2021

Ji Luo (jluo006@ucr.edu), University of California, Riverside
Kanok Boriboonsomsin, University of California, Riverside
Barry Wallerstein, University of California, Riverside
Chao Wang, University of California, Riverside
Matthew Barth, University of California, Riverside

Intelligent Transportation System (ITS) technology is increasingly used to improve traffic safety and mobility. In recent years, a number of ITS applications have also been developed to reduce environmental impacts from greenhouse gases and other air pollutant. Typical eco-friendly ITS applications (e.g., eco-routing) focus on reducing vehicle fuel consumption and emissions. However, the impact of pollutant emissions from vehicles on public health also depends on how much of those emissions disperse into communities where people live, work, and participate in outdoor activities, and be exposed by community members. To date, eco-friendly ITS applications have rarely been developed from a pollutant exposure reduction viewpoint. In this study, we have developed a vehicle routing method for reducing pollutant exposure to the localized population along the roadways, and applied it to heavy-duty diesel truck trips in the Carson and East Los Angeles neighborhoods in Southern California. For each trip, we calculated the low exposure route and compared it to the shortest distance route. For the Carson neighborhood, the low exposure route would result in a travel distance increase of no more than 10% in 226 of 1,100 trips (21%). For these trips, the low exposure route would reduce population exposure to fine particulate matter (PM 2.5 ) and nitrogen oxides (NO x ) emissions by about 40%. For the East Los Angeles neighborhood, such an effective low exposure route was found in 25 out of 224 trips (11%). For these trips, the low exposure route would reduce population exposure to PM2.5 and NO x emissions by about 49%.

Weichang Yuan (wyuan5@ncsu.edu), North Carolina State University
H. Christopher Frey, North Carolina State University
Tongchuan Wei, North Carolina State University

Eco-driving offers opportunities to improve fuel efficiency and mitigate tailpipe emissions for existing vehicles and future autonomous vehicles (AVs).  However, eco-driving related fuel use and emission rates (FUERs) reduction potential has not been quantified based on achievable improvements in vehicle operation relative to average real-world driving.  The objective is to quantify light-duty gasoline vehicle FUERs reduction potential based on real-world speed trajectories and eco-driving.  Inter-run variability in 232 measured trajectories was quantified for an example signalized intersection road segment.  Segment-average FUERs of CO 2 , CO, hydrocarbons (HC), NO x , and particulate matter (PM) for each trajectory were estimated based on a vehicle specific power modal model calibrated to 15 U.S. Tier 3 passenger cars.  For each species, eco-driving reduction potential was quantified for the trajectory that had the lowest rate compared to the average rate estimated among all trajectories.  Multiple sensitivity cases were analyzed based on differing constraints for entry and exist speeds, and differing metrics for eco-driving including the best trajectory, the n th best trajectory, or the partial mean of multiple trajectories.  Reduction potential was sensitive to differences in entry and exit speeds.  The most robust estimate of reduction potential was based on the partial means of trajectories that were in the central 50% range of the distributions of entry and exit speeds.  The reduction potential was 21% for fuel use and CO 2 , 40% for CO, 20% for HC, NO x , and PM.  The best eco-driving trajectory can differ among species.

Kyoungho Ahn, Virginia Polytechnic Institute and State University (Virginia Tech)
Youssef Bichiou, Virginia Polytechnic Institute and State University (Virginia Tech)
Mohamed Farag, Virginia Polytechnic Institute and State University (Virginia Tech)
Hesham Rakha, Virginia Polytechnic Institute and State University (Virginia Tech)

This paper develops and investigates the impacts of multi-objective Nash optimum (user equilibrium) traffic assignment on a large-scale network for battery electric vehicles (BEVs) and internal combustion engine vehicles (ICEVs) in a microscopic traffic simulation environment. Eco-routing is a technique that finds the most energy efficient route. ICEV and BEV energy consumption patterns are significantly different with regard to their sensitivity to driving cycles. Unlike ICEVs, BEVs are more energy efficient on low-speed arterial trips compared to highway trips. Different energy consumption patterns require different eco-routing strategies for ICEVs and BEVs. This study found that eco-routing could reduce energy consumption for BEVs but also significantly increases their average travel time. The simulation study found that multi-objective routing could reduce the energy consumption of BEVs by 13.5%, 14.2%, 12.9%, and 10.7%, as well as the fuel consumption of ICEVs by 0.1%, 4.3%, 3.4%, and 10.6% for “not congested, “slightly congested,” “moderately congested,” and “highly congested” conditions, respectively. The study also found that multi-objective user equilibrium routing reduced the average vehicle travel time by up to 10.1% compared to the standard user equilibrium traffic assignment for the highly congested conditions, producing a solution closer to the system optimum traffic assignment. The results indicate that the multi-objective eco-routing can effectively reduce fuel/energy consumption with minimum impacts on travel times for both BEVs and ICEVs.

Ata Khan, Carleton University
Greg Kent, City of Ottawa
Omar Choudhry, City of Ottawa

The objective of Eco-Drive is to reduce fuel consumption and resulting emissions using technology and favourable human factors. On the technology side, communication and traffic control technologies have advanced well in terms of capability and their costs have become affordable for supporting infrastructure-to-vehicle (I2V) connection for eco-driving in a signalized network. On the human factors side, there is a growing interest across the world in advising drivers to take eco-drive actions by making the effective use of the green phase of the signal cycle time in order to save fuel and reduce emissions. This paper describes large scale real-world research studies in Ottawa (Canada) in this subject, sponsored by Transport Canada, Ontario Ministry of Transportation, and the City of Ottawa. The technology and methods that support the Green Light Optimized Signal Advice (GLOSA) research were defined. Field study data were analyzed for speed trajectories, fuel consumption, and GLOSA compliance. Greenhouse emissions and fuel cost changes were computed. An anonymous questionnaire study investigated driver perception of the usefulness of the signal data displayed on an in vehicle unit (IVU) as advice on driving adjustment decisions made under prevailing traffic conditions. Results are favourable in terms of user acceptance of GLOSA, reduction in fuel consumption, emissions, and fuel cost. The GLOSA data analysis and driver questionnaire results have mutual consistency. The findings of simulation and on-line research studies reviewed in this paper can be verified with the results of the large-scale real world investigations reported in this paper.

Ivana Tasic, Chalmers University of Technology
Paula Jungbjer, Chalmers tekniska hogskola Institutionen for arkitektur och samhallsbyggnadsteknik
Mattias Blomgren, Chalmers tekniska hogskola Institutionen for arkitektur och samhallsbyggnadsteknik
Bara Gudmunsdottir, Chalmers tekniska hogskola Institutionen for arkitektur och samhallsbyggnadsteknik

The electric urban transit is a growing research area, and a major interest for sustainable transportation solutions industry. Electrification, together with congestion charging, and sustainable mobility management techniques, is a path towards reducing traffic emissions. This study explores a method for facilitating urban transit electrification by using a zone management system which implements low emission and low speed zones, with the purpose of reducing traffic emissions and traffic injuries. The zone management system implemented on an electric bus line in Gothenburg, Sweden is observed. The research team explored macroscopic speed-emissions relationships and total traffic emissions sensitivity to traffic composition and speeds. Then a microscopic simulation model was developed to evaluate the possibilities to improve the existing zone management system by accounting for traffic-related and other air pollution sources. The study results show that the implementation of the current spatially fixed low emission zones is feasible in stable traffic conditions for low and high-speed range values and will lead to effective emission reduction. For urban traffic conditions with larger speed variability a dynamic zone management system primarily focused on speeds is worthy of exploring. Further research efforts should focus on better integration of various air pollution data sources to make urban transit lines more sensitive to environmental conditions.

Namita Singh (namita.msu@gmail.com), Indian Institute of Technology, Bombay
Trupti Mishra, Indian Institute of Technology, Bombay
Rangan Banerjee, Indian Institute of Technology, Bombay

The present study analyzed emission from India's road transport sector for the reference year 2016 and the target year 2025 using the bottom-up activity approach model to analyze the mitigation potential of retrofit and scrappage programs in 2025 on the pollutants CO2, CO, PM and NOx from vehicle categories. The analysis gave the share of newly registered vehicle categories and identified the significant emitters from the given years. The bottom-up approach was also used to predict the future stock of private, public, IPT, and freight vehicles in 2025. According to the results, the retrofit program was more effective in reducing vehicle pollution than the scrappage program. Retrofit programs such as the shift to BS-VI from BS-IV, increase in CNG vehicles, and fuel efficiency improvement reduced vehicle emissions significantly compared to the removal of old vehicles from the fleet. The study suggests a combination of different strategies with co-benefit effects for effective reduction of CO2 (greenhouse gas) and CO, PM, and NOx (air pollution) emissions from the road transport sector of India.

Mohammad Miralinaghi (smiralin@purdue.edu), Purdue University
Gonçalo Homem de Almeida Correia, Technische Universiteit Delft
Sania E. Seilabi, Purdue University
Samuel Labi, Purdue University

Electric vehicles (EVs) are generally considered as a viable alternative to internal combustion engine vehicles (ICEVs) because EVs can help curb vehicular emissions in urbanized regions. However, transport agencies face the challenge of designing the location and capacities of EV charging stations in metropolitan areas, in a manner that addresses the decision-related concerns of the agencies and the road users. This paper proposes a framework to address this question by managing travel demand. This study incorporates the vehicle-purchasing behavior in combination with charging/refueling station locations and route decision-making behavior of travelers. In this framework, the transport agency decision-maker seeks to gradually provide new electric charging stations at selected locations and/or repurposing existing gas stations. A bi-level mathematical model is developed. At the upper level, the transport decision-maker seeks to minimize vehicular emissions by determining the optimal locations of the electric charging stations and their optimal capacities subject to a budget constraint. At the lower level, travelers choose the vehicle type (ICEV vs. EV) and routes, and a diffusion model is used to characterize such choices. The bi-level model is solved using an active-set algorithm. The results of the numerical experiments suggest that urban EV patronage, and hence, vehicular emissions, are very sensitive to the availability and capacity of EV charging stations. The results of the paper can be used by transport agencies to achieve optimal design (location and capacities) of EV charging stations and to support transport vehicle policies that contribute to the long-term reduction of urban emissions.

Abdullah Alarfaj (aarfaj@andrew.cmu.edu), Carnegie Mellon University
W. Michael Griffin, Carnegie Mellon University
Constantine Samaras, Carnegie Mellon University

Deeply decarbonizing the transport sector is an essential element in any climate stabilization scenario, but requires a major transition in energy use, vehicle technology, and enabling infrastructure. This uncertainty presents a challenge for transportation decarbonization decision-making and identifying the robust pathways and barriers to achieving climate policy objectives is needed. This research examines the pathways for meeting transport climate mitigation targets in the U.S. under deep uncertainty by assessing the implications of transitions in the passenger transportation sector. Using a robust decision-making (RDM) method, we examined two strategies on when to introduce fuel economy improvements, electricity decarbonization, and EV adoption. The Immediate Action strategy represents introducing these improvements immediately, while the Delayed Action follows base case projections, then starts the interventions in year 2024. Further, we considered three distinct scenarios for effects of COVID-19 on LDV sales and VMT by assuming varying rates of recovery. We find that even with Immediate Action, only 23% of the 1000 simulated LDV future cases achieve a midcentury climate target of at least 80% CO 2 reduction from 2005 levels. Out of these cases, the requirements for maximum allowable electricity carbon intensity and minimum travel electrification were 220 g CO 2 /kWh and 69% miles electrification, respectively. Delayed Action increased cumulative CO 2 emissions almost all the simulated futures and it caused the 2030 emissions reduction goal to be infeasible. The few cases for which the Delayed Action resulted in emission reductions were ones with delayed interventions being less stringent than the base case projection.

Bobin Wang (bobinwang@bjtu.edu.cn), Ecole Polytechnique de Montreal
E. Owen Waygood, Ecole Polytechnique de Montreal
Ricardo Daziano, Cornell University
Zachary Patterson, Concordia University

Emissions from motorized vehicles are a major source of greenhouse gas emissions that could be reduced by purchasing more efficient vehicles. The objective of this study is to determine the effects of framing of GHG emissions information on people’s willingness-to-pay for transportation emissions reductions. Differences in response strength by province and attitudes towards climate change are also considered to understand the heterogeneity of the population in Canada. Six different framing techniques were developed following goal framing theory and applied to the current Natural Resources Canada (NRCan) vehicle labels for gasoline, plug-in hybrid, and electric vehicles. Previous work applied gain and norm framing. In this experiment, hedonic framing is added along with policy questions on carbon taxes and higher reduction objectives. Discrete choice experiments with 1,800 Canadian drivers were used to determine willingness-to-pay (WTP) for CO2 emissions reductions. The results show that all of the framings led to higher WTP than the current NRCan label. Of the frames tested, the two hedonic additions of color and emoticons resulted in the greatest increases in WTP. A significant difference was found between residents of different provinces, with residents of Quebec having WTP values (with the highest WTP of nearly CA$600/ton, roughly US$ 400/ton)) hundreds of dollars higher than the western provinces of Saskatchewan and Alberta where belief and support are lower for climate change. This research will directly inform policies on how best to present information to consumers that will reduce greenhouse gas emissions.

Prasanna Humagain, Utah State University
Patrick Singleton (patrick.singleton@usu.edu), Utah State University

Much research has shown how transportation sources contribute to air pollution, which negatively affects the environment and public health. In this paper, we investigate an understudied topic—how air quality or air quality perceptions impact travel behaviors or transportation choices—with a unique focus on individual-level (rather than aggregate-level) data. This allows us to incorporate perceptual aspects that can inform the design of effective behavior-based strategies. Specifically, we utilize a multi-day travel diary dataset, collected during winter 2019 from over 300 residents of Cache Valley in northern Utah, a small urban area that is in non-attainment for small particulate matter due to wintertime inversions. Our panel data regression analyses find no changes in the number of trips or total travel time that participants reported on days with better or worse air quality, suggesting that individual-level travel behavior is fairly stable regardless of air quality levels. Perceived air quality was positively correlated with measured air quality, and seemed to be affected by awareness of air quality impacts. Overall, our results suggest that residents are somewhat aware of air quality issues (but perhaps not air quality programs), but do not make measurable travel behavior changes in response to episodes of poor air quality (likely due to various constraints). “Hard” policies that involve restrictions and disincentives may be more effective than “soft” informational and encouragement strategies, but they receive less public support, which challenges efforts to improve air quality through travel behavior change.

Xinghua Liu, Tongji University
Ye Li, Tongji University
Wenxiang Li, University of Shanghai for Science and Technology
Jing Fan, Tongji University
Zhiyong Shen, Tongji University

Alarming levels of air pollutions from transportation sector impose a substantial health hazard on people worldwide. With the easy accessibility of information and communications technologies, ridesplitting, a form of ridesourcing services via smartphone applications, enables passengers with similar origins and destinations to be matched to the same driver. It is regarded as a promising travel mode that can mitigate pollutant emissions. However, due to a lack of quantitative analysis, the environmental benefits of ridesplitting have not been rigorously justified. As vast amounts of detailed datasets become increasingly available, this paper quantifies environmental benefits of ridesplitting leveraging observed ridesourcing data in Chengdu of China as a case study. First, by computing the travel distances of both ridesplitting trips and the corresponding trips under non-ridesplitting condition based on the corrected trajectory and order datasets, the distribution of travel distances saved by ridesplitting is obtained. Then, CO, HC, and NOx emission factors are calculated by a localized MOVES model, and emission reductions from each ridesplitting trip can be analyzed. Results highlighted that 90.85% of ridesplitting trips can save travel distance. The average travel distance of ridesplitting trips is 13.371 km, constituting a decrease of approximately 22% as compared with that of non-ridesplitting. In addition, each ridesplitting trip can reduce CO, NOx, and HC emissions by 11.903 g, 0.774 g, and 1.600 g on average, respectively. This study provides an improved understanding of the quantitative benefits of ridesplitting and theoretical guidance for government decision-making in green transport planning.

Serena Alexander (serena.alexander@sjsu.edu), San Jose State University
Asha Agrawal, San Jose State University
Benjamin Clark, University of Oregon

This paper focuses on how cities can use climate action plans (CAPs) to ensure that on-demand mobility and autonomous vehicles (AVs) help reduce, rather than increase, greenhouse gas (GHG) emissions and inequitable impacts from the transportation system. We employed a three-pronged research strategy involving: 1) an analysis of the current literature on on-demand mobility and AVs; 2) a systematic content analysis of 23 CAPs and general plans developed by municipalities in California; and 3) a comparison of findings from the literature and content analysis of plans to identify opportunities for GHG emissions reduction and mobility equity. Findings indicate that policy and planning discussions should consider the synergies between AVs and on-demand mobility as two closely related emerging mobility trends, as well as the key factors (e.g. vehicle electrification, fuel efficiency, use and ownership, access and distribution, etc.) that determine whether deployment of AVs would help reduce GHG emissions from transportation. Additionally, AVs and on-demand mobility can potentially contribute to a more equitable transportation system by improving independence and quality of life for individuals with disabilities and the elderly, enhancing access to transit, and helping alleviate the geographic gap in public transportation services. Although municipal CAPs and general plans in California have adopted several strategies and programs relevant to AVs and on-demand mobility, several untapped opportunities exist to harness the GHG emissions reduction and social benefits potential of AVs and on-demand mobility.

Zoe Le Hong, University of British Columbia
Naomi Zimmerman (nzimmerman@mech.ubc.ca), University of British Columbia

Connected and autonomous vehicles (CAVs) are soon to become a part of roadways, and much like the introduction of the automobile, are predicted to massively transform the transportation industry, trends in mobility habits, and supporting infrastructure. This study explores total vehicle fleet emissions changes due to increasing levels of CAV diffusion, over the years 2030 to 2040 in Metro Vancouver, using the US Environmental Protection Agency’s MOtor Vehicle Emission Simulator (MOVES), a motor vehicle emission modeling system. We assessed the impacts across a range of scenarios and investigated the relative impacts of vehicle kilometers travelled, transit use, and diffusion rate with a local sensitivity analysis. The scenarios encompassed a range of possible effects of CAVs on traffic patterns, which are a reflection not only of the vehicle-level emissions changes of CAVs, but of user modality choices in response to autonomous transportation. We found that CAVs have the potential to significantly impact future emissions in both positive and negative ways. In the best case, CAVs decreased greenhouse gas emissions by an additional 45% compared to no-CAV conditions in 2040. Conversely, a 10% increase in greenhouse gas emissions could be realized if autonomy provokes increased use of personal vehicles. This trend is similar for other pollutants, with effects ranging from emissions savings of up to 66% (NOx ) to emissions increases of up to 21% (PM10 ). The sensitivity analysis indicates a significant impact of VKT habits on emissions, and shows that impact on emissions is most significant between 1-5% diffusion levels.

Jeffrey Ang-Olson, ICF
Philip Sheehy, ICF
Alison Linder, Southern California Association of Governments
Steven Smith, San Bernardino County Transportation Authority

San Bernardino County, California, faces urgent challenges associated with air quality and climate change. Public agencies have established aggressive goals to reduce nitrogen oxide (NOx) and greenhouse gas (GHG) emissions from on-road vehicles. With a variety of vehicle technology and fuel options available to reduce emissions and their different benefits and costs, government agencies need more information to support a transition to cleaner vehicles and fuels. This analysis was intended to help these agencies with their decision making. The paper describes the development and application of an analysis tool for the purpose of quantifying the emissions and cost impacts of alternative paths to clean vehicle and fuels implementation. Five scenarios are analyzed and compared to a baseline scenario through 2040. None of the scenarios achieve the NOx and GHG emission reduction targets for 2030 that are consistent with state and regional goals, highlighting the challenging nature of those targets. Scenarios involving accelerated introduction of electric vehicles and, for medium and heavy-duty vehicles, natural gas vehicles can both achieve large NOx and GHG reductions at similar net costs. A more aggressive electrification scenario can achieve 2040 GHG emissions reductions commensurate with state goals. A scenario involving greater use and lower carbon intensity of liquid biofuels, coupled with the introduction of low-NOx diesel engine technology, can also achieve significant NOx and GHG emission reductions. However, unlike electrification and natural gas scenarios, this scenario does not result in net cost savings because it would not reduce vehicle operating costs.

Jonn Axsen (jaxsen@gmail.com), Simon Fraser University
Michael Wolinetz, Navius Research

We explore the role that road pricing could play in achieving deep GHG mitigation targets, including cordon zones, fuel taxes, VKT fees, and ZEV zones. Focusing on the city government perspective, our framework summarizes evidence on policy effectiveness (reduction of GHGs and VKT), as well as cost-effectiveness, equity, political acceptability, and implementation process – while also considering potential policy interactions. The appropriateness of road pricing design varies widely by region, though we offer some general findings. First, stringent road pricing can be effective for GHG mitigation in the short- and long-run, especially for passenger vehicles (less so for freight), while also playing a complementary role to vehicle efficiency regulations (mitigating rebound effects). Second, while climate benefits are maximized with a carbon tax, fuel tax or per VKT fee, cordon pricing seems more practical and acceptable for many cases. Third, both equity and political acceptability can be improved by careful design of exemptions and usage of revenues – though the ideal design seems to vary considerably by region. Fourth, the most economically efficient design might not be the most equitable, requiring a tradeoff among policy goals. Finally, the implementation process seems to be highly important for acceptability, which should include consultation with all relevant stakeholders, clear communication of benefits, potentially a referendum and demonstration phase, and ideally agreement among political parties. There is much less evidence for ZEV zones, which don’t seem necessary for GHG mitigation, though offering ZEV exemptions for road pricing may be an effective design feature.

Manuel Frondel, RWI - Leibniz-Institut fur Wirtschaftsforschung
Clemens Marggraf, RWI - Leibniz-Institut fur Wirtschaftsforschung
Stephan Sommer, RWI - Leibniz-Institut fur Wirtschaftsforschung
Colin Vance (vance@rwi-essen.de), RWI

A large proportion of pollutants originating from the road transport sector is generated during the so-called cold-start phase of driving, that is, the first few minutes of driving after a car has stood idle for several hours. Drawing on data from the German Mobility Panel, this paper analyzes the factors that affect the frequency of cold starts, approximated here by the number of tours that a household takes over the course of a week. Based on fixed-effects panel estimations, we find a negative and statistically significant effect of fuel prices on the number of tours in large city centers, with some evidence for a higher magnitude among middle income households than among wealthy or poor households. Using our estimates to explore the implications for air quality arising from fuel price increases stipulated under Germany's Climate Program 2030, we find improvements in air quality even for modest fuel price increases of just 10 cents per liter. This outcome  lends support to using carbon pricing as a tool to improve local air quality.

Qian Yu (yuqian@chd.edu.cn), Chang'an University
Tiezhu Li, Southeast University
Yuanqing Wang, Chang'an University
Na Wu, Chang'an University

Due to stop-and-go events, bus stops and intersections are often treated as “hot spots” for air pollution. The design of bus stops and signal controls should be optimized to reduce emissions in urban corridors. The objective of this study was to analyze the impact of bus-stop platforms and transit signal priority (TSP) on vehicle emissions. Second-by-second emissions data were first collected from buses using a portable emission measurement system (PEMS) . Microscopic traffic simulation was then used to estimate overall traffic emissions under seven scenarios with different bus stops and signal control strategies. The results of PEMS tests showed that there were no significant differences between bus operation and emissions generated near two different types of bus stops. As the bus arrives, idles, and departs from a bus-stop, the driving cycles and emission characteristics are essentially the same for the two types of bus stops. Optimization of platform design would hardly reduce bus emissions and fuel consumption (FC). However, bus emissions are proportional to control delay at intersections. So, bus emissions decrease can be achieved by implementation of TSP at intersections. The simulation results showed that bus bays reduced the emissions of other heavy-duty diesel vehicles (HDVs) in this study. However, bus bays were not effective during rush hours. It is essential to consider the number of bus lines, length of platform, and dynamic characteristics of traffic flows before deciding on which type of bus stop is suitable. Additionally, appropriate TSP can reduce emission factors by about 20%, which improves bus operation and emissions without significant negative impact on other vehicles.

I-Yun Lisa Hsieh (iyhsieh@ntu.edu.tw), National Taiwan University
Guillaume Chossière, Massachusetts Institute of Technology (MIT)
Emre Gençer, Massachusetts Institute of Technology (MIT)
Hao Chen, Renmin University of China
Steven Barrett, Massachusetts Institute of Technology (MIT)
William Green, Massachusetts Institute of Technology (MIT)

Electric vehicles (EVs) are a promising pathway to providing cleaner passenger mobility. China provides substantial supports to increase EV market share. This study provides an extensive analysis of the currently unclear environmental and health benefits of these incentives. EVs in China have modest cradle-to-gate CO2 benefits (on average 29%) compared to conventional internal combustion engine vehicles (ICEVs) but have similar carbon emissions relative to hybrid electric vehicles. Well-to-wheel air pollutant emissions assessment shows that emissions associated with ICEVs are mainly from gasoline production, not the tailpipe, suggesting tighter emissions controls on refineries are needed to combat air pollution problems effectively. By integrating a vehicle fleet model into policy scenario analysis, we quantify climate and health impacts at the provincial level. Under the current policy scenario, private vehicle stock in China will reach 302 million in 2030 (22% lower than no EV policy scenario). Full implementation of current EV policy mix will reduce the public’s long-term exposure to air pollution, leading to more than 15,000 premature deaths avoided in 2030. We monetize the climate change mitigation and health benefits associated with the EV policy; the benefits are not uniform across China, in part because weather patterns and the geographical location of the emission sources cause certain provinces’ populations to be more affected by increased air pollution. Refinery emissions have significant impacts, suggesting that regulations to reduce these emissions could have large benefits. Some differences in the effects of the EV policy on different provinces are quantified.

Mohammad Tayarani (mt789@cornell.edu), Cornell University
Amirhossein Baghestani, City College of New York
Mahdieh Allahviranloo, City College of New York
H. Gao, Cornell University

Transportation demand management strategies aim to influence people’s travel behavior and to optimally manage travel activity. Congestion pricing is set to change travel patterns by disincentivizing driving during a specific time period and in a specific geographical area. While quantifying the air quality impacts of congestion pricing has been widely investigated, there is still a gap in the existing literature on the temporal and spatial variation of air quality due to congestion pricing strategies. In this study, we introduce a robust framework to evaluate the impacts of congestion pricing schemes on air quality and exposure to pollution in the New York City metropolitan area. We find that under the low charging scheme, the PM2.5 concentration decreases by 5–25% in areas located mostly in the Central Manhattan area and on the edges of the charging zones. Under the high charging scheme, the PM2.5 concentration decreases by more than 10% in almost all of New York City. The 24-hour cumulative air pollution concentration for the entire study area decreases, from the current value of 4.17 μg/m3, to 3.88 µg/m3 and 3.37 µg/m3 under the low and high charging schemes, respectively. Our results indicate a non-linear relation between adaptation of travel behavior to the congestion charging strategy and the consequent changes in air quality and the cumulative exposure to pollution. Our counterintuitive findings harbor reservations about the health and economic impacts of congestion pricing and accentuate the need to integrate health and urban planning into transportation planning.