Transitions to Alternative Vehicles and Fuels
Transitioning Away from Oil: What Role for Electrics and Plug-Ins?
The fuel substitution approach is advocated by Section b of the Energy Policy Act of EPACT , and has been an important impetus for research and demonstration projects and for fleet programs to help alternative fuels enter the US transportation market. A comprehensive listing of measures including tax credits and state and local initiatives can be found in McNutt and Rodgers Department of Energy DOE estimate the technical and economic feasibility producing sufficient alternative and replacement fuels to replace, on an energy equivalent basis, at least 10 percent of gasoline use by the year ; and at least 30 percent by the year EPACT, a , b.
Petroleum is displaced by the use of neat alternative fuels as well as through the use of reformulated and oxygenated gasolines which contain natural gas, hydrogen, and alcohol and ether-oxygenates. The recent strong interest in hydrogen-fueled vehicles reflects its promise for both gasoline substitution, improved end-use fuel efficiency and a set of diverse domestic supply options. This study determined, among other things, that p. However, this estimated feasibility is based upon a number of assumptions that may not be realized without additional alternative-fuel initiatives.
This is despite efforts by the Department of Energy to promote alternative fuel use in government fleets and other markets and despite the 1.
Transitions to Alternative Vehicles and Fuels
As described in detail below, it is also quite unlikely that the 30 percent displacement goal for the year will be met. This shortfall from the projections based on comparative-static analyses can largely be attributed to an incomplete understanding of the magnitude and importance of certain key dynamic or transitional impediments to alternative fuels. This earlier work provided critical foundations for our transitional analysis. Transitional Analysis As recognized in DOE's own analysis, past studies of the alternative fuel AF and AFV penetration either assumed mature markets with large-scale vehicle production and the widespread availability of alternative fuels at retail stations, or assumed immature markets and small scale production.
Early studies of the AFV market can be grouped into those which are static, single year snapshots Sperling, ; National Research Council, ; USDOE, , and those which are multiyear analyses Fulton , Rubin , and Kazimi a, b , still with limited degrees of dynamic detail. Obviously, the static analyses are limited in that they cannot assess the feasibility or cost of a transition to the new long run equilibrium. Furthermore, in many cases their conclusions, as well as those of most early dynamic models, reflect exogenous assumptions regarding fuel and vehicle prices or AFV penetration rates or both.
Those results, in fact, can turn out to be misleading.
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This is because barriers to new fuels and technologies are real and, for the case of transportation technologies, economically important. The overall objective of the TAFV model is to assess the competitive market outcome over time, without and with possible new policy initiatives. Rather than taking fuel and vehicle prices and penetration rates as an input, they are determined from market conditions.
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Operationally, this is equivalent to maximizing consumer and producer surplus well-being from transportation services provided by the light duty vehicles cars and trucks and a variety of possible fuels. The TAFV model characterizes interactions among fuel providers, vehicle producers, fuel retailers, private vehicle purchases and fleet vehicle programs. Each of the supply sectors is represented by a single period cost function defined for each time period, region, fuel, and vehicle type.
Examples include: vehicle production costs; fuel production or conversion costs; fuel retailing costs; raw material supply costs; and sharing or mix costs associated with vehicle and fuel choices. The cost functions summarize the way in which changing levels of activities, inputs, and outputs affect the costs for each supply module, and implicitly define the cost minimizing behavioral relationships among the model's variables. Benefits in this model come from the satisfaction of final demand for transportation services as determined from projections of light duty vehicle fuel use excluding diesel for to given in the Annual Energy Outlook.
The total demand for light duty fuel is satisfied by the use of existing used vehicles and the purchase and use of new vehicles. The use of older vehicles is limited by the stock of each vehicle type given a fixed, age adjusted use profile. Each year, to the extent that existing vehicle stocks are insufficient to satisfy the demand for transportation services, a mix of new vehicles is purchased.
Vehicle choice is based on upfront vehicle capital costs, nonprime vehicle attributes and expected lifetime nested fuel choice costs. Fuel choices must be made for the vehicles that are dual or flexibly fueled. Since vehicle and fuel choice is endogenous, it is important to specify which fuel and vehicle characteristics are considered in the fuel and vehicle choice submodules, and which characteristics are endogenously determined.
A more detailed presentation of assumptions and data sources can be found in Leiby and Rubin , , and Key Transitional Phenomena From preliminary analysis and discussions with experts, we identified key areas that could strongly affect the transition to alternative fuels and vehicles.
These include the costs to consumers of limited fuel infrastructure and retail availability of alternative fuels; scale economies for vehicle production and fuel retailing; the implications for consumer behavior of initially limited AFV model choice and diversity; the prospect for technological improvement cost-reduction through learning-by-doing; and any costs to consumers from being unfamiliar with a new technology.
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Because of their potential importance, all these transitional barriers, except for those related to consumer unfamiliarity, have been explicitly modeled. We did not model the costs of consumer acceptance for new technologies since we had little information to make realistic parameter estimates. As our results below suggest, omitting this additional possible cost is unlikely to have altered any of our qualitative results, since the AFV market has a difficult time getting started given the transitional barriers that we do include.
There is, however, little empirical evidence as to the possible size of these costs. Our approach is to use work by Greene who asked the following question in two national surveys: "Suppose your car could use gasoline or a new fuel that worked just as well as gasoline. If the new fuel costs 25 or 10, or 5 cents LESS per gallon but was sold at just one in 50 or 20, or 5 stations, what percent of the time would you but this new fuel? Fuel Availability IV. The vehicles are either dedicated to a particular fuel type or are capable of using both gasoline and the respective alternative fuel.
The one exception is electricity, which is a direct fuel input only to dedicated EVs. AFV costs are calculated from engineering-economic estimates of the incremental cost of each AFV fuel technology compared to conventional vehicle technology EEA, Here "mature" means that, for a given production scale, further production experience will not reduce per unit vehicle production costs at a rate significantly faster than it would for conventional vehicles.
There do exist, however, substantial economies of scale in vehicle production. That is, there are sharp reductions in per unit vehicle cost with larger scale production. Scale economies occur greatly around 10, vehicles produced per year, with full economies are achieved at , vehicles per year. We therefore model per unit vehicle production costs as a declining function of the installed production capacity available in each year. The volume of production in any given year is constrained by the level of cumulative capacity investment less capacity decay.
This means that vehicle prices and manufacturing capacity are endogenous variables. This has the advantage of admitting the positive feedback effects from policies that encourage the early adoption and hence larger scale production of AFVs. Offering, for example, methanol fuel technology on only a single model will put methanol vehicles at a disadvantage compared to gasoline vehicles, all else equal.
At the same time, offering methanol capability on several different models is expensive because it lowers plant scale for any overall level of production. Thus, during the transition period for a new vehicle technology there is an inherent tension between providing model diversity and achieving economies of scale. Rather than predetermining the number of makes and models offered with alternative fuel capability, we endogenize the level of model diversity by balancing the additional production costs off against the additional consumer satisfaction.
This is accomplished by defining a variable that represents the number of makes and models for each vehicle-fuel type produced.
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On the vehicle production side we divide the total industry production capacity for each vehicle-fuel type by this diversity variable to get average production scale for that vehicle type; on the consumer side we incorporate the diversity variable into our multinomial choice framework. The value of diversity to consumers depends on the order in which vehicle manufacturers introduce a new fuel or vehicle technology to their existing model lines.
This is because different models have market penetrations that vary from a few thousand per year for specialty cars to well over one hundred thousand for some popular cars and pickup trucks. In the simulation model we assume that the AF technology is offered on the most popular model first. Thus the significance of the initially limited model diversity for new vehicle technology depends upon the strategy that manufacturers adopt when introducing the technology.
New approach to modeling large-scale transitions to alternative fuels and vehicles
If the technology is introduced on a new, unfamiliar, and made-to-purpose vehicle such as Honda initially did with the Insight HEV , then the deterrent effect of limited model diversity is far greater than if manufacturer takes a chance by introducing the technology on its most popular vehicle such as the Honda subsequently did with the hybrid Civic. This phenomenon is observed in various industrial situations where it is described as a learning curve, progress function, or experience curve.
The theory of LBD was first exposited by Arrow At the same time, however, these rates must be used with great caution.
This is because, as McDonald and Schrattenholzer note, the empirical literature varies in its methodologies and data sources by which learning rates are calculated. Sometimes the dependent variable is price, rather than cost, and price is influenced by supply and demand factors not related to learning.
Notwithstanding these limitations, LBD, as documented the empirical literature, appears to be an important component of technology change and cost reduction. The existence of substantial learning may also be important for determining good public policies designed to spur new technologies. The Partnership for a New Generation of Vehicles and FreedomCAR programs are classic examples of this research-based approach to advanced automotive design.
If cost reductions are also gained via LBD, then the impact of learning and technological change on the timing of abatement efforts is ambiguous. The same reasoning may be applied to policies for promoting new vehicle technologies. Depending on the particular technologies and assumptions, it may be optimal to act sooner, implement technologies, to learn and thereby lower future costs.
If the endogenous learning-by-doing rate is sufficiently rapid, LBD proponents argue that forcing a sharp divergence from existing technologies by performance mandates could induce otherwise uneconomic technologies to become economically viable.