Access the publisher's website Management Science (forthcoming)
The transportation sector’s carbon footprint and dependence on oil are of deep concern to policy makers in many countries. Use of all-electric drive-trains is arguably the most realistic medium-term solution to address these concerns. However, motorist anxiety induced by an electric vehicle’s limited range and high battery cost have constrained consumer adoption. A novel switchingstation- based solution is touted as a promising remedy. Vehicles use standardized batteries that, when depleted, can be switched for fully charged batteries at switching stations and motorists only pay for battery use. We build a model that highlights the key mechanisms driving adoption and use of electric vehicles in this new switching-station-based electric vehicle system and contrast it with conventional electric vehicles. Our model employs results from repairable item inventory theory to capture switching station operation; we embed this model in a behavioral model of motorist use and adoption. Switchingstation systems effectively transfer range risk from motorists to the station operator, who, on account of statistical economies of scale, can better manage it. We find that this transfer of risk can lead to higher electric vehicle adoption than in a conventional system, but it also encourages more driving than a conventional system does. We calibrate our models with motorist behavior data, electric vehicle technology data, operation costs and emissions data to estimate the relative effectiveness of the two systems under the status quo and other plausible future scenarios. We find that the system that is more effective at reducing emissions is often less effective at reducing oil dependence, and the misalignment between the two objectives is most severe when the energy mix is coal heavy and with advanced battery technology. Increases in gasoline prices (by imposition of taxes, for instance) are much more effective in reducing carbon emissions while battery-price reducing policy interventions are more effective for reducing oil dependence. Taken together, our results help a policy maker identify the superior system for achieving the desired objectives. They also highlight that policy makers should not conflate the dual objectives of oil dependence and emissions reductions as the preferred system and the policy interventions that further that system may be different for the two objectives.