The four prio-scenarios are designed to test the impacts of using the respective energy carriers at a very large scale (within the limits of what the authors deem theoretically possible) by 2050. This is intended 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 using 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, using discussions with industry 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 industry and the only transport segments using direct electricity are 50% of cars and buses. We do not provide any details on whether or how the energy transition takes place in transportation and industry 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 small 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 using hydrogen in industry.
Fuel mix represents a future where many types of fuels coexist. E-fuel is used for aviation corresponding to the Refuel EU Aviation regulation and electricity and hydrogen is used on some shorter distances. Shipping uses 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 using batteries. In this scenario, direct hydrogen use is avoided and it is only used to some extent ‘indirectly’ through e-fuels. Where battery electric propulsion might not be possible because of technical limitations, this scenario uses a mix similar to fuel mix. H2 prio has a high share of indirect electrification. Gaseous hydrogen is used in heavy road transport and liquid hydrogen is used 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 used 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 intended to account for some onboard energy use 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 used 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 industry was modelled separately using the SVENG model, as well as water use and land use. Maps of demand nodes are given in Extended 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 used for long-haul trucks, shipping and aviation. Energy demand from short-distance transportation modes, such as cars, buses 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 used 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 buses, was also handled differently. Liquid fuel demand was calculated using the total Europe transportation work from the JRC IDEES Europe-dataset47 and, for transportation electricity, data from Multinode were used, see ref. 21.
For the long-distance transportation segments covered in SVENG, individual linear regression models were built for each country, for national and international road transport, all different shipping segments and aviation. These correlate outgoing transportation work from Eurostat48,49,50,51,52 with gross domestic product based on purchasing power parity (GDPPPP) from the World Bank53,54. A growth factor for transportation work in each country and segment is determined by applying this model to a value for projected GDPPPP 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 using the same methodology on total values for all Europe. For transportation work growth and its influence on electricity demand for cars and buses, 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, using 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 using 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 buses) 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 used reported data from EU MRV57 to calculate energy use 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 uses for aircraft in the different segments have been provided in discussion with industry 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 use 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 used 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 using 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 using 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 using 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 using SVENG, as shown 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 used 6-h time steps, using the baseline electricity and heat use 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 use in the same time step, summing the total value per region and dividing by the total electricity use 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 use modelling
The land requirements for electricity production in each scenario are calculated on the basis of the electricity generation mix modelled in Multinode. Land use factors for thermal and hydropower are from ref. 34 based on UNECE35. Land use for offshore wind is also taken from the latter. The land use factor for onshore wind is chosen as the median infrastructural land use factor presented by Turkovska et al.33 (3.2 m2 MWh−1). This is a lot smaller than the average wind farm size presented by Ritchie34 at 99 m2 MWh−1 and also than the average land use intensity that can be derived from the ENSPRESO dataset62 at 55 m2 MWh−1. The chosen value only represents land use for permanent infrastructure and technology connected to wind power generation, which means we are considering the potential for co-generation of values, such as forestry 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 use 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 end 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 use for solar power. Different land use intensities for different assumptions are presented and discussed further in Supplementary Note 3.
Total land use from biofuels production is calculated using the EU average land use 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 used for biofuels to be counted towards targets in the Refuel EU Aviation legislation. Jet fuel has a higher feedstock need 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 use 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 together with 4 years of other crops. This output is recalculated to biofuels using a factor of 0.22 kg of ethanol per kilogram of miscanthus dry mass, estimated from ref. 66.
Reporting summary
Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.