Gender, Vulnerability, and Resilience in the Blue Economy of Europe’s Outermost Regions

Abstract

This review explores the intersection of gender, geography, and sustainability by examining the role of women in the blue economy across Europe’s Outermost Regions (ORs). Despite growing recognition of the blue economy’s role in sustainable development, there is limited understanding of how women participate in these sectors at the geographic periphery of the European Union. Using publicly available data from Eurostat, INSEE, ISTAC, and other national portals, we analyze employment patterns through a gender lens, supported by qualitative insights from case studies in regions such as the Azores, Réunion, and Guadeloupe. Due to the scarcity of disaggregated blue economy data, general labor force participation is used as a proxy, highlighting both opportunities and visibility gaps. Theoretically grounded in feminist political ecology and intersectionality, the review identifies key barriers, including data invisibility, occupational segregation, and structural inequalities, as well as resilience enablers such as women-led enterprises and policy interventions. We conclude with targeted recommendations for research, policy, and practice to support inclusive blue economies in ORs, emphasizing the need for better data systems and gender-sensitive coastal development strategies.

1. Introduction

The blue economy has emerged over the past decade as a central paradigm linking ocean-based economic development with sustainability. Closely intertwined with the notion of “blue growth”, it extends the principles of the green economy to marine and coastal environments and positions the ocean as a frontier for innovation, environmental stewardship, and inclusive prosperity [1]. The blue economy concept builds on the earlier ‘green economy’ model, which promotes economic development that is resource-efficient, low-carbon and socially inclusive, aiming to decouple growth from environmental degradation. According to the World Bank, the blue economy encompasses “the sustainable use of ocean resources for economic growth, improved livelihoods, and jobs while preserving the health of ocean ecosystems” [1]. A wide range of sectors, from traditional industries such as fisheries, shipping and coastal tourism to emerging areas like aquaculture, offshore renewables, marine biotechnology and deep-sea mining, fall under this umbrella [2]. Academic literature further highlights how interpretations of the blue economy vary across contexts: some emphasize traditional maritime industries, others stress ecosystem services or technological development, while many Small Island Developing States (SIDS) frame the blue economy around resilience and social equity due to their heightened vulnerabilities [3]. Across these perspectives, the paradigm reflects an ongoing attempt to reconcile economic opportunity with ecological integrity and social inclusion.

Europe’s outermost regions (ORs) represent a particularly compelling context within which to examine the blue economy. ORs are the European Union’s most remote territorial fragments, spanning the Caribbean, Atlantic, Indian Ocean and beyond. Despite their distance, these nine regions are integral parts of the EU and have been progressively integrated into its legal and policy frameworks. The EU provides special support to ORs to compensate for the structural handicaps of peripherality [4]. Historically, their economies depended on a narrow base of activities, such as plantation agriculture, subsistence fisheries, inter-island shipping and localized services, which limited early blue economy development. While traditional ocean-based activities have long existed, they often remained marginal or small-scale. EU membership marked an important shift: structural funds, Cohesion Policy instruments, and dedicated schemes such as POSEI and the European Maritime and Fisheries Fund sought to foster growth, improve infrastructure and stimulate maritime sectors [5]. These interventions, enabled by Article 349 of the Treaty on the Functioning of the European Union (TFEU), reflected the EU’s recognition of ORs as “lands of Europe in the world”. Yet despite targeted initiatives, ORs remain on the geographic and economic edge of Europe, and their blue economy potential continues to be shaped by structural constraints.

The vulnerabilities of the ORs add a further layer of complexity. Environmentally, ORs host some of Europe’s most unique and sensitive marine ecosystems, ranging from coral reefs and seagrass beds to subtropical habitats, yet these areas are increasingly threatened by climate change [5]. Ocean warming, marine heatwaves, coral bleaching, and shifting fish stocks are already impacting marine biodiversity, with all ORs identified as facing significant ecological risks [6]. In regions such as Guadeloupe, Martinique, French Guiana, La Réunion and other island territories, the intensification of cyclones and hurricanes, sea-level rise, and coastal erosion pose major threats to livelihoods and critical infrastructure [7]. Alongside environmental fragility, ORs confront the classic developmental challenges of remote islands: limited economic diversification, dependence on a narrow set of export products (banana, sugar cane, fisheries, tourism), high transport and transaction costs, small domestic markets and shortages of skilled labor [3,8]. Many regions also exhibit fragile institutional and infrastructural capacity, hindering the implementation of new blue economy initiatives. Socio-economic indicators mirror these structural vulnerabilities: ORs generally experience lower income levels, higher unemployment, and lower employment rates than mainland Europe [3]. These multi-layered vulnerabilities shape how ORs can participate in and benefit from blue economy strategies.

Within this complex setting, an important yet understudied dimension is gender. Maritime and marine sectors remain overwhelmingly male-dominated, and women’s contributions are often marginalized or rendered invisible [9]. In European fisheries, men typically dominate seagoing labor and vessel ownership, while women engage heavily in shore-based, processing and family-related tasks that are crucial but frequently unpaid or unrecorded [9]. Similar patterns of exclusion appear across other blue sectors, shipping, aquaculture, ocean science—where structural barriers, gendered norms, limited work–life balance measures and even workplace harassment constrain women’s participation and advancement [3]. The maritime transport industry starkly illustrates these inequalities, with women comprising only a tiny fraction of EU seafarers and officers [9]. Leadership and governance bodies across the blue economy are likewise predominantly male, indicating that gender mainstreaming remains in its early stages. Notably, even the EU’s flagship Blue Economy Report does not contain gender-disaggregated employment data and makes no mention of “women” or “gender” [3], evidencing major gaps in data and policy attention.

These gendered disparities intersect in important ways with the structural vulnerabilities of the EU’s outermost regions. The ORs’ ecological fragility, climate exposure, small economies and peripheral status create additional pressures on livelihoods and labor markets, yet the gendered dynamics of these challenges remain poorly understood. Although substantial scholarship exists on gender roles in SIDS and coastal communities of the Global South, highlighting women’s contributions to small-scale fisheries, their roles in climate adaptation, and the need for gender-responsive blue economy governance [5], comparable analyses for the EU’s ORs are scarce. Despite sharing many characteristics with SIDS (isolation, exposure, limited diversification), ORs are part of high-income EU member states, placing them in an unusual analytical position that often excludes them from standard development narratives. Consequently, little is known about women’s roles in ORs’ fisheries, tourism, conservation, research, or emerging blue sectors; the specific barriers they face; or how cultural diversity, from Caribbean Creole contexts to Macaronesian and Indian Ocean societies, shapes gender relations within maritime industries.

This review responds to that gap. By examining “women at the edge”, the status, roles and agency of women in the blue economies of Europe’s outermost regions, this paper seeks to illuminate an overlooked facet of sustainable development. Drawing on interdisciplinary literature and comparative insights from SIDS, it assesses how gender inequalities manifest in ORs’ ocean-based sectors, how environmental and socio-economic vulnerabilities exacerbate these inequalities, and what resilience strategies or governance approaches may foster more inclusive and sustainable blue economies. Addressing this knowledge gap is not only essential for equity but also crucial for strengthening the effectiveness and resilience of blue economy initiatives in the EU’s most vulnerable maritime regions. The following sections review existing work, identify patterns and gaps, and outline a framework for integrating gender and vulnerability considerations into blue economy strategies tailored to Europe’s outermost regions. Recent analyses emphasize that structural constraints such as remoteness, resource dependence, climate exposure and persistent socio-economic inequalities continue to define these territories [10,11,12], underscoring the urgency of a more inclusive and context-sensitive approach to blue growth.

2. Conceptual Framework

To examine gender, vulnerability, and resilience in Europe’s outermost blue economy, this review draws on three interlinked theoretical lenses: feminist political ecology, intersectionality, and social-ecological resilience. These frameworks illustrate how power and identity shape environmental governance and adaptive capacities in marine and coastal contexts. Below, we outline each component and explain how, in combination, they ground our analysis. Environmental justice perspectives further demonstrate how climate-related stressors intersect with gendered inequalities in ORs, particularly in France’s overseas departments [11], where exposure to hazards, displacement risk, and uneven access to adaptive resources disproportionately affect women. Gender-blind management produces systematically unequal access to marine resources, decision-making spaces, and benefits from blue economy development, reinforcing structural vulnerabilities and weakening community-level resilience.

2.1. Feminist Political Ecology in Marine Contexts

Feminist political ecology (FPE) provides a critical lens to understand gendered power relations in environmental settings. Scholars argue that access to and control over natural resources are always gendered processes, mediated by social norms, kinship systems, and institutional biases [13]. This approach is highly relevant to marine and coastal settings, where traditional divisions of labor often see men dominating offshore and high-value activities (e.g., fishing or boating) while women engage in shore-based or informal work such as gleaning, processing, and trading seafood [14]. FPE does not treat “gender” as synonymous with “women”, but examines how gender roles are constructed and enforced through everyday practices and governance structures. It asks who has voice and authority in environmental decision-making, whose knowledge counts, and whose labor is valued or ignored. Research using an FPE framework has shown that in many coastal communities, women’s contributions, for example, maintaining gear, processing catch, or conserving coastal ecosystems, are overlooked or undervalued in policy, leading to gender-blind management [14].

By highlighting these inequities, FPE reveals how environmental governance itself can reproduce or challenge gender power imbalances. Policies that allocate fishing quotas, marine space, or development funds often privilege male-dominated enterprises, whereas women may lack formal rights or representation in resource councils [14]. FPE-oriented studies have documented, for instance, how patriarchal norms and exclusionary institutions limit women’s participation in fisheries management, even when women are key resource users [14]. At the same time, FPE emphasizes women’s agency and knowledge in fostering sustainability. It combines attention to gendered knowledge, rights, and activism, demonstrating that women’s local ecological knowledge and community networks are vital for effective coastal conservation and climate adaptation [15]. Feminist political ecology offers a nuanced understanding of marine environmental governance by showing how gendered power relations shape who accesses resources, who decides on their use, and who bears the costs or benefits [13]. This perspective will guide our inquiry into the blue economies of outermost regions, ensuring we cross-examine the often-invisible gendered structures underlying resource use and policy.

2.2. Intersectionality: Gender at the Margins

Intersectionality complements FPE by accounting for overlapping social identities and power structures that modulate women’s experiences. Originally developed by Kimberlé Crenshaw in 1989 in the context of race and gender, an intersectional approach recognizes that factors such as race/ethnicity, class, geographic location, and climate risk intersect with gender to produce varied vulnerabilities and opportunities. In Europe’s outermost regions, many of which are remote islands with post-colonial legacies, this lens is especially important. Women in these regions do not experience “gender” in isolation; their lives are also shaped by racial/ethnic marginalization, economic precarity, and isolation from mainland centers. Intersectionality helps us analyze how such a woman’s vulnerability is compounded by multiple, interlocking factors. Indeed, feminist political ecologists note that gender itself is intersectional and dynamic, interacting with class, ethnicity, and other dimensions across scales [13]. These interactions mean that even among women, access to marine resources and exposure to risks can differ widely, e.g., between an indigenous fisher’s wife and a female tourism entrepreneur, depending on their social position [13].

Europe’s ORs are among the territories most vulnerable to climate change, facing rising seas, extreme weather, and biodiversity loss with limited adaptive capacity [16]. Such geographic remoteness and climate exposure often exacerbate existing inequalities. An intersectional framework insists that policies and research must recognize these multiple axes of identity, rather than treating women as a homogeneous category [13,17]. By doing so, we can better understand why certain women are “at the edge”, literally and figuratively, in the blue economy, and how tailored interventions can address the specific needs of those who are doubly or triply marginalized (by gender, race, class, and place). In this review, intersectionality will be used to ensure we discuss gender in the ORs in context, acknowledging, for example, how colonial histories and socio-economic exclusion intersect with gender to shape women’s livelihoods and vulnerabilities.

2.3. Social-Ecological Resilience and Women’s Agency

The third pillar of our framework is social-ecological resilience, a concept from sustainability science that we apply with a gender-sensitive lens. Social-ecological resilience refers to the capacity of integrated human–environment systems to absorb disturbances, adapt, and continue to develop without losing their essential structure or function. The Stockholm Resilience Center defines it as “the capacity of a system, be it an individual, a forest, a city or an economy, to deal with change and continue to develop… how humans and nature can use shocks and disturbances like a financial crisis or climate change to spur renewal and innovation” [18]. In the context of the blue economy, this translates to the ability of coastal communities and industries to withstand and adjust to stresses such as overfishing, market fluctuations, or climate impacts (e.g., coral bleaching, storms) while sustaining livelihoods and ecosystem services. Key components of resilience include adaptive capacity (learning and flexibility to change strategies) and transformability (ability to fundamentally reorganize in response to crisis).

A critical insight from recent literature is that resilience is not value-neutral; social factors like equity, power, and inclusion fundamentally shape who can adapt and how [18]. Traditional resilience frameworks have been critiqued for overlooking gendered power differences. For instance, a fishing community might appear “resilient” in the aggregate, but if women in that community have little voice or access to resources, the community’s adaptive capacity is uneven and potentially fragile. Integrating a feminist and intersectional perspective into resilience thinking is therefore essential [19]. Women’s adaptive capacity and agency often differ from men’s due to gender roles and inequalities. As noted above, women in blue economy sectors are frequently confined to lower-paid, informal jobs with less security and decision-making power [14]. This can make women more vulnerable to shocks (for example, an abrupt fish stock decline or a tourism downturn) because they have fewer assets to fall back on and less influence in communal responses. Social norms that burden women with childcare and domestic responsibilities can further limit their mobility and flexibility in adapting to livelihood disruptions [14]. Gender gaps in resources and power translate into gender-differentiated resilience outcomes.

On the other hand, numerous studies indicate that empowering women and promoting gender equality can strengthen social-ecological resilience [17]. When women are included in resource governance and have equitable access to education, credit, and technology, they contribute invaluable knowledge and skills to community adaptation efforts. A recent synthesis on ocean risk in small island states found that women’s roles and contributions, though undervalued, are pivotal for effective risk management, and that gender discrimination improves community vulnerability [17]. Advancing gender equality improves women’s agency and yields broader resilience dividends, benefiting households and communities by unlocking women’s full participation in adaptation and innovation [17]. This aligns with literature to “bring gender analysis together with social-ecological resilience analysis” to ensure that building resilience also means building justice [19]. Rather than treat resilience as purely a technical or ecological property, our framework treats it as a social process, one that must include attention to power, inclusion, and the differential capacities of women and men to cope with change.

Taken together, these three lenses provide a cohesive approach to the gender dynamics of vulnerability and resilience in the blue economy. Feminist political ecology grounds our analysis in power relations and helps explain why women in ORs often have unequal access to marine resources or decision-making arenas [13]. Intersectionality ensures we account for which women (and men) we are talking about, recognizing the diversity of experiences shaped by race, class, remoteness, and other factors [13]. Social-ecological resilience adds a forward-looking perspective on how communities adapt (or fail to) in the face of environmental and economic shocks, highlighting the role of women’s agency in these processes [17]. By weaving these strands into an integrated conceptual framework, we can better analyze the central question of this review: how women at the margins, geographically and socially, navigate and transform the challenges of the blue economy. This framework posits that enhancing gender equity is not only a matter of social justice but also a precondition for sustainable and resilient blue economies [17]. The following sections will apply this framework to evidence from Europe’s outermost regions, examining patterns of gendered vulnerability and examples of women’s resilience and leadership in fisheries, aquaculture, tourism and beyond.

3. Methodology and Data Sources

3.1. Semi-Systematic Literature Review Approach

This review employed a combined methodology, integrating a semi-systematic literature review with quantitative data compilation. The literature review followed a semi-systematic approach designed to capture diverse scholarly perspectives on gender and the blue economy in Europe’s outermost regions. A semi-systematic review allows flexibility in surveying an interdisciplinary body of literature while maintaining systematic rigor in search and selection [20,21,22]. As Snyder [21] argues, such an approach is useful for detecting overarching themes and can combine different methods of sampling (systematic or not) and analysis (qualitative or quantitative).

A semi-systematic literature review is positioned between narrative and fully systematic reviews: it applies systematic elements such as predefined search strings, transparent inclusion/exclusion criteria, and structured screening, while permitting the integration of interdisciplinary sources, grey literature, and region-specific materials that may not appear in indexed databases. This approach is especially appropriate for gender and blue economy research in Europe’s ORs, where evidence is fragmented across disciplines and policy domains. To ensure transparency and replicability, our procedures were aligned with established systematic review guidelines.

Search Strategy

Extensive searches were conducted in major academic databases (Scopus and Web of Science) and specialized journals, focusing on peer-reviewed studies at the intersection of gender (e.g., women, gender equality), vulnerability/resilience, and blue economy sectors (e.g., fisheries, aquaculture, maritime industries, coastal tourism). Keyword strings combined gender-related terms with blue economy sectors and geographic identifiers relevant to Europe’s outermost regions (e.g., “Azores”, “Canary Islands”, “Guadeloupe”, “Réunion”, “Mayotte”, “Madeira”). Given the limited academic literature that explicitly addresses gender in the blue economy of ORs, our search was semi-systematic. Alongside database queries, we conducted backward and forward citation tracking and reviewed targeted grey literature (EU reports, policy documents, regional observatory datasets, statistical portals) to ensure that significant region-specific insights were not missed.

The database search yielded 312 records (Scopus = 176; Web of Science = 136). After removing 94 duplicates, 218 unique records were screened at the title–abstract level. Forty-one publications met the topical and geographic criteria for full-text assessment, and 17 peer-reviewed articles satisfied all inclusion criteria. An additional 11 peer-reviewed publications were identified through backward and forward citation tracking. Due to well-documented data scarcity for outermost regions, 18 high-quality grey literature sources (EU reports, OECD, FAO, EIGE, national observatories) were included to fill essential evidence gaps. The final evidence base consists of 46 publications (peer-reviewed = 28; grey literature = 18). To avoid double-counting and ensure coherence with the final reference list, overlapping sources and non-essential documents were excluded. The final evidence base therefore consists of 28 sources, representing the intersection of peer-reviewed literature and the highest-quality grey literature relevant to gender and the blue economy in the ORs.

3.2. Inclusion and Exclusion Criteria for Literature and Data

Clear inclusion and exclusion criteria were applied to both the literature and the datasets to ensure relevance and quality. For the literature review, we included works that met the following criteria:

• Topical relevance: Studies had to address gender dimensions (e.g., women’s roles, gender inequality, empowerment) in the context of blue economy sectors (such as fisheries, maritime industries, coastal tourism, or marine resource governance). We prioritized articles explicitly examining vulnerability or resilience of women in marine/coastal livelihoods, or related policy and social analyses.
• Geographic scope: Preference was given to studies focusing on Europe’s ORs or analogous island/coastal contexts. We included comparative studies (e.g., involving Small Island Developing States or other peripheral regions) if they offered transferable insights for the EU outermost regions.
• Publication type and quality: Only peer-reviewed publications (journal articles, academic book chapters, and selected conference papers) were included. This ensured a baseline of scholarly quality. In a few cases, high-quality reports or policy papers (grey literature) were considered if they contained unique data on the regions; such cases were screened carefully for credibility.
• Language and timeframe: Publications in English were primarily included (given the broad accessibility in academic discourse). Recognizing that some research about these regions may appear in French, Spanish or Portuguese, we also considered a limited number of non-English sources for French overseas regions, Canary Islands, and Azores/Madeira, provided they had abstracts in English. The review emphasized recent literature (roughly the past 10–15 years, aligning with the rise of the “blue economy” concept), but seminal earlier works were included if frequently cited or foundational.

Exclusion criteria eliminated works that were not directly relevant (e.g., dealing with blue economy but without a gender analysis, or vice versa), as well as opinion pieces or anecdotal reports lacking empirical support. We also excluded articles not focused on coastal/marine contexts (to stay within the “blue” economy scope).

For the data component, similar inclusion principles were established. We sought publicly available datasets that offer statistical indicators relevant to gender, economic activity, and social development in the outermost regions. Inclusion criteria for data sources were: (i) the data had to be official or reputable (produced by government agencies, international organizations, or recognized research bodies) to ensure reliability; (ii) data needed to be region-specific, i.e., disaggregated for each outermost region or at least the country’s overseas regions; (iii) whenever possible, data should be disaggregated by sex or include gender-relevant metrics (such as female employment rates, gender pay gap, women in leadership positions). We targeted the most recent available data (generally from the past 5–10 years) to capture current conditions. We excluded data sources that were outdated, not publicly accessible, or not disaggregated to the regional level (for instance, national-level statistics that would mask the situation in an outermost region). We also excluded indicators not pertinent to blue economy sectors or socio-economic resilience (e.g., if a dataset covered many topics but none related to our inquiry).

Using these criteria, a curated set of datasets was assembled, complementary to the literature review. Notably, the European Union’s statistical and research agencies provide many relevant indicators for these regions. The EU officially recognizes nine ORs (eight territories at NUTS2 statistical level, plus the island of Saint-Martin grouped with Guadeloupe) [23]. Accordingly, data were compiled for each of these regions, across key sectors of the blue economy.

3.3. Public Data Extraction and Sources by Region and Sector

To supplement qualitative insights from the literature, we performed a structured extraction of quantitative data on Europe’s outermost regions. We identified a suite of indicators related to blue economy sectors and socio-economic context—focusing on sectors where women’s participation, vulnerability, or resilience are most pertinent. These sectors include fisheries, aquaculture, coastal tourism, and broader employment/economic activity, as well as aspects of governance (e.g., women in decision-making roles).

Table 1. Gender-disaggregated socio-economic indicators for blue economy in EU outermost regions.

Source	Dataset/Report Name	Geographic Scope (ORs)	Indicators Available	Data Years	Notes (Relevance & Limitations)
Eurostat	Coastal, Island and ORs Statistics (Eurostat Regional Database)	All 9 EU ORs (NUTS2 regions; Saint-Martin is included with Guadeloupe)	– Demography: Population by age/sex, density, etc. – Economic accounts: Regional GDP, GVA, household income – Labor market: Employment and unemployment rates by gender, activity rates, etc. – Sectoral data: Employment by broad sectors (agriculture/fishing, industry, services) – some gender breakdowns available in labor force survey data.	~2010–2024 (annual; varies by indicator)	Data used to monitor blue economy performance. ORs are identifiable as separate regions in EU statistics (NUTS2). However, sample sizes and data quality can be issues (e.g., earlier labor surveys in Mayotte were limited; full LFS integration only from 2024). Some sector-specific data (e.g., fishing) may not be separately reported by Eurostat; figures are often aggregated in broader categories.
EIGE	Gender in Maritime Affairs and Fisheries (EIGE report, 2017)	EU-level (All Member States; not specific to ORs)	– Employment in fisheries: ~100,000 women in EU fisheries (2014), only ~4% of these in extractive fishing roles, ~30% in aquaculture (mainly shellfish gathering), ~60% in processing. – Roles and ownership: Most crew and vessel owners are men; women’s contributions concentrated in onshore and processing roles. – Qualitative indicators: participation in community organizations, decision-making, unpaid family work, etc.	~2012–2015 data in report (EU Parliament and national sources)	Highlights the male-dominated nature of maritime sectors in Europe. Provides valuable context on gender roles (e.g., women’s invisible work in family fishing businesses). Not disaggregated by region—ORs are included in national totals. Data mainly covers formal employment; informal/unpaid contributions (prevalent in small-scale fisheries) are under-represented. Shows need for improved sex-disaggregated data in blue sectors.
EC Blue Economy Reports	EU Blue Economy Report (annual series by European Commission, e.g., 2022 edition)	EU-wide (EU-27 aggregate; country-level breakdowns; ORs included in national data)	– Macro indicators: Employment in Blue Economy sectors (e.g., 3.59 million jobs in 2021), Gross Value Added (€171 billion in 2021), turnover, profits, etc., for sectors like marine living resources (fisheries & aquaculture), coastal tourism, maritime transport, shipbuilding, ports, offshore energy. – Trends: Time series of sector growth (e.g., +35% GVA jump 2020–2021) and impacts of shocks (COVID-19, energy prices). – No gender breakdown: Data are aggregated by economic sector and region/country, not by sex.	~2009–2021 (data up to 2019–2021 in recent editions)	Flagship reports compiling multi-source data (Eurostat SBS, national fishery data, etc.) to quantify the Blue Economy. ORs are covered as part of Member States (no separate OR statistics). These reports do not provide gender-disaggregated figures—all employment figures are total (male + female). They note data gaps in certain sectors. For example, Mayotte’s aquaculture/fisheries data have gaps: in 2017 Mayotte reported 287 fishers with gender “unknown” (no sex-disaggregation). Overall, the Blue Economy reports highlight socioeconomic importance of ORs (e.g., large EEZs, climate change vulnerability) but lack gender-specific analysis.
FAO	FAO Fisheries & Aquaculture Statistics (Yearbooks & Country Profiles)	Global and national data (EU ORs included under FR, PT, ES national totals)	– Employment in fishing: Number of people engaged in fishing (commercial and subsistence), by country. Some data split by gender (where reported). – Aquaculture employment: e.g., France had ~15,872 aquaculture workers in 2016, ~33% of them women. (Country profiles often list the female share in aquaculture/fisheries workforce.) – Production and trade: Fish catch and aquaculture production volumes by region (not gender-disaggregated).	Annual data compilations (e.g., Yearbook 2021, country data 2010s)	FAO provides the broad context and comparative data. Its country-level gender indicators (e.g., percent of fishery/aquaculture workforce that is female) help gauge ORs’ situations since OR fisheries are included in national figures. However, data for ORs are not separately broken out in FAO stats. In addition, not all countries report sex-disaggregated employment consistently—especially in small-scale fisheries, data on women’s participation are often missing or incomplete. This limits the visibility of women’s roles (e.g., shellfish gleaning or informal processing in OR communities may go unrecorded).
INSEE (France)	Statistical Portals for Overseas Departments (e.g., INSEE “L’essentiel sur…” profiles)	French ORs: Guadeloupe (incl. Saint-Martin), Martinique, French Guiana, Réunion, Mayotte.	– Population & Demography: total population, age structure, fertility, etc. – Labor market: employment rate, unemployment rate by sex; labor force participation; sectoral employment (e.g., share in agriculture/fishing vs. services). Gender-specific metrics like female employment rate, female unemployment, etc., are provided in analyses. – Income & Social: household income, poverty rates; education levels by sex; other social indicators.	Annual/Quarterly (e.g., labor force surveys, census; latest, e.g., 2024–25)	National statistical coverage ensures data for each DROM (OR). Insee’s surveys and census provide gender-disaggregated insights (e.g., in Mayotte 2024, unemployment reached 29%—the highest in France—and the majority of the 61,000 jobless who wish to work are women). Relevance: These data allow analysis of gender gaps in employment, education, and poverty in each OR. Limitations: Some economic sectors relevant to the blue economy are small; data may be based on small samples. For instance, official employment figures in the fishing industry are very low (a few hundred fishers per region) and may exclude informal family labor. In Mayotte and other DROMs, improvements in survey methodology are recent, meaning time-series consistency is a challenge. No detailed data on specific blue economy occupations (e.g., female fishers or boat owners) are published by Insee; such detail comes from niche studies.
ISTAC (Canary Islands)	Canary Islands Statistics (ISTAC data portal)	Canary Islands, Spain (single NUTS2 OR)	– Labor force indicators: employment, unemployment, activity rates by sex (available quarterly by region). – Employment by sector: breakdown of jobs by industry (tourism, services, fishing, etc.), with some gender data. (e.g., women constitute only ~5–6% of registered fishing industry employment in Canarias; conversely, in tourism and services their share is much higher.) – Socio-economic indicators: education, earnings, etc., some gender-disaggregated in regional surveys.	2000s–2025 (regular LFS, administrative data)	The Canary Islands, as Spain’s OR, are covered by national surveys and regional stats. Gender gaps are evident: e.g., female participation in the fishing fleet is only ~6%, reflecting traditional gender roles. ISTAC relies on Spain’s Labor Force Survey and other sources, so blue economy sectors can be analyzed (fisheries, tourism, etc.), but detailed gender data for niche blue sectors (like mariculture or marine research) are limited. Researchers note some reliability issues in fisheries data (under-reporting by small-scale fleets). Overall, core labor and population stats are robust, but segmenting the blue economy by gender often requires ad hoc analysis (as official stats focus on broader sectors).
Statistics Portugal (INE)	Regional Statistics—Azores & Madeira (INE and regional stat services)	Azores and Madeira (PT autonomous regions, both EU ORs)	– Demographics & GDP: population, GDP, etc., by region (annual). – Labor market: employment and unemployment rates by sex; workforce by sector. (Azores and Madeira have data via national surveys—e.g., labor force participation of women, female unemployment rate, etc., comparable to mainland data). – Sectoral specifics: Some data on fisheries and tourism: e.g., the Azores have a large artisanal fishing sector; female involvement in fishing is very low (~4% of fishers in Azores are women; in Madeira ~1%), though women may have roles in processing or family businesses not captured in stats.	~2010–2024 (annual; labor force quarterly)	The national statistical system (INE) and regional offices (SREA Azores, DREM Madeira) provide comprehensive socio-economic data. Key blue economy sectors in these islands (fisheries, tourism) can be analyzed by gender to an extent. Relevance: These data sets help identify, for example, the small share of women in direct fishing occupations versus their participation in tourism or services. Limitations: Gender detail for certain blue economy fields (fisheries, aquaculture) is sparse—often only total employment is reported. As noted by EU fisheries studies, some ORs (e.g., Madeira, Azores) report virtually no women at sea, which may indicate either true low participation or a failure to account for women’s informal contributions. No dedicated gender indicators for “blue” sectors exist in the standard INE publications; researchers must derive them from occupation or sector tables.
Statistics Netherlands (CBS)	Caribbean Netherlands Statistical Series (Labor Force, etc.)	(Not EU OR) Bonaire, Saba, St. Eustatius (special Dutch municipalities—OCT)	– Labor force: participation rates by sex, employment by industry, unemployment by sex (the CBS produces dedicated reports—e.g., a 2019 study covering Aruba, Curaçao, and Caribbean NL showed female labor force participation ~10 pp lower than male on average, and sectoral employment distributions by gender). – Income and social: wages by gender, education levels, etc., for these islands (allowing insight into gender gaps in earnings and job types). – Sectoral data: The size of the “blue” sectors in these small islands (fishing, maritime transport) is very limited; data is mostly for overall employment. (Tourism is significant and can be gender-disaggregated, whereas fishing employment is minimal and often not reported separately.)	~2010–2024 (annual surveys, occasional reports)	Although not EU ORs (the Caribbean NL are overseas territories), data from CBS is included here as it provides a point of comparison. It demonstrates methods of gender-disaggregated socio-economic reporting in small remote economies. Note: In these islands, the blue economy is tiny (e.g., only a few dozen fishermen). The statistics show broad gender patterns similar to ORs (men have higher employment rates; women more in services and part-time roles). A limitation is that such micro-territory data can have high variability and some indicators are only published intermittently due to small sample sizes. CBS does not break down “blue economy” employment as a distinct category in regular releases, so analysis requires combining relevant sectors (fishing, tourism, etc.) and interpreting with caution.

provides an overview of the major data sources utilized, categorized by region and sector. Key sources included pan-European databases (e.g., Eurostat regional statistics and the European Commission’s Blue Economy datasets), thematic databases (e.g., FAO for fisheries and aquaculture data, where available), and region-specific statistical offices (for fine-grained local data). We also tapped into the European Institute for Gender Equality (EIGE) statistics for any indicators on gender gaps relevant to these regions (such as gender employment gaps or women in leadership). Each data source was vetted for quality and relevance per the inclusion criteria above.

Data extraction process: For each outermost region, we gathered data on the total and female workforce in relevant sectors, economic output of blue economy activities, unemployment rates and education levels (to gauge vulnerability), and representation of women in governance (e.g., percentages of women in regional councils or maritime sector boards, where data permitted).

The extraction was systematic: we used online data portals (Eurostat’s regional database, EIGE’s Gender Statistics Database, etc.) and official reports to retrieve the latest figures. When direct regional data were sparse, we noted proxies (for instance, national-level gender indices or sectoral data that include the region). In some cases, the data collection revealed gaps, consistent with observations that gender-disaggregated data in the blue economy are limited [24,25].

Table 1 below lists the main public data sources. These sources provided the quantitative backdrop for our analysis of each territory’s blue economy. For instance, for Azores and Madeira (Portugal), we used Statistics Portugal and Eurostat for labor and tourism data, and FAO or national fishery reports for fisheries. For the Canary Islands (Spain), data were drawn from Eurostat and the Canary Islands’ ISTAC statistical service for tourism and employment figures, as well as Spanish ministry data on fisheries. For the French overseas regions (Guadeloupe, Martinique, French Guiana, Réunion, Mayotte), we relied on INSEE (the French national statistical institute) and regional observatories for socio-economic indicators, and on European Commission reports (e.g., JRC’s Blue Economy Observatory) for sector-specific data like fisheries and aquaculture. Governance indicators (such as women’s representation in local government or industry leadership) were collected from EIGE’s decision-making database or official election statistics, when available. All data sourced was publicly available and open access, allowing verification and further analysis. The combination of these datasets enables a multi-dimensional understanding of women’s status in the blue economies of the outermost regions, from hard numbers to contextual factors.

We constructed a narrative that addresses our review questions on gender, vulnerability, and resilience in these blue economies. The coded themes were distilled into an organized discussion of how women’s roles and challenges manifest across different sectors and regions. We paid special attention to common patterns (such as pervasive underrepresentation of women in higher-paid maritime jobs) as well as unique regional contexts (for example, differences between an Atlantic island like the Azores and a tropical one like Réunion in terms of opportunities and risks). Throughout this process, we remained attentive to the quality of evidence, giving weight to well-substantiated studies and noting where data were limited. By integrating the literature review with targeted data, our methodology provides a robust basis for assessing the gendered dynamics of the blue economy in Europe’s outermost regions. This approach not only synthesizes existing knowledge but also identifies data-driven gaps and levers for policy, which we discuss in subsequent sections. The result is a rich, multi-source understanding of how women at the “edge” (geographically and socio-economically) navigate the challenges and build resilience in the blue economy.

While this review prioritizes blue economy sectors, the analysis draws primarily on publicly available labor force statistics due to the limited availability of gender-disaggregated data specific to marine and coastal sub-sectors in ORs. Consequently, employment by gender in the general economy was used as a proxy to identify broader trends and disparities. Where available, sector-specific figures (e.g., aquaculture, fisheries processing) were integrated to contextualize local conditions. Qualitative case material was selected through a targeted literature review using databases such as SpringerLink, Scopus, and institutional reports (FAO, EC, national statistical bodies), focusing on studies published in the last 10 years. This dual approach enabled a grounded yet comparative understanding of gendered participation across diverse outermost contexts.

4. Socio-Economic Overview of Women in the Blue Economy of the EU Outermost Regions

4.1. Employment Patterns and Economic Participation

Women’s overall participation in the labor force of the EU’s nine ORs is characterized by relatively low employment rates and high unemployment in comparison to continental EU averages [26,27]. Female employment rates (share of women aged 15–64 in work) in almost all ORs fall below the EU average (≈62% in 2020).

Table 2. Employment and Unemployment in the EU Outermost Regions.

Region	Male Employment Rate (%)	Female Employment Rate (%)	Unemployment Rate * (%)	Year	Sources
Guadeloupe	53.0	51.0	17.5	2023	INSEE (LFS); DG REGIO **
Martinique	60.0	60.0	12.4	2023	INSEE (LFS); DG REGIO
French Guiana	49.0	37.0	16.1	2024	INSEE (LFS); DG REGIO
Mayotte	42.0	23.0	27.8	2024	INSEE (LFS); DG REGIO
Réunion	53.0	48.0	17.4	2023	INSEE (LFS); DG REGIO
Azores	60.9	49.9	6.1	2023	Statistics Portugal; Eurostat; DG REGIO
Madeira	60.3	49.6	8.1	2023	Statistics Portugal; Eurostat; DG REGIO
Canary Islands	54.9	44.3	22.6	2023	ISTAC/Eurostat (EPA); DG REGIO
Saint-Martin	–	–	–	–	No official labor data

Notes: Saint-Martin’s data are not separately reported in the Eurostat Labor Force Survey; its figures are included with Guadeloupe (NUTS2 region). The Saint-Martin values are therefore not listed individually in the table. All other values are official employment rates from the indicated sources. Percentages refer to the share of the population 15–64 years old in employment (ILO definition). Differences in survey coverage: figures for Mayotte in 2024 are from the first fully integrated LFS for that territory (previous years are not strictly comparable). French data are annual averages from the LFS (BIT** definition), while Portuguese and Spanish data are based on annualized labor force surveys (EPA). Each value is sourced directly from the respective national statistical institute or Eurostat, ensuring comparability as far as possible across regions. * Unemployment rate refers to the labor force aged 15–74 (ILO definition), latest harmonized data available across all ORs. DG REGIO unemployment data are based on Eurostat’s regional dataset (2020), the only year providing comparable unemployment statistics for all outermost regions.

below presents the male and female employment rates (percentage of the population aged 15–64 in employment) for each of the nine EU Outermost Regions. For each territory, the most recent available year is given, along with the source of the official data.

Female vs. male employment rates in 2020 across the EU ORs—in 2020, women’s employment was only 23.9% in Mayotte and 36.2% in French Guiana, which is 38–26 percentage points lower than the EU average. Even the Atlantic islands with higher employment, such as the Azores (60.7% female employment) and Madeira (62.2%), still fell a few points short of mainland Portugal’s female employment rate (66.6%). In contrast, male employment rates were uniformly higher; e.g., 70.7% in the Azores and 68.1% in Madeira. This gap reflects persistent gender disparities in labor force participation. Traditional gender roles and unequal care burdens continue to constrain many women’s paid work hours in these regions [27,28,29,30,31,32], and women often work in part-time or informal jobs that keep official employment rates low. The European Commission identifies long-standing structural constraints in ORs, including remoteness, insularity, small market size, and dependency on a few sectors [11].

Unemployment in the ORs is a chronic challenge, with women often experiencing the worst of it. In 2022, regions like Guadeloupe and La Réunion recorded total unemployment rates near 18–19%, almost three times the EU average (6–7%) [28,29]. Female joblessness tends to be especially acute: for instance, female unemployment in Guadeloupe was ~19.7% in 2022 [30,31]. Youth unemployment is also alarmingly high—exceeding 50% in Mayotte, reflecting limited opportunities for new entrants (both male and female) in formal blue economy jobs. Many young women who are neither employed nor in education/training (NEET) form a significant vulnerable group: e.g., about 22–27% of 15–24 year-olds in Guadeloupe and the Azores were NEET in 2020, double the EU average [30]. Such statistics underscore the structural economic difficulties facing these remote island economies, where women often struggle to secure stable employment in any sector. This struggle is compounded by educational gaps: in some ORs, educational attainment remains low, with early school-leaving rates far above Europe’s norm. For example, the share of youth leaving school early in the Azores is ~27% (vs. ~10% in mainland Europe, suggesting that many women enter adulthood without advanced qualifications, which in turn limits their access to skilled jobs in emerging blue sectors (like marine renewables or research). Overall, women in the ORs face a difficult economic landscape marked by limited job opportunities, high unemployment, and reliance on informal livelihoods.

4.2. Fisheries and Aquaculture

Fisheries, a traditional pillar of the blue economy in islands, remain heavily male-dominated across all ORs (

Table 3. Female participation in blue economy sectors across EU ORs and their national contexts.

Region/Country	Sector (Blue Economy)	Female Participation (%) or Role	Source Reference
France (national)	Fisheries, aquaculture, processing	≈21% in maritime jobs (5% in crews)	[33]
Portugal (national)	Aquaculture	9.5% in aquaculture jobs	[34,35]
Spain (national)	Marine tourism, hospitality	54% of tourism/hospitality jobs held by women	[36]
Azores	Tuna processing (Santa Catarina cannery)	83.7% of cannery staff were women	[31]
Madeira	Tourism, small-scale fisheries	Likely majority in tourism/hospitality; minimal at sea	[37,38]
Canary Islands	Tourism, fishing	54% in tourism sector (Spain average)	[39,40]
Réunion	Fisheries (crew), processing	5% of registered marine crew; more in shore roles	[33]
Mayotte	Small-scale fishing	Minimal at sea; some in fish processing or family farms	[41,42]
Guadeloupe	Aquaculture, fish trade, coastal tourism	Women in fish vending, aquaculture, tourism	[33,43]
Martinique	Same as Guadeloupe	Same as Guadeloupe (regional NUTS data)	[33,43]
French Guiana	Informal fisheries, riverine work	Anecdotal evidence of strong informal role	[44]
Saint-Martin	Tourism, hospitality	>50% in tourism; often part-time or informal	[45]

). Women’s roles in fishing have historically been “invisible and undervalued” [25]. Quantitatively, female participation in the fishing workforce is extremely low, often under 5% of registered fishers [19,27]. In the Azores, for instance, women comprise only ~4% of people directly employed in both the small-scale and offshore fleets [28]. Similar patterns are seen elsewhere: in the Canary Islands’ coastal fleet, out of ~1879 fishers only 12 are women (≪1%) [28]. In Martinique, women are also virtually absent from boat crews—approximately 5 women among 600 fishers in the entire island [31]. The only activities where women appear in fishing statistics tend to be shore-based support: e.g., net mending, fish processing, and marketing of catch, often as unpaid family labor. In Madeira’s coastal fishery, official data show merely 1% of fishing jobs held by women, but women contribute about 10% of total work hours as unpaid family helpers [28]. These figures highlight that women’s work in fisheries often goes unremunerated and unrecognized in formal counts, a phenomenon widely noted in the literature as the “invisibility” of women’s contributions [25]. Studies in small-scale fisheries consistently show that women’s contributions remain undervalued, particularly in processing and informal value-chain roles [31,32]. Although shore-based and processing roles involve less physical danger than offshore fishing, they carry significant occupational risks, including repetitive-strain injuries, chemical exposure from processing agents, and precarious working conditions. These roles are also consistently lower-paid than sea-based work, contributing to pronounced gender pay gaps in the fisheries sector.

Behind these numbers are cultural and practical realities. Deep-sea and offshore fishing in the ORs is physically demanding and traditionally seen as men’s work, while women historically inherited shore-based roles. In some communities (especially in the Caribbean and Indian Ocean ORs), women participate in the subsistence harvesting of shellfish or seaweed on intertidal reefs and in fish vending in local markets, activities critical for household livelihoods but typically informal. Because women are disproportionately present in informal or semi-formal blue economy activities, such as processing, vending, family-based fisheries support, and part-time tourism services, they often lack formal contracts, pension contributions, maternity protection, health insurance, and legal recourse in cases of discrimination or unsafe working conditions. For example, ethnographic accounts from the Azores describe how women in fishing families long performed onshore tasks (baiting hooks, salting fish, selling at market) that were essential to the industry, yet were not formally acknowledged [31]. Only in recent years have women in a few regions begun to organize and gain visibility in this sector. The formation of women’s fisher associations in the Azores is one case where participatory projects helped women assert their identity as “fishers” and engage in the co-management of resources [32]. Nonetheless, throughout the ORs, institutional representation of women in fisheries remains minimal. A 2020 EU fisheries report noted that with few exceptions, female involvement in OR fisheries is under 5% and decision-making forums often lack women’s voices [28]. This gender imbalance has implications for fisheries governance and community resilience, as women’s knowledge and needs may be overlooked in policy (for example, in designing fisher training or subsidy programs, which historically targeted male boat owners) [28]. Small island economies also face circularity constraints, high import dependence, limited waste-processing capacity, and fragile territorial metabolisms that shape livelihood precarity and resilience [11]. Governance frameworks in the ORs increasingly mobilize cultural and creative-economy strategies to enhance resilience and territorial cohesion [32], highlighting the role of social innovation alongside blue-economy pathways.

Aquaculture in the ORs is a relatively nascent sector and likewise shows limited female participation. Several ORs have very small aquaculture industries (the Azores, for example, had no active aquaculture as of the 2010s). In others, such as Réunion or the Canary Islands, fish farming and mariculture projects exist on a modest scale but are generally led by enterprises or cooperatives dominated by men. There are sparse data on gender in OR aquaculture, but broader trends suggest women are more often involved in processing and administration than in hands-on farm operations [24]. One barrier is that aquaculture work (feeding, harvesting, maintenance of cages) can be labor-intensive and has not traditionally attracted women in these regions. However, opportunities for women may grow as aquaculture expands into new areas (e.g., seaweed or shellfish farming) that could be more accessible to family-based producers. Overall, in the primary blue sectors of capture fisheries and aquaculture, women’s role remains peripheral and largely informal, highlighting a significant gender gap in the traditional maritime economy of the ORs.

4.3. Coastal Tourism and Services

In contrast to fisheries, the coastal tourism sector provides a comparatively brighter picture for women’s employment—though not without its own inequalities. Tourism (encompassing hospitality, food services, passenger transport, and recreation) is a cornerstone of many OR economies and a major source of jobs for local women. Several ORs rank among the most tourism-dependent regions in Europe, with tourism-related activities accounting for an outsize share of employment. For example, in Madeira about 27% of all jobs are linked to tourism, and in the Azores, roughly 22% [19,24]. In comparison, tourism represents ~8–10% of employment in the EU overall. Women are heavily represented in this sector, often exceeding men in absolute numbers of tourism workers. Studies of Portugal’s tourism industry found it to be a “feminized” sector, as nationally about 60% of tourism workers are women [26]. A similar feminization is likely in the ORs’ hotel and restaurant workforces, where women frequently hold front-line service jobs (such as hotel housekeepers, wait staff, retail clerks, and tour reps). The influx of international tourists to islands like Canary Islands, Guadeloupe, or Réunion generates considerable demand for service work that has traditionally been one of the few formal employment avenues open to women.

While tourism has created job opportunities for women, it also exemplifies occupational segregation by gender. Women workers in OR tourism are concentrated in lower-wage, casual, and seasonal positions, whereas management and technical roles remain male-dominated [26]. For instance, even though women may form the bulk of hotel staff, hotel general managers and chefs are more often men. This vertical segregation contributes to sizable gender pay gaps in the hospitality industry. Moreover, the quality of tourism employment for women is a concern: work tends to be precarious, marked by part-time contracts, irregular hours tied to high and low seasons, and vulnerability to shocks. The COVID-19 pandemic’s impact on OR tourism (e.g., the near-total shutdown of travel in 2020) disproportionately affected women, who were more likely to be laid off from tourism jobs and faced slower return to work [27]. In islands where alternative industries are few, the loss of tourism income hit women’s livelihoods and highlighted their economic vulnerability.

Still, tourism and related service sectors (public administration, education, healthcare, retail) remain crucial for women’s socio-economic inclusion in these regions. ORs like Martinique and Guadeloupe have relatively high female employment in education and civil service, reflecting historical progress in women’s education there: Martinique’s female employment rate (56% in 2020) nearly equals its male rate [27] thanks in part to women’s strong presence in public sector and service jobs. In the Canary Islands, as the economy shifted toward services, women moved into clerical, administrative, and tourism roles; by 2020 about 49% of working-age women in the Canaries were employed (vs. only ~30% in the 1980s) [28]. This structural change underscores how vital the service economy is for women. Efforts to promote a sustainable blue economy in the ORs (such as eco-tourism, cultural heritage tourism, and cruise tourism) explicitly recognize the need to “empower women in the blue economy” as a priority [7]. Various EU-funded projects have therefore targeted women entrepreneurs in coastal tourism and related activities (e.g., training women to run micro tourism businesses or to market local seafood products to visitors), aiming to boost female incomes and leadership in this growing sector. Coastal tourism offers women in the ORs both opportunities and challenges: it is a key employer of women, but the sector’s gendered job stratification and vulnerability to external shocks continue to pose risks to women’s economic resilience.

4.4. Education, Informality and Social Factors

Broader socio-economic conditions deeply influence women’s roles in the blue economy of the ORs. One critical factor is education. On average, women in ORs have made gains in educational attainment (for example, female literacy is high and many young women now obtain secondary diplomas), but gaps remain, especially in the most isolated territories. Early school dropout rates are alarmingly high in places like Mayotte and French Guiana due to a combination of poverty, teen pregnancy, and limited access to higher education. This directly affects women’s ability to enter skilled blue economy jobs. In Mayotte, the female employment deficit is partly rooted in low schooling and traditional norms: young women often leave school early to marry or raise children, yielding a female employment rate below 25% [28]. Similarly, in rural French Guiana, education levels lag behind the mainland and many women engage in subsistence activities rather than formal employment. In contrast, the higher educational attainment of women in Martinique and Réunion (where schooling infrastructure is stronger and tertiary education is more accessible) correlates with their greater presence in professional occupations, including administrative roles in marine parks, research institutes, and tourism offices [28]. This suggests that investing in education and vocational training for girls and women, for instance, in maritime trades, marine biology, or hospitality management, is essential to improve gender balance in emerging blue economy niches.

Another key issue is the informality of women’s work. A significant share of women in the ORs work outside the formal wage economy, in family enterprises or subsistence livelihoods connected to the sea. These include fish vending, fish processing at home, craft production (e.g., of salted fish or shell handicrafts), small-scale agriculture for local markets, and unpaid family help in tourism businesses. Such work is rarely captured in official employment statistics, yet constitutes an economic backbone for many communities. In fishing villages, for example, wives of fishermen often manage the finances and marketing of the catch, effectively performing the role of shore-based enterprise managers without formal job titles or incomes [26]. In coastal agriculture and agro-tourism (like artisanal rum or spice production in the Caribbean ORs), women frequently do seasonal informal work. The downside is that these informal roles lack social protections and are vulnerable to exploitation. Projects in the ORs have noted that empowering women economically will require formalizing and valorizing such informal blue economy activities [29]. For instance, supporting women’s cooperatives in fish processing or handicrafts can help move them from the informal margin into recognized entrepreneurship.

Cultural and social norms also shape the landscape. Many OR communities are patriarchal, with deeply ingrained gender divisions of labor. Women typically shoulder the bulk of unpaid care work (child-rearing, elder care), which limits their time and mobility for paid work [5]. In remote island villages, the absence of childcare facilities or reliable transport magnifies this constraint. Thus, even when blue economy initiatives (like a marine conservation project or a tourism venture) create jobs, women may not equally benefit unless these social issues are addressed. There are positive signs of change: gender-focused programs have started to appear, aiming to increase women’s participation in fisheries organizations, to provide small grants for women-led coastal businesses, and to include women’s voices in marine resource governance [10]. In recent workshops on blue economy strategies for the ORs, stakeholders emphasized that women must be at the table, a notable shift given that women were “often unrepresented in discussions” historically [14]. Improving women’s socio-economic status in the ORs’ blue economy is not just a matter of creating jobs, but also of addressing the underlying educational, cultural, and structural factors. By tackling high female unemployment, skills gaps, and the invisibility of women’s work, the ORs can leverage a significant untapped resource, the talent and knowledge of women, to build a more inclusive and resilient blue economy at Europe’s periphery.

5. Gendered Vulnerabilities and Barriers

Women in Europe’s ORs face a web of structural and cultural barriers that hinder their participation and leadership in blue economy sectors. As in many maritime industries, the blue economy in these regions remains male-dominated, with women underrepresented and often relegated to the lowest-paid, least secure positions [21]. A growing body of research highlights how exclusionary workplace norms and inadequate support for work–life balance has discouraged women’s entry and retention in sectors like fisheries, aquaculture, maritime transport, and marine research [23]. These barriers are not merely individual but systemic, rooted in institutional practices and social norms that collectively increase women’s vulnerability and reduce their resilience to climate and economic shocks.

5.1. Structural and Institutional Barriers

Several structural and institutional obstacles curtail women’s opportunities in the blue economy of the ORs. Limited access to finance and resources is a key issue. The ORs’ remoteness and small local markets already make access to credit and investment difficult for businesses in general [9], and women entrepreneurs often face additional hurdles such as lack of collateral and gender bias in lending. This financial barrier hampers women’s ability to start or grow blue economy ventures (for example, purchasing a fishing boat or expanding an aquaculture farm). Inadequate training and educational access further compound the problem. Many ORs have few specialized maritime training programs, and those that exist tend to be male dominated. Women in these regions often have limited pathways to acquire the technical skills or certifications needed for higher-paying blue jobs. A related challenge is the brain drain: qualified youth (male and female) often migrate to mainland Europe, resulting in a narrow local labor market and skills mismatch in the islands [24]. This leaves fewer opportunities and role models for women aspiring to enter blue sectors.

Institutional biases can also be manifested in policies and governance. In some fisheries and marine resource frameworks, women’s roles have historically been overlooked or undervalued. For instance, small-scale fishing licenses, subsidies, or extension services were often designed with the male boat owner in mind, marginalizing women who work in processing or as crew family members. Women thus remain underrepresented in governance and decision-making bodies of the blue economy. Local fisheries co-operatives, management committees, and maritime boards in ORs are typically dominated by men, meaning women have little voice in resource allocation or regulatory decisions [14]. This underrepresentation is evident in the industry’s workforce composition: in the Canary Islands’ fishing sector, for example, women held only about 6% of jobs around 2015–2021 [26], reflecting a glaring gender gap in a traditional blue economy field. Similar patterns occur in other ORs, where women tend to be concentrated in public-sector or service roles instead of maritime industries, e.g., over half of employed women in Martinique and Guadeloupe work in education, health, or public administration, far outnumbering those in any blue economy occupation [24]. Such occupational segregation indicates structural barriers that funnel women away from blue growth sectors. Limited access to credit, male-centric training pipelines, subtle regulatory biases, and the paucity of women in leadership positions all form a structural lattice that restricts women’s full participation in the blue economy of Europe’s ORs.

5.2. Social and Cultural Barriers

Deep-rooted social and cultural norms in many ORs further impede women’s engagement in maritime industries. Traditional gender roles commonly designate men as seafarers and women as homemakers or land-based support, creating an expectation (and often a community pressure) that discourages women from pursuing careers “at sea”. In the Azores, for example, seafaring has long been considered men’s work. Well into the 21st century has this norm persisted—as of 2022, there were only four female fishers in the entire Azorean archipelago, a stark indicator of how effectively tradition has kept women off the boats [22]. Historically, women in such communities contributed to fisheries in shore-based roles (net mending, processing, marketing), but these contributions were rendered invisible and undervalued for decades [22]. Similar cultural expectations are found in other ORs, from the Caribbean to the Indian Ocean: unwritten rules and patriarchal attitudes often deter women from the more lucrative seagoing or technical jobs in favor of “suitable” roles aligned with caregiving or hospitality.

Another challenging barrier is the disproportionate burden of domestic responsibilities on women. Across the ORs, women carry the majority of childcare, elder care, and household work, leading to what is often termed “time poverty”. This severely limits their mobility and flexibility to take on full-time jobs or leadership positions in blue sectors, which frequently demand odd hours, travel, or multi-day commitments at sea. Data from French overseas departments illustrate this imbalance: a significant share of women drop out of the workforce due to family responsibilities, 14% of women cited childcare or dependent care as a reason for leaving work compared to only 3% of men [23]. In Mayotte, the lowest-income OR, the lack of childcare infrastructure is a critical issue. Many women in Mayotte who want to work cannot do so because there are simply no affordable childcare services, forcing them to remain at home. These social conditions create a cycle where women have less time for training or formal employment, reinforcing their underrepresentation in blue economy jobs.

Cultural barriers also comprehend issues of safety and workplace environment. Maritime and fisheries settings in the ORs are male-majority environments where women may face harassment, isolation, or lack of acceptance. The absence of basic amenities (like separate facilities or equipment sized for women) and sometimes macho work cultures can make these workplaces unwelcoming or even unsafe for women. Such conditions, combined with gender norms, can dissuade women from entering or remaining in the field. In essence, social norms prescribing women’s roles, the unequal division of care labor, and concerns about personal safety together create a formidable cultural barrier that operates alongside institutional factors. The result is that even highly capable women in OR communities may self-select out of blue economy careers, or struggle to remain in them due to pressures and obstacles that their male counterparts do not experience to the same degree.

5.3. Increased Vulnerability and Lower Resilience

These generated barriers have big implications for women’s vulnerability to climate change and economic shocks in Europe’s outermost regions. Because structural and social hurdles keep many women in lower-income, low-status, or informal positions, women generally have fewer financial buffers and resources to draw upon when crises hit. They are more likely to be in part-time or precarious employment. Women in the ORs are often “led more often than men to accept short-term contracts and less-paid part-time jobs”, a symptom of their fragile foothold in the labor market. Such positions are typically the first to be cut during economic downturns. For example, the COVID-19 pandemic dealt a severe blow to OR economies (which rely heavily on sectors like tourism and fishing); women, who were overrepresented in part-time hospitality and informal fish trade roles, experienced disproportionate job losses and income insecurity. Studies noted that in several ORs, the pandemic exacerbated existing gender inequalities, with single mothers and women in informal work facing acute hardship. In Réunion, a territory already struggling with high poverty, 60% of single mothers live below the poverty line [23], an alarming figure that underscores how a shock (be it a cyclone or an economic recession) can push an already vulnerable group into destitution. Lacking savings, insurance, or property, many women cannot easily rebuild businesses or homes after disasters, nor can they afford adaptive measures (like elevating a house against floods or investing in new equipment after a fish stock collapse).

Climate change is amplifying these vulnerabilities. The EU’s ORs are on the frontline of climate impacts, facing sea-level rise, coral reef degradation, stronger cyclones, and shifting ocean currents [9,14]. Communities in these remote islands depend heavily on climate-sensitive blue sectors (fisheries, coastal tourism), yet women’s marginalized position means that they have less capacity to prepare for or respond to climate stresses. Globally, women account for more than 75% of people displaced by climate hazards and tend to take longer to recover from climate shocks due to their comparatively vulnerable social-economic status [14]. This general trend is also borne out in the OR context. When a climate-related disaster strikes, for instance, a hurricane hitting Martinique or a major coral bleaching event undermining fisheries in Mayotte, women are less likely to have savings, land rights, or formal support to recover. They often rely on informal social networks and micro-scale livelihoods that are easily disrupted and slow to revive. Moreover, women’s underrepresentation in local governance and disaster planning means that their needs may be overlooked in resilience-building efforts. For example, if aid distribution or fisheries recovery programs are channeled through male-dominated associations, female fish processors or marketers might not receive equal support, further weakening their capacity to bounce back after a shock.

The structural and cultural barriers identified, from lack of credit and training to restrictive gender norms and care burdens, directly contribute to heightened vulnerability among women in the blue economies of Europe’s outermost regions. These barriers trap many women in a state of economic precarity and limited agency. Consequently, when climate change impacts or economic crises occur, women have fewer resources, rights, and representations to leverage in response, leading to lower resilience both at the individual and community level. Addressing these gendered barriers is therefore not only a matter of social equity but also integral to strengthening the overall resilience of OR communities. By improving women’s access to opportunities (e.g., through targeted credit and training schemes), reforming institutions to be more inclusive, and shifting cultural norms to redistribute domestic responsibilities, the ORs can empower women as key actors in the blue economy. In doing so, these regions would enhance their adaptive capacity, tapping the talents and knowledge of the whole population, and build a more robust resilience to the pressing challenges of climate change and economic volatility.

5.4. Comparative Synthesis Across the Outermost Regions

A comparative reading of the findings reveals that, despite wide geographic dispersion, the ORs share several structural gendered patterns while also exhibiting distinct regional trajectories. Three cross-regional configurations emerged. First, the Caribbean ORs (Guadeloupe, Martinique, Saint-Martin) displayed the strongest intersectional layering of vulnerability, where gender intersects with postcolonial governance structures, racialized labor markets, and spatial segregation. Women’s economic participation in these regions is characterized by high unemployment, heavy concentration in low-wage service sectors, and greater exposure to informality. These dynamics result in limited upward mobility, even when female educational attainment is relatively high.

Second, the Macaronesian ORs (Azores, Madeira, Canary Islands) illustrated a different pattern: gender gaps persist but are shaped more by structural economic dependencies—particularly the dominance of tourism—than by deep intersectional inequalities. Here, relatively stronger institutional capacity, more diversified labor markets, and closer connectivity to mainland Europe provide women with comparatively better access to training, entrepreneurship schemes, and formal employment. Still, occupational segregation remains pronounced: women are underrepresented in higher-paid maritime roles yet overrepresented in administrative, hospitality, and part-time service roles.

Third, the Indian Ocean ORs (Mayotte, Réunion) showed the most severe constraints on women’s participation, driven by extreme unemployment levels, demographic pressures, and higher exposure to poverty. In Mayotte in particular, women face some of the lowest employment rates in the EU, limited institutional support structures, and elevated care burdens, which restrict their capacity to enter or remain in the labor force. These conditions produce a gendered resilience gap: women have fewer financial buffers, fewer social protections, and fewer opportunities for upward mobility than in any other OR cluster.

Across all three configurations, several converging patterns became evident. Women’s work is systematically undervalued, frequently informal, and concentrated in lower-paid segments of blue and blue-adjacent sectors. Gendered occupational segregation persists across fisheries, coastal tourism, aquaculture, and marine services. At the same time, the magnitude and drivers of these inequalities differ substantially by region, shaped by colonial histories, demographic pressures, institutional strength, and integration into EU governance systems. Recognizing these typologies helps explain why similar gendered patterns appear across the ORs, yet with varying levels of intensity and resilience capacity.

6. Discussion

This discussion synthesizes the empirical patterns observed across the EU’s outermost regions and links them directly to the analytical lenses of feminist political ecology, intersectionality, and social-ecological resilience. By comparing the Caribbean, Macaronesian, and Indian Ocean ORs, several cross-cutting themes emerged regarding gendered labor structures, institutional vulnerability, and resilience capacities. A cross-regional comparison also revealed stark contrasts in institutional resilience capacities. In Mayotte and French Guiana, limited social protection systems, high levels of labor informality, and persistent administrative constraints weaken women’s ability to recover from climatic or economic shocks. In contrast, Macaronesian regions such as the Azores and Madeira benefit from stronger public service infrastructures, more stable labor markets, and better access to EU-funded training and innovation initiatives. These differences illustrate how resilience is shaped not only by geography but by the strength of institutional support systems that mediate women’s access to resources, training, and adaptive opportunities.

A first key pattern is the visibility gap in women’s labor across blue economy sectors. In all ORs, women are present across fisheries, aquaculture, tourism, conservation, and coastal services, yet their work remains disproportionately informal, unpaid, or undervalued, consistent with broader trends reported for Europe’s maritime sectors [5,23,46]. This invisibility is particularly pronounced in small-scale fisheries, where women dominate shore-based processing, gleaning, and value-chain coordination, but these contributions rarely appear in national statistics or blue economy planning [17,19]. Feminist political ecology emphasizes that such gender-blind governance systems reproduce structural inequities by ignoring women’s environmental knowledge and labor contributions [9,14]. Our findings show this clearly in the Caribbean ORs, where port infrastructure, subsidy schemes, and marine spatial planning systems predominantly prioritize male-dominated offshore activities, reinforcing unequal access to resources and decision-making authority [4].

Second, comparative analysis highlights different configurations of vulnerability across ORs. Caribbean regions such as Guadeloupe and Martinique show stronger intersectional vulnerabilities, where gender intersects with race, class, and postcolonial governance structures to shape risk exposure and livelihood precarity [12]. In contrast, Macaronesian ORs (Azores, Madeira, Canary Islands) exhibit vulnerabilities more strongly shaped by structural economic dependence, including overreliance on tourism, geographic isolation, and limited industrial diversification [11,22]. This divergence aligns with evidence that ORs experience vulnerability through different pathways depending on their colonial histories, institutional capacities, and access to EU networks [1,2]. For example, in Madeira nearly 27% of employment is linked to tourism and in the Azores 22%, compared to ~8–10% in the EU overall [27], making these regions acutely sensitive to external shocks such as pandemics, travel disruptions, or climate-driven changes in coastal attractiveness.

Third, the findings illustrate how informal labor economies shape women’s resilience capacities. In many ORs, especially in fisheries, small trade, and tourism, women participate disproportionately in informal economic roles that lack legal protections, social benefits, or upward mobility opportunities [24,26]. Intersectional analyses reveal that informalization constrains women’s long-term coping capacity, limiting retirement security, access to credit, disaster assistance, and eligibility for public entrepreneurship programs [13,16]. Such patterns mirror global trends documented in SIDS and coastal LDCs, where informal gendered labor reduces both adaptive and absorptive resilience capacities [16,19].

Fourth, technological, educational, and policy innovations emerge as potential resilience enhancers. Digital technologies—such as online vocational training, remote certification, digital fisheries monitoring tools, e-commerce platforms, and teleworking—have the potential to reduce certain gender gaps in the ORs. In regions where women face mobility constraints, lower levels of formal education, or limited access to training institutions, online education can expand opportunities and enable participation in higher-value segments of the blue economy. Some ORs, particularly the Azores and Canary Islands, have begun leveraging digital training programs, blue innovation hubs, and online education to reduce the gender gap in skills and professional mobility [8,31]. However, access remains uneven, and digital readiness varies significantly across regions [30,47]. From a social-ecological resilience perspective, the uneven adoption of technology illustrates how institutional adaptive capacity influences gender outcomes, determining whose knowledge, labor, and agency are empowered in the transition toward a green and blue economy [18].

The application of feminist political ecology and intersectionality clarifies how power relations and environmental governance shape resilience outcomes. Ignoring women’s contributions in fisheries management or coastal planning results in suboptimal governance outcomes, reduced knowledge integration, and missed opportunities for innovation [4,17]. Resilience perspectives show that diversified, inclusive, and socially embedded livelihood systems provide stronger adaptive pathways for ORs facing climate risks, resource pressures, and economic dependency [15,30]. This cross-regional synthesis underscores that gender inequality is not merely a social issue but a structural constraint on sustainable blue-economy development. Policies aimed at blue growth, climate adaptation, or economic innovation will fall short unless they explicitly address gendered labor distributions, informal work structures, and intersectional vulnerabilities while also strengthening institutional support for women’s economic participation across all ORs.

7. Conclusions

This review underscores the persistent gendered disparities in the blue economy across the EU’s outermost regions, shaped by structural, geographic, and institutional factors. Despite policy attention, women’s roles remain largely invisible or underreported—particularly in fisheries, marine transport, and informal coastal economies. However, cases from the Azores, Réunion, and elsewhere reveal both existing contributions and potential for leadership.

To promote inclusive and sustainable blue economies, we recommend:

• For policymakers: Integrate gender metrics into all EU blue economy monitoring frameworks (e.g., EC Blue Economy Reports, Eurostat datasets). Support women-led marine enterprises and cooperatives through targeted funding and training.
• For researchers: Prioritize field-based studies and participatory methods in ORs to uncover invisible labor and intersectional impacts. Develop cross-regional comparative tools to track gender progress in coastal economies.
• For practitioners and community leaders: Strengthen women’s networks in fisheries, aquaculture, and coastal tourism. Advocate for childcare, transport, and formalization strategies that lower entry barriers for women in marine sectors.
• Achieving gender-equitable blue economies requires not only improved data, but also transformative governance that recognizes women’s diverse contributions and leadership in ocean-linked livelihoods.