A new review published in Frontiers in Energy Research has now taken the most comprehensive look yet at how researchers actually model those costs. Engineers at the University of Wollongong in Australia systematically surveyed 99 peer-reviewed studies published mainly between 2000 and 2026, covering cost models for both fixed foundations such as jackets and monopiles and floating platforms including spar buoys, semi-submersibles and tension leg platforms. Their verdict is striking: the field’s favourite cost model, in two out of three cases, is also its crudest. Material costs appear in 98 percent of the studies reviewed, but 66.7 percent of studies rely on material cost alone, essentially equating the price of a platform with the price of its steel.

That weight-based approach has an obvious appeal. It requires nothing more than structural mass and a steel price, which makes it ideal for early-stage design studies and for comparing optimisation algorithms. The review documents how researchers have used it to pit genetic algorithms against particle swarm optimisation, colliding bodies optimisation, simulated annealing, ant colony methods and gradient-based sequential quadratic programming, all racing to shave tonnes off jacket legs and braces. It also underpins topology optimisation, where material is redistributed within a design space rather than resized member by member. But the simplicity comes at a price, because the mass of steel tells you almost nothing about what it actually costs to turn that steel into a finished structure bolted to the seabed.

Manufacturing is the most glaring omission. Processes such as blasting, cutting, welding and painting can account for more than 30 percent of the total production cost of welded steel structures, and for offshore wind turbines specifically, welding and bending make up 46 percent of manufacturing costs. Yet only 29 percent of the reviewed studies include manufacturing at all. The detailed models that do exist mostly descend from a time-based costing framework developed by Jármai and Farkas, which estimates the hours needed for preparation, assembly, tacking, welding, flattening, painting and cutting, and multiplies them by labour and equipment rates. Adaptations of this method have been applied to ring-stiffened cylindrical shells for platform columns, spar hulls and semi-submersibles, while other studies estimate weld volumes from the geometry of tubular joints or use activity-based costing borrowed from shipyards. Simpler studies multiply material cost by a manufacturing complexity factor, with values ranging from 1.1 for a spar buoy to 4.0 for a jacket relative to a monopile baseline.

Transport and installation fare even worse. These operations typically consume 10 to 16 percent of total CAPEX, yet they appear in only 16 and 20 percent of studies respectively. The review attributes this neglect to the sheer difficulty of modelling them: costs hinge on vessel day rates, weather windows, towing distances and mobilisation fees, all of which fluctuate with unsteady market conditions. The most rigorous approaches calculate vessel-based costs from day rates, travel time and weather downtime, distinguishing between floating platforms that can be towed to site by tugboats and fixed foundations that demand specialised barges and heavy-lift vessels. At the other extreme, some studies lump transport and installation into a single term pegged to structural mass or to turbine capacity, at rates such as 2.5 million euros per megawatt for spar platforms. Most strikingly, only 14.1 percent of the reviewed studies include all four CAPEX components together.

To make the comparison rigorous, the Australian team introduced a new quantitative framework combining cost model completeness, modelling detail and structural complexity, producing a composite score that ranges from zero to one. Plotted against the number of CAPEX components each study covers, the results reveal a field clustered in the bottom-left corner: most work considers material cost only, and even studies that cover more components rarely model them in full detail. The top-right corner, representing complete CAPEX coverage with fully detailed modelling of every component, is empty. Fewer than 5 percent of studies combine all four components with high detail scores, and none achieved a fully detailed treatment across the board.

The review also exposes asymmetries between fixed and floating technology. Offshore wind dominates the literature, appearing in 77 percent of the studies, and jackets and semi-submersibles attract the most attention because their complex welding and assembly make cost modelling more critical. Yet internal structures of floating platforms, such as stiffeners and bulkheads that are essential for structural integrity, show up in only 12 percent of CAPEX calculations, always alongside the hull, which itself appears in 96 percent of models. Mooring lines are considered in half of the floating platform studies and anchors in just 30 percent. On the fixed side, pile detail is included in only a small share of jacket cost models, and monopiles and tension leg platforms remain comparatively understudied despite their industrial importance.

Uncertainty adds another layer of neglect. Every CAPEX model, however detailed, ultimately depends on steel prices, labour rates and vessel day rates that swing with markets and geography, yet most studies treat these inputs as fixed constants. Only a handful of the reviewed works tested the consequences, using probabilistic distributions, bounded sensitivity ranges of around 10 percent, or one-at-a-time parameter sweeps. Across those that did, material and substructure costs emerged most often as the dominant driver of the final estimate. The review argues that robust optimisation, in which cost uncertainty is built directly into the design process rather than examined afterwards, remains largely unexplored for offshore support structures.

The recommendations that follow are straightforward. Future cost models should cover manufacturing, transportation and installation together rather than defaulting to steel mass, treat internal floating platform components as standard rather than optional, adopt detailed vessel-based transport and installation models especially for fixed foundations, model jacket piles explicitly, and extend established manufacturing costing methods beyond the dominant Jármai-Farkas lineage. The authors also call for cutting, assembly and coating to receive more consistent attention, since these operations shape real fabrication bills even if welding dominates.

Why does this matter beyond the journals? Steel jacket support structures alone can account for 19 percent of the total cost of an offshore wind farm, and as floating wind pushes into deeper waters with new platform concepts appearing every year, the economic penalty of optimising against an incomplete cost picture grows. A design that minimises tonnes of steel may unwittingly inflate welding hours, barge days or weather-exposed installation windows. By mapping exactly where the literature’s blind spots lie, this review gives designers and researchers a checklist for building cost models that reflect what an offshore project actually costs, a small change in method that could translate into large savings across a rapidly expanding global industry.

Subject of Research: CAPEX cost models for steel support structures of offshore energy platforms

Article Title: Cost-driven design and optimization of steel support structures for offshore energy platforms: a review of CAPEX cost models

Article References: Asgari Motlagh, A., McCarthy, T., & Ahmed, A. (2026). Cost-driven design and optimization of steel support structures for offshore energy platforms: a review of CAPEX cost models. Frontiers in Energy Research, 14, Article 1917780. https://doi.org/10.3389/fenrg.2026.1917780

Image Credits: AI Generated

DOI: 10.3389/fenrg.2026.1917780

Keywords: offshore wind, CAPEX, cost models, steel structures, jacket platforms, monopiles, semi-submersibles, floating wind, manufacturing costs, installation, structural optimisation, offshore energy

The core problem is deceptively simple. Environmental regulators have long required developers to assess not just the direct footprint of a project but the combined burden it adds to everything else pressing on an ecosystem, from shipping noise to climate-driven warming. Yet the review finds that for marine renewable energy, effects are typically measured one by one: the underwater noise of a turbine, the electromagnetic field of an export cable, the collision risk posed to fish and marine mammals by rotating tidal blades. What is rarely asked is how these effects stack on top of each other, on top of other industries, and on top of natural variability. The authors argue that without rigorous cumulative assessment, the industry could trigger unintended consequences that undermine the very sustainability goals, from clean energy to climate action and healthier oceans, that motivate it.

The marine environment makes this accounting uniquely hard. Unlike land, the ocean is fluid and dispersive, spreading impacts far beyond the physical footprint of any installation. Many marine species shift habitats across their life histories, making some ecosystems exquisitely sensitive to disturbance at particular stages. Add the accelerating pace of ocean industrialization, what researchers have called the blue acceleration, and the potential for cascading, unpredictable effects multiplies. The review emphasizes that cumulative effects can arise not only from multiple projects of the same kind, such as a cluster of wave energy converters, but from interactions across sectors: fisheries, shipping, tourism, offshore wind, and the overarching pressure of climate change. Effects may also appear only after a lag, or emerge only when a second activity unlocks the harm of the first.

Not all cumulative effects are additive, and this is where the science gets genuinely tricky. The review catalogues several interaction types. Additive effects are the intuitive case: the combined outcome equals the sum of the parts, and this pattern tends to dominate in physical systems. But ecosystems frequently behave nonlinearly. Synergistic effects occur when two stressors amplify each other, as when mortality from boat strikes and fishing combines with climate-driven heat stress to push a population below the threshold where it can reproduce. Dominant effects arise when one process overwhelms everything else, such as sediment smothering a seagrass meadow regardless of how ideal other conditions are. Antagonistic effects, where stressors dampen one another, can even flip an expected harm into a benefit. Two decades of research suggest these nonlinear outcomes are the norm in ecosystems, not the exception, yet most assessment tools in routine use cannot represent them.

The history of the field explains some of the current awkwardness. Cumulative effects entered regulatory consciousness in the 1970s and 1980s as terrestrial industrialization accelerated, but progress stalled for years because ecosystem-scale analysis demanded large datasets and heavy computation. By the 1990s, tiered risk assessment had become standard practice, beginning with qualitative risk tables before progressing to quantitative methods. Marine cumulative assessment surged from the 2000s, often driven by European policy such as the Marine Strategy Framework Directive. Of 154 marine assessments reviewed in one earlier synthesis, the majority simply combined geographic information system maps of overlapping human activities with expert judgments about how those activities interact. Expert elicitation remains a workhorse, and the review notes it is currently the most widely used route for bringing Indigenous knowledge into cumulative effects assessment, though repeatability remains a persistent weakness that structured techniques such as the Delphi method try to address.

The review organizes fifteen assessment methods along a gradient of increasing computational complexity. At the simple end sit interaction matrices and map-based overlays, including the weighted cumulative pressure mapping approach popularized by a landmark 2008 global study of human impacts on the ocean, variants of which account for more than half of all published assessments. These methods are transparent and digestible, which explains their appeal to stretched regulatory agencies, but they treat effects as linear and additive and cannot capture feedbacks or indirect pathways. In the middle tier are qualitative system models, notably loop analysis using signed digraphs, which have been applied to offshore wind in the Bay of Seine and elsewhere. These networks of positive and negative links between pressures and ecosystem components can be tested for stability and probed with perturbation analysis, and they pair naturally with Bayesian belief networks that attach conditional probabilities to each causal link.

At the demanding end lie fully dynamic numerical models: models of intermediate complexity focused on a handful of interacting species, and whole-of-system ecosystem models that track age-structured populations or biomass pools across space and time. Originally built for fisheries and climate research, these platforms are now being redirected at marine energy. Ecospace, an ecosystem model with fisheries roots, has been used to project cumulative impacts of combined aquaculture and marine energy platforms off Scotland, while spatiotemporal analyses have mapped offshore wind development scenarios across the North Sea basin. The review also flags an incoming wave of artificial intelligence: machine learning models already predict hydrodynamic and water quality responses relevant to energy siting, and large language models are being used to assemble ecosystem models far faster than manual parameterization allows. The authors predict AI and machine learning will make substantial contributions to cumulative assessment within a decade, while cautioning that models must still remain interpretable to the human decision-makers who rely on them.

Project-level practice, however, lags far behind the research frontier. Many consenting documents still amount to a cursory statement that no significant cumulative effects are anticipated, a formula the review’s authors describe as opaque and high-level. The critique echoes twenty years of published complaints: shifting baselines, single-industry focus, superficial expert assurances, and poor communication of uncertainty. Part of the fix, the authors argue, is structural. Nesting project-level assessments within strategic regional assessments, as maritime spatial planning frameworks increasingly attempt, would spread the analytical burden and ensure system-wide effects are not missed. A Baltic Sea case study, which embedded cumulative assessment at multiple scales into spatial planning and explicitly included marine energy among the activities considered, is held up as a template that balances rigor with pragmatism.

The recommendations that close the review are pointed. Assessments should use standardized terminology and terms of reference across jurisdictions; risk criteria should be set with stakeholders before analysis begins; predictive models should underpin assessments wherever possible; and every assumption, limitation, and uncertainty should be documented transparently, ideally communicated through IPCC-style confidence matrices. Centralized, interoperable data platforms, from the United States’ PRIMRE to Europe’s MARENDATA and France’s ResCORE, should be maintained and linked to reduce duplicated monitoring effort. Field studies must disentangle single-pressure from multi-pressure responses, and region-specific reference points defining acceptable levels of effect need to be developed, potentially borrowing methods from fisheries management. Crucially, the authors stress that assessments cannot remain a planning formality: they must be validated against post-deployment monitoring, shifting from hazard analysis to genuine evaluation of realized risk. For an industry still small enough to learn cheaply, the message is clear: borrow the hard-won methods of offshore wind, fisheries, and even terrestrial mining now, before the arrays grow too large for trial and error.

Subject of Research: Cumulative effects assessment methods for marine renewable energy development

Article Title: Marine renewable energy and cumulative effects assessments: Summary of the state of play

Article References: Fulton, E. A., Hemery, L. G., Copping, A. E., Farr, H. K., Fox, J., Garavelli, L., Hasselman, D. J., Miller, R., & O’Hagan, A. M. (2026). Marine renewable energy and cumulative effects assessments: Summary of the state of play. Environmental Challenges, 25, Article 101674. https://doi.org/10.1016/j.envc.2026.101674

Image Credits: AI Generated

DOI: 10.1016/j.envc.2026.101674

Keywords: marine renewable energy, cumulative effects assessment, environmental impact assessment, offshore energy, marine spatial planning, ecosystem modeling, tidal energy, wave energy, ocean stressors, Bayesian networks, marine ecosystems, regulatory policy