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1 SUMMARY This report presents work completed for NCHRP 25-25 Task 115: Estimates of Emissions Reductions from Future Fleet Changes for Use in Air Quality Models. The goal of this work was to quantify emission changes of criteria pollutants, mobile source air toxics (MSATs), and greenhouse gases (GHGs) as a result of varying future battery electric vehicle (BEV) and fuel cell electric vehicle (FCEV) adoption rates in the U.S. This work aimed to provide state departments of transportation (DOTs) and metropolitan planning organizations (MPOs) with technical insights, while identifying emission reduction strategies and their relative effectiveness that could inform policy decisions to advance penetration of zero-emission vehicles (ZEV) into the light-duty vehicle fleet. This work included (1) a literature review to identify and review how various programs affect ZEV adoption (ZEVs represent BEVs and FCEVs in this work), and support the development of scenarios used in quantifying future emissions reductions resulting from adoption of ZEVs; (2) development of analysis scenarios used to demonstrate different levels of ZEV adoption given different future policy, consumer, technology, and infrastructure assumptions; (3) application of the Market Acceptance of Advanced Automotive Technologies (MA3T) model (public version V20190404), developed by the Oak Ridge National Laboratory (ORNL), to estimate ZEV populations in 2040 for the analysis scenarios; and (4) modeling of emissions using the MOtor Vehicles Emission Simulator (MOVES2014b) for each analysis scenario to estimate emissions reductions (compared to a Base Case scenario) as a result of increased ZEV adoption. The literature focused on consumer preferences, policies, technology, and infrastructure that impact ZEV adoption. Much of the literature reviewed covered all advanced technology vehicles (ATVs), including hybrid electric vehicles (HEVs) and plug-in hybrid electric vehicles (PHEVs), in addition to ZEVs. Although the results of the literature review include findings for other ATVs, the emissions assessment for this report focuses exclusively on ZEVs. In general, annual ATV sales are expected to increase over the coming decades. In particular, the literature review indicates that passenger vehicles and trucks that are not internal combustion engine vehicles (ICEVs) are expected to increase from approximately 3% of the vehicle population in 2020 to 15% of the population in 2040. That forecasted 2040 market share of 15% differs from the modeling results in this study, which are only for ZEVs (discussed later). While there are many factors responsible for forecasted changes in the number of ATVs, this research identified the following factors as having the greatest importance as drivers and barriers to ATV adoption: ⢠Drivers o High occupancy vehicle (HOV) lane access o High vehicle performance and reliability o Long driving range o Cost parity between ATVs and ICEVs, including purchase cost and fuel cost ⢠Barriers o Higher purchase cost o Lack of home charging availability o Lack of knowledge about BEVs o Limited vehicle model choice and availability o Limited vehicle range
2 o Negative attitude towards new technology Several other factors play a supporting role in driving ATV adoption. Technology changes continue to drive down purchase cost for ATVs. Policies, such as rebates and tax credits, may also reduce the effective purchase cost of ATVs. Infrastructure initiatives that increase the availability of electric vehicle service equipment (EVSE; for example, EV charging stations) and other alternative fuels also help to reduce barriers to consumer ATV adoption. These factors were found to closely match inputs used in MA3T to influence changes in ATV adoption. The MA3T model is a comprehensive consumer choice model that forecasts future ZEV populations by integrating several behavioral models with certain known technology, infrastructure, and policy parameters. The model uses state- and regional-level inputs but is best used for analysis of nationwide outputs because it is calibrated to nationwide data. The modelâs baseline forecasts were comparable to ZEV populations estimated using other data sources, and it was found to be suitable for use in this study. Four key scenarios were developed: 1. Base Case: business as usual (without changes to default MA3T parameter inputs) 2. Substantial Expansion of Infrastructure: expansion of EVSE beyond recent and pending improvements 3. Advanced Use of Incentives: wide implementation of high-impact incentive policies/programs 4. Accelerated Achievement of Cost Parity: accelerated reduction of battery cost and increased gasoline cost The Base Case scenario was used as a point of comparison for the three alternative scenarios with expected greater ZEV adoption. The three alternative scenarios were developed with changes in the underlying factors that would likely result in the largest changes in ZEV adoption. They represent a range of scenarios, including those that reflect historical fluctuations in gasoline price, advancements in ZEV technology supported by recent trends, operational changes in factors such as HOV lane management, and others. While a confluence of factors may lead to synergistic growth in ZEV adoption, in this study, the value of only one input parameter was adjusted in each set of simulations. Although MA3T does have important feedback loops built into the model, modeling combined effects by adjusting multiple parameters in a single simulation was beyond the scope of this study. A total of 49 MA3T V20190404 sensitivity simulations were conducted for this project, each corresponding to one analysis scenario and one or more model input parameters. Three simulations were conducted for each scenario and input parameter(s) combination, representing âLowâ, âMediumâ, and âHighâ adjustments to the default value(s) of the model parameter(s). The parameters adjusted and levels of adjustment were informed by evidence gathered in the literature review. The large parameter adjustments were used to better understand the relative importance of each parameter within the MA3T model; these large adjustments do not necessarily reflect a likely future condition, but rather a possible future condition given technology and known investments as of 2019. The largest parameter adjustments were used to demonstrate the largest possible increases in ZEV adoption given technology that is currently feasible. The analysis is not intended to make a case for any particular future outcome, but to illustrate the effect of each scenario on future emissions. The analysis also does not capture emissions associated with the energy source used for each of the vehicle types, or âwheel-to-wellâ emissions. It focuses solely on changes to vehicle fleet exhaust emissions.
3 The relative increases in ZEV populations modeled by MA3T are generally consistent with the findings in the literature review (e.g., the literature shows that purchase cost is the greatest barrier to ZEV adoption, and the largest ZEV population increases modeled with MA3T [and corresponding reductions in emissions] are for the cost parity scenarios). The modeled percentage of ZEVs relative to the total U.S. light-duty vehicle population in the Base Case scenario was 9% in 2040 (in contrast to the forecasted 15% identified in the literature review that included all ATVs). Although the percentage of ZEVs did not increase from the Base Case for several of the simulations, it increased to 12% on average across all simulations, and to 40% for the simulation with the greatest increase. California leads the 50 states and Washington D.C. in terms of total ATV sales, with 1,269,877 ATV sales (60% HEV, 22% BEV, 18% PHEV, and 0.4% FCEV) between 2011 and 2018. That value is five times higher than ATV sales in Florida (231,360), which accounts for the second most ATV sales for a state over the time period. There is substantial uncertainty surrounding the modeled outcomes presented here. The main objective of this work was not so much to forecast a precise expected future fraction of the vehicle fleet that will become ZEVs, as to enable readers to understand what key factors influence the degree to which ZEVs will penetrate the vehicle fleet. By default, MA3T assumes that there are no FCEVs on the market. To obtain FCEV sales and population estimates from MA3T, numerous input parameters would be required and the model would need to be recalibrated, which was beyond the scope of this project. The 2019 Annual Energy Outlook (AEO) forecasts that FCEVs will account for less than half a percent of the total light-duty vehicle fleet in 2040, and it is highly uncertain what the FCEV technology and supporting infrastructure will be at that time. The MOVES2014b model was used to calculate light-duty vehicle exhaust emissions for all analysis scenarios using vehicle populations and sales estimated by MA3T. Other emission processes, such as start emissions, evaporative emissions, and refueling emissions, were not modeled. The pollutants included in the analysis were criteria pollutants (nitrogen oxides [NOx], carbon monoxide [CO], particulate matter [PM2.5 and PM10]), total gaseous hydrocarbons (HCs), MSATs (1,3-butadiene, acetaldehyde, acrolein, benzene, ethylbenzene, formaldehyde, naphthalene [gaseous and particulate]), and GHGs (carbon dioxide [CO2], methane [CH4], and nitrous oxide [N2O]). Since the emissions focus of this study was vehicle exhaust, the modeling did not include emissions of PM from tire wear, brake wear, and re-entrained road dust, which are independent of vehicle fuel type. An analysis year of 2040 was used for all four modeling scenarios, and ZEV population estimates for the year 2040 were taken from the MA3T model and used with MOVES2014b to calculate emissions for the year 2040. Importantly, while estimated emissions decreased with increased ZEV population in the three alternative scenarios compared to the Base Case, the level of decrease was not as great as the change in total ZEV population. Emissions reductions are driven by the change in conventional vehicle population, which remains a larger portion of the total light-duty fleet even in 2040. For example, if ZEVs account for 10% of the light-duty vehicle population, doubling that percentage to 20% corresponds to only a 10% decrease in conventional vehicle population, and emissions are directly related to the change in conventional vehicle population. The emissions reductions were roughly the same across pollutants in each simulation. The highest level of reduction for any of the scenarios was equal to a 23% decrease in CO2 emissions, associated with vehicle manufacturer cost parity. In general, the maximum modeled emissions reductions achieved across the simulations in the infrastructure scenarios and most Incentive/Policy scenarios were approximately 2-3%
4 in 2040. Although financial incentives like tax credits and rebates contribute to the reduction of overall ZEV cost, the MA3T model generally estimated moderate increases in the ZEV population as a result of changes to those inputs, with the greatest increases due to extension of the duration of rebates. It is possible that the longer a credit or rebate applies, the greater its effectiveness will be, as consumers have a longer period to learn about rebate and tax credit programs and consider how those will affect their net cost for ZEV vehicle ownership. Indeed, findings in the literature review suggest that consumer education plays an important role in making rebates and tax credits effective. The sensitivity testing completed here indicated the relative importance of the various factors represented in the modeling scenarios. The findings suggest that, to support efforts to promote adoption of ZEVs, state DOTs and MPOs should consider the following implications from the simulations (listed in order of importance): ⢠Cost Parity simulations, specifically the High and Medium cases of the vehicle manufacturer cost simulations and gasoline price simulations, resulted in the greatest reductions of light-duty vehicle emissions. ⢠Long-duration rebates in states that did not have a rebate as of 2019 resulted in the next highest reductions of light-duty vehicle emissions. ⢠After the Low case of the gasoline price simulation set, the greatest change in HOV lane access (i.e., extended access to HOV lanes from 2014-2030) had the next highest emissions reductions. DOTs and MPOs may have less influence over ZEV purchase and fuel costs compared to ZEV infrastructure expansion. Infrastructure initiatives that state DOTs might support are important for increasing ZEV adoption, and the modeling results for the infrastructure scenarios show moderate reductions in criteria pollutants, MSATs, and GHGs. Also, DOTs and MPOs may make recommendations for advancing policies that support ZEV adoption, such as HOV lane access and rebate programs, and could provide resources to address the need for consumer awareness and education. This report provides state DOTs and MPOs detailed estimates of the potential effectiveness of various factors that affect ZEV adoption for reducing light-duty vehicle exhaust emissions.
