Ziming Yan

The heavy-duty transport sector—one of the most emissions-intensive and operationally demanding segments of global mobility—faces mounting pressure to improve environmental outcomes while maintaining cost efficiency. Addressing this dual challenge requires not only innovative powertrain technologies but also region-specific evaluations that account for varying fuel prices, infrastructure readiness, and policy environments. This presentation introduces the Heavy-Duty Truck Total Cost of Ownership (HDTTCO) model, a robust techno-economic and life cycle assessment (TEA-LCA) framework developed to evaluate the economic and environmental performance of emerging heavy-duty powertrains across different markets.

The model compares diesel, battery electric, hydrogen internal combustion (H2-ICE), fuel cell electric (FCE), and mobile carbon capture (MCC) technologies over a 15-year operational period, with detailed treatment of fuel costs, carbon intensity, vehicle incentives, and regulatory policies. Applied to both the United States of America (USA) and the Kingdom of Saudi Arabia (KSA), the model illustrates how no single technology universally dominates across regions.

In KSA, MCC emerges as the most viable near-to mid-term solution due to its strong cost-effectiveness and compatibility with existing fuel infrastructure. Policy levers such as CO₂ unloading support, higher weight allowances, or carbon pricing could further strengthen its competitiveness. In contrast, in the U.S., high diesel prices enhance the economic attractiveness of alternative powertrains. While BEV, FCEV, and hydrogen combustion technologies hold significant long-term potential for emission reductions, their widespread adoption is contingent on continued cost declines and infrastructure build-out.

The results underscore the need for strategic planning tailored to local conditions and demonstrate how modeling tools like HDTTCO can guide investment decisions, policy design, and sustainable fleet strategy in hard-to-abate transport sectors.