areas.