Saudi Arabia's green leap: unlocking the climate potential of the Haramain High-Speed Railway through occupancy optimization and renewable energy adoption

Introduction This study presents the first comprehensive evaluation of the CO 2 mitigation potential of the Haramain High-Speed Railway (HHR) system in Saudi Arabia. Focusing on the 450 km Makkah–Madinah corridor, it addresses the pressing need to assess operational and energy-related factors influencing high-speed rail (HSR) decarbonization in fossil-dependent economies. Methods A dynamic scenario-based mathematical modeling approach was applied, examining four operational parameters: train energy efficiency (0.03–0.07 kWh/pkm), grid emission factor (0.65–0.10 kgCO 2 /kWh), renewable energy (RE) adoption (0–100%), and occupancy rates (25–100%). Real-world operational losses were incorporated through a 15% system efficiency adjustment. Multiple scenarios were simulated to capture nonlinear interactions between energy sourcing and passenger dynamics. Results Three key findings emerged. First, in worst-case conditions (low occupancy and a fossil-heavy grid), the HHR system could result in a net increase of 187 kT of CO 2 annually compared to buses. Conversely, in best-case conditions (high occupancy and 100% renewable energy), it could achieve savings of 285 kT of CO 2 per year, which equates to a saving of 11.93 kg per passenger for the entire journey of 450 km. Second, occupancy rates exert an outsized influence on carbon performance. In multiple scenarios, ridership optimization yields greater emissions reductions than RE integration alone, particularly when grid decarbonization is partial. Third, the study identifies scenario-specific climate-positive thresholds: net CO 2 savings are achieved when occupancy exceeds 70–75% under the current grid mix, or 45–50% when RE adoption reaches 50% Discussion These thresholds highlight the nonlinear interplay between energy sourcing and ridership dynamics, offering critical insights for Vision 2030 transport planning. By integrating operational variability and passenger behavior, the methodology provides a practical toolkit for aligning clean energy investments with ridership incentives and forecasting emissions under real-world conditions. Contributing to UN-SDGs 9, 11, and 13, the study establishes a foundational reference for future research on decarbonization thresholds in HSR systems, particularly in Middle Eastern and arid-region contexts.