consumer heterogeneity and the development of environmentallyfriendly technologies

Technological Forecasting & Social Change 76 (2009) 533–551

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Technological Forecasting & Social Change

Consumer heterogeneity and the development of environmentallyfriendly technologies☆

Paul Windrum a,⁎, Tommaso Ciarli b,c, Chris Birchenhall d

a Manchester Metropolitan University Business School, Aytoun Building, Aytoun Street, Manchester M1 3GH, UKb Manchester Metropolitan University Business School, Manchester, UKc Max Planck Institute for Economics, Jena, Germanyd University of Manchester, Manchester, UK

a r t i c l e i n f o

☆ The authors gratefully acknowledge funding throuand Humanities Programme of the EU 7th Frameworkimprove the original draft of the paper.⁎ Corresponding author.

E-mail address: [email protected] (P. Windr1 For an in-depth discussion of the empirical validati

of Computational Economics, edited by Birchenhall et a

0040-1625/$ – see front matter © 2008 Elsevier Inc.doi:10.1016/j.techfore.2008.04.011

a b s t r a c t

Article history:Received 14 September 2007Received in revised form 16 April 2008Accepted 22 April 2008

The paper examines the effect of heterogeneous consumer demand on the generation anddiffusion of environmentally benign technology paradigms. The history of the shift from horse-based to car-based transport provides the basis for an empirically grounded multi-agent modelof sequential technology competitions. Firms compete on price, product quality, and theenvironmental sustainability of their products, and improve their market position throughproduct innovation. The trajectory of product innovation is shaped by the distribution ofheterogeneous consumer preferences with regards to quality, price, and the environmentalimpact of consumption. The distribution of consumer preferences determines whether cleanerdesigns are developed within a technology paradigm, whether new, more environmentallybenign paradigms are developed, and whether these new paradigms replace older,environmentally harmful technology paradigms.

© 2008 Elsevier Inc. All rights reserved.

Keywords:Heterogeneous consumer preferencesInnovationEnvironmental technologiesParadigm substitutions

1. Introduction

Under what conditions can new, more environmentally friendly technologies displace established, strongly pollutingtechnologies? In this paper we focus on the role played by heterogeneous consumer preferences in the development of newtechnology paradigms. In particular, we examine the way in which current pollution, created by the use of an establishedtechnology, stimulates some consumers to search for new, cleaner technologies. Herein lies a potentially important source ofendogenous technological and environmental change. How influential are highly concerned ‘green consumers’ (eco-warriors)who seek to develop radically new lifestyles? Are other individuals able to emulate these radical environmental consumers?Alternatively, is it more realistic for everyone to improve their consumption patterns slightly, rather than attempt to shift to aradically new lifestyle? What type of policies should government develop under these different conditions?

To address these questions, we develop an empirically grounded model of sequential technology competition. Empiricallygrounded modelling uses empirical data, in the form of datasets and case studies, to inform the development of a model's microfeatures (the features and behaviours of agents, and their interactions) and the set of industry/macro level observations that areused to test the validity of outputs generated by the model.1 The empirical case study that guides our modelling process is thehistory of the switch from horse-based transport systems to car-based transport systems. This case study provides empirical data

gh the Public-Private Services Innovation (ServPPIN) project, funded through the Socio-Economic Science. We thank the two anonymous journal referees for their excellent comments and suggestions on how to

um).on of simulation models, the interested reader is referred to a series of papers contained in the special issuel. [1].

All rights reserved.

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http://dx.doi.org/10.1016/j.techfore.2008.04.011

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534 P. Windrum et al. / Technological Forecasting & Social Change 76 (2009) 533–551

on the link between environmental pollution and the search for new, cleaner transport technologies. The switch required afundamental change in the preferences and actions of households, matched with a set of new technology firms that were able andwilling to innovate and produce a set of new technology products.

The structure of the paper is as follows. Section 2 considers the history of the switch from horse-based to car-based transportsystems. This provides a set of stylised empirical facts that guide the modelling exercise in Section 3; notably, in modelling thebehaviours of consumers and firms, the interactions between these agents with respect to new technology paradigms can beanalysed. In themodel,firms compete through price, product quality (service characteristics), and the environmental sustainabilityof their goods, and engage in innovation in order to improve their market position. The trajectory of innovation is shaped byconsumer demand and by a set of technological constraints. Hence, the distribution of consumer preferences, with respect toproduct quality and the environmental impact of consumption, plays a key role the development of new, cleaner technologyparadigms.

The model is novel in a number of respects. First the adoption, development and diffusion of new technology paradigms areendogenous within the model. Second, the focus is placed on the role played by consumers in the search for, and development of,new, more environmentally friendly paradigms. Stimulated by the negative impact of pollution on utility, consumers must decidewhether to experiment with new technology paradigms that have the potential to be environmentally cleaner. They need to weighthe promise of a new paradigm against the actual environmental performance of the earliest new technology designs. Givenheterogeneity of preferences, the particular distribution of consumer preferences that exists will determine whether or not atechnological substitution occurs.

This discussion paves the way for an examination, in Section 4, of the impact of heterogeneous consumer preferences onparadigm substitutions and, hence, on global environmental pollution. First, we examine the effect of heterogeneity inenvironmental preferences. This is achieved by considering alternative standard deviations in environmental utility over a set ofconsumer preference distributions. The impact is evaluated with respect to the global level of pollution that is generated in themodel for a given period of time. Second, we examine the impact of differences in the mean level of environmental utility for a setof consumer preference distributions. Section 5 of the paper concludes.

2. Pollution, consumption, and the evolution of transport

Themodern industrial city was born in the 19th century. Steampower enabledmanufacturing to be concentrated in cities; citiesthat provided both local markets of labour and consumers. The concentration of manufacturing, in turn, made it even moreattractive for the population to move from the countryside, and there was a dramatic growth of cities in Europe and in the USA. InBritain, the birthplace of industrialisation, much of the 19th centurywas concernedwith the growth and development of industrialcities. When Queen Victoria ascended the throne in 1837 most Britons lived in the countryside. By the end of the century, 80% ofBritons lived in towns or cities. New industrial cities were born: Manchester, Glasgow, and Leeds. Others, such as Liverpool andLondon were reshaped and transformed. This unparalleled urban growth was accompanied by immense health and hygieneproblems. Swelling urban populations produced unprecedented densities of horse and human excrement, much of which found itsway on to public streets. Sanitation, water supplies, and notions of public health were non-existent. 15% of all children could expectto die before their first birthday. Differences in child mortality and life expectancy between urban and rural dwellers were clear.Figures published in the Lancet in 1843 revealed that the life expectancy of a labourer in Liverpoolwas just 15, while in rural Rutlandit was 38 [2].

Since the middle ages in Europe, epidemics and diseases such as cholera, malaria, and tuberculosis were thought to be linkedto ‘miasmas’ — a poisonous vapour created by decaying organic material. In the 19th century, health theorists morphedthe notion. Using their knowledge of the respiratory process, they “placed the blame on exhaled carbon dioxide in unventilatedrooms and sewer gas, an often colorless, odorless gas given off by inadequately flushed plumbing or poorly cleaned privies”[[3], p. 24].

The solution was proper ventilation to remove these gases. This gave rise to a ‘public hygiene movement’. The movement wasreshaped and given further momentum by improving scientific knowledge. In 1854, John Snow identified a connection betweencholera and contaminated water supplies. This led to the funding of scientific studies that established our modern understandingof bacteria and disease. The public hygiene movement led to a seismic shift in public opinion and to fundamental changes in urbanliving. The public hygiene movement led to the removal of human excrement from the street. City planning departments werecreated, charged with the responsibility of changing the urban city itself, in order to reduce disease and improve health. In Europeand the USA, the target was densely packed, ill ventilated row houses that were inhabited by the city working classes. Other socialgroups viewed these as the source of moral and social ill health, as well as physical ill health. Planners removed these tenementsand worked with private sector firms to open up spaces and let in ‘healthy’ light and air in the rebuild [4,5].

It was against this background that the car was born in the mid-1880s. The one remaining source of organic pollution was thehorse. Whereas the disease and pollution associated with horses was understood by the end of the 19th century, the pollutionassociatedwith car usewas not. Consequently, cars were perceived to be the cleaner option. Therewas another factor favouring thecar — the desire for ‘suburban living’. This new concept was a child of the public health movement. Suburban living representedboth the moral and the physically healthy alternative to the inner city.

In the medieval European city, ‘suburbs’ (which literally means ‘homes under defensive city walls’) had been prohibited, or atleast heavily discouraged, because they could give shelter to an attacking army.With urban landwithin the city walls at a premium,therewas little physical space for the rich to segregate themselves from the poor. The pattern of segregation that existed within the

535P. Windrum et al. / Technological Forecasting & Social Change 76 (2009) 533–551

city walls was the opposite of the modern city. Because transportationwas primitive, and most people walked to work, city centreresidences commanded higher prices. Relatively poorer people tended to reside on the more remote outskirts.

This pattern started to change as horse drawn carriages and wagons improved but it was with the introduction of the railroadsand the electric tram (streetcar) that change really began. Railways enabled the upper classes to relocate to the countryside and tocommute to work on a daily basis. From the 1890s, the electric tram enabled the middle classes to relocate to newly built suburbs.Travel by tram was much faster than by horse and, unlike train travel, daily tram travel was affordable for the middle classes. Themaximum speed of an electric tramwas 12 mph compared to 4 mph for a horse drawn omnibus. The increase in speed translatedinto a significant expansion of the radius of land accessible for settlement, from 12.26 square miles with the horse drawn omnibusto 113.86 miles with the electric tram [6]. In practice, however, the desire for suburban living largely remained an unsatisfied ‘newtaste’ until the advent and development of the car. Benz produced the first patented car with a petrol-driven, internal combustionengine in 1886. However, this early design was very different to the standard car design that we know today. It was a threewheeled, open chassis design using a tiller steering arrangement (the same principle as on a canal boat) to control a single frontwheel. It had a 1 cylinder, 1 litre engine offering just 1 brake horse power (bhp) and a top speed of 9 mph. The engine was locatedunderneath the driver/passenger bench. With a 1.5 litre fuel tank its range was just 5 miles— rather problematic given that petrolstations didn't exist. It was not until the 1920s, through a series of radical product and process innovations, that the standard carconfiguration we know today was established [7]. By the 1930s the car had become the dominant form of urban travel in the USA.In Western Europe the transition occurred shortly after WWII. The car facilitated the emergence of a new type of consumerlifestyle: the suburban car commuter.

The evolution of transport highlights two important aspects of paradigm substitutions. The first is the role played byintermediate technologies. The pollution and congestion created by horse-based transportation provided a ‘window ofopportunity’ for new transport technologies. In fact it was the electric tram, not the car, which displaced the horse as theprimary form of mass urban transport. The diffusion of the electric tram led to the disappearance of the horse drawn omnibus. Dueto relatively low fares and its greater speed, the electric tram made it possible for the middle classes to start experimenting withsuburban living, far from the polluted city centre. Horses remained in use in private taxis, freight transport, and in the countrysidewhere electric power lines were too expensive to erect, given low population densities.

The electric tramplayed an important role in the transformation of the city centre. The upper andmiddle classesmay have startedto vacate the city centre as a place of residency, but they continued to use it as a centre for business and entertainment activity. This ledto the transformation of the city centre into the ‘central business district’ that we know today, with its concentration of business andentertainment activities such as department stores, theatres,museums and cinemas. In addition to facilitating the initial exploration ofsuburban living, the electric tram enhanced, and further developed, the concept of ‘high speed transport’. This concept was firstintroduced with train travel between cities. Now the notion of high speed travel was being applied within the city for the first time[3,8]. While the electric tram was subsequently displaced by the car (and in this sense represents an ‘intermediate technology’), itplayed an essential role in the wider transformation of the concept of travel, and in the organisation of the modern city [8].

This leads us to a second important aspect of paradigm substitutions: the existence of ‘deep path dependency’ acrossparadigms. As well as offering something that is different to the old technologies which they displace, new technologies sharecertain features with the previous technology. Paradigmatic substitutions therefore involve sequences of technologies that unfold,one from another. Technically, the electric tramwas amotorised version of the horse drawn omnibus. However its speed and rangemeant the concept of high speed transport could be applied to urban travel for the first time. This was essential for the earlyexploration of suburban living. The electric tram paved the way for the car. Technically the earliest cars also owed a debt toprevious horse technologies — they were known as horseless carriages, and some of the very earliest designs were indeed horsebuggies powered by an engine. But what differentiated the car from the previous paradigmwas the fact that it was a strictly privatetransport vehicle; one that would enable the middle classes to really engage in, and develop, a distinctive, suburban lifestyle basedon private consumption goods.

Given their importance, both aspects of paradigm substitutions need to be captured within a model of paradigmsubstitutions. The ‘window of opportunity’ is operationalised in our model in the following way. There is an optimalenvironmental-technology design within each paradigm. This optimal design combines minimum environmental impact withhigh product quality and low price. Over time, through a process of product innovation, one or more firms will identify andproduce this optimal design. Once identified, no further improvements can be made with respect to environmental pollution.Ongoing sales of the optimal design leads to a steady increase in the rate of global pollution. This negative externalityadversely affects average consumer utility. A ‘window of opportunity’ opens because consumers have an incentive toexperiment with new, less polluting technology paradigms. This is an ongoing process. As we have seen, the disease and filthassociated with high levels of horse pollution led to experimentation with electric trams. As electric trams diffused, so the levelof urban pollution decreased, but horses were still in use because trams were not economical to run in all areas. Once theoptimum level of environmental impact with electric trams was reached, another window of opportunity opened for a new,more environmentally benign, mode of transport.

With regards to ‘deep path dependency’ across these environmental–technological paradigms, we follow Windrum andBirchenhall [9] and model paradigmatic substitutions as sequential competitions between sets of technology goods that shareat least one product feature, or ‘service characteristic’. For example, as discussed, the electric tram combined an electric motorwith the carriage of the horse drawn omnibus. It was still a public transport vehicle but was less polluting per mile, and wasfaster than a horse drawn omnibus. This enabled users to engage in suburban living for the first time. The car is a privateconsumption good. The car offered greater geographical flexibility; unlike tram travel, car drivers could travel to any urban or


rural destination, at any time. After some initial R&D, cars were also faster than electric trams. The diffusion of the car finallyenabled the complete removal of horses in urban areas.

We note that the substitution of environmental–technological paradigms being described here is subtly different to Dosi's originaldiscussionof ‘technological paradigms’ and ‘trajectories’. Dosi defines a technologicalparadigmas “a ‘model’ anda ‘pattern’of solutionsof selected technological problems based on selected principles derived fromnatural science and selectedmaterial technologies” [[10],p. 152]. For Dosi, the focus is engineering knowledge and R&D. A technology is “a set of pieces of knowledge both directly ‘practical’(related to concrete problems and devices) and theoretical (both practically applicable although not necessarily already applied),know-howmethods, procedures, experience of success and failure and also, of course, physical devices and equipment” [[10], p. 151].The emphasis placed on engineering knowledge and practice contrasts with our discussion, which places the emphasis on theinteraction between the environment and the technologies that make up a paradigm. The distinction is particularly clear when onecompares Dosi's discussion of the drivers of new technology paradigms with our discussion of windows of opportunity. According toDosi, engineers focus on improvements in the technical performance of a product, which improve consumers' direct utility. Given theexistence of a technical ceiling that prevents further improvement in theperformance of a particulardesign, engineers realise theneedto engage in a radical redesign of the core technologies that are used in the product. This leads to the development of a new set ofconceptual models and solutions, thereby establishing a new paradigmwith a new technological trajectory. Contrast this discussionwith our discussion of windows of opportunity for new environmental-technology paradigms. This observes that, while technicalquality and price are factors, a key long termdriver for technological change is the environmental impact of existing technology goods.This is why we prefer to consider them as environmental–technological paradigms.

The history of transport provides us with empirical insight into paradigm substitutions and a set of stylised facts regarding therelationship betweenpollution, consumption, and experimentationwith new, potentiallymore environmentally benign technologies:

i) There is an unfolding of new technological paradigms from old paradigms, facilitating the evolution of differentconsumption opportunities and lifestyles over time.

ii) Performance characteristics and environmental pollution are tightly interconnected. It was not simply the servicecharacteristics of the car (speed, flexibility, private consumption) that led to its rapid adoption after WWII. Demand for thecar was closely tied to the demand for healthy suburban living, driven by a desire to escape the disease and filth of faecespollution in city centres. Cars enabled the middle class to truly explore a new set of healthier consumption opportunitiesassociated with suburban living.

iii) New technology firms are born that champion innovation and develop the new technology paradigm. They seek to unseatand replace old technology firms to become the dominant industry players [11,12]. This may take some time. It tooknumerous innovations over a 30 year period before the standard car configuration that we know today was established [7].

iv) In addition to reducing individual consumers' exposure to pollution, new technologies offer the promise of reducing globalpollution levels for all. Hence, the car was originally perceived to be a new, ‘cleaner’ technology. In adopting the newlifestyles associatedwith new transport technologies, the consumer has a subjective evaluation of the promised reduction inpollution, and of the impact of their consumption, e.g. by reducing the use of horses in transport. Note that when theexposure to pollution is reduced, concern about the future environmental impact of current consumption is also reduced—

that is, unless a new environmental problem is raised by scientists and the media, and is perceived to be an issue byconsumers.

Of course, our image of the car has subsequently changed due to a better scientific understanding of how car emissionscontribute to greenhouse gases and global warming, and of its impact on individual human health (carcinogens and respiratoryillness). Once again, pollution is a key factor driving change in transport technologies. This changed perception has induced a newinterest in, and exploration of, alternative modes of transportation. These include a renewed interest in public transport as well asnew concepts such as multi-modal transport. There is also a changing attitude towards the car itself. This is reflected in theintroduction of congestion charging in London, and the tighter regulation of exhaust emissions across Europe, the USA, and inother parts of the world. In response, car manufacturers are now actively engaged in the development of alternative, less polluting,alternatives to the petrol-driven internal combustion engine.

3. Modelling paradigm substitutions

In this section we outline the key elements of our simulation model.2 The model captures the set of stylised facts on pollution,consumption, and experimentationwith new paradigms discussed in Section 2. Using this model we will explore, in Section 4, theeffect of heterogeneous consumer preferences on paradigm substitutions and, hence, environmental pollution. Heterogeneouspreferences are important because these strongly influence, for a given set of technological and cost constraints, whether firmsproduce more, or less, environmentally friendly consumer products.

As noted, the model is a development of the Windrum and Birchenhall [9] model of sequential technology competitions. Thereare three important developments. First, in addition to direct and indirect utility, consumer preferences take into account theenvironmental pollution that is generated as a consequence of consuming rival technology products, and the environmental‘promise’ of each technology paradigm. In this way, one can consider the environmental impact of different distributions of

2 For a more technical discussion, the interested reader is referred to the description provided in the second of our papers in this Special Issue.


consumer preferences. Second, the development and diffusion of new technology paradigms are endogenouswithin this model. Bycontrast, Windrum and Birchenhall [9] modelled these as exogenous shocks. Third, environmental-technology paradigms aremodelled using a pseudo-NK landscape. Firms explore the dimensions of paradigm landscapes, which are given by servicecharacteristics and by the environmental impact of different combinations of service characteristics.

The model presented in this paper contains two types of interacting market agents: firms and household consumers. Theenvironmental and technological beliefs and actions of each type of agent are shaped over time by the beliefs and actions of theother. This interaction leads to ‘co-evolutionary learning’. Products are the objects via which different sets of agents communicatetheir expectations, mentalities, desires, and competences. Technological change is the consequence of inter-agent learning. Ratherthan being an independent causal factor, a technology product is a mediation device. It is this inter-agent mediation that leads totechnological change. Technological substitutions occur as a consequence of consumers and firms interactively learning about thepossibilities associated with the production, consumption, and environmental impacts of old and new technologies. A paradigmsubstitution involves far more than the replacement of one set of technology products with another. It involves the displacement ofexisting patterns of demand and supply with new patterns. In summary, it is a gestalt shift. This is the type of radical shift referredto by Schumpeter [13,14] in his ‘gales of creative destruction’ metaphor. The change is widespread in reach, and deep in impact.

3.1. Pollution

The case study provides a number of empirical observations regarding pollution, its understanding, and its impact on consumerdemand. To start with, the environmental ‘promise’ of a new paradigm is a key factor that affects the behaviour of firms andconsumers. This ‘promise’ is exogenously given for a particular technology paradigm. It is estimated by scientists, based on currentscientific knowledge. Of course, an initial estimatemay be inaccurate but will be improved as scientific information is updated overtime, and a better understanding of the true impact of a technology on the environment is developed. Horse drawn transport wasubiquitous and the pollution due to horsemanure and urinewas well understood by the end of the 19th century. By contrast, giventhe small numbers initially in use, the environmental impact of the car went unrecognised. The true environmental impact of thepetrol engine was only recognised when there was mass consumption of the car. In the current version of the model we simplifyand assume that scientific information on the true environmental impact of each technology paradigm is accurate from the outset.

The environmental promise of a paradigm is modelled in the following way. A pseudo-NK framework represents theenvironmental impact of interactions between different combinations of service characteristics. Landscape fitness is inverselyrelated to the environmental pollution that a particular combination of service characteristics produces. Hence, the global peakwithin a paradigm landscape is the minimal environmental impact of a paradigm — i.e. its environmental promise. Throughproduct innovation, firms explore this complex landscape of non-linear interactions between service characteristics andenvironmental impact.3 The environmental promise is easier to reach when there is a high degree of modularity in technologydesigns, i.e. when a change in one service characteristic does not affect the impact of other characteristics on environmental fitness.

In the current version of the model, the landscape is perfectly modular with respect to product characteristics. This has twoimplications. First, there exists a smooth, global environmental peak in each paradigm (i.e. a global optimum)4 that firms can, inprinciple, reach. Second, firms can increase the overall environmental fitness of their designs by improving the environmentalfitness of one product characteristic. The assumption of perfect modularity allows us to analyse firms' behaviour as a pure responseto the market incentives of consumers. Lock-in to old paradigms can occur, but this is determined by consumer preferences. This isnot the case in non-modular landscapes, where lock-in is also partly determined by technological complexity.

Whether firms reach this environmental optimum depends on the rewards to innovation. These are strongly influenced byconsumer preferences. The decision to move toward less polluting designs or, alternatively, to designs that major on high qualityservice characteristics depends on: (i) the relation between service characteristics (the position of the environmental optimum inthe landscape), and (ii) consumers' preferences towards service characteristics, towards the environmental impact ofconsumption, and towards product price. We know, from research on industry lifecycles, that the locus of competitive advantagein the early stages of a new technology product tends to be the improvement of service characteristics. This was certainly the casefor car manufacturers [18]. Hence, the actual environmental performance of current designs can diverge from the environmentaloptimum for long periods of time. Still, the global environmental optimum is an attractor point within the paradigm landscape.Pollution increases as more consumers adopt the technology. Increasing pollution negatively impacts consumers, leading to a fallin utility. This induces firms to develop cleaner versions of their products. Given long enough, one or more firms will identify theoptimal (i.e. pollution minimising) design configuration in our model.

3.2. Consumer demand

Following Lancaster [19,20], and Saviotti andMetcalfe [21], wemodel a product as a set of service characteristics that yield directutility to consumerswhen the product is consumed. Relative product quality can therefore be defined as better/worse performance

3 The pseudo-NK framework is a continuous variable version of the (more familiar) binary NK framework. This allows one to consider semi-modular (and semi-interdependent) technological landscapes with real value variables and graduated interdependencies (i.e. all modules interact with each other, but to a partialdegree). Further, the pseudo-NK framework allows one to exogenously determine the optimal point of the landscape. This is important to showmovements in thetechnological frontier (i.e. the optimal location). For a technical discussion of the pseudo-NK landscape approach, see Valente [15] and Ciarli et al. [16].

4 This is similar to an NK landscape with low correlation, as discussed by Kauffman et al. [17].

http://www.springerlink.com/content/mk56044l37k30531/?p=6ea72da03e81446a80bb385d8ebf2bd1&pi=4


over a set of service characteristics. In our model there is a heterogeneous population of consumers that place different weights onthese service characteristics (quality) x, on price p, and on the environmental impact of consumption s. Following Windrum andBirchenhall [9], wemodel this heterogeneity as a distribution of individual consumers over a limited number of consumer groups or‘consumer classes’. Associated with each consumer class is a utility function. Each class differs with respect to the relativeweight itplaces on quality, price, and environmental impact. A key advantage of this set up is that it allows one to calibrate the model, basedon empirical evidence of the distributions that exist in different markets, and to consider alternative scenarios.

Formally, the utility function of each consumer class is modelled as

U x;p; sð Þ = d !xð Þ + v m − pð Þ + e s !xð Þð Þ ð1Þ

d !xð Þ is the direct utility provided by the design vector!x, V(m−p) is the indirect utility of purchasing the good (m is initial
whereincome of the consumer and p is the price of the good), and e s !xð Þð Þ is the environmental utility provided by the environmentalfitness (i.e. pollution level) of the design vector !x. Note here that a higher fitness obtained by a firm's design implies a lowerenvironmental impact of that design in consumption.
In considering the negative externality of pollution, the current model differs to previous models of sequential technologycompetitions, such as Malerba et al. [22], and Shy [23]. These earlier models considered positive Arthur-type network externalities,where utility is positively related to the installed user base of a technology. Pollution is a negative network externality and, as such,an important driver behind consumers' willingness to experiment with new, cleaner technology products.

A second feature that distinguishes our model fromMalerba et al. [22] is the treatment of quality and price. Our model does notassume, as Malerba et al. do, that new technology goods are always superior in quality/price performance to old technology goods,or (hence) that all new firms have a performance advantage over old technology firms. Furthermore, Malerba et al. treat quality asa simple integer value. We unpack performance quality, using the service characteristics approach, into a vector of servicecharacteristics !x that contain complex, non-linear relationships. Perceived quality depends on the valuations that are placed onthese service characteristics by the utility functions of an existing set of consumer classes.

‘Direct utility’ is the utility gained by consuming the set of service characteristics embodied in a good. ‘Indirect utility’ is theutility a consumer obtains from spending residual income (income minus the price paid) on other goods. The higher the price of agood, the less money the consumer can spend in other markets, and the lower is his/her indirect utility. There is an important linkhere between indirect utility and indirect network effects. Indirect network effects are the static economies of scale built up overtime by old technology firms. Clearly, these are not initially available to new technology start-ups [7,24].

‘Environmental utility’ e s !xð Þð Þ is the utility associated with the environmental sustainability of the consumption good. This is acomplex term that takes into account (i) the environmental fitness of the design, i.e. the pollution generated through consumingone unit of a particular design over its lifetime; (ii) the promised sustainability of the paradigm to which the design pertains(attainable with a specific combination of service characteristics); (iii) the current level of global pollution that has accumulatedover time due to past consumption by all consumers (i.e. the negative network externality); (iv) the consumer's discount rate; and(v) the relative risk aversion of the consumer towards the environmental impact of consuming a particular technology design.

Wemodel the environmental utility component as a composite function that reflects the hyperbolic risk aversion of consumersto the global pollution generated by consuming technology goods. Ej(sj) is the perceived environment impact of a specifictechnology good.

Ej sið Þ = ηpjs !xið Þ

1 +^s zð Þ− s !xið Þ + 1− ηpj� �

^s zð Þ ð2Þ

te that this is a relative evaluation, since each technology is being compared to the sustainability of the best technology
No currently available on the market ŝ(z) attimet. Ej(si) is thus a combination of the sustainability of the technologygood (relativeto the cleanest technology design currently available), and the current environmental performance of a technology vis-à-vis its‘technological promise’. ηp is a weighting (between 0 and 1) that consumers attach to the current sustainability of design i relativeto its ‘technological promise’.
This specification of the consumer utility function captures three key stylised facts about environmental pollution andconsumer preferences raised in the empirical case study. The first is the important role played by the environmental ‘promise’ ofnew technologies. Given lags in understanding and information about the true impact of new technology paradigms, earlyconsumer adoption may largely be based on its initial environmental ‘promise’. These environmental promises are exogenouslygiven, based on the current state of scientific knowledge.

The second stylised fact is the existence of a difference between the environmental promise and the level of pollution producedby the current generation of new technology products. As discussed, early innovative effort may be placed on improving servicecharacteristics or reducing price rather than improving pollution performance. However, ongoing pollution will reduce averageconsumer utility at an increasing rate: the more pollution increases, the more consumers become aware of the risks of pollution,and the higher the loss of utility for a marginal increase in the environmental impact of the consumed good. We model thisrelationship as a hyperbolic utility function. When the environmental fitness of a product reaches a minimum threshold,consumers receive negative utility when they purchase and consume the product. Referring back to the case study, consumerswere suffering negative utility due to the environmental pollution and disease generated by horse urine and faeces in cities. This


motivated urban populations to move out of town, and to seek alternative means of transportation to achieve this. In the present,we see the emphasis that firms are placing on the environmental benefits of their consumption goods: solar watches,environmentally friendly air companies, non-polluting garments, and so on. In the long run, developing environmentally poordesigns adversely affects producers, due to the increasing global pollution that this produces.

The third fact captured by the model is the key role played by an initial group of consumers who place a high value onenvironmental utility. These ‘eco warriors’ may be sufficient in number to form a distinct ‘consumer class’ that champions thedevelopment of a new paradigm. If a firm produces a technology product that (closely) matches the preferences of this consumerclass, then a stable niche will form. If this niche is successful – i.e. it becomes relatively well serviced vis-à-vis other consumerclasses in the current population – then the niche can grow and other individuals will be attracted to this consumer class.

This is modelled in the following way. As in Windrum and Birchenhall [9,25], the population of individual consumers areinitially distributed evenly across a number of consumer classes. The preferences of each class differ with respect to direct, indirectand environmental utility. Each class can be thought of as a distinct ‘consumer type’, each with a distinct lifestyle that is facilitatedby a particular type of technology product. Individual consumers are attracted to consumer classes that enjoy high utility. Overtime, consumer types that are consistently well serviced by the existing set of technology designs will attract increasing numbersof individual consumers. By contrast, individuals will move away from types that are not well serviced. This shifting of individualconsumers across consumer classes, according to the relative fitness of classes, is captured using a replicator algorithm.

This approach is attractive in that it captures the dynamics of consumer choice that wewish tomodel. First, individuals are freeto explore alternative patterns of consumption over time. In the model this occurs when individual consumers switchconsumption patterns, from one consumer class to another. Second, heterogeneity of consumption depends on the existence ofalternative consumer lifestyles that individuals can choose between. Third, there are two opposing forces that affect the degree ofconsumer heterogeneity. On the one hand, consumer classes that are well serviced will attract increasing numbers of individualconsumers over time, while consumer classes that are poorly serviced lose individual consumers and, in the limit, disappearcompletely. Hence, there is a tendency for intra-paradigm competition to reduce variety over time (also see [26] on this point). Onthe other hand, the introduction of new paradigms increases variety by offering individual consumers new consumptionopportunities and lifestyles. Hence, inter-paradigm competition increases, initially at least, heterogeneity of consumer classes. Ofcourse, when one paradigm completely replaces another, the variety (i.e. heterogeneity) of available consumer types declines.

3.3. Firms: production and innovation

Firms compete by offering consumer designs that are a distinct point within a multi-dimensional space of servicecharacteristic/price/environmental performance. Product innovation is the means by which firms search this multi-dimensionalspace. As well as differingwith respect to their environmental impact, alternative paradigms differ with respect to the set of servicecharacteristics that are possible [9,25]. Hence, some service characteristics are available with both new and old technology goods,but some are only available using one or other technology.

Initially, all firms in the model are endowed with identical levels of capacity and wealth. Firms are heterogeneous with respectto the quality of the service characteristics that make up their product designs, and the consumer class that they target. These arerandomly generated at the outset. In each period, every firm has a current design, a productive capacity (setting an upper limit onoutput), and a non-negative inventory of stock that is carried over from the previous period. Product inventories set the maximumnumber of goods that a firm can currently sell in the market. The price of its design is determined by a fixed mark-up on the unitcost of production (i.e. prices do not adjust to clear the market). Fixed mark-up pricing is a stylised fact that features in manymodels of innovation (e.g. [22,27]. The classic empirical research on mark-up pricing is Hall and Hitch [28], with more recentstudies by Blinder [29], and Hall et al. [30] finding widespread use of this cost-based pricing rule. In the current version of themodel we assume there is a common and constant mark-up for all firms.

Unit cost includes a fixed cost for innovation, divided by the firm's level of production, and an average variable cost that is afunction of the vector of service characteristics offered by the design. The average variable cost of a design is thus independent ofthe level of production. As noted already, there is an important link between the indirect network effects enjoyed by old technologyfirms, and consumers' indirect utility. Given that old technology firms have had time to build market share and to exploit scaleeconomies, while new technology start-ups clearly have not, the supply prices of old technology firms are likely to be lower thannew technology firms. This advantage may be counterbalanced if there are consumers who are willing to pay a high demand pricefor amore environmentally friendly good (as discussed in Section 3.2). If this event, awindow of opportunity openswith economicincentives for new technology firms to experiment with new, potentially cleaner technologies.

Given that all firms in our model face the same underlying technology and cost functions, and that firms have fixed mark-ups,the only means by which they can improve their competitive position is through innovation. Product innovation involves thecreation and evaluation of new designs with different combinations of service characteristics, different prices, and differentialenvironmental impact (fitness).

Successful firms are those that develop designs that closely match the preferences for direct utility, indirect utility, andenvironmental utility of their target consumer type. These firms enjoy higher levels of sales and productionwith, as a consequence,lower average fixed costs and lower supply prices. Profits are added to current wealth. Growth of productive capacity, to meetincreasing consumer demand, is financed from current wealth. A firmwith relatively high levels of sales, and relatively high profits,will be able to finance a higher growth of capacity. Hence, successful firms grow over time. By contrast, loss making firms mustcover themselves by drawing on their current stock of monetarywealth. Once this monetary wealth is exhausted, firms can finance

http://papers.ssrn.com/sol3/papers.cfm?abstract_id=114948


production by selling off part of their production capacity. Once this capacity is exhausted, the firm is bankrupt and exits themarket.

In order to keep the model relatively simple, we assume that, by funding and performing R&D, firms attempt to mutate allservice characteristics. There is a certain probability that this R&Dwill be successful; i.e. not all R&D that is conducted will have apositive outcome. We assume that this probability of success is the same for all firms. If successful, an innovating firm ends upwith a proposal for a new product design. The values of one or more service characteristics of this proposed design will bedifferent to the design currently in production. Using its knowledge of the utility function of its target consumer class, theinnovating firm evaluates the likely demand for this proposed design. If the market performance – price, characteristic quality,and environmental impact – of the proposed design is superior to the firm's current design, then the new design is accepted andput into production.

3.4. Introduction of a new technology paradigm

To investigate the possibility of paradigmatic succession – the breaking away from an established dominant firm/consumerclass combination – we introduce a new technology paradigm. As noted, the search for new technology paradigms is endogenouswithin our model. Initially, populations of firms and consumers can only explore the technological landscape of one paradigm(with its given combinations of service characteristics and environmental impact). The opening of a new technological landscape istriggered by one or more old technology firms reaching the global optimum contained in the first technological landscape. Therefollows a period of time, after which a new scientific discovery or an engineering breakthrough facilitates the introduction of a newtechnology paradigm. In the pseudo-NK framework, this new paradigm takes the form of a different technology landscape,containing a different set of interactions between service characteristics, and an environmental global optimumwhich is higher inheight. New technology firms enter, produce new technology designs, and explore this new, technologically determined landscapethrough product innovation.

Initially, both the old and the new landscape co-exist. Picking up on issues raised in Section 2, a key feature of the newtechnology designs is that they offer new characteristics, previously unavailable with old technology designs. This is implementedin the model as an extension of the service characteristic space. The other defining feature of the new paradigm is that its globalenvironmental peak is higher than that in the old technology paradigm. In other words, it has the potential to be moreenvironmentally benign. Of course, whether this global peak (the ‘promise’) is reached, or whether there is lock-in to a sub-optimalenvironmental solution, is the key question that we are addressing here.

A set of new consumer classes are also created. These place a positive weight on the new service characteristics offered by thenew technology designs. The direct utility component of these new class preferences is randomly assigned. As before, each firm isassigned a single consumer class, which it targets throughout its lifetime. Thus, new consumer classes are targeted by newtechnology firms. Importantly, the initial designs of the new technology firms are randomly initialised. Hence, the designs mayinitially be inferior to the designs of the old technology firms. This distinguishes our model from Malerba et al. [22] (see above).

Having discussed the key features of themodel, the next section of the paper examines the properties of themodel, and reportson a series of simulations that explore the impact of heterogeneous preferences. Specifically, we examine a set of alternativescenarios that explore the environmental impact of heterogeneity in the standard deviation of environmental preferences, and inthe mean average of environmental preferences of consumer classes.

4. Results

4.1. Sensitivity analysis

Prior to discussing the main findings, we report on sensitivity tests for robustness which have been conducted on a benchmarkinitialisation. In order to thoroughly assess the properties of any simulation model, one needs to perform a detailed sensitivityanalysis. Sensitivity analysis entails a careful investigation of how the outputs of a model vary when one alters its inputs [31–33].Set against the need for statistical confidence of one's results, is the cost of computation time.

We have performed sensitivity analysis on the initial conditions and on across-run variability induced by the random elementsover 50 simulation runs, using a benchmark configuration with average values of the critical parameters. The reader is referred tothe ‘Note on robustness’ in Appendix A for a detailed discussion of the sensitivity analysis. Here we note that a comparison of theconfidence intervals for 50 runs, 25 runs, and 10 runs indicates that averages drawn from 10 runs provide a set of results that areacceptably robust while being computationally viable. This may initially appear to be a small number of simulation runs, but oneshould recognise that in each run there is a large population of firms and a large population of consumers: 25 firms, and 500individual consumers distributed over 100 consumer classes. Each simulation run lasts for 3000 iterations (time steps), and anaverage of 7 technology paradigm changes occur during this period (see Fig. 1 below). It would therefore be better to think of eachsimulation run as a time series containing a large number of observations. Averaging overmultiple simulation runs further increasesthe explanatory power of the parameters and variables that are analysed, ensuring the results are not distorted by a few, atypicalsimulation outputs.

Given this sensitivity analysis, we consider 10 time series simulations to represent a good trade-off between establishingrobustness of results and computational effort. Hence, unless otherwise stated, each individual reported result is an average valuedrawn from 10 time series simulations. With regards to the simulation exercises reported in Sections 4.2 and 4.3 below, each

Fig. 1. Aggregate pollution, paradigms, and consumer utility.


simulation time series contains a population of 25 firms, and 500 consumers. At the outset, all firms start with equal endowments,and equal shares of sales and consumers. Consumer classes start with equal numbers of individual consumers.

4.2. General findings

A few comments on the underlying dynamics of the model are useful here. Fig. 1 shows the evolution of the globalenvironmental impact (pollution), the number of paradigms that emerged, and the average level of consumer utility acrossconsumer classes over 3000 periods. The results clearly indicate the importance of the environmental utility component withinclass utility functions. Despite the fact that consumer utility is linearly increasing with improvements in service characteristics(direct utility) and with a decrease in price (indirect utility), there is a decreasing rate of pollution as consumers switch to new,more environmentally benign technologies.

This sustained reduction in pollution over time is facilitated by the discovery of new techno-environmental paradigms.Reaching the highest environmental fitness in one paradigm opens up scientific/engineering research into a new paradigm thathas a higher level of environmental fitness and sustainability. Depending on consumer preferences regarding the trade-offbetween service characteristics, price, and environmental fitness, firms may have an even stronger incentive to reach the newenvironmental peak (compared to firms in previous technology landscapes) if this increases the average utility of its targetconsumer type. This, in turn, opens the way for the discovery of a new paradigm.

With reference to Fig. 1, the observed downturns in average utility are caused by firms moving away from the environmentalpeak of a landscape. This is due to consumer preferences of the existing population of consumer classes placing greater weight ondirect utility than on environmental performance. Firms respond to these incentives and focus on developing designs withsuperior service characteristics, to the detriment of environmental performance. Only when a new paradigm arises that happens tohave a higher quality of characteristics and a higher environmental peak, do firms have an incentive to develop moreenvironmentally benign designs. Within such a landscape, average consumer utility increases as firms move toward the landscapepeak. When one or more firms identify the optimal design within this landscape, the way is opened for the discovery of a newparadigm, and so on. As previously noted, one needs to bear in mind that these results apply when environmental-technologylandscapes are fully modular. If lock-in to a sub-optimal technology occurs, this is due to consumer preferences. If the landscape isnon-modular then lock-in may also be due to the specificities of technologies.

A second issue is the relationship between environmental performance, direct utility, and indirect utility. In our other paperin this Special Issue we consider in detail the effect of consumer trade-offs on paradigm substitutions and, hence, pollution. Fornow we note that firms have, on the one hand, an incentive to improve the quality of service characteristics because thisincreases the direct utility of their target consumers. On the other hand, the indirect utility of consumer preferences meansthere is an incentive for firms to reduce prices. Depending on the given structure of a particular technology landscape, theenvironmental performance of new technology designs may reinforce consumer preferences for higher direct utility, while inother cases they may reinforce consumer preferences for higher indirect utility. There is no reason, empirically, to assume exante that eco-friendly goods will have lower performing service characteristics or have higher supply prices. The extent to whichfirms improve the environmental fitness of their designs depends on the relationship between environmental performance,service characteristics, and price. Results show that, on average, firms develop a series of designs that cycle around the optimalenvironmental point in the technological landscape. Firms with the highest market shares develop designs that are moreenvironmentally benign.

A third feature of the outputs generated by the model relates to the hyperbolic relationship between global pollution andenvironmental utility. While all three components of the utility function are characterised by decreasing returns, environmentalutility takes a hyperbolic form. When consumers perceive the cumulated level of global pollution to be highly risky, average utilityrapidly drops and can turn negative. Under these circumstances, as global pollution continues to increase, so small changes in the

5 There is strong pair-wise correlation (statistically significant at the 99\% level) between the two series. Note that it does not make sense to test thecointegration, vector autoregression, impulse responses, or the direction of causality. This is because the series are averages values drawn from different series osimulation runs, in which different VAR may apply.

Fig. 2. a. Concentration of markets and consumers classes. Indicates the Standardized Herfindahl Index (0bHIb1) for firms' market shares and the share oconsumer classes — across simulation average and standard deviation. b. Relationship between consumer classes and market concentration. Indicates theestimated firm HI, given consumer class HI.


f

environmental performance of a firm's design can significantly raise average utility. This improvement in environmentalperformance will offset any negative consequences for direct or indirect utility. This is why, on average, the rate of paradigmsubstitutions increases over time.

A fourth feature of the model is a tendency for the market to converge to an oligopoly: a few successful firms tend to supply asmall number of consumer classes. Note that these findings are consistent with the results of Windrum and Birchenhall [9,25], andwith the general stylised facts of the industry lifecycle [18,34]. In the model this is due to the replicator algorithm that operates onconsumer classes. In the long run, demand and supply side dynamics are strongly correlated: they co-evolve. As Fig. 2a indicates,individual consumers converge towards a few consumer classes, leading to high market concentration as consumers purchasedesigns from just a few successful firms. The linear correlation between the two series is shown in Fig. 2b.5

Examining Fig. 2a and bmore closely, a few aspects deserve particular attention.Within a single technology paradigm, there is atendency for themarket to converge to a single design/consumer class/firm. This can be seen in Fig. 2a during the first 200 periods.However, following the introduction of a new technology paradigm, long term supply is, on average, divided between two firms—one old technology producer and one new technology producer.

The other interesting finding is that firm concentration is, on average, stronger than the concentration in consumer classes. Thismeans that, on average, a few firms are able to sell to more than one consumer class. This indicates that the preferences of anumber of surviving consumer classes are satisfied by the product design of one firm. Note that the standard deviation across runsincreases for both series, and stabilises in the long run, indicating that markets may end up with different degrees of oligopoly.

f

Fig. 3. Aggregate levels of pollution generated for alternative standard deviations of environmental preferences across the consumer population.


Finally, the absence of full market concentration (on average) is related to the presence of competing paradigms. Variety is alsomaintained through differentiation in the production and consumption of competing paradigms. Sequences of new paradigmsover time ensure that this variety is maintained in the long run.

Let us now consider a set of scenarios that explore the impact of heterogeneous consumer preferences on paradigmsubstitutions and, hence, levels of global pollution.

4.3. Scenario 1. Presence of a group of environmentally concerned consumers

As discussed in Sections 2 and 3, the existence of a class of environmentally concerned consumers (that have a highenvironmental preference), may affect the probability of a technological substitution occurring and, hence, the global level ofenvironmental pollution that is produced. If a group of ‘eco warriors’ that champion the development of a new, potentially morebenign technology are serviced by a design that (closely) matches the preferences of this consumer class, then a stable niche willform. Over time this niche will grow if increasing numbers of individual consumers are attracted to this consumer class lifestyleoffering an above-average utility. The net result would be a general ‘greening’ of consumption. As noted earlier, this presumes thatindividual consumers are free to switch between consumer classes (lifestyles). The role of ‘champions’ in the diffusion process hasbeen highlighted by Rogers [35]. From a policy perspective, it would be sensible, under this scenario, to target and supportenvironmentally concerned consumer classes.

In order to examine scenario 1, we compare the environmental impact of a set of alternative consumer class populations whichdiffer with respect to the distribution of environmental preferences. The mean level of environmental utility is the same for eachpopulation; what differs between the populations is the standard deviation in environmental preferences. Populations for whichthe difference between the least and themost environmentally concerned class is larger have a higher standard deviation. Also, thehigher the standard deviation, the higher the environmental preference of the most concerned consumer class. In this way, we cancompare the effect of populations with higher (lower) standard deviations on the level of pollution that is generated for a givenperiod of time. This is the first proposition that will be examined:

Proposition 1. The higher the standard deviation of environmental utility in a population of consumer classes, the lower the globalpollution that is generated.

Fig. 3 shows the levels of global environmental pollution generated by class populations with the same mean value of η=0.5but with differing standard deviations of environmental preferences. Note that the lower graph focuses on the pollution levels inthe last 1000 periods. Consumer classes are divided into sets, each set initially containing five consumer classes. The sets areordered with respect to their environmental preferences.

The findings clearly indicate that the higher the standard deviation in environmental utility across the initial population ofconsumer classes, the lower is the level of global pollution generated. Again it is worth emphasising that this result depends onindividual consumers being able to switch between consumer classes, imitating more environmentally concerned peers.

The relationship between environmental preferences, heterogeneity and global pollution is examined in more detail by Fig. 4aand b. Fig. 4a is a scatter plot for the 10 time series results. This indicates a negative relationship between the standard deviation ofenvironmental preferences and the final level of global pollution. Fig. 4b plots the results of a (simple) polynomial prediction ofglobal environmental impact, given the different standard deviations of environmental preferences within the population ofconsumer classes.

Fig. 4. Relationship between the standard deviation of environmental preferences and global pollution. a. Scatter plot. b. Simple polynomial prediction.


Turning to the heterogeneity of environmental preferences within the initial population of classes, we find that a positive(linear) relationship exists between initial heterogeneity and the final average (non-weighted) level of consumers' utility. This isindicated in Fig. 5a. As noted, there is a tendency in this model for intra-paradigm competition to result in convergence to a singleconsumer group with the highest environmental preference. A number of classes can co-exist, provided these have an equally highenvironmental preference (Fig. 5b).

We clearly observe this pattern when comparing the distribution of consumers across the sets of consumer classes (Fig. 6).Starting from the same baseline distribution in the initial period, consumer distributions with zero standard deviation changerandomly through time, i.e. no specific pattern of consumption is detected. Conversely, when there is high initial heterogeneitywithin the population, the distribution is increasingly skewed towards classes with a high preference for environmentally benigngoods. This is due to the hyperbolic relationship between pollution and environmental utility previously discussed.Whenpollutionis high, and increasing, the high environmental utility gained from improvements in the environmental performance of technologydesigns dwarfs the potential utility gains to be had from improving service characteristics. Firms respond to the strong marketincentives provided by their target consumer classes, and focus their innovation accordingly. This focus on the environmentalperformance of new designs means, in turn, that classes with the highest environmental preferences are better serviced thanclasses with lower environmental preferences. Hence the final result. The greater the initial heterogeneity of consumerpreferences, the stronger the tendency for individual consumers to be attracted a consumer class with a high environmentalpreference (Fig. 6b).

Fig. 6 shows the changing distributions of consumers across groups of classes, ordered by preference toward environmentalimpact (from low to high). Fig. 6a is the case where there is zero standard deviation across groups. In Fig. 6b there is the maximumstandard deviation between consumer groups (≈0.3).

Fig. 5. The effect of initial environmental preferences heterogeneity on final consumer utility and market concentration. a. Average level of utility in the consumepopulation. b. Average firm and consumer concentration.


r

4.4. Scenario 2. Effect of different average levels of environmental preference

Let us now consider the alternative scenario where the initial population of consumer classes does not contain a class ofenvironmentally concerned consumers. Scenario 2 is the counterfactual to scenario 1. Suppose the environmental preferences of allclasses are identical (i.e. homogeneous). In this scenario, policy makers would need to focus on policies that raise themean level ofenvironmental utility across all consumer classes. Ex ante, one would expect that paradigm substitutions are more likely to occurwhen the population contains consumer classes with higher mean levels of utility, the result being lower levels of global pollution.This leads us to the second proposition to be examined:

Proposition 2. The higher the mean level of environmental utility, the lower the global pollution that is generated.

In order to investigate Proposition 2, we compare the global impact of a population of consumer class populations which differwith respect to the mean level of environmental preferences. Within each population, the environmental preferences of allconsumer classes are homogeneous (i.e. there is zero standard deviation across the classes within each population). By comparingpopulations of homogeneous classes, we can readily compare the global environmental impact of different mean values ofhomogeneous environmental preferences. The findings are presented in Fig. 7. Note that thick lines indicate the globalenvironmental impact with very low and very high mean averages (environmental preference ranging between 0.1 and 0.9 acrossclasses).

Examining Fig. 7, we find that a non-linear relationship exists between the mean level of environmental preferences and thelevel of global pollution. The relationship between mean environmental preferences, the final level of pollution, the final level ofaverage consumer utility, and the number of paradigms is further illuminated by Fig. 8 below. Fig. 8a plots final average levels ofconsumer utility against mean environmental preferences, while Fig. 8b plots the number of techno-environmental paradigmsthat are discovered by firms against the mean environmental preferences of consumer classes.

Fig. 6. Distribution of consumers across classes through time: homogeneous versus heterogeneous environmental preferences. a. Zero standard deviation. bMaximum standard deviation.

Fig. 7. Aggregate pollution with different mean levels of environmental preferences (populations of homogeneous classes).


.

On the one hand, we observe that the final average level of utility enjoyed by consumers increases linearly with respect to themean level of environmental preferences in the population of consumer classes. On the other hand, we observe that theaccumulation of pollution reaches its minimum level in Fig. 8a for intermediate values of environmental preferences, thereafterincreasing with higher mean environmental preferences. The net result is a U-shaped relationship. Referring to Fig. 8b, we see that,for very high average levels of environmental preferences, a quite low number of new paradigms arise in a given period of time.This is important because lower numbers of new paradigms means a slower rate of pollution improvement is achieved. Takentogether, the findings partially confirm Proposition 2.

Finally, let us compare the pollution generated in scenarios 1 and 2.We observe that a lower rate of pollution occurs when thereare homogeneous populations with relatively high environmental preferences (i.e. that does not contain a distinct ‘eco-warrior’group) than for heterogeneous populations containing one or more classes with the same level of high environmental preference.

Fig. 8. Effect of average environmental preferences on global pollution and consumer utility. a. Environmental impact and utility. b. Number of paradigms.


This is an unanticipated finding, and one that requires serious consideration. We know, in scenario 1 with heterogeneousenvironmental preferences, that individual consumers will eventually converge to the eco-warrior class with the highest level ofenvironmental preference. In other words, in the limit all individual consumers will join the eco-warrior class. The key point is thatthis takes a certain amount of time to occur. By contrast, in scenario 2, all classes have this level of environmental preference. As aconsequence, the level of pollution created in scenario 2 is far lower than in scenario 1.

5. Conclusions

The findings presented in this paper have important relevance for discussions currently taking place in the USA and in Europeregarding the potential for markets to develop low-carbon, high-efficiency goods and services through the combined power ofconsumer choice and technological innovation. The importance of consumer demand lies in its power to induce firms to innovateand develop new technology designs that are more (or, alternatively, less) environmentally benign. In order to examine thisproposition, the paper considered the impact of heterogeneous consumer preferences on the development of cleaner productdesigns within a technology paradigm, on the development of new, more environmentally benign paradigms, and the substitutionof old, polluting paradigms by these new paradigms.

In order to tackle the issue, we considered the environmental implications of different distributions of consumer preferencesfor environmentally sustainable goods. This investigationwas performed by considering a number of alternative scenarioswithinan empirically grounded model of sequential technology competitions. The model contains a set of salient, stylised factsregarding pollution, consumer preferences, and the development of new technology paradigms that are provided by the casehistory of the transition from horse-based to car-based transport systems. In the model, firms compete through price, productquality, and the environmental sustainability of their goods, and innovate in order to improve their market position. Thetrajectory of innovation is shaped by the distribution of preferences across consumer classes for environmental utility, directutility, and indirect utility.


A first consideration is the nature of the technological landscape inwhich innovation takes place. In our model we considered aperfectly modular pseudo-NK landscape of service characteristics and environmental pollution. Only under these circumstances isthe pattern of firm innovation purely driven by the distribution of preferences within a population of consumer classes. In the caseof non-modular technologies, firms face complex non-linearities in service characteristics that can lead to lock-ins to sub-optimal,polluting designs within a paradigm, and to polluting technology paradigms. Lock-ins are also possible with perfectly modulartechnologies but, as the results clearly have shown, firms in these circumstances are highly responsive to the incentives providedby consumer preferences. When lock-ins occur, they are solely determined by consumer preferences, and how consumerpreferences evolve over time.

Given perfectly modular paradigm landscapes, we examined the impact of alternative distributions of consumer classpreferences – specifically, in the standard deviation and the mean average of the distribution of environmental preferences – onparadigm substitutions and, hence, on environmental pollution. The results clearly indicate that the heterogeneity of preferences isa key factor determining the global level of pollution that is generated over time. In this respect, the contention that competitivemarkets can, in principle, produce low-carbon and high-efficiency goods and services is supported. Having said this, the findings ofthe paper clearly indicate that significant variations in global pollution can be generated by relatively small differences in the initialdistributions of consumer preferences. One should therefore be cautious about relying on consumer induced innovation to achievea particular (target) level of pollution reduction.

In general, the findings supported our initial propositions concerning the expected impact on pollution of changes in standarddeviation and in themean average of environmental preferences respectively. It was found that the larger the standard deviation ofthe initial distribution of consumer classes' preferences, the lower the global level of pollution generated over a given time period;and the higher the mean average of the initial distribution of consumer classes' preferences, the lower the level of pollutiongenerated in a given time period.

In testing these propositions we set up distributions of preferences in such a way that we could consider the merits ofconsumer champions (eco-warriors) in the development and diffusion of new, more environmentally friendly consumerlifestyles. The environmental utility of the most environmentally concerned user class is higher (lower), the greater (less) isthe standard deviation of a heterogeneous population of environmental preferences (the mean average kept constant). Hence,the finding that lower levels of global final pollution are generated for distributions with larger standard deviations – i.e. fordistributions containing environmentally concerned eco-warriors – is an important finding. Of course, this result cruciallydepends on the assumption that individual consumers can freely move from one consumer class to another. In our model thisoccurs through the replicator dynamic algorithm that operates on consumer classes: individual consumers tend to migratetoward consumer classes that have a higher environmental utility. Given the relative weight that this consumer class places onenvironmental performance, firms have a stronger incentive to focus innovation on this aspect than on improving eitherservice characteristics or price.

This set up also allowed us to compare the levels of pollution generated under this scenario with those generated under thealternative scenario that consumer classes are homogeneous with respect to their environmental preferences. The findingsindicate that a higher level of pollution is generated by a heterogeneous population containing with an eco-warrior class with arelatively high level of environmental preference than by a homogeneous distribution with the same level of environmentalpreference. Initially, this result seems surprising. On reflection, however, it becomes clear that the key factor is the time it takes forconvergence to occur within a heterogeneous population of environmental preferences.

This opens an important policy issue. Different policies are required, depending on the distribution of consumers'environmental preferences and on the rate of pollution reduction that is required. If, as in the first scenario, there is significantvariance within the population of environmental preferences, it makes sense to assist the development and diffusion ofnew, radical lifestyles, i.e. encouraging the process by which the lifestyles of the eco-warriors become mainstream. By contrast, if,as in the second scenario, environmental preferences are relatively homogenous then policy should support a shift in the lifestylesof many consumer classes. Finally, the comparative analysis indicates that if large reductions in the rate of environmental pollutionare required then it is more effective to develop policies that encourage moderate changes in the lifestyles of many consumerclasses rather than supporting the (relatively slow) diffusion of a more radical environmental lifestyle.

To summarise, the findings highlight the importance of heterogeneous consumer preferences. Specifically, the initialdistribution of class preferences, and whether (or not) individual consumers can switch between consumer classes over time. Wehave shown that different distributions of consumer preferences for environmental sustainability fundamentally influence thetype innovation undertaken by firms and the rate of paradigm substitution. The net consequence is a quantitative difference in therate of global pollution that is generated over time.

As the findings have shown, the existence of a ‘green’ consumer group (eco-warriors), promoting a new lifestyle, may be akey part of the story, as it was in the history of the car. However, this need not always be the case. We have seen that significantimprovements in global pollution occur in the model when there are differences in the mean levels of environmentalpreferences across all consumer classes. Indeed, one of the most important findings is that such shifts can have a far greater netimpact on pollution than the diffusion of radical environmental preferences through the consumer population. In other words,it may be far better for everyone to immediately improve their consumption patterns slightly, today, than to engage in a morehard core, but more arduous and therefore slower, adjustment towards a zero impact lifestyle. Issues of consumer distributionsand adjustment processes have important implications for government policy. A range of policy options are required, for thesuccess of each option will depend on the distribution of consumer within the population, and on the rates of consumeradjustment.


Looking ahead, other aspects of our co-evolutionary model of paradigm substitutions need to be examined in order to furtherdevelop this discussion. In the second of our papers in this Special Issue [36], we consider the effect of trade-offs in consumerpreferences between direct utility, indirect utility, and environmental utility. In Section 4.2 of this paper (‘General Findings’) wenoted that trade-offs in consumer preferences between environmental performance, direct utility, and indirect utility effect thedirection of firm innovation and, as a consequence, whether environmentally efficient designs are developed within an existingparadigm, andwhether a paradigm substitution occurs. Both, of course, have significant implications for the level of environmentalpollution that is generated over time. For this reason, it is important to consider consumer heterogeneity with respect to thesetrade-offs, and to consider the implications for the findings presented in this paper.

Appendix A

A1. Note on robustness

Fig. A1 maps the results from 50 different runs obtained with a set of benchmark settings, and different random averages overthe 50 runs.

Note that the light series of lines represent the 50 different runs. The dark series of lines represent averages over 50, 25 (2series) and 10 runs (5 series). Given the variability over the different runs, random averages of 50, 25 or 10 series do not showqualitatively different results.

While the spread between the single runs is not negligible, the difference between averages (randomly grouping the results)over different sample sizes is quite small. In particular, averages from the first and last groups of 25 runs almost exactly overlap theaverage over 50 runs. We then map the averages over the first, second, and up to the fifth group of ten runs: although there isevidence of some difference between random averages over 10 runs, this is quite negligible.

We then proceed to analyse the confidence intervals (CI) of the above averages over 50, 25 and 10 runs from the same sample ofsimulation runs. This is presented in Fig. A2 below.

Confidence regions for averages between 50, 25 and 10 runs are represented on one graph. These confidence regions overlapfrom thewidest to the smallest (in different grey scales). Note that the series of averages lies within the confidence region of the 50runs. Hence, they lie underneath the confidence region of the 50 runs series. The intersection of all 10 run averages is larger thanthe 50 runs average CI by one standard deviation.

A comparison of the different confidence intervals indicates that intersecting the confidence regions of the 10 run averages isonly slightly larger than the confidence region of the 50 run averages.

Finally, Fig. A3 compares the standard deviation of the different averages. Only in one instance do the 10 run averages have ahigher standard deviation.

Given the relatively small difference that would be obtained by randomly drawing any 10 runs average with respect toany 50 runs average, and the exponential difference in computation time between 10 and 50 runs, together with the quite large

Fig. A1. Results from 50 different simulation runs, and averages. a. Environmental impact and utility. b. Number of paradigms.

http://dx.doi.org/doi:10.1016/j.techfore.2008.04.012

Fig. A3. Standard deviations of the different averages.

Fig. A2. Confidence intervals (CI) for different averages over 50 runs.


sample size of agents we represent – for this kind of agent-based model – we decide to present results with the 10 runaverages.

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Paul Windrum, PhD is a Reader at Manchester Metropolitan University Business School and a Visiting Professor at Max Planck Institute for Economics, Jena,Germany.

Tommaso Ciarli, PhD is a Post-Doctoral Research Fellow at Manchester Metropolitan University Business School.

Chris Birchenhall, is a Senior Lecturer in Computational Economics at the University of Manchester, founding member of the Society of Computational Economicsand is an associate editor of the Computational Economics journal.

consumer heterogeneity and the development of environmentallyfriendly technologies

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