Dynamic urban transport taxes and subsidies for the transition to electric mobility

Abstract

Transport is a major contributor to pollutant gas emissions, significantly degrading air quality and exacerbating global climate change. In response to these environmental challenges, many countries have promoted the adoption of electric vehicles (EVs) as a local solution.

However, a trade-off exists between the environmental benefits of EV adoption and the increased congestion they may cause due to their low operational costs. This research aims to identify the optimal schedule of taxes and subsidies for private vehicles and public transport to address externalities such as congestion and emissions effectively.

The methodology employs the Generalized Bass Model with dynamic variables such as the price differential between electric and conventional cars and their respective operational costs. This approach determines the adoption of each vehicle type over time and integrates with a mode-choice model to identify the modal equilibrium that maximizes social welfare.

The proposed methodology was applied to data from Santiago, Chile, under five scenarios incorporating transport policies such as transit subsidies, car tolls, and EV purchase incentives.

Key findings highlight the need for early planning of congestion pricing measures for EVs. Results show that car tolls are lower when most of the population owns combustion vehicles, as the low costs of EVs exacerbate congestion as adoption increases. However, in every scenario, a future dominated by electric cars is more congested than a present with only conventional cars.

The analysis of EV subsidies revealed only a modest acceleration in adoption rates with a fixed subsidy, yielding negligible differences in social benefits compared to scenarios without subsidies. Finally, the results emphasize the need for more precise parameters for dynamic adoption effects, as the current model exhibits limited sensitivity to variations induced by transport policies.