Developing a Monitoring and Evaluation Framework for Sustainable Maritime Spatial Planning: A Stakeholder-Driven Approach

Abstract

Effective monitoring and evaluation (M&E) are essential for ensuring that Maritime Spatial Planning (MSP) contributes to the sustainable development of the blue economy while maintaining alignment with institutional frameworks. The study presented in this paper develops a stakeholder-driven M&E framework for sustainable MSP, emphasizing a participatory methodology to enhance the relevance and applicability of performance assessment. Using a structured mutual learning approach, the research engaged stakeholders in two iterative rounds: the first identified key strategic objectives for a sustainable blue economy through dialogue and a complementary questionnaire survey, while the second refined these into corresponding specific objectives. This process was applied in the context of a case study in Greece, where MSP implementation is shaped by national and EU regulatory frameworks and the socio-economic dynamics of the coastal and maritime sectors. The case study provided a practical testing ground for the proposed methodology, involving stakeholders from government, industry, and civil society to ensure a comprehensive perspective. The insights gained informed the design of a key performance indicator (KPI) framework, integrating qualitative and quantitative metrics tailored to the regional maritime governance landscape. These metrics were selected based on the SMARTIE (Specific, Measurable, Achievable, Relevant, Time-Bound, Inclusive, Equitable) criteria and were clearly aligned with the established objectives. The frequency of measurements, appropriate data collection methods, and indicative data sources were also defined to provide a complete KPIs framework. This stakeholder-driven methodology strengthens the adaptive capacity of MSP by ensuring continuous assessment and revision aligned with sustainability objectives and facilitating ex ante, intermediate, and ex post evaluations. The proposed framework is scalable and transferable, offering a systematic approach to improving policy coherence and decision-making across different geographic, administrative, and sectoral contexts, enabling sustainable governance and maritime governance.

1. Introduction

The blue economy is essential for coastal and island nations and regions, significantly contributing to local, regional, and national economies. The marine environment provides critical resources for these regions, both economically and socially, underscoring the need for sustainable management to preserve its ecosystems, functions, and services. To achieve this, the blue economy must be managed in alignment with various policy frameworks and land-based activities. Maritime Spatial Planning (MSP) serves as a valuable tool to address these objectives by fostering equitable and balanced development that respects the marine environment and ensures long-term sustainability through efficient monitoring, evaluation, and adaptive planning [1].

The complexity of the blue economy arises from the diverse stakeholders engaged in its economic, social, and environmental aspects, as well as the increasing demand for marine space and resources. Therefore, the effective allocation of marine space and resources, aligned with stakeholder interests and needs, is essential for minimizing conflicts and fostering synergies. This is facilitated through participatory processes embedded in MSP, promoting a multisectoral, inclusive approach that ensures active stakeholder engagement.

Despite the broad stakeholder participation in MSP, variations in knowledge levels and sectoral expertise present challenges for effective engagement. A mutual learning approach has shown promise in addressing these challenges by fostering a shared understanding, bridging knowledge gaps, and strengthening the capacity within the planning area [2,3,4,5,6]. This approach enhances equity and balance among stakeholders, improving the reliability and validity of the MSP outcomes [2,7,8,9].

Mutual learning has been integrated into MSP for several purposes, including identifying stakeholder priorities, facilitating knowledge exchange, and improving participatory decision-making [2,7,8,9]. This approach helps planners gain deeper insights into stakeholders’ perspectives, collect qualitative and quantitative data, and promote stakeholder commitment to MSP implementation. Stakeholders, in turn, benefit from knowledge sharing, an enhanced awareness of MSP benefits, and the identification of potential collaborations. Mutual learning can be adapted to various spatial scales (i.e., local, regional, transregional, national, cross-border, etc.), enabling knowledge transfer and capacity building and promoting socio-economic cohesion [10,11,12,13,14,15,16,17,18,19].

Several mutual learning methods have been used, particularly in the Baltic Sea Region (BSR), with Germany and Latvia being pioneers in employing relevant exercises [20,21]. These methods include participatory processes like individual or group meetings [9], open forums, mutual learning events or exchange activities [22], informal dialogues [23,24], and multisectoral events [25]. These can be one-time events or regular and recurrent interactions, such as expert working groups [26], which enhance mutual learning through continuous capacity building and collaboration [9,24]. Practical tools like topic papers, conflict/synergy tables [3], data portals [27], and digital decision-support tools [28,29,30] further strengthen the learning process and improve decision-making.

The literature demonstrates that the mutual learning approach can be applied to various MSP phases. Kyvelou [31] emphasized its importance in the early phases of MSP development. Varjopuro [32] noted that the initial planning cycle inherently serves as a learning process, aligning with the principles of mutual learning. Aps et al. [29] and Varjopuro et al. [30] highlighted its role in addressing conflicts between scientific and societal perspectives, while Bonnevie et al. [28] integrated it into an advanced decision-support tool for assessing cumulative impacts in the BSR. In the BSR, mutual learning is regularly employed to foster dialogue around draft MSP plans, ensuring inclusivity and stakeholder-driven outcomes [33].

Monitoring and evaluation (M&E), as a phase of MSP, tracks the progress of objectives, assesses the efficiency and effectiveness of the plan, and evaluates its outcomes, generating new knowledge to support plan adaptation to changing conditions [34,35] and reducing uncertainty [36,37]. It is a complex process linked to different timescales and phases of MSP [34,36,38], as well as to cross-cutting processes such as participatory processes [36,39], which can support baseline identification, improve data quality, and foster long-term engagement in MSP [40,41]. Given the importance of M&E from the early stages of MSP, mutual learning can enhance these processes by improving the identification of objectives, an understanding of current conditions, and the integration of stakeholder needs. The alignment of M&E activities with MSP’s preplanning and planning phases strengthens baseline assessments and facilitates ex ante, intermediate, and ex post evaluations [42,43,44,45,46].

Several M&E frameworks have been developed in alignment with the requirements of the EU MSP Directive [1], e.g., [36,39,40,47,48,49,50,51,52,53,54,55,56,57,58,59], many of which were supported by EU funding. The role of M&E is an information provider for plan adaptation; thus, its ‘corrective function’ is well established, identifying areas for improvement [59]. The literature also distinguishes between the types of M&E, with Schultz-Zehden [60] promoting both conformance and performance evaluation.

Most existing M&E frameworks recognize the importance of linking M&E to other MSP phases, often connecting it to the entire planning cycle, from preplanning to plan implementation [35,49,58,61,62], and promoting ex ante, intermediate, and ex post evaluations. Ex ante assessment considers priorities and alternative scenarios during the preplanning and planning stages; intermediate evaluation occurs during the implementation; and ex post evaluation assesses the final outcomes, informs plan revision, and initiates the next planning cycle [63,64,65].

Integrating M&E early in the MSP process facilitates the systematic linking of clear, specific, and measurable outcomes to objectives [35,59], often through the use of indicators [49,51,61,66]. Several frameworks use established indicator sets, such as Kannen et al. [65], who applied Integrated Coastal Zone Management (ICZM) sustainable development indicators and Marine Strategy Framework Directive (MSFD) Good Environmental Status (GES) descriptors. Others complement indicators with checklists, evaluation questions, evaluation criteria [35,51,55,62,64,67], and baselines and interim targets [35,66], while also depending on data availability [66].

Objectives are usually formulated using the SMART (Specific, Measurable, Achievable, Relevant, Time-Bound) [61] or SMARTIE (Inclusive and Equitable) criteria [45,68]. Most M&E frameworks align with one or more MSP principles [62], addressing transparent planning [64,67], the ecosystem-based approach (EBA) [62,64,67], land–sea interaction [62], coherence, e.g., [34], legal compliance [35,64,67], and stakeholder engagement [35,47,51,56,60,61,62,66,69]. Carneiro [56] emphasized the importance of communicating the M&E results, while Matczak et al. [69] highlighted the role of institutional capacity. Multiple frameworks also account for contextual factors, such as socio-economic, environmental, and governance conditions [35,56].

Cormier et al. [64,67] grounded their framework in risk management principles, whereas Varjopuro [47,51] proposed a theory-based M&E using the Theory of Change, which strongly depends on stakeholder engagement. M&E frameworks have been applied at various planning scales, including cross-border [56,61,63] and national levels [32,69]. Stelzenmüller et al. [58] focused on spatial dimensions, developing a framework for spatially managed areas that is transferrable to MSP due to its generic structure and EBA basis. This framework emphasizes the use of spatial tools, such as cumulative impact assessment (CIA) and pressure–state relationships.

Timing is another critical aspect of M&E, requiring clearly defined timelines [56,60] along with the need for regular, recurring evaluations [60].

However, Varjopuro [51] noted that applying a standardized M&E framework in cross-border MSP may be unrealistic due to different national objectives, highlighting the need for co-evolutionary evaluation. Stelzenmüller et al. [40] supported this view, suggesting that M&E should be tailored to specific place-based needs to enhance knowledge generation. Varjopuro et al. [32] further argued that while indicators are useful for deriving lessons, they are not sufficient on their own and must be clearly linked to MSP objectives and socio-economic and environmental developments. Stelzenmüller et al. [58] also pointed out that aligning objectives with indicators has not always yielded clear conclusions about the effectiveness of the management actions, largely due to data limitations.

Finally, Avgerinou-Kolonias et al. [49] stressed that since M&E spans all MSP phases, the different types of indicators should be used accordingly. Their framework proposes using state indicators to establish baselines during preplanning, process indicators during planning and implementation, and performance indicators to assess outcomes.

This paper explores the integration of mutual learning into MSP’s monitoring and evaluation process. Specifically, it identifies key qualitative and quantitative performance indicators (KPIs) for the blue economy and its interactions with institutional, environmental, and social elements by providing clear links between objectives and KPIs, enabling the recurrent collection of reliable data, and better aligning the planning and implementation phases. A case study in Greece demonstrates this approach, illustrating its practical implications. However, it must be emphasized that the framework presented is only a part of a larger corresponding framework and does not aim to provide a comprehensive set of KPIs. Section 2 describes the context of the case study, followed by an elaboration of the methods applied in Section 3. Findings are presented in Section 4 and discussed in Section 5, and conclusions are outlined in Section 6.

2. Case Study Context

Maritime Spatial Planning is integral to supporting blue economy activities, considering, among other factors, land–sea interactions and ensuring that social and environmental objectives are met. This is particularly relevant in Greece, a coastal nation with an extensive and complex geographical landscape, largely due to its numerous islands. However, Greece faces significant challenges in MSP implementation, as its national MSP strategy was institutionalized for the first time in April 2025 [70], while no official MSP plans addressing regional, subregional, and local levels have been regulated. Considering that 12 out of its 13 regions (NUTS 2) are coastal or insular, MSP plays a crucial role in supporting local economies and promoting sustainable development.

From a socio-economic perspective, Greece’s islands account for 18% of the nation’s land area and accommodate 14% of the total population, according to the 2021 census [71,72,73]. In addition, the Greek blue economy generated around 446,000 jobs in key sectors such as coastal tourism, marine resources, maritime transport, port activities, and shipbuilding and repair, contributing over EUR 6 billion in Gross Added Value (GVA) in 2021 [74]. However, despite their economic significance, Greece’s coastal and insular regions face substantial socio-economic and territorial disparities [75,76], underscoring the potential of MSP to enhance regional cohesion and equitable development.

Furthermore, the existing national MSP strategy provides a general strategic vision, along with the goals, objectives, and guidelines for organizing and developing human activities in Greek marine space, while ensuring the protection of the marine environment. Regional and local MSP plans, still pending in Greece, are expected to refine these strategic objectives into specific ones, in accordance with regional, subregional, and local specificities. At these levels, the plans are also anticipated to promote cross-sectoral synergies by reflecting respective priorities for the use of marine space, thus identifying potential compatibilities [70].

The national strategy emphasizes that participatory processes in MSP are essential for ensuring the resilience and health of marine ecosystems, while marine governance should guarantee equitable access to marine space. At the national and transregional scales, these processes are to be conducted as public consultations involving only government representatives. In contrast, at the regional and local levels, participatory processes should engage all marine users, including government actors, academia (e.g., universities and research institutions), industry associations, non-governmental organizations, and civil society. Their engagement should aim to inform and consult the planning process, with recommended tools including information days, the participatory geographic information system (PGIS), and workshops. At all planning scales, all stakeholders, including the general public, should be clearly and transparently informed about the planning process from the early stages of MSP [70].

M&E should be incorporated systematically and carried out continuously to support the necessary revisions. Specifically, M&E should consistently integrate changes in the sociocultural, economic, and environmental aspects relevant to MSP and support evaluation through both qualitative and quantitative indicators. The national MSP strategy highlights the potential contribution of an open access geodatabase to support M&E. It recommends establishing an observatory as a mechanism for collecting M&E-related data, tracking trends and impacts using KPIs, and overseeing coordinating among key MSP stakeholders [70].

This paper builds upon insights from the Mutual Learning Process (MLP) implemented within the context of the BLUEAIR project, funded by the INTERREG ADRION programme, which was implemented from December 2021 to July 2023. The project’s MLP engaged stakeholders through national and transnational participatory activities aimed at identifying regional and macroregional challenges and opportunities in the blue economy while fostering synergies across the Adriatic and Ionian Region (AIR). Implemented between February 2022 and April 2023, the MLP focused on regional and national scales, engaging stakeholders from all blue economy sectors and all quadruple helices (QHs), with each helix representing government, industry, academia, and civil society actors, respectively, across Bosnia–Herzegovina, Slovenia, Croatia, Greece, Italy, and Montenegro. The findings were validated at the macroregional level through consultations with stakeholder representatives from across the region.

Most of the AIR countries (Slovenia, Croatia, Greece, Italy) have a strong blue economy with significant potential for further development. In contrast, Bosnia–Herzegovina and Montenegro possess considerable relevant resources that remain underutilized. Specifically, Bosnia–Herzegovina has a relatively short coastline compared to the other AIR countries. However, it already hosts blue economy activities with potential for further development, particularly in the sectors of coastal tourism and living resources, including both marine aquaculture and fisheries [77].

Montenegro, on the other hand, despite its longer coastline, has limited development across most blue economy sectors such as maritime transport and fisheries, with tourism being the only significant contributor to the country’s growth [78]. Croatia has a more established blue economy, primarily driven by coastal tourism, followed by living resources and maritime transport [74]. However, its MSP process remains ongoing, with no plan yet adopted for its Exclusive Economic Zone (EEZ) [79].

In Italy, coastal tourism is also the dominant blue economy sector, followed by maritime transport, shipbuilding and repair, and marine living resources [74]. Italy completed its MSP process in 2024 [79]. Slovenia also demonstrates a strong blue economy, led by port activities, followed by coastal tourism, marine living resources, and shipbuilding and repair [74], with its MSP process completed since 2021 [79].

The results of this MLP initially aimed to contribute to shaping regional, national, and macroregional institutional frameworks by defining common strategic priorities for strengthening a sustainable blue economy (SBE) in alignment with the stakeholders’ objectives across the AIR. At the regional and national levels, these insights can inform regional and national Smart Specialization Strategies, as well as the development of potential SBE strategies and MSP plans. Similarly, the identified common priorities can support the formulation of macroregional frameworks while also enhancing transregional cooperation in SBE and MSP. Focusing on the Greek MSP context and recent developments in the national institutional framework, this case study addresses relevant policy gaps regarding how participatory processes can be facilitated to better align M&E with earlier MSP stages and the needs of stakeholders.

3. Research Approach and Methodology

3.1. Mutual Learning Process

The mutual learning approach enhances participatory processes by fostering the exchange of knowledge, insights, and perspectives through horizontal, two-way interactions among stakeholders [4,8,80]. It promotes equity and balanced power dynamics [2,7,8,9], ensuring that all participants have a voice and are heard during the participatory process [4,81,82]. This is essential for producing valid and representative results, promoting trust [10,11,12,13,14,15,16,17,18,19], and encouraging active stakeholder participation while ensuring the legitimacy of the planning process [81,82].

Based on these principles, the MLP in this study employed mixed methods to facilitate stakeholder interactions [83,84]. Dialogue enabled effective two-way interactions [4,8,80], while questionnaire surveys quantified the outcomes [83,84].

The MLP designed and applied in this case study consisted of two rounds of national stakeholder engagement (Figure 1). The first round focused on identifying and prioritizing strategic objectives for enhancing blue economy development in Greece, using a questionnaire and a dialogue session to explore relevant challenges and opportunities for economic development, and the creation and leveraging of synergies. The second round further specified these objectives through additional dialogue sessions.

Figure 1 illustrates the MLP steps implemented in this case study, providing a structured approach for monitoring and evaluation. The framework aligns M&E with the preplanning, planning, adaptation, and revision phases of the MSP process, facilitating data, information, and knowledge exchange.

Both rounds were supplemented with training sessions for the participating stakeholders, aimed at achieving a comprehensive understanding of the key topics of the participatory process to the fullest extent possible. The stakeholders represented all the sectors of the blue economy with existing and potential activities in Greece, encompassing all (QH) actors across various geographic and administrative units at regional and national scales. The outcomes were synthesized and further analyzed to support the monitoring and evaluation of the blue economy within the framework of MSP. This process contributed to the development of a KPI framework, supporting the ex ante, intermediate, and ex post evaluations of the blue economy through MSP in both qualitative and quantitative terms, enabling the monitoring and evaluation of the relevant aspects in line with the key principles of MSP, SBE, and key indicator criteria (SMARTIE criteria).

3.2. Target Groups

Participatory processes should be inclusive, open to the public, and ensure representation of all the relevant stakeholders with vested interests [45,85]. For mutual learning to be successful, this approach depends on multidisciplinarity, requiring the engagement of a diverse group of stakeholders, sufficient in numbers and with a strong knowledge base [86]. In MSP, mutual learning has facilitated knowledge exchange across different stakeholder groups (i.e., among planners, between planners and authorities, and between planners and stakeholders), underscoring the value of all stakeholders’ contributions in a science-based framework [19,33,87,88].

Identifying whom to engage in this process to ensure these principles are met is essential. This assessment used commonly accepted stakeholder typologies, particularly (a) the QH model [89,90], (b) sub-categories of the QH model [84], and (c) blue economy sectors and activities [91,92,93], following the statistical classification of economic activities in the European Community (NACE classification) [94], as stakeholders’ interests in MSP and the blue economy are shaped by their sectors, while on the contrary, these sectors are critical for MSP as core economic activities at the local, regional, national, and international scales.

The activities primarily involved stakeholders from regional and national levels in Greece. Potential participants were identified and selected through purposive sampling, based on the planner’s judgement of stakeholder eligibility [95]. Additional selection criteria included stakeholder expertise, institutional affiliation, and years of experience. These criteria were applied to the representatives of all QH groups, who were personally invited to participate.

To improve outreach to civil society, particularly the general public, the first MLP round was also promoted through social media, enhancing the inclusiveness of the process for stakeholders not directly involved in SBE and MSP but with an interest in them.

However, while the sampling technique supported the design of an inclusive MLP, the final statistical representativeness of all eligible stakeholder typologies depended on the availability and willingness of the invited stakeholders to participate. To mitigate the underrepresentation of specific QH actors, a large pool of potential stakeholders was created. Despite these efforts, some limitations in achieving a balanced representation, particularly of all QH actors, remained and are acknowledged in the interpretation of the MLP findings.

The future iterations of the MLP could benefit from a more formalized stakeholder mapping and sampling process to enhance representativeness.

3.3. Participants’ Training

Mutual learning involves the bilateral exchange of knowledge, information, and insights, meaning that both planners and participants should acquire new knowledge. In this context, a training session was held in each round of stakeholder engagement to introduce participants to mutual learning and the blue economy within the framework of MSP. These sessions defined and expanded on key topics relevant to each round’s objectives (e.g., marine uses, land–sea interaction, MSP’s alignment with other horizontal and vertical policies, synergies, conflicts, and the balanced development of the blue economy). While achieving a complete mutual understanding of these topics is unlikely [89], the training sessions aimed to establish a common level of familiarity, enabling participants to provide relevant insights valuable for planning, monitoring, and evaluating the blue economy within MSP.

3.4. First Round: Identifying Strategic Objectives for Strengthening the Blue Economy

The first round of stakeholder engagement aimed to identify shared strategic objectives for strengthening the blue economy within the MSP framework. Representatives from all stakeholder categories outlined in Section 3.2 participated. Following a preparatory training session, participants completed a questionnaire designed to identify common challenges and opportunities for enhancing economic activities and fostering synergies at sectoral, cross-sectoral, local, regional, and national levels. The entire questionnaire is provided in Appendix A. The questionnaire results were further discussed, allowing participants to share additional insights not captured in the questionnaire and to finalize the prioritization of the identified strategic objectives.

This round, held in May 2022, engaged 24 representatives from all QH and blue economy activities, who actively contributed to the dialogue session and the prioritization of common strategic objectives. Out of these participants, 18 had already responded to the questionnaire in writing, providing additional insights into the challenges and opportunities for enhancing economic activities and fostering synergies (Figure 2 and Figure 3). The stakeholder could represent more than one sector, while stakeholders that fell under the ‘Other’ category (BE13) represented transport economics, freight services, and information systems and services for blue data.

The dialogue session was facilitated by a moderator who first presented the results of the questionnaire survey and then used its open-ended questions as a guide, posing them to each discussant using the go-round technique [96]. This method ensured that all stakeholders had the opportunity to share their insights. Next, following the same technique, the moderator asked participants whether they agreed with the priorities identified through the questionnaire. In cases of conflicting opinions, stakeholders were invited to justify their views, aiming for a consensus to be reached. The dialogue concluded with a list of the top four priority strategic objectives, following an inductive content analysis to identify recurring themes and perspectives. These objectives were related to (a) challenges for blue economy development, (b) challenges for relevant synergies, (c) opportunities for blue economy development, and (d) opportunities for corresponding synergies.

3.5. Second Round: Identifying Specific Objectives for Strengthening the Blue Economy

The second round of stakeholder engagement aimed to further specify the top-priority objectives identified in the first round. This round focused on the key priorities derived from the first four strategic objectives, assessing the current and future capacity of the Greek blue economy to address them effectively. A training session was conducted at the beginning of this round to introduce participants to the objectives of the activity and familiarize them with key topics in the MLP, as outlined in Section 3.3. Stakeholders representing all blue economy sectors and QH actors at regional and national levels participated in this round’s dialogue session, helping to refine the specific objectives. The dialogue was guided by open-ended questions on the current and future capacity of the blue economy sectors to address the key strategic priorities identified in the first round of the MLP, allowing stakeholders to further refine them into specific objectives. It was also facilitated by a moderator, who again used the go-round technique to enable all stakeholders to share their insights, provide specific examples related to the strategic objectives, and contribute to their refinement into concrete objectives.

This round took place in January 2023, engaging 22 stakeholder representatives from all the QH and blue economy sectors (

Table 1. Distribution of discussants of the second round of the MLP per QH actor and per blue economy sector (n = 22).

Stakeholders’ Typology	Number of Discussants (n = 22)
QH actors
Industry (QH1)	13
Academia (QH2)	3
Civil society (QH3)	4
Government (QH4)	2
Blue economy
Coastal tourism (BE1)	12
Waterborne transport and port activities (BE2)	21
Management of marine ecosystem services (BE3)	7
Infrastructure and maritime works (BE4)	11
Marine living resources (BE5)	10
Marine renewable energy (BE6)	10
Public services and governance (BE7)	7
Shipbuilding and ship repair (BE8)	18
Maritime surveillance (BE9)	13
Marine bioeconomy and biotechnology (BE10)	5
Seabed mining (BE11)	9
Offshore oil and gas (BE12)	10

Notes: The discussants could represent more than one blue economy sector. ‘Marine renewable energy’ includes all relevant technologies like offshore wind energy, ocean energy, and desalination.

).

This round was transcribed and analyzed using an inductive qualitative content analysis approach. The responses were subjected to open coding to identify meaningful information, which was then grouped into categories that reflected shared themes and perspectives. The analysis resulted in a list of specific objectives that elaborated on the key strategic objectives prioritized in the first round (the top four strategic objectives).

3.6. Development of a KPI Framework for the Monitoring and Evaluation of the Blue Economy Through MSP in Greece

Findings from both rounds of stakeholder engagement were synthesized and analyzed to create a KPI framework. This framework consists of clusters of qualitative and quantitative indicators designed to monitor and evaluate the progress of SBE-related objectives within the framework of MSP. Each KPI is defined by key elements, including the type of measurement (i.e., unit of measurement and scale), frequency of measurement, spatial visualization format (i.e., spatial units and geometry), data collection methods (i.e., participatory process and secondary data collection), and indicative sources of data and information. Figure 4 illustrates the conceptual framework for the identification of the KPIs and the potential links between the strategic and specific objectives and the indicators. The codification of the specific and strategic objectives, the KPI clusters, and the KPIs are indicative, while the links show potential connections between them. More specifically, the specific objectives are derived from the strategic objectives as defined above, while the KPIs are selected to measure at least one of the identified specific objectives. The dashed lines indicate that specific objectives can address more than one strategic objective if their subject is broad enough. Likewise, some KPIs are expected to be capable of measuring more than one specific objective.

The KPIs and the overall framework were subjected to self-evaluation using a set of criteria based on the SMARTIE framework. This process defined how each criterion could be applied within the context of this case study (

Table 2. Self-evaluation criteria of the KPIs based on the SMARTIE framework.

Criteria	Application in This Case Study
Specific	The indicator is easily understood and interpreted by all relevant stakeholders regardless of their background and familiarity with MSP.
Measurable	The indicator should be quantitatively or qualitatively measurable and comparable across different timeframes, contexts (i.e., spatial, institutional, etc.), and relevant existing indicators.
Achievable	Parameters like cost-effectiveness, availability of data, and required time for data collection are considered when selecting indicators.
Relevant	The indicator’s relevance is assessed based on the Greek MSP context, and the expectations, needs, and priorities identified by stakeholders in this MLP.
Time-Bound	The indicator is dynamic and measurable across different time frames, and its frequency can be defined based on the duration of each MSP phase of each planning cycle.
Inclusive/Equitable	The validity and usefulness of the KPI framework for all MSP stakeholders in Greece are assessed to ensure inclusiveness and equity.

Source: authors’ own elaboration on data from [45,68].

). The framework was designed to align with the key principles of MSP [45,85,97] and SBE [98], serving as assessment criteria to evaluate the suitability of the KPIs for this case study’s objectives (

Table 3. Alignment of KPI framework with key MSP and SBE principles.

MSP and SBE Principles	Application in This Case Study
Sustainability (S)	The indicator measures progress toward economic, social, or environmental objectives promoting sustainability, aligning with institutional frameworks or stakeholder objectives.
Spatial approach (SA)	The indicator includes a spatial dimension, which can be measured qualitatively and quantitatively.
Ecosystem-based approach (EBA)	The economic aspects of MSP (blue economy) are addressed primarily, but a broader ecosystem-based approach could be incorporated in future applications.
Integrated approach (IA)	The approach incorporates all QH actors and uses an integrated questionnaire to address various blue economy sectors in the MSP framework.
Strategic planning (SP)	The indicator measures long-term objectives related to the MSP planning area, though not all objectives need to be long-term.
Precautionary principle (PP)	The precautionary principle is adhered to by involving stakeholders with environmental and social interests.
Participatory approach (PA)	The MLP process ensures that KPIs were developed through a participatory approach, involving stakeholders at all levels.
Evidence- and science-based decision-making (ESB)	The MLP method is scientifically sound, and the data collection methods should be scientifically validated.
Cross-border and transboundary cooperation (CBTB)	The MLP encourages cooperation between stakeholders in different regions in Greece and can be considered to promote cross-border cooperation.
Land–sea interaction and coherence (LSI)	The involvement of stakeholders from all blue economy sectors, including non-maritime activities, ensuring that land–sea interaction is considered.
Four-dimensional approach (4D)	Engaging stakeholders involved in seasonal activities ensured the incorporation of the temporal dimension, particularly seasonality, into the indicators.
Adaptive and iterative approach (AIA)	The primary role of the indicators is to contribute to MSP adaptation and revision, facilitating the new planning cycle.

Source: authors’ own elaboration of data retrieved from [45,85,97,98].

).

4. Findings

4.1. Strategic Objectives for the Greek Blue Economy

The first round of the MLP investigated the challenges and opportunities for enhancing the Greek blue economy, creating new synergies and leveraging existing ones at the sectoral and cross-sectoral levels.

Table 4. Ranking of challenges and opportunities for blue economy development and synergies based on the questionnaire survey results.

Ranking	Response (Codification)	Number of Responses (n = 18)
Challenges for blue economy development
1	Conservation of the marine environment (CB2)	11
2	Regulatory uncertainty (CB4)	10
3	High costs of technology development (CB3)	9
4	Limitations in sustainable management (CB1)	7
5	Negative or reluctant general public perceptions (CB5)	6
6	Resistance to change practices or regulations (CB6)	6
7	Inadequate governance and services (CB7)	4
Challenges for blue economy synergies
1	Insufficient cooperation among quadruple helix actors (CC3)	14
2	Low knowledge capacity (CC1)	13
3	Lack of technology and knowledge transfer (CC2)	12
4	Uncoordinated cross-border governance and services (CC8)	9
5	Limited innovation entrepreneurship (CC4)	7
6	Lagging compliance with/adaptation to EU policies (CC6)	7
7	Limited access to funding (CC7)	7
8	Lack of international cluster/networks (CC9)	7
9	Insufficient networking (CC5)	6
Opportunities for blue economy development
1	Growing demand for marine resources (OB1)	15
2	Increasing demand for ‘cleaner’ technologies (OB4)	11
3	Extension of circular economy initiatives to the sustainable blue economy (SBE) (OB5)	11
4	New funding opportunities for blue economy projects (OB3)	9
5	Geopolitical considerations (OB6)	7
6	Demand for newer and cheaper technologies (OB2)	4
Opportunities for blue economy synergies
1	Cooperation in RD&I in SBE (OC1)	12
2	Cooperation and networking within the industry and between industry and academia (OC2)	7
3	SBE communities of all QH actors (OC3)	7
4	Collaboration projects adapted to challenges and demands (OC4)	7
5	Increased potential for innovation scale-up (OC5)	7
6	Funding opportunities from EU programmes (OC8)	7
7	Increased efficiency through shared infrastructure on land or in ports (cross-sectoral) (OC9)	7
8	Potential for value chain development (OC6)	5
9	Access to finance and opportunities for business creation (OC11)	5
10	Improved coordination and governance through common standards and frameworks (OC12)	4
11	Capacity building in SBE (i.e., blue skills) (OC13)	4
12	Creation of thematic partnerships (OC10)	3
13	Opportunities for industry competitiveness and sustainability (OC7)	2

Source: authors’ own elaboration.

summarizes the results of the questionnaire survey, ranking the available responses for each subject.

The results of the questionnaire survey were discussed in the subsequent dialogue session to finalize the prioritization of the responses, collecting further insights from the stakeholders. This process resulted in a set of 12 prioritized strategic objectives. They are intended to be addressed, monitored, and evaluated within the framework of MSP and other relevant institutional frameworks (

Table 5. Key strategic objectives for SBE development and synergies to be addressed, monitored, and evaluated in the framework of MSP.

Ranking	Strategic Objective	Description—Key Results of the Dialogue Session
1	Conservation of the marine environment (SO1)	Requires alignment of current and future human activities impacting the marine environment with relevant national and EU policies.
2	Low knowledge capacity (SO2)	Refers mainly to blue technologies, where limited knowledge constrains stakeholders from adopting innovations, enhancing activities, and building partnerships.
3	Increasing demand for ‘cleaner’ technologies (SO3)	Encourages the utilization of existing technologies and the development of new ones.
4	Cooperation for R&D&I in the context of the sustainable blue economy (SO4)	Stimulates and mobilizes stakeholders from existing cooperations to strengthen current synergies and encourages the creation of new ones.
5	Regulatory uncertainty (SO5)	Limits SBE development by hindering investments.
6	Insufficient cooperation among QH actors (SO6)	Emphasizes the importance of synergy creation through cooperation among all QH actors.
7	Growing demand for marine resources (SO7)	Promotes innovation for sustainable resource management.
8	Collaboration projects adapted to challenges and demands (SO8)	Stimulates synergies through the utilization of tailor-made initiatives.
9	High costs of blue technology development (SO9)	Limits SBE development and competitiveness, which rely on technological innovations.
10	Lack of technology and knowledge transfer (SO10)	Hinders balanced development and exacerbates regional disparities.
11	Extension of circular economy initiatives to SBE (SO11)	Utilizes relevant initiatives to support SBE development and innovation.
12	Funding opportunities from EU programmes (SO12)	Supports SBE development by stimulating cooperation and synergies.

Source: authors’ own elaboration.

).

4.2. Specific Objectives for the Greek Blue Economy

The second round of the MLP further refined the strategic objectives identified in the first round. During this phase, stakeholders assessed the current and future capacity of the Greek blue economy to achieve the top-priority objectives. This approach led to the formulation of 29 specific objectives linked to the four highest-priority strategic objectives.

4.2.1. Challenges for Blue Economy Development

The conservation of the marine environment in Greece faces significant challenges enhanced by the lack of an established institutional framework at that time (2023), such as a dedicated SBE strategy and MSP. These frameworks are critical for managing synergies and conflicts at sectoral and cross-sectoral scales, mobilizing funding resources, and aligning technological advancements with environmental conservation goals. Key challenges include the role of environmental impact assessment (EIA) in evaluating new investments, particularly in infrastructure projects with long lifecycles (i.e., floating wind platforms and seaplane platforms), and the environmental impact of waterborne transport, including shipbuilding and repair activities. The adoption of international environmental regulations is essential to mitigate these impacts.

Existing initiatives can address these challenges by supporting marine environment monitoring through data collection (i.e., seawater quality), informing institutional frameworks to align with socio-economic and environmental needs, and raising environmental awareness with industry networks. Encouraging the adoption of Environment, Social, and Governance (ESG) criteria was highlighted as a key step toward sustainable operations. Technological challenges include infrastructure developments in the marine environment (i.e., floating wind platforms), the transition to renewable energy and alternative fuels in waterborne transport, which will impact related sectors like shipbuilding and ports, and increased coastal tourism, driving new investments like marine development. Opportunities for addressing these challenges are provided by academia and research communities that shape research, development, and innovation (R&D&I) agendas to address industry needs and policy requirements, promote marine environment conservation (i.e., water pollution mitigation), and advance climate neutrality initiatives. Technological innovations were recognized as vital solutions that require supportive regulatory and funding mechanisms.

4.2.2. Challenges for Blue Economy Synergies

A significant barrier to fostering synergies in the blue economy is the limited knowledge capacity regarding blue technologies, particularly concerning their role in advancing green and digital transitions, which are central policy objectives. Addressing this challenge requires the promotion of innovative entrepreneurial processes and collaboration to enhance national competitiveness and self-sufficiency. Industry networks and associations play a pivotal role in facilitating sectoral and cross-sectoral cooperation. They can provide training opportunities, attract young professionals to blue economy careers, and foster environmental awareness, all of which are necessary to build a sustainable and innovation-driven blue economy.

4.2.3. Opportunities for Blue Economy Development

The growing demand for ‘cleaner’ technologies presents significant opportunities for economic growth and innovation. This demand is driving the renewal of Greece’s vessel fleet, enhancing shipbuilding and repair activities, and supporting the development of a green shipyard. It also encourages improvements in energy efficiency, including new data management techniques for ports (e.g., big data applications) and shore-side electricity supply.

Other opportunities include enhancing port reception facilities (PRFs) to support sustainable fisheries management (i.e., recycling passively fished nets and the proper disposal of end-of-lifecycle equipment) and strengthening aquaculture through biotechnology integration. Digital communication platforms for real-time data collection and exchange were also identified as key tools for addressing sectoral challenges.

Despite these opportunities, challenges remain, such as the lack of energy supply infrastructure in ports, which is a critical barrier to advancing electromobility in waterborne transport. Initiatives aimed at developing electric ferries and ship-to-port energy connections are underway but require infrastructure improvements, particularly in small ports.

4.2.4. Opportunities for Blue Economy Synergies

Research, development, and innovation collaborations offer significant opportunities for fostering synergies and driving economic growth. Innovative solutions that align research with industry needs can improve profitability while advancing scientific progress.

The importance of existing tools like tailor-made blue technologies, knowledge and innovation communities, academic and research networks, and digital communication platforms was emphasized. These mechanisms facilitate collaboration among industries and between industry and academia, enhance innovation and entrepreneurship, and support sustainable development within the blue economy.

4.2.5. Specific Objectives for Enhancement of and Synergies Within the Greek Blue Economy

The synthesis of findings from the second round of the MLP resulted in a comprehensive list of 29 specific objectives elaborating on the four high-priority strategic objectives identified in the first round. These objectives summarize stakeholders’ insights (

Table 6. Strategic and specific objectives for enhancing the SBE and relevant synergies as identified through the MLP.

Code	Strategic Objective (First Round of MLP)	Code (2nd Level)	Specific Objective (Second Round of MLP)
SO1	Conservation of the marine environment	SO1.1	Establish institutional frameworks related to the blue economy (i.e., SBE and MSP)
		SO1.2	Improve the alignment of technology deployment with marine environment conservation
		SO1.3	Formulate common regulations for environmental impact assessment on the marine environment
		SO1.4	Enhance the management of synergies and conflicts at sectoral and cross-sectoral levels
		SO1.5	Improve the allocation and mobilization of funding resources
		SO1.6	Adopt alternative energy sources
		SO1.7	Leverage existing initiatives to support decision-making and policy formulation
		SO1.8	Raise awareness about marine environmental conservation
SO2	Low knowledge capacity	SO2.1	Enhance knowledge transfer through collaboration
		SO2.2	Utilize existing initiatives for education and training
		SO2.3	Leverage existing initiatives for environmental awareness
		SO2.4	Exploit existing initiatives for knowledge transfer
		SO2.5	Use existing initiatives to foster synergies
		SO2.6	Promote blue entrepreneurship
SO3	Increasing demand for ‘cleaner’ technologies	SO3.1	Utilize existing initiatives for technological development (i.e., electric ferries and biotechnology)
		SO3.2	Leverage existing initiatives to support decision-making and policy development
		SO3.3	Exploit existing initiatives that promote digitalization (i.e., real-time data collection and exchange, etc.)
		SO3.4	Implement environmental frameworks (i.e., PRFs)
		SO3.5	Renew the existing fleet with vessels equipped with ‘cleaner’ technologies
		SO3.6	Incorporate electromobility in waterborne transport
		SO3.7	Establish electricity supply infrastructures at ports (i.e., SSE/OPS)
		SO3.8	Revitalize the shipbuilding and repair sector
		SO3.9	Promote green shipyards
SO4	Cooperation for R&D&I in the context of the sustainable blue economy	SO4.1	Enhance innovation and technology development to strengthen the economy and R&D&I
		SO4.2	Utilize existing academic and research networks
		SO4.3	Leverage existing knowledge and innovation initiatives
		SO4.4	Exploit existing cross-sectoral synergies
		SO4.5	Use digital communication platforms to foster synergies
		SO4.6	Leverage existing digital communication tools to promote innovation and entrepreneurship

Source: authors’ own elaboration.

). Some objectives overlap, such as SO1.7 and SO3.2, but they have been retained in the final list as they address distinct strategic objectives. Furthermore, several objectives are interdependent; for example, the adoption of electromobility in waterborne transport relies heavily on the availability of electricity supply infrastructure at ports.

4.3. A KPI Framework for Monitoring and Evaluating the SBE Through the MSP in Greece

In alignment with the key principles of the MSP and SBE, the synthesis of findings from both rounds of the MLP led to the development of a KPI framework, summarized in Figure 5 and

Table 7. Proposed KPI framework on the monitoring and evaluation of the SBE strategic and specific objectives in the framework of the MSP.

KPI Description and Objectives	Indicator Type	Data Collection Method and Indicative Source
Cluster A: ‘Environmental indicators’
Subcluster A1: ‘Conservation of the marine environment’
A1.1. Implementation of port reception facilities (PRFs) in Greece: Progress of implementing PRFs in Greece, following the respective framework. [Specific objectives addressed: SO1.3; SO3.3; SO3.4].	Qualitative (Likert) and quantitative (number of PRFs)	Participatory process (including Greek National Competent Authority for SafeSeaNet 1 (GNC SSN); secondary data collection (i.e., Greek National Maritime Single Window (NMSW))
A1.2. Number of Waste Reception and Management Plans for PRFs: Progress of developing plans for receiving and managing waste disposals at Greek PRFs. [Specific objectives addressed: SO1.3; SO3.4].	Quantitative (number of plans)
A1.3. Waste amount disposed at PRFs: The amount of waste disposal at Greek PRFs in total and per waste type (i.e., passively fished waste; cargo residue; oily water; sewage; etc.). [Specific objectives addressed: SO1.3; SO1.8; SO3.3; SO3.4].	Quantitative (amount in m3)
A1.4. Active initiatives that support environmental objectives: The existence and proven exploitation of initiatives supporting objectives relating to the marine environment in total, per field, and per initiative type, covering different spatial scales. [Specific objectives addressed: SO1.2; SO1.8; SO2.3].	Qualitative (binary, Likert, description) and quantitative (number of initiatives)	Participatory process (including all QH actors); secondary data collection (i.e., project inventories, etc.)
Cluster B: ‘Blue economy indicators’
Subcluster B1: ‘Entrepreneurship’
B1.1. Entrepreneurship in the blue economy: Existence of blue entrepreneurship in total and per (sub)sector, covering different spatial scalability (i.e., regional and national) and entrepreneurship types. [Specific objective addressed: SO2.6].	Qualitative (binary, Likert, description) and quantitative (EUR, number of businesses, etc.)	Participatory process (including all QH actors and sectors); secondary data collection (i.e., statistical authorities, etc.)
B1.2. Increase/Change in entrepreneurship in the blue economy: Increase or decrease in blue entrepreneurship in total, per (sub)sector, and per entrepreneurship type. [Specific objective addressed: SO2.6].
B1.3. Contribution of blue entrepreneurship to Greece’s self-sufficiency and competitiveness: The contribution of blue entrepreneurship to national self-sufficiency at national, regional, and local levels, and its competitiveness at the international level. [Specific objective addressed: SO2.6].	Qualitative (binary, Likert, description) and quantitative (EUR, number of employees, GVA, etc.)
B1.4. Active existence of initiatives for promoting entrepreneurship in the blue economy: The existence and proven exploitation of initiatives promoting blue entrepreneurship in total, per initiative type, and per (sub)sector and covering different spatial scalability. [Specific objectives addressed: SO2.6; SO4.6].	Qualitative (binary, Likert, description) and quantitative (number of initiatives)	Participatory process (including all QH actors and sectors); secondary data collection (i.e., project inventories, etc.)
Subcluster B2: ‘Funding opportunities’
B2.1. Amount of funds dedicated to blue economy activities: The amount of funds offered to all QH actors for activities related to the blue economy, including relevant land-based activities, measured in total, per topic, per QH actor, and per (sub)sector. [Specific objective addressed: SO1.5].	Quantitative (EUR)	Participatory process (including mainly authorities); secondary data collection (i.e., ECAS, etc.)
Cluster C: ‘Technology indicators’
Subcluster C1: ‘Development and use of ‘cleaner’ technologies’
C1.1. Incorporation of ‘cleaner’ technologies in vessel fleet: Progress incorporating ‘cleaner’ technologies in the Greek vessel fleet, covering operating and ordered vessels, different technologies, and vessel types. [Specific objectives addressed: SO1.2; SO1.6; SO3.5; SO3.6].	Qualitative (binary, Likert, description), and quantitative (number of vessels)	Participatory process (including stakeholders from waterborne transport and port); secondary data collection (i.e., Clarkson’s database, etc.)
C1.2. Incorporation of ‘cleaner’ energy supply infrastructures in Greek ports: Progress of ports’ greening, supporting waterborne transport’s respective transition, measured in total, per technology type, per vessel type, and per port type and size. [Specific objectives addressed: SO1.2; SO1.6; SO3.5; SO3.6; SO3.7].	Qualitative (binary, Likert, description), and quantitative (number of infrastructures)
C1.3. Progress of green shipyards in Greece: Progress incorporating ‘cleaner’ technologies in shipbuilding and repair, also covering the sector’s practices, and measured per technology and subsector. [Specific objectives addressed: SO1.2; SO1.6; SO3.5; SO3.6; SO3.7].	Qualitative (binary, Likert, description), and quantitative (number)	Participatory process (including stakeholders from shipbuilding and repair); secondary data collection (i.e., Shipyards Directory, etc.)
Subcluster C2: ‘Technological deployment in the marine environment’
C2.1. Permanent infrastructures in the marine environment: The construction of permanent infrastructures as an indirect measure of human activities’ impact on the marine environment, measured per technology and (sub)sector. [Specific objective addressed: SO1.2].	Qualitative (binary and description), and quantitative (number of infrastructures)	Participatory process (including all QH actors and sectors); secondary data collection (i.e., statistical authorities, official national geoportals 2, etc.)
Subcluster C3: ‘Support of technological innovation’
C3.1. Active initiatives that support technological innovation: The existence and proven exploitation of initiatives supporting technology and innovation in the blue economy in total, per (sub)sector, and per initiative type, covering different spatial scalability. [Specific objectives addressed: SO1.2; SO3.1; SO4.1; SO4.2; SO4.3].	Qualitative (binary, Likert, description) and quantitative (number of initiatives)	Participatory process (including all QH actors); secondary data collection (i.e., project inventories, etc.)
Cluster D: ‘Governance and social indicators’
Subcluster D1: ‘Institutional framework’
D1.1. Institutional framework related to the blue economy: Establishment of institutional frameworks on the blue economy covering their wide range, like strategies, plans, frameworks, and all (sub)sectors. [Specific objectives addressed: SO1.1; SO1.6; SO1.7; SO3.1; SO3.2].	Qualitative (binary, Likert, description), and quantitative (number of frameworks)	Participatory process (including all QH actors); secondary data collection (i.e., desk research, etc.)
D1.2. Established frameworks on assessing and addressing the blue economy’s environmental impact: Establishment of common standardized processes for assessing and addressing the blue economy’s impact on the marine environment in total and per (sub)sector. [Specific objectives addressed: SO1.1; SO1.2; SO1.6; SO1.7; SO3.1; SO3.2; SO3.3; SO3.4; SO3.5; SO3.6; SO3.7; SO3.9; SO4.2].	Qualitative (binary, Likert, description), and quantitative (number of frameworks, etc.)
D1.3. Active initiatives’ contribution to policy making: The existence and proven exploitation of initiatives supporting policy making in total, per (sub)sector, and per initiative type. [Specific objectives addressed: SO1.7; SO3.2].	Qualitative (binary, Likert, description), and Quantitative (number of initiatives)	Participatory process (including all QH actors); secondary data collection (i.e., project inventories, etc.)
Subcluster D2: ‘Capacity building’
D2.1. Education and training in the blue economy: The existence of education and training offers about the blue economy in total, per field, per (sub)sector, per occupational profile, per trainee profile, per EQF level, and per type of education and training mode. [Specific objectives addressed: SO1.8; SO2.2; SO2.3; SO2.4; SO4.1].	Qualitative (binary and description), and quantitative (number of offers)	Participatory process (including all QH actors); secondary data collection (i.e., desk research, etc.)
D2.2. Completion of education and training in the blue economy: The successful completion of education and training offers about the blue economy, indirectly measuring capacity building and the potential contribution to the blue economy, as measured in D2.1. [Specific objectives addressed: SO1.8; SO4.1].	Qualitative (binary and description), and quantitative (number of offers)
D2.3. Absorption of newly educated or trained individuals in blue economy positions: Absorption of newly skilled, reskilled, or upskilled professionals in blue economy jobs, indirectly measuring capacity building in the blue economy, as measured in D2.1. [Specific objectives addressed: SO1.8; SO2.2; SO2.3; SO2.4; SO4.1].	Qualitative (binary and description), and quantitative (number of new employees)
Subcluster D3” ‘Synergies and conflicts’
D3.1. Active initiatives that support synergies in the blue economy: The existence and proven exploitation of initiatives supporting synergies in the blue economy in total, per (sub)sector(s), and per initiative type and covering different spatial scalability. [Specific objectives addressed: SO1.4; SO2.1; SO2.5; SO4.3; SO4.4; SO4.5].	Qualitative (binary, Likert, description) and quantitative (number of initiatives)	Participatory process (including all QH actors); secondary data collection (i.e., project inventories, etc.)

¹ the Greek National Competent Authority for SafeSeaNet is the Department of Maritime Surveillance and Vessel Traffic Management of the Directorate of Safety of Navigation of the Hellenic Ministry of Maritime Affairs and Insular Policy. ² official geoportals include geospatial data validated by competent authorities. Such national portals with MSP-related layers are the geoportal of the Hellenic Ministry of Environment and Energy and the geoportal of THAL-CHOR2. Source: authors’ own elaboration of data retrieved from [99,100,101,102].

. This framework comprises 21 indicative KPIs, categorized into four thematic clusters, each linked to one or more specific objectives identified through the MLP.

The framework defines the nature of each indicator (qualitative or quantitative), along with measurement methods, data collection approaches, indicative data sources, time references, and spatial visualization capabilities. Its comprehensive design ensures that the KPIs adhere to the SMARTIE criteria, effectively covering all relevant evaluation dimensions and facilitating the systematic monitoring and assessment of SBE implementation within the MSP. The detailed KPI framework is provided in Appendix B.

Figure 5 illustrates the links between strategic objectives and KPIs. Next to each KPI, the corresponding specific objectives are summarized. The links are color-coded based on the KPI cluster: green for cluster A, red for cluster B, black for cluster C, and magenta for cluster D.

Cluster A addresses environmental performance by tracking progress in implementing established institutional frameworks that contribute to conserving the marine environment, like PRFs. Relevant advancements at Greek ports are measured and evaluated through indicators such as the volume of disposed waste, indicating efforts to prevent the pollution of the marine environment by different waste types. The measurement of initiatives that promote environmental conservation is also included in this cluster. This cluster emphasizes the monitoring and evaluation of the environmental objectives prioritized by the participating stakeholders like the conservation of the marine environment (SO1) but also the objectives that promote capacity building (SO2) and the exploitation of demand for ‘cleaner’ technologies (SO3), measuring the availability of relevant initiatives (i.e., waste reduction technologies), their practical adoption, and their impact (i.e., reduction in waste).

Cluster B focuses on economic performance, emphasizing blue entrepreneurship and the availability of funding opportunities as drivers of economic activity. Indicative KPIs measure current entrepreneurial activity and its contribution to the national economy, particularly Greece’s self-sufficiency, considering factors like GVA and employment, and its international competitiveness. This cluster is strongly linked to stakeholders’ objectives on capacity building through promoting entrepreneurship (SO2.6), cooperation for R&D&I (SO4) through digital communication platforms aimed at enhancing entrepreneurship, and the conservation of the marine environment (SO1), addressing the allocation of available funds, which are essential for improving the economic performance of all QH actors.

Cluster C evaluates technological performance, measuring advancement in the blue economy in alignment with current and future environmental objectives. The proposed KPIs assess the adoption of ‘cleaner’ technologies in specific blue economy sectors strongly associated with climate neutrality, like waterborne transport and shipbuilding. These KPIs attempt to indirectly measure the blue economy’s impact on the marine environment, assuming that permanent infrastructures inherently have adverse effects due to their occupation of marine space. This cluster addresses stakeholders’ objectives for the conservation of the marine environment (SO1), exploiting opportunities from the increasing demand for ‘cleaner’ technologies (SO3) and cooperation for R&D&I (SO4). Multiple stakeholders’ specific objectives are measured by the selected KPIs, highlighting the contribution of innovation and technological advancements to almost all dimensions of the MSP from economic (i.e., enabler of competitiveness; improved operational efficiency) and environmental (i.e., required for compliance with environmental policies) to social (i.e., social innovations that support communication) and governance (i.e., improved cross-sectoral coordination and cooperation).

Cluster D emphasizes governance, focusing on the MSP-related dimensions of the SBE, particularly its social ones. It measures the integration of the blue economy into institutional frameworks, like MSP plans or other sectoral and cross-sectoral frameworks related to the SBE. Furthermore, it evaluates the progress in establishing common frameworks to address shared challenges, like assessing and mitigating the impact of human activities on the marine environment, thereby standardizing relevant practices. Both measurements are within the interests of stakeholders to address uncertainty through such frameworks (i.e., for deploying relevant investments) but also support them on how to conserve the marine environment by continuing and further developing their activities. This cluster’s KPIs also cover capacity building, synergy creation, and conflict resolution, which are integral to governance in the SBE and MSP and are closely linked to social aspects, which stakeholders perceive as essential for further developing their blue economy activities. Capacity building includes KPIs for education and training in SBE-related fields, accounting for newly skilled, reskilled, and upskilled individuals employed in relevant jobs that benefit all QH actors, both employees and employers. Regarding synergies and conflicts, KPIs consider both sectoral and cross-sectoral aspects, extending beyond spatial dimensions to include partnerships, communities, and other forms of collaboration. All strategic objectives are more or less addressed by this cluster of KPIs, highlighting the strong influence of social dimensions in the SBE and MSP but also the role of governance in addressing cross-sectoral objectives among different QH actors.

Each cluster includes at least one KPI that measures existing, active initiatives that are being exploited to support the blue economy in line with environmental, social, technological, and governance principles and objectives. Relevant initiatives may include technological solutions like tools, infrastructures, and technology deployments and social solutions like collaboration networks or platforms. Most KPIs can be measured both qualitatively and quantitatively, with collection methods including participatory processes to provide insights, inform planners, and indicate reliable and validated data sources. Such sources may include MSP-related authorities, SBE-related stakeholders, and all QH actors, as appropriate.

Quantitative KPIs include numerical measurements using specific units, informed through secondary data collection methods. Existing data sources, like geoportals, databases, data catalogues, and project inventories provided by validated institutions like competent authorities and statistical authorities, can support these measurements. All KPIs can be measured and compared across different time periods, enabling ex ante, intermediate, and ex post evaluations. They can be visualized at least at the national scale, with several also applicable to other spatial scales too, following geographic or administrative boundaries. For instance, KPI A1.1, which refers to the ‘Implementation of port reception facilities (PRFs) in Greece’, can be monitored and measured in its quantitative format across multiple spatial scales. At the local level, data can be analyzed using the United Nations Code for Trade and Transport Locations (UN/LOCODEs) to identify individual ports with PRFs. Additionally, the number of island ports equipped with PRFs can further reflect local-level implementation utilizing both geographic and administrative criteria (i.e., NUTS III or lower level). At the regional scale, aligned with Greece’s administrative units, such as municipal port funds, the number of ports with PRFs within each unit can be assessed. Finally, at the national level, the total number of ports with PRFs across the country provides an aggregate measure of implementation.

5. Discussion and Policy Impact

This case study applies an MLP to engage blue economy stakeholders at the national level, identifying relevant strategic and specific objectives and developing a KPI framework to support monitoring and evaluation within the MSP framework. The questionnaire survey facilitated the production of the initial results that laid the basis for the dialogue in the first MLP round. Since the stakeholder engagement methodology along with the questionnaire of this MLP have been applied in the AIR, they are transferrable to other spatial contexts, including local, regional, national, transregional, and cross-border levels. This transferability enables a comparative analysis between Greece’s results with those of the AIR, supporting the development and benchmarking of relevant KPIs at broader scales. Comparing the results of the survey between Greece and the rest of the AIR countries showed that the country presents several common challenges and opportunities, although their prioritization may differ. More specifically, the challenges for the development of the blue economy included in the top three priorities are (a) the conservation of the marine environment (CB2) shared with Bosnia–Herzegovina, Slovenia, and Montenegro, (b) the regulatory uncertainty (CB4) shared with Italy, Croatia, and Montenegro, and (c) the high costs of technology development (CB3) shared with all the AIR countries. The three top challenges for synergy creation in the blue economy include (a) insufficient cooperation among QH actors (CC3) shared with all AIR countries, (b) a low knowledge capacity (CC1) shared with Slovenia, Bosnia–Herzegovina, Croatia, and Montenegro, and (c) a lack of technology and knowledge transfer (CC2) shared with Italy, Bosnia–Herzegovina, and Montenegro. The three top opportunities for blue economy development include (a) a growing demand for marine resources (OB1) shared with Bosnia–Herzegovina, Montenegro, Croatia, and Slovenia, (b) an increasing demand for ‘cleaner’ technologies (OB4) shared with all AIR countries, and (c) an extension of circular economy initiatives to the sustainable blue economy (SBE) (OB5) shared with Italy, Slovenia, and Croatia. The top three opportunities for synergy creation in the blue economy include (a) cooperation in RD&I in the SBE (OC1) shared with Italy, Croatia, Bosnia–Herzegovina, and Montenegro, (b) cooperation and networking within the industry and between industry and academia (OC2) shared with all AIR countries, and (c) SBE Communities of all QH actors (OC3) shared with Italy and Slovenia [103,104,105,106,107].

However, as mentioned above, the prioritization of these challenges and opportunities differ, particularly when addressing them at the macroregional scale, as it has been derived from the validation of these results from the stakeholders’ representatives of the entire AIR macroregion. For instance, at the macroregional scale, new funding opportunities for blue economy projects (OB3) were among the top three opportunities for blue economy development within the AIR. Likewise, funding opportunities from EU programmes (OC8) were within the top three opportunities for synergy creation in the blue economy within the AIR [108].

From a policy perspective, this MLP provides a tested methodology for integrating evidence-based decision-making into policy formulation and for actively engaging the relevant stakeholders to share their insights and jointly shape relevant priorities to be addressed, monitored, and evaluated through MSP. Considering the MLP’s successful implementation, it can be considered as a potential practice of the participatory process to be officially adopted in the Greek MSP system. Although the current national MSP strategy [70] defines the objectives of the relevant participatory processes and the types of stakeholders to be involved in them at each planning scale along with a few recommended tools for supporting the operational part of these processes, it can also incorporate the MLP in these tools, considering its efficiency and effectiveness according to its purposes but also its benefits according to the literature. This may also be achieved through the development of a respective framework for shaping how, when, and why MSP participatory processes should be conducted, which may be institutionalized at both national and European levels (i.e., through a respective directive) [70,109,110], considering and thus facilitating the relevant processes and bearing in mind that knowledge exchange and stakeholder collaboration encourage active engagement and enhance the legitimacy of planning efforts [111,112]. This MLP also provides a tested model for institutionalizing the planning stages of M&E, thus contributing to the establishment of formal frameworks and guidelines for ‘monitoring and evaluation’ at the preplanning stage of MSP, ‘stakeholder participation’ as an integral part of MSP implementation, and ‘alignment of monitoring and evaluation’ with other MSP phases.

Focusing on the results of the strategic and specific objectives, stakeholders jointly concluded with four key strategic objectives that reflect their MSP-related interests and needs, with the first one highlighting the challenges they face regarding the development of their activities due to the conservation of the marine environment (SO1), as already clarified by the respective specific objectives. This strategic objective is established by several national and European institutional frameworks but also endorsed by the recently institutionalized national MSP strategy of Greece [70] (i.e., increase in marine protected areas from 20% to 30% of the Greek marine space, etc.), stressing the significance of setting relevant countermeasures through MSP that can ensure environmental conservation in balance with economic development. The second one addresses the challenges that remain unsolved due to a low knowledge capacity (SO2), which can be addressed through utilizing existing initiatives as defined above, knowledge transfer and cooperation. Relevant recommendations have already been included in the national MSP strategy [70], promoting the exploitation of relevant initiatives (i.e., research results) and cooperation, emphasizing transregional cooperation for addressing common challenges in shared marine environments, scientific cooperation among academic stakeholders, and cross-sectoral cooperation among government stakeholders. Cooperation among government actors is also recommended to monitor and coordinate through the suggested observatory for M&E MSP in Greece [70]. Of course, these recommendations can be broadened to include the rest of the QH actors (i.e., industry and civil society) to meet the MSP principles related to social equity.

The third strategic objective enhances the existing opportunities derived from the increasing demand for ‘cleaner’ technologies (SO3). It is strongly linked to SO2, promoting the practical adoption of existing initiatives and the development of new ones according to stakeholders’ needs like improved environmental performance (i.e., energy efficiency, zero waste, etc.). SO3 primarily aims for the practical adoption of these initiatives, while SO2 mainly encourages knowledge transfer to support capacity building. Relevant recommendations are provided through the national MSP strategy [70], mainly addressing biotechnology, renewable energy, and the greening of ports (i.e., the installation of cold ironing), but they can expand to other blue economy activities that can be benefitted by relevant recommendations. The last strategic objective in priority is promoting cooperation for R&D&I focused on the SBE (SO4). This is also strongly linked to SO2, this time emphasizing the creation of synergies, particularly for R&D&I purposes, including social innovation, too. Synergy creation, particularly at the cross-sectoral level, is a requirement by the EU MSP Directive; however, it can be further encouraged through the Greek MSP strategy and plans; thus, it can be incorporated in their strategic and specific objectives and addressed through appropriate actions and measures, exploiting for instance the multi-use concept.

The proposed KPIs address multiple dimensions of MSP, including the economic, environmental, social, governance, technological, and institutional aspects. Emphasis was placed on leveraging the existing initiatives, demonstrating that viable solutions already exist but require further institutional support. The role of institutional frameworks as enablers of economic, environmental, social, and innovation-driven objectives is explicitly recognized within the KPIs. Entrepreneurship emerged as a key economic driver, with the potential to strengthen local, regional, and national development while enhancing Greece’s international competitiveness. Stakeholders also underscored the need to align technological and innovation advancements with environmental policy objectives. Meanwhile, social considerations are addressed primarily through social innovation mechanisms, such as collaborative communities and knowledge-sharing networks aimed at capacity-building and partnership development.

Relevant limitations for implementing the MLP and the proposed KPI framework include the absence of a specific M&E framework for MSP in Greece. Despite the recently institutionalized national MSP strategy in Greece, it lacks a specific M&E programme that will monitor and evaluate progress to the general strategic objectives set in this strategy. Additionally, although this strategy aims at providing strategic guidelines that can be further refined in regional and local MSP plans along with additional objectives, actions, and measures aligned with both national strategy and regional and local needs, the methodology and the results of this study can support future regional and local MSP plans, though the results require an update. Although the MLP engaged representatives of government, the practical implementation of the KPI framework at the regional and local levels also requires the identification of synergies or conflicts with other relevant institutional frameworks (i.e., regional smart specialization strategies, etc.) to ensure MSP’s integrative approach. The proposed KPI framework can inform M&E frameworks for national, regional, and local MSP, although there is a significant difference in detail between the proposed framework and the national MSP strategy in both the terms of priorities identified and the specificities of the M&E process.

Future research should focus on extending the MLP’s application to the environmental and social dimensions of MSP in Greece. While the current KPI framework primarily addresses economic aspects, expanding it to incorporate environmental and social objectives would create a more holistic and integrated evaluation framework. This expansion would also validate the transferability of the process to other MSP-related contexts.

A critical step involves implementing the proposed data collection methods to establish the KPI baselines [68]. These baseline measurements will facilitate benchmarking, ensuring that future evaluations remain data-driven and evidence-based [113,114]. Standardizing data requirements and establishing metadata frameworks [109,115,116,117] will be essential for ensuring consistency and comparability across monitoring efforts. At the institutional level, identifying and designating competent authorities for data collection and validation is crucial. A structured governance framework will ensure that monitoring and evaluation efforts use high-quality, reliable data, supporting the long-term success of MSP implementation. These implications should be appropriately addressed by Greek MSP authorities, ideally at the national scale, to ensure that relevant standards and frameworks will apply to lower planning scales, regardless of their regional MSP competent authorities that still need to be officially defined.

The proposed KPIs can be visualized cartographically, facilitating the presentation and communication of their objectives, the interpretation of their results, and their correlation with the spatial dimensions of the blue economy and other MSP-related aspects, as well as corresponding decision-making and policy formulation. Their visualization can be facilitated through geospatial data infrastructures like geoportals and dashboards, defining how relevant data should be integrated, processed, analyzed, and presented through an appropriate framework. Several best practices have already been implemented across Europe, allowing open access to all relevant stakeholders and fostering their interaction on MSP-related topics like the participatory geographic information system (PGIS) developed within the framework of the GAP and GAP2 projects [29,30], data portals like the BalticRIM, which focuses on Mutual Learning Processes [27], and decision-support tools (DSTs) like the MYTILUS tool, which assesses cumulative environmental impacts on the marine environment in the BSR [28]. The relevant data infrastructure framework is closely linked to definitions provided through the aforementioned data and metadata frameworks. Existing infrastructures operating under the responsibility of the national MSP competent authority in Greece (i.e., the Hellenic Ministry of Energy and Environment) can be complemented to host relevant geodatabases and services, thus enhancing the link of MSP with terrestrial planning and facilitating collaboration and alignment with other relevant authorities at both national and regional levels.

Monitoring and evaluation support the review, adaptation, and revision of MSP, and thus it needs to be a continuous and systematic procedure [38]. In this context, the proposed MLP requires recurrent, regular, and iterative stakeholder engagement processes that can ensure continuous knowledge exchange. The significance of this recurrency lies also in the fact that the MSP-related aspects are dynamic and continuously changing, including not only the sociocultural, economic, and environmental conditions but also stakeholders’ respective interests and expectations, and most importantly, any changes in the institutional framework related to MSP that may affect both conditions and stakeholders’ objectives. The literature suggests that systematic engagement maximizes participatory efficiency and effectiveness [4,8,80]. Several methodologies have been implemented to ensure the regularity of participatory processes, including expert working groups [26] or the VASAB process, which stimulates systematic mutual learning at the macroregional level [118]. As Greece continues to advance its MSP implementation, a commitment to the iterative refinement of the KPI framework and active stakeholder engagement will be crucial. Establishing a culture of continuous learning and adaptation will enable MSP to effectively address the emerging challenges and opportunities in the blue economy.

Through formulating an official M&E framework that specifies and supports how M&E should be implemented within the MSP context in Greece, the competent body of each M&E programme should also be defined. This body will ensure the successful design and implementation of the M&E programme according to the specificities set by the M&E framework and the objectives of the MSP strategy and plans. The establishment of this body should ideally be completed at the very beginning of the MSP process, facilitating its alignment of M&E with the rest of the MSP stages.

6. Conclusions

The blue economy is a complex and dynamic domain that requires a management approach that ensures socio-economic equity, the balanced distribution of marine space among its users, environmental sustainability, and alignment with broader institutional frameworks. MSP is a key mechanism for achieving these objectives, provided it is supported by robust and continuous monitoring and evaluation processes and inclusive and representative stakeholder engagement.

This paper presents findings from a two-round MLP, with the first round concluding with a set of four strategic objectives aimed at enhancing blue economy development and cross-sectoral synergies creation, pointing out the significance of the conservation of the marine environment, capacity building, seizing opportunities from the increasing demand for ‘cleaner’ technologies, and cooperation for R&D&I. The second MLP round refined these strategic objectives in 29 specific ones that facilitated the development of a KPI framework for monitoring and evaluating the blue economy within Greece’s MSP framework. This framework included 21 indicative qualitative and quantitative indicators clearly linked to the objectives identified, aligned with the SBE and MSP principles, adhering to the SMARTIE criteria, and classified into four thematic clusters that reflect the environmental, economic, technological, social, and governance dimensions. These indicators enable assessments across different geographic and administrative levels, with clearly defined data collection methods and the sources of relevant information, the frequency of their measurement, and the method of visualization. The participatory approach ensured that the findings align with stakeholder needs and expectations, complementing the basis for continuous monitoring and evaluation through qualitative and quantitative indicators.

The proposed approach strengthens the integration of MSP and preplanning and planning activities with monitoring, evaluation, and participatory processes. To institutionalize this alignment, appropriate frameworks for each phase of MSP must be formalized and systematized. This includes establishing continuous MLPs for shaping and measuring targeted KPIs, developing robust data, metadata, and infrastructure frameworks, and designating competent authorities responsible for data collection and validation.

Furthermore, the MLP approach demonstrated in this paper is highly transferable to other institutional contexts, both vertically and horizontally, within the broader blue economy and marine governance landscape. Its applicability allows for applications beyond economic dimensions, extending to the environmental and social aspects of MSP. This makes it a valuable tool for enhancing comprehensive planning, assessment, and decision-making. The scalability of the methodology also enables its implementation at local, regional, national, macroregional, transregional, and cross-border levels, supporting a more cohesive and harmonized approach to sustainable blue economy management through MSP.