pith. sign in

arxiv: 2505.09277 · v2 · pith:PSAOYN2Unew · submitted 2025-05-14 · ⚛️ physics.soc-ph

A Minimal Methanol Backstop for High Electrification Scenarios

Philipp Glaum , Fabian Neumann , Markus Millinger , Tom Brown This is my paper

Pith reviewed 2026-05-22 15:35 UTC · model grok-4.3

classification ⚛️ physics.soc-ph
keywords methanol backstopenergy system modelcarbon neutralityelectrificationhydrogen economyhard-to-electrify sectorsEuropean energy system
0
0 comments X

The pith

Methanol backstop raises total system costs by 2.4% over hydrogen in carbon-neutral European models.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper explores how to meet residual fuel needs in sectors like aviation, shipping and backup power once land transport and buildings are largely electrified. It proposes a minimal methanol backstop that produces the liquid fuel from hydrogen and carbon monoxide while drawing in biogenic carbon from biomass wastes. A European energy system model run under strict carbon-neutral constraints finds that this methanol pathway increases overall costs by 2.4 percent relative to a hydrogen pathway. The extra cost stays below 6 percent in sensitivity tests. The authors conclude that the modest premium is acceptable because methanol avoids the transport, storage and coordination problems that accompany hydrogen infrastructure.

Core claim

Using a European energy system model constrained to be carbon-neutral, methanol-based systems increase total system costs by 2.4% relative to hydrogen-based systems, an increase that remains below 6% across sensitivities. The modest cost premium is justified by methanol's advantages as a liquid fuel that is easy to store and transport and that integrates biogenic carbon from decentralized biomass wastes and residues without requiring major new infrastructure.

What carries the argument

Minimal methanol backstop: a liquid-fuel supply route that meets residual demand in highly electrified systems by combining hydrogen with carbon monoxide and biogenic carbon sources.

If this is right

  • Methanol supplies aviation, shipping and backup power as a storable liquid without new dedicated pipelines or large-scale storage facilities.
  • Biogenic carbon from waste and residue biomass can be incorporated directly into the fuel chain.
  • High-electrification pathways can proceed with lower risk of infrastructure lock-in.
  • Total system costs remain competitive even when input assumptions on costs or efficiencies are varied.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Coordination difficulties between producers and consumers may be lower than in a hydrogen-centric system because methanol uses existing liquid-fuel logistics.
  • The same modeling approach could be applied to other large regions to check whether biomass availability changes the cost comparison.
  • Policy standards for sustainable aviation and marine fuels could favor methanol routes if the modeled cost gap holds in practice.

Load-bearing premise

The energy system model and its input data accurately capture real-world costs, efficiencies, infrastructure requirements, and integration challenges for both methanol and hydrogen pathways under carbon-neutral constraints.

What would settle it

Empirical data from a full-scale demonstration project or updated cost database showing that actual hydrogen infrastructure and coordination costs are substantially lower than modeled, or that methanol distribution and end-use conversion add more than a 6% system-wide penalty.

Figures

Figures reproduced from arXiv: 2505.09277 by Fabian Neumann, Markus Millinger, Philipp Glaum, Tom Brown.

Figure 1
Figure 1. Figure 1: Methanol production and consumption pathways. Own figure created using icons designed by OpenMoji [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Comparison of total annual system costs for the different scenarios. The panel A shows the absolute cost of the ‘all networks’ scenario. The panel B shows the cost increases and decreases of the other scenarios by component relative to the ‘all networks’ scenario. The net absolute and relative cost difference is shown at the top of each bar. A breakdown of the cost groups is given in Table S3. Default scen… view at source ↗
Figure 3
Figure 3. Figure 3: Energy balances of the different scenarios for hydrogen, methane, methanol and oil. The positive values show supply and the negative values show consumption. The bold number above each bar shows the total supply or con￾sumption in TWh/a. MtO, MtA and MtK are competitive with the green naphtha to steam cracker route for HVC and Fischer￾Tropsch for kerosene production because of the higher product selectivit… view at source ↗
Figure 4
Figure 4. Figure 4: Methanol storage levels and backup power plant dispatch. Storage profile is shown as a solid line. The stacked area chart shows the dispatch of methanol CHP and backup power plants. The storage behavior of methanol is shown in [PITH_FULL_IMAGE:figures/full_fig_p009_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Supply fractions of secondary/final demand in residential and industry heat, road transport, aviation, shipping, and high-value chemicals (HVC). The demands for residential and industry heat are in TWh thermal, while the demands for transport and HVC are in TWh oil equivalent. the majority of the methanol is used in shipping (55%), the production of aviation fuels (67%) and high￾value chemicals (76%). Meth… view at source ↗
Figure 6
Figure 6. Figure 6: Merit order curve of levelized costs for different methanol routes and final demands. The costs are disag￾gregated into cost elements and consider the endogenous CO2 price for fossil routes. Costs are reported as demand-weighted averages across regions and time. For technologies that store captured carbon, resulting revenue streams are represented as negative cost elements. The dashed line indicates the ne… view at source ↗
Figure 7
Figure 7. Figure 7: Sankey diagram of annual carbon flows in the ‘minimal methanol backstop’ scenario. of biogenic CO2 is used for biomethanol and e-biomethanol production, and 57 Mt/a is captured. Biogas enters the system with 115 Mt/a biogenic CO2 , mostly converted to e-biomethanol. In total, 200 Mt/a of fossil CO2 enters the system, 70 Mt/a from fossil oil and 130 Mt/a through fossil process emissions in industry like cem… view at source ↗
Figure 8
Figure 8. Figure 8: Geographical patterns of methanol production and consumption in the ‘all networks’ and ‘minimal methanol backstop’ scenario. Upper semi-circles show the production of methanol by technology route, while the lower semi-circles show the consumption of methanol by application. The choropleth layer shows demand-weighted average hy￾drogen prices per region. the presence of large flexible loads in the form of el… view at source ↗
Figure 9
Figure 9. Figure 9: Cost sensitivity for the different sensitivity settings and the scenarios. The top panel shows the cost differ￾ence between the ‘all network’ and the ‘minimal methanol backstop’ scenario for the different sensitivity settings. The lower panel compares the absolute cost difference for the ‘minimal methanol backstop’ scenario between the default setting (with CO2 network) and the other sensitivity settings. … view at source ↗
Figure 10
Figure 10. Figure 10: Sensitivity of CO2 prices for sensitivity settings. The spider web charts show the variation in CO2 prices for the ‘all networks’ and the ‘minimal methanol backstop’ scenario in the different sensitivity settings. Discussion The final energy supply in net-zero scenarios tends to be dominated by electricity, followed by carbona￾ceous liquids for long-haul transport and the chemical industry. This result is… view at source ↗
read the original abstract

Electrification of sectors such as land transport and building heating is a cost-effective pathway to deep decarbonization. However, some sectors still require energy-dense fuels -- including aviation, shipping and backup power -- or chemical feedstocks. While a 'hydrogen economy' is often proposed to fill these hard-to-electrify gaps, it faces challenges in transport, storage, and infrastructure coordination. We introduce a 'minimal methanol backstop' to supply residual demand in highly-electrified systems. As a liquid fuel, methanol is easy to store and transport, and avoids infrastructure lock-in. Produced from hydrogen and carbon monoxide, it can help integrate biogenic carbon from decentralized biomass wastes and residues. Using a European energy system model constrained to be carbon-neutral, we show that methanol-based systems increase total system costs by 2.4% relative to hydrogen-based systems, an increase that remains below 6% across sensitivities. We argue that this modest cost premium is justified by reduced infrastructure complexity.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The manuscript proposes a 'minimal methanol backstop' to meet residual demand for energy-dense fuels and feedstocks in highly electrified, carbon-neutral European energy systems. Using a constrained optimization model, it reports that methanol-based configurations raise total system costs by 2.4% relative to hydrogen-based ones, with the premium remaining below 6% across sensitivities. The authors argue this modest increase is justified by methanol's advantages in storage, transport, and avoidance of infrastructure lock-in while facilitating integration of biogenic carbon from wastes and residues.

Significance. If the model parameterization proves robust, the work supplies a concrete, quantitative comparison that could inform infrastructure choices for hard-to-abate sectors such as aviation, shipping, and backup power. The use of a carbon-neutral constrained European energy system model together with sensitivity testing constitutes a clear methodological strength and supports the central claim of a limited cost differential.

major comments (2)
  1. [Abstract] Abstract: The central quantitative claim of a 2.4% cost increase (and <6% bound in sensitivities) is load-bearing for the paper's argument yet rests on the model's internal encoding of hydrogen transport/storage coordination penalties versus methanol's liquid-fuel advantages and the efficiency losses associated with producing methanol from electrolytic hydrogen plus biogenic CO2. No details on these cost vectors, efficiency assumptions, or external calibration against independent infrastructure studies are supplied in the abstract, leaving the result's sensitivity to parameterization unverified.
  2. [Model and results sections] Model and results sections: The weakest assumption—that the energy system model accurately captures real-world costs, efficiencies, infrastructure requirements, and integration challenges—is not shown to have been validated outside the modeling framework. If key infrastructure or carbon-accounting parameters deviate from deployment data, the reported modest premium could move outside the <6% sensitivity band, directly affecting the policy-relevant conclusion.
minor comments (1)
  1. [Introduction] The phrase 'minimal methanol backstop' is introduced without an explicit operational definition; adding a short clarifying sentence in the introduction would improve accessibility.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive review and recommendation for major revision. We address each major comment below and have revised the manuscript to improve clarity on assumptions and parameterization.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The central quantitative claim of a 2.4% cost increase (and <6% bound in sensitivities) is load-bearing for the paper's argument yet rests on the model's internal encoding of hydrogen transport/storage coordination penalties versus methanol's liquid-fuel advantages and the efficiency losses associated with producing methanol from electrolytic hydrogen plus biogenic CO2. No details on these cost vectors, efficiency assumptions, or external calibration against independent infrastructure studies are supplied in the abstract, leaving the result's sensitivity to parameterization unverified.

    Authors: We agree that the abstract would benefit from brief additional context on the key assumptions. In the revised version, we have updated the abstract to note the efficiency losses in methanol synthesis from electrolytic hydrogen and biogenic CO2, as well as the model's encoding of methanol's storage and transport advantages relative to hydrogen infrastructure penalties. Full details on cost vectors, efficiencies, and citations to external infrastructure studies (e.g., IRENA and IEA reports) remain in the methods and supplementary material. revision: yes

  2. Referee: [Model and results sections] Model and results sections: The weakest assumption—that the energy system model accurately captures real-world costs, efficiencies, infrastructure requirements, and integration challenges—is not shown to have been validated outside the modeling framework. If key infrastructure or carbon-accounting parameters deviate from deployment data, the reported modest premium could move outside the <6% sensitivity band, directly affecting the policy-relevant conclusion.

    Authors: We acknowledge that the manuscript does not present a comprehensive external validation of the full integrated model against real-world deployment data, which is a genuine limitation for any large-scale energy system optimization study due to data scarcity. The model parameters are drawn from and cross-checked against multiple peer-reviewed sources and reports on hydrogen and methanol infrastructure costs and efficiencies; we have now added explicit citations and a dedicated paragraph in the methods section discussing these sources and the associated uncertainties. The existing sensitivity analysis already tests deviations in key parameters while keeping the cost premium below 6%. We have also expanded the discussion of model limitations. revision: partial

Circularity Check

0 steps flagged

No circularity: cost comparison obtained from independent model optimization

full rationale

The paper derives its 2.4% cost premium by executing a carbon-neutral constrained optimization in an energy system model for methanol versus hydrogen scenarios. This constitutes a forward simulation whose output is not equivalent to any input parameter by construction. No self-definitional loops, fitted quantities renamed as predictions, or load-bearing self-citations appear in the provided derivation chain. The model structure and external cost/efficiency data remain independent of the headline result, rendering the finding self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

Central claim depends on validity of an energy system optimization model whose parameters, data inputs, and structural assumptions are not detailed in the abstract; no new entities are postulated.

free parameters (1)
  • Model cost and efficiency parameters
    Typical energy system models contain numerous cost, efficiency, and capacity parameters that are either fitted or drawn from external datasets.
axioms (1)
  • domain assumption The chosen energy system model structure and data accurately represent European infrastructure costs and integration for methanol and hydrogen under carbon neutrality.
    Invoked by the decision to run the model and report its outputs as policy-relevant.

pith-pipeline@v0.9.0 · 5697 in / 1312 out tokens · 48704 ms · 2026-05-22T15:35:39.862690+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

What do these tags mean?
matches
The paper's claim is directly supported by a theorem in the formal canon.
supports
The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends
The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses
The paper appears to rely on the theorem as machinery.
contradicts
The paper's claim conflicts with a theorem or certificate in the canon.
unclear
Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

83 extracted references · 83 canonical work pages · 2 internal anchors

  1. [1]

    Pathak, R

    M. Pathak, R. Slade, P. Shukla, J. Skea, R. Pichs-Madruga, D. Ürge-Vorsatz, Technical summary, in: P. Shukla, J. Skea, R. Slade, A. A. Khourdajie, R. van Diemen, D. McCollum, M. Pathak, S. Some, P. Vyas, R. Fradera, M. Belkacemi, A. Hasija, G. Lisboa, S. Luz, J. Malley (Eds.), Climate Change 2022: Mitigation of Climate Change. Contribution of Working Grou...

    work page doi:10.1017/9781009157926.002 2022

  2. [2]

    rep., International Energy Agency (2024)

    IEA, World Energy Outlook 2024, Tech. rep., International Energy Agency (2024). URLhttps://www.iea.org/reports/world-energy-outlook-2024 27

    work page 2024

  3. [3]

    URLhttps://eur-lex.europa.eu/legal-content/EN/TXT/?uri=COM:2024:63:FIN

    European Commission, Securing our future: Europe’s 2040 climate target and path to climate neu- trality by 2050 building a sustainable, just and prosperous society, European Commission - Commu- nication (2024). URLhttps://eur-lex.europa.eu/legal-content/EN/TXT/?uri=COM:2024:63:FIN

    work page 2040

  4. [4]

    S. Link, A. Stephan, D. Speth, P. Plötz, Rapidly declining costs of truck batteries and fuel cells enable large-scale road freight electrification, Nature Energy 9 (8) (2024) 1032–1039.doi:10.1038/ s41560-024-01531-9. URLhttps://doi.org/10.1038/s41560-024-01531-9

    work page doi:10.1038/s41560-024-01531-9 2024

  5. [6]

    Zeyen, S

    E. Zeyen, S. Kalweit, M. Victoria, T. Brown, Shifting burdens: how delayed decarbonisation of road transport affects other sectoral emission reductions, Environmental Research Letters 20 (4) (2025) 044044.doi:10.1088/1748-9326/adc290. URLhttps://dx.doi.org/10.1088/1748-9326/adc290

    work page doi:10.1088/1748-9326/adc290 2025

  6. [7]

    rep., International Energy Agency (2025)

    IEA, Global EV Outlook 2025, Tech. rep., International Energy Agency (2025). URLhttps://www.iea.org/reports/global-ev-outlook-2025

    work page 2025

  7. [8]

    A. W. Schäfer, S. R. H. Barrett, K. Doyme, L. M. Dray, A. R. Gnadt, R. Self, A. O’Sullivan, A. P. Synodinos, A. J. Torija, Technological, economic and environmental prospects of all-electric aircraft, Nature Energy 4 (2) (2019) 160–166.doi:10.1038/s41560-018-0294-x. URLhttps://doi.org/10.1038/s41560-018-0294-x

    work page doi:10.1038/s41560-018-0294-x 2019

  8. [9]

    Brown, D

    T. Brown, D. Schlachtberger, A. Kies, S. Schramm, M. Greiner, Synergies of sector coupling and transmission reinforcement in a cost-optimised, highly renewable European energy system, Energy 160 (2018) 720–739.doi:10.1016/j.energy.2018.06.222

    work page doi:10.1016/j.energy.2018.06.222 2018

  9. [10]

    Madeddu, F

    S. Madeddu, F. Ueckerdt, M. Pehl, J. Peterseim, M. Lord, K. A. Kumar, C. Krüger, G. Luderer, The CO2 reduction potential for the European industry via direct electrification of heat supply (power- to-heat), Environmental Research Letters 15 (12) (2020) 124004.doi:10.1088/1748-9326/abbd02. URLhttps://dx.doi.org/10.1088/1748-9326/abbd02

    work page doi:10.1088/1748-9326/abbd02 2020

  10. [11]

    J. O. Bockris, A hydrogen economy, Science 176 (4041) (1972) 1323–1323.arXiv:https://www.science. org/doi/pdf/10.1126/science.176.4041.1323,doi:10.1126/science.176.4041.1323. URLhttps://www.science.org/doi/abs/10.1126/science.176.4041.1323

    work page doi:10.1126/science.176.4041.1323 1972

  11. [12]

    V. Vogl, M. Åhman, L. J. Nilsson, Assessment of hydrogen direct reduction for fossil-free steelmak- ing, Journal of Cleaner Production 203 (2018) 736–745.doi:https://doi.org/10.1016/j.jclepro.2018.08. 279. URLhttps://www.sciencedirect.com/science/article/pii/S0959652618326301 28

    work page doi:10.1016/j.jclepro.2018.08 2018

  12. [13]

    van der Zwaan, A

    B. van der Zwaan, A. Fattahi, F. Dalla Longa, M. Dekker, D. van Vuuren, R. Pietzcker, R. Ro- drigues, F. Schreyer, D. Huppmann, J. Emmerling, S. Pfenninger, F. Lombardi, P. Fragkos, M. Kan- navou, T. Fotiou, G. Tolios, W. Usher, Electricity- and hydrogen-driven energy system sector- coupling in net-zero CO2 emission pathways, Nature Communications 16 (1) ...

    work page doi:10.1038/s41467-025-56365-0 2025

  13. [14]

    Haldane, Daedelus or science and the future, Heretics Society, Cambridge, 1923

    J. Haldane, Daedelus or science and the future, Heretics Society, Cambridge, 1923. URLhttp://bactra.org/Daedalus.html

    work page 1923

  14. [15]

    Johnson, M

    N. Johnson, M. Liebreich, D. M. Kammen, P. Ekins, R. McKenna, I. Staffell, Realistic roles for hydrogen in the future energy transition, Nature Reviews Clean Technology (2025).doi:10.1038/ s44359-025-00050-4. URLhttps://doi.org/10.1038/s44359-025-00050-4

    work page doi:10.1038/s44359-025-00050-4 2025

  15. [16]

    Martin, I

    P. Martin, I. B. Ocko, S. Esquivel-Elizondo, R. Kupers, D. Cebon, T. Baxter, S. P. Hamburg, A review of challenges with using the natural gas system for hydrogen, Energy Science & Engineering 12 (10) (2024) 3995–4009.doi:10.1002/ese3.1861

    work page doi:10.1002/ese3.1861 2024

  16. [17]

    Frischmuth, M

    F. Frischmuth, M. Berghoff, M. Braun, P. Härtel, Quantifying seasonal hydrogen storage demands under cost and market uptake uncertainties in energy system transformation pathways, Applied Energy 375 (2024) 123991.doi:10.1016/j.apenergy.2024.123991. URLhttps://doi.org/10.1016/j.apenergy.2024.123991

    work page doi:10.1016/j.apenergy.2024.123991 2024

  17. [18]

    M. Sand, R. B. Skeie, M. Sandstad, S. Krishnan, G. Myhre, H. Bryant, R. Derwent, D. Hauglustaine, F. Paulot, M. Prather, D. Stevenson, A multi-model assessment of the global warming potential of hydrogen, Communications Earth & Environment 4 (1) (2023) 203.doi:10.1038/s43247-023-00857-8. URLhttps://doi.org/10.1038/s43247-023-00857-8

    work page doi:10.1038/s43247-023-00857-8 2023

  18. [19]

    S. Bube, N. Bullerdiek, S. Voß, M. Kaltschmitt, Kerosene production from power-based syngas – A technical comparison of the Fischer-Tropsch and methanol pathway, Fuel 366 (2024) 131269.doi: 10.1016/j.fuel.2024.131269

    work page doi:10.1016/j.fuel.2024.131269 2024

  19. [20]

    URLhttps://www.iea.org/data-and-statistics/data-tools/etp-clean-energy-technology-guide

    ETP Clean Energy Technology Guide – Data Tools (2025). URLhttps://www.iea.org/data-and-statistics/data-tools/etp-clean-energy-technology-guide

    work page 2025

  20. [21]

    Brown, J

    T. Brown, J. Hampp, Ultra-long-duration energy storage anywhere: Methanol with carbon cycling, Joule 7 (11) (2023) 2414–2420.doi:10.1016/j.joule.2023.10.001

    work page doi:10.1016/j.joule.2023.10.001 2023

  21. [22]

    Bertau, H

    M. Bertau, H. Offermanns, L. Plass, F. Schmidt, H.-J. Wernicke (Eds.), Methanol: The Basic Chemical and Energy Feedstock of the Future: Asinger’s Vision Today, 1st Edition, Springer Berlin, Heidelberg, 2014.doi:10.1007/978-3-642-39709-7. URLhttps://doi.org/10.1007/978-3-642-39709-7

    work page doi:10.1007/978-3-642-39709-7 2014

  22. [23]

    URLhttps://github.com/hfg-gmuend/openmoji

    Hfg-gmuend/openmoji (2025). URLhttps://github.com/hfg-gmuend/openmoji

    work page 2025

  23. [24]

    J. Cui, R. Yu, H. Wang, Y. Wang, J. Tong, A Comparative Study of Methanol and Methane Combus- tion in a Gas Turbine Combustor, Energies 18 (7) (2025) 1765.doi:10.3390/en18071765. 29

    work page doi:10.3390/en18071765 2025

  24. [25]

    Asinger, Methanol — Chemie- und Energierohstoff: Die Mobilisation der Kohle, 1st Edition, Springer Berlin, Heidelberg, 1986.doi:https://doi.org/10.1007/978-3-642-70763-6

    F. Asinger, Methanol — Chemie- und Energierohstoff: Die Mobilisation der Kohle, 1st Edition, Springer Berlin, Heidelberg, 1986.doi:https://doi.org/10.1007/978-3-642-70763-6

    work page doi:10.1007/978-3-642-70763-6 1986

  25. [26]

    G. A. Olah, The methanol economy, Chemical & Engineering News 81 (38) (2003) 5, doi: 10.1021/cen-v081n038.p005.doi:10.1021/cen-v081n038.p005. URLhttps://doi.org/10.1021/cen-v081n038.p005

    work page doi:10.1021/cen-v081n038.p005.doi:10.1021/cen-v081n038.p005 2003

  26. [27]

    G. A. Olah, Beyond oil and gas: The methanol economy, Angewandte Chemie International Edition 44 (18) (2005) 2636–2639.doi:10.1002/anie.200462121. URLhttps://doi.org/10.1002/anie.200462121

    work page doi:10.1002/anie.200462121 2005

  27. [28]

    Blanco, W

    H. Blanco, W. Nijs, J. Ruf, A. Faaij, Potential for hydrogen and Power-to-Liquid in a low-carbon EU energy system using cost optimization, Applied Energy 232 (2018) 617 – 639.doi:10.1016/j.apenergy. 2018.09.216. URLhttps://doi.org/10.1016/j.apenergy.2018.09.216

    work page doi:10.1016/j.apenergy 2018

  28. [29]

    V. G. Johnsen, F. B. Bornemann, A. Nemati, S. Linderoth, H. Lund Frandsen, Efficient onboard car- bon capture system using methanol-fueled solid oxide fuel cells, International Journal of Hydrogen Energy 92 (2024) 1000–1020.doi:https://doi.org/10.1016/j.ijhydene.2024.10.100. URLhttps://www.sciencedirect.com/science/article/pii/S0360319924042927

    work page doi:10.1016/j.ijhydene.2024.10.100 2024

  29. [30]

    Brazzola, T

    N. Brazzola, T. Zurbriggen, A. Odenweller, F. Ueckerdt, J. Rogelj, Assessing the feasibility of economy-scale direct air capture deployment by 2050 (Jul. 2025).doi:10.21203/rs.3.rs-6165238/v1. URLhttps://doi.org/10.21203/rs.3.rs-6165238/v1

    work page doi:10.21203/rs.3.rs-6165238/v1 2050

  30. [31]

    Gustafsson, Centralized or decentralized? How to exploit Sweden’s agricultural biomethane potential, Biofuels (2024) 1–12doi:10.1080/17597269.2024.2318515

    M. Gustafsson, Centralized or decentralized? How to exploit Sweden’s agricultural biomethane potential, Biofuels (2024) 1–12doi:10.1080/17597269.2024.2318515

    work page doi:10.1080/17597269.2024.2318515 2024

  31. [32]

    Matschoss, M

    P. Matschoss, M. Steubing, J. Pertagnol, Y. Zheng, B. Wern, M. Dotzauer, D. Thrän, A consolidated potential analysis of bio-methane and e-methane using two different methods for a medium-term renewable gas supply in Germany, Energy, Sustainability and Society 10 (1) (2020) 41.doi:10.1186/ s13705-020-00276-z

    work page 2020

  32. [33]

    Korberg, S

    A. Korberg, S. Brynolf, M. Grahn, I. Skov, Techno-economic assessment of advanced fuels and propulsion systems in future fossil-free ships, Renewable and Sustainable Energy Reviews 142 (2021) 110861.doi:10.1016/j.rser.2021.110861. URLhttps://doi.org/10.1016/j.rser.2021.110861

    work page doi:10.1016/j.rser.2021.110861 2021

  33. [34]

    URLhttps://www.methanol.org/methanol-price-supply-demand/

    Methanol Institute and Argus, Renewable methanol plant database (2024). URLhttps://www.methanol.org/methanol-price-supply-demand/

    work page 2024

  34. [35]

    Methanex, The global methanol leader: Corporate presentation, Tech. rep. (2024). URLhttps://www.methanex.com/wp-content/uploads/MEOH-Investor-Presentation-March-2024. pdf

    work page 2024

  35. [36]

    Liang, D

    J. Liang, D. Liu, S. Xu, M. Ye, Comparison of light olefins production routes in China: Combining techno-economics and security analysis, Chemical Engineering Research and Design 194 (2023) 225– 241.doi:10.1016/j.cherd.2023.04.037. URLhttps://doi.org/10.1016/j.cherd.2023.04.037 30

    work page doi:10.1016/j.cherd.2023.04.037 2023

  36. [37]

    Global Maritime Forum, Zero-emission shipping fuels: A guide to methanol and ammonia, Tech. rep. (2025). URLhttps://globalmaritimeforum.org/news/zero-emission-shipping-fuels-methanol-and-ammonia/

    work page 2025

  37. [38]

    URLhttps://www.methanol.org/renewable/

    Methanol Institute and GENA Solutions Oy, Renewable methanol plant database (2025). URLhttps://www.methanol.org/renewable/

    work page 2025

  38. [39]

    URLhttps://eur-lex.europa.eu/legal-content/EN/TXT/?uri=COM:2024:62:FIN

    European Commission, Towards an ambitious Industrial Carbon Management for the EU, European Commission - Communication (2024). URLhttps://eur-lex.europa.eu/legal-content/EN/TXT/?uri=COM:2024:62:FIN

    work page 2024

  39. [40]

    Schreyer, F

    F. Schreyer, F. Ueckerdt, R. Pietzcker, A. Odenweller, A. Merfort, R. Rodrigues, J. Strefler, F. Lécuyer, G. Luderer, From net-zero to zero-fossil in transforming the EU energy system, Nature Communica- tions 16 (1) (2025) 10700.doi:10.1038/s41467-025-66682-z. URLhttps://doi.org/10.1038/s41467-025-66682-z

    work page doi:10.1038/s41467-025-66682-z 2025

  40. [41]

    M. J. Gidden, S. Joshi, J. J. Armitage, A.-B. Christ, M. Boettcher, E. Brutschin, A. C. Köberle, K. Riahi, H. J. Schellnhuber, C.-F. Schleussner, J. Rogelj, A prudent planetary limit for geologic carbon storage, Nature 645 (8079) (2025) 124–132.doi:10.1038/s41586-025-09423-y. URLhttps://doi.org/10.1038/s41586-025-09423-y

    work page doi:10.1038/s41586-025-09423-y 2025

  41. [42]

    P. Ruiz, W. Nijs, D. Tarvydas, A. Sgobbi, A. Zucker, R. Pilli, R. Jonsson, A. Camia, C. Thiel, C. Hoyer- Klick, F. Dalla Longa, T. Kober, J. Badger, P. Volker, B. S. Elbersen, A. Brosowski, D. Thrän, ENSPRESO - an open, EU-28 wide, transparent and coherent database of wind, solar and biomass energy poten- tials, Energy Strategy Reviews 26 (2019) 100379.do...

    work page doi:10.1016/j.esr.2019.100379 2019

  42. [43]

    URLhttps://www.entsoe.eu/outlooks/tyndp/2024/

    ENTSO-E, Ten Year Network Development Plan 2024 (2024). URLhttps://www.entsoe.eu/outlooks/tyndp/2024/

    work page 2024

  43. [44]

    Rehfeldt, S

    M. Rehfeldt, S. Bußmann, T. Fleiter, Direct electrification of industrial process heat. An assessment of technologies, potentials and future prospects for the EU. Study on behalf of Agora Industry. (2024). URLhttps://www.agora-industry.org/fileadmin/Projects/2023/2023-20_IND_Electrification_ Industrial_Heat/A-IND_329_04_Electrification_Industrial_Heat_WEB.pdf

    work page 2024

  44. [45]

    M. Zier, P. Stenzel, L. Kotzur, D. Stolten, A review of decarbonization options for the glass industry, Energy Conversion and Management: X 10 (2021) 100083.doi:10.1016/j.ecmx.2021.100083. URLhttps://doi.org/10.1016/j.ecmx.2021.100083

    work page doi:10.1016/j.ecmx.2021.100083 2021

  45. [46]

    Hofmann, C

    F. Hofmann, C. Tries, F. Neumann, E. Zeyen, T. Brown, H 2 and CO 2 network strategies for the European energy system, Nature Energy 10 (6) (2025) 715–724.doi:10.1038/s41560-025-01752-6. URLhttps://doi.org/10.1038/s41560-025-01752-6

    work page doi:10.1038/s41560-025-01752-6 2025

  46. [47]

    P. C. Verpoort, L. Gast, A. Hofmann, F. Ueckerdt, Impact of global heterogeneity of renewable energy supply on heavy industrial production and green value chains, Nature Energy 9 (4) (2024) 491–503. doi:10.1038/s41560-024-01492-z. URLhttps://doi.org/10.1038/s41560-024-01492-z 31

    work page doi:10.1038/s41560-024-01492-z 2024

  47. [48]

    Neumann, J

    F. Neumann, J. Hampp, T. Brown, Green energy and steel imports reduce Europe’s net-zero infras- tructure needs, Nature Communications 16 (1) (2025) 5302.doi:10.1038/s41467-025-60652-1. URLhttps://www.nature.com/articles/s41467-025-60652-1

    work page doi:10.1038/s41467-025-60652-1 2025

  48. [49]

    für Wirtschaft, Fortschreibung der Nationalen Wasserstoffstrategie (2023)

    B.-B. für Wirtschaft, Fortschreibung der Nationalen Wasserstoffstrategie (2023). URLhttps://www.bmwk.de/Redaktion/DE/Publikationen/Energie/ fortschreibung-nationale-wasserstoffstrategie.html

    work page 2023

  49. [50]

    URLhttps://www.bmwk.de/Navigation/DE/Wasserstoff/wasserstoffstrategie.html

    One-Stop-Shop - Wasserstoff - Wasserstoff. URLhttps://www.bmwk.de/Navigation/DE/Wasserstoff/wasserstoffstrategie.html

    work page

  50. [51]

    URLhttps://www.h2-global.org

    H2Global’s Foundation. URLhttps://www.h2-global.org

    work page

  51. [52]

    L. Göke, J. Wohland, S. Moret, A. Bardow, The liquid buffer: Multi-year storage for defossilization and energy security under climate uncertainty (2025).arXiv:2511.13513. URLhttps://arxiv.org/abs/2511.13513

    work page arXiv 2025

  52. [53]

    URLhttps://www.ieabioenergy.com/blog/publications/state-of-the-biogas-industry-in-12-member-countries-of-iea-bioenergy-task-37/

    State of the biogas industry in 12 member countries of IEA Bioenergy Task 37 – Bioenergy. URLhttps://www.ieabioenergy.com/blog/publications/state-of-the-biogas-industry-in-12-member-countries-of-iea-bioenergy-task-37/

    work page

  53. [54]

    URLhttps://www.maersk.com/news/articles/2024/10/30/maersk-signs-long-term-methanol-sourcing-deal

    Maersk Signs Long-Term Methanol Sourcing Deal. URLhttps://www.maersk.com/news/articles/2024/10/30/maersk-signs-long-term-methanol-sourcing-deal

    work page 2024

  54. [55]

    Luderer, F

    G. Luderer, F. Bartels, T. Brown, C. Aulich, F. Benke, T. Fleiter, F. Frank, H. Ganal, J. Geis, N. Ger- hardt, T. Gnann, A. Gunnemann, R. Hasse, A. Herbst, S. Herkel, J. Hoppe, C. Kost, M. Krail, M. Lind- ner, M. Neuwirth, H. Nolte, R. Pietzcker, P. Plötz, M. Rehfeldt, F. Schreyer, T. Seibold, C. Senkpiel, D. Sörgel, D. Speth, B. Steffen, P. C. Verpoort, ...

    work page doi:10.48485/pik.2025.003 2045

  55. [56]

    M. M. Frysztacki, J. Hörsch, V. Hagenmeyer, T. Brown, The strong effect of network resolution on electricity system models with high shares of wind and solar, Applied Energy 291 (2021) 116726. doi:10.1016/j.apenergy.2021.116726

    work page doi:10.1016/j.apenergy.2021.116726 2021

  56. [57]

    Brown, T

    T. Brown, T. Bischof-Niemz, K. Blok, C. Breyer, H. Lund, B. Mathiesen, Response to ‘burden of proof: A comprehensive review of the feasibility of 100% renewable-electricity systems’, Renewable and Sustainable Energy Reviews 92 (2018) 834 – 847.doi:10.1016/j.rser.2018.04.113. URLhttps://doi.org/10.1016/j.rser.2018.04.113

    work page doi:10.1016/j.rser.2018.04.113 2018

  57. [58]

    Brown, The Minimal Methanol Backstop: Frequently Asked Questions (2025)

    T. Brown, The Minimal Methanol Backstop: Frequently Asked Questions (2025). URLhttps://nworbmot.org/blog/methanol-faq.html

    work page 2025

  58. [59]

    PyPSA-Eur: An Open Optimisation Model of the European Transmission System

    J. Hörsch, F. Hofmann, D. Schlachtberger, T. Brown, PyPSA-Eur: An Open Optimisation Model of the European Transmission System, Energy Strategy Reviews 22 (2018) 207–215.arXiv:1806.01613, doi:10.1016/j.esr.2018.08.012

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.1016/j.esr.2018.08.012 2018

  59. [60]

    Brown, M

    T. Brown, M. Victoria, E. Zeyen, F. Hofmann, F. Neumann, M. Frysztacki, J. Hampp, D. Schlacht- berger, J. Hörsch, PyPSA-Eur: An open sector-coupled optimisation model of the European energy 32 system (Feb. 2024). URLhttps://pypsa-eur.readthedocs.io/en/latest/index.html#workflow

    work page 2024

  60. [61]

    Mölder, K

    F. Mölder, K. P. Jablonski, B. Letcher, M. B. Hall, C. H. Tomkins-Tinch, V. Sochat, J. Forster, S. Lee, S. O. Twardziok, A. Kanitz, A. Wilm, M. Holtgrewe, S. Rahmann, S. Nahnsen, J. Köster, Sustainable data analysis with Snakemake, F1000Research 10 (2021) 33.doi:10.12688/f1000research.29032.1

    work page doi:10.12688/f1000research.29032.1 2021

  61. [62]

    PyPSA: Python for Power System Analysis

    T. Brown, J. Hörsch, D. Schlachtberger, PyPSA: Python for Power System Analysis, Journal of Open Research Software 6 (2018) 4.arXiv:1707.09913,doi:10.5334/jors.188

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.5334/jors.188 2018

  62. [63]

    Hofmann, Linopy: Linear optimization with n-dimensional labeled variables, Journal of Open Source Software 8 (84) (2023) 4823.doi:10.21105/joss.04823

    F. Hofmann, Linopy: Linear optimization with n-dimensional labeled variables, Journal of Open Source Software 8 (84) (2023) 4823.doi:10.21105/joss.04823

    work page doi:10.21105/joss.04823 2023

  63. [64]

    Tchung-Ming, L

    Joint Research Centre (European Commission), S. Tchung-Ming, L. Mantzos, T. Wiesenthal, N. A. Matei, M. Rozsai, JRC-IDEES: Integrated Database of the European Energy Sector : Methodological Note, Publications Office of the European Union, 2017. URLhttps://data.europa.eu/doi/10.2760/182725

    work page doi:10.2760/182725 2017

  64. [65]

    Hofmann, J

    F. Hofmann, J. Hampp, F. Neumann, T. Brown, J. Hörsch, Atlite: A Lightweight Python Package for Calculating Renewable Power Potentials and Time Series, Journal of Open Source Software 6 (62) (2021) 3294.doi:10.21105/joss.03294

    work page doi:10.21105/joss.03294 2021

  65. [66]

    URLhttps://planet.osm.org

    OpenStreetMap contributors, Planet dump retrieved from https://planet.osm.org (2017). URLhttps://planet.osm.org

    work page 2017

  66. [67]

    Xiong, D

    B. Xiong, D. Fioriti, F. Neumann, I. Riepin, T. Brown, Modelling the high-voltage grid using open data for Europe and beyond, Scientific Data 12 (1) (2025) 277.doi:10.1038/s41597-025-04550-7

    work page doi:10.1038/s41597-025-04550-7 2025

  67. [68]

    URLhttps://technology-data.readthedocs.io/en/latest/

    Aarhus University, Technische Universität Berlin, PyPSA Technology data (2023). URLhttps://technology-data.readthedocs.io/en/latest/

    work page 2023

  68. [69]

    Danish Energy Agency, Technology Data, Tech. rep. (2022). URLhttps://ens.dk/en/our-services/projections-and-models/technology-data

    work page 2022

  69. [70]

    URLhttps://github.com/p-glaum/technology-data

    Techno-economic assumptions for 2030 (2025). URLhttps://github.com/p-glaum/technology-data

    work page 2030

  70. [71]

    Hoffmann, L

    M. Hoffmann, L. Kotzur, D. Stolten, The Pareto-optimal temporal aggregation of energy system models, Applied Energy 315 (2022) 119029.doi:10.1016/j.apenergy.2022.119029

    work page doi:10.1016/j.apenergy.2022.119029 2022

  71. [72]

    M. M. Frysztacki, G. Recht, T. Brown, A comparison of clustering methods for the spatial reduction of renewable electricity optimisation models of Europe, Energy Informatics 5 (1) (2022) 4.doi:10. 1186/s42162-022-00187-7. URLhttps://doi.org/10.1186/s42162-022-00187-7

    work page doi:10.1186/s42162-022-00187-7 2022

  72. [73]

    Neumann, E

    F. Neumann, E. Zeyen, M. Victoria, T. Brown, The potential role of a hydrogen network in Europe, Joule 7 (8) (2023) 1793–1817.doi:10.1016/j.joule.2023.06.016. URLhttps://doi.org/10.1016/j.joule.2023.06.016 33

    work page doi:10.1016/j.joule.2023.06.016 2023

  73. [74]

    S. Bube, L. Sens, C. Drawer, M. Kaltschmitt, Power and biogas to methanol – A techno-economic analysis of carbon-maximized green methanol production via two reforming approaches, Energy Conversion and Management 304 (2024) 118220.doi:10.1016/j.enconman.2024.118220

    work page doi:10.1016/j.enconman.2024.118220 2024

  74. [75]

    Grupa Lotos, Yield structures of selected Central European refineries (Mar. 2018). URLhttps://www.statista.com/statistics/685062/yield-structure-central-european-refineries/

    work page 2018

  75. [76]

    D. H. König, N. Baucks, R.-U. Dietrich, A. Wörner, Simulation and evaluation of a process concept for the generation of synthetic fuel from CO2 and H2, Energy 91 (2015) 833–841.doi:10.1016/j.energy. 2015.08.099

    work page doi:10.1016/j.energy 2015

  76. [77]

    Lechtenböhmer, L

    S. Lechtenböhmer, L. J. Nilsson, M. Åhman, C. Schneider, Decarbonising the energy intensive basic materials industry through electrification – Implications for future EU electricity demand, Energy 115 (2016-11-15) 1623–1631.doi:10.1016/j.energy.2016.07.110. URLhttps://www.sciencedirect.com/science/article/pii/S0360544216310295

    work page doi:10.1016/j.energy.2016.07.110 2016

  77. [78]

    V. Dias, M. Pochet, F. Contino, H. Jeanmart, Energy and Economic Costs of Chemical Storage, Fron- tiers in Mechanical Engineering 6 (May 2020).doi:10.3389/fmech.2020.00021

    work page doi:10.3389/fmech.2020.00021 2020

  78. [79]

    Bioenergy potentials for EU and neighbouring countries., Tech

    Pablo Ruiz, Alessandra Sgobbi, Wouter Nijs, Christian Thiel, Francesco Dalla Longa, Tom Kober, Berien Elbersen, Geerten Hengeveld, The JRC-EU-TIMES model. Bioenergy potentials for EU and neighbouring countries., Tech. rep., IET-JRC, ECN, Alterra (2015). URLhttps://publications.jrc.ec.europa.eu/repository/bitstream/JRC98626/biomass%20potentials% 20in%20eur...

    work page 2015

  79. [80]

    Zeyen, V

    E. Zeyen, V. Hagenmeyer, T. Brown, Mitigating heat demand peaks in buildings in a highly renew- able European energy system, Energy 231 (2021) 120784.doi:10.1016/j.energy.2021.120784. URLhttps://doi.org/10.1016/j.energy.2021.120784

    work page doi:10.1016/j.energy.2021.120784 2021

  80. [81]

    Persson, S

    U. Persson, S. Werner, Heat distribution and the future competitiveness of district heating, Applied Energy 88 (3) (2011) 568 – 576.doi:10.1016/j.apenergy.2010.09.020. URLhttps://doi.org/10.1016/j.apenergy.2010.09.020

    work page doi:10.1016/j.apenergy.2010.09.020 2011

Showing first 80 references.