A Binary Discounting Method for Economic Evaluation of Hydrogen Projects: Applicability Study Based on Levelized Cost of Hydrogen (LCOH)

Abstract

Hydrogen is increasingly recognized as a key element of the transition to a low-carbon energy system, leading to a growing interest in accurate and sustainable assessment of its economic viability. Levelized Cost of Hydrogen (LCOH) is one of the most widely used metrics for comparing hydrogen production technologies and informing investment decisions. However, traditional LCOH calculation methods apply a single discount rate to all cash flows without distinguishing between the risks associated with outflows and inflows. This approach may yield a systematic overestimation of costs, especially in capital-intensive projects. In this study, we adapt a binary cash flow discounting model, previously proposed in the finance literature, for hydrogen energy systems. The model employs two distinct discount rates, one for costs and one for revenues, with a rate structure based on the required return and the risk-free rate, thereby ensuring that arbitrage conditions are not present. Our approach allows the range of possible LCOH values to be determined, eliminating the methodological errors inherent in traditional formulas. A numerical analysis is performed to assess the impact of a change in the general rate of return on the final LCOH value. The method is tested on five typical hydrogen production technologies with fixed productivity and cost parameters. The results show that the traditional approach consistently overestimates costs, whereas the binary model provides a more balanced and risk-adjusted representation of costs, particularly for projects with high capital expenditures. These findings may be useful for investors, policymakers, and researchers developing tools to support and evaluate hydrogen energy projects.

1. Introduction

Given the rising global energy demands, the depletion of conventional fuel sources, and the urgent need to mitigate climate change, hydrogen is becoming a key element of the future low-carbon energy system [1]. With its high energy intensity, versatility, and zero combustion emissions, hydrogen can play a crucial role in the decarbonization of various economic sectors, including industry, transport, power generation, and heating [2,3]. Its application holds particular relevance in regions where electrification is difficult or uneconomical, making hydrogen an integral part of a sustainable energy transition [4,5].

The most important aspect of hydrogen energy realization is an accurate assessment of the economic feasibility of its production, which has a significant impact on investment decisions and policy making [6]. The Levelized Cost of Hydrogen (LCOH) is one of the most important integral indicators used to assess the economic viability of various hydrogen production technologies. Due to its ability to take into account total capital and operating costs with the time value of money, this indicator is widely used in academic studies, industry reports and investment analysis [7,8]. It enables comparison between different production routes, informs tariff policies, and supports decision-making within the framework of government transfers and infrastructure planning [9].

Levelized Cost of Hydrogen (LCOH) is a quantitative measure that allocates the total lifetime costs of a hydrogen production system to each unit of production. The core principle underlying this methodology involves adjusting the costs incurred at different points in time within a project for their time value and then establishing a proportional relationship to the hydrogen production volume in the respective periods, thus creating a standard for measuring unit costs that can be used for comparisons [10]. The logical operation of LCOH comes from the discounted cash flow (DCF) method used in capital budgeting. Essentially, all future costs are calculated at their present value and allocated to each period of production, thus establishing a single scale of cost [11,12].

In this design, cash flows at different stages of a project are compressed to a representative cost expression, allowing investors to estimate the average economic burden of a long-term project from a current perspective. In contrast to traditional marginal cost or static pricing approaches, LCOH provides a dynamic inter-period cost framework that emphasizes the synergies between time, risk, and outcomes in cost estimates [13,14]. However, in this framework, costs are discounted uniformly to present value in the form of future cash flows whose value systematically decreases as the discount rate increases. Therefore, an increase in the discount rate usually reflects an increase in the level of uncertainty and risk faced by the project [15]. In accordance with this dynamic, increased risk should lead to more cautious estimates of future costs, rather than reflecting their economic weight in lower present values. Second, traditional LCOH calculations are based on using a single discount rate for all types of cash flows (including capital and operating costs and production). In hydrogen production projects, capital and operating costs tend to have a high degree of certainty because they are based on contractual terms, fixed equipment purchases, and standardized operating schedules [16,17]. At the same time, actual hydrogen production volumes depend on variables such as power supply stability, capacity utilization, maintenance strategy and other operational factors [18,19]. This makes production (or revenue) streams more susceptible to risk compared to costs. Although such flows are often set as fixed in modeling, in practice, their risk is higher. Thus, applying a single discount rate to all flows ignores differences in risk and may systematically distort valuation results.

In discounting logic, the present value of future cash flows will systematically decrease as the discount rate increases [20]. In terms of costs, initial one-time capital expenditures (CAPEX) are less sensitive to the discount rate, while subsequent ongoing operating costs (OPEX), especially electricity costs, decrease significantly as the discount rate increases, presumably leading to a decrease in the present value of costs. However, the denominator in the LCOH structure, the discounted hydrogen production, is set as a constant value throughout the cycle in most model scenarios. In other words, the discounted production value decreases faster than the present value of costs, resulting in higher unit costs, which manifests as a systematic overestimation of LCOH.

Of greater concern, the use of a single discount rate masks the responsiveness of changes in project risk to outcomes. Although an increase in the discount rate reflects a significant increase in investment risk, because discounting acts on both the numerator and denominator, the value of LCOH changes very little, not reflecting the economic inappropriateness of high-risk projects, but instead may create a false sense of economic attractiveness due to the lower present value of costs. This “decoupling of risk and outcome” undermines the effectiveness of LCOH as a project selection and decision support tool.

The present work aims to eliminate this methodological limitation by adapting the binary cash flow discounting model proposed by S.G. Galevskiy, within the framework of real asset valuation, to the problems of LCOH calculation. Unlike the traditional approach, the binary model assumes the use of two different discount rates: the reflection of the risk of cash outflows (costs), and the risk of receipts (income from hydrogen production) [21]. The relationship between these rates is formalized based on the requirement of no arbitrage, taking into account the risk-free rate of return. The methodology not only corrects the structural overestimation of unit costs through a single discount rate but also increases the sensitivity of the model to changes in risk and provides a more rational quantitative tool for evaluating hydrogen energy investments. This study fills a gap in the traditional LCOH model, which cannot effectively account for risk, and offers a new perspective and methodology for future economic evaluation of hydrogen energy.

2. Materials and Methods

2.1. Traditional Definition of LCOH

The basic design of the LCOH model is based on the most basic logic of cost-benefit analysis: dividing total life-cycle costs by total production over the same period to determine the average cost of producing a unit of hydrogen [18]. In the context of hydrogen project evaluation, this relationship can be formalized as follows:

$$\mathrm{L}\mathrm{C}\mathrm{O}\mathrm{H}={\displaystyle \frac{{\sum }_{\mathrm{t}=0}^{\mathrm{T}}({\mathrm{C}\mathrm{a}\mathrm{p}\mathrm{e}\mathrm{x}}_{\mathrm{t}}+{\mathrm{O}\mathrm{p}\mathrm{e}\mathrm{x}}_{\mathrm{t}}+{\mathrm{F}\mathrm{u}\mathrm{e}\mathrm{l}\mathrm{C}\mathrm{o}\mathrm{s}\mathrm{t}}_{\mathrm{t}})/{(1+\mathrm{r})}^{\mathrm{t}}}{{\sum }_{\mathrm{t}=1}^{\mathrm{T}}{\mathrm{H}}_{\mathrm{t}}/{(1+\mathrm{r})}^{\mathrm{t}}}},$$

(1)

where ${\mathrm{C}\mathrm{a}\mathrm{p}\mathrm{e}\mathrm{x}}_{\mathrm{t}}$—annual capital costs (USD/year) for hydrogen production equipment calculated using the annual cost method and discount rate; ${\mathrm{O}\mathrm{p}\mathrm{e}\mathrm{x}}_{\mathrm{t}}$—operation and maintenance costs of hydrogen production technology (USD/year); ${\mathrm{F}\mathrm{u}\mathrm{e}\mathrm{l}\mathrm{C}\mathrm{o}\mathrm{s}\mathrm{t}}_{\mathrm{t}}$—energy consumption costs.

The expression structure corresponds to the standard “discounted cost/discounted productivity” scheme but is improved to take into account the specificity of the hydrogen production project cost structure [22,23]. In particular, life cycle costs are divided into three categories: initial and incremental investments (Capex), ongoing operation and maintenance costs (Opex), and energy consumption costs (Fuel Cost). Among them, energy costs, as the most significant and sensitive component of hydrogen production costs, often account for 60–80% of the total costs, especially in processes such as hydrogen production from electrolytic water or high-temperature gas reactions [24,25]. Given their high volatility, regardless of the Levelized Cost of Hydrogen from electrolytic water or high-temperature gas reactions, energy costs are the most important and sensitive component of hydrogen production costs.

This structural separation is not only closer to the actual operational logic of hydrogen energy projects, but also provides a basis for the subsequent implementation of a differentiated discounting approach. It should be noted that, although Fuel Cost can be considered a subset of Opex from a financial accounting perspective, the independent construction of the project more effectively reflects its crucial role in project economics in economic modeling, which aligns with the mainstream practice of international energy economics research.

2.2. Binary Discount Model

In the discounting model proposed by Galevskiy, a project with real assets (e.g., a hydrogen production system) is analogous to a portfolio consisting of two parts: a revenue asset, representing the economic potential of the project’s future output, and a liability, representing the various types of costs to be incurred over the life of the project. To reasonably reflect the difference in risk between the two types of cash flows, the model is discounted using two separate discount rates: the outflows (discount rate—rc) represent the cost of hydrogen projects; the inflow (discount rate—rs) represents the volume of hydrogen produced by the hydrogen project. The realization of the production volume is subject to fluctuations in renewable energy, reductions in equipment efficiency, and potential capacity constraint strategies, and is much more uncertain than the outflow volume. Therefore, rs should include a risk premium [26]. A similar binary discounting framework was also employed by Ponomarenko et al. (2022) in evaluating oil and gas field development projects, where the model effectively accounted for both market volatility and engineering risks in long-term investment analysis [21,27]. Thus, after introducing the binary cash flow discounting model, the conventional LCOH can be formed into the following hydrogen production cost model:

$${\mathrm{L}\mathrm{C}\mathrm{O}\mathrm{H}}_{\mathrm{B}\mathrm{D}}={\displaystyle \frac{{\sum }_{\mathrm{t}=0}^{\mathrm{T}}({\mathrm{C}\mathrm{a}\mathrm{p}\mathrm{e}\mathrm{x}}_{\mathrm{t}}+{\mathrm{O}\mathrm{p}\mathrm{e}\mathrm{x}}_{\mathrm{t}}+{\mathrm{F}\mathrm{u}\mathrm{e}\mathrm{l}\mathrm{C}\mathrm{o}\mathrm{s}\mathrm{t}}_{\mathrm{t}})/{(1+{\mathrm{r}}_{\mathrm{c}})}^{\mathrm{t}}}{{\sum }_{\mathrm{t}=1}^{\mathrm{T}}{\mathrm{H}}_{\mathrm{t}}/{(1+{\mathrm{r}}_{\mathrm{s}})}^{\mathrm{t}}}},$$

(2)

To ensure model consistency with a single discounting framework (e.g., WACC or investor expected return r), the binary discount rate is not arbitrary but must satisfy the arbitrage equilibrium equation and risk ranking constraints put forward by Galevskiy. Based on this framework, the authors further suggest that the overall discount rate r for a project (understood as the investor’s expected return or weighted average cost of capital (WACC)) should be treated as a weighted average return on a “portfolio of assets and liabilities” rather than being used directly to discount all cash flows. In other words, the proportion of the discounted value of production and the discounted value of costs in the overall portfolio should be used as weights for the corresponding discount rates that

$${\displaystyle \frac{{(\mathrm{I}}_0+\mathrm{C})}{{\mathrm{I}}_0}}\times {\mathrm{r}}_{\mathrm{s}}-{\displaystyle \frac{\mathrm{C}}{{\mathrm{I}}_0}}\times {\mathrm{r}}_{\mathrm{c}}=\mathrm{r},$$

(3)

where ${\mathrm{I}}_0$—initial investment (e.g., initial one-time cost of building a plant, equipment, etc.); C—Total cost of subsequent capital outflow (e.g., operation and maintenance, equipment replacement, fuel, etc.); r—total expected return on capital for the project—discount rate.

In selecting the discount rate, the risk-free discount rate, rf [28], was introduced as a benchmark to ensure the reasonableness of the discounted results. According to the principle of financial theory, “the higher the risk, the higher the required return,” the uncertainty in income is mainly due to market price fluctuations, and the uncertainty in costs is due to the excess of costs over expectations [28]. Therefore, the following constraints are introduced in the model:

$${\mathrm{r}}_{\mathrm{c}}\le {\mathrm{r}}_{\mathrm{f}}\le {\mathrm{r}}_{\mathrm{s}},$$

(4)

The risk-free rate, typically defined as the yield on long-term government bonds, represents the lower bound of the social time value of money. In the traditional discounting framework, a unified discount rate r is applied to all cash flows. This rate comprises the risk-free rate rf and a risk premium R, such that r = rf + R. However, applying the same discount rate to both cash inflows and outflows fails to account for the fundamentally different ways in which these flows respond to risk.

For inflows (i.e., revenues), it is logically consistent to assume that increased risk leads to a higher risk premium R, thereby reducing the present value of the inflows, appropriately reflecting the higher uncertainty. Accordingly, the discount rate for inflows should exceed the risk-free rate and may be expressed as rs = rf + R.

Conversely, applying this same logic to outflows (i.e., costs) produces economically inconsistent results. Using r = rf + R for outflows implies that increased risk would reduce the present value of costs, which is counterintuitive—Higher uncertainty should not make expenses appear smaller. To properly account for the impact of risk on outflows, the model should adopt an inverse relationship: risk leads to a reduction in the discount rate applied to outflows, thereby increasing their present value. Therefore, the discount rate for outflows should be less than the risk-free rate, represented as rc = rf − R′, where R′ is a risk premium specifically reflecting the uncertainty associated with outflows. In this way, when risk increases, a lower rc leads to a higher present value of costs, which is logically consistent with the notion that greater uncertainty should imply higher effective expenditures.

The model is built on a strict mathematical foundation: the present value of benefits PVs is a function of the risk premium λ = rs − rf, which is strictly monotonically decreasing and convex (i.e., decreasing marginal); while the present value of costs PVc is a function of the downward adjustment of the discount rate μ =rf − rc, which is strictly monotonically increasing and concave (i.e., decreasing marginal) [29]. In other words, as rs increases, the present value of benefits will continue to decrease, but at an increasingly slower rate. In contrast, the present value of costs will continue to increase as rc decreases, but at an increasingly slower rate.

This set of nonlinear marginal effects determines that, for a fixed expected rate of return r, there can be a number of combinations of rs and rc that satisfy the risk equilibrium conditions, so that the final LCOH is no longer a single value but falls within a closed interval with clear upper and lower bounds. These upper and lower bounds of LCOH are not predefined but are obtained as computational results by solving for all feasible combinations of rs and rc that satisfy the model’s equilibrium and risk constraints. In particular, when rs = rf, rc ≤ rf, discounted benefits are the most conservative (least strongly discounted) and discounted costs are the most aggressive (most strongly discounted), where the present value of benefits is the largest and the present value of costs is the smallest, so that the LCOH reaches its lowest possible value. Conversely, at rc = rf and rs ≥ rf, the discounting of costs is the most conservative, and the discounting of benefits is the most aggressive, resulting in the lowest present value of benefits and the highest present value of costs, thereby pushing LCOH to its highest possible value.

At the same time, it is important to note that the binary discounting framework used in this study is based on a set of explicit theoretical and operational assumptions that define its validity and scope of application.

Separation of identifiable cash flow categories: The model assumes that inflows and outflows can be independently identified, predicted, and evaluated over the project’s life cycle. This structural distinction is critical for applying different discount rates.

Static discount rate structure: It is assumed that the discount rate remains constant throughout the project period. This model does not include dynamic re-estimation of risk or interest rate adjustments over time.

No arbitrage equilibrium constraint: The model requires that the combined application of the inflow and outflow discount rates satisfies the no-arbitrage condition, i.e., the weighted average of these discount rates equals the total required rate of return (r). This constraint ensures consistency with investment pricing theory.

Simplified financial environment: The model excludes secondary financial impacts such as taxes, depreciation, asset terminal values, and inflation. The purpose is to conduct a comparative analysis under the assumptions of fixed prices and real-time horizons.

Project autonomy and input stability: The hydrogen project technology is independently assessed, with no cross-dependencies or scenario feedback. Input variables such as production, energy prices, and cost coefficients remain constant or follow a predetermined schedule.

Therefore, this model is suitable for early-stage technical and economic assessments of hydrogen production projects, especially when investment decisions must be made under conditions of limited market data and uncertainty. The model is most appropriate when production and cost structures are well-defined and long-term trends are assumed to be stable. However, its application may be limited under conditions of highly dynamic financing conditions, policy instability, or nonlinear feedback between project inflows and outflows.

However, unlike the traditional method using a single discount rate, the model can not only provide a “point estimate” based on the risk structure, but more importantly, it can construct a reasonable and mathematically closed interval of LCOH variation according to the risk characteristics of the project, which reflects the uncertainty of project costs under different risk perceptions and provides a richer basis for manager’s decision making. This reflects the uncertainty of project costs under different risk perceptions. Furthermore, especially in the case of hydrogen energy projects with large capital costs, long life cycles, and high production volatility, the introduction of a structured discount rate has significant theoretical significance and practical importance for more accurate estimation of unit costs and investment risk.

2.3. Calculation Methodology and Data Sources

To compare the impact of the binary cash flow discounting model on different hydrogen production routes, five routes are selected in this study: alkaline water electrolysis (ALK), proton exchange membrane (PEM) water electrolysis, solid oxide electrolysis (SOEC), natural gas steam reforming (SMR), and advanced carbon capture scheme (SMR + CCUS) [30]. This paper uses the parameters presented in

Table 1. Basic application parameters of different hydrogen production technologies.

Parameter Category	Parameter Description	ALK	PEM	SOEC	SMR	SMR + CCUS	Source
Annual production ($Q_{H2}$)	kg	20,000,000	20,000,000	20,000,000	20,000,000	20,000,000	-
Project execution time(T)	year	20	20	20	20	20	-
Lead time per year(h)	hour	4000	4000	4000	8000	8000	[31]
Electricity (RES)/natural gas price λ	USD/kWh	0.033	0.033	0.033	0.0239	0.0239	[32]
Average electricity prices for industry (CCUS)	USD/kWh	-	-	-	-	0.0795	[33]
Average system CAPEX	USD/kW. USD/kW	539.65	809.475	863.44	500	900	[34,35]
Average system efficiency (η)	kW/kg	54	60	40	44.5	41	[36,37]
CCUS system power consumption (99%) ${\eta }_{ccus}$	kWh/kg	-	-	-	-	0.6	[32]
OPEX (Share of CAPEX)	%	3%	3%	3%	4%	3.5%	[38]

Source: Compiled by the author from data [31,32,33,34,35,36,37,38].

. All values are given in USD as of 2023. The calculations assume that prices remain nominally constant throughout the life of the project.

To ensure a fair comparison of the different technologies, the same conditions are set. The hydrogen project is assumed to be implemented with an initial annual production capacity of 20,000 tons and a projected lifetime of 20 years. In the context of electrolysis technology, the SOEC implementation period is limited to five years due to technical reasons, necessitating three additional capital investments over the 20-year project cycle.

Calculation of installed capacity and capital costs. The installed capacity (in kW) for each technology is determined based on the given annual production volume and specific energy intensity [34,36]:

$$\mathrm{P}={\displaystyle \frac{{\mathrm{Q}}_{\mathrm{H}2}\times \mathsf{\eta }}{\mathrm{h}}},$$

(5)

where:

• $Q_{H2}$—annual volume of hydrogen (kg);
• η—specific energy consumption (kWh/kg);
• h—the number of operating hours per year.

Total initial investment:

$${\mathrm{I}}_0=\mathrm{C}\mathrm{A}\mathrm{P}\mathrm{E}\mathrm{X}\times \mathrm{P},$$

(6)

Operating expenses (OPEX)

$$\mathrm{O}\mathrm{P}\mathrm{E}\mathrm{X}=\mathsf{\varphi }\mathrm{\ast }{\mathrm{I}}_0,$$

(7)

where ϕ is the norm of operating costs.

Because the energy costs for hydrogen power projects are typically high, they are listed separately in the formula and are not included in operating costs.

For electrolysis technology:

$${\mathrm{C}\mathrm{O}\mathrm{S}\mathrm{T}}_{\mathrm{F}\mathrm{u}\mathrm{e}\mathrm{l}}={\mathrm{Q}}_{\mathrm{H}2}\times {\mathsf{\lambda }}_{\mathrm{r}\mathrm{e}}\times \mathsf{\eta },$$

(8)

where ${\mathsf{\lambda }}_{\mathrm{r}\mathrm{e}}$ is the price of electricity (RES) [39].

For SMR + CCUS:

$${\mathrm{C}\mathrm{O}\mathrm{S}\mathrm{T}}_{\mathrm{F}\mathrm{u}\mathrm{e}\mathrm{l}}={\mathrm{Q}}_{\mathrm{H}2}\times {\mathsf{\lambda }}_{\mathrm{g}}\times \mathsf{\eta }+{\mathrm{Q}}_{\mathrm{H}2}\times {\mathsf{\lambda }}_{\mathrm{e}}\times {\mathsf{\eta }}_{\mathrm{c}\mathrm{c}\mathrm{u}\mathrm{s}},$$

(9)

where ${\mathsf{\lambda }}_{\mathrm{g}}$—price for natural gas. ${\mathsf{\lambda }}_{\mathrm{e}}$—price of electricity for industry. ${\mathsf{\eta }}_{\mathrm{c}\mathrm{c}\mathrm{u}\mathrm{s}}$—power consumption of the CCUS system at a carbon capture rate of 99% [40].

For technologies with CCUS, the industrial electricity tariff is additionally taken into account.

Based on the resulting costs, a stream of cash outflows is generated for each year. Inflow streams are defined as either income from hydrogen sales or as the nominal equivalent of an investment return.

In the computational model, it is assumed that:

• In year 0, only capital expenditures are counted ${\mathrm{I}}_0$;
• In years 1 through 20, the values of operating costs and output are repeated annually;
• The calculations do not take into account residual values and tax effects.

The purpose of building this model is to create a reasonable calculation base for quantitative analysis of hydrogen technologies using a binary cash flow discounting model. Based on the entered parameters—technical, economic, and operational—cash flows reflecting the real conditions of the project functioning are formed. Then, for each technology, the Levelized Cost of Hydrogen (LCOH) is calculated in three variants: in the traditional approach with a single discount rate, as well as within the binary model—with minimum and maximum values of LCOH, due to different ratios between the rates of inflow and outflow flows.

The stepwise implementation of the model—from parameter input to constraint-based discounting and LCOH interval estimation—is described in detail in this section and can be directly reproduced from the presented equations.

3. Results

Based on input parameters and the theoretical binary discounting model, this section presents the calculation results for five technologies at a 5% risk-free discount rate, as determined by the yield on U.S. government bonds as of June 2025 [41]. The purpose is to construct an interval of possible LCOH values in units of 1% based on a change in the discount rate r from 5% to 25% and analyze the behavior of the boundaries of this interval, and also differentiate between the results of the LCOH formula of the binary cash flow discounting model and the traditional LCOH formula.

In the alkaline electrolysis (ALK) technology, the conventional LCOH value curve shows a steady upward trend as the discount rate increases from 5% to 25%. Throughout the observation period, the conventional LCOH value remains above the maximum LCOH value and maintains a stable distance from the upper and lower limits. The maximum and minimum values also increase synchronously with the rise in the discount rate, and the three curves maintain a stable interval, indicating a relatively uniform distribution of the technology’s valuation range at different discount rates, shown in Figure 1.

In the proton exchange membrane (PEM) electrolysis technology, the conventional LCOH curve remains well above the maximum value throughout the discount rate range, and the distance from the upper and lower limits continues to increase. As shown in Figure 2, the three curves grow rapidly with an increasing discount rate, demonstrating a steep trend. In particular, the gap between the traditional LCOH value and the maximum value becomes increasingly prominent as the discount rate exceeds 15%, indicating that the valuation bias widens with increasing discount rates.

In hydrogen production using solid oxide electrolysis (SOEC), the conventional LCOH value remains below the minimum value in the low discount rate range (5% to 12%). When the discount rate reaches 13%, it enters the range defined by the minimum and maximum values, and as the discount rate increases further, it gradually approaches and converges to LCOH-max, as shown in Figure 3. This is the only curve in the figure that crosses the assessment interval from below, while other technology pathways remain outside the interval throughout.

In natural gas steam reforming (SMR) technology, the three curves follow each other closely, rising steadily as the discount rate increases. The conventional value of LCOH remains slightly above the maximum value, but at a small distance, almost overlapping it. The distance between the three curves is extremely small, therefore indicating an extremely small range of graphical error, as shown in Figure 4. This means that the difference between the traditional LCOH estimate and the upper and lower bounds is extremely small and almost imperceptible in the figure.

With SMR + CCUS, the traditional LCOH curve remains above the maximum value throughout the discount rate range, albeit with small deviations from the upper and lower bounds. The trend remains consistent and relatively stable. The distance between the three curves is constant throughout the range, indicating that the valuation increases steadily with increasing discount rate and shows a stable trend, as shown in Figure 5.

4. Discussion

It is important to emphasize that the selected discount rate range of 5% to 25% in this study is not arbitrary. It reflects the actual investment evaluation practices for hydrogen energy projects across different countries and jurisdictions. According to industry analyses and official reports, in developed economies such as the United States and the European Union, discount rates for energy infrastructure projects typically range from 6% to 10%, with 8–9% being most commonly applied. In China, depending on the sector and source of financing, discount rates generally fall between 8% and 12% [42]. In contrast, developing or high-volatility economies tend to employ significantly higher discount rates. For example, in Russia, the applicable discount rate in the energy sector reached 20–24% in 2024–2025 due to elevated central bank interest rates and macroeconomic risk [43].

Therefore, the adopted range of 5% to 25% covers both low-risk benchmarks and high-risk investment environments, offering a comprehensive framework that accommodates stable, emerging, and transitional economies. This broader spectrum enhances the relevance of the sensitivity analysis of LCOH with respect to capital cost and risk, thereby increasing the practical applicability of the study’s findings in a global context.

As the discount rate increases from 5% to 25%, the Levelized Cost of Hydrogen (LCOH) for all technology pathways shows a steady upward trend, with the three LCOH estimation curves (LCOH-trad, LCOH-min, and LCOH-max) following a similar overall trajectory. In addition, across estimations, LCOH-trad remains consistently above LCOH-min, and there are no instances where traditional methods underestimate costs. Notably, with the exception of SOEC, traditional LCOH-trad estimates for the other four technologies (ALK, PEM, SMR, and SMR + CCUS) remain above the estimated range (defined by LCOH-min and LCOH-max) throughout the discount rate range, consistently exceeding the upper bound. This indicates a persistent overestimation of costs. For the PEM technology in particular, this deviation increased markedly as the discount rate increased, with the gap between the traditional estimate and the range becoming much larger than for the other technologies.

Among the five scenarios, only the SOEC technology exhibited a unique inflection point: its LCOH-trad was below the estimated range at lower discount rates (5% to 12%), entered the range between the upper and lower bounds at a discount rate of 13%, and then continued to rise toward the upper boundary. This phenomenon of “crossing the lower boundary of the range” has not been observed in other technologies and is a distinguishing feature of this case. In addition, the three LCOH estimation curves for the SMR technology are virtually identical, and the difference between the LCOH-trad and the upper and lower bounds is extremely small, making them virtually indistinguishable on the graph. This indicates minimal variation in the SMR technology estimates under different discount rates. The SMR path+ CCUS also shows similar stability, albeit with small deviations, but the trend remains unchanged.

SOEC technology represents a unique case where traditional LCOH valuation does not suffer from the over-discounting problem seen in other technologies. The SOEC has a relatively short life of only five years, and four independent capital investments are made during that time. This unique feature allows SOECs to avoid the typical over-discounting problems that accompany long-term capital-intensive projects such as PEM technology. In fact, the apparent underestimation of costs by the traditional method in the SOEC scenario stems from its discounting of future capital reinvestments. Since most costs occur in later project years (e.g., second, third, and fourth SOEC installations), the traditional model applies heavy discounting to these future outflows, which significantly reduces their present value. As a result, the total discounted cost appears artificially low, despite the actual capital burden being substantial. This effect is less visible in technologies with large upfront CAPEX, where discounting is applied to early costs that are less affected by the discount rate. The binary model, by assigning a lower discount rate to cost outflows, partially offsets this distortion and yields a more conservative estimate, even for short-lived technologies.

While the results for SOEC may seem to confirm the accuracy of the traditional LCOH calculation in this case, this does not mean that the traditional method is generally applicable. The traditional method assumes stable cash flows over the long term, but this is not the reality for new technologies such as SOEC. Thus, while the relatively short life of SOECs and multiple capital investments make the traditional method adequate in this particular case, this is a result of the fact that the SOEC cash flow structure avoids the systematic errors inherent in the traditional approach. This does not mean that the traditional method is appropriate for all new or capital-intensive technologies.

The results of the study show that traditional LCOH valuation methods can lead to overly conservative cost projections, especially for technologies such as PEM. For investors and policymakers, this can mean underestimating potential returns on investment and overestimating financial risk. As the market matures and more obvious risks emerge, the model is adjusted to reflect these changes, offering a more accurate estimate of future costs. Policymakers may also consider applying such a model when designing support mechanisms for hydrogen energy projects, such as subsidies or incentives that take into account the specific financial risks of each technology.

While the results provide valuable information, several assumptions and limitations affect the generalizability of the findings. One key assumption in this study is the use of constant discount rates throughout the analysis. In reality, discount rates can fluctuate depending on external factors such as inflation, policy changes, or market conditions. Therefore, applying the model to real-world scenarios may require additional calibration to account for such fluctuations. In addition, while the binary cash flow discounting model provides a more dynamic cost estimate, it remains sensitive to certain parameters, including estimates of capital expenditures (CAPEX) and operating costs (OPEX). Fluctuations in energy prices and hydrogen output—both key variables in the LCOH formulation—may also significantly affect the results. For example, higher electricity or fuel costs would increase the present value of outflows, while lower hydrogen output would amplify unit costs. These effects could widen the LCOH range and reinforce the relevance of a risk-adjusted modeling framework. Future research should focus on validating the model using real data and incorporating additional variables that may impact hydrogen production costs, such as regional market conditions or technological advancements in hydrogen production methods.

Moreover, the binary discounting model has intrinsic drawbacks. It introduces additional complexity by requiring the specification of two discount rates and a risk allocation structure, which may be difficult to estimate accurately in practice. The added computational burden and potential for subjective input further limit its practical adoption in standardized policy or industry evaluations.

5. Conclusions

This study presents and formalizes a binary cash flow discounting model used to calculate the costs of hydrogen production (BD-LCOH). At the theoretical level, it is shown that the separate discounting of inflows (rate rs) and outflows (rate rc) allows for accounting for risk asymmetry between the two types of cash flows, avoiding the systematic distortions inherent in traditional single-rate models. The BD-LCOH analytical formula gives upper and lower bounds on costs that depend on the combination of discount rates and project parameters.

Detailed cash flow models were developed for five industrial process routes—ALK, PEM, SOEC, SMR, and SMR + CCUS—for a 20-year period. The same production conditions (20,000 tons of H₂/year) and a single corridor of discount rates (r = 5–25%) were assumed. Numerical calculations showed that for all technologies, the conventional LCOH increased with increasing r, but its value was consistently higher than BD-LCOH-max and sometimes outside the credible interval. The largest overestimation was observed for PEM (∆LCOH_trad ≈ USD 2.1/kg), moderate overestimation for ALK and SOEC, and minimal overestimation for SMR and SMR + CCUS. In addition, the range of uncertainty (LCOH_max–LCOH_min) was largest for PEM and smallest for SMR.

The modeling results support the hypothesis that the classical approach does not have sufficient risk sensitivity. The use of a single discount rate leads to overestimation (and underestimation for SOEC at low r values), which can lead to erroneous rejection or approval of projects. In contrast, the binary cash flow discounting model generates a credible range for BD-LCOH and provides a more accurate differentiation of technologies based on investment attractiveness, which is particularly important in an environment of high uncertainty and limited capital.

A central innovation of this study is the correction of a fundamental methodological flaw in the traditional LCOH framework. In conventional models, an increase in the discount rate leads to a reduction in the present value of costs, implying that higher uncertainty results in lower projected expenditures—a conclusion that contradicts basic economic reasoning. The binary discounting model addresses this inconsistency by applying distinct discount rates to inflows and outflows, thereby ensuring that variations in discounting produce coherent and economically interpretable outcomes. This modification transforms the LCOH from a single-point estimate into a bounded interval, thereby enhancing the robustness and reliability of investment appraisals.

Future work should focus on further validating the binary cash flow discounting model using real data and extending it to include more dynamic factors, such as fluctuating market conditions, regional policy influences, and technological advances. This may also include the use of time-varying discount rates to better reflect the changing financial conditions over the project life cycle. While the current study does not explicitly incorporate policy instruments or financial support schemes, the risk-sensitive framework established here provides a conceptual foundation for future research in these areas. By distinguishing between revenue-side and cost-side risks, the model may support the development of more targeted subsidy designs or public-private investment mechanisms tailored to the financial characteristics of different hydrogen technologies. Policymakers and investors may consider using this model to better assess the economic viability of hydrogen projects under uncertainty.