The EU’s policy landscape is set to accelerate the adoption of zero-emission trucks in the coming years, with battery electric trucks (BETs) leading the way due to their stronger economic performance compared with other alternative powertrains. Assessing the effectiveness of these policies in terms of the number of BETs put on European roads remains challenging, because the economic drivers impacting technology adoption vary by vehicle model, freight application, and Member State. Reliable estimates of BET uptake are key for ensuring that freight electrification aligns with charging infrastructure deployment and grid expansion, which often operate in different planning and investment timelines. Vehicle technology diffusion modeling—which is used to generate estimates of vehicle uptake—is an important tool for policymakers to evaluate policies targeting BET uptake and support coordinated development of the freight electrification ecosystem.
This study presents a policy-sensitive diffusion modeling framework, the HDV-DIFFUSE model, tailored to the EU’s VECTO truck classes. By using total cost of ownership (TCO) and linking policy-driven changes in trucking economics to technology adoption, the model projects BET uptake across the EU27+3 for all truck classes under various policy scenarios, including the Emission Trading System (ETS2) and the Eurovignette Directive. An overview of the model structure is shown in Figure ES1.
Key capabilities of the model include:
- Quantifying the impact of individual policies on BET cost-competitiveness and adoption at a highly granular level: By capturing vehicle, operational, and country-specific parameters, the model can assess how individual policy measures impact specific TCO components and BET adoption across segments, which in turn enables policymakers to develop tailored policies for specific applications, vehicle types, and countries.
- Determining the pace and timing of market diffusion: By capturing economic variables, such as charging prices or carbon pricing schemes, the model can estimate BET adoption trajectories over time, revealing periods of quick growth or stagnated diffusion. Understanding these dynamics is crucial to align BET diffusion with investment and planning on charging infrastructure and grid energization.
- Identifying how and why policy effectiveness varies across countries and truck classes: By accounting for differences in market conditions such as fuel and charging prices, as well as the operational profiles of the vehicles (e.g., regional or long-haul) and policy implementations, the model captures an array of policy-relevant differences among Member States, allowing policymakers to quantify policy impact across different EU countries. For instance, the same policy measures (full road toll exemptions and ETS2) result in a BET diffusion of 66% in Germany by 2030, compared with 16% in Poland
Source: theicct.org
