Energy Economics & Economic Policy

Widespread acceptance of electric vehicles requires a comprehensive investigation of the charging infrastructure, which is still relatively under-researched, particularly in the context of studies in the field of economics. Charging behavior differs significantly from conventional refueling practices, which poses new challenges.

In this SNF project, Prof. Dr. Doina Radulescu (KPM), Jing Li (Tufts University) and Jan Braun (KPM) are initially investigating the impact of regulatory standards on the type of sockets installed and the spread of e-vehicles. The compatibility of charging standards and thus an interoperable fast charging infrastructure can have a decisive influence on the introduction of e-vehicles. One way to achieve this is through legislation. For example, European Union regulations (Directive 2014/94/EU) stipulate that all charging stations built after 2018 must be at least compatible with the standard chosen by the EU. In particular, alternating current (AC) normal and high-power charging stations must be equipped with at least type 2 sockets or vehicle plugs, while direct current (DC) high-power charging stations must be equipped with at least “Combo2” combined charging system plugs. Therefore, we assess the effectiveness of such legislation with regard to the installed plug types. In a second step, we estimate the impact of this standardization on the uptake of EVs, as the availability of suitable charging stations is one of the most important success factors for the acceptance of this new technology.

Second, we analyze the impact of competition between charging station operators on market dynamics, including pricing strategies. We develop an empirical model of consumer demand for charging and coordinated investments in charging stations. Drivers' demand for charging depends on the availability of local charging stations. Furthermore, we estimate the impact of spatial competition between providers on the applied charging tariffs.

By combining theory and empirical analysis, the project sheds light on critical issues related to the charging infrastructure for electric vehicles.

Recent years have seen increased interest in the distribution of household energy expenditure burdens across income levels. Using data from the Swiss Household Budget Survey (2006-2017) and the European Household Budget Survey (2010, 2015, 2020), Prof. Dr. Doina Radulescu and Ivan Ackermann quantify energy poverty and assess the regressivity of household energy expenditures, including the impact of recent energy price surges.

In 2017, the energy expenditure shares of equivalent income were 8.6% for the lowest income quintile and 2.6% for the highest in Switzerland, placing it among the lowest burdened compared to most EU economies. Energy poverty, defined as households with energy expenditures exceeding 10% of their income, is lowest in Switzerland and Luxembourg. However, using the criterion of expenditures exceeding twice the national median, Switzerland had one of the highest values at 17%.

We also examined the inequality of the energy expenditure burden. Switzerland shows relatively high values for the convexity of energy expenditure shares and Kakwani index implying a higher degree of regressivity compared to the majority of the EU counterparts.

Equivalent income was the primary factor in energy expenditure inequality, contributing over 50% to the overall concentration index. Age of household head and household size contributed negatively. This suggests that policies beyond incomebased transfers may be necessary to address the adverse effects of an increasing energy expenditure burden. Furthermore, our findings underscore the importance of using multiple measures to accurately assess the allocation of energy expenditure burdens within the population.

Transport sector CO₂ emissions account for a large proportion of overall emissions and have continuously increased in recent decades. Even though electric cars accounted for around 18% of all cars sold in 2023, the share of electric vehicles (EVs) in the overall stock of cars worldwide is considerably smaller, despite a wide range of government support programs.

In the SNSF project “Household Preferences for Electric Vehicles and Renewable Energy and the Effect of These Technologies on Electricity Demand” (2020-2023), Prof. Dr. Doina Radulescu and Patrick Bigler investigated household-specific socio-demographic factors that encourage households to switch to hybrid or electric cars. They also investigate the environmental and equity effects of instruments such as  feebates on annual vehicle registration taxes or of upfront price subsidies for electric cars. The research team computes the optimal feebate-subsidy combination that promotes the spread of electric cars, while simultaneously ensuring sufficient revenue to finance the road transport infrastructure and taking equity aspects into account. The empirical analysis is carried out using data on households and cars for the Canton of Bern (see also publication Environmental, Redistributive and Revenue Effects of Policies Promoting Fuel Efficient and Electric Vehicles with Nicola Pavanini and Fabian Feger Review of Economic Studies, February 2022).

In this project, Prof. Dr. Doina Radulescu and Ivan Ackermann use cross-sectional and longitudinal microdata from the European-wide Statistics on Income and Living Conditions (SILC) to investigate the likelihood of households living in neighborhoods affected by pollution and assess how this varies with socio-demographic characteristics. This is important to understand which households benefit from better environmental quality through the energy transition.

Further, the authors intend to merge actual air pollution satellite data to the survey data from the socio-economic panel (SOEP) in Germany which incluedes the same question about air pollution perception as in the SILC data. This allows us them do the same regressions as with the perceived air pollution and compare the relationships with the socio-economic variables. Subject to being able to merge the SILC data with data to regional variation in information on air quality like the closing or building of a coal fired power plant or Smog-alarm, they identify the effect of information on the discrepancy between actual and perceived air pollution.