Data from 2022 indicates that medium- and heavy-duty electric trucks accounted for just 0.4 percent of all new truck registrations in the United States. The widespread adoption of medium- and heavy-duty electric vehicles may be hindered by various obstacles, including the high initial cost of electric trucks and buses, the high cost and limited availability of charging infrastructure, and complexities surrounding electricity tariffs. Regulatory impact analyzes using models such as the Kinetic Vehicle Emissions Simulator, the EMission FACtor model, or TCO-based models often fail to fully capture some of these obstacles, particularly those that require changes in logistics, behavior, or learning by Fleet managers.
For example, the EMission FACtor model is based on sales forecasts from the US Energy Information Administration Annual energy forecast To assess the impact of regulatory measures, the model does not take into account how these regulations affect fleet purchasing decisions. Consider California’s Advanced Clean Truck Regulation, which requires manufacturers to sell a certain percentage of zero-emission vehicles. If there are significant barriers to EV adoption, the model will likely lead to overly optimistic forecasts for new EV sales.
While ownership-based models effectively capture the total cost Visible While obstacles, such as the initial price of vehicles and chargers, do include less obvious obstacles such as fleet owners’ preferences for specific vehicle features, nor transition costs such as those that arise from having to navigate complex electricity tariffs. By not incorporating fleet owners’ preferences or switching costs into their models, regulators risk underestimating costs or overestimating the effectiveness of regulation.
Furthermore, total cost of ownership-based models do not adjust new and used vehicle prices and sales forecasts in response to stringent regulations. Because the regulations target new trucks only, these policies may contribute to increasing the initial costs of purchasing these vehicles (and may affect vehicle prices on the used market); Therefore, these models will not take into account the very real possibility that fleets will continue to use older vehicles for extended periods. In fact, historical data reveals a steady increase in truck life and a decline in scrap rates.
To explore this issue more directly, we analyzed vehicle registration records from five different years: 2002, 2007, 2012, 2017, and 2022. Figure 1 shows five-year scrappage rates for specific classes of medium and heavy vehicles, broken down by vehicle age for those years. Each line represents a different five-year time period. For each age group, the values show the percentage of trucks that were taken off the road during that five-year period. For example, the yellow curve indicates that 10 percent of 50-year-old trucks registered in 2017 were taken off the road before 2022. Furthermore, the endpoints of the curves on the x-axis indicate the age of the oldest vehicles that were registered in each general.
