Association for Public Policy Analysis & Management

Increasing market share of battery electric and plug-in hybrid vehicles imply a rise in demand for electricity. Since emissions from electricity production are not zero and vary by the time of the day depending on the resources used to generate power, it is critical to ensure that households charge their vehicles when less carbon-intensive resources are available. Electricity pricing is an important tool at the disposal of policymakers to affect household charging behavior. This paper studies the impact of a time-of-use price schedule on electric vehicle charging behavior and consequently on carbon dioxide emissions from electricity generation. Using longitudinal data of carbon dioxide emissions in the California ISO region and data from a pricing experiment conducted by a utility company in the same area, a model of carbon dioxide emissions and demand for electricity for EV charging is estimated.

The utility company randomly assigned its customers who owned a Nissan Leaf (an electric vehicle) to three different treatment groups. Each treatment group got a different time-of-use price schedule. The difference is in the ratio between the peak and super off-peak price of electricity. Information from the pricing experiment allows estimation of the price elasticity of demand using the Almost Ideal Demand System (AIDS) model. The price elasticity measure is subsequently used in the panel regression to estimate how emissions may change if electric vehicle owners are shifted from block pricing to time-of-use pricing.

Comparative analysis of emissions under the traditional tiered pricing scheme and time-of-use pricing reveal that total greenhouse gas emissions are lower under the latter pricing strategy. Using the EPA social cost of carbon of $41 per ton, the savings in terms of emission cost is estimated to be $5.80.

The discussion is pertinent as utility companies, on one hand, are increasingly moving towards time-of-use pricing while on the other there is a rising share of electric vehicles on the road.