Social and Livelihood Dependencies on Ocean Ecosystems

Field Value
Circular ID TG-2.3
Version 8.0
Badge Applied
Status Draft
Last Updated 2026-05-20

1. Outcome

This Circular provides guidance on compiling indicators of social and livelihood dependencies on ocean ecosystems, enabling practitioners to quantify how populations, households, and communities rely on marine and coastal natural capital for economic sustenance, food security, cultural identity, and wellbeing. Ocean ecosystems underpin the livelihoods of hundreds of millions of people globally, particularly in coastal communities where fishing, aquaculture, tourism, and other marine-dependent activities provide employment, food security, and cultural sustenance[1]. Understanding and measuring these dependencies is essential for identifying vulnerable populations, assessing the social dimensions of ocean sustainability, designing policies that protect both marine ecosystems and the communities that depend on them, and informing just transition planning as coastal economies adapt to environmental and economic change. The indicators are framed throughout in poverty-alleviation terms—following the integrated ocean-poverty account approach of Burnside (2026)[2]—and are designed to feed disaster risk reduction processes under the Sendai Framework for Disaster Risk Reduction 2015-2030[3], where evidence on ocean-dependent populations is required for resilience planning, loss-and-damage assessment, and recovery prioritisation.

Upon completing this Circular, readers will understand how to compile dependency indicators across four key dimensions: employment dependency (measuring reliance on ocean-dependent sectors for income and jobs), nutritional dependency (quantifying contributions of marine resources to food security and dietary adequacy), cultural dependency (documenting non-material values and traditional practices), and vulnerability (assessing exposure and adaptive capacity of ocean-dependent populations). The guidance supports compilation of indicators aligned with policy frameworks including SDG 1 (No Poverty), SDG 2 (Zero Hunger)--particularly Target 2.3, which explicitly references fishers among the small-scale food producers whose productivity and incomes should be doubled[4]--SDG 5 (Gender Equality) through its treatment of women's roles in fisheries value chains, SDG 8 (Decent Work and Economic Growth), SDG 10 (Reduced Inequalities) through its attention to equity dimensions of ocean access, and SDG 14 (Life Below Water)--particularly Target 14.7 on increasing "the economic benefits to small island developing States and least developed countries from the sustainable use of marine resources"[5].

This Circular provides decision-relevant indicators for coastal poverty assessment, just transition planning as marine industries adapt to climate change and sustainability constraints, food security monitoring in fish-dependent populations, and the design of small-scale fisheries co-management arrangements. It connects the ecosystem service flows documented in TG-3.2 Flows from Environment to Economy to the social accounting approaches described in TG-3.5 Social Accounts, providing the methodological bridge between biophysical ecosystem contributions and human wellbeing outcomes. It draws on the economic activity classifications established in TG-3.3 Economic Activity and complements TG-1.5 Fisheries Management by extending the analysis from resource extraction to the full social system of dependencies. For the foundational framework and standards overview, see TG-0.1 General Introduction and TG-0.2 Standards Overview.

2. Requirements

This Circular requires familiarity with:

For guidance on measuring ecosystem service flows from ocean ecosystems to the economy, see TG-3.2 Flows from Environment to Economy. For documentation of traditional marine knowledge and customary practices, see TG-3.6 Traditional Knowledge Accounts. For detailed guidance on labour market measurement, including the concepts of compensation of employees and employment status, see the 2025 SNA Chapter 16 on Labour[6].

3. Guidance Material

Ocean ecosystems provide essential contributions to human livelihoods through multiple pathways: directly through employment in ocean-based industries; through provisioning of food and other marine products; through cultural, spiritual, and recreational benefits; and through the regulating services that protect coastal communities from hazards[7]. The SEEA Ecosystem Accounting framework describes these contributions as ecosystem services--"the contributions of ecosystems to the benefits that are used in economic and other human activity"[8]. This Circular provides guidance on measuring the human dependencies on these services, focusing on the social and livelihood dimensions that extend beyond purely economic measures.

While TG-3.5 Social Accounts establishes the general framework for social accounting in the ocean context--covering wellbeing, employment, equity, and vulnerability as broad accounting dimensions--this Circular applies that framework specifically to the measurement of livelihood dependencies. The distinct contribution of TG-2.3 is its focus on the dependency relationship itself: how populations, sectors, and communities rely on ocean ecosystems, and how changes in ecosystem condition translate into livelihood impacts. Where TG-3.5 asks "what is the social state?", this Circular asks "how dependent are communities on ocean ecosystems, and what happens if those ecosystems change?"

The Statistical Framework for Measuring the Sustainability of Tourism (SF-MST) observes that measuring sustainability requires understanding "the range of direct and indirect effects and the wide spectrum of stakeholders involved"[9]. This principle applies equally to ocean dependencies: understanding the full scope of how people rely on ocean ecosystems requires systematic measurement across multiple dimensions including employment, nutrition, culture, and vulnerability.

3.1 Livelihood Dependency Framework

A livelihood dependency framework organizes the multiple ways in which individuals, households, and communities rely on ocean ecosystems for their economic, nutritional, cultural, and social sustenance. The framework distinguishes between direct and indirect dependencies, and between material and non-material dimensions of dependency. The regulating-services pathway shown in Figure 2.3.1 (ocean ecosystems -> regulating services -> coastal protection) is operationalised in Section 3.4 using the SEEA-EA storm-mitigation service flow, rather than as a freestanding dependency indicator.

Figure 2.3.1: Livelihood dependency framework--pathways from ocean ecosystem services to wellbeing outcomes

3.1.1 Defining ocean-dependent livelihoods

Ocean-dependent livelihoods encompass all forms of work and subsistence activities that rely substantially on marine and coastal ecosystems. The 2025 SNA recognizes that measuring wellbeing and sustainability requires extending measurement "beyond income and consumption to include...human capital as a produced asset"[10], capturing the skills, knowledge, and capabilities that enable people to derive livelihoods from ocean resources. Human capital in the context of ocean-dependent livelihoods includes not only formal education but also the traditional ecological knowledge, navigation skills, and resource management capabilities that coastal communities develop through sustained engagement with marine environments.

Ocean-dependent livelihoods can be classified into three tiers. This classification parallels but does not replicate the SF-MST distinction between "direct effects" and "indirect and induced effects"[11]: while the SF-MST categories apply to economic impact measurement, the dependency tiers below describe the directness of the livelihood relationship to ocean ecosystems. Compilers should document which classification they adopt and how it maps to SF-MST or other frameworks used in their context.

Primary dependencies: Livelihoods directly engaged in harvesting marine resources or providing services within marine ecosystems. These include:

Secondary dependencies: Livelihoods in sectors that process, distribute, or add value to marine resources:

Tertiary dependencies: Livelihoods supported by the multiplier effects of ocean-based economic activity:

These tiers correspond to the ocean economy industry classifications detailed in TG-3.3 Economic Activity, which provides the ISIC-based framework for identifying and measuring ocean-dependent, ocean-related, and partially ocean-related industries.

3.1.2 Dependency indicators

Dependency indicators quantify the extent to which populations, sectors, or regions rely on ocean ecosystems. Key indicator categories include:

Employment dependency indicators:

Nutritional dependency indicators:

Economic dependency indicators:

Cultural dependency indicators:

The SEEA Ecosystem Accounting framework supports these indicators by providing the methodology for measuring ecosystem service flows. The SEEA EA notes that "the ecosystem accounting framework also supports the recording of flows of intermediate services, which are flows of services between and within ecosystem assets...Recording these flows supports an understanding of the dependencies among ecosystem assets"[12]. Extending this logic to social dependencies, recording the flows of ecosystem services to human users supports understanding of livelihood dependencies.

3.2 Employment Indicators

Employment in ocean-dependent sectors represents one of the most tangible measures of livelihood dependency. The SF-MST provides detailed guidance on employment measurement that can be adapted for ocean accounting[13]. While TG-3.5 Social Accounts covers general ocean sector employment measurement, this section focuses specifically on compiling employment as a dependency indicator--that is, measuring how reliant communities and populations are on ocean-based employment rather than simply counting jobs.

3.2.1 Direct employment

Direct employment encompasses all persons engaged in activities that harvest marine resources or operate within marine spaces. SDG indicator 14.7.1 measures "Sustainable fisheries as a proportion of GDP in small island developing States, least developed countries and all countries"[14], while employment measures provide the complementary livelihood dimension.

Key direct employment categories for ocean accounting include:

Fisheries employment:

SDG Target 2.3 calls for doubling "the agricultural productivity and incomes of small-scale food producers, in particular women, indigenous peoples, family farmers, pastoralists and fishers"[15]. Employment accounts should distinguish small-scale fisheries from industrial operations to support monitoring of this target.

The SEEA for Agriculture, Forestry and Fisheries (SEEA AFF) provides an integrated framework describing how "biophysical and management information relevant to agriculture, forestry and fisheries production can be integrated into the statistical framework"[16]. This framework supports linking employment data to production and ecosystem condition data. For guidance on integrating fisheries stock assessment data with employment accounts, see TG-1.5 Fisheries Management.

Marine tourism employment: SDG Target 8.9 calls for implementing "policies to promote sustainable tourism that creates jobs"[17]. Marine tourism employment includes:

The SF-MST notes that "employment in tourism industries is known for the fact that it often consists of a small core of permanent staff complemented with staff with a temporary contract and on-call workers"[18]. This seasonal and precarious employment pattern is common across marine tourism and affects vulnerability assessments.

Other direct ocean employment:

Classification note—"ocean-operating" versus "ocean-dependent" employment: Maritime transport workers (ISIC 50) use the ocean as their operating environment but do not depend on ocean ecosystem services in the same way as fishers or aquaculture workers. Accordingly, ISIC 50 is assigned to the secondary tier rather than direct employment. Within ISIC 50, compilers should distinguish two subcategories where 4-digit data are available: inter-island ferry and coastal passenger services (ISIC 50.10) are classified as "high ocean-operating intensity" within the secondary tier; deep-sea cargo shipping (ISIC 50.20) is classified as "low ocean-ecosystem dependency" within the secondary tier. These subcategories should be reported separately in secondary employment tables to enable analysts to identify the sub-population with stronger livelihood dependency on ocean conditions. This classification follows the principle that tier assignment is determined by economic activity type and dependency on ocean ecosystem services, not physical proximity to the ocean, ensuring comparability with TG-3.3 Economic Activity classification.

3.2.2 Indirect employment

Indirect employment arises in sectors that supply goods and services to ocean-dependent industries. The SF-MST describes these as activities "in the supply chain of tourism characteristic products"[19]--for ocean accounting, the analogous concept encompasses supply chains for fishing, aquaculture, marine tourism, and other ocean sectors.

Measuring indirect employment requires either:

The SF-MST notes that full measurement of indirect effects "would also require a single reference location to have information on all of the other locations that are connected"[20]. For practical compilation, countries may focus on key supply chain sectors known to have significant connections to ocean industries.

Key indirect employment categories include:

3.2.3 Induced employment

Induced employment results from the spending of wages earned in ocean-dependent sectors. When fishers, tourism workers, or seafood processors spend their incomes on housing, food, education, and other goods and services, additional employment is generated throughout the economy.

Induced effects are typically estimated using economic multipliers derived from input-output models. The SF-MST recommends that "at relevant places...there is discussion of the types of indirect and induced effects that might be considered as part of a wider analysis"[21]. For ocean dependency indicators, induced employment estimates can illustrate the broader economic significance of ocean-based livelihoods but should be presented separately from direct and indirect measures.

Table 1 summarizes the data requirements and methodological approaches for compiling employment dependency indicators across the three tiers.

Tier Scope Primary Data Sources Estimation Method Key Challenges
Direct Persons harvesting marine resources or operating in marine spaces Labour force surveys, fisheries registries, vessel crew records Direct count from administrative/survey data Informal employment, subsistence fishers underreported
Indirect Supply chain employment serving ocean industries (including ISIC 50 maritime transport) Establishment surveys, input-output tables Supply chain tracing via I-O analysis or surveys Boundary definition, partial attribution
Induced Employment from spending of ocean sector wages Household expenditure surveys, I-O multipliers Multiplier-based estimation from I-O models Multiplier uncertainty, double-counting risk

Table 1: Employment dependency indicator data requirements by tier

3.2.4 Employment characteristics

Beyond counting employment, ocean accounts should characterize employment quality using the decent work framework dimensions described in TG-3.5 Social Accounts. The 2025 SNA provides comprehensive guidance on labour market measurement, including employment status, compensation of employees, and the distinction between formal and informal employment[22]. Key characteristics include:

Characteristic Relevance to Ocean Employment
Sex Women play significant but often underrecognized roles in fish processing and gleaning
Age Many fisheries face aging workforce issues; youth employment in marine tourism
Employment status High rates of self-employment in small-scale fisheries
Full-time/part-time Seasonal variation particularly in tourism and some fisheries
Formal/informal High informality rates in small-scale fisheries and coastal tourism
Geographic location Concentration in coastal communities affects spatial development patterns

Table 2: Employment characteristics relevant to ocean dependency analysis

The SF-MST recommends that employment data be compiled with these characteristics to assess social sustainability[23]. For ocean accounts, such disaggregation reveals important patterns--for example, the high proportion of informal employment in small-scale fisheries, or the seasonal concentration of marine tourism employment.

3.3 Food Security and Nutrition

Marine ecosystems contribute fundamentally to global food security, providing essential protein, micronutrients, and fatty acids that support healthy diets[24]. Understanding nutritional dependencies on ocean ecosystems is essential for both food security policy and for assessing the human welfare implications of ecosystem change. This section addresses the dependency dimension of marine food provisioning; for the ecosystem service flow perspective, see TG-3.2 Flows from Environment to Economy, Section 3.1 on provisioning services.

3.3.1 Protein dependency

Fish and other aquatic foods are a primary source of animal protein for billions of people globally, with particularly high consumption in coastal regions, Small Island Developing States, and countries with extensive traditional fishing cultures[25]. SDG Target 2.1 calls for ending hunger and ensuring "access by all people...to safe, nutritious and sufficient food all year round"[26].

Key protein dependency indicators include:

Per capita consumption:

Population-level dependencies:

Nutritional quality:

The SEEA Ecosystem Accounting framework treats biomass provisioning services as ecosystem contributions to "the growth of biomass of marine animals (e.g. fish and shellfish) that are captured for nutrition"[27]. Nutritional dependency indicators measure the human welfare dimension of these provisioning services.

3.3.2 Subsistence harvesting

Beyond commercial fisheries, subsistence harvesting provides essential food for coastal and island communities, often without being captured in formal economic statistics. Subsistence fishing includes:

The SEEA EA notes that ecosystem services include contributions to both marketed and non-marketed benefits[28]. For subsistence harvesting, the ecosystem service (fish provisioning) flows directly to households without entering market transactions. This treatment is consistent with the ecosystem service flow accounting in TG-3.2 Flows from Environment to Economy, which records provisioning services regardless of whether the resulting benefits enter markets. It also aligns with the artisanal fisheries coverage in TG-1.5 Fisheries Management, which addresses small-scale fisheries governance alongside industrial operations. SDG Target 14.b emphasizes the importance of providing "access for small-scale artisanal fishers to marine resources and markets"[29].

Measuring subsistence dependencies requires:

The monetary value of subsistence harvests can be imputed using local market prices for equivalent products. This valuation allows integration with economic accounts and comparison with formal sector production. The 2025 SNA provides guidance on valuing own-account production for inclusion in household income and consumption measures[30].

3.3.3 Food security vulnerability

Food security encompasses four dimensions: availability, access, utilization, and stability[31]. Table 3.3.3 below summarises how ocean ecosystem dependencies affect each dimension.

Dimension Description
Availability Marine ecosystems supply fish and other aquatic foods. Changes in ecosystem condition (overfishing, habitat loss, climate impacts) directly affect food availability.
Access Poverty, market structure, and governance arrangements determine which populations can access marine foods. Equity in access is addressed in TG-3.5 Social Accounts.
Utilization Safe food handling, nutritional knowledge, and cooking practices affect how marine foods contribute to nutrition.
Stability Seasonal variation in fish availability, stock fluctuations, and climate variability affect the reliability of marine food supplies.

Indicators of food security vulnerability related to ocean ecosystems include:

3.4 Coastal Protection—Operationalising SEEA-EA Storm Mitigation

Coastal ecosystems—coral reefs, mangroves, saltmarshes, seagrass meadows, oyster reefs, and coastal dunes—attenuate waves, dissipate storm surge, and stabilise shorelines. Communities behind these features depend on the regulating services they supply for physical protection of dwellings, infrastructure, and lives. The "regulating services -> coastal protection" arm in Figure 2.3.1 is operationalised here.

The 2019 discussion paper on coastal protection dependency indicators (Crossman, Nedkov, Brander) has been superseded by the adopted SEEA Ecosystem Accounting standard (UN et al., 2021; current consolidated edition December 2024). The standard does not specify dependency indicators in the original proposed form and deliberately does not endorse specific datasets or platforms. Critically, SEEA-EA Table 6.3 splits the previously combined "water flow regulation for mitigating river and coastal flooding" service into three separate reference services: Water flow regulation, Flood control, and Storm mitigation[32]. The two indicators originally considered for a dedicated coastal-protection dependency sub-section—population exposure in the absence of natural coastal protection, and the value of avoided damage—are absorbed into standard service-flow accounting rather than treated as freestanding dependency metrics:

Chapter 14 of the SEEA-EA (Tables 14.3--14.4) lists indicator categories but no named indicators of this kind. Reference-list and capacity concepts sit in Chapter 6 §6.5 and Appendix A6.1.

3.4.1 Populating the SPA/SBA structure for storm mitigation

Compilers should populate the storm-mitigation service entry in the Chapter 7 physical SUT as follows:

A SEEA-compatible national compilation of the storm-mitigation service typically combines the following inputs. None are mandated by SEEA-EA; the standard is deliberately data-agnostic. These are the de facto templates that national compilations have used.

3.4.3 Anchor worked examples

Three published national-scale applications are recommended as templates:

3.4.4 Illustrative composite where SBA data are unavailable

Where the SBA-based service-flow approach cannot yet be populated (typically because elevation, population, or hydrodynamic modelling capacity are unavailable), compilers may report an illustrative composite dependency index drawing on the Arkema et al. (2013) approach as an interim measure. The composite is calculated as:

Coastal protection dependence = m × (n × (1 − o × p × q)) / 2

where:

The (1 − o × p × q) term means that where substitutes are strong, measured dependence on coastal ecosystems drops; where substitutes are weak, exposure and susceptibility translate more directly into dependence. The illustrative composite is not a substitute for the SPA/SBA service-flow approach in §3.4.1. Compilers using it should clearly flag the indicator as a transitional measure and document a plan to migrate to SEEA-EA service-flow accounting as elevation, population, and hydrodynamic inputs become available.

3.5 Cultural and Recreational Dependencies

Beyond material livelihoods, ocean ecosystems support non-material dimensions of human wellbeing through cultural connections, recreational opportunities, and spiritual significance. The SEEA EA describes cultural services as "experiential and intangible services related to the perceived or actual qualities of ecosystems whose existence and functioning contribute to a range of cultural benefits"[34]. While TG-3.2 Flows from Environment to Economy documents the ecosystem service flows themselves (Section 3.1.3 on cultural services), this section addresses the human dependency dimension--how communities and populations rely on these cultural services for their wellbeing and identity.

3.5.1 Cultural ecosystem services

Marine cultural services encompass:

Recreation-related services: The ecosystem contributions to recreational activities including swimming, diving, snorkelling, surfing, recreational fishing, and wildlife watching[35]. Coral reefs, beaches, and marine protected areas attract visitors seeking marine recreation experiences. The SEEA EA notes that "recreation-related services are considered final ecosystem services since they are directly enjoyed by people"[36].

Indicators of recreational dependency include:

Visual amenity services: The contribution of marine seascapes to aesthetic enjoyment and property values[37]. Coastal views affect residential amenity, tourism attractiveness, and community identity.

Education and research services: The contribution of marine ecosystems to scientific research, environmental education, and the generation of knowledge[38]. Marine research stations, coastal field sites, and marine education programs depend on functioning ocean ecosystems.

Spiritual, artistic, and symbolic services: The contributions to cultural identity, spiritual practices, and artistic inspiration[39]. For many coastal and island cultures, the ocean holds profound spiritual significance that cannot be adequately captured through quantitative indicators.

3.5.2 Cultural identity and traditional practices

For Indigenous Peoples and traditional coastal communities, cultural dependencies on ocean ecosystems may be the most fundamental dimension of human-ocean relationships. The TNFD notes that "Indigenous Peoples and Local Communities manage or have tenure over" significant proportions of remaining intact natural areas[40], with analogous relationships existing for traditional marine territories.

Cultural identity dependencies include:

Measuring cultural dependencies requires participatory and qualitative approaches, as described in TG-3.5 Social Accounts and TG-3.6 Traditional Knowledge Accounts. Both circulars emphasize the need for community-led methods that respect Indigenous data sovereignty. Quantitative proxies may include:

3.5.3 Tourism and recreation economies

Marine tourism represents a significant economic expression of cultural and recreational dependencies. SDG Target 14.7 calls for increasing "the economic benefits to small island developing States and least developed countries from the sustainable use of marine resources, including through sustainable management of fisheries, aquaculture and tourism"[41].

Tourism dependency indicators connect to the broader ocean economy measurement described in TG-2.5 Ocean Economy Structure:

The SF-MST provides the statistical framework for "linking ecosystem accounting to measures of tourism activity"[42], enabling integration of cultural ecosystem service flows with tourism economic accounts.

3.6 Vulnerability Indicators

Livelihood dependencies create vulnerabilities when the ecosystems or resources upon which livelihoods depend are subject to degradation, depletion, or disruption. The IPCC defines vulnerability as "the propensity or predisposition to be adversely affected. Vulnerability encompasses a variety of concepts and elements, including sensitivity or susceptibility to harm and lack of capacity to cope and adapt"[43]. TG-3.5 Social Accounts provides the general vulnerability and resilience framework for ocean social accounts; this section applies that framework specifically to livelihood dependencies, focusing on how to measure the vulnerability that arises from reliance on ocean ecosystems.

Ocean-dependent communities face multiple risks:

Ecological risks: Overfishing, habitat degradation, pollution, and invasive species can reduce the productivity of marine ecosystems and the services they provide. The SEEA EA describes ecosystem condition indicators that track these changes (see TG-2.1 Biophysical Indicators).

Climate risks: Ocean warming, acidification, sea level rise, and changing storm patterns directly affect marine ecosystems and coastal communities. SDG Target 14.2 calls for managing marine ecosystems "including by strengthening their resilience"[44]. SDG Target 1.5 calls for building "the resilience of the poor and those in vulnerable situations and reduce their exposure and vulnerability to climate-related extreme events"[45].

Economic risks: Volatile commodity prices, changing trade policies, and market disruptions can undermine the economic viability of ocean-dependent livelihoods.

Governance risks: Weak or inequitable governance arrangements may fail to protect community access rights or ensure sustainable management.

The following indicators measure exposure across these risk categories:

3.6.2 Sensitivity indicators

Sensitivity measures how significantly populations would be affected by changes in ocean ecosystems or disruptions to ocean-based activities:

Economic sensitivity:

Nutritional sensitivity:

Social sensitivity:

3.6.3 Adaptive capacity indicators

Adaptive capacity encompasses the resources, institutions, and capabilities that enable communities to respond to change:

Human capital:

Social capital:

Financial capital:

Institutional capital:

The SF-MST notes that vulnerability assessment should consider "the dependence of tourism activity on a given water supply and the associated potential vulnerability of tourism activity"[47]. The same principle applies to ocean dependencies: understanding the specific ecosystem services on which livelihoods depend, and the current and projected condition of those services, enables targeted vulnerability assessment.

3.6.4 Livelihood vulnerability indicator summary

Table 3 provides a summary of livelihood vulnerability indicators organized by the exposure-sensitivity-adaptive capacity framework. Each indicator is linked to the account type within the Ocean Accounts framework that supplies the relevant data.

Vulnerability Component Indicator Data Source Account Link
Exposure Storm frequency/intensity Climate data Governance accounts
Exposure Fish stock variability Stock assessments Asset accounts
Sensitivity % income from ocean Household surveys Economic accounts
Sensitivity Dietary fish dependence Consumption surveys Social accounts
Adaptive capacity Skill diversity Labour surveys Social accounts
Adaptive capacity Asset ownership Household surveys Economic accounts

Table 3: Livelihood vulnerability indicators by component

3.6.5 Composite vulnerability indices

Vulnerability indicators across exposure, sensitivity, and adaptive capacity dimensions can be combined into composite vulnerability indices that identify priority populations or areas for policy attention. The IPCC vulnerability framework[43:1] and the TNFD approach to dependency analysis[40:1] both provide conceptual foundations for such indices in the ocean context. Composite vulnerability indices should:

For guidance on integrated indicator frameworks and quality assurance of composite indices, see TG-0.7 Quality Assurance.

4. Compilation Considerations

4.1 Data sources

Social and livelihood dependency indicators draw on diverse data sources:

Household surveys: Labour force surveys provide employment data; living standards surveys capture consumption and income patterns; specialized coastal community surveys can provide targeted dependency information.

Administrative records: Fishing licenses, vessel registrations, marine worker registrations, and tourism statistics provide administrative data on ocean sector participation.

Fisheries data: Catch statistics, vessel monitoring data, and stock assessments provide physical measures of resource extraction that can be linked to employment and livelihood data.

Participatory assessments: Community consultations and participatory mapping document dependencies not captured in formal statistics, particularly for subsistence activities and cultural dimensions.

For detailed guidance on data sources and collection methods, see TG-4.2 Survey Methods and TG-4.3 Administrative Data.

4.1.1 Community sense-checking of data sources

Secondary data sources alone are not sufficient to determine which dependencies are captured in the account and which are systematically obscured. Before compilation begins, compilers should undertake a structured sense-check with communities in the spatial units covered by the account. The purpose is not validation of results (which occurs at the compilation stage; Section 4.2), but verification of whether the source list and its assumed coverage match community experience.

The minimum standard is one structured engagement per spatial unit, disaggregated to include separate sessions or sub-groups for women, youth, elders, and persons with disabilities. Mixed sessions alone do not meet the standard, because the dependency categories most likely to be missed by official sources (e.g. gleaning, shore-based processing, subsistence harvesting by women and children, ceremonial use) are precisely those that mixed sessions are least likely to surface. Where capacity permits, additional disaggregation by livelihood type, migration status, or Indigenous identity is appropriate. The methodological approach is consistent with that established in the GOAP Social Accounts methodology[48], which treats disaggregated consultation as a quality assurance requirement rather than a procedural courtesy.

Three questions structure the sense-check at the source stage:

(1) Coverage. Does each proposed secondary source list or count the people who actually undertake ocean-dependent activities in this place? Findings on systematic undercounts feed directly into the adjustment factors applied in Section 4.2 (in particular Step 1 sub-step 2 on informal and subsistence employment, and Step 3 sub-step 3 on sensitivity indicators).

(2) Legitimacy of additional sources. Are there community-held data sources—community monitoring programmes, women's cooperative records, traditional catch records, locally maintained registers—that should be incorporated alongside official sources? The conditions under which traditional and Indigenous knowledge holdings can enter the account are governed by TG-3.6 Traditional Knowledge Accounts, including Free, Prior and Informed Consent and the principle that the decision to share rests with knowledge holders.

(3) Disaggregation that matters. At what spatial and demographic level does the data need to be disaggregated for the policy question to be answerable? Communities can identify when district-level aggregation obscures settlement-level vulnerability, or when household-head reporting misses the labour of other household members. These findings shape the spatial unit chosen in Section 4.2 Step 3 sub-step 1.

Each engagement should be documented with the date, the groups consulted, the questions asked, and the findings—particularly any source-coverage gaps that will require adjustment or qualitative annotation downstream. This documentation forms part of the metadata required under Section 4.2 Step 5. For detailed survey and consultation protocols, see TG-4.2 Survey Methods and TG-4.3 Administrative Data.

4.1.2 Integration with existing survey systems

The dependency indicators in this Circular are designed as an analytical overlay on existing national household survey systems, not as a new statistical operation. Most of the variables needed are already collected by national statistical offices for other purposes. The compiler's task is principally one of recoding, cross-tabulation, and concordance, supplemented by a small number of genuinely new variables. Reframing the work in this way—as integration rather than new collection—is essential to keeping it tractable for NSOs already operating at capacity, and is consistent with the integrated ocean-poverty account framework set out by Burnside (2026)[2:1].

The principal survey instruments and their roles are:

The genuinely new variables for which existing survey instruments typically do not provide coverage—and which therefore require either supplementary modules, dedicated coastal community surveys, or community sense-check engagement (Section 4.1.1)—are limited and identifiable. The principal ones are:

The integration approach has two consequences that compilers should make explicit in publication metadata. First, the indicators are an analytical layer on existing official statistics—not a separate accounting operation—and so are bound by the reference-year, vintage, and revision conventions of their underlying sources (see Section 4.2 Step 5). Second, the dependency indicators are framed throughout in poverty-alleviation terms and are designed to feed into integrated ocean-poverty assessments rather than to substitute for ecosystem-condition indicators. Empirical illustrations that have driven the indicator design include the Inhambane Bay studies, where small-scale fisheries provide 60-70% of household income and 70-80% of dietary protein in coastal villages; the Lake Illawarra estuary case, where ecosystem degradation translated directly into livelihood loss without the dependency relationship having been measured ex ante; and the Nippon Causeway / Tarawa case, where infrastructure decisions taken without dependency evidence led to documented harm to subsistence harvesting (Burnside, 2026)[2:2]. These cases—where ex ante dependency evidence would have changed the decision—are the standing reference for the indicator set.

4.1.3 Institutional coordination and inter-agency data reconciliation

Compilation of TG-2.3 indicators draws on data held by multiple agencies—the NSO (household and labour surveys, census), fisheries ministry (catch, vessel, licence registers), tourism ministry or TSA compiler (visitor arrivals, accommodation employment), environment or marine affairs ministry (MPA, stock, and ecosystem-condition data), and community-held sources legitimised under Section 4.1.1. Without an explicit coordination arrangement, the dependency indicators will either be incompilable (because no single agency holds the data) or will be compiled inconsistently between cycles (because the agency lead changes). The 2025 SNA provides extensive guidance on inter-agency coordination for satellite accounts (Chapter 21, especially para 21.28; Chapter 4 paras 4.252-4.254), and the SEEA-EA Chapter 14 and SEEA-AFF set out the analogous arrangements for environmental-economic and food-system accounts.

Compiling lead. The default compiling lead for TG-2.3 indicators is the National Statistical Office, in its role as the institutional custodian of the survey programme on which the indicators principally depend (Section 4.1.2) and the convening authority for satellite-account work under SNA 2025 Chapter 21. Where this default does not hold—for example, where the NSO does not have the marine-domain capacity or where another agency (a national ocean accounts unit, a Ministry of the Sea, a Pacific Community statistical hub, a research institute compiling on behalf of government) is better placed—the alternative arrangement must be documented and the rationale recorded, consistent with TG-0.1 General Introduction to Ocean Accounts §3.7 on compiling-lead determination.

Minimum coordination arrangement. A standing coordination mechanism is required. The minimum acceptable form is a written instrument—a standing inter-agency working group or a Memorandum of Understanding—with the NSO and at minimum the fisheries, tourism, and environment portfolios as parties, with a documented process for incorporating community-held sources legitimised under Section 4.1.1. The instrument should specify: data flows and update cycles; the agency authoritative for each variable; the procedure for resolving inconsistencies between sources; and the publication and revision schedule. SNA 2025 Chapter 4 paras 4.252-4.254 set out the institutional sectoring underlying these arrangements.

Reconciliation hierarchy for same-population-in-multiple-registries. A common compilation problem is that the same population (typically small-scale fishers) appears in multiple registries (fisheries licence register, LFS, fishing-cooperative records, community catch registers) with different counts and different coverage definitions. The recommended reconciliation hierarchy is:

  1. Source closest to the dependency relationship. Where one source is operationally closer to the activity being measured (e.g. a community catch register for subsistence harvesting; a fisheries licence register for licensed commercial activity), that source takes precedence for the relevant sub-population.
  2. Broadest informal coverage where (1) does not discriminate. Where multiple sources are equally close to the relationship, the source with the broadest informal coverage takes precedence; the resulting gap relative to narrower sources is recorded in metadata.
  3. Cross-tabulation where unique IDs permit. Where personal or vessel identifiers allow linkage across registers, the indicator is compiled from the cross-tabulated unique-person count rather than from any single register.
  4. Reported as range where unresolvable. Where steps 1-3 do not resolve the inconsistency, the indicator is reported as a range bounded by the highest and lowest defensible source counts, with metadata identifying the sources and the unresolved coverage difference.

Concordance table. Each compilation cycle should publish a concordance table mapping the operational definitions used in the dependency indicators to ISIC Rev. 4 industry codes, ISCO occupation codes, national fisheries register categories, and the TSA tourism-characteristic-activity classification. This concordance is the practical instrument that makes inter-agency reconciliation possible and is consistent with the satellite-account requirements in SNA 2025 Chapter 21 paras 21.59-21.62 and Chapter 16 on labour.

4.2 Compilation Procedure

Note on thresholds: All thresholds in this section are illustrative. Compilers must derive context-specific thresholds using local data and expert judgement before applying them to national or sub-national reporting.

Note on community sense-checking. The compilation procedure below produces preliminary indicator values from secondary data. These preliminary values must be sense-checked with communities in the spatial unit before publication. Sense-checking is not a sign-off: findings from sense-check engagements must be capable of revising preliminary values, flagging them with qualitative annotation, or adding supplementary indicators. A sense-check that cannot change the published account is not meeting the standard. Sense-check engagements at the compilation stage build on the source-level sense-check described in Section 4.1.1 and should, where possible, reach the same groups consulted at that stage. Compilers should treat findings under one of four categories, documenting which applies to each indicator:

  1. Confirmation. Community feedback corroborates the preliminary value. The indicator is published with metadata recording the validation date and groups consulted.
  2. Calibration within documented bounds. A specific component is identified as over- or under-stated for a known reason (typically a systematic undercount in the secondary source). The value is revised within a documented range—the secondary source providing one bound, the community estimate providing the other—and published as a range with a reported point estimate. The metadata records the reason for adjustment.
  3. Flagging without revision. The community identifies that the score, while numerically defensible from secondary data, misses a dimension that cannot be quantified within the indicator set (for example, spiritual ties to a particular reef, intergenerational transmission of marine knowledge, displacement losses already absorbed). The numerical score is published unchanged with a qualitative annotation explicitly naming what the score does not capture.
  4. Supplementation. A community-identified indicator is added as a supplementary measure alongside the standard set. It does not replace a standard indicator, but appears in the national table and feeds into the next revision of this Circular and the forthcoming Accounting for People Circular[49].

This section provides a step-by-step procedure for compiling the core livelihood dependency indicators described in Section 3. The procedure assumes that basic ocean economy accounts have been compiled following TG-3.3 Economic Activity and that relevant household survey data are available. Compilers should adapt the procedure to national data circumstances.

Step 1: Compile ocean employment dependency ratio

Objective: Measure the share of total employment attributable to ocean-dependent sectors.

Data requirements:

Procedure:

  1. Extract employment counts for ocean-dependent industries from labour force survey:

    • ISIC 0311 (Marine fishing)
    • ISIC 0321 (Marine aquaculture)
    • Coastal tourism share of ISIC 55 (Accommodation), 56 (Food and beverage services), 79 (Travel agencies)

    Note: ISIC 50 (Water transport) is assigned to the secondary tier, not this direct employment list (see Section 3.2.1 for full ISIC 50 classification).

  2. Adjust for informal and subsistence employment using fisheries registry:

    • Cross-check survey totals against fishing license/registration counts
    • If survey underestimates artisanal/small-scale fishers, apply adjustment factor based on registry coverage
    • Document adjustment methodology in metadata

2a. Compile post-harvest and value chain employment with sex/gender disaggregation. Direct-harvest employment counts (sub-step 1) systematically understate the labour engaged in ocean-dependent livelihoods because the post-harvest and shore-based value chain is disproportionately performed by women and persons in informal arrangements. Compilers must compile a separate count of value-chain employment, with the following scope and disaggregation:

Cross-references: TG-3.13 Gender Equality and Social Inclusion for the disaggregation framework; TG-3.15 for the related value-chain treatment. Counts produced under this sub-step are preliminary and remain subject to revision under Section 4.1.1 / Step 1 sub-step 6 community sense-check.

  1. Sum employment across ocean industries to derive direct ocean employment total

  2. Compute ocean employment dependency ratio:

    Ocean employment ratio = Direct ocean employment / Total employment

  3. Report both absolute employment count and percentage share

  4. Community sense-check on employment count. Before publishing the ratio, present the disaggregated employment counts (by ISIC category, by sex, by formal/informal status) to community sense-check sessions disaggregated by sex and age. Record findings on whether categories of work are systematically under- or over-counted, particularly informal and gendered work such as gleaning, shore-based processing, and net-mending. Apply the four-category treatment (confirmation, calibration, flagging, supplementation) and document the basis for any revision in metadata.

Output: Ocean employment dependency ratio (percent), with metadata documenting survey source, reference period, any adjustments for informal employment, sex/gender-disaggregated value-chain employment counts (sub-step 2a), and the four-category treatment applied following community sense-check.

Step 2: Compile fish protein dependency ratio

Objective: Measure the contribution of marine-sourced protein to total animal protein supply.

Data requirements:

Procedure:

  1. Compile total fish supply from food balance sheet:

    • Domestic catch (commercial + estimated subsistence)
    • Plus imports, minus exports
    • Minus non-food uses (fishmeal, pet food, waste)
    • Equals fish available for human consumption (tonnes live-weight equivalent)

  2. Convert live-weight supply to edible weight using species-specific edible yield coefficients (FAO Food Composition Tables recommended; typical range 0.45--0.65). Where species-level yield data are unavailable, apply a default yield coefficient of 0.50 and flag the indicator as a low-confidence estimate in metadata.

    Fish available (edible weight, tonnes) = Fish available (live-weight, tonnes) × Edible yield coefficient

  3. Apply protein content factor to edible weight only:

    Fish protein supply (tonnes) = Fish available (edible weight, tonnes) × Average protein content factor

    Use species-specific factors where possible; otherwise apply regional average (typically 0.16--0.20, with 0.18 as common benchmark for edible weight, wet-weight basis per FAO FCT conventions). Protein content factors must not be applied to live-weight or round-weight data.

  4. Compile total animal protein supply from food balance sheet:

    • Sum protein from meat, fish, dairy, eggs
    • Align reference period with fish data

  5. Compute fish protein dependency ratio:

    Fish protein dependency = Fish protein supply / Total animal protein supply

  6. Compute per capita fish protein:

    Per capita fish protein (kg/year) = Fish protein supply / Population

  7. Community sense-check on dietary reliance. Present the preliminary per capita fish protein supply and the protein dependency ratio to disaggregated community sense-check sessions. Pay particular attention to seasonality: where the household consumption survey reference period misses seasonal lows in fish availability, communities can identify this directly and the indicator should be flagged or supplemented with a seasonal range. Where subsistence harvesting is significant, sense-check sessions should verify whether the imputed subsistence component reflects actual household consumption. Apply the four-category treatment and document accordingly.

Output: Fish protein dependency ratio (percent) and per capita fish protein supply (kg/year), with metadata on protein conversion factors, data sources, and the four-category treatment applied following community sense-check.

Step 3: Compile livelihood vulnerability index

Objective: Produce composite vulnerability index for ocean-dependent coastal community using exposure, sensitivity, and adaptive capacity components.

Data requirements:

Procedure:

  1. Define the spatial unit for vulnerability assessment:

    • Coastal administrative unit (district, municipality)
    • Or specific coastal community
    • Document spatial boundaries and population

  2. Compile and normalize exposure indicators (scale 0--1, higher = greater exposure):

    • Flood risk: Share of settlement in 1-in-50-year flood zone
    • Cyclone frequency: Annual average cyclone landfalls (past 20 years), normalized by maximum observed
    • Stock risk: Share of local catch from stocks assessed as overfished or uncertain
    • Compute exposure score as simple average of normalized indicators

  3. Compile and normalize sensitivity indicators (scale 0--1, higher = greater sensitivity):

    • Income dependence: Average share of household income from fishing/ocean sectors (from household survey)
    • Housing quality: Share of households in informal or non-code housing
    • Dietary dependence: Share of animal protein from fish (from household consumption survey)
    • Compute sensitivity score as simple average of normalized indicators

  4. Compile and normalize adaptive capacity indicators (scale 0--1, higher = greater capacity):

    • Educational attainment: Average years of schooling, normalized by national average
    • Financial inclusion: Share of households with savings account or insurance coverage
    • Livelihood diversity: Shannon diversity index of employment across industries, normalized
    • Compute adaptive capacity score as simple average of normalized indicators

  5. Compute composite vulnerability index:

    Vulnerability index = (Exposure + Sensitivity + (1 - Adaptive Capacity)) / 3

    The formula inverts adaptive capacity (using 1 - AC) because higher capacity reduces net vulnerability. This produces a true 0--1 range where 0 indicates no vulnerability and 1 indicates maximum vulnerability.

  6. Interpret result using threshold classification (all thresholds are illustrative—see note at start of Section 4.2):

    • 0.00--0.33: Low vulnerability
    • 0.33--0.55: Moderate vulnerability
    • 0.55--1.00: High vulnerability

  7. Community sense-check on component scores and composite index. Present preliminary component scores (exposure, sensitivity, adaptive capacity) and the composite index to disaggregated community sense-check sessions. Three questions structure the engagement:

    • Does the composite score reflect lived experience of vulnerability in this place?
    • Which component is most over- or under-stated, and why?
    • What is missing from the indicator set that the community would include?

    The threshold classification (low / moderate / high) is the most consequential element to sense-check, because it drives policy targeting. A community whose composite score falls near a threshold boundary may experience itself as more or less vulnerable than the classification suggests, for reasons the index does not capture; this finding must appear in metadata even where the score itself is unchanged. Apply the four-category treatment to each component and to the composite. Where supplementation occurs—that is, where a community identifies an indicator the index should include—record the supplementary indicator in the national table and flag it for consideration in the next revision of this Circular and in the forthcoming Accounting for People Circular[49:1].

Output: Composite livelihood vulnerability index (0--1 scale) with component scores, spatial unit definition, interpretation guide, and the four-category treatment applied following community sense-check.

Step 5: Document methodology and metadata

For all compiled indicators, document:

Reference-year metadata block. A single reference-year field is insufficient for indicators whose underlying sources update at different frequencies. For each compiled indicator, the metadata block must record the following eight fields rather than a single year stamp:

  1. Indicator's published reference year—the year to which the published indicator value is attributed.
  2. Anchor source and observation year—the binding source for the indicator and the year of its underlying observation. The anchor source per indicator is: HIES for the livelihood vulnerability index (because the index is sensitivity-led and sensitivity depends on household income and asset variables only the HIES provides); LFS for the ocean employment dependency ratio (because it is the only source providing the industry-by-employment cross-tabulation on a comparable basis); Food balance sheet for the fish protein dependency ratio. The Population and Housing Census is not an anchor source for any TG-2.3 indicator because the inter-censal interval is too long to keep an anchor current; the census is used only to provide small-area population denominators.
  3. Every contributing source—listed with its reference year and update frequency.
  4. Temporal gap in years—the largest difference, in years, between the published reference year and the observation year of any contributing source.
  5. Bridging method—the method used to bring older contributing sources up to the published reference year. The standard method is proportional Denton temporal disaggregation (IMF Quarterly National Accounts Manual, Chapter 6), which preserves both the lower-frequency benchmark and the higher-frequency indicator's shape. Pro rata extrapolation is rejected as a bridging method because it does not preserve the benchmark's covariance with auxiliary information. On methodology changes in the contributing source, the indicator series is back-cast rather than spliced, so that pre- and post-change values remain comparable.
  6. Indicator series used for extrapolation—the higher-frequency series (typically LFS, climate, or stock-status data) used to bring older HIES or FBS observations forward.
  7. Update type—routine update, benchmark update, or comprehensive revision, in the SNA 2025 sense (paras 20.92, 20.95-20.98, 20.36, 20.101; satellite-account treatment in paras 21.59-21.62, 21.67).
  8. Confidence flag—where the temporal gap exceeds five years between the anchor source's observation year and the published reference year, the indicator is flagged as low confidence in the publication. Gaps of three to five years are flagged as medium confidence; gaps under three years as high confidence.

A worked example of a vintage label, where the HIES observation is from 2024 but the indicator is published with respect to reference year 2024 in publication year 2026, is: "reference year 2024, vintage 2026, routine update, medium confidence". The reference-year metadata block is a publication-side artefact attached to each compiled indicator and is the principal mechanism by which the analytical layer described in Section 4.1.2 stays consistent with the revision conventions of the official statistics on which it is built (see also SNA 2025 paras 21.28 and 21.59-21.62).

This documentation supports transparency, enables indicator updates in subsequent periods, and provides the evidence base for indicator reform when the Accounting for People Circular is finalised.

The dependency indicators in this Circular share survey-side update cycles with the official statistics from which they are derived. Table 2 sets out the recommended compilation frequency and maximum acceptable data vintage for each indicator, together with the primary data source and natural update cycle of that source.

Indicator Primary data source Natural update cycle Recommended compilation frequency Maximum data vintage Notes
Ocean employment dependency ratio Labour Force Survey (LFS) Annual or biennial Annual where LFS is annual; otherwise biennial 2 years (annual LFS); 4 years (biennial LFS) Industry-by-employment cross-tabulation routinely available
Fish protein dependency ratio Food Balance Sheet (annual) + HIES (3-5 year) FBS annual; HIES 3-5 year Annual ratio; HIES-based household-level disaggregation refreshed on HIES cycle 2 years (FBS); 5 years (HIES) Use FBS as the routine series; refresh household-level component on HIES cycle
Livelihood vulnerability index HIES (anchor) + annual climate and stock data HIES 3-5 year; climate and stock annual Composite re-run annually using updated climate and stock data with most recent HIES; full refresh on HIES cycle 5 years (HIES anchor) Sensitivity components carry the longest vintage and dominate the index's currency status

Table 2: Recommended compilation frequency and maximum data vintage by indicator

Two principles govern the practical use of Table 2:

  1. Sub-indicators update independently; the composite is re-run on the most recent vintage of each. A compiler is not required to wait for the slowest-moving input before re-running the composite. The composite is re-run each year using the most recent vintage of each contributing source, with the reference-year metadata block (Step 5) recording the vintage of each contributing input.
  2. Over-vintage indicators carry a metadata flag rather than being suppressed. Where a contributing source has not been refreshed within the maximum vintage in Table 2, the indicator is published with a confidence flag rather than withdrawn. Suppression of an indicator deprives users of the most recent defensible estimate; flagging preserves the estimate while making its vintage status transparent. This is consistent with SNA 2025 Chapter 21 guidance on satellite-account publication where source vintages differ from the core-account release.

4.3 Worked Example

This section presents a worked numerical example for compiling the three core dependency indicators described in the compilation procedure (Section 4.2). The example uses synthetic data for a hypothetical Small Island Developing State (SIDS) to illustrate the calculation steps. Compilers should substitute national data sources as described in Section 4.1.

Employment dependency indicators

The ocean employment ratio measures the share of total employment attributable to ocean-dependent sectors. Direct ocean employment is compiled by summing employment across primary dependency categories using data from labour force surveys, fisheries registries, and tourism statistics.

Step 1—Sum direct ocean employment by sector:

Sector Employment (persons)
Fishing (commercial and artisanal) 12,000
Aquaculture 3,500
Maritime transport 8,000
Coastal tourism 25,000
Seafood processing 6,000
Total direct ocean employment 54,500

Step 2—Compute the ocean employment ratio:

Ocean employment ratio = Ocean employment / Total employment

= 54,500 / 420,000 = 13.0%

Step 3—Estimate total ocean-dependent employment using an indirect multiplier:

Input-output analysis for the national economy yields an ocean sector employment multiplier of 1.8, meaning that each direct ocean job supports an additional 0.8 jobs in supply chains and induced spending.

Total ocean-dependent jobs = 54,500 × 1.8 = 98,100

Total ocean-dependent employment share = 98,100 / 420,000 = 23.4%

Compilers should report both the direct ratio (13.0%) and the multiplier-adjusted ratio (23.4%), noting the multiplier source and methodology. Multiplier estimates are sensitive to the scope of industries included and should be validated against input-output tables as described in TG-3.3 Economic Activity.

Nutritional dependency indicators

The fish protein dependency ratio measures the contribution of marine-sourced protein to total animal protein supply. Data are drawn from food balance sheets and household consumption surveys.

Step 1—Estimate fish available for human consumption:

The food balance sheet reports annual fish supply of 45,000 tonnes (live-weight equivalent) after accounting for imports, exports, and non-food uses.

Step 2—Convert live-weight supply to edible weight:

Apply an edible yield coefficient to convert from live-weight to the edible portion. Using a yield coefficient of 0.50 (appropriate as a default where species-level data are unavailable; compilers should use species-specific FAO FCT coefficients where possible):

Fish available (edible weight) = 45,000 tonnes × 0.50 = 22,500 tonnes edible weight

Step 3—Apply protein content factor to edible weight:

Fish protein supply = Fish available (edible weight) × Average protein content factor

= 22,500 tonnes × 0.18 = 4,050 tonnes fish protein

The protein content factor of 0.18 (18% of edible weight, wet-weight basis per FAO FCT conventions) is a standard conversion factor for mixed fish species; compilers should adjust based on national catch composition (edible-weight-only application: Section 4.2 Step 2).

Step 4—Compute the fish protein dependency ratio:

Fish protein dependency ratio = Fish protein supply / Total animal protein supply

= 4,050 / 32,000 = 12.7%

Step 5—Compute per capita fish protein supply:

Per capita fish protein = Fish protein supply / Population

= 4,050 tonnes / 2.1 million persons = 1.93 kg per capita per year

A fish protein dependency ratio of 12.7% indicates meaningful nutritional reliance on marine resources. Values above 20% are commonly used as thresholds for identifying fish-dependent populations. Per capita fish protein supply can be compared against FAO global averages (approximately 3.3 kg per capita per year) to contextualize national dependency levels. Note that the two-stage conversion (live-weight to edible weight, then edible weight to protein) is essential for comparability: omitting the edible weight step would overstate fish protein supply by approximately 100% in this example.

Livelihood vulnerability index

A composite livelihood vulnerability index aggregates exposure, sensitivity, and adaptive capacity indicators into a single summary measure, following the IPCC vulnerability framework referenced in Section 3.6. Each component is normalized to a 0--1 scale where higher values indicate greater exposure, greater sensitivity, or greater adaptive capacity respectively.

Step 1—Compile component scores for a coastal fishing community:

Note: This example uses a reduced indicator set for brevity. Compilers should include all indicators listed in the compilation procedure (Section 4.2), including stock risk for exposure and dietary dependence for sensitivity.

Component Sub-indicator Score
Exposure Flood risk (share of settlement in 1-in-50-year flood zone) 0.68
Cyclone frequency (events per decade, normalized) 0.76
Exposure average 0.72
Sensitivity Income dependence on fishing (% of household income) 0.70
Housing quality (% informal or non-code construction) 0.60
Sensitivity average 0.65
Adaptive capacity Educational attainment (years, normalized) 0.50
Savings and insurance coverage (% of households) 0.35
Alternative livelihood options (diversity index) 0.50
Adaptive capacity average 0.45

Step 2—Compute the composite vulnerability index (apply the formula from Section 4.2 Step 5):

= (0.72 + 0.65 + (1 - 0.45)) / 3 = (0.72 + 0.65 + 0.55) / 3 = 1.92 / 3 = 0.64

Step 3—Interpret the result:

A vulnerability index of 0.64 indicates high vulnerability on the 0--1 scale (formula inversion: Section 4.2 Step 5). Applying the illustrative threshold classification from Section 4.2 Step 6:

The example community scores high on exposure (0.72) and sensitivity (0.65) but has limited adaptive capacity (0.45), driven primarily by low savings and insurance coverage (0.35). The high vulnerability score (0.64) reflects the combination of elevated exposure and sensitivity with constrained adaptive capacity. Policy interventions targeting financial inclusion and livelihood diversification would improve adaptive capacity and reduce the composite vulnerability score.

For guidance on weighting, normalization methods, and quality assurance of composite indices, see TG-2.1 Indicator Design Principles and TG-0.7 Quality Assurance.

4.4 Decision Use Cases

This section describes the policy decision contexts where livelihood dependency indicators compiled following this Circular directly inform management choices. The use cases illustrate how dependency indicators translate into actionable policy insights.

Use Case 1: Coastal poverty targeting

Decision context: A national poverty reduction programme seeks to identify coastal communities requiring targeted support. Standard poverty headcount data exist but do not reveal which poor households also face high exposure to ocean ecosystem degradation.

Dependency indicators used:

Application: Combine poverty mapping with dependency indicators to identify communities that are both poor and highly dependent on ocean ecosystems. Prioritize these "high-dependency, high-poverty" communities for interventions addressing both poverty and ecosystem resilience. Communities with high dependency but low poverty may require different policy responses focused on sustaining livelihoods rather than poverty alleviation.

Policy outcome: Improved targeting of social protection, livelihood diversification programmes, and ecosystem restoration investments to communities where both poverty and ecosystem dependency are high.

Use Case 2: Just transition planning for fisheries reform

Decision context: A country is implementing new fisheries regulations to reduce overfishing, which will reduce total allowable catch and eliminate some fishing licenses. Policy-makers need to identify which communities and workers will be most affected and design compensation and transition support.

Dependency indicators used:

Application: Map the spatial distribution of fishing-dependent employment and household income shares against the proposed license reductions. Identify communities where a high proportion of employment depends on fishing (for example, >30%, though compilers should establish context-specific thresholds based on national circumstances) and where adaptive capacity (education, alternative skills) is low. Design differentiated transition packages: early retirement for older fishers near retirement age; retraining and job placement for younger workers with transferable skills; direct income support for households with high fishing income dependency and limited alternatives.

Policy outcome: Socially equitable fisheries reform that protects livelihoods while achieving sustainability objectives, with compensation and support tailored to community-specific dependency profiles.

Use Case 3: Food security monitoring in coastal zones

Decision context: A Ministry of Health monitors national nutrition indicators and seeks to understand whether coastal populations face distinct food security risks related to marine ecosystem change or fishing access restrictions.

Dependency indicators used:

Application: Identify coastal regions where a high share of animal protein comes from fish (for example, >40%, though compilers should establish context-specific thresholds based on national circumstances) and where subsistence fishing provides a substantial share of household consumption (for example, >20%). Establish nutrition surveillance in these high-dependency zones to track changes in dietary adequacy alongside monitoring of fish stock status and coastal access. If fish protein dependency is high and fish stocks are declining, trigger contingency food security responses (school feeding programmes with alternative protein sources, nutrition supplementation, support for alternative protein production).

Policy outcome: Proactive food security interventions in fish-dependent populations, preventing malnutrition before it occurs by linking nutrition surveillance to ecosystem condition monitoring.

Use Case 4: Marine protected area design with livelihood safeguards

Decision context: A conservation agency plans to expand marine protected areas (MPAs) to meet biodiversity targets, but must ensure that MPA boundaries and management rules do not disproportionately harm fishing-dependent communities.

Dependency indicators used:

Application: Overlay proposed MPA boundaries with maps of fishing dependency and cultural significance. Identify coastal communities that currently derive >50% of income from areas proposed for strict protection. Adjust MPA boundaries to avoid complete exclusion of high-dependency communities, or design co-management zones where sustainable fishing and cultural practices continue under community governance. Where displacement is unavoidable, quantify affected livelihoods and design compensation mechanisms including alternative livelihood support and benefit-sharing from MPA tourism revenues.

Policy outcome: Conservation gains achieved without imposing uncompensated livelihood losses, with MPA governance structures that recognize and support the dependencies of coastal communities on marine ecosystems.

Use Case 5: Indigenous customary sea rights (deferred—placeholder)

A customary sea rights use case (Indigenous tenure, FPIC, UNDRIP) is in development. Drafting is deferred until Indigenous co-authors and reviewers are confirmed as part of the writing team with authority over the text, consistent with the principle stated in TG-3.6 Traditional Knowledge Accounts that guidance on Indigenous knowledge and tenure should be written with, not about, the communities concerned. Practitioners from the jurisdictions named in the original proposal (Fiji, Vanuatu, and ideally others such as Aotearoa and the Torres Strait) would be the appropriate people to lead or co-lead this use case. The omission is recorded here as a finding rather than a gap: writing a use case on customary sea rights without Indigenous co-authors would contradict the framing this Circular already adopts elsewhere on Indigenous data sovereignty and community-led methods.

Use Case 6: Post-disaster needs assessment (PDNA)

Decision context: Following a major cyclone, storm surge, or coastal flooding event, a government convenes a Post-Disaster Needs Assessment to quantify damage and loss across affected sectors and to plan recovery. The PDNA Fisheries and Coastal Livelihoods sector requires evidence on the dependent population, the magnitude of livelihood loss, and the recovery cost. The Sendai Framework for Disaster Risk Reduction 2015-2030[3:1] frames this work as the operational expression of disaster resilience commitments. TG-2.3 supplies the dependency baseline against which damage and loss are measured.

Dependency indicators used (PDNA phase mapping):

PDNA phase Indicators required TG-2.3 source
Baseline (pre-event) Ocean employment dependency ratio; fish protein dependency ratio; livelihood vulnerability index Compiled per Section 4.2
Rapid assessment (72-hour) Pre-event indicator values, affected-population overlay Pre-existing values + spatial extent of event
Damage and loss Loss of employment, loss of subsistence harvest, dependency-weighted loss valuation Pre-event dependency × event extent
Recovery planning Vulnerability index components; integration with the social protection register (SDG 1.3.1) Section 3.6 / 4.2

Application: TG-2.3 indicators supply the pre-event baseline against which the PDNA Fisheries and Coastal Livelihoods sector module quantifies damage and loss. The dependency indicators are inputs to the PDNA methodology—they do not replace the World Bank / UN / EU PDNA Fisheries and Coastal Livelihoods sector guidance note (the sector-specific damage and loss costing methodology). The illustrative magnitudes documented in the Inhambane Bay case (small-scale fisheries providing 60-70% of household income and 70-80% of dietary protein in coastal villages; Burnside, 2026)[2:3] indicate the order of magnitude that dependency baselines can take in SSF-reliant coastal economies and the corresponding scale of livelihood loss that an event in such a setting can impose.

Policy outcome: PDNA recovery costings that reflect the dependency baseline of affected populations, supporting Loss & Damage Fund applications and recovery resourcing decisions that are calibrated to the magnitude of pre-event reliance on ocean ecosystems.

Note on scope. The PDNA-sector worked-table crosswalk in this use case, including its precise alignment with the most recent World Bank / UN / EU PDNA Fisheries and Coastal Livelihoods sector guidance note, is to be confirmed in consultation with the GOAP Secretariat and CSDR. The use case is therefore presented as the operational frame for the indicator set rather than as a substitute for the PDNA sector methodology.

Use Case 7: Marine spatial planning and ESIA

Decision context: A marine spatial planning (MSP) authority and a project developer have a concurrent need for dependency evidence over the same spatial area: the MSP authority requires the human-uses analysis to underpin allocation of the marine area between competing uses (Step 4 of the IOC-UNESCO MSP guidance[50][51]), while the developer requires an Environmental and Social Impact Assessment (ESIA) under IFC Performance Standard 5[52] and the Equator Principles IV[53] to identify economically displaced persons and design livelihood restoration. The use case is framed throughout from the government / MSP-authority perspective, with the ESIA application as a downstream use of the same indicator stack.

Dependency indicators used:

Application (MSP): Under Step 4 of the IOC-UNESCO MSP guidance, the dependency indicators populate the human-uses analysis that the spatial allocation depends on. The MSP authority overlays the dependency layers against proposed allocations—offshore wind concessions, MPA zoning, shipping lanes, aquaculture sites—to identify spatial conflicts and the populations whose livelihoods the allocation will displace, sustain, or transform.

Application (ESIA / IFC PS5): The same indicator stack supplies the ESIA's baseline. Specifically, it identifies the economically displaced persons whose livelihood restoration the developer is obliged to plan; it provides the pre-project baseline against which livelihood restoration is measured (PS5 paragraph 28); and the livelihood vulnerability index, in conjunction with the dependency indicators, supports identification of "particularly vulnerable" status (PS5 paragraph 27).

Worked illustration—offshore-wind concession. A 200 km² concession overlaps a fishing ground that supplies 18% of the affected coastal community's fishing effort, in a community whose livelihood vulnerability index is greater than 0.55. The MSP authority's response is either a fisheries-compatible turbine array (with spacing, foundations, and access arrangements that retain fishing access) or an offset package tied to the dependency magnitude (livelihood restoration funded at a level scaled to the 18% effort displaced and the vulnerability classification of the affected community). Cross-reference: this use case is the developer-and-MSP-authority counterpart to Use Case 4 (MPA design with livelihood safeguards), and shares the underlying spatial dependency methodology.

Policy outcome: MSP allocations and ESIA mitigation plans that are calibrated to the dependency magnitude they affect, reducing the incidence of post-allocation livelihood disputes and supporting the IFC PS5 / Equator Principles IV obligations on economically displaced persons.

Use Case 8: Fishing access agreements (DWFN / SIDS)

Decision context: A Small Island Developing State government is renewing an access agreement with a distant-water fishing nation (DWFN) under, for example, the Pacific Islands Forum Fisheries Agency Vessel Day Scheme[54], with PNA Office minimum benchmark prices[55] as a floor. The negotiating government requires evidence on the social and nutritional floor (the level of access below which domestic livelihoods and food security are compromised) and on the fiscal floor (the level of access fees below which the agreement does not deliver an adequate share of resource rent to the coastal state).

Dependency indicators used:

Application: The dependency indicators inform the negotiation in three ways. First, the fish protein dependency ratio establishes the social floor below which domestic catch retained for local consumption must not fall. Second, the government revenue dependency indicator establishes the fiscal floor against which access-fee levels are evaluated, with the PNA Vessel Day Scheme benchmark prices as a defensible minimum. Third, the employment dependency in domestic processing and transhipment indicates whether the agreement should require minimum domestic landings, crewing, or transhipment to sustain shore-based employment, consistent with FAO Voluntary Guidelines for Securing Sustainable Small-Scale Fisheries (the SSF Guidelines), Chapters 5 and 7[56].

Note on SNA 2025 asymmetry. Under SNA 2025, output from a DWFN vessel is allocated to the residency of the vessel operator (the DWFN), while access fees received by the coastal state are recorded as resource rent. Conventional GDP accounting therefore systematically understates the economic significance of the resource to the coastal state. This is precisely the asymmetry that TG-2.3 dependency indicators are designed to surface: by quantifying domestic livelihood, protein, and revenue dependency on the resource, the indicators provide the evidence base that GDP accounts cannot. Distributional consequences for domestic SSF (gear conflict, stock pressure, displacement of local fleets) cross-reference Use Case 2 on just transition and, where relevant, the deferred Use Case 5.

Policy outcome: Access-agreement renewals in which the negotiating government has explicit, defensible floors for domestic catch retention, access-fee levels, and shore-based employment, replacing the present asymmetric position in which DWFNs are typically the better-resourced negotiating party.

Use Case 9: Climate adaptation planning and NAP

Decision context: A government compiles a National Adaptation Plan (NAP) and prepares finance submissions to the Green Climate Fund, the Adaptation Fund, and the Loss and Damage Fund. The NAP needs ocean-dependent evidence over a 10-20 year planning horizon, aligned with the Nationally Determined Contribution (NDC) cycle.

Dependency indicators used:

Application: A trajectory analysis overlays current dependency indicators against projected ecosystem condition. Projections are drawn from IPCC AR6 regional climate projections, downscaled where available, and from national stock-status assessments under climate scenarios. The product of "today's dependency" and "projected ecosystem trajectory" identifies populations and sectors with the greatest projected livelihood loss over the planning horizon, which in turn drives the NAP's adaptation-investment prioritisation and the loss-and-damage component of climate-finance submissions.

Indicator → climate-finance criterion mapping:

TG-2.3 indicator NAP / climate-finance criterion
Livelihood vulnerability index Particularly Vulnerable Group identification
Ocean employment dependency × projected ecosystem condition Just transition investment case
Fish protein dependency × projected stock trajectory Food-security adaptation investment case
Coastal protection dependency × SLR projection Nature-based coastal protection investment case

Distinction from Use Case 2. Use Case 2 (just transition for fisheries reform) addresses policy-triggered transition (a regulator-imposed reduction in total allowable catch). Use Case 9 addresses ecosystem-driven transition (climate-driven change in ecosystem condition), and is loss-and-damage oriented rather than reform-compensation oriented. Both use the same dependency indicators; the trigger and the policy instrument differ.

Data limitations. Climate-projection-based use of dependency indicators must be presented as scenario ranges, not point estimates. The community sense-check protocol established in Section 4.1.1 applies to the projected-change scenarios: communities have direct evidence on whether the trajectory implied by a climate scenario is consistent with their observation of stock, season, and storm patterns over the recent past, and this evidence must be capable of revising the scenario set used in the NAP.

Policy outcome: NAP investment portfolios and climate-finance submissions that are calibrated to projected dependency loss rather than to current-condition dependency alone, with explicit scenario uncertainty surfaced rather than collapsed into point estimates.

Decision context: A government statistical agency, regulator, or financial system supervisor uses national or sub-national dependency indicators to support the Taskforce on Nature-related Financial Disclosures (TNFD) LEAP framework[57]—Locate, Evaluate, Assess, Prepare—particularly the Locate and Evaluate phases at national and sub-national scale.

Dependency indicators used:

Application: National and sub-national TG-2.3 indicators serve as screening inputs and contextual baselines for TNFD-aligned disclosure at the entity level. They locate the high-dependency, high-vulnerability geographies that financial institutions and corporates should examine in greater detail under the Locate and Evaluate phases, and they provide the contextual baseline against which entity-level data are interpreted.

Constraints on use. Three constraints govern this use case and must be made explicit in any application:

  1. Scale mismatch. TG-2.3 indicators are compiled at national or sub-national scale. They do not, by themselves, satisfy project-level due diligence requirements. Where a district-level indicator flags a population as high-dependency or high-vulnerability, the appropriate response is finer-scale data collection at the affected community, not treatment of the national or district indicator as sufficient.
  2. IFC PS7 is a safeguard standard, not a measurement framework. TG-2.3 indicators inform the screening process by which Indigenous Peoples-related risk is identified; they do not meet PS7 obligations. PS7 obligations—including Free, Prior and Informed Consent—attach at the project level and to the entity undertaking the activity, and they require entity-level due diligence consistent with IFC PS7[58].
  3. Deferred-use-case boundary. This use case must not pre-empt deferred Use Case 5 on Indigenous customary sea rights, particularly given the PS7 FPIC provisions. Any TNFD-disclosure application that engages Indigenous tenure, knowledge, or rights is bound by the principle stated under Use Case 5 that drafting and use must be led with, not about, the communities concerned.

Note on scope. The scope of this use case—the extent to which TG-2.3 indicators can be used to support TNFD disclosure beyond screening, and the boundary between disclosure use and the deferred Indigenous use case—is to be confirmed in consultation with the GOAP Secretariat and CSDR.

Policy outcome: A defensible boundary for the use of national and sub-national ocean dependency indicators in TNFD-aligned disclosure: as screening inputs that direct entity-level due diligence rather than as substitutes for it.

Use Case 11: Scenario analysis (cross-cutting)

Decision context: A government, regulator, or research institute requires a defensible mechanism for asking "what if" questions of the dependency indicator set—for example, "what would the ocean employment dependency ratio equal if the total allowable catch fell by 30%?"—without overstating the analytical confidence of the answer. The use case is cross-cutting: it sits alongside the policy-specific use cases above and supports them with a common scenario-analysis frame.

Methodological frame. This use case is framed as a sensitivity table, not a predictive model. The Circular's stance against causal inference from cross-sectional dependency indicators (Section 3.6.5; Section 4.2) is preserved: a scenario analysis built on these indicators reports the magnitude of indicator change implied by a given input change, under stated and explicit assumptions, and does not represent itself as a forecast of the policy outcome.

Indicator linkages. A scenario passes through three Circulars in sequence: biophysical inputs from TG-2.1 Biophysical Indicators (stock, extent, condition); ecosystem service flows from TG-3.2 Flows from Environment to Economy (the service-flow transformation); and the social outcomes that TG-2.3 measures (employment, protein, vulnerability). All three core TG-2.3 indicators are addressed—the scenario frame is not restricted to a single indicator. The synthetic SIDS figures in Section 4.3 (54,500 direct ocean employment; 4,050 tonnes fish protein supply; vulnerability index 0.64) provide the parameter base for the worked scenarios below.

Worked example—TAC reduction. Using the Section 4.3 SIDS parameters, a 30% reduction in TAC propagates through the indicator set as follows. Direct ocean employment in fishing falls by approximately 3,600 jobs (30% of 12,000), reducing the ocean employment dependency ratio from 13.0% to approximately 12.1% before multiplier effects. The fish protein dependency ratio is recomputed against a reduced domestic catch input to the food balance sheet; sensitivity to the trade response is large, and the scenario must report a range bounded by full import substitution at one end and unsubstituted domestic shortfall at the other. The livelihood vulnerability index increases through both the sensitivity channel (income dependence on a contracting sector) and, indirectly, through the adaptive-capacity channel as livelihood diversity is forced to adjust on a shortened time frame.

Explicit limitations.

Policy outcome: A defensible cross-cutting tool for scenario interrogation of the dependency indicator set, preserving the Circular's anti-causal stance while allowing decision-makers to bound the magnitude of indicator change implied by alternative input assumptions.

These use cases demonstrate that livelihood dependency indicators are not merely descriptive statistics but provide the empirical foundation for designing socially informed, equitable ocean governance decisions.

4.5 Spatial considerations

Livelihood dependencies are spatially concentrated in coastal communities, requiring attention to geographic disaggregation:

4.6 Temporal considerations

Dependencies may vary seasonally (monsoon fishing patterns, tourism seasons) and change over time (shifts in employment structure, declining fish stocks). Time series data enable:

5. Acknowledgements

This Circular has been approved for public circulation and comment by the GOAP Technical Experts Group in accordance with the Circular Publication Procedure.

Authors: [To be confirmed]

Reviewers: [To be confirmed]

6. References

Section 3.4 (illustrative composite) references:

  1. FAO, The State of World Fisheries and Aquaculture 2024. Globally, fisheries and aquaculture employ over 60 million people, with hundreds of millions more dependent on the sector for nutrition and livelihoods. ↩︎

  2. Burnside, D. (2026). Integrated Ocean-Poverty Account Framework. Working paper; cited illustrative cases (Inhambane Bay; Lake Illawarra; Nippon Causeway / Tarawa) provide the empirical motivation for the poverty-alleviation framing and integration-with-existing-survey-systems approach adopted in Section 1, Section 4.1.2, and Use Case 6. ↩︎ ↩︎ ↩︎ ↩︎

  3. UNDRR (2015). Sendai Framework for Disaster Risk Reduction 2015-2030. Adopted by the UN General Assembly in resolution A/RES/69/283. The Framework provides the policy reference under which TG-2.3 dependency indicators feed disaster risk reduction, resilience planning, and Loss & Damage processes (see Use Case 6). ↩︎ ↩︎

  4. United Nations, Transforming our world: the 2030 Agenda for Sustainable Development, SDG Target 2.3. "By 2030, double the agricultural productivity and incomes of small-scale food producers, in particular women, indigenous peoples, family farmers, pastoralists and fishers." ↩︎

  5. United Nations, Transforming our world: the 2030 Agenda for Sustainable Development, SDG Target 14.7. ↩︎

  6. United Nations et al. (2025). System of National Accounts 2025, Chapter 16 on Labour. Provides comprehensive guidance on employment status, remuneration of employees, and the distinction between formal and informal employment. ↩︎

  7. SEEA Ecosystem Accounting, para. 2.15. "Benefits are the goods and services that are ultimately used and enjoyed by people and society. The benefits to which ecosystem services contribute may be captured in current measures of production (e.g. food, water, energy, recreation) or may be outside such measures." ↩︎

  8. SEEA Ecosystem Accounting, para. 2.14. "Ecosystem services are the contributions of ecosystems to the benefits that are used in economic and other human activity." ↩︎

  9. SF-MST, para. 1.1. "Given the range of direct and indirect effects and the wide spectrum of stakeholders involved, there is a need for an integrated approach to tourism development, management and monitoring." ↩︎

  10. System of National Accounts 2025, Preface. The 2025 SNA broadens the national accounts framework "to better account for elements affecting wellbeing and sustainability." ↩︎

  11. SF-MST, para. 2.57. The framework distinguishes "direct effects" from "indirect and induced effects" for measuring sustainability. ↩︎

  12. SEEA Ecosystem Accounting, para. 2.31. ↩︎

  13. SF-MST, Chapter 3. "Linkages between measures of employment in tourism" provides detailed guidance adaptable to ocean sector employment. ↩︎

  14. United Nations, Global indicator framework for SDGs, Indicator 14.7.1. ↩︎

  15. United Nations, 2030 Agenda for Sustainable Development, SDG Target 2.3. ↩︎

  16. SEEA for Agriculture, Forestry and Fisheries, para. 2. ↩︎

  17. United Nations, 2030 Agenda for Sustainable Development, SDG Target 8.9. ↩︎

  18. SF-MST, para. 5.80. ↩︎

  19. SF-MST, para. 2.57. ↩︎

  20. SF-MST, para. 2.58. ↩︎

  21. SF-MST, para. 2.58. ↩︎

  22. System of National Accounts 2025, Chapter 16 on Labour. Provides comprehensive guidance on employment status, remuneration of employees, and the treatment of self-employed and informal workers in national accounts. ↩︎

  23. SF-MST, Table 5.3. ↩︎

  24. FAO, The State of World Fisheries and Aquaculture 2024. Fish provides over 3.3 billion people with at least 20% of their average animal protein intake. ↩︎

  25. Hicks et al. (2019), "Harnessing global fisheries to tackle micronutrient deficiencies." Nature 574: 95-98. ↩︎

  26. United Nations, 2030 Agenda for Sustainable Development, SDG Target 2.1. ↩︎

  27. SEEA Ecosystem Accounting, Table 6.3. ↩︎

  28. SEEA Ecosystem Accounting, para. 2.15. "The benefits to which ecosystem services contribute may be captured in current measures of production (e.g. food, water, energy, recreation) or may be outside such measures (e.g. clean water, clean air, flood protection)." ↩︎

  29. United Nations, 2030 Agenda for Sustainable Development, SDG Target 14.b. ↩︎

  30. System of National Accounts 2025, guidance on own-account production. ↩︎

  31. FAO, Food Security Framework. ↩︎

  32. SEEA Ecosystem Accounting (UN et al., 2021; consolidated edition December 2024), Table 6.3. The previously combined "water flow regulation for mitigating river and coastal flooding" service is split into three reference services: Water flow regulation, Flood control, and Storm mitigation. See https://seea.un.org/sites/seea.un.org/files/documents/EA/seea_ea_f124_web_12dec24.pdf. ↩︎

  33. SEEA Ecosystem Accounting, Chapter 7 (physical supply-and-use tables, SPA/SBA structure) and Chapter 9 §9.3.6 (expected-expenditure / avoided-damage methods) and §9.4 (service-specific valuation). The forward-references in this section to a future Accounting for People Circular (post-September 2026) and the GOAP Social Accounts methodology supplement, rather than replace, SEEA-EA guidance. ↩︎

  34. SEEA Ecosystem Accounting, para. 6.51. ↩︎

  35. SEEA Ecosystem Accounting, Table 6.3, row 18. ↩︎

  36. SEEA Ecosystem Accounting, Table 6.3, entry for "Recreation-related services." ↩︎

  37. SEEA Ecosystem Accounting, Table 6.3, row 19. ↩︎

  38. SEEA Ecosystem Accounting, Table 6.3, rows 21-22. ↩︎

  39. SEEA Ecosystem Accounting, Table 6.3, rows 20, 23. ↩︎

  40. TNFD Recommendations, Box 1. ↩︎ ↩︎

  41. United Nations, 2030 Agenda for Sustainable Development, SDG Target 14.7. ↩︎

  42. SEEA Ecosystem Accounting, para. 1.66. "Measuring the Sustainability of Tourism website...provides guidance on linking ecosystem accounting to measures of tourism activity." ↩︎

  43. IPCC, 2022, Annex II: Glossary. ↩︎ ↩︎

  44. United Nations, 2030 Agenda for Sustainable Development, SDG Target 14.2. ↩︎

  45. United Nations, 2030 Agenda for Sustainable Development, SDG Target 1.5. ↩︎

  46. United Nations, Global indicator framework for SDGs, Indicator 1.3.1 measures "Proportion of population covered by social protection floors/systems." SDG 1.3.1 is distinct from the poverty headcount ratio (SDG 1.1.1 / 1.2.1), which measures the prevalence of poverty rather than the institutional coverage of social protection. ↩︎

  47. SF-MST, para. 2.6. ↩︎

  48. GOAP Social Accounts methodology. The community sense-checking protocol in Section 4.1.1 and Section 4.2 is consistent with the disaggregated-consultation quality assurance approach established in that methodology, which treats disaggregated consultation as a quality assurance requirement rather than a procedural courtesy. ↩︎

  49. A future GOAP Accounting for People Circular (planned post-September 2026) will provide the consolidated standard for socially-disaggregated indicators of ocean dependency, including community sense-checking protocols. Until then, this Circular records community-identified indicators added through Section 4.2 Step 3 supplementation as candidates for inclusion in that future Circular. ↩︎ ↩︎ ↩︎

  50. Ehler, C. and Douvere, F. (2009). Marine Spatial Planning: a step-by-step approach toward ecosystem-based management. Intergovernmental Oceanographic Commission and Man and the Biosphere Programme. IOC Manual and Guides No. 53, ICAM Dossier No. 6. UNESCO. ↩︎

  51. UNESCO-IOC / European Commission (2021). MSPglobal International Guide on Marine/Maritime Spatial Planning. IOC Manuals and Guides No. 89. ↩︎

  52. International Finance Corporation (2012). Performance Standard 5: Land Acquisition and Involuntary Resettlement. PS5 paragraphs 27 and 28 are referenced in Use Case 7 for particularly-vulnerable identification and livelihood-restoration baselines respectively. ↩︎

  53. Equator Principles Association (2020). The Equator Principles IV. ↩︎

  54. Pacific Islands Forum Fisheries Agency. Vessel Day Scheme. See https://www.ffa.int/. ↩︎

  55. Parties to the Nauru Agreement (PNA) Office. PNA Office minimum benchmark price for vessel days under the Vessel Day Scheme. ↩︎

  56. FAO (2015). Voluntary Guidelines for Securing Sustainable Small-Scale Fisheries in the Context of Food Security and Poverty Eradication (the SSF Guidelines), particularly Chapters 5 (Governance of tenure in small-scale fisheries and resource management) and 7 (Value chains, post-harvest and trade). ↩︎

  57. Taskforce on Nature-related Financial Disclosures (2023). Recommendations of the Taskforce on Nature-related Financial Disclosures, including the LEAP approach (Locate, Evaluate, Assess, Prepare) and the Metrics and Targets pillar. ↩︎

  58. International Finance Corporation (2012). Performance Standard 7: Indigenous Peoples. Including the Free, Prior and Informed Consent provisions referenced in Use Case 10 and the boundary condition vis-à-vis deferred Use Case 5. ↩︎