1. Introduction
As a result of greenhouse gas emissions (GHGs) (mainly CO
2) emitted through human activities, global warming and anthropogenic climate change has drawn worldwide concern [
1]. The transportation sector emitted about 20% of global CO
2 emissions in 2012, and an even higher percentage in developed countries such as the United States, members of the E.U., Japan, and others [
2]. The transport sector produces the second largest share of CO
2 emissions among all sectors in the E.U., in which passenger cars are responsible for about 70% of total CO
2 emissions [
3]. Transportation represented 26% of total U.S. GHG emissions in 2014, and within the transportation sector, light-duty vehicles were by far the largest category, with 61% of GHG emissions [
4]. Lopes Toledo and Lèbre La Rovere [
5] have estimated that individual motorized transport accounts for 60% of total emissions from the urban transportation sector. Vierth et al. [
6] have shown that road transportation contributes by far the most to emission costs in Sweden.
Electric vehicles (EVs) show promise for improving the environmental sustainability of the transport system since, as opposed to conventional vehicles, they have no tailpipe exhaust gas emissions [
7]. Zero-emission vehicles (ZEVs) have been found to potentially reduce greenhouse gas emissions by up to 60% under ideal conditions [
8]. However, different cities may have conditions that are characterized by diversity in landforms, congestion patterns, driving styles, etc., and policies that support the adoption of ZEVs would need to take these differences into account to effectively contribute to CO
2 emissions reduction efforts [
9]. A comprehensive analysis is needed to understand the extent of the benefits that the deployment of ZEVs could generate and how these benefits are spatially distributed for a given region or state. A number of practical tools have been developed for estimating CO
2 emissions from transportation, including MOBILE6, COMMUTER, the Motor Vehicle Emissions Simulator (MOVES), and the Intelligent Transportation Systems Deployment Analysis System (IDAS) [
10]. Some researchers have also built GHG emission models on the basis of car ownership, vehicle kilometers traveled (VKTs), and CO
2 emissions factors [
1,
11,
12,
13,
14]. While these models are capable of estimating CO
2 emissions, they are not suitable for analyzing the effects of e.g. ZEV cost on mode choice, destination choice, travel path choice, and resulting travel patterns.
Travel behavior and the resulting GHG emissions are affected by factors such as fuel economy and the ZEV adoption rate, which consists of a high level of uncertainty. Schipper [
15] has stated that fuel economy technology is not the only factor that can yield significant reductions in CO
2 emissions and that it will be difficult for technology alone to lower CO
2 emissions from the transport sector because of the increasing number of vehicles and vehicle kilometers traveled. Gerard et al. [
16] found that people interested in hybrids are much “greener” than are diesel enthusiasts, and hybrid drivers log fewer annual miles and have a higher percentage of in-city driving. Akar and Guldmann [
17] found that an SUV, a pickup truck, a van, or a hybrid are likely used and result in producing more vehicle miles traveled (VMTs). Yu et al. [
18] examined the rebound effects when replacing current vehicles with a plug-in hybrid and electric vehicles in Japan and found that improvements in vehicle efficiency caused a rebound effect in the transport sector, such as increased vehicle kilometers traveled and frequency of car use, as well as in the consumption of domestic goods. Mishina and Muromachi [
19] have concluded that the potential reductions in CO
2 emissions offered by the higher tested fuel economy of hybrid electric vehicles (HEVs) have been offset markedly by the deterioration in test fuel economy and the direct rebound effects in real traffic over a certain period. Langbroek et al. [
7] found that EV users make significantly more trips and choose driving for a significantly larger percentage of their total travel distance than conventional vehicle users. This research suggests a rebound effect, that is, EVs will still consume a considerable amount of energy and contribute to other external effects such as congestion. Thus, the benefits of EVs in reducing GHG emissions should be explored carefully, considering such rebound effects.
The Greenhouse Gas Emissions Reduction Act (GGRA), enacted by the State of Maryland in 2009 required the state to achieve a 25% reduction in statewide GHG emissions from 2006 levels by 2020, and the GGRA of 2016 set a new benchmark goal of a 40% reduction in emissions from 2006 levels by 2030 [
20]. To what extent could ZEV deployment strategies achieve this ambitious GHG emission reduction goal? This paper aims to answer this question by quantifying the amount of CO
2 emissions from road passenger transport by varying ZEV ownership and cost levels and analyzing whether ZEV deployment strategies could achieve the GHG emissions reduction goal in the state of Maryland by 2030. A modeling platform, the Mobile Emissions Model (MEM), developed by integrating the Maryland Statewide Transportation Model (MSTM) and the Environmental Protection Agency’s MOVES model, was used for this analysis. The MSTM is a multilayer model that works at regional, statewide, and urban levels, and uses a traditional four-step travel forecasting process with the addition of a time-of-day model. MOVES estimates emissions from mobile sources by using data such as climate, fuel economy, and other variables. The MEM estimates transportation emissions by applying emissions rates from the MOVES model to MSTM-generated traffic flows [
21]. We utilized this modeling platform to conduct various ZEV adoption scenarios to gain insight into, e.g., travel behavior changes, VMTs, and carbon dioxide equivalents (CO
2Eqs).
This paper is organized as follows:
Section 2 gives the details of the methods we developed to incorporate ZEVs into the MSTM and MEM and describes a set of ZEV scenarios designed to estimate the impacts on CO
2 emissions.
Section 3 provides a detailed analysis of scenario results at various geographic scales.
Section 4 discusses the practical and policy implications of the results and tries to answer the research question. The last section summarizes the main contributions, limitations, and future research directions.
4. Discussion and Conclusions
In this study, we showed that ZEVs could play a significant role in reducing CO2 emissions from road passenger transport at different levels.
The scenario results presented in this paper demonstrated that all ZEV ownership levels led to an increase in DA trips by ZEV users and moderately longer travel distances because of our reduced operating cost and parking cost assumptions. From the 2015 base year level, total VMT increased by approximately 21.71% to 31.52%, and only in two high-ZEV ownership scenarios did statewide CO2Eq emissions drop by 16.35% and 15.80%, respectively. From the 2030 baseline level, total VMT increased by approximately 0.47% to 8.06%, and the changes in CO2Eq emissions was between −32.50% and −10.92%. Compared to the percentage of households with ZEVs, 14.49% (low-ZEV ownership), 23.18% (medium-ZEV ownership), and 43.14% (high-ZEV ownership), the change in CO2Eq emissions was less than the corresponding increase in percentage of ZEV ownership. The main reason was that we did not consider the impact of truck trips and long-distance trips on CO2Eq emissions, and truck and long-distance trips also make up a large part of all MSTM trips. Thus, in order to achieve a pre-established goal of emissions reductions, more aggressive measures and policies should be implemented, such as encouraging ICEV users to choose ZEVs and using more clean freight vehicles.
In contrast with other research [
1,
11,
13,
25,
33], we calculated the CO
2Eq emissions per unit area and per VMT in each county and per mile by facility type. The estimated results showed that the high-ZEV ownership scenarios could reduce the CO
2Eq emissions per VMT (shown in
Table 5) among all counties and the gaps in CO
2Eq emissions per mile among six road types (shown in
Figure 8). In recent years, public transportation’s role in reducing GHG emissions has gained renewed attention [
34], so convenient and competitive public transport options should be provided to attract more car users to transit, especially in dense metropolitan areas. In counties with high emissions per VMT values, which are rural and where typically more low-income households reside, subsidies can be provided to incentivize ZEV purchasing. The results by road type can guide policies to improve environmental quality and address environmental justice questions. For example, road types with high emissions per mile, such as minor arterials, freeways, and collectors, can be given priority when deploying ZEV charging infrastructure to support their use, and residents along them can be given incentives.
Increasing the use of ZEVs led to lower statewide and regional GHG emissions, and it shortened the gaps in emissions per unit area or per VMT among different counties. The operational costs and parking costs under a ZEV ownership scenario had no significant effect on statewide CO2Eq emissions, likely due to the rebound effect from congestion. To reduce CO2 emissions, priority should be given to encouraging more households to switch to ZEVs. Although our results showed that the operational costs and parking costs had no significant effect on emissions, the lower cost would attract more households to own ZEVs.
This research established a ZEV deployment policy testing model based on Maryland. Our analysis had some limitations. First, all results were concluded under suggested ZEV ownership assumptions: a ZEV ownership estimation model should be established in further work. Second, we used the same nested-logit mode choice model with different operational costs and parking costs for analyzing the behavioral changes of households with ZEVs. Little is known about potential behavioral changes that occur after adopting electric vehicles or behavioral differences between EV users and non-EV users [
7]. Charging facility availability, driving range limitation, and other factors have impacts on ZEV adoption. As data becomes available, ZEVs, as a new mode, should be added to the mode choice model, and the model parameters should be recalibrated and revalidated. Third, there are certainly more variables that may have a significant impact on GHG distribution, such as the deployment of zero-emission trucks and autonomous vehicles. Further research is also needed to determine other variables and their effects on GHG emissions.