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arxiv: 2606.25945 · v1 · pith:ES5QOJ5Lnew · submitted 2026-06-24 · 🧮 math.OC · cs.CE

A Methodology for Integrating Life Cycle Assessment into a Multidisciplinary Design Analysis and Optimization Framework for Sustainable Launcher Development

Alice De Oliveira , Mathieu Balesdent , Lo\"ic Brevault , Annafederica Urbano This is my paper

Pith reviewed 2026-06-25 19:18 UTC · model grok-4.3

classification 🧮 math.OC cs.CE
keywords life cycle assessmentMDAOlaunch vehicle designparametric inventoriesenvironmental impactmulti-objective optimizationsustainable launcher development
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The pith

Parametric life-cycle inventories let LCA run as a discipline inside launch vehicle MDAO optimization.

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

The paper develops a method that adds environmental life cycle assessment directly into the coupled optimization used for early rocket design. It builds inventories whose values change with the design variables and coupling data so that the full chain from component production through propellant manufacture, transport, and flight emissions becomes part of the same calculation loop. When the approach is applied to a sample expendable vehicle, the optimizer produces designs in which gains in one environmental measure often increase another, showing that the choice of which impact to minimize must be stated explicitly.

Core claim

The methodology integrates an LCA discipline within an MDAO framework for launch vehicle design. The approach relies on parametric life-cycle inventories depending on design and coupling variables, covering component and propellant production as well as transport to the launch site. Launch emissions are evaluated from optimized trajectory profiles and characterized in terms of climate change impact. The methodology is illustrated on a representative expendable launch vehicle, where multi-objective optimizations assess trade-offs between performance and environmental indicators and results highlight antagonistic behaviors among environmental impact categories.

What carries the argument

Parametric life-cycle inventories that scale with design and coupling variables, inserted as an additional discipline inside the MDAO framework.

If this is right

  • Multi-objective optimizations can now trade performance metrics against environmental indicators from the start of design.
  • Antagonistic behaviors among different environmental impact categories become visible inside the design space.
  • The generic method supplies a foundation for adding LCA to early-stage launch vehicle architecture studies.
  • Trade-offs among performance, cost, and environmental indicators can be explored together in one framework.

Where Pith is reading between the lines

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

  • The same parametric structure could be extended to reusable vehicles by adding new inventory terms for recovery and refurbishment operations.
  • Mission-specific trajectory data already inside the optimizer might be used to test how different orbital targets change the environmental ranking of a given launcher.
  • Adding standardized space-industry material databases would reduce the uncertainty now carried by the parametric inventories.
  • Running the optimization with cost as a third objective would show whether the performance-environment trade-offs remain stable when economic constraints are added.

Load-bearing premise

Parametric life-cycle inventories can be defined accurately from design and coupling variables so that they represent the full life-cycle impacts without introducing large modeling errors.

What would settle it

Run a full detailed LCA after the optimization finishes on the same vehicle configuration and compare the numerical impact scores against the values the parametric model produced during the run; large differences would falsify the claim that the inventories are accurate enough.

Figures

Figures reproduced from arXiv: 2606.25945 by Alice De Oliveira, Annafederica Urbano, Lo\"ic Brevault, Mathieu Balesdent.

Figure 1
Figure 1. Figure 1: Description of the phases of the LCA methodology, adapted from ISO [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 3
Figure 3. Figure 3: Typical MDF formulation with integration of LCA considerations. [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 2
Figure 2. Figure 2: Typical MDF formulation for a launch vehicle design process. In this [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 4
Figure 4. Figure 4: System boundaries for the expendable launch vehicle design, adapted from [ [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Description of the LCA discipline. out in parallel as they are not dependent on each other. The methodology is described in the next paragraphs. 3.2.1. Construction of the parametric inventories As mentioned previously, the critical aspect of the method￾ology defined in this paper is the dynamic inventory analysis 8 [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Process tree for the engine production. In gray are represented the background processes (generated via LCI databases), in blue the foreground processes [PITH_FULL_IMAGE:figures/full_fig_p009_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Definition of primary and secondary emissions, adapted from James et al. [49]. [PITH_FULL_IMAGE:figures/full_fig_p010_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Ascent trajectory for the baseline (min GLOW) TSTO launch vehicle (propelled phase). [PITH_FULL_IMAGE:figures/full_fig_p014_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Pareto front between GWP and GLOW. In blue are represented the [PITH_FULL_IMAGE:figures/full_fig_p015_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Left: Contribution of each impact category to the PEF single score for the min GLOW solution ( [PITH_FULL_IMAGE:figures/full_fig_p017_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Pareto front between water use and GLOW. In blue are represented [PITH_FULL_IMAGE:figures/full_fig_p017_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Left: Contribution of each impact category to the PEF single score for the min GLOW solution ( [PITH_FULL_IMAGE:figures/full_fig_p018_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Pareto front between PEF and GLOW. In blue are represented the [PITH_FULL_IMAGE:figures/full_fig_p019_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Left: Contribution of each impact category to the PEF single score for the min GLOW solution ( [PITH_FULL_IMAGE:figures/full_fig_p020_14.png] view at source ↗
read the original abstract

The increasing number of orbital and sub-orbital launches makes it necessary to investigate the environmental impacts of launch vehicles and incorporate eco-design considerations into their development. In response, the European Space Agency has promoted Life Cycle Assessment (LCA) as a standardization methodology to mitigate environmental impacts of present and future space missions. This need is further amplified in the NewSpace, where numerous configurations and innovative technologies are explored, reinforcing the importance of integrating environmental considerations. At early design stages, launch vehicle architecture can be formalized through a multi-physics optimization problem based on Multidisciplinary Design Analysis and Optimization (MDAO) methods, where disciplines such as propulsion, aerodynamics, structure, and trajectory are coupled to obtain trade-offs among candidate configurations. This paper proposes a methodology to integrate an LCA discipline within an MDAO framework for launch vehicle design. The approach relies on parametric life-cycle inventories depending on design and coupling variables, covering component and propellant production as well as transport to the launch site. Launch emissions are evaluated from optimized trajectory profiles and characterized in terms of climate change impact. The methodology is illustrated on a representative expendable launch vehicle, where multi-objective optimizations assess trade-offs between performance and environmental indicators. Results highlight antagonistic behaviors among environmental impact categories, emphasizing the importance of carefully defining environmental objectives in eco-design studies. The generic nature of the methodology lays the foundation for integrating LCA into early-stage launch vehicle design, enabling exploration of trade-offs between performance, cost, and environmental considerations.

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 paper proposes a methodology to integrate Life Cycle Assessment (LCA) as an additional discipline within a Multidisciplinary Design Analysis and Optimization (MDAO) framework for launch vehicle design. Parametric life-cycle inventories are constructed as functions of design and coupling variables to cover component and propellant production plus transport; launch emissions are computed from optimized trajectories and characterized via climate-change metrics. The approach is demonstrated on a representative expendable launch vehicle through multi-objective optimizations that reveal trade-offs among performance and environmental impact categories.

Significance. If the parametric mappings prove robust, the framework would enable systematic exploration of environmental trade-offs at the earliest design stages, directly supporting ESA’s LCA standardization efforts and eco-design in the NewSpace context. The explicit coupling of trajectory-derived emissions and the reported antagonistic behaviors among impact categories constitute a concrete, falsifiable illustration that could serve as a template for subsequent studies.

major comments (2)
  1. [Methods] Methods section: while the paper states that parametric life-cycle inventories are defined explicitly from design and coupling variables, it does not report any sensitivity analysis or uncertainty quantification on the functional forms chosen for material quantities, propellant production factors, or transport distances; this directly affects the claim that the inventories can be coupled “without introducing significant modeling errors.”
  2. [Results] Results section: the multi-objective optimizations are said to exhibit antagonistic behaviors among environmental impact categories, yet no quantitative metrics (e.g., Pareto-front distances, correlation coefficients, or normalized trade-off slopes) are supplied to substantiate the strength or consistency of these antagonisms across the design space.
minor comments (2)
  1. [Abstract] Abstract and introduction: the phrase “antagonistic behaviors among environmental impact categories” is used without naming the specific categories (e.g., climate change vs. resource depletion) or indicating the direction of the observed trade-offs.
  2. The manuscript would benefit from a short table or appendix listing the exact design variables, coupling variables, and inventory parameters that enter the parametric LCI functions, even if the underlying data sources are referenced rather than reproduced.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments and positive evaluation of the manuscript. We address each major comment below and indicate the corresponding revisions.

read point-by-point responses

  1. Referee: [Methods] Methods section: while the paper states that parametric life-cycle inventories are defined explicitly from design and coupling variables, it does not report any sensitivity analysis or uncertainty quantification on the functional forms chosen for material quantities, propellant production factors, or transport distances; this directly affects the claim that the inventories can be coupled “without introducing significant modeling errors.”

    Authors: We acknowledge that explicit sensitivity or uncertainty quantification on the chosen functional forms would further support the claim of negligible modeling errors. The parametric inventories rely on linear scaling relations and emission factors drawn from established databases (Ecoinvent and engineering handbooks), which are standard in the LCA community. To address the point directly, the revised manuscript will add a short sensitivity subsection in Methods that perturbs the principal parameters (material quantities, production factors, transport distances) by representative ranges and reports the resulting variation in inventory entries. revision: yes

  2. Referee: [Results] Results section: the multi-objective optimizations are said to exhibit antagonistic behaviors among environmental impact categories, yet no quantitative metrics (e.g., Pareto-front distances, correlation coefficients, or normalized trade-off slopes) are supplied to substantiate the strength or consistency of these antagonisms across the design space.

    Authors: We agree that quantitative descriptors would strengthen the presentation of the observed antagonisms. The revised Results section will report Pearson correlation coefficients between the environmental impact categories across the Pareto set and will include normalized trade-off slopes extracted from the fronts to quantify the strength and consistency of the antagonisms. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper presents a methodology for coupling parametric life-cycle inventories (defined explicitly from design and coupling variables) into an MDAO framework, with trajectory-based emissions evaluated from optimized profiles. No derivation step reduces by construction to a fitted input, self-definition, or self-citation chain; the parametric mappings and multi-objective trade-off results are constructed independently of the target outputs. The central claim remains a generic framework illustrated on one vehicle, with no load-bearing reliance on prior author work that would collapse the result to its inputs.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

Abstract provides insufficient detail to identify specific free parameters or invented entities; the approach relies on standard domain assumptions from MDAO and LCA fields without introducing new entities.

axioms (2)
  • domain assumption Standard assumptions in Life Cycle Assessment for inventory data accuracy and completeness hold when made parametric on design variables.
    The methodology depends on the validity of extending LCA inventory methods to depend on MDAO coupling variables.
  • domain assumption MDAO frameworks can incorporate an additional LCA discipline while maintaining numerical stability and convergence.
    Assumed that adding the environmental discipline does not break the existing optimization coupling.

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