Life Cycle Assessment of Shipbuilding Materials and Potential Exposure Under the EU CBAM: Scenario-Based Assessment and Strategic Responses

Abstract

This study evaluates the environmental impacts of shipbuilding materials through life cycle assessment (LCA) and assesses potential exposure under the EU Carbon Border Adjustment Mechanism (CBAM). Three representative vessel types, a pure car and truck carrier (PCTC), a bulk carrier, and a container ship, were analyzed across scenarios reflecting different steelmaking routes, recycling rates, and regional energy mixes. Results show that structural steel (AH36, EH36, DH36, A/B grades) overwhelmingly dominates embedded emissions, while aluminium and copper contribute secondarily but with high sensitivity to recycling and energy pathways. Coatings, polymers, and yard processes add smaller but non-negligible effects. Scenario-based CBAM cost estimates for 2026–2030 indicate rising liabilities, with container vessels facing the highest exposure, followed by bulk carriers and PCTCs. The findings highlight the strategic importance of steel sourcing, recycling strategies, and verifiable supply chain data for reducing embedded emissions and mitigating financial risks. While operational emissions still dominate the life cycle, the relative importance of construction-phase emissions will grow as shipping decarbonizes. Current EU-level discussions on extending CBAM to maritime services, together with recognition of domestic carbon pricing as a potential pathway to reduce liabilities, underscore regulatory uncertainty and emphasize the need for harmonized methods, transparent datasets, and digital integration to support decarbonization.

1. Introduction

1.1. Background

Environmental regulation is increasingly extending beyond direct operational emissions to include the embedded carbon of traded goods. A prominent example is the European Union’s Carbon Border Adjustment Mechanism (CBAM), which will enter full implementation on 1 January 2026 [1]. CBAM imposes a carbon price on carbon-intensive imports to align their cost with EU-produced goods. Among its initial target products, steel and aluminium are of direct relevance to shipbuilding, where they constitute the majority of structural and outfitting materials [2].

For the shipbuilding industry, this means that the embedded emissions of construction materials in vessels delivered to European owners may, under certain conditions, become subject to CBAM reporting and certificate surrender. While the specific treatment of ship newbuildings has not been determined, a vessel can effectively be regarded as a large import of steel: a Korean- or Chinese-built ship entering the EU market represents tens of thousands of tons of primary steel. Without verifiable LCA data, such ships may be assigned conservative default emission factors, resulting in higher CBAM liabilities [3]. This dynamic could reshape procurement contracts, with clauses on material origin and embedded carbon footprints becoming standard practice.

Cradle-to-gate life cycle assessment (LCA) is therefore increasingly important for shipbuilding. A shipyard able to demonstrate low-carbon steel procurement (e.g., electric arc furnace over blast furnace) and renewable electricity use may be able to reduce the CBAM liability of its European customers, potentially gaining a cost advantage in tenders. Moreover, the influence of CBAM is expected to extend beyond the EU: steel suppliers worldwide are beginning to disclose product carbon footprints [4], and shipyards will be expected to integrate these data into vessel-level LCA models as part of competitive positioning.

1.2. Current Issue

1.2.1. Implications of CBAM Uncertainty

Shipbuilding is among the most material-intensive industries, with steel alone typically accounting for 75–85% of a vessel’s lightweight [5,6,7,8]. The emission factor of hull steel, however, varies by more than 20% depending on production route—blast furnace/basic oxygen furnace (BF–BOF) versus electric arc furnace (EAF) [9]. For a large ship, this difference translates into several million euros in CBAM liabilities. Default values under CBAM are set higher than average emissions, penalizing yards that cannot document their supply chains [10].

This introduces both regulatory and commercial risks for major shipbuilding exporters such as South Korea, Japan, and China. Transparent and yard-specific LCA approaches are therefore critical. Beyond compliance, shipyards that can credibly demonstrate the use of certified low-carbon steel, higher scrap content, or renewable-powered fabrication may reduce CBAM exposure while marketing these practices as ESG differentiators.

1.2.2. Maritime LCA and Prior Studies

In the maritime sector, LCA has been most widely applied to the operational phase. Recent IMO guidelines on life cycle GHG intensity of marine fuels [11,12] formalized well-to-tank (WtT), tank-to-wake (TtW), and well-to-wake (WtW) accounting. WtT covers upstream processes including feedstock extraction, processing or synthesis, transport, and bunkering. TtW covers use-phase emissions from onboard combustion or conversion processes. WtW is the sum of both stages, representing the full fuel pathway from extraction to use. By clearly defining these boundaries, the IMO guidelines harmonize fuel pathway assessments, reduce methodological ambiguity, and enable more consistent integration of LCA results into maritime operational and regulatory decision-making. These advances improved harmonization of fuel pathway assessments and accelerated the integration of LCA into fuel and operational decision-making [13,14].

By contrast, the shipbuilding phase remains comparatively underexplored. Early initiatives quantified embedded emissions of ship steel, but their results are fragmented, with inconsistent boundaries and assumptions. Full-vessel LCAs [15,16] indicate that as operational emissions decline, the relative share of construction-phase emissions can increase significantly (up to ~40% in forward-looking projections). At present, however, operational emissions remain the dominant contributor to lifetime ship GHGs, with construction-related emissions forming a smaller but increasingly important share. Yet, gaps remain in yard-level inventories, fabrication processes, coating systems, and regional recycling practices [17].

1.3. Research Gap

Life cycle assessment (LCA) methodologies for fuel pathways and operational emissions (well-to-tank, tank-to-wake, well-to-wake) are now relatively mature, supported by ISO 14040/44 standards and recent IMO guidelines [18,19]. By contrast, the shipbuilding phase remains comparatively underexplored. Most prior studies focused on fuel choice, propulsion efficiency, or voyage optimization, leaving upstream and construction-related impacts insufficiently characterized.

Critical data gaps persist in several areas: (i) steel grades and fabrication processes, (ii) yard-level electricity consumption, (iii) coatings and outfitting modules, and (iv) maintenance and refit cycles. End-of-life modeling is also inconsistent, with wide variation in recycling versus disposal assumptions. Moreover, uncertainty is rarely propagated into decision metrics, which limits the practical value of existing studies for procurement strategies, regulatory compliance, or cost–benefit evaluations.

This omission is increasingly consequential. As operational emissions decline through efficiency measures and low- or zero-carbon fuels, the relative importance of construction materials intensifies. Full-vessel LCAs to date consistently indicate that structural steel, aluminium, composites, and coating systems dominate a vessel’s embodied carbon footprint, together accounting for the majority of construction-phase GHG emissions [11,13,17]. Yet, the absence of harmonized, comparative datasets across vessel types leaves shipyards, owners, and regulators without reliable benchmarks for assessing exposure or mitigating carbon leakage risks.

From a regulatory perspective, this gap is amplified by the EU Carbon Border Adjustment Mechanism (CBAM), where embedded emissions could directly influence procurement costs. Without rigorous, reproducible, and yard-specific LCAs, penalty exposure may vary widely depending on methodological assumptions or supply chain transparency, creating both regulatory and commercial risks for major exporting nations.

Addressing this shortfall requires targeted, case-based LCAs across multiple vessel types, explicitly quantifying embodied emissions, identifying carbon leakage points, and evaluating mitigation strategies such as high-scrap steel use or renewable-powered fabrication. Such work is essential not only to ensure regulatory compliance and cost competitiveness, but also to provide shipyards, owners, and policymakers with the robust evidence needed to guide sustainable material choices and achieve genuine life cycle decarbonization. This gap is therefore not only academic, but also regulatory and financial, as CBAM may directly tie embedded emissions in shipbuilding to monetary costs, making robust and transparent LCA methods indispensable for competitiveness and compliance.

In response, this study develops a scenario-based cradle-to-gate LCA framework for shipbuilding materials and applies it to three representative vessel classes (a PCTC, a bulk carrier, and a container ship). By quantifying construction-phase emissions and translating them into indicative EUR/ship exposures along EU CBAM price trajectories, the study illustrates how material-level LCA can be linked with regulatory and financial implications. The novelty further lies in harmonizing inventories across ship types and structuring scenarios into near-term, mid-term, and exploratory tiers. This provides not only methodological rigor, but also forward-looking insights for policymakers and industry stakeholders in light of possible extensions of CBAM or similar carbon pricing mechanisms to shipbuilding materials and newbuildings.

1.4. Objectives and Contributions

Methodologically, the study couples a harmonized, scenario-based cradle-to-gate LCA with an ETS-linked CBAM cost translation, offering a generalizable procedure for embedded-carbon exposure assessment.

2. Methodology (Overview and Study Boundary)

Figure 1 presents a structured methodology for conducting a life cycle assessment (LCA) in the context of shipbuilding. The framework consists of five steps: (1) data collection, (2) inventory compilation, (3) sensitivity and scenario analysis, (4) cross-application to various ship types, and (5) carbon cost estimation under the EU Carbon Border Adjustment Mechanism (CBAM). Unless otherwise specified, the system boundary is defined as cradle-to-gate, encompassing raw material production, transport, fabrication at the shipyard, and coating application. All quantitative outputs are interpreted as illustrative ranges rather than forecasts and are intended for sensitivity and decision support purposes; for any compliance or contracting use, yard-specific, auditable datasets must replace proxy averages.

In life cycle assessment (LCA), the functional unit (FU) is defined as the quantified reference unit that describes the function of a product system, thereby providing a consistent and comparable basis across alternatives (ISO 14040/44). In the context of shipbuilding, typical examples include “1 ton of hull structural steel produced,” “1 ship constructed within the cradle-to-gate boundary,” or “1 ton of vessel lightweight.” These units establish a standardized reference for assessing the environmental burdens of different design or sourcing strategies.

Building on this general principle, the present study specifies the FU in two complementary ways to capture both material-level comparability and vessel-level scale. Results are therefore reported as follows:

• total greenhouse gas (GHG) emissions normalized per ton of hull structure steel (primary FU, kg CO₂-eq/t steel), and
• total emissions per ship (kg CO₂-eq/ship) to reflect absolute scale.

The methodology follows ISO 14040/44 standards and uses terminology and workflows widely adopted in the maritime sector.

2.1. Step 1: Data Collection (Ship and Materials)

The analysis covers typical newbuild vessels such as pure car and truck carriers (PCTC), bulk carriers, and container ships. Lightweight (t-LW) is allocated across key material groups: hull and deck structure steel, aluminium (ramps/superstructure), internal and outfitting steel, copper and brass, piping systems, coatings/paints, and machinery/outfitting.

To define the effective LCA boundary, the reported lightweight of each vessel was adjusted by applying representative reduction factors: 98% for PCTC, 97% for bulk carrier, and 95% for container vessel. These values were derived from a comparative review of detailed lightweight breakdowns of representative newbuildings in each segment, where non-construction items, such as main engines, outfitting unrelated to primary structure, operational fluids, furnishings, cables, and loose equipment, were systematically excluded. The resulting adjustment factors were conservatively standardized across vessel types to ensure consistency and rounded downward to avoid underestimating material-related impacts. While not intended to represent a continuous trend, they provide a pragmatic and transparent boundary definition for cradle-to-gate shipbuilding LCAs.

In this study, lightweight refers to the vessel’s total light displacement, including structure, machinery, outfitting, and fixed equipment. Within this total, hull structure weight denotes only the mass of the primary structural steel forming the hull and deck. The LCA boundary is defined at the adjusted lightweight level, rather than at hull structure weight alone. This distinction ensures clarity when interpreting results expressed per lightweight versus per ton of hull steel.

If detailed weight breakdowns are not available, proxy data are applied based on general arrangement drawings and yard estimates. Material profiles: Each material is assigned a cradle-to-gate emission factor (EF_m, kg CO₂-eq/kg), defined by material grade and production route (e.g., blast furnace–basic oxygen furnace [BF–BOF] vs. electric arc furnace [EAF] for steel; regional grid mix for aluminium; and copper refining process). Regional tagging supports scenario-based adjustments. Environmental product declarations (EPDs) and supplier-specific data are prioritized, followed by reputable life cycle inventory (LCI) databases.

Supporting flows: Yard-level energy use (e.g., welding, cutting, and painting), transportation of materials (road, rail, and short-sea), and other inputs, such as paint and welding consumables, are included where available. Volatile organic compound (VOC) losses and waste generation are also tracked.

Data assurance (purpose and limitations): All datasets are reviewed for temporal, geographic, and technological relevance, with explicit logging of assumptions. The proxy values used here are for exploratory sensitivity analysis; for application, they must be replaced by yard-specific, verifiable primary data (e.g., meters, batch records, and logistics documents), particularly if recognition of domestic carbon pricing or verification affects CBAM exposure.

Table 1. Harmonized (ship type—normalized) lightweight (LW) allocation by material category (%) *.

Material Category	PCTC (Approx. %)	Bulk Carrier (Approx. %)	Container Vessel (Approx. %)
Hull structure steel	45.0	55.0	50.0
Deck structure steel	20.0	15.0	18.0
Aluminium (ramp, access)	5.0	0.0	0.5
Internal structure steel	10.0	10.0	12.0
Outfitting steel	8.0	7.0	6.0
Copper and brass	1.0	0.5	1.0
Piping (steel, CuNi, etc.)	4.0	4.0	5.0
Coating and paint	3.0	2.5	3.0
Machinery/outfitting	4.0	6.0	4.5

* Notes: Shares denote the proportion of ship lightweight (LW) allocated to each material category for three archetype classes (PCTC, bulk carrier, and container vessel). Values were harmonized from an anonymized set of standard designs per class; see Methods (data harmonization) for details.

harmonized (ship type—normalized) lightweight (LW) allocation by material category (%) presents the harmonized allocation of vessel lightweight across material categories for three representative ship types: Figure 2 (PCTC), Figure 3 (bulker), and Figure 4 (container). The values are expressed relative to the LCA boundary–adjusted lightweight (i.e., 98% for PCTC, 97% for bulk carrier, and 95% for container vessel, after excluding non-construction items such as main engines, operational fluids, furnishings, cables, and loose equipment).

The data show that hull and deck structure steels dominate the material inventory across all vessel types. In our dataset of over 30 anonymized newbuilding designs, hull structure steel alone consistently represents about 45–55% of the adjusted lightweight, with deck structures contributing an additional 15–20%. These shares are broadly consistent with, but more specifically, more grounded than, general findings in prior literature that reported total steel accounting for 75–85% of lightweight.

Aluminium contributes significantly in PCTCs due to ramps and access structures, but only marginally in container vessels and negligibly in bulk carriers. Internal and outfitting steels, together with piping, account for another 20–25%, while coatings, copper/brass, and machinery/outfitting make up the remaining 10–15%.

By normalizing each vessel type to its adjusted lightweight, the table provides a consistent basis for applying cradle-to-gate emission factors in subsequent LCA calculations. This ensures that the reported category shares are directly aligned with the functional unit of the study and avoids confusion with unadjusted lightweight or with hull-steel-only subsets.

For PCTCs, aluminium alloys used in ramps and access structures contribute an additional 5% to the material mix, while bulkers show no significant aluminium use. Container vessels include minimal aluminium (~0.5%), primarily in minor outfitting applications. Internal structural steel, outfitting steel, and piping together contribute ~20–25% across vessel types, while coating, machinery, and copper/brass components comprise the remaining 10–15%.

To support a consistent life cycle assessment (LCA) modeling framework, the following assumption was applied. If multiple materials under a single category share the same GWP emission factor (kg CO₂-eq/kg), the category percentage was proportionally distributed equally among them. This assumption enables a harmonized treatment of material-based emissions where itemized mass data are either proprietary or unavailable and avoids the bias of overestimating the impact of any one component in aggregated categories (e.g., carbon steel brackets vs. stiffeners). While this may reduce granularity, it provides a transparent and replicable basis for upstream emissions mapping—especially important when conducting scenario-based sensitivity analyses across supply chain pathways or regulatory exposure (e.g., CBAM).

To enable fair comparison across vessel types, lightweight breakdowns were harmonized based on anonymized reference designs. Median values were used to reduce shipyard-specific variation. A typical mix of hull steel grades (e.g., AH36, EH36, and A/B plate) was assumed, and results were expressed relative to hull structure weight. Sensitivity tests were conducted for critical input variables such as steelmaking route, scrap content, grid emission factor, and coating VOC content.

2.2. Step 2: Life Cycle Inventory (LCI) Construction

The build-stage GHG emissions (GWP_build) are calculated according to attributional LCA practice (ISO 14040/44) as shown below:

$$GW{P}_{build}=\sum _m\left(M_m\times E{F}_m,base\times {\alpha }_m,scen\right)+E_{yard}\times E{F}_{grid}+G_{consumables}$$

(1)

where

• M_m: mass of material m
• EF_m_base: baseline emission factor
• α_m,scen: scenario-based adjustment factor
• E_yard: shipyard energy use
• EF_grid: emission intensity of the electricity used
• G_consumables: emissions from paint, welding, and other consumables.

Optional end-of-life (EoL) modeling includes material recovery or recycling credits, particularly for steel. However, unless otherwise stated, the primary boundary remains cradle-to-gate.

Reported outputs include the following:

• total CO₂-equivalent emissions per ship,
• emissions per ton of hull structure steel (main reference unit),
• optionally, emissions per ton of lightweight or per ton-nautical mile.

GWP (100-year horizon) is used as the main metric, though supporting indicators (e.g., acidification, particulate matter, toxicity) are recorded for reference.

Life Cycle Inventory (LCI) Setup

The life cycle inventory (LCI) for each vessel was established based on detailed material breakdowns. Figure 5 illustrates the modeled inventory for a pure car and truck carrier (PCTC) developed in GaBi. The model captures major structural steels (AH36, EH36, and A/B grade plates), deck stiffeners, aluminium alloys, internal structural members, and outfitting components such as copper wiring, piping systems (CuNi, carbon steel, and SUS), coating materials, and machinery items (ventilation units, ramp motors, and cables). This visualization provides a clear overview of the mass contribution of each category, expressed in kilograms, and directly maps to emission factors used in the subsequent life cycle impact assessment. The inventory confirms that the majority of the ship’s embedded carbon arises from hull structural steels, while secondary but non-negligible contributions stem from aluminium, copper, paints, and outfitting items.

By including such a comprehensive inventory, the study ensures methodological transparency and allows for reproducibility of the scenario-based LCA calculations. This also highlights the capacity of GaBi to capture the hierarchical structure of ship components, making it a suitable tool for scaling across vessel classes.

Table 2. LCA intensity values for different hull structures and materials.

Category	Material	LCA Value (kg CO2-eq/kg)	Correct Source(s)	Justification
Hull structure steel	AH36 steel plate	2.3	Worldsteel LCI (Hot Rolled Plate), GaBi	Produced via BOF/EAF + hot rolling > 6 mm; alloying has negligible GWP effect vs. process emissions
Hull structure steel	EH36 steel plate	2.3	Worldsteel LCI, GaBi	Same process route as AH36; composition differences minor
Hull structure steel	A/B grade steel plate	2.3	Worldsteel LCI, GaBi	Same as AH36, chemistry differences negligible
Deck structure steel	AH36 deck stiffeners	2.3	Worldsteel LCI, GaBi	Hot rolled stiffener sections; dataset aligned
Deck structure steel	DH36 deck stiffeners	2.3	Worldsteel LCI (Sections), GaBi	HR section route matches
Deck structure steel	Deck plating	2.3	Worldsteel LCI, GaBi	Same as AH36 plates
Aluminium (ramp, access)	Aluminium 5083	10.5	European Aluminium EPD, GaBi	Marine-grade 5xxx alloy; dataset scope matches
Aluminium (ramp, access)	Aluminium 6061	10.5	European Aluminium EPD, GaBi	6xxx alloy, rolled; consistent with dataset
Internal structure steel	Mild steel	2.2	Worldsteel LCI (HR coil/section), GaBi	Generic low-carbon HR coil
Internal structure steel	Structural members	2.2	Worldsteel LCI, GaBi	Rolled sections; dataset aligned
Internal structure steel	Girders	2.2	Worldsteel LCI, GaBi	Channels/beams from same route
Outfitting steel	Carbon steel brackets	2.2	Worldsteel LCI, GaBi	Fabrication within HR section dataset scope
Outfitting steel	Stiffeners	2.2	Worldsteel LCI, GaBi	Plate-based stiffeners; same route
Outfitting steel	Grating	2.2	Worldsteel LCI, GaBi	HR section dataset
Copper and brass	Electrical copper wiring	4.5	ICA LCA (cathode copper), Ecoinvent	Cu rod drawing → conductor, dataset aligned
Copper and brass	Brass fittings	3.8	Brass EPD, GaBi	Cu-Zn alloy fittings, dataset covers
Piping (Steel, CuNi, etc.)	CuNi pipes	3.0	ICA (copper), Nickel Institute, GaBi	Weighted Cu-Ni alloy composition
Piping (Steel, CuNi, etc.)	Carbon steel pipes	2.2	Worldsteel LCI, GaBi	Welded/seamless pipe manufacturing dataset
Piping (Steel, CuNi, etc.)	SUS pipes	6.1	Outokumpu Stainless EPD, GaBi	Austenitic Cr-Ni stainless pipes
Coating and paint	Polyurethane	5.0	PlasticsEurope Eco-profile, GaBi	PU resin binder dataset matches marine coating binder
Coating and paint	Epoxy	6.0	Generic coating EPD, GaBi	Epoxy resin production, marine grade
Coating and paint	Anticorrosive paints	6.5	Literature review, GaBi	Resin + pigment stage; marine anticorrosive formulations
Machinery/ outfitting	Ventilation units	4.5	HVAC unit EPD, GaBi	Mixed metal/plastic assembly dataset
Machinery/ outfitting	Ramp motors	6.5	Electric motor EPD, GaBi	Copper winding, casing, assembly
Machinery/ outfitting	Cables	5.0	Cable EPD, GaBi	Copper wiring + insulation, dataset scope

indicates the LCA intensity values for different hull structures.

However, the datasets applied here serve primarily for exploratory scenario analysis; application-level use (e.g., for compliance or procurement contracts) requires substitution with yard-specific, auditable primary data such as meter readings, batch records, and transport documentation. Results should therefore be interpreted as illustrative ranges rather than forecasts.

The LCA inventory analysis was conducted for three ship types—PCTC, bulker, and container ship—using the GaBi [20] software platform. GaBi is a widely used LCA modeling tool that provides extensive unit process datasets for metals, energy systems, and chemicals. It enables regionalized modeling of production routes, scrap ratios, and grid electricity mixes, which is critical for scenario-based analysis in shipbuilding. GaBi was selected for this study because of its transparent dataset pedigree (Worldsteel, European Aluminium, ICA, PlasticsEurope, etc.) and its suitability for testing sensitivity to sourcing and recycling assumptions. For each vessel, the material composition was systematically quantified, with steel plates (AH36, EH36, DH36, and A/B grade) representing the majority share, followed by aluminium alloys, copper-based materials, polymers, coatings, and auxiliary equipment. While the absolute quantities vary with ship design and size, the overall trend remains consistent: steel dominates the inventory, while aluminium, copper, polymers, and coatings act as important secondary contributors.

Overall, the diagram illustrates how the inventory is dominated by steel products, with aluminium, copper, polymers, and auxiliary equipment providing additional contributions, thereby forming the detailed material basis for subsequent impact assessment within the LCA framework.

2.3. Step 3: Sensitivity Analysis (Input Ranges and Scenarios)

It should be noted that in this study the term “sensitivity analysis” refers to a pragmatic combination of deterministic one-at-a-time (OAT) parameter variation and structured scenario testing. The objective is to identify which assumptions most strongly influence embedded GHG emissions and CBAM exposure, thereby providing clear directional insights for decision-making. This differs from formal probabilistic sensitivity indices, but is widely applied in exploratory LCA studies where the primary purpose is to support policy and procurement strategies.

This step includes two types of testing:

• Input variation: Key parameters such as steel production method, scrap ratio, aluminium electricity source, coating type, and recycling rate were perturbed individually in a one-at-a-time (OAT) fashion. The resulting absolute changes in total embedded GHG emissions and EUR/ship CBAM exposure were quantified. Results are presented as sensitivity bar charts, ranking the relative influence of each factor.
• Scenario testing: grouped assumptions are applied to reflect realistic procurement or design strategies, complementing the OAT analysis by illustrating how combined measures shift total outcomes.

In addition, the overall spread of results was also visualized using boxplots to show the range of variability across scenarios, including maximum and minimum values. To ensure robustness in the absence of full probabilistic uncertainty quantification, all parameter variations and scenario rules were applied in a deliberately conservative manner. This approach was intended to avoid underestimation of CBAM exposure so that the reported ranges provide reliable directional insights for decision-making even under simplified deterministic assumptions.

We apply a series of realistic scenarios that reflect procurement or design decisions, such as the following:

• using steel from EU/EAF routes,
• selecting high-scrap content,
• applying VOC-free or bio-based paints,
• shortening supply chains,
• or reducing structure mass.

Each case is compared to a baseline and expressed both in absolute values and in terms of impact per ton of hull structure steel. These groupings reflect different decision levers procurement, design, and compliance and allow clearer communication of findings. Adjustment coefficients for each scenario are listed in

Table 3. Various LCA scenarios for sensitivity analysis.

Scenarios	Description	Rationale (Why Considered/What It Shows)	Adjustment Rule
A—technological optimization	Improved efficiency in material processing and yard energy use without changing suppliers.	Represents “low-hanging fruit”: marginal improvements achievable with current suppliers and processes; provides realistic minimum-gain scenario.	All materials ×0.90
B—Regional average (EU/EPD)	Applies Environmental Product Declaration (EPD) or EU-average emission factors for major materials.	Reflects a greener procurement pathway aligned with European supply chains; shows compliance-level carbon intensity if certified data are used.	Steel ×0.85; Al ×0.76; Cu ×0.78; brass ×0.84; coatings ×0.85
C—Conservative baseline	Assumes poor supply chains with +15% higher emission factors.	Serves as an upper-bound stress test; highlights penalty risk if verification is weak or sourcing is uncontrolled.	All materials ×1.15
D—Strategic material shift	Active substitution to lower-impact material grades or suppliers.	Demonstrates effect of deliberate design/procurement strategy; connects directly to ESG-driven corporate choices.	All materials ×0.80
E—Green compliance	Selection aligned with ecolabel or owner green-procurement rules.	Shows real-world requirement from charterers/financiers; provides benchmark for market-driven decarbonization.	Steel ×0.85; Al ×0.70; Cu/brass ×0.75; coatings ×0.80; cables ×0.90
F—EU-origin steel only	All hull and deck steel sourced from EU EAF-based suppliers.	Illustrates strong leverage of steel sourcing on CBAM exposure; demonstrates potential advantage of certified EU supply.	Steel ×0.80
G—High-emission source	Assumes coal-intensive or uncontrolled global supply chains (+25%).	Provides worst-case bound; useful for quantifying maximum CBAM penalty exposure.	All materials ×1.25
H—High-scrap steel	Steel produced with ~80% recycled scrap via EAF route.	Tests realistic pathway of steel sector decarbonization; shows benefits of circularity and high scrap availability.	Steel ×0.70
I—Low-scrap aluminium	Aluminium sourced primarily from virgin production (low recycling rate).	Highlights sensitivity of aluminium to recycling; quantifies penalty if recycled content is not available.	Al ×1.40
J—VOC-free coatings	Use of solvent-free or water-based paints.	Addresses health/environmental co-benefits; shows effect of coating system switch on total GWP.	Coatings ×0.65
K—Owner EPD compliance	Materials restricted to certified EPD-compliant suppliers.	Simulates procurement rules of major shipowners; ensures transparency and comparability across supply chains.	Steel ×0.85; Al ×0.75; Cu/brass ×0.80; coatings ×0.85
L—Electric copper refining	Copper produced with clean electricity (renewable-powered smelting).	Tests impact of decarbonized electricity in energy-intensive non-ferrous metals.	Cu/Brass ×0.75
M—Korea–domestic mix	Emission factors based on domestic (Korea) industry-average datasets.	Reflects sourcing pathway common to Korean yards; important baseline for East Asia vs. EU comparison.	Steel/aluminium/copper/brass × regional Korea LCI values
N—Recycled plastics in equipment	Introduces recycled plastic share in outfitting/equipment components.	Captures emerging trend of recycled polymers; quantifies potential but smaller contribution.	Plastics in equipment ×0.70
O—Full IMO GHG compliance	Assumes materials and processes aligned with IMO decarbonization targets.	Provides forward-looking compliance pathway; shows alignment with sectoral regulation beyond CBAM.	All materials ×0.70 (as per IMO-aligned pathway)
P—100% green steel (hydrogen DRI, −50%)	Steel produced entirely via hydrogen-based direct reduced iron (H2-DRI) combined with EAF route.	Represents one of the most transformative decarbonization pathways. Targets the dominant material in shipbuilding; ~65% lower EF vs. BF–BOF. Demonstrates how large-scale adoption of H2-DRI can dramatically cut embedded emissions and CBAM liability.	Steel ×0.50
Q—Low-carbon steel (H2-DRI)	BF–BOF steelmaking route with ~60–70% carbon capture efficiency.	Transitional pathway for regions where BF–BOF remains dominant. Delivers meaningful reductions without full technological replacement; pragmatic but capital-intensive.	Steel ×0.60
R—Aluminium closed-loop recycling (−45%)	Aluminium fully supplied via closed-loop recycling streams (e.g., ramps, outfitting).	Strong circular economy strategy; ~50% reduction vs. global average aluminium. Reduces reliance on virgin smelting and lowers CBAM exposure.	Al ×0.55
S—Multi-metal recycling synergy (−30%)	Bundled recycling of multiple inputs: scrap-based EAF steel, recycled aluminium, renewable-sourced copper, and recycled plastics.	Demonstrates synergy effects across materials. Shows that multi-stream circularity can deliver cumulative reductions larger than individual measures.	Steel ×0.75; Al ×0.65; Cu/brass ×0.70.
T—Renewable shipyard electricity	Yard painting and fabrication powered by renewables.	Captures indirect but controllable emissions; reflects green-shipyard initiatives.	Per-kg EF unchanged; yard energy factor adjusted
U—Bio-based coatings	Substitution of petroleum-based resins with bio-based binders.	Demonstrates effect of renewable feedstocks; aligns with emerging ecolabels.	Coatings ×0.60
V—Low-carbon logistics	Optimized transport modes and load factors for material supply.	Reflects incremental reductions through logistics efficiency; highlights scope-3 control potential.	All materials ×0.95 (excl. W)
W—Localized supply Chain	All key suppliers within 500 km radius.	Tests effect of localization; relevant to nearshoring and regional content rules.	All materials ×0.90 (excl. V)
X—Circular copper program	Full closed-loop recycling of copper/brass components.	Illustrates long-term circularity potential in non-ferrous metals.	Cu/Brass ×0.50
Y—Material substitution	Lightweight redesign reduces material mass by 5–10%.	Represents design-driven reductions; connects directly to naval architecture trade-offs.	Mass ×(1–δ); EF unchanged

Note: Detailed descriptions and rationle for each scenario (A–Y) are proivded in Appendix A.

. To capture the variability in material sourcing, design strategies, and yard practices, a structured set of scenarios was developed. Each scenario includes (i) a description of the assumed condition, (ii) the rationale for its inclusion, and (iii) the adjustment rule applied in the LCA model.

Beyond technical and procurement scenarios, policy-oriented cases were also considered conceptually, such as the possible recognition of domestic carbon pricing schemes or the extension of CBAM scope to maritime transport services. These were not quantified in detail but provide important context for interpreting results.

Scenario tiering for clarity (added): We classify scenarios by feasibility/maturity to avoid equal weighting: (i) near-term (≤2030)—e.g., high-scrap steel, EU/EAF sourcing, VOC-free/bio-based coatings, logistics optimization; (ii) mid-term (2030s)—e.g., CCUS-equipped BF-BOF, partial H₂-DRI, closed-loop copper; (iii) exploratory (post-2035+)—e.g., 100% H₂-DRI steel, full closed-loop aluminium. This prevents misinterpretation of exploratory cases as immediately actionable levers.

2.4. Step 4: Carbon Cost Exposure Under CBAM

To link environmental results with economic implications, embedded emissions from key inputs (e.g., steel, aluminium, coatings) are translated into an estimated carbon cost under the EU CBAM framework (2), using a direct linear conversion with the certificate price (EUR/tCO₂) as defined in the EU CBAM Regulation:

$$CBA{M}_{cost}=\sum _i\left(C{O}_2,i^{embedded}\times CBAM\_rat{e}_i\right)$$

(2)

Here, the embedded CO₂ is derived from the build-phase LCA outputs (Step 2) and varies by material source and production route. The CBAM rate reflects the expected levy per ton of CO₂ equivalent. The CBAM price is directly linked to the EU Emissions Trading System (EU ETS) allowance price. In this study, we assume a gradual increase from EUR 95/tCO₂ in 2026 to EUR 149/tCO₂ in 2030, consistent with current EU ETS futures and policy outlooks. These values are used as illustrative price points; regulatory scope/coverage may evolve, and actual liabilities will depend on verified data acceptance and scope decisions.

Key outputs include the following:

• Total CBAM exposure per ship (EUR/ship)
• Breakdown by material category and region of origin
• Scenario-based comparisons to quantify potential cost avoidance from low-carbon sourcing strategies.

This step connects technical LCA findings with regulatory and financial risk assessment, supporting decision-making for shipyards and shipowners under tightening carbon border measures. Accordingly, CBAM cost outcomes are presented as illustrative exposures (not forecasts), highlighting avoidable costs under lower-emission pathways.

Where CO₂_i is the embedded carbon per material or system and CBAM_rate_i is the expected levy per ton of CO₂. Results show potential savings from adopting lower-emission materials or production routes. A range of design and sourcing scenarios were constructed to reflect practical procurement or regulatory conditions. These include shifts in material origin, recycled content, coating systems, logistics routing, and structural design.

To improve clarity, the scenarios are grouped into five thematic categories:

• Baseline and stress range adjustment—to test uncertainty bounds (e.g., A, C, and G);
• Material-specific procurement strategies—assessing impact from sourcing choices for steel, aluminium, and copper (e.g., B, F, H, etc.);
• Coating system alternatives—VOC-free or renewable coatings and energy inputs (e.g., E, J, T, and U);
• Logistics and supply chain optimization—localized or efficient transport (V, W);
• Structural redesign and material reduction—design-based material savings or substitution (D, M, N, and Y).

This grouping enables scenario interpretation not only by numerical outcome, but also by strategic decision level (procurement, design, or regulation-driven). The methodological framework defined in Steps 2 and 3 provides the foundation for the subsequent case study results. Specifically, the material-specific emission factors and inventory structure established in Table 2 form the baseline values for ship-level greenhouse gas calculations. The scenario adjustment rules summarized in Table 3 are then systematically applied to these baseline inventories to generate a range of outcomes under different sourcing, design, and compliance pathways. In the Section 3, these inputs are translated into vessel-specific assessments of total embedded emissions and CBAM cost exposures. Figures 6 and 7 summarize the total GWP and LCA intensity distributions of shipbuilding materials, respectively, while Figures 8–12 visualize how material-level contributions vary across scenarios. Tables 4–6 present the corresponding scenario-based penalty outcomes for three representative ship types. (Detailed numerical results are discussed in Section 3: Results.)

2.5. Step 5: Interpretation of CBAM Cost Exposures

The final stage of the methodology does not introduce new calculations but positions the CBAM cost estimates generated in Step 4 for interpretation. In practice, the monetary exposure values are intended to inform decision-making rather than stand as abstract figures. Therefore, Step 5 establishes a link between the quantitative assessment and its relevance to different stakeholders, preparing the ground for the results and discussion chapters.

Specifically, the CBAM cost outputs are framed as potential liabilities for shipowners, competitiveness factors for shipyards, and policy signals for regulators. This framing is scope-bound to construction-phase embedded emissions; integration with operational policies (e.g., ETS/FuelEU) is identified as future work.

3. Results

Detailed descriptions of scenarios A–Y, including their assumptions and adjustment rules, are provided in Section 2.3 (Methodology, Table 3). Accordingly, the focus here is on comparative outcomes, scenario-dependent variations, and interpretation of results, rather than repeating the scenario definitions. For clarity, scenarios are interpreted by maturity tier: near-term (immediately actionable by 2030), mid-term (technically feasible within the 2030s), and exploratory (post-2035 or emerging). Monetary values are presented as illustrative exposures rather than forecasts and depend on data verification and regulatory scope. This study focuses on CBAM-only construction-phase exposures; interactions with IMO/FuelEU/ESG are beyond scope.

3.1. Base Assessment (Step 1–2)

In this study, three vessel classes were selected as representative case studies: a 7000 CEU PCTC in the automobile carrier segment, a 200 k DWT bulk carrier in the dry cargo segment, and a 20,000 TEU container vessel in the liner shipping segment. These classes were chosen because they reflect mainstream sizes frequently adopted in global shipbuilding markets, thereby serving as realistic benchmarks for assessing embedded emissions and CBAM exposure.

Across the three reference vessel classes, the material inventories confirm that steel overwhelmingly dominates the cradle-to-gate embedded emissions, typically representing about 90–95% of the total footprint under baseline assumptions. Aluminium contributes only a small share of vessel lightweight, but due to its much higher emission factor, its relative impact on embedded carbon can become more pronounced, especially for PCTCs where ramps and access structures increase aluminium use. Other materials, such as copper, coatings, polymers, and yard energy inputs, collectively account for the remaining few percent. While minor in share compared to steel, these categories carry scenario-specific importance for example under low recycling or carbon-intensive electricity assumptions.

3.1.1. Baseline Assessment Pure Car and Truck Carrier (PCTC, ~7000 CEU (Car Equivalent Unit))

The PCTC case study represents the automobile transport segment, where lightweight structural design is dominated by hull steel and outfitting, with relatively higher aluminium shares compared to bulk carriers. The reference vessel has a lightweight of approximately 25,000 tons, of which 98% (24,500 tons) was considered within the cradle-to-gate LCA boundary (excluding fluids, furnishings, and other non-cradle-to-gate items).

Under the conservative baseline assumption of blast furnace/basic oxygen furnace (BF/BOF) steel production at global average emission factors, cradle-to-gate emissions are estimated at ~84.5 kt CO₂-eq. At the 2026 ETS-linked CBAM price, the corresponding baseline exposure aligns with Table 4’s conservative baseline (Scenario C) at ~EUR 7.98 million per ship. Steel alone accounts for ~78% of total embedded emissions, underscoring its decisive role in determining CBAM liabilities for this vessel type.

3.1.2. Baseline Assessment Bulk Carrier (200 k DWT)

The 200 k DWT bulk carrier represents one of the most steel-intensive vessel classes in the global fleet. The reference vessel has a lightweight of approximately 29,800 tons, of which 97% (28,906 tons) was included within the LCA boundary. Hull and deck structures dominate the material mass, making steelmaking routes and scrap content the principal drivers of embedded GHG emissions and CBAM exposure.

Compared with the PCTC case, the significantly larger steel mass amplifies the dispersion of costs across scenarios, particularly as the EU ETS-linked CBAM price escalates (e.g., EUR 95→149/tCO₂ from 2026 to 2030).

3.1.3. Baseline Assessment 20,000 TEU Class Container Vessel

The 20,000 TEU container vessel represents the mainstream class in the liner shipping segment, frequently adopted by global carriers for fleet expansion programs. The reference vessel has a lightweight of approximately 35,300 tons, of which 95% (33,535 tons) was considered within the LCA boundary.

Its exceptionally large hull dimensions and reinforcement requirements for deck structures and longitudinal strength result in very high raw material demand. Consequently, baseline CBAM exposure per ship is higher than for the PCTC and the 200 k DWT bulker, consistent with Table 6’s central scenarios, making this vessel a critical stress test for decarbonization and compliance strategies. Given the capital-intensive nature of the container sector, even modest per-hull penalties accumulate rapidly in series orders.

3.2. Sensitivity Analysis and CBAM Cost Exposure (Step 3)

Scenario maturity tiers used in interpretation: near-term (A, B, E, F, H, J, K, N, T, V, W, and Y), mid-term (L, O, Q, R, and S), and exploratory/stress or contextual (P as exploratory; C, G, I, and M as conservative/high-emission/regional context). This avoids reading exploratory cases as immediately actionable.

3.2.1. 7000 CEU PCTC—Scenario Results (2026–2030, Million EUR/Ship CBAM Penalty)

In 2026, PCTC exposures range from ~EUR 4.70 M (Scenario P: 100% green steel, hydrogen DRI) to ~EUR 8.67 M (Scenario G: high-emission sourcing). By 2030, the spread widens to ~EUR 7.38 M (P) to ~EUR 13.60 M (G). Mid-range compliance-oriented strategies such as Scenarios B (regional average), E (green compliance), and K (owner EPD compliance) cluster around EUR 9–10 M. These results confirm that steel sourcing is the dominant driver of CBAM exposure, while aluminium recycling and coating/logistics measures exert secondary but noticeable effects.

Table 4. PCTC CBAM penalties (million EUR/ship) under Scenarios A–Y (2026–2030) *.

Scenario	Description	2026 (EUR 95/t)	2027 (EUR 120/t)	2028 (EUR 130/t)	2029 (EUR 140/t)	2030 (EUR 149/t)
A	Technological optimization (−10%)	6.24	7.89	8.54	9.2	9.79
B	Regional average (EU/EPD)	5.86	7.4	8.02	8.64	9.19
C	Conservative baseline (+15%)	7.98	10.08	10.92	11.76	12.51
D	Strategic material shift (−20%)	5.55	7.01	7.59	8.18	8.7
E	Green compliance	5.79	7.31	7.92	8.53	9.08
F	EU-origin steel only	6.04	7.63	8.27	8.91	9.48
G	High-emission source (+25%)	8.67	10.95	11.87	12.78	13.6
H	High-scrap steel (80%)	5.6	7.07	7.66	8.25	8.78
I	Low-scrap aluminium (+40%)	7.43	9.38	10.16	10.94	11.65
J	VOC-free paints	6.81	8.6	9.32	10.04	10.68
K	Owner EPD compliance	5.89	7.44	8.06	8.68	9.23
L	Electric copper refining	6.91	8.73	9.46	10.19	10.84
M	Korea–domestic mix	6.45	8.15	8.83	9.51	10.12
N	Recycled plastics in equipment	6.91	8.73	9.45	10.18	10.83
O	Full IMO GHG compliance	4.86	6.13	6.65	7.16	7.62
P	100% green steel (hydrogen DRI)	4.7	5.94	6.43	6.93	7.38
Q	CCUS-equipped steelmaking	5.37	6.79	7.35	7.92	8.43
R	Aluminium closed-loop recycling	6.39	8.07	8.74	9.41	10.02
S	Multi-metal recycling synergy	5.36	6.77	7.34	7.9	8.41
T	Renewable shipyard electricity	6.86	8.67	9.39	10.12	10.77
U	Bio-based coatings	6.79	8.58	9.3	10.01	10.65
V	Low-carbon logistics	6.59	8.32	9.02	9.71	10.34
W	Localized supply chain (≤500 km)	6.24	7.89	8.54	9.2	9.79
X	Circular copper program	6.89	8.7	9.43	10.15	10.8
Y	Advanced material substitution	6.59	8.32	9.02	9.71	10.34

* Notes: CBAM exposures are illustrative (not forecasts). Values read from manuscript Table 4, Table 5 and Table 6 under the assumed ETS-linked CBAM price path EUR 95→149/tCO₂ (2026→2030); actual liabilities depend on regulatory scope/exemptions and verification of supplier-specific data.

presents CBAM exposure by scenario and year. The vertical axis lists procurement, design, and regulatory options, while the horizontal axis represents the EU ETS price trajectory. Relative values across rows indicate cost sensitivity of alternative strategies, and the year-on-year slope shows exposure to rising carbon prices.

3.2.2. Bulk Carrier 200 k DWT—Scenario Results (2026–2030, Million EUR/Ship CBAM Penalty)

For the bulk carrier, 2026 exposures range from ~EUR 4.38 M (Scenario P) to ~EUR 8.90 M (Scenario G). By 2030, the spread increases to ~EUR 6.86 M (P) to ~EUR 13.95 M (G). Mid-range strategies such as Scenarios B, E, and K converge around ~EUR 11 M, providing a realistic compliance baseline. The larger steel mass of the bulk carrier amplifies the leverage of steelmaking choices, while aluminium and coating pathways introduce additional variability of ~EUR 1–2 M.

Table 5 presents the projected CBAM penalties under scenarios A–Y for the bulk carrier. In 2030, Bulker exposures cluster around ~EUR 8–13 M for mid-range strategies, with worst-case ~EUR 13.95 M (G) and best-case ~EUR 6.86–7.81 M (P/O) as read from Table 5, depending on the package.

Table 5. Bulk carrier CBAM penalties (million EUR/ship) under scenarios A–Y, 2026–2030 *.

Scenario	Description	2026 (EUR 95/t)	2027 (EUR 120/t)	2028 (EUR 130/t)	2029 (EUR 140/t)	2030 (EUR 149/t)
A	Technological optimization (−10%)	6.4	8.09	8.76	9.44	10.04
B	Regional average (EU/EPD)	6.18	7.81	8.46	9.11	9.7
C	Conservative baseline (+15%)	8.18	10.34	11.2	12.06	12.84
D	Strategic material shift (−20%)	5.69	7.19	7.79	8.39	8.93
E	Green compliance	6.18	7.81	8.46	9.11	9.7
F	EU-origin steel only	6.02	7.6	8.24	8.87	9.44
G	High-emission source (+25%)	8.9	11.24	12.17	13.11	13.95
H	High-scrap steel (80%)	5.47	6.91	7.49	8.06	8.58
I	Low-scrap aluminium (+40%)	7.12	8.99	9.74	10.49	11.16
J	VOC-free paints	6.99	8.83	9.57	10.3	10.97
K	Owner EPD compliance	6.23	7.87	8.53	9.18	9.77
L	Electric copper refining	7.1	8.97	9.72	10.47	11.14
M	Korea–domestic mix	6.53	8.25	8.94	9.63	10.25
N	Recycled plastics in equipment	7.06	8.92	9.67	10.41	11.08
O	Full IMO GHG compliance	4.98	6.29	6.82	7.34	7.81
P	100% green steel (hydrogen DRI)	4.38	5.53	5.99	6.45	6.86
Q	CCUS-equipped steelmaking	5.2	6.57	7.11	7.66	8.15
R	Aluminium closed-loop recycling	7.12	8.99	9.74	10.49	11.16
S	Multi-metal recycling synergy	5.73	7.24	7.84	8.44	8.99
T	Renewable shipyard electricity	7.05	8.9	9.64	10.38	11.05
U	Bio-based coatings	6.97	8.81	9.54	10.28	10.94
V	Low-carbon logistics	6.76	8.54	9.25	9.96	10.6
W	Localized supply chain (≤500 km)	6.4	8.09	8.76	9.44	10.04
X	Circular copper program	6.81	8.96	9.7	10.45	11.12
Y	Advanced material substitution	6.76	8.54	9.25	9.96	10.6

* Notes: CBAM exposures are illustrative (not forecasts). Values read from manuscript Table 4, Table 5 and Table 6 under the assumed ETS-linked CBAM price path 95→149 EUR/tCO₂ (2026→2030); actual liabilities depend on regulatory scope/exemptions and verification of supplier-specific data.

Table 5. Bulk carrier CBAM penalties (million EUR/ship) under scenarios A–Y, 2026–2030 *.

Scenario	Description	2026 (EUR 95/t)	2027 (EUR 120/t)	2028 (EUR 130/t)	2029 (EUR 140/t)	2030 (EUR 149/t)
A	Technological optimization (−10%)	6.4	8.09	8.76	9.44	10.04
B	Regional average (EU/EPD)	6.18	7.81	8.46	9.11	9.7
C	Conservative baseline (+15%)	8.18	10.34	11.2	12.06	12.84
D	Strategic material shift (−20%)	5.69	7.19	7.79	8.39	8.93
E	Green compliance	6.18	7.81	8.46	9.11	9.7
F	EU-origin steel only	6.02	7.6	8.24	8.87	9.44
G	High-emission source (+25%)	8.9	11.24	12.17	13.11	13.95
H	High-scrap steel (80%)	5.47	6.91	7.49	8.06	8.58
I	Low-scrap aluminium (+40%)	7.12	8.99	9.74	10.49	11.16
J	VOC-free paints	6.99	8.83	9.57	10.3	10.97
K	Owner EPD compliance	6.23	7.87	8.53	9.18	9.77
L	Electric copper refining	7.1	8.97	9.72	10.47	11.14
M	Korea–domestic mix	6.53	8.25	8.94	9.63	10.25
N	Recycled plastics in equipment	7.06	8.92	9.67	10.41	11.08
O	Full IMO GHG compliance	4.98	6.29	6.82	7.34	7.81
P	100% green steel (hydrogen DRI)	4.38	5.53	5.99	6.45	6.86
Q	CCUS-equipped steelmaking	5.2	6.57	7.11	7.66	8.15
R	Aluminium closed-loop recycling	7.12	8.99	9.74	10.49	11.16
S	Multi-metal recycling synergy	5.73	7.24	7.84	8.44	8.99
T	Renewable shipyard electricity	7.05	8.9	9.64	10.38	11.05
U	Bio-based coatings	6.97	8.81	9.54	10.28	10.94
V	Low-carbon logistics	6.76	8.54	9.25	9.96	10.6
W	Localized supply chain (≤500 km)	6.4	8.09	8.76	9.44	10.04
X	Circular copper program	6.81	8.96	9.7	10.45	11.12
Y	Advanced material substitution	6.76	8.54	9.25	9.96	10.6

* Notes: CBAM exposures are illustrative (not forecasts). Values read from manuscript Table 4, Table 5 and Table 6 under the assumed ETS-linked CBAM price path 95→149 EUR/tCO₂ (2026→2030); actual liabilities depend on regulatory scope/exemptions and verification of supplier-specific data.

3.2.3. 20,000 TEU Class Container Vessel—Scenario Results (2026–2030, Million EUR/Ship CBAM Penalty)

For the container vessel, 2026 exposures range from ~EUR 5.24 M (Scenario P) to ~EUR 10.52 M (Scenario G). By 2030, the spread expands further, from ~EUR 8.21 M (P) to ~EUR 16.50 M (G). Mid-range compliance strategies (Scenarios B, E, and K) consistently cluster around EUR 11–12 M. The container vessel shows the highest absolute exposure among the three cases, reflecting its extreme material intensity. Steel sourcing again dominates the variance, while aluminium pathways (Scenarios I and R) and coating systems (Scenarios J and U) add an additional EUR 1–3 M variability.

Table 6 presents the projected CBAM penalties for the 20,000 TEU container vessel under scenarios A–Y for the period 2026–2030. Container exposures in 2026 range from ~EUR 5.24–10.52 M (P vs. G) and expand toward ~EUR 8.21–16.50 M by 2030 (P vs. G), per Table 6.

Table 6. Container vessel CBAM penalties (million EUR/ship) under scenarios A–Y, 2026–2030 *.

Scenario	Description	2026 (EUR 95/t)	2027 (EUR 120/t)	2028 (EUR 130/t)	2029 (EUR 140/t)	2030 (EUR 149/t)
A	Technological optimization (−10%)	7.57	9.57	10.37	11.16	11.88
B	Regional average (EU/EPD)	7.25	9.16	9.93	10.69	11.38
C	Conservative baseline (+15%)	9.68	12.23	13.24	14.26	15.18
D	Strategic material shift (−20%)	6.73	8.51	9.21	9.92	10.56
E	Green compliance	7.26	9.17	9.93	10.7	11.38
F	EU-origin steel only	7.14	9.02	9.78	10.53	11.21
G	High-emission source (+25%)	10.52	13.29	14.4	15.5	16.5
H	High-scrap steel (80%)	6.51	8.22	8.91	9.59	10.21
I	Low-scrap aluminium (+40%)	8.48	10.72	11.61	12.5	13.31
J	VOC-free paints	8.24	10.41	11.28	12.15	12.93
K	Owner EPD compliance	7.32	9.25	10.02	10.79	11.48
L	Electric copper refining	8.38	10.59	11.47	12.35	13.15
M	Korea–domestic mix	7.73	9.77	10.58	11.39	12.13
N	Recycled plastics in equipment	8.37	10.57	11.45	12.34	13.13
O	Full IMO GHG compliance	5.89	7.44	8.06	8.68	9.24
P	100% green steel (hydrogen DRI)	5.24	6.61	7.16	7.72	8.21
Q	CCUS-equipped steelmaking	6.19	7.82	8.47	9.12	9.71
R	Aluminium closed-loop recycling	8.34	10.54	11.41	12.29	13.08
S	Multi-metal recycling synergy	6.73	8.5	9.21	9.91	10.55
T	Renewable shipyard electricity	8.32	10.51	11.38	12.26	13.05
U	Bio-based coatings	8.22	10.38	11.25	12.11	12.89
V	Low-carbon logistics	8	10.1	10.94	11.78	12.54
W	Localized supply chain (≤500 km)	7.57	9.57	10.37	11.16	11.88
X	Circular copper program	8.35	10.55	11.43	12.31	13.1
Y	Advanced material substitution	8	10.1	10.94	11.78	12.54

* Notes: CBAM exposures are illustrative (not forecasts). Values read from manuscript Table 4, Table 5 and Table 6 under the assumed ETS-linked CBAM price path EUR 95→149/tCO₂ (2026→2030); actual liabilities depend on regulatory scope/exemptions and verification of supplier-specific data.

Table 6. Container vessel CBAM penalties (million EUR/ship) under scenarios A–Y, 2026–2030 *.

Scenario	Description	2026 (EUR 95/t)	2027 (EUR 120/t)	2028 (EUR 130/t)	2029 (EUR 140/t)	2030 (EUR 149/t)
A	Technological optimization (−10%)	7.57	9.57	10.37	11.16	11.88
B	Regional average (EU/EPD)	7.25	9.16	9.93	10.69	11.38
C	Conservative baseline (+15%)	9.68	12.23	13.24	14.26	15.18
D	Strategic material shift (−20%)	6.73	8.51	9.21	9.92	10.56
E	Green compliance	7.26	9.17	9.93	10.7	11.38
F	EU-origin steel only	7.14	9.02	9.78	10.53	11.21
G	High-emission source (+25%)	10.52	13.29	14.4	15.5	16.5
H	High-scrap steel (80%)	6.51	8.22	8.91	9.59	10.21
I	Low-scrap aluminium (+40%)	8.48	10.72	11.61	12.5	13.31
J	VOC-free paints	8.24	10.41	11.28	12.15	12.93
K	Owner EPD compliance	7.32	9.25	10.02	10.79	11.48
L	Electric copper refining	8.38	10.59	11.47	12.35	13.15
M	Korea–domestic mix	7.73	9.77	10.58	11.39	12.13
N	Recycled plastics in equipment	8.37	10.57	11.45	12.34	13.13
O	Full IMO GHG compliance	5.89	7.44	8.06	8.68	9.24
P	100% green steel (hydrogen DRI)	5.24	6.61	7.16	7.72	8.21
Q	CCUS-equipped steelmaking	6.19	7.82	8.47	9.12	9.71
R	Aluminium closed-loop recycling	8.34	10.54	11.41	12.29	13.08
S	Multi-metal recycling synergy	6.73	8.5	9.21	9.91	10.55
T	Renewable shipyard electricity	8.32	10.51	11.38	12.26	13.05
U	Bio-based coatings	8.22	10.38	11.25	12.11	12.89
V	Low-carbon logistics	8	10.1	10.94	11.78	12.54
W	Localized supply chain (≤500 km)	7.57	9.57	10.37	11.16	11.88
X	Circular copper program	8.35	10.55	11.43	12.31	13.1
Y	Advanced material substitution	8	10.1	10.94	11.78	12.54

* Notes: CBAM exposures are illustrative (not forecasts). Values read from manuscript Table 4, Table 5 and Table 6 under the assumed ETS-linked CBAM price path EUR 95→149/tCO₂ (2026→2030); actual liabilities depend on regulatory scope/exemptions and verification of supplier-specific data.

Figure 6 illustrates the total global warming potential (GWP, expressed in kg CO₂-eq) associated with the material composition of three ship types: PCTC, bulker, and container. Each bar represents the overall GWP for one vessel type, with different colored segments indicating the proportional contribution of individual materials.

Across all three ship types, steel products dominate the environmental footprint, particularly AH36, EH36, FH36, and A/B grade steel plates, together contributing more than half of the total GWP. For PCTC, AH36 and EH36 each contribute around 12%, with additional notable shares from aluminium alloys (6061 and 5083, ~9% each) and deck stiffeners (4–5%). For the bulker, the share of steel is even more pronounced, with AH36, DH36, and A/B grade steel plates each contributing ~15–16%, while aluminium has a smaller share compared to PCTC. The container ship shows a similar trend to the bulker, with steel plates again dominating (~15–16%), though aluminium (6061, 5083) and other auxiliary materials, such as polymers and coatings, make up a slightly larger share compared to the bulker.

Secondary contributors across all vessels include copper-based components (wiring, brass fittings, and CuNi pipes), polymers (polyurethane, epoxy), and auxiliary equipment (ventilation units, ramp motors, and cables), each typically accounting for 2–4% of the total GWP.

In summary, the chart highlights that while steel plates are the primary driver of shipbuilding-related GWP across all ship types, the relative importance of aluminium, polymers, and auxiliary components differs by vessel, with PCTC showing comparatively higher contributions from lightweight materials such as aluminium.

The scenario spreads in Figure 6 and Figure 7 therefore frame materials as a rising strategic lever. Figure 7 illustrates the variation in LCA intensity values for different shipbuilding materials across multiple scenarios. Steel-based materials, such as AH36, EH36, and DH36 plates, as well as structural members and stiffeners, exhibit relatively low and consistent impacts, with values clustered around 1.5–2.5 and showing minimal sensitivity to scenario assumptions. In contrast, aluminium alloys (e.g., 5083, 6061) display the highest variability, with median values near 10 and ranges extending up to 15, reflecting their strong dependence on electricity mix and recycling assumptions. Polymers and coatings, including polyurethane, epoxy, and anticorrosive paints, also demonstrate considerable variability (ranging from about 2 to 8), indicating scenario-driven uncertainties in chemical production and disposal stages. Intermediate variability is observed for materials such as copper wiring, brass fittings, and stainless-steel pipes, with ranges typically between 3 and 6. Overall, the results suggest that while steel provides a stable and robust baseline in LCA assessments, aluminium and polymers emerge as hotspots with high environmental intensity and sensitivity to scenario conditions, highlighting the importance of carefully considering supply chain assumptions and regional factors when evaluating these materials.

Across Figure 8, Figure 9, Figure 10, Figure 11 and Figure 12, the material-related LCA results for multiple scenarios (A–Y) consistently show that steel plates (AH36, EH36, DH36, and A/B grade) dominate the environmental impacts across all ship types—PCTC, bulker, and container—owing to their extensive use in hull and structural construction. Aluminium alloys (5083, 6061, and deck plating) and copper-based materials (wiring, brass fittings, and CuNi pipes) emerge as the next most significant contributors, although their relative importance varies depending on scenario assumptions, such as energy mix, recycling rates, and background datasets. Polymers (polyurethane, epoxy) and protective coatings (anticorrosive paints) contribute moderately but display noticeable scenario sensitivity, reflecting variability in production and disposal assumptions. While the overall ranking of materials remains stable—with steel consistently driving the baseline footprint—secondary contributors, such as aluminium, copper, and polymers, fluctuate in magnitude across scenarios, shaping the comparative impacts between ship types. Scenario-specific differences are highlighted, such as higher aluminium-related impacts in Scenarios C, I, T, and U, elevated epoxy contributions in Scenarios I and J, or increased steel dominance for PCTC in Scenario P. Finally, Scenario Y confirms the broader trend: steel remains the primary driver of LCA impacts, while aluminium, copper, and polymers play important but variable secondary roles influenced by scenario conditions.

Figure 13 presents the carbon border adjustment mechanism (CBAM) penalty projections for three vessel types—PCTC, bulker, and container—across the years 2025 to 2030, evaluated under multiple LCA scenarios (A–Y). Each panel (a–f) shows box plots with the mean, median, interquartile range, and scenario-specific results.

Across all years, the penalties cluster between 6,000,000 and 12,000,000 EUR-equivalent, with gradual increases toward 2030. Container vessels generally exhibit the highest median penalties, followed by bulkers, while PCTCs show slightly lower but still comparable values. The spread of results reflects scenario sensitivity, where assumptions such as energy mix, recycling rates, and material sourcing cause variability. Notably, outliers appear in most years, indicating that certain scenarios (e.g., high-carbon aluminium production or limited recycling) could lead to substantially higher penalties.

Overall, the results suggest that while the CBAM penalty burden will rise steadily through 2030, the magnitude and variability differ among ship types, with container ships facing the greatest exposure, underscoring the importance of material choices and decarbonization strategies in mitigating compliance costs.

Table 4, Table 5 and Table 6 and Figure 8, Figure 9, Figure 10, Figure 11 and Figure 12 present the quantified CBAM penalties derived from the material inventories and scenario rules defined in the methodology. To place these results in context, the following section interprets them from an embedded-carbon cost perspective, highlighting key drivers, sensitivities, and stakeholder implications.

3.3. Interpretation of CBAM Cost Exposure (Embedded Carbon Cost Lens) (Step 4–5)

3.3.1. Interpretation of Scenario—PCTC

The results reveal a broad range of CBAM exposure. In the most favorable case, Scenario P (100% green steel, hydrogen DRI) reduces penalties to EUR 4.7–7.4 M between 2026 and 2030. In contrast, Scenario G (high-emission sourcing) raises exposure to EUR 8.7–13.6 M, nearly twice the best-case level. Intermediate cases, including A (technological optimization), B (regional average), E (green compliance), and K (owner EPD compliance), fall in the EUR 5.8–9.2 M range, corresponding to reductions of 20–30% relative to the conservative baseline (Scenario C). Scenario H (high scrap content) achieves savings comparable to Scenario D (strategic material shift), highlighting recycling as a cost-effective pathway. The spread between the best- and worst-case trajectories widens over time. By 2030, the difference between the best- and worst-case trajectories amounts to about 6.2 million euros per vessel, a figure large enough to influence contract margins and financing decisions.

These findings confirm that green steel pathways offer the strongest and most immediate leverage, whereas full hydrogen-based steelmaking should be regarded as an exploratory long-term option. In practice, only incremental improvements, such as high-scrap content or certified EAF supply are near-term actionable, while large-scale hydrogen DRI adoption lies in the mid- to long-term horizon.

3.3.2. Sensitivity and Stakeholder Implications—PCTC

(a)

Sensitivity to Carbon Price Trajectories

The PCTC case is highly sensitive to carbon price escalation. The spread between best- and worst-case outcomes grows from EUR 3.9 M in 2026 to EUR 6.2 M by 2030. Given that newbuilding prices typically range from EUR 70 to 100 M, this variation corresponds to ~6–9% of capital cost. Early adoption of decarbonized steel (Scenarios P, Q, and D) provides compounding benefits: shifting from the conservative baseline (C) to 100% green steel (P) avoids ~EUR 5 M per ship cumulatively over 2026–2030. For a fleet of 10–15 vessels, this translates into EUR 50–75 M in avoided penalties, comparable to the annual profit margin of a major car carrier.

(b)

Sensitivity to Material Pathways

While steel dominates exposure, secondary pathways also contribute. Aluminium plays a particularly important role: a 40% increase in scrap intensity (Scenario I) raises exposure to EUR 11.6 M by 2030, approaching the level of the high-emission steel case (G). Copper pathways (Scenarios L, X) shift exposure by ~EUR 0.5–0.8 M per ship, and coatings (Scenarios J, U) reduce penalties by ~EUR 0.5 M by 2030. Logistics-related measures (Scenarios V, W, and Y), including low-carbon or localized supply chains, provide additional reductions of ~EUR 0.6–0.8 M by 2030. For export-oriented shipyards, these logistics measures represent a material yet often overlooked contributor.

(c)

Stakeholder Implications

• For shipowners:

The PCTC analysis makes clear that strategic steel choices dominate exposure. A shipowner contracting vessels at a Korean or Chinese yard without green steel guarantees faces penalties in the EUR 10–13 M range per ship by 2030. Conversely, contracting with guaranteed hydrogen DRI steel could cut exposure by nearly half. The financial case for green procurement is thus unambiguous.

• For shipyards:

Shipyards stand at the interface of compliance and competitiveness. Offering green steel, scrap-based routes, or certified logistics packages will increasingly be decisive in tenders. The difference between scenarios D/H (scrap steel, −20%) and G (high-emission) is more than EUR 5 M by 2030 per vessel, a spread large enough to determine whether a yard secures or loses contracts to European rivals.

• For regulators:

From the regulatory perspective, the wide range of outcomes validates CBAM’s role in discriminating between clean and carbon-intensive imports. The penalty gap observed in PCTC results ensures that green steel adoption will be rewarded, while high-emission sourcing will face proportionally higher costs. The PCTC segment, with its reliance on automotive trade into the EU, is especially vulnerable, making it a likely first mover in adopting certified low-carbon supply chains.

(d)

Uncertainty and Robustness

Sensitivity analysis confirms that while steel dominates the variance, complementary measures, such as recycling (S), coatings (U), and logistics (V), help narrow the uncertainty band. Even under pessimistic assumptions (Scenario G), combining such measures can mitigate exposure by 15–20%. This demonstrates the robustness of LCA-based scenario analysis in capturing both best-case opportunities and credible worst-case risks. In practice, market adoption is likely to cluster around intermediate pathways, such as Scenario E (green compliance) and Scenario K (owner EPD compliance), which represent realistic compliance strategies.

3.3.3. Interpretation of Scenario—Bulk Carrier

The results for the bulk carrier case reveal a broad and widening penalty range as the CBAM carbon price rises. In 2026, the spread between the best-performing case (Scenario P, EUR 4.38 M) and the worst-performing case (Scenario G, EUR 8.90 M) already amounts to EUR 4.5 M per ship. By 2030, this spread grows further: scenarios based on low-carbon or compliance pathways such as P or O converge in the EUR 6.9 M–7.8 M range, whereas carbon-intensive pathways such as G and C escalate liabilities to between EUR 12.8 M and EUR 16.5 M. The resulting gap of more than 8 million euros per vessel demonstrates how decisively steel sourcing determines competitiveness under CBAM.

The best-performing outcomes are consistently found in Scenario P (100% green steel, hydrogen DRI) and Scenario O (full IMO GHG compliance), which limit exposure to EUR 8.2–9.2 M by 2030. By contrast, the highest exposures are observed in Scenario G (high-emission source) and Scenario C (conservative baseline), where penalties escalate to EUR 16.5 M and EUR 15.2 M, respectively. Steel sourcing thus emerges as the decisive factor: the transition from coal-intensive BF–BOF to hydrogen-based DRI routes yields more than EUR 8 M in avoided penalties per vessel by 2030.

Secondary materials also exert notable influence. Aluminium pathways (Scenarios I and R) result in penalties above EUR 13 M when recycling rates are constrained, while coatings and copper-related options (Scenarios J, U, L, and X) shift exposure by EUR 2–3 M, a significant effect given the scale of outfitting in large bulk carriers. Intermediate compliance-oriented strategies, such as Scenarios B (regional average), E (green compliance), and K (owner EPD compliance), converge around EUR 11.3–11.5 M by 2030, providing a realistic baseline for operators when full adoption of green steel is not feasible.

Overall, bulk carrier exposures are shaped almost entirely by steel sourcing choices. Short-term reductions are realistically achievable through measures such as higher scrap ratios or regional EAF sourcing, while CCUS and partial hydrogen DRI integration remain mid-term prospects. A full transition to 100% hydrogen-based steelmaking should be interpreted as a long-term exploratory scenario rather than an immediate solution.

3.3.4. Sensitivity and Stakeholder Implications—Bulk Carrier

(a)

Sensitivity to Carbon Price Trajectories

Because bulk carriers are among the most steel-intensive ship types, their exposure is highly sensitive to carbon price escalation. Each EUR 10/tCO₂ increment increases CBAM costs by ~EUR 0.5–0.7 M per vessel in central scenarios (B/E/K). For series orders of 10–15 vessels, this translates into EUR 10–20 M in additional liability, materially affecting competitiveness in European markets.

(b)

Sensitivity to Material Pathways

While steel remains the dominant driver, aluminium exerts a secondary but material influence. In Scenarios I and R, limited recycling increases exposure beyond EUR 13 M by 2030, nearly matching the effect of poor steel sourcing. Coatings and copper pathways shift results by EUR 1–3 M per vessel, showing that non-structural materials, though smaller in volume, are non-trivial in determining CBAM liabilities.

(c)

Stakeholder Implications

For shipowners, the difference between worst- and best-case outcomes exceeds EUR 8 M per vessel by 2030. For a five-vessel order, this equates to EUR 40–45 M in avoided penalties, an amount that directly affects financing conditions and long-term charter competitiveness. For shipyards, securing certified EU/EAF or hydrogen DRI steel supply chains becomes a decisive advantage, as bundled offerings that combine low-carbon steel with renewable electricity or circular aluminium can reduce liabilities from ~EUR 15 M to ~EUR 8–9 M per vessel. For regulators, the bulk carrier case validates CBAM’s intended effect of discriminating between carbon-intensive and low-carbon imports, demonstrating how transparent verification procedures reward green pathways while penalizing uncontrolled sourcing.

(d)

Uncertainty and Robustness

Although steel dominates the variance, complementary measures such as multi-metal recycling (Scenario S), bio-based coatings (U), or localized logistics (V) narrow the uncertainty band by 10–20%. Even in pessimistic scenarios (G), combining such measures reduces exposure by several million euros. Moreover, the convergence of B/E/K scenarios shows that compliance-oriented sourcing strategies provide a reliable baseline, making them attractive as pragmatic near-term options.

3.3.5. Interpretation of Scenario—Container Vessel

The container vessel case presents the largest absolute CBAM exposure among the three ship types studied, reflecting both its size and material intensity. In 2026, reliance on hydrogen-based DRI steel (Scenario P) would limit exposure to approximately EUR 5.24 million, whereas sourcing from high-emission supply chains (Scenario G) would raise liabilities to nearly EUR 10.52 million. By 2030, this divergence becomes even more pronounced: vessels built with low-carbon steel under Scenario P incur around EUR 8.21 million, while those relying on carbon-intensive pathways under Scenario G face up to EUR 16.50 million. The resulting spread of more than EUR 8 million per ship is large enough to reshape series order economics, influencing both contract margins and fleet financing strategies.

The best-performing outcomes are consistently observed in Scenario P (100% green steel, hydrogen DRI) and Scenario O (full IMO GHG compliance), which converge in the EUR 8.2–9.2 million range by 2030. By contrast, the worst-performing outcomes occur under Scenario G (high-emission source) and Scenario C (conservative baseline), where penalties escalate to EUR 15.2–16.5 million. This wide gap illustrates how decisively steel supply chains determine CBAM exposure for ultra-large container vessels. Even intermediate compliance-oriented strategies such as Scenario B (regional average), Scenario E (green compliance), and Scenario K (owner EPD compliance) converge around EUR 11.3–11.5 million by 2030, offering a pragmatic baseline for operators when full adoption of green steel is not yet feasible.

Aluminium also emerges as a significant secondary driver. In scenarios with constrained recycling, such as I (low-scrap aluminium) and R (closed-loop aluminium recycling), exposure rises above EUR 13 million by 2030, approaching the level of unfavorable steel scenarios. Copper and coating systems shift results by EUR 1–2 million, showing that even smaller material categories become non-trivial when scaled to the outfitting scope of container vessels.

For shipowners, the wide spread between best- and worst-case outcomes highlights the strategic stakes of procurement choices. The difference of more than EUR 7–8 million per hull by 2030 can accumulate to EUR 70–80 million across a ten-vessel series, directly affecting financing conditions and charter competitiveness. For shipyards, the ability to guarantee green steel and circular aluminium supply chains becomes a critical differentiator, with bundled offerings reducing exposure from ~EUR 16 million to ~EUR 8–9 million per vessel. For regulators, the container vessel case illustrates CBAM’s strongest leverage: the most capital-intensive ship type also shows the greatest divergence between high- and low-carbon pathways, reinforcing the incentive for verifiable low-carbon sourcing.

Scenario spreads further confirm that while steel dominates, secondary materials significantly affect mid-range outcomes. Even under pessimistic assumptions (Scenario G), adoption of recycling (Scenario S, multi-metal), bio-based coatings (Scenario U), or low-carbon logistics (Scenario V) can reduce exposure by 15–20%. Intermediate scenarios such as B, E, and K consistently cluster, providing a robust baseline for compliance strategies where full green steel adoption may not yet be realistic.

3.3.6. Sensitivity and Stakeholder Implications—Container Vessel

(a)

Sensitivity to Carbon Price Trajectories

Exposure is highly elastic to carbon price increases. Each EUR 10/tCO₂ increment adds ~EUR 0.4–0.6 M per vessel in mid-range scenarios. For series orders of 10–12 ships, this translates into EUR 15–20 M in additional liabilities, demonstrating how policy-driven tightening of carbon prices can substantially affect market dynamics.

(b)

Sensitivity to Material Pathways

Steel sourcing accounts for the greatest variability, with hydrogen DRI routes reducing exposure by over EUR 7 M relative to coal-based pathways. Aluminium, however, is also critical: low-recycling pathways (I, R) push exposure above EUR 11 M, while closed-loop recycling mitigates costs significantly. Copper (L, X) and coatings (J, U) shift liabilities by EUR 1–2 M, reflecting the impact of non-ferrous and chemical inputs at scale.

(c)

Stakeholder Implications

For shipowners, exposure differences between best- and worst-case scenarios exceed EUR 7 M per ship by 2030. For a fleet of 10–12 vessels, this equates to EUR 70–85 M in avoided penalties, making procurement strategies decisive for long-term competitiveness. For shipyards, the ability to guarantee green steel and circular aluminium supply chains becomes a critical differentiator, with bundled offerings reducing exposure from ~EUR 14 M to ~EUR 7–8 M per vessel. For regulators, the container vessel case illustrates CBAM’s strongest leverage, as the most capital-intensive ship type shows the greatest divergence between high- and low-carbon pathways, reinforcing the incentive for verifiable low-carbon sourcing.

(d)

Uncertainty and Robustness

Scenario spreads confirm that while steel dominates, secondary materials significantly affect mid-range outcomes. Even under pessimistic assumptions (Scenario G), the adoption of recycling (S, X), bio-based coatings (U), or low-carbon logistics (V) reduces exposure by 15–20%. Intermediate scenarios such as B, E, and K converge consistently, providing a robust baseline for compliance strategies where full green steel adoption may not yet be feasible.

To synthesize the scenario-based results across vessel types,

Table 7. Summary of CBAM exposure across vessel types under scenario analysis (2026–2030).

Vessel Type	Exposure Range (EUR/Ship, 2026–2030)	Key Drivers of Variability	Best-Performing Scenario(s)	Worst-Performing Scenario(s)
PCTC (~7000 CEU)	EUR 4.7 M–EUR 13.6 M	Steel sourcing (BF–BOF vs. H2-DRI), aluminium recycling, coating choice	Scenario P (100% green steel, H2-DRI); Scenario H (high-scrap steel)	Scenario G (high-emission sourcing); Scenario I (low-scrap aluminium)
Bulk carrier (200 k DWT)	EUR 4.4 M–EUR 14.0 M	Steel mass intensity, aluminium pathways, copper/coatings secondary effects	Scenario P (100% green steel, H2-DRI); Scenario O (IMO GHG compliance)	Scenario G (high-emission sourcing); Scenario C (conservative baseline)
Container vessel (20,000 TEU)	EUR 5.2 M–EUR 16.5 M	Steel sourcing, aluminium recycling, copper and coating systems	Scenario P (100% green Steel, H2-DRI); Scenario O (IMO GHG compliance)	Scenario G (high-emission sourcing); Scenario C (conservative baseline)

provides a comparative summary. It highlights the overall exposure ranges, principal drivers of variability, and the best- and worst-performing scenarios for PCTC, bulk carriers, and container vessels. The results confirm that steel sourcing remains the decisive determinant across all vessel types, while aluminium, copper, and coatings act as secondary but material contributors. The table also illustrates that, despite differences in scale, all vessel classes show similar structural patterns: low-carbon steel pathways consistently yield the lowest exposures, while uncontrolled or high-emission supply chains result in the highest penalties.

4. Discussion

4.1. Original Contributions to the Industry

(a)

Research impacts for enhancing LCA-based shipbuilding

This research makes several novel contributions at the intersection of life cycle assessment (LCA), shipbuilding economics, and regulatory compliance. First, it integrates LCA into the early-stage design process, rather than applying it only as a retrospective evaluation. By coupling material inventories with carbon pricing pathways under the Carbon Border Adjustment Mechanism (CBAM), the study provides a quantitative decision support framework for shipyards and owners during concept selection and design iteration.

Second, the introduction of a scenario-based risk assessment framework distinguishes this work from existing studies. By applying multiple scenarios covering technological, regional, and compliance pathways, stakeholders can examine both best- and worst-case trajectories. Importantly, by classifying scenarios into near-term, mid-term, and exploratory tiers, the study also avoids misinterpretation of long-term options as immediate solutions. This creates the foundation for risk-based design, highlighting robust strategies such as green steel and high-scrap steel, and identifying strategies that are fragile under policy or material market uncertainty.

Third, by structuring the model in a parametric and vessel-specific manner, the study demonstrates a scalable logic: lightweight is first allocated across key material categories, grouped into representative steel grades and other inputs, and then multiplied by corresponding emission factors. This enables not only ship-specific calculations, but also straightforward extension to other vessel types and sizes. The adaptability of this framework reinforces its value as a generalizable tool for embedded carbon accounting in shipbuilding. It also highlights the relevance of shipbuilding within the broader policy and trade context of CBAM, where such quantitative models can support evidence-based decision-making and help anticipate how regulatory design may influence both environmental outcomes and competitiveness in international markets.

Beyond shipbuilding, this framework has potential downstream relevance, for example in automotive or renewable energy infrastructure where steel and aluminium supply chains are decisive for CBAM liabilities.

(b)

Digitalization and Model-Based Approval

The ongoing digital transformation in classification, particularly the shift from document-based to 3D model-based approval (MBA), provides a natural environment for embedding LCA. As ship designs grow more complex, sustainability assessments need to be integrated into the same digital models used for structural and safety checks, ensuring that carbon performance is evaluated in real time rather than retrospectively.

Material inventories parameterized within digital models could, in principle, be connected to emission factor databases, enabling real-time estimation of CBAM exposure alongside compliance verification. This opens the possibility for approval processes to evolve from static rule checking into platforms that also provide insights into sustainability and cost implications.

(c)

OCX as a Practical Enabler of Integration

Among digitalization initiatives, the open class exchange (OCX) format stands out as a particularly promising enabler. OCX provides a standardized interface for exchanging 3D approval data, ensuring interoperability across designers, yards, and classification societies. Extending OCX with an LCA module would represent a tangible pathway for embedding sustainability into digital workflows.

Such an extension could support:

• Automatic mapping of BOM and process data to emission factor databases.
• Scenario-based evaluation of alternative material and sourcing options within the digital design workflow.
• Benchmarking of design alternatives for both carbon and cost performance.

For shipyards, 3D model-based OCX-enabled integration would provide a rapid sustainability overview at the earliest design stages. For shipowners, it would deliver transparent accounting of embedded carbon liabilities. For classification societies, it offers a potential pathway to evolve from traditional compliance verification toward value-added decision support, bridging safety, economics, and sustainability in a unified digital framework. In practice, however, such integration should be regarded as a conceptual pathway rather than an operational capability at this stage. Its practical deployment will depend on the availability of transparent and verifiable datasets and collaborative development, which remain priorities for future research.

4.2. Recommendations for Industry and Policy

The results suggest several practical measures for shipyards, owners, and regulators.

• Shipyards should adopt LCA-informed design practices to pre-empt CBAM penalties. Even a modest material substitution or sourcing decision can result in several million euros of avoided exposure over a newbuild.
• Shipowners are advised to integrate embedded emissions into procurement and contracting decisions. Vessels optimized for lower CBAM exposure may secure favorable financing conditions and improve long-term competitiveness in European trades.
• Regulators and classification societies should facilitate the adoption of transparent and standardized methodologies for embedded carbon accounting, ensuring comparability across designs and regions. Equally important, they should strengthen requirements for transparent and verifiable emission data, as credible datasets will determine whether domestic carbon pricing schemes in exporting countries can be recognized under CBAM. Such mechanisms would help avoid double regulation while maintaining environmental integrity.

The broader implication is that LCA must be viewed as a strategic instrument, not merely a reporting requirement. Embedding it into procurement and design processes enhances resilience against tightening climate policies and carbon price escalation. For the maritime industry, this means LCA should evolve into a strategic tool supporting procurement, financing, and regulatory alignment, rather than remaining a compliance exercise. It should also be emphasized that this study is scoped exclusively to CBAM-related construction-phase emissions. Possible downstream extensions of CBAM to product-level carbon reporting, and broader interactions with IMO regulations, ESG finance, and trade/FTA frameworks, were not modeled here and remain outside the scope of this analysis. Nevertheless, these potential developments highlight the importance of transparent and verifiable LCA data as a prerequisite for any future policy expansion.

The current regulatory landscape in shipping combines instruments that address different phases of the vessel life cycle. At the operational level, the IMO’s GHG fuel intensity (GFI) standard sets a global trajectory for reducing the well-to-wake carbon intensity of marine fuels. Within the EU, FuelEU Maritime enforces progressively stricter GHG intensity limits for onboard energy use, while the EU ETS extends carbon pricing to voyages connected to European ports. These measures push shipowners toward alternative fuels and efficiency improvements in operation, while the EU CBAM targets the construction phase by assigning a carbon cost to embedded emissions in steel, aluminium, and other inputs. In principle, the frameworks are complementary: CBAM regulates upstream material emissions, whereas FuelEU and ETS cover downstream operational emissions. In practice, however, their overlap creates uncertainty. Compliance costs may accumulate at both the building and operational stages, and alignment between EU instruments and the IMO framework remains uncertain. According to insights from EU policy experts, these regulations are shaped not only by environmental ambition, but also by political considerations such as industrial protection and trade interests. This political dimension means that shipyards and shipowners face not only technical compliance challenges, but also shifting regulatory signals. As a result, flexible, scenario-based strategies covering the full life cycle are essential for managing both regulatory risk and long-term decarbonization planning.

4.3. Limitations of the Study

Several limitations of this study must be acknowledged to properly frame the scope and applicability of the results.

• Data limitations

The model relies primarily on published emission factors (e.g., Worldsteel, EPD, and GaBi/Ecoinvent databases) and generalized recycling rates, with only limited access to primary yard-level datasets. While some shipyard data were consulted, they were deliberately generalized into conservative approximate values. Critical inputs, such as yard-level electricity consumption, coating application, welding energy use, and block transport logistics, could not be verified with auditable records. Accordingly, the numerical results should be interpreted strictly as proxy-based sensitivity ranges intended for illustrative benchmarking, rather than compliance-ready inventories. For any applied or contractual use, these proxy values must be replaced with yard-specific, verifiable measurements (e.g., meter readings, batch records, and logistics documents). Future research in collaboration with shipyards will be essential to integrate such primary data.

• Uncertainty analysis

The sensitivity analysis in this study was deliberately simplified. We applied a one-at-a-time (OAT) variation in key parameters and visualized the spread of results using bar charts and boxplots to illustrate the range, including maximum and minimum values across scenarios. No derivative-based or normalized sensitivity indices were applied. The sensitivity analysis should therefore be understood as deterministic and scenario-based, designed to highlight the dominant drivers of variability for CBAM exposure. While this differs from probabilistic methods commonly used in uncertainty analysis, it provides sufficiently clear insights for decision support. More rigorous probabilistic approaches, such as Monte Carlo simulations or other global sensitivity indices, can complement this framework in future work, particularly once higher-resolution primary datasets become available.

• Vessel scope

The analysis was limited to three representative vessel types (a 7000 CEU PCTC, a 200 k DWT bulk carrier, and a 20,000 TEU container vessel). While these are mainstream classes in the market, other segments such as LNG carriers, cruise ships, or offshore units may exhibit different material profiles and sensitivities. The results presented here should not be generalized to all ship types without further validation.

• Regulatory modeling

CBAM was modeled as a simple linear penalty (EUR/tCO₂) linked to the EU ETS carbon price path. In practice, CBAM may include transitional exemptions, differentiated tariffs, scope adjustments, or recognition of domestic carbon pricing schemes. Such complexities could alter exposure profiles, meaning that actual liabilities may diverge from the scenario-based estimates presented here.

• Primary data gaps

We acknowledge the absence of high-resolution process-level datasets from shipyards, such as welding process energy, paint booth emissions, waste streams, or scrap handling. These data will be essential for moving from indicative scenario-based estimates to compliance-ready LCAs.

• Policy scope

This study was deliberately scoped to construction-phase embedded emissions under the EU CBAM. Broader interactions with other policy frameworks such as IMO’s lifecycle GHG fuel intensity standard, the EU ETS, FuelEU Maritime, ESG-linked finance, or trade/FTA dynamics were only briefly discussed and not quantitatively modeled. Future research should integrate construction- and operation-phase emissions into a holistic lifecycle compliance framework.

4.4. Suggestions for Future Research

Future work can build on this study in several directions:

• Expansion of vessel scope and inventory detail. Applying the framework to additional ship types (e.g., LNG carriers, offshore units, and cruise ships) and refining inventories for specific materials, such as advanced composites or alternative steels, will improve generalizability and provide more robust benchmarks across segments.
• Collaborative acquisition of process-level datasets. A critical next step is to combine yard-level measurements (e.g., welding energy use, coating application, and block transport logistics) with established LCA databases. Such collaborative efforts with shipyards and research institutes can replace proxies with verifiable data and significantly enhance the accuracy of scenario-based assessments.
• Regional differentiation of emission factors. Future research should explicitly capture supplier origin and production processes (e.g., EAF vs. BF–BOF steelmaking, aluminium under different grid intensities, copper refining routes, and coating systems) to benchmark procurement strategies across regions and supply chains.
• Integration of operational and construction phases. Linking embedded carbon (CBAM) with operational emissions regulated under EU ETS and FuelEU Maritime can create a holistic lifecycle compliance model, enabling optimization across the full life cycle rather than in isolation.
• Dynamic policy modeling. Probabilistic approaches should be applied to capture carbon price volatility, transitional exemptions, and evolving regulatory designs. This would allow stakeholders to evaluate exposure under multiple regulatory futures rather than a single deterministic pathway.
• Investigating digital pathways for automation. Future research may explore extending standardized data exchange formats, such as the open class exchange (OCX), with sustainability modules. Pursuing these digital pathways could enable semi-automated mapping of the bills of materials (BOM) and bills of process (BOP) to emission factor databases, lowering the manual workload of LCAs and embedding carbon accounting directly into design, approval, and verification workflows.

5. Conclusions

This study addressed one of the most pressing challenges in maritime decarbonization: how to evaluate and reduce embedded carbon emissions in shipbuilding under the forthcoming Carbon Border Adjustment Mechanism (CBAM). By developing a scenario-based life cycle assessment (LCA) framework and applying it to three representative vessel types (PCTC 7000 CEU, 200 k DWT bulk carrier, and 20,000 TEU container vessel), the research generated both methodological advances and practical insights for the shipping industry.

(1)

Integration of LCA into early-stage ship design

The first major finding is that LCA can be systematically integrated into early-stage design and approval workflows. By mapping material inventories (steel, aluminium, coatings, copper, etc.) to emission factors and CBAM-linked carbon costs, this study demonstrates that embedded carbon can be quantified before procurement or construction begins. The results consistently show that steel accounts for the overwhelming majority of embedded emissions, while the cumulative share of other materials remains below ~10%, though it is still relevant for targeted mitigation strategies. This represents a shift from retrospective, reporting-oriented LCAs to design-oriented, decision-support tools. Such integration has the potential to reshape how shipyards and owners evaluate alternative design and sourcing strategies, ensuring that sustainability is considered alongside safety, cost, and performance.

(2)

Scenario-based risk and sensitivity analysis

The second major finding is the utility of a comprehensive scenario framework for assessing variability and uncertainty. By applying scenarios ranging from incremental efficiency improvements to systemic shifts such as hydrogen-based steelmaking, the study shows that CBAM exposure for a single vessel can swing by tens of millions of euros depending on the chosen pathway. Importantly, the scenarios were organized into near-term, mid-term, and exploratory tiers, avoiding misinterpretation of long-term options as immediately actionable levers. This structure enables stakeholders to understand not only point estimates, but also the distribution of risks, highlighting strategies that remain robust across policy and market uncertainties (e.g., high-scrap steel and hydrogen-based green steel).

(3)

Implications for shipyards, owners, and regulators

The third finding concerns the practical implications for different stakeholder groups. For shipyards, the results show that relatively small adjustments in sourcing strategies (such as substituting EU-origin steel or incorporating recycled inputs) can deliver substantial reductions in CBAM penalties, enhancing competitiveness in European export markets. For shipowners, the results underscore the importance of evaluating embedded emissions at the contracting stage: CBAM costs are not abstract future liabilities, but direct financial exposures that can influence charter competitiveness, financing conditions, and reputational standing. For regulators and classification societies, the research highlights the need for standardized methodologies to ensure comparability across ship types, yards, and sourcing strategies. It should be emphasized that these findings are scoped exclusively to CBAM-related construction-phase emissions; interactions with other regulatory instruments such as IMO or FuelEU were beyond this study’s scope.

(4)

Digitalization as an enabler for adoption

Finally, the study finds that the long-term success of shipbuilding LCAs depends on their digital integration into emerging design and approval workflows. Embedding LCA modules into 3D model-based environments could enable real-time assessment of CBAM exposure during design iterations. The open class exchange (OCX) format illustrates the feasibility of linking bills of materials (BOM) and bills of process (BOP) directly with 3D models and mapping them to emission factor registers. While this study only outlines the concept, such digital pathways could transform approval platforms from static compliance tools into strategic decision-support systems, providing stakeholders with simultaneous insights on cost, risk, and sustainability.

In conclusion, shipbuilding is no longer outside the scope of carbon pricing. With CBAM implementation approaching, embedded emissions are expected to become increasingly important economic considerations. By providing a structured LCA-based framework, tested across scenarios and vessel types, the research equips shipyards, owners, and regulators with the tools to anticipate and mitigate these costs. The results should be interpreted as indicative ranges rather than forecasts, reflecting the limits of current data. Nevertheless, the framework and its digital extensions represent not only a methodological innovation, but also a strategic opportunity: to transform classification into a facilitator of sustainability-by-design, bridging the gap between compliance, economics, and decarbonization.