ESU-services, Kanzleistrasse 4, CH - 8610 Uster, Switzerland;
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In the last few years a dependence of LCI concepts and models on the LCA goal has been identified (Udo de Haes & Wrisberg 1997, Frischknecht 1998, Weidema et al. 1999, Curran et al. 2002). One main distinction is made between models that describe a state of the flows of the economic system (attributional LCI) and models that describe changes in the flows within the economic system caused by a decision made or planned (consequential LCI). Curran et al. (2002) made a second major distinction that covers the time aspect. LCIs may be retrospective (describing past situations or changes) or prospective (describing expected situations or changes). Hence, an LCI may be
- retrospective attributional (how was the situation in the past, e.g. environmental reports?)
- retrospective consequential (how did a decision in the past change the situation?)
- prospective attributional (how will the situation be in the future?)
- prospective consequential (how will a decision change the situation in the future?)
In the following I will concentrate on the differences between attributional and consequential LCI models.
The outline of attributional LCI models has been described in depth by Heijungs (1997). Attributional LCI models are used to describe for instance the life cycle of one litre of Max Havelaar orange juice consumed in Switzerland in 2001. It is assumed that this litre is part of the total consumption volume in Switzerland (3'500'000 litres) and not an extra litre. Inputs and outputs will be determined based on the average production situation for the 3.5 mio. litres sold in 2001. The product system of such an attributional analysis comprises (theoretically) all farmers involved in harvesting oranges in 2001 for Max Havelaar, all factories producing Max Havelaar orange juice in 2001, all factories producing packaging materials for this juice, etc.
The result of such an LCI (or LCA) provides information about the environmental impacts of farmers, producers, carriers, etc. that can be attributed to the consumption of an average litre of orange juice purchased in 2001.
The outline of LCI models that describe the changes of a situation caused by a decision, called "consequential approach", has been extensively discussed during an LCA workshop on electricity data in LCI held in Cincinnati, Ohio, USA and during the Internet Life Cycle Assessment - Life Cycle Management (InLCA-LCM) conference in May 2002 (Ekvall 2002). In the final report of the electricity workshop (Curran et al. 2002) the consequential approach is defined as an attempt to estimate how flows to and from the environment will change as a result of a decision. A consequential LCA aims to answer the question whether the decision to purchase for instance a litre of Max Havelaar orange juice (instead of conventional orange juice, instead of apple juice, instead of tap water, etc.) leads to reduced or increased CO2-emissions, nitrate emissions to water, etc. on a global level. For that purpose, factories and farmers need to be identified which will change their production volume due to that particular change in demand. Opposite to the attributional approach, actors (farmers, producers, carriers etc.), that are not affected by a change in demand, are not part of the product system of a consequential LCA.
In other words, the product system does not comprise the world average of orange farmers but the ones that will increase or decrease their production. It may well include apple farmers as well, if an increase in Max Havelaar orange juice consumption is at the expense of apple juice. It may even include only (selected) conventional orange farmers if the production capacity of Max Havelaar farmers is constrained. In that case a decision to purchase labelled orange juice leads to increased sales (and production) of conventional orange juice, because sales of orange juice as a whole increases but Max Havelaar farmers cannot supply the additional demand. Hence, the additional litre of Max Havelaar orange juice will be charged with the environmental impacts of an additional litre of conventional orange juice.
We recognise that the consequential approach aims to link micro-economic actions with macro-economic consequences (what happens in the different markets that are affected by my decision?). It requires an LCA that considers market reactions, production volume developments, technology developments etc. This information may be delivered by a set of (pre-defined) conditions, by one or several scenarios or with the help of dynamic models. In any case an embedding in a broader range of socio-economic interdependence is required (Suh, 2002a). The hybrid LCA developed at CML and presented at the last discussion forum in April 2002 (Suh 2002b) may be an option in describing the LCA system embedded in a broader socio-economic interdependence for a complex modeling. It can be non-linear, it can consider capital inducement and it can be extended to computational general equilibrium (CGE) models.
The result of a consequential LCI provides information about how an individual (consumption or investment) decision will influence the (global) environment and whether the purchase of a supposed environmentally friendly product is likely to lead to a reduction in environmental impacts.
An alternative definition of the consequential approach remains on the micro-economic level and is described in Frischknecht (1998). In contrast to the interpretation of the consequential approach described above, the alternative definition of the approach uses the financial and contractual relations between economic actors (b2b relations) as the main basis of information. Applied on our case study, namely the decision whether or not to buy Max Havelaar orange juice (instead of conventional juice or instead of apple juice), the product system would be modelled as follows: If a consumer chooses to purchase a certain product or service he or she is entitled (or obliged) to accept the environmental impacts related to its production.
As a consequence - and this is the main difference to the consequential approach described above -, the orange juice LCI includes Max Havelaar farmers, producers, carriers, etc. in any case, even if they were not able or not obliged to adjust their total production (my extra consumption might be compensated by a reduced consumption by someone else). The alternatively defined consequential approach attributes particular economic activities, that are linked to the product via economic and contractual relations, to an individual additional (or reduced) consumption. The consequential approach as defined in the previous section links a (consumption or investment) decision to its affected economic activities irrespective of the fact whether these affected activities are actually required for the product consumed or invested in, and irrespective of the fact whether direct economic and contractual links to the purchased product exist.
The alternatively defined consequential LCA supports an efficient allocation of scarce environmental resources (similar to the price system, that helps to allocate the traditional economic resources labour and capital). This alone of course does not reduce environmental pressure. Supporting measures introduced on a macro-economic level are of course necessary. An environmental policy is required that defines reduction targets on emissions and resource consumptions or on environmental impacts (such as global warming). The relative scarcity of the environmental resources can then be operationalised for LCA with the help of a life cycle impact assessment method.
If we conduct a prospective LCA, scenarios are required irrespective of the concept applied. However, the breadth of required scenario information differs substantially. In a prospective attributional LCA predictions about technology development, about technology mixes, and average supply situations in the relevant moment in time are required. Hereby information is required for all technologies that contribute in relevant portions to future mixes.
In a prospective consequential LCA predictions about technology development are also needed but only for marginal technologies (technologies/production sites that change their output or are put in or out of operation due to a macro-economic change in demand). Additionally, macro-economic information such as the developments in relevant markets (whether growing, saturated, or shrinking), about marginal technologies and marginal production sites, about final consumption levels, economic growth rates, and eventual market constraints are required.
If we intend to use LCA for decision support, the concept of attributional LCA is of limited use. But before we adopt a consequential approach for environmental decision support, we first need a basic discussion about the role and capabilities of LCA.
From my point of view the main question is whether (product) LCA is suited to assess the global effects on the environment of individual decisions (consequential approach, linking micro-economic actions to macro-economic (environmental) consequences) or rather a tool to efficiently allocate scarce environmental resources to individual products and services (alternative definition of the consequential approach). The latter requires supporting measures, namely a (national or international) environmental policy that defines reduction targets for the emission of pollutants and the extraction of resources. I refrain from answering the question which one of the two definitions of a consequential approach delivers environmental information that is more relevant to society.
Curran, M.A., Mann, M. and Norris G. (2002) Report on the International Workshop on Electricity Data for Life Cycle Inventories, US Environmental Protection Agency, Cincinnati, USA; report, slides and individual contributions can be retrieved from http://www.sylvatica.com/ElectricityWorkshop.htm
Ekvall, T. (2002) Limitations of Consequential LCA, paper presentat at InLCA-LCM 2002, abstract and slides retrieved from http://www.lcacenter.org/lca-lcm/session-methods.html, online discussion contributions retrieved from http://www.lcacenter.org/lca-lcm/messages-methods.html
Frischknecht, R. (1998) Life Cycle Inventory Analysis for Decision-Making; Scope-dependent Inventory System Models and Context-specific Joint Product Allocation, Ph.D. thesis No. 12599, ETH Zürich
Heijungs, R. (1997) Economic Drama and the Environmental Stage; Formal derivation of algorithmic tools for environmental analysis and decision-support from a unified epistemological principle, Ph.D. thesis, University Leiden
Suh, S. (2002a) Personal communication, 29.08.2002
Suh, S. (2002b) The Hybrid Approach Merging IO and Process LCA, presented at the 16th Discussion Forum on Life Cycle Assessment: Input-output Life Cycle Assessment, April 10, 2002, Ecole Polytechnique Fédérale de Lausanne
Udo de Haes, H.A., Wrisberg, N. (1997) Life Cycle Assessment: Stae-of-the-Art and Research Priorities; Results of LCANET, a Concerted Action in the Environment and Climate Programme (DGXII), LCA Documents Vol. 1, ecomed Publishers, Landsberg
Weidema, B.P., Frees, N., Nielsen, A.-M. (1999) Marginal Production Technologies for Life Cycle Inventories, in Int. Journal of Life Cycle Assessment, Vol. 4, No. 1, pp. 48-56
Dep. of Energy Conversion, Chalmers University of Technology, SE-412 96 Göteborg, Sweden; phone +46-31-772 1445; fax +46-31-772 3592; email firstname.lastname@example.org
The methodology of effect-oriented or consequential LCA – as defined in Cincinnati last year (Curran et al. 2001) – is designed to describe how the environmental burdens are affected by changes that are made in the product system. This is distinguished from attributional LCA, which describes the environmental burdens of the product system as such. Consequential LCA can be used for studying effects of past as well as future actions. It can be used for decision-making as well as for learning purposes. In the former case, the study describes the effects of specific actions. In the latter case, it describes effects of possible actions - in other words the sphere of influence of the decision-maker.
However, consequential LCA is not adequate when information on the consequences of actions is irrelevant or insufficient. The results from a consequential LCA can sometimes seem unfair and we do, in some cases, base our decisions on a sense of responsibility that goes beyond the consequences of our actions. Furthermore, if each decision-maker focuses on the effects of her own actions only, there is a risk that larger systems are suboptimised. In these cases, an attributional LCA may generate relevant information.
The methodology of consequential LCA – as applied by, i.e., Ekvall et al. (1998) – also includes important aspects that are attributional rather than consequential. For example, it is typically assumed that the increase in demand for a product by one customer results in the same increase in production. The actual effects on the market for this product are likely to be more complex. An analysis of these effects could be based on the concept of price elasticity, perhaps utilizing partial equilibrium models. It may also be possible to study economy-wide effects using general equilibrium models. Such models would probably not resemble what we mean by LCA.
Curran, M.A., Mann, M. and Norris G. (2001) Report on the International Workshop on Electricity Data for Life Cycle Inventories, US Environmental Protection Agency, Cincinnati, USA.
Ekvall, T., Frees, N., Nielsen, P.H., Person, L., Ryberg, A., Weidema, B., Wesnæs, M. and Widheden, J. (1998) Life cycle assessment on packaging systems for beer and soft drinks – Main report. Danish Environmental Protection Agency, Copenhagen, Denmark.
Assessing future energy and transport systems is of major importance for providing information on potential environmental bottlenecks of the various life cycle stages of innovative technologies, for determining competitive advantages compared to conventional technologies and for developing scenarios of future, environmentally benign energy and transport systems.
Future systems are typically characterised by a cleaner and more efficient use phase, by a more complex system design (e. g. use of coproducts, cascade design, etc.), by a trend towards decentralised systems and renewable primary energy carriers and by the use of new concepts and materials. Due to these characteristics, the LCA analyst is confronted with methodological problems, particularly with data gaps and the requirement of forecasting and anticipation of future developments, but also with the need to consider infrastructure and decentralisation effects. In this presentation, the LCA of fuel cells and other future systems will be used as an illustration for problems arising in the LCA practice and approaches to deal with these aspects. Amongst others, the use of cost estimation methods to assess future production processes will be presented.
In Life-Cycle Assessment (LCA), decision makers are often faced with tradeoffs between current and future impacts. One typical example is waste incineration, where immediate emissions to the air from the incineration process have to be weighted against future emissions of slag landfills. Long-term impacts are either completely taken into account or they are entirely disregarded in case of a temporal cut-off. Temporal cut-offs are a special case of discounting. In this paper, discounting is defined as valuing damages differently at different points of time using a positive or negative discount rate. Apart from temporal cut-offs, discounting has rarely been applied in LCA so far. It is the goal of this paper to discuss the concept of discounting and its applicability in the context of LCA. For this purpose, we first review the arguments for discounting and its principles in economic sciences. Discounting in economics can be motivated by pure time preference, productivity of capital, diminishing marginal utility of consumption, and uncertainties. The nominal discount rate additionally includes changes in the price level. These arguments and their justification are discussed in the context of environmental impacts harming future generations. It is concluded that discounting across generations because of pure time preference contradicts fundamental ethical values and should therefore not be applied in LCA. However, it has to be acknowledged that in reality decision makers often use positive discount rates because of pure time preference - either because they might profit from imposing environmental damage on others instead of themselves or because people in the far future are not of immediate concern to them. Discounting because of the productivity of capital assumes a relationship between monetary values and environmental impact. If such a relationship is accepted, discounting could be applied. However, future generations should be compensated for the environmental damage. It is likely that they would demand a higher compensation if the per capita income increases in the future. As both the compensation and the discount rate are related to economic growth, the overall discount rate might be close to zero. It is shown that the overall discount rate might even be negative considering that the required compensation could increase (even to infinite) if natural assets remain scarce whereas the utility of consumption diminishes with increasing income. Uncertainties could justify both positive and negative discount rates. Since the relationship between uncertainties and the magnitude of damage is generally not exponential, we recommend to model changes in the magnitude of damage in scenario analysis instead of considering it in discounting (which requires an exponential relationship in the case of a constant discount rate). We investigated the influence of discounting in a case study of heavy metal emissions from slag landfills. It could be shown that even small discount rates of less than 1% lead to a significant reduction of the impact score whereas negative discount rates inflate the results. Discounting is only applicable when temporally differentiated data is available. In some cases, such a temporal differentiation is necessary to take sound decisions, especially when long emission periods are involved. An example is the disposal of nuclear or heavy metal containing waste. In these cases, the results might completely depend on the discount rate. This paper helps to structure arguments and thus to support the decision about whether or not discounting should be applied in an LCA.
Life Cycle Assessments are assessing a product over its whole life cycle – well known. Products from railway manufacturing may face a complicated life, lasting for more than 25 years, and including a complex structure of maintenance processes, both preventive and corrective (i.e. repair processes). In life cycle costing, a discounting of future costs is an often used option, which is in line with good practice in investment cost accounting. In Life Cycle Assessment, it is not. It is even so that introducing time in the life cycle is not common in LCA. Why should it? And is it that feasible, for complex life cycles?
We display two different algorithms for calculating a Life Cycle Assessment over time, show and discuss their requirements, and show an LCA calculation over time, performed for a German train’s component. We discuss these results in comparison to an LCA for the same component, calculated without a timely resolution. This comparison reveals four main advantages, and also disadvantages, of ‘introducing time’ in LCA results.
The advantages are of (i) methodical nature (coherence with methods from the area of financial accounting), (ii) to allow a general inclusion of future’s uncertainty, (iii) to be able to care for the timely behaviour of substances, (iv) to be able to incorporate prognoses on the future environment and also on processes, and, finally, (v) to enable the user to express his or her views on the future in the results of an LCA (coherence with the user’s perspective on the future).
We are looking very forward to discussing and hopefully presenting our ideas at the Forum.
The new information and communication technologies are bringing both opportunities and risks for the goal of sustainable development. Our unit analyses these interrelationships in an interdisciplinary team. The main questions thereby are: What can the digital revolution contribute to sustainable development? Which new dangers for humans and the environment could come out of it? One of the instruments used by our group is LCA. Thereby, with the goal of using the opportunities and minimizing the risks,
Possibilities as well as limits of the use of LCA for answering the above rised questions will be highlighted with examples from ongoning research at EMPA. Future research areas and open questions for a more appropriate use of LCA in the area of the modern information society will be raised and discussed.
French.-German Institute for Environmental Research (DFIU) – University of Karlsruhe (TH)
Hertzstr. 16 – 76187 Karlsruhe - Germany
Ziel des EU-geförderten Projekts ISACOAT ist die Untersuchung zukünftiger struktureller Veränderungen der Organisation und Funktionsweise der Wertschöpfungskette der Metalloberflächenbehandlung mit dem Schwerpunkt auf dem Bereich der Lackierung, und
deren Konsequenzen für kleine und mittlere Unternehmen (KMU), die durch neue umweltfreundliche Produkte und Techniken verursacht werden. Schwerpunkt der laufenden ersten Arbeitsphase von ISACOAT ist es, eine Übersicht über die Branche, eingesetzte Produkte und Technologien, sowie relevante Einflussfaktoren auf die Entwicklung der Branche zu erarbeiten. Als erste Ergebnisse werden die Wertschöpfungskette der Metalllackierung in der EU sowie die damit verbundenen Materialflüsse und VOC-Emissionen präsentiert. Am Beispiel der Farb- und Lackindustrie werden wichtige Einflussfaktoren auf die Branche und die Marktentwicklung der vergangenen
10 Jahre aufgezeigt. Die zukünftige Entwicklung des Sektors soll durch eine integrierte Analyse von zwei mittelfristigen (Betrachtungshorizont 10 Jahre) sowie zwei langfristigen (Betrachtungshorizont 20 Jahre) Szenarios analysiert werden. Beispielhaft werden die Rahmenbedingungen des Szenarios "long-term, vertical integration" mit einer Verschiebung vom Produkt- zum Serviceangebot und KMUs als Nischenanbietern anhand der Wertschöpfungskette skizziert. Zur Entscheidungsunterstützung für die langfristige strategische Planung stehen neben der Szenarioanalyse vielfältige Instrumente zur Verfügung. Ihre Anwendung in der Praxis wird anhand von Fallbeispielen (Interviews) evaluiert.
An dem Projekt sind insgesamt 14 Partner beteiligt: