Hydrogen Production Cost Forecasts since the 1970s and Implications for Technological Development

Miyagawa, Tomonori; Goto, Mika

doi:10.3390/en15124375

Open AccessEditor’s ChoiceReview

Hydrogen Production Cost Forecasts since the 1970s and Implications for Technological Development

by

Tomonori Miyagawa

and

Mika Goto

^*

Department of Innovation Science, School of Environment and Society, Tokyo Institute of Technology, 3-3-6, Shibaura, Minato-ku, Tokyo 108-0023, Japan

^*

Author to whom correspondence should be addressed.

Energies 2022, 15(12), 4375; https://doi.org/10.3390/en15124375

Submission received: 29 May 2022 / Revised: 13 June 2022 / Accepted: 14 June 2022 / Published: 15 June 2022

(This article belongs to the Section A5: Hydrogen Energy)

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Abstract

:

This study reviews the extant literature on hydrogen production cost forecasts to identify and analyze the historical trend of such forecasts in order to explore the feasibility of wider adoption. Hydrogen is an important energy source that can be used to achieve a carbon-neutral society, but the widespread adoption of hydrogen production technologies is hampered by the high costs. The production costs vary depending on the technology employed: gray, renewable electrolysis, or biomass. The study identifies 174 production cost forecast data points from articles published between 1979 and 2020 and makes a comparative assessment using non-parametric statistical tests. The results show three different cost forecast trends across technologies. First, the production cost of gray hydrogen showed an increasing trend until 2015, but started declining after 2015. Second, the renewable electrolysis hydrogen cost was the highest of all, but has shown a gradual declining trend since 2015. Finally, the biomass hydrogen cost has been relatively cheaper up until 2015, after which it became the highest. Renewable electrolysis and biomass hydrogen will be potential candidates (as principal drivers) to reduce CO₂ emissions in the future, but renewable electrolysis hydrogen is more promising in this regard due to its declining production cost trend. Gray hydrogen can also be an alternative candidate to renewable electrolysis hydrogen because it can be equipped with carbon capture storage (CCS) to produce blue hydrogen, although we need to consider additional production costs incurred by the introduction of CCS. The study discusses the technological development and policy implications of the results on hydrogen production costs.

Keywords:

production cost forecast; gray hydrogen; renewable electrolysis hydrogen; biomass hydrogen; historical trend

1. Introduction

Since the Paris Agreement came into effect in November 2016 and entered its implementation phase in 2020, leaders, policymakers, and society have been concerned about meeting the Agreement’s goal of carbon neutrality by 2050, reducing greenhouse gas emissions. Renewable energy generation has steadily increased in many countries, with its cost coming down progressively. However, the deployment of renewable energy has been a constraint for the transmission grid owing to the intermittency of solar photovoltaics (PV) and wind power generation. Alternative renewable energies, such as biomass, can avoid the intermittency problem, but the production costs of such alternatives are still higher than that of solar PV and wind power energy.

Hydrogen has the potential to solve both carbon emission and electricity intermittency problems. It can replace fossil fuels in economic activities, such as energy, transportation, building, and industries. Hydrogen can be produced by water electrolysis using abundant renewable electricity, adequately mitigating the problem of transmission grid constraints.

Academic researchers have debated the techno-economic aspects of hydrogen for decades. Maggio et al. (2019) [1] discussed the use of hydrogen to balance the mismatch between renewable energy production and demand; however, they only reviewed 12 articles to analyze hydrogen production cost forecasts from 2008 to 2017. Therefore, it was thought that if we looked into a larger number of papers covering a longer period, the trends and conclusions might be different. This was the motivation for this research. We reviewed approximately 300 articles on renewable energy system-based hydrogen (published between 2008 and 2017) that also analyzed the costs of hydrogen production from electrolysis, steam methane reforming (SMR), and other processes. The general conclusion from these studies was that, while there were progressive cost improvements over time, as expected, the actual reported costs did not change significantly.

We first focused on the articles analyzing hydrogen delivery costs, as the total costs delivered to the final consumers could then be compared with incumbent alternatives, such as petrol for automobiles. For the search process, we selected the keywords, “hydrogen”, “economic*”, and “supply chain”, to identify articles containing techno- or socio-economic analyses of the hydrogen supply chain, as summarized in Table 1. The asterisk attached to a keyword indicates a wildcard that can be replaced with any word. The search identified 98 articles matching the keywords.

The search found articles on various technologies in the supply chain. The range of technologies covered by the screening was wide, as shown in Table 1 and summarized in Table 2. The variety of potential combinations of technologies in the supply chain, however, made it difficult to find a trend in the cost forecast because the possible combinations were too broad to identify the practical trends from a limited number of datasets.

Therefore, we decided to narrow down the focus to hydrogen production costs with different technologies, along with the year of publication of the study. With increased awareness of carbon emission reductions, the number of published articles can only increase with time. Until recently, renewable electricity was more expensive than other generation technologies; therefore, renewable electrolysis technology has been ignored in the research on hydrogen mass production of hydrogen, while incumbent hydrocarbon or coal- and gas-based hydrogen have been intensively discussed. However, the cost of hydrogen production is likely to reduce over time, regardless of the technologies applied.

The subsequent sections describe the trends in hydrogen production forecasts from 1979 to 2020, organized as follows: Section 2 explains hydrogen production technologies and their production costs; Section 3 describes the analytical method used in this study; Section 4 summarizes the findings of this study; Section 5 concludes the paper.

2. Hydrogen Production Technologies and Cost Estimations

This section briefly explains hydrogen production costs from a technological perspective and discusses the cost forecast method.

2.1. Hydrogen Production Technologies

Hydrogen production methods can be classified into two categories: hydrocarbon-based and non-hydrocarbon-based (Sharma, Agarwal, and Jain (2021)) [2]. The methods using fossil fuels are SMR, coal gasification, and biomass gasification. Currently, hydrogen is produced almost exclusively from fossil fuels through SMR (Balat (2008)) [3], which is the most popular process for producing hydrogen from natural gas. It requires a high process temperature, and burning natural gas is the most common practice for providing the required heat. Steam reformation of natural gas produces hydrogen-rich gas.

SMR is a three-step hydrogen production process. The main steps adopted in this method are reforming, shift-conversion gas purification, and methanation. In the first step, the SMR method produces a mixture of carbon oxide (CO) and hydrogen; that is, methane is catalytically reformed at elevated temperatures and pressures to produce a syngas mixture of H₂ and CO. A catalytic shift reaction is then performed to combine CO and H₂O to arrive at the H₂ product. The hydrogen duct is purified via absorption, and the reforming step is described by [CH₄ + H₂O → CO + 3H₂].

Hydrogen can be produced in various other ways: (a) gasification of coal and syngas or artificial water gas [CO + H₂] from coal can be reformed to hydrogen as [CO + H₂O → H₂ + CO₂]; (b) biomass gasification, steam gasification, etc., described as [Biomass → H₂ + CO₂ + CO + N₂]; and, (c) water and steam electrolysis are classified into two types of water electrolyzers, alkaline electrolyte and polymer electrolyte membrane (PEM), and the net reaction for producing hydrogen and oxygen by water electrolysis is described as [H₂O → H₂ + 1/2O₂].

2.2. Cost Estimation Methods

The method of calculating hydrogen production cost varies with the production technology and process. However, it can be summarized, as applicable to most production technologies and processes, as follows (Lysenko, Sadaka, and Brown (2012)) [4].

(Hydrogen production cost = annual operating cost/annual hydrogen production), in which (Annual operating cost = annual capital charge + total direct expenses + total indirect costs), (Annual capital charge = annualized fixed capital + annualized working capital), (Total direct expenses = raw material + operating labor + supervisory labor + maintenance and repair cost + operating supplies + laboratory charges + patents and royalties), and (Total indirect costs = overhead + local taxes + insurance + general expenses).

The methods of sourcing input data vary but can be mostly grouped into two: the H2A method and reference-to-peer analysis. Although this study does not differentiate between these methods when comparing hydrogen production cost forecasts, we briefly address their characteristics.

The H2A method was defined by the US Department of Energy (DOE) [5]. The procedure is reliable and transparent because it is based on transparent reporting of process design assumptions and a consistent cost analysis methodology for the production of hydrogen. Data on capital and operating costs and financial parameters are provided by the DOE as default values. Users can enter their own values instead of the given default values, so they are stable, flexible, and useful for cost analysis users. The models use a standard discounted cash flow rate of return (ROR) analysis methodology to determine the hydrogen selling cost, depending on the desired internal ROR.

Another popular method, peer analysis, refers to peer articles that analyze and provide cost data in projects with similar production methods and regions. Both methods are viable for hydrogen cost analysis, but the H2A method is more transparent and reliable, while peer references are likely to suffer from ambiguity of the original data source, different estimation times, and currency conversions.

3. Methods

This study used Elsevier’s Scopus search engine to review academic articles investigating hydrogen production costs published up to January 2021. First, we set keywords for “hydrogen production costs” and other fields, as listed in Table 3.

The search identified 144 articles that matched the selected options. Figure 1 shows the historical trend in the publication of these 144 articles over the years. The number of articles significantly increased after 2000, perhaps due to the increasing interest in hydrogen. Interestingly, as the figure shows, the number of articles increased and decreased in waves, once around 2010 and then after 2015. We also investigated the reasons for this volatility.

Thereafter, we summarized the hydrogen costs estimated in these articles. Appendix A Table A1 summarizes the forecasted hydrogen production costs, as identified from 174 cases of hydrogen production costs in 114 articles; 30 articles did not provide the estimated hydrogen production costs. Each hydrogen production cost was converted to US dollars per kilo of hydrogen, using the conversion rate described in Table 4.

The collected production cost data were classified by (1) the year of publication of the article, (2) the period of projection of the hydrogen production costs in the articles (i.e., the projected time), and (3) the hydrogen production method.

Based on the above classifications, this study analyzed the trend of historical hydrogen production cost forecasts using non-parametric tests. Specifically, we used the Kruskal–Wallis rank-sum test and Wilcoxon rank-sum test with respect to (a) the chronological order of the years of publication and (b) production methods. Production methods are classified as (i) gray hydrogen (coal gasification and SMR), (ii) renewable electrolysis hydrogen (solar electrolysis and wind electrolysis), (iii) biomass hydrogen (biomass gasification and biomass reforming), and (iv) others (biomass fermentation, chemical looping, electrolysis other than renewables, solar thermochemical, waste gasification, reforming, etc.). Non-parametric analyses were performed using STATA software version 16.1.

The collected data are summarized in Table 5. Figure 2 plots the data on the scatter chart, with a horizontal axis (X) of projected time (forecast years) and a vertical axis (Y) of the forecasted production costs.

We performed non-parametric tests (Kruskal–Wallis rank-sum test and Wilcoxon rank-sum test) in four steps. Note that these tests sort the cost forecasts from cheaper to higher (more expensive) ones and provide them with sequential ranks. Thus, for example, a cost ranked 10 is cheaper than that ranked 100. First, we applied the Wilcoxon rank-sum test to the production cost forecast of each production method (i.e., gray, renewable electrolysis, and biomass) by comparing them with all other categories over the full period to allow us to examine whether the production cost of each method was statistically different from the others (see Figure 3).

In the second step, we conducted the Wilcoxon rank-sum test, as in the first step, but for different groups of the years of publication (before 2000, 2001–2010, 2011–2015, and 2016–2020), to compare the changes in the costs over time. It is ideal to compare the cost data not only in publication years, but also in combination with the forecast years and regions in the second step, as the production costs of gray and renewable electrolysis hydrogen are highly dependent on forecast years and regions, and whether cheap gas, coal, or renewable electricity are available, which vary according to region. However, the 174 data samples are not sufficient to analyze fragmented categories with combinations of forecast years and regions. Therefore, as Figure 4 shows, this study focused on publication years, recognizing the limitation that there are no segregations of forecast years and regions in the same categories of the publication year ranges (before 2000, 2001–2010, 2011–2015, and 2016–2020).

The third step analyzed each production method using the Kruskal–Wallis test to identify whether there were significant differences among the time categories (see Figure 5).

Finally, in the fourth step, as Figure 6 shows, each production method was analyzed using the Wilcoxon rank-sum test between the adjacent time categories to identify which time category was different (or had changed) from the adjacent time categories.

4. Results

We present the results of the non-parametric tests in the four steps presented in Section 3 for each production method of gray, renewable electrolysis, and biomass, as follows.

4.1. Gray Hydrogen

4.1.1. Wilcoxon Rank-Sum (First and Second Step) Tests between Gray Hydrogen and Other Hydrogen Types over Time

Table 6 presents the results of the first and second step tests. The columns from left to right indicate the results for the entire period, i.e., before 2000, 2001–2010, 2011–2015, and 2016–2020. Over the entire period, the null hypothesis was rejected at the 1% significance level, which reveals that the gray hydrogen samples differed from those of the other types in the entire period at the 1% significance level. In particular, the result of the rank-sum of gray hydrogen (2225) was lower than the expected rank-sum (3850), which indicates that the cost of gray hydrogen tends to be lower than that of the other hydrogen types.

Similar to the entire period, the other columns show the results of the rank-sum tests for the four different time categories. The null hypothesis was rejected for all categories at either the 1% or 5% levels. In addition, each rank-sum (i.e., 38, 190.5, 160, and 183) was far lower than expected (i.e., 63, 337.5, 299, and 330). These test results, therefore, confirm that the costs of gray hydrogen are different from (and always lower than) those of the other hydrogens in any time category.

4.1.2. Kruskal–Wallis Test for Gray Hydrogen for Different Periods (Third Test)

In the third step, the different categories of gray hydrogen production costs were examined using the Kruskal–Wallis test. Table 7 shows that the null hypothesis (i.e., all samples across categories are identical) is rejected at the 1% significance level (p = 0.0053). Therefore, we consider that not all production costs are identical for all categories of the published years.

4.1.3. Wilcoxon Rank-Sum Test for Gray Hydrogen for Different Periods (Fourth Step)

The next is the fourth step, which is to identify the category that is not identical to the other adjacent categories of published years. In other words, this test can show how the gray hydrogen cost forecast has evolved. Table 8 shows that only the adjacent categories before 2000 and 2001–2010 were rejected at the 5% significance level, and those between 2001–2010 and 2011–2015, and between 2011–2015 and 2016–2020 were not rejected at effective significance levels. It is interesting to note that the significant difference between categories decreased over time. Further, when we look at the gap between the observed rank-sum and the expected sum before 2000 and 2001–2010, and between 2001–2010 and 2011–2015, the observed rank-sum was higher than that of the expected one in the later period (i.e., 197 > 165, 222 > 188.5), while for comparison, between 2011–2015 and 2016–2020, the rank-sum of the latter period was lower than that of the expected (i.e., 108 < 120). This indicates that the gray hydrogen production cost was forecasted to increase until 2015, but this trend of the forecasted cost was reversed (to decline after 2016).

4.2. Renewable Electrolysis Hydrogen

This section performs non-parametric tests for renewable electrolysis hydrogen using the same procedure as gray hydrogen.

4.2.1. Wilcoxon Rank-Sum (First and Second Step) Tests between Renewable Electrolysis Hydrogen and Other Hydrogens over Time

Table 9 presents the results of the first and the second step tests. The columns indicate the results for the entire period before 2000, 2001–2010, 2011–2015, and 2016–2020, from left to right.

For the entire period, the null hypothesis was rejected at the 1% significance level, which confirms that the samples of renewable electrolysis hydrogen were different from those of the other hydrogens in the entire period at the 1% significance level. In addition, the rank-sum of renewable electrolysis hydrogen (5628) is higher than the expected rank-sum (4200), which indicates that the costs of renewable electrolysis hydrogen tend to be higher than those of other hydrogens.

Similar to the entire period, the other columns show the rank-sum test results in the four different time categories (before 2000, 2001–2010, 2011–2015, and 2016–2020). The null hypothesis was rejected at the 1%, 5%, or 10% levels. Each rank-sum (48, 282, 338.5, and 1009.5) was far higher than the expected value (31.5, 202.5, 230, and 858). These test results, therefore, confirm that the costs of renewable electrolysis hydrogen are different from and constantly higher than those of the other hydrogens in any time category.

4.2.2. Kruskal–Wallis Test for Renewable Electrolysis Hydrogen for Different Periods (Third Test)

In the third step, the different categories of renewable electrolysis hydrogen production costs were examined using the Kruskal–Wallis test. Table 10 shows that the null hypothesis (i.e., all samples across categories were identical) was not rejected at the 10% significance level (the p-value was 0.6573). This implies that the production cost of renewable electrolysis hydrogen did not evolve or it varied materially over time.

4.2.3. Wilcoxon Rank-Sum Test for Renewable Electrolysis Hydrogen for Different Periods (Fourth Step)

Thereafter, we conducted the fourth step test to identify which category was not identical to the other categories of publication years. Table 11 presents the results of the Wilcoxon rank-sum test. The table demonstrates that none of the null hypotheses were rejected, which indicates that the adjacent time categories did not reveal a significant difference in the forecasted production costs. However, the absolute z-values increased over time; in particular, the z-value during 2016–2020 (0.954) was higher than the previous periods (0.185, 0.245). Further, the rank-sum of 2016–2020 (454) was lower than its expected value (481), which indicates that the production cost forecasts for 2016–2020 were lower than those for 2011–2015. Therefore, these tests showed that the production costs of renewable electrolysis hydrogen gradually declined after 2015, although the changes were not statistically significant at the 10% level.

4.3. Biomass Hydrogen

4.3.1. Wilcoxon Rank-Sum (First and Second Step) Test between Biomass Hydrogen and Other Hydrogens over Time

Table 12 presents the results of the first and second step tests. The columns indicate the results for all periods before 2000, 2001–2010, 2011–2015, and 2016–2020, from left to right.

The null hypothesis—that there is no difference between the production costs of biomass and the other hydrogens—was not rejected for the entire period, even at the 10% significance level. This means that the samples of biomass hydrogen were not materially different from those of other hydrogens during this period. The result was also confirmed for biomass hydrogen by comparing the rank-sum (2257.5) with the expected rank-sum (2538), where they were not significantly different from each other. This indicates that the cost of biomass hydrogen is not significantly lower than those of other hydrogens.

Similarly, the other columns show the results of the rank-sum tests in different time categories (i.e., before 2000, 2001–2010, 2011–2015, and 2016–2020). Only the test for the period 2011–2015 was rejected at the 5% significance level, and the others were not rejected at the 10% significance level. Each observed rank-sum until 2015 (i.e., 38.5, 134.5, and 78) was lower than the expected value (i.e., 42, 180, and 138), whereas the rank-sum after 2016 (i.e., 427.5) was higher than the expected value (i.e., 363). This indicates that the cost of biomass hydrogen was lower than those of the other hydrogens until 2015 but increased after 2016.

4.3.2. Kruskal–Wallis Test for Biomass Hydrogen for Different Periods (Third Step)

In the third step, the different categories of biomass hydrogen production costs were examined using the Kruskal–Wallis test. Table 13 shows that the null hypothesis (i.e., all samples across categories were identical) was rejected at the 1% significance level (p = 0.0008), indicating that the production cost of biomass hydrogen varied significantly over time.

4.3.3. Wilcoxon Rank-Sum Test for Biomass Hydrogen for Different Periods (Fourth Step)

The Wilcoxon rank-sum tests in the adjacent time periods in Table 14 show that the rank-sum of the later period category (i.e., 66, 49, and 129) was always higher than expected (i.e., 52, 45, and 99), which indicates that the production cost of biomass hydrogen increased over time compared to the adjacent previous time periods. The absolute values of z fluctuated over time at 2.378, 0.516, and 3.015 for each comparison. Thus, at least two comparisons (before 2000 vs. 2001–2010 and 2011–2015 vs. 2016–2020) reveal statistically significant differences between the two adjacent time categories.

5. Discussion

5.1. Gray Hydrogen

From the non-parametric test results, we confirm that the production cost of gray hydrogen was lower than that of other hydrogens during the study period. We surmise that this is due to the trend of the low cost of fuel (coal and gas). However, based on the articles’ publication years, it can be concluded that the trend of the production cost of gray hydrogen increased until 2010, and started declining after 2015.

This change in trend can be linked to the fuel cost. Until 2010, the cost of resources, such as coal and gas, was expected to increase in the future, but after 2010, there was an expectation of price depression, which was reflected in the decline in fuel cost in the mid-2010s.

Figure 7 and Figure 8 show the price trends of coal and gas between 1990 and 2020, averaging those in Europe, the US, and Japan. Fuel cost units in these figures were converted from the original data sources to make the values consistent with hydrogen production costs presented in Table 5 and Figure 2, using conversion rates in Table 4. Price trends became volatile after 2005 and were mostly subject to the global economic situation (of China’s economic growth, in particular). However, the trend of the 4-year rolling average mostly corresponded to the estimation of gray hydrogen, as described in Section 4, where prices rose until 2010 and plateaued between 2011 and 2015, after which prices decreased gradually.

In order to compare Figure 7 and Figure 8, which plot the trends of actual coal and gas prices with those used for gray hydrogen cost forecasts in articles reviewed in this study, we extracted gas prices assumed in those articles and plotted them in Figure 9. The reason why we focused on gas prices is due to their higher levels compared to coal prices, thereby higher impacts were expected on gray hydrogen costs. Although the price levels are somewhat higher in Figure 9 than those in Figure 8, it is interesting to observe that the cost trend over time in Figure 9 (cost assumption) is roughly aligned with Figure 8 (actual cost), particularly with the UK trend, except for recent data in 2018 (higher cost case) and 2020. This implies that gray hydrogen cost forecasts in the articles properly assumed fuel prices in the calculations and we needed to consider future fuel cost developments when we assessed the gray hydrogen production costs.

5.2. Renewable Electrolysis Hydrogen

The non-parametric test results confirm that the production cost of renewable electrolysis hydrogen was higher than that of the other hydrogens over the study period. We estimate that this is due to the higher supply costs of renewable power, such as solar PV and wind, and capital expenditure in electrolyzers. When we compare the different time categories of the published articles, we could see that the production costs did not change significantly, which demonstrates that the renewable electrolysis hydrogen cost was not yet sufficiently low. However, articles published after 2015 tended to show a gradual decline in costs. We assume that these were influenced by declining renewable power costs (International Renewable Energy Agency (2020)) [8] and capital expenditures of electrolyzers (Christensen (2020)) [9].

Figure 10 shows the historical trend of the global, weighted-average utility scale, and the leveled cost of energy (LCOE) using various technologies. It provides evidence that the costs of solar and wind energy decreased over time. In particular, the LCOE of solar PV sharply declined, while those of offshore and onshore wind gradually declined.

Figure 11 shows the historical trend of the capital expenditures in electrolyzers in 2020 (US dollars). The expenditures are for two different technologies: alkaline electrolysis (AE) and proton exchange membranes (PEM). The data show that although there is a gradual declining trend in capital expenditures, the trend is not clear because of the low coefficient of determination (R²), AE: R² = 0.0383, REM: R² = 0.2024.

Therefore, we assume that the cost reduction of renewable electrolysis hydrogen can be attributed to the reduction in the renewable power generation cost rather than in electrolyzer capital expenditures.

5.3. Biomass Hydrogen

Non-parametric tests confirm that the biomass hydrogen production cost was not significantly different from the other hydrogen types, but its cost was lower. Interestingly, its production cost increased over time and exceeded that of other hydrogens after 2015. This is because the main inputs of biomass hydrogen production are agricultural products, and the costs of agricultural products and labor increased, especially after 2015 (Food and Agriculture Organization of the United Nations (2020) [10], International Labour Organization (2020) [11]).

As shown in Figure 9, the historical trend of biomass power generation costs between 2010 and 2020 did not directly correspond to biomass hydrogen production costs. This relates to the technical aspect of hydrogen production because biomass hydrogen is mostly produced from biomass gasification rather than water electrolysis from biomass power generation.

6. Conclusions

This study investigated 174 hydrogen production cost data points from 114 articles published between 1979 and 2020. The non-parametric tests of these data present certain trends in the historical hydrogen cost forecasts. Gray hydrogen was the cheapest hydrogen production method, and its cost increased over time, while this trend reversed after 2015 due to weak fuel resource cost forecasts. The cost of renewable electrolysis hydrogen was always higher than that of the other hydrogens but started showing a gradual declining trend after 2015 because of the reduced renewable electricity cost and electrolyzer capital expenditure. The cost of biomass hydrogen increased similar to that of gray hydrogen and became higher than that of the others after 2015, due to increasing costs of agricultural products and labor. Renewable electrolysis hydrogen and biomass hydrogen will be potential candidates (as principal drivers) to reduce CO₂ emissions in the future, but renewable electrolysis hydrogen is more promising in this regard due to its declining production cost trend. Moreover, given that the production cost of gray hydrogen is showing a declining trend, it can be an alternative candidate to renewable electrolysis hydrogen because it can be equipped with carbon capture storage (CCS) to produce blue hydrogen, although we need to consider how much the cost of gray hydrogen will increase if the cost of CCS is added. The cost level of CCS depends on regions and local conditions, e.g., see International Energy Agency (2020) [12] on the cost trend of CCS that evidenced the case of a 35% cost reduction per CO₂ ton between 2014 and 2017 (Boundary Dam and Petra Nova in Canada). With the declining trend of the CCS cost, however, the total production cost of gray hydrogen with CCS will need to be competitive with renewable electrolysis hydrogen if we are to consider it as a future option.

There is a future task related to the data classification in this study. We conducted this study using 174 hydrogen production cost datasets but did not use the information on the cost forecast projection year and geographical regions (e.g., US, EU, or Asia). As pointed out in Section 3, these classifications with the projection year and regional segmentation can provide more detailed findings on the change in production costs, since those of gray and renewable electrolysis hydrogens are highly dependent on the availability of cheap gas, coal, or renewable electricity, which vary across subject regions. Moreover, the analysis of production cost forecasts in different projected years should be useful to investigate how production costs could change in the future. To conduct this extended study and achieve a more segmented analysis in a statistically significant manner, we need to expand the data source with an increased number of data points with detailed classifications by region and the forecasted year.

Funding

This research was funded by the Japan Society for the Promotion of Science (JSPS) through a Grant-in-Aid for Scientific Research (KAKENHI) 20KK0106.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Table A1. List of production cost forecasts in articles.

Authors	Issued Year	Projected Timing	Production Method	Country	Region	Production Cost Forecast
Beghi G.E. [13]	1979	1977	Electrolysis			USD 7–8/GJ
Broggi A. et al. [14]	1981	1996	Electrolysis			USD 8.40–9.21/GJ
Carleson G. [15]	1982	1985	Biomass (gasification)	Sweden	Europe	USD 9.80/GJ
Carleson G. [15]	1982	1985	Coal gasification	Sweden	Europe	USD 5.05/GJ
Carleson G. [15]	1982	1985	Electrolysis	Sweden	Europe	USD 9.65/GJ
Carleson G. [15]	1982	1985	SMR	Sweden	Europe	USD 4.45/GJ
Estève D. et al. [16]	1982	1986	Electrolysis (solar)			USD 1.4/m³
Estève D. et al. [16]	1982	1995	Electrolysis (solar)			USD 0.5/m³
Carleson G. [15]	1982	2005	Biomass (gasification)	Sweden	Europe	USD 10.70/GJ
Carleson G. [15]	1982	2005	Coal gasification	Sweden	Europe	USD 6.30/GJ
Carleson G. [15]	1982	2005	Electrolysis	Sweden	Europe	USD 11.60/GJ
Carleson G. [15]	1982	2005	SMR	Sweden	Europe	US7.80/GJ
Carleson G. [15]	1982	2025	Biomass (gasification)	Sweden	Europe	USD 11.6/GJ
Carleson G. [15]	1982	2025	Coal gasification	Sweden	Europe	USD 8.0/GJ
Carleson G. [15]	1982	2025	Electrolysis	Sweden	Europe	USD 14.1/GJ
Carleson G. [15]	1982	2025	SMR	Sweden	Europe	US12.4/GJ
Krikorian O.H. et al. [17]	1983	1980	Electrolysis	US	N America	USD 11.53–14.43/GJ
Aochi A. et al. [18]	1989	1985	Electrolysis	Japan	Asia	JPY 77.2/m³
Ogden J.M. [19]	1991	2000	Electrolysis (solar)	US	N America	USD 9–14/GJ
Shiga H. et al. [20]	1998	1995	Biomass (gasification)	India	Asia	JPY 310/GJ
Ewan B.C.R. et al. [21]	2005	2005	Coal gasification	UK	Europe	USD 1.621/kg
Ewan B.C.R. et al. [21]	2005	2005	Coal gasification + CCS	UK	Europe	USD 3.114/kg
Da Silva E.P. et al. [22]	2005	2005	Electrolysis	Brazil	S America	USD 10.3/kg
Ewan B.C.R. et al. [21]	2005	2005	Electrolysis (solar)	UK	Europe	USD 14.95/kg
Ewan B.C.R. et al. [21]	2005	2005	Electrolysis (wind)	UK	Europe	USD 6.081/kg
Chen Z. et al. [23]	2005	2005	SMR	US	N America	USD 0.74–0.97/kg
Ewan B.C.R. et al. [21]	2005	2005	SMR	UK	Europe	USD 0.982/kg
Ewan B.C.R. et al. [21]	2005	2005	SMR + CCS	UK	Europe	USD 1.575/kg
Chen Z. et al. [24]	2006	2006	SMR	US	N America	USD 0.74–0.97/kg
Giaconia A. et al. [25]	2007	2005	Electrolysis	Italy	Europe	USD 7.531/kg
Dowaki K. et al. [26]	2007	2007	Biomass (gasification)	Japan	Asia	USD 5.75–7.86/kg
Forsberg P. et al. [27]	2007	2010	SMR (@ refueling station)	US	N America	USD 6.0/kg
Forsberg P. et al. [27]	2007	2030	SMR (@ refueling station)	US	N America	USD 4.1/kg
Lv P. et al. [28]	2008	2007	Biomass (gasification)	China	Asia	USD 1.69/kg
Balat M. [3]	2008	2008	Biomass	Turkey	Middle East	USD 10–14/GJ
Muradov N. et al. [29]	2008	2008	Biomass (reforming)	USA	N America	USD 3.00/kg
Balat M. [3]	2008	2008	Coal gasification	Turkey	Middle East	USD 10–12/GJ
Graf D. et al. [30]	2008	2008	Electrolysis	Germany	Europe	EUR 5.8/kg
Charvin P. et al. [31]	2008	2008	Electrolysis	France	Europe	USD 7.98/kg
Balat M. [3]	2008	2008	Electrolysis	Turkey	Middle East	USD 8/kg
Rivera-Tinoco R. et al. [32]	2008	2008	Electrolysis (biomass)	France	Europe	EUR 2.32/kg
Rodrigues Halmeman M.C. et al. [33]	2008	2008	Electrolysis (biomass)	Brazil	S America	USD 0.50 m³
Balat M. [3]	2008	2008	SMR	Turkey	Middle East	USD 1.5/kg
Pilavachi P.A. et al. [34]	2009	2004	Electrolysis (hydraulic)	Greece	Europe	USD 1.25/kg
Pilavachi P.A. et al. [34]	2009	2004	Electrolysis (solar)	Greece	Europe	USD 16.00/kg
Lewis M.A. et al. [35]	2009	2005	Electrolysis	US	N America	USD 3.30/kg
Pilavachi P.A. et al. [34]	2009	2007	Biomass (gasification)	Greece	Europe	USD 23.78/kg ¹
Pilavachi P.A. et al. [34]	2009	2007	Coal gasification	Greece	Europe	USD 22.37/kg ²
Pilavachi P.A. et al. [34]	2009	2007	Electrolysis (wind)	Greece	Europe	USD 36.75/kg ³
Pilavachi P.A. et al. [34]	2009	2007	Smr	Greece	Europe	USD 32.75/kg ⁴
Pregger T. et al. [36]	2009	2020	Biomass (gasification)	Germany	Europe	EUR 3.2–13ct/kWh
Pregger T. et al. [36]	2009	2020	Coal gasification + CCS	Germany	Europe	EUR 4.2–8.6ct/kWh
Pregger T. et al. [36]	2009	2020	Electrolysis (solar)	Germany	Europe	EUR 20–22ct/kWh
Pregger T. et al. [36]	2009	2020	Electrolysis (wind)	Germany	Europe	EUR 7–8ct/kWh
Pregger T. et al. [36]	2009	2020	SMR	Germany	Europe	EUR 3.6–7.7ct/kWh
Pregger T. et al. [36]	2009	2020	SMR + CCS	Germany	Europe	EUR 4.0–9.1ct/kWh
Wang Z.L. et al. [37]	2010	2002	Electrolysis	Canada	N America	USD 2.41/kg
Wang Z.L. et al. [37]	2010	2002	SMR	Canada	N America	USD 2.67/kg
Ljunggren M. et al. [38]	2010	2010	Biomass (fermentation)	Sweden	Europe	EUR 19.93/kg
Lemus R.G. et al. [6]	2010	2010	Biomass (gasification)	Spain	Europe	USD 44–82/GJ
Lemus R.G. et al. [6]	2010	2010	Electrolysis (hydraulic)	Spain	Europe	USD 45–66/GJ
Lemus R.G. et al. [6]	2010	2010	Electrolysis (solar)	Spain	Europe	USD 41.7–166/GJ
Lemus R.G. et al. [6]	2010	2010	Electrolysis (wind)	Spain	Europe	USD 34.0–75.2/GJ
Leybros J. et al. [39]	2010	2010	Hybrid-sulfur cycle	France	Europe	EUR 6.6/kg
Lemus R.G. et al. [6]	2010	2010	SMR	Spain	Europe	USD 6.9/GJ
Leybros J. et al. [40]	2010	2010	Sulfur–Iodine	France	Europe	EUR 12/kg
Lemus R.G. et al. [6]	2010	2020	Biomass (gasification)	Spain	Europe	USD 18.3/GJ
Lemus R.G. et al. [6]	2010	2030	Biomass (gasification)	Spain	Europe	USD 13–17/GJ
Ljunggren M. et al. [41]	2011	2011	Biomass (fermentation)	Sweden	Europe	EUR 51.0/kg
Müller S. et al. [42]	2011	2011	Biomass (gasification)	Austria	Europe	EUR 54/MWh
Huisman G.H. et al. [43]	2011	2011	Biomass (gasification)	Sweden	Europe	EUR 14.4/GJ
Mansilla C. et al. [44]	2011	2011	Electrolysis	France	Europe	EUR 3.27/kg
Balta M.T. et al. [45]	2011	2011	Electrolysis (geothermal)	Turkey	Middle East	USD 1.446–2.706/kg
Lu Y. et al. [46]	2011	2011	Electrolysis (solar)	China	Asia	RMB38.46/kg
Menanteau P. et al. [47]	2011	2011	Electrolysis (wind)	France	Europe	EUR 4–12/kg
Holtermann T. et al. [48]	2011	2011	Photobioreactor	Germany	Europe	EUR 50/MWh
Corgnale C. et al. [49]	2011	2015	Solar thermochemical	US	N America	USD 4.80/kg
Huisman G.H. et al. [43]	2011	2020	Biomass (gasification)	Sweden	Europe	EUR 12.8/GJ
Corgnale C. et al. [49]	2011	2025	Solar thermochemical	US	N America	USD 3.19/kg
Gim B. et al. [50]	2012	2006	Electrolysis (wind)	Korea	Asia	USD 6.55/kg
Gim B. et al. [50]	2012	2006	Electrolysis (wind)	US	N America	USD 4.80/kg
Gim B. et al. [50]	2012	2006	SMR	Korea	Asia	USD 4.87/kg
Gim B. et al. [50]	2012	2006	SMR	US	N America	USD 3.00/kg
Lysenko S. et al. [4]	2012	2012	Biomass (gasification)	US	N America	USD 2.23–3.01/kg
Becker W.L. et al. [51]	2012	2012	Biomass (gasification)	US	N America	USD 1.6/kg
Becker W.L. et al. [51]	2012	2012	CHHP	US	N America	USD 4.4/kg
Chiuta S. et al. [52]	2012	2012	Coal gasification	South Africa	Africa	USD 3/kg
Mansilla C. et al. [53]	2012	2012	Electrolysis	France	Europe	EUR 2.90/kg
Becker W.L. et al. [51]	2012	2012	Electrolysis	US	N America	USD 4.17/kg
Genç G. et al. [54]	2012	2012	Electrolysis (wind)	Turkey	Middle East	USD 8.5/kg
Becker W.L. et al. [51]	2012	2012	SMR	US	N America	USD 1.4/kg
Liberatore R. et al. [55]	2012	2012	Solar sulfur–iodine thermochemical	Italy	Europe	EUR 8.3/kg
Gim B. et al. [50]	2012	2017	Electrolysis (wind)	Korea	Asia	USD 4.23/kg
Gim B. et al. [50]	2012	2017	Electrolysis (wind)	US	N America	USD 2.80/kg
Gim B. et al. [50]	2012	2017	SMR	Korea	Asia	USD 3.56/kg
Gim B. et al. [50]	2012	2017	SMR	US	N America	USD 2.00/kg
Mansilla C. et al. [56]	2013	2012	Electrolysis	France	Europe	EUR 3.37/kg
Mansilla C. et al. [56]	2013	2012	Electrolysis	Germany	Europe	EUR 3.23/kg
Mansilla C. et al. [56]	2013	2012	Electrolysis	Spain	Europe	EUR 3.52/kg
Wu W. et al. [57]	2013	2012	SMR	Taiwan	Asia	USD 5.67/kmol
Olateju B. et al. [58]	2013	2013	Coal gasification	Canada	N America	USD 1.78/kg
Olateju B. et al. [58]	2013	2013	Coal gasification + CCS	Canada	N America	USD 2.11/kg
Olateju B. et al. [58]	2013	2013	Coal gasification + EOR	Canada	N America	USD 1.61/kg
Olateju B. et al. [58]	2013	2013	SMR	Canada	N America	USD 1.73/kg
Olateju B. et al. [58]	2013	2013	SMR + CCS	Canada	N America	USD 2.14/kg
Urbaniec K. et al. [59]	2014	2014	Biomass (fermentation)	Poland	Europe	EUR 9.30/kg
Brown D. et al. [60]	2014	2014	Biomass (gasification)	Canada	N America	USD 3.01/kg
Olateju B. et al. [61]	2014	2014	Electrolysis (wind)	Canada	N America	USD 7.84/kg
Guo L.J. et al. [62]	2015	2015	Coal gasification	China	Asia	USD 0.111m³
Bennoua S. et al. [63]	2015	2015	Electrolysis	France	Europe	EUR 3.0–4.5/kg
Matzen M. et al. [64]	2015	2015	Electrolysis (wind)	US	N America	USD 3.74–5.86/kg
Galera S. et al. [65]	2015	2015	Water reforming	Spain	Europe	USD 5.36/kg
Loisel R. et al. [66]	2015	2030	Electrolysis (wind)	France	Europe	EUR 4.2–13.0/kg
Han W. et al. [67]	2016	2016	Biomass (fermentation)	China	Asia	USD 14.89/kg
Abuşoğlu A. et al. [68]	2016	2016	Electrolysis (biomass)	Turkey	Middle East	USD 3.31–15.63/kg
Olateju B. et al. [69]	2016	2016	Electrolysis (hydraulic)	Canada	N America	USD 1.18–2.43/kg
Southall G.D. et al. [70]	2016	2016	Electrolysis (renewable)	UK	Europe	GBP 4.98/kg
Olateju B. et al. [71]	2016	2016	Electrolysis (wind)	Canada	N America	USD 3.37–9.0/kg
AlRafea K. et al. [72]	2016	2016	Electrolysis (wind)	Canada	N America	USD 4.0–5.0/kg
Oh T.H. [73]	2016	2016	Formic acid	Korea	Asia	USD 380/kg ⁵
Olateju B. et al. [71]	2016	2016	SMR	Canada	N America	USD 1.87–2.60/kg
Shafiee A. et al. [74]	2016	2016	SMR	Saudi Arabia	Middle East	USD 1.98/kg
Couto N.D. et al. [75]	2016	2016	Waste gasification	Portugal	Europe	EUR 2.66/kg
Chang W.-C. et al. [76]	2016	2016	Waste hydrogen from coked coal	Taiwan	Asia	USD 0.69–0.78/m³
Gillessen B. et al. [77]	2017	2015	Electrolysis (solar)	Germany	Europe	EUR 0.057/kWh
Wang G.-R. et al. [78]	2017	2017	Biomass (gasification)	China	Asia	EUR 2.4–3.85/kg
Lei Y. et al. [79]	2017	2017	Biomass (reforming)	China	Asia	USD 0.231–0.465/kWh
Madeira J.G.F. et al. [80]	2017	2017	Biomass (reforming)	Brazil	S America	USD 0.13–0.24/kWh
Lee B. et al. [81]	2017	2017	Electrolysis	Korea	Asia	USD 7.72/kg
Yuksel Y.E. et al. [82]	2017	2017	Electrolysis (geothermal)	Turkey	Middle East	USD 1.1/kg
Lee B. et al. [81]	2017	2017	SMR	Korea	Asia	USD 7.59/kg
Ozcan H. et al. [83]	2017	2017	Water thermochemical	Canada	N America	USD 3.67/kg
Gillessen B. et al. [77]	2017	2030	Electrolysis (solar)	Germany	Europe	EUR 0.034/kWh
Schweitzer D. et al. [84]	2018	2018	Biomass (gasification)	Germany	Europe	EUR 6–10/kg
Di Marcoberardino G. et al. [85]	2018	2018	Biomass (reforming)	Italy	Europe	EUR 4–4.1/kg
Lin K.-W. et al. [86]	2018	2018	Biomass (reforming)	Taiwan	Asia	USD 5.1–5.66/kmol
Di Marcoberardino G. et al. [87]	2018	2018	Biomass (reforming)	Italy	Europe	EUR 4.2–5.0/kg
Ajanovic A. et al. [88]	2018	2018	Electrolysis (renewable)	Austria	Europe	EUR 0.07–0.12/kWh
Andresen L. et al. [89]	2018	2018	Electrolysis (renewable)	Germany	Europe	EUR 2.00–4.55/kg
Robinius M. et al. [90]	2018	2018	Electrolysis (renewable)	Germany	Europe	EUR 4–7.5/kg
Boudries R. [91]	2018	2018	Electrolysis (solar)	Algeria	Africa	USD 1.5–2.0/kg
Touili S. et al. [92]	2018	2018	Electrolysis (solar)	Morocco	Africa	USD 4.64–5.79/kg
Arora A. et al. [93]	2018	2018	SMR	US	N America	USD 2.13–3.12/kg
Riva L. et al. [94]	2018	2018	SMR		Europe	EUR 0.178/m³
Lee D.-Y. et al. [95]	2018	2018	Steam cracking	US	N America	USD 0.9–1.1/kg
Keipi T. et al. [96]	2018	2018	Thermal decomposition of methane	Finland	Europe	EUR 72/MWh
Ajanovic A.et al. [88]	2018	2050	Electrolysis (renewable)	Austria	Europe	EUR 0.06–0.09/kWh
Grabarczyk R. et al. [97]	2019	2019	Biomass (fermentation)	Poland	Europe	EUR 32.68/kg
Becker W.L. et al. [98]	2019	2019	Biomass (gasification)	US	N America	USD 2.48/kg
Rau F. et al. [99]	2019	2019	Biomass (reforming)	Germany	Europe	EUR 2.90–5.32/kg
Di Marcoberardino G. et al. [100]	2019	2019	Biomass (reforming)	Italy	Europe	EUR 4.01–4.11/kg
Chisalita D.-A. et al. [101]	2019	2019	Chemical looping	Romania	Europe	EUR 41.84/MWH
Bahzad H. et al. [102]	2019	2019	Chemical looping	UK	Europe	USD 1.16–2.10/kg
Bahzad H. et al. [103]	2019	2019	Chemical looping	UK	Europe	USD 1.41–1.62/kg
González Rodríguez D. et al. [104]	2019	2019	Electrolysis (nuclear)	Brazil	S America	USD 4.8–5.96/kg
Timmerberg S. et al. [105]	2019	2019	Electrolysis (renewable)	Algeria, Morocco, Libya	Africa	EUR 45–99/MWh
Grüger F. et al. [106]	2019	2019	Electrolysis (renewable)	Germany	Europe	EUR 11.52–13.42/kg
Arellano-Garcia H. et al. [107]	2019	2019	Electrolysis (solar)		Europe	USD 10.9–11.0/kg
Micena R.P. et al. [108]	2019	2019	Electrolysis (solar)	Brazil	S America	USD 8.96–13.55/kg
Becker W.L. et al. [98]	2019	2019	Electrolysis (wind)	US	N America	USD 6.71/kg
Guerra O.J. et al. [109]	2019	2020	Electrolysis	UA	N America	USD 2.6–12.3/kg
Kikuchi Y. et al. [110]	2019	2030	Electrolysis (solar)	Japan	Asia	JPY 17.42–26.39/m³
Khzouz M. et al. [111]	2020	2020	SMR	UK	Europe	USD 0.9/kg
Khzouz M. et al. [111]	2020	2020	Electrolysis	UK	Europe	USD 2.92/kg
Tolley T.E. et al. [112]	2020	2020	SMR	US	N America	USD 1.10/gge
He Y. et al. [113]	2020	2020	Chemical looping	China	Asia	USD 32.87/MWh
Armijo J. et al. [114]	2020	2020	Electrolysis (renewable)	Chile, Argentine	S America	USD 1.94–2.33/kg
Coleman D. et al. [115]	2020	2020	Electrolysis (wind)	Germany	Europe	EUR 3.50/kg
Schnuelle C. et al. [116]	2020	2020	Electrolysis (solar)	Germany	Europe	EUR 5.00/kg
Schnuelle C. et al. [116]	2020	2020	Electrolysis (wind)	Germany	Europe	EUR 4.33/kg
Roussanaly S. et al. [117]	2020	2020	SMR	Norway	Europe	EUR 12.2/m³
Roussanaly S. et al. [117]	2020	2020	SMR + CCS	Norway	Europe	EUR 18.1ct/m³
Lux B. et al. [118]	2020	2050	Electrolysis (renewable)		Europe	EUR 110/MWh
Matute G. et al. [119]	2021	2020	Electrolysis	Spain	Europe	EUR 3.0/kg
Kazi M.-K. et al. [120]	2021	2020	Electrolysis (renewable)	Qatar	Middle East	USD 10.0/kg
Herwarts S. et al. [121]	2021	2020	Electrolysis (wind)	Germany	Europe	EUR 6.4/kg
Koleva M. et al. [122]	2021	2020	Electrolysis (solar)	US	N America	USD 6.59/kg
Koleva M. et al. [122]	2021	2020	SMR	US	N America	USD 1.34/kg
de Souza T.A.Z. et al. [123]	2021	2020	Biomass (reforming)	Brazil	S America	USD 2.42–5.26/kg

Note: The numbers after author names refer to the reference numbers. ¹ This case was excluded from the analysis as an outlier. ² Ditto. ³ Ditto. ⁴ Ditto. ⁵ This case was excluded from the analysis as an outlier.

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Figure 1. Trend of the number of articles published from 1975 to 2021. Note: The horizontal axis represents the publication year. The figure for 2021 is only for January.

Figure 2. Scatter chart of forecast years and hydrogen production costs. Note: others include biomass fermentation, chemical looping, electrolysis other than renewables, solar thermochemical, waste gasification, reforming, etc.

Figure 3. Wilcoxon rank-sum test between gray/renewable electrolysis/biomass hydrogen and all the others over time. Note: numbers in parentheses indicate the number of samples (cost forecasts) included in the category.

Figure 4. Wilcoxon rank-sum test between gray/renewable electrolysis/biomass hydrogen and all of the others in the different time categories. Note: numbers in parentheses indicate the number of samples (cost forecasts) included in the category.

Figure 5. Kruskal–Wallis test in each gray/renewable electrolysis/biomass hydrogen. Note: numbers in parentheses indicate the number of samples (cost forecasts) included in the category.

Figure 6. Wilcoxon rank-sum test in each gray/renewable electrolysis/biomass between different time categories. Note: numbers in parentheses indicate the number of samples (cost forecasts) included in the category.

Figure 7. Trend of coal prices in Europe, US, Japan, and the 4-year rolling average. Source: created by the authors using data from BP Statistical Review of World Energy (July 2021) [7].

Figure 8. Trend of gas prices in the UK, US, Japan, and the 4-year rolling average. Source: created by the authors using data from the BP Statistical Review of World Energy (July 2021) [7].

Figure 9. Scatter chart of gas costs assumed in SMR gray hydrogen.

Figure 10. Trend of global, weighted-average, levelized renewable power generation costs in 2010–2020. Source: created by the authors using data from Renewable Power Generation Costs in 2020 (IRENA) [8].

Figure 11. Trend of capital expenditures in electrolyzes (AE and PEM). Data source: created by the authors using data from Christensen (2020) [9].

Table 1. Search process.

Field	Option Introduced
Keywords	“hydrogen” AND “economic *” ¹ AND “supply chain”
Search in	Title, abstract, keywords
Period explored	Open
Type of documents	Articles
Language	English
Database	Scopus

¹ * indicates a wildcard.

Table 2. Variety of supply chains.

Production Technology	Storage	Delivery Mode	Refilling
Coal gasification	Liquid	Tanker truck	Liquid refilling
Gas reformer (SMR)	Compressed gas	Tube trailer	Gas refilling
Biomass gasification	Ammonia	Pipeline	Onsite reforming
Electrolysis		Railway
		Ship

Table 3. Search strategy in Scopus site.

Field	Option Selected
Keywords	“hydrogen production cost”
Search in	Title, abstract, keywords
Period explored	Open
Type of documents	Articles
Language	English
Database	Scopus

Table 4. Conversion rate.

Hydrogen conversion	1 Gigajoule (GJ) = 8.333 kg
	1 Cubic meter (m³) = 0.08988 kg
	1 Megawatt hour (MWh) = 30.0 kg
	1 kilo mol (kmol) = 2.02 kg
Exchange rates	Purchasing power parity (OECD)

Sources: created by the authors using data from Lemus and Martínez Duart (2010) [6] for the first three conversion rates, convertworld.com (https://www.convertworld.com/, accessed on 20 September 2020) for the kilo mol conversion rate, and OECD (https://data.oecd.org/conversion/purchasing-power-parities-ppp.htm, accessed on 20 September 2020) for the exchange rates.

Table 5. Summary of data.

(USD/kg)	Total	Gray (Coal and Gas)	Renewable Electrolysis	Biomass (Gasification and Reforming)	Others
Number of Samples	174	44	48	29	53
Average	4.81	2.08	6.05	3.18	6.85
Max.	65.35	7.59	16.31	8.38	65.35
Min.	0.21	0.53	1.08	0.21	0.84
Stdev.	6.70	1.41	3.82	2.03	10.81

Note: Max., Min., and Stdev. denote maximum, minimum, and standard deviation, respectively.

Table 6. Wilcoxon rank-sum test between gray hydrogen and all other hydrogen types over time.

	Obs.	R. sum	Ex.	Obs.	R. sum	Ex.	Obs.	R. sum	Ex.	Obs.	R. sum	Ex.	Obs.	R. sum	Ex.
	Entire Period			–2000			2001–2010			2011–2015			2016–2020
Gray	44	2225	3850	6	38	63	15	190.5	337.5	13	160	299	10	183	330
Others	130	13,000	11,375	14	172	147	29	799.5	652.5	32	875	736	55	1962	1815
Total	174	15,225	15,225	20	210	210	44	990	990	45	1035	1035	65	2145	2145
z value	5.626 ***			2.063 **			3.640 ***			3.481 ***			2.673 ***

Note: Obs. is the number of observations; R. sum is the rank-sum value; Ex. is the expected rank-sum value. *** and ** indicate significance at the 1% and 5% levels, respectively.

Table 7. Kruskal–Wallis test for gray hydrogen.

Published Years	Number of Observations	Rank-Sum
−2000	6	41
2001–2010	15	320
2011–2015	13	375
2016–2020	10	254
Chi-squared value: 12.732 with 3 d.f.
p-value: 0.0053

Note: d.f. denotes the degree of freedom.

Table 8. Wilcoxon rank-sum test for gray hydrogen in different time categories.

	Obs.	R. sum	Ex.	Obs.	R. sum	Ex.	Obs.	R. sum	Ex.
	–2000 vs. 2001–2010			2001–2010 vs. 2011–2015			2011–2015 vs. 2016–2020
Earlier	6	34	66	15	184	217.5	13	168	156
Later	15	197	165	13	222	188.5	10	108	120
Total	21	231	231	28	406	406	23	276	276
z value	−2.492 **			−1.544			0.744

Note: Obs. is the number of observations; R. sum is the rank-sum value; Ex. is the expected value. ** indicates significance at the 5% level.

Table 9. Wilcoxon rank-sum test between renewable electrolysis hydrogen and the other hydrogen types over time.

	Obs.	R. sum	Ex.	Obs.	R. sum	Ex.	Obs.	R. sum	Ex.	Obs.	R. sum	Ex.	Obs.	R. sum	Ex.
	Entire Period			–2000			2001–2010			2011–2015			2016–2020
R. Elec	48	5628	4200	3	48	31.5	9	282	202.5	10	338.5	230	26	1009.5	858
Others	126	9597	11,025	17	162	178.5	35	708	787.5	35	696.5	805	39	1135.5	1287
Total	174	15,225	15,225	20	210	210	44	990	990	45	1035	1035	65	2145	2145
z value	−4.808 ***			−1.747 *			−2.313 **			−2.962 ***			−2.029 **

Note: Obs. is the number of observations; R. sum is the rank-sum value; Ex. is the expected value. ***, **, and * indicate the significance levels at 1%, 5%, and 10%, respectively.

Table 10. Kruskal–Wallis equality of the production test for renewable electrolysis hydrogen.

R.Elec Issued	Obs	Rank-Sum
−2000	3	73.5
2001–2010	9	254.5
2011–2015	10	269
2016–2020	26	579
chi-squared value: 1.609 with 3 d.f.
p-value: 0.6573

Note: d.f. denotes the degree of freedom.

Table 11. Wilcoxon rank-sum test for renewable electrolysis hydrogen for different periods.

	Obs.	R. sum	Ex.	Obs.	R. sum	Ex.	Obs.	R. sum	Ex.
	–2000 vs. 2001–2010			2001–2010 vs. 2011–2015			2011–2015 vs. 2016–2020
Earlier	3	18.5	19.5	9	93	90	10	212	185
Later	9	59.5	58.5	10	97	100	26	454	481
Total	12	78	78	19	190	190	36	666	666
z value	−0.185			0.245			0.954

Note: Obs. is the number of observations; R. sum is the rank-sum value; Ex. is the expected value.

Table 12. Wilcoxon rank-sum test between biomass hydrogen and other hydrogen types over time.

	Obs.	R. sum	Ex.	Obs.	R. sum	Ex.	Obs.	R. sum	Ex.	Obs.	R. sum	Ex.	Obs.	R. sum	Ex.
	Entire Period			–2000			2001–2010			2011–2015			2016–2020
Biomass	29	2257.5	2538	4	38.5	42	8	134.5	180	6	78	138	11	427.5	363
Others	145	12,968	12,688	16	171.5	168	36	855.5	810	39	957	897	54	1717.5	1782
Total	174	15,225	15,225	20	210	210	44	990	990	45	1035	1035	65	2145	2145
z value	1.131			0.331			1.385			2.003 **			−1.128

Note: Obs. is the number of observations; R. sum is the rank-sum value; Ex. is the expected value. ** indicates significance at the 5% level.

Table 13. Kruskal–Wallis equality of production test for biomass hydrogen.

Biomass Issued	Obs	Rank-Sum
−2000	4	12
2001–2010	8	103
2011–2015	6	76
2016–2020	11	244
chi-squared = 16.719 with 3 d.f.
probability = 0.0008

Table 14. Wilcoxon rank-sum test for biomass hydrogen between pre-2000 and 2001–2010.

	Obs.	R. sum	Ex.	Obs.	R. sum	Ex.	Obs.	R. sum	Ex.
	–2000 vs. 2001–2010			2001–2010 vs. 2011–2015			2011–2015 vs. 2016–2020
Earlier	4	12	26	8	56	60	6	24	54
Later	8	66	52	6	49	45	11	129	99
Total	12	78	78	14	105	105	17	153	153
z value	−2.378 **			-0.516			−3.015 ***

Note: Obs. is the number of observations; R. sum is the rank-sum value; Ex. is the expected value. *** indicates significance at the 1% level, ** is at the 5% level, respectively.

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Miyagawa, T.; Goto, M. Hydrogen Production Cost Forecasts since the 1970s and Implications for Technological Development. Energies 2022, 15, 4375. https://doi.org/10.3390/en15124375

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Miyagawa T, Goto M. Hydrogen Production Cost Forecasts since the 1970s and Implications for Technological Development. Energies. 2022; 15(12):4375. https://doi.org/10.3390/en15124375

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Miyagawa, Tomonori, and Mika Goto. 2022. "Hydrogen Production Cost Forecasts since the 1970s and Implications for Technological Development" Energies 15, no. 12: 4375. https://doi.org/10.3390/en15124375

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Miyagawa, T., & Goto, M. (2022). Hydrogen Production Cost Forecasts since the 1970s and Implications for Technological Development. Energies, 15(12), 4375. https://doi.org/10.3390/en15124375

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Hydrogen Production Cost Forecasts since the 1970s and Implications for Technological Development

Abstract

1. Introduction

2. Hydrogen Production Technologies and Cost Estimations

2.1. Hydrogen Production Technologies

2.2. Cost Estimation Methods

3. Methods

4. Results

4.1. Gray Hydrogen

4.1.1. Wilcoxon Rank-Sum (First and Second Step) Tests between Gray Hydrogen and Other Hydrogen Types over Time

4.1.2. Kruskal–Wallis Test for Gray Hydrogen for Different Periods (Third Test)

4.1.3. Wilcoxon Rank-Sum Test for Gray Hydrogen for Different Periods (Fourth Step)

4.2. Renewable Electrolysis Hydrogen

4.2.1. Wilcoxon Rank-Sum (First and Second Step) Tests between Renewable Electrolysis Hydrogen and Other Hydrogens over Time

4.2.2. Kruskal–Wallis Test for Renewable Electrolysis Hydrogen for Different Periods (Third Test)

4.2.3. Wilcoxon Rank-Sum Test for Renewable Electrolysis Hydrogen for Different Periods (Fourth Step)

4.3. Biomass Hydrogen

4.3.1. Wilcoxon Rank-Sum (First and Second Step) Test between Biomass Hydrogen and Other Hydrogens over Time

4.3.2. Kruskal–Wallis Test for Biomass Hydrogen for Different Periods (Third Step)

4.3.3. Wilcoxon Rank-Sum Test for Biomass Hydrogen for Different Periods (Fourth Step)

5. Discussion

5.1. Gray Hydrogen

5.2. Renewable Electrolysis Hydrogen

5.3. Biomass Hydrogen

6. Conclusions

Funding

Institutional Review Board Statement

Informed Consent Statement

Data Availability Statement

Conflicts of Interest

Appendix A

References

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