journal cleaner prod

Journal of Cleaner Production 15 (2007) 1469e1481www.elsevier.com/locate/jclepro

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

Abstract

In this paper we develop a typology of consequences that can be used for environmental assessments of investment in technologies. As anillustration we estimate how the inclusion of different causeeeffect chains could affect the estimated greenhouse gas emissions resulting frombuying 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 findings 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 environmentalassessments of investment in emerging technologies should not only include effects resulting from marginal change of the current system butalso marginal contributions to radical system change.� 2006 Elsevier Ltd. All rights reserved.

Keywords: Fuel cell; Life-cycle assessment; Consequential; Experience curve; Climate change

1. Introduction

Mitigation of climatic change while sustaining worldwideeconomic growth will require a radical reduction of the green-house gas (GHG) intensity of energy and transport systems[1]. As a consequence, there is a need for development andlarge-scale diffusion of a range of new technologies.

Different technologies are competing for investments andenvironmental arguments are crucial to legitimise the moneyspent by governments and firms. To gain credibility, argumentsare often supported by environmental assessments of themerits and drawbacks of different options. The quality and val-idity of the used assessment studies could therefore be of greatimportance.

Life-cycle assessment (LCA) is a methodology that is fre-quently used to assess products and technologies and it strives

* Corresponding author. Tel.: þ46 31 772 8612; fax: þ46 31 772 2172.

E-mail address: [email protected] (B.A. Sanden).

0959-6526/$ - see front matter � 2006 Elsevier Ltd. All rights reserved.

doi:10.1016/j.jclepro.2006.03.005

to give a complete picture of the environmental impact. How-ever, standard LCA methodology is developed to answer ques-tions about environmental impacts of the current (or historical)production and use of one unit of a product or minor product orprocess changes. When this methodology is used (unmodified)to provide answers to questions about strategic technologicalchoices, i.e. not decisions that aim at improving a processwithin an existing technological environment, but with thelong-term goal of changing large-scale technological systems,the result is of little value and in the worst case interpretationsof the result may be grossly misleading.

Within the LCA research community, the standard method-ology has earlier been criticised for not being designed tosupport decisions about where to go in the future [2e7]. Con-sequential (or change-oriented) LCA that investigates thelikely environmental consequences of a decision has been pro-posed as a more appropriate method [4]. However, Ekvall [8]concludes that consequential LCA has several limitations indescribing effects of change. Ekvall as well as de Haes et al.[9] see the need for new hybrid methodologies where LCAs

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1470 B.A. Sanden, M. Karlstrom / Journal of Cleaner Production 15 (2007) 1469e1481

are linked to and combined with other types of assessmenttools. This raises the question of what consequences or causeeeffect chains to include in such hybrid methodologies.

In this paper we develop a typology of consequences thatcan be used for environmental assessments of an investmentdecision. In particular, which we believe is a novel idea in thiscontext, we include effects resulting from positive feedback(or increasing returns to adoption). As an illustration we estimatehow the inclusion of different causeeeffect chains could affectthe estimated GHG emissions resulting from buying and usinga fuel cell bus today. To make a quantitative assessment of ef-fects resulting from positive feedback from adoption we use sce-narios and experience curves. We discuss how our findings affectthe usefulness and limitations of consequential LCA, and whatassessment methodologies in general need to be considered tosupport strategic technology choice.

2. LCA typology

An assessment should contribute meaningful information toa specific situation. For every assessment there is a need toconsider the intended application of the result [10]. In theLCA methodology research community there has been an in-creasing understanding that the application of the result of anassessment has consequences for methodological choices, andin order to clarify when different methodological choices aresuitable, several attempts have been made to classify LCAsinto different types [5e7,11]. In particular, a distinctionbetween two perspectives has been highlighted. Lately, thedistinction has been clarified but some confusion still prevails.

The first type is LCAs aiming at mapping the environmen-tal impacts that a product can be made accountable for, socalled ‘accounting’ [2], ‘descriptive’ [7], ‘retrospective’ [11]or ‘attributional’ LCA [8]. The second is LCAs aiming atdescribing the consequences of changes, so called ‘change-oriented’ [7], ‘prospective’ [11] or ‘consequential’ LCA [8].The first perspective is assumed to look backwards at effectsthat have occurred. The latter perspective is assumed to beforward-looking.

A problem with this subdivision is that at least two dimen-sions are lumped together. The first dimension is time. Wewould simply suggest that some studies are retrospective,looking back at historic environmental impact, while othersare prospective, looking forward at future environmental im-pact. To be more general, it is a matter of specifying a pointin time, or a time period, for which the study is valid, be it1985, 2005 or 2025. In this respect, retrospective studies andprospective 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 responsi-bility for environmental impacts is shared between the object ofstudy and other products and services. A product can be lookedupon as being part of a given situation or steady state. The prod-uct is responsible for a share of the total environmental impact inthe state e a share is attributed to the product. In this case, it isreasonable to use average data for inputs. Therefore, LCAs ofthis kind, made for different products, are additive in principal.

Alternatively, it is possible to apply a change-oriented or conse-quential perspective, i.e. the addition of a unit of the productchanges the state. The product is then responsible for how theenvironmental impact is affected when the state is changed. Inthis case it is reasonable to use marginal data. We follow Ekvall[8] and Rebitzer and co-workers [12] and use the terms attribu-tional and consequential LCA for these two types.1

In our terminology, the traditional accounting type of LCAis attributional and retrospective (or relevant for the presentstate, 2005, and recent historical states). However, it is alsopossible to investigate how a technology or product would per-form in a different steady state, for example a future state(2025), a historic state (1985) or a fictional ‘stylised state’characterised by changed performance and a different techno-logical environment, e.g. different background systems [13].Thus attributional LCA can be prospective, i.e. prospectivestudies do not have to be comparisons of changes on the mar-gin of the current state but could also be comparisons of prod-ucts and processes in future steady states.

Even though consequential LCA is commonly used to inves-tigate the future environmental consequences of a decision today(2005), in principle, it would be possible to use a retrospectivechange-oriented perspective to track the environmental conse-quences of a historic choice (e.g. in 1985) or to speculate aboutthe consequences of a future choice (e.g. in 2025).

Since we here are concerned with strategic technologychoice we find it useful to suggest one more distinction: thatbetween product and technology LCA, where the former seeksto investigate the impact of a specific product, plant or produc-tion process, while the latter is an assessment of a more gen-eral technology. For the former, data valid for a specificsituation, or state, could generate a useful result, while forthe latter the impacts under many different and more generalcircumstances are of greater value. For consequential technol-ogy LCA a broader spectrum of causeeeffect chains are of in-terest than for consequential product LCA.

In our view both attributional and consequential technologyLCA could be used to support decisions on strategic technol-ogy choice. Prospective attributional technology LCAs couldbe used to analyse the general performance of a technologyunder different circumstances. The key methodological prob-lem is to analyse the technology in a relevant state or scenarioof consecutive states [12,13].

In this paper we wish to demonstrate how the environmen-tal consequences of a single decision today could be assessed.For this we need a consequential technology LCA. Then thekey methodological problem is to select which consequencesor causeeeffect chains should be included and find ways toquantify their environmental impact.2

1 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 dif-

ferent 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.

1471B.A. Sanden, M. Karlstrom / Journal of Cleaner Production 15 (2007) 1469e1481

3. A typology of consequences

A first order of consequences is the proportionate relationsbetween the use, production and waste handling of a product(one functional unit) and environmental impacts (e.g. emis-sions). These are causeeeffect chains made up of physicalflows.

A second order of consequences and a more indirect type ofeffects that takes into account the economic flows related tothe physical flows has been identified. In the literature on con-sequential LCA it has been observed that an increased demandon the margin does not have to imply that one more unit of thedemanded good is supplied (see for example Ref. [14]).Instead increased demand for an input that is constrainedwill induce the production of a substitute. As a consequence,marginal data (the last unit produced) differ from averagedata. An increased production could also affect the demandfor the product and related products [14]. In this way conse-quential LCA has started to bring in elements of equilibriumthinking and negative feedback mechanisms stemming fromconstrained (or fixed) supply. These types of effects are prop-agated by a price mechanism controlling supply and demandrelationships. Since this mechanism is based on constraintsand negative feedback, the assessed action leads to a shift toa new equilibrium between demand and supply of differentgoods and services. The marginal environmental effect inthis sense is the difference between the old and the new stateof equilibrium.

It is not only production capacity, or the supply flow, thatcan be constrained. Also stocks of resources are constrained.We therefore suggest that one could argue for that the demandfor a limited resource leads to the use of less limited resource(or ‘back-stop resource’) on the ‘stock-margin’, albeit delayedin time. As in the case of arguments grounded in equilibriummodels this argument presumes that preferences and availabletechnologies are fixed.

While the causeeeffect relationships based on models ofequilibrium between supply and demand are borrowed fromneoclassical economics, we suggest that a third order of con-sequences could be brought in from theories of technicalchange.3 These effects depend on the cumulative build-up ofstocks and structures, such as physical structures, institutionsand actors and networks, leading to altered availability andcost of technologies and to changed preferences [17]. First,an investment in a technology could change physical struc-tures such as manufacturing equipment and physical infra-structure. This will affect future costs and have implicationsfor future technology choice and thus future environmentalimpact. Second, a technology is also part of an institutionalsystem, or rule system that guides actions. There are cognitiveas well normative rules. Cognitive rules in the form of explicitand tacit knowledge are decisive for technical change.

3 See for example Freeman [16] for an overview of the economics of tech-

nical change and Bijker et al. [17] for a seminal collection of articles on soci-

ology and history approaches to the study of technical change.

Increasing the stock of knowledge and experience couldhave implications not only for the further development ofthe technology in a specific application. It could also affectthe use of the technology in other applications. Knowledgegeneration occurs in all parts of the life cycle and includes dif-ferent elements of technology development, learning by doingand learning by using. Knowledge propagates through differ-ent actors, some taking active part in the production chainsuch as manufacturers and users, others acting on a more gen-eral level such as universities. The adaptation of hard norma-tive rules such as laws and regulations are often necessary forthe successful adoption of a new technology and normallycomes as a result of early investments. Influencing soft cogni-tive and normative rules such as beliefs, visions, attitudes andnorms is also a prerequisite for change. Finally, all cognitiveand normative rules as well as physical structures are changedby actors and networks of actors, which themselves evolvewith technology adoption to form advocacy coalitions [18,19].

The investment in a new technology often affects these sys-tems in ways that are beneficial for further investments in thetechnology. In the literature on technical change such positivefeedback mechanisms, or positive returns to adoption, havebeen given a prime role in the development of a new technol-ogy.4 On the producer side, economies of scale, learning bydoing and incremental product development lead to increasedperformance to cost ratio when more systems are produced.On the user side, learning by using, decreased uncertaintyover cost and performance, and economies of scale or user net-works will decrease the hesitation to invest in a new technol-ogy and lower the cost and increase the benefits of using it.Over time, technical systems, regulations, the educational sys-tem and political power will be adapted to the new technology.In this way early investments in a radically new technologycan set in motion a self-re-enforcing process towards radicalsystem change. Thus, the environmental effects of the invest-ment can go far beyond the direct effects and marginal equilib-rium shifts.

4. An illustrative case: GHG effects of afuel cell bus investment

We have selected fuel cell buses as a case since it is an im-mature technology that is linked to many surrounding systems.This creates a rich environment for demonstrations of causalrelationships. In addition, several LCAs, in this area oftentermed ‘‘well-to-wheel’’ studies, have been made to quantifythe environmental differences between different vehicle andfuel configurations, and results tend to vary a great deal[23e35].

Results differ between studies depending on assumptions onfuel supply systems, performance of propulsion systems andvehicle types. These are in turn dependent on time frameand geographical scope. In most studies a fairly short

4 Overviews of different positive feedback mechanisms have been published

elsewhere [20e22].


time-perspective is chosen as well as a specific geographicalsetting. This limits their usefulness as tools for strategic technol-ogy choice and guidance for long-term radical change of thetransport system. One source of confusion is that in most cases,it is not clear what type of LCA is conducted, if the studies arebased on an attributional (state-oriented) or consequential(change-oriented) perspective, and it is seldom clear on whatgrounds specific states and consequences have been chosen.

In this section we take a consequential perspective and tryto assess the importance of different causeeeffect chains lead-ing from an investment in fuel cell buses today to changedGHG emissions.

4.1. A brief system description and first order effects

Fuel cell propulsion systems are in a pre-commercialisationphase and they have mainly been developed for demonstrationvehicles. In 2002, only 200 light-duty vehicles and approxi-mately 30 fuel cell buses had been built and operated world-wide [36,37]. To reach a commercial phase, fuel cellsystems have several hurdles to overcome. The cost of solidpolymer fuel cells (SPFCs), the preferred fuel cell for tractionpurposes, is large compared to the current cost of internalcombustion engines and according to US Department ofEnergy ‘‘the life/reliability characteristics of fuel cell technol-ogies have not been verified satisfactorily’’ [38].

Hydrogen is the preferred fuel in SPFC systems sincehydrogen has a very high chemical reactivity and close tozero emissions during use. However, there are problems asso-ciated with developing hydrogen as a fuel, such as high pro-duction costs, lack of infrastructure and standards, concernsabout safety and uncertain consumer acceptance and a needfor more cost-effective storage technology [38]. Hence, thedevelopment of a large-scale fuel-cell-hydrogen system willrequire radical system innovation and take considerable time.

The fuel cell propulsion technology is clearly embedded ina larger fuel-vehicle-transport system and it is the configura-tion of the whole system that determines the GHG emissions.To track the consequences of an investment in the fuel cellpropulsion technology we need to understand how the technol-ogy is embedded in and linked to different systems (Fig. 1).

The contribution from fuel cell production and waste han-dling is not insignificant. Pehnt [39] presents an LCA of fuelcells that includes the manufacturing of fuel cell stacks andthe use phase of a fuel cell vehicle using hydrogen from naturalgas. With no recycling of platinum group metals (PGM), the cur-rent German electricity mix and a driving range of 150,000 km/year, the stack production was responsible for 23% of total GHGemissions, while for 75% recycling of PGM and electricity fromhydropower the figure decreased to 10%.

Nevertheless, the life-cycle emission of greenhouse gasesof hydrogen-powered fuel cells is mainly dependent on howthe hydrogen is produced. Hydrogen can be produced fromdifferent feedstock (fossil hydrocarbons, biomass and water)and different energy sources (hydrocarbons, nuclear energyand solar energy) using different technologies such as steam

reforming, gasification, electrolysis, thermolysis or photolysis.Ogden [40] makes a distinction between short-term options,such as steam reforming of natural gas and electrolysis usingconventional electricity production, and long-term optionswith zero net CO2 emissions such as gasified biomass and wa-ter split by various renewable energy sources, nuclear poweror even coal if large-scale CO2 sequestration is feasible. TheGHG emission from different ‘‘well-to-pump’’ hydrogen sup-ply system varies a lot. Wang [41] estimates that the GHGemissions of a fuel cell vehicle relative to a reformulatedgasoline vehicle vary from 100% reduction to a 60% increasedepending on hydrogen fuel production pathway.

A well-to-wheel study also takes into account how effi-ciently the fuel is used in the vehicle. Emissions are normallyspecified in relation to one vehicle kilometre (vkm). Asa consequence changes of transport demand are ignored andthe vehicle type and driving cycle are kept fixed for all alter-natives. This is practical but perhaps a bit misleading sincenew propulsion technologies could allow for new vehicledesigns and concepts and change how vehicles are used andcombined to satisfy a transport demand and even the transportdemand could be affected.5 New functions could also beadded. A vehicle with fuel cells and an electrical engine couldfor example be plugged into the electricity grid to supply peakpower. Changing the vehicle design could also imply effectsfrom changed vehicle production and waste handling.

An estimate of first order effects of buying a fuel cell businstead of an advanced diesel bus today (assuming similarbus types and use and disregarding differences in emissionsfrom the production of the propulsion system) is given byKarlstrom [44]. The emissions of CO2-equivalents for a fuelcell bus running on hydrogen produced from steam reformingof natural gas and an advanced diesel bus are estimated at896 g/vkm and 1255 g/vkm, respectively. A first order effectaccording to this estimate is thus a reduction by about360 gCO2-eq/vkm.

4.2. Second order effects: constraints, negativefeedback and equilibrium adjustments

4.2.1. Constrained capacity and market adjustments on thesupply side

The main impact consequential thinking has had on LCApractices so far is the occasional, and often arbitrary, use ofmarginal data for inputs. Limited or fixed supply of an input(or production factor) leads to a different effect on the marginthan what is attributed to the functional unit in an attributionalLCA [45].

5 Pehnt [42] provides a summary of the main differences with regards to

energy use between fuel cell propulsion systems and conventional ICE, which

result in different performances under different operating conditions. Johansson

and Alvfors [43] simulated the fuel consumption of a fuel cell bus and a diesel bus

of similar weight operated in the CBD14 drive cycle and the real bus route 85 in

Goteborg, Sweden. They demonstrated that the reduction of fuel consumption

that was reached by using the fuel cell bus differed. For the CBD14 cycle and

bus route 85 the reductions were 33% and 37%, respectively.


Hydrogen production

Fuel cell &drive train use

Fuel cell and drivetrain production

Vehicle use

Fuel cell and drive trainwaste handling

Vehicle waste handling

Transport

Vehicle production

Fig. 1. The fuel cell propulsion technology is linked to and embedded in several other systems.

Electricity production provides a standard example. A fuelcell bus in Sweden that runs on hydrogen produced by electrol-ysis uses electricity from the Nordic electricity grid. This ismainly produced from hydro- and nuclear power. However, hy-dro- and nuclear power capacity is constrained. On the margin,other electricity sources will be applied to supply the last unitof electricity that is required for the electrolysis. Coal power isnormally assumed to be this marginal technology. In principlethis could be viewed upon as a matter of price elasticity. Anincreased demand raises the price of electricity. This is com-pensated by increased production from different sources, in-creased efficiency of energy use or decreased demand forenergy services from other end uses. The mix of effects de-pends on the price elasticity of the different measures.

Hydrogen produced from bioenergy provides a secondexample. Currently, bioenergy production is not severely con-strained but with increased use, land area for bioenergy produc-tion is likely to become a limiting factor. In this case, biofuelscould lay hands on biomass that otherwise could have beenused to replace coal for heat and/or electricity production. Inthis case the marginal effect would not be near zero GHG emis-sions but the emissions from coal combustion, which could behigher than the replaced emissions from a diesel bus.

A third example is platinum that is used as a catalyst inSPFC. An increased demand for platinum would not necessar-ily imply more mining of PGM mineral. It could also inducea price increase that partly stimulates more platinum extrac-tion but also stimulates recycling and reduces the platinum de-mand from other sectors. If other materials replace platinum inthese sectors, the marginal effect is partly the increased pro-duction of these other materials.

The main idea here is that a constrained input is replaced bya marginal input that is not constrained (see Refs. [14] and[45] for discussions on marginal input and marginaltechnology).6

6 In the case of decreased use, the marginal technology is the technology

that is first removed, that is, a technology that is not fixed.

4.2.2. Constrained stocks of inputsThe examples given above relate to capacity constraints.

We would suggest that also stock constraints can generate sim-ilar effects. One could argue that the use of natural gas todaydecreases the natural gas resource stock and when resourcesare depleted or too expensive to extract, coal, which is lessconstrained in terms of resource availability, will be used in-stead of natural gas. A necessary assumption for this argumentis that natural gas is so limited and desirable that it is depletedat a point when coal is still in use. This assumption seemsplausible. CO2-stabilisation scenarios above 450 ppmv implycumulative emissions above 630 Gt C (1991e2100) whilethe resource base of conventional oil and gas contains onlyabout 500 Gt C [46]. A level as low as 450 ppmv CO2 is con-sidered to be a very tough target [47]. A stabilisation at550 ppmv implies cumulative emissions of 870e990 Gt C[46]. Thus, if natural gas or oil is used to replace renewables,which indefinitely could replace coal, the marginal effect oncumulative CO2 emissions is likely to be the emissions associ-ated with coal conversion.

4.2.3. Second order effects on the supply side: someconclusions

In conclusion, the selection of a marginal primary energysource is somewhat arbitrary. It is possible to find argumentsthat coal is used on the margin independently if hydrogen isproduced from steam reforming of natural gas or electrolysis.The hydrogen fuel cell bus would replace a diesel bus that, fol-lowing the same line of argument, also could imply more coaluse on the ‘stock-margin’.7 Depending on different conversionefficiencies, the exact GHG emissions would differ somewhat.The bottom line is that well-to-wheel energy efficiency (withwide system boundaries) is a good indicator of the GHG

7 This assumes a constant set of technological options and costs. Assuming

a different development it is possible to assume other marginal technologies.

Wind could be a marginal electricity technology in the current system and so-

lar could replace coal on the stock margin if other scenarios are assumed.


emissions in most cases. However, if energy from a renewablesource (not stock constrained) that has not reached a capacitylimit (not capacity constrained) is used, then it is difficult tofind arguments for not using the GHG intensity of its specificfuel cycle.

For input materials of specific interest that are a part of thetechnology in focus, such as platinum in the case of SPFC, itcould be relevant to make a closer analysis of the causeeeffectchains due to specific market relationships.

4.2.4. Constrained budgets and marketadjustments on the demand side

Constraints and negative feedback mechanism are not onlyat work on the supply side but could also affect the demand fordifferent kinds of vehicles and transport. Such effects are sel-dom taken into account in LCA, since LCA are normally con-cerned with the environmental impact that can be associated toone unit of a product. However, the investment in a fuel cellbus could have environmental consequences that are propa-gated on the user side and affect emissions of the vehicle fleet.

The transport system consists of several parts and their re-lationships. The consequences of an investment or a policycould influence the behaviour of the system in a counterintui-tive way, due to feedbacks and synergy effects. Effects on fac-tors such as total demand for transport, travel patterns, modalchanges, load factors, vehicle stock changes and speed couldinfluence the GHG emissions. Transport models that describesuch effects and that are linked to models of emissions arereviewed by IPCC [48] and the US Department of Transporta-tion [49]. Here we will only consider some basic relationships.An important class of such effects are related to constrainedbudgets.

An investment in an expensive fuel cell bus could result inless or postponed investments in new less polluting conven-tional buses and thus affect the environmental impact of thebus fleet negatively. Since prototype fuel cell buses currentlycost in the order of five times more than a diesel bus [44]this effect is not insignificant. A hypothetical example: Ifbuses that are 10% more energy efficient costs 10% morethan a less energy efficient alternative, then the money savedby not buying the fuel cell bus would be enough to buy 50 en-ergy efficient buses instead of the less efficient alternative.This would in turn save five times more GHG emissionseven if hydrogen were produced with zero GHG emissions.This effect is normally not included as a part of the LCAbut is a natural component of a cost-benefit analysis that theLCA could be part of. Nevertheless it could be included asa consequence in consequential LCA.

One way to avoid crowding out of investments due to theconstrained budget of the bus company is to increase the in-come to the bus company by increasing the price of bus trans-port. If the cost is taken by a municipality with a limitedbudget, it could result in less bus kilometres and thus in loweremissions if the load factor is increased. However, it is likelythat it will also result in fewer bus kilometres travelled due tothe decreased service level. Some of this will result in lesstransport, while some of the transport demand instead will

be transferred to a different transport mode, e.g. cars. Thiscould increase total emissions. Alternatively, the increasedcost could be transferred to bus passengers. This would alsoresult in partly less transport and partly a shift of transportmode. This effect is a matter of price elasticity of transportand cross-elasticity between buses, cars, bicycles and othertransport modes.

To exemplify the effect of modal shift, assume that a citybuys fuel cell buses instead of diesel buses. Assume furtherthat the average life cycle GHG emissions for cars are approx-imately 195 g/vkm and the occupancy rate of cars and busesare 1.4 and 17, respectively. If the cross-elasticity of car traveldemand with respect to bus fares is 0.09 [50], and the bus faresare doubled to cover some of the losses from more expensivebuses, the GHG reduction of shifting from advanced dieselbuses to fuel cell buses is decreased from 360 gCO2-eq/vkm(see Section 4.1) to about 150 gCO2-eq/vkm.8

The argument of demand adjustments can be taken beyondthe micro economic price elasticities used above.9 If moremoney is spent on bus fares, cars and fuel, less will be spenton other goods, implying less emissions from those activities,and vice versa. This leads us out of micro economic elasticitythinking to a macro economic effect and the GHG emission in-tensity of the entire economy [8,51,52]. A euro or dollar notspent on one thing must be spent on something else.10 Whatis the true GHG reduction from buying or not buying a specificproduct in a closed economy?

4.3. Third order effects: learning, positivefeedback and system change

In the previous section we were concerned with effects thatresulted in a shift to a new equilibrium between supply anddemand in the short term, in most cases controlled by a pricemechanism. Since the effects emerge due to constraints andnegative feedback the result of including them is normallyjust a moderation of the original LCA result and the effectis reversible.11 In contrast, effects leading to positive feedbackhave the power to change systems radically and irreversibly.As a consequence, they can have a larger effect on the resultof the assessment.

4.3.1. Quantifying the technology learning effectLCAs are quantitative assessments. A critical question is if

effects magnified by positive feedback can be quantified andcompared to other effects. Many of the cumulative effects

8 It is assumed that no corresponding reduction of bus roundtrips takes

place.9 There could also be non-monetary effects of a similar kind. It is possible to

imagine a psychological effect. If I buy an environmental friendly car I can use

it more since it does not cause as much harm. In this case it is not the budget

constraint that is relaxed but conscience constraints. This effect would lead to

emissions that are larger than what an LCA of one functional unit would

indicate.10 This is sometimes referred to as a ‘rebound effect’.11 One exception that is not reversible is the effect resulting from constrained

stock of an input.


stemming from positive feedback cannot be quantified, but thelearning and scale effect of increased adoption can be quanti-fied in terms of cost reductions by using an experience curve.This could be translated into a realisation of a scenario wherefuture emissions are avoided.

Such an assessment would require a two-step procedure.First, the potential for avoided future emissions of large-scaleadoption of the technology is estimated. Second, the contribu-tion from one investment (one functional unit) to the realisa-tion of the future potential is assessed.

To estimate the potential for avoided future emissions X(ton CO2-eq.), at least one baseline scenario without the stud-ied technology has to be constructed. This should then be com-pared to scenarios where diffusion of the studied technologyresults in lower emissions. These scenarios would be charac-terised by final saturation levels (final technology potential)and the speed of diffusion. A probability parameter p (-) couldalso be related to each scenario. We do not elaborate on how toestimate this parameter here, but keep it as an unknown. Aswill become evident in the example in the next section, thisis reasonably satisfactory since we are here only interestedin estimates of orders of magnitude and not precise figures.

There is probably no single best procedure for allocatinga share of the future emission reduction to a single investment.Here we suggest one procedure that we think is intuitively ap-pealing. If a certain amount of investments is needed to realiselearning and scale economies to an extent that the cost of theemerging technology is bought down to a competitive level,the single investment could be credited a share of the emissionreduction in proportion to its share of the total required learn-ing investment.

The required learning investment could be estimated withan experience curve. The experience curve is an empirical re-lationship between cumulative production S (e.g. kW for fuelcells) and unit cost c (US$/kW for fuel cells) that has beenobserved for a number of technologies [53]. Cumulativeproduction leads to lower costs through learning and scaleeconomies.12 The experience curve is typically expressed interms of a progress ratio rP (-), i.e. the relative cost reductionfor each doubling of cumulative production. A progress ratioof 0.8 implies a cost reduction of 20% for each doubling ofcumulative production. The future unit cost can then be de-scribed in terms of future cumulative production, currentunit cost c0 (US$/kW), current cumulative production S0

(kW) and an experience index b.

c¼ c0

�S

S0

�b

ð1aÞ

S¼ S0

�c

c0

�1=b

ð1bÞ

12 The experience curve that describes how total costs decrease with cumula-

tive production, is a generalisation of the learning curve, which describes how

labour costs decrease with cumulative production.

where

b¼ ln rP

ln 2: ð2Þ

Early in a technology’s life when only a few units have beenproduced it is difficult to establish an experience curve. Then itis advisable to decompose the technology into different partsand procedures and search for recorded progress ratios forsimilar parts and procedures (see for example Ref. [54]).

For each large-scale potential future market that is identi-fied in the first step, one needs to establish a target cost c1

(US$/kW), i.e. the cost when the technology becomes compet-itive on the large-scale market. The cumulative production S1

(kW) that is required to reach that target cost can be calculatedfrom Eq. (1b). The total extra cost C1 (US$), or learning in-vestment that is required to ‘buy down’ the cost of the technol-ogy can then be calculated from

C1 ¼ZS1

0

ðcðSÞ � c1ÞdS ¼ �b

1þ bc1S1: ð3Þ

The cost C1 of buying down the unit cost of the technology isthen the cost for realising the low emission scenario and avoidemission X1. The extra cost for investing in one unit of the newtechnology at present is c0� c1. The potential future avoidedemissions can then be allocated to a current investment ofone unit taking the probability factor p into account

x0 ¼c0� c1

C1

X1p¼ 1þ b

�b

c0� c1

c1

X1p

S1

ðton CO2-eq:=kWÞ: ð4Þ

If the amount of cumulative production S1, which is required tomake the technology competitive, is small in comparison tothe future potential market and thus to the potential to reduceemissions X, this technology learning effect can be large if theprobability of realising the scenario is not too small. Early in-vestments, when c0 still is large in comparison to c1 are givena larger share of future emission reduction. When competitive-ness is reached, technology learning effects can no longer beput down to an investment’s credit.

4.3.2. The technology learning effect and the fuel cell busTo exemplify the methodology, let us once again consider

the fuel cell bus and hydrogen fuel. First, we construct two di-verging background scenarios of the total energy system tocreate a context for bus scenarios.

A low cost (‘business-as-usual’) scenario of the world en-ergy system is a coal scenario as sketched in Fig. 2. In mostscenarios of future energy systems (for example most of theSRES scenarios developed for the IPCC [47]), it is assumedthat population growth and economic growth will create an in-creased demand for energy that is moderated by energy effi-ciency gains, resulting in an annual demand for primaryenergy at about 1000 EJ towards the end of this century.Over this time period oil and gas resources will dwindle.


0

200

400

600

800

1000

1200

1850

EJ/y

ear

1900 1950 2000 2050 2100 2150 2200year

UnknownCoalNatural gasOilBio energyNuclear and intermittent renewables

Fig. 2. A ‘business-as-usual’ scenario of the global energy supply system. When oil and gas resources dwindle, coal comes to dominate the energy system. Ultimate

oil resources are put to 17,200 EJ (3 trillion barrels), ultimate gas resources to 25,000 EJ and coal to 130,000 EJ [46,55]. Bioenergy is limited to 100 EJ/year and

nuclear, hydropower and intermittent renewables shows a slight increase to 70 EJ.

Bioenergy supply could possibly increase to 100 or 200 EJ andelectricity from intermittent renewables could take a share ofperhaps 20% of the electricity market. If advanced nuclear(such as fusion or breeders) and hydrogen from renewable en-ergy sources are not available the remainder would be filled upwith coal converted into electricity and synthetic fuels. Finally,towards the end of the 23rd century also coal resources willdwindle. The CO2 emissions of this ‘business-as-usual’scenario is staggering.

An alternative future is depicted in Fig. 3. Instead of the fu-ture based on conventional coal, a hydrogen option is realised.The hydrogen could be produced from water and solar energy(and other intermittent renewables), decarbonised coal (withCO2 sequestration) or possibly nuclear energy.

A fuel cell scenario in the bus sector is illustrated inFig. 4. Global bus travel more than doubles and pass20,000 billion pkm/year during the second half of the century[56,57]. The FC bus stock grows from 30 buses in 2002 tomarket dominance after 2050. The market growth is rapid,initially about 30% per year. Nevertheless, FC buses do nothave a substantial impact on the market until after 2030,and consequently the impact on emissions is small up tothis point. The major impact comes after 2050, when oiland gas resources are on decline. As a consequence and inaccordance with the above outlined scenarios, it is possibleto approximate the impact of FC hydrogen buses as theavoidance of let us say 100 years of the use of synthetic fuelsfrom coal. If we assume that the fuel in the business-as usualscenario is synthetic diesel produced in chemical factories,where excess carbon dioxide is captured and sequestered,an emission factor corresponding to current diesel buses canbe used. Following Shafer and Victor [58] we use an averageemission factor of 50 gCO2-eq./pkm.13 The avoided emissionsin the fuel cell scenario then amount to in the order of100 Gton CO2.

13 Current recorded emission factors vary considerably between different

regions mainly because of varying load factors.

If we further assume an initial cost of US$ 2000/kW, an ini-tial cumulative production of 30 FC buses a 200 kW each anda progress ratio of 0.8, a target cost of US$ 150/kW is reachedafter a cumulative production of about 20 million kW of fuelcell stacks.14 This corresponds to 100,000 large buses. If weassume a load factor of 15 persons/bus and an annual bus driv-ing distance of 50,000 km this in turn corresponds to a marketof some 75 billion pkm or a tiny fraction of the global bustransport market. In the scenario in Fig. 4 the required amountof cumulative production of buses is reached in 2025, i.e. be-fore the main growth phase.

If we use Eq. (4), the avoided future emissions that canbe allocated to an investment in a fuel cell bus today becomes25 million ton CO2 per bus or 50 ton CO2/vkm (assuming500,000 vkm/bus over a lifetime of 10 years). This figure ismore than 100,000 times larger than the short-term first orderCO2-emission reduction effect of 360 gCO2-eq./vkm for theswitch from advanced diesel to fuel cells fuelled by hydrogenfrom natural gas. Even if the probability factor is only 1% thelong-term technology learning effect is still more than 1000times larger than the short-term effect.15

The technology learning effect could be even larger ifwe take into account that the scope of learning could extendoutside the bus application. Fuel cell stacks for buses are pre-sumably not very different from those produced for cars. Theavoided emissions from five billion cars16 run on syntheticfuels from coal for 100 years amount to 750 Gton CO2

if we assume an annual driving range of 15,000 km and

14 The cost of SPFC manufactured in small quantities around the year 2000

has been estimated at about US$ 2000/kW [54,59], compared to the current

cost of internal combustion engines of US$ 25e35/kW for cars and about

US$ 150/kW for buses. The costs of mass-produced SPFC systems are difficult

to predict. Carlson et al. [60] estimated the cost of fuel cell systems using

manufacturing costs’ analyses at $ 195e325/kW if 500,000 units/year were

produced and Tsuchiya and Kobayashi [54] used an experience curve approach

to estimate the cost at US$ 15e145/kW after five million units produced.15 One percent is as an arbitrary figure, but a figure that we (the authors) in-

tuitively (based on our experience and beliefs) consider to be on the lower end.16 European car density for 10 billion people [57].


0

200

400

600

800

1000

1200

1850 1900 1950 2000 2050 2100 2150 2200year

EJ/y

ear

Hydrogen (from solar, nuclear orcoal with carbon sequstration)

CoalNatural gasOilBio energyNuclear and intermittentrenewables

Fig. 3. A hydrogen scenario of the global energy supply system. During the first half of the 21st century hydrogen use would increase by 30% per year to reach

a significant share after 2050.

100 gCO2-eq./vkm for a future car.17 The ultimate limit of thescope of learning is that commercialised fuel cells are a prereq-uisite for the whole hydrogen economy. As depicted in Figs. 2and 3 the total CO2-credit for avoiding the coal option is about9000 Gton CO2.

The future potential is key for this argument. If there is any-thing that limits the potential of the assessed technology thefuture potential emissions’ reduction must be lowered accord-ingly. For SPFC, platinum availability could be such an issue.The development of a fleet of five billion fuel cell cars is likelyto require an annual platinum demand in the high growth pe-riod (2040e2070) that exceeds current mining rates by a factorof 10 or more and the total amount of required platinum metalexceeds the reserve base even if very low platinum loadingsare reached [61]. An interesting issue is if the scope of learn-ing can be extended to other fuel cell technologies that do notrequire platinum. An intriguing question is if SPFC could actas a bridging technology that could pave the way for a fuel celltechnology with even greater potential or if it could act asdead-end technology that could lock out fuel cell technologieswith greater potential [15,21,22,62].

In conclusion, it is inherently difficult to assess the effects ofpositive feedback mechanisms since they make the world unsta-ble and evolving far away from equilibrium. In principle, any ac-tion can set in motion a process that changes large-scale events(the butterfly effect Lorenz (1972) cited by Hilborn [63]).18

17 It should be noted that power train of the car is less expensive (in the order

of US$ 30/kW). To reach such a low cost would require a cumulative produc-

tion of in the order of 7 billion kW of fuel cells or 35 million buses or 140

million cars if an initial volume of 30 buses (200 kW) and 200 cars

(50 kW) and a progress ratio of 0.80.18 Following this reasoning one investment could change the course of history

and thus not only be credited for a share of a future emission reduction but the

whole reduction in a particular scenario. However, the probability that a particular

single investment is this decisive is likely to be very low and completely unknow-

able. In addition, one could argue that the following investments are necessary for

the first to have this impact. The advantage with the proposed method in Section

4.3.1 is that it relies on an empirically observed relationship between investments

and economic performance and it does result in a large allocation of future emis-

sion reductions to the first seminal investments in a new technology.

Moreover, the result derived in this section is very sensitiveto changes in some parameters, in particular the progress ratio.However, we believe that we have demonstrated here that if dy-namic learning and scale effects are not taken into account atall in assessments of emerging technologies, the most impor-tant effect could have been left out.

4.3.3. Change and lock in of surrounding systemsFuel cells are not the only prerequisite for materialising

carbon-free bus transport. The hydrogen also has to be pro-duced in a carbon-neutral fuel cycle. Thus, learning and scaleeffects are equally important in areas such as solar energy orcarbon sequestration. Investments in these technologies thusdeserve a share of the future emission reduction.

However, investments in fuel-cell technology could alsoinduce change in different surrounding systems throughpositive feedback mechanisms. The production of hydrogenfrom multiple sources could be stimulated by the existenceof a market for the hydrogen and transport demand couldchange due to the new options that the new technology makespossible.

For a single actor it is difficult to support a complete newsystem including new propulsion technology and new fuel in-frastructure. For radical change of interlinked systems onemust accept that while being in an early learning phase thenew system has to grow within the old system [64,65]. Hydro-gen from natural gas is often viewed as a ‘transition’ or ‘bridg-ing’ technology that allow for learning in hydrogen applicationssuch as fuel cells. However, this also introduces the risk of get-ting stuck with the transition technology, that it becomes a deadend instead of a bridge, since positive feedback mechanismleads to technology lock in (for example due to sunk costs,political power of advocates and bounded rationality). Whatwas once thought of as a stepping-stone becomes an islandwhere technological development is stranded.

In Section 4.2.4, the price elasticity of transport was dis-cussed. It has been noticed that the long-term price elasticityis around three times larger than the short-term price elasticity[50]. The reasons for this are that long-term decisions are


0

5000

10000

15000

20000

25000

2000 2050 2100 2150year

Billio

n bu

s pk

m p

er y

ear

Total

FC buses

Fig. 4. Future bus scenario with penetration of fuel cell buses (bus transport demand scenario is derived from Azar et al. [57] and Schafer and Victor [58]).

influenced, such as the desirability for citizens to live nearwork if fuel costs are high and vice versa. If such long lastingstructures (such as city plans) can be influenced not only byprices but also by more technical aspects and the type of ser-vices a new transport technology can provide, very differentfutures can result from different investments (see Hultenet al. [66], Karlstrom et al. [67] and Elzen [68] for two inter-esting approaches to address this issue). The possibility thata new technology bares the seeds of a radically different soci-ety, which was the case with the steam engine in the 18th cen-tury, the internal combustion engine in the beginning of 20thcentury or the transistor 50 years ago,19 creates a profoundchallenge for any assessment that claims to assess the environ-mental consequences of an investment in something new (seealso footnote 18).

4.3.4. Competing technologies and the sailing ship effectFinally, we acknowledge a consequence of an investment

that is somewhat difficult to categorise but that could havea significant impact. In history of technology it goes underthe name ‘‘the sailing ship effect’’. When the steam shipsemerged the performance of sailing ships were improved.The effect is difficult to verify since there may be multiplecauseeeffect chains leading to technology improvement[69]. However, if a new promising technology is demon-strated, it is likely that the development of the old technologyis intensified. In this way, a few investments could have a largeimpact, not only through the creation of a radically new sys-tem in the long term but by inducing incremental improve-ments in the large fleets of its incumbent competitors.

19 See for example Grubler [70] and Freeman and Luca [71] for analyses of

technology clusters, techno-economic paradigms and four to five consecutive

industrial revolutions that created the industrial society.

5. The relative importance of different effectsover the technology life cycle

The relative importance of the second and third order ef-fects is likely to change over the life cycle of a technology.It has been observed that the diffusion of technologies (andmany other phenomena) tends to follow an s-shaped curve(as in Fig. 5) [70]. The technology life cycle can thus bedivided into different phases. (This ‘life cycle’ should notbe mixed up with the life cycle from cradle to grave of anobject that is referred to in the concept of ‘life-cycle assess-ment’.) For a mature technology that has realised much of itslearning or growth potential, positive feedback does little butdefending the technology. In this case, negative feedbackmechanisms, i.e. effects due to equilibrium adjustments,could be more important.20 For an emerging technologythat still is in a formation phase, the direct environmental im-pact is small and negative feedback is lacking or does little tomodify the small total impact of the technology. Instead themajor environmental consequences of an investment stemfrom positive feedback mechanisms that could influence thegrowth pattern and realise a future potential. This simple sub-division could work as a rule of thumb on where to focus inenvironmental assessments of investments such as consequen-tial technology LCA.

6. Conclusion

Life-cycle assessments are used to support decisions, notonly decisions on incremental improvements of mature prod-ucts but strategic decisions on technology choice. One result

20 A mature technology could, however, start to diffuse on new markets and

thereby start to climb a new s-curve. Then, positive feedback on the user side

will be important. The diffusion of a second car in the household could be an

example of this. (We are thankful to Tomas Ekvall for pointing this out.)


of a recent and influential well-to-wheel study [35] was spreadin the following wording:

‘‘Using hydrogen as a road transport fuel will increaseEurope’s greenhouse gas emissions rather than cut them,according to new collaborative research by the Brussels-based association of European oil companies, Concawe,and its automotive industry counterpart, Eucar.’’ (Interna-tional oil daily, 2004).

This ambitious well-to-wheel study could thus be used toargue that fuel cell vehicles and hydrogen fuel is not a wayto reduce greenhouse gas emissions in the transport sectorbut the opposite. However, the study has a short time horizonfrom a climate change perspective and attributional and conse-quential perspectives are mixed. This opens up for somewhatmisleading interpretations as the one in the quote.

In this paper we argue that LCAs aiming to support strategicdecisions on technology choice, in particular in the area of green-house gas emissions, need to apply a methodology that is adaptedto the problem and include long-term effects. For attributional (orstate-oriented) LCAs this implies that future states could be morerelevant than the current situation as basis for analysis. For con-sequential (or change-oriented) LCAs, or for any environmentalassessment of the effects of an investment, an implication is thatnew classes of consequences need to be included.

In this article we list and discuss different causeeeffectchains that could be taken into account in a consequentialassessment. We make a distinction between first order direct ef-fects described by physical relationships, second order effectsdue to equilibrium shifts controlled by price mechanisms andnegative feedback in surrounding systems and third order ef-fects due to learning and structural change, i.e. effects magni-fied by positive feedback. We argue that the third order effectsare more important for emerging technologies while second or-der effects could be more important for mature technologies.The effects due to constraints and negative feedback lead toa marginal shift of equilibrium which results in a moderationof the first order LCA result and the effect is reversible.21 In

Emerging Mature

Negative feedback

Positive feedback

time

market

penetration

Fig. 5. The technology life cycle and the relative importance of positive and

negative feedback mechanisms.

21 We also suggest that one could include marginal effects related to limited

stocks of resources (see Section 4.2.2).

contrast, positive feedback mechanisms have the power tochange systems radically. As a consequence, they can havea larger effect on the result of the assessment.

To illustrate this point, a methodology based on scenariosand an experience curve is used to quantify the potential futurereduction of CO2 emissions that could be allocated to aninvestment in a fuel cell bus today. The technology learningeffect of an investment now in fuel cell buses was estimatedat 50 ton CO2/vkm, a staggering number if compared to theshort-term first order direct emission reduction of 360 gCO2-eq./vkm for a switch from advanced diesel to fuel cells fuelledby hydrogen from natural gas. Even if the probability factor isonly 1% the long-term technology learning effect is still morethan 1000 times larger than the short-term effect.

The magnitude of the estimated value resides on a numberof assumptions. The future emission reduction potential islarge, the potential cost is low enough to be competitive, thecost reduction with increased scale and learning is fast enough(the experience curve is steep), the scope of learning of the in-vestment is broad and the investment is taken early in the tech-nology’s life cycle. Different technologies and investmentswill score differently according to these criteria.

Even if this calculation of emission reductions resultingfrom positive feedback effects is somewhat speculative themain lesson is that environmental assessments of emergingtechnologies that only take into account short-term effectscould be grossly misleading. Unfortunately, the most relevantaspects are not always synonymous to the aspects that are eas-iest to quantify and for which validated data are available. Theenvironmental effects of investments in emerging technologiesare inherently uncertain. If one refrains from including largebut uncertain aspects, and only include what can be found‘in the beam of light’, one should consider not doing the studyat all, or at least be very clear about the limited conclusionsthat can be drawn from the study.

A constructive approach is to conduct attributional LCAsbased on relevant future states or scenarios of consecutivestates to guide the direction of actions. To assess decisionsdirectly, a consequential perspective is needed. However, ifconsequential LCA is to be of any value for strategic technol-ogy choice, in particular in the area of GHG emissions, itshould not only include effects resulting from marginal changeof the current system but also marginal contributions to radicalsystem change. Sometimes there is a trade-off between the twoeffects. Then it could be wise to remember that if you aregoing to build a skyscraper you start by digging a hole.

Acknowledgements

The financial support of the Swedish Foundation forStrategic Environmental Research, MISTRA, the CompetenceCentre for Environmental Assessment of Product and MaterialSystems (CPM), Chalmers Environmental Initiative, Goteborgand Energy Ltd. Research Foundation are gratefully acknowl-edged. We also wish to thank Roland Clift, Tomas Ekvall andtwo anonymous reviewers for valuable remarks.


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