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arxiv: 2509.10734 · v1 · submitted 2025-09-12 · 📡 eess.SY · cs.SY· physics.soc-ph

Multi-sectoral Impacts of H2 and Synthetic Fuels Adoption for Heavy-duty Transportation Decarbonization

Youssef Shaker , Jun Wen Law , Audun Botterud , Dharik Mallapragada This is my paper

Pith reviewed 2026-05-18 16:43 UTC · model grok-4.3

classification 📡 eess.SY cs.SYphysics.soc-ph
keywords heavy-duty vehicleshydrogensynthetic fuelsdecarbonizationmulti-sector modelingCO2 storagedirect air captureWestern Europe
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The pith

Without CO2 storage, heavy-duty vehicles must switch from liquid fossil fuels to meet deep decarbonization targets across power, hydrogen, and transport sectors.

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

The paper evaluates the system-wide effects of decarbonizing heavy-duty vehicles through either hydrogen or synthetic liquid fuels derived from hydrogen and CO2. It connects a bottom-up transport model that generates final energy demand scenarios to a capacity expansion model that optimizes power, hydrogen, and CO2 infrastructure for Western Europe in 2040 under strict emission limits. Key findings indicate that absent CO2 storage options, replacing fossil liquids in trucks becomes necessary to satisfy the overall decarbonization constraint. Hydrogen use in heavy-duty vehicles lowers total costs and fossil liquid needs yet may raise natural gas consumption, while synthetic fuels increase direct air capture requirements and overall expenses.

Core claim

In the absence of CO2 storage, substitution of liquid fossil fuels in HDVs is essential to meet the deep decarbonization constraint across the modeled power, H2 and transport sectors. Additionally, utilizing H2 HDVs reduces decarbonization costs and fossil liquids demand, but could increase natural gas consumption. While H2 HDV adoption reduces the need for direct air capture (DAC), synthetic fuel adoption increases DAC investments and total system costs.

What carries the argument

Soft-linking of a bottom-up transport demand model that produces final energy demand scenarios with a multi-sectoral capacity expansion model that co-optimizes power, H2, and CO2 supply chains under technological and policy constraints.

If this is right

  • Substitution of liquid fossil fuels in HDVs becomes necessary to meet deep decarbonization without CO2 storage.
  • H2 HDVs lower overall decarbonization costs and reduce fossil liquids demand.
  • H2 HDVs may increase natural gas consumption in the broader system.
  • H2 HDV adoption reduces direct air capture investments while synthetic fuel adoption increases them and raises total system costs.

Where Pith is reading between the lines

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

  • The results imply that the presence or absence of CO2 storage infrastructure strongly shapes the preferred HDV decarbonization route.
  • Trade-offs between H2 and synthetic fuels would likely shift if the transport model endogenously responded to energy prices.
  • Similar multi-sector effects could appear in other hard-to-electrify segments such as aviation or shipping when synthetic fuels are considered.

Load-bearing premise

The bottom-up transport demand model produces accurate final energy demand scenarios for the same service demand that can be directly soft-linked into the multi-sectoral capacity expansion model without significant feedback or inconsistency.

What would settle it

A scenario run in which energy system costs feed back into the transport demand model and change the optimal share of H2 versus synthetic fuel HDVs under the same deep decarbonization targets.

Figures

Figures reproduced from arXiv: 2509.10734 by Audun Botterud, Dharik Mallapragada, Jun Wen Law, Youssef Shaker.

Figure 1
Figure 1. Figure 1: Overview of supply and demand-side modeling used for this study. a) Modeling approach to estimate transportation final energy demand by fuel type and vehicle sub-category, illustrated for the heavy-duty vehicle segment (HDV). Blue boxes are data inputs, while orange boxes are calculated values. Loading factor represents the fraction of vehicle loading capacity used on average. Input data is sourced from th… view at source ↗
Figure 2
Figure 2. Figure 2: a) 10-node model representation of the Western European region for the supply-side modeling, highlighting the initial power transfer capacities between the regions (as of 2020, which is assumed to be the built capacity in 2040) and regional distribution of non-transport electricity and H2 demand for modeled 2040 demand scenarios. The size of each bubble represents the non-transportation demand for power (b… view at source ↗
Figure 3
Figure 3. Figure 3: a) Summary of core scenarios evaluated. The y-axis represents varying levels of H2 HDV adoption (between 0 and 142 TWh of H2 consumption), while the x-axis represents varying levels of synthetic fuel adoption (between 0 and 128 TWh of synthetic Diesel consumption). All scenarios are equivalent from an emissions capping perspective with a cap of 103 MtCO2/y. The HDV fleet represents all vehicle types with g… view at source ↗
Figure 4
Figure 4. Figure 4: Transportation final energy consumption across different H2 HDV adoption scenarios. HDV energy use, included in the category Heavy-duty Vehicles and Light Commercial Vehicles (HDVs & LCVs), represents 71-76% of the category’s energy consumption and 36-42% of road transportation final energy consumption. Two-wheelers are excluded from the diagram as their demand is negligible compared to other vehicle categ… view at source ↗
Figure 5
Figure 5. Figure 5: Power and H2 generation for baseline and no CO2 sequestration scenarios under no synthetic fuel adoption. The left set of charts shows power generation and the right set of charts shows H2 generation. Within each panel, the amount of H2 HDV adoption increases moving from left to right. Corresponding capacity charts are shown in Figure A.1 [PITH_FULL_IMAGE:figures/full_fig_p013_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: System CO2 balance under varying levels of H2 HDV adoption and no SF adoption (Scenario Set 1). The subfigure on the left shows the CO2 balance under no CO2 sequestration availability, while the one on the right shows the CO2 balance under baseline CO2 storage availability. Within each subplot the H2 HDV adoption level increases left to right. The leftward column in each subfigure represents CO2 input into… view at source ↗
Figure 7
Figure 7. Figure 7: Annualized bulk-system costs under varying levels of H2 HDV adoption and no SF adoption. The subfigure on the left shows the cost breakdown under no CO2 sequestration availability, while the one on the right shows the cost breakdown under baseline CO2 sequestration availability. Within each subplot the H2 HDV adoption level increases left to right. The costs do not include vehicle replacement or H2 distrib… view at source ↗
Figure 8
Figure 8. Figure 8: Trade-off between natural gas (NG) and liquid fossil fuel utilization. The subfigure on the top shows the relationship for the H2 HDV scenarios (i.e. scenario set 1), while the one on the bottom shows the relationship for SF adoption scenarios (i.e. scenario set 2). Within each subplot the amount of natural gas consumption can be examined on the x-axis, while the amount of liquid fossil fuel consumption ca… view at source ↗
Figure 9
Figure 9. Figure 9: Power and hydrogen generation for baseline and no CO2 sequestration scenarios under medium H2 HDV adoption and varying scenarios of synthetic fuel adoption. The left set of charts shows power generation and the right set of charts shows H2 generation. Within each panel, the amount of synthetic fuel adoption increases moving from left to right. Total system emissions constrained to 103 MtCO2/y. Shaker et al… view at source ↗
Figure 10
Figure 10. Figure 10: System CO2 balance under varying levels of SF adoption and medium H2 HDV adoption and varying scenarios of synthetic fuel adoption. The subfigure on the left shows the CO2 balance under no CO2 sequestration availability, while the one on the right shows the CO2 balance under baseline CO2 sequestration availability. Within each subplot the SF adoption level increases left to right. The leftward column repr… view at source ↗
Figure 11
Figure 11. Figure 11: Annualized bulk-system costs under varying levels of SF adoption and medium H2 HDV adoption and varying scenarios of synthetic fuel adoption. The subfigure on the left shows the cost breakdown under no CO2 sequestration availability, while the one on the right shows the cost breakdown under baseline CO2 sequestration availability. Within each subplot the SF adoption level increases left to right. The cost… view at source ↗
Figure 12
Figure 12. Figure 12: System emissions balance. All terms are assumed to be positive in value. In the absence of CO2 storage, deep decarbonization of power, H2 and transportation sectors without liquid fossil fuel substitution (using H2 , SFs, or other methods not considered in this study) may not be viable, as illustrated by the infeasible outcomes from the modeled scenarios mimicking these conditions. This finding reinforces… view at source ↗
read the original abstract

Policies focused on deep decarbonization of regional economies emphasize electricity sector decarbonization alongside electrification of end-uses. There is growing interest in utilizing hydrogen (H2) produced via electricity to displace fossil fuels in difficult-to-electrify sectors. One such case is heavy-duty vehicles (HDV), which represent a substantial and growing share of transport emissions as light-duty vehicles electrify. Here, we assess the bulk energy system impact of decarbonizing the HDV segment via either H2, or drop-in synthetic liquid fuels produced from H2 and CO2. Our analysis soft-links two modeling approaches: (a) a bottom-up transport demand model producing a variety of final energy demand scenarios for the same service demand and (b) a multi-sectoral capacity expansion model that co-optimizes power, H2 and CO2 supply chains under technological and policy constraints to meet exogenous final energy demands. Through a case study of Western Europe in 2040 under deep decarbonization constraints, we quantify the energy system implications of different levels of H2 and synthetic fuels adoption in the HDV sector under scenarios with and without CO2 sequestration. In the absence of CO2 storage, substitution of liquid fossil fuels in HDVs is essential to meet the deep decarbonization constraint across the modeled power, H2 and transport sectors. Additionally, utilizing H2 HDVs reduces decarbonization costs and fossil liquids demand, but could increase natural gas consumption. While H2 HDV adoption reduces the need for direct air capture (DAC), synthetic fuel adoption increases DAC investments and total system costs. The study highlights the trade-offs across transport decarbonization pathways, and underscores the importance of multi-sectoral consideration in decarbonization studies.

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 / 2 minor

Summary. The manuscript soft-links a bottom-up transport demand model that generates final energy demand scenarios for heavy-duty vehicles (HDVs) under fixed service demand with a multi-sectoral capacity expansion model that co-optimizes power, hydrogen, and CO2 supply chains. Applied to Western Europe in 2040 under deep decarbonization constraints, the study compares scenarios with varying H2 and synthetic fuel adoption shares in HDVs, with and without CO2 sequestration. Central findings are that fossil liquid substitution in HDVs is essential without CO2 storage, H2 HDVs lower system costs and fossil demand (while potentially raising natural gas use) and reduce direct air capture (DAC) needs, whereas synthetic fuels raise DAC investments and total costs.

Significance. If the soft-link is robust, the work usefully quantifies multi-sectoral trade-offs for decarbonizing hard-to-abate HDV transport, crediting the explicit co-optimization of power/H2/CO2 chains and the with/without sequestration scenario design. It demonstrates the value of integrated assessment for identifying cost and DAC differences between H2 and synthetic pathways.

major comments (2)
  1. [Modeling Framework] The modeling framework treats HDV final energy demands as exogenous outputs from the bottom-up transport model that are passed as fixed inputs to the capacity expansion model. This one-way soft-link assumes the resulting endogenous electricity, H2, and CO2 prices and availability do not materially alter optimal HDV fleet composition, utilization, or service demand. Without reported iteration, consistency checks, or sensitivity tests on this assumption, the reported cost savings, natural-gas increases, and DAC reductions for H2 versus synthetic scenarios rest on an unexamined premise and may not be robust.
  2. [Abstract and Results] The abstract and results sections present adoption levels of H2 and synthetic fuels as scenario inputs rather than fitted or endogenous outputs. The central claims on essential substitution without CO2 storage and the differential DAC/cost impacts therefore depend on post-hoc scenario choices whose influence on the quantitative outcomes is not quantified or tested.
minor comments (2)
  1. [Methods] Additional detail on data sources, technology cost assumptions, and validation of both the transport demand and capacity expansion models would improve transparency and allow readers to assess the strength of the quantitative results.
  2. [Scenario Design] Clarify how the deep decarbonization constraint is enforced across the three sectors and whether any sensitivity to the 2040 time horizon or regional boundaries is explored.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive and detailed comments on our manuscript. We address each major comment below and outline revisions that will strengthen the presentation of our scenario-based analysis.

read point-by-point responses

  1. Referee: [Modeling Framework] The modeling framework treats HDV final energy demands as exogenous outputs from the bottom-up transport model that are passed as fixed inputs to the capacity expansion model. This one-way soft-link assumes the resulting endogenous electricity, H2, and CO2 prices and availability do not materially alter optimal HDV fleet composition, utilization, or service demand. Without reported iteration, consistency checks, or sensitivity tests on this assumption, the reported cost savings, natural-gas increases, and DAC reductions for H2 versus synthetic scenarios rest on an unexamined premise and may not be robust.

    Authors: We agree that the soft-link is one-directional with fixed final energy demands and that we have not performed iteration or explicit consistency checks between the models. This structure is intentional: the bottom-up transport model generates a set of final-energy scenarios for fixed service demand and varying technology shares, which are then used as inputs to the capacity-expansion model to isolate and quantify supply-side trade-offs across power, hydrogen, and CO2 infrastructure. Full endogenous co-optimization of fleet composition would require a fundamentally different integrated framework and is outside the present scope. To address the concern, we will add a new limitations subsection that explicitly discusses the one-way linkage assumption and its implications for the reported cost, natural-gas, and DAC results. We will also include a sensitivity analysis varying HDV service demand and fuel-price responsiveness to test robustness of the key differentials. revision: partial

  2. Referee: [Abstract and Results] The abstract and results sections present adoption levels of H2 and synthetic fuels as scenario inputs rather than fitted or endogenous outputs. The central claims on essential substitution without CO2 storage and the differential DAC/cost impacts therefore depend on post-hoc scenario choices whose influence on the quantitative outcomes is not quantified or tested.

    Authors: The study is explicitly designed as a scenario analysis in which adoption shares are treated as exogenous inputs to explore the system consequences of different H2 versus synthetic-fuel pathways under the same service demand. This allows transparent comparison of the multi-sectoral implications without embedding a specific adoption mechanism. The claims (e.g., necessity of liquid substitution without CO2 storage, cost and DAC differences) are therefore conditional on the chosen shares. We will revise the abstract and results sections to state this framing more clearly and will add quantitative sensitivity results showing how the reported cost, fossil-demand, natural-gas, and DAC metrics vary across the range of adoption levels examined. revision: yes

Circularity Check

0 steps flagged

Exogenous demands and scenario inputs keep derivation self-contained

full rationale

The paper generates final energy demand scenarios for HDVs exogenously via a bottom-up transport model and passes them as fixed inputs to a multi-sectoral capacity expansion model that co-optimizes supply chains to meet those demands under chosen constraints. Adoption levels of H2 HDVs versus synthetic fuels are defined as scenario parameters rather than fitted or derived outputs. No equations or steps reduce any reported result (costs, DAC requirements, fuel substitutions) to a quantity defined by the paper's own fitted parameters or self-referential definitions. The analysis is therefore a conditional scenario comparison whose central claims do not collapse to its inputs by construction.

Axiom & Free-Parameter Ledger

2 free parameters · 2 axioms · 0 invented entities

The analysis rests on standard energy-system assumptions about technology costs, demand projections, and policy targets that are drawn from prior literature rather than derived within the paper.

free parameters (2)
  • technology costs and performance parameters
    Costs and efficiencies for H2 production, DAC, fuel synthesis, and vehicle technologies are taken as inputs calibrated from external sources.
  • HDV adoption shares for H2 and synthetic fuels
    Different levels of adoption are imposed as exogenous scenario definitions rather than emerging from the model.
axioms (2)
  • domain assumption Deep decarbonization targets for Western Europe power, H2, and transport sectors by 2040
    The capacity-expansion model is constrained to meet specified emissions limits under the chosen scenarios.
  • domain assumption Exogenous final energy demands from the bottom-up transport model are consistent with the capacity-expansion model's representation
    Demands are produced separately and fed forward without iterative feedback.

pith-pipeline@v0.9.0 · 5863 in / 1431 out tokens · 43061 ms · 2026-05-18T16:43:23.748924+00:00 · methodology

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