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Potential and risks of hydrogen-based e-fuels in climate change mitigation

Abstract

E-fuels promise to replace fossil fuels with renewable electricity without the demand-side transformations required for a direct electrification. However, e-fuels’ versatility is counterbalanced by their fragile climate effectiveness, high costs and uncertain availability. E-fuel mitigation costs are €800–1,200 per tCO2. Large-scale deployment could reduce costs to €20–270 per tCO2 until 2050, yet it is unlikely that e-fuels will become cheap and abundant early enough. Neglecting demand-side transformations threatens to lock in a fossil-fuel dependency if e-fuels fall short of expectations. Sensible climate policy supports e-fuel deployment while hedging against the risk of their unavailability at large scale. Policies should be guided by a ‘merit order of end uses’ that prioritizes hydrogen and e-fuels for sectors that are inaccessible to direct electrification.

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Fig. 1: Basic principle of e-fuels in an energy system.
Fig. 2: Energy efficiencies for major conversion steps from electricity input to useful energy.
Fig. 3: Life-cycle GHG emissions for different fuels and transport applications as a function of the life-cycle carbon intensities of electricity used for battery charging, hydrogen and e-fuel production.
Fig. 4: Levelized cost and fuel-switching CO2 prices of e-fuels.
Fig. 5: Marginal abatement cost curves (that is, fuel-switching CO2 prices).

Data availability

The life-cycle analysis for passenger cars and trucks can be reproduced with the open-source tools carculator51 and carculator_truck68. The electrolysis cost and efficiency data are available in ref. 59. All other data are available from the corresponding author on request.

Code availability

The modified version of ecoinvent used in this analysis is generated from ecoinvent 3.7.1, which is available at https://github.com/romainsacchi/premise. The modified version is available from the authors on reasonable request.

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Acknowledgements

We thank J. Knapp, S. Bi, F. Schreyer, F. Benke, A. Odenweller, B. Pfluger, J. Strefler, F. Stöckl and S. Madeddu for their critical reviews, valuable discussion and comments, and C. Wang for sharing Australian electricity price data. We gratefully acknowledge funding from the START project (FKZ03EK3046A), from the Kopernikus-Ariadne project (FKZ 03SFK5A) by the German Federal Ministry of Education and Research and from PSI’s ESI platform.

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Contributions

F.U. and G.L. designed the study and derived the main conclusions. F.U. coordinated the work, conducted the cost calculations and efficiency comparisons, derived the main figures and did most of the writing. G.L. substantially contributed to the writing. R.S., C.B. and A.D. carried out the life-cycle GHG analysis and produced the associated figures. J.E. conducted the majority of the literature review and contributed to the data curation and code development. All coauthors reviewed and edited the text.

Corresponding author

Correspondence to Falko Ueckerdt.

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The authors declare no competing interests.

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Peer review information Nature Climate Change thanks Pilar Lisbona and Yuanrong Zhou for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Levelized cost and fuel-switching CO2 prices of e-fuels with zero emissions approximation.

Same as Fig. 4, but here based on the assumption that e-fuels (including those that are fossil CCU based) are evaluated as if they would not cause GHG emissions. a, Levelized cost (and its components) and fuel-switching CO2 prices for e-methane (shipped from Northwest Africa to northwestern European ports, based on DAC) for 2020–25, 2030 and 2050, in comparison to European wholesale market natural gas prices for 2010–20. The + shows total costs. The box plots indicate uncertainties based on a sensitivity study (see Supplementary Information S2). b, same as ‘a’, but for e-gasoline compared to wholesale gasoline prices. c, Levelized costs (including CO2 costs) of e-fuels and fossil fuels for 2030 as a function of CO2 prices. The + on y axis are the direct costs (without CO2) shown in panel a and b. The slopes represent the life-cycle carbon intensities of the respective fuels. The circles mark the intersections of fossil and e-fuel costs, which are the break-even points that determine fuel-switching CO2 prices (shown on the 2nd y axis in a and b). d, Fuel-switching CO2 prices in time, for e-fuels and hydrogen, in comparison to CO2 price trajectories of global 1.5–2 °C climate mitigation scenarios62. Uncertainty ribbons of the e-fuels lines represent 25th-75th percentiles. Note that when calculating fuel-switching CO2 prices we compare costs (for e-fuels) with wholesale prices (for fossil fuels). We hereby take a system planner perspective on climate mitigation seeking for a cost-efficient energy transformation. The extent to which e-fuel costs translate into e-fuel prices depend on competition, structure and regulation of future e-fuel markets.

Extended Data Fig. 2 Levelized cost and fuel-switching CO2 prices.

a, for e-methane (hydrogen shipped from Northwest Africa to northwestern European ports, based on fossil CCU) for 2020–25, 2030 and 2050, in comparison to European wholesale market natural gas prices for 2010–20. The + shows total costs. The box plots indicate uncertainties based on a sensitivity study (see S5). b, same as ‘a’, but for e-gasoline compared to wholesale gasoline prices. c, same as ‘a’, but for liquefied hydrogen compared to natural gas and d, to gasoline. Hydrogen is no perfect substitute to fossil fuels and thus requires additional costs for an end-use transformation, which are not reflected in the cost bars and fuel-switching CO2 prices.

Extended Data Fig. 3 Marginal abatement cost curves including hydrogen (that is, fuel-switching CO2 prices).

Same as main Fig. 5, but here also including direct use of hydrogen. In 2020–25 and for e-methane (replacing natural gas), liquid e-fuels (replacing fossil liquids) and hydrogen (replacing liquids or gases) from the cost calculations shown in Fig. 4 and Extended Data Fig. 2, as well as direct electrification alternatives (green, illustrative curve) across non-electric energy and industrial sectors in the OECD (2014 energy end-use data from IEA ETP 201763). The additional end-use transformation costs of using hydrogen are illustrative only. Shaded areas represent uncertainty ranges. The three categories of energy end uses are sorted according to the costs of directly electrifying the respective applications (horizontal sorting from low to high costs of direct electrification). Within each of the four categories, the sectors are sorted according to their size.

Extended Data Fig. 4 Life-cycle GHG emissions: Sensitivity analyses associated with CO2 sources.

Left, different assumptions for heat supply for DAC. Waste heat (for example from an integration with a renewable-based hydrocarbon synthesis) is GHG emission free. Market heat refers to an average mix of heat sources used by the petrochemical industry in the EU. Natural gas heat is heat only provided by natural gas boilers. Right, different assumptions for fossil-CCU pathways on the attribution of direct exhaust of fossil CO2 emissions between the e-fuel application and the fossil CO2 source application where CO2 is captured. For example, ‘alloc 0:100’ refers to 0 % allocated to the e-fuel application and 100 % to the fossil CO2 source application. The rest of the figure is the same as main Fig. 3: Life-cycle GHG emissions for light-duty vehicles (left), heavy-duty trucks (middle), and planes (right), as a function of the carbon intensity of electricity used for battery charging, hydrogen and e-fuel production. Comparing e-fuel options (CO2 from DAC or fossil CCU), hydrogen fuel cells (H2 from electrolysis), direct electrification with batteries and fossil options, all of which is based on anticipated technological progress in 2030 and 2050 using the life-cycle assessment model carculator51. Vertical lines show carbon intensities of electricity for selected geographies (for 2017–18). The secondary x axis (bottom) translates the carbon intensity of electricity into an equivalent share of renewable electricity generation (equal shares of wind and solar PV electricity, where the remaining non-renewable generation is natural gas and coal electricity in equal shares).

Extended Data Fig. 5 Life-cycle GHG emissions: Sensitivity analyses for 2030 and 2050 technology.

Life-cycle GHG emissions for light-duty vehicles (left), heavy-duty trucks (middle), and planes (right), as a function of the carbon intensity of electricity used for battery charging, hydrogen and e-fuel production. Comparing e-fuel options (CO2 from DAC or fossil CCU), hydrogen fuel cells (H2 from electrolysis), direct electrification with batteries and fossil options, all of which is based on anticipated technological progress in 2030 and 2050 using the life-cycle assessment model carculator51 and carculator_truck68. Vertical lines show carbon intensities of electricity for selected geographies (for 2017–18). The secondary x axis (bottom) translates the carbon intensity of electricity into an equivalent share of renewable electricity generation (equal shares of wind and solar PV electricity, where the remaining non-renewable generation is natural gas and coal electricity in equal shares).

Extended Data Fig. 6 Breakdown of overall life-cycle GHG emissions of 2030 and 2050 medium-sized passenger vehicles for carbon intensities of electricity for several regions, quantified using the LCA model presented in Sacchi et al.51.

Numbers in each panel title are the GHG intensity of average electricity supply mixes (for 2017–18)68. In the legend, ‘EoL’: End of life (of vehicles); ‘energy chain’ represents net emissions associated with fuel supply. CO2 for e-fuels is supplied via DAC or fossil CCU.

Extended Data Fig. 7 Breakdown of overall life-cycle GHG emissions of 2030 and 2050 heavy-duty trucks for carbon intensities of electricity for several regions, quantified using the LCA model presented in Sacchi et al.68.

Numbers in each panel title are the GHG intensity of average electricity supply mixes (for 2017–18)50. In the legend, ‘EoL’: End of life (of vehicles); ‘energy chain’ represents net emissions associated with fuel supply. CO2 for e-fuels is supplied via DAC or fossil CCU.

Extended Data Fig. 8 Breakdown of overall life-cycle GHG emissions of 2030 and 2050 long-distance planes for carbon intensities of electricity for several regions, quantified using the LCA model presented in Sacchi et al.51.

Numbers in each panel title are the GHG intensity of average electricity supply mixes (for 2017–18)50. In the legend, ‘EoL’: End of life (of vehicles); ‘energy chain’ represents net emissions associated with fuel supply. CO2 for e-fuels is supplied via DAC or fossil CCU. ‘LNB’: Long-Nose Body.

Extended Data Fig. 9 Life-cycle GHG emissions of fuels for 2030 and 2050.

This includes all upstream as well as combustion related (direct) emissions without specifying the end-use application or energy service. These values are the basis for the main specification of calculating fuel-switching CO2 prices presented in Figs. 4, 5 and Extended Data Figs. 2 and 3.

Extended Data Fig. 10 Literature review: electrolysis data.

Specific capacity costs (left) and efficiencies (right) of electrolysis (PEMEC, AEC, SOEC) based on a literature review59.

Supplementary information

Supplementary Information

Supplementary S1 and S2, and references.

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Ueckerdt, F., Bauer, C., Dirnaichner, A. et al. Potential and risks of hydrogen-based e-fuels in climate change mitigation. Nat. Clim. Chang. 11, 384–393 (2021). https://doi.org/10.1038/s41558-021-01032-7

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