Ali Alderete Peralta, Nazmiye Balta-Ozkan (p)

Jaewon Lim

Electrification of transport sector is essential for UK’s meeting Climate Change Act goals and reducing its emissions to net-zero. The Committee on Climate Change, the government’s independent advisory body on climate change, expects electric vehicles (EVs) to account for 97% of car sales (equalling to 43% of car fleet) in 2030 so that UK is on track towards this target. A number of policy measures include the government banning the sale of new petrol and diesel cars in 2030 and some hybrid models in 2035 and the introduction of grants towards charging infrastructure. These subsidies, coupled with increasing environmental awareness resulted in new battery electric vehicle registrations (51,000) surpass diesel cars for the first time in Great Britain during the third quarter of 2021. Yet, the impact of EVs on UK power demands is going to be quite significant. The Committee on Climate Change expects UK’s power demand to double or even triple by 2050. For the management of power networks, it matters where these vehicles are charged. As EVs are stationary for around 95% of their service life, they can provide power back to the grid via vehicle-to-grid applications when they are not on the move. By aligning charging and discharging decisions in tandem with the availability of renewable resources, EVs, in principle, can contribute to smart operation of power networks. This becomes particularly more important for densely populated urban areas where opportunities to utilise renewable resources may be quite limited. A key factor that will determine the amount of ‘flexible’ power from EVs is individuals’ transport choices and their commuting patterns. While the impacts of Covid-19 pandemic on commuting patterns is highly uncertain, EVs, may increase or decrease demands for power in urban areas. This paper for the first time brings spatially explicit commuting patterns and EV registration datasets together to develop a socio-economically tuned approach to analyse the impacts of EVs on urban demands for energy. Utilising data-driven approaches, the results present the scale of power displaced across geographically connected areas and how EVs may support the cost-effective operation of networks in congested areas.

Brian H. Kim (p), Taejune Mo

Nazmiye Balta-Ozkan

This study analyzes the demand system and social welfare for electric vehicles and determine the appropriate subsidy amount for fuel cell electric vehicles to achieve the goal of the “Hydrogen Economy Revitalization Roadmap” announced by the Korean government. The demand system was analyzed by applying the nested logit model. The price elasticity of each automobile model was examined from the estimation results, and the demand and supply functions of the automobile market were derived to obtain the amounts of consumer surplus, producer surplus, and government tax revenue in a new equilibrium state. The data used for the analysis include sales volume, price, vehicle specifications of the model sold from January 2016 to May 2020, the number of charging stations, subsidies for purchasing eco-friendly cars by year, and consumer price index. All coefficients derived through the nested logit model were statistically significant, and the price elasticity and the differences in markup by group also reflected logical result as expected. However, the achievement of the goal for “Hydrogen Economy Revitalization Roadmap” seems not possible to achieve through changes in purchasing subsidies due to the low-price elasticity of fuel cell electric vehicles. On the other hand, the social surplus, based on the newly adjusted target value, was found that the “Hydrogen Economy Revitalization Roadmap” policy still had a positive effect in terms of overall social welfare, despite the large subsidies for hydrogen refueling stations. This suggests that, in addition to the purchase subsidy support policy, the government's active policy promotion such as the steady promotion of electric vehicles and the establishment of charging infrastructure should be consistently considered for the positive social welfare effects.

Jaewon Lim (p), DooHwan Won, Sandy Dall'erba

Brian H. Kim

According to the U.S. Energy Information Administration (EIA), U.S. carbon dioxide emissions from energy sources hit their 25-year low at 5,134 million metric tons in 2017. However, for the first time, the total metric tonnage of CO2 emissions from transportation exceeded that from electric power. Each state in the U.S. has a different set of policies to meet the tailpipe emission standards. In 2004, CARB (California Air Resources Board) approved the nation’s first GHG (Greenhouse Gas) emission standards specifically for cars. There are 13 other “CARB states” that follow California’s more restrictive standards with the aim to control CO2 emissions specifically from vehicles. The purpose of this paper is to examine the impact of CARB’s tailpipe emission standard policy on the reduction of CO2 emissions emanating from the transportation sector. This policy is controversial and creates a conflict between the Trump administration’s EPA and the CARB states. Using a spatial panel dataset for 49 U.S. states over 1987-2015, we estimate both a Spatial Lag Model (SAR or SLM) and a Spatial AutoRegressive with additional AutoRefressive error structure (SARAR or SAC) as the latter captures the effect of omitted variables from the former. The estimation results of SAC indicate the expected effects of all three policy-related variables. CARB’s stricter standards have the largest effects among the alternative policy approaches tested in this study. Local spatial effects of CARB’s standards show the presence of spatial effects among neighboring states. All three policy approaches (CARB’s emission standard, gasoline price policy, and fuel efficiency) should be combined to reach the CO2 emission reduction goal set by these CARB’s states. Evidence of intestate spillovers indicates that a state needs to collaborate with its neighbors as envisioned by the partnerships among EPA, NHTSA, and California. This approach provides a viable path to achieve the ambitious but still feasible CO2 reduction goal set by CARB and adopted by EPA in 2012.