Resource requirements and consequences of large-scale hydrogen utilize in Europe

Scenario definition

The four prio-scenarios are designed to test the impacts of utilizing the respective energy carriers at a very large scale (within the limits of what the authors deem theoretically possible) by 2050. This is intfinished to represent ‘extreme’ cases where society decides to prioritize certain energy carriers in transportation. The fuel mix scenario, as the name suggests, represent a future with a low emission transportation system utilizing a mix of energy carriers. The shares for each fuel type remain constant across all vehicles in each transportation segment for each scenario. The transportation segments are listed in Supplementary Note 4 and the assumed energy mix in the five designed scenarios is presented visually, for each transportation segment, in Supplementary Fig. 4. In all scenarios, the included industries (ammonia, steel, HVC and refineries) are considered to transition completely to processes based on hydrogen. Scenario designs represent exploratory narratives⁴³ based on a qualitative-to-quantitative approach⁴⁴, utilizing discussions with indusattempt and author assumptions based on more than 20 years of research in the field, motivated below.

For some of the analyses, a special case labelled ‘no transition’ is also included. In this case, no hydrogen usage has been included for transportation or indusattempt and the only transport segments utilizing direct electricity are 50% of cars and butilizes. We do not provide any details on whether or how the energy transition takes place in transportation and indusattempt in this case. It is included to clarify the impact of hydrogen usage across all sectors. Since the biofuel prio scenario includes almost no hydrogen for transportation (only a compact amount of hydrogen in refineries for production of biogenic jet fuel), this could be compared with the no transition case to examine the effect of only utilizing hydrogen in indusattempt.

Fuel mix represents a future where many types of fuels coexist. E-fuel is utilized for aviation corresponding to the Refuel EU Aviation regulation and electricity and hydrogen is utilized on some shorter distances. Shipping utilizes a mix of fuels, with a slight preference for ammonia, in all segments except passenger transportation due to its toxicity. The four remaining scenarios represent futures where one energy carrier is prioritized. Elec prio has a high share of directly electrified vehicles utilizing batteries. In this scenario, direct hydrogen utilize is avoided and it is only utilized to some extent ‘indirectly’ through e-fuels. Where battery electric propulsion might not be possible becautilize of technical limitations, this scenario utilizes a mix similar to fuel mix. H2 prio has a high share of indirect electrification. Gaseous hydrogen is utilized in heavy road transport and liquid hydrogen is utilized for shipping and aviation, considering projections that hydrogen may be able to serve medium-distance flights⁴⁵. This scenario emphasizes carbon-free energy carriers and the segments of shipping where direct liquid hydrogen might be technically insufficient, ammonia is utilized instead. E-fuel prio has a high share of vehicles and vessels running on e-fuels and biofuel prio has a high share of vehicles and vessels running on biofuels. In all scenarios, a large part of road traffic, as well as a minor share of some shipping segments operating on short distances, are electrified. This is also intfinished to account for some onboard energy utilize being offset by onshore power.

Hydrogen demand is distributed to specific geospatial node locations for each scenario. There are 10,077 nodes in H2 prio and 4,312 nodes in fuel mix, since hydrogen is utilized directly in trucks, shipping and aviation. There are 276 nodes in the remaining scenarios.

Modelling methods

This study is primarily based on two models: the SVENG model for simulating energy demand from transportation in 2050 (presented for trucks in ref. ²⁰) and the Multinode model for modelling electricity system investments and dispatch year 2050. Outputs from the Multinode model are, for example, electricity generation and investments from different technologies and marginal electricity costs for different regions around Europe²¹. Hydrogen demand for indusattempt was modelled separately utilizing the SVENG model, as well as water utilize and land utilize. Maps of demand nodes are given in Extfinished Data Figs. 1–5 and a summary of total relative electricity system costs per scenario is given in Supplementary Note 2.

Demand modelling

The main modules of SVENG build on origin–destination data for calculating energy demand for individual trips and allocating this demand to specific geographical sites. This is utilized for long-haul trucks, shipping and aviation. Energy demand from short-distance transportation modes, such as cars, butilizes and regional trucks, is estimated on the basis of average distance regional transportation work. All industries considered in this study, steel, ammonia and HVC production, are considered to transition completely to hydrogen-based processes. Their hydrogen demand is determined in relation to their output. Detailed descriptions of demand modelling are given in following sections, complemented by further detail in Supplementary Note 4.

We utilized logistics origin–destination data from ref. ⁴⁶ to represent individual trips in heavy-duty road transport, shipping and aviation. Short-distance trucks were modelled separately on the basis of aggregate transport work, which is described further in Supplementary Note 4. Transportation work, when calculating demand for liquid fuels and electric propulsion for cars and butilizes, was also handled differently. Liquid fuel demand was calculated utilizing the total Europe transportation work from the JRC IDEES Europe-dataset⁴⁷ and, for transportation electricity, data from Multinode were utilized, see ref. ²¹.

For the long-distance transportation segments covered in SVENG, individual linear regression models were built for each counattempt, for national and international road transport, all different shipping segments and aviation. These correlate outgoing transportation work from Eurostat^{48,49,50,51,52} with gross domestic product based on purchasing power parity (GDP_PPP) from the World Bank^53,54. A growth factor for transportation work in each counattempt and segment is determined by applying this model to a value for projected GDP_PPP in 2050 under the shared socioeconomic pathway scenario 2 (SSP 2), modeled by IIASA⁵⁵. Further details on the linear regression modes are given in Supplementary Note 4 (with descriptions of some exceptions). Car and bus transport, considered in bulk when calculating liquid fuel demand, is projected onto 2050 utilizing the same methodology on total values for all Europe. For transportation work growth and its influence on electricity demand for cars and butilizes, see ref. ²¹.

Direct electricity for battery electric vehicles and electricity for hydrogen production, was allocated to the geographical location of charging points and hydrogen refuelling stations, respectively. Electricity for hydrogen production for further conversion into e-fuels is modelled according to geographic points for European refineries and fuel production is considered to be distributed to fuel production sites according to EU-ETS reported emissions as described in Supplementary Note 2. Production of e-ammonia is distributed onto current ammonia plants around Europe, according to current ammonia production, listed with references in Supplementary Data.

Road energy demand is allocated for electricity and direct hydrogen to truck stop locations from ref. ⁵⁶, utilizing the SVENG model as described in ref. ²⁰. The SVENG model has for this work also been adapted to model geospatial charging distribution for long-haul trucks utilizing electric drivetrains, in addition to hydrogen refuelling that is modelled in ref. ²⁰. In addition, we have added local and shorter-distance regional freight. Road passenger transportation (cars and butilizes) has also been added, calculated in bulk for all Europe. These additions to the SVENG model are explained further in Supplementary Note 4.

Shipping energy demand is calculated individually for all ship routes starting in Europe. Electricity demand and hydrogen demand, for the entire route, is allocated back to the starting port. We utilized reported data from EU MRV⁵⁷ to calculate energy utilize per unit of transportation work (tkm) for the different considered shipping segments. The resulting values are given in Supplementary Tables 6 and 7.

Aviation energy demand is also calculated individually for each aviation route starting in Europe, multiplied by the modelled annual number of aircraft. Again, electricity and hydrogen demand, for the entire route, is allocated back to the starting airport. Energy utilizes for aircraft in the different segments have been provided in discussion with indusattempt experts. These are described in Supplementary Table 10.

For road transport, all liquid fuels are considered to be produced through the Fischer–Tropsch process. For shipping, the biofuel pathway is represented by biomethanol and the e-fuels pathway includes both e-methanol and e-ammonia, as indicated in Supplementary Fig. 4. For aviation, the biofuel pathway is ethanol-to-jet and the e-fuels pathway is e-methanol-to-jet. Propulsion efficiency, production efficiency and hydrogen usage are given in Supplementary Tables 3, 8, 9 and 11.

Hydrogen utilize for steel, ammonia and HVC production is calculated in relation to facility output, with further details and data given in Supplementary Note 4. Ammonia production, specifically, has been gathered from several sources, each of which is listed in Supplementary Data. After calculating the annual energy demand for each node (representing, for example, a hydrogen refuelling station or a steel plant), this demand was combined with an hourly operational profile. The profile represents demand as a full-year 8,760-h time-step series in each node, with profiles varying between node type. These data are supplied along with the article for each scenario, to allow modelling the energy system with an hourly resolution. The details around these data are explained further in Supplementary Note 4.

Water stress modelling

Water resources are subject to different kinds of pressure. Water stress risk relates available water to total withdrawal of water, of which some is returned to the source. Water depletion risk relates available water to total consumption of water, which is the part that is embedded in the product, lost as steam and so on, and not returned²². For every kilogram of hydrogen produced, 30 l of water is estimated to be withdrawn from the source⁵⁸, 15 l of which is consumed and not returned to the basin²⁵.

These pressures are, for each scenario, calculated from the total hydrogen demand from all nodes in each sub-basin throughout Europe. The Aqueduct 4.0 dataset⁵⁹ contains data on projected future freshwater risk for each of these sub-basins²². Each sub-basin in that dataset is assessed according to different types of projected risks for water management under different scenarios, from low to extremely high. Their business-as-usual scenario for 2050 is utilized for the assessments in this study.

When comparing water stress and water depletion, respectively, to the pressures in the Aqueduct 4.0 database, the annual additional water pressure is calculated by comparing annually projected available water with annually projected water withdrawal. The water for hydrogen production is, for both comparisons, added to the water withdrawal, since projected water depletion is not included in the dataset. This is done for each sub-basin separately and compared with the Aqueduct modelled annual water availability.

Electricity system modelling

Electricity system investments and dispatch are modelled utilizing the Multinode electricity system model. A recent description of this model is given in ref. ²¹. Multinode is a cost-minimizing linear optimization system built in GAMS, solved utilizing Cplex. It is essentially a so-called ‘greenfield’ model, meaning that it builds an optimal electricity system 2050 from scratch without considering the current power generation fleet. However, some details such as current hydropower and transmission capacity are included as a starting point for the model. This is further elaborated in ref. ²¹. The model includes 50 regions on national and sub-national levels in Europe, specified geographically in ref. ⁶⁰. Also, electrolyser capacity and hydrogen storage are modelled utilizing this model. The Multinode model is run for the year 2050. A breakdown of hydrogen demand simulated in SVENG for each scenario, as well as details on electricity generation and investments, are elaborated in Supplementary Fig. 1. The baseline electricity demand, to which the hydrogen demand in this study is added, are taken from ref. ²¹. Technology costs for electricity and hydrogen generation and storage are largely based on data from the Danish Energy Agency⁶¹ and are provided in Supplementary Tables 1 and 2. Like refs. ^9,11,21, we consider the regions to be self-sufficient in their hydrogen production and consumption and no trade in hydrogen is done between the regions. Electricity, however, can be traded between regions, according to the transmission capacity invested.

After the annual hydrogen and transportation electricity demand has been modelled on the node level utilizing SVENG, as displayn in Fig. 1, these are aggregated region by region and added to the Multinode optimization model, on top of the baseline 2050 load. In this study we utilized 6-h time steps, utilizing the baseline electricity and heat utilize profiles from ref. ²¹. Belarus, Ukraine and the Balkans, although simulated in SVENG and thus part of the hydrogen demand dataset, are not part of the Multinode model and therefore left out of the electricity generation and cost assessment.

The average marginal electricity cost is the weighted marginal value of electricity (€₂₀₂₄) in each time step, for each region. Costs do not include other costs like taxes, or local or regional electricity distribution costs. Average weighted marginal cost is calculated for each region by multiplying the average hourly marginal cost per 6-h time step with the electricity utilize in the same time step, summing the total value per region and dividing by the total electricity utilize in that region. The European average marginal cost is weighted according to electricity demand for each region. The electricity generation investment costs have been annualized considering total investment cost and technical lifetime for each technology.

Land utilize modelling

The land requirements for electricity production in each scenario are calculated on the basis of the electricity generation mix modelled in Multinode. Land utilize factors for thermal and hydropower are from ref. ³⁴ based on UNECE³⁵. Land utilize for offshore wind is also taken from the latter. The land utilize factor for onshore wind is chosen as the median infrastructural land utilize factor presented by Turkovska et al.³³ (3.2 m² MWh⁻¹). This is a lot compacter than the average wind farm size presented by Ritchie³⁴ at 99 m² MWh⁻¹ and also than the average land utilize intensity that can be derived from the ENSPRESO dataset⁶² at 55 m² MWh⁻¹. The chosen value only represents land utilize for permanent infrastructure and technology connected to wind power generation, which means we are considering the potential for co-generation of values, such as foresattempt or farming, between power generation installations. Using a larger factor would thus be misrepresentative for this comparison. As in one of the ENSPRESO datasets, solar power land utilize intensity was calculated assuming 170 MW km⁻² technology, which at a capacity factor of 0.12 requires 5.6 m² MWh⁻¹. This is lower than the ground-mounted PV figures from ref. ³⁴ and at the higher finish of the spectrum for rooftop photovoltaic (PV). Since rooftop solar is the dominating kind in the EU⁶³, with lower acceptance issues considering deployment compared with ground-mounted solar and with a large continued potential for further deployment⁶⁴, we consider this factor to be representative of the total land utilize for solar power. Different land utilize intensities for different assumptions are presented and discussed further in Supplementary Note 3.

Total land utilize from biofuels production is calculated utilizing the EU average land utilize intensity, 59 GJ of biofuels per hectare, modelled for a mix of crops under the ILUC directive restriction of maximum 7% conventional biofuels in the Globiom report⁶⁵. Biomass from alternative feedstock streams (Annex IX feedstocks + miscanthus, as described below) are considered to first offset jet fuel, since this regulation requires Annex IX feedstocks to be utilized for biofuels to be counted towards tarobtains in the Refuel EU Aviation legislation. Jet fuel has a higher feedstock required per unit of energy than road and shipping biofuel, due to conversion via ethanol-to-jet. Any remaining biomass from alternative streams then offset road and shipping feedstocks. The domestic availability of Annex IX feedstocks for utilize in the EU transportation sector in 2050 have been estimated by Soler³¹ and the minimum estimate comprise the ‘residue potential’ case in Fig. 6. The annual miscanthus feedstock is assumed from Englund et al.³² where they modelled annual miscanthus output in a 3-yr rotation toobtainher with 4 years of other crops. This output is recalculated to biofuels utilizing a factor of 0.22 kg of ethanol per kilogram of miscanthus dry mass, estimated from ref. ⁶⁶.