Leveraging advanced marine design and decision-making methods to address the maritime decarbonization challenge

Leveraging advanced marine design and decision-making methods to address the maritime decarbonization challenge Joseph Burgoyne1,* and Thomas A. McKenney1 1 University of Michigan, Ann Arbor, MI, USA Abstract. Global climate targets and stricter maritime regulations have accelerated the urgency for shipping to decarbonize. The consequent challenge lies not only in the immaturity of possible solutions but in the cascading uncertainties that shape investment, design, and operational choices. This paper examines how uncertainty has been addressed in maritime decarbonization research and what lessons can be drawn from industry. This review maps commercial, regulatory, technological, and priority uncertainty against the analytical methods most often applied, from scenario analysis, multi-criteria decision analysis(MCDA), to optimization and simulation. The review shows a heavy reliance on scenario-based exploration, frequent use of MCDA to navigate competing priorities, and regulatory uncertainties often being framed in ineffective static views. It also highlights the implicit hybridization of methods, which is often underreported and limits transparency. To move from description to action, this study introduces a structured decision framework that adapts existing uncertainty postures into a decision tree format. An illustrative case demonstrates how the framework works to align organizational context – resources, risk tolerance, timelines – with appropriate methods to guide overall strategy. The contribution lies in linking analytical rigor with decision postures, offering practical support for different stakeholders navigating the uncertainty of maritime decarbonization. Keywords: Maritime decarbonization; decision-making under uncertainty; strategic framework; scenario anlaysis, stakeholder context; regulatory uncertiany; commercial uncertainty; technolgoical uncertainty 1 Introduction The maritime industry faces an urgent need to decarbonize in response to global climate targets, including the Paris Agreement and the IMO's net-zero emissions goal by 2050. This challenge requires a substantial shift in approach, driven by the emergence of alternative fuels, new technologies, and evolving regulatory and market conditions. Industry stakeholders must enhance their decision-making processes for ship design and operation, focusing on energy efficiency, reduced emissions, and carbon pricing. While various technologies are being developed to achieve maritime decarbonization, they introduce new economic and technical considerations, including increased ship costs and complexity, reliability concerns, and greater uncertainty overall. The significance of these emerging considerations for major shipping segments creates a gap that academic research, with its experience in complex ship design and decision-making under uncertainty, is well-positioned to address. Industry reports on maritime decarbonization provide regularly updated overviews on energy efficiency, alternative fuels, and general pathways to achieve sector-wide decarbonization by 2050, mainly serving regulators and offering helpful but non-specific background for ship designers and operators . Meanwhile, academic research increasingly explores sustainability, decarbonization, and complex ship design, especially the challenges of defining requirements for technologically advanced and operationally diverse vessels such as naval combatants and, more recently, commercial ships like container ships, bulk carriers, and tankers, which are now facing similar complexities due to decarbonization demands [4], [5], [6]. Ultimately, there is an imperative for immediate action, suggesting that a combination of regulatory compliance, technical progress, and innovative commercial strategies is essential to decarbonize the maritime industry. Decision-making under uncertainty is an important aspect that requires new strategies, tools, and a holistic approach that includes design, operation, and fleet-level perspectives. Considering the complexity and dynamic nature of the decarbonization challenge, the maritime industry can employ a blend of strategic approaches to manage uncertainties. * Correspondence to: [email protected] 16th International Symposium on Practical Design of Ships and Other Floating Structures PRADS 2025 Ann Arbor, MI, USA, October 19th – 23rd 2025 2 This paper builds upon “The Impact of Maritime Decarbonization on Ship Design: State-of-the-Art-Report” presented at the International Marine Design Conference in June 2024 [5] by: • Providing a framework to guide the selection of a decision-making approach, • Reviewing in detail existing methods for decision-making under uncertainty and their current applications to maritime decarbonization, and • Identifying remaining gaps in addressing the complete decarbonization challenge. This research aims to stimulate further discussion and foster collaboration between academia and industry in tackling the urgent maritime decarbonization challenge and caters to both researchers and practitioners, providing actionable insights for their daily work. For researchers, industry developments are moving quickly, and help is needed to bring relevant use cases and ensure they are addressing the right challenges and applying their research in the most effective way possible to maximize impact. This includes clearly identifying the best applications for given methods available or under development. For practitioners, there is a need to bring ship design methods and decision-making processes into the normal way of working to best handle the inherent uncertainties and dynamics associated with the maritime decarbonization challenge. 2 The Maritime Decarbonization Challenge The industry’s decarbonization challenge can be captured in three main statements: • First, we need to act now in an industry that is not easy to change. • Second, there are many options, most of which are not fully mature. • And third, key stakeholders must handle uncertainty and dynamic conditions. Shipping accounts for roughly 3% of global emissions and is considered a “hard-to-abate” sector [7]. If maritime emissions are not reduced, the sector may be responsible for a greater share of global emissions by 2050, as other sectors such as power and road transport decarbonize at a faster pace [1], [2], [3]. Historical resistance to change, long vessel lifespans, high capital intensity, and slow-moving global governance constrain swift transformation. Yet the sector is now entering a period of accelerating transformation, spurred by regulatory shifts – especially the IMO’s enhanced targets for net-zero emissions by 2050 and stricter regional policies like those from the EU – that are accelerating change and raising compliance risks. Responses to this rising pressure are multifaceted and require development in three main areas: regulation, technical, and commercial. Regulatory measures are becoming stricter and more complex, creating direct compliance requirements and indirect market signals. Technical solutions are proliferating, from energy efficiency measures to new fuel systems, but remain at varying levels of maturity and integration complexity. Commercial viability underpins adoption, forcing owners and operators to weigh uncertain fuel supply chains and volatile pricing. Even further, these uncertainties are not experienced in isolation. The complex relationships between maritime stakeholders results in decision-making being interdependent across different actors. Figure 1. Maritime decarbonization ship design cause-and-effect chain (McKenney 2024) 3 Figure 1 summarizes the maritime decarbonization challenge within the ship design context as a cause-andeffect chain, where regulatory pressures drive shifts in design objectives, which in turn stimulate the adoption of technical solutions and introduce new layers of emerging considerations. At each stage, uncertainties accumulate, regulatory shifts alter compliance requirements and future baselines; design emphasis toward efficiency and emissions reduction forces trade-offs in performance; technical solutions such as alternative fuels or EE technologies carry risk of immaturity, integration complexity, and uncertainty regarding long-term viability. These compounding uncertainties do not simply complicate the technical task of design, but directly shape the commercial and strategic decisions that follow. The chain not only highly the drivers of decarbonization but also the expanding decision space in which shipowners, ports, and designers must operate under deep and persistent uncertainty. 2.1 Approaching Decision-Making Decision-making under uncertainty is widely studied, but what makes the maritime case distinct is the scale and speed of change being demanded in a traditionally conservative industry. The sector has long relied on incrementalism: deterministic forecasts, efficiency-focused upgrades, and stepwise regulatory compliance. These approaches proved effective in relatively stable contexts, where fuel markets were predictable, and regulations advanced gradually. However, today, evolving regulation and consequent commercial volatility combine to overwhelm incremental approaches, leaving decision makers exposed. The decarbonization challenge forces stakeholders to confront questions beyond technical feasibility or shortterm profitability. Shipowners, ports, and technology developers each face high-stakes choices, from retrofits and infrastructure planning to piloting new technologies, often under unclear regulatory or market conditions. These decisions not only require thorough analysis but also depend on organizational strategy and are shaped by resources, capabilities, timelines, and risk tolerance. The same uncertainty may be tolerable for a diversified fleet owner, yet existential for a smaller operator. Strengthening these decisions requires grounding strategy in analytical support. Sophisticated methods increasingly explored in academia, i.e., scenario analysis, simulations, and multi-criteria evaluation, offer rigor in comparing trade-offs and testing robustness. However, their impact depends on how they are aligned with the posture of the decision-maker. A first mover, a cautious follower, and a hedger may face the same uncertainty yet require different analytical approaches to act effectively. This recognition frames the purpose of the following literature review. Mapping current patterns in how methods are applied to different uncertainty types is a necessary first step toward integrating analytical methods with strategic decision-making. Section 3, therefore, reviews how the literature has engaged with commercial, regulatory, and technological uncertainties, and evaluates the analytical methods most often employed in response. 3 Literature Review: Method Use in Maritime Decarbonization This section reviews how various decision-making methods have been applied in maritime contexts to date, and what patterns emerge from their usage. To ground these observations in evidence, an extensive literature review was conducted, surveying over 50 papers focused on decision-making under uncertainty with an emphasis on maritime decarbonization. While intended to build a representative picture of how uncertainty is addressed in maritime decarbonization and planning, it must be noted that this data set may not capture every individual experience or approach – the results should be read as a reflection of broader patterns rather than a definitive census. This body of literature spans from techno-economic models, ship design studies, fuel selection frameworks, and life cycle assessments, and reflects a wide range of academic and applied perspectives. The objectives of the review were to: • Investigate how and when uncertainty is acknowledged in maritime decarbonization research, • Map the methods employed to address uncertainty in maritime and comparable complex systems, • Identify recurring patterns, emerging practices, and methodological gaps that can inform future tool selection. 4 3.1 Increasing Academic Engagement Over the past 20 years, there has been a growing academic and commercial response to the challenges of maritime decarbonization and the broader problem of decision-making under uncertainty. Early contributions in the mid-to-late 2000s and early 2010s were limited in number and scope, often focused on discerning what stakeholders’ priorities should be and from sustainability planning in adjacent fields such as forestry, transportation, or power systems [8], [9]. Figure 2. Number of publications on decision-making under uncertainty in decarbonization and sustainability, cumulative bars composed of maritime-focused publications and others, as notated. Following 2015, a clear shift occurred, with a more focused wave of research most likely tied to emission regulation, the emergence of alternative fuels, and a clearer definition of policy in the shipping sector. The adoption and enactment of policies and advisements such as the IMO’s initial GHG strategy and the EU’s Fit for 55 packages may have prompted the surge in studies targeting maritime-specific applications[10]. Since 2020, the volume of published work addressing uncertainty in maritime decarbonization has accelerated dramatically. This rise in engagement reflects recognition that uncertainty is not a peripheral concern in maritime decarbonization; rather, it is central to the problem. 3.2 Uncertainty Themes in Literature To better understand how the studies have characterized and addressed uncertainty, this section explores the specific types of uncertainty of interest within the reviewed literature. Uncertainty in maritime decarbonization was most frequently framed around three dominant categories: commercial, regulatory, and technological. One of the broadest categories, commercial, includes factors such as fuel price, life cycle cost, carbon tax, market considerations, and investment timing. Regulatory uncertainty stems from the ambiguity of the scope, strength, and timing of local and global policy. Technological uncertainty refers to the still-developing maturity, availability, and long-term viability of emerging fuel and propulsion options. Priority uncertainty captures the ambiguity of stakeholder values and objectives. Unlike commercial or technological uncertainties, which can more easily be quantified, priority uncertainty reflects shifting and contested views on which outcomes—cost, compliance, performance, safety— should drive decisions. In maritime decarbonization, these priorities differ across actors and evolve as markets and regulations change. No. Publications by Year 2000 2005 2010 2015 2020 2025 Yea r 0 1 2 3 4 5 6 7 No. of Publications Maritime Non-Maritime 5 Figure 3. Percent acknowledgement of uncertainty type seen in the literature. Commercial uncertainty was the most prevalent category, accounting for nearly 40% of mentions across the reviewed studies. This reflects the capital-intensive, asset-heavy nature of the maritime industry, where long investment cycles can magnify exposure and risk from financial variables such as fuel price volatility, carbon pricing, and market demand shifts. Studies like Vergara Paredes et al. and Aspen et al. emphasize fuel price and lifecycle cost projections as central to determining the viability of alternative fuel pathways for maritime decarbonization [11], [12]. Investment timing uncertainty also appeared frequently, especially in analyses weighing retrofit options versus newbuilds in anticipation of future regulatory schemes and consequent carbon costs [11]. The second most dominant category was technological uncertainty, encompassing questions of technology maturity, availability, and overall viability in the operational lifespan of marine assets. This high level of acknowledgement not only reflects the now rapidly evolving technology landscape, but the apprehension in the industry to adopt them, due to uncertainty in fuel availability and compatibility locking owners into suboptimal pathways. It is important to note how the reviewed studies not only focused on the uncertainty of the performance of new technologies, as seen in Chae et al, but also the flexibility in retrofitting and the robustness of these technologies as seen in Foretich et al. [13], [14]. Regulatory uncertainty, while showing in 20% of cases, is almost certainly underrepresented relative to its importance in industry discourse. However, many studies frame regulatory changes indirectly through their economic impacts, i.e., carbon taxing, emissions trading, or compliance costing, as opposed to standalone policy uncertainty. With the screening criteria used in the literature review, those instances were classified under commercial as opposed to regulatory, shifting the overall weighting of these categories. Secondly, regulatory uncertainty is often broadly mentioned but directly implemented as fixed scenario inputs rather than as uncertain variables to be explored themselves. Uncertainty Acknowledged (%) Commercial Priority Regulatory Technological Uncertainty Type 0 5 10 15 20 25 30 35 Acknowledged (%) 6 3.3 Methods Employed to Handle Uncertainty The methods used across the reviewed papers to acknowledge uncertainty were categorized into six overarching groups, based on their underlying approach to uncertainty modeling. The groupings used for the literature review are as follows: • Scenario-Based Methods o Exploratory approaches for addressing deep uncertainty through alternative futures and pathways. • Multi-Criteria Decision Analysis (MCDA) o Tools for structuring trade-offs among alternatives, often incorporating stakeholder preferences or weighted criteria. • Performance-Based Tools o Structured assessment frameworks (e.g., Life Cycle Assessments (LCA), Techno-economic Analysis (TEA) that embed uncertainty in performance evaluation. • Simulation-Based Methods o Probabilistic modeling of system dynamics, capturing variability through stochastic or agentbased simulations. • Optimization-Based Methods o Quantitative approaches for finding optimal solutions under constraints, including robust and stochastic optimization. Figure 4. Percent method usage by type in the literature. Figure 4 displays the percent contribution of each method group across the reviewed papers. The distribution highlights multiple interesting patterns in how the field has chosen or been constrained to handle uncertainty in maritime decarbonization decision-making. The first of which is the domination of Scenario-Based methods, reflecting the field’s recognition that the deep uncertainties around fuel availability, technology maturation, and regulatory evaluation often demand exploratory rather than predictive approaches. The lack of reliable forecasts and projections and the long-term planning horizons inherent to the maritime industry again strongly support the prevalence of scenario thinking seen within the literature. Complementary to this, Optimization-Based methods represented under 4% of contributions, suggesting that traditional optimization approaches are less suitable for the vague and unstructured challenges seen in maritime decarbonization. Method Contributions (%) MCDA Optimization-Based Performance-Based Scenario-Based Simulation-Based Method Group 0 5 10 15 20 25 30 35 Contributions (%) 7 The second most employed method was MCDA, present in just under a quarter of all the publications. The frequency of MCDA speaks to the complex stakeholder environment and the unclear and often competing objectives seen in maritime decarbonization decisions, such as cost, emissions, safety, and reliability. The strong presence of MCDA suggests that researchers and practitioners in this area are actively grappling with how to balance the technical evaluations with qualitative stakeholder input and preference-driven trade-offs. Scenariobased and MCDA methods are also more intuitive and can be explained easily without detailed knowledge of the underlying methodology, unlike Optimizationand Simulation-based methods, for example. 3.4 Mapping Method Types to Uncertainty Types Figure 5 plots the number of times method types have been used to address the uncertainty categories identified in the review. The purpose of this mapping is to reveal which combinations of uncertainty type and decision support, or modeling method, have been studied and which have received little or no attention. Figure 5. Method employed to handle type of uncertainty in literature heat map. Consistent with the distribution seen in Figure 4 (method contribution chart), commercial and technological uncertainties were the most widely assessed overall, with research employing methods across all categories. The strongest pairing in the dataset occurred between commercial uncertainties and scenario-based methods, followed by optimization-based and performance-based tools. Technological uncertainties were the second most common category, appearing 21 times, most frequently approached with MCDA and scenario-based methods. Priority uncertainties appeared less often but were dominated by MCDA, with all other methods showing minimal connections. MCDA was second only to scenario-based methods overall in terms of frequency of use. Clusters of low activity were concentrated in the often less directly quantifiable uncertainty types of priority and regulatory. Regulatory uncertainties showed no recorded pairings with MCDA or performance-based tools, and only small counts with optimization-based, simulation-based, and scenario-based methods. Performancebased, simulation-based, and optimization-based methods also had minimal application to priority uncertainties, which were overwhelmingly handled through MCDA. MCDA Optimization-based Performance-based Scenario-based Simulation-based Method Type Commercial Priority Regulatory Technological Uncertainty Type Uncertainty Type vs Method Type Co-occurrence 0 5 10 15 8 These patterns, particularly the concentration of scenario work in commercial contexts and the spread of technological uncertainties across multiple methods, are examined further in the discussion to understand their drivers and implications for decision-making under uncertainty. 4 Discussion 4.1 Increasing Engagement: To What & Why? The dramatic increase in publications addressing uncertainty in maritime decarbonization reflects far more than just academic momentum. Commercial interest derived from rising regulatory pressure has forced the realization of decarbonization no longer being a distant or optional objective. Early literature in this space primarily revolved around identifying what one should care about and what might be possible. In recent years, that framing has transformed to a more operationally focused question of: given the uncertainties that define upcoming decisions, how can one make sound choices? This transition shows both pressure within the industry and the adoption of approaches from other fields that have long faced planning under uncertainty. The resulting decision-making landscape has matured greatly. What once revolved around generalized scenario mapping or MCDA rankings has evolved into structured decision workflows, combining scenario development, performance modeling, simulations of dynamic behaviors, and optimization for sizing or configuration. This “chaining” of methods provides clearer links between defined uncertainties and resulting decisions, a feature less frequently seen in earlier work. Within these structured workflows, commercial and technological uncertainties continue to dominate. This is unsurprising: operational expenses are a central driver of shipping investment, and both commercial variables (fuel price trajectories, carbon costs, utilization rates) and technical factors (technology maturity, integration risks, reliability) can swing total costs far more than marginal changes in efficiency. These technological uncertainties can also compound potential exposure by delaying adoption or forcing expensive redundancies as technology develops. In this environment, decision-making becomes as much about managing future exposure as it is about hitting absolute performance targets. This is exactly where scenario-based methods and more robustness-oriented frameworks like robust decision making become essential, where ensuring options remain defensible across a spectrum of unpredictable variability is paramount. Scenario methods hold the planning stage, creating a space of plausible futures where directly applied tools such as optimization, simulation, and performance-based tools can work within to configure systems, test sensitivities, and even further structure tradeoffs through MCDA. 4.2 Method Uncertainty Clustering: Insights & Implications The co-occurrence mapping shows a few clear clusters, again with the strongest concentration between commercial uncertainties and scenario-based methods. This pairing reflects the complex reality of the current and future market in literature. As Moshiul et al (2023) note, “Fuel price volatility and carbon cost exposure remain the most influential and least controllable drivers in pathway selection”, making exploratory methods a more defensible starting point than deterministic and single-value forecasts [15]. This tendency parallels practices in finance, where scenario-based approaches like real options analysis have emerged precisely because deterministic forecasting methods have consistently failed to capture market volatility and structural shifts [16]. Real options analysis itself represents a form of scenario thinking, where adaptive capacity is valued over optimization for singular futures. This approach has proven far more robust than traditional net present value calculation in volatile markets [17]. This overall pairing is effective because commercial uncertainties often exhibit deep and structural uncertainty where historical patterns are not proven to predict future conditions. However, the heightened focus on commercial uncertainties has prompted a wide range of methodological treatments beyond scenario thinking. Optimization and simulation approaches have also been employed frequently, but most often with scenario methods and MCDA being used at early stages to frame further analysis. As seen with Wang & Teo and Zwaginga & Pruyn, fleet-level cost planning frequently begins with stochastic fuel price distributions that are subsequently tested in optimization frameworks for fleet renewal and deployment [18], [19]. In addition, the literature has identified these distributions as “often subjective constructs and are founded on 9 exploratory and scenario thinking [20]. This hybridization is not an exception but has increasingly become the norm. 4.2.1 Hybridization: From Implicit to Explicit The clustering patterns presented reveal several consistent trends that point toward both the prevalence and limitations of current methodological approaches in maritime decarbonization. The pattern of scenario methods shaping initial exploratory stages while other methods drive applied analysis suggests a broader reality not fully captured in the literature review. Most methods and models are, to some extent, implicitly hybrid. In this context, hybrid refers to combining multiple method types in the pursuit of reducing and testing uncertainty parameters. While a more limited subset of publications explicitly describes their approaches as hybrid (e.g., combining scenario exploration with simulation), in practice, most efforts rely on upstream assumptions and inputs drawn from exploratory work. As Kwakkel & Haasnoot (2019) point out, “simulation models for operational reliability require demand, fuel price, and technology performance distributions – all of which are scenario artifacts in their own right [21].” The distinction between pure and hybrid methods is often more semantic than practical and is far more prevalent than clustering analysis alone might suggest. 4.2.2 Priority Uncertainty and MCDA The priority uncertainty cluster shows the most intuitive method usage, with MCDA approaches having a heavy concentration. Priority uncertainty stems from conflicting stakeholder valuations, where varying stakeholder priorities and risk tolerances can clash. This dilemma requires structured and fair methods of preference elicitation and accounting, which is the primary strength of MCDA methods such as Analytical Hierarchy Process (AHP), Technique for Order Preference by Similarity (TOPSIS), Multi-criteria Optimization and Compromise Solution (VIKOR), and Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE). However, the isolation of priority uncertainty from other studied method types reveals a potential blind spot: stakeholder preferences are rarely independent of developing technical and commercial realities, yet few studies combine priority elicitation with simulation or scenario-based exploration. One study that does is Baudry et al.’s (2018) range-based Multi-Actor Multi-Criteria Analysis (MAMCA) framework, where stakeholder preference analysis is combined with Monte Carlo simulations to explore how stakeholder preference propagates across scenarios [22]. However, these integrated approaches remain underrepresented in maritime decarbonization literature overall. The seemingly static treatment of stakeholder priorities misses the dynamic reality where stakeholder attitudes can shift drastically as market and regulatory conditions evolve. For example, a port authority’s weighing of cost efficiency and environmental performance might change entirely if carbon pricing reaches $200/tonne versus $50/tonne, or if lowor zero-emission fuel availability is abundant or scarce. Regulatory or commercial shifts like these can fundamentally alter stakeholder priorities and downstream decisions. Advancing methods to capture these dynamics is a critical area for further research in maritime decarbonization decision-making. 4.2.3 Regulatory Uncertainties’ Static Acknowledgment A more jarring pattern in the clustering is the overall weak engagement with regulatory uncertainty. This is counterintuitive given the fundamental importance of regulation as a central driver of decarbonization overall. However, as previously mentioned, regulatory uncertainty is often voiced through commercial measures, where policy outcomes are treated as static conditions, i.e., carbon pricing of X amount in year Y. Doing so can facilitate decision making where wicked and intractable uncertainties can be treated in much more analytically manageable forms. However, this trend denies the dynamic and political processes in which these regulations take place; they shift based on industry behavior and emission outcomes. Policy can adapt to the very behaviors being modeled, and the feedback loops between industry decisions and regulatory response can be easily ignored. Some studies call out this limitation, as in Psaraftis et al. (2018), noting that when assessing regulatory uncertainty, many approaches “do not include the dynamic feedback that might exist between measures due to their interactions [23]” . Dynamic policy modeling approaches like agent-based adaptation or integrated assessment models from climate, economy, and energy markets present strong areas for possible method transfer to maritime decarbonization contexts. While these clusters illustrate how different methods align with specific uncertainties, they also highlight the limitations of current academic work. Hybridization is increasingly present, but often framed implicitly, leaving 16 [37] K. Andersson, S. Brynolf, J. Hansson, and M. Grahn, “Criteria and Decision Support for A Sustainable Choice of Alternative Marine Fuels,” Sustainability, vol. 12, no. 9, p. 3623, Apr. 2020, doi: 10.3390/su12093623. [38] L. Berger and M. Marinacci, “Model Uncertainty in Climate Change Economics: A Review and Proposed Framework for Future Research,” Environ. 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