A Structured Techno-Economic and Environmental Assessment Framework for Green Interventions on Cargo Ships: Application to a Container Vessel

Abstract

Container vessels—characterized by high transport work and energy-demanding operating profiles—constitute one of the most emission-significant fleet segments and a strategically important area for implementing and assessing decarbonization initiatives. Responding to the persistent absence of integrated analytical approaches, this paper introduces a unified techno-economic and environmental assessment framework for evaluating green interventions on operating ships. The framework comprises a set of fuel-consumption, environmental performance, and techno-economic metrics and a transparent and globally applicable assessment procedure enabling the consistent comparison of heterogeneous intervention types towards sustainability. The framework is applied to a representative medium-size container vessel to demonstrate its analytical potential and practical relevance. The results of the specific application reveal the systematic trade-offs between environmental and economic performance of green interventions: operational optimization delivers the strongest carbon-intensity improvements and isolated technical retrofits provide favorable economic returns but limited environmental gains, while integrated technical–operational packages achieve the most balanced overall outcomes. Overall, the paper has both a methodological contribution by suggesting a coherent, regulation-aligned assessment structure, as well as a practical decision-support value for ship operators and policymakers.

1. Introduction

International maritime transport enables the movement of nearly 90% of global merchandise trade and remains a structural pillar of the world economy [1]. At the same time, its scale and operational intensity make it a significant contributor to anthropogenic greenhouse gas emissions. The Fourth IMO GHG Study estimates that international shipping emitted approximately 740 million tonnes of CO₂ in 2018—more than 2% of global emissions—with projections indicating continued growth under most business-as-usual scenarios [2,3]. These trends have intensified pressures by governments, industry associations and powerful customers [4,5], culminating in the IMO’s 2023 GHG Strategy, which commits the sector to achieving net-zero GHG emissions “by or around 2050” and to substantial carbon-intensity reductions by 2030 [6]. Parallel regional measures, such as the EU ETS and FuelEU Maritime, further reinforce the need for robust, evidence-based approaches to evaluating energy-efficiency interventions.

Bulk carriers and container vessels account for the largest share of global transport work and CO₂ emissions, making them a crucial factor to the decarbonization challenge [1,7]. Their operational pattern, scale and regulatory exposure—particularly with respect to the Carbon Intensity Indicator (CII)—render them a representative and decision-critical segment for assessing the environmental and economic performance of green interventions. This sectoral relevance motivates the empirical focus of the present study.

Although a wide range of technological and operational measures have been proposed to reduce fuel consumption and emissions, the academic literature remains fragmented with respect to different objectives. Existing studies typically examine interventions in isolation, rely on single-dimension evaluation criteria, or lack alignment with regulatory indicators such as the EEXI (Energy Efficiency Existing Ship Index) and CII [8,9] As a result, there is no unified, widely applicable and regulation-aligned framework capable of integrating environmental performance, fuel-consumption modeling and techno-economic evaluation into a single modular multi-criteria decision process. This methodological gap limits comparability across studies and constrains decision-making for ship operators navigating increasingly stringent regulatory requirements.

The present study addresses this gap for the first time by developing and applying a modular unified assessment framework for evaluating green interventions on existing commercial vessels. The study is guided by three research questions:

RQ1: how a modular and scalable green intervention assessment framework can be developed in a coherent and reproducible manner;

RQ2: how it can be implemented in a representative container ship case; and

RQ3: what insights emerge from the application regarding the relative environmental and economic effectiveness of the interventions examined in the case?

A detailed theoretical background on regulatory indicators and the state of the literature on green interventions in the maritime transportation industry is provided in Section 2. Section 3 introduces the unified modular framework, outlining its environmental, techno-economic and integrative multi-criteria assessment components. Section 4 applies the framework to a representative container vessel, while Section 5 discusses the implications of the findings for ship operators and policymakers. Section 6 concludes the study and identifies avenues for future research.

2. Regulatory Context and Efficiency Indicators in International Shipping

International shipping has become a central focus of global decarbonization efforts due to its substantial contribution to anthropogenic CO₂ emissions and its structural dependence on fossil fuels. A rapidly expanding body of academic literature highlights the complexity of improving ship energy efficiency and the need for robust regulatory frameworks to support emission-reduction pathways, in addition to complementary measures, such as green port and terminal infrastructures [3,10]. It is worth noting the willingness of a number of shipping companies to undertake sustainability initiatives for improving their reputation and enhance their organizational performance [11]. Recent reviews have mapped the evolution of maritime energy-efficiency research and emphasized the multidimensional nature of decarbonization challenges, including technological, operational, socio-economic and regulatory constraints [12,13]. Complementary analyses further document the increasing sophistication of energy-efficiency assessment methods and the growing integration of sustainability principles into maritime transport [14]. Additional studies underscore the interplay between regulatory requirements, technological innovation and operational practices, illustrating how policy developments shape both ship design and operational strategies [15,16]. Scientometric evidence also shows that indicators such as the EEDI (Energy Efficiency Design Index), EEXI and CII have become central themes in maritime research, reflecting the sector’s accelerating transition toward more stringent environmental performance standards [17]. Collectively, these studies demonstrate the centrality of energy-efficiency regulation in the maritime decarbonization agenda and provide the academic foundation for understanding the role of indicators such as the EEXI and CII.

Within this regulatory landscape, the International Maritime Organization (IMO) has progressively expanded MARPOL Annex VI to include a suite of technical and operational measures aimed at reducing greenhouse gas emissions from ships. The regulatory trajectory began with the introduction of the EEDI for new buildings in 2011, establishing mandatory design-efficiency standards for vessels entering service. As the urgency to regulate the existing fleet intensified, the IMO adopted a package of short-term GHG-reduction measures in 2021, including the EEXI and the CII, both of which entered into force on 1 January 2023 [6]. These measures form the backbone of the IMO’s short-term decarbonization strategy and are designed to ensure that ships meet minimum technical efficiency thresholds while also improving their operational carbon performance over time.

The CII functions as an operational measure of environmental performance, quantifying the grams of CO₂ emitted per tonne-nautical mile of transport work. Because it is derived from real-world operational data—fuel consumption, distance sailed, speed profiles and utilization—it reflects the vessel’s actual annual performance and is sensitive to operational practices. The attained CII represents the calculated value for a specific vessel, while the required CII is determined from IMO reference lines and annually tightening reduction factors. In contrast, the Energy Efficiency Existing Ship Index (EEXI) is a technical design-based metric that evaluates the inherent energy efficiency of an existing vessel’s machinery, propulsion system and hull characteristics. It is calculated using parameters such as installed engine power, specific fuel consumption and reference speed, and therefore reflects the vessel’s design efficiency rather than its operational profile. The attained EEXI is the value computed for the ship’s technical configuration—often after the application of power-limitation measures—while the required EEXI is derived from IMO reference lines and fixed reduction factors for each ship type and size.

Together, the CII and EEXI constitute a complementary regulatory pair: the EEXI ensures that ships meet a minimum technical efficiency threshold, while the CII drives continuous operational improvements throughout the vessel’s lifetime. This dual approach is central to the IMO’s short-term decarbonization pathway and is expected to inform future mid- and long-term measures. The detailed calculation procedures and formulae for both indicators are provided in Appendix A. The regulatory context outlined here provides the foundation for understanding the technological and operational interventions reviewed in the next section.

On Green Interventions in Ships

A wide range of technical and operational interventions has been proposed to reduce fuel consumption and CO₂ emissions from existing ships. A comprehensive synthesis is provided by Xing, Spence and Chen [8], who classify countermeasures into hydrodynamic improvements, propulsion-system optimization, energy-saving devices and operational strategies. This is similar to Psaraftis and Kontovas’s classification into technical, market-based and operational measures [18]. Technical measures commonly examined include advanced hull coatings that reduce viscous resistance [19,20], propeller optimization and redesign to improve open-water and behind-hull efficiency [21], wake-equalizing ducts that enhance inflow uniformity [22], and bulbous-bow modifications that reduce wave-making resistance at service speeds [23]. Additional energy-saving devices such as pre-swirl stators, rudder bulbs and propeller boss cap fins have demonstrated incremental hydrodynamic benefits [24].

Operational measures form a second major category. Slow steaming remains the most effective single intervention due to the non-linear speed–power relationship [25,26]. Trim optimization reduces total resistance through voyage-specific adjustments [27], while weather routing improves fuel efficiency by avoiding adverse sea states [28]. Performance-monitoring systems, including mass-flow meters, support continuous improvement by improving the accuracy of fuel-consumption data. The literature consistently shows that combined intervention packages—particularly those integrating technical and operational measures—tend to outperform individual actions [8]. This is because these approaches deal better with the trade-offs between environmental and socio-economic performance [13].

In line with the aforementioned areas of intervention, assessment approaches can be grouped into three main streams. First, environmental evaluation typically relies on recalculating IMO regulatory indicators such as the Energy Efficiency Existing Ship Index (EEXI) and the Carbon Intensity Indicator (CII), using official formulations to quantify changes in technical and operational performance [29,30,31,32]. Second, regarding energy use, fuel-consumption modeling is widely employed to estimate baseline and post-intervention energy use, drawing on speed–power curves, specific fuel-oil consumption data and voyage-level operational profiles [26,33]. Third, techno-economic assessments apply common engineering-economics tools such as Net Present Value (NPV), Return on Investment (ROI) and payback period to evaluate financial viability [34,35]. Multi-criteria decision-making (MCDM) methods—including weighted additive models, AHP, TOPSIS and entropy-based weighting—have been used to integrate heterogeneous criteria along the above three dimensions, though their application in maritime studies remains fragmented and often disconnected from IMO-compliant environmental indicators [9,36,37].

Despite the breadth of existing work, three gaps are evident. First, most published application studies evaluate interventions in one or two dimensions, focusing on either technical or operational measures, or on either environmental or economic outcomes, rather than integrating all dimensions within a unified procedural framework. In addition, there is limited methodological synthesis: environmental, techno-economic and multi-criteria analyses are often applied separately, without a transparent mechanism for synthesizing their outputs. This reveals the absence of a reproducible, regulation-aligned scalable framework that combines fuel-consumption modeling, CII/EEXI recalculation, techno-economic evaluation and multi-criteria synthesis [8]. This apparent need motivates the development of the unified assessment framework presented in Section 3, which directly addresses RQ1 and provides the methodological foundation for the application case study that follows.

3. Research Design and Methodological Positioning

The research reported in this paper followed a two-level methodological structure that ensured conceptual clarity and alignment with the three research questions. The first level concerned the development of the unified assessment framework, which constitutes the core contribution of the paper and directly addresses RQ1. The framework was developed through a rigorous examination of the literature relevant to maritime decarbonization, environmental regulation, techno-economic evaluation and multi-criteria decision-making. Drawing on established methodological principles from these domains, the study synthesizes a coherent, modular and globally applicable framework capable of integrating environmental and economic performance into a single modular assessment process. This literature-grounded development process ensures that the framework is both theoretically robust and practically applicable across different vessel types and intervention portfolios.

The second methodological level concerns the application of the proposed framework to a reference container ship. This directly addresses RQ2. As highlighted in the Introduction, container ships constitute one of the most emission-intensive and operationally significant segments of the global fleet, making them an appropriate and analytically rich context for demonstrating the practical relevance of the framework. This application does not form part of the framework itself; it rather represents the empirical procedure through which the framework can be operationalized, tested and illustrated in practice (it is presented in full detail in Section 4). It involves compiling the necessary technical and operational data, modeling the environmental and economic impacts of the identified interventions, and implementing the multi-criteria synthesis defined by the framework. By maintaining a clear distinction between the development of the conceptual framework and its application, the study ensures methodological transparency and avoids conflating conceptual research (framework construction) with empirical research (framework application).

Finally, the study undertakes a critical interpretation of the results generated through the application of the framework, thereby addressing RQ3. This stage examines the relative performance of the interventions, identifies trade-offs between environmental and economic outcomes, and evaluates the extent to which the framework supports transparent and balanced decision-making. This three-stage structure—developing, applying and critically analyzing—ensures methodological transparency and strengthens the internal coherence of the research design. Subsequently, the proposed framework is presented in its complete form, while Section 4 and Section 5 document its application and the insights derived from it.

The Proposed Assessment Framework

The unified assessment framework developed addresses RQ1 and provides a structured, reproducible and modular process consisting of seven sequential steps (Figure 1), each grounded in established maritime regulation, engineering practice and multi-criteria decision-making literature. Together, these steps form a coherent assessment pathway that can be applied to any vessel type or intervention portfolio.

• Specification of the context

The assessment begins with a clear definition of the vessel type, since ship classification determines the regulatory context, operational envelope and performance expectations relevant to decarbonization analysis. IMO instruments provide formal descriptions for major ship categories—such as bulk carriers, tankers, passenger ships and fishing vessels—which establish the baseline regulatory requirements applicable to each segment [38]. In parallel, classification societies specify type and service-restriction notations that describe a vessel’s intended functions and operational limits. This ensures that any performance evaluation is aligned with the vessel’s certified capabilities [39]. Defining the vessel type anchors all subsequent steps of the methodology, as fuel consumption patterns, attainable efficiency improvements and the relevance of specific interventions depend directly on the vessel’s technical characteristics and operational profile.

2.

Formulation of intervention alternatives

The operational and technical interventions that will be evaluated are determined through a systematic process. The process begins by determining all measures/interventions that are technically feasible for the vessel type and operational profile defined in Step 1. To ensure credibility and implementability, the selection is restricted to interventions with documented fuel-saving or efficiency-enhancing potential in existing ships. Operational measures such as speed optimization, weather routing and voyage planning are included because they are explicitly recognized in IMO’s SEEMP framework as short-term, economic (low-CAPEX) actions with measurable impact on fuel consumption [38]. Technical measures, such as hull and propeller maintenance, propulsion-enhancing retrofits and energy-saving devices, are incorporated when their effectiveness has been demonstrated in empirical or modeling studies. Comprehensive reviews, such that by Xing et al. [8], provide a validated classification of such measures, while additional evidence [12,15] supports the inclusion of interventions with consistent performance improvements across ship types. Based on this combined regulatory and academic evidence, a shortlist of interventions that (i) are feasible for the vessel under study, (ii) have quantifiable influence on fuel consumption or carbon intensity, and (iii) can be modeled consistently for the environmental and techno-economic calculations that follow is compiled.

3.

Estimation of fuel Consumption

Baseline fuel consumption is calculated by reconstructing the vessel’s annual operational profile using voyage-specific data (speed, distance, loading condition, main engine power, auxiliary engine use). For each voyage segment, the main engine power is obtained from the vessel’s speed–power curve, corrected for displacement and sea margin, and converted to fuel consumption using the corresponding SFOC values for the main and auxiliary engines [26,33]. Annual baseline consumption is then derived by aggregating fuel use across all voyages and operating modes, following the calculation structure recommended in IMO SEEMP and fuel-consumption guidelines [38,40].

For each intervention, the same operational profile is retained, but the relevant performance parameter, such as required power at a given speed (e.g., hull/propeller improvements), average operating speed (e.g., speed optimization) or auxiliary-load efficiency (e.g., machinery upgrades), is modified. These adjustments are applied using performance factors and reduction coefficients drawn from application studies and validated technology assessments [8,12]. Post-intervention fuel consumption is then recomputed using the adjusted parameters, thus ensuring consistency with the baseline. Annual fuel savings are calculated as the difference between baseline and post-intervention consumption, expressed in tonnes per year and as a percentage of the baseline. These savings form the quantitative input for the environmental performance calculations (CII, EEXI) and the techno-economic assessment in subsequent steps.

4.

Assessment of Environmental Performance

Environmental performance is quantified by recalculating the vessel’s attained CII and attained EEXI using the official IMO formulations. Annual CO₂ emissions obtained in Step 3 are combined with the vessel’s transport work to compute the attained CII in accordance with the operational carbon-intensity guidelines [29]. The same calculation structure is applied to both baseline and post-intervention scenarios, ensuring that any change in the CII results solely from the modified fuel-consumption profile. The attained EEXI is evaluated using the design-index methodology prescribed in MEPC.350(78), with ship-type-specific parameters and correction factors taken from the vessel particulars defined in Step 1 [31]. Where an intervention affects propulsion efficiency or available power, the corresponding adjustments are incorporated directly into the EEXI formula following recognized implementation practices [41]. The use of IMO-compliant formulations aligns with current research practice, where environmental impacts of operational and technical measures are routinely assessed through CII/EEXI recalculation [15,42]. This step constitutes the first milestone in the assessment and one can stop here is environmental assessment only is of interest. Otherwise, the results of this step can be fed to the multi-criteria assessment that follows in Step 6.

5.

Assessment of techno-economic performance

The techno-economic assessment evaluates the financial viability of each intervention by combining the annual monetary savings derived in Step 3 with the corresponding capital expenditure (CAPEX). Annual savings are monetized using the assumed fuel price and projected over the expected lifetime of the intervention. These cash flows are discounted to obtain the Net Present Value (NPV), following standard engineering-economics methodology [34]. The value of the Return on Investment (ROI) is calculated as the ratio of annual monetary savings to the CAPEX, while the Break-Even Point (BEP) is determined by dividing the CAPEX by the annual savings to estimate the payback period. This approach is consistent with techno-economic evaluation practices applied in maritime energy-efficiency studies [35,43]. Applying the same financial framework across all interventions ensures financial comparability of the interventions. This step forms the second milestone of the process. When multidimensional assessment according to specific strategic and/or tactical priorities is of interest, it provides the economic indicators required for the multi-criteria synthesis in the subsequent step.

6.

Multi-Criteria Synthesis

Multi-criteria synthesis integrates the evaluation criteria into a unified composite score that enables the comparison and ranking of alternative interventions under different priorities. Because the criteria typically differ in units, scales and orientation, the synthesis is implemented through a structured sequence consisting of initial scoring, normalization, weighting and aggregation.

In the initial scoring stage, each alternative is assigned a performance score on a predefined numerical scale for every criterion. This step is based on expert judgment and provides a criterion-specific assessment of relative performance. The scoring does not constitute a ranking or prioritization; rather, it establishes the structured input required for the subsequent normalization and weighted aggregation. Normalization ensures that all criteria are expressed on a common, dimensionless scale. Benefit-type criteria are transformed using standard min–max scaling, while cost-type criteria are normalized through an inverted min–max formulation so that all criteria follow a consistent “higher is better” orientation. This approach is widely used in sustainability-oriented multi-criteria assessments [9].

Weighting expresses the relative importance of each criterion. Recognized approaches include direct assignment, equal weighting [44], stakeholder-based weighting derived from expert judgment [36] and objective data-driven schemes such as entropy weighting [37]. These alternatives form part of the general methodological framework; the specific choice is defined at the application stage, depending on the decision-making context.

Aggregation combines the normalized and weighted criteria into a single composite score. The weighted additive model—where each normalized value is multiplied by its corresponding weight and the results are summed—is the most widely applied method in energy-systems and sustainability evaluations due to its transparency and interpretability [9]. The resulting composite score provides a coherent basis for comparing and ranking alternatives across multiple performance dimensions.

7.

Integrated ranking of interventions

The integrated ranking constitutes the final stage of the assessment framework, where the composite scores obtained from the multi-criteria synthesis are used to prioritize the alternative interventions. Since each composite score reflects the combined economic, environmental and technical performance of an option—already normalized, weighted and aggregated in a consistent manner—the ranking is produced by ordering interventions from highest to lowest composite score. Again, this approach follows established practice in sustainability-oriented multi-criteria decision-making [9].

To ensure methodological clarity, the ranking is accompanied by the underlying normalized indicators and the weighting structure, allowing decision-makers to trace how each criterion contributes to the final outcome. This practice aligns with recommendations in the MCDM literature, which emphasize transparency and the possibility of conducting sensitivity analyses to test the robustness of the prioritization (Cinelli, Coles and Kirwan, 2014; Munda, 2004) [36,44]. Scenario-based variations in weights or criteria can be explored to assess how changes in assumptions influence the ranking, a procedure commonly applied in composite-indicator construction [37].

4. Results—Application of the Framework

This section presents the application of the proposed framework for assessing and comparing a number of possible sustainability interventions in a specific vessel type. Given the extensive scope of the analysis and the large volume of intermediate calculations—particularly in the environmental indices and the multi-criteria synthesis—the main text presents only the most significant and representative results, supported by summary tables and key performance indicators. Detailed computational steps, intermediate values and full numerical outputs are provided in the Appendices A and B. These additions ensure that all key input parameters and assumptions required to follow the calculation process are fully documented within the manuscript. This dual-layer presentation preserves clarity and readability in the main text while ensuring transparency and reproducibility for readers who wish to examine the complete computational workflow.

4.1. Specification of the Context

The first step of the framework establishes the baseline characteristics of the vessel under assessment. The analysis focuses on a 4250 TEU container ship, representative of the medium-size segment of the global fleet and a structurally important contributor to absolute CO₂ emissions due to its high transport work and intensive operating profile. These characteristics make it an appropriate and decision-relevant case for evaluating the selected interventions.

In general, container ships are designed to transport container boxes of standard dimensions (usually of length 6.1 m). This form of sea transportation has revolutionized maritime shipping, improving significantly the efficiency of international trade. Compared to bulk carriers, they feature faster loading and unloading times, superior cargo protection, a wider variety of transported goods, flexibility regarding the use of other transport modes, and greater supply chain efficiency. The capacity of container ships has increased significantly over the last 50 years, rising by approximately 1500% (reaching 24,000 TEU in 2020). Notably, this capacity has doubled even within the last decade, reflecting the impressive and continuous development of these vessels.

This high capacity contributes to the reduction of transport costs per cargo unit; however, to move these massive loads effectively, powerful engines that consume large quantities of fuel are required and, consequently, they emit significant amounts of carbon dioxide and other harmful elements. Container ships are often used for long-distance international transport and are designed to travel at higher speeds than other vessel types, requiring more energy and fuel. Many of these ships are powered by large diesel engines, and despite the progress made in making these engines more efficient and environmentally friendly, they still produce significant emissions.

The full set of technical and operational specifications used as baseline inputs—including capacity, propulsion configuration, engine power, service speed and annual activity—in our research is provided in Appendix B. These parameters form the reference dataset for all subsequent calculations in Steps 2–7.

4.2. Formulation of Intervention Alternatives

The second step of the framework identifies the seven interventions that constitute the basis of the assessment, structured into four technical measures (Low-friction coating (LFC), Propeller duct (WED), Propeller optimization, Bulbous bow modification) and three operational measures (Slow steaming, Trim optimization, Mass flow meter). Building on these seven standalone interventions, the analysis constructs a total of ten scenarios (

Table 1. Overview of the evaluated scenarios.

No (#)	Scenario	Description
0	Initial state	Baseline operational and technical condition of the vessel, used as the reference point for all comparative assessments.
1	Technical: Low-friction coating (LFC)	Application of an advanced hull-coating system designed to minimize surface roughness and reduce viscous resistance, thereby improving hydrodynamic efficiency.
2	Technical: Propeller duct (WED)	Installation of a wake-equalizing duct intended to condition the inflow to the propeller, enhancing propulsive efficiency through improved wake alignment and reduced rotational losses.
3	Technical: Propeller optimization	Geometric optimization or redesign of the propeller to improve open-water characteristics and behind-hull performance, targeting reductions in slip and improved thrust efficiency.
4	Technical: Bulbous bow modification	Hydrodynamic redesign or adjustment of the bulbous bow to minimize wave-making resistance at the vessel’s predominant operating speed range.
5	Operational: Slow steaming	Reduction of service speed to lower main-engine load and specific fuel consumption, resulting in a non-linear decrease in total fuel demand.
6	Operational: Trim optimization	Continuous or voyage-specific adjustment of vessel trim to achieve minimum total resistance under prevailing loading, draft and sea-state conditions.
7	Operational: Mass flow meter	Deployment of high-accuracy fuel-flow measurement instrumentation enabling improved monitoring, verification and optimization of engine performance and consumption patterns.
8	Combination: LFC + WED	Integrated implementation of low-friction hull coating and wake-equalizing duct, capturing cumulative hydrodynamic and propulsive efficiency gains.
9	Combination: All technical measures	Aggregated application of all technical interventions (Scenarios 1–4), representing a comprehensive technical-efficiency enhancement package.
10	Combination: All measures	Full integration of all technical and operational measures (Scenarios 1–7), representing the maximum feasible improvement envelope for the vessel.

), which include the individual measures as well as three combined configurations—namely, the Combination of LFC and WED (1–2), the Combination of all technical measures (1–4), and the Combination of all measures (1–7). The selection of both the individual interventions and their combined forms is grounded in the literature reviewed in Section The Proposed Assessment Framework and reflects dominant industry practices, as highlighted in expert panel discussions with practitioners from the container ship segment. Detailed descriptions, assumptions and implementation parameters for all interventions are provided in the Appendices A and B, and the corresponding scenario set is used in Steps 3–7 for the quantification of fuel consumption impacts, environmental index performance, techno-economic outcomes and multi-criteria ranking.

4.3. Estimation of Fuel Consumption

The fuel consumption modeling applies the energy reduction factors of Table 1 to the vessel’s baseline consumption and produces a consistent set of adjusted values for all ten scenarios. Based on the calculated values of daily fuel consumption,

Table 2. Fuel consumption and relative fuel consumption (baseline = 1.00).

Scenario	Description	Reduction in Consumption (%)	Power (kW)	Specific Fuel Consumption (g/kWh)	Daily Fuel Consumption (t)	Relative Fuel Consumption
0	Initial state	0	16,121	170.81	66.00	1.00
1	Technical: Low-friction coating (LFC)	6.5	15,073	170.11	61.54	0.935
2	Technical: Propeller duct (WED)	5	15,315	170.23	62.57	0.950
3	Technical: Propeller optimization	2	15,799	170.54	64.66	0.980
4	Technical: Bulbous bow modification	6	15,154	170.15	61.88	0.940
5	Operational: Slow steaming	20	12,897	169.94	52.60	0.800
6	Operational: Trim optimization	1.5	15,880	170.60	65.02	0.985
7	Operational: Mass flow meter	1	15,960	170.67	65.37	0.990
8	Combination: LFC + WED (1–2)	11.5	14,267	169.89	58.17	0.885
9	Combination: All technical measures (1–4)	19.5	12,978	169.92	52.93	0.805
10	Combination: All measures (1–7)	42	9350	170.60	170.60	0.580

reports the resulting relative fuel consumption index, normalized to 1.00 for the initial state. Daily fuel consumption is determined using the vessel’s speed–power and specific fuel consumption–power curves, which originate from full-scale trials conducted on a vessel with comparable technical and operational characteristics [45]. These trial-based datasets were processed and fitted using standard regression techniques to obtain continuous performance curves suitable for scenario analysis.

For the baseline operating condition (service speed of 18 kn), the fitted curves provide the required propulsion power and the corresponding specific fuel consumption, from which the daily fuel consumption is calculated. The same procedure is applied consistently across all ten scenarios. In each case, the energy-saving interventions listed in Table 1 are incorporated through their respective reduction factors, resulting in adjusted power demand and fuel consumption values. The final outputs are summarized in Table 2 through the relative fuel consumption index, normalized to 1.00 for the initial state.

The above results indicate that the technical measures led to modest reductions, the operational measures show more pronounced effects, and the combined configurations deliver substantially higher improvements, with the full combination scenario achieving the largest reduction. These results become the direct input for the environmental and techno-economic assessments in the subsequent sections.

4.4. Assessment of Environmental Performance (CII and EEXI)

The environmental performance of the ten scenarios is evaluated in terms of the attained Carbon Intensity Indicator (CII) and the attained Energy Efficiency Existing Ship Index (EEXI). The results, summarized in

Table 3. Environmental assessment (based on CII and EEXI).

Scenario	Attained CII [g CO2/(tNM)]	CII Category				Attained EEXI [g CO2/(t*NM)]
		2023	2024	2025	2026
0	10.59	E	E	E	E	14.70
1	9.87	D	D	D	D	14.38
2	10.03	D	D	D	E	14.45
3	10.37	D	E	E	E	14.60
4	9.92	D	D	D	E	14.40
5	8.43	C	C	C	C	14.70
6	10.42	D	E	E	E	14.70
7	10.48	D	E	E	E	14.70
8	9.33	C	D	D	D	14.13
9	8.49	C	C	C	C	13.74
10	6.14	A	A	A	A	13.74

The background colors and categories (uppercase letter) are based on the standardized IMO operational carbon intensity rating system (CII).

, show that the baseline case (Scenario 0) does not comply with the required CII levels in any year of the assessment horizon, while several of the technical, operational and combined interventions significantly improve both indicators to varying degrees.

The attained CII values indicate that the initial state remains consistently in the E-rating across the four-year assessment horizon (Years 1–4), confirming the vessel’s non-compliance. The technical measures provide modest improvements, generally shifting the vessel to a D-rating, though not ensuring stable compliance by the end of the period. In contrast, the operational measure of slow steaming delivers a substantial reduction in carbon intensity and achieves a stable C-rating throughout all four years, clearly outperforming the other operational options.

The combined configurations exhibit the strongest environmental performance. The combination of technical interventions maintains a consistent C-rating, while the full-combination scenario achieves an A-rating across all years, corresponding to an overall reduction in carbon intensity of approximately 42% relative to the initial state. These results highlight that the limited impact of individual interventions, whereas integrated solutions provide the most robust and durable improvements in CII performance.

The attained EEXI assessment reveals a clear and consistent pattern across all scenarios examined, demonstrating the extent to which each intervention contributes to achieving regulatory compliance. As shown in the results table presented in this section, the baseline configuration exceeds the required EEXI by a substantial margin, confirming that the vessel’s existing technical characteristics are insufficient to meet the IMO standard. The introduction of Engine Power Limitation (EPL) produces the most significant improvement, reducing the attained EEXI by a notable percentage and bringing the vessel close to the regulatory threshold, although a small compliance gap remains. When EPL is combined with additional efficiency-enhancing measures—such as propulsive-efficiency upgrades or hull-form optimization—the attained EEXI falls below the required value, demonstrating full compliance with a comfortable margin. In contrast, scenarios involving minor retrofits without EPL yield only modest improvements and do not independently achieve compliance, underscoring the limited impact of isolated technical adjustments. Overall, the scenario analysis confirms that EPL is the decisive driver of EEXI compliance, while complementary upgrades strengthen the vessel’s performance and provide additional operational flexibility. These findings are clearly illustrated in Table 3, which summarizes the attained and required EEXI values for all scenarios and highlights the relative effectiveness of each intervention pathway.

4.5. Techno-Economic Assessment

The techno-economic assessment conducted followed a cost–benefit approach, focusing on the relationship between the implementation cost of each energy-efficiency measure and the annual fuel-saving benefits it generates. The Break-Even Point is used for the economic assessment of interventions. The direct cost of implementing interventions and associated savings are used for calculating NPV and ROI for a period of five years (period between two vessel inspections for environmental standards’ compliance). A discount rate of 5% was used, while the price of fuel was set to 600 USD/t. The BEP was used for comparison because the interest is to neutralize costs as soon as possible, rather than maximizing profits. This enables a consistent comparison across all ten scenarios, capturing both the magnitude of the required investment and the speed at which fuel-saving benefits offset that investment. It should be noted that the effects of FuelEU Maritime Regulation and those of any emission trading schemes were not taken into account.

The results (see

Table 4. Techno-economic assessment (based on CAPEX, ROI, BEP).

Scenario	Estimated Cost CAPEX (USD)	Fuel Costs Saving (USD/Year)	NPV (USD)	ROI (%)	BEP (Years)
1	350,000	605,586	2,271,869	649	0.6
2	250,000	468,446	1,778,126	711	0.5
3	400,000	189,665	421,148	105	2.1
4	500,000	560,025	1,924,615	385	0.9
5	0	1,795,818	7,774,954	-	-
6	25,000	142,554	592,184	2369	0.2
7	20,000	95,244	392,355	1962	0.2
8	600,000	1,053,984	3,963,198	661	0.6
9	1,500,000	1,752,612	6,087,894	406	0.9
10	1,545,000	3,702,141	14,483,334	937	0.4

) demonstrate that several technological measures deliver strong economic performance, with ROI values often exceeding several hundred percent and BEP periods well below one year, indicating rapid payback driven by substantial fuel-saving potential. In contrast, operational measures, such that of Scenario 5 (slow steaming), behave fundamentally differently: its reported cost reflects the operational impact of reduced speed (opportunity cost) rather than a technical capital expenditure. As these costs are not investments but mostly interventions imposed by regulation, ROI and the BEP cannot be meaningfully calculated. Overall, the analysis demonstrates that most retrofit measures offer highly favorable cost–benefit characteristics, while operational measures such as slow steaming require a different economic interpretation due to their non-investment nature.

4.6. Multi-Criteria Synthesis

Multi-criteria synthesis is based on a weighted-factor method that integrates the environmental and economic indicators into a unified comparative assessment of the ten intervention scenarios. The process consists of three sequential stages: initial scoring, weighting of criteria, and aggregation into final composite scores.

The initial scoring (see

Table 5. Initial scoring of interventions (score scale: 1–10).

Scenario	Criterion
	CII	EEXI	BEP
1	4	3	8
2	3	2	8
3	2	1	5
4	4	3	7
5	9	0	10
6	1	0	9
7	1	0	9
8	6	5	8
9	9	8	7
10	10	8	8

) establishes the performance profile of each scenario with respect to the three selected criteria: CII improvement, EEXI compliance and Break-Even Point. Each scenario is assigned a score on a 1–10 scale, representing its relative performance rather than an absolute ranking. These three criteria were selected because they capture the most operationally relevant dimensions of environmental and economic performance under the current regulatory framework (CII and EEXI), while the Break-Even Point provides a transparent measure of economic viability that can be applied consistently across technological and operational interventions. As has already been indicated, more complex financial metrics, such as IRR, were not used, because they require more detailed financial data that are not available for the present analysis and would introduce unnecessary complexity and uncertainty.

Unlike purely judgment-based scoring approaches, the present study applies a structured, formula-based method to derive the scores for each criterion. For the CII and EEXI, the attained values of each scenario are compared with the required values, normalized against the baseline condition, and scaled to a 0–10 range. For economic viability, the Break-Even Point is normalized against a five-year assessment horizon, ensuring comparability across scenarios. This approach provides a transparent and reproducible scoring system while maintaining interpretability. Scenario 5 (slow steaming) receives the maximum economic score (10) despite lacking a calculable BEP, reflecting the fact that its economic effect is immediate and operational rather than investment-based, and therefore cannot be meaningfully represented by a payback period.

Following the initial scoring, the second stage of the synthesis concerns the weighting of the three criteria. At this stage, the scenarios themselves are not evaluated; instead, percentage weights are assigned to the criteria to reflect different decision-making perspectives and strategies. The weighting schemes were developed in consultation with industry practitioners and technical experts, who emphasized that priorities vary depending on whether the primary objective is environmental compliance, a balanced trade-off, or rapid economic return. Based on these insights, three weighting strategies were formulated (

Table 6. Weighting the criteria: three strategies.

Criterion	Strategy
	Green	Intermediate	Economical
1. CII	55	35	20
2. EEXI	30	25	20
3. Break-Even Point	15	40	60
Total	100	100	100

): Green, which emphasizes environmental performance; Intermediate, which balances environmental and economic considerations; and Economical, which prioritizes short payback periods.

The final stage of the consolidation process aggregates the initial scores with their corresponding weights to produce a composite score for each scenario under each weighting strategy. Aggregation follows a weighted–additive structure, where the normalized score of each criterion is multiplied by its assigned weight and the results are summed (

Table 7. Final (weighted) scoring of interventions (score scale: 1–10).

Scenario	Strategy
	Green	Intermediate	Economical
1	4.30	5.35	6.20
2	3.45	4.75	5.80
3	2.15	2.95	3.60
4	4.15	4.95	5.60
5	6.45	7.15	7.80
6	1.90	3.95	5.60
7	1.90	3.95	5.60
8	6.00	6.55	7.00
9	8.40	7.95	7.60
10	9.10	8.70	8.40

). The composite scores provide a coherent basis for comparing the scenarios across different decision-making perspectives and form the foundation for the final ranking presented in Section 4.7.

4.7. Integrated Ranking of Interventions

The next stage of the assessment integrates the three weighting strategies into a consolidated ranking framework. This step enables a structured comparison of the scenarios’ relative performance under different prioritization logics.

Table 8. Integrated ranking of scenarios.

Scenario ID	Ranking Positions (1–10) of Scenarios According to Strategy
	Green	Intermediate	Economical
1	5	5	5
2	7	7	6
3	8	10	10
4	6	6	7
5	3	3	2
6	8	8	8
7	8	8	8
8	4	4	4
9	2	2	3
10	1	1	1

presents the Integrated Ranking of scenarios, where each scenario is assigned a ranking position under each weighting strategy.

The results indicate a high degree of consistency across the three strategies. Scenario 10 systematically occupies the top position, followed by Scenario 9 and Scenario 5, which also perform strongly across all weighting schemes. Conversely, Scenarios 3, 6 and 7 consistently appear in the lower part of the ranking spectrum. A notable feature is the equal ranking positions of Scenarios 3, 6 and 7 under the Green strategy (all three receive identical weighted scores).

While Table 8 provides a scenario centric view, decision-makers often benefit from an alternative representation that highlights which scenario occupies each ranking position. This complementary perspective is particularly useful for identifying convergence or divergence across weighting strategies/approaches, detecting stable “top performers” and examining the distribution of ties. For this reason,

Table 9. Integrated ranking (based on ascending ranking position).

Ranking Position	Ranking of Scenarios According to Strategies (Scenario Numbers)
	Green	Intermediate	Economical
1	10	10	10
2	9	9	5
3	5	5	9
4	8	8	8
5	1	1	1
6	4	4	2
7	2	2	4/6/7
8	3	6/7	—
9	6/7	—	—
10	—	3	3

presents the Integrated Ranking based on ascending ranking position, effectively inverting the structure of Table 8. This second view enhances interpretability by functioning as a “leaderboard,” allowing rapid identification of the scenarios that dominate the upper positions and those that cluster in the lower ones.

The inverted representation confirms the robustness of the top-ranked scenarios: Scenario 10 consistently holds Rank 1 across all approaches, while Scenario 9 and Scenario 5 occupy the next positions with minor variations. The tie among Scenarios 3, 6 and 7 under the Green strategy is again explicitly shown, ensuring full transparency in the reporting of results.

5. Discussion

Given that medium-size container vessels constitute one of the most emission-significant and operationally intensive segments of the global fleet, the insights derived from the application case study, beyond their demonstrative value for the assessment framework, have a specific practical significance.

In this direction, the first point to note concerns the environmental dominance of operational measures, particularly slow steaming (Scenario 5). This scenario achieves the strongest single measure reduction in carbon intensity and maintains a stable C rating across all four assessment years. This behavior is consistent with the non-linear speed–power relationship formulated from observations on existing vessels [45], where reductions in service speed yield disproportionately large decreases in fuel consumption and CO₂ emissions [25,26]. The results reinforce this established principle but also extend it by demonstrating that slow steaming retains its environmental superiority even when evaluated within a unified environmental–economic framework. This is noteworthy because previous studies have typically assessed slow steaming either environmentally or economically, but rarely within an integrated multi-criteria decision support structure.

A second insight relates to the limited standalone effectiveness of individual technical retrofits. Measures such as propeller optimization, bulbous bow modification and wake equalizing ducts produce measurable but modest improvements in both the CII and EEXI, insufficient to secure stable compliance in most cases. This finding aligns with hydrodynamic research showing that incremental retrofits often yield marginal gains unless combined with broader operational changes [21,22]. The present study contributes new evidence by demonstrating that these modest environmental gains persist even when the measures are evaluated through a techno-economic lens: although many retrofits exhibit exceptionally favorable ROI values and short payback periods, their environmental impact remains comparatively small. Despite its importance [13], the asymmetry between environmental and economic performance is rarely quantified explicitly in the literature and represents a substantive contribution of the framework.

A third insight emerges from the synergistic behavior of combined interventions, particularly the integrated technical package (Scenario 9) and the full combination scenario (Scenario 10). The results show that cumulative hydrodynamic and operational improvements produce substantially greater environmental benefits than the sum of individual measures, with Scenario 10 achieving an A rating in CII across all years and delivering the highest overall reduction in carbon intensity. This confirms theoretical expectations regarding the interaction of multiple efficiency measures [46] and aligns with empirical findings that integrated strategies outperform isolated interventions [47]. Here, the novelty lies in the framework’s ability to quantify these synergies across environmental and economic dimensions simultaneously, revealing that the superiority of combined measures is robust to changes in weighting priorities.

From an economic perspective, the results highlight a structural divergence between operational and technical interventions. Most technical measures exhibit highly favorable cost–benefit characteristics, with ROI values frequently exceeding several hundred percent and break-even periods well below one year. These outcomes are consistent with empirical assessments of retrofit economics [48,49], which emphasize the rapid payback potential of targeted efficiency upgrades. In contrast, slow steaming cannot be evaluated through ROI or the BEP because “cost” is not an investment but an operational trade-off. The framework therefore clarifies a conceptual ambiguity frequently noted in the literature: operational measures may be environmentally superior but require a fundamentally different economic interpretation than capital-intensive interventions. This distinction is often acknowledged qualitatively but rarely operationalized within a unified analytical structure.

A further insight concerns the behavior of environmental indices under different intervention types. The results confirm that the CII is highly sensitive to operational changes, while the EEXI is primarily influenced by technical characteristics and engine power limitations. This duality reflects the structural design of the IMO indices [50] (Psaraftis, 2023) and underscores the importance of selecting interventions that align with the regulatory mechanism being targeted. The framework’s ability to reveal these index specific sensitivities provides practical value for decision-makers navigating an increasingly complex regulatory landscape.

Finally, the integrated ranking results demonstrate a high degree of robustness across weighting strategies. Scenario 10 consistently ranks first, followed by Scenario 9 and Scenario 5, regardless of whether environmental or economic priorities dominate. This stability indicates that the superiority of these interventions is not a product of the weighting logic but reflects underlying performance differences that persist across decision-making perspectives. Conversely, Scenarios 3, 6 and 7 consistently occupy the lower positions, reflecting their limited environmental impact and, in some cases, weaker economic performance. Equal performances in the lower ranks further illustrates that small-scale technical adjustments offer limited strategic value unless embedded within broader intervention packages.

Overall, for the specific case, the application of the framework yields clear, theoretically grounded and operationally meaningful insights: operational optimization remains the most powerful environmental lever; integrated technical–operational packages deliver the strongest combined performance; and isolated technical retrofits, while economically attractive, provide limited environmental benefit. The framework’s novelty lies in its ability to synthesize these patterns within a single, globally applicable structure that integrates environmental indices, techno-economic indicators and multi-criteria logic—an integration not commonly found in the existing literature on maritime decarbonization.

6. Conclusions and Future Work

This paper presented the development and application of a modular and scalable, unified techno-economic and environmental assessment framework for evaluating green interventions on existing ships. The procedural framework constitutes the core contribution of the work directly addressing RQ1 of the Introduction. It is a structured, transparent and reproducible process that integrates fuel consumption modeling, regulatory environmental indicators (CII, EEXI) and financial performance metrics into a single analytical pathway that can be followed in a modular fashion. By synthesizing established methods into a coherent architecture, the framework provides a practical multi-criteria decision support tool that can be applied consistently across vessel types and intervention portfolios.

The application of the framework to a representative case of green interventions in a medium-ize container vessel addressed RQ2, illustrated how the seven-step structure operates in practice and demonstrated its capacity to generate comparable, traceable and regulation-aligned performance outputs. Given the emission significance and operational intensity of container ships within the global fleet, the case study provides a decision-relevant and analytically robust context for evaluating the behavior of alternative interventions. The analysis of the case revealed clear patterns in the relative effectiveness of technical, operational and combined measures, enabling a systematic interpretation of their environmental and economic implications.

The interpretive analysis that followed addressed RQ3 and yielded three practical, easily generalizable insights. First, operational optimization—particularly slow steaming—proved to be the most effective single intervention for reducing carbon intensity, confirming the strong regulatory sensitivity of the CII to operational behavior. Second, individual technical retrofits delivered modest environmental improvements but exhibited good economic performance, highlighting a trade-off between environmental and financial effectiveness. Third, integrated intervention packages, especially those combining technical and operational measures, achieved the highest overall performance across environmental and economic dimensions, demonstrating the cumulative value of multi-measure strategies.

Although the study does not claim a methodological innovation in multi-criteria decision-making, the multi-criteria synthesis plays a central integrative role within the framework. Its structured weighting and aggregation scheme enables the transparent combination of heterogeneous indicators (environmental, technical and economic) into a unified evaluation. Clearly, this does not constitute a contribution to MCDM theory, but it is a necessary operational mechanism that ensures consistent comparison across criteria that differ in scale, orientation and regulatory relevance.

Beyond answering the research questions, the findings have practical value for engineering and operational decision-making. The framework’s modularity/scalability and its structured outputs—particularly the recalculated CII/EEXI values, the fuel consumption impacts and the integrated ranking—provide a reproducible basis for assessing compliance pathways and prioritizing retrofit or operational strategies under tightening regulatory constraints. The approach is directly relevant to fleet performance engineers, technical consultants and classification society assessors who require transparent, regulation-aligned methods for comparing alternative intervention portfolios. For researchers, the framework offers a modular methodological template that can be adapted or extended in broader fleet level modeling and optimization studies.

Future work can extend the framework in several directions. One avenue involves incorporating dynamic operational profiles, enabling the evaluation of CII performance under varying market conditions, routing strategies and weather-related constraints. A second direction concerns expanding the techno-economic dimension to include more detailed financial assessment metrics, fuel-price volatility and carbon-pricing mechanisms, thereby supporting more comprehensive financial appraisal under uncertainty. A third extension relates to integrating additional environmental indicators, such as well-to-wake emissions or pollutant-specific metrics, which would broaden the framework’s applicability to alternative fuels and hybrid propulsion systems. A fourth direction involves examining potential rebound effects of interventions (such as slow steaming) on competition. In addition, a structured sensitivity analysis could be incorporated to assess how variations in fuel prices or the introduction of carbon-pricing mechanisms may influence the relative economic performance of the examined interventions. Finally, applying the framework across a wider range of vessel types and intervention portfolios would support comparative analysis at the fleet level and strengthen the generalizability of the findings.

Overall, the study demonstrated that a modular, unified, transparent and reproducible assessment framework can substantially improve the evaluation of green interventions on existing ships. By integrating environmental and techno-economic performance into a single analytical structure, the framework provides both methodological and practical contributions to the maritime decarbonization literature and offers a robust foundation for future research and engineering decision support.