The United States aims to achieve a net-zero emissions economy, with clean hydrogen playing a crucial role. The U.S. Hydrogen Energy Earthshot seeks to reduce the cost of clean hydrogen to $1 per kilogram within a decade. This research focuses on blue hydrogen, produced from fossil fuels with CCS, as a near-term solution. While offering a pathway to decarbonization, blue hydrogen faces scrutiny due to potential methane leakage. The study leverages experience curves to analyze the cost trajectory of blue hydrogen production, considering the impact of the Inflation Reduction Act (IRA) of 2022, which provides tax credits for carbon sequestration and clean hydrogen. The IRA's incentives, including Section 45V for clean hydrogen production and the enhanced Section 45Q for carbon sequestration, are expected to accelerate the adoption of blue hydrogen. The study's significance lies in its quantitative assessment of the learning-by-doing effect on blue hydrogen costs and its evaluation of the effectiveness of IRA incentives in reaching the Earthshot goal. Understanding these factors is vital for informed policymaking and investment decisions in the emerging hydrogen economy.

Existing literature highlights the potential of clean hydrogen to significantly reduce emissions by 2050. Studies project substantial job creation in the hydrogen economy. Government investments, such as the $9.5 billion appropriated under the U.S. Bipartisan Infrastructure Law, are driving the development of regional clean hydrogen hubs. However, debates exist regarding the environmental competitiveness of blue hydrogen due to potential methane leakage. Previous research has investigated learning curves for SMR, gasification, and CCS, but comprehensive assessments of overall blue hydrogen technological learning and the impact of tax incentives are limited. The authors note the use of learning rates from pioneering studies, such as Schoots et al. (2007) and Rubin et al. (2015), which are often applied in various applications, despite potential limitations and need for further refinement. This study aims to fill this gap by providing a thorough assessment of blue hydrogen technological learning and examining the potential effect of tax incentives.

The study employs a multi-faceted approach. First, it characterizes greenhouse gas emissions and costs of current blue hydrogen production technologies (SMR and coal gasification with CCS) based on data from the National Energy Technology Laboratory (NETL). A component-based learning curve model is developed to project future costs, accounting for learning-by-doing in individual subsystems (SMR, CCS, etc.). Learning rates and initial installed capacities are determined from several studies. The model considers the IRA's 45Q and 45V tax credits, estimating their impact on levelized cost of hydrogen (LCOH). Parametric analyses are performed to assess the sensitivity of LCOH to key factors such as natural gas price, carbon capture cost uncertainties, learning rates, and inflation. A diffusion-of-innovation model, based on an S-shaped curve, is used to estimate the time-based diffusion of gas-based blue hydrogen production capacity. The model incorporates various parameters, including the saturation level of annual installed capacity, growth rate, and initial installed capacity. Regression analysis is used to estimate the coefficients of this diffusion model based on data from the International Energy Agency and other sources. The LCOH is calculated using standard levelized cost formulas, which incorporate capital costs, operating and maintenance costs, and fuel costs. The effect of inflation is examined by estimating the LCOH in both real and nominal dollars. The authors explicitly describe the assumptions and parameters used in the models, including project book lifetime, capacity factor, fuel prices, fixed charge rates, and financial parameters such as the weighted average cost of capital (WACC). Detailed information on cost breakdowns, initial installed capacities, learning rates, and financial parameters is provided in supplementary tables.

The study finds that the levelized cost of hydrogen (LCOH) for SMR with CCS is significantly lower than that for coal gasification with CCS. Learning-by-doing alone can reduce production costs, but achieving the $1/kg H₂ target without tax incentives is challenging. The breakeven cumulative production capacity required to reach this target highly depends on the tax credit type, natural gas price, inflation rate, and learning rates. The 45Q tax credit for carbon sequestration is more effective than the 45V tax credit for clean hydrogen production. With the 45Q tax credit, gas-based blue hydrogen from SMR with CCS is more likely to reach the $1/kg H₂ target than coal-based production. Sensitivity analysis reveals a substantial impact of natural gas prices on LCOH; cheaper gas greatly reduces the capacity required to reach the target. Uncertainties in carbon removal system costs also affect LCOH, resulting in a range of breakeven cumulative capacity. Higher learning rates, particularly in O&M costs for SMR, PSA, and CO₂ compression, decrease the required capacity. Inflation significantly impacts the LCOH, potentially preventing the $1/kg H₂ target from being met, even with tax incentives and increased learning rates. Time-based diffusion analysis suggests that reaching the 10 MMTA annual clean hydrogen production target by 2030 may be difficult based on current projections, even though gas-based blue hydrogen is projected to comprise a large share of low-carbon hydrogen production. Analyses consider various scenarios, exploring the effects of different learning rates, tax credits (45Q and 45V), and natural gas prices on the required cumulative production capacity to reach the $1/kg H₂ target.

The findings highlight the importance of policy incentives, technological advancements, and resource planning in achieving the Hydrogen Energy Earthshot. The strong influence of natural gas prices emphasizes the need for strategies to ensure affordable and reliable natural gas supply. The sensitivity to learning rates underscores the importance of continued R&D efforts, especially focusing on improving the efficiency and reducing the cost of CCS. The comparison of 45Q and 45V credits informs policy design in maximizing the cost reduction potential. The significant impact of inflation necessitates considering inflation in cost projections and planning for long-term investments. The results concerning the time-based diffusion of production capacity emphasize the need for accelerated deployment of blue hydrogen projects to meet the ambitious 2030 target. The study acknowledges the limitations of using simplified models and constant learning rates to project future cost trends. Further research could incorporate more complex models, probabilistic approaches, and dynamic learning rates. Additionally, the environmental implications of methane leakage during blue hydrogen production should be carefully addressed.

This study demonstrates that achieving the U.S. Hydrogen Energy Earthshot for blue hydrogen is feasible but highly dependent on several factors. Policy levers such as tax credits, particularly the 45Q credit, are crucial in driving cost reductions. Continued investments in R&D to improve CCS efficiency and reduce uncertainties are essential. Strategic planning considering natural gas prices, inflation, and accelerated technology deployment is necessary. Retrofitting CCS to existing infrastructure could offer cost-effective near-term solutions. Future research should focus on more sophisticated modeling techniques and account for the full lifecycle environmental impact of blue hydrogen, including methane emissions. Regional hydrogen hub development, aligning hydrogen supply with demand, and implementing robust methane abatement strategies are crucial for realizing a sustainable and cost-competitive hydrogen economy.

The study relies on simplified models and constant learning rates, potentially underestimating or overestimating the true future costs. Uncertainties exist in the projections, stemming from the variability of input parameters such as natural gas prices and learning rates. The model does not explicitly account for potential technological breakthroughs that could significantly alter the cost trajectory of blue hydrogen production. The analysis focuses mainly on gas-based blue hydrogen, with limited detailed exploration of coal-based alternatives. The model assumes a constant and uniform application of tax credits across all projects and over time, which might not accurately reflect the real-world implementation.