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Financial Analytics: Science and Experience
 

State support to electric vehicle development: Subsidizing vs infrastructure incentives

Vol. 12, Iss. 4, DECEMBER 2019

Received: 27 May 2019

Received in revised form: 28 June 2019

Accepted: 19 July 2019

Available online: 29 November 2019

Subject Heading: Economic policy

JEL Classification: O32, Q55, R42

Pages: 372–387

https://doi.org/10.24891/fa.12.4.372

Ratner S.V. V.A. Trapeznikov Institute of Control Sciences, Russian Academy of Sciences (ICS RAS), Moscow, Russian Federation
lanaratner@ipu.ru

https://orcid.org/0000-0003-3485-5595

Iosifov V.V. Kuban State Technological University (KUBSTU) Krasnodar, Russian Federation
iosifov_v@mail.ru

ORCID id: not available

Subject The article considers State incentives for electric vehicle (EV) technologies development. Most European countries use a set of measures, including purchase subsidy, benefits for registration, ownership, use of infrastructure, etc. All of them together are effective, however, it is difficult to assess the effectiveness of each measure individually, as many of them are complementary.
Objectives The study aims to develop a methodology to assess the effectiveness of government support measures based on their combinations and complementarity.
Methods We employ the dispersion analysis as the main statistical method of the study. The information base was the data on the level of development of electric vehicles and measures of State support applied in the EU countries.
Results We developed a special scheme of the experiment plan for two-factor dispersion analysis. It includes two series of calculations under different methods of data selection.
Conclusions Testing of the proposed method showed that the most effective measures to support electric vehicles in Russia include the support to companies that buy electric cars, and the financial backing to first buyers that can be combined with other benefits reducing the cost of EV operation.

Keywords: electric transport, State incentives, efficiency, experimental design, two-factor analysis of variance

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