Floating solar photovoltaic energy for coastal areas: A siting methodology

Floating solar photovoltaic energy for coastal areas: A siting methodology D.M. Fouz a , R. Carballo b,* , I. L´ opez b , B. ´ Alvarez b , G. Iglesias c,d a PROePLA (GI-1716), ´ Area de Proyectos de Ingeniería, EPSE, Campus Terra, Univ. de Santiago de Compostela, 27002, Lugo, Spain b ´ Area de Ingeniería Hidr´ aulica, EPSE, Campus Terra, Univ. de Santiago de Compostela, 27002, Lugo, Spain c School of Engineering and Architecture & MaREI, Environmental Research Institute, University College Cork, College Road, P43 C573, Cork, Ireland d School of Engineering, Computing and Mathematics, University of Plymouth, Marine Building, Drake Circus, PL4 8AA, Plymouth, UK HIGHLIGHTS •The novel Integrated Floating Solar Energy (IFSE) index is developed. •A method for site selection of FPV systems in coastal areas is applied. •The IFSE index is applied to the Ría de Vigo. •Nearshore areas for FPV systems operation in the Ría de Vigo are identified. ARTICLE INFO Keywords: Floating solar energy FPV systems Nearshore areas Estuaries CAPEX ABSTRACT The decision-making process at the early stages of marine renewable energy projects typically faces considerable uncertainties. Amongst them, identifying the most suitable cost-effective locations is a key aspect, for which several novel indices have been proposed recently. In the case of floating solar photovoltaic (FPV) energy, a nascent technology, efforts have focused on offshore sites, possibly to the detriment of nearshore options of high interest such as estuaries. Granted, coastal areas present specific challenges, not least their high environmental value and the coexistence of many uses of the marine space, the compatibility of which ought to be accurately assessed. In this work, the Integrated Floating Solar Energy (IFSE) index is proposed for siting FPV farms in coastal areas. This novel index is based on a comprehensive methodology, which considers the aspects relevant to the decision-making process: (i) the exploitable resource, (ii) the costs of installation, and (iii) the socioeconomic and environmental aspects. The IFSE index is applied to a case study in the Ría de Vigo, an estuary in NW Spain, leading to the delimitation of an area of about 7 km 2 as that with the highest interest, and showing the capability of this index to significantly contribute to the decision-making process for the deployment of FPV farms in coastal areas. 1. Introduction The diversification and decarbonisation of the energy mix stand out as key efforts of the international community towards reaching climate neutrality objectives (Ghorbani et al., 2024). In this context, renewable energies, and especially solar and wind energy, have reached remarkable quotas of carbon-free energy production (Sarkodie et al., 2023); however, land scarcity constitutes a major difficulty for their further expansion in developed countries (Fang et al., 2024), with traditional renewables being subject to endless controversy between different stakeholders (e.g., energy vs. food production debate) (Turney and Fthenakis, 2011). As a result, marine and coastal environments have been postulated as potential areas for installing solar (Martínez and Iglesias, 2024a) and wind (Thomas et al., 2025) as well as wave (L´ opez, I. et al., 2024) and hydrokinetic (Carballo et al., 2024) energy farms. In this context, floating solar photovoltaic (FPV) energy is undergoing an impressive growth, with an average increment per year in terms of installed power of approx. 130 % over the last decade (Rosa-Clot and Tina, 2020). Despite the novelty of the installation of FPV technology in marine and coastal areas, commercial FPV systems have already been effectively deployed in different freshwater bodies worldwide (Wei et al., 2025), revealing a significant number of promising locations in regions with high levels of insolation (e.g., Elminshawy et al., 2024). These facilities have shown promising advantages in * Corresponding author. E-mail address: [email protected] (R. Carballo). Contents lists available at ScienceDirect Journal of Cleaner Production journal homepage: www.elsevier.com/locate/jclepro https://doi.org/10.1016/j.jclepro.2025.146733 Received 30 April 2025; Received in revised form 15 September 2025; Accepted 26 September 2025 Journal of Cleaner Production 528 (2025) 146733 Available online 1 October 2025 0959-6526/© 2025 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ ). comparison with ground-mounted PV (GPV) facilities, such as their higher efficiency, which is enhanced by the cooler marine and coastal environments, or the minimisation of shading losses resulting from the open nature of these areas (Claus and L´ opez, 2022). Nevertheless, in spite of their advantages, the levelized cost of energy (LCOE) of FPV technology (i.e., 0.05–0.10 € /kWh) is still higher than GPV systems (i.e., 0.03–0.05 € /kWh) (Oliveira-Pinto and Stokkermans, 2020). On these grounds, extensive research efforts are focused on reducing its LCOE figures through the development of new technology (e.g., L´ opez, M. et al., 2024) or the improvement in its installation (e.g., Claus and L´ opez, 2023), thus leading to optimize the performance of FPV systems (e.g., Elminshawy et al., 2024), along with their co-location with other renewable energy resources (Luhaniwal et al., 2024), especially wind energy (e.g., Huang and Iglesias, 2024). Despite their value, these works are usually focused on the installation of FPV systems in offshore environments, disregarding their deployment in nearshore areas or semi-enclosed waterbodies, such as estuaries or bays, which have shown to be promising areas with lower LCOE (Martínez and Iglesias, 2024b). These types of waterbodies constitute complex coastal environments, usually characterised by an intricate geomorphology (i.e., shallow to intermediate water depths, including significant intertidal and emerged areas in their inner part, as well as narrow cross-sections), where an intense socioeconomic activity (e.g., aquaculture, shipping, etc.) coexists with several protected areas (e.g., Natura 2000), often in a limited available area. This results in abrupt spatial variations (i.e., over short distances) of the main constraints for the installation and operation of FPV systems, which strongly influence the resulting LCOE figures. In this regard, recent works have proposed a new FPV system adapted to estuarine areas (L´ opez, M. et al., 2024). Therefore, the definition of reliable siting procedures for FPV technology in coastal areas is a key step towards its further development. Along with R&D activities focused on its technological development, these site-selection methods could constitute a starting point for the definition of more complex procedures (e.g., cost-effective analyses), specifically designed for the accurate computation of the LCOE of FPV systems in advanced-stage projects, as is the case of other marine renewable energy resources, such as wave (deCastro et al., 2024), hydrokinetic (Fouz et al., 2023) or offshore wind (Martínez and Iglesias, 2024c) energy, or offshore solar farms (Filgueira-Vizoso et al., 2025), and finally lead to the growth of the FPV sector with coastal deployments. The main weakness of early-stage projects is the large number of uncertainties involved in their decision-making process. In order to reduce the uncertainties and facilitate the decision-making process, several novel indices have been recently proposed in the context of different marine renewable energy resources (e.g., Penalba et al., 2021), especially in the case of offshore wind energy (e.g., Bahaj et al., 2020), with particular emphasis on its co-location with wave energy farms (e.g., Majidi et al., 2025) and FPV systems (e.g., Frías-Paredes and Gast´ on-- Romeo, 2025), including their long-term analysis (e.g., Ulazia et al., 2023). Amongst the proposed indexes to analyse MRE projects, the IHE (Integrated Hydrokinetic Energy) index, originally developed for the site selection of hydrokinetic energy projects (Fouz et al., 2022), stands out for its ease of application to different coastal regions of interest, including nearshore areas such as estuaries or bays. The IHE index provides accurate and reliable results and thereby constitutes a first step towards conducting cost-effective analyses (Fouz et al., 2023). Overall, its application leads to the selection of the most appropriate area for energy exploitation in a coastal region, irrespective of the energy conversion technology and farm layout, by using a holistic approach integrating (Fouz et al., 2022): (i) the exploitable resource, (ii) the installation costs as a function of the coastal configuration, and (iii) the main socioeconomic and environmental constraints. This index was previously applied to identify the most appropriate areas for energy exploitation in tidally-driven estuaries, such as the Shannon Estuary (W Ireland) (Fouz et al., 2022), and subsequently adapted for its application to coastal areas whose hydrodynamics are dominated by large fluvial discharges, thereby subject to high seasonality, as is the case of the Mi˜ no Estuary (NW Iberian Peninsula) (Carballo et al., 2024). In this work, and following a similar approach, a new index for siting FPV systems in estuarine areas, the IFSE (Integrated Floating Solar Energy) index, is developed by analysing not only the available energy resource, but also the exploitable resource, along with the installation costs, socioeconomic and environment aspects. The application of this new index is illustrated through a case study in the Ría de Vigo (NW Spain) (Fig. 1), a mesotidal coastal area with a surface area of 176 km 2 , characterised by its sheltered conditions resulting from the natural barrier constituted by the Cíes Islands, whose favourable environmental conditions for FPV operation have been highlighted in previous works, focusing on its high irradiance levels throughout the year and moderate wave climate (L´ opez, M. et al., 2024). The Ría de Vigo is the southernmost of the Galician Lower Rias (Rías Baixas), former tectonic valleys flooded during the Cenozoic era. It is geographically delimited between Capes Silleiro (42◦06 ′ 44 ″ N 8◦53 ′ 57 ″ W) and Home (42◦15 ′ 10 ″ N 8◦52 ′ 12 ″ W) to the north and south, respectively, being usually divided in three different parts: (i) the inner ria, to the east of Punta Arro´ as and Punta A Guía, (ii) the middle ria, to the west of the inner ria extending up to Cape Balea and do Mar, and (iii) the outer ria, to the west of the middle ria. According to the K¨ oppen–Geiger–Pohl Climatic Classification (Peel et al., 2007), it presents a transitional climate between the warm-summer Mediterranean climate (Csb) and the oceanic climate (Cfb), tending more towards the first. As a result of its latitude (i.e., around 42◦N), the Ría de Vigo presents average annual figures of global horizontal irradiance (GHI) of about 175–195 Wm –2 (L´ opez, M. et al., 2022), which corresponds to a relatively high resource availability (e.g., GHI ranges in Spain between Fig. 1. Ría de Vigo (b) located in NW Spain (a). D.M. Fouz et al. Journal of Cleaner Production 528 (2025) 146733 2 147 and 235 Wm –2 ), indicating its suitability for energy exploitation and therefore for FPV operation. Previous works have analysed the suitability of the Ría de Vigo for FPV operation, and in particular for the installation of a new concept of FPV system, HelioSea, providing a first simplified estimate of LCOE figures resulting from its operation (L´ opez, M. et al., 2024), but without developing a detailed site selection. As a result, LCOE between 0.16 and 0.27 € /kWh have been preliminary estimated, i.e., around three times higher than typical offshore FPV operation with relatively higher commercial maturity. Thus, and despite the valuable interest of these initial approaches, the application, for the first time, of holistic site selection procedures to this coastal area, i.e., considering not only the energy resource but also other aspects such as the coastal configuration or the main socioeconomic and environmental restraints, could shed some light on the decision-making for FPV operation in the Ría de Vigo and, more generally, in nearshore coastal environments, providing key information as a preliminary step towards its accurate cost-effective analysis. This article is organised as follows. First, in Section 2, a general overview of the IFSE index is presented. Then, in Sections 3 to 5, the different aspects and terms considered in the index are explained in detail. Next, in Section 6, the main results of the application of the IFSE index to the Ría de Vigo are presented and discussed in detail. Finally, in Section 7, the major conclusions and findings of the development of the index and its subsequent application are drawn. 2. General description of the method: an overview of the IFSE index The IFSE index developed in this work aims to assess the location of FPV systems at early project stages. To this end, a similar approach to the so-called Integrated Hydrokinetic Energy (IHE) index is followed, being conceived as a holistic tool for identifying appropriate locations for hydrokinetic energy projects in coastal regions. This index considers all the relevant aspects involved in the decision-making process, namely (Fouz et al., 2022): (i) the exploitable energy resource, (ii) the main installation costs resulting from coastal configuration by applying a geospatial cost penalty function, C gp , and (iii) socioeconomic and environmental (S&E) aspects through a geospatial water use penalty function, U gp . The IHE index has shown to be a powerful tool, successfully applied for identifying the best sites for installing hydrokinetic energy farms in different coastal areas, including both shallow (Carballo et al., 2024) and deep areas (Fouz et al., 2022), constituting a first step towards their detailed cost-effective analysis (Fouz et al., 2023). However, the IHE index cannot be directly applied to other marine renewable energies, such as FPV. Based on the IHE index, the Integrated Floating Solar Energy (IFSE) index is defined, being expressed as: IFSE=SE Cgp,sUgp ,(1) where SE stands for the Solar Energy resource index, C gp,s is the geospatial cost penalty function adapted to solar energy exploitation and Fig. 2. Flowchart describing the whole method for applying the IFSE index. D.M. Fouz et al. Journal of Cleaner Production 528 (2025) 146733 3 U gp represents the geospatial water use penalty function, accounting for the abovementioned aspects (i) to (iii), respectively, and applying the same physical interpretation as the IHE index, i.e., values of SE or IFSE ≥1 indicate suitability for installing FPV systems, with larger values indicating better locations, with C gp,s and U gp functions ranging between 0 and 1. In the following sections, the development of the parameters constituting the IFSE index and their application are explained in detail. In order to help the reader to understand the development and application of the IFSE index, a flowchart describing the whole procedure is provided in Fig. 2. Due to complexity in the definition of the procedure, the three involved functions are described in separate sections, namely: (i) the exploitable resource, SE (Section 3), (ii) the costs of installation, C gp,s (Section 4), and (ii) the socioeconomic and environmental aspects, U gp (Section 5). The IFSE index is conceived as a spatial planning tool which aims to address uncertainties throughout the decision-making processes at early FPV project stages. Therefore, it is necessary to highlight that final FPV farm designs would require detailed LCOE analysis of various technologies and array distribution combinations. 3. Solar energy (SE) resource index 3.1. Development of SE index The first step towards the development of the IFSE index is to define the Solar Energy (SE) resource index as an indicator which should accurately characterise the suitability of coastal areas for solar energy exploitation from the point of view of the exploitability of the energy resource. With this aim, the SE index is defined as: SE =Eg Eg,ref Cep,(2) where E g represents the yearly averaged value of the GHI over a horizontal surface considering both day and night-time (Fig. 3), E g,ref is a threshold value of E g representing the minimum energy resource required for exploitation suitability (i.e., a threshold energy resource level), and C ep is a parameter which models the effects of the cooler marine environment on the performance of FPV systems. C ep is expressed as (Marion, 2010): Cep =1+ α P(TC−TSTC),(3) where α P = − 0.36 % ◦C −1 is the PV system temperature coefficient of power (Martínez and Iglesias, 2024a), T C is the spatial distribution of the operating temperature of the PV system, and T STC =25 ◦C stands for the operating temperature at standard test conditions (L´ opez, M. et al., 2022). In the present application, T C is modelled by (Kalogirou, 2023): TC=30 +0.0175×(Eg−300)+1.14 × (Ta−25),(4) being T a the spatial distribution of the ambient temperature, which is obtained from the Copernicus Climate Change Service (C3S) and computed by the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis products (ERA5) for the period 1991–2020 (Hersbach et al., 2020). In this work, E g is retrieved from PVGIS (Photovoltaic Geographical Information System), a web GIS tool widely used for the analysis of PV technology in Europe (Huld et al., 2012). This tool stores solar radiation data obtained from satellite operation. In particular, the present application resorts to CMSAF (Climate Monitoring Satellite Application Facility) data (Schulz et al., 2009), based on Meteosat geostationary satellite images which are processed to obtain different GHI datasets (Amillo et al., 2014). Likewise, E g,ref is set to 100 Wm –2 (Yang et al., 2024). The period of interest for the analysis of E g can be adapted (e.g., shorter time periods, such as seasonal or monthly scenarios), similar to E g,ref . As mentioned, the SE index indicates suitability for solar energy exploitation in areas where SE ≥1. The upper limit of the SE index (and thus of the IFSE index) is expected to be about 2.5, which would correspond with desertic areas with a GHI of about 250 Wm –2 . As a result, this dimensionless metric constitutes a reliable indicator for the assessment of the exploitability of the solar energy resource at coastal locations of interest. Fig. 3. Spatial distribution of E g throughout the Ría de Vigo. Fig. 4. Spatial distribution of the SE index throughout the Ría de Vigo. D.M. Fouz et al. Journal of Cleaner Production 528 (2025) 146733 4 3.2. Application of the SE index to the Ría de Vigo On the basis of the above procedure (Section 3.1), the spatial distribution of the SE index in the Ría Vigo is computed (Fig. 4). As can be observed, it is virtually homogeneous within the whole coastal area, mainly as a consequence of the reduced spatial variations of E g (Fig. 3), ranging between 1.8 and 2.0, and reaching its maximum value in the surroundings of the Baiona Bay (located in the southern margin of the ria, closer to its mouth, and limited by Cape Silleiro and Punta Lameda). Given the homogeneity of the SE index, the spatial distribution of the IFSE index, and therefore the identification of the most appropriate locations for solar energy exploitation in the Ría de Vigo, are expected to be highly dependent on the spatial distribution of the installation costs (C gp,s , Section 4) and water uses (U gp , Section 5) within the ria. 4. Geospatial cost penalty function (C gp,s ) 4.1. Development of C gp,s The next step involves adapting the geospatial cost penalty function (previously C gp ) to the technical requirements of solar energy exploitation, denoted as C gp,s . This new function, independent of a specific FPV technology or layout, identifies and penalises coastal areas where the deployment of FPV systems would incur larger installation costs by means of a detailed parameterisation of the main capital expenditures (CAPEX) as a function of the geomorphological parameters on which they depend. The CAPEX encompasses the expenses incurred during the construction or fabrication and installation of an industrial facility or, in other words, prior to its operation stage. In the case of marine renewable energy projects, this cost term usually groups the expenses of (Dalton et al., 2015): (i) energy conversion technology, (ii) cabling, (iii) foundations or moorings and anchors, (iv) deployment procedure, and (v) electrical grid connection. More specifically, the influence of the coastal configuration on CAPEX has been thoroughly studied in previous works for the particular case of marine renewable energy projects, identifying cabling and mooring as the main CAPEX drivers with a strong dependence on geomorphological aspects such as water depth and shoreline distance (Fouz et al., 2022). Therefore, these CAPEX terms and geomorphological variables are retained in this work for the development of C gp,s . Cable costs are usually subdivided into inter-array cable, which connects the different strands and floating structures of a specific FPV plant layout, and exporting cable, which transports the energy yield to an onshore substation (Martínez and Iglesias, 2024b). The first is outside the scope of this work given that it is highly dependent on the specific farm layout, which contradicts the technology-agnostic aim of the IFSE index. Exporting cable costs (hereinafter, cable costs), C ec , can be parametrised as a function of shoreline distance, being computed as (Martínez and Iglesias, 2024b): Cec =lecCu,ecPT≈lCu,ecPT,(5) where l ec is the total cable length, which is accurately approximated by the shoreline distance, l, C u,ec represents the unitary cost per unity of cable length and installed power (C u,ec =2.18 € (MW m) −1 ), and P T stands for the total plant power. Mooring costs include the expenses of both mooring lines and anchorages required for limiting the movements of floating platforms ensuring their structural stability. Given their easily manufacturing and economic feasibility, recent works resort to tension-legged substructures composed by four taut mooring lines and the corresponding anchorage points constituted by concrete dead weights in the form of gravity anchors (Martínez and Iglesias, 2024b). Thus, the cost of mooring and anchoring, C ma , is calculated as the sum of the costs of the mooring lines, C m , and anchoring system, C a , as follows (Martínez and Iglesias, 2024b): Cma =Cm+Ca=lmlCu,mlNmlNfs +Wu,dwCu,dwNdwNfs,(6) where l ml is the length of mooring lines, which is computed as the water depth adjusted by a generic draft for FPV installations, C u,ml represents the cost per unity of length for the mooring lines (C u,ml =57.6 € m −1 ), N ml stands for the number of mooring lines per floating structure, N fs is the number of floating structures, W u,dw represents the unitary weight per unit of concrete dead weights (W u,dw =23 ton), C u,dw stands for the cost per unit of weight for the concrete dead weights (C u,dw =44 € ton −1 ), and N dw is the number of concrete dead weights per substructure. Therefore, the total plant CAPEX function, C plant ( € ), can be computed by combining and rearranging Eqs. (5) and (6) as: Cplant =Cec +Cma =βl+γNfsd+δNfs,(7) where β =C u,ec P T , γ =230.4 and δ =1398.4 are coefficients that result of rearranging the constant terms in Eqs. (5) and (6), and d represents the water depth (i.e., the real water depth, given the effects of the abovementioned draft adjustment are modelled by γ and δ). The influence of the coastal configuration —defined by its most representative parameters, i.e., water depth, d, and shoreline distance, l— on the total CAPEX is illustrated by Eq. (7). Nevertheless, and despite its interest, Eq. (7) depends on site-specific parameters resulting from a detailed plant layout (i.e., P T and N fs ), which require explicit definition, and therefore does not represent all possible FPV system configurations. To ensure that the resulting function will be broadly applicable to any FPV system deployed in any coastal area of interest, these site-specific parameters should be standardised, for which the following approach is applied. Both parameters, P T and N fs , are closely interconnected. Once the total plant power is established, the required number of PV panels to achieve this power must be determined, which in turn determines the necessary number of floating structures to support them. Some of the issues involved in defining these technical aspects may be unclear at early project stages, so the current trends and prospects of FPV technology should be carefully considered to ensure the reliable standardisation of the site-specific parameters (i.e., P T and N fs ). In this regard, based on previous works, and with a particular emphasis on FPV operation in nearshore coastal environments, a total plant power P T =1 MW is retained for further analyses (Trapani and Red´ on Santaf´ e, 2015). Regarding the number of floating structures required to support the number PV panels necessary to achieve the total plant power (i.e., 1 MW), it strongly depends on the final layout of the array (e.g., number of rows and columns, arrangement of the different strands, etc.), and therefore is also dependent on the site-specific availability of useable surface in a given coastal area, which in turn is highly influenced by the coastal configuration and the interactions among the different water uses. Thus, its definition constitutes a complex task if conducted with independence of the final layout. To avoid this complexity, in this work a wide range of real FPV projects is considered in order to accurately characterise the relation between the total plant power and the number of floating structures required to support the number of PV panels necessary to attain it (Islam et al., 2023; L´ opez, M. et al., 2024; Martínez and Iglesias, 2024a, 2024b; Trapani and Red´ on Santaf´ e, 2015). Once conducted this analysis, the results are parametrised by unit of installed power, and N fs can be obtained as a generic parameter (i.e., number of floating structures per MW of installed power) to be applied to FPV projects at early stage. Thus, the standardisation of the site-specific parameters as described, leads to the generalisation of Eq. (7). The generalised total CAPEX function, C plant,g ( € ), is obtained after the operation and rearrangement of the terms in Eq. (7), which now reads: Cplant,g=βgl+γgd+δg,(8) where β g =2.18, γ g =3456 and δ g =20976 are the constant coefficients D.M. Fouz et al. Journal of Cleaner Production 528 (2025) 146733 5 resulting from generalising and rearranging the constant terms in Eqs. (5) and (6). Eq. (8) provides a generalised expression applicable to any coastal area of interest, enabling an estimated assessment of CAPEX based on the shoreline distance and water depth at any given site. Building upon this, the geospatial cost penalty function, now adapted for solar energy exploitation through FPV technology, C gp,s , establishes a relationship between the installation costs and geomorphological aspects (i.e., shoreline distance and water depth), being expressed as: Cgp,s=1−Cplant,g Cplant,g,ref ,(9) where C plant,g,ref is a threshold value for Eq. (8), beyond which energy conversion becomes unfeasible, resulting from a large shoreline distance, water depth, or a combination of both factors. Based on previous works, the limiting value for the shoreline distance is set to l =7500 m (Carballo et al., 2024), and in the case of water depth, d =100 m according to specific developments for FPV technology (Garrod et al., 2024). In addition, areas not suited for FPV systems, i.e., very close to the shoreline or with reduced water depths (l <500 m or d <2 m) are disregarded. Fig. 5 shows the representation of C gp,s (Eq. (9)) for a generic domain Fig. 5. Representation of C gp,s (Eq. (9)) for a generic domain. Fig. 6. Spatial distribution of the shoreline distance (a) and water depth (b) throughout the Ría de Vigo. Fig. 7. Spatial distribution of C gp,s throughout the Ría de Vigo. D.M. Fouz et al. Journal of Cleaner Production 528 (2025) 146733 6 up to the abovementioned limiting values for shoreline distance and water depth. As can be observed, the water depth has a more important role as CAPEX driver than the shoreline distance resulting from γ g ≫ β g (Eq. (8)). On the other hand, it is important to note that δ g >0 (Eq. (8)) and thus there always exist a reduced penalisation, i.e., C gp,s <0.95, which results from the installation of FPV systems instead of GPV technology, even in an idealised location where l =d ≈0, which could correspond with a port or a specific site at the shoreline. 4.2. Application of C gp,s to the Ría de Vigo Fig. 6 illustrates the spatial distribution of the shoreline distance and water depth in the Ría de Vigo, required for the computation of C gp,s shown in Fig. 7. As a result of the coastal configuration, the largest penalisation takes place in the surroundings of the mouth of the ria, and increasing to the west of Cíes Islands, where C gp,s figures of less than 0.25 are found. On the other hand, the inner and middle ria are less restrictive according to their geomorphological conditions resulting in an overall lower penalisation (i.e., C gp,s of approx. 0.7–0.8). However, specific areas such as the deeper central channel in the middle ria, present significant higher penalisation (C gp,s ≈0.5–0.6). 5. Geospatial water use penalty function (U gp ) 5.1. Development of U gp The last step towards the development of the IFSE index is the computation of the geospatial cost penalty function, U gp . In this case, given it considers socioeconomic and environmental aspects directly resulting from a multicriteria characterisation of the area of interest (i. e., independent of the energy conversion technology considered), no specific adaptations are required for its straightforward application to solar energy analysis. U gp provides a reliable indicator to assess the coexistence between energy exploitation and the surrounding existing or potential coastal uses. Two different steps are required for its computation: (i) the identification and characterisation of the main socioeconomic (e.g., fishing, shipping, etc.) and environmental (e.g., protected areas, such as Natura Fig. 8. Spatial distribution of the main water uses throughout the Ría de Vigo. D.M. Fouz et al. Journal of Cleaner Production 528 (2025) 146733 7 2000 Network) water uses, and (ii) their quantitative assessment, defining the value of U gp function in these areas. Finally, if a superposition of uses is present in a given area, specific aggregation methods should be defined. In the present work, the most restrictive value of U gp (i.e., the minimum) is retained (Carballo et al., 2024). An alternatively method could consist in adding the various penalisation up to a limiting value of U gp =0. The quantitative assessment of the U gp function for the different coastal uses identified (step ii) is not straightforward and requires a comprehensive approach adapted to the specific characteristics of each coastal use. For the sake of simplicity, the reader is referred to previous works (e.g., Fouz et al., 2022) for further details regarding the definition of U gp for each specific use. Regarding the particular case of the Ría de Vigo, it hosts a large number of socioeconomic and environmental water uses, highlighting the intense activity taking place in the Port of Vigo, one of the most important port areas in northern Spain in terms of maritime transport (e. g., shipping containers) and fishing, along with the high environmental value of the Cíes archipelago. Moreover, the comprehensive work conducted by the Port Authority of Vigo (APVigo), which manages several port facilities within the ria, makes available a large amount of geospatial data relative to port activities and zoning, which are suitable for being analysed through the U gp function. As a result, the following water uses are considered in this work. On the one hand, as socioeconomic activities: (i) shellfishing, (ii) mussel farms (i.e., traditional rafts for its cultivation), (iii) bivalve mollusc gathering (i.e., other species, such as oysters or clams), (iv) fishing Fig. 9. Spatial distribution of U gp values for each main water use considered. D.M. Fouz et al. Journal of Cleaner Production 528 (2025) 146733 8 grounds, (v) navigable waters and/or navigation channels, (vi) ferry routes, (vii) anchoring zones (i.e., those delimited and managed by APVigo), and finally, (viii) port zone I (i.e., the public water surface comprising the interior of harbour waters, including all areas that are sheltered either naturally or by the effect of seawalls, managed by APVigo). On the other hand, as environmental aspects: (i) Natura 2000 Network (ii) National Parks (i.e., a protection figure from the Spanish legal framework, such as Cíes archipelago), and (iii) Zones of Special Protection of Natural Values (ZSPNV) (i.e., a regional protection figure which applies to those areas whose natural, cultural, scientific, educational or landscape value makes it necessary to ensure their conservation and which do not have any other specific protection; however, in the particular case of Galicia, it also includes the Natura 2000 Network, being overlapped in extensive areas). For the sake of simplicity, Figs. 8 and 9 show the spatial distribution of the main water uses in the Ría de Vigo and their resulting U gp value, respectively. The U gp values for all considered water uses are presented in Table 1. 5.2. Application of U gp to the Ría de Vigo The spatial distribution of U gp in the Ría de Vigo, computed as introduced in Section 5.1, is shown in Fig. 10. As a result of the high number of water uses coexisting in the Ría de Vigo, a large part of the inner and middle ria is considered as a restricted area (i.e., U gp =0), where the energy exploitation is incompatible with the existing socioeconomic and environmental restraints. On the contrary, there is a large area in the outer ria, closer to its south margin, and in particular to the south of the southernmost branch of the navigation channel, between Cape do Mar and Punta Lameda, which presents medium to high U gp values (i.e., U gp ≈0.6–0.9), indicating suitability for energy exploitation. Likewise, there are specific areas also located in the Soth margin of the mouth of the Ría, in the surroundings of the Baiona Bay, which also present similar figures of U gp parameter. 6. IFSE index The integration of the different terms constituting the IFSE index (i. e., SE in Section 3, C gp,s in Section 4, and U gp in Section 5) leads to the computation of its spatial distribution in the Ría de Vigo following Eq. (1). Its physical interpretation, as previously introduced, is straightforward: the higher the IFSE index, the better the site for solar energy exploitation, with IFSE ≥1 indicating suitability. In consequence, IFSE =1 would represent a suitability threshold indicating locations with a minimum level of energy resource (i.e., SE =1), and without Table 1 U gp values considered for socioeconomic and environmental water uses in the Ría de Vigo. Water use U gp Shellfishing 0.30 Ferry routes 0 Anchoring zones 0 Port zone I 0 Natura 2000 Network 0.90 National Parks 0 Fig. 10. Spatial distribution of U gp throughout the Ría de Vigo. Fig. 11. Spatial distribution of the IFSE index (a) and the resulting areas of interest (A1 to A4) (b). D.M. Fouz et al. Journal of Cleaner Production 528 (2025) 146733 9