Effects of battery chemistry and performance on the life cycle greenhouse gas intensity of electric mobility

Lithium traction batteries are a key enabling technology for plug-in electric vehicles (PEVs). Traction battery manufacture contributes to vehicle production emissions, and battery performance can have significant effects on life cycle greenhouse gas (GHG) emissions for PEVs. To assess emissions from PEVs, a life cycle perspective that accounts for vehicle production and operation is needed. However, the contribution of batteries to life cycle emissions hinge on a number of factors that are largely absent from previous analyses, notably the interaction of battery chemistry alternatives and the number of electric vehicle kilometers of travel (e-VKT) delivered by a battery. We compare life cycle GHG emissions from lithium-based traction batteries for vehicles using a probabilistic approach based on 24 hypothetical vehicles modeled on the current US market. We simulate life-cycle emissions for five commercial lithium chemistries. Examining these chemistries leads to estimates of emissions from battery production of 194–494 kg CO₂ equivalent (CO₂e) per kWh of battery capacity. Combined battery production and fuel cycle emissions intensity for plug-in hybrid electric vehicles is 226–386 g CO₂e/e-VKT, and for all-electric vehicles 148–254 g CO₂e/e-VKT. This compares to emissions for vehicle operation alone of 140–244 g CO₂e/e-VKT for grid-charged electric vehicles. Emissions estimates are highly dependent on the emissions intensity of the operating grid, but other upstream factors including material production emissions, and operating conditions including battery cycle life and climate, also affect life cycle GHG performance. Overall, we find battery production is 5–15% of vehicle operation GHG emissions on an e-VKT basis.     

Influence of driving patterns on vehicle emissions: A case study for Latin American cities

On-board real-time emission experiments were conducted on 78 light-duty vehicles in Bogota. Direct emissions of carbon monoxide (CO), carbon dioxide (CO₂), nitrogen oxides (NO_x) and hydrocarbons (HC) were measured. The relationship between such emissions and vehicle specific power (VSP) was established. The experimental matrix included both gasoline-powered and retrofit dual fuel (gasoline–natural gas) vehicles. The results confirm that VSP is an appropriate metric to obtain correlations between driving patterns and air pollutant emissions. Ninety-five percent of the time vehicles in Bogota operate in a VSP between −15.2 and 17.7 kW ton⁻¹, and 50% of the time they operate between −2.9 and 1.2 kW ton⁻¹, representing low engine-load and near-idling conditions, respectively. When engines are subjected to higher loads, pollutant emissions increase significantly. This demonstrates the relevance of reviewing smog check programs and command-and-control measures in Latin America, which are widely based on static (i.e., idling) emissions testing. The effect of different driving patterns on the city’s emissions inventory was determined using VSP and numerical simulations. For example, improving vehicle flow and reducing sudden and frequent accelerations could curb annual emissions in Bogota by up to 12% for CO₂, 13% for CO and HC, and 24% for NO_x. This also represents possible fuel consumption savings of between 35 and 85 million gallons per year and total potential economic benefits of up to 1400 million dollars per year.     

Analysis of fuel consumption and pollutant emissions of regulated and alternative driving cycles based on real-world measurements

Discrepancies between real-world use of vehicles and certification cycles are a known issue. This paper presents an analysis of vehicle fuel consumption and pollutant emissions of the European certification cycle (NEDC) and the proposed worldwide harmonized light vehicles test procedure (WLTP) Class 3 cycle using data collected on-road. Sixteen light duty vehicles equipped with different propulsion technologies (spark-ignition engine, compression-ignition engine, parallel hybrid and full hybrid) were monitored using a portable emission measurement system under real-world driving conditions. The on-road data obtained, combined with the Vehicle Specific Power (VSP) methodology, was used to recreate the dynamic conditions of the NEDC and WLTP Class 3 cycle. Individual vehicle certification values of fuel consumption, CO₂, HC and NO_x emissions were compared with test cycle estimates based on road measurements. The fuel consumption calculated from on-road data is, on average, 23.9% and 16.3% higher than certification values for the recreated NEDC and WLTP Class 3 cycle, respectively. Estimated HC emissions are lower in gasoline and hybrid vehicles than certification values. Diesel vehicles present higher estimated NO_x emissions compared to current certification values (322% and 326% higher for NO_x and 244% and 247% higher for HC + NO_x for NEDC and WLTP Class 3 cycle, respectively).     

Wireless charging in California: Range,recharge, and vehicle electrification

This research evaluated the potential for wireless dynamic charging (charging while moving) to address range and recharge issues of modern electric vehicles by considering travel to regional destinations in California. A 200-mile electric vehicle with a real range of 160 miles plus 40 miles reserve was assumed to be used by consumers in concert with static and dynamic charging as a strict substitute for gasoline vehicle travel. Different combinations of wireless charging power (20–120 kW) and vehicle range (100–300 miles) were evaluated. One of the results highlighted in the research indicated that travel between popular destinations could be accomplished with a 200-mile EV and a 40 kW dynamic wireless charging system at a cost of about $2.5 billion. System cost for a 200-mile EV could be reduced to less than $1 billion if wireless vehicle charging power levels were increased to 100 kW or greater. For vehicles consuming 138 kWh of dynamic energy per year on a 40 kW dynamic system, the capital cost of $2.5 billion plus yearly energy costs could be recouped over a 20-year period at an average cost to each vehicle owner of $512 per year at a volume of 300,000 vehicles or $168 per year at a volume of 1,000,000 vehicles. Cost comparisons of dynamic charging, increased battery capacity, and gasoline refueling were presented. Dynamic charging, coupled with strategic wayside static charging, was shown to be more cost effective to the consumer over a 10-year period than gasoline refueling at $2.50 or $4.00 per gallon. Notably, even at very low battery prices of $100 per kWh, the research showed that dynamic charging can be a more cost effective approach to extending range than increasing battery capacity.     

Age related annual crash incidence rate ratios in professional drivers of heavy goods vehicles

ObjectivesEvidence concerning crash risk for older heavy vehicle drivers is sparse, making it difficult to assess if it is prudent to encourage older drivers to remain in the workforce in a climate of labour shortages. The objective of this study was to estimate annual crash rate ratios of older male heavy vehicle drivers relative to their middle aged peers.MethodsData utilized in this study includes all crashes meeting inclusion criteria involving heavy goods vehicles, categorised as rigid trucks and articulated trucks; this data was recorded by the New South Wales Roads and Traffic Authority. The exposure to the risk of a crash was represented by distance travelled for each vehicle type and year, by age of driver, as estimated by the Australian Survey of Motor Vehicle Use. Negative binomial regression modelling was applied to estimate annual crash incidence rate ratios for male drivers in various age groups.ResultsA total of 26,146 crashes occurred in New South Wales during 1999–2006, involving a total of 54,191 vehicles; removing observations that did not meet the inclusion criteria, 19,736 observations remained representing 12,501 crashes. For rigid trucks, the incidence rate ratio for drivers aged 65+ years, compared to 45–54 year olds, was 0.74 (95% CI 0.51, 0.98). For articulated trucks, the annual crash incidence rate ratio for drivers aged 65+ years compared to 45–54 year olds was 1.4 (95% CI 0.96, 1.9), and that for drivers aged 55–64 years compared to 45–54 year olds was 1.1 (95% CI 0.83, 1.3).ConclusionsOlder male professional drivers of heavy goods vehicles have lower risk of crashes in rigid vehicles, possibly due to accrued driving experience and self-selection of healthy individuals remaining in the workforce. Thus, encouraging these drivers to remain in the workforce is appropriate in the climate of labour shortages, as this study provides evidence that to do so would not endanger road safety.     

China’s electric car frustrations

By 2020, the vehicle population in China will likely exceed 280 million—exacerbating national energy security, urban air pollution, and traffic congestion. In response, many local and regional governments in China are pursuing an expanding array of measures to restrain growth in personal vehicle ownership and, along with the central government, reducing emissions and energy use of vehicles. One prominent strategy is the promotion of new energy vehicles, especially plug-in electric vehicles (PEVs). Large subsidies were offered—up to $27,600 (171,000 RMB) per vehicle in some regions, including almost $9200 (57,000 RMB) from the central government—which suggests that China is making a major commitment to PEVs. But sales have been meager. In 2013, only 17,600 PEVs, mostly buses and utility trucks, were sold, less than 0.1% of total civilian vehicle sales. Several factors explain the failure of PEV sales to take off: (1) protectionism by local governments; (2) uncertainty over which electric-drive vehicle technologies to promote and what consumers are willing to pay, (3) lagging investments in charging infrastructure, and (4) conservative investment behavior by automakers and battery manufacturers. The central government issued directives to local governments in late 2013 to reduce barriers to out-of-town companies, resulting in modest sales increases in early 2014, but a more coherent, broader, and effective set of policies, incentives, and strategies are needed to overcome consumer and industry resistance and the lack of charging infrastructure.     

Preferences for alternative fuel vehicles by Dutch local governments

Using a choice model, we estimate the preferences for alternative fuel vehicles by Dutch local governments. The analysis shows that local governments are willing to pay between 25% and 50% extra for an alternative fuel vehicle without a serious loss of utility. Further, local emissions are an important criterion on which to base a decision, especially for municipalities and provinces. We also calculate the utility for a number of prominent alternative fuel vehicles. We find that show that local governments value the battery electric vehicle and biogas internal combustion engine equally. It is important, however, that the time to refuel for electric vehicles is reduced to about 30 min.     

A new offline optimization approach for designing a fuel cell hybrid bus

In this study a hydrogen powered fuel cell hybrid bus is optimized in terms of the powertrain components and in terms of the energy management strategy. Firstly the vehicle is optimized aiming to minimize the cost of its powertrain components, in an official driving cycle. The optimization variables in powertrain component design are different models and sizes of fuel cells, of electric motors and controllers, and batteries. After the component design, an energy management strategy (EMS) optimization is performed in the official driving cycle and in two real measured driving cycles, aiming to minimize the fuel consumption. The EMS optimization is based on the control of the battery’s state-of-charge. The real driving cycles are representative of bus driving in urban routes within Lisbon and Oporto Portuguese cities. A real-coded genetic algorithm is developed to perform the optimization, and linked with the vehicle simulation software ADVISOR. The trade-off between cost increase and fuel consumption reduction is discussed in the lifetime of the designed bus and compared to a conventional diesel bus. Although the cost of the optimized hybrid powertrain (62,230 €) achieves 9 times the cost of a conventional diesel bus, the improved efficiency of such powertrain achieved 36% and 34% of lower energy consumption for the real driving cycles, OportoDC and LisbonDC, which can originate savings of around 0.43 €/km and 0.37 €/km respectively. The optimization methodology presented in this work, aside being an offline method, demonstrated great improvements in performance and energy consumption in real driving cycles, and can be a great advantage in the design of a hybrid vehicle.     

Isocyanic acid and ammonia in vehicle emissions

Vehicles are considered to be an important source of ammonia (NH₃) and isocyanic acid (HNCO). HNCO and NH₃ have been shown to be toxic compounds. Moreover, NH₃ is also a precursor in the formation of atmospheric secondary aerosols. For that reason, real-time vehicular emissions from a series of Euro 5 and Euro 6 light-duty vehicles, including spark ignition (gasoline and flex-fuel), compression ignition (diesel) and a plug-in electric hybrid, were investigated at 23 and −7 °C over the new World harmonized Light-duty vehicle Test Cycle (WLTC) in the Vehicle Emission Laboratory at the European Commission Joint Research Centre Ispra, Italy. The median HNCO emissions obtained for the studied fleet over the WLTC were 1.4 mg km⁻¹ at 23 °C and 6 mg km⁻¹ at −7 °C. The fleet median NH₃ emission factors were 10 mg km⁻¹ and 21 mg km⁻¹ at 23 and −7 °C, respectively. The obtained results show that even though three-way catalyst (TWC), selective catalytic reduction (SCR), and NOx storage catalyst (NSC) are effective systems to reduce NOx vehicular emissions, they also lead to considerable emissions of the byproducts NH₃ and/or HNCO. It is also shown that diesel light-duty vehicles equipped with SCR can present NH₃ emission factors as high as gasoline light-duty vehicles at both, 23 and −7 °C over the WLTC. Therefore, with the introduction in the market of this DeNOx technology, vehicular NH₃ emissions will increase further.     

Vehicle attribute trade-offs to meet the 2025 CAFE fuel economy target

The literature analyzes changes in vehicle attributes that can improve fuel economy to meet Corporate Average Fuel Economy (CAFE) standards. However, these analyses exclude either vehicle price, size, acceleration or technology advancement. A more comprehensive examination of the trade-offs among these attributes is needed, this case study focuses on technically feasible modifications to a reference 2012 vehicle to meet the 2025 fuel economy target. Scenarios developed to examine uncertainty in technology advancement indicate that expected technology cost reductions over time will be insufficient to offset the costs of additional fuel efficiency technologies that could be used to meet the 2025 fuel economy target while maintaining other vehicle attributes. The mid-price scenario results show the targeted 66% increase in fuel economy from 2012 to 2025 can be achieved with (i) a 10% ($2070) vehicle price increase (lightweight hybrid electric vehicle), (ii) a 31% (2.9 second) increase in the 0–97 km/h (60 mph) acceleration time (smaller engine), or (iii) a 17% (700 L) decrease in interior volume (smaller body) while maintaining other vehicle attributes. These results are consistent with those obtained using methods that generalize the US light-duty vehicle fleet, but are not a forecast of future vehicle attributes because combinations of less perceptible changes to vehicle price, acceleration and size would also be feasible. This study shows there are numerous ways that 2025 fuel economy targets can be met; therefore, the trade-offs quantified provide important insights on the implications of future CAFE standards.     

Socially optimal electric driving range of plug-in hybrid electric vehicles

This study determines the optimal electric driving range of plug-in hybrid electric vehicles (PHEVs) that minimizes the daily cost borne by the society when using this technology. An optimization framework is developed and applied to datasets representing the US market. Results indicate that the optimal range is 16 miles with an average social cost of $3.19 per day when exclusively charging at home, compared to $3.27 per day of driving a conventional vehicle. The optimal range is found to be sensitive to the cost of battery packs and the price of gasoline. When workplace charging is available, the optimal electric driving range surprisingly increases from 16 to 22 miles, as larger batteries would allow drivers to better take advantage of the charging opportunities to achieve longer electrified travel distances, yielding social cost savings. If workplace charging is available, the optimal density is to deploy a workplace charger for every 3.66 vehicles. Moreover, the diversification of the battery size, i.e., introducing a pair and triple of electric driving ranges to the market, could further decrease the average societal cost per PHEV by 7.45% and 11.5% respectively.     

Fuel savings on a heavy vehicle via aerodynamic drag reduction

In this numerical study, the fuel-saving potentials of drag-reducing devices retrofitted on heavy vehicles are analysed. Realistic on-road operations are taken into account by simulating typical driving routes on long-haul and urban distributions; variations in vehicle weight are also considered. Results show that the performance of these aerodynamic devices depend both on their functions and how the vehicles are operated. Vehicles on long-haul routes generally save twice as much fuel as those driven in urban areas. The fuel reductions from using selected devices individually on a large truck range from less than 1% to almost 9% of the fuel cost of a vehicle doing an annual mileage is 80,000 miles.     

How might potential future plug-in electric vehicle buyers differ from current “Pioneer” owners?

The entry of various plug-in electric vehicles (PEVs) into the passenger vehicle sector provides novel opportunities to learn about the potential for future PEV markets. However, early PEV buyers (or “Pioneers”) can substantially differ from present conventional vehicle owners that have interest in purchasing PEVs in the future (or the “Potential Early Mainstream buyers”). To compare the characteristics, preferences, and motivations of Pioneers and Potential Early Mainstream buyers, we draw data from the Canadian Plug-in Electric Vehicle Study, a three-part mixed-mode survey with samples of PEV owners (n = 94) and conventional new vehicle buyers (n = 1754). We identify several significant differences in household characteristics, including income, education, and recharge access. In terms of preferences, Pioneers express extremely high valuation of PEVs and prefer pure battery electric vehicle (BEV) designs over plug-in hybrid electric (PHEV) designs. In contrast, Potential Early Mainstream respondents prefer PHEVs. Both Pioneer and Potential Early Mainstream respondents are similarly cautious about controlled charging programs, but Pioneers place five times as much value on using electricity generated from renewable sources than the Potential Early Mainstream. Pioneers also tend to have different motivations, including significantly higher levels of environmental concern, and higher engagement in environment- and technology-oriented lifestyles. Policymakers, automakers, and electric utilities that anticipate a transition to electric mobility ought to consider how potential future PEV buyers may differ in their vehicle preferences, usage and motivations relative to current PEV owners.     

Statistical modeling of Electric Vehicle electricity consumption in the Victorian EV Trial,Australia

The market share of Electric Vehicles (EVs), an attractive alternative to conventional vehicles, is expected to exceed 30% of all vehicles by 2033 in Australia. Although the expected EV uptake may place greater burdens on electricity networks, the potential impacts contributed by different EV user categories and vehicle models to peak loads at different times during the day are not well understood. This paper addresses the issue through statistical analysis of the charge events in the Victorian EV Trial in Australia as well as modeling the charging behaviors according to participant categories and vehicle models. The analysis was performed on 4933 charge events that were recorded by both private and public Electric Vehicle Supply Equipment. In total, these events consumed over 33 MW h of energy over 12,170 h by the 178 trial participants, out of which about 70% were household participants while the others were fleet participants. Based on a range of EV uptake scenarios and modeled charging behaviors from the trial, the power demand in the summer of 2032/33 was estimated for all of Victoria. The results of the simulations show that the broad scale uptake of EVs produces a relatively small increase in overall power demand (estimated to be between 5.72% and 9.79% in 2032/33).     

Delay to major street through vehicles at two-way stop-controlled intersections

This paper describes a methodology for predicting the delay to major street through vehicles at two-way stop-controlled intersections. This delay is incurred when major street left-turn demand exceeds the available storage area and blocks the adjacent through lane. The through lane blockage problem does not generally occur with significant frequency on streets with divided cross sections that have left-turn bays or lanes; however, it frequently occurs on undivided streets due to their lack of left-turn storage. To minimize this delay, through drivers often merge with vehicles in the adjacent through lane—if there is an adequate gap for them to safely merge into. If there is no merge opportunity, then the through drivers will stay in the inside lane until the queue ahead dissipates. The through vehicle delay predicted by the methodology was found to be relatively small (i.e. less than 5 s veh⁻¹) when compared with delays commonly incurred by non-priority movements at unsignalized intersections. However, when expressed in terms of total vehicle hours of delay, the effect can be quite significant. In general, through vehicle delay increases with increasing approach flow rate and left-turn percentage. However, at flow rates in excess of about 1400 veh h⁻¹, delays increase very rapidly and there is evidence that larger left-turn percentages may have lower delays. ©     

Fuel economy improvements for urban driving: Hybrid vs. intelligent vehicles

The quest for more fuel-efficient vehicles is being driven by the increasing price of oil. Hybrid electric powertrains have established a presence in the marketplace primarily based on the promise of fuel savings through the use of an electric motor in place of the internal combustion engine during different stages of driving. However, these fuel savings associated with hybrid vehicle operation come at the tradeoff of a significantly increased initial vehicle cost due to the increased complexity of the powertrain. On the other hand, telematics-enabled vehicles may use a relatively cheap sensor network to develop information about the traffic environment in which they are operating, and subsequently adjust their drive cycle to improve fuel economy based on this information – thereby representing ‘intelligent’ use of existing powertrain technology to reduce fuel consumption. In this paper, hybrid and intelligent technologies using different amounts of traffic flow information are compared in terms of fuel economy over common urban drive cycles. In order to develop a fair comparison between the technologies, an optimal (for urban driving) hybrid vehicle that matches the performance characteristics of the baseline intelligent vehicle is used. The fuel economy of the optimal hybrid is found to have an average of 20% improvement relative to the baseline vehicle across three different urban drive cycles. Feedforward information about traffic flow supplied by telematics capability is then used to develop alternative driving cycles firstly under the assumption there are no constraints on the intelligent vehicle’s path, and then taking into account in the presence of ‘un-intelligent’ vehicles on the road. It is observed that with telematic capability, the fuel economy improvements equal that achievable with a hybrid configuration with as little as 7 s traffic look-ahead capability, and can be as great as 33% improvement relative to the un-intelligent baseline drivetrain. As a final investigation, the two technologies are combined and the potential for using feedforward information from a sensor network with a hybrid drivetrain is discussed.     

The problem of cold starts: A closer look at mobile source emissions levels

While the phenomenon of excess vehicle emissions from cold-start conditions is well known, the magnitude and duration of this phenomenon is often unclear due to the complex chemical processes involved and uncertainty in the literature on this subject. This paper synthesizes key findings regarding the influence of ambient and engine temperatures on light-duty vehicle (LDV) emissions. Existing literature, as well as analytical tools like the U.S. Environmental Protection Agency’s Motor Vehicle Emission Simulator (MOVES), indicate that while total vehicle emissions have dropped significantly in recent years, those associated with cold starts can still constitute up to 80% for some pollutant species. Starting emissions are consistently found to make up a high proportion of total transportation-related methane (CH₄), nitrous oxide (N₂O), and volatile organic compounds (VOCs). After 3–4 min of vehicle operation, both the engine coolant and the catalytic converter have generally warmed, and emissions are significantly lower. This effect lasts roughly 45 min after the engine is shut off, though the cooling rate depends greatly on the emission species and ambient temperature. Electrically (pre-)heated catalysts, using the bigger batteries available on hybrid drivetrains and plug-in vehicles, may be the most cost-effective technology to bring down a sizable share of mobile source emissions. Trip chaining (to keep engines warm) and shifting to non-motorized modes for shorter trips, where the cold start can dominate emissions, are also valuable tactics.     

Effects of driving style on the fuel consumption of city buses under different road conditions and vehicle masses

The variance in fuel consumption caused by driving style (DS) difference exceeds 10% and reaches a maximum of 20% under different road conditions, even for experienced bus drivers. To study the influence of DS on fuel consumption, a method for summarizing DS characteristic parameters on the basis of vehicle-engine combined model is proposed. With this method, the author proposes 26 DS characteristic parameters related to fuel consumption in the accelerating, normal running, and decelerating processes of vehicles. The influence of DS characteristic parameters on fuel consumption under different road conditions and vehicle masses is quantitatively analyzed on the basis of real driving data over 100,000 km. Analysis results show that the influence of DS characteristic parameters on fuel consumption changes with road condition and vehicle mass, with road condition serving a more important function. However, the DS characteristics in the accelerating process of vehicles are decisive for fuel consumption under different conditions. This study also calculates the minimum sample size necessary for analyzing the effect of DS characteristics on fuel consumption. The statistical analysis based on the real driving data over 2500 km can determine the influence of DS on fuel consumption under a given power-train configuration and road condition. The analysis results can be employed to evaluate the fuel consumption of drivers, as well as to guide the design of Driver Advisory System for Eco-driving directly.     

Regional climate impact of aerosols emitted by transportation modes and potential effects of policies on demand and emissions

The transportation system is one of the main sectors with significant climate impact. In the U.S. it is the second main emitter of carbon dioxide. Its impact in terms of emission of carbon dioxide is well recognized. But a number of aerosol species have a non-negligible impact. The radiative forcing due to these species needs to be quantified. A radiative transfer code is used. Remote sensing data is retrieved to characterize different regions. The radiative forcing efficiency for black carbon are 396 ± 200 W/m²/AOD for the ground mode and 531 ± 190 W/m²/AOD for the air transportation, under clear sky conditions. The radiative forcing due to contrail is 0.14 ± 0.06 W/m² per percent coverage. Based on the forcing from the different species emitted by each mode of transportation, policies may be envisioned. These policies may affect demand and emissions of different modes of transportation. Demand and fleet models are used to quantify these interdependencies. Depending on the fuel price of each mode, mode shifts and overall demand reduction occur, and more fuel efficient vehicles are introduced in the fleet at a faster rate. With the introduction of more fuel efficient vehicles, the effect of fuel price on demand is attenuated. An increase in fuel price of 50 cents per gallon, scaled based on the radiative forcing of each mode, results in up to 5% reduction in emissions and 6% reduction in radiative forcing. With technologies, significant reduction in climate impact may be achieved.     

Evaluating policies to reduce greenhouse gas emissions from private transportation

This paper proposes a model system to forecast household greenhouse gas emissions (GHGEs) from private transportation. The proposed model combines an integrated discrete-continuous car ownership model with MOVES 2014. Four modeling components are calibrated and applied to the calculation of GHGEs: vehicle quantity, vehicle type and vintage, miles traveled, and rates of GHGEs. The model is applied to the Washington D.C. Metropolitan Area. Three tax schemes are evaluated: vehicle ownership tax, purchase tax and fuel tax. We calculate that the average GHGEs per vehicle is 5.15 tons of carbon dioxide-equivalent (CO₂E) gases. Our results show that: (a) a fuel tax is the most effective way to reduce vehicle GHGEs, especially for households with fewer vehicles; (b) a purchase tax reduces vehicle GHGEs mainly by decreasing vehicle quantity for households with more vehicles; and (c) an ownership tax reduces vehicle GHGEs by decreasing both vehicle quantity and miles traveled.