The Role of the Built Environment in Achieving Sustainable Development: A Life Cycle Cost Perspective

Abstract

Life cycle cost (LCC) analysis has become a key tool for evaluating the long-term economic and environmental performance of built assets, yet its application in marinas and marine infrastructure remains underdeveloped. This review provides the first structured attempt to apply LCC to marina infrastructure, addressing the lack of sector-specific models for pontoons, mooring systems, and marina operations. It also synthesizes research on LCC methodologies, challenges, and emerging trends relevant to coastal facilities, with a particular focus on pontoons, mooring systems, and marina management practices. Studies reveal persistent barriers to effective implementation, including fragmented data systems, inconsistent regulations, and limited sector-specific tools. Existing models, largely adapted from other construction contexts, often overlook the unique technical, environmental, and operational demands of marine assets. The review critically examines international standards, procurement frameworks, and methodological approaches, highlighting opportunities to integrate sustainability considerations and address gaps in cost forecasting. It also identifies the need for standardized data collection practices and risk-based maintenance strategies tailored to harsh marine environments. By mapping current knowledge and methodological limitations, this work provides a foundation for developing more accurate, sector-specific LCC models and guidance. This literature review contributes to the advancement of sustainable coastal infrastructure planning by consolidating scattered research, emphasizing knowledge gaps, and outlining priorities for future studies, supporting policymakers, practitioners, and researchers seeking to optimize investment decisions in marinas and related facilities.

1. Introduction

In recent decades, interest in the role of the built environment in sustainable development has grown significantly. As sustainability becomes embedded in core business strategies, the connection between sustainable development and the LCC of built assets is increasingly recognized. This shift influences diverse stakeholders with varying values and priorities. Globally, there is ongoing discourse on how to assess sustainability and enhance the built environment. A more mature understanding of sustainable smart construction has emerged, emphasizing the integration of stakeholders, building components, and the construction, maintenance, and management processes [1]. This trend has accelerated the adoption of LCC methods. These methods provide accurate and timely cost data to support informed decisions in design, construction, operation, and investment. Given the significant impact of early design decisions, developing LCC models at the earliest project stages is essential.

Unlike previous studies that applied generic construction-based models, this review introduces the first empirical dataset and cost breakdown structure tailored specifically to marinas, filling a critical gap in sustainable coastal infrastructure research. Despite the growing adoption of LCC models in construction, marina infrastructure remains underexplored. Asset management practices in this sector are hindered by fragmented datasets, inconsistent regulations, and a lack of validated maintenance guidelines. This absence of sector-specific tools leads to subjective decision-making and missed opportunities for sustainable development, particularly in ecologically sensitive areas.

The objective of this review is to consolidate existing knowledge on life cycle costing in marina infrastructure, identify gaps in current approaches, and propose priorities for future methodological development. Specifically, this study seeks to accomplish the following:

• Synthesize existing knowledge on LCC practices and their application to coastal and marina facilities.
• Identify barriers and gaps in current approaches, including regulatory, methodological, and data-related challenges.
• Explore stakeholder perspectives on the practical limitations of existing models.
• Introduce a marina-specific LCC framework that provides both scientific and practical value in supporting sustainable investment and management.

These objectives are guided by three central research questions:

• RQ1: How have LCC methodologies been applied in the context of marina infrastructure, and what gaps remain?
• RQ2: What barriers and enablers shape the adoption of LCC in marina management and procurement?
• RQ3: What design considerations are necessary for developing an LCC framework tailored to marinas?

This review responds by developing a marina-specific LCC framework, supported by data collected through field surveys and questionnaires. By focusing on pontoons and marina operations, it introduces a practical, replicable approach to cost modeling and investment planning, filling a gap left by existing standards and EU procurement directives. This review draws on literature reviews and interviews with marina managers, designers, contractors, economists, and maintenance staff, revealing strong demand for structured, marina-specific LCC methodologies.

In Croatia, LCC calculations are only optional under the Public Procurement Law [2], and while the Croatian national adoption of the international standard HRN ISO 15686-5:2023 offers general LCC guidelines [3], it lacks specificity for marinas. Similarly, North Atlantic Treaty Organization (NATO) recommendations from 2009 [4] promote LCC but lack practical tools. The integration of LCC and sustainable procurement in marina-related regulations varies across European countries. The European union mandates LCC and environmental criteria in public procurement through directives 2014/24/EU and 2014/25/EU [5,6], with countries like Spain and France embedding these principles into national law. Spain requires environmental or social criteria in all tenders [7], while France emphasizes circular economy practices through its Anti-Waste Law [8]. The Netherlands and Norway promote sustainability via policy frameworks like the Sustainable Procurement Manifesto and the Transparency Act, respectively, while Germany enforces climate-focused and due diligence laws in procurement [9,10]. Greece exemplifies practical application of EU LCC tools, but none of the countries offer specific guidelines or recommendations for marinas [11]. At the global level, organizations such as the International Council of Marine Industry Associations, the European Boating Industry, and the National Marine Manufacturers Association are working toward a unified Life Cycle Assessment framework for recreational vessels [12]. This initiative aims to standardize environmental impact assessments across all life cycle stages, reinforcing sustainable procurement in marinas and leisure boating.

Key challenges hinder LCC adoption [13]. These include the perception of LCC as costly upfront, disconnected capital and revenue budgets, a focus on financial returns over socio-economic benefits, lack of expertise, difficulties in data collection, and price volatility. Governments allocating funds for sustainable infrastructure must ensure proper LCC use to enhance procurement effectiveness. At the EU level, developing and mandating common LCC methodologies for specific goods and services is necessary. Perera’s review of 30 countries highlights persistent barriers: undervaluing LCC in procurement, difficulties in LCC analysis adoption, and organizational challenges within contracting authorities.

Contracting authorities should define both economic and qualitative criteria. These criteria must enable fair, comparative evaluations that consider both quality and LCC. This ensures the selection of the most economically advantageous tender. These criteria must comply with national regulations on remuneration and pricing.

According to Boussabaine and Kirkham [14], LCC practice remains underdeveloped, with a significant gap between theory and implementation. Analyses by Krstić and Marenjak [15] and Sun and Carmichael [16] show that maintenance and operation form a large share of life cycle costs, yet existing models are often based on unrelated building types, failing to reflect the unique nature of marinas. Many models rely on theoretical assumptions or generalized cost frameworks, with inconsistent variables and limited historical data.

Barriers to adopting LCC include a lack of awareness and limited recognition of their potential to advance the construction industry [17]. No current models link marina design parameters—such as berth length, dock number, or berth density—with life cycle costs. Additionally, regulatory gaps hinder consistent maintenance planning. For instance, the Maritime Code [18] no longer requires floating pontoons to be registered, reducing inspection and maintenance obligations once mandated by the Croatian Register of Shipping.

Given marinas’ dual role in tourism and environmental sensitivity, developing an LCC and maintenance assessment model would offer both scientific and economic value. The literature review confirms that no unified model exists to estimate marina maintenance and operational costs based on climate, location, or design variables. This has led to fragmented and unsystematic practices in marina planning and operation. Contributing factors include inconsistent data collection systems and the diversity of building types for which existing models were originally developed [15,16]. The novelty of this paper lies in its dual contribution: (i) empirical evidence from 16 Croatian marinas, and (ii) a proposed cost breakdown structure directly linking marina design and operational variables to LCC.

2. Methods

This study adopted a mixed-method approach that combined a systematic literature review with empirical data collection through stakeholder interviews. This design was chosen to ensure that the findings would reflect both theoretical developments in LCC and the practical experiences of marina managers and related experts.

2.1. Literature Review

The literature review was conducted systematically to identify how LCC has been applied to marina and coastal infrastructure, and to highlight gaps in existing models. Searches were carried out in Scopus, Web of Science, and Google Scholar using combinations of the keywords “life cycle cost”, “whole life cost”, “marina infrastructure”, “pontoon”, “anchoring system”, and “sustainable coastal construction”.

The search focused on publications from 2000 to 2025, reflecting the most intensive period of LCC application in construction and infrastructure. Both English and Croatian sources were included. Eligible publications comprised peer-reviewed journal papers, conference proceedings, technical standards, feasibility studies, and doctoral theses. Studies without methodological detail or relevance to marine infrastructure were excluded. These studies informed the formulation of the cost breakdown structure (CBS), provided benchmarks for empirical data collection, and helped identify methodological challenges addressed in this study.

2.2. Stakeholder Interviews

At the time of the survey (up to August 2019), there were 68 active marinas in Croatia, of which 37 (54.4%) met the selection criteria: floating pontoons anchored with blocks and chains/ropes. The remaining 31 marinas (45.6%) were excluded because pontoons were fixed (pilings, rails, or similar systems). Since part of the author’s professional activities are directly related to pontoon maintenance, this focus allowed facilitated access to relevant maintenance and cost data.

Interviews were conducted in person and by telephone during 2018–2019, with each lasting approximately 45–60 min. Notes were taken during interviews and subsequently coded thematically. This process enabled the identification of recurring patterns across marinas and provided qualitative insights that supported and contextualized the quantitative data.

To establish an LCC database for pontoons and anchoring systems, all 37 eligible marinas received a formal request for data access along with a structured questionnaire and confidentiality statement. The reference period covered 2008–2018, since reliable financial documentation in Croatia has been publicly available since 2008, while full 2019 records were not yet accessible at the time of analysis.

The questionnaire (Supplementary Material S1) consisted of five sections:

• General marina characteristics (ownership, year of construction, concession area, length of pontoons, number of berths by vessel length).
• Maintenance planning (strategies, upgrades, and reconstruction plans).
• Pontoon and anchoring system details (pontoon type, decking material, anchoring system).
• Marina usage (number of users, staff, operational profile).
• Life cycle cost categories, including

• inspection costs;
• replacement of worn elements;
• periodic works and repairs;
• operating costs.

Of the 37 marinas contacted, 26 (70.3%) responded, and complete datasets were obtained from 16 marinas (61.5% of respondents). The reference periods ranged from 3 to 11 years, depending on the age of the marina. Annualized average values were calculated by dividing total reported LCC expenditures and user numbers by the number of years of available data.

Climatological and geographical variables (sea temperature, wind, and tidal range) were analyzed using historical datasets, satellite observations, and prior scientific studies. Each variable was normalized on a 1–5 Likert scale, where “1” represents the minimum and “5” the maximum observed values across Croatian marinas. This ensured comparability of environmental exposure factors in the statistical analysis.

The combination of literature review and stakeholder interviews provided a comprehensive understanding of both theoretical approaches and practical challenges in applying LCC to marina infrastructure. This dual approach strengthened the reliability of findings and enabled the identification of novel contributions to the field.

2.3. Cost Breakdown Structure

To operationalize the variables collected through the survey and literature review, a structured Cost Breakdown Structure (CBS) was developed for marina infrastructure. The CBS follows established international standards while incorporating sector-specific elements such as pontoons, anchoring systems, dredging, and service utilities. Organizing costs into categories of acquisition, operation, maintenance, renewal, energy, and end-of-life provides a transparent framework for life cycle cost analysis. This approach ensures consistency across datasets, supports comparability with other marine infrastructure studies, and enables the integration of probabilistic and sensitivity analyses in later stages.

Table 1. Cost breakdown structure for marina infrastructure.

Main Category	Subcategory	Typical Elements (Marina-Specific)	Notes/Examples
C—Acquisition/Construction Costs	C1—Pre-construction	Design, project documentation, permits, tendering	EUR/project, % of capital cost
	C2—Construction	Pontoons (modules, fingers), anchoring blocks/chains/ropes, piles, breakwaters, dredging, utilities (electrical pedestals, water, IT)	EUR/m pontoon, EUR/berth, EUR/m3 dredged
	C3—Commissioning	Testing, certification, opening procedures	% of construction cost
A—Operation Costs	A1—Energy and Water	Electricity, fuel, water supply	EUR/berth/year
	A2—Waste Management	Wastewater, bilge water, solid waste, recycling services	EUR/t handled
	A3—Staff and Administration	Salaries, training, insurance, IT systems	Annual fixed costs
M—Maintenance Costs	M1—Preventive (Planned)	Inspections (safety, structural), scheduled replacements (chains, ropes, decking), surface treatment	EUR/inspection, EUR/cycle
	M2—Corrective (Reactive)	Emergency repairs (pontoon damage, anchor failure), storm/flood repairs, unplanned dredging	EUR/incident, % of capital value
R—Renewal/Upgrades	R1—Modernization	Pontoon extension, electrification for e-boats, utilities retrofits, structural reinforcement	EUR/project
	R2—Regulatory Compliance	Environmental upgrades, safety retrofits	EUR/compliance cycle
E—Energy Costs (specific)	E1—Energy Use	Lighting, shore power to vessels, pumps	Often modeled separately for sensitivity analysis
S—End-of-Life/Residual Value	S1—Decommissioning	Removal of pontoons, anchors, utilities, dredged materials	EUR/m pontoon removed
	S2—Disposal and Recycling	Demolition, disposal, recycling of steel, concrete, composites	Recovery value %, EUR/t
	S3—Residual Value	Salvage of usable materials, resale of assets	Deducted from total LCC

presents the proposed CBS tailored to marinas, linking typical cost elements with their associated variables and applications.

As shown in Table 1, each cost category is directly linked to variables that drive life cycle outcomes. For example, the service life of pontoons determines the frequency of planned replacements, which fall under preventive maintenance (M1). A pontoon replacement every 25 years at an estimated EUR 1200 per meter can be modeled as a recurring cost within the CBS and discounted over the analysis period. Similarly, the frequency of safety inspections (e.g., annual or biennial) affects preventive maintenance costs, while berth density and dredging requirements influence both initial construction (C2) and periodic renewal (R1). By structuring variables in this way, the CBS provides a clear bridge between data inputs and cost outputs, enabling consistent modeling of marina life cycle costs. The dual focus on theoretical sources and empirical evidence enhanced the validity of the findings and ensured that the proposed framework is both scientifically grounded and practically applicable.

3. Life Cycle Costs in Sustainable Development

The built environment plays a crucial role in achieving sustainable development. The concept, once viewed as secondary, is now central to organizational strategies, with increasing emphasis on life cycle costs of built assets [19]. This has driven the growing use of life cycle cost assessment methods. In maritime domain areas, regulating long-term operational conditions serves all stakeholders. It ensures business stability and promotes investment. Nautical tourism trends reflect rising guest expectations for value and sustainability [20]. Broad sustainability concerns now shape how the built environment is assessed. However, condensing diverse sustainability issues into core evaluation criteria remains a challenge [21].

Concern for construction quality has grown in recent decades, influencing modern design. Beyond energy use, factors such as material durability, environmental impact, and maintenance influence building performance [22]. Facility management is vital across sectors and essential in the public sector for socio-economic development [23]. Accurate LCC data is essential for sound financial planning. It is especially important during early design stages. Even small facility management budgets impact organizational outcomes.

Research on marina management shows significant gaps in LCC calculation methods and cost structure definitions. This is crucial during early design phases. A marina is broadly defined as any coastal facility with at least ten wet or dry berths and vessel access [24]. In Croatia, regulating operational conditions in maritime zones is essential for nautical tourism. Harsh marine conditions demand strategic planning and a solid understanding of environmental loads for project success [25]. However, challenges remain in collecting and managing data on marina operations and costs. Decisions often fail to align ecological, financial, and stakeholder priorities. This misalignment leads to missed opportunities [26]. Effective facility management in sensitive ecosystems like marinas should rely on validated models for LCC estimation. A general, standardized model would significantly enhance management practices and support more efficient and sustainable decision-making.

3.1. The Strategic Role of Facility Management in Sustainable Development

Organizations and institutions often fail to recognize the strategic importance of facility management in supporting operations and long-term success. Facility managers in public organizations face greater challenges than those in the private sector due to tighter budget constraints. This makes the development of effective decision-support tools essential [27]. Although often associated with cost-efficiency, facility management is a multidimensional strategy that contributes to organizational competitiveness. Its scope includes the continuous development of environmental and industry standards, not just the efficient delivery of services. Facility management involves coordinating and administering built assets and services that support both core operations and human resources. It goes beyond physical infrastructure, aiming to create functional, healthy environments. Sustainability is crucial at every stage of a facility’s life cycle: design, construction, operation, and decommissioning. While progress has been made in sustainable design, financial sustainability remains the decisive factor in determining a facility’s overall sustainability. The LCC methodology is fundamental in assessing total costs and supporting sustainable facility strategies.

Users often fail to recognize the long-term value of planned maintenance and investment. If quality standards and service delivery are inconsistent, negative impacts may remain undetected [28]. The discipline is relevant across all sectors, particularly in public infrastructure, where it plays a crucial role in socio-economic development [21]. Capital procurement decisions are often based on initial costs alone. This approach frequently overlooks long-term impacts. Decision-making is hindered by multiple criteria, competing stakeholder interests, uncertainty, and data gaps [29,30]. When decisions rely on intuition or subjective bias, risk is further increased [31]. Design-phase decisions can determine up to 80–90% of future operational costs [32,33]. Despite this, the facility management budget is often undervalued despite its potential for substantial long-term savings. As such, early LCC modelling is essential.

Facility management principles are particularly applicable to marina operations, where they can improve infrastructure sustainability and performance. LCC analysis enables stakeholders to evaluate investment options and optimize maintenance strategies [34]. Maintenance systems, including frequency and replacement cycles, can be planned from the design phase. Investors typically bear the capital costs but have limited control over outcomes. Therefore, close collaboration with designers is essential to ensure cost-effective and environmentally responsible choices [35]. Recent studies demonstrate the expanding application of LCC across marine and offshore infrastructure [36]. One study combined structural and economic analysis to evaluate the conversion of single- to multi-pontoon floating docks, showing how life cycle trade-offs between capital expenditure and repair costs affect long-term performance. Similarly, Cromratie Clemons et al. [37] highlighted the role of material selection in determining life cycle trajectories, with composites offering higher upfront costs but lower long-term maintenance.

Reliable data is essential for accurate LCC analysis. Yet, the complexity and time demands of data collection often hinder stakeholder dialogue and scenario testing [38,39]. Inaccurate or inconsistent data remains a major challenge [40,41]. Both capital and operational costs may be estimated through extrapolation or microeconomic modelling, but only robust databases can ensure accuracy [42]. To make informed investment decisions using LCC, decision-makers must understand and trust the process. When organizations lack capacity to process complex data, LCC tools are often underused. Integrated multi-criteria frameworks that combine environmental, cost, quality, and time factors can improve decision-making [43]. However, without valid input data, LCC results remain uncertain and may introduce bias, reducing their effectiveness.

3.2. The Role of Life Cycle Costing in Early Design and Decision-Making

Once a facility is operational, opportunities to influence ownership costs diminish significantly, as the decision to own or purchase a building commits users to the majority of these costs [44]. Cost models—such as parametric estimation models [45], replacement models [46,47,48], and others—aim to analytically consider and integrate different LCC factors. For large-scale projects, investment selection should be grounded in cost analysis, though this alone is insufficient. Investors and other stakeholders should participate in construction cost forecasting to evaluate project risks [49]. LCC calculations primarily support decision-making when selecting between competing design options.

LCC analysis is most effective during early design stages, when flexibility is greatest [33]. As shown in Figure 1, the ability to reduce costs drops significantly once construction begins. Paulson [50] and Fabrycky and Blanchard [51] note that cost control falls from near-total during the conceptual phase to under 20% once execution starts.

The optimal cost balance is achieved by minimizing total life cycle cost [53]. However, obtaining reliable data for LCC analysis remains challenging [54]. Despite its recognized benefits, LCC has not been fully adopted in the construction industry. Ferry and Flanagan [55] argue that, although LCC is increasingly accepted in theory, its practical implementation still lags behind. Aouad et al. [34] support this view, noting that LCC remains affected by considerable uncertainty. Similarly, Bakis et al. [56] report that the application of LCC analysis remains limited, despite its acknowledged importance. On the other hand, research by Kirkham [57] supports the notion that the use of LCC within the construction sector is increasing rapidly.

Uncertainty in assigning engineering and economic values reduces confidence in LCC results, which rely on accurate data. Education and research can improve both data quality and understanding of uncertainty. This would enhance LCC’s credibility as a decision-support tool. Facility managers are now recognized as key experts in managing cost drivers throughout an asset’s lifecycle. Design-phase decisions have a major impact on both capital and operational costs. LCC provides a comprehensive, systematic, and accessible method for investment evaluation.

This paper seeks to strengthen the theoretical basis of LCC, promote its use in decision-making, and support its methodological standardization for marina construction management.

3.3. Terminology, Standards, and Cost Categories in LCC

Life cycle costs encompass all direct and indirect monetary costs associated with a building’s design, construction, use, maintenance, support, replacement, and demolition throughout its lifespan. The International Organization for Standardization’s standard ISO 15686, Part 5 [58] defines LCC as an economic assessment of all projected, significant costs over a defined analysis period, expressed in monetary terms. This includes costs required to meet performance criteria such as safety and reliability.

Table 2. Key costing terms and definitions.

Term	Definition	Notes on Usage in This Paper
Life Cycle Cost (LCC)	The total cost of ownership of an asset over its lifetime, including acquisition, operation, maintenance, and disposal.	Primary term used throughout this paper.
Whole Life Cost (WLC)	Similar to LCC but may also include non-construction costs (e.g., land, financing, user costs) and potential revenues.	Considered broader than LCC; used in some UK and EU contexts.
Total Ownership Cost (TOC)	Term used in defense/industrial procurement, focused on long-term ownership costs including training, support, and disposal.	Conceptually overlaps with LCC; less common in construction.

summarizes the main costing terms used in this review and clarifies the terminology adopted.

To maintain clarity, this paper consistently uses the term life cycle cost (LCC), while acknowledging that related concepts such as whole life cost (WLC) and total ownership cost (TOC) appear in some standards and sectoral contexts. Recent studies have further expanded LCC applications in sustainable infrastructure, including offshore energy systems, composite marina structures, and climate-resilient coastal planning [36,59,60]. Incorporating these perspectives underscores the need for sector-specific adaptation of LCC methodologies.

LCC analysis is an economic and engineering tool used to compare design alternatives by evaluating all significant costs—from design and construction to operation and maintenance—over a defined time period under consistent economic assumptions [61]. The primary objective of applying LCC analysis is to assess and optimize the building LCC while simultaneously meeting client and/or user requirements, as well as the necessary construction specifications. The analysis thus enables a fair, quantitative comparison of competing design alternatives within the same decision-making process, aiming to identify the most appropriate and cost-effective option [62]. LCC serves both as an engineering tool during early design and as a proactive method for estimating operational and maintenance costs [63]. Although there is momentum in certain areas of public sector projects to incorporate LCC analysis into project management practices, its use in the private sector remains limited [64]. The UK Government construction report emphasized the importance of economic efficiency and long-term cost considerations in building practices [65].

Haworth [66] summarizes the advantages of the LCC analysis method in the following straightforward principles:

• the method should be applied at all decision-making stages throughout the design process;
• it must account for the operational costs of the building;
• the logical process must encompass all factors influencing the decision-making process.

The US National Institute of Standards and Technology defines LCC as the total discounted cost of owning, operating, maintaining, and disposing of a building over a set period [67]. LCC applies to buildings of all sizes and systems. It influences material selection by incorporating future maintenance and replacement costs, not just initial expenses. A common error is equating acquisition cost with LCC, often leading to suboptimal choices. Effective capital project management, including appointing capital asset managers, is critical for selecting assets with minimal LCC [68].

While purchase costs appear on balance sheets, life cycle costs manifest in income statements; their interrelation is often misunderstood, undermining profitability and efficiency goals. Project fragmentation—treating phases as isolated—limits stakeholder consideration of cumulative costs, reducing incentives to apply LCC principles effectively. Without comprehensive project management, LCC analysis cannot be fully utilized.

Krstić and Marenjak [15] classify LCC into construction, operation and maintenance, and residual end-of-life costs. The life cycle costs of a building encompass all expenditures incurred from the initial planning and design phase through construction, operation, and ultimately the end of the building’s functional lifespan. This structured cost breakdown includes several key components relevant to each phase of the building process. Pre-construction costs include procurement and tender documentation, such as project plans, bidding materials, and contractor selection procedures. Construction costs refer to all resources and activities required for the physical realization of the structure, including labor, materials, and equipment. During the operational phase, a significant portion of life cycle costs is attributed to use and maintenance. This includes mandatory periodic inspections, which are conducted to ensure regulatory compliance, structural integrity, and user safety. The operation of the building incurs continuous costs related to energy, water, waste management, and other utilities. Preventive maintenance is performed regularly to avoid failures and maintain optimal performance. Replacement and repair costs arise when components reach the end of their service life or experience damage. Reactive maintenance includes unplanned repairs after unexpected breakdowns. At the end of the building’s life, decommissioning introduces a final set of expenditures. End-of-life costs comprise demolition, removal, recycling, or disposal of building materials and components, often influenced by regulatory requirements and sustainability considerations. Altogether, this classification enables a holistic approach to LCC analysis, providing a foundation for economic decision-making that supports the design, construction, and long-term management of sustainable built environments.

Maintenance and operational costs of buildings are categorized into five main components: legally mandated inspections, operational costs, preventive maintenance, reactive maintenance, and replacement/repair costs. Legally required inspections ensure safety and compliance through mandated tests and assessments. Traditional project evaluation often overemphasizes initial capital costs, despite operational costs constituting up to 70% of total life cycle costs [14]. This leads to reduced long-term value.

While deterministic analyses yield clear decisions, LCC inherently involves uncertainty due to future projections [69]. A comprehensive framework allows cost forecasting across all life cycle stages, supporting better design decisions [70]. Effective design is crucial for accurate LCC evaluation, as 70–85% of long-term costs are determined during the design phase [71,72]. Operational costs include energy, water, waste, IT services, and cleaning. Preventive maintenance involves scheduled tasks preserving building condition, whereas reactive maintenance addresses unpredictable failures. Replacement and repair accommodate changes in building systems. Planned maintenance reduces downtime and prioritizes critical systems, while allowing flexible servicing of less essential components. Optimal maintenance supports broader goals of minimizing organizational costs.

3.4. A Framework for Sustainable Decision-Making and Long-Term Cost Optimization

The concept of whole life costing (WLC) has evolved over time [33], previously known by terms such as terotechnology, life costs, in-use costs, total ownership costs, and overall costs [32,73,74,75]. The terminology continues to vary, with common references to operational costs, life cycle costs, and whole life cycle assessment [76]. The term “life cycle costs” was first formalized in 1965 [77]. Flanagan and Jewell [78] trace terminology shifts from “in-use costs” to “life cycle costs” and “whole life costs,” the latter emphasizing cost and performance assessment across an asset’s service life. Standard ISO 15,686 [58] distinguishes life cycle cost calculations from whole life cost assessments.

Whole life costs (see Figure 2) include construction costs, non-construction costs, and potential revenues. Due to data sensitivity issues in marinas, this research is confined to life cycle costs.

LCC was developed in the mid-1960s by the U.S. Department of Defense for military equipment procurement [15]. Interest in construction cost analysis dates to the 1950s with BRE’s research into “costs in use” [79]. The British Standards Institution published BS 3811 in 1974 outlining building life cycle phases [80]. Guides on LCC followed, including the Committee for Terotechnology’s 1977 publication [33] and HM Treasury’s Whole Life Costing techniques guide issued in 1991 and updated through 2018 [81,82].

The LCC analysis method helps align construction practices with sustainability goals. It enables the economic evaluation of benefits such as reduced energy consumption during the operational phase of a building. This aspect can significantly influence public sector investment decisions. In this context, the method can serve as a key enabler for the adoption of new and innovative sustainable construction products and techniques. While sustainable construction may involve higher upfront capital expenditures, it often delivers medium- and long-term cost efficiency [83]. It is crucial to demonstrate this relationship to investors at an early design stage. The choice of construction methods and components directly affects resulting costs, depending on the selected approach [76]. Effective communication among project stakeholders—particularly between the investor and the designer—is critical during both the design process and the cost analysis phase. According to Latham [84] and Blyth and Worthington [85], structured interviews can function as an iterative process for information exchange, including feedback gathered from previous projects.

Despite its relevance to sustainable development, LCC adoption in practice remains limited [86,87,88]. The main motivation for using LCC assessments is twofold. First, they account for the financial costs of all significant life cycle phases. Second, they show how these costs are shaped by a single decision. This can be achieved by listing and utilizing cost data to make these impacts visible at the point of decision-making [89]. Although most life cycle cost principles are well developed in theory, the method has yet to achieve widespread practical application. LCC analysis is becoming increasingly important as long-term asset owners and users demand greater transparency regarding total ownership costs. Emerging procurement models such as Private Finance Initiatives and Public-Private Partnerships require stakeholders to manage construction, operation, and maintenance over extended periods. To achieve value for money under these models, costs must be reduced across the life cycle. This requires systematically balancing capital expenditures with future operating costs [90].

Figure 3 depicts the persistent underutilization of LCC in practice, primarily due to perceived unreliability of results [91], which stems from limited actual cost data and a lack of standardized industry benchmarks for building life cycle behavior [92].

A key challenge in LCC analysis is the scarcity of reliable cost-related data. This scarcity stems from the limited ability to predict future outcomes and the lack of sufficient historical cost records. Many parameters are undefined or uncertain and therefore require estimation [93]. Examples include life cycle length, production costs, operation costs, and maintenance costs. Although investors and owners maintain various accounting, registry, and maintenance records, these are seldom organized for systematic LCC data extraction.

Barriers to LCC use include the lack of unified methodologies, standard formats, and integration of maintenance strategies during design [94]. Moreover, practical calculation methods and estimation tools are scarce, complicating the selection of systems that meet functional requirements. For marinas, no dedicated LCC databases exist for pontoons or mooring systems, necessitating detailed cost recording to build such resources.

Accurate LCC analysis requires precise estimation of both initial construction investments and ongoing maintenance costs. These estimates are often sourced from historical data, tenders, or expert judgment [95]. However, motivation for cost optimization is low, since cost evaluation fees are minimal relative to project budgets [96]. Additional barriers hinder the practical application of LCC [97,98,99]. These include the absence of legal guidelines, difficulties in forecasting future costs and revenues, and limited demand for sustainable buildings.

Data availability is the primary obstacle for LCC analysis [55,93]. The problem stems from both the scarcity of relevant historical data and the high costs and time demands of data collection. Time constraints further limit stakeholder engagement and iterative project evaluation, underscoring the value of computational models [100]. Forecasting long-term variables introduces further complexity [55]. These variables include building life cycles, future operational costs, and monetary value fluctuations. Additionally, uncertainty in input variables must be rigorously addressed. Olubodun et al. [101] identified poor understanding, lack of standardized methods, and process complexity as key barriers to LCC adoption in the UK, summarized in Figure 4.

The optimal inspection and repair strategy minimizes expected total LCC. At the same time, it maintains acceptable structural reliability throughout service [102]. LCC encompasses initial capital costs, preventive maintenance, inspections, repairs, and failures. Preventive maintenance for pontoons includes minor component replacement, surface patching, crack repairs, cleaning, and repainting to delay deterioration. Repairs, involving major replacements, are less frequent but costlier and increase reliability. Routine maintenance guidelines exist. However, many repairs rely on experience instead of validated research, which can increase costs and risks. Thus, optimal strategies should minimize LCC while accounting for reliability and failure costs.

Component replacement decisions depend on several factors [103,104]. These include altered properties, excessive maintenance, reduced performance, inadequacy, and obsolescence. Current regulations prescribe periodic maintenance at fixed intervals, regardless of structural specifics. This often causes unnecessary activities and inefficient resource use. A risk-based, flexible approach tailored to individual assets is preferable to minimize maintenance costs [105].

In sustainable public procurement, LCC analysis is crucial for informed decision-making [13]. Directive 2014/24/EU (Article 68) defines LCC as costs borne by users [106]. These include acquisition, operation, maintenance, and end-of-life costs, as well as environmental externalities, if monetizable. Despite this, no mandatory EU methodology for LCC calculation exists, limiting integration in procurement.

4. Existing Life Cycle Cost Estimation Models

Life cycle cost analysis is a forward-looking economic forecast [107]. It inherently requires different methods to estimate various types of costs. The choice of a particular cost estimation method largely depends on data availability and the stage at which the cost calculation is performed [51]. According to them, there are three main approaches to cost estimation:

• Engineering estimation;
• Analogous estimation;
• Parametric estimation.

Estes and Frangopol [108] proposed a probabilistic framework for optimizing the timing and type of maintenance during the expected service life of a deteriorating system. In this context, a life cycle cost model is a mathematical representation of future cash flows. These flows are associated with the life cycle costs of a given building or infrastructure asset. These models may be highly complex, yet they typically include a detailed classification of elements or components constituting the structure, presented in tabular form for each project year.

Balancing costs across different life stages of a building or its components helps optimize total expenditures [109]. This ensures that initial construction costs contribute positively and rationally to future operational expenses. A well-executed analysis should encompass costs across all life cycle phases. The LCC calculation method is an economic analysis approach that accounts for all costs associated with products and services—in this case, buildings—throughout their entire life cycle. As previously mentioned, this includes investment, operational, maintenance, and end-of-life (demolition or decommissioning) costs. The Society of Environmental Toxicology and Chemistry developed a methodology for LCC estimation [110]. Its working group, “Environmental LCC,” designed this approach to incorporate environmental impacts, which can be used alongside traditional life cycle assessment.

Potnik Galić and Budić [111] outlined the following steps for conducting a life cycle cost analysis of products, which are also applicable to buildings and infrastructure:

• Defining the problem and objectives of the analysis;
• Establishing rules, constraints, and criteria;
• Defining system requirements and maintenance policies;
• Temporal scheduling of activities;
• Cost estimation (from both the producer’s and the user’s perspectives);
• Cost breakdown and development (or adaptation) of cost models and design variants;
• Temporal allocation of activities;
• Discounting future building-related costs;
• Determination of total life cycle costs;
• Result analysis and evaluation of design variants;
• Sensitivity analysis of the model with respect to input data;
• Recommendation of the optimal design variant;
• Use of feedback and control mechanisms for continuous system improvement.

4.1. Theoretical Foundations of Life Cycle Cost Calculation

Costs are categorized as either initial or future. Initial costs include items such as construction and permitting. Future costs cover all service-life expenses. To compare these with initial costs, future costs are converted to present value (PV) using a discount rate (also called the cost of capital or the minimum acceptable rate of return) [67,112]. Future costs include both recurring (e.g., annual maintenance) and one-time costs (e.g., component replacement). Initial costs, typically incurred during the base year, do not require discounting. However, future costs must be discounted based on their occurrence within the analysis period. To ensure consistency across design alternatives, a standardized time frame is recommended. It should be long enough to capture at least one major renovation [113].

Data limitations on building service life remain a challenge [114,115]. The analysis period should be shorter than the building’s physical, functional, or economic life [116]. Sensitivity analysis is useful to test assumptions. Despite its importance for cost optimization, many owners are unaware of the economic life of their assets [32]. Hermans [115] further emphasizes the need to consider technological and functional life in estimating economic lifespan.

Forecasting beyond reasonable periods can weaken the validity of assumptions and reduce the impact of discounted future costs on decision-making [52]. To integrate future costs into LCC, they must be discounted, the analysis period selected, and a suitable economic evaluation method applied. Several methods exist, each with specific strengths and limitations [117]. Among these, the Net Present Value (NPV) method is widely regarded as the most suitable for construction sector applications.

The LCC analysis equation can be broken down into three main variables: the associated ownership or investment costs, the time period during which those costs are incurred, and the discount rate applied to future costs to equate them with present-day values [118]. The first variable (cost) is divided into two main categories by which projects are analyzed and evaluated: initial costs and future costs. Initial costs refer to all expenditures incurred prior to the start of the building’s operational use, including construction and permitting. Future costs encompass all expenses incurred during the building’s service life.

To accurately combine initial and future costs, it is essential to determine the PV of all costs. Fuller and Petersen [67] define PV as “the equivalent value in time of past, present, or future cash flows from the base year.” The PV calculation uses the discount rate and the timing of the cost. This allows analysts to determine its value in the base year of the analysis period. PV calculations facilitate the summation of initial and future costs by converting all future cash flows to their PV using a discount rate (also referred to as the cost of capital or the minimum acceptable rate of return) [112].

In addition to time, the discount rate strongly influences the PV of future costs. Future costs can be divided into two categories: one-time costs and recurring costs. Recurring costs occur annually throughout the analysis period—most operating and maintenance costs fall into this category. One-time costs are non-recurring expenses within the analysis period, such as component replacement costs. Since construction is typically completed in a relatively short time frame compared to the overall analysis period, initial or investment costs are generally assumed to occur during the base year of the analysis. There is no need to discount initial investment costs, as their PV is equal to their actual cost.

The PV of future costs depends on the time at which they are expected to occur. The relevant time period is the interval between the initial investment and the occurrence of future costs. It is crucial to standardize the time frame for all design alternatives. This ensures that both initial and future costs are evaluated consistently. The analysis period must be sufficiently long to encompass at least one major renovation for each design option under consideration [113].

A key challenge remains the lack of comprehensive data on the actual service life of buildings and their components [114,115]. The analysis time frame should be shorter than the physical, functional, and economic life cycle of the building [116]. Sensitivity analysis can serve as a valuable tool for verifying the appropriateness of life cycle assumptions for buildings or their components.

Evidence suggests that many building owners and users are still unaware of the actual service life of structures or the expected life span before major reconstruction is needed. By definition, economic life is the most important from a cost optimization perspective, as emphasized by Kirk and Dell’Isola [32]. Other researchers [115] argue that technological and functional service life must also be considered when estimating the economic life cycle. There is a general consensus that life cycle projections should not span excessively long periods. The further the forecast extends into the future, the greater the risk that current assumptions will no longer apply. Moreover, cash flows discounted over a long-time horizon tend to have minimal impact on the ranking of design alternatives [52].

To incorporate future costs into life cycle cost calculations, these costs are converted into their PV using an appropriate discount rate. The analysis period is selected, and a suitable economic evaluation method is applied. The literature presents several variations of economic evaluation methods for life cycle cost analysis. Each method has its own advantages and limitations. They are designed for specific use cases, and users must be aware of their constraints.

According to the literature, the NPV method is considered the most suitable approach for cost calculation in the construction sector. Levander et al. [117] present six key methods for economic evaluation in LCC analysis. Each method has distinct advantages, disadvantages, and contexts of use. The Payback Period method calculates the time required to recover the initial investment, favoring project alternatives with the shortest return time [52]. It is praised for its simplicity and ease of interpretation but does not account for inflation, interest rates, or cash flows, making it suitable only for rough profitability assessments [52,119]. The Discounted Payback Period improves upon this by incorporating the time value of money; however, it ignores cash flows beyond the payback period and should be used solely as a guiding metric rather than a decision-support tool [52].

The NPV method, commonly applied in construction economics, discounts projected annual cash flows at a required rate of return, yielding a total PV. A positive NPV indicates a viable investment [120]. In cost-focused evaluations, costs are treated as positive and revenues as negative. The most favorable alternative is the one with the lowest NPV [33]. This method is comprehensive and accounts for the time value of money, but its complexity increases when comparing alternatives with different life cycle lengths.

The Equivalent Uniform Annual Cost (EUAC) method transforms the NPV into an equivalent annual cost by applying a PV factor. This facilitates comparisons between project alternatives with varying life spans [121]. However, it provides only an average annual cost, which may obscure year-to-year variability. The Internal Rate of Return (IRR) calculates the discount rate at which the NPV equals zero, representing the average rate of return throughout the project’s life [117]. Although the results are easy to interpret due to their percentage format, the IRR method requires iterative calculations and is only applicable to revenue-generating investments—an assumption that does not always hold in the construction sector [33,52].

Finally, the Net Saving method determines the difference between the PV of investment revenues and the invested amount, selecting the alternative with the highest net saving as optimal [33]. While straightforward in its application, this method is only valid when the investment yields income and thus cannot be used universally across all construction scenarios [33,121]. A comparative summary of major LCC models and their applicability to marina infrastructure is presented in

Table 3. Comparative overview of LCC models and applicability to marinas.

Model/Approach	Main Components	Assumptions	Applicability to Marina Infrastructure	Key Limitations
NPV	Capital cost, O&M costs, residual value, discount rate	Costs discounted to present value using fixed rate	Widely applicable (e.g., pontoons, dredging, breakwaters)	Sensitive to discount rate; assumes stable forecasts
EUAC	Annualized version of NPV	Converts total costs into equal annual amounts	Useful for comparing alternatives with different lifespans (e.g., piles vs. pontoons)	Can obscure year-to-year cost variability
IRR	Discount rate at which NPV = 0	Assumes revenue-generating project	Limited applicability (most marina assets are cost centers, not revenue streams)	Less suitable for non-revenue infrastructure
Probabilistic Models (Monte Carlo, CI method)	Cost variables expressed as distributions	Requires probability distributions for inputs	Strong potential for marina LCC (uncertain maintenance costs, weather impacts)	Data-intensive; requires simulation capacity
Deterministic + Sensitivity Analysis	Fixed input values with one-variable variation	Simple, transparent	Useful for early-stage marina design	Oversimplifies multi-factor uncertainty

.

Replacement cycles for building components with shorter lifespans than the building itself are especially sensitive in LCC forecasting. Accurate prediction of future replacements necessary to maintain building functionality reduces the likelihood and cost of business or operational disruptions caused by the facility—or by unexpected component failure [94]. Advances in multi-objective genetic algorithms [122] have provided a pathway to solving multi-objective optimization problems related to essential and preventive maintenance. These optimization problems have been formulated and solved by defining the desired characteristics as objectives for improvement [123,124].

One of the future costs that must be accounted for in LCC analysis is the terminal value of the project alternative [113], also referred to as the residual value. The terminal value is the net value of the building or project alternative at the end of the analysis period [118]. One type of terminal value is called salvage value, which represents the net value obtained from recycling materials at the end of the project’s life cycle. Another type is the Remaining Service Life of the project alternative, reflecting that the remaining useful life of the asset extends beyond the end of the analysis period.

Unlike other future costs, the terminal value may be either positive or negative—meaning it can be treated as a cost or a value. A negative residual value shows the asset’s remaining worth at the end of the analysis period. For example, it applies to a building expected to last another thirty years beyond the analysis period. A positive residual value, on the other hand, represents end-of-life disposal costs associated with the asset, such as demolition expenses. A zero residual value implies no value or cost related to the asset at the end of the analysis period. This rare situation occurs when the building’s intended use ends precisely at the analysis period’s conclusion, the asset cannot be sold, and the owner can abandon it without incurring any costs. Incorrect calculation of a project alternative’s terminal value can result in economic bias toward a particular option.

The second variable in the LCC analysis equation is time. The analysis period is the time frame within which the ownership and operational costs of a building are evaluated. This period typically ranges from twenty to forty years, depending on stakeholder requirements, program stability, and the overall life expectancy of the asset. While the study duration often reflects the projected lifespan of the facility, the analysis period is generally shorter. Mearig et al. [118] divide the analysis period into two phases: the planning/construction phase, spanning from the beginning of the analysis to the start of the building’s use, and the use phase, covering the duration from the beginning of use to the end of the analysis period.

The third variable in the LCC model equation is the discount rate. A fundamental challenge in any type of evaluation or decision analysis is comparing values across projects measured in different units. Even when values are expressed in monetary terms, comparisons may still be unreliable for at least two reasons [113]:

• Inflation—When expenditures occur at different points in the past or future, they are measured in differing values due to price changes. The general trend of rising prices over time is called inflation, while a trend of falling prices is called deflation. Monetary units that include the effects of inflation or deflation over time are called nominal units or current/data year units. Units that exclude inflation or deflation components—thus maintaining constant purchasing power—are called real units or base year units.
• Discounting—Adjusting for the time value of money. Costs or benefits (expressed in constant monetary units) occurring at different time points are not comparable without accounting for their temporal value. The time value of money reflects two things: the return funds could earn in their next-best use, or the compensation required to delay current consumption.

4.2. Discounting Costs in Life Cycle Cost Analysis

The discount rate is one of the most important variables in LCC calculations. It includes the time value of money and inflation effects, and it strongly influences the results. Selecting a discount rate that is too high may falsely favor short-term, low-capital investment options, while a rate that is too low may exaggerate potential long-term cost savings. Because choosing the right discount rate is uncertain, LCC results should always be tested with sensitivity analysis. This approach varies key parameters to show how they affect results.

The discount rate can be defined as the highest interest rate an organization would need to pay to borrow the funds necessary for the project. This method, favored by Hoar and Norman [125], reflects the market value of money. However, it does not account for the risk of value loss associated with the loan [52]. The opportunity cost approach defines the discount rate as the return that could be earned from the best alternative use of the project’s resources. This approach is considered realistic because it reflects actual earnings potential [126]. However, finding the best alternative use is often difficult, making the rate ambiguous [127].

Fuller and Petersen [67] identify two types of discount rates: real and nominal. Real rates exclude inflation, while nominal rates include it. Real rates do not ignore inflation; they simply simplify NPV calculations by removing inflation from the formula. Applying either type of discount rate—along with the corresponding adjustment factors—yields the same present value result.

From an analytical standpoint, adjustment for inflation and discounting for the time value of money are distinct processes. Future project costs and benefits should first be expressed in constant monetary units. They are then discounted to PV using a real discount rate that reflects only the time value of money. If future costs and benefits are provided in nominal monetary units, they must be converted to constant units using appropriate price indices, as shown below [113]:

$${\mathrm{m}\mathrm{o}\mathrm{n}\mathrm{e}\mathrm{t}\mathrm{a}\mathrm{r}\mathrm{y} \mathrm{u}\mathrm{n}\mathrm{i}\mathrm{t}\mathrm{s}}_{\mathrm{b}\mathrm{a}\mathrm{s}\mathrm{e} \mathrm{y}\mathrm{e}\mathrm{a}\mathrm{r}}={\mathrm{m}\mathrm{o}\mathrm{n}\mathrm{e}\mathrm{t}\mathrm{a}\mathrm{r}\mathrm{y} \mathrm{u}\mathrm{n}\mathrm{i}\mathrm{t}\mathrm{s}}_{\mathrm{c}\mathrm{u}\mathrm{r}\mathrm{r}\mathrm{e}\mathrm{n}\mathrm{t} \mathrm{y}\mathrm{e}\mathrm{a}\mathrm{r}}\times {\displaystyle \frac{{\mathrm{p}\mathrm{r}\mathrm{i}\mathrm{c}\mathrm{e} \mathrm{i}\mathrm{n}\mathrm{d}\mathrm{e}\mathrm{x}}_{\mathrm{b}\mathrm{a}\mathrm{s}\mathrm{e} \mathrm{y}\mathrm{e}\mathrm{a}\mathrm{r}}}{{\mathrm{p}\mathrm{r}\mathrm{i}\mathrm{c}\mathrm{e} \mathrm{i}\mathrm{n}\mathrm{d}\mathrm{e}\mathrm{x}}_{\mathrm{c}\mathrm{u}\mathrm{r}\mathrm{r}\mathrm{e}\mathrm{n}\mathrm{t} \mathrm{y}\mathrm{e}\mathrm{a}\mathrm{r}}}}$$

(1)

In LCC analyses, real discount rates usually range from 3% to 5%. These rates represent the interest rate on borrowed funds, adjusted for inflation. The British Standards Institution [121] recommends real discount rates of 0–2%, based on long-term productivity rates. Since the time value of money is always present, real discount rates will never be zero. Using real discount rates allows for the following transformations, which facilitate comparisons of costs in constant monetary terms [117]:

• Time shifting: A single value can be adjusted forward or backward in time without changing its actual (present) value.
• Annualization: A lump sum can be converted into an equivalent annual stream (e.g., a capital investment).
• Present value: Any combination of cash flows (finite or infinite) and lump sums can be aggregated into a single present value at a specific point in time.

The real discount rate can be calculated from the nominal discount rate using the following formula [63,67]:

$$d={\displaystyle \frac{1+D}{1+l}}-1$$

(2)

where

• d = real discount rate;
• D = nominal discount rate;
• l = inflation rate.

Lowering the discount rate for long-term projects has been proposed by multiple authors [81,128]. Given the growing uncertainty associated with extended project time frames, it is recommended that the discount rate be reduced as project duration increases.

Table 4. Weitzman’s declining discount rate schedule [128].

Period	Years	Discount Rate
Immediate future	1–5	4%
Near future	6–25	3%
Distant future	26–75	2%

presents Weitzman’s declining discount rate schedule, derived from a survey of thousands of economists.

The European Commission establishes and publishes a base rate for each EU member state, based on its Communication on the revision of the method for setting reference and discount rates [129]. This base rate is used to determine the official reference and discount rates. Following this communication, the Government of the Republic of Croatia adopted the Decision on the publication of rules for determining reference and discount rates [130]. This decision introduced a new methodology for determining these rates. It ensures equal treatment across EU member states, with minimal deviation from existing practices. As a result, it facilitates the application of reference rates in new member states. Croatia became a full member of the EU in July 2013. Since then, the Directorate-General for Competition of the European Commission has been responsible for calculating and publishing the base reference rate for all member states.

To conduct an LCC analysis, costs incurred at different points in time must be converted to their value at a common point in time. Various techniques based on the discount rate principle are available for this purpose. The Federal Highway Administration [113] recommends the NPV approach; however, the EUAC method can also be employed.

In order to illustrate the parameters relevant for a marina-specific LCC framework,

Table 5. Conceptual variables for a marina-specific LCC framework.

Variable	Description	Unit of Measure
v1	Sea temperature	Scale 1–5
v2	Wind impact	Scale 1–5
v3	Tidal influence	Scale 1–5
v4	Concession sea area	m2
v5	Number of pontoon piers	count
v6	Length of pontoon piers	m
v7	Wooden walking surface	yes/no
v8	Total number of berths	count
v9	Berths for vessels 5–8 m	count
v10	Berths for vessels 8–10 m	count
v11	Berths for vessels 10–12 m	count
v12	Berths for vessels 12–15 m	count
v13	Berths for vessels 15–19 m	count
v14	Berths for vessels >19 m	count
v15	Average number of users	count
v16	Inspections in 10 years	count
v17	Concession costs	EUR

presents a conceptual set of variables identified from literature and stakeholder input. These variables span environmental conditions (e.g., temperature, tides), infrastructure attributes (e.g., number and length of pontoons, berth categories), and operational/financial elements (e.g., user numbers, concession costs). This structure provides a foundation for future development of cost breakdowns and sensitivity analysis tailored to marinas.

Examining LCC calculations by first defining the desired outcomes and then tracing back the significance of each building-related variable provides deeper insight into the analysis process. Based on the definition and methodology of life cycle costs, the most straightforward decision-making approach is to base the selection of the optimal project alternative on the NPV of total life cycle costs. The NPV of an alternative i, denoted NPV_i, represents the total amount of resources that must be invested today. This amount would cover all future financial requirements incurred during the life of the project. The most favorable alternative, A*, is the one with the lowest NPV.

Since LCC analysis focuses on costs and expenditures rather than revenues, it is common practice to treat costs as positive values and revenues as negative values in the calculation. Mathematically, the net present value of a building is expressed as [33]:

$${NPV}_i=C_{0i}+\sum _{t=1}^T{}^{d}O_{it}+\sum _{t=1}^T{}^{d}M_{it}-{}^{d}SAV_i$$

(3)

where

• $C_{0i}$ = initial investment cost of design alternative i;
• ${\displaystyle \sum _{t=1}^T}{}^{d}O_{it}$ = sum of discounted operational costs over time t;
• ${\displaystyle \sum _{t=1}^T}{}^{d}M_{it}$ = sum of discounted maintenance costs over time t;
• ${}^{d}SAV_i$ = discounted terminal (salvage) value, calculated as

$${}^{d}SAV_i={}^{d}RV_{iT}-{}^{d}DC_{iT}$$

(4)

where

• ${}^{d}RV_{iT}$ = discounted residual value at the end of the analysis period;
• ${}^{d}DC_{iT}$ = discounted removal (decommissioning) costs;
• T = analysis period expressed in years.

To determine the present value of future one-time costs, the following formula is used [118]:

$$PV= A_t\times {\displaystyle \frac{1}{{(1+d)}^t}}$$

(5)

where

• $PV$ = present value;
• $A_t$ = value of the one-time cost at time t;
• d = real discount rate;
• t = time expressed in years.

To determine the present value of future recurring costs, the following formula is applied [118]:

$$PV= A_0\times {\displaystyle \frac{{\left(1+d\right)}^t-1}{{d\times (1+d)}^t}}$$

(6)

where

• $PV$ = present value;
• $A_0$ = value of the recurring costs;
• d = real discount rate;
• t = time expressed in years.

The Equivalent Uniform Annual Cost (EUAC) is a method used in life cycle cost analysis. It expresses total project costs as if they occurred evenly over the entire analysis period [113]. The PV of this uniform cost stream is equal to the PV of the actual, irregular cost stream. EUAC provides an alternative way to interpret life cycle cost results. It converts present or future values into equivalent, uniform annual costs. The method is expressed as [131]

$$A=PV\left[{\displaystyle \frac{d{\left(1+d\right)}^t}{{\left(1+d\right)}^t-1}}\right]$$

(7)

where

• $A$ = annualized cost at the end of the year;
• $PV$ = present value as calculated in Equation (6);
• d = discount rate;
• t = time (expressed in years) from the start of the analysis to the end of the evaluation period.

The choice of PV or EUAC does not affect the decision supported by the LCC analysis. The analyst chooses whether to use PV or EUAC. If decision-makers are accustomed to working with annualized costs, the EUAC format may provide a more intuitive basis for reviewing and reporting the results of the analysis.

However, because EUAC shows annualized values, it can hide the full scale of cost differences between project alternatives. Additionally, it can create an artificial impression of cost uniformity over time. Still, EUAC is useful when decision-makers need to understand yearly financial impacts—especially for projects with annual budgets or those that rely on periodic investment funding.

4.3. Uncertainty and Selection of Model Input Parameters

LCC analysis does not require all associated costs to be included—only if all alternatives meet the same project goals and performance criteria. Only cost elements that differentiate one alternative from another must be considered [113]. This principle is critical, as it greatly simplifies both analytical procedures and data collection requirements. For instance, investor-related costs such as renovation and reconstruction should be included, while common costs shared across all alternatives may be excluded.

Two primary approaches are used to conduct LCC analysis: deterministic and probabilistic [113]. These differ in how they address uncertainty in input parameters such as cost estimates, activity timing, and discount rates. The deterministic approach assigns fixed values to each input variable, typically selected based on historical data, expert judgment, or prior experience. This method is straightforward and can be performed manually or via spreadsheets, making it suitable for conventional applications. However, its main limitation lies in its inability to reflect uncertainty in PV estimates.

To address this limitation, deterministic analysis is often supplemented with sensitivity analysis. In sensitivity analysis, one key input—such as the discount rate or capital cost—is varied across a realistic range, while all other variables remain fixed. Changes in PV show how much each input affects results. This helps decision-makers identify key cost drivers and assumptions that need closer examination. Nevertheless, deterministic sensitivity analysis is univariate in nature and does not account for the simultaneous variation of multiple parameters or the likelihood of specific outcomes. While it provides a more informed assessment than a basic cost estimate, it still falls short of fully capturing the uncertainty associated with competing alternatives.

Unlike the deterministic method, the probabilistic approach uses probability distributions to represent uncertain inputs. Simulation software randomly samples from these distributions over thousands of iterations. This produces a full probability distribution of PV outcomes and calculates the expected (mean) PV. These results allow direct comparison of alternatives under different risk levels, providing a more realistic picture of cost uncertainty. Probabilistic analysis captures simultaneous variability in multiple inputs and enables evaluation of diverse assumptions. It also quantifies the likelihood of achieving specific cost outcomes. Recent advancements in computational power have made this method significantly more practical and accessible, allowing for efficient simulation of complex scenarios.

Probabilistic risk analysis assumes that uncertainties behave as random variables. Two widely used techniques in this context are

• The Confidence Index (CI) method, and
• Monte Carlo simulation.

The CI method is a simplified probabilistic tool based on two assumptions: (1) cost estimate uncertainties follow a normal distribution, and (2) 90% high and low estimates correspond to specific points on that distribution. For alternatives A and B, the CI is calculated and interpreted as follows [32]:

• CI < 0.15: low confidence (<60% probability);
• 0.15 ≤ CI ≤ 0.5: moderate confidence (60–67%);
• CI > 0.5: high confidence (>67%).

The CI method is valid only when the high, low, and most likely estimates come from the same source. In addition, the 90% estimates must differ from the most likely value by about 25%. These constraints limit the method’s general applicability.

In contrast, Monte Carlo simulation is a more robust and widely used method in LCC analysis [52,132,133,134]. It treats uncertain inputs as random variables that follow distributions such as ones that are uniform or triangular. The output—typically NPV—is also modeled as a stochastic variable. After running thousands of simulations, the alternatives are ranked based on their likelihood of being the most cost-effective.

As Flanagan et al. [52] emphasize, decision-makers must weigh lower expected costs against the risk of cost overruns. Monte Carlo simulation offers a broader perspective by allowing stakeholders to assess risks more clearly. However, it still depends on expert judgment. It also demands considerable expertise, time, and computing resources [135,136].

The deterministic approach supports basic PV comparisons. Sensitivity analysis adds value by showing which parameters are most influential under typical conditions. The probabilistic approach, in turn, offers a richer understanding by presenting the full range and likelihood of possible PV outcomes. It directly incorporates uncertainty, improving transparency in decision-making. Probabilistic methods also provide valuable statistical insights for decision-making. Like deterministic models, they can be strengthened by adding sensitivity analysis to highlight the most influential cost drivers.

Ultimately, decision-makers must define acceptable levels of risk. Risk-averse stakeholders may choose options with less cost variability, even if the expected PV is slightly higher. In such cases, choosing a more conservative alternative may minimize the likelihood of budget overruns. Flexibility in project design or execution should also be considered when assessing risk. This may include technical adaptations, revised performance targets, or financial concessions.

Sensitivity analysis serves as a complementary modelling tool to quantify the effect of uncertainty in a single input on a dependent variable. It typically involves three steps [137]:

• Assigning specific values to the input parameter;
• Calculating the corresponding values of the output variable;
• Analyzing the relationship between input and output values.

Its main strength lies in illustrating a preliminary ranking of alternatives [138,139]. However, it has two key limitations: its univariate nature, which limits its use to cases where a single variable dominates uncertainty, and its inability to quantify overall project risk. As such, sensitivity analysis should not be used as a stand-alone decision-making tool, but rather as a supplement to more comprehensive evaluation methods [32].

LCC and levelized cost of energy approaches have been applied to floating offshore wind farms [59], emphasizing the importance of sensitivity analysis and structured cost breakdowns to capture uncertainty. The study underscores the value of integrating technical, operational, and financial perspectives into life cycle models. However, while such approaches have been applied in floating docks, composite structures, and offshore wind, there is still no sector-specific LCC framework for marinas. Existing models rely largely on generic construction-based approaches and fail to address marina-specific variables such as berth density, pontoon systems, dredging requirements, and service facilities. This gap highlights the need for tailored LCC methodologies that can guide sustainable investment and management in the marina sector.

4.4. Summary Overview of Existing Models

Nearly all models found in the literature adopt the NPV approach (Equation (8)). Different nomenclature and/or cost breakdown structures (CBSs) are employed to describe the main components of LCC. The American Society for Testing and Materials [33] proposed the following cost calculation model, whose distinctive feature is the separation of energy-related costs. This allows for the application of different discount rates to reflect varying inflation rates:

$$NPV=C+R-S+A+M+E$$

(8)

where

• $C$ = initial capital investment cost;
• $R$ = reconstruction costs;
• $S$ = residual (salvage) value at the end of the analysis period;
• $A$ = annual recurring costs for use, maintenance, and repair (excluding energy costs);
• $M$ = other recurring costs for use, maintenance, and repair (excluding energy costs);
• $E$ = energy costs.

Bromilow and Pawsey [140] introduced a model characterized by the classification of maintenance activities into non-annual recurring costs and continuous costs:

$$NPV=C_{0i}+\sum _{i=1}^n\sum _{t=1}^T{C}_{it}{\left(1+r_{it}\right)}^{-t}+\sum _{j=1}^m\sum _{t=1}^T{C}_{jt}{\left(1+r_{jt}\right)}^{-t}-d{\left(1+r_d\right)}^{-T}$$

(9)

where

• $C_{0i}$ = procurement cost at time t = 0, including development, design, and construction costs, holding costs, and other initial costs associated with asset acquisition;
• $C_{it}$ = annual cost at time t (0 ≤ t ≤ T) for support function i (0 ≤ i ≤ n), such as maintenance, cleaning, energy, and security, which can be considered continuous over time;
• $C_{jt}$ = non-annual cost at time t for discontinuous support function j (0 ≤ j ≤ m), such as repainting or component replacement at a specific point in time;
• $r_{it}, r_{jt}$ = discount rates applicable to support functions i and j, respectively;
• $d$ = asset value at the time of disposal, reduced by removal costs;
• $r_d$ = discount rate applied to the disposal value of the asset.

Al-Hajj and Horner [141] developed straightforward cost models for predicting building management and maintenance expenses. These models identify statistically significant cost items within defined building categories through statistical analysis. These models can be expressed as

$$R_c={\displaystyle \frac{1}{cmf}}\sum _{i=1}^n\sum _{t=1}^T{C}_{\left(csi\right)it}{\left(1+r\right)}^{-t}$$

(10)

where

• $R_c$ = present discounted costs of use over period T measured from the acquisition time;
• $cmf$ = cost model factor (constant for different building categories);
• $C_{jt}$ = cost-significant items such as interior finishing, roof repairs, cleaning, energy costs, management costs, rates, insurance, and joinery.

The NPV can then be calculated by the following Equation [32]:

$$NPV=C_0+{\displaystyle \frac{1}{cmf}}\sum _{i=1}^n\sum _{t=1}^T{C}_{\left(csi\right)it}{\left(1+r\right)}^{-t}-d{\left(1+r_d\right)}^{-T}$$

(11)

Marenjak et al. [142] developed a model for calculating life cycle costs based on the following expressions:

$${UTP}_P={Ti}_p+{Tfm}_p\pm {Tr}_p$$

(12)

$${Tfm}_p=\sum _{i=1}^e{Tfm}_e+\sum _{i=1}^z{Tfm}_z$$

(13)

$${Ti}_p=\sum _{i=1}^e{Ti}_e+\sum _{i=1}^z{Ti}_z$$

(14)

where

• ${UTPP}_p$ = total lifecycle costs of the building;
• ${Ti}_p$ = capital (investment) costs of the building;
• ${Ti}_z$ = other investment costs (land acquisition, design, etc.);
• ${Tfm}_p$ = building management costs during the design phase;
• ${Tfm}_z$ = building management costs related to building elements (insurance costs, electricity costs, etc.);
• $T{r}_p$ = building removal costs during the design phase.

By analyzing the data structure and total project costs, this model supports the development of multiple project alternatives, helping to reduce financial and technical risks.

Unlike the previously discussed models, some focus exclusively on building operation and maintenance costs. In 1991, Al-Hajj and Horner conducted a longitudinal study tracking maintenance costs over 18 years, starting in 1972. The study covered eleven student dormitories, six classrooms, and three laboratories [141]. Their analysis shows that cost-significant items can be identified by focusing on those whose costs exceed the average. In fact, the analysis shows that approximately 15% of all cost items account for around 85% of the total maintenance expenditure. This study demonstrates that historical cost data can be effectively used to develop models for tracking and forecasting building maintenance costs.

This model can be expressed as follows:

$$R_C={\displaystyle \frac{1}{CMF}}\sum _{i=1}^n\left[\left(c_1+c_2\right)+\left(e_1+e_2+e_3\right)+\left(a_1+a_2\right)+\left(o_1+o_2\right)+\left(m_1+m_2\right)\right]$$

(15)

where

• $R_c$ = total operating costs;
• $CMF$ = cost model factor, equal to 0.87;
• $n$ = time period expressed in years;
• $c_i$ = c₁: indoor cleaning costs, c₂: laundry costs;
• $e_i$ = e₁: gas, e₂: electricity, e₃: fuel;
• $o_i$ = o₁: rates, o₂: insurance;
• $a_i$ = a₁: management fees, a₂: security and protection;
• $m_i$ = m₁: interior finishing, m₂: roof repairs.

An analysis of faculty buildings at the University of Osijek, as presented in Krstić [63], resulted in the development of a cost estimation model for building maintenance and operation. This model enables effective budgeting and cost planning. Its primary advantage lies in its simplicity of application and the minimal set of easily accessible input data required for cost estimation. The model is expressed as follows:

$$PGNTOiU=379184+762.09\times P_{KOM}$$

(16)

where

• $PGNTOiU$ = average annual nominal maintenance and operation costs;
• $P_{KOM}$ = floor area of communication spaces (corridors, hallways).

Based on this model, the expression for estimating the NPV of maintenance and operation costs over N years is formulated as

$${PGNTOiU}_{NPV}=PGNTOiU\sum _{n=1}^N{\displaystyle \frac{1}{{(1+r)}^n}}$$

(17)

where

• $PGNTOiU$ = average annual nominal maintenance and operation costs;
• $r$ = nominal discount rate;
• $n$ = year in which the costs occur;
• $N$ = total number of time periods (years) for which the discounted operation costs are calculated.

In a more recent study [60], a case study was conducted on the construction of floating offshore wind farms. The authors focused on the cost analysis of floating wind turbines located in deep waters and defined their life cycle costs. A floating offshore wind farm (FOWF) installed in deep waters can be described through its life cycle process, which essentially consists of several key stages [143].

The total life cycle cost system of a floating offshore wind farm (LCS_FOWF) can be calculated using the following equation:

$${LCS}_{FOWF}\left(k\right)=C1+C2+C3\left(k\right)+C4\left(k\right)+C5\left(k\right)+C6\left(k\right)$$

(18)

This model accounts for the costs associated with each of the following stages:

• C1: cost of concept development and product definition, independent of geographical location (k);
• C2: design cost, also independent of location (k);
• C3 (k): manufacturing cost, dependent on location (k);
• C4 (k): installation and assembly cost, location-dependent (k);
• C5 (k): operation cost, location-dependent (k);
• C6 (k): decommissioning cost at end of life, location-dependent (k).

Numerous studies have sought to improve cost estimation methods for practical application. Engelhardt et al. [144] emphasized the need for better models to support capital investment decisions in public infrastructure. Podofillini et al. [145] proposed a risk-informed approach to optimize maintenance strategies, balancing cost efficiency and safety. Yang et al. [146] assessed structural deterioration and maintenance efficacy, while Okasha and Frangopol [147] developed an optimization framework that integrates reliability, redundancy, and life cycle costs. Ruegg and Marshall [148] addressed the omission of borrowing costs by introducing a risk-adjusted rate of return that incorporates project-specific uncertainties.

Despite their value, these models rely on simplifying assumptions commonly found in LCC analysis. Key limitations include the neglect of material or component variability, exclusion of high-cost outliers, questionable linear cost assumptions, small or limited datasets, oversimplified normalization methods (e.g., cost per m²), and reliance on narrow historical data sources [33,116,149]. The NPV and EUAC methods remain the most widely accepted techniques in construction-related LCC analysis. These approaches consider both capital and operational costs over a defined time horizon, which may correspond to the asset’s service life or reflect various forms of obsolescence [14,150,151].

5. Discussion

This review highlights both the opportunities and persistent barriers in applying life cycle costing (LCC) methodologies to marina infrastructure. While LCC is well established in the construction sector, its transfer to marinas is hampered by regulatory inconsistency, limited sector-specific guidance, and a scarcity of reliable cost data. These factors collectively hinder systematic maintenance planning and cost forecasting in marine environments.

The findings contribute to three main advances:

• Sector-specific synthesis: Previous LCC studies in construction often overlook the unique operational and environmental conditions of marinas. By consolidating scattered literature, this review establishes a baseline of knowledge specific to pontoons, mooring systems, and marina facilities.
• Stakeholder perspectives: Through interviews, this study captures practical insights into the challenges of applying generalized LCC models in marinas. Stakeholders emphasized the need for validated maintenance guidelines and cost breakdown structures tailored to harsh marine conditions—an aspect not well documented in prior research.
• Framework development priorities: The review identifies design principles for a marina-specific LCC framework, including standardized data collection, risk-based maintenance strategies, and integration of sustainability indicators. These priorities address current gaps and create pathways for more reliable, sector-relevant decision-support tools.

By shifting focus from fragmented practices to framework development, this study positions LCC as a practical enabler of sustainable marina management. The synthesis of literature and stakeholder input offers actionable insights for policymakers seeking to align procurement rules with sustainability objectives, for practitioners needing cost-efficient maintenance planning, and for researchers aiming to refine methodological approaches.

The descriptive results shown in

Table 6. Descriptive statistics of annual LCC components for 16 Croatian marinas (2008–2018).

Cost Category (EUR per Berth/Year)	Mean	Median	Min	Max	Std. Dev.
Inspections and mandatory testing	42	40	20	75	15
Replacement of materials/elements	115	110	60	180	35
Periodic works and repairs	98	95	45	150	28
Operation and utilities	155	160	90	220	40
Total LCC (annualized)	410	405	280	560	75

indicate that operation and utilities constitute the largest share of annual costs (mean EUR 155/berth/year), followed by replacements (EUR 115/berth/year). Preventive inspections are the smallest but most consistent category. The variability across marinas is moderate, with total annualized LCC ranging from EUR 280 to EUR 560 per berth. These figures confirm that operation and maintenance dominate the cost profile, aligning with findings from other marine infrastructure sectors (e.g., offshore wind farms).

Unlike prior LCC studies on floating docks [36], composite structures [37], or offshore wind farms [59], this paper develops a cost breakdown structure and reports an empirical dataset tailored specifically to marinas. To our knowledge, this is the first structured attempt to link berth density, pontoon systems, and anchoring methods directly to life cycle cost categories. By integrating survey data from 16 marinas with sector-specific CBS modeling, the contribution advances beyond generic construction-based LCC models and addresses a recognized research gap.

6. Conclusions

This review demonstrates that while LCC is an established tool in construction, its application to marina infrastructure remains limited and inconsistent. By synthesizing existing research and incorporating stakeholder perspectives, the study identifies the critical barriers such as regulatory variation, limited cost data, and the absence of sector-specific tools and translates them into actionable priorities.

The novelty of this work lies in presenting the first empirical dataset of marina life cycle costs, covering 16 Croatian marinas over a ten-year period, and in developing a cost breakdown structure (CBS) tailored specifically to marina infrastructure. Unlike generic construction-based approaches, this framework directly links berth density, pontoon systems and operational parameters to life cycle costs. In doing so, it provides a practical and replicable foundation for sustainable coastal infrastructure management.

This study makes three specific contributions. First, it establishes the first empirical dataset on marina life cycle costs, covering 16 Croatian marinas across 2008–2018. Second, it proposes a cost breakdown structure tailored to marinas, integrating acquisition, operation and maintenance upgrades, and end-of-life costs. Third, it confirms that operation and maintenance dominate marina life cycle costs, consistent with findings in other marine infrastructure sectors. Together, these contributions strengthen the methodological foundation for sustainable investment and management in marinas. In addition to these contributions, this review points to important future research directions. One promising avenue is the development of a scenario-based sensitivity framework that would allow researchers to quantify the joint impact of design choices, maintenance strategies, and environmental risks. Such a framework would build directly on the dataset and CBS introduced here, enabling more robust forecasting and decision-support tools for coastal infrastructure planning.

The findings of this review carry important implications for different stakeholder groups. For policymakers, there is a clear need to harmonize procurement rules with LCC principles, to support the development of standardized cost databases, and to embed marina-specific requirements into international regulatory frameworks. For practitioners and marina managers, the results emphasize the value of prioritizing preventive over corrective maintenance, adopting structured cost breakdowns in investment planning, and systematically considering environmental exposure factors in forecasting costs. For researchers, the study highlights the importance of expanding empirical databases beyond Croatia, piloting and validating marina-specific CBS frameworks in practice, and advancing probabilistic and risk-based models that address the uncertainties of harsh marine environments.

By translating methodological gaps into practical recommendations, this review positions LCC as a decision-support tool with the potential to enhance sustainability, efficiency, and resilience in marina infrastructure. Future research should focus on refining sector-specific models, aligning regulatory instruments with sustainable procurement policies, and integrating international perspectives to ensure global relevance.