Inaugurated in 2021 as a cornerstone of Saudi Vision 2030, the SGI is a comprehensive sustainability program targeting carbon emission reductions, large-scale afforestation, and the protection of terrestrial and marine ecosystems (Saudi Green Initiative, 2023a,b). Central to its strategy is the Circular Carbon Economy (CCE) framework, complemented by ambitious RE projects, with the collective goal of eliminating 278 million tons of CO₂ annually by 2030 (Saudipedia, 2024). A detailed breakdown of all initiatives aligned with SGI's strategic dimensions is provided in Table 1.

Launched in 2016 under Saudi Vision 2030, the NREP is a transformative initiative to diversify the Kingdom's energy mix by scaling renewable sources to 50% of electricity generation by 2030, with natural gas supplying the remainder.

The NREP drives progress across three pillars (Saudipedia, 2024):

Tangible progress (2024 update)

The HHR, being so young, has earned several prestigious awards in recognition of remarkable achievements, ensuring operational excellence and serving more than 60 million passengers since 2018. Moreover, it qualifies as a landmark transformational project of Saudi Arabia (First HSR for desert climate/arid regions), seamlessly blending modern technology with cultural heritage. Table 4 presents all unique details of key awards in a range of categories, such as engineering innovation, sustainability, cultural integration, and societal impact.

The HHR operates at speeds of up to 300 km/h, placing it among the fastest rail systems globally, comparable with leading global rail systems, with details given in Table 5.

This study evaluates the CO₂ reduction potential of the HHR using a dynamic multiscenario emission model built around four critical variables:

To reflect both benchmark comparisons and operational realism, two modeling regimes are constructed with details given in Table 6:

Benchmarking is performed against diesel buses, which is the most prevalent and standardized intercity transport mode operating in the Makkah–Madinah route. Although air travel and private cars are commonly used in other corridors, their inclusion was assessed and ruled out due to limited operational data, inconsistent emissions profiles, and route-specific irrelevance:

Buses thus represent the most appropriate comparator due to uniformity, route overlapping, and consistent energy/emission metrics. Now, to assess the superior performance of the HHR, the conventional diesel bus is considered as a benchmarking transportation mode, with benchmarking equations given by:

where Bus fuel intensity = 2.68 kgCO₂/L and bus emission factor = 0.102 kgCO₂/pkm. Emission factors and energy intensities were derived from authoritative sources, including the IPCC Sixth Assessment Report (Intergovernmental Panel on Climate Change, 2022), IEA emissions factors database (IEA, 2022), and empirical studies such as Urban Mobility India (Rawat et al., 2024) and CARB EMFAC documentation (California Air Resources Board, 2019). Bus fuel intensity and per-passenger emission rates were validated using the EPA GHG Hub (United States Environmental Protection Agency, 2025) and the UK Department for Environment, Food and Rural Affairs based estimates (Department for Energy Security Net Zero, 2024 ).

Four distinct scenarios are developed and detailed in Table 7 for comprehensive emissions and savings analysis to quantify the environmental benefits of the HHR compared with diesel bus transport over a 450-km intercity route.

Moreover, CO₂ emission savings per passenger are calculated by integrating both fixed and variable energy efficiencies for train and bus systems. These calculations are evaluated across dynamic occupancy levels [25, 50, 75, 100] and varying rates of RE adoption [0, 25, 50, 75, 100]. The analysis is conducted under the assumption that parameters are held independent to isolate the marginal impact of each variable, such as occupancy rate, RE penetration, and energy intensity.

The emissions savings are the difference between bus and train emissions, given by:

with positive values indicating net environmental benefit in favor of the train. While the indirect benefits of RE integration, such as enhanced grid performance and its impact on train operations, are acknowledged, they fall outside the scope of the current linear modeling framework. These dynamics will be addressed in future work using multiagent simulation and co-optimization approaches.

Scenario 1 serves as a baseline, assuming fixed energy efficiencies for both modes of transportation. The train operates at 15 kWh/km with a fully regenerative braking system, while the bus maintains a constant emission factor of 0.102 kg CO₂/passenger-km. Train CO₂ emissions remain consistent across all occupancy levels for each RE penetration rate indicating optimal emission distribution. At 0% RE, the train emits 14.63 kg CO₂/passenger, decreasing linearly with RE adoption and reaching zero emissions under a fully renewable grid (Figure 1a). Bus emissions remain fixed at 45.9 kg CO₂/passenger, unaffected by either occupancy or RE penetration (Figure 1b). Comparatively, the train emits up to 68% less CO₂ than the diesel bus under non-renewable conditions (Figures 1a, b). With both transportation modes efficiencies held constant, CO₂ savings are identical across all occupancy levels for a given RE penetration. Maximum savings of 45.9 kg CO₂/passenger occur at 100% RE adoption. Each 25% increase in RE penetration results in a consistent savings increment of approximately 3.66 kg CO₂/passenger, further amplifying the train's advantage over diesel buses (Figure 1c).

The train consistently outperforms the bus in emissions performance, independent of occupancy level. As RE penetration increases, the gap widens significantly, culminating in CO₂ savings ranging from 31.28 kg/passenger (worst case at 0% RE) to 45.9 kg/passenger (best case at 100% RE).

Scenario 2 introduces variable energy efficiency for trains based on occupancy levels, while bus emissions remain constant across all conditions. This dynamic model highlights the enhanced emissions performance gained through increased train utilization and RE adoption. Train emissions vary inversely with occupancy and RE penetration. At low occupancy (25%) and no RE adoption (0%), emissions peak at 42.09 kg CO₂/passenger, approaching bus levels. However, train emissions drop sharply as occupancy and RE adoption rise. For instance, at 100% occupancy and 50% RE adoption, emissions fall to 5.26 kg CO₂/passenger, representing an 88% reduction compared with the diesel bus case (Figure 2a). Bus emissions are fixed at 45.9 kg CO₂/passenger across all occupancy and RE adoption rate, offering no improvements from increased load or renewable integration. CO₂ savings grow significantly with train occupancy and RE adoption. Savings reach 14.34 kg CO₂/passenger at 25% occupancy and 25% RE adoption, while full occupancy and RE penetration yield the maximum 45.9 kg CO₂/passenger saved (Figure 2b). Even moderate occupancy levels (50%–75%) paired with RE adoption over 50% allow the train to outperform the diesel bus substantially.

Scenario 2 demonstrates strong synergy between high train occupancy and RE adoption in reducing emissions. The system is especially sensitive at low RE levels, where occupancy is the dominant driver of performance. CO₂ savings span from 3.81 kg/passenger (worst case) to 45.9 kg/passenger (best case), confirming that meaningful mitigation requires exceeding 50% thresholds in both RE adoption and occupancy (Figure 2c).

Scenario 3 models fixed train energy efficiency (15 kWh/km) while accounting for variable bus emissions influenced by occupancy levels. Diesel bus emissions decrease with higher occupancy due to load-sharing benefits, while train emissions stay constant across occupancy levels but drop progressively with greater RE adoption.

At low RE penetration (0%–25%) and low occupancy (25%), diesel buses outperform trains. For example, with 0% RE adoption and 25% occupancy, the train emits 2.69 kg CO₂/passenger more than the bus (Figures 3a, b). Net CO₂ savings from rail become positive around 50%−75% RE adoption, depending on occupancy level. At 50% occupancy and 75% RE, the train saves 2.31 kg CO₂/passenger compared to the bus. At 100% RE, electric trains outperform diesel buses across all occupancy levels, with savings ranging from 2.98 to 11.93 kg CO₂/passenger (Figure 3c). Electric trains are disadvantaged when both occupancy and RE levels are low. However, as RE adoption increases, even at static train efficiency, trains begin to outperform buses, especially when both occupancy and RE exceed 50%.

Scenario 3 underscores the pivotal role of clean electricity in reducing train emissions. Although low occupancy paired with fossil-based grids hampers rail performance, full RE integration reverses this trend completely. Figure 3c highlights a savings range from −11.64 kg CO₂/passenger (worst case) to 11.93 kg CO₂/passenger (best case), demonstrating that grid decarbonization can unlock HHR full environmental potential.

This scenario reflects the most realistic modeling, capturing how both train and bus emissions vary with occupancy and RE adoption. Unlike earlier scenarios, train emissions here adjust dynamically with occupancy, and bus emissions continue to drop at higher occupancy due to load-sharing. At 0% RE and 25% train occupancy, the train emits 30.15 kg CO₂/passenger more than a diesel bus, which is the worst-case emissions gap (Figures 4a, b). The emissions gap begins to close as RE integration exceeds 50%, and at 75% RE adoption, with just 25% occupancy, trains start outperforming buses with a savings of 1.41 kg CO₂/passenger. At 100% RE, train emissions hit zero, delivering CO₂ savings across all occupancy levels ranging from 2.98 to 11.93 kg CO₂/passenger (Figure 4c).

This dynamic scenario highlights the interplay of occupancy and grid cleanliness in determining transport sustainability. Trains underperform when fossil energy dominates, and occupancy is low. However, trains consistently outperform even under minimal occupancy for RE adoption ≥75% and achieve complete decarbonization at 100% RE adoption, reinforcing the power of clean grids and public transport optimization. Diesel buses maintain steady emissions regardless of RE adoption but benefit from increasing occupancy.

In conclusion, across all scenarios, RE adoption emerges as the dominant driver for reducing transport emissions. Train performance is highly sensitive to both occupancy rate and RE adoption levels, but emissions reach zero under full RE adoption. Diesel buses benefit from higher occupancy but remain carbon-intensive and are outperformed when RE penetration exceeds 75%. The shift consistently favors electric trains when grid decarbonization and passenger density align. CO₂ savings span from −30.15 to +45.9 kg/passenger across cases. Advancing RE adoption and public transport use is therefore critical to climate-optimized mobility. This aligns directly with UN SDG 7.3 and 11.2, promoting energy efficiency and sustainable transport systems to ensure affordable, clean energy and inclusive urban mobility.

This section evaluates annual carbon savings across four scenarios (see Figure 5), assuming 24 daily round-trip trains with a daily passenger capacity of 20,016 and accounting for a 15% operational efficiency loss.

Building on the annual savings analysis presented in Section 5.5, the data clearly demonstrate that occupancy is not merely a secondary operational factor, but the principal determinant of emission outcomes in electrified transport systems. Scenarios 3 and 4 reveal that even with 100% RE adoption, low ridership can result in net-negative annual CO₂ savings as steep as −187 kT, while optimized occupancy delivers annual CO₂ saving gains of up to +285 kT. The following targeted strategies translate these findings into policy-relevant interventions:

These recommendations are supported by recent global best practices. For example, (Zhou et al. 2024) developed a multi-objective optimization framework to enhance passing capacity in HSR hub stations, specifically for multi-directional train routes. (Feng et al. 2022) underscored the importance of robust scheduling, integrated timetabling and coupling optimization for managing fluctuating passenger demand in HSR networks. Additionally, (Zheng and Zhang 2024) applied a multi-objective optimization approach to improve station track utilization, focusing on operational robustness under stochastic intervals. Finally, (Chen et al. 2025) proposed a two-level spatiotemporal network model to enhance timetable resilience in HSR systems by strengthening their ability to absorb disturbances. Building on these global insights, this study moves beyond operational modeling to quantify emissions under dynamic occupancy and renewable energy conditions which is a critical gap often overlooked in HSR sustainability literature. Unlike recent works (Zhou et al., 2024; Feng et al., 2022; Zheng and Zhang, 2024; Chen et al., 2025) that treat infrastructure and scheduling as separate domains, this study results reveal the compounded environmental value of integrating occupancy strategies with clean energy adoption. Far from a logistical detail, occupancy optimization emerges as a strategic fulcrum for rail decarbonization. Hence, by embedding these mechanisms into the daily operations of HHR, Saudi Arabia can fully leverage its HSR investments to accelerate climate gains and advance the net-zero pillars of Vision 2030.