Question:
You are given the full text of a scientific research paper.

    Summarize the **research methods** used in the study in a way that is informative for policy audiences. Your response should:
    - Describe the overall study design, data sources, tools, and models used
    - Avoid unnecessary technical details or equations
    - Use accessible but precise language

    Write a short paragraph suitable for the “Scientific Research Study Methods” section of a policy brief.

    Scientific Research Paper:
    Nature Energy | Volume 10 | January 2025 | 110–123
110
nature energy
Article
https://doi.org/10.1038/s41560-024-01684-7
The green hydrogen ambition and
implementation gap

Adrian Odenweller
  1,2
 & Falko Ueckerdt
  1
Green hydrogen is critical for decarbonizing hard-to-electrify sectors,
but it faces high costs and investment risks. Here we define and quantify
the green hydrogen ambition and implementation gap, showing that
meeting hydrogen expectations will remain challenging despite surging
announcements of projects and subsidies. Tracking 190 projects over
3 years, we identify a wide 2023 implementation gap with only 7% of global
capacity announcements finished on schedule. In contrast, the 2030
ambition gap towards 1.5 °C scenarios has been gradually closing as the
announced project pipeline has nearly tripled to 422 GW within 3 years.
However, we estimate that, without carbon pricing, realizing all these
projects would require global subsidies of US$1.3 trillion (US$0.8–2.6
trillion range), far exceeding announced subsidies. Given past and future
implementation gaps, policymakers must prepare for prolonged green
hydrogen scarcity. Policy support needs to secure hydrogen investments,
but should focus on applications where hydrogen is indispensable.
There is a widespread consensus among scientists1–5, industry6 and
increasingly also policymakers7 that green hydrogen, produced from
renewable electricity via electrolysis, is critical for reducing emissions
in end-use applications that defy straightforward electrification. Addi-
tionally, hydrogen is a promising candidate for long-duration energy
storage of renewables8,9 and the precursor to all electrofuels10, which are
highly versatile yet costly11. Consequently, policy measures to stimulate
the ramp-up of the hydrogen market are gaining momentum as more
than 40 governments have already adopted hydrogen strategies1,7.
Prominent examples are the supply-side subsidies implemented
through the the US Inflation Reduction Act12 and the EU Hydrogen
Bank13. Such policy support is urgently required: to meet the median
ambition in 1.5 °C scenarios, namely, 350 GW by 2030, green hydrogen
production needs to grow 380-fold, more than doubling each year.
However, implementation is not going according to plan.
Following a surge of enthusiasm14,15, the green hydrogen market
and associated expectations have recently entered a phase of consoli-
dation16 as high costs17,18, limited demand19 and lagging implementa-
tion of support policies1 are hampering deployment. Shortfalls in the
announced deployment of electrolysers, the key component for green
hydrogen production, are representative of the systemic challenges
of scaling up supply, demand and infrastructure at the same time. In
2022, instead of the 2.8 GW electrolysis capacity initially announced,
eventually only 0.62 GW was realized on time (Fig. 1a). Similarly, in
2023, of the 7.1 GW initially announced, only an estimated 0.92 GW was
realized and operational. In stark contrast to these recent setbacks,
announced future growth rates of green hydrogen have increased
substantially over the past 3 years, indicating a backlog of projects as
well as further increasing ambition (Fig. 1b). This raises questions such
as whether recent failure rates and the looming ‘valley of death’20 can
be overcome to meet updated project announcements, whether the
expected role of hydrogen in ambitious climate change mitigation
scenarios has changed and what plausible implementation pathways
exist given currently announced hydrogen support policies.
In this paper, we structure and analyse the past and future chal-
lenges of the nascent green hydrogen industry by introducing and
quantifying the green hydrogen ambition and implementation gap.
This builds on the well-established concepts of emissions gaps21 and
recent extensions towards a carbon dioxide removal gap22. Looking
back, we define the past implementation gap as the difference between
announced and eventually realized capacity in 2022 and 2023 (Fig. 1a).
Looking ahead to 2030, we define the ambition gap as the difference
Received: 1 June 2024
Accepted: 12 November 2024
Published online: 14 January 2025
 Check for updates
1Potsdam Institute for Climate Impact Research, Member of the Leibniz Association, Potsdam, Germany. 2Global Energy Systems Analysis, Technical
University of Berlin, Berlin, Germany.
 e-mail: adrian.odenweller@pik-potsdam.de

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added capacity was eventually installed and operational, leading to an
overall success rate of 7% (Fig. 3a). Furthermore, comparing announce-
ments by 2021 with the final outcome reveals that virtually no project
announced in 2021 was realized on time in 2023, with 86% experienc-
ing delays and 14% disappearing altogether (Fig. 3b). Similarly, of the
projects announced in 2022, only 3% were realized on time, with 76%
delayed and 21% disappearing (Fig. 3c). Projects in the feasibility study
or concept stage almost always had a success rate of zero, implying that
projects announced without a final investment decision (FID) in 2021
or 2022 were never realized on time in 2023 (Fig. 3b,c). Across all years
of announcement, even projects that had secured FIDs, or that were
already under construction, were mostly delayed or had disappeared
(Fig. 3b–d). The success rate varies by region, with projects in North
America equivalent to the global average, European projects below
average, Asian projects above average and a success rate of zero for Aus-
tralian projects (Supplementary Figs. 7–10). On the global level, these
high failure rates are not compensated by an influx of newly announced
projects or projects that were delayed from previous years (grey bars
in Fig. 3a), such that a dramatic green hydrogen implementation gap
of almost 4 GW remained in 2023.
The low success rates of green hydrogen projects are not unique
to the year 2023. In 2022, the overall success rate was 6%, with simi-
lar patterns of delay and disappearance of projects over time (Sup-
plementary Fig. 5). The high failure rates in 2022 and 2023 may be
attributed to supply chain disruptions caused by COVID-19, surging
electricity prices during the European energy crisis and rising global
interest rates. However, in Europe, the energy crisis was also seen as an
opportunity to accelerate green hydrogen deployment, although this
has yet to materialize (Supplementary Fig. 9). Considering the project
announcements for 2024, it remains questionable whether the more
than 12 GW currently announced will be realized on time (Supplemen-
tary Fig. 6). Although nearly 5 GW (40%) has already achieved an FID or
is under construction, this was also the case for project announcements
between 1.5 °C scenario requirements and announced projects and
find that it has been gradually closing in the past 3 years for most sce-
narios (Fig. 1b). However, this has been accompanied by a widening
future implementation gap, which we define as the difference between
announced projects and projects that are backed by policies in 2030
(Fig. 1b). Analysing the competition between green hydrogen (and
hydrogen-based electrofuels) and incumbent fossil competitors across
14 end-use sectors, we estimate that realizing all green hydrogen pro-
jects would require subsidies, or alternative policies such as end-use
quotas, for at least another decade, even with ambitious carbon pricing
and potentially indefinitely without. This paper is structured around
these three gaps and concludes with a discussion of policy implications
to safeguard climate targets against uncertain green hydrogen supply.
The wide green hydrogen implementation gap in
2022 and 2023
Green hydrogen project announcements reveal two opposing trends
over the past 3 years. First, there has been a notable short-term setback,
with capacities diminishing as projects approach their announced
launch year (Fig. 2a). This trend of downward-adjusted expectations
persists in both 2022 and 2023, indicating a dramatic green hydro-
gen implementation gap in recent years. Second, however, this trend
reverses from 2024 onwards, with project announcements increasing
steadily over the past 3 years (Fig. 2b). This steep mid-term growth of
announcements is mostly driven by Europe, which accounts for the
largest share of announced capacity by 2030, followed by Australia and
Central and South America (Fig. 2d). These opposing trends raise the
question as to whether future promises can overcome past setbacks. We
address this question in the next section, following the quantification
of the 2022 and 2023 green hydrogen implementation gaps.
Tracking 190 individual green hydrogen projects announced glob-
ally for 2023 over the past 3 years (Methods), we observe a substantial
implementation gap as only 0.3 GW of the initially announced 4.3 GW
2022
2023
0
5
10
Year
Capacity (GW)
Project announcements
Announcements by 2021
Announcements by 2022
Announcements by 2023
Realized projects
Past green hydrogen implementation gap
a
1.5 °C
scenario
requirements
0
200
400
600
800
2020
2021
2022
2023
2024
2024
2025
2026
2027
2028
2029
2030
Year
Capacity (GW)
Project announcements
Announcements by 2021
Announcements by 2022
Announcements by 2023
Supported by implemented demand policies
and announced subsidies
2030 green hydrogen ambition and implementation gaps
b
 2030
ambition
gap
(2)
 2030
implementation
gap
(3)
 Past
implementation
gap
(1)
Fig. 1 | The green hydrogen ambition and implementation gaps in the past
and the future. a, Past green hydrogen implementation gaps in 2022 and 2023,
defined as the difference between project announcements and realized projects
(denoted as (1), also see Fig. 3). Realized projects in 2023 show the outcome
of project announcements by 2023, based on our own research (Methods).
b, Green hydrogen ambition and implementation gaps in 2030. We define
the 2030 ambition gap as the difference between 1.5 °C scenarios and project
announcements (denoted as (2), also see Fig. 4). The depicted data range shows
the IEA Net Zero Emissions by 2050 scenarios, while the full analysis includes
further scenarios (Fig. 4a, Extended Data Fig. 1 and Methods). We define the
2030 green hydrogen implementation gap as the difference between project
announcements and our estimate of projects that are either supported by
implemented demand-side policies or by currently announced subsidies
(denoted as (3), see Fig. 5 and Supplementary Fig. 17). The black line indicates
our central estimate and the light grey corridor indicates the uncertainty range
spanned by the sensitivity analysis. Green hydrogen project announcements
are displayed in terms of electrical input capacity of electrolysers. Project
announcements are based on three snapshots of the IEA Hydrogen Projects
Database, which we have validated comprehensively (see Methods,
Supplementary Table 1 and Supplementary Figs. 1–4). The dashed curve between
a and b connects the same data point in 2024 and illustrates the different y-axis
scale between project announcements until 2024 (a) and until 2030 (b).

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made in 2022 for 2023, of which only 8% were completed on schedule
(Fig. 3c). It will take some more years to determine whether the recent
implementation gaps were exceptions caused by unusual global events,
or the unfortunate norm.
Substantial implementation gaps may be common for emerging
energy technologies in the early stages of technology diffusion, as large
projects almost always exceed their budget and run behind schedule23.
However, while research has identified similarly high failure rates for
complex and customized technologies24 such as carbon capture and
storage25, this does not apply to highly modular technologies such
as solar photovoltaics (PV) and wind power23,26. For green hydrogen,
recent evidence suggests that while the mass-producible electrolyser
stack is highly modular, other components of the electrolyser system
and the overall green hydrogen production plant are more complex and
require customization17, making them more prone to budget and time
overrun23. As long as the underlying uncertainties remain unresolved,
policymakers should avoid relying solely on project announcements
to assess progress on green hydrogen.
Apart from the unsettled question of electrolyser modularity,
three tangible factors contribute to the low success rate of green hydro-
gen projects. First, cost estimates for electrolysers have recently surged
due to increasing equipment and financial costs1, and because only
the electrolyser stack may be set for rapid cost reductions24. Second,
analysts have observed a lack of offtake agreements19, which could arise
from a limited willingness to pay for costly green hydrogen. Further-
more, required hydrogen end-use investments, such as transforming
steel production from a blast furnace to a direct reduction route, are
often difficult to reverse and therefore pose the risk of becoming locked
into an expensive and potentially scarce energy carrier. Third, bridg-
ing the substantial cost gap and reducing investment risks requires
hydrogen-specific support policies and regulation, even in countries
with ambitious carbon pricing27. However, lagging implementation of
support policies1 and regulatory uncertainty regarding green hydrogen
production standards in the European Union (EU) and the United States,
although crucial to ensure climate benefits28,29, have hampered growth.
What implications does the sobering track record of past project
announcements have for the future of green hydrogen in ambitious
climate change mitigation scenarios? To explore these ramifica-
tions, we next focus on the mid-term horizon towards 2030. First, we
provide an overview of electrolysis requirements in 1.5 °C scenarios,
introducing the 2030 green hydrogen ambition gap. Second, we
analyse the economic viability of surging project announcements
Announcements
by 2021
Announcements
by 2022
Outcome
in 2023
0
5
10
Capacity (GW)
Projects until 2024 by status
a
Announcements
by 2021
Announcements
by 2022
Announcements
by 2023
0
100
200
300
400
Capacity (GW)
Projects from 2024 by status
b
0
5
10
Year
Capacity (GW)
Projects until 2024 by region
c
0
100
200
300
400
2020
2021
2022
2023
2024
2024
2025
2026
2027
2028
2029
2030
Year
Year
2020
2021
2022
2023
2024
2024
2025
2026
2027
2028
2029
2030
Year
Capacity (GW)
Projects from 2024 by region
d
Status
Concept
Feasibility study
FID/construction
Operational
Region
Asia
Australia
C and S America
Europe
MENA
North America
Other
Fig. 2 | Green hydrogen project announcements by 2021, 2022 and 2023.
a,b, Project announcements by status from 2020–2024 (a) and 2024–2030 (b).
c,d, Project announcements by region from 2020–2024 (c) and 2024–2030
(d). For each year there are three bars. The left bar shows announcements by
2021, the middle bar shows announcements by 2022 and the right bar shows
announcements by 2023, each of which corresponds to different project
database snapshots (Methods). Two main trends are visible. First, in 2022 and
2023, project announcements decrease strongly as the year of project launch
approaches (a,c), leading to a wide green hydrogen implementation gap (see
Fig. 3 and Supplementary Fig. 5). Second, after 2024, this pattern reverses as the
project pipeline has surged over the past 3 years (b,d), thereby gradually closing
the green hydrogen ambition gap to 1.5 °C scenarios (see Fig. 4). However, the
vast majority of projects have not secured an FID yet (b), which gives rise to the
2030 green hydrogen implementation gap due to a mismatch of required and
announced policies (see Fig. 5). In contrast to Figs. 1a and 3, this figure does not
show the outcome of project announcements for 2023. C and S America, Central
and South America; MENA, Middle East and North Africa. Region mapping is
available in ref. 67.

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and estimate the subsidy volumes that would be required
to realize all projects, leading to the 2030 green hydrogen imple-
mentation gap.
The closing 2030 green hydrogen ambition gap
Comparing green hydrogen project announcements with 1.5 °C sce-
narios, we find that the green hydrogen ambition gap for 2030 has
been gradually closing over the past 3 years (Fig. 4). Due to a stead-
ily growing project pipeline, the gap has already closed for most
scenarios, including the median of both the integrated assessment
model (IAM) scenarios (169 GW) and the institutional and corporate
scenarios (350 GW).
Green hydrogen requirements vary substantially across different
1.5 °C scenarios, consistent with previous research30 (Fig. 4a). For 2030,
this lack of consensus leads to an enormous range of 3–1,072 GW for
the IAM scenarios and 30–1,016 GW for the institutional and corpo-
rate scenarios (excluding an outlier of 1,700 GW), with correspond-
ing interquartile ranges of 38–375 and 203–655 GW, respectively.
Announcements
by 2021
Announcements
by 2022
Announcements
by 2023
Outcome
in 2024
Announcements
by 2021 VERSUS outcome in 2024
0
25
50
75
100
Share of capacity additions in 2023 (%)
Total
4.3 GW
FID/
construction
Feasibility
study
Concept
By status
3.1 GW
b
0.7
GW
0.5
GW
Announcements
by 2022 VERSUS outcome in 2024
0
25
50
75
100
Share of capacity additions in 2023 (%)
Total
2.6 GW
FID/
construction
Feasibility
study
Concept
By status
1.2 GW
1.2 GW
c
0.2
GW
Announcements
by 2023 VERSUS outcome in 2024
0
25
50
75
100
Share of capacity additions in 2023 (%)
Total
0.6 GW
FID/
construction
Feasibility
study
By status
0.5 GW
d
Outcome
Disappeared
Delayed
On time
0.1
GW
 Green hydrogen
(1)
implementation
gap in 2023
4.28 GW
announced by 2021
2.6 GW
announced by 2022
1.01 GW
announced by 2023
0.3 GW
operational as of 2024
FID/
construction
Feasibility
study
Concept
New/
delayed
New/
delayed
Delayed
to 2024+
Disappeared
New
On time
0
1
2
3
4
5
Capacity additions announced for 2023 (GW)
Tracking global green hydrogen projects announced for 2023
a
Fig. 3 | The 2023 green hydrogen implementation gap. a, Sankey diagram
showing the development of green hydrogen projects announced for 2023 in
terms of added electrolysis capacity (n = 190). The bars show different snapshots
of the underlying project database, where, for example, ‘Announcements by
2021’ refers to the database published in 2021 and therefore contains project
announcements made by 2021 (Methods). In 2021, 4.3 GW of new capacity was
announced to be installed in 2023. This was revised downward to 2.6 GW in 2022,
and again to 1 GW in 2023. Finally, in 2024, it became clear that only 0.3 GW of
new capacity had been installed and was operational in 2023. This results in
a green hydrogen implementation gap of almost 4 GW in 2023. In contrast to
Fig. 2, this figure additionally shows the outcome of project announcements
for 2023 as ‘Outcome in 2024’, based on our own research (Methods). The
outcome in 2024 refers only to projects that were included in the 2023 database.
Additional projects that were missing in the 2023 database could change the
success rate. b–d, Percentage rates of success, delay and disappearance of
uncertain green hydrogen projects announced to launch in 2023, comparing
announcements by 2021 with the outcome in 2024 (b), announcements by 2022
with the outcome in 2024 (c) and announcements by 2023 with the outcome in
2024 (d). In b–d, the left panel shows the total share and the right panel shows the
disaggregation by status. As indicated by the horizontal whiskers at the bottom,
the widths of the bars in the right panels correspond to the share of the total
capacity (also compare with a). Within each colour band, individual projects
are shown as segments, ordered by size. The ‘disappeared’ outcome category
contains projects that appeared in one database, but were absent in subsequent
databases. This includes cancelled or discontinued projects, but may also be due
to other reasons (Supplementary Note 1).

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This heterogeneity results from two key uncertainties. First, the pace
at which the nascent green hydrogen value chain can be scaled up is
highly uncertain31, particularly as project announcements have been
a poor indicator of growth. However, to reach 1.5 °C scenarios by 2030,
green hydrogen would need to experience unprecedented growth
rates (Extended Data Fig. 1a,c). Second, although evidence shows that
hydrogen and electrofuels are promising for decarbonizing maritime
shipping32, aviation33 and steel34, substantial uncertainty remains con-
cerning the competition with alternative mitigation options such as
direct electrification, biofuels or carbon capture and storage35–37. This
structural uncertainty also persists in the long run, explaining the high
heterogeneity until 2050 (Extended Data Fig. 1b,d).
Despite the high heterogeneity, a notable trend emerges in a subset
of the 1.5 °C scenarios: the International Energy Agency (IEA) Net Zero
Emissions by 2050 Scenario (NZE), which has been updated annually
over the past 3 years38–40, indicates a steady downward revision of
required electrolysis for 2030 (Fig. 4b). This adjustment reflects recent
setbacks for green hydrogen and the rapid progress of competing miti-
gation options, particularly the deep electrification of road transport
as well as industrial and residential heat40. Meanwhile, the 2030 green
hydrogen project pipeline has nearly tripled from 161 GW to 422 GW,
surpassing the requirements for 1.5 °C in 48 of the 60 IAM scenarios, and
9 of the 15 institutional and corporate scenarios. As a result, the green
hydrogen ambition gap in 2030 has already closed for 60–80% of the
scenarios and can be expected to close soon for the IEA NZE scenario.
Although the convergence of project announcements and 1.5 °C
scenarios is encouraging, the past green hydrogen implementation
gaps in 2022 and 2023 cast doubt on the reliability of ever-increasing
project announcements. Of the 422 GW announced by 2030, 97% are
still in the concept or feasibility study phase, which have exhibited criti-
cally insufficient success rates in the past (see the previous section).
Achieving the level of ambition required in 1.5 °C scenarios hinges on
overcoming these high failure rates. Yet, how much policy support
would be required to realize all project announcements?
Estimating the 2030 green hydrogen
implementation gap
The flipside of the closing of the green hydrogen ambition gap is the
widening future green hydrogen implementation gap in 2030, which
we define as the difference between project announcements and pro-
jects that are supported by policies. In this context, we estimated the
policy support required to realize all 422 GW of green hydrogen project
announcements by 2030. Modelling pay-as-bid market premium auc-
tions, we estimated the required subsidies across 14 end-use sectors rep-
resented in the projects database (Extended Data Fig. 2). We modelled
the competition between four green products (green hydrogen, plus
three hydrogen-based synthetic electrofuels, e-methanol, e-kerosene
and e-methane) and five incumbent fossil competitors (natural gas, grey
hydrogen, grey methanol, kerosene and diesel). For each end use, we
calculated the gradually declining cost gap between the green product
and its fossil competitor, considering higher efficiencies of hydrogen if
applicable (Extended Data Table 1) and accounting for end-use-specific
transport and storage costs (Supplementary Table 2). We explored
the impact of more progressive and more conservative parameter
values, which cover wide ranges for green products (Extended Data
Table 2) and fossil competitors (Extended Data Table 3). For the latter,
0
300
600
900
IAM
scenarios
Institutional and
corporate scenarios
Capacity in 2030 (GW)
Green hydrogen in 1.5°C scenarios in 2030
a
n = 528
n = 788
n = 1,168
NZE 2021
NZE 2022
NZE 2023
Increasing
green hydrogen
project pipeline
 Green hydrogen
(2)
ambition gap in 2030
Decreasing role of
green hydrogen in
IEA Net Zero Emissions
scenarios
0
300
600
900
2021
2022
2023
Year of announcement/publication
Capacity in 2030 (GW)
Green hydrogen ambition gap in 2030
b
Project status
Concept
Feasibility study
FID/construction
Operational
Fig. 4 | The closing green hydrogen ambition gap in 2030. a, Electrolysis
capacity requirements for 2030 in 1.5 °C scenarios from IAMs (n = 60) and
from institutional and corporate 1.5 °C scenarios (n = 15), excluding one outlier
scenario with a capacity of 1,700 GW in 2030 (see Extended Data Fig. 1). Each
dot represents one scenario. Red dots indicate the IEA NZE scenarios (b). The
whiskers indicate the range of capacities, 3–1,072 GW for the IAM scenarios and
30–1,016 GW for the institutional and corporate scenarios, underlining the high
uncertainty around mid-term green hydrogen deployment. The boxes indicate
the upper and lower quartiles, spanning the interquartile range of 38–375 GW
for the IAM scenarios and 203–655 GW for the institutional and corporate
scenarios. The horizontal line inside each box indicates the median at 169 and
350 GW, respectively. For the IAM scenarios, it remains uncertain whether
models explicitly represent different hydrogen applications and whether the
results have been vetted. When estimating the required subsidies for a 1.5 °C
scenario, we therefore only used the institutional and corporate scenarios
(Methods and Supplementary Figs. 11 and 13). Extended Data Fig. 1 shows data
for all of the scenarios over time. b, Electrolysis capacity requirements in the
IEA NZE scenarios and the project pipeline for 2030. Only the NZE scenarios
provide annually updated electrolysis capacity in 2030 over the past 3 years.
The x axis shows the year of announcement of the projects database and the year
of publication of the NZE scenarios. Individual projects are shown as segments
within the coloured bars. For the NZE scenarios, the green hydrogen ambition
gap in 2030 has gradually closed as (1) the project pipeline for 2030 has almost
tripled in the past 3 years and (2) the NZE scenarios in the past 3 years show a
decreasing role of green hydrogen by 2030. For 80% of the IAM scenarios and
60% of the institutional and corporate scenarios, the 2030 ambition gap has
already closed. However, more than 97% of the announced project capacity in
2030 is not yet backed by an FID.

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we also assessed the impact of a high carbon price in line with EU cli-
mate targets. To recover their costs, green hydrogen and electrofuel
projects must sell at their respective levelized costs throughout the
payback period (see illustrative explanation in Extended Data Fig. 3).
Assuming that offtakers are broadly not willing to pay a premium for
green products, the cost gap determines the specific per-megawatt
hour subsidy required. To estimate the total required subsidies, for
each end use, we tracked all project announcements throughout their
payback period and combined this vintage tracking with the cost gap
between the levelized cost of the projects and the corresponding fossil
fuel cost. Our model includes the impact of end-use-specific imple-
mented demand-side policies, which reduce subsidy requirements
Without carbon price
With ambitious carbon price
0
50
100
150
Year
Specific cost (US$ MWhLHV
–1)
Specific cost (US$ MWhLHV
–1)
Specific cost (US$ MWhLHV
–1)
Specific cost (US$ MWhLHV
–1)
Green hydrogen versus natural gas
a
0
50
100
150
2024
2030
2035
2040
2045
2024
2030
2035
2040
2045
Year
Year
2024
2030
2035
2040
2045
2024
2030
2035
2040
2045
Year
Year
2024
2030
2035
2040
2045
2024
2030
2035
2040
2045
Year
Green hydrogen versus natural gas
b
End uses (214 GW)
Other industry
Power
Grid injection
CHP
Domestic heat
Iron and steel
NA
0
50
100
150
Green hydrogen versus grey hydrogen
c
0
50
100
150
Green hydrogen versus grey hydrogen
d
End uses (171 GW)
Ammonia
Refining
Biofuels
Total cost
Green hydrogen
Natural gas/grey hydrogen
Green hydrogen
Electricity
Transport and storage
Fixed O&M
Investment: stack
Investment: balance of plant
Natural gas/grey hydrogen
CO2 price
Transport and storage
Price
0
25
50
75
100
0
500
1,000
1,500
Annual subsidies (US$ billion yr–1)
Cumulative subsidies (US$ billion)
Required subsidies for all projects by 2030
e
0
25
50
75
100
0
500
1,000
1,500
Annual subsidies (US$ billion yr–1)
Cumulative subsidies (US$ billion)
Required subsidies for all projects by 2030
f
Annual subsidies (left axis)
Projects 2029–2030
Projects 2027–2028
Projects 2024–2026
Cumulative subsidies (right axis)
Projects until 2030
Projects until 2028
Projects until 2026
/
/
/
  Green hydrogen
implementation gap
in 2030
US$0.5 trillion
(US$0.1–2.0
trillion)
0
500
1,000
1,500
Required
without
carbon price
Required
with ambitious
carbon price
Announced
(BNEF)
Cumulative subsidies (US$ billion)
Required cumulative subsidies
for all projects by 2030
g
US$1.3 trillion
(US$0.8–2.6 trillion)
(3)
Fig. 5 | The green hydrogen implementation gap in 2030. a–d, Cost gap between
green hydrogen and natural gas (a,b) and between green hydrogen and grey
hydrogen (c,d) without carbon pricing (a,c) and with an ambitious carbon price
pathway (b,d) that is in line with reaching EU climate targets41 (US$149 tCO2
−1
in 2030, US$246 tCO2
−1 in 2040 and US$407 tCO2
−1 in 2050, see Extended Data
Table 3). These two markets cover over 90% of the project announcements by
2030 (Extended Data Fig. 2). The represented end uses are shown next to each
row. Extended Data Fig. 5 displays the full set of competition across all end uses,
covering four other markets and different hydrogen-based electrofuels. The
red double-headed arrows and the light-red shading indicate the cost gap that
needs to be bridged by subsidies. The stacked bars indicate the decomposition
of the LCOH and the total cost of the fossil competitor for selected years (2024,
2030, 2035, 2040 and 2045). For easier visualization, the LCOH bar is shown on
the left and the fossil competitor bar on the right. Our 2030 LCOH estimates are
in line with recent studies (see Extended Data Fig. 4). LHV, lower hydrogen value.
O&M, operations and maintenance. CHP, combined heat and power. NA, not
available (end use unknown). e,f, Subsidies required to bridge the cost gap across
all end uses to realize all project announcements until 2030 on time, without
carbon pricing (e) and with carbon pricing (f). The bars show the required annual
subsidies (left axis) and the lines show the required cumulative subsidies (right
axis). g, Cumulative subsidies required to realize all project announcements by
2030 compared with globally announced hydrogen subsidies as of September
2023 from BloombergNEF (BNEF)43. Our estimate takes currently implemented
demand-side policies into account (see Methods and Supplementary Fig. 15).
Without carbon pricing, US$1.3 trillion of subsidies are required to realize all
projects announced until 2030 (the values in parentheses show the ranges
of more progressive and conservative parameters, see Extended Data Fig. 6).
Note that e and f show only the subsidies required for green hydrogen project
announcements until 2030. Staying on a 1.5 °C scenario requires substantial
further subsidies after 2030 (Supplementary Fig. 16 and Table 1).

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by increasing the willingness to pay (Supplementary Fig. 15 and Meth-
ods) but also incur macroeconomic costs (Supplementary Note 5).
Across all end uses, the competitiveness analysis reveals a sub-
stantial and prolonged cost gap between all green products and their
respective fossil competitors. This is exemplified by the competition
between green hydrogen and natural gas, which is relevant for end uses
such as industry, power and grid injection (Fig. 5a,b), as well as between
green hydrogen and grey hydrogen, covering the end uses ammonia,
refining and some biofuel routes (Fig. 5c,d). Together, these account
for over 90% of the announced electrolyser capacity by 2030 (Extended
Data Fig. 2). In contrast, project announcements for electrofuels remain
limited, which may be due to a larger cost gap to the fossil competi-
tors in the respective end uses (Extended Data Fig. 5g–l). Without
carbon pricing, the cost gap between green hydrogen and natural gas
of US$150 MWh−1 in 2024 implies that green hydrogen is initially more
than seven times as expensive as natural gas (Fig. 5a), while the cost gap
between green hydrogen and grey hydrogen is only slightly lower at
US$121 MWh−1 in 2024 (Fig. 5c). As green hydrogen costs decrease, the
cost gap gradually reduces, but typically prevails also into the long term.
This pattern holds across all end uses. Without carbon pricing, in our
central estimate, no green product becomes competitive with its fossil
competitor until 2050. This is robust across a wide range of progressive
and conservative parameter values (Extended Data Fig. 5, left column).
In contrast, under an ambitious carbon price pathway in line with
EU climate targets41 (US$149 tCO2
−1 in 2030, US$246 tCO2
−1 in 2040 and
US$407 tCO2
−1 in 2050, see Extended Data Table 3), green products
gradually achieve cost parity with their fossil competitors. While the
exact timing of cost parity remains highly uncertain, a relative sequence
of hydrogen end-use competitiveness can be derived (Fig. 5b,d and
Extended Data Fig. 5, right column). In our central estimate, green
hydrogen first becomes competitive with grey hydrogen in 2034 (for
example, for ammonia and refining), followed by green hydrogen
becoming competitive with diesel in 2037 (for mobility), e-methanol
becoming competitive with grey methanol in 2043 (for example, for
chemicals), and green hydrogen becoming competitive with natural gas
in 2044 (for example, for industry and power). In our central estimate,
e-kerosene and e-methane narrowly miss reaching cost parity with their
fossil competitors by 2050 (Extended Data Fig. 5h,l). Thus, even with
ambitious carbon pricing, the cost gap persists for at least one decade,
depending on the end use and the scenario. Sustained support policies
complementing carbon pricing are therefore essential to foster green
hydrogen growth and reduce investment risks.
The main drivers of green hydrogen costs are electricity prices and
electrolyser investment costs (Fig. 5a–d). For electrofuels produced
from green hydrogen and renewable carbon, these two factors domi-
nate the overall costs (Extended Data Fig. 5g–l). Although electrolyser
investment costs have recently surged1,17, this trend is expected to
reverse soon due to learning by doing and economies of scale. Note
again that to estimate the volume of required subsidies, we considered
a scenario where all project announcements until 2030 are realized
on time, while after 2030, cost reductions are driven by the median
electrolysis capacity in 1.5 °C scenarios (Methods and Supplementary
Fig. 11). This leads to rapidly falling electrolyser costs (Supplementary
Fig. 12). We used a payback period of 15 years to calculate the levelized
costs (Methods), as well as to estimate the required subsidies (Extended
Data Fig. 3); this period represents the typical length of implemented
policy support such as auctions42 and is therefore more relevant for
investment decisions than the technical lifetime. Our 2030 levelized
costs of green hydrogen (LCOHs) are consistent with recent studies
(Extended Data Fig. 4).
The annual subsidies required to realize all project announce-
ments across all end uses by 2030 are bell-shaped, with the height and
timing of the peak varying by scenario (Fig. 5e,f, left axis). Without
carbon pricing, the required annual subsidies rise sharply to a plateau
of around US$90 billion per year throughout the 2030s (Fig. 5e). With
carbon pricing, the required annual subsidies peak at US$44 billion
per year in 2030 (Fig. 5f). The resulting cumulative subsidies for all
422 GW by 2030 follow an S curve (Fig. 5e,f, right axis). In our central
estimate, the required cumulative subsidies are US$1.3 trillion without
carbon pricing and US$0.5 trillion with carbon pricing, subject to
considerable uncertainty (Table 1 and Extended Data Fig. 6). However,
these figures only pertain to the 2030 project pipeline. Aligning green
hydrogen with 1.5 °C scenarios after 2030 would require substantially
higher subsidies, rising to US$9.3 trillion (US$4.2–17.7 trillion range)
without carbon pricing by 2050 (Table 1 and Supplementary Fig. 16).
Due to a substantial discrepancy between required and announced
subsidies, a wide 2030 green hydrogen implementation gap arises
(Fig. 5g and Table 1). The cumulative subsidies required to realize all
project announcements by 2030 exceed currently announced subsi-
dies, estimated at US$308 billion as of September 202343, by over 300%
without carbon pricing and by over 60% without. There are counteract-
ing uncertainties regarding this estimate, as announced subsidies are
likely to increase in the future, but challenges may arise during their
implementation (Methods). Even if all currently announced global
subsidies were immediately available, without carbon pricing this
would only support 61 GW (32–106 GW range) by 2030 (Supplemen-
tary Fig. 17). Depending on the scenario, implemented demand-side
policies could support a similar share of project announcements,
Table 1 | Estimating the 2030 green hydrogen implementation gap for different scenarios
Without carbon price
With ambitious carbon price
Central
Progressive
Conservative
Central
Progressive
Conservative
Green hydrogen project
announcements by 2030
Required total cumulative
subsidies (US$)
1.3 trillion
0.8 trillion
2.6 trillion
0.5 trillion
0.1 trillion
2.0 trillion
Announced subsidies (US$)
0.3 trillion
Implementation gap (US$)
1.0 trillion
0.5 trillion
2.3 trillion
0.2 trillion
0
1.7 trillion
Green hydrogen scale-up
until 2050 (median 1.5 °C
scenario)
Required cumulative subsidies
by 2050 (US$)
9.3 trillion
4.2 trillion
17.7 trillion
2.4 trillion
0.1 trillion
12.4 trillion
Average specific subsidies in
2050 (US$ MWh−1)
98
35
214
8
0
119
For project announcements by 2030, the table shows required total cumulative subsidies (which are required until 2045, see Fig. 5), announced subsidies and the resulting implementation
gap in terms of the missing subsidies that would be required to realize all project announcements from 2024 to 2030 on time. Without carbon pricing, there is a substantial 2030
implementation gap. Even with carbon prices in line with reaching EU climate targets41 (US$149 tCO2
−1 in 2030, US$246 tCO2
−1 in 2040 and US$407 tCO2
−1 in 2050, see Extended Data Table 3),
the implementation gap only closes for the progressive scenario. Beyond 2030, we modelled the green hydrogen scale-up until 2050 by using the median of all institutional and corporate
1.5 °C scenarios (Extended Data Fig. 1a,b) and the end-use shares from the IEA NZE Scenario (Supplementary Fig. 13). For this scenario, the table shows the required cumulative subsidies by
2050 (Supplementary Fig. 16) and the required average specific subsidies in 2050 (Extended Data Fig. 7, differentiated by green hydrogen and electrofuels). Without carbon pricing, green
hydrogen and electrofuels require subsidies until 2050 across all end uses, leading to enormous required cumulative subsidies by 2050, as well as substantial average specific subsidies even
in 2050, which may be required indefinitely for some applications. With ambitious carbon pricing, the required cumulative subsidies by 2050 strongly depend on the scenario.

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underlining the crucial role of demand-side regulation for fostering
green hydrogen growth.
Our results indicate that permanently subsidizing green hydrogen
and electrofuels to compete with cheap fossil fuels would likely end up
being prohibitively expensive in the long term, highlighting the key role
of carbon pricing in closing the cost gap. Without carbon pricing, green
hydrogen growth in line with the 1.5 °C scenario median requires annual
subsidies that far exceed the historical support of solar PV and wind
(Extended Data Fig. 7a). In particular, without carbon pricing, green
hydrogen and electrofuels likely require subsidies until at least 2050
(Extended Data Fig. 7c and Table 1). In contrast, under an ambitious
carbon price pathway, the required green hydrogen and electrofuel
subsidies could remain in the same range historically observed for solar
PV and wind, with per-megawatt hour subsidies steadily decreasing
until 2050 (Extended Data Fig. 7b,d).
Discussion and conclusion
The past and future of green hydrogen is characterized by three gaps,
reflecting the challenges of scaling-up a novel and as yet uncompeti-
tive energy carrier that requires dedicated policy support. First, the
2023 implementation gap shows that only 7% of initially announced
green hydrogen capacity was eventually realized. Second, the 2030
ambition gap has gradually closed over the past 3 years as the project
pipeline increasingly exceeds the requirements in 1.5 °C scenarios.
Third however, this has led to a wide 2030 implementation gap as
enormous subsidies would be required to realize all of the projects
by 2030, and even more to put green hydrogen on track for 1.5 °C in
the long term.
The high past failure rates indicate a limited reliability of project
announcements published by industry, which may announce green
hydrogen projects for strategic reasons, such as raising attention or
attracting subsidies. Although sobering, this can provide valuable
insights for realistic scale-up analyses of green hydrogen31 and other
low-carbon energy technologies in feasibility studies44–46, some of
which45 have recently faced criticism for lacking statistical rigour47.
Our results are particularly useful for analyses that use uncertain
project announcements as input data25,48. System planners, policy-
makers and society should interpret the increasingly steep growth
suggested by recent project announcements with caution, focusing
on scale-up challenges, such as lacking competitiveness and the need
for policy support.
To close the green hydrogen implementation gap, policymakers
need to bridge the cost gap to fossil fuels and de-risk hydrogen invest-
ments. This requires a balanced policy mix and a robust strategy to
navigate the following three key uncertainties and risks.
First, the huge past and future implementation gaps indicate that
green hydrogen will likely fall short of 1.5 °C scenarios. Even if policy
support is strengthened, it remains uncertain whether this would be
sufficient to drive the necessary hydrogen investments. Realizing cur-
rent project announcements would require unprecedented growth
rates (Extended Data Fig. 1a,c), exceeding even the fastest-growing
energy technology in history, namely, solar PV. Given that green hydro-
gen technologies are more complex, less standardizable and require
new infrastructure, all of which slow down technology diffusion24,
realizing such unprecedented growth is unlikely.
Second, current hydrogen policy instruments often seek to spur
hydrogen investments by bridging the cost gap to fossil fuels through
supply-side subsidies such as fixed-premium auctions. However, as we
have shown, this approach requires not only excessive subsidy volumes
but also strong perseverance as policy support could be required for
several decades, or even indefinitely without carbon pricing or strong
demand-side regulation. Subsidies for near-term green hydrogen pro-
duction are often framed within a narrative of kickstarting a ‘hydrogen
economy’ through a short policy push, after which green hydrogen
becomes cost-competitive and scales up on its own. However, this
critically depends on optimistic assumptions about technology cost
reductions, which stands in contrast to recent cost increases of electro-
lysers1. Without ambitious cost reductions, the ‘kickstarting’ narrative
is misleading and raises false hopes.
Third, the primary role of hydrogen in climate change mitigation
is to replace fossil fuels in hard-to-electrify sectors. However, strong
political support for hydrogen is often accompanied by overconfi-
dence in its potential15, resulting in conflicting visions about its future
role. Many global climate change mitigation scenarios show a modest
long-term share of hydrogen of 5–15% in final energy2,40,49, focusing on
key end uses where hydrogen is highly valuable due to a lack of alterna-
tives5. In stark contrast, incumbent actors in gas, heat, industry and
transport tend to endorse a wide use of hydrogen across sectors50, even
in end uses such as residential heat, where electrification is cheaper,
more efficient and readily available2,40,49,51. Uncertainties remain around
the role of hydrogen in complementing the electrification of heavy
transport and industrial heat11,35,40.
Disregarding these uncertainties and risks, and instead focusing
on supply-side subsidies with the expectation of abundant low-cost
green hydrogen in the future, risks crowding out readily available and
more economical options, thereby delaying climate change mitiga-
tion. To minimize these risks while safeguarding the scale-up of green
hydrogen, we draw two key policy conclusions.
First, supply-side subsidies, which reduce the investment risk
of electrolysis projects, should be complemented by demand-side
policies that guide hydrogen to its most valuable use cases by increas-
ing their willingness to pay. The benefit of demand-side measures is
illustrated by the European Hydrogen Bank’s recent inaugural auction,
which resulted in surprisingly low successful bids of €0.37–0.48 kg−1
(ref. 52) compared with a similar auction in the UK, which received only
high bids equivalent to €9.40 kg−1 (ref. 53). Aside from regional hetero-
geneity, this stark difference may be attributed to the EU’s demand-side
quotas, such as the mandatory 42% green hydrogen share of all hydro-
gen used in industry by 2030 under the Renewable Energy Directive III
(ref. 54), and mandates for hydrogen-based electrofuels under ReFu-
elEU Aviation55 and FuelEU Maritime56 regulations. Although they incur
macroeconomic costs (Supplementary Note 5), demand-side policies
can reduce the pressure on supply-side subsidies, helping to close the
implementation gap.
Second, policymakers should plan the transition from subsidies
to market mechanisms. In the short run, achieving rapid near-term
hydrogen growth is crucial to keep 1.5 °C scenarios within reach. This
requires strong policies, such as subsidies to directly bridge the cost
gap, minimize investment risks and initialize a hydrogen market. How-
ever, as hydrogen technologies and markets mature, policy support
should shift to market-based mechanisms to (1) reduce policy costs, (2)
reveal the full hydrogen costs to markets and consumers, and (3) create
a level playing field with other mitigation options. The most important
technology-neutral strategy is ambitious carbon pricing. However, as
carbon prices are currently too low and too uncertain in the future,
complementary instruments are required to de-risk the remaining
uncertainties. These include technology-neutral auctions of carbon
contracts for difference57, which hedge investors against unpredict-
able prices by covering the difference between emissions abatement
costs and carbon prices, as well as tradable, technology-neutral quotas
for, for example, low-carbon materials, fostering green lead markets.
In summary, a comprehensive policy strategy for green hydro-
gen should include targeted demand-side measures and a gradual
transition from subsidies to market mechanisms. In the short term,
this would de-risk early investment at manageable costs, guiding
hydrogen to its most valuable use cases. In the long term, this would
transfer investment risks and competition between hydrogen and
other mitigation options to the market, thereby establishing a cred-
ible commitment for climate change mitigation while spurring green
hydrogen growth.

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Methods
Overview
Our approach was split into three parts. First, we tracked green
hydrogen project announcements to quantify the green hydrogen
implementation gap in 2022 and 2023. Second, we compared project
announcements with 1.5 °C scenarios to show the 2030 green hydrogen
ambition gap. Third, we modelled the pay-as-bid market premium and
estimated required subsidies using a competitiveness analysis of four
green products and five fossil competitors across 14 end uses, which
led to the 2030 green hydrogen implementation gap.
Green hydrogen projects database
We used data of electrolysis project announcements from the IEA
Hydrogen Production Projects and Infrastructure Database58 (previ-
ously called the IEA Hydrogen Projects Database), incorporating three
database snapshots from 2021, 2022 and 2023. We only included pro-
ject announcements for electrolysers that included a year of project
launch, had a meaningful status (not ‘Other’ or ‘Other/Unknown’) and
reported a capacity value. We did not filter for the type of electricity as
this was often unknown. These criteria led to 612 projects in the 2021
snapshot, 877 projects in the 2022 snapshot and 1,265 projects in the
2023 snapshot. In the 2023 snapshot, only a single status category was
reported for projects that were either under construction or had an FID
(‘FID/Construction’). To ensure consistent status categories across all
snapshots, we merged the ‘FID’ and ‘Under construction’ categories
in the 2021 and 2022 snapshots. Projects with a ‘DEMO’ status were
allocated as ‘Operational’, ‘FID/Construction’ or ‘Decommissioned’,
depending on whether they were still running, announced for the future
or had been decommissioned, respectively. We note that the ‘Concept’
category is very broadly defined with an unspecified credibility bar
for inclusion, while the ‘Feasibility study’ category may also contain
projects for which a feasibility study is planned, but has not yet started.
Confidential projects were distributed to all regions in proportion to
the share of capacity from non-confidential projects, but could not be
tracked across database snapshots.
Data quality validation
We conducted a comprehensive, structured and fully documented
data quality validation of the green hydrogen project announcements,
manually validating 524 project entries across all three database ver-
sions. For projects announced for 2022 or 2023, we covered at least
90% of the announced capacity, while for projects announced for
2024–2030, we covered at least 75% of the announced capacity in
all three database versions (Supplementary Table 1). In addition, we
manually verified the fate of all projects announced to launch in 2023
in the database published in October 2023 (Fig. 3). Note that we did not
attempt to identify missing projects, implying that the success rate
may change if projects that were realized in 2023 were missing from
the most recent database version included in this analysis, published
in October 2023. During the data validation, we adjusted the size of a
project if it was not operating at its nameplate capacity, which was the
case for the world’s largest green hydrogen project, Sinopec Kuqa in
China. The data quality validation procedure is described in detail in
Supplementary Note 1.
Tracking green hydrogen projects
Each project has a unique reference number that stays the same
across all database snapshots, as confirmed by the IEA in personal
correspondence. This enabled us to track the development of pro-
ject announcements over time (see Fig. 3 for projects announced for
2023, Supplementary Fig. 5 for projects announced for 2022 and Sup-
plementary Fig. 6 for projects announced for 2024). Supplementary
Figs. 7–10 also show the 2023 project tracking for those regions that
have at least ten trackable project entries. We accounted for chang-
ing capacity of projects between two database snapshots by adding
dummy projects, which are, however, not explicitly shown in the Sankey
diagrams for simplicity. The reported rates of disappearance, delay and
success (Fig. 3b–d and Supplementary Fig. 5b, c) only refer to projects
announced in 2021, 2022 and 2023, respectively.
Green hydrogen in 1.5 °C scenarios
As an indicator of green hydrogen requirements in stringent climate
mitigation scenarios, we collected electrolysis capacity values from
a wide range of 1.5 °C scenarios, including (1) IAM scenarios and (2)
institutional and corporate scenarios (Extended Data Fig. 1). For the
IAM scenarios, we used the IPCC AR6 Scenarios Database59 (category
C1) as well as the Network for Greening the Financial System (NGFS)
dataset60 (Version 4.2, the ‘Net Zero 2050’ and ‘Low demand’ scenarios).
We excluded IAM scenarios that always report zero electrolysis capac-
ity (or zero electrolytic hydrogen production) or, in any period from
2025, report a value that is lower than the operational electrolysis
capacity in 2023. We also omitted scenarios from the NGFS project
that included climate damages as this is only reported by one model.
For the institutional and corporate scenarios, due to limited report-
ing of numerical data in text or tables, in some cases we resorted to
extracting data from graphics using WebPlotDigitizer, which has been
shown to be reliable61. All datasets are available via GitHub (see the
Data availability statement). If electrolysis capacity was not directly
reported, we converted production quantities into electrolysis capac-
ity, assuming 3,750 full load hours, 69% efficiency and the lower heating
value of hydrogen, 33.33 kWh kg−1. For IAM scenarios, we transformed
the reported hydrogen output capacity to the corresponding input
capacity of the electrolyser using the efficiency of 69%. Due to these
approximations, reported electrolysis requirements in 1.5 °C scenarios
are inherently uncertain.
Modelling pay-as-bid market premiums
To quantify the future green hydrogen implementation gap, we devel-
oped a model of the required pay-as-bid market premiums for green
hydrogen projects (Extended Data Fig. 3). First, we mapped each of the
14 end-use categories from the green hydrogen projects database to
the competition between a green product and a fossil competitor, cov-
ering four green products (green hydrogen, e-methanol, e-kerosene
and e-methane) and five fossil competitors (grey hydrogen, natural
gas, grey methanol, diesel and kerosene), as shown in Extended Data
Table 1. For projects without a designated end use, we assumed that
green hydrogen competes with natural gas. Second, we calculated the
levelized cost of all green products (Extended Data Table 2) and the
prices of all fossil competitors with and without an ambitious carbon
price pathway that is in line with EU climate targets41 (Extended Data
Table 3). Details on these costs and prices are explained in the fol-
lowing sections. Third, we incorporated demand-side policies such
as end-use quotas, which increase the willingness to pay for green
products and thereby reduce required policy costs (Supplementary
Fig. 15). Finally, for each end use, we estimated the required subsi-
dies based on (1) vintage tracking of project announcements and (2)
the cost gap between the green product and the fossil competitor
(Extended Data Fig. 3).
We included global estimates of implemented demand-side poli-
cies in 2030 across four end uses, provided by the IEA1, which we con-
verted into the corresponding electrolysis capacities using the lower
heating value, as well as the full load hours and efficiencies of the
respective scenario. We proportionally distributed these estimates
of electrolysis capacity that are supported by demand-side regulation
in 2030 according to the project announcements from 2024–2030
(Supplementary Fig. 15).
If the capacity supported by demand-side policies exceeded the
announced capacity, which is the case for refining and synthetic fuels,
we omitted the difference, assuming that demand-side policies are
end-use specific.

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To estimate the required annual subsidies, we combined these
components. As shown in Fig. 5a–d and Extended Data Fig. 5, for each
end use, the instantaneous cost gap (Δpt) between the levelized cost
of the green product in year t (LCOXt) and the price of the fossil com-
petitor (pfossil
t
) is given as:
Δpt = LCOXt −pfossil
t
(1)
However, this cannot be used directly to estimate subsidies. As
illustrated in Extended Data Fig. 3, a green hydrogen or electrofuel
project completed in year t′ must sell the green product at LCOXt′ for
the entire duration of the payback period τ to recover its costs. The
required annual subsidies accumulate over time due to projects built
in previous years. For example, in 2026, projects that were built in 2024
face a cost gap of LCOX2024 −pfossil
2026, projects that were built in 2025 face
a cost gap of LCOX2025 −pfossil
2026 and projects that were built in 2026 face
a cost gap of LCOX2026 −pfossil
2026. These cost gaps have to be bridged for
the electrolysis capacity built in the corresponding year t′, denoted as
ΔCt′ (accounting for capacity supported by demand-side policies). For
each end use, with electrolyser full load hours FLHH2, electrolyser effi-
ciency ηH2 and payback period τ, the required annual subsidy (Sannual
t
)
in year t is given as:
Sannual
t
=
t
∑
t′=max{2024,t−τ+1}
ΔCt′ × FLHH2,t′ × ηH2,t′ × max {0, LCOXt′ −pfossil
t
}
(2)
Note that for subsidies in year t, only the price of the fossil competi-
tor (pfossil
t
) refers to the same year t, whereas all other parameters refer
to the year t′ in which the project was built. Thus, the realization of
green hydrogen projects built in the year t′ requires subsidy payments
for the full payback period [t′, t′ + τ) as long as LCOXt′ > pfossil
t
. For end
uses where the green product and the fossil competitor are not used
thermally, we included the relative efficiency improvement of using
the green product over the fossil competitor, ηgreen
LHV /ηfossil
LHV , adjusting the
LCOX accordingly (Extended Data Table 1). Note that for green hydro-
gen, we denote LCOX as LCOH. Correspondingly, the required cumula-
tive subsidies until year t (Scumulative
t
) are given by:
Scumulative
t
=
t
∑
t′=2024
Sannual
t′
(3)
We show in Fig. 5e–g and Extended Data Fig. 6 the required annual
and cumulative subsidies as the sum over all end uses.
To analyse what would be required for a 1.5 °C scenario, after 2030
we used the median of the institutional and corporate 1.5 °C scenarios
for ΔCt′ (Extended Data Fig. 1b and Supplementary Fig. 11). To determine
the sectoral allocation of the overall capacity to the 14 end uses after
2030, we used the green hydrogen end-use shares of the IEA NZE Sce-
nario40 (Supplementary Fig. 13). The results for this 1.5 °C scenario until
2050 are presented in Supplementary Fig. 16.
Levelized costs of green products
For all green products, we first calculated LCOH for each year from 2024
using the annuity method and broadly following the system boundaries
outlined in ref. 62 (for the parameters, see Extended Data Table 2), but
adding end-use-specific transport and storage costs (Supplementary
Table 2). Omitting time indices, the LCOH was calculated as:
LCOH =
1
ηH2
{ [a (r, τ) + FOMH2]
IBOP
FLHH2
+ [a (r, τstack)
+FOMH2]
Istack
FLHH2
+ pelec} + VOMH2
(4)
where ηH2 denotes the electrolyser efficiency, a(r, τ) =
r
1−(1+r)
−τ  is the
annuity factor, r is the cost of capital, τ is the payback period in years
(which can be shorter than the technical lifetime), τstack is the lifetime
of the electrolyser stack in years, FOMH2 is the fixed operation and
maintenance costs as a percentage of the specific investment costs,
IBOP is the specific investment cost of the electrolyser’s balance of plant
(BOP) and other engineering work, Istack is the specific investment cost
of the electrolyser stack, FLHH2 is the electrolysis full load hours, pelec is
the price of electricity and VOMH2 is the variable operation and main-
tenance costs, which are transport and storage costs (Supplementary
Table 2). Both IBOP and Istack relate to the electrical input capacity of the
electrolyser (US$ kWel
−1).
The electricity price paid by electrolysers is highly dependent on
the specific supply case and the regulatory definition of green hydro-
gen with respect to spatio-temporal matching and additionality28,29.
Flexible operation and a direct connection to a renewable energy
source reduces the price as electrolysers can tap into hours when
electricity is cheap and abundant. Grid-connected electrolysers need
to pay grid fees on top of electricity prices, but can run at higher full
load hours. Furthermore, stationary batteries can extend the electro-
lyser’s full load hours by providing a buffer for renewable electricity,
but require additional investments. While hourly energy system models
can represent these effects in detail28, we accounted for them in an
aggregated manner by using the same broad range of electricity prices
as in ref. 27. This ensures high traceability of results, while still captur-
ing the effects of system heterogeneity. Further discussion is provided
in Supplementary Note 2, while Supplementary Note 3 discusses how
energy system models could learn from our results.
We separated the total specific investments costs of the electro-
lyser (I) into Istack and IBOP because (1) the stack needs to be replaced ear-
lier than the rest of the electrolyser, such that we included two annuities
in equation (4)62, and (2) the stack is much more modular and therefore
more susceptible to cost improvements17, which we included through
different learning rates. Technological learning reduces specific invest-
ment costs of both IBOP and Istack in year t (It) according to
It = I2023(
Ct
C2023
)
log2(1−LR)
(5)
where I2023 denotes the investment costs in 2023, Ct denotes the global
cumulative electrolysis capacity in year t, C2023 = 0.92 GW installed
capacity in 2023 and LR denotes the learning rate. Technological learn-
ing is driven by cumulative project announcements until 2030 and
subsequently by the median 1.5 °C scenario (Supplementary Fig. 11).
Thus, electrolyser costs fall quickly (Supplementary Fig. 12).
For electrofuels derived from green hydrogen (e-kerosene,
e-methanol and e-methane), the corresponding LCOX are
LCOX = [a (r, τ) + FOMX]
IX
FLHX
+
pH2
ηX
+ pCO2εX + VOMX
(6)
where FOMX represents fixed operation and maintenance costs, IX is
the specific investment cost of the electrofuel synthesis plant (in terms
of electrofuel output), FLHX is the full load hours of the synthesis plant,
pH2 = LCOH −VOMH2 is the price of hydrogen (that is, the LCOH without
transport and storage costs), ηX is the synthesis energy efficiency, pCO2
is the price of renewable CO2 (not the carbon price of emissions), εX is
the CO2 intensity of the electrofuel and VOMX is the end-use-specific
transport and storage costs (Supplementary Table 2).
The price of renewable CO2, which can either come from bio-
genic sources or from direct air capture, is an uncertain but important
cost component for the production of carbon-neutral electrofuels
(Extended Data Fig. 5g–l). While biogenic carbon can initially be as
cheap as US$30 tCO2
−1, it likely faces availability limits such that it could
quickly become more expensive as demand increases (see, for example,
Fig. 6.3 in ref. 63). In contrast, direct air capture is more scalable, but
currently faces very high costs in the order of US$500–1,000 tCO2
−1,

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which could reduce to approximately US$300 tCO2
−1 once the scale of
1 GtCO2 yr−1 is reached in the long term64, although this is again subject
to substantial uncertainty. In our central estimate, we set the average
cost of renewable carbon to US$200 tCO2
−1, which reflects the differ-
ent CO2 sources reported in electrofuel projects, while the progres-
sive and conservative sensitivity scenarios covered a wide range of
US$30–300 tCO2
−1.
Prices of fossil competitors
We collected harmonized data on prices for all fossil competitors rep-
resented in our pay-as-bid market premium model for 2024, 2030 and
2050 (for parameters, see Extended Data Table 3), using linear interpola-
tion in between. For natural gas, our cost estimate was the average of
the EU trading point Title Transfer Facility in the Netherlands and the US
trading point Henry Hub, using spot market prices in 2024 and future
prices in 2030. For 2050, we used the gas price from the IEA NZE 1.5 °C
scenario40. For grey hydrogen and grey methanol, which are produced
from natural gas, we first collected current prices for 2024. To ensure
internal consistency with natural gas prices, we then calculated the cor-
responding specific fixed costs in 2024, which reflect the per-megawatt
hour capital costs associated with the synthesis plant. Assuming that
these stay constant, for 2030 and 2050 we inferred the price of grey
hydrogen and grey methanol by adding the corresponding variable
costs, that is, the natural gas price divided by the efficiency. We pro-
ceeded similarly for kerosene and diesel, using crude oil spot and future
prices as the reference for 2024 and 2030, respectively, while for 2050
we again used the oil price from the IEA NZE 1.5 °C scenario. This calibra-
tion ensured that prices for fossil products are internally consistent.
Last, we differentiated between scenarios without and with ambi-
tious carbon pricing. For the latter, we used a carbon price pathway
that is in line with EU climate targets in the sectors covered by the EU
Emissions Trading System, such as industry and energy supply41. The
CO2 cost per megawatt hour of the fossil competitor is the product of
the emissions intensity, including upstream methane emissions for
natural gas, grey hydrogen and grey methanol27, and the carbon price
per tonne of CO2. We denote the total cost as pfossil, which includes CO2
costs if applicable. In addition, for natural gas, we considered grid fees
of US$5 MWh−1 based on ref. 65 (Supplementary Table 2).
Limitations
As the quality of the data of the IEA Hydrogen Production and Infra-
structure Projects Database58 may be limited, we conducted a com-
prehensive data validation (see the section ‘Data quality validation’,
Supplementary Note 1, Supplementary Table 1 and Supplementary
Figs. 1–4). Nevertheless, some errors may remain, particularly for
smaller projects that were not checked. In general, there are counter-
acting uncertainties related to project announcements. On the one
hand, the database may underestimate projects, as we verified only
existing entries and did not conduct research to identify potentially
missing projects. On the other hand, the database may include projects
that are no longer active, as it is often unclear if and when a project has
been scrapped.
The quality of the data of the electrolysis requirements in 1.5 °C
scenarios is limited due to heterogeneous sources and limited numeri-
cal reporting of the scenario data accompanying the reports. In sev-
eral cases, we had to infer electrolysis capacity from green hydrogen
production values, also for IAM scenarios. Thus, Fig. 4 and Extended
Data Fig. 1 show only estimates of electrolysis capacity using publicly
available data and should not be interpreted as numerically exact.
Modelling the pay-as-bid market premium to estimate subsidies
required several simplifications. First, although we distinguished
between 14 end-use applications, four green products and five fossil
competitors, we did not account for regional differences in hydrogen
production costs. Our estimates can be interpreted as cross-regional
averages. Note that our sensitivity ranges are large enough to contain
the regional cost heterogeneity found in GIS-based analyses66. Sec-
ond, we neglected additional end-use transformation costs, which
are typically small or even zero, for example, for drop-in electrofu-
els. Some applications can simply replace grey with green hydrogen
with no additional costs (for example, ammonia production), while
additional investment costs in other applications are low compared
with fossil applications (for example, direct reduced iron-based steel
plants or hydrogen boilers). Third, we calculated levelized costs using
constant electricity prices, assuming that green hydrogen projects
require new dedicated renewable energy plants or long-term con-
tracted power-purchase agreements that deliver electricity at stable
prices. Similarly, for electrofuels, this implies dedicated electrolysers
or long-term contracts that deliver green hydrogen at constant prices.
Fourth, we did not consider the option that projects could pay back a
part of the received subsidies once they are profitable relative to their
fossil competitor in the future because this would require a contract
for differences that allows for this option. Fifth, we did not include
factors other than costs that influence the project realization as this
was outside the scope of this analysis. Sixth, we did not incorporate
the competition of green hydrogen with blue hydrogen and other
mitigation options, which we discuss in Supplementary Note 4. Last, we
assumed that demand-side policies directly translate into electrolysis
capacity without the need for additional subsidies.
The quality of the data of global announced hydrogen subsidies
from BloombergNEF (BNEF) may be limited and will likely soon be
outdated. The estimate for US subsidies is particularly uncertain as the
production tax credits of the Inflation Reduction Act12 are uncapped
such that BNEF bases their US subsidy estimates on hydrogen pro-
ject announcements. Furthermore, the tracked subsidies cover not
only green hydrogen but also other sources of low-carbon hydrogen,
which we optimistically compared to subsidy requirements only for
green hydrogen project announcements. The global subsidy volume
of US$308 billion for low-carbon hydrogen as of September 2023
therefore serves only as a snapshot. Although this figure will be out-
dated soon, it still offers a valuable reference point. However, it should
be interpreted with caution as the implementation of these subsidies
will critically depend on future government commitments to foster
the hydrogen market ramp-up.
Data availability
All of the data are publicly available via GitHub at https://github.com/
aodenweller/green-hydrogen-gap and via Zenodo at https://doi.
org/10.5281/zenodo.14041796 (ref. 67). All of the data files include a
column that indicates the original source.
Code availability
The R model code used to perform the analyses and produce all figures
is available via GitHub at https://github.com/aodenweller/green-hydro
gen-gap and via Zenodo at https://doi.org/10.5281/zenodo.14041796
(ref. 67).
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Acknowledgements
We gratefully acknowledge funding from the Kopernikus-Projekt
Ariadne through the German Federal Ministry of Education and
Research (grant nos. 03SFK5A and 03SFK5A0-2, A.O. and F.U.) and the
HyValue project (grant no. 333151, F.U.). We thank the IEA, in particular
J. M. Bermudez, for providing the Hydrogen Projects Database and for
answering related questions. We thank R. Pietzcker for brainstorming
figures, P. Verpoort for cost parameters and J. Sitarz for carbon price
data. Following the IEA’s Creative Commons license, we note that this
is a work derived by us from IEA material and we are solely liable and
responsible for this derived work. The derived work is not endorsed by
the IEA in any manner.
Author contributions
A.O. suggested the initial research question, which F.U. extended.
A.O. and F.U. collected cost data, while A.O. collected all other data.
A.O. performed the analyses and created the figures. A.O. and
F.U. interpreted the results. A.O. wrote the paper with contributions
from F.U.
Funding
Open access funding provided by Potsdam-Institut für
Klimafolgenforschung (PIK) e.V.
Competing interests
The authors declare no competing interests.
Additional information
Extended data is available for this paper at
https://doi.org/10.1038/s41560-024-01684-7.

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Supplementary information The online version contains supplementary
material available at https://doi.org/10.1038/s41560-024-01684-7.
Correspondence and requests for materials should be addressed to
Adrian Odenweller.
Peer review information Nature Energy thanks Paul Wolfram,
Sebastian Zwickl-Bernhard and the other, anonymous, reviewer(s) for
their contribution to the peer review of this work.
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Extended Data Fig. 1 | Green hydrogen electrolysis capacity in 1.5 °C scenarios.
a-b, Institutional and corporate 1.5 °C scenarios. c-d, Integrated assessment
model (IAM) scenarios. IAM scenarios from the IPCC AR6 Database59 only
include scenarios in category C1 (1.5 °C). IMP stands for illustrative mitigation
pathway. IMP-LD focusses on demand, IMP-Ren on renewable energy, and
IMP-SP on sustainable development. NGFS stands for Network for Greening
the Financial System – a project that provides regularly updated IAM scenarios
from different models60. We exclude scenarios, which either always report
zero green hydrogen, or which in any year from 2025 report a lower capacity
than has already been realised in 2023. If scenarios do not report electrolysis
capacity, we convert production quantities into corresponding electrolysis
capacity, which implies uncertainties (see Methods). The scenarios show a very
wide range, particularly in 2030, underlining the high uncertainty surrounding
the green hydrogen market ramp-up. Furthermore, panels a and c show that
unprecedented growth rates would be required to achieve the 1.5 °C scenarios in
2030 (apart from a few IAM scenarios that report limited use of green hydrogen
in 2030). Figure 4a depicts the distribution of these two scenario groups in 2030.
For the IAM scenarios, we are uncertain to what extent the different models
explicitly represent the different hydrogen applications and whether hydrogen
results have been vetted in detail. Therefore, in our analysis of required subsidies
for 1.5 °C, we use the median of the institutional and corporate scenarios (see
Supplementary Figure 11 and Supplementary Figure 13).

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Extended Data Fig. 2 | Designated end uses of project announcements.
a, Projects until 2024 by end use. b, Projects from 2024 until 2030 by end use.
The IEA Hydrogen Production and Infrastructure Projects Database 202358
distinguishes 14 end use categories (plus one category for projects that have no
designated end use). Projects can be assigned to multiple end uses, although
a single end use is most common (see Supplementary Figure 14). If a project
contains more than one end use, we distribute the announced capacity evenly
among them. Natural gas and grey hydrogen emerge as the most important
competitors of green hydrogen and electrofuels, covering over 90% of the
project pipeline in 2030. For each end use, we model the competition between
the corresponding green product and the fossil competitor (see Extended
Data Table 1). Notably, in the end uses refining and synthetic fuels, project
announcements are insufficient for the quantities required under already
implemented demand-side regulation (Supplementary Figure 15). Furthermore,
in 2030 there is a substantial mismatch between the designated end uses in the
project announcements and the end use shares according to the IEA NZE scenario
(Supplementary Figure 13a).

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Extended Data Fig. 3 | Illustration of our model of the pay-as-bid market
premium. This figure shows our the exemplary competition between green
hydrogen and natural gas with carbon pricing in our central estimate. For
illustration, we show how to calculate required subsidies for projects built in
2024, and for projects built in 2035. In both cases, projects have to sell at their
respective LCOH for the entire payback period (dashed green horizontal lines).
The specific cost gap decreases due to rising natural gas prices, which increase
due to carbon pricing. This cost gap defines the required subsidies for projects
built in the corresponding year. For projects built in 2024, subsidies are required
throughout their payback period (red shaded area and red arrows). For projects
built in 2035, subsidies are required until 2047, which is when the LCOH of
projects built in 2035 intersects with the price of natural gas (purple shaded
area and purple arrows). Notably, this is longer than the instantaneous cost gap
between green hydrogen costs and natural gas prices would suggest as natural
gas becomes more expensive than green hydrogen already in 2043. However,
this implies that only projects built after 2043 do not require any subsidies. In
order to calculate annual subsidies for a given year, we therefore need to track
all projects built in previous years, multiplied by the corresponding cost gap,
which is the gap between the constant LCOH of those years and the current
price of natural gas. In Fig. 5e, f and Extended Data Figure 6 we show the sum of
these annual subsidies across all end use sectors. Cumulative subsidies are then
calculated by adding up annual subsidies over all previous years (see Methods).

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Extended Data Fig. 4 | Levelised cost of green hydrogen in 2030 compared
to recent studies. The filled circle indicates the central estimate (if available)
and the vertical line indicates the range (if available). Colours indicate the
organisation. Our 2030 LCOH estimates are shown in black on the left, and are
in line with most recent studies. The lower end of the range corresponds to the
progressive scenario in the end-uses ammonia, refining and biofuels, for which
we do not include transport and storage costs (see Supplementary Table S2). The
upper end of the range corresponds to the conservative scenario in the end-uses
industry, power, grid injection, CHP, domestic heat, iron & steel, and mobility, for
which we include transport and storage costs of 20 $/MWh, based on literature
values. Studies that report very low LCOH values, for example BNEF, likely do
not include transport and storage costs, which are however critical for a full
assessment of hydrogen competitiveness in our model. Note that Capgemini did
not calculate LCOHs, but rather conducted a global survey among more than 100
companies in the hydrogen industry. The data for this figure, including sources
and the full name of the organisations, is available on GitHub.

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Extended Data Fig. 5 | Cost gap between green products and fossil competitors
by end uses. Next to the brace, we list all end uses represented by the competition
in that row, showing project announcements in GW by 2030 within these end
uses (see Extended Data Figure 2). The green and brown line show our central
estimate, while the shaded green and brown areas represent the uncertainty
range spanned by the conservative and progressive scenarios. a-f, Cost gap
between green hydrogen and fossil competitors, without carbon prices (left
column) and with carbon prices (right column) that are in line with reaching EU
climate targets41 (149 $/tCO2 in 2030, 246 $/tCO2 in 2040, 407 $/tCO2 in 2050,
see Extended Data Table 3). Panels a-f belong to the legend at the top. Panels a-d
represent more than 90% of the total announced capacity by 2030 and are also
shown in Fig. 5a–d. In the case of mobility, the LCOH has been adjusted for the
slightly higher end-use efficiency of hydrogen trucks in comparison to diesel
trucks (e-f, also see Extended Data Table 1), whereas this is for simplicity not
visualised here for iron & steel, which is grouped with other end uses in a-b.
g-l, Cost gap between different hydrogen-based electrofuels and fossil
competitors, without carbon prices (left column) and with carbon prices (right
column). Panels g-l belong to the legend at the bottom. Without carbon pricing,
no green product becomes cost competitive with its fossil competitor. With
carbon pricing, a distinct sequence emerges that is however characterised
by high uncertainty. In the central estimate, green hydrogen first becomes
competitive with grey hydrogen in 2034 (d), then with diesel in 2037 (f), after
which e-methanol becomes competitive with grey methanol in 2043 (j), followed
by green hydrogen becoming competitive with natural gas in 2044 (b). In the
central estimate, e-kerosene and e-methane narrowly miss reaching cost parity
with their fossil competitor by 2050 (h,l).

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③
③
③
Extended Data Fig. 6 | The 2030 green hydrogen implementation gap by
scenarios. The bars show annual subsidies required to realise all project
announcements by 2030, while the line shows the corresponding cumulative
subsidies. The left column shows results without carbon pricing, and the central
column shows results with carbon pricing in line with reaching EU climate
targets41 (149 $/tCO2 in 2030, 246 $/tCO2 in 2040, 407 $/tCO2 in 2050, see
Extended Data Table 3). The right column shows how the cumulative subsidies
compare to currently announced global subsidies according to BNEF43.
a-c, Central estimate (also shown in Fig. 5e–g). d-f, Progressive scenario,
g-i, Conservative scenario. As summarised in Table 1, the size of the 2030 green
hydrogen implementation gap strongly depends on the scenario. However,
without carbon pricing there always remains a substantial gap of $1.0 trillion
in the central estimate (c), with uncertainties ranging from $0.5 trillion in the
progressive scenario (f) to $2.3 trillion in the conservative scenario (i).
Even with ambitious global carbon pricing, the gap only closes in the progressive
scenario (f).

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Extended Data Fig. 7 | Required subsidies for green hydrogen and electrofuels
compared with historical and projected support for solar PV and wind. The
green and purple line show our central estimate, while the corresponding shaded
areas depict the range spanned by the progressive and conservative scenario.
Until 2030, the build-out of green hydrogen and electrofuels follows the project
announcements, while after 2030 it follows the median of the institutional and
corporate 1.5 °C scenarios (Supplementary Figure 11). Similarly, until 2030, end
uses are obtained from the project announcements (Extended Data Figure 1),
while after 2030 end use shares follow the shares of the IEA NZE 1.5 °C scenario40
(Supplementary Figure 13b). Data for historical and projected support for solar
PV and wind is obtained from the IEA Renewables 2023 report, estimated via the
cost difference between solar PV/wind and fossil fuel power plants68. The values
correspond to the LCOE approach, not the value-adjusted LCOE approach.
a-b, Total annual support without carbon pricing (a) and with ambitious carbon
pricing (b) in line with reaching EU climate targets41 (149 $/tCO2 in 2030,
246 $/tCO2 in 2040, 407 $/tCO2 in 2050, see Extended Data Table 3). Without
carbon pricing, required annual support in a 1.5 °C scenario quickly exceeds
historically observed support for solar PV and wind power (a). With carbon
pricing, total annual support could be limited to the same order of magnitude,
although large uncertainties prevail (b). Note that the historical and projected
support for wind power turns negative in 2022 as it is estimated from the
difference between the generation costs of wind and from fossil fuels68.
In 2022, the energy crisis led to an unprecedented surge of natural gas prices,
particularly in Europe, leading to a negative estimate of policy support.
c-d, Relative support (per MWh) without carbon pricing (c) and with carbon
pricing (d). When calculating relative support for green hydrogen and
electrofuels, we exclude production that is backed by demand-side policies
and therefore does not require subsidies. Without carbon pricing, green
hydrogen and electrofuels require support until at least 2050, and potentially
indefinitely (c). Due to additional conversion losses, electrofuels require higher
specific support. Overall, relative support is in the same order of magnitude as
historically observed for solar PV and wind. With carbon pricing, the specific
subsidy requirements of green hydrogen and electrofuels steadily decrease
towards 2050, reaching zero for green hydrogen in our central estimate (d).
Figure adapted from ref. 68 under a Creative Commons license CC BY 4.0.

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Extended Data Table 1 | Mapping of project end uses to the competition of green products and fossil competitors
Sorted by capacity of project announcements until 2030. We provide short explanations for some end use sectors: For iron & steel, production sites have to operate on the Direct Reduced
Iron (DRI) route, where hydrogen reduces iron ore to produce DRI, which is then used as a feedstock in electric arc furnaces (EAF) or occasionally in blast furnaces to produce steel. In DRI
plants, natural gas could easily replace hydrogen as the reduction agent, making natural gas the main fossil competitor, not coal, which is used in the blast-furnace (BF) route. For other
industry, we assume that hydrogen replaces natural gas in providing process heat. For mobility, we assume that hydrogen is used for heavy duty vehicles such that fuel cell electric trucks
compete with conventional diesel trucks. For synthetic fuels, we assume they will be used primarily in aviation, making e-kerosene the green product and kerosene the fossil competitor. For
end uses where the green product is not used thermally and thus cannot be compared with the fossil competitor using the lower heating value (LHV), we include the relative efficiency
between the green product (ηgreen
LHV ) and the fossil competitor (ηfossil
LHV ). This applies to iron & steel and mobility. Note that we neglect additional transformation costs in end uses in cases where
using hydrogen requires converting end use applications (other industry, mobility, power, CHP, and domestic heat, also see Methods)69,70.

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Extended Data Table 2 | Parameters to calculate the levelised costs of green hydrogen and hydrogen-based electrofuels
The upper part of the table displays all parameters required to calculate the LCOH (see Methods). The central estimate column shows the values for the figures presented in the main text
(central estimate), while the “scenario range” column shows the values used in the sensitivity studies (progressive and conservative scenario). Please refer to the Excel file on the GitHub
repository for further details regarding the sensitivity scenarios. The payback period and weighted average cost of capital are used to calculate the annuity and remain the same for both green
hydrogen and electrofuels. The lower part of the table displays all parameters required to calculate the levelised costs (LCOX) of the green hydrogen derivatives, e-methanol, e-kerosene and
e-methane (see Methods). Shared parameters for all derivatives are the green hydrogen price (obtained from the LCOH calculation), the renewable CO2 supply price, the full-load hours of the
synthesis, and FOM costs. All values in MWh refer to the respective lower heating value71–77.

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Extended Data Table 3 | Parameters to calculate the total cost of the fossil competitors
All values in MWh refer to the respective lower heating value. The natural gas price and the oil price in 2050 are obtained from the IEA NZE 1.5 °C scenario40. For grey hydrogen and grey
methanol, which are produced from natural gas, we ensure internal consistency with the natural gas price by calibrating the specific fixed costs in 2024, which we then use for 2030 and 2050
(see Methods). For kerosene and diesel in 2030 and 2050, we proceed similarly, using crude oil as the reference point. All values, sources and further comments are available in the Excel file
provided on the GitHub repository78,79.

    Scientific Research Study Methods:

Answer: In our study, we start by tracking 190 individual global green hydrogen projects announced for 2023 over a period of three years. This tracking builds on three consecutive and manually validated versions of the IEA Hydrogen Production Projects Database, each providing unique project identifiers across versions. Next, we collect data on 1.5 °C scenarios, drawing from integrated assessment models (primarily from the IPCC) as well as from institutional and corporate sources like the IEA, BloombergNEF, and the Hydrogen Council. Lastly, we estimate the subsidies that would be required to realize all global project announcements until 2030, developing a model of the required policy support per unit of green hydrogen. For each of the 14 designated end‑uses for green hydrogen projects, we calculate the cost gap between the green product and its corresponding fossil competitor. This cost gap, together with the volume and timing of project announcements, determines the total required subsidies.