Journal article
Electric vehicles: Market penetration and positive externalities
Technological forecasting & social change, Vol.24(2), pp.137-152
1983
DOI: 10.1016/0040-1625(83)90028-8
Abstract
In this paper, we develop a methodology for approximating the market penetration potential for electric vehicles (EVs). The model is dynamic in nature and explicitly accounts for the competitive effects of conventional vehicles. Unlike many previous models, it does not require an estimation of the time required to achieve a 50% market share. Instead, the model allows one to endogenously determine the rate of market penetration, as well as the ceiling level. We note that some electric vehicle characteristics limit marketability to consumers unaffected by the disadvantages associated with operating EVs. These characteristics are accounted for in the model by the use of a substitution index. The addition of the substitution index allows one to explicitly account for variations in prices, climate, and geographic contour, all of which affect market potential. Electric vehicles are expected to result in positive social benefits by way of reduced pollution emissions and because of the concomitant decrease in the consumption of gasoline that market adoption would bring about. However, the social benefits will not be captured in the market and will not, therefore, encourage the use of these alternatives to conventional vehicles. This means that the rate of adoption of electric vehicles will be slower than is socially optimal, which is a potential argument for government subsidies for electric vehicles. In this paper, we explore a method for analyzing the benefits associated with the adoption of electric vehicles.
Details
- Title: Subtitle
- Electric vehicles: Market penetration and positive externalities
- Creators
- Robert Giese - Argonne National LaboratoryPhilip C. Jones - Northwestern UniversityBrent G. Kroetch - American University
- Resource Type
- Journal article
- Publication Details
- Technological forecasting & social change, Vol.24(2), pp.137-152
- DOI
- 10.1016/0040-1625(83)90028-8
- ISSN
- 0040-1625
- eISSN
- 1873-5509
- Publisher
- Elsevier Inc
- Number of pages
- 16
- Language
- English
- Date published
- 1983
- Academic Unit
- Business Analytics
- Record Identifier
- 9984963207702771
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