Contrastación de hipótesis Hipótesis General

Technological and economic replacement approaches, based on classical economic theories of technological change, argue new technologies replace older

technologies because they perform better, subject to the users’ preferences, and have a lower price (Grübler et al., 1999). These models are also based on theories of rational choice and utility maximisation. Individuals and firms are treated as rational actors that will choose a new technology because it will provide more utility than the existing technology (Arrow, 1962).

These approaches also assume that technologies will improve both in performance and in reducing costs due to the effects of learning-by-doing and learning-by-using (Nakićenović, 2002; Grübler and Gritsevskyi, 1997). The theory of learning-by-doing assumes that costs decline with cumulative experience (Arrow, 1962) and this

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decline can be graphed as an experience curve. The cost of a new technology is determined by the formula:

(∑

)

Where is cost of the T^th unit, is the initial unit cost, is the s^th unit produced, and is the experience parameter (Schwoon, 2006a).

Cumulative production is used as a proxy for cumulative experience. An analysis of historical trends indicates that learning rates in the range of 5% to 25% for a doubling in cumulative production can be expected (Leiby and Rubin, 2004;

Schwoon, 2006a)¹². The use of experience curves has been criticised because of the inability to separate the effects of learning-by-doing on changing costs from those of economies of scale or research and development expenditures (Leiby and Rubin, 2004). Experience curves have also been criticised because they relate to changes in costs, but as prices are determined by supply and demand, the changes in costs may not be reflected in the prices of the new technology. There are also difficulties in using experience curves for forecasting when the curve is based on a limited data set because small variations in the estimated value of the experience parameter can lead to major variations in the estimated rate of learning. In turn, this can have a significant impact on projected success of the new technology (Schwoon, 2006a).

The use of these approaches has been criticised because they assume that user preferences are fixed, so they do not account for changing preferences over time. It is also assumed that new technologies will have to compete directly with old

technologies. However, historical analysis shows that rather than directly entering the market, a new technology often emerges in a specialised niche where it has a comparative advantage and then develops from there. These approaches have also been criticised for treating the new and old technologies as completely distinct and competitive. This view does not take account of the possibility that many new technologies are often used to augment the existing technology as an interim

12 IEA (2000) and McDonald & Schrattenholzer (2001) provide a useful summary of the experience curves for energy technologies.

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measure until the time where the new technology improves or the supporting systems and infrastructure develop. An example of this kind of hybridisation is the introduction of the coal fired steam ship, which at first was incorporated into sailing ships (Geels, 2004; Geels et al., 2008)¹³.

The rational choice theory of decision making behaviour that underpins these approaches has been questioned as it is often observed that decision makers act in ways that do not appear to be economically rational (Camerer and Loewenstein, 2004). As a response to this apparent irrational behaviour, the concept of bounded rationality has been developed. This concept holds that rather than implementing a decision making process which will result in the maximum utility, a decision maker often uses decision heuristics which make the decision making process manageable and provide an adequate, if not optimal, answer (Conlisk, 1996; Wilson and

Dowlatabadi, 2007).

Replacement approaches of technological change that incorporate bounded rationality have been developed in evolutionary economics. These models recognise that organisations and firms comprise human beings who have limited cognitive capacities and therefore use rules and heuristic routines to make sense of a complex world. Rules and routines are shared within groups, providing a degree of coordination and stability. These rules and routines form a technological regime and focus the activities of firms and organisations in particular directions. This is the basis of technological path dependency and the stability of technological regimes over time (Dosi and Nelson, 1994). The development of technologies is the outcome of a selection process where the variety in routines and research

directions across organisations results in different innovation outcomes. Successful innovations are selected by the market, survive, and are further developed (Geels, 2004; Metcalfe, 1994). The approaches based on evolutionary economics are focused on the process of innovation, competition between innovators, and the creation of diversity in technologies and technological systems.

13 The PHEV is potentially another example of this approach to technological development.

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These approaches have been criticised as having little to say about the selection process itself, which determines success or failure of technologies, other than to say that it occurs through competition in the market (Geels, 2010).

The literature review indicates that the AFV studies using technological and economic replacement approaches can be categorised as economic cost models, agent based models (ABMs), and discrete choice models.

Economic cost models of replacement

Economic cost models of replacement have been widely used, often in conjunction with logistic diffusion models, to estimate the market penetration of new

technologies (Packey, 1993).

In cost models, the market share of each type of vehicle is determined by a formula of the general form:

∑

Where is the market share of the new technology i, Ci is the cost of the new technology i, and v is a number that reflects penetration ability of the new

technology. A larger value of v indicates a greater penetration or attractiveness of the new technology (Jaccard et al., 2004; Christidis et al., 2003; Packey, 1993). The cost of the new technology is the present value of the capital and operating costs over the life of the new technology, or, in some studies, the period of vehicle ownership. The values for v and the discount rate are usually determined by a literature review, the researcher’s judgement, meta-analysis, or discrete choice surveys (Jaccard et al., 2004). To reflect the uncertainty surrounding the value of v, Christidis et al. (2003) modelled the value by using random draws from a Weibull distribution.

A limitation of the economic cost model is that the rate of penetration of the new technology is solely determined by the capital and operating costs of the new technology and does not take into account any other values that may impact on the uptake of the new technology. Jaccard et al. (2004) attempted to expand the cost

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model by adding a parameter to the cost function that attempted to capture the intangible aspects of the alternative. Baxter et al. (2009) expanded the cost model by first dividing the New Zealand car buying population into groups based on the Rogers typology e.g. innovators, early adopters, etc. Then the cost model was applied to each group, with each group being given a weighting intended to reflect that group’s inherent preference for EVs.

Agent based modelling

A number of recent studies have used ABMs to simulate the market penetration of AFVs. These models are used to simulate the effects of the interactions between car buyers on their purchase decisions (Cui et al., 2011; Eppstein et al., 2011). These models can be extended to also include the effect on car buyers’ purchase decisions from their interactions with vehicle manufacturers (Schwoon, 2006b; Zhang et al., 2011), and the Government and fuel producers (Sullivan et al., 2009). In these models, the consumers, manufacturers, and other groups are treated as self-organising automata whose behaviour can be modelled by using deterministic or stochastic functions. The interactions between the automata are simulated by using differential mathematical equations designed to mimic processes such as

persuasion, sanctioning, and imitation.

The ABMs used in the AFV studies assume that the consumer agents will attempt to maximise their utility given budget constraints and that the utility derived from a type of car will increase with the increasing uptake of the same type of car by the neighbouring agents within the model. A number of studies have used discrete choice models to estimate consumer utility for the vehicle types in ABMs, but with output adjusted to take account of the neighbourhood effects (Cui et al., 2011;

Eppstein et al., 2011).

The power of ABMs is that they can demonstrate interesting emergent and other complex phenomena (Macy and Willer, 2002). ABMs also have the potential to more accurately reflect the complex behaviours that underpin the process of technological change. However, because this real world behaviour is complex and based on psychologies that are both bounded and subjective it is often difficult for

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the modeller to quantify, and validate their models. The complexity demonstrated in ABMs also means that their output is highly sensitive to the initial conditions and to any small variations in the interaction rules as specified by the modeller. As a result, ABMs can demonstrate behaviour that is not observed in the real world (Castle and Crooks, 2006). Zellner (2008) argues that the role for ABMs is not as predictive models, which aim to reproduce patterns in time and space, but to assist in exploring and explaining observed phenomena.

Discrete choice models

Discrete choice models have been used since the 1979 energy crisis to estimate the demand for AFVs (Train, 1980; Beggs et al., 1981) and this research has resulted in a substantial and methodologically sophisticated literature.

Discrete choice models are based on economic theories of utility maximisation and random utility theory. They bring together insights from the field of psychophysical discrimination (Thurstone, 1927), traditional microeconomic theory of consumer choice, and Lancaster’s theory of consumer demand (Louviere et al., 2000, pp. 2-7;

Lancaster, 1966). Discrete choice theory is discussed in more detail in chapter 3.

Discrete choice models estimate the probability of a decision maker choosing one product or service out of a set of products or services based on the decision maker’s preferences for attributes of the products or services and the personal characteristics of the decision maker. The preferences or taste for the attributes in the model are derived from revealed preference data or, in the case of new

products, stated preference data (Hensher et al., 2005, pp. 88-99).

These models are demand models and do not explicitly take into account the effect of constrained product supply on choice behaviour¹⁴. It is also assumed that, when these models are used for prediction, consumers’ preferences will stay constant over the forecasting period (Geels, 2004; Wilson and Dowlatabadi, 2007).

14 It is possible that models based on revealed preference data do take into account the effect of the availability of the product on choice behaviour through the alternative specific constants.

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These models have been used, inter alia, in: (1) economics, where they have been used to study labour force participation, residential location, and house tenure status; (2) marketing, where they have been used to study purchase incidence and brand marketing; and (3) transportation studies, where they have been used to study mode choice, destination choice, car ownership, travel demand (Bhat et al., 2008), and car choice (De Jong et al., 2004).

The first use of discrete choice models to study the demand for EVs was by Train (1980). Train’s study comprised the development of a multinomial logit (MNL) model based on revealed preference data. The MNL model was then used to predict the household demand for EVs entering the United States market from 1980 to 2025. The results of this very early model indicated that there would be a low uptake of EVs, comprising less than 3% of the United States light passenger vehicle (LPV) fleet by 2025.

Beggs et al. (1981) developed an ordered logit model using survey data to estimate the preferences of United States car buyers. Although the model was not used to project EV uptake, survey respondents placed a very high negative value on the limited driving range of EVs, which the lower running costs did not offset.

Hensher (1982) modelled the uptake of EVs in Sydney based on a three attribute model (EV purchase price, petrol fuel cost, and EV driving range). The focus of this study was the estimation of the elasticities of the various attributes in the model and market shares were not estimated, but the model did indicate that driving range and petrol price were significant factors when choosing an EV, but purchase price was not. This latter result was probably influenced by the range of the

purchase price attribute levels used in the survey, which was limited to 30% lower, or 30% higher, than a conventional internal combustion engine vehicle (ICEV).

Calfee (1985) developed a discrete choice model using stated preference data from a small sample collected in Berkeley, California. The model’s attributes consisted of vehicle price, EV driving range, EV top speed, and operating (fuel) costs. The study found that EVs, with the modest performance of the technology of that time, would

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not gain any significant market share. However, the results of the study indicated that EVs with ranges greater than 240 km may have a place in the market.

Train (1986, pp. 134-191) developed an approach to estimate both household car purchase and use behaviour that comprised the development of a hierarchy of choice models. The hierarchy operated sequentially by estimating for a household:

(1) whether a car would be bought or sold; (2) the number of cars bought or sold;

(3) the type of car bought (if two or more cars were bought the choice model was applied to each purchase); and (4) finally the amount of travel undertaken by each car held. The output from the choice models is conditional on the characteristics of the household and took into account the changing character of the household. To apply the model, a number of synthetic households were created that were intended to be representative of the types of ‘typical’ households in the general population. To model car ownership behaviour at the population level, the survey sample of ‘typical’ households was reweighted to reflect the proportions of these types of households in the general population. For each time period, changes in each ‘typical’ household were first estimated, and, then using the updated household characteristics, the car choice behaviour was estimated. Then, the updated household and car ownership characteristics were carried over for use in the next modelling period.

Golob et al. (1996) used the approach developed by Train to model the Californian LPV fleet. They extended the scope of the models to include cars in business fleets.

The models comprised a comprehensive set of car holding, transaction and use models that were used to forecast the uptake and use of biofuel cars, natural gas cars, and EVs into the Californian LPV fleet taking into account the effects of the changing structures of households and businesses, and car technology.

Hazard functions were used to simulate the changing structure of the households over time. These functions estimated, for each household type, changes in

household income, employment status, education, marital status, ethnicity, fertility and mortality, number of children, and ages of the occupants. A duration model was then used to simulate the age of cars held by the households and the

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probability that they would be replaced. A similar approach was used for fleet operators, but to simulate the changes in the profile of different business types, a separate business forecast model was used.

The discrete choice models used by Golob et al. (1996) in the original models were MNL models. However, later research using the same data found that replacing these models with mixed multinomial logit models resulted in a better overall model performance and more realistic substitution patterns between car alternatives (Brownstone et al., 2000; Brownstone and Train, 1999).

Holding and transaction models are complex. The data requirements for these models are significant because a large amount of data is required on the

characteristics of households and businesses. These data are often not available from existing sources and must be collected in addition to the stated preference data on car choices. The data for the Californian study required three survey phases. The first phase was to collect general data on household structure, car ownership, usage, and purchase intentions. The second phase consisted of a questionnaire customised to each household, with more detailed questions on household membership and car usage. This phase also contained two stated choice experiments. The third phase involved a final interview and additional questions about AFVs. For the fleet survey, the researchers had access to the vehicle register of the Californian Department of Motor Vehicles, which allowed them to match cars to fleet owners. This was followed by a two phase survey process similar to the first two phases of the household survey (Golob et al., 1996).

An alternative approach, which is less data intensive, is to: (1) develop econometric models by using aggregate economic data to forecast the demand for new cars; and (2) use the discrete choice model to allocate this demand between different types of car. The output for the discrete choice model can then be used in conjunction with an aggregate car market model to model the changing car stock (De Jong et al., 2004). This type of approach is used by the Oak Ridge National Laboratory in its Transitional Alternative Fuels and Vehicles model (Greene, 2001), the European Commission’s TREMOVE model (De Ceuster et al., 2007), and the United States

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Department of Energy’s AFV Demand Sector sub-module of the United States National Energy Modeling System (Office of Integrated Analysis and Forecasting, 2009).

Leaver and Gillingham (2010) used a similar approach, but incorporated a conditional logit model into a multi-regional integrated energy dynamic systems model. The conditional logit model comprised one attribute that represented the annualised cost of purchasing and operating the vehicle alternative. The value of this parameter was derived from estimates of the elasticity of new ICEVs. To

account for differences in preferences between the AFVs in the model and ICEVs an intrinsic preference parameter was also included in the model. The value of this parameter was adjusted based on the modellers’ judgement.