Pathways to global hydrogen production within planetary boundaries

Domain: chemistry
Published: 2026-03-05
Editorial outcome: major_revision

Abstract
Hydrogen is a key lever for decarbonising hard-to-electrify sectors. Mitigation scenarios project an essential role for
hydrogen in returning within the climate change planetary boundary. Yet, its planetary footprint remains unclear,
particularly when Earth system interactions are considered. Here, we quantify the planetary footprint of global
clean hydrogen production using a bottom-up system model coupled with an Earth system interaction model. From
2025 to 2050, even under the most favourable scenarios, global hydrogen production is likely unsustainable,
confirming previous findings. We further show that Earth system interactions amplify hydrogen’s planetary
footprint. While electrolytic and abated fossil production are complementary, bio-based production is an order of
magnitude more unsustainable, as opposed to current trends. Our results contrast with the current focus on “green
hydrogen” which does not inform on its sustainability. Achieving sustainable hydrogen production will require the
reconsideration of viable production pathways, starting with the decarbonisation of existing production capacity.

Introduction
The planetary boundaries framework emerges from Earth system science1. It delimits a safe operating space (SOS)
for human activities across nine critical processes guiding the stability and resilience of the Earth system1,2.
Currently, seven of these boundaries have been transgressed1–3. Outside the SOS, there is an increasing risk of
exceeding Earth-system tipping points, possibly triggering irreversible cascading effects1,4. In this context, there is a
global consensus to limit global warming well below 1.5 degree Celsius (°C) relative to preindustrial levels5–7. This
requires a rapid transition towards a sustainable economy, in which clean hydrogen is regarded as a key
decarbonisation lever for hard-to-electrify sectors and industries7–9. Terminology of key concepts is provided in
Supplementary Note 1.

Hydrogen could provide decarbonisation options for hard-to -electrify industries such as steelmaking, chemicals,
fertilisers, e-fuel synthesis, and transportation8,10,11. Consequently, mitigation scenarios limiting global warming to
1.5°C require a manifold increase in global clean hydrogen production7,12–15. Clean hydrogen production refers to
electrolytic, abated fossil and bio-based pathways16–18. Given the broad range of future applications for hydrogen,
ensuring that it remains within its dedicated share of the SOS (hereafter referred to as environmental space only) is
crucial19.

Recent absolute environmental sustainability assessments (hereafter referred to as sustainability assessment only)
found hydrogen production pathways to be unsustainable, with several planetary boundary transgressions20–23 (see
Supplementary Note 2). However, these studies20–23 assessed these pathways independently. Global hydrogen
production is unlikely to be provided by a single route or process but rather by a collection of processes operating
in a production system. Therefore, quantifying its sustainability remains a significant gap.

Recent climate change literature24,25 implies that only climate change mitigation scenarios leading to 1.9 watt per
square metre (W m-2 ) nameplate effective radiative forcing by 2100 (thereafter referred to as mitigation scenarios
only), could enable human operation within the climate change boundary. These scenarios are complementary to
additional mitigation measures for biodiversity and ecosystems26. Climate change and biosphere integrity are core
Earth-system processes with significant influence on the stability of the Earth-system which characterised the
Holocene epoch since the last glaciation1,27,28. These scenarios contain the necessary production volumes to
achieve the climate target. Therefore, environmental space allocation based on mitigation scenarios is essential. Yet
no study has comprehensively used this important contextualisation for allocating an environmental space to
global hydrogen production.

Sustainability assessment studies quantify planetary footprints of anthropogenic systems, but overlook Earth
system interactions29,30. The Earth system is composed of biophysically interacting processes, with amplifying or
stabilising feedbacks27,31. These feedbacks can ultimately shrink the SOS27. Implementing this important aspect
could reveal the effective planetary footprint of anthropogenic systems1,2,27,31. However, this aspect remains largely
unexplored in sustainability assessment studies.

Here, we address these gaps by quantifying the effective planetary footprint of global clean hydrogen production.
To this end, we develop a bottom-up production system model based on thirteen hydrogen production pathways to
minimise its effective planetary footprint. We use a mitigation scenario ensemble (n=32) from the
Intergovernmental Panel on Climate Change Sixth Assessment Report (thereafter referred to as the AR6
ensemble)7,32 to define future production volumes and assign an environmental space to clean hydrogen
production based on its future utility. An overview of the approach is illustrated in Fig. 1. Our results show that
global clean hydrogen production is likely unsustainable with several planetary boundary transgressions, amplified
by biophysical interactions. Our assessment reveals that bio-based hydrogen production can amplify the planetary
footprint owing to significant burden shifts. In contrast, electrolytic and abated-fossil pathways can remain within
most planetary boundaries provided concomitant carbon capture and sequestration.

Results
An environmental space for global clean hydrogen production
Our AR6 ensemble (n=32) implies a brief transgression of the high-risk threshold and sustained emission reduction
by 2100. The ensemble shows CO₂ concentrations rising to 453 (437-474) parts per million (ppm) by 2045 before
falling to ~401 (396-425) ppm by 2100 (Fig. 2a). This trajectory is consistent with the long-term target but briefly
overshoots the high-risk boundary (450 ppm) owing to historical emissions inertia. In the longer term, the CO₂
concentration decreases towards the planetary boundary (350 ppm) until it is reached in ~2150. By contrast, 2.6 W
m⁻² (~2°C warming) trajectories remain above the high-risk threshold for decades and stabilise in the zone of
increasing risks for centuries post-2100. These projections imply a significant decarbonisation of sectors and
industries, in which clean hydrogen could play an important role.

Decarbonisation efforts imply a rapid increase in clean hydrogen production volumes. Production volumes need to
increase from 2.58 (0.001-8.7) million metric tonnes of hydrogen per year (MtH₂ yr⁻¹) in 2025, to ~225 (68-460)
MtH₂ yr⁻¹ by 2050 (Fig. 2b). Recent studies12,16,33 report that only 0.8-0.95 MtH₂ yr⁻¹ was produced from clean
sources in 2025. Our AR6 ensemble, therefore, shows a current implementation gap of 1.63-1.78 MtH₂ yr⁻¹.
Meeting these production volumes would imply a compound annual growth rate of 19.6 (53.3 – 17.2) % from 2025
to 2050. These projections align with recent findings12 but remain lower than projections from the International
Energy Agency (IEA)'s net-zero scenario13 (NZE) for two reasons. First, the IEA integrates historical fossil production,
while most AR67 scenarios focus on clean hydrogen production12. Second, the role of hydrogen scales with
mitigation ambition – NZE target by 2050, whereas our ensemble follows a 1.9 W m⁻² trajectory by 2100. This
difference also shows how the assessment focus could shape the role of hydrogen in future energy systems.

A shrinking environmental space requires eco-efficiency improvements. The environmental space for global
hydrogen production reflects both the declining emission intensities of the energy system and the rising share of
hydrogen within it (Fig. 2c). Accordingly, the space available in 2025 is 9.53(6.74–13.78) percent of the SOS per year
and per giga metric ton of hydrogen (%SOS yr⁻¹ Gt H₂⁻¹), but by 2050, falls to 1.6 (0.76-5.57) %SOS yr⁻¹ Gt H₂⁻¹. Over
the 2025–2050 period, the average allocation is 4.54(2.8-9.26) %SOS GtH₂⁻¹. This contraction implies that hydrogen
production can only be sustainable if the system’s eco-efficiency improves at the same pace as the environmental
space shrinks.

Key hydrogen production pathways
The future production mix largely relies on abated fossil and electrolytic pathways. Optimisation results show that
together with the steam reforming (SMR) of fossil methane with carbon capture and sequestration (CCS), all water
electrolysis production routes are involved in the future production mix (Fig. 3b). In 2025, our results show that
24% (1.1 MtH2) needs to be produced from proton exchange membrane electrolysis (PEM), while alkaline
electrolysis (AE) provides 23% (~1.1 MtH2). Solid oxide electrolysis provides a relatively small share of the total
production (3% for 0.13 MtH2). The total contribution from water electrolysis is 49% (2.3 MtH2) of the total
production of hydrogen, while SMR-CCS provides 31% (1.44 MtH2), and SMR of biomethane with CCS is 20% (0.92
MtH2). For comparison, the IEA clean hydrogen projects database33 shows a higher reliance on water electrolysis
(54%) and SMR-CCS (43%). By 2050, the optimal production system relies on 63% (145 MtH2) of the total
production from water electrolysis, while the remaining 37% (84 MtH2) is provided by SMR-CCS. These results
indicate the strong reliance on both electrolysis and SMR-CCS, while bio-based production acts as a complement
when both the electrolytic and SMR-CCS pathways reach full capacity – consistent with recent projections14,16,18.

Water electrolysis implies a rapidly growing demand for low-emission electricity. Hydrogen production volumes
require a power mix dominated by wind, solar and hydropower, complemented by other low-emission alternatives
(Fig. 3d). In 2025, the system requires 0.1 petawatt-hour per year (PWh yr⁻¹) low-emission electricity. By 2050, total
electricity demand reaches 7.44 PWh yr⁻¹, equivalent to 83.3% of the current (202334) global production capacity.
This translates to 1.65 PWh yr⁻¹ (22%) and 4 PWh yr⁻¹ (54%) supplied by wind and solar energies alone. The power
mix also requires 1.17 PWh yr⁻¹ (16%) and 0.6 PWh yr⁻¹ (8%) from hydro and geothermal, respectively. This future
demand for renewable electricity is critical to minimise the planetary footprint. Without it, the optimal solution
shifts toward alternative routes, including bio-based production, thereby increasing pressure on the climate change
and biosphere integrity Earth system processes.

Planetary footprint of global clean hydrogen production
Without Earth system interactions, global hydrogen production is unsustainable across most boundaries. If
implemented, the production system would transgress six of nine planetary boundaries already in 2025 (Fig. 3a).
The production mix would transgress ~14 (10.5 - 21.5) times its dedicated environmental space for climate change
in 2025 and up to ~34 (11.9-93.7) times by 2050. Despite the low-emission nature of the system, transgressions
increase across all boundaries. For instance, we find a slight transgression representing 1.1 (0.82-1.56) times the
environmental space for biosphere integrity in 2025, while in 2050, this value increases to 4.2 (2.1-8.11). A more
significant evolution of the transgression is observed for the phosphorus biochemical flow boundaries, where the
impact dramatically increases from 23 (17-30.5) times in 2025 to 52 (31-164.6) times in 2050. The transgression
pattern reflects the mismatch between the shrinking environmental space (Fig. 2c) and the environmental
intensities of several processes (Fig. 4). More broadly, this illustrates the eco-efficiency mismatch between
prospective life cycle assessments (LCAs) and those assumed in mitigation scenarios.

Freshwater use remains within sustainable limits. In 2050, the optimal hydrogen production system requires ~9.2
cubic kilometre (km³) of blue water (41.4 km³ GtH₂⁻¹), consistent with previous estimates9,35 of 43.2 km³ GtH₂⁻¹ and
the 35.7–53 km³ GtH₂⁻¹ range36. Most of this demand (~45%) is embodied by solar power, primarily during silicon
manufacturing, cooling and purification (Fig. 4a). Water consumed directly in electrolysis accounts for ~25%,
reflecting both the reaction stoichiometry and cooling requirements. However, cooling water demand can be
substantially higher depending on meteorological conditions and water quality37,38, underlining that these global
results are likely an underestimation.

Land-system change is also within sustainable limits. At scale, the system requires ~58 square kilometre (km²),
equivalent to 1.43 × 10⁻4 % of global forested land39. Most of this impact (82%) arises from the construction of
natural gas pipelines for the SMR–CCS process (Fig. 4a). However, these values are likely underestimated.
Background inventories for wind and solar assume transformation of pastureland only, thereby excluding prior
conversion of forested land to pasture, as well as natural reforestation of pastures29. These omissions limit the
accuracy of current land-system change characterisation29.

Electricity supply and SMR-CCS are the dominant environmental hotspots. For climate change and ocean
acidification, embodied impacts of the renewable electricity mix account for ~40% of transgressions (Fig. 4a). Most
originate from manufacturing, where non-renewable energy is required at several stages; despite technological
improvements by 2050, embodied emissions remain non-negligible at scale. Solar thermal, although contributing
little to the overall supply, accounts for ~17% of these impacts, mainly due to industrial heat demand. Wind and
solar PV dominate pressures on biogeochemical flows, with nitrogen and phosphorus emissions linked to copper
mining and processing, including the use of explosives and sulfidic tailings. Direct emissions from SMR-CCS
contribute to ~32% of climate- and ocean-related transgressions, arising from carbon oxidation in reforming, the
water–gas shift reaction, and fuel combustion in the reformer furnace40. Even with CCS, SMR emits ~2.6 kilogram
CO₂ (kgCO₂) per kilogram of hydrogen (kgH₂⁻¹) in addition to embodied burdens. The natural gas feedstock account
for ~15% the total impacts, as both fossil extraction and biogas upgrading release significant carbon emissions.
Overall, renewable electricity and SMR–CCS together drive ~78% of the footprint across boundaries, except for
stratospheric ozone depletion, where end-of-life treatment and transport of scrap steel from alkaline electrolyser
recycling accounts for ~41% of impacts.

With Earth system biophysical interactions, the planetary footprint is strongly amplified. These interactions (via
feedback loops) can either stabilise or amplify pressures. In 2025, the amplification drives previously negligible
impacts on freshwater and land-system change to transgressions of 5.5 times (4.3–6.6) and 11 times (8.6–13.2)
their boundaries, respectively (Fig. 3c). Core boundaries are affected even more. Climate change transgressions
rise by 83%, and biosphere integrity increases by 1511%. Amplification increases towards 2050, owing to the
contraction of environmental space, and results in a more even distribution of pressures (equilibrium) across Earth-
system processes (Fig. 3b) – revealing the effective system-wide footprint of individual processes (Fig. 4b).

This equilibrium has critical implications. Alleviating pressures on one boundary, such as climate change, at the
expense of another, yields limited benefits because of feedback loops propagating impacts across the Earth-system,
e.g., bio-based production30,41,42(Fig. 4b). Thus, capturing the effective planetary footprint requires sustainability
assessments to include biophysical interactions.

Sustainability of alternative hydrogen production systems
Electrolytic hydrogen production shows the lowest planetary footprint. The electrolytic system is composed of
proton exchange membrane (~60%), alkaline (~29%), and solid oxide (~10%) electrolysis technologies in 2050
(Supplementary Fig.18). This system would require ~12 PWh of electricity, exceeding today’s renewable output of
~8.9 PWh and approaching half of the solar and wind generation projected by the IEA18 for 2035 (18.2–23.2 PWh).
Despite the consequent renewable electricity demand, this system transgresses 5 out of 9 planetary boundaries
(Fig. 5a). For instance, the impact on climate change exceeds the environmental space ~12 times while ocean
acidification is exceeded ~4 times. Biosphere integrity boundary is also exceeded ~2 times. These transgressions
originate from embodied emissions of renewable electricity generation technologies. With biophysical interactions
(Fig. 5b), the climate change and biosphere integrity boundaries are transgressed ~41 and ~26 times. In comparison
to abated fossil and bio-based production, electrolytic production continues to show the lowest planetary
footprint, making it the closest pathway to sustainability, provided a matched deployment of renewable electricity
capacity.

Abated-fossil production performs similarly to electrolysis. In 2050, the fossil-based system consists primarily of
SMR-CCS (~96%) and coal gasification with CCS (~4%) (Supplementary Fig.22). This system transgresses five
planetary boundaries in 2050. Climate change is transgressed ~41 times, and ocean acidification ~14 times, owing
to the remaining direct CO₂ emissions from reforming reactions and combustion in reformer furnaces in the SMR
process. Additional transgressions occur in phosphorus biochemical flows (transgressed by ~40 times), and
biosphere integrity (exceeded by ~3 times). While CCS reduces pressures compared with unabated fossil
production, the system remains unsustainable in absolute terms. With biophysical interactions (Fig. 5b), the
planetary footprint of abated-fossil is in the same order of magnitude of the electrolytic production system –
climate change and biosphere integrity transgressed ~58 times and ~42 times, respectively. Given the current clean
hydrogen implementation gap43, these results imply that retrofitting existing fossil capacity with CCS18 to
complement electrolysis could be meaningful. However, this requires sufficient CO₂ storage capacity44 and ensuring
that potential CCS lock-in45,46 does not hinder the complete phase out of fossil fuel (Discussion).

Bio-based hydrogen production amplifies planetary boundary transgressions. At scale, the biomass-based system
includes steam biomethane reforming with (~30%) and without (~67%) CCS as well as the gasification of (poplar)
woodchips with CCS (3%) (Supplementary Fig.20). Under this configuration, the system transgresses six planetary
boundaries, with particularly high impact on climate change (exceeded ~174 times) and biosphere integrity
(exceeded ~16 times) (Fig. 5a). These impacts reflect on the use of wood chips (~80%) of biosphere integrity
impact, and emissions from biomethane upgrading, reforming and water-gas shift reactions (~98% climate change
and ocean acidification impact). With biophysical interactions, the footprint is further amplified (Fig. 5b). Unlike
electrolysis and fossil-CCS systems, bio-based production shows the worst planetary footprint – climate change and
biosphere integrity transgressed ~229 and 180 times – consistent with its exclusion from the reference production
system (Fig. 3b). Further information in these alternative production systems is provided in Supplementary Note
10.

Direct air capture and sequestration to mitigate the planetary footprint
Direct air capture and sequestration (DACS) can mitigate most boundary transgressions. With biophysical
interactions, DACS provides a mechanism to stabilise the effective planetary footprint of hydrogen production by
mitigating climate change. Optimisation results show that DACS has the potential to reduce transgressions in nearly
all categories (Fig. 6). In 2025, a 5.31 kgCO₂ kgH₂-1 DACS rate is required, while in 2050, it decreases to 3.86 kgCO₂
kgH₂-1 (Fig. 6a-b, d and g-i). On average, ~4.13 kgCO₂ kgH₂-1 yr-1 must be captured between 2025 and 2050 to
maintain hydrogen production within its allocated environmental space. At scale, this represents 0.93 giga metric
ton of CO2 per year (GtCO2 yr-1). DACS, however, is unable to offset pressures on biochemical flows owing to the
system’s reliance on renewable electricity. The significant nitrogen and phosphorus emissions that occur during
copper mining, for both wind and solar power infrastructure, outweigh DACS benefits for these Earth system
processes (Fig. 6e and f).

Concurrent DACS can increase the system’s electricity requirement significantly. Optimisation consistently favours
heat pump-based DACS systems powered by renewable energy. Generally, after CO2 capture, heat is required in
DAC systems to release CO2 from the binding sorbent or solvent47. In 2050, the DACS energy consumption is 0.95
kilowatt hour (kWh) per kgCO2. Reaching sustainability in most boundaries at scale (i.e., 225MtH2 yr-1) would
require an additional 2.2PWh of electricity (9.75PWh total) for capturing and sequestrating CO2. This estimation is
relatively high but remains lower than the 12TWh for hydrogen production estimated in the IEA NZE scenario18.
The required DACS rate could vary strongly depending on the system configuration. The electrolysis-based system
requires ~4 kgCO₂ kgH₂-1 yr-1 (Supplementary Fig. 24), compared with ~5.3 kgCO₂ kgH₂-1 yr-1 for a fossil with CCS-
based system and ~17 kgCO₂ kgH₂-1 yr-1 for a bio-based system (Supplementary Fig. 25 and Supplementary Fig. 26).
These DACS rates reflect the underlying emission drivers. For the electrolytic system, the DACS rate would primarily
offset the embodied impacts of renewable energy, while for the fossil with CCS system, the remaining direct
emissions of carbon dioxide would be mitigated. The relatively high DACS rate for bio-based production can be
explained by the emission intensities of processes involved, as well as the impact on biosphere integrity. These
DACS rates would require 14.23PWh, 2.58PWh, and 3.84PWh of total renewable electricity for the electrolytic,
abated fossil and bio-based systems, respectively. Compared to the 12PWh projected in the IEA NZE scenario, these
results highlight that achieving sustainability for hydrogen cannot rely on water electrolysis alone unless a massive
increase in renewable electricity capacity is deployed concomitantly.

Hydrogen leakages can reduce the benefits of carbon capture and sequestration. Hydrogen is a difficult gas to
manage, and leakages could be significant12,48,49. At an optimal DACS rate (i. e., 3.86 kgCO₂ kgH₂⁻¹ in 2050), if 10% of
production is leaked, the impact on climate change could increase by ~480% (Supplementary Fig.27). To offset
these leakages, an additional 0.63 kgCO₂ kgH₂⁻¹ would be necessary (Supplementary Note 11), thereby increasing
the system electricity requirement. At scale (i.e., ~225MtH2), this represents an additional 0.142GtCO2 captured
and sequestrated, requiring 0.135PWh of renewable electricity. Given potential constraints in both DACS and
renewable energy capacity18,44, achieving sustainability for hydrogen will also depend on controlling potential H₂
leakages.

Methods
Goal and scope of the study
This study aims to quantify the effective planetary footprint of global hydrogen production and identify key
production pathways. More specifically, we focus on future clean hydrogen production volumes expected to
replace the current fossil-based production capacity16. To this end, we conduct an absolute environmental
sustainability assessment aligned with recommendations from Bjørn et al.87 and based on an attributional cradle-
to-gate prospective LCA. We define the functional unit as the “global annual hydrogen production with optional
direct air capture and sequestration of carbon dioxide” with no further specifications on the thermodynamic state
or quality. The temporal scope spans from 2025 to 2050 with a 5-year time step, as the hydrogen economy is
expected to grow sharply in that time frame45.

Future hydrogen production volumes are consistent with a 1.9-2.25 W m-2 target by 2100. We obtained these
projections from a scenario ensemble (n=32) derived from the AR67,32. From the same ensemble, we further
derived a utilitarian-based downscaling approach to allocate an environmental space to global hydrogen
production. Such an approach defines an environmental space and quantifies hydrogen production volumes. This
robust approach is also in line with some of the recent recommendations from de Bortoli et al.78.

Thirteen hydrogen production processes were considered for building the life cycle inventory. These include key
electrolytic-, abated-fossil-, and bio-based pathways (Supplementary Table 6). Background data are sourced from
the Ecoinvent 3.9.1 "cut-off by classification" database. Following the approach of Bachmann et al.30, whenever
possible, the selected data focuses on the "World" and "GLO" locations. When background inventories are
unavailable at these locations, conservative locations were selected, such as Europe "EUR" and "Europe without
Switzerland". Alternatively, for the same reason, the rest of the world (ROW) location is chosen as a proxy. When
data was unavailable at these locations, Germany (DE) was chosen. This is because Germany is likely to become one
of the largest hydrogen producers in Europe9,54,89. Here, it is worth noting that this approach introduces
uncertainties, as the processes involved may not accurately reflect global averages.

We obtain prospective LCA matrices using the premise software v2.2.679 and aggregated all meaningful inventories
to decrease computation times. In a subsequent step, we reduced the number of matrices by creating sampling
matrices using Triangular distributions with the Julia package Distributions.jl90. More specifically, we opted for
triangular distributions to remain within the bounds of the selected scenarios. The matrices obtained would
contain the minimums, maximums, and modes, thereby reducing the number of matrices that need to be
manipulated. The modes were defined as the median of values across scenarios. The approach is further described
in Supplementary Note 7. However, we do not account for uncertainties in the background system following the
conclusions from Marsh et al.83. Similarly, we do not account for uncertainties in the foreground system, as process
parameters could not be retrieved along with matrices. We also do not account for uncertainties from
characterisation, as we could not obtain uncertainty parameters from previous work29,30,85. Thus, the uncertainty
range reflects values across selected mitigation scenarios only.

We used a linear programming (LP) model to minimise the effective planetary footprint of global hydrogen
production. The LP model selects and identifies the optimal scale of technology options to minimise transgression
across planetary boundaries, considering biophysical interactions. The model is built using the Julia programming
language91 v1.10, JuMP.jl92 v1.27.0, and CPLEX as the optimisation solver. Modelling constraints were directly
sourced from the scenario ensemble derived from the AR67,32.

Mitigation scenarios ensemble definition
As previously mentioned, we derived a scenario ensemble (n=32) from the AR67,32 consistent with 1.9-2.25 W m-2
total radiative forcing by 2100. To ensure consistency, we computed future radiative forcing values using the FaIR93
v2.2.1 base model – replicating the AR66 results. These were compared with the radiative forcing concentration for
atmospheric CO2 reported values from the AR66.

Next, we screened mitigation scenarios in the AR6 dataset32. We selected the “AR6 climate diagnostics|Effective
Radiative Forcing|FaIRv1.6.2|50.0th Percentile” ( W m-2) variable and imposed a maximum of 2.25 W m-2 (average
between 1.9 and 2.6 W m-2) variable in 2100. From the AR66, the 1.9 W m-2 by 2100 target represents a nameplate
category of scenarios6. From each scenario, we extracted the "Emissions|CO2" variable (MtCO2 yr-1) for each year.
Because these scenarios do not provide the forcing and concentration of CO2, these had to be computed for the
ensemble validation.

For scenario ensemble validation, we modelled the integrated impulse response function of CO2 49 (Equation (1))
which we used to derive the concentration 𝐶𝑡 (Equation (2)) and radiative forcing 𝐹𝑡 (Equation (3)) for CO2. In these
equations, 𝑘𝑡 the cumulative decay of CO2 for a year 𝑡 in the period 𝑇, 𝜏𝑖 and 𝛼𝑖 are the lifetime and decay
parameters, respectively. Further, 𝑞𝑎𝑖𝑟 is the air mixing ratio48, 𝑀𝐶𝑂2 the molar mass and 𝜀𝐶𝑂2 the effective radiative
forcing of CO2. Parameters for Equation (1) to Equation (3) are available in Supplementary Table 13. Lastly, for
validation, we compared the computed values from the ensemble with results from FaIR93 v2.2.1 modelling and the
AR6 projections6.

𝑘𝑡 = 𝛼0 𝑡 + ∑ 𝛼𝑖 𝜏𝑖 (1 − exp(𝑡 / 𝜏𝑖)) ∀𝑡 ∈ 𝑇 (1)
𝑖=1

𝐶𝑡 = (𝑞𝑎𝑖𝑟 / 𝑀𝐶𝑂2) ∑ 𝑘𝑡−𝑦+1 𝑒𝑦 (2)
𝑦=1

𝐹𝑡 = 𝜀𝐶𝑂2 𝐶𝑡 (3)

The AR6 ensemble is independent of the prospective LCA scenario ensemble we derived from premise Python
package v2.2.679. We computed radiative forcing and atmospheric concentration of CO2 trajectories for both
ensemble and compared them against a FaIR93 v2.2.1 model and the AR6 projections6 for validation. We found
four matching scenarios, namely REMIND SSP1- SSP2- and SSP5-, PkBudg500, as well as IMAGE SSP2-1.9. TIAM-UCL
SSP2-1.9 was available but excluded due to an important mismatch with a 1.9 W m-2 target by 2100. Ultimately, we
also excluded the IMAGE SSP2-1.9 scenario due to a dimension mismatch in the LCA matrices, which we were
unable to resolve.

To summarise, we derived the AR6 ensemble (n=32) consistent with a 1.9-2.25 W m-2 target by 2100, which
includes future hydrogen production volumes and technology constraints, such as the availability of wind and solar
electricity. Prospective LCA matrices were derived from a smaller ensemble (n=3) consisting of REMIND SSPx-
PkBudg500 scenarios. Both ensembles were validated against the 1.9-2.25 W m-2 target by 2100 (Supplementary
Note 9).

Hydrogen production pathways
Each production pathway was modelled as a cradle-to-gate process consistent with our system boundaries. More
details on the production pathways are available in Supplementary Note 3, and life cycle inventories are provided in
Supplementary Table 6.

Electrolysis technologies were included. Low-temperature technologies cover alkaline electrolysis (AE) and proton
exchange membrane (PEM) electrolysis, both operating at 60–80 °C55. AE is a mature and cost-effective technology,
while PEM systems offer higher efficiency and compact design but are constrained by the availability of iridium and
platinum catalysts55,56. Anion exchange membrane (AEM) electrolysis is an emerging option, but it was not
considered here due to its limited commercial maturity. High-temperature electrolysis was represented by solid
oxide electrolysis cells (SOEC), which operate at 700–1,000 °C using steam to reduce electricity demand. SOECs
achieve higher efficiencies but face material degradation challenges that limit their current commercial
deployment15,55.

Methane-based production pathways include steam methane reforming (SMR) and methane pyrolysis (MP). SMR
was included as it is the current dominant production route. The process uses nickel catalysts at 700–1,000 °C and
can be combined with carbon capture and storage (CCS)40. MP was also considered as a low-carbon alternative,
splitting methane into hydrogen and solid carbon. MP requires more methane input on a stoichiometric basis and
is technically challenging to implement, but it avoids carbon oxidation. For these processes, methane can be
sourced from fossil origins or from upgraded biogas.

Coal and biomass gasification production pathways were also considered. Coal gasification operates at
temperatures ranging from 800 to 1,300 °C and produces syngas, a mixture composed of H₂, CO, and CO₂45.
Biomass gasification uses feedstocks such as wood chips or residues and generates syngas alongside char and tar.
Both processes employ water-gas shift reactions and syngas purification to maximise hydrogen yield94. CCS can be
applied to reduce the emission intensity of both coal- and biomass-based hydrogen.

Electricity supply
The electricity generation mix reflects the scenario ensemble projections (Supplementary Fig. 14). The scenario
ensemble indicates a decline in coal and unabated gas, offset by rapid growth in wind and solar, and with more
moderate but sustained roles for hydro, nuclear and biomass. A complete list of the inventories used is provided in
the Supplementary Table 7.

We include renewables and nuclear sources as low-emission alternatives, consistent with long-term
decarbonisation trends. These include onshore wind turbines, solar photovoltaic and solar thermal plants, run-of-
river hydropower, geothermal power, and nuclear power from three reactor configurations: boiling water,
pressurised water, and heavy water-moderated pressurised reactors.

Carbon dioxide capture and sequestration
Carbon capture and storage technologies act as decarbonisation levers to achieve carbon neutrality before 205095.
CCS can be combined with several hydrogen production processes, such as SMR, biomethane steam reforming, and
coal and biomass gasification. For processes with significant carbon outputs, such as steam methane reforming and
coal gasification, CCS can be used at plant sites to capture CO2 directly from flue gas (CO2-rich exhaust gas)40.
Inventories we use from the premise software v2.2.6 already include CCS (e.g., SMR, biomass gasification with
CCS).

For additional carbon capture, we use the direct air capture and sequestration of carbon dioxide (DACS). We
consider large-scale (1 MtCO2) solvent-based systems with heat either supplied by a heat pump or industrial
steam47. Inventories for these processes are listed in the Supplementary Table 8.

Planetary boundary impact characterisation
We assess the direct impact of global hydrogen production on 8 out of the 9 Earth-system processes defined in the
planetary boundaries framework. These are climate change, ocean acidification, atmospheric aerosol loading,
freshwater use, biogeochemical flow (phosphorus and nitrogen), stratospheric ozone depletion, land-system
change and changes in biosphere integrity. All characterisation factors originate from Bachmann et al.30 initially
based on the work of Ryberg et al.29 and Galán-Martín et al85. In addition, we calculated a hydrogen
characterisation factor for climate change to evaluate the effect of potential leakages (Supplementary Note 8).

Although the recent progress on the definition of control variables for novel entities96 is encouraging, we do not
assess the impact on this Earth-system process since we could not define a boundary value29,96. Also, as previously
mentioned, we do not account for uncertainties of the characterisation method due to a lack of data. Additionally,
we exclude biogenic carbon accounting as there is likely a temporal mismatch between carbon uptake and emission
during hydrogen production.

Although crucially important, the environmental response (including feedback loops in the Earth system) was not
modelled in previous characterisation methods29,30,85 owing to the inherent complexity. In this work, we extend
previous methods by integrating the interaction model from Lade et al.27. This simple model, while at an infancy
stage of development27, would account for amplifying or stabilising feedbacks between different Earth-system
processes. Ultimately, this would lead to a certain impact equilibrium that is not captured in conventional
sustainability assessments (Supplementary Note 6).

Building on the above, we define the interaction matrix 𝚪∈ ℝ𝑘×𝑧 as in Equation (6). In this Equation 𝐁∈ℝ𝑧×𝑘 is
the biophysical interaction matrix, whose coefficients 𝐵𝑧𝑘 represent the influence of a biophysical system 𝑧 on a
receiving biophysical system 𝑘 and 𝐈∈ℝ𝑧×𝑘 the identity matrix. 𝐁 was sourced from Lade et al.27. The formulation
in Equation (4), in the form of the Leontief matrix, would account for feedback loops between Earth system
processes. In this sense, the matrix coefficients 𝚪𝑘𝑧 represent the interaction between an originating biophysical
system 𝑧 and a receiving one 𝑘. 𝚪 acts as a weighting matrix to inform the optimisation model of feedback loops
between Earth-system processes (Supplementary Note 7). 𝚪 matrix can also be used in traditional absolute
environmental sustainability assessment studies. We also provide optimisation results for human-mediated
interactions in Supplementary Note 12. It is important to note that we were unable to derive sufficient uncertainty
parameters for the interaction matrix and therefore unable propagate uncertainties for this aspect.

(𝐈−𝐁)−𝟏 = 𝚪 (4)

Environmental space definition for global hydrogen production
To be sustainable, global hydrogen production must remain within its dedicated environmental space. To define
this space, we allocate a share of the global SOS to global hydrogen production using a utilitarian downscaling
principle. Based on two downscaling steps, this principle aims to capture (i) the necessary emission reduction of
the energy supply system and (ii) the growing share of hydrogen energy in the energy supply system. For this, we
extract value from the AR632 ensemble (see the goal and scope section). To describe the computational structure,
let 𝑢 be a mitigation scenario part of the scenario ensemble 𝑈 and 𝑡 a year part of the period 𝑇.

For point (i), we extracted the variables "Emissions|CO2|Gross" representing total gross CO2 emissions and
"Emissions|CO2|Gross|Energy|Supply" representing the energy supply system. For each scenario and year, we
take the share of emissions of the energy supply system over total emissions to form the matrix 𝛜𝐒𝐄 ∈ℝ𝑢×𝑡.
Similarly, for point (ii), we extracted the variables “Secondary Energy”, “Secondary Energy|Hydrogen”, to take the
ratio of secondary energy in the form of hydrogen over the total secondary energy to obtain 𝐩𝐇𝟐 ∈ℝ𝑢×𝑡 matrix.
From these arrays, we derive the allocation matrix 𝛂𝐇𝟐 ∈ ℝ𝑢×𝑡 using Equation (5) and normalise it with hydrogen
production volumes. The list of scenarios considered in the ensemble is provided in the source data.

Subsequently, we define the SOS vector Δ𝐱𝐏𝐁 ∈ℝ𝑧 which represent the difference between the natural
background x0,z and boundary value xPB,z of an Earth-system process 𝑧 (i.e., ΔxPB,z = xPB,z - x0,z). We provide in
Supplementary Table 4 values from refs2,30. In Equation (6), we compute the outer product of Δ𝐱𝐏𝐁 and 𝛂𝐇𝟐 to
obtain the environmental space array 𝛚∈ℝ𝑧×𝑢×𝑡. In these equations, ⊙ and ⊗ are the Hadamard element-wise
multiplication and the outer product operators, respectively.

𝛜𝐒𝐄 ⊙ 𝐩𝐇𝟐 = 𝛂𝐇𝟐 (5)

Δ𝐱𝐏𝐁 ⊗ 𝛂𝐇𝟐 = 𝛚 (6)

In a subsequent step, we use Distributions.jl90 to create a sampling matrix 𝛿𝛚∈ℝ𝑧×𝑡 populated with triangular
distributions (Supplementary Note 7). This step reduces the scenario dimension 𝑢 where the triangular distribution
parameters are the minimum, maximum, and median of the scenario dimension. The sampling matrix is then used
in the stochastic LP model97 (Supplementary Fig.15).

Minimising the planetary footprint with boundary interactions
To minimise the planetary footprint of global hydrogen production, we based our LP approach on the stochastic
technology choice model (TCM) from Kätelhön et al.97. The TCM is based on the computational structure of LCA98. It
enables LCA-based process-system engineering design50,97 as demonstrated in several studies30,50,74. The overall
approach is described in detail by refs50,97.

In computational LCA98, the technosphere matrix 𝐀∈ℝ𝑖×𝑗 represent the exchanges of reference flow 𝑖 between
processes 𝑗 in the technosphere. Its coefficient 𝐴𝑖𝑗 represents the consumption
(𝐴𝑖𝑗 <0) or production (𝐴𝑖𝑗 >0) of reference flows 𝑖 per process 𝑗. To allow the technology choice model to
select and scale technology options, degrees of freedom (doj) must be created in the technosphere
(Supplementary Note 7). For this, we performed the row elimination of similar reference flows in the technosphere
matrix, such that there are effectively more processes than products (𝑖 <𝑗) in matrix 𝐀∗ whose 𝑑𝑜𝑗 =𝑗−𝑖.
Further, 𝐟∈ℝ𝑖 is the demand vector representing the total output of the system. It typically corresponds to the
functional unit where only one 𝑖 entry is populated with the reference flow (i.e., scaled functional unit). Here, we
set this entry to be 1 𝑘𝑔𝐻2 𝑦𝑟−1. The annual production of hydrogen derived from the scenario ensemble is
reflected in the normalised environmental space vector 𝛚∈ℝ𝑧×𝑢×𝑡. 𝒔∈ℝ𝑗 is the scaling vector of processes in
the technosphere to fulfil the demand set by the functional unit. Similarly, the biosphere 𝐁̃ ∈ℝ𝑒×𝑗 is composed of
coefficients 𝐵̃𝑒𝑗 representing the extraction (𝐵̃𝑒𝑗 <0) or emission (𝐵̃𝑒𝑗 >0) of elementary flows 𝑒 per process 𝑗.
𝐐∈ℝ𝑧×𝑒 is the characterisation matrix, whose elements 𝑄𝑧𝑒 characterise elementary flows 𝑒 into environmental
impacts on the Earth system process 𝑧.

Using the premise software v2.2.6, we generated 𝐀 and 𝐁̃ for each year of the period and each scenario (18
matrices for 𝐀 and 𝑩̃). Subsequently, we computed sampling matrices 𝛿𝐀∗ ∈ℝ𝑖×𝑗×𝑡, 𝛿𝐁̃ ∈ℝ𝑒×𝑗×𝑡 and 𝛿𝐜∈ℝ𝑗×𝑡
where 𝐜 is the process constraint vector. We provide detailed replication steps in Supplementary Note 7. For each
year, we generated 500 samples of 𝛿𝛚∈ℝ𝑧×𝑡, 𝛿𝐀∗ ∈ℝ𝑖×𝑗×𝑡, 𝛿𝐁̃ ∈ℝ𝑒×𝑗×𝑡 and 𝛿𝐜∈ℝ𝑗×𝑡 to run the TCM from
Equation (7) to Equation (12). Therefore, in the formulation below, we omit the time dimension from the indices.
The overall process is illustrated in Supplementary Fig.16.

The objective of the optimisation framework is to minimise 𝐱∈ℝ𝑘 whose elements 𝑥𝑘 represent the normalised
state of control variables of 𝑘 biophysical systems (Equation (7)). 𝚪∈ ℝ𝑘×𝑧 is the interaction matrix (Equation (4))
and 𝐝∈ℝ𝑧 is the normalised direct human impact vector. When no interactions are considered 𝑘 =𝑧 and thus,
𝐱=𝐝. The normalised direct human impact is calculated using Equation (8), where the scaling vector 𝐬∈ℝ𝑗 is the
decision variable in the TCM. In Equation (8), ⊘ is the Hadamard division. The annual production of hydrogen is set
in Equation (9) in the demand vector 𝐟∈ℝ𝑖 (Supplementary Note 7). Equation (10) provides a lower bound by
ensuring that the scale product of processes cannot be negative. To limit the scale of technologies in the system,
we constrain hydrogen and electricity production processes using Equations (11) and Equation (12). In these
equations, the superscripts, 𝑒𝑙 and 𝐻2 indicate the set of electricity and hydrogen production processes,
respectively. More details on the modelling of the constraint vector are available in Supplementary Note 7.

min 𝐱 = 𝚪𝐝 (7)

s.t. 𝐬
𝐐𝐁̃𝐬 ⊘ 𝛚 = 𝐝 (8)

𝐀∗𝐬 = 𝐟 (9)

𝐬𝐣 ≥ 𝟎 (10)

𝐬H2 ≤ 𝐜H2 (11)

𝐬el ≤ 𝐜el (12)

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The manuscript presents a modeling-based assessment of global hydrogen production and its sustainability within planetary boundaries. While the topic is relevant, the study in its current form raises concerns that prevent it from being suitable for publication in Nature Communications. The paper relies entirely on a modeling approach that remains unclear in its hypotheses, methodological choices, and model specifics, leading to surprising and unexplained results. For instance, the authors find that hydrogen production worsens ocean acidification, yet it is unclear whether this is simply a consequence of fossil fuel use or whether other mechanisms are at play. At the same time, they suggest that hydrogen reduces pressure on freshwater resources, which contradicts existing literature indicating hydrogen’s high water footprint compared to other energy sources (Olaitan et al., 2024, STOTEN). With such a simulation-based experiment, it is difficult to determine whether these outcomes reflect real-world mechanisms or are merely artifacts of model assumptions. The model appears to function as a black box, with results being presented quantitatively but without sufficient critical interpretation.\nThe manuscript also overlooks key physical processes that could significantly alter its conclusions. Notably, hydrogen is an indirect greenhouse gas, yet this effect is neither referenced nor incorporated into the model, raising concerns about the completeness of the analysis. Additionally, the choice of a 1.5°C scenario is questionable, as this target is increasingly viewed as unattainable. The rationale for focusing on this specific pathway is not clearly articulated, nor is its relevance to the broader discussion of planetary boundaries and hydrogen sustainability.\nWhile planetary boundaries provide a useful rhetorical framework for discussing environmental sustainability, they remain more of a conceptual tool than a precise scientific paradigm. This distinction should be made clearer in the text.\nBeyond these scientific concerns, the manuscript is difficult to follow due to its dense structure and convoluted explanations. Methodological details are scattered throughout the text, making it challenging for the reader to assess the rigor of the approach. The conclusions, while ambitious, remain highly speculative given the limitations of the study. Without a clearer justification of assumptions, a more transparent discussion of uncertainties, and some degree of empirical validation, the paper does not meet the publication standards of Nature Communications.", "reviewer_2": "The authors provide a comprehensive analysis of the environmental impacts of future global hydrogen production using a planetary boundary-based prospective LCA framework combined with mathematical optimization. I found the article interesting and believe it has the potential to make a contribution to both the LCA and hydrogen literature. However, a key concern is that the methodology is difficult to follow. I outline my specific comments below.\n1. The methodological approach is quite difficult to follow. First, it is unclear how your bottom-up hydrogen production system model integrates with the annual hydrogen demand in the SSP1-PkBudg500 scenario. If REMIND already considers hydrogen, then its scenario results include a specific technology mix that meets this demand while satisfying the climate target and other constraints within the model. If I understood it correctly, your model determine a new optimal mix that minimizes the planetary footprint (or only for the electricity mix that supplies electrolytic hydrogen production?). If so, I was wondering to what extent is this consistent? Will the new optimal mix ensure that the interactions between the hydrogen sector and other sectors in the IAM scenario remain coherent? For example, in the SI it is stated that “allows the model to go beyond the capacity initially defined by the SSP”. For instance, could your optimized hydrogen production mix lead to excessively high system costs, potentially affecting the results of the IAM model?\n2. By focusing on the global scale, the authors rely on LCIs that reflect global, European, RoW, or German averages. This introduces significant uncertainty, particularly for electrolytic hydrogen powered by renewable energy, given the large variability in capacity factors. As shown in studies such as Wei et al. or de Kleijne et al. (works that the authors cite), the carbon footprint (and potentially other impacts) of hydrogen production can vary widely even across major regions. Similarly, the impacts of various power generation technologies like coal, natural gas, or hydro can vary widely depending on the location. While the authors acknowledge this limitation (page 17, line 543) and note that these uncertainties are not addressed in this work, I believe this issue has major implications and warrants further discussion. For instance, to what extent do the average datasets used in this study accurately represent a truly global average process?\n3. The implementation of interactions between planetary boundaries is not entirely clear. At page 7, line 225, the authors provide the following example of biophysically mediated interactions: “clearing land leads to carbon dioxide emission to the atmosphere. Thus, a significant change in the LSC control variable would influence the boundary for CC.” This raises the question of whether these additional CO2 emissions from land clearing should be accounted for in the life cycle inventory, thereby directly contributing to the quantification of the CC planetary boundary. If these emissions are captured within the LCI, what is the added value of explicitly modeling these interactions? I suggest that the authors further clarify their rationale and provide a more detailed explanation of how these interactions are integrated into their approach.\n4. The readability of the Results section could be improved. Presenting the planetary footprint right away, without first describing the optimal hydrogen production mix, provides little context for understanding the results. I suggest first introducing the hydrogen mix that minimizes the planetary footprint (i.e., the current Figure 3) before discussing its planetary footprint (current Figure 2).\n5. A key takeaway from the study is that “using biomass-based sources for hydrogen production will significantly impact the biosphere integrity boundary. This result directly contradicts hydrogen applications relying on biomass”. However, if I understand correctly, wood chip is the only biomass feedstock considered for the gasification route. This overlooks other biomass residues that do not compete for land (e.g., with food) and/or have lower or no land requirements. Could the authors further elaborate on how their choice of biomass feedstock influences this conclusion?\n6. On page 16, line 503, the authors suggest that \"it is better to focus on a certification scheme informing the planetary footprint of hydrogen (impact on all planetary boundaries)\". While this sounds like a great idea, simply mentioning it does not add much depth. I wonder, for example, to what extent such an approach is practically feasible. I believe the authors should elaborate further on how they envision the implementation of such a certification scheme.\n7. On page 17, line 521, the authors suggest that their approach could help strengthen the integration between LCA and IAM. While this has been a long-standing topic in the literature, practical progress has been limited. Other studies have already developed optimization models on top of IAM scenarios (e.g., the PULPO framework cited by the authors). Therefore, it remains unclear how their approach advances this integration. Similar to the certification scheme, the authors should provide further clarification on their specific contribution.\n8. I believe the authors could better emphasize the novel findings in the abstract. For instance, on page 9, line 275, they state: “The addition of interaction parameters to evaluate the absolute sustainability of hydrogen shows a strong amplification of impact.” This appears to be a key novel finding of the study, yet it is not mentioned in the abstract.\nOther minor comments:\n- Page 3, line 125, please note that the hydrogen production scenarios assessed in Wei et al. are based on IEA scenarios.\n- At several places across the manuscript, there is a mix between carbon dioxide removal (CDR, removal of CO2 from the atmosphere) and carbon capture and storage. For example, at page 6 line 201, biomass with carbon capture and storage would be a more appropriate term than “biomass with a carbon removal”.\n- Page 9, line 287: I don’t believe “the correct distribution of processes” is the right term here. I suggest removing “correct”.\n- Page 18, line 572: Please revise the text since the functional unit is something that you define within your LCA, and not something that is applied to.\n- Page 18, line 572: “REMIND SSP1-PkBudg500 was selected as the main model”. Clarify that REMIND is the model and SSP1-PkBudg500 is the scenario.\n- Page 18, line 579: “Such an approach aims to define a reference model that quantifies the role of hydrogen irrespective of a scenario”. I don’t see the point of this, since the whole analysis is based on the SSP1-PkBudg500 scenario (plus SSP2 and 5 as sensitivity analysis). I don’t think your analysis study hydrogen irrespective of the scenario (perhaps the allocated SOS yes).", "reviewer_3": "This article investigates whether current plans to expand hydrogen production are compatible with operating within the Earth’s safe operating space. The work also develops an optimization model to investigate whether an optimized portfolio of hydrogen production technologies could safely operate within the ecological capacity of the planet. The topic is highly relevant and timely. Below I provide some comments that require some attention before proceeding any further:\n- Previous works already investigated the planetary boundaries impact of hydrogen production, yet the authors critically assessed them concluding that further refinements were still necessary to draw more meaningful conclusions. However, the final conclusion they present in this work, namely that future hydrogen production is unsustainable, remains somehow very similar. I still see the value of the analysis, but it needs to be more clearly stated in the abstract and conclusions. Moreover, the current study is also affected by a range of methodological choices and uncertainties, some of which were not analyzed with enough depth, as discussed next.\n- Different sharing principles should be explored to study the robustness of the results depending on the downscaling approach.\n- The calculations are affected by several uncertainty sources, including uncertainties in the background, foreground, characterization factors and absolute thresholds. Please describe all of them in detail and explain how they were accounted for, which remains unclear to me (it seems some were considered and others not, and an exhaustive discussion of this topic is missing).\n- The authors use the TCM model to perform the calculations. They mention that a data aggregation step was carried out when building the LP. Please provide more details, as it is very hard to understand how the model works. This aggregation inevitably leads to some information loss, which should be discussed.\n- It remains unclear how the premise data is integrated into the LP, and whether the scenario in premise is fully consistent with the IAM scenario for which the calculations were done. Moreover, I wonder why the authors did not explore other scenarios, at least the ones available in premise.\n- The authors let the model optimize the amount of DAC required. I wonder if this leads to an inconsistency in the model, as the premise scenario in the background assumes already a given DAC capacity that might differ from the optimal capacity found by the LP model. If so, how would this affect the results?\nOverall, this is a nice piece that goes beyond previous studies on a highly relevant topic connected to future energy systems design. The work would benefit from a more exhaustive description of the methods, perhaps complemented by additional calculations to further evaluate the effect of uncertainties. This would enhance the robustness and relevance of the findings."}, "author_response": "Michaël Lejeune1,2, Sami Kara1,2*, Michael Zwicky Hauschild4,5*, Sareh Shahrabifarahani1, Rahman Daiyan2,3*\n1Sustainability in Manufacturing and Life Cycle Engineering Research Group, School of Mechanical and Manufacturing\nEngineering, The University of New South Wales, Sydney 2052, Australia.\n2Australian Research Council, Training Centre for the Global Hydrogen Economy, Sydney 2052, Australia.\n3School of Minerals and Energy Engineering, The University of New South Wales, Sydney 2052, Australia.\n4Section for Quantitative Sustainability Assessment (QSA), Department of Environmental and Resource Engineering,\nTechnical University of Denmark, Kgs, Lyngby, Denmark.\n5Centre for Absolute Sustainability, Technical University of Denmark, Kgs, Lyngby, Denmark.\nDear reviewers of the manuscript “Pathways to global hydrogen production within planetary boundaries”\nwith ID: NCOMMS-25-07779. We appreciate your constructive comments and thank you for taking the time to\nreview our work.\nWe confirm that we have addressed each comment in the revised manuscript as outlined in the sections\nbelow. Please find the reviewers’ comments in blue, our response in black, and the changes made to address the\ncomments in red, with key revisions highlighted in grey. Also, for clarity, we highlight references to revisions or\nto line number in blue.\nList of revisions ................................................................................................................................................... 3\nGeneral major revisions ...................................................................................................................................... 4\nReviewers’ comments ......................................................................................................................................... 7\nReviewer 1 - Comment 1 to 5 .......................................................................................................................... 7\nReviewer 2 – Comment 6 to 20 ...................................................................................................................... 15\nReviewer 3 – Comment 21 to 28 .................................................................................................................... 25\nReferences ........................................................................................................................................................ 39\nRevision 1. Manuscript clarity and readability and interpretation improvements ................................................ 4\nRevision 2. Robustness improvements ................................................................................................................ 4\nRevision 4. Correction of interaction matrices..................................................................................................... 6\nRevision 4. Clarification of main assumptions .................................................................................................... 8\nRevision 5. Process impact contributions ........................................................................................................... 8\nRevision 8. Contextualisation of future scenario in relation to planetary boundaries and hydrogen sustainability\n......................................................................................................................................................................... 11\nRevision 9. Influence of hydrogen emissions .................................................................................................... 12\nRevision 10. Discussion on global average processes ........................................................................................ 17\nRevision 12. Interaction model justification. ..................................................................................................... 18\nRevision 13. Bio-based production system ........................................................................................................ 20\nRevision 14. improved discussion on hydrogen green certification. ................................................................... 22\nRevision 15. Discussion on future work based on the technology choice model and climate modules. ............. 23\nRevision 16. Reshape the summary to emphasis novel findings. ....................................................................... 23\nRevision 17: clarification on previous work conclusion. ..................................................................................... 25\nRevision 18. Discussion on the allocation approach .......................................................................................... 27\nRevision 19. Uncertainty clarification ................................................................................................................ 28\nRevision 20. Prospective technology choice model clarification ........................................................................ 31\nRevision 21. Scenario ensembles validation ...................................................................................................... 36\nRevision 1. Manuscript clarity and readability, and interpretation improvements\nFor clarity improvements, we have significantly revamped the Manuscript and Supplementary File. First, we\nremoved the excessive use of acronyms, which made the manuscript dense and hard to read. In particular, for\nthe broad readership of Nature Communications. Instead, we introduced a simpler terminology (Supplementary\nInformation S1). We believe these improvements convey our findings more effectively while preserving the\nscientific integrity of the content. We have also shortened the manuscript to remain within the 5000-word limit\n(introduction, results and discussion). Some background knowledge we initially provided has been transferred\nto the Supplementary File.\nIn the initial version of the manuscript, a comparison was made between interaction scenarios (i.e., no\ninteractions, with interactions, and with human-mediated interactions). However, the influence on the human-\nmediated interaction scenario showed only a slight difference from the biophysical interaction scenario. This\ndid not necessarily require a special focus in the main manuscript, and as a result, key takeaways became unclear.\nFor this reason, we have also transferred these additional results to the Supplementary File.\nFollowing Comment 2, Comment 7, Comment 25, and Comment 26, the methodology section has been\nrevised to be clearer while including more details. At the same time, we expanded the methodologies, including\ndetailed replication steps in the Supplementary File.\nIn the initial version of the manuscript, Figure 5 was difficult to interpret for some readers. This figure aimed\nto assess the additional capacity required for water electrolysis to achieve absolute sustainability within specific\nboundaries. This was, however, in relation to REMIND-SSPx-PkBudg500 scenarios only. As discussed in Revision\n2, we removed the reliance on a specific scenario. Instead, we provide additional system configurations if\nhydrogen was produced entirely from water electrolysis, biomass and abated fossil fuels. We have, therefore,\nremoved Figure 5 and the related section from the manuscript.\nFollowing Comment 2, we also provide a better interpretation of our results by, for instance, and whenever\npossible, comparing them with projections from the literature, as well as those of the International Energy\nAgency (IEA)1,2 or the International Renewable Energy Agency (IRENA).\nRevision 2. Robustness improvements\nFirst, a new environmental target range was raised 1.9-2.25 W m-2 by 2100. This is because 1.9 W m-2 is a\nnameplate target that is more indicative of the CO2 forcing only (Table AIII.4a - AR64). Several scenarios with a\ntotal radiative forcing above 1.9 W m-2 but below 2.6 W m-2 can still be categorised as 1.9 W m-2 scenarios. To\n4\naddress this aspect, we have set the new target to 2.25 W m-2, which is the mean value between 1.9 W m-2 and\n2.6 W m-2. This allowed for more scenarios to be considered in our assessment (n=32, compared to the initial\nn=20). We have also performed a validation analysis by testing the derived scenario ensemble against a\nsimulation using the finite amplitude impulse response (FaIR) model5 and AR6 data6. We mention this from line\n473 to 475 of the manuscript:\n“Next, we screened scenarios in the AR6 dataset7, selected the “AR6 climate diagnostics|Effective Radiative\nForcing|FaIRv1.6.2|50.0th Percentile”( W m-2) and imposed a maximum of 2.25 W m-2 (average between 1.9 and\n2.6 W m-2) variable in 2100.”\nA recent review (as of July 2025) by de Bortoli et al.8 stressed the importance of understanding the limitations\nof integrated assessment model scenarios for prospective life cycle assessment. While some recommendations\nfrom this review are outside the scope of this study, we have implemented significant changes to align our work\nwith these recommendations.\nFor instance, from the prospective life cycle assessment aspect of our work, the reliance on a specific scenario\n(SSP1-PkBudg500) has been removed. Instead, our new analysis considers a “premise” scenario ensemble that\nachieves 1.9-2.25 W m-2 by 2100. It is worth noting that the premise scenario ensemble is still based on SSPx-\nPkBudg500 scenarios. We mention this from line 501 to 503 of the manuscript:\n“On the other hand, prospective life cycle assessment matrices were derived from a smaller ensemble (n=3)\nconsisting of REMIND SSPx-PkBudg500 scenarios.“\nAll selected scenarios were systematically checked against the climate target by modelling both the expected\natmospheric carbon dioxide concentration and radiative forcing, as discussed in Revision 18. From line 503 to\n504 of the manuscript:\n“We also ensured that all ensembles are consistent with the 1.9-2.25 W m-2 target by 2100 (Supplementary\nMethods S7.2 and Supplementary Fig.17).”\nIn our stochastic implementation of the technology choice model, we sample across the range of scenarios in\nthe ensembles. We mention this from line 637 to 640 of the manuscript:\n“Using the premise software, we generated and for each year of the period and each scenario (18\nmatrices for and ) (Supplementary Method S\n𝐀𝐀\n8.2). S 𝐁𝐁ubsequently, we computed sampling matrices\n∗\n, 𝐀𝐀 𝐁𝐁 and where is the process constraint vector. We provide detailed replic 𝛿𝛿 a 𝐀𝐀 tio ∈ n\n𝑖𝑖×𝑗𝑗×𝑡𝑡 𝑒𝑒×𝑗𝑗×𝑡𝑡 𝑗𝑗×𝑡𝑡\nℝ\nsteps in\n𝛿𝛿\nSu 𝐁𝐁p\n∈\nple\nℝ\nmentary Me\n𝛿𝛿\nt\n𝐜𝐜\nho\n∈\nd\nℝ\ns S7.4.”\n𝐜𝐜\nAll references to a specific IAM scenario have been removed, and results are now presented quantitatively\nbased on the range of results from several scenarios, rather than specific ones. We believe that this approach\naddresses the concerns raised in Comment 7 and Comment 27. We clarify that our results, based on this new\napproach, are not dependent on any specific scenario.\nassessment remain lacking8, our approach is quantitative and does not aim to address ethical issues embedded\nin future scenarios. Instead, our study focuses on a subset of scenarios (those leading to 1.9 W m-2 by 2100) that\nalign with the trajectories presented in the Intergovernmental Panel on Climate Change's Sixth Assessment\nReport (AR6)4 .\nRevision 3. Correction of interaction matrices\nIn the initial version of the manuscript, the interaction matrices should have been transposed as indicated by\nLade et al.10. Consequently, our results focused on originating impacts instead of receiving impacts. We have\ncorrected this issue. The main difference primarily relates to stratospheric ozone depletion, which now shows\nnegative results. Conversely, the impact on freshwater use is now positive. We have corrected the matrices and\nupdated Supplementary Fig.8 accordingly. Additionally, the initial matrices lacked an interaction parameter\nbetween phosphorus and nitrogen biochemical flows. Each one of these Earth system processes can\nindependently influence others10. This aspect was also corrected.\nReviewer 1 - Comment 1 to 5\nComment 1: The manuscript presents a modeling-based assessment of global hydrogen production and its\nsustainability within planetary boundaries. While the topic is relevant, the study in its current form raises\nconcerns that prevent it from being suitable for publication in Nature Communications.\nThank you for your interest in our work, for the time invested in reviewing and for your constructive remarks.\nThe critical comments provided additional grounds for improving the clarity of our work.\nComment 2: The paper relies entirely on a modeling approach that remains unclear in its hypotheses,\nmethodological choices, and model specifics, leading to surprising and unexplained results. For instance, the\nauthors find that hydrogen production worsens ocean acidification, yet it is unclear whether this is simply a\nconsequence of fossil fuel use or whether other mechanisms are at play. At the same time, they suggest that\nhydrogen reduces pressure on freshwater resources, which contradicts existing literature indicating hydrogen’s\nhigh-water footprint compared to other energy sources (Olaitan et al., 2024, STOTEN). With such a simulation-\nbased experiment, it is difficult to determine whether these outcomes reflect real-world mechanisms or are\nmerely artifacts of model assumptions. The model appears to function as a black box, with results being\npresented quantitatively but without sufficient critical interpretation.\nThe overall comment relates to a lack of clarity that we understand and aim to address in\nRevision 1. However, we find important to clarify that our work focuses on finding a hydrogen production system\nwithin planetary boundaries. This implies that the absolute reference for comparison is the environmental space\ndelimited by planetary boundaries10–12. To that extent, our work assesses whether, in sustainable future scenarios\n(those leading to 1.9-2.25 W/m2 in 2100), global hydrogen production could be sustainable. From that basis, the\nwork of Olaitan et al.13, while being informative for a comparison between energy sources, does not relate to the\naim of our work. As mentioned, global production of hydrogen has a dedicated environmental space for\ncomparison. In simple terms, global hydrogen production could require more water than other energy sources\nwhile still being within its dedicated environmental space. On that basis, we respectfully disagree that our work\ncontradicts existing literature, as the reference for comparison is simply different.\nRegarding the underlying mechanisms (process impact contribution), we understand the criticism and agree\nthat this critical interpretation could have been better explained. In the initial version of the manuscript, we\nprovided all impact contributions in the supplementary materials. This made it challenging for the reader to\nunderstand which process has the most significant impact. We have therefore added the impact contribution of\nprocesses to the planetary footprint in the main text (Revision 5). In this revision, we clearly demonstrate the\nprocesses causing impacts with and without biophysical interactions. We also explain what the main contributors\nand the root cause of the impact.\nthe underlying data used in this work originates from peer-reviewed life cycle assessment studies, which we\nsystematically checked. In fact, the same data is also used in previous studies14–17 and quantitatively reflects real-\nworld mechanisms. Furthermore, the main assumptions in this work concern environmental space allocation\nand constraints of hydrogen production pathways. We derived these variables from a scenario ensemble derived\nfrom the IPCC AR6 dataset6. While there is currently no scientific consensus on a correct approach to allocate an\nenvironmental space, we have clearly defined and motivated our main assumption in the initial manuscript (line\n803 to 806). This aspect acts on the magnitude of transgressions but does not alter the system configuration\ndefined by the optimisation. Our optimisation objective is centred on environmental sustainability rather than\nminimising costs. Our results can therefore differ from the non-optimal “real-world” system configurations and\nshould be interpreted as pathways for hydrogen production within planetary boundaries. However, we agree\nthat a comparison between our results and future projections from well-known institutions (e.g., IEA1,2 or\nIRENA) for validation could have been made. We implemented this aspect as part of Revision 1.\nAlso, we agree that the initial version of the manuscript lacked sufficient critical interpretations. However,\nregarding the “black box” comment, our model is based on the technology choice model1–4 (linear programming)\nand relies on the set of processes and technology constraints, which we detailed in the initial and revised versions\nof the supplementary file. While the optimisation appears to function as a black box, its inner workings and\nresults can be critically interpreted. We assume this aspect to be covered in the general Revision 1 we conducted.\nTo summarise, we agree that the initial version of the manuscript lacked clarity, in particular for result\ninterpretation, which we address thoroughly in Revision 1. We also agree that our assumptions were not\nexplicitly clear. For this, we implemented Revision 4. We also provide additional explanations and results for the\nunderlying impact contributions in the main text (Revision 5).\nRevision 4. Clarification of main assumptions\nFrom line 265 to 271 of the manuscript, we list key assumptions:\n“The assessment is based on a set of key assumptions. First, the environmental space was defined by\nassuming a utilitarian downscaling principle, which captures emission reductions and the growing role of\nhydrogen in future scenarios. Second, we assume that the biophysical interaction model10 is representative\nenough to capture potential environmental trade-offs. Third, we assumed that selected hydrogen production\npathways and renewable electricity supply would be representative of future clean hydrogen production\nsystems. Lastly, we do not consider biogenic carbon accounting for bio-based production.”\nWe also provide a summary of all assumptions and methodological choices in Supplementary Methods S4.\nRevision 5. Process impact contributions\nmechanisms:\n“Electricity supply and SMR-CCS are the dominant environmental hotspots. For climate change and ocean\nacidification, embodied impacts of the renewable electricity mix account for ~40% of transgressions (Fig. a).\nMost originate from manufacturing, where non-renewable energy is required at several stages; despite\ntechnological improvements by 2050, embodied emissions remain non-negligible at scale. Solar thermal,\nalthough contributing little to overall supply, accounts for ~17% of these impacts, mainly due to industrial heat\ndemand. Wind and solar PV dominate pressures on biogeochemical flows, with nitrogen and phosphorus\nemissions linked to copper mining and processing, including the use of explosives and sulfidic tailings. Direct\nemissions from SMR-CCS contribute to ~32% of climate- and ocean-related transgressions, arising from carbon\noxidation in reforming, the water–gas shift reaction, and fuel combustion in the reformer furnace18. Even with\nCCS, SMR emits ~2.6 kgCO₂ kgH₂⁻¹ in addition to embodied burdens. Natural gas feedstock adds another ~15%\nof total impacts, as both fossil extraction and biogas upgrading release significant carbon emissions. Overall,\nrenewable electricity and SMR–CCS together drive ~78% of the footprint across boundaries, except for\nstratospheric ozone depletion, where end-of-life treatment and transport of scrap steel from alkaline\nelectrolyser recycling accounts for ~41% of impacts.”\nWe also provide Fig.4 as shown below.\nmodelled planetary boundaries without biophysical system interactions. b Process contribution to the impact on all modelled\nplanetary boundaries with biophysical system interactions. Results are based on median values across future scenarios and\nlowly influential processes were grouped in “others” based on 5% cutoff criteria. Process names reflect life cycle assessment\ninventories from the Ecoinvent 3.9.1. “Cutoff by classification” database updated with the premise19 software.\nconclusions. Notably, hydrogen is an indirect greenhouse gas, yet this effect is neither referenced nor\nincorporated into the model, raising concerns about the completeness of the analysis. Additionally, the choice\nof a 1.5°C scenario is questionable, as this target is increasingly viewed as unattainable. The rationale for focusing\non this specific pathway is not clearly articulated, nor is its relevance to the broader discussion of planetary\nboundaries and hydrogen sustainability.\nWe understand the concern regarding potential hydrogen emissions. Hydrogen is indeed an indirect\ngreenhouse gas, as explained in several studies20–23. In the state-of-the-art planetary boundary characterisation\nfactors from refs24–26, there is still no characterisation method for hydrogen emitted to the atmosphere. As this\ncould be a crucial aspect of future global hydrogen production, it needs to be considered. Therefore, we added\na paragraph in the manuscript to evaluate the implications of potential hydrogen leakages (Revision 7).\nRegarding the focus on a scenario limiting climate change to 1.5°C, we agree that this aspect could have been\nbetter articulated in relation to planetary boundaries and the sustainability of hydrogen production. While some\nallusions to 1.5°C scenarios were made in the initial version of the manuscript, in Revision 1, these were removed.\nWhile a recent study27 highlights that limiting global warming to 1.5°C is unlikely, global temperature increase\nremains an environmental response variable. In our work, we focus on the radiative forcing control variable. Fig.\n1 of Steffen et al.28 provides a clear visual representation of the difference between control and response\nvariables. In short, the scenarios we use could overshoot the 1.5°C global warming limit while achieving the 1.9\nW m-2 by 2100. This aspect was illustrated in Figure 1 of the initial Supplementary File. However, to address your\nconcern, we have implemented Revision 6 where the link between the scenario, planetary boundaries, and\nhydrogen sustainability is clearly shown.\nTo summarise, we agree that our approach should have included potential hydrogen emissions. Revision 7\nwas implemented accordingly. Also, the rationale and relevance for focusing on 1.9 W m-2 is addressed in Revision\n6.\nRevision 6. Contextualisation of future scenario in relation to planetary boundaries and hydrogen\nsustainability\nFrom line 50 to 59 of the manuscript:\n“Recent climate change literature4,29 implies that only future scenarios leading to 1.9 W m-2 nameplate effective\nradiative forcing by 2100 (thereafter referred to as future scenarios only) enable human operation within the\nclimate change boundary. These scenarios are complementary to additional mitigation measures for biodiversity\nand ecosystems30. Climate change and biosphere integrity are core Earth-system processes with significant\ninfluence on the stability of the Earth-system that has characterised the Holocene epoch since the last\nglaciation10,11,31. These scenarios also contain the necessary production volumes to achieve the climate target.\nAllocating an environmental space for global hydrogen production based on future scenarios is therefore\n11\nessential. Yet, no study has comprehensively used this important contextualisation for allocating an\nenvironmental space to global hydrogen production.“\nFig. 2| Environmental space for global hydrogen production linked with a climate target. a Alignment of scenario ensemble\n(n=32) with path a towards operation within climate change planetary boundary compared with projections from the AR632\nand FaIR33 modelling results (n=66). The black line represents the median value, the inner uncertainty range is the 25th-75th\ninterquartile range (IQR), while the light-shaded area represents the 5th-95th IQR. b Hydrogen production volumes (excluding\nunabated fossil fuels) from the scenario ensemble compared to future projections9 (n=17) and IEA16. Historical data and\nproject data from refs1,34. c Dynamic downscaling factor for the environmental space of global hydrogen production. Historical\ndata from the National Oceanic and Atmospheric Administration (NOAA) 35 and AR6-ssp119 projections from ref32.\nRevision 7. Influence of hydrogen emissions\nFrom line 254 to 261of the manuscript:\n“Hydrogen leakages can reduce the benefits of carbon capture and sequestration. Hydrogen is a difficult gas to\nmanage, and leakages could be significant9,20,21. At an optimal DACS rate (i. e., 3.86 kgCO₂ kgH₂⁻¹ in 2050), if 10%\nof production is leaked, the impact on climate change could increase by ~480% (Supplementary Fig.27). To offset\nthese leakages, an additional 0.63 kgCO₂ kgH₂⁻¹ would be necessary, thereby increasing the system electricity\nrequirement. At scale (i.e., 225MtH2), this represents an additional 0.142GtCO2 captured and sequestrated,\nrequiring 0.135PWh of renewable electricity. Given potential constraints in both DACS and renewable energy\ncapacity2,36, achieving sustainability for hydrogen will also depend on controlling potential H₂ leakages.”\nsolution. Results are relative to the optimal solution found for the energy imbalance planetary boundary only.\nComment 4: While planetary boundaries provide a useful rhetorical framework for discussing\nenvironmental sustainability, they remain more of a conceptual tool than a precise scientific paradigm. This\ndistinction should be made clearer in the text.\nThank you for your comment. At their cores, both the planetary boundaries and life cycle assessment are\nframeworks of a quantitative nature31,37. We focus on the quantitative environmental sustainability of global\nhydrogen production and use the planetary boundaries as a reference for absolute limits. On several occasions\nin the manuscript, we emphasise the quantitative nature of our work (lines 19, 48, 60, 66, 263, 424,438 of the\nrevised manuscript). With these mentions, we believe that the quantitative nature of our work is sufficiently\nclear and does not require further clarification on the precision of the planetary boundary framework.\nComment 5: Beyond these scientific concerns, the manuscript is difficult to follow due to its dense structure\nand convoluted explanations. Methodological details are scattered throughout the text, making it challenging for\nthe reader to assess the rigor of the approach. The conclusions, while ambitious, remain highly speculative given\nthe limitations of the study. Without a clearer justification of assumptions, a more transparent discussion of\n13\nuncertainties, and some degree of empirical validation, the paper does not meet the publication standards of\nNature Communications.\nThank you for your constructive comments and review, which helped improve the quality and transparency\nof our work. To summarise, in Revision 1, we enhanced the clarity and readability of the manuscript. In Revision\n4, we clarify our assumptions. In Revision 5, we provide process impact contributions to explain underlying\nmechanisms and improve the critical interpretation of our results. In Revision 6 we provide a better rationale for\nthe use of 1.9W m-2 scenarios. Lastly, in Revision 7, we characterised hydrogen emissions and explained how\nleakages can potentially influence our results. With these revisions, we believe we have addressed all concerns\nraised.\nComment 6: The authors provide a comprehensive analysis of the environmental impacts of future global\nhydrogen production using a planetary boundary-based prospective LCA framework combined with\nmathematical optimization. I found the article interesting and believe it has the potential to make a contribution\nto both the LCA and hydrogen literature. However, a key concern is that the methodology is difficult to follow. I\noutline my specific comments below.\nThank you for your interest in our work and for taking the time to review it carefully.\nComment 7: The methodological approach is quite difficult to follow. First, it is unclear how your bottom-\nup hydrogen production system model integrates with the annual hydrogen demand in the SSP1-PkBudg500\nscenario. If REMIND already considers hydrogen, then its scenario results include a specific technology mix that\nmeets this demand while satisfying the climate target and other constraints within the model. If I understood it\ncorrectly, your model determine a new optimal mix that minimizes the planetary footprint (or only for the\nelectricity mix that supplies electrolytic hydrogen production?). If so, I was wondering to what extent is this\nconsistent? Will the new optimal mix ensure that the interactions between the hydrogen sector and other sectors\nin the IAM scenario remain coherent? For example, in the SI it is stated that “allows the model to go beyond the\ncapacity initially defined by the SSP”. For instance, could your optimized hydrogen production mix lead to\nexcessively high system costs, potentially affecting the results of the IAM model?\nThank you for this comment. We acknowledge the lack of clarity in the first version of the manuscript, which\nwe addressed in Revision 1. In line with this, we added more detail on the approach and provided a simplified\nexample code for reproducibility (Comment 25 and Revision 17).\nFirst, it is essential to note that we have made a significant change to the manuscript to eliminate reliance on\na specific scenario (Revision 2). This follows the recent criticisms and recommendations from de Bortoli et al.8.\nIn this new version, the demand for hydrogen and technology constraints are no longer bound to a specific\nscenario, but rather estimated by an ensemble of IAM scenarios (n=32), which we also derived from the IPCC\nAR66,7 ensemble.\nRegarding the link between the hydrogen production volumes and our model, this is done by imposing\nequation (1) as a constraint in our optimisation model (Line 848 of the initial version of the manuscript).\nPractically, it is simply done by setting the annual production volume for hydrogen in the demand vector . The\nindex would refer to the row index o\n𝑓𝑓\nf𝑖𝑖 the demand vector where the reference flow is hydrogen. Overa\n𝐟𝐟\nll, the\nscaling vector (decision variable in our optimisation model) will be forced to scale all processes to match the\n𝑖𝑖 𝐟𝐟\nannual production volumes. This process is done for every year of the period assessed.\n𝒔𝒔\n𝐀𝐀𝐀𝐀=𝐟𝐟 (1)\n“SE|Hydrogen|+|Electricity”, which represents secondary energy in the form of hydrogen generated from\nelectricity. This variable would only indicate how much hydrogen is produced via electricity, but does not include\nfurther specificity on the technology mix. Our work provides this specificity by building a bottom-up production\nsystem to minimise the planetary footprint.\nWhile we base our analysis on IAM scenarios, our aim is not to preserve the integrity of these scenarios (e.g.,\ninteractions with other sectors, system costs, etc.). There is an inherent mismatch between what IAM scenarios\nproject in terms of emissions or technical performance and what life cycle assessment reveals38,39. From that\nbasis only, the integrity of the scenario cannot be preserved. Instead, we isolate global hydrogen production in\nthese IAM scenarios to conduct our quantitative assessment. We provide specific answers to your questions\nbelow.\n“Is this consistent?”. The trajectory to return within the planetary boundary for climate change is reflected in\nthe hydrogen production volume. Our new system defines the technology to produce hydrogen without altering\nproduction volumes.\n“Will the new optimal mix ensure that the interactions between the hydrogen sector and other sectors in the\nIAM scenario remain coherent?”. Production volumes are not altered, thus interactions between sectors should\nremain consistent. However, the demand for renewable energy or natural gas could be higher, depending on the\nsystem configuration we find through optimisation. Additionally, we do not address the economic aspects. IAM\nscenarios, depending on how costs are treated, may opt for different technology options that fulfil the climate\ntarget while maximising socioeconomic welfare. Assessing this aspect would require the inclusion of costs, as\nwell as the demand (or use) for hydrogen, which is beyond our scope. Rather, we assess hydrogen production in\nisolation using production volumes from a range of future scenarios (n=32).\n“For instance, could your optimized hydrogen production mix lead to excessively high system costs, potentially\naffecting the results of the IAM model?”. Since we do not address the economics, the conservative answer is yes.\nAs previously mentioned, this type of analysis would require a comprehensive economy-wide energy system\nanalysis.\nComment 8 : By focusing on the global scale, the authors rely on LCIs that reflect global, European, RoW, or\nGerman averages. This introduces significant uncertainty, particularly for electrolytic hydrogen powered by\nrenewable energy, given the large variability in capacity factors. As shown in studies such as Wei et al. or de\nKleijne et al. (works that the authors cite), the carbon footprint (and potentially other impacts) of hydrogen\nproduction can vary widely even across major regions. Similarly, the impacts of various power generation\ntechnologies like coal, natural gas, or hydro can vary widely depending on the location. While the authors\nacknowledge this limitation (page 17, line 543) and note that these uncertainties are not addressed in this work,\nI believe this issue has major implications and warrants further discussion. For instance, to what extent do the\naverage datasets used in this study accurately represent a truly global average process?\nwhether it is for the present time (2025) or prospectively. Indeed, several studies show that the location where\nhydrogen is produced matters14,16,17,40. The variation in capacity factors could be quite significant from one\nlocation to another. However, for this global assessment, we rely on the global “World” inventories from\nPremise19. These inventories would match the world market mix defined in each scenario. That approach is\nsimilar to the one used by Wei et al.16. When a global inventory is not available, we follow the same approach as\nBachmann et al.26, using conservative locations. For the hydrogen production mix, we tested inventories from\nvarious locations and found no significant influence. Transgression levels remain quantitatively similar. For the\nelectrical mix, we note only one difference: when selecting wind power from Germany, the system relies more\non wind power than solar power, owing to the lesser impact of wind at that location. Yet, this switch remains in\nthe same proportions and transgression levels remain quantitatively similar. However, we agree that this topic\ncould be further discussed. We implemented Revision 8 accordingly.\nRevision 8. Discussion on global average processes\nFrom line 389 to 398 of the manuscript, we added a discussion on the global averages we used:\n“Uncertainty arises from prospective life cycle assessment and regional variability in renewable performance.\nOur inventories are based on the Premise software19, which does not fully capture geospatial parameters such\nas capacity factors and process conditions that strongly influence LCA results14,17,41. Although global in scope,\nour analysis relies on scenario-based market mixes19 that may not reflect regional performance. Some electricity\nsources, such as wind, solar and hydropower, are location-specific, using conservative “rest of the world”\ninventories may therefore bias results. For example, solar power appears more eco-efficient than wind in RoW\ninventories, while the opposite is observed in Germany. While this introduces uncertainty, the overall\ntransgression levels for hydrogen production remain robust. Extending the analysis to regional scales would\nbetter capture these geospatial dynamics14,17.”\nComment 9: The implementation of interactions between planetary boundaries is not entirely clear. At page\n7, line 225, the authors provide the following example of biophysically mediated interactions: “clearing land leads\nto carbon dioxide emission to the atmosphere. Thus, a significant change in the LSC control variable would\ninfluence the boundary for CC.” This raises the question of whether these additional CO2 emissions from land\nclearing should be accounted for in the life cycle inventory, thereby directly contributing to the quantification of\nthe CC planetary boundary. If these emissions are captured within the LCI, what is the added value of explicitly\nmodelling these interactions? I suggest that the authors further clarify their rationale and provide a more\ndetailed explanation of how these interactions are integrated into their approach.\nThank you for raising this important aspect. In our study, the interaction model serves as an essential guide\nfor our optimisation model to prioritise the minimisation of impact from influential Earth-system processes. This\n17\nis important as it could ultimately render the safe operating space smaller than what is currently reported in\nseveral studies12,31. These interactions would account for feedback loops that could have an amplifying or\nstabilising influence on other boundaries10,42–45. Beyond life cycle optimisation, the interactions are operational\nfor any absolute environmental sustainability assessment studies focusing on planetary boundaries.\nGiven the above, simply adding indirect emissions such as those from land clearing to the inventory of\nprocesses would capture the independent environmental pressure but not the amplifying or stabilising response\ndue to feedback loops with other Earth-system processes. In addition, such an approach would assume the\nindependence of land clearing CO2 emissions from all other Earth-system processes beyond climate change. Land\nclearing CO2 emissions are only part of the mechanisms captured by the interaction model10,46. The climate\nresponse due to changes in other state control variables would also be quite difficult to capture at a life cycle\ninventory stage, and for several reasons. First, to adjust elementary flows to account for interactions – thereby\ncontributing directly to an impact category – LCA practitioners would need to know in advance the environmental\nresponse of Earth-system processes (feedback loop). Second, there will be an issue of proportionality between\naffected Earth system processes, and a normalisation step would be required for all elementary flows involved.\nThird, interaction parameters between all elementary flows would have to be defined for known interactions\nand for each biophysical system. The ecoinvent database lists more than 4700 elementary flows; attempting to\nmodel the environmental response at an LCI stage would be quite challenging and tedious.\nAt the impact characterisation stage, to the best of our knowledge, there are no impact characterisation\nmethods that integrate these feedback loops between Earth-system processes. This is particularly the case for\nplanetary boundary-based characterisation methods, where environmental pressure is modelled but the\nresponse is not. It is worth noting that significant uncertainty remains for the interaction model due to its\ninfancy10. However, Earth-system processes are strongly interdependent10,43–46 and assessing them\nindependently poses a greater risk of overlooking the true planetary footprint of human activities. Hence, until\nrecent changes43,44 are operational for absolute environmental sustainability assessments, our approach can be\nseen as an initial and conservative one.\nIn summary, the interaction model focuses on the interdependent environmental response rather than the\nindependent environmental pressure. The impact characterisation captures the environmental pressure, while\nthe interdependent response is done by the interaction model. We have also clarified that modelling emissions\ndirectly at an LCI stage could be challenging compared to our more straightforward approach. In Revision 9, we\nclarify the added value of the interaction model to this extent.\nRevision 9. Interaction model justification.\nIn the Discussion, from line 373 to 377, we justify the novelty the interaction model tackles:\n“We advance sustainability assessments by integrating interactions between Earth-system processes. Previous\nstudies captured costs or climate-oriented trade-offs but neglected biophysical interactions38,47–49. Our\n18\nframework enables life cycle optimisation guided by these interactions, thereby identifying environmental\nburden shifts and feedback loops between Earth-system processes.”\nSimilarly, in the Methods, from line 564 to 570:\n“Although crucially important, the environmental response (including feedback loops in the Earth system) is not\nmodelled in previous characterisation methods24–26 owing to the inherent complexity. In this work, we extend\nprevious methods by integrating the interaction model from Lade et al.10. This simple model, while at an infancy\nstage of development10, would account for amplifying or stabilising feedbacks between different Earth-system\nprocesses. Ultimately, this would lead to a certain impact equilibrium that is not captured in conventional\nsustainability assessments (Supplementary Method S7). ”\nAnd in the review, we provide in Supplementary Information S2.3, from line 148 to 152:\n“Herein, we address this significant gap by integrating the planetary boundaries interaction model developed\nby Lade et al.10 with a stochastic TCM50. By incorporating this model, the TCM is provided with meaningful\nguidance (weights) for minimising the planetary footprint. More specifically, with this approach, the optimisation\nis informed by the biophysical interaction weights and would opt and scale of processes to avoid significant shifts\nin environmental burden.”\nComment 10: The readability of the Results section could be improved. Presenting the planetary footprint\nright away, without first describing the optimal hydrogen production mix, provides little context for\nunderstanding the results. I suggest first introducing the hydrogen mix that minimizes the planetary footprint\n(i.e., the current Figure 3) before discussing its planetary footprint (current Figure 2).\nThank you for your constructive suggestion to improve the result section. In the revised version of the\nmanuscript (Revision 1), we followed your suggestion to improve readability.\nComment 11: A key takeaway from the study is that “using biomass-based sources for hydrogen production\nwill significantly impact the biosphere integrity boundary. This result directly contradicts hydrogen applications\n19\nrelying on biomass”. However, if I understand correctly, wood chip is the only biomass feedstock considered for\nthe gasification route. This overlooks other biomass residues that do not compete for land (e.g., with food)\nand/or have lower or no land requirements. Could the authors further elaborate on how their choice of biomass\nfeedstock influences this conclusion?\nThank you for raising this important topic. A wide range of biomass sources could be used to produce\nhydrogen18,51. In the initial version of the manuscript, we put an emphasis (in text) on the dry wood chips (poplar\nbiomass) gasification as our modelling results favoured this production pathway for biomass-based production.\nHowever, another bio-based process was considered: biogas generation was included, from the treatment of\nsewage sludge via anaerobic digestion, upgraded via amine scrubbing, with and without carbon capture and\nsequestration (CCS).\nIn our study, we use the ecoinvent cut-off by classification system mode and in this system model, waste flows\nare burden-free. This implies that the biogas obtained from sewage sludge would be equivalent to biogas\nobtained from waste food, residues or manure. However, biogas upgrading (via amine scrubbing) to methane\nand the subsequent conversion of methane to hydrogen (steam methane reforming - SMR) are not burden-free.\nThese are required processes regardless of the biogas origin. Carbon dioxide and methane emissions from these\nprocesses (with or without carbon capture and storage (CCS)) made biogas-based production less favourable\nthan the gasification of wood chips with CCS. However, with corrected interaction matrices (Revision 3) and the\nnew technology constraint approach we use, the SMR of biogas, with and without CCS, is now preferred. The\ngasification of woodchips would primarily impact biosphere integrity. In the updated interaction matrices, the\nimpact on biosphere integrity is significantly more impactful. Thus, biogas-based production becomes more\nfavourable.\nAs said above, we did not include other potential sources of biomass for gasification, as this aspect would\nrequire the process modelling of the new biomass feedstock. We consider this aspect beyond our scope. We\nbelieve that the gasification of wood chips as well as biogas (here burden-free), sufficiently represent potential\nbiomass feedstocks for bio-based hydrogen production. To illustrate this, we developed a bio-based production\nsystem, as explained Revision 10.\nRevision 10. Bio-based production system\nIn the Result section, we added Fig.5 for comparison between different kinds of systems.\nelectrolytic, bio and Fossil-CCS-based production pathways. a Scenario without biophysical interactions. b Scenario including\nbiophysical interactions. Uncertainty ranges are based on a future scenario ensemble (n=32) and prospective life cycle\nassessment scenario ensemble (n=3). The plain bars extend to the 5th quantile; middle lines represent the median and the\ntop bar the 95th quantile. The black dashed circle represents the safe operating space.\nFrom line 212 to 222 in the result section, we detail:\n“Bio-based hydrogen production amplifies planetary boundary transgressions. At scale, the biomass-based\nsystem includes steam biomethane reforming with (~30%) and without (~67%) CCS as well as the gasification of\n(poplar) woodchips with CCS (3%) (Supplementary Fig.20). Under this configuration, the system transgresses six\nplanetary boundaries, with particularly high impact on climate change (exceeded ~174 times) and biosphere\nintegrity (exceeded ~16 times) (Fig. 5a). These impacts reflect on the use of wood chips (~80%) of biosphere\nintegrity impact), and emissions from biomethane upgrading, reforming and water-gas shift reactions (~98%\nclimate change and ocean acidification impact). With biophysical interactions, the footprint is further amplified\n(Fig. 5b). Unlike electrolysis and fossil-CCS systems, bio-based production shows the worst planetary footprint –\nclimate change and biosphere integrity transgressed ~229 and 180 times – consistent with its exclusion from\nthe reference production system (Fig. 3b).”\nIn the Supplementary File, section S12.1, line 826 to 830:\n“It is worth noting that we use ecoinvent, the “cutoff by classification” system model. In this system model,\nwaste flows are considered burden-free. This information is important since biogas production (prior cleaning\nand conditioning) is treated as a waste flow in our model; it is therefore burden-free. This also implies that all\nsources of biogas (e.g., sludge, manure) would return the same impact. Waste biogas upgrading, however, is not\nburden-free, as previously detailed.”\nComment 12: On page 16, line 503, the authors suggest that \"it is better to focus on a certification scheme\ninforming the planetary footprint of hydrogen (impact on all planetary boundaries)\". While this sounds like a\ngreat idea, simply mentioning it does not add much depth. I wonder, for example, to what extent such an\napproach is practically feasible. I believe the authors should elaborate further on how they envision the\nimplementation of such a certification scheme.\nThank you for your comment. We agree that this statement could have been discussed more effectively. This\nparticular topic is part of ongoing work. However, we elaborate more on this aspect in Revision 11.\nRevision 11. improved discussion on hydrogen green certification.\nIn the discussion section from line 359 to 364:\n“The concept of “green” hydrogen only indicates feedstock origin, but provides no information on its effective\nplanetary footprint14,16,52. This has direct implications for certification schemes9,52, which currently assume no\nemission intensity for green hydrogen9. Future work could shift towards planetary footprint certification. A\ntarget state of the Earth system set in the planetary boundaries interaction model10 can be used to this extent.”\nComment 13: On page 17, line 521, the authors suggest that their approach could help strengthen the\nintegration between LCA and IAM. While this has been a long-standing topic in the literature, practical progress\n22\nhas been limited. Other studies have already developed optimization models on top of IAM scenarios (e.g., the\nPULPO framework cited by the authors). Therefore, it remains unclear how their approach advances this\nintegration. Similar to the certification scheme, the authors should provide further clarification on their specific\ncontribution.\nThank you for your comment. In light of the recent work by de Bortoli et al.8 and the inherent mismatch\nbetween LCA and IAM scenarios, we have decided to withdraw this statement. Our approach can be expanded\nto model energy systems49. The approach would still require exogenous parameters, such as demand for energy\nand population growth. However, more importantly, by utilising future projections from climate models5, it is\npossible to impose constraints on the emission of greenhouse gases (elementary flows) from a life cycle\nperspective. While the system would focus on a return within the climate change planetary boundary, from a life\ncycle perspective, the optimised system would provide valuable projections for other impact categories\n(freshwater use, land-use, biogeochemical flow, etc) which are often not captured by IAM scenarios. This is part\nof our ongoing work. We revised the discussion of that topic in Revision 12.\nRevision 12. Discussion on future work based on the technology choice model and climate modules.\nIn the discussion section, from line 384 to 386:\n“Future research should refine interaction models with dynamic nonlinear responses43,45, updated values from\nthe AR632, and extend the optimisation to couple energy system models49 with simplified climate modules8.”\nComment 14: I believe the authors could better emphasize the novel findings in the abstract. For instance,\non page 9, line 275, they state: “The addition of interaction parameters to evaluate the absolute sustainability of\nhydrogen shows a strong amplification of impact.” This appears to be a key novel finding of the study, yet it is\nnot mentioned in the abstract.\nThank you for your suggestion, this has been implemented in Revision 13.\nRevision 13. Reshape the summary to emphasis novel findings.\nIn the Summary from line 22 to 23:\n“We further show that Earth-system process interactions amplify hydrogen’s planetary footprint.”\nComment 15: Page 3, line 125, please note that the hydrogen production scenarios assessed in Wei et al. are\nbased on IEA scenarios.\nof REMIND scenarios, i.e., NDC, SSP2-PkBudg1150, SSP1-PkBudg500, with the technology shares of IEA scenarios,\ni.e., STEPS, APS, NZE. After reading Wei et al.16 again, we agree that the authors adapted the REMIND scenarios\nto the IEA scenarios. However, this statement was ultimately removed from the revised manuscript to limit the\nmanuscript length (Revision 1).\nComment 16: At several places across the manuscript, there is a mix between carbon dioxide removal (CDR, removal of CO2 from the atmosphere) and carbon capture and storage. For example, at page 6 line 201, biomass with carbon capture and storage would be a more appropriate term than “biomass with a carbon removal”.\nThank you for your remark. This was implemented in Revision 1.\nComment 17: Page 9, line 287: I don’t believe “the correct distribution of processes” is the right term here. I\nsuggest removing “correct”.\nThank you, this was corrected in Revision 1.\nComment 18: Page 18, line 572: Please revise the text since the functional unit is something that you define\nwithin your LCA, and not something that is applied to.\nThank you, this was corrected in Revision 1.\nComment 19: Page 18, line 572: “REMIND SSP1-PkBudg500 was selected as the main model”. Clarify that\nREMIND is the model and SSP1-PkBudg500 is the scenario.\nThank you for this, we implemented this in Revision 1.\nComment 20: Page 18, line 579: “Such an approach aims to define a reference model that quantifies the role\nof hydrogen irrespective of a scenario”. I don’t see the point of this, since the whole analysis is based on the\nSSP1-PkBudg500 scenario (plus SSP2 and 5 as sensitivity analysis). I don’t think your analysis study hydrogen\nirrespective of the scenario (perhaps the allocated SOS yes).\nThank you for your constructive feedback. Since we removed the specific focus on IAM scenarios in Revision\n2, our new analysis is now independent of specific IAM scenarios. The demand and technology constraints are\nbased on a range of scenarios from the IPCC AR6 rather than a specific one (n = 32). For prospective LCA data,\nwe use the set of REMIND SSPx-PkBudg500 scenarios (n=3) from which we compute the minimum, maximum and\nmedian values across the range of scenarios. We also validate the ensemble against the trajectory set by the\nIPCC AR6 report, ensuring it returns within the CO2 concentration boundary by 2150.\nComment 21: This article investigates whether current plans to expand hydrogen production are compatible\nwith operating within the Earth’s safe operating space. The work also develops an optimization model to\ninvestigate whether an optimized portfolio of hydrogen production technologies could safely operate within the\necological capacity of the planet. The topic is highly relevant and timely. Below I provide some comments that\nrequire some attention before proceeding any further:\nWe appreciate your interest in our work and thank you for the careful review you provided. We provide\ndetailed responses to the comments below.\nComment 22: Previous works already investigated the planetary boundaries impact of hydrogen production,\nyet the authors critically assessed them concluding that further refinements were still necessary to draw more\nmeaningful conclusions. However, the final conclusion they present in this work, namely that future hydrogen\nproduction is unsustainable, remains somehow very similar. I still see the value of the analysis, but it needs to be\nmore clearly stated in the abstract and conclusions. Moreover, the current study is also affected by a range of\nmethodological choices and uncertainties, some of which were not analyzed with enough depth, as discussed\nnext.\nThank you for your comment. Indeed, while previous work14,16,17,53–55,55,56 took a different perspective, our\noverall assessment leads to the same conclusion. We therefore implement Revision 14 for clarity.\nRevision 14: clarification on previous work conclusion.\nIn the Summary, from line 20 to 22:\n“From 2025 to 2050, even under the most favourable scenarios, global hydrogen production is unsustainable,\nconfirming previous findings.”\nIn the discussion, from line 280 to 283:\n“Our findings confirm this mismatch, showing that environmental space for global hydrogen production\nshrinks faster than eco-efficiencies improve, rendering the production system unsustainable. Different, yet\nsimilar conclusions have been drawn in other studies10,18–20,42,49,50.”\nComment 23: Different sharing principles should be explored to study the robustness of the results\ndepending on the downscaling approach.\nwhich there is currently no scientific consensus26,57. We understand very well the need to test our results against\nother sharing principles for robustness. However, data availability remains a limiting factor for the\noperationalisation and inclusion of more downscaling principles57. We detail the challenges we encountered\nwhen exploring more scenarios next.\nFirst, the prospective allocation should be consistent with a return within the climate change boundary (as\nwe motivate from line 50 to 59). Our downscaling approach is therefore limited to the data available in IAM\nscenarios.\nThe second challenge relates to the use of common metrics not only across/within sectors, but also across\nIAM scenarios. This is because several future pathways are possible, and it is a common practice to define an\nuncertainty range using an ensemble of IAM scenarios6,58. This is also recommended by the recent review from\nde Bortoli et al.8 on the use of IAM scenarios. In our work, at global level, it is based on gross CO2 emission\n( ) as it is readily available in all IAM models. In contrast, economic metrics such as gross domestic\n−1\nproduct and final consumption expenditure are not harmonised across IAM scenarios, and we could not\n𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀2 𝑦𝑦𝑟𝑟\nconsistently retrieve or derive principles based on these variables. At a global level, for the energy supply, the\nsecondary energy ( ) is the only metric that could be retrieved from all IAM models and is available for all\n−1\nconversion processes. This highlights the severely constrained margin for exploring sharing principles while\n𝐸𝐸𝐸𝐸 𝑦𝑦𝑟𝑟\nremaining within the bounds of IAM scenarios.\nGiven these relatively small margins, several downscaling principles were rejected as they would have either\nclashed with the global and prospective nature of the assessment or deviated from the scenario objective\n(Supplementary Table 4). It is important to note that introducing exogenous data would create more\nuncertainties (i.e., break scenario consistency).\nOur downscaling approach aims to capture the utility of hydrogen in future energy supply systems. For this,\nwe established clear goals (line 589 to 591). The downscaling approach has to (i) be at global level, (ii) capture\nthe necessary decrease in emissions from the scenario, (iii) capture the growing/needed utility of hydrogen in\nthe future global energy supply system9,59.\nSome REMIND-based scenarios include the expected gross emissions for hydrogen supply. It is therefore\npossible to calculate the ratio of hydrogen supply emissions over the total emissions in these scenarios. However,\nwe did not adopt this approach for three reasons. First, the set of scenarios that includes this output variable is\nsmall and inconsistent with the scenario variables from the IPCC AR6 scenarios. Second, this would introduce a\nsignificant bias towards REMIND models. Third, these scenarios anticipate that hydrogen will be nearly emission-\nfree by 2050. Using an emission-based principle would imply no environmental space for hydrogen by 2040-\n2050, thereby increasing transgression levels (Figure 1).\nWe therefore believe that we have investigated all possible downscaling approaches applicable to our work\nand given the IPCC AR6 data. In Revision 15, we clarify all uncertainties related to our downscaling approach.\nthis work. (a) Emission-based allocation principle. (b) Emission and costs-based allocation. (c) utilitarian principle.\nRevision 15. Discussion on the allocation approach\nIn the Discussion, from line 407 to 421:\n“Our downscaling approach is climate-oriented. We applied a utilitarian allocation based on the global climate\ntarget (AR6 ensemble), without distinguishing between global and regional control variables such as CO₂\nconcentration and freshwater use. Yet, nine interdependent Earth-system processes define the global SOS, and\nallocating based on climate only can distort requirements for other boundaries. For example, agriculture would\nrequire proportionally more space in biogeochemical flows than other sectors, but a climate-based allocation\ncannot capture this distinction. This creates uncertainty for boundaries with strong regional or sectoral drivers,\nincluding freshwater use, land system change and biogeochemical flows.\nMore comprehensive approaches are needed. Allocating environmental space per planetary boundary would\nprovide a fairer basis, but there is no consensus on appropriate methods26,57. Several sharing principles could in\ntheory, apply60, yet their operationality remains limited, particularly in prospective studies where data\navailability constrains implementation (Supplementary Information S5.2). Because of these limitations, we\nlimited our assessment to a utilitarian downscaling approach. Thus, uncertainty arises from this methodological\nchoice and underscores the broader need to define fair and just allocation principles61.”\nbackground, foreground, characterization factors and absolute thresholds. Please describe all of them in detail\nand explain how they were accounted for, which remains unclear to me (it seems some were considered and\nothers not, and an exhaustive discussion of this topic is missing).\nThank you for your important comment. We agree that in the initial version of the manuscript, some\nuncertainties were not comprehensively discussed nor clearly stated. We therefore implemented Revision 16.\nWe briefly describe how we addressed uncertainties below.\nRegarding the uncertainties in the background system, we purposefully excluded this uncertainty as it would\ntechnically create more uncertainty62. Indeed, the traditional approach is to perform random sampling of\ncoefficients in the LCA database using Montecarlo analysis and assuming their independence. However, these\ncoefficients are strongly correlated and cannot be sampled randomly without creating large mass and energy\nimbalance62. We note a recent paper on the topic from Kim et al.63, but their approach is hardly operational\nwith our optimisation framework. We therefore excluded background system uncertainties for this reason. For\nthe characterisation part, we relied on the characterisation factors from Ryberg et al.24 and Bachmann et al.26,\nwho used biosphere integrity characterisation factors from Galan Martin et al.25. None of these studies has\nprovided uncertainties for characterisation factors, and we were unable to determine these uncertainties.\nWe include uncertainties related to the environmental space for global hydrogen production across our AR6-\nderived scenario ensemble. More scenarios were included (n=32, instead of n=20 in the initial version) to\nincrease diversity in IAM models and mitigate bias towards REMIND scenarios. As discussed in comment 23, we\nwere unable to derive more downscaling principles from this scenario ensemble. This is another source of\nuncertainty, which we discuss in Revision 15.\nAlso, premise scenarios are not part of the AR6 dataset (as pointed out in Comment 26). We therefore had to\nvalidate these against the climate objective we defined (1.9W m-2 forcing by 2100). This aspect is discussed in.\nFrom this validation, we were able to use only 3 scenarios (REMIND SSPx-PkBudg500 scenarios). There is a\nsignificant bias towards REMIND models, which raises some uncertainties in this aspect.\nBeyond the above, the interaction model is still in its early stages of development, and several interaction\nparameters lack uncertainties. The authors also omitted some interactions due to a lack of data or understanding\nof interaction mechanisms10. We were unable to use these uncertainties confidently and therefore excluded\nthem from our analysis.\nRevision 16. Uncertainty clarification\nIn the Discussion from line 375 to 378, we mention the high uncertainty of the interaction model:\n28\n“Our framework enables life cycle optimisation guided by these interactions, thereby identifying\nenvironmental burden shifts and feedback loops between Earth-system processes. The approach provides new\ninsights into amplification and stabilisation effects but carry significant uncertainties.”\nFrom line 400 to 407 in the discussion we discuss the uncertainties from prospective life cycle assessment:\n“Additional uncertainties arise from LCA data and characterisation factors. We account for variation across\nprospective scenarios but exclude background system uncertainties due to potential mass and energy\nimbalances62. While Kim et al.63 partially addressed this issue, their approach is hardly compatible with our\noptimisation model. Foreground uncertainties were also excluded because process parameters could not be\nretrieved. Similarly, uncertainties in planetary boundary characterisation factors were not considered, as\nuncertainty parameters are not available in the literature24–26. Future work should therefore focus on a regional\nassessment, while integrating background, foreground and characterisation uncertainties to enhance\nrobustness.”\nIn the methods, from line 451 to 463, we explain the uncertainties considered:\n“We obtain prospective life cycle assessment matrices using the premise software19 and aggregated all\nmeaningful inventories to decrease computation times. In a subsequent step, we reduced the number of\nmatrices by creating sampling matrices using Triangular distributions with the Julia package Distributions.jl64.\nMore specifically, we opted for triangular distributions to remain within the bounds of the selected scenarios.\nThe matrices obtained would contain the minimums, maximums, and modes, thereby reducing the number of\nmatrices that need to be manipulated. The modes were defined as the median of values across scenarios. The\napproach is described in Supplementary Methods S7. However, we do not account for uncertainties in the\nbackground system following the conclusions from Marsh et al.62. Similarly, we do not account for uncertainties\nin the foreground system, as process parameters could not be retrieved along with matrices. We also do not\naccount for uncertainties from characterisation, as we could not obtain uncertainty parameters from previous\nwork24–26. Thus, the uncertainty range reflects values across selected future scenarios only.”\nLastly, we provide a clear table that summarises all these uncertainties in Supplementary Table 13.\nSupplementary Table 13| Summary of uncertainties considered in this work. The triangular distribution (Tr(min, max,\nmode)) is systematically used to represent uncertainty distribution across scenarios.\nUncertainty types\nScenario ensemble Life cycle assessment\nTechnosphere Tr(Min, Max, Median), (n=3) Not considered Not considered\nBiosphere Tr(Min, Max, Median), (n=3) Not considered Not considered\nCharacterisation Not applicable Not considered\nTechnology\nTr(65th, 95th , 95th), (n=32) Not applicable\nConstraints\nEnvironmental\nspace normalised\nwith hydrogen Tr(Min, Max, Median), (n=32) Not applicable\nproduction\nvolumes\nInteraction matrix Not considered Not applicable\nComment 25: The authors use the TCM model to perform the calculations. They mention that a data\naggregation step was carried out when building the LP. Please provide more details, as it is very hard to\nunderstand how the model works. This aggregation inevitably leads to some information loss, which should be\ndiscussed.\nThank you for your remark. We agree and reshape our method section to be more comprehensive as part of\nRevision 1. Additionally, we recognise that without a simple example, the model appears to be quite challenging\nto implement. In reality, calculations are quite simple. The main difficulty relates more to pre-processing, tracking\nand organising data for interpreting results. To address this, we provide a Jupyter notebook “5_TCM.ipynb” at\nthe GitHub repository along with a tutorial video\n(Revision 17).\na natural step in the computation of inventories and impact scores in LCA65. All background inventories can be\ntraced back using the scaling factor found through optimisation. For example, let be the scaling factor for\nproton exchange membrane (PEM) electrolysis with a reference flow of 1kgH2. For t\n𝑠𝑠\nh𝑝𝑝e𝑒𝑒 𝑝𝑝processes involved, there\nis a direct proportionality between the scaling factor and the reference flow ( 1 : 1kgH2). From there, one\ncan set the reference flow of a new inventory calculation to find the u\n𝑠𝑠\nn𝑝𝑝a𝑒𝑒g𝑝𝑝gregated inventory for PEM\nelectroly\n𝑠𝑠\ns𝑝𝑝is𝑒𝑒.𝑝𝑝 The same can be done iteratively to backtrack all inventories. The only requirement is access to the\noriginal database (e.g., Ecoinvent). In that sense, the data loss would encompass all background inventories of\nprocesses that ultimately do not contribute to the overall inventory calculation. Given this information, the\naggregation we perform can be seen as a preliminary step to simplify calculations, improve computation speed\nand allow, according to the Ecoinvent End-User Licence Agreement (EULA), to make our results sharable to some\nextent.\nLechtenberg et al. 38 highlight data loss in aggregation. It is, however, important to clarify that this relates to\ntheir approach only. The authors include all background processes that are aggregated in our approach. Their\napproach assumes feedback loops between the foreground and background systems. For this, they require a\nlarger extent of the database. Feedback loops would, for example, consider the use of low emission hydrogen\n(Foreground), in processes upstream of the supply chain of its supply chain (Background). This would, in turn,\nlower the emission intensity of the hydrogen produced. We find this aspect novel and important, but note that\nit is beyond our scope, which ends at the production gate. This approach would also be technically challenging\nto implement at a global scale, considering the number of constraints required for the optimisation model to\nprovide realistic results. Lastly, we implemented stochasticity, which is not (to our knowledge) implemented in\nPULPO38.\nIn summary, we clarified that no data is “technically” lost in aggregation, and we therefore believe that further\ndiscussion is required. Aggregation is a natural step in building the life cycle inventory of a product system, and\nall inventories can be traced back if practitioners have access to the original dataset. We also clarified what\nLechtenberg et al.38 implied by ‘loss of data’, which, in their case, refers to background system data used in a\nvalue chain or economy-wide optimisation. Lastly, we implemented Revision 17 to provide better clarity on the\noptimisation approach.\nRevision 17. Prospective technology choice model clarification\nFor this revision, we have increased the level of detail in the Methods and replication steps in the\nSupplementary File. First, we added dimensions to all tensors (vectors, matrices, and higher ranks). For instance,\nthe interaction matrix is now written , where the dimensions are clearly shown. We have also made\n𝑘𝑘×𝑧𝑧\nthe optimisation equations more intuitive as part of Revision 1, (in the methods from line 659 to 664).\n𝚪𝚪∈ ℝ\nMore importantly, for this assessment, we relied on an in-house software for data processing that will be part\nof the ongoing work we are carrying out. This code is not central to this analysis, as it primarily relates to data\n31\nmanipulation, which experienced practitioners of absolute environmental sustainability should be able to code.\nAdditionally, this software is not optimal and can be quite unintuitive for external users. For these reasons, it is\nnot provided. However, the optimisation structure code is provided in the GitHub Repository:\nhttps://github.com/Michael-ljn/pathways-H2-prod-within-PBs/blob/1st-\nreview/code/Main/modules/optimisation.jl\nWe understand this can be a strong limitation for replication. For this reason, we provide a tutorial video with\ncode to replicate our work using ActivityBrowser and JuMP.jl. This will be publicly available in the GitHub\nrepository listed above. We believe this will address any replication concerns raised in Comment 7 and Comment\n25.\nIn addition to this, we provide in Supplementary Method S8, a step-by-step implementation including data\ngeneration using premise19:\nand (b) its rectangularised form. The rectangulation process merges identical reference flows onto the same row to obtain\nmore processes than reference flows. This can simply be done by picking out rows with identical reference flows (a matrix is\nobtained) and summing these rows together (a vector is obtained). The summed reference flows are added back to the\ntechnosphere matrix via concatenation. The process is done iteratively for every choice made in the inventory.\nSupplementary Fig. 12| Rectangulation approach of prospective technosphere matrices\nWe also detail how hydrogen production volumes were integrated (supplementary Method S8.5.1)\n“The demand for hydrogen can theoretically be set in the demand vector . However, we instead set the\n𝑖𝑖\nreference flow to . This allows not only for the quicker interpretation of the scaling vector\n𝐟𝐟∈ℝ\n−1\nbut also for faste𝑓𝑓rℑ 𝐻𝐻c2om=p1u t𝑘𝑘a𝑘𝑘ti𝐻𝐻on2 𝑦𝑦t𝑟𝑟imes and fewer numerical instabilities. Instead, to represent production\nvolumes in the optimisation model, we normalise the environmental space vector with the\n𝑧𝑧×𝑢𝑢×𝑡𝑡\n𝚚𝚚∈ℝ\n34\nproduction volumes. Consequently, equation 13 will force the optimisation to scale processes to deliver\nand will be composed of values ranging from 0 to 1. “\n−1\n1 𝑘𝑘𝑘𝑘𝐻𝐻2 𝑦𝑦𝑟𝑟 𝐀𝐀𝔰𝔰H2\n∗\n𝐀𝐀 𝐀𝐀=𝐟𝐟 (13)\nLastly, the structure of the optimisation code is illustrated in Supplementary Fig.16.\nSupplementary Fig. 16| Structure of the prospective technology choice program implemented in Julia66.\npremise is fully consistent with the IAM scenario for which the calculations were done. Moreover, I wonder why\nthe authors did not explore other scenarios, at least the ones available in premise.\nThank you for your important remark. We believe that Revision 17, along with the clarifying improvements in\nRevision 1 address this concern.\nRegarding the scenario ensemble consistency, we agree that this aspect is quite important (as raised in\nComment 25) and requires some sort of validation. For validation, we used the planetary boundaries control\nvariables for climate change (CO2 concentration and radiative forcing). For this, we modelled the impulse\nresponse function for CO2 and used the emissions of each scenario in the ensembles. We compare these\nscenarios against the required trajectory from the IPCC AR64. We adopted all scenarios below 2.25 W m-2 by\n2100. We found only four acceptable scenarios from the premise ensemble (REMIND SSPx-PkBudg500 and\nIMAGE SSP2-1.19). During our calculations, we needed to exclude IMAGE SSP2-1.19 from the ensemble due to a\nmatrix dimension mismatch that we were unable to resolve. This validation also allowed us to flag the TIAM-\nUCLSSP2-1.19 as inconsistent. Indeed, the scenario does not lead to 1.19-2.25 W m-2 forcing by 2100.\nPremise19 also allows the use of custom scenarios. However, we were unable to use the IPCC AR6 scenarios\nin premise19 for two reasons. First, computing LCA matrices for 32 scenarios over 6 years would have required\nmanipulating 96GB of data, which is well beyond our computing resources. Second, premise19 relies on some\nspecific scenario output names from scenarios. In the IPCC AR6 dataset, these variable names differ from the\nones defined in premise19. It was therefore not possible to compute model LCA matrices using IPCC AR6\nscenarios.\nIn summary, we have clarified our premise-LP integration approach in Revision 17, and provided a guide to\nplicate the work. We have also implemented an approach to validate the consistency of scenario ensembles in\nRevision 18.\nRevision 18. Scenario ensembles validation\nScenario ensemble validation is represented in Supplementary Fig. 17, available in Supplementary Results\nS11.\nagainst planetary boundaries for climate change and validated with IPCC AR6 data4. Simulated with FAIR33 for\na wide range of SSP1-1.9 (n=66) for the 1750 to 2050 period. (a) the total radiative forcing and, (b) atmospheric\nconcentration in carbon dioxide. Uncertainties for the SSP1-2.6 variables omitted in this figure.\nComment 27: The authors let the model optimize the amount of DAC required. I wonder if this leads to an\ninconsistency in the model, as the premise scenario in the background assumes already a given DAC capacity\nthat might differ from the optimal capacity found by the LP model. If so, how would this affect the results?\nThank you for your remark. We understand the concern about this aspect. In this new version of our work,\nwe have removed the reliance on specific scenarios for robustness (Revision 2).\nHowever, it is important to clarify that we assess hydrogen production in isolation (Comment 7). We use IAM\nscenarios to extract future values contextualised with our climate objective. From the AR6-derived scenario\nensemble, we extract production volumes and technology constraints and derive an environmental space for\nglobal hydrogen production. From the premise ensemble, we compute prospective LCA matrices required for\nour assessment. Our aim is to assess the quantitative planetary footprint of global hydrogen production\ncontextualised with these future scenarios. We therefore do not address any potential scenario consistency\nissues that may arise from implementing an additional DAC capacity or a higher energy requirement. Instead,\nour work shows the discrepancy between what IAM scenarios project and what prospective life cycle assessment\nreveals.\nconnected to future energy systems design. The work would benefit from a more exhaustive description of the\nmethods, perhaps complemented by additional calculations to further evaluate the effect of uncertainties. This\nwould enhance the robustness and relevance of the findings.\nWe appreciate your interest in our work and thank you for taking the time to review it. Your constructive\ncomments directly contributed to the inclusion of more stochasticity for robustness and the validation step to\nensure scenario ensembles are consistent. In addition, your comments helped refine the manuscript for more\nclarity on important aspects.\n1. IEA. Global Hydrogen Review 2025. https://www.iea.org/reports/global-hydrogen-review-2025 (2025).\n2. IEA. World Energy Outlook 2024. (2024).\n3. IRENA. World Energy Transitions Outlook 2023: 1.5°C Pathway. www.irena.org/publications (2023).\n4. Intergovernmental Panel On Climate Change (Ipcc). Climate Change 2021 – The Physical Science Basis:\nWorking Group I Contribution to the Sixth Assessment Report of the Intergovernmental Panel on Climate\nChange. (Cambridge University Press, 2023). doi:10.1017/9781009157896.\n5. Leach, N. J. et al. 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SIAM\nRev. 59, 65–98 (2017).\nplanetary boundaries\nResponse letter\nMichaël Lejeune1,2, Sami Kara1,2*, Michael Zwicky Hauschild4,5*, Sareh Shahrabifarahani1, Rahman\nDaiyan2,3*\n1Sustainability in Manufacturing and Life Cycle Engineering Research Group, School of Mechanical and\nManufacturing Engineering, The University of New South Wales, Sydney 2052, Australia.\n2Australian Research Council, Training Centre for the Global Hydrogen Economy, Sydney 2052, Australia.\n3School of Minerals and Energy Engineering, The University of New South Wales, Sydney 2052, Australia.\n4Section for Quantitative Sustainability Assessment (QSA), Department of Environmental and Resource\nEngineering, Technical University of Denmark, Kgs, Lyngby, Denmark.\n5Centre for Absolute Sustainability, Technical University of Denmark, Kgs, Lyngby, Denmark.\nDear editor and reviewers of the manuscript “Pathways to global hydrogen production within\nplanetary boundaries” with ID: NCOMMS-25-07779A. We appreciate your constructive comments\nand thank you for taking the time to review our work.\nWe confirm that we have addressed each comment in the revised manuscript as outlined in the\nsections below. Please find the reviewers’ comments in blue, our response in black, and the changes\nmade to address the comments in red, with key revisions highlighted in grey. Also, for clarity, we\nhighlight references to revisions or to line number in blue.\nReviewer 2 - Comment 1 to 8\nComment 1: The authors have made commendable efforts to improve the clarity of the\nmethodology and findings, and to address reviewers’ comments. The revised version is significantly\nclearer and more accessible. However, after reviewing this improved manuscript, I have additional\ntechnical concerns.\nThank you for your interest in our work and for the time invested to review it. We address your\nadditional concerns below.\nComment 2: At a more fundamental level, I have serious reservations about the suitability of\nprospective LCA combined with planetary boundaries to determine whether a product system can\nbe considered “absolutely” sustainable in the future (in contrast to the more common application to\ndetermine whether product system A performs relatively better than B). An inherent limitation of\nprospective LCAs is that the results and conclusions are only valid under the specific conditions\nrepresented by the background and foreground LCIs. Many background processes remain\nunchanged even when using premise, meaning that by 2050 they may still reflect current (or in many\ncases outdated) practices. This is particularly important given that the authors identify embodied\nemissions from the manufacturing of renewable electricity technologies as a key driver of hydrogen’s\nunsustainability. In this context, several upstream processes such as heat sources in industrial\nprocesses are often not updated in premise. Similarly, emissions of nitrogen and phosphorus from\ncopper mining and the management of sulfidic tailings are highly uncertain and remain largely\nunchanged. While these assumptions may be acceptable for comparative LCAs of product systems, I\nbelieve they pose significant limitations when applied to absolute sustainability. These assumptions\nmay be enough for addressing the research question “How do different hydrogen production\npathways perform under a given set of predefined conditions?” Such an analysis provides important\ninsights into relative performances across alternatives and future hotspots that deserve attention\n(e.g., that sulfidic tailings from copper mining need to be managed in a better way). In contrast, I find\nit problematic to conclude that a product system is “absolutely” unsustainable by 2025 due to\nimpacts associated with industrial practices representative of conditions from 25-30 years back. The\nauthors acknowledge the limitations of potentially unrepresentative LCIs. However, I believe this\nraises a more fundamental question about what “absolute unsustainable” truly represents in the\ncontext of prospective LCAs (and how fair it is maybe).\nunderstand your concern as we investigated this aspect and tried to find potential solutions to\nmitigate the biogeochemical emissions of renewables. Ultimately, we were unable to address it due\nto a lack of data (line 332) – we do not know how these emissions would be tackled in the future of\nmining.\nAs correctly mentioned, premise has inherent limitations regarding the processes that can be\n“updated”. When basing the “update” on IAM scenarios, it is only possible to update processes if the\ncorresponding data is available in scenarios used. Emissions resulting from copper mining or other\nactivities with relatively low climate change impacts are generally not represented in IAM scenarios.\nThis explains their omission from the “update”. From this basis, one can easily conclude that this\nissue entirely stems from the prospective LCA part of our analysis. Thus, it would naturally be a\nlimitation in all prospective environmental sustainability assessments, both absolute and relative. If\nthese limitations are acceptable in pLCA, they should also be acceptable in AESA.\nTo be more specific, in a relative assessment, when answering the following research question:\n“How do different hydrogen production pathways perform under a given set of predefined\nconditions?”, the predefined conditions are limited to the scenario data. Non-updated processes\ncould also shape the conclusions (and fairness). For example, two processes (say, A and B), could rely\non different upstream processes while providing the same function. If some upstream processes of A\nare omitted in the premise update, B’s relative environmental performance could be found more eco-\nefficient and also shape conclusions. It is thus a problem inherent to IAM-based pLCA, and not\nspecifically prospective AESA. To the best of our knowledge, it is very difficult (if not impossible) to\nupdate the entire background system data prospectively. This would require knowing the future\ntechnical performance of thousands of processes. Despite these limitations, premise is an accepted\nsoftware because it can improve the accuracy of prospective impact scores for given environmental\nscenarios, and for studies into the future. The alternative to using prospective approaches like\npremise would be to assume that the environmental performance of all technologies will remain at\nits current state into the future.\nThat said, we understand your concern relates to the sharp sustainability conclusion we derive\nfrom our results given this inherent limitation. We therefore implement revision 1 to put emphasis\non the likelihood of sustainability for hydrogen production and that our conclusions are solely based\non our model and known limitations.\nRevision 1. Added limitations to sustainability results\nAt line 21, 72,and 283, where we report our key takeaways, we added “likely unsustainable” to\nsoften the sharp sustainability statement previously made. We use “likely” to highlight what is\nsupported by our results while leaving enough room for uncertainties.\nHowever, our results depend on technologies that remain unproven at scale1–3 and do not account\nfor potential future improvements or breakthroughs in the upstream hydrogen value chain.\nAt line 347, we added:\nHowever, this is subject to future technological options that may not be accurately represented in\nthis work.\nIn addition, from line 417 to 428, we expand the discussion on that particular topic:\nUncertainty also arises from the representation of future technologies in prospective background\nsystems. The Premise software can only reflect future efficiency improvements for processes present\nin future scenarios. It thereby excludes improvement processes that are not represented in these\nscenarios and ignores potential technological breakthroughs. For instance, global hydrogen\nproduction could approach sustainability if future processes substantially reduce nitrogen and\nphosphorus emissions associated with renewable electricity technologies. Similarly, this assessment\ndoes not account for emerging capillary-feed4 electrolysis designs or advances in abated fossil\npathways, including electrified reformer furnaces5 or improved pre- and post-combustion carbon\ncapture. The omission of such developments introduces additional uncertainty for our results.\nNevertheless, updating background system data using Premise remains a state-of-the-art approach\nfor improving the accuracy of future life-cycle impacts under a given climate target.\nComment 3: I also have a question about carbon accounting in the case of biogas for biomethane\nproduction followed by reforming to hydrogen coupled with CCS. The authors explain that they do\nnot account for biogenic carbon. However, biomethane with CCS is potentially a negative emissions\ntechnology. Yet, since the authors used the ecoinvent cut-off system model, the CO2 uptake during\nbiomass growth is disconnected from the waste management process (i.e., it is accounted for in the\ncorresponding agricultural activity). How did the authors ensure a correct carbon balance? I believe\nthis is important since the results suggest that “bio-based production shows the worst planetary 223\nfootprint – climate change and biosphere integrity transgressed ~229 and 180 times” (line 222).\nThank you for your comment on this important aspect that deserves more clarifications. We\nprovide the rationale for this important assumption below.\nFirst, as correctly pointed out in the comment, under the cut-off system model, the uptake and\nemissions are reported in the original activity. Our process for hydrogen production from\nbiomethane is based on Antonini et al.6. While the authors expanded their system boundaries to add\nthe uptake, we refrain from adopting such an approach. For secondary biomass e.g., waste organic\nfood, sewage sludge, manure, it is more relevant to look at the avoided/created emissions owing to\n4\nrecycling secondary biomass7. This is to avoid the misleading conclusion that secondary biomass\nwould have had the same impact if not used. It could be true when looking at long time horizons, but\nnot on an annual basis as imposed by planetary boundaries (Figure.1). In the case of fuel production,\ncarbon emissions necessarily occur at a much faster rate than the uptake. To sustain such a\nproduction, more biomass need to be used continuously.\nSecond, for the planetary boundary for climate impacts, peak environmental pressures matter\nmore in our case than a net carbon balance over 100 years. Unlike traditional LCA that focuses on the\nimpact created during a product system’s life cycle, in planetary boundaries based LCA, the\nassessment is on annual pressure basis. As pointed out by Ryberg et al.8, time integrated impacts\nmethods are not suitable for the control variables of planetary boundaries. If we apply sufficient\npressure on Earth system processes in a given year, the environmental response may lead to\nirreversible changes that cannot be captured by time integrated approaches. For this reason,\nplanetary boundaries-based characterisation methods rely on continuous annual emission rates\nrather than a pulse emission decay over 100 years8,9.\nThat said if we emit a large amount of biogenic carbon today, until it is recaptured by carbon sinks,\nit has the same effect on the Earth system as fossil carbon emissions. Current carbon accounting\napproaches based on GWP100, overlook this aspect inherent to planetary boundaries. While we\nagree that biogenic carbon accounting is an important aspect, due to the lack of proper accounting\napproaches with regards to planetary boundaries and the implications of our results, we believe it is\nmore conservative not to discount biogenic carbon.\nTo exemplify this aspect further, we can compare the global temperature response to 1GtC\nemitted as a pulse during hydrogen production or spread over a 30-year period in landfill (Figure.1).\nHere we chose landfill as it is the most common disposal of waste that will not be recycled in any way.\nLandfills provide anaerobic conditions. It could take 5 to 7 years to reach peak emissions and decades\nfor carbon ultimately going to the atmosphere to be completely released10. The 30-years, evenly\ndistributed emission period can be seen as conservative. To be more conservative, we can also\nassume that all the carbon from landfill is emitted to the air, while in reality, some would end up in\nsoils. For a fair comparison with hydrogen production, we can reasonably assume that landfills are\nproperly managed with all methane emissions being flared. As can be seen in Figure.1, at long time\nhorizons (≥100years), both responses would be similar which confirms current carbon accounting\napproaches used in LCA. However, in the short term for nearly 25 years, there is a large positive\ndifference, more relevant for an assessment on planetary boundaries, that is overlooked when\nlooking at long time horizons.\nThis short illustrative example is not perfect and has many flaws, but it clearly shows that current\ncarbon accounting approaches from traditional life cycle assessment are not directly applicable for\nan assessment on planetary boundaries. Also, there is a potential asymmetry between carbon\n5\nemissions and uptake that deserves further attention11. These aspects require further\nmethodological work that we consider out of scope.\nFigure 1. Comparison of global temperature response to 1GtC from pulse and spread emission profiles. On a\n100 years horizon, both approach would lead to the same results. On a yearly basis, current carbon accounting\napproaches fail to represent the pressure on Earth system processes. Model was sourced from Ricke and\nCaldeira12.\nOverall, our assumption to treat biogenic carbon the same as fossil carbon is conservative. Future\nwork is needed to investigate this aspect more thoroughly. Despite this assumption, our results\nremain consistent with most projections from the IRENA13 or IEA14–16. To clarify this aspect more in\ndetail in the manuscript, we implement Revision 2.\nRevision 2. Clarified biogenic carbon assumption\nAt line 279:\nLastly, we conservatively do not discount CO emissions from biogenic carbon for bio-based\n2\nproduction to avoid potentially misleading conclusions in relation to planetary boundaries\n(Supplementary Note 4).\nFrom line 356 to 363:\nThe temporal mismatch between carbon uptake during cultivation and immediate release during\nproduction could result in a cumulative positive carbon balance17. In addition, planetary boundaries\nhave an assessment on an annual basis while current biogenic carbon accounting approaches focus\n6\non a time horizon (e.g., 100 years). A sufficiently high environmental pressure in a given year could\ntrigger irreversible tipping points during the biomass rotation period. For these reasons, and to\navoid potentially misleading conclusions we conservatively do not discount biogenic carbon.\nFurther research is required on this particular topic.\nComment 4: I find the use of IAM scenario outputs in the revised version somewhat confusing. The\nauthors state that they align with recommendations from a recently published Bortoli et al paper by\nusing an ensemble of 32 IAM scenarios to define future global hydrogen production volumes and the\ncorresponding allocated SOS. However, for the prospective LCA modelling, they rely on 3 IAM\nscenarios provided within premise. This is acknowledged at line 495. Although both sets of scenarios\nare consistent in terms of their climate targets (i.e., achieving a radiative forcing of 1.9–2.25 W/m²),\nthis approach introduces a substantial inconsistency in terms of hydrogen demand and sectoral\nstructure. For example, I believe the electricity mix in each IAM scenario is influenced by the level of\nhydrogen demand assumed in that scenario. Therefore, using the range of hydrogen demand from\nthe 32-scenario ensemble may not be representative of the sectoral dynamics embedded in the 3\npremise scenarios.\nThank you for your comment on this interesting point. We understand the concern, however, as\npreviously said, we do not assess the embedded sectoral dynamics of each scenario. If a scenario\nmeets our climate target, this implies that it has an underlying sectoral configuration that enables it.\nThe heterogeneity of sectoral configurations is also not a substantial inconsistency, quite the\nopposite, it provides robustness to our outcomes. The point for comparison across scenarios is not\n“how similar should be their sectoral configurations”, but rather what trends appear across scenarios\nwhen they are built to meet the climate target. These trends include hydrogen production volumes,\nelectrical mix configurations, and in the case of premise, expected prospective impact scores.\nTo be more specific, all 32 scenarios in our AR6-based ensemble have different sectoral\nconfigurations, with different electrical mixes and different hydrogen production volumes. The\ncommon point between all these scenarios is their sectoral configurations aligned for the fulfillment\nof the climate target. The approach is similar to that of the IPCC AR618,19 where scenarios are classified\nby categories – e.g., C1 for scenarios limiting global warming to 1.5°C, C2 for 2°C. From each category,\na need for hydrogen, electricity generation sources or carbon management technologies be\nestablished quantitatively – a good example for hydrogen is shown in figure Fig. 4b in Johnson et al.3.\nSimilarly, when using Premise, prospective and quantitative impact scores are derived from\nunderlying sectoral configurations designed to meet the climate target. Ideally, the entire\n7\nassessment would be based on a single scenario ensemble. However, technical limitations (i.e.,\ndimension mismatch) restricted the Premise ensemble to the REMIND PkBudg500 scenarios (n=3).\nDespite the small premise sample size, Supplementary Fig. 17 shows that the uncertainty range of\nthe Premise ensemble adequately covers that of the broader AR6 ensemble by 2100. Therefore,\nsampling impact scores from this range in our Monte Carlo analysis should sufficiently represent the\nscores derived from the AR6 ensemble.\nIn summary, even if hydrogen production volumes and sectoral dynamics from the Premise\nensemble is different from the AR6 ensemble, our uncertainty range ensures the consistency of\nimpacts scores with the climate targets.\nComment 5: The results indicate that the optimal hydrogen mix for 2025 requires approximately\n49% to come from electrolysis. However, it is well known that as of 2024, green hydrogen produced\nvia electrolysis accounts for about 1%. It would be helpful if the authors clarified in the manuscript\nthat these figures represent a hypothetical snapshot of what would be needed today to remain within\nthe allocated SOS. Moreover, this raises questions about whether the authors should have calibrated\ntheir model against empirical data to ensure consistency between modelled projections and current\nrealities.\nThank you for this remark. It is indeed well known that current hydrogen production volumes\n(fossil based) are around 100MtH yr−1 (illustrated in Fig.2b)14,15. On the other hand, in 2025, hydrogen\n2\nproduction from clean sources (not just electrolysis) is 0.8-0.95 MtH yr−1 which matches your 1%\n2\nobservation15. However, our study focuses on future clean hydrogen production only. Fig.2b shows\nthat in 2025, the median value across scenarios is 2.58 MtH yr−1 (0.001-8.7 MtH yr−1 range) which is\n2 2\napproximately consistent with the historical production reported above. Considering the\nmisalignment between our median value and current realities, we point out at line 91 an\nimplementation gap of 1.63-1.78 MtH yr−1. Also, in the IEA clean hydrogen projects database15, the\n2\nproportion of electrolysis projects is 42% in 2024 and 54% in 2025, which provides some sort of\nvalidation for the 49% found via optimisation.\nBesides that, we refrain from calibrating the model with current production volumes. This is to\nhighlight what should have been implemented and the implementation gap (line 91). Our\nMontecarlo analysis calculates the effective planetary footprint using production volumes sampled\nfrom an uncertainty range that covers historical values. This ensures that, despite the\nimplementation gap, our results remain stochastically consistent with present-day realities\nThe above said, at line 107, it is clearly indicated the paragraph refers to optimisation results rather\nthan a report on historical production. To add further differentiation, we implemented Revision 2.\nAt line 114, we added:\n“For comparison, the IEA clean hydrogen projects database15 shows a higher reliance water\nelectrolysis (54%) and SMR-CCS (43%)”\nWhich should further indicate how our results compare to historical data for 2025. To clarify this\nmore, we also specified throughout the manuscript that our assessment focuses on clean hydrogen\nproduction.\nComment 6: Line 432: the authors indicate that their “system-based approach can also be\ninterpreted as consequential to some extent”. This consequential approach does not seem to align\nwith current recommendations for “absolute sustainability assessment”, where it is stated that “An\nimportant requirement, no matter the scale of the activity studied, is that the accounting approach\nfollows the attributional accounting philosophy”\n(https://publications.jrc.ec.europa.eu/repository/handle/JRC142099). The idea of a consequential\napproach is also repeated at line 315.\nThank you for this comment. Because the technology choice model is an inherent consequential\napproach20,21, this comment was initially made to qualify the LCA type while noting this other\nperspective. However, we agree that it can be confusing and conflicting with the AESA guideline.\nAs this is not central to the analysis, we removed the consequential LCA statements.\nComment 7: Line 433: “the functional unit as the “global annual hydrogen production with a\ncarbon capture option”. This is not very clear, as the optimal annual hydrogen production include\nseveral pathways such as electrolytic hydrogen. Could the authors be more precise about the\nmeaning of “with a carbon capture option”?\nThank you for your raising this point. We implemented revision 3 to be make the functional unit\nmore precise.\nRevision 4. Functional unit clarification\nAt line 459, we updated the functional unit to : “global annual hydrogen production with optional\ndirect air capture and sequestration of carbon dioxide”\nperformance comparable to electrolysis and can serve as a bridging pathway”… which however is\nentirely dependent on the conditions assumed in the background system for the raw materials\nsupply and manufacturing of renewable technologies.\nThank you for your remark. We addressed this point in Revision 1.\nReviewer 3 – Comment 9\nComment 9: The authors successfully addressed my previous comments, so the manuscript is\nnow ready for publication.\nThank you for your interest in our work and for the time invested to review it. We truly appreciate\nthe constructive comments provided to improve the quality and robustness of our work.\n1. IEA. Global Hydrogen Review 2025. https://www.iea.org/reports/global-hydrogen-review-2025 (2025).\n2. IEA. World Energy Outlook 2024. (2024).\n3. IRENA. World Energy Transitions Outlook 2023: 1.5°C Pathway. (International Renewable Energy\nAgency IRENA, Abu Dhabi, 2023).\n4. Intergovernmental Panel On Climate Change (Ipcc). Climate Change 2021 – The Physical Science Basis:\nWorking Group I Contribution to the Sixth Assessment Report of the Intergovernmental Panel on Climate\nChange. (Cambridge University Press, 2023). doi:10.1017/9781009157896.\n5. Leach, N. J. et al. FaIRv2.0.0: a generalized impulse response model for climate uncertainty and future\nscenario exploration. Geosci. Model Dev. 14, 3007–3036 (2021).\n6. Climate Change 2022: Mitigation of Climate Change. (IPCC, Geneva, 2022).\n7. Byers, E. et al. AR6 Scenarios Database. Integrated Assessment Modeling Consortium &\nInternational Institute for Applied Systems Analysis https://doi.org/10.5281/ZENODO.5886911 (2022).\n8. de Bortoli, A., Chanel, A., Chabas, C., Greffe, T. & Louineau, E. More rationality and inclusivity are imperative\nin reference transition scenarios based on IAMs and shared socioeconomic pathways - recommendations for\nprospective LCA. Renew. Sustain. Energy Rev. 222, 115924 (2025).\n9. Johnson, N. et al. Realistic roles for hydrogen in the future energy transition. Nat. Rev. Clean\nTechnol. 1–21 (2025) doi:10.1038/s44359-025-00050-4.\n10. US EPA, O. Basic Information about Landfill Gas. https://www.epa.gov/lmop/basic-\ninformation-about-landfill-gas (2016).\n11. Zickfeld, K., Azevedo, D., Mathesius, S. & Matthews, H. D. Asymmetry in the climate–carbon cycle\nresponse to positive and negative CO2 emissions. Nat. Clim. Change 11, 613–617 (2021).\n12. Ricke, K. L. & Caldeira, K. Maximum warming occurs about one decade after a carbon dioxide\nemission. Environ. Res. Lett. 9, 124002 (2014).\n13. IRENA. World Energy Transitions Outlook 2023: 1.5°C Pathway. (International Renewable Energy\nAgency IRENA, Abu Dhabi, 2023).\n14. IEA. Global Hydrogen Review 2025. https://www.iea.org/reports/global-hydrogen-review-2025\n(2025).\n15. IEA. Hydrogen Production and Infrastructure Projects Database - Data product. (2025).\n16. IEA. World Energy Outlook 2024. (2024).\n17. Busch, P., Kendall, A. & Lipman, T. A systematic review of life cycle greenhouse gas intensity values\nfor hydrogen production pathways. Renew. Sustain. Energy Rev. 184, 113588 (2023).\n18. Kikstra, J. S. et al. The IPCC Sixth Assessment Report WGIII climate assessment of mitigation\npathways: from emissions to global temperatures. Preprint at https://doi.org/10.5194/egusphere-\n2022-471 (2022).\n19. Byers, E. et al. AR6 Scenarios Database. Integrated Assessment Modeling Consortium &\nInternational Institute for Applied Systems Analysis https://doi.org/10.5281/ZENODO.5886911 (2022).\n20. Kätelhön, A., Bardow, A. & Suh, S. Stochastic Technology Choice Model for Consequential\nLife Cycle Assessment. Environ. Sci. Technol. 50, 12575–12583 (2016).\n12\n21. Lechtenberg, F. et al. PULPO: A framework for efficient integration of life cycle inventory models\ninto life cycle product optimization. J. Ind. Ecol. https://doi.org/10.1111/jiec.13561 (2024)\ndoi:10.1111/jiec.13561.\nplanetary boundaries\nResponse letter\nMichaël Lejeune1,2, Sami Kara1,2*, Michael Zwicky Hauschild4,5*, Sareh Shahrabifarahani1, Rahman\nDaiyan2,3*\n1Sustainability in Manufacturing and Life Cycle Engineering Research Group, School of Mechanical and\nManufacturing Engineering, The University of New South Wales, Sydney 2052, Australia.\n2Australian Research Council, Training Centre for the Global Hydrogen Economy, Sydney 2052, Australia.\n3School of Minerals and Energy Engineering, The University of New South Wales, Sydney 2052, Australia.\n4Section for Quantitative Sustainability Assessment (QSA), Department of Environmental and Resource\nEngineering, Technical University of Denmark, Kgs, Lyngby, Denmark.\n5Centre for Absolute Sustainability, Technical University of Denmark, Kgs, Lyngby, Denmark.\nDear editor and reviewers of the manuscript “Pathways to global hydrogen production within\nplanetary boundaries” with ID: NCOMMS-25-07779B. We appreciate your constructive comments\nand thank you for taking the time to review our work. You will find below our response.\nReviewer 2\nThe authors have addressed all my comments from the second round. Overall, I think this is an\ninteresting contribution to the literature that can fuel further discussion on the environmental\nsustainability of an hydrogen economy but which also raises important methodological questions\nthat require further discussion and refinement.\nWe thank you for your interest and we value the time invested reviewing our work. We truly\nappreciate the constructive comments raised which helped improve the quality, clarity, and\nrobustness of our work. We also hope that this study can spark further discussion and work to\naddress the methodological limits we faced.\n2"}, {"round": 2, "reviewer_comments": {"reviewer_2": "The authors have made commendable efforts to improve the clarity of the methodology and findings, and to address reviewers’ comments. The revised version is significantly clearer and more accessible. However, after reviewing this improved manuscript, I have additional technical concerns.\n1. At a more fundamental level, I have serious reservations about the suitability of prospective LCA combined with planetary boundaries to determine whether a product system can be considered “absolutely” sustainable in the future (in contrast to the more common application to determine whether product system A performs relatively better than B). An inherent limitation of prospective LCAs is that the results and conclusions are only valid under the specific conditions represented by the background and foreground LCIs. Many background processes remain unchanged even when using premise, meaning that by 2050 they may still reflect current (or in many cases outdated) practices. This is particularly important given that the authors identify embodied emissions from the manufacturing of renewable electricity technologies as a key driver of hydrogen’s unsustainability. In this context, several upstream processes such as heat sources in industrial processes are often not updated in premise. Similarly, emissions of nitrogen and phosphorus from copper mining and the management of sulfidic tailings are highly uncertain and remain largely unchanged. While these assumptions may be acceptable for comparative LCAs of product systems, I believe they pose significant limitations when applied to absolute sustainability. These assumptions may be enough for addressing the research question “How do different hydrogen production pathways perform under a given set of predefined conditions?” Such an analysis provides important insights into relative performances across alternatives and future hotspots that deserve attention (e.g., that sulfidic tailings from copper mining need to be managed in a better way). In contrast, I find it problematic to conclude that a product system is “absolutely” unsustainable by 2025 due to impacts associated with industrial practices representative of conditions from 25-30 years back. The authors acknowledge the limitations of potentially unrepresentative LCIs. However, I believe this raises a more fundamental question about what “absolute unsustainable” truly represents in the context of prospective LCAs (and how fair it is maybe).\n2. I also have a question about carbon accounting in the case of biogas for biomethane production followed by reforming to hydrogen coupled with CCS. The authors explain that they do not account for biogenic carbon. However, biomethane with CCS is potentially a negative emissions technology. Yet, since the authors used the ecoinvent cut-off system model, the CO2 uptake during biomass growth is disconnected from the waste management process (i.e., it is accounted for in the corresponding agricultural activity). How did the authors ensure a correct carbon balance? I believe this is important since the results suggest that “bio-based production shows the worst planetary 223 footprint – climate change and biosphere integrity transgressed ~229 and 180 times” (line 222).\n3. I find the use of IAM scenario outputs in the revised version somewhat confusing. The authors state that they align with recommendations from a recently published Bortoli et al paper by using an ensemble of 32 IAM scenarios to define future global hydrogen production volumes and the corresponding allocated SOS. However, for the prospective LCA modelling, they rely on 3 IAM scenarios provided within premise. This is acknowledged at line 495. Although both sets of scenarios are consistent in terms of their climate targets (i.e., achieving a radiative forcing of 1.9–2.25 W/m²), this approach introduces a substantial inconsistency in terms of hydrogen demand and sectoral structure. For example, I believe the electricity mix in each IAM scenario is influenced by the level of hydrogen demand assumed in that scenario. Therefore, using the range of hydrogen demand from the 32-scenario ensemble may not be representative of the sectoral dynamics embedded in the 3 premise scenarios.\n4. The results indicate that the optimal hydrogen mix for 2025 requires approximately 49% to come from electrolysis. However, it is well known that as of 2024, green hydrogen produced via electrolysis accounts for about 1%. It would be helpful if the authors clarified in the manuscript that these figures represent a hypothetical snapshot of what would be needed today to remain within the allocated SOS. Moreover, this raises questions about whether the authors should have calibrated their model against empirical data to ensure consistency between modelled projections and current realities.\nOther minor comments:\n1. Line 432: the authors indicate that their “system-based approach can also be interpreted as consequential to some extent”. This consequential approach does not seem to align with current recommendations for “absolute sustainability assessment”, where it is stated that “An important requirement, no matter the scale of the activity studied, is that the accounting approach follows the attributional accounting philosophy”\n(https://publications.jrc.ec.europa.eu/repository/handle/JRC142099). The idea of a consequential approach is also repeated at line 315.\n2. Line 433: “the functional unit as the “global annual hydrogen production with a carbon capture option”. This is not very clear, as the optimal annual hydrogen production include several pathways such as electrolytic hydrogen. Could the authors be more precise about the meaning of “with a carbon capture option”?\n3. Line 336: “Our results confirm that abated fossil production has an environmental performance comparable to electrolysis and can serve as a bridging pathway”… which however is entirely dependent on the conditions assumed in the background system for the raw materials supply and manufacturing of renewable technologies.", "reviewer_3": "The authors successfully addressed my previous comments, so the manuscript is now ready for publication."}, "author_response": "Pathways to global hydrogen production within\nplanetary boundaries\nResponse letter\nMichaël Lejeune1,2, Sami Kara1,2*, Michael Zwicky Hauschild4,5*, Sareh Shahrabifarahani1, Rahman\nDaiyan2,3*\n1Sustainability in Manufacturing and Life Cycle Engineering Research Group, School of Mechanical and\nManufacturing Engineering, The University of New South Wales, Sydney 2052, Australia.\n2Australian Research Council, Training Centre for the Global Hydrogen Economy, Sydney 2052, Australia.\n3School of Minerals and Energy Engineering, The University of New South Wales, Sydney 2052, Australia.\n4Section for Quantitative Sustainability Assessment (QSA), Department of Environmental and Resource\nEngineering, Technical University of Denmark, Kgs, Lyngby, Denmark.\n5Centre for Absolute Sustainability, Technical University of Denmark, Kgs, Lyngby, Denmark.\nDear editor and reviewers of the manuscript “Pathways to global hydrogen production within\nplanetary boundaries” with ID: NCOMMS-25-07779A. We appreciate your constructive comments\nand thank you for taking the time to review our work.\nWe confirm that we have addressed each comment in the revised manuscript as outlined in the\nsections below. Please find the reviewers’ comments in blue, our response in black, and the changes\nmade to address the comments in red, with key revisions highlighted in grey. Also, for clarity, we\nhighlight references to revisions or to line number in blue.\nReviewer 2 - Comment 1 to 8\nComment 1: The authors have made commendable efforts to improve the clarity of the\nmethodology and findings, and to address reviewers’ comments. The revised version is significantly\nclearer and more accessible. However, after reviewing this improved manuscript, I have additional\ntechnical concerns.\nThank you for your interest in our work and for the time invested to review it. We address your\nadditional concerns below.\nComment 2: At a more fundamental level, I have serious reservations about the suitability of\nprospective LCA combined with planetary boundaries to determine whether a product system can\nbe considered “absolutely” sustainable in the future (in contrast to the more common application to\ndetermine whether product system A performs relatively better than B). An inherent limitation of\nprospective LCAs is that the results and conclusions are only valid under the specific conditions\nrepresented by the background and foreground LCIs. Many background processes remain\nunchanged even when using premise, meaning that by 2050 they may still reflect current (or in many\ncases outdated) practices. This is particularly important given that the authors identify embodied\nemissions from the manufacturing of renewable electricity technologies as a key driver of hydrogen’s\nunsustainability. In this context, several upstream processes such as heat sources in industrial\nprocesses are often not updated in premise. Similarly, emissions of nitrogen and phosphorus from\ncopper mining and the management of sulfidic tailings are highly uncertain and remain largely\nunchanged. While these assumptions may be acceptable for comparative LCAs of product systems, I\nbelieve they pose significant limitations when applied to absolute sustainability. These assumptions\nmay be enough for addressing the research question “How do different hydrogen production\npathways perform under a given set of predefined conditions?” Such an analysis provides important\ninsights into relative performances across alternatives and future hotspots that deserve attention\n(e.g., that sulfidic tailings from copper mining need to be managed in a better way). In contrast, I find\nit problematic to conclude that a product system is “absolutely” unsustainable by 2025 due to\nimpacts associated with industrial practices representative of conditions from 25-30 years back. The\nauthors acknowledge the limitations of potentially unrepresentative LCIs. However, I believe this\nraises a more fundamental question about what “absolute unsustainable” truly represents in the\ncontext of prospective LCAs (and how fair it is maybe).\nunderstand your concern as we investigated this aspect and tried to find potential solutions to\nmitigate the biogeochemical emissions of renewables. Ultimately, we were unable to address it due\nto a lack of data (line 332) – we do not know how these emissions would be tackled in the future of\nmining.\nAs correctly mentioned, premise has inherent limitations regarding the processes that can be\n“updated”. When basing the “update” on IAM scenarios, it is only possible to update processes if the\ncorresponding data is available in scenarios used. Emissions resulting from copper mining or other\nactivities with relatively low climate change impacts are generally not represented in IAM scenarios.\nThis explains their omission from the “update”. From this basis, one can easily conclude that this\nissue entirely stems from the prospective LCA part of our analysis. Thus, it would naturally be a\nlimitation in all prospective environmental sustainability assessments, both absolute and relative. If\nthese limitations are acceptable in pLCA, they should also be acceptable in AESA.\nTo be more specific, in a relative assessment, when answering the following research question:\n“How do different hydrogen production pathways perform under a given set of predefined\nconditions?”, the predefined conditions are limited to the scenario data. Non-updated processes\ncould also shape the conclusions (and fairness). For example, two processes (say, A and B), could rely\non different upstream processes while providing the same function. If some upstream processes of A\nare omitted in the premise update, B’s relative environmental performance could be found more eco-\nefficient and also shape conclusions. It is thus a problem inherent to IAM-based pLCA, and not\nspecifically prospective AESA. To the best of our knowledge, it is very difficult (if not impossible) to\nupdate the entire background system data prospectively. This would require knowing the future\ntechnical performance of thousands of processes. Despite these limitations, premise is an accepted\nsoftware because it can improve the accuracy of prospective impact scores for given environmental\nscenarios, and for studies into the future. The alternative to using prospective approaches like\npremise would be to assume that the environmental performance of all technologies will remain at\nits current state into the future.\nThat said, we understand your concern relates to the sharp sustainability conclusion we derive\nfrom our results given this inherent limitation. We therefore implement revision 1 to put emphasis\non the likelihood of sustainability for hydrogen production and that our conclusions are solely based\non our model and known limitations.\nRevision 1. Added limitations to sustainability results\nAt line 21, 72,and 283, where we report our key takeaways, we added “likely unsustainable” to\nsoften the sharp sustainability statement previously made. We use “likely” to highlight what is\nsupported by our results while leaving enough room for uncertainties.\nHowever, our results depend on technologies that remain unproven at scale1–3 and do not account\nfor potential future improvements or breakthroughs in the upstream hydrogen value chain.\nAt line 347, we added:\nHowever, this is subject to future technological options that may not be accurately represented in\nthis work.\nIn addition, from line 417 to 428, we expand the discussion on that particular topic:\nUncertainty also arises from the representation of future technologies in prospective background\nsystems. The Premise software can only reflect future efficiency improvements for processes present\nin future scenarios. It thereby excludes improvement processes that are not represented in these\nscenarios and ignores potential technological breakthroughs. For instance, global hydrogen\nproduction could approach sustainability if future processes substantially reduce nitrogen and\nphosphorus emissions associated with renewable electricity technologies. Similarly, this assessment\ndoes not account for emerging capillary-feed4 electrolysis designs or advances in abated fossil\npathways, including electrified reformer furnaces5 or improved pre- and post-combustion carbon\ncapture. The omission of such developments introduces additional uncertainty for our results.\nNevertheless, updating background system data using Premise remains a state-of-the-art approach\nfor improving the accuracy of future life-cycle impacts under a given climate target.\nComment 3: I also have a question about carbon accounting in the case of biogas for biomethane\nproduction followed by reforming to hydrogen coupled with CCS. The authors explain that they do\nnot account for biogenic carbon. However, biomethane with CCS is potentially a negative emissions\ntechnology. Yet, since the authors used the ecoinvent cut-off system model, the CO2 uptake during\nbiomass growth is disconnected from the waste management process (i.e., it is accounted for in the\ncorresponding agricultural activity). How did the authors ensure a correct carbon balance? I believe\nthis is important since the results suggest that “bio-based production shows the worst planetary 223\nfootprint – climate change and biosphere integrity transgressed ~229 and 180 times” (line 222).\nThank you for your comment on this important aspect that deserves more clarifications. We\nprovide the rationale for this important assumption below.\nFirst, as correctly pointed out in the comment, under the cut-off system model, the uptake and\nemissions are reported in the original activity. Our process for hydrogen production from\nbiomethane is based on Antonini et al.6. While the authors expanded their system boundaries to add\nthe uptake, we refrain from adopting such an approach. For secondary biomass e.g., waste organic\nfood, sewage sludge, manure, it is more relevant to look at the avoided/created emissions owing to\n4\nrecycling secondary biomass7. This is to avoid the misleading conclusion that secondary biomass\nwould have had the same impact if not used. It could be true when looking at long time horizons, but\nnot on an annual basis as imposed by planetary boundaries (Figure.1). In the case of fuel production,\ncarbon emissions necessarily occur at a much faster rate than the uptake. To sustain such a\nproduction, more biomass need to be used continuously.\nSecond, for the planetary boundary for climate impacts, peak environmental pressures matter\nmore in our case than a net carbon balance over 100 years. Unlike traditional LCA that focuses on the\nimpact created during a product system’s life cycle, in planetary boundaries based LCA, the\nassessment is on annual pressure basis. As pointed out by Ryberg et al.8, time integrated impacts\nmethods are not suitable for the control variables of planetary boundaries. If we apply sufficient\npressure on Earth system processes in a given year, the environmental response may lead to\nirreversible changes that cannot be captured by time integrated approaches. For this reason,\nplanetary boundaries-based characterisation methods rely on continuous annual emission rates\nrather than a pulse emission decay over 100 years8,9.\nThat said if we emit a large amount of biogenic carbon today, until it is recaptured by carbon sinks,\nit has the same effect on the Earth system as fossil carbon emissions. Current carbon accounting\napproaches based on GWP100, overlook this aspect inherent to planetary boundaries. While we\nagree that biogenic carbon accounting is an important aspect, due to the lack of proper accounting\napproaches with regards to planetary boundaries and the implications of our results, we believe it is\nmore conservative not to discount biogenic carbon.\nTo exemplify this aspect further, we can compare the global temperature response to 1GtC\nemitted as a pulse during hydrogen production or spread over a 30-year period in landfill (Figure.1).\nHere we chose landfill as it is the most common disposal of waste that will not be recycled in any way.\nLandfills provide anaerobic conditions. It could take 5 to 7 years to reach peak emissions and decades\nfor carbon ultimately going to the atmosphere to be completely released10. The 30-years, evenly\ndistributed emission period can be seen as conservative. To be more conservative, we can also\nassume that all the carbon from landfill is emitted to the air, while in reality, some would end up in\nsoils. For a fair comparison with hydrogen production, we can reasonably assume that landfills are\nproperly managed with all methane emissions being flared. As can be seen in Figure.1, at long time\nhorizons (≥100years), both responses would be similar which confirms current carbon accounting\napproaches used in LCA. However, in the short term for nearly 25 years, there is a large positive\ndifference, more relevant for an assessment on planetary boundaries, that is overlooked when\nlooking at long time horizons.\nThis short illustrative example is not perfect and has many flaws, but it clearly shows that current\ncarbon accounting approaches from traditional life cycle assessment are not directly applicable for\nan assessment on planetary boundaries. Also, there is a potential asymmetry between carbon\n5\nemissions and uptake that deserves further attention11. These aspects require further\nmethodological work that we consider out of scope.\nFigure 1. Comparison of global temperature response to 1GtC from pulse and spread emission profiles. On a\n100 years horizon, both approach would lead to the same results. On a yearly basis, current carbon accounting\napproaches fail to represent the pressure on Earth system processes. Model was sourced from Ricke and\nCaldeira12.\nOverall, our assumption to treat biogenic carbon the same as fossil carbon is conservative. Future\nwork is needed to investigate this aspect more thoroughly. Despite this assumption, our results\nremain consistent with most projections from the IRENA13 or IEA14–16. To clarify this aspect more in\ndetail in the manuscript, we implement Revision 2.\nRevision 2. Clarified biogenic carbon assumption\nAt line 279:\nLastly, we conservatively do not discount CO emissions from biogenic carbon for bio-based\n2\nproduction to avoid potentially misleading conclusions in relation to planetary boundaries\n(Supplementary Note 4).\nFrom line 356 to 363:\nThe temporal mismatch between carbon uptake during cultivation and immediate release during\nproduction could result in a cumulative positive carbon balance17. In addition, planetary boundaries\nhave an assessment on an annual basis while current biogenic carbon accounting approaches focus\n6\non a time horizon (e.g., 100 years). A sufficiently high environmental pressure in a given year could\ntrigger irreversible tipping points during the biomass rotation period. For these reasons, and to\navoid potentially misleading conclusions we conservatively do not discount biogenic carbon.\nFurther research is required on this particular topic.\nComment 4: I find the use of IAM scenario outputs in the revised version somewhat confusing. The\nauthors state that they align with recommendations from a recently published Bortoli et al paper by\nusing an ensemble of 32 IAM scenarios to define future global hydrogen production volumes and the\ncorresponding allocated SOS. However, for the prospective LCA modelling, they rely on 3 IAM\nscenarios provided within premise. This is acknowledged at line 495. Although both sets of scenarios\nare consistent in terms of their climate targets (i.e., achieving a radiative forcing of 1.9–2.25 W/m²),\nthis approach introduces a substantial inconsistency in terms of hydrogen demand and sectoral\nstructure. For example, I believe the electricity mix in each IAM scenario is influenced by the level of\nhydrogen demand assumed in that scenario. Therefore, using the range of hydrogen demand from\nthe 32-scenario ensemble may not be representative of the sectoral dynamics embedded in the 3\npremise scenarios.\nThank you for your comment on this interesting point. We understand the concern, however, as\npreviously said, we do not assess the embedded sectoral dynamics of each scenario. If a scenario\nmeets our climate target, this implies that it has an underlying sectoral configuration that enables it.\nThe heterogeneity of sectoral configurations is also not a substantial inconsistency, quite the\nopposite, it provides robustness to our outcomes. The point for comparison across scenarios is not\n“how similar should be their sectoral configurations”, but rather what trends appear across scenarios\nwhen they are built to meet the climate target. These trends include hydrogen production volumes,\nelectrical mix configurations, and in the case of premise, expected prospective impact scores.\nTo be more specific, all 32 scenarios in our AR6-based ensemble have different sectoral\nconfigurations, with different electrical mixes and different hydrogen production volumes. The\ncommon point between all these scenarios is their sectoral configurations aligned for the fulfillment\nof the climate target. The approach is similar to that of the IPCC AR618,19 where scenarios are classified\nby categories – e.g., C1 for scenarios limiting global warming to 1.5°C, C2 for 2°C. From each category,\na need for hydrogen, electricity generation sources or carbon management technologies be\nestablished quantitatively – a good example for hydrogen is shown in figure Fig. 4b in Johnson et al.3.\nSimilarly, when using Premise, prospective and quantitative impact scores are derived from\nunderlying sectoral configurations designed to meet the climate target. Ideally, the entire\n7\nassessment would be based on a single scenario ensemble. However, technical limitations (i.e.,\ndimension mismatch) restricted the Premise ensemble to the REMIND PkBudg500 scenarios (n=3).\nDespite the small premise sample size, Supplementary Fig. 17 shows that the uncertainty range of\nthe Premise ensemble adequately covers that of the broader AR6 ensemble by 2100. Therefore,\nsampling impact scores from this range in our Monte Carlo analysis should sufficiently represent the\nscores derived from the AR6 ensemble.\nIn summary, even if hydrogen production volumes and sectoral dynamics from the Premise\nensemble is different from the AR6 ensemble, our uncertainty range ensures the consistency of\nimpacts scores with the climate targets.\nComment 5: The results indicate that the optimal hydrogen mix for 2025 requires approximately\n49% to come from electrolysis. However, it is well known that as of 2024, green hydrogen produced\nvia electrolysis accounts for about 1%. It would be helpful if the authors clarified in the manuscript\nthat these figures represent a hypothetical snapshot of what would be needed today to remain within\nthe allocated SOS. Moreover, this raises questions about whether the authors should have calibrated\ntheir model against empirical data to ensure consistency between modelled projections and current\nrealities.\nThank you for this remark. It is indeed well known that current hydrogen production volumes\n(fossil based) are around 100MtH yr−1 (illustrated in Fig.2b)14,15. On the other hand, in 2025, hydrogen\n2\nproduction from clean sources (not just electrolysis) is 0.8-0.95 MtH yr−1 which matches your 1%\n2\nobservation15. However, our study focuses on future clean hydrogen production only. Fig.2b shows\nthat in 2025, the median value across scenarios is 2.58 MtH yr−1 (0.001-8.7 MtH yr−1 range) which is\n2 2\napproximately consistent with the historical production reported above. Considering the\nmisalignment between our median value and current realities, we point out at line 91 an\nimplementation gap of 1.63-1.78 MtH yr−1. Also, in the IEA clean hydrogen projects database15, the\n2\nproportion of electrolysis projects is 42% in 2024 and 54% in 2025, which provides some sort of\nvalidation for the 49% found via optimisation.\nBesides that, we refrain from calibrating the model with current production volumes. This is to\nhighlight what should have been implemented and the implementation gap (line 91). Our\nMontecarlo analysis calculates the effective planetary footprint using production volumes sampled\nfrom an uncertainty range that covers historical values. This ensures that, despite the\nimplementation gap, our results remain stochastically consistent with present-day realities\nThe above said, at line 107, it is clearly indicated the paragraph refers to optimisation results rather\nthan a report on historical production. To add further differentiation, we implemented Revision 2.\nAt line 114, we added:\n“For comparison, the IEA clean hydrogen projects database15 shows a higher reliance water\nelectrolysis (54%) and SMR-CCS (43%)”\nWhich should further indicate how our results compare to historical data for 2025. To clarify this\nmore, we also specified throughout the manuscript that our assessment focuses on clean hydrogen\nproduction.\nComment 6: Line 432: the authors indicate that their “system-based approach can also be\ninterpreted as consequential to some extent”. This consequential approach does not seem to align\nwith current recommendations for “absolute sustainability assessment”, where it is stated that “An\nimportant requirement, no matter the scale of the activity studied, is that the accounting approach\nfollows the attributional accounting philosophy”\n(https://publications.jrc.ec.europa.eu/repository/handle/JRC142099). The idea of a consequential\napproach is also repeated at line 315.\nThank you for this comment. Because the technology choice model is an inherent consequential\napproach20,21, this comment was initially made to qualify the LCA type while noting this other\nperspective. However, we agree that it can be confusing and conflicting with the AESA guideline.\nAs this is not central to the analysis, we removed the consequential LCA statements.\nComment 7: Line 433: “the functional unit as the “global annual hydrogen production with a\ncarbon capture option”. This is not very clear, as the optimal annual hydrogen production include\nseveral pathways such as electrolytic hydrogen. Could the authors be more precise about the\nmeaning of “with a carbon capture option”?\nThank you for your raising this point. We implemented revision 3 to be make the functional unit\nmore precise.\nRevision 4. Functional unit clarification\nAt line 459, we updated the functional unit to : “global annual hydrogen production with optional\ndirect air capture and sequestration of carbon dioxide”\nperformance comparable to electrolysis and can serve as a bridging pathway”… which however is\nentirely dependent on the conditions assumed in the background system for the raw materials\nsupply and manufacturing of renewable technologies.\nThank you for your remark. We addressed this point in Revision 1.\nReviewer 3 – Comment 9\nComment 9: The authors successfully addressed my previous comments, so the manuscript is\nnow ready for publication.\nThank you for your interest in our work and for the time invested to review it. We truly appreciate\nthe constructive comments provided to improve the quality and robustness of our work.\n1. IEA. Global Hydrogen Review 2025. https://www.iea.org/reports/global-hydrogen-review-2025 (2025).\n2. IEA. World Energy Outlook 2024. (2024).\n3. IRENA. World Energy Transitions Outlook 2023: 1.5°C Pathway. (International Renewable Energy\nAgency IRENA, Abu Dhabi, 2023).\n4. Intergovernmental Panel On Climate Change (Ipcc). Climate Change 2021 – The Physical Science Basis:\nWorking Group I Contribution to the Sixth Assessment Report of the Intergovernmental Panel on Climate\nChange. (Cambridge University Press, 2023). doi:10.1017/9781009157896.\n5. Leach, N. J. et al. FaIRv2.0.0: a generalized impulse response model for climate uncertainty and future\nscenario exploration. Geosci. Model Dev. 14, 3007–3036 (2021).\n6. Climate Change 2022: Mitigation of Climate Change. (IPCC, Geneva, 2022).\n7. Byers, E. et al. AR6 Scenarios Database. Integrated Assessment Modeling Consortium &\nInternational Institute for Applied Systems Analysis https://doi.org/10.5281/ZENODO.5886911 (2022).\n8. de Bortoli, A., Chanel, A., Chabas, C., Greffe, T. & Louineau, E. More rationality and inclusivity are imperative\nin reference transition scenarios based on IAMs and shared socioeconomic pathways - recommendations for\nprospective LCA. Renew. Sustain. Energy Rev. 222, 115924 (2025).\n9. Johnson, N. et al. Realistic roles for hydrogen in the future energy transition. Nat. Rev. Clean\nTechnol. 1–21 (2025) doi:10.1038/s44359-025-00050-4.\n10. US EPA, O. Basic Information about Landfill Gas. https://www.epa.gov/lmop/basic-\ninformation-about-landfill-gas (2016).\n11. Zickfeld, K., Azevedo, D., Mathesius, S. & Matthews, H. D. Asymmetry in the climate–carbon cycle\nresponse to positive and negative CO2 emissions. Nat. Clim. Change 11, 613–617 (2021).\n12. Ricke, K. L. & Caldeira, K. Maximum warming occurs about one decade after a carbon dioxide\nemission. Environ. Res. Lett. 9, 124002 (2014).\n13. IRENA. World Energy Transitions Outlook 2023: 1.5°C Pathway. (International Renewable Energy\nAgency IRENA, Abu Dhabi, 2023).\n14. IEA. Global Hydrogen Review 2025. https://www.iea.org/reports/global-hydrogen-review-2025\n(2025).\n15. IEA. Hydrogen Production and Infrastructure Projects Database - Data product. (2025).\n16. IEA. World Energy Outlook 2024. (2024).\n17. Busch, P., Kendall, A. & Lipman, T. A systematic review of life cycle greenhouse gas intensity values\nfor hydrogen production pathways. Renew. Sustain. Energy Rev. 184, 113588 (2023).\n18. Kikstra, J. S. et al. The IPCC Sixth Assessment Report WGIII climate assessment of mitigation\npathways: from emissions to global temperatures. Preprint at https://doi.org/10.5194/egusphere-\n2022-471 (2022).\n19. Byers, E. et al. AR6 Scenarios Database. Integrated Assessment Modeling Consortium &\nInternational Institute for Applied Systems Analysis https://doi.org/10.5281/ZENODO.5886911 (2022).\n20. Kätelhön, A., Bardow, A. & Suh, S. Stochastic Technology Choice Model for Consequential\nLife Cycle Assessment. Environ. Sci. Technol. 50, 12575–12583 (2016).\n12\n21. Lechtenberg, F. et al. PULPO: A framework for efficient integration of life cycle inventory models\ninto life cycle product optimization. J. Ind. Ecol. https://doi.org/10.1111/jiec.13561 (2024)\ndoi:10.1111/jiec.13561.\nplanetary boundaries\nResponse letter\nMichaël Lejeune1,2, Sami Kara1,2*, Michael Zwicky Hauschild4,5*, Sareh Shahrabifarahani1, Rahman\nDaiyan2,3*\n1Sustainability in Manufacturing and Life Cycle Engineering Research Group, School of Mechanical and\nManufacturing Engineering, The University of New South Wales, Sydney 2052, Australia.\n2Australian Research Council, Training Centre for the Global Hydrogen Economy, Sydney 2052, Australia.\n3School of Minerals and Energy Engineering, The University of New South Wales, Sydney 2052, Australia.\n4Section for Quantitative Sustainability Assessment (QSA), Department of Environmental and Resource\nEngineering, Technical University of Denmark, Kgs, Lyngby, Denmark.\n5Centre for Absolute Sustainability, Technical University of Denmark, Kgs, Lyngby, Denmark.\nDear editor and reviewers of the manuscript “Pathways to global hydrogen production within\nplanetary boundaries” with ID: NCOMMS-25-07779B. We appreciate your constructive comments\nand thank you for taking the time to review our work. You will find below our response.\nReviewer 2\nThe authors have addressed all my comments from the second round. Overall, I think this is an\ninteresting contribution to the literature that can fuel further discussion on the environmental\nsustainability of an hydrogen economy but which also raises important methodological questions\nthat require further discussion and refinement.\nWe thank you for your interest and we value the time invested reviewing our work. We truly\nappreciate the constructive comments raised which helped improve the quality, clarity, and\nrobustness of our work. We also hope that this study can spark further discussion and work to\naddress the methodological limits we faced.\n2"}, {"round": 3, "reviewer_comments": {"reviewer_2": "The authors have addressed all my comments from the second round. Overall, I think this is an interesting contribution to the literature that can fuel further discussion on the environmental sustainability of an hydrogen economy but which also raises important methodological questions that require further discussion and refinement."}, "author_response": "Pathways to global hydrogen production within\nplanetary boundaries\nResponse letter\nMichaël Lejeune1,2, Sami Kara1,2*, Michael Zwicky Hauschild4,5*, Sareh Shahrabifarahani1, Rahman\nDaiyan2,3*\n1Sustainability in Manufacturing and Life Cycle Engineering Research Group, School of Mechanical and\nManufacturing Engineering, The University of New South Wales, Sydney 2052, Australia.\n2Australian Research Council, Training Centre for the Global Hydrogen Economy, Sydney 2052, Australia.\n3School of Minerals and Energy Engineering, The University of New South Wales, Sydney 2052, Australia.\n4Section for Quantitative Sustainability Assessment (QSA), Department of Environmental and Resource\nEngineering, Technical University of Denmark, Kgs, Lyngby, Denmark.\n5Centre for Absolute Sustainability, Technical University of Denmark, Kgs, Lyngby, Denmark.\nDear editor and reviewers of the manuscript “Pathways to global hydrogen production within\nplanetary boundaries” with ID: NCOMMS-25-07779B. We appreciate your constructive comments\nand thank you for taking the time to review our work. You will find below our response.\nReviewer 2\nThe authors have addressed all my comments from the second round. Overall, I think this is an\ninteresting contribution to the literature that can fuel further discussion on the environmental\nsustainability of an hydrogen economy but which also raises important methodological questions\nthat require further discussion and refinement.\nWe thank you for your interest and we value the time invested reviewing our work. We truly\nappreciate the constructive comments raised which helped improve the quality, clarity, and\nrobustness of our work. We also hope that this study can spark further discussion and work to\naddress the methodological limits we faced.\n2"}]

Decision letter