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<p>Journal of Cleaner Production 15 (2007) 1469e1481 www.elsevier.com/locate/jclepro</p> <p>Positive and negative feedback in consequential life-cycle assessment Bjorn A. Sanden*, Magnus Karlstrom Division of Environmental Systems Analysis, Department of Energy and Environment, Chalmers University of Technology, SE-412 96 Goteborg, Sweden Received 6 July 2005; accepted 23 March 2006 Available online 30 May 2006</p> <p>Abstract In this paper we develop a typology of consequences that can be used for environmental assessments of investment in technologies. As an illustration we estimate how the inclusion of different causeeeffect chains could affect the estimated greenhouse gas emissions resulting from buying and using a fuel cell bus today. In contrast to earlier studies, we include causeeeffect chains containing positive feedback from adoption (e.g. economies of scale and learning). We discuss how our ndings affect the usefulness and limitations of consequential life-cycle assessment (LCA) and how LCA methodology in more general can be used to support strategic technology choice. A major conclusion is that environmental assessments of investment in emerging technologies should not only include effects resulting from marginal change of the current system but also marginal contributions to radical system change. 2006 Elsevier Ltd. All rights reserved.Keywords: Fuel cell; Life-cycle assessment; Consequential; Experience curve; Climate change</p> <p>1. Introduction Mitigation of climatic change while sustaining worldwide economic growth will require a radical reduction of the greenhouse gas (GHG) intensity of energy and transport systems [1]. As a consequence, there is a need for development and large-scale diffusion of a range of new technologies. Different technologies are competing for investments and environmental arguments are crucial to legitimise the money spent by governments and rms. To gain credibility, arguments are often supported by environmental assessments of the merits and drawbacks of different options. The quality and validity of the used assessment studies could therefore be of great importance. Life-cycle assessment (LCA) is a methodology that is frequently used to assess products and technologies and it strives</p> <p>* Corresponding author. Tel.: 46 31 772 8612; fax: 46 31 772 2172. E-mail address: bjorn.sanden@chalmers.se (B.A. Sanden). 0959-6526/$ - see front matter 2006 Elsevier Ltd. All rights reserved. doi:10.1016/j.jclepro.2006.03.005</p> <p>to give a complete picture of the environmental impact. However, standard LCA methodology is developed to answer questions about environmental impacts of the current (or historical) production and use of one unit of a product or minor product or process changes. When this methodology is used (unmodied) to provide answers to questions about strategic technological choices, i.e. not decisions that aim at improving a process within an existing technological environment, but with the long-term goal of changing large-scale technological systems, the result is of little value and in the worst case interpretations of the result may be grossly misleading. Within the LCA research community, the standard methodology has earlier been criticised for not being designed to support decisions about where to go in the future [2e7]. Consequential (or change-oriented) LCA that investigates the likely environmental consequences of a decision has been proposed as a more appropriate method [4]. However, Ekvall [8] concludes that consequential LCA has several limitations in describing effects of change. Ekvall as well as de Haes et al. [9] see the need for new hybrid methodologies where LCAs</p> <p>1470</p> <p>B.A. Sanden, M. Karlstrom / Journal of Cleaner Production 15 (2007) 1469e1481 </p> <p>are linked to and combined with other types of assessment tools. This raises the question of what consequences or causee effect chains to include in such hybrid methodologies. In this paper we develop a typology of consequences that can be used for environmental assessments of an investment decision. In particular, which we believe is a novel idea in this context, we include effects resulting from positive feedback (or increasing returns to adoption). As an illustration we estimate how the inclusion of different causeeeffect chains could affect the estimated GHG emissions resulting from buying and using a fuel cell bus today. To make a quantitative assessment of effects resulting from positive feedback from adoption we use scenarios and experience curves. We discuss how our ndings affect the usefulness and limitations of consequential LCA, and what assessment methodologies in general need to be considered to support strategic technology choice. 2. LCA typology An assessment should contribute meaningful information to a specic situation. For every assessment there is a need to consider the intended application of the result [10]. In the LCA methodology research community there has been an increasing understanding that the application of the result of an assessment has consequences for methodological choices, and in order to clarify when different methodological choices are suitable, several attempts have been made to classify LCAs into different types [5e7,11]. In particular, a distinction between two perspectives has been highlighted. Lately, the distinction has been claried but some confusion still prevails. The rst type is LCAs aiming at mapping the environmental impacts that a product can be made accountable for, so called accounting [2], descriptive [7], retrospective [11] or attributional LCA [8]. The second is LCAs aiming at describing the consequences of changes, so called changeoriented [7], prospective [11] or consequential LCA [8]. The rst perspective is assumed to look backwards at effects that have occurred. The latter perspective is assumed to be forward-looking. A problem with this subdivision is that at least two dimensions are lumped together. The rst dimension is time. We would simply suggest that some studies are retrospective, looking back at historic environmental impact, while others are prospective, looking forward at future environmental impact. To be more general, it is a matter of specifying a point in time, or a time period, for which the study is valid, be it 1985, 2005 or 2025. In this respect, retrospective studies and prospective studies only differ with regards to uncertainty (we know more, but not everything about the past). The second dimension is responsibility, i.e. how the responsibility for environmental impacts is shared between the object of study and other products and services. A product can be looked upon as being part of a given situation or steady state. The product is responsible for a share of the total environmental impact in the state e a share is attributed to the product. In this case, it is reasonable to use average data for inputs. Therefore, LCAs of this kind, made for different products, are additive in principal.</p> <p>Alternatively, it is possible to apply a change-oriented or consequential perspective, i.e. the addition of a unit of the product changes the state. The product is then responsible for how the environmental impact is affected when the state is changed. In this case it is reasonable to use marginal data. We follow Ekvall [8] and Rebitzer and co-workers [12] and use the terms attributional and consequential LCA for these two types.1 In our terminology, the traditional accounting type of LCA is attributional and retrospective (or relevant for the present state, 2005, and recent historical states). However, it is also possible to investigate how a technology or product would perform in a different steady state, for example a future state (2025), a historic state (1985) or a ctional stylised state characterised by changed performance and a different technological environment, e.g. different background systems [13]. Thus attributional LCA can be prospective, i.e. prospective studies do not have to be comparisons of changes on the margin of the current state but could also be comparisons of products and processes in future steady states. Even though consequential LCA is commonly used to investigate the future environmental consequences of a decision today (2005), in principle, it would be possible to use a retrospective change-oriented perspective to track the environmental consequences of a historic choice (e.g. in 1985) or to speculate about the consequences of a future choice (e.g. in 2025). Since we here are concerned with strategic technology choice we nd it useful to suggest one more distinction: that between product and technology LCA, where the former seeks to investigate the impact of a specic product, plant or production process, while the latter is an assessment of a more general technology. For the former, data valid for a specic situation, or state, could generate a useful result, while for the latter the impacts under many different and more general circumstances are of greater value. For consequential technology LCA a broader spectrum of causeeeffect chains are of interest than for consequential product LCA. In our view both attributional and consequential technology LCA could be used to support decisions on strategic technology choice. Prospective attributional technology LCAs could be used to analyse the general performance of a technology under different circumstances. The key methodological problem is to analyse the technology in a relevant state or scenario of consecutive states [12,13]. In this paper we wish to demonstrate how the environmental consequences of a single decision today could be assessed. For this we need a consequential technology LCA. Then the key methodological problem is to select which consequences or causeeeffect chains should be included and nd ways to quantify their environmental impact.2</p> <p>We have earlier used the terms state-oriented and change-oriented [13]. The differentiation between the time and responsibility dimensions makes it possible for us to use the terms attributional and consequential in a slightly different way than for example Rebitzer and co-workers [12, p. 705]. 2 In this paper we do not attempt to go beyond the inventory phase. We only investigate what emissions an action leads to, excluding any further impact assessment.</p> <p>1</p> <p>B.A. Sanden, M. Karlstrom / Journal of Cleaner Production 15 (2007) 1469e1481 </p> <p>1471</p> <p>3. A typology of consequences A rst order of consequences is the proportionate relations between the use, production and waste handling of a product (one functional unit) and environmental impacts (e.g. emissions). These are causeeeffect chains made up of physical ows. A second order of consequences and a more indirect type of effects that takes into account the economic ows related to the physical ows has been identied. In the literature on consequential LCA it has been observed that an increased demand on the margin does not have to imply that one more unit of the demanded good is supplied (see for example Ref. [14]). Instead increased demand for an input that is constrained will induce the production of a substitute. As a consequence, marginal data (the last unit produced) differ from average data. An increased production could also affect the demand for the product and related products [14]. In this way consequential LCA has started to bring in elements of equilibrium thinking and negative feedback mechanisms stemming from constrained (or xed) supply. These types of effects are propagated by a price mechanism controlling supply and demand relationships. Since this mechanism is based on constraints and negative feedback, the assessed action leads to a shift to a new equilibrium between demand and supply of different goods and services. The marginal environmental effect in this sense is the difference between the old and the new state of equilibrium. It is not only production capacity, or the supply ow, that can be constrained. Also stocks of resources are constrained. We therefore suggest that one could argue for that the demand for a limited resource leads to the use of less limited resource (or back-stop resource) on the stock-margin, albeit delayed in time. As in the case of arguments grounded in equilibrium models this argument presumes that preferences and available technologies are xed. While the causeeeffect relationships based on models of equilibrium between supply and demand are borrowed from neoclassical economics, we suggest that a third order of consequences could be brought in from theories of technical change.3 These effects depend on the cumulative build-up of stocks and structures, such as physical structures, institutions and actors and networks, leading to altered availability and cost of technologies and to changed preferences [17]. First, an investment in a technology could change physical structures such as manufacturing equipment and physical infrastructure. This will affect future costs and have implications for future technology choice and thus future environmental impact. Second, a technology is also part of an institutional system, or rule system that guides actions. There are cognitive as well normative rules. Cognitive rules in the form of explicit and tacit knowledge are decisive for technical change.</p> <p>Increasing the stock of knowledge and experience could have implications not only for the further development of the technology in a specic application. It could also affect the use of the technology in other applications. Knowledge generation occurs in all parts of the life cycle and includes different elements of technology development, learning by doing and learning by using. Knowledge propagates through different actors, some taking active part in the production chain such as manufacturers and users, others acting on a more general level such as universities. The adaptation of hard normative rules such as laws and regulations are often necessary for the successful adoption of a new technology and normally comes as a result of early investments. Inuencing soft cognitive and normative rules such as beliefs, visions, attitudes and norms is also a prerequisite for change. Finally, all cognitive and normative rules as well as physical structures are changed by actors and networks of actors, which themselves evolve with technology adoption to form advocacy coalitions [18,19]. The investment in a new technology often affects these systems in ways that are benecial for further investments in the technology. In the literature on technical change such positive feedback mechanisms, or positive returns to adoption, have been given a prime role in the development of a new technology.4 On the producer side, economies of scale, learning by doing and incremental product development lead to increased performance to cost ratio when more systems are produced. On the user side, learning by using, decreased uncertainty over cost and performance, and economies...</p>