Socio-Economic Analytical Frameworks for Marine Spatial Planning: Evaluating Tools and Methodologies for Sustainable Decision Making

Abstract

Marine strategy requires a high level of understanding of the interrelationships and processes occurring between the various social and ecological systems that coexist in the same environment, given the high degree of complexity of such a system. Maritime Spatial Planning (MSP) is a process for implementing ecosystem-based management in the marine and coastal environment, expressing the ambitious goal of protecting the natural capital of the oceans and promoting sustainable economic growth in the maritime sectors from a holistic perspective. One of the main challenges in MSP is to identify methods and tools for integrated assessments of socio-economic aspects with physical and environmental variables, attempting to assess the economic costs and benefits of a plan and to detect a cause-and-effect relationship between MSP and the performance of the blue economy. Depending on the specific features of areas where planning takes place and the objectives of the assessment, there is a wide spectrum of different approaches and tools that allow the assessment of the economic impact of MSP with varying levels of precision and detail. The objective of this paper is to present a comprehensive analytical framework that will facilitate the understanding of, and development of, appropriate socio-economic instruments and analyses for estimating the economic effects associated with MSP. A comprehensive information base will be constructed based on the findings of studies and analyses conducted by research institutions and relevant national and international organizations. This is intended to identify the principal characteristics, scope, strengths, and weaknesses of decision-support tools in order to identify best practices for measuring socio-economic impacts of management plans and to facilitate a holistic view of marine ecosystems.

1. Introduction

Maritime Spatial Planning (MSP) represents a strategic framework aimed at implementing ecosystem-based management across marine and coastal environments [1]. One of the principal challenges in MSP is to identify methods and tools that facilitate integrated assessments encompassing both socio-economic and physical-environmental variables. The range of available approaches and tools for assessing the economic impacts of MSP is extensive, reflecting the varied characteristics of different regions and the specific goals of each assessment. While the precision and granularity of these tools vary according to circumstances, they remain instrumental for the continuous monitoring process required by the plans. This is a highly complex and detailed process that must consider the intricacies and interdependencies among various economic sectors, which are often insufficiently represented in official statistics due to limitations in sectoral and regional specificity. Notably, European MSP efforts to date have often prioritized environmental assessments, generally neglecting comprehensive socio-economic evaluation within the processes of co-planning, monitoring, and adaptation.

This study presents a comprehensive analysis of the prevailing techniques employed to assess and quantify the contribution of blue economy sectors within MSP, with a particular focus on their applicability in the European context. The objective is to identify the key characteristics, scope, strengths, and weaknesses of decision-support tools to assess the impact of MSP on performance of marine-based activities and maritime-related activities. The principal objective of this research is the construction of a comprehensive analysis framework that integrates socio-economic factors with physical and environmental variables within the domain of MSP.

Following an initial discussion on the necessity of incorporating socio-economic evaluation into European MSP strategies, the article describes the methodology employed in classifying estimation methods in Section 3. Section 4 provides an in-depth exploration of specific models designed to represent socio-economic structure within MSP, identifying main critical aspects, type of impact, and area of application. Section 5 and Section 6 present the results and offer a summary of the key conclusions.

2. The EU Framework of MSP

The Maritime Spatial Planning Directive (MSPD, 2014/89/EU) is a central European policy designed to foster the sustainable development of maritime economies and the responsible use of marine resources. It encourages an ecosystem-based approach to ensure the harmonious coexistence and integrated management of various activities and uses that affect the seas and coasts. Under this directive, Member States are required to develop and implement Maritime Spatial Plans (MSP) for their national waters. These plans aim to promote a long-term sustainable balance between human activities and natural ecosystems, taking into account all relevant economic sectors and ecological components within a given marine area.

While the MSPD was designed to provide a comprehensive and flexible framework for managing economic activities in EU waters, the absence of clear definitions for key MSP concepts and specific guidance for creating national plans has resulted in a fragmented approach among Member States in their marine strategies [2]. The complexity of MSP is heightened by the diverse interests involved, the dynamic nature of marine environments, and the multidisciplinary understanding required to grasp how the seas evolve. The increasing reliance on marine resources, coupled with a limited understanding of the socio-environmental impacts of activities, demands a deep comprehension of the interrelationships and processes between the social and ecological systems that share the same space. Despite the mandate for MSP in Europe since 2021 and the significant efforts invested in studies, projects, and data collection, only in recent years has the literature begun to focus on quantitative aspects, such as evaluating the economic costs and benefits of plans and establishing cause–effect relationships between MSP and the performance of the blue economy [3].

From the MSPD perspective, a novel approach to management has emerged, where the evaluation of competing uses of the sea and its resources is seen as a dynamic and adaptive process. This process is marked by significant uncertainty due to the challenges of quantifying marine resources and services, both in monetary and non-monetary terms, and understanding their interactions [4]. To assess the economic implications of a management plan and determine its social desirability, it is crucial to evaluate the costs and benefits related to the private and public goods and services involved in MSP. This assessment allows for an accurate determination of whether the plan yields a net economic benefit to society [5].

Given the unique characteristics of the areas where planning occurs and the specific objectives of the assessment, there is no single method for evaluating economic impacts. Instead, a broad array of approaches and tools is available, each offering varying levels of precision and detail in assessing the economic impact of MSP [6]. Advanced models, which aim to quantify the multiple uses and values of marine and coastal ecosystems, as well as to evaluate land–sea interactions and potential cause-and-effect relationships over different time periods, have only recently become more common. This trend is partly due to the advent of software that simulates ecosystem dynamics, making it easier to study marine ecosystems in a holistic manner [7].

The literature on the socio-economic impacts of MSP has primarily concentrated on two key areas: market goods and services (including those from the blue economy and the broader economy) and ecosystem services [8]. The blue economy encompasses established sectors such as marine living resources, non-living marine resources, renewable energy, port activities, shipbuilding and repair, maritime transport, and coastal tourism. It also includes emerging and innovative sectors related to renewable energy (e.g., ocean energy, offshore wind energy, floating solar energy, and offshore hydrogen generation), blue biotechnology (e.g., seaweed cultivation), desalination, maritime defense, security and surveillance, as well as research and infrastructure. The assessment methods for the blue economy sectors have focused on the economic performance of marine-based activities occurring directly within the marine environment (e.g., fishing, maritime transport), maritime-related activities (e.g., port activities, shipbuilding), which are traditional contributors to the maritime economy, and innovative and emerging sectors that offer new investment opportunities for coastal area development. More recently, some studies have sought to evaluate indirect effects from supplier sectors and induced effects across various economic sectors in some European areas [9,10]. However, attempts to carry out a comprehensive assessment of the methods and approaches used in MSP with a focus on the blue economy have so far been relatively limited.

3. Methodology

The analysis entailed a comparison of the principal tools and most commonly employed methodologies for the economic valuation of sectors within the blue economy. We concentrated on the approaches used to identify and quantify the economic activities within MSP and to estimate the direct, indirect, and induced socio-economic impacts. These include direct impacts from changes in maritime-related sectors, indirect impacts from sectors supplying inputs to the blue economy, and induced impacts on the broader economy [3].

For each evaluation method, the analysis examined critical aspects such as information availability, type of impact, and area of application. This activity, which involved gathering information from studies and analyses by research institutions and relevant authorities, is crucial for identifying suitable decision-support tools for MSP, in accordance with the availability of information and the characteristics of the maritime areas in question.

4. Evaluating Models Applied to the Blue Economy Sectors

The following paragraphs present the results of the research conducted. This involved the compilation of an information base from studies and analyses carried out by institutions, including the European Commission, World Bank, National Oceanic and Atmospheric Administration (NOAA), Organisation for Economic Co-operation and Development (OECD), and United Nations (UN).

Table 1. Summary of tools for MSP socio-economic analysis.

Tool	Objective	Data Need	Weaknesses
Reference point indicators	Current economic activity; input for other tools	Time series of target indicators	Selection of an appropriate geographical scale; no interactions amongst the different sectors
Counterfactual approach	Estimation of a causal direct effect of a policy	Time series of target indicators before and after the MSP intervention	Reliable control group; no interactions amongst the different sectors
Input–output models (IOM)	Ex ante evaluation of direct, indirect, and induced impacts. Explicit consideration of inter-industry transactions	Product inputs and destination of outputs for economic sectors	Static and deterministic model, requiring adaptation for simulation analyses; disaggregation of variables at geographic and industrial levels
Environmental input–output models (EIOM)	Direct and indirect environmental impacts associated with the consumption of goods and services	Mix physical and monetary indicators, as pollutant emissions and volumes of raw materials	Same assumptions of the IOM: homogeneity of products and prices, constant returns to scale and allocations on investments
Social accounting matrix (SAM) and BioSAMs	Allows for the capture of intermediate and final demand transactions	Indicators of multi-regional and sectoral flows	Disaggregated regional and sectorial indicators
Integrated agent-based modelling (ABM) system	Simulation of individual interactions for evaluating the response of systems under different scenarios	Indicator variables corresponding to specific management objectives (information on stock sizes for the primary fishery target group, port congestion, water quality)	Matching scales of indicators; available data to directly support assumptions about the individual agent choices
Cost–benefit analysis (CBA)	Comparative analysis for assessing tradeoffs of alternative management decision	Tangible and intangible costs and benefits associated with a scenario or management plan	Choice of the social discount rate
Multi-criteria decision analysis (MCDA)	Evaluation process for the assessment of alternative measures/scenarios	Multiple evaluation criteria and their relative importance, elicitation of preferences	Inadequate selection of MCDA techniques; inappropriate use of the weighting method
General equilibrium ecosystem model (GEEM) and bio-economic modelling (BEM)	Mathematical representation of biological and economic systems, which typically links economic and biological components and parameters together	Each operational module is characterized by several interconnection components including functional relationships among input and output variables, which are generally developed through ad hoc software	Results are highly dependent on key parameters and assumptions

offers a concise classification of alternative models, organized by their objectives (such as the type of impact and area of application), data requirements, and limitations. For each approach, basic introductory concepts are highlighted to summarize the underlying logic and their applications in analysis and forecasting.

4.1. Socio-Economic Indicators

While single indicators may not fully capture the complexity of ecosystems [11], socio-economic indicators are frequently used in quantitative analyses and are often compared with appropriate reference points (RP) or threshold values. Thresholds can be linked to either a critical condition, known as the limit reference point (LRP), which should be avoided or not exceeded, or to an optimal condition, known as the target reference point (TRP), which the system should aim to achieve [12].

Profitability indicators are typically calculated based on trends in net income (revenue minus costs), net profit, the ratio of current revenue to breakeven revenue, and the return on fixed tangible assets. For employment, the key indicators are the number of jobs and the average gross salary. The impact of a specific economic activity on the national economy is often measured by the direct value added and the balance of trade.

Socio-economic indicators are an essential element of bio-economic modelling, which uses a combination of biological and economic variables or parameters to simulate a social-ecological system. This approach enables the evaluation of different management strategies or the modelling of the impact of external factors (such as rising oil prices or climate change shocks) [13]. The economic impact of maritime spatial planning has been assessed in several Northern European countries through an EC study, which analyzed time series data on production, value added, employment, and investment costs. The integration of biological and economic indicators offers a valuable tool for assessing marine areas, particularly in addressing issues like overfishing and environmental degradation [10].

Collecting socio-economic indicators is crucial for understanding the current distribution of maritime sectors within the marine areas under a specific planning zone. These indicators are fundamental for implementing assessment methodologies that evaluate the social, economic, and environmental impacts of various MSP options. This process aids in identifying potential spatial conflicts or synergies, where different maritime and coastal activities and ecosystem services can coexist or even benefit one another [14]. These indicators are also used as inputs for further analyses, including input–output, cost–benefit, and counterfactual analyses. For example, to establish baseline levels and reference points for scenario analysis, a trend analysis of the production value of marine activities is performed both before and after the implementation of MSP, alongside an assessment of the rates of change that have occurred.

However, as outlined in the MSPD, data and indicators should encompass all elements directly or indirectly related to a specific site or geographical area covered by the Marine Spatial Planning (MSP). Additionally, given the economic significance of maritime activities and their connection to the sea, determining the most appropriate territorial scale for collecting and aggregating indicators can be challenging. The impact assessment of the blue economy fundamentally includes identifying territorial boundaries within which certain maritime or non-maritime economic activities take place [15]. This task is complex and involves trade-offs, primarily due to the lack of spatially explicit socio-economic data (in terms of availability, quality, and accuracy) and the difficulty in separating the marine and terrestrial components of key economic activities. The main challenges include the diversity of definitions and statistical representations of marine sectors, as well as the limited availability and quality of data [16].

In many cases, MSP is carried out at the regional and/or sea-basin level, which requires highly detailed geographical resolution. Conversely, certain activities, such as port operations, often extend beyond municipal administrative boundaries. Furthermore, other blue economy activities, like those related to transport and energy extraction, hold national strategic importance, making them difficult to align with the administrative borders of municipalities, provinces, or regions.

Choosing an appropriate geographical scale and scope for socio-economic indicators is vital for the planning and evaluation stages of marine spatial planning (MSP). Moreover, this selection is equally important for the subsequent monitoring process.

4.2. The Counterfactual Approach

The rates of change of socio-economic indicators can also be used for the creation of a counterfactual scenario and the estimation of the direct economic impact of MSP. A counterfactual analysis is typically conducted to evaluate the causal effect of a policy on desired outcomes. The causal effect is defined as the difference between the outcomes observed under the policy (actual situation) and the outcomes that would have occurred if the policy had not been implemented (counterfactual scenario). Since it is impossible to observe the same units under both scenarios simultaneously, the counterfactual analysis focuses on identifying an appropriate control group of non-participants with characteristics similar to those of participants in the policy intervention.

A policy is an intervention targeted at a specific population with the goal of inducing change in a defined state or behaviour. This definition highlights three key elements of a policy intervention [17]:

(a)

A target population: this refers to a clearly defined set of units (such as individuals, households, firms, or geographic areas) on which the intervention is applied at a specific time.

(b)

An intervention: this consists of one or more actions (treatments) whose impact on the outcome of interest is compared to a scenario without intervention. Generally, the analysis is confined to a single action, represented by a binary variable (treatment vs. non-treatment or participation vs. non-participation). Those exposed to the intervention are called participants (treated), while those not involved in the programme are classified as non-participants (non-treated).

(c)

An outcome variable: this is an observable and measurable characteristic of the population units that the intervention might impact.

The objective of impact evaluation is to study cause-and-effect relationships. This is carried out to ascertain whether and to what extent participation in the programme has affected the outcome variable, with all other factors held constant. The response to this research question is obtained by deducting the outcome value subsequent to intervention exposure from the value that would have been observed in the absence of treatment (net difference).

A randomized field experiment represents the most valid methodology for establishing the effects of an intervention, as this approach ensures that each unit has an equal probability of being included in the control or intervention groups. This enables outcomes to be observed for both groups, with any identified differences being attributed to the intervention. In the event that the randomization design is not feasible, evaluators have the option of employing alternative designs. These approaches, referred to as quasi-experimental or observational studies, involve the comparison of target units that have received the intervention with a control group of selected targets or potential targets not receiving the intervention. A suitable counterfactual analysis can be conducted with a reasonable degree of confidence if the latter groups are comparable to the intervention group with respect to relevant characteristics. Conversely, selection bias arises when participants and non-participants exhibit differences in significant characteristics that are associated with both status and outcome.

In the context of MSP, a counterfactual approach has been undertaken in four European case studies (Belgium, Germany, Scotland, and Norway). This entailed the formulation of a baseline scenario, based on the assumption that the growth in production value in a given sector would occur at the same average rate as that observed in the years preceding the implementation of the plan [6]. Subsequently, the initial direct economic impact is calculated as the difference between the actual post MSP value and the hypothetical baseline value. In the absence of both the pre- and post-MSP time series, an alternative approach may be adopted. This would entail establishing a control group comprising neighbouring economies with similar environmental, economic, and social contexts, but where MSP regulation remains untested.

4.3. Input–Output Model Framework (IOM)

The input–output model (IOM) framework, or structural interdependency tables, are crucial analytical tools in economics, designed to analyze the intricate web of relationships between different sectors within an economy. These models are indispensable for understanding the impact of economic activities in Maritime Spatial Planning (MSP). By revealing how changes in one industry affect others and the broader economy, IOM provides a detailed view of the economic structure. The models capture the interactions between production, consumption, and income distribution, allowing policymakers and researchers to evaluate economic strategies, simulate the effects of economic shocks, and plan for sustainable development. Furthermore, IOMs facilitate the assessment of how shifts in demand or production within a single sector can trigger ripple effects across the entire economy, aiding in decision making for investments, economic policies, and resource allocation. These models are vital in scenario analysis, helping predict economic outcomes under various conditions and design policies that promote balanced and resilient economic growth.

IOMs represent the accounting of financial flows within a specific economic system (such as a region) over a defined period (e.g., a year) [18]. The basic unit of the table is the economic sector or production branch. These models show the quantity of goods and services produced (output) by each sector that is used as inputs by other sectors in their production processes. The primary purpose of these models is to elucidate the structure of a particular economic system and the interrelationships among its various sectors. It is a linear modelling approach that examines the economic production cycle by analyzing the relative relationships between the flow of production inputs and the resultant flow or destination of produced outputs in an economy. The model comprises a system of linear equations, each describing how an industry’s product is distributed throughout the economy. It can simulate the direct, indirect, and induced impacts of a specific policy on various economic indicators, such as employment, Gross Value Added (GVA), and the balance of trade [19].

An input–output model is composed of various matrices that interact to describe the economic relationships between the sectors of an economy (Figure 1):

• Inter-industrial input matrix (or technological matrix) z_ij represents the flows of goods and services produced by sector i that is demanded and destined for sector j. The row total for sector i thus represents the quantity of goods/services from sector i (totD_i) that is demanded by all industries within the economic system. Conversely, the column total for sector i (totO_i) represents the quantity produced by sector i necessary to satisfy the inter-industrial and final demand of the economic system.
• Final demand matrix f expresses the flow of goods and services d_ij produced by economic sectors and demanded by the different types of final consumers considered in the economic system (e.g., households, government). The row total (totDj) captures the total quantity demanded by the final components of the economic system. The column total (totFj) reflects the total amount of various goods and services demanded by each component. This matrix allows for the analysis of how external demand affects the internal economy.
• Import matrix tracks the inputs that are imported from other countries for each sector of the economy. In an input–output model, it is crucial to distinguish between domestic inputs and imported ones to properly analyse the external dependencies of an economy and the impact of trade policies.
• Value-added matrix reports values related to the remuneration of productive factors (wages and salaries, taxes, interests, rents, and profits) used in the production processes of the n sectors. The individual element v_ij represents the remuneration of the productive factor i for services rendered within sector j. The row total for factor i (e.g., totL) captures the total resources paid to a specific type of productive factor (labour, capital, land, etc.). The column total for sector j (totvj), on the other hand, represents the number of resources absorbed by all components of the added value to produce the quantity produced by sector j. This table serves as a critical tool for understanding the distribution of income within the sectors of an economy. It breaks down how much each sector contributes to the overall value added in the economy through its use of different productive factors. This breakdown helps in assessing the economic contribution of each sector, not just in terms of gross output, but more importantly, in terms of the value added to the production process. This is essential for economic analysis, policy making, and understanding sectoral interdependencies in economic activities.

The traditional IOM approach is therefore useful for the ex ante evaluation of alternative management measures. The model is based on the assumption of general equilibrium for all industries included in the analysis, as well as constant input coefficients:

(a)

Each good is used for production (i.e., as an input in other sectors) or for final demand; final demand is assumed for at least one of the goods produced;

(b)

Each sector produces only one good;

(c)

Inputs are a linear function of outputs;

(d)

The quantities of resources supplied are adjusted to demand on the basis of relationships that take into account production techniques but not prices;

(e)

Supply prices depend on unit factor costs, including discounts for production techniques, but not on quantities exchanged.

It is possible to calculate a unique multiplier for each sector of the economy by manipulating the empirical IOM. These multipliers can be used to estimate the economic impact of alternative policies or changes in the local economy, and are useful for preliminary policy analysis [20]. In particular, it is possible to produce a range of multipliers, from output to employment multipliers, using a set of fixed ratios where the following occurs:

• The output multiplier for industry i measures the sum of direct and indirect requirements from all sectors needed to deliver an additional unit of output of i to final demand.
• The income multiplier measures the total change in income throughout the economy from a unit change in final demand for any given sector.
• The employment multiplier measures the total change in employment for a one-unit change in labour in a given sector.

The construction of multipliers allows the multiplier effect to be decomposed into three parts: (1) initial (or direct) effects, (2) indirect effects, and (3) induced effects for a given sector within the local area. Direct effects represent the initial change in industry. Indirect effects are changes in inter-industry transactions as supplying industries respond to increased demand from the directly affected industries. Induced effects reflect changes in local spending which arise from changes in income in the directly and indirectly affected industries, such as the impact of wages and salaries on the local economy.

A better understanding of the industry under study and its relationship to the wider economy can be gained by decomposing the multiplier into its induced and indirect effects. Induced effects are larger than indirect effects, for example, in more labour-intensive industries. Industries with high wages and salaries also tend to have higher induced effects [21].

Utilizing the IOM framework, various studies have explored the effects of reducing fishing or aquaculture production in Europe. Morrissey and O’Donoghue (2013) performed a 10-sector disaggregation of the marine economy based on the Irish input–output table to analyze the forward and backward linkages within a combined capture fisheries and aquaculture sector [22]. Building on this, Grealis et al. (2017) concentrated on Ireland’s final demand matrix to disaggregate aquaculture and calculate aquaculture multipliers [23].

Surís-Regueiro et al. (2021) applied the IOM to estimate direct, indirect, and induced effects of MSP in three Northern European countries. Instead of using the traditional model based solely on final demand, they also used supply-based models, assuming that the effect of MSP on the maritime sectors can be considered as a supply shock [9]. This means that part of the total output of the economic sectors directly affected by MSP is exogenously determined and their final demand is endogenous. The remaining industries are assumed to remain exogenous in their final demands. This particular model, designated a mixed endogenous–exogenous IOM, has been extensively employed in numerous empirical studies pertaining to the economic consequences of activities associated with the utilization and exploitation of natural resources. A notable limitation of this approach, however, is the assumption that the regional economies of the countries studied (German Baltic Sea, Norwegian North Sea, and Skagerrak) have similar cross-sectoral flows as their respective economies as a whole. It is also assumed that marine activities operate with the same input coefficients and the same capacity to generate GVA and employment per unit of output as the average of the corresponding sector when grouped in mixed IO sectors (combining marine activities with other non-marine activities).

In conclusion, while the IOM is a powerful tool for estimating the socio-economic impacts of MSP, the traditional deterministic model requires a number of “adjustments” to take into account several factors. These include the sub-national and sub-regional scales of marine and coastal planning, the sectoral disaggregation of blue economy activities, and the inclusion of a “dynamic” component to capture factors such as technological change, price dynamics, investment, and capital accumulation.

4.4. Environmental Input–Output Models (EIOM)

Input–output models are valuable tools in environmental studies due to their ability to provide a comprehensive view of the economic system and trace the origins of both direct and indirect inputs used in production processes. By linking economic and environmental variables on both the production and consumption sides, these models offer insights into the environmental impacts of economic activities. Specifically, environmental input–output models (EIOM) extend the traditional input–output approach with additional datasets and assumptions, helping to assess the environmental footprint of these activities, with a focus on the externalities they generate [24]. EIOMs can be categorized into three primary types:

• Generalized input–output models: These models expand the matrix of technical coefficients by adding extra rows and/or columns to reflect activities related to pollution generation and reduction. They are typically divided into two types—models used for impact analysis and those used for planning applications.
• Environmentally extended input–output models: These models extend the inter-industry framework to include additional “ecosystem” sectors, capturing flows between economic and ecosystem sectors similarly to an inter-regional input–output model. They have been increasingly developed to address various environmental issues, from local air and water pollution to global challenges like greenhouse gas emissions and climate change.
• Commodity-by-industry models: These models treat environmental factors as “commodities” within a commodity-by-industry input–output table. While these categories often overlap and are combined in practice, they help distinguish the main features of these models—either by extending the technical coefficient matrix to reflect environmental impacts or by incorporating additional “ecosystem” sectors that record flows between the environment and the economy.

From a production perspective, it is possible to determine the quantity of natural resources and the extent of pollution involved in the creation of goods and services, including both direct and indirect inputs. On the demand side, environmental variables, such as natural resources and pollution, can be measured within the consumption of goods and services in the economy (Figure 2). While IOMs track the flow of goods and services across industries by recording the monetary transactions associated with production and consumption, EIOMs are particularly suited for analyzing the supply chain-related environmental impacts of these activities.

The environmental and social extensions of the input–output approach are commonly used to calculate the climate, water, and material resource footprints of production and consumption at national or regional levels. This allows for a comprehensive examination of various factors associated with economic activities and policies, such as employment, pollution, water usage, and capital expenditure [25].

EIOM is a well-established method within applied economics and industrial ecology for analyzing global value chains, and it plays a significant role in environmental economics for estimating the environmental footprints of specific agents, such as the global greenhouse gas emissions associated with household consumption in a particular country. A critical consideration in environmental modelling is the selection of an appropriate unit of measurement for environmental or ecological quantities, which may be expressed in physical units or through monetary valuation. Additionally, as with the standard IOM, extended environmental analysis is predicated on several assumptions that must be critically evaluated when interpreting the outcomes of such studies:

• Homogeneity of products: it is assumed that each economic activity produces only one physically homogeneous product in calculations based on the standard I-O model. However, in reality, the high level of aggregation of activities leads to heterogeneous outputs, and many industries also produce by-products.
• Homogeneity of prices: it is assumed that each industry sells its characteristic output to all other economic activities and to the final consumer at a constant price.
• Constant economies of scale: it is assumed that all inputs and outputs increase proportionally, maintaining constant economies of scale.
• Allocation of investments: the results obtained for the resource equivalents of exports are significantly influenced by the manner in which investments are incorporated and whether they are depreciated.

Understanding and addressing these methodological issues will be crucial for advancing research in this domain.

In their 2017 study, Huang et al. [26] explored the applicability of the EIO approach for the decision making regarding the ocean-related activities in Xiamen Bay, specifically focusing on two leading marine industries in the area: marine transportation and waterfront tourism. This study assessed the economic output impacts and various environmental effects, including energy consumption, air emissions, freshwater use, and water pollutant discharge, associated with these two sectors, utilizing the 2007 regional input–output (IO) table as a basis. Furthermore, a comparative static analysis was conducted to simulate the influences of expected economic growth and structural change on selected economic and environmental parameters. The results indicated that the economic multiplier is larger for waterfront tourism than marine transportation. Additionally, for the same final demand, the tourism sector generates larger economic impacts. With regard to environmental implications, the economic activities associated with waterfront tourism generally present smaller environmental footprints per unit of gross output than those with marine transportation.

The EIO framework represents a promising approach to conducting systematic and structured analyses on a macroscopic scale in the context of marine spatial planning, as it allows for the integration of socio-economic and environmental systems, as well as the implementation of policy scenarios. Nevertheless, as with the conventional IOM, the primary challenge lies in the estimation of model parameters tailored to regional and marine sectoral disaggregation which might imply overestimations [10].

4.5. Social Accounting Matrix (SAM)

A SAM is an array of data, organized as a grid or matrix, which represents the flows of resources between different economic entities (such as productive sectors and institutional units) over a specified period of time (usually one year). It is a natural extension of input–output tables through the addition of matrices which take into account the relationships between functional distribution, income personnel, and the composition of consumption expenditure. In this way, the whole process of income generation can be captured, as well as the behaviour of households in terms of consumption and investment, since all flows in this cycle can be described quantitatively. A SAM model thus captures income flowing from industrial production sectors to factors of production (such as labour and capital) and then on to institutional sectors such as households and different levels of government, overcoming one of the most critical limitations of the IOM. The model can be used to study the distributional effects of a policy or an exogenous shock on non-industrial sectors and on different types of institutions [27]. The model also captures the endogenous demand for goods and services by these institutions (governments and households). Therefore, the resulting multipliers can include effects that an IO model cannot capture.

SAMs represent the economic process and highlight its circularity in a flexible manner. The disaggregation of individual accounting blocks allows for the identification of specific interdependencies that would remain hidden in traditional statements. Moreover, with the availability of statistical information, one can choose the classification type suitable for specific economic system analysis needs. The economic agents present in the SAM include institutional sectors defined as decision-making centres, articulated into families, businesses, non-profit entities, and public administration.

To use the SAM as a model for analysis and forecasting, it is necessary to separate endogenous from exogenous accounts, using criteria that depend on the specific subject. The rest of the world account and the investment (or capital formation) account are usually considered exogenous. The constructed model allows the evaluation of the impact of exogenous variables on the economic system, affecting all endogenous variables within the system. It is possible to look at the impact on a number of key variables, such as GDP, income, consumption, as well as the spillover effects on the rest of the world.

A SAM has six basic groups of accounts:

—

Activities or commodities (or both, separated);

—

(Production) factors;

—

(Private) institutions—households and corporations/enterprises;

—

Government (public institution);

—

(Combined) capital accounts;

—

Accounts for the rest of the world.

The final dimensions of the matrix are determined by the level of disaggregation of these six basic groups.

Several scientific studies have employed social accounting matrix (SAM) models to explore various sectors, particularly following the BioSAM approach. This approach entails a highly detailed sectoral breakdown of the agricultural and agrifood industries, while also explicitly representing current biomass utilization in the bioenergy, biochemical, and bio-based industries [28]. For instance, Kim and Seung (2020) utilized a SAM to compare the economic contributions of wild fisheries and aquaculture in Gyeong-Nam Province, Korea, specifically to mitigate the risk of double counting inherent in the conventional Leontief demand-driven model [29]. Additionally, Waters et al. (2014) employed a multi-regional SAM model encompassing three regions—Alaska, the West Coast, and the rest of the United States—to estimate the multi-regional contributions of the head and gut fishing fleet in terms of changes in output, employment, and income [27].

The flows that are represented in a SAM can be modulated according to the information that is available and the objectives of the analysis. For this reason, there is no standard form for SAMs, apart from the fact that they are square matrices, which leads to problems of uniformity, definition, and communication within the scientific community. However, based on the information available and the ultimate purpose of their use, this flexibility makes SAMs highly adaptable to specific contexts and for scenario analyses. Consequently, the main difficulty in collecting the data and constructing the matrix is to include a large proportion of multi-regional flows, which occur in many sectors.

4.6. Agent-Based Models (ABM)

Agent-based models belong to the family of computational models used to simulate the behaviour of complex phenomena through the interaction of autonomous individual entities called agents [30]. These models are widely used in various fields, including social sciences, economics, biology, ecology, and engineering, to explore emerging dynamics and collective behaviours. The most common features of agent-based models can be summarized as follows [31]:

• Agent autonomy: agents act autonomously and make decisions based on internal rules or behaviours. This autonomy enables the representation of individuality and diversity within the system.
• Interaction between agents: agents interact with each other as well as with the environment around them. These interactions can be direct or indirect, resulting in unpredictable collective behaviours that emerge from the sum of the agents’ actions.
• Adaptation and learning: agents can adapt to the environment and learn over time, modifying their behaviour in response to changes. This capability captures the evolutionary dynamics of systems.
• Heterogeneity: agents can differ from each other, contributing to the complexity and diversity of the system represented by the model, in terms of characteristics, behaviours, and decision rules.
• Complex pattern emergence: ABMs are known for their ability to generate emergent phenomena, namely system behaviours that are not easily predicted at the level of the individual agent, but which emerge from the overall dynamics of the system.
• Sensitivity to initial conditions: ABMs can be sensitive to initial conditions and small parameter variations, reflecting the chaotic nature of many complex systems.
• Validation and calibration: due to their complexity, validation and calibration of these models is a major challenge. To ensure a realistic representation of observed real-world behaviour, careful comparison with empirical data is required.
• Applicability and flexibility: ABMS are flexible and can have applications in a wide range of sectors and domains. They can be used for future scenario exploration, hypothesis testing, and policy design.

Overall, agent-based models provide a powerful tool for exploring the complexity of dynamic systems through the simulation of individual interactions, leading to a better understanding of complex phenomena across various scientific disciplines. ABMs have been used to investigate various aspects of marine planning in a number of scientific papers. McDonald et al. (2008) focused on four sectors: oil and gas, conservation, fisheries, and urban and industrial development, and provided a selection of cross-sectoral scenarios, management strategies, and computer representations [32]. These included projected future sectoral activities and impacts, and sectoral responses to alternative management strategies. Bailey et al. (2019) studied marine fisheries as an example of complex human-environment systems based on the POSEIDON model, a computational approach to policy design, which includes an adaptive agent-based representation of a fishing fleet coupled to a simplified marine ecology model. The agents (fishing vessels) respond adaptively as a group to their environment (including policy constraints), rather than having programmed responses based on empirical data [33]. In the presence of complex policy scenarios, this conceptual model reproduced a wide range of fleet behaviours observed in real fisheries. However, a numerical optimization model cannot anticipate all the potential mechanisms or behavioural characteristics associated with the agent, such as forward planning to discount predictably bad decisions or risky options.

4.7. Cost–Benefit Analysis (CBA)

CBA is commonly used to quantify in monetary terms the costs and benefits of alternative management decisions, allowing for comparative analysis of available options. It essentially consists of comparing the costs and benefits of an investment to determine whether it is economically and/or socially feasible to implement. This is an ex ante evaluation approach, as it is in principle carried out before the investment is made. In economics, a policy is considered socially desirable if the total benefits outweigh the costs. Considering that the allocation of scarce resources, such as part of the marine space, involves a trade-off between different uses, CBA is a suitable tool for comparing benefits and costs [5] and is the most commonly used method for estimating the economic impacts of implementing MSP [10].

A full cost–benefit analysis would require collecting a wide range of information to be able to monetize all the tangible and intangible costs and benefits associated with MSP and to compare the opportunity costs of alternative public investments. Therefore, in the marine socio-environmental system, which is characterized by a large number of actors and interactions over a long-term planning horizon, CBA is often used as a complementary tool for trade-off analysis to assess whether a plan contributes to improving social welfare compared to an alternative scenario without MSP [3]. The main drawback of using CBA is that it fails to provide integrated economic valuations, i.e., in the case of environmental investments such as coastal restoration, the ability to identify all the benefits, including the economic value of the new state of the asset, and not just the economic value of the restored environmental asset. Because CBA can only consider a limited number of causes and ecosystem interactions, it has been defined as a “narrow-system boundary tool” that should be combined in environmental management with other more holistic approaches such as the environmental input–output matrix [34].

In employing CBA as a decision-support instrument in scenario analysis, three criteria warrant particular consideration [5]:

• It is essential to consider all parties, whether directly or indirectly affected by the MSP agreement, as recipients of benefits or bearers of costs.
• The full implications of MSP policies, including intrinsic and non-market values, should be reflected in the estimation of costs and benefits.
• The relevant social discount rate, i.e., the social rate at which representatives of the current generation discount the welfare of future generations.

In particular, the choice of discount rate is very complex. The social discount rate reflects the opportunity cost of capital from an inter-temporal perspective for society as a whole and is used to discount economic costs and benefits of investments. In other words, it reflects society’s view of how future benefits and costs should be valued against those of the present. In that sense, any discount rate involves a value judgment about the future and affects the weight given to future benefits or costs. A zero social rate of time preference is derived from the assumption that the utilities occurring at each moment are equally weighted, i.e., that current and future consumption are indifferent from a utility point of view. In contrast, a positive discount factor indicates that current consumption should be preferred to future consumption, while a negative discount rate indicates the reverse. In a perfectly competitive economy and in equilibrium, the social discount rate would coincide with the financial discount rate, which would be equal to the interest rate on the financial market. However, because capital markets are distorted, this is not the case in practice [35]. It is therefore intuitive to conclude that the discount rate is a critical element in cost–benefit analysis when costs and benefits differ in their distribution over time and the expected outcomes of MSP processes (e.g., climate change effects or coastal erosion) are usually projected over long-time horizons.

Various approaches have been proposed in the literature to estimate the social discount rate. The social rate of time preference—the rate at which society is willing to postpone a unit of current consumption in exchange for more future consumption—is the most popular in the MSP literature. The rationale behind this approach is that the government should consider the well-being of both current and future generations when formulating an optimal planning programme, with a focus on aligning individual consumption preferences. Other approaches exist and could be used for intertemporal discounting, including probabilistic risk assessment, which is widely used to develop cost–benefit analysis under uncertain MSP scenarios, i.e., as a sensitivity/uncertainty analysis to evaluate a range of scenarios with different discount rates [3].

4.8. Multi-Criteria Decision Analysis (MCDA)

Multi-criteria evaluation supplies a powerful framework for the implementation of the incommensurability principle, since it may be inter/multi-disciplinary, participatory (involving local community) and transparent (being criteria presented in their original form) [36]. Therefore, it turns out to be an appropriate assessment framework for micro and macro sustainability policies.

MCDA allows for increasing the degree of congruence and coherence between the decision-process evolution and actors’ value systems and objectives, helping in contexts characterized by ambiguity, uncertainty, multiple perspectives, and possibilities [37]. It refers to formal approaches that consider multiple criteria within decision-making processes. These evaluation methods provide a structured framework for discussion which can contribute to the management/resolution of conflicts between stakeholders (with different economic, social, and environmental interests) and to the optimization of resources, making participation processes for public policies more transparent, and improving the availability of information for managers and other actors involved [38]. Used for the evaluation of alternatives on the basis of multiple criteria or factors, MCDA helps to rank or select the best alternative based on the preferences and importance of each criterion by systematically considering different dimensions, assigning weights to criteria, and applying different decision models (such as weighted sum and analytic hierarchy process).

Multi-criteria methodologies allow to consider a wide range of criteria, including social and environmental ones, and not just the monetary dimension, thus providing an integrated assessment of the issues concerned. They use quantitative and qualitative information to meet multiple objectives, to better understand outcomes, impacts, and overall performance.

MCDA is related to the Pareto assumption about the multiplicity of interests, often conflicting. The Pareto optimal set and its trade-offs provide useful reference and information for decision makers. The chosen alternative is usually not the result of a formal maximization problem. Rather, it is a subjective assessment of the relative importance of the objectives by the decision makers. Therefore, rather than proposing a single optimal solution, the multi-objective approach focuses on informing decision makers about the range of effective choices and the consequences of different options [39].

Multi-criteria methods can be distinguished according to different approaches: using a single synthesizing criterion, without incomparability; synthesizing by outranking with incomparability; trial-and-error interaction [40]. The analytic hierarchy process/analytic network process, multi attribute value theory/multi attribute utility theory, and Evamix are among the methods based on the single-criterion approach. Moreover, multi-criteria methods are either discrete or continuous, according to a finite or infinite set of alternatives [41].

There are continuous methods (linear programming, goal programming, compromising programming), fuzzy methods (using imprecise/uncertain information), and soft systems methodology (structured approach to systems analysis, action-oriented, based on the participation of heterogeneous groups) [38].

The analytic hierarchy process (AHP) is a pairwise comparison-based MCDA method. The weighting procedure of AHP is often used for defining the relative importance between decision criteria.

ELECTRE, ELimination Et Choix Traduisant la REalité, is a non-compensatory multi-attribute decision-making method, based on the comparison of alternatives considering individual criteria. Differently from proper compensatory methods, in the ELECTRE method, the weights are coefficients of importance, not criteria substitution rates [42].

NAIADE, Novel Approach to Imprecise Assessment and Decision Environments, is a discrete multi-criteria evaluation method, dealing with quantitative and qualitative data, suitable for planning problems characterized by a high degree of uncertainty, in relation to complex spatial, social, and economic interactions [43]. The focus is on evaluation of alternative scenarios, on the basis of decision criteria and stakeholders’ judgement.

Early data analysis, based on problem structuring according to sustainability criteria and indicators, may be further attested by the application of evaluation methods, in order to better understand interactions and identify useful decision rules [44]. Rough set approaches turn out particularly useful when ordinary statistical methods are not convenient/effective. Advanced extensions, related to the above approach, have been implemented, also in the field of multi-criteria decision making (with particular reference to the dominance-based rough set approach) [45].

MCDA is emerging as an area of growing interest in fisheries management, aquaculture, and marine conservation. A wide range of objectives have been identified, such as resource allocation and marine-protected area planning, to support decision making in a variety of marine scenarios [46]. Participatory MCDA, which refers to decision-making processes that promote effective stakeholder participation, is becoming more widespread. The basic steps for an appropriate participative MCDA are the following: exploring the decision problem for common understanding; establishing objectives and criteria through inclusive and transparent participative approaches; developing alternatives together with stakeholders; assessing consequences using local knowledge and user experience, not just decision makers and experts; establishing weights (of importance) of objectives, evaluating trade-off); prioritizing alternatives using MCDA methods [47].

Participative multi-criteria decision analysis has been increasingly applied in marine multi-objective management situations. Almost one hundred publications about applications in marine management (fisheries or aquaculture) and marine conservation had been carried out until 2015. Approximately one third of these studies explicitly involved stakeholders at one or more stages of the process [46]: mostly participation, but also consultation, mainly at the stage of defining objectives and criteria, and eliciting weights (of importance).

In the context of collaborative decision making, multi-criteria analysis can be useful to improve the governance of protected areas, which are characterized by strong conflicts between stakeholders, by clarifying values and objectives and promoting mutual understanding and social learning. Among the applications of MCDA to MSP, the Delphi method is used in combination with other techniques for stakeholder involvement. Sauve’ et al. (2022) implemented a process to assess the pros and cons of alternative coastal defence measures by identifying and weighting 16 criteria through the PROMETHEE method [48].

In the planning and management of marine or coastal areas, challenges frequently arise in evaluating and assigning value to the pressures exerted by various spatial uses or non-uses and their consequent impacts on ecosystem components. Additionally, trade-off issues often emerge, particularly when balancing different, and sometimes conflicting, objectives related to human activities. In such scenarios, multi-criteria decision analysis proves to be an invaluable tool. MCDA facilitates a systematic analysis and structuring of the decision-making process by organizing it into hierarchical decision elements—namely goals, objectives, and criteria—against which the performance of alternative options is assessed [49].

In case of spatial settings, linking MCDA to geographic information systems (GIS) turns out as very useful for the spatial analysis and the problem visualization. The above combination, that is spatial MCDA, may also be useful within offshore spatial planning and marine settings, in case of planning new uses or reorganizing existing ones, with reference to adequate site selection [49]. Spatial MCDA helps to concretize issues of marine and coastal spatial planning; in particular, the use of spatial data and analyses together with expressed decision criteria and objectives may help in identifying potentially suitable places for planned offshore installations. The above procedure is consistent with the participatory nature of MSP, since the effective involvement of stakeholders and experts may be enhanced through the use of mapping. The scarce spatial data infrastructures and related accessibility suggest to subject spatial MCDA to proper uncertainty and sensitivity analyses.

In-depth studies about Strategic Environmental Assessment (SEA) and its supporting methodologies show multi-criteria decision analysis to be the most SEA-coherent approach. The potential of MCDA for SEA already emerged twenty years ago, in terms of evaluation of various intervention alternatives, considering heterogeneous criteria and multiple/diverging standpoints [50]. No single MCDA method can be applied to every possible problem, but the wide series of procedures offers many different operational opportunities.

Among the MCDA methods, the ELECTRE family shows high suitability to the SEA evaluation phase, also considering possible integration with other multi-criteria evaluation methods [50]. The ELECTRE method is a widely used decision-making tool, applied within several areas, from transportation to environmental protection, with “natural resources and environmental management” turning out as the most popular application area. It is able to handle imprecise and uncertain data, to deal with complex and conflicting criteria, to accommodate various decision-making scenarios, through the refined computational process, the outranking approach, the consideration of indifference, and preference thresholds [42]. The disadvantages are related to the possible impact of threshold values on the final results, the time-consuming nature, and the requirement of a metric scale for the discordance index.

Multi-criteria decision analysis, combined with other assessment processes and participatory techniques, may be a valuable support for MSP and management authorities.

4.9. Computable General Equilibrium Model (CGE)

The CGE model is an analytical tool used to study the impact of economic, political, and environmental changes on the general equilibrium of an economy as a whole. It consists of a system of equations describing an economy and the interactions between its components. Equations describing the supply behaviour of producers and the demand behaviour of consumers are derived directly from economic theory. Both exogenous and endogenous variables and budget constraints are included in a CGE model. All the equations in the model are solved at the same time (comparative static models) to find an equilibrium at the national level where, at given prices, the quantities of supply and demand are the same in each market. To conduct experiments with a CGE model, economists change one or more exogenous variables and re-solve the CGE model to find new values for the endogenous variables. In this way, it is possible to observe the effect of the exogenous change, or “economic shock”, on the market equilibrium and draw conclusions [51].

A CGE model describes production decisions in different industry sectors. While a partial equilibrium model assumes that incomes and prices in the rest of the economy are fixed, a CGE model describes how changes in the supply and demand of a good in one market can lead to changes in employment and wages, and hence in household income and expenditure, throughout the rest of the economic system. It also describes how the behaviour of the whole economic system can be affected by changes in the prices of other goods and services in the economy. This means that, in addition to demand from producers and consumers, demand from other economic actors such as the state, investors, and foreign trade is taken into account.

Since a CGE model represents all the microeconomic activities in an economy, the sum of these activities describes the macroeconomic behaviour of an economy. These activities include Gross Domestic Product (GDP), aggregate saving and investment, the trade balance, and, in some CGE models, the government fiscal deficit or surplus.

The term “computable” is therefore used to describe the ability of this type of model to quantify the economic relationships that exist in the economic system and to measure the shocks that can be imagined to it. The ability to quantify values associated with “what if” scenarios allows economists to make useful and relevant contributions to economic policy debates. CGE modellers have provided influential analyses of the costs and benefits of government policies such as trade agreements like North American Free Trade (NAFTA) agreements, emission control programmes, and the agreement to admit China to the World Trade Organisation (WTO).

In a CGE model, “general” means that the model simultaneously considers all economic activities in an economy, including production, consumption, employment, taxes, savings and trade, and their interrelationships, for example, if an increase in the price of fuel changes the cost of production of manufactured goods.

In a CGE model, equilibrium occurs at that set of prices at which all producers, consumers, workers, and investors are satisfied with the quantities of goods they produce and consume, the industry in which they work, the amount of capital they save and invest, and so on. The CGE model’s equilibrium must also satisfy some important macroeconomic market-clearing constraints, generally requiring that aggregate supply of goods and services equals aggregate demand, that all labour and capital are employed, and that national or global saving equals investment spending.

As for the input–output model, a “standard” CGE model is static (single period), representing a single country with a fixed endowment of production factors, such as labour and capital. Such shocks, for instance, a tax, may prompt reallocation of productive resources in ways that may not always be optimal. Static models can certainly provide insights into how potential winners and losers may be affected by an economic shock. On the other hand, there is a notable shortcoming in that they do not delineate the adjustment trajectory. The adjustment process may potentially result in periods of unemployment and displacement, which could have implications for society. It is important to consider these potential consequences, regardless of the expected benefits in the new equilibrium.

4.10. General Equilibrium Ecosystem Model (GEEM) and Bio-Economic Modelling (BEM)

With the development of bio-economic modelling, which is widely applied in fisheries management, CGE models have incorporated natural capital and ecosystem services. A BEM can be described as a mathematical representation of biological and economic systems, which typically links economic and biological components and parameters together. The biological dimension represents the natural resource, whereas the economic component characterizes the resource users, for example, the fishermen. Modelling the technical interactions and links between these two is of critical importance and represents a significant challenge, given that it encompasses both biological variability and human behavioural aspects. Model complexity is predominantly driven by the underlying assumptions on system dynamics, interactions and feedback mechanisms, key parameters, data availability, as well as their relationships, interactions, and feedback mechanisms [13]. A bioeconomic model is therefore defined by a number of interconnecting components, including functional relationships between input and output variables. The aforementioned relationships are typically developed through the utilization of specific software designed for this purpose. In each simulation, the variables representing fishing effort (e.g., fishing days, number of vessels, gross tonnage, and engine power) interact with the biological module via the pressure module. This interaction is represented by fishing effort and fishing mortality, which serve as the interface between the biological and economic modules. The biological model is thus influenced by the pressure exerted on key metrics, including the number of individuals in a given stock, the spawning stock biomass of a fish population, and the size and age structure of that population. This ultimately affects the catches, which are comprised of both landings and discards. The landing component of catches (simulated on the basis of functions simulating the evolution of the biomass and the demographic structure for each stock) is incorporated into the economic module for the estimation of economic variables and indicators. The primary output of the economic module is represented by profits, which can influence fishing behaviour (within the behavioural module) and modify the levels of capacity (number of vessels, vessel tonnage) and activity (days at sea) for the subsequent simulation step.

In the ecosystem framework, ecological modelling represents an evolution of bio-economic models, as it is capable of evaluating the cumulative impacts of environmental and human activities on marine food webs, which cannot be answered by single species models. The model represents an ecosystem in which the interconnection between trophic groups is based on biomass. These groups are linked by mass transfers [52]. The model is founded upon the principle of mass balance, which posits that the production of any given prey is equal to the biomass consumed by predators, plus the biomass caught and any exports from the system.

Waters and Seung (2010) constructed a CGE to investigate the impact of two exogenous shocks on fishing activities in Alaska: a reduction in the allowed catch of pollock and an increase in fuel prices [53]. This study examined shocks from the supply side, utilizing fixed-price models. The 2011 study conducted by Chen et al. utilized CGE to examine the economic impact of the construction of the Taipei Port Container Terminal situated in northern Taiwan. This study has proved a significant source of information in formulating maritime and regional navigation policies, as well as in assisting business managers in strategic planning [54]. In their 2014 study, Allan et al. employed multi-sectoral economic models to investigate the potential consequences of the 2010–2020 plan for the development of marine energy capacity in the Pentland Firth and Orkney Waters region, situated off the northern coast of Scotland. It was shown that the conventional input–output modelling method substantially overestimates the impact on employment and value added when compared to the computable general equilibrium approach, which explicitly models the short-term scarcity of productive resources [55]. In a comprehensive socio-economic–ecological framework, Wang et al. (2020) evaluated input and output control policies for fisheries management in the Pearl River Delta in China [56]. A computable general equilibrium (CGE) model, linked to an Ecopath with Ecosim model, was proposed as a means of assessing changes in economic and social indicators [57].

In essence, the Ecopath is a mass balance food web model that allows nodes in the food web to be classified as groups (such as feeding guilds), species, or stages of species (such as juveniles and adults). For each node within the food web, calculations are made in relation to the respective levels of production and consumption. The production of a node in the food web can be expressed as a function of catch, predation, net migration, biomass accumulation, and other forms of mortality. Similarly, consumption can be expressed as a function of production and respiration, as well as unassimilated food. As with conventional bio-economic modelling techniques used in fisheries management, the basic economic parameters incorporated into the marine ecosystem model include the profitability of economic activities that exert pressure on the region under study.

With reference to the European context, Qu et al. (2023) constructed a computable general equilibrium (CGE) model that incorporates natural capital and ecosystem services. This model was developed to anticipate potential conflicts and synergies associated with the development of offshore wind farms in proximity to existing marine activities in Scotland [58]. The study indicated that the expansion of offshore wind farms has a markedly adverse impact on the fishing industry, while fish stocks experience a slight benefit as the catch is reduced. Furthermore, the increase in fish stocks resulting from the implementation of closed areas and the artificial barrier effect could confer benefits to the fishing sector, with a potential ripple effect across the entire economy. The combined impact of offshore wind farm expansion and increased fish stocks highlights the potential benefits of the multi-use of marine spaces by offshore wind farms and fishing activities.

5. Results

In this review, we employed a systematic approach, beginning with the simplest and most general evaluation analysis based on the collection of indicators and progressing to the more complex field of general equilibrium ecosystem models. This latter approach emphasises the interconnections between the diverse economic and ecological elements of the coastal marine system. It shares the same flexibility as other sophisticated computational approaches, such as the social accounting matrix or integrated agent-based modelling, and represents a more comprehensive perspective.

The comparison on decision-support tools for MSP, in fact, revealed considerable heterogeneity across methods in terms of complexity, applicability, and implementation challenges. The majority of these methods are dependent on the particular characteristics of the economic sectors being investigated, as well as the inherent features of the planning areas. The limited adaptability of these approaches raises concerns about the replicability of the results to other social and ecological systems [59].

A variety of analytical tools are available, including those that utilize stochastic or econometric models, such as counterfactual analysis or bio-economic models based on time series of biological and economic indicators. The objective of these tools is to assess the direct impacts of policies in the short and longer terms, albeit that their scope is usually limited to a restricted range of sectors, such as fisheries and aquaculture. Other commonly employed models, such as input–output analysis, though deterministic and static by nature, enable the calculation of both direct and indirect effects resulting from shifts between different economic sectors. Furthermore, these models facilitate the mapping of intersectoral transactions, thereby providing a comprehensive overview of the economic interdependencies within a region and among a wide range of sectors. Models such as input–output models, the social accounting matrix, and general equilibrium models are particularly useful for defining the current state of an economy or a status quo scenario. The aforementioned models may also be used to predict the consequences of external shocks, such as those caused by the implementation of management measures related to MSP, thereby offering a comprehensive framework for anticipating potential future developments. However, the inclusion of the dynamic component in this type of model requires the availability of a very large number of bio-physical and economic indicators.

Additionally, quantitative analyses based on the collection of environmental and socio-economic indicators and qualitative approaches like cost–benefit analysis and multi-criteria analysis serve as valuable supplements to decision-making processes across various scenarios and overcome the limitations of quantitative tools. Multi-criteria decision analysis can contribute to a better understanding of social-ecological systems and facilitate decision making through transparent and integrated approaches, considering stakeholder preferences.

Nevertheless, a common limitation of all the methods analyzed is the lack of data at appropriate spatial and sectorial scales to estimate the relationship between the different socio-ecological components of the system under investigation. The distinctive character of the blue economy, coupled with the heterogeneity of national and regional contexts, renders the aggregation of reliable and comparable data a challenging endeavour. This, in turn, may potentially compromise the accuracy of the resulting insights. At the same time, the need to include in the analysis the inter-linkages between the different uses of the sea implies the need for the integration of different analysis tools that allow for a comprehensive socio-economic assessment of MSP [10]. The selection of the spatial dimension of the socio-economic data inevitably involves a compromise between the geographical scale and the sectoral disaggregation. The European official statistics frequently lack socio-economic data at the subnational level for emerging blue sectors such as offshore renewable energy, underwater activities, and marine biotechnology. Conversely, data for established blue economy sectors with national importance (e.g., extractive industries, maritime transport) collected at a very high geographical granularity may not accurately reflect their actual relevance for the national economy.

In order to provide more precise forecasts and pertinent data for planning and economic policy, researchers have initiated the development of more flexible and adaptive models that can integrate interregional exchanges, disaggregated sub-sectorial indicators, and temporal variability of coefficients. To cite one example, the environmental input–output approach to marine spatial planning represents a novel contribution to the field of the blue economy and marine policy studies. This approach considers the sub-regional dimension of economic interactions within each area, including environmental considerations for future scenarios. Consequently, it can be readily incorporated into the System of Environmental–Economic Accounting (SEEA) framework, a widely used method for integrating economic and environmental data and connect the satellite account of marine-dependent industries with ecosystem services valuation, to account for the stocks and health of the resources that support the marine economy [60]. The SEEA offers a comprehensive and multipurpose understanding of the relationship between the economy and the environment, including the stocks of environmental assets and their changes. This is achieved by aligning with the concept of added value as used in the field of national accounting. As a result, the framework that emerges is one that can be used consistently to integrate the values of economic activities reliant on ocean and coastal resources with non-market activities and corresponding ecosystem services.

6. Conclusions

Incorporating socio-economic elements into the various stages of MSP is a major challenge. This activity requires the use of heterogeneous and interdisciplinary analytical tools capable of adapting to the inherently complex ecological system under consideration, while simulating its intricate interactions and dynamics.

A review of the most commonly used socio-economic approaches within the domain of MSP reveals that there is no single method; rather, a set of methodologies enables a holistic understanding of the complex ecological and socio-economic processes that interact at the marine and coastal scale. This comprehension is vital for the successful execution of the managerial measures encompassed within the plans, hence necessitating constant monitoring and interdisciplinary involvement.

In order to construct an integrated assessment framework, it is evident that the various approaches examined, including those of a more sophisticated nature, need to be mutually integrated so as to overcome the potential limitations of these instruments. One such limitation is the restricted availability of statistical data, which could therefore be supplemented by qualitative assessments provided by experts and stakeholders. Furthermore, the study emphasized the pivotal role of the type and quantity of available data in selecting an optimal analytical model.