Global Ocean Accounts Partnership Secretariat · GOAP Technical Guidance on Ocean Accounting · circular
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. The indicators are framed in poverty-alleviation terms, following the integrated ocean-poverty account approach of Burnside (2026), and are designed to feed disaster risk reduction processes under the Sendai Framework for Disaster Risk Reduction 2015-2030, where evidence on ocean-dependent populations is required for resilience planning, loss-and-damage assessment, and recovery prioritisation.
The indicators cover four dimensions: employment dependency, nutritional dependency, cultural dependency, and vulnerability (exposure, sensitivity and adaptive capacity of ocean-dependent populations). The guidance supports compilation of indicators aligned with 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 — 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”.
This Circular provides decision-relevant indicators for coastal poverty assessment, just transition planning, 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, and so supplies 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.
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.
Ocean ecosystems contribute to human livelihoods through multiple pathways: (1) employment in ocean-based industries, (2) provisioning of food and other marine products, (3) cultural, spiritual, and recreational benefits, and (4) the regulating services that protect coastal communities from hazards. 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”. 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.
Whilst TG-3.5 Social Accounts establishes the general framework for social accounting in the ocean context, 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?”
A livelihood dependency framework organises 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
Source TG-2.3 (livelihood dependency framework), Section 3 (indicator design).
Ocean-dependent livelihoods encompass all forms of work and subsistence activities that rely substantially on marine and coastal ecosystems. The 2025 SNA recognises that measuring wellbeing and sustainability requires extending measurement “beyond income and consumption to include…human capital as a produced asset”. That extension covers the skills, knowledge, and capabilities that enable people to derive livelihoods from ocean resources. Human capital in the context of ocean-dependent livelihoods extends beyond formal education to 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”: whilst 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.
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:
Recording the flows of ecosystem services to human users supports understanding of livelihood dependencies, and extends the logic of SEEA EA para. 2.31 on intermediate service flows.
Whilst TG-3.5 Social Accounts covers general ocean sector employment measurement, this section focuses specifically on compiling employment as a dependency indicator. The measure of interest is how reliant communities and populations are on ocean-based employment, which a headcount of jobs alone does not capture.
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”, whilst 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”. 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”. 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”. Marine tourism employment includes:
Marine tourism employment is commonly seasonal and precarious, with a small core of permanent staff complemented by temporary and on-call workers. This pattern 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 same 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. That principle preserves comparability with TG-3.3 Economic Activity classification.
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”. For ocean accounting, the analogous concept encompasses supply chains for fishing, aquaculture, marine tourism, and other ocean sectors.
Measuring indirect employment requires either:
For practical compilation, countries may focus on the supply chain sectors with known connections to ocean industries.
Key indirect employment categories include:
Induced employment results from the spending of wages earned in ocean-dependent sectors. Induced effects are typically estimated using economic multipliers derived from input-output models. 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 summarises the data requirements and methodological approaches for compiling employment dependency indicators across the three tiers.
Table 1: Employment dependency indicator data requirements by tier
Beyond counting employment, ocean accounts should characterise employment quality using the decent work framework dimensions described in TG-3.5 Social Accounts. The 2025 SNA provides detailed guidance on labour market measurement, including employment status, compensation of employees, and the distinction between formal and informal employment. Key characteristics include:
Table 2: Employment characteristics relevant to ocean dependency analysis
Such disaggregation reveals patterns that aggregate counts obscure: the high proportion of informal employment in small-scale fisheries, or the seasonal concentration of marine tourism employment.
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.
Fish and other aquatic foods are a primary source of animal protein, with particularly high consumption in coastal regions, Small Island Developing States, and countries with extensive traditional fishing cultures, . SDG Target 2.1 calls for ending hunger and ensuring “access by all people…to safe, nutritious and sufficient food all year round”.
Key protein dependency indicators include:
Per capita consumption:
Population-level dependencies:
Nutritional quality:
Nutritional dependency indicators measure the human welfare dimension of the biomass provisioning services described in SEEA EA Table 6.3.
Beyond commercial fisheries, subsistence harvesting provides essential food for coastal and island communities, often without being captured in formal economic statistics. Subsistence fishing includes:
For subsistence harvesting, the ecosystem service 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 and with the artisanal fisheries coverage in TG-1.5 Fisheries Management. SDG Target 14.b emphasises the importance of providing “access for small-scale artisanal fishers to marine resources and markets”.
Measuring subsistence dependencies requires:
The monetary value of subsistence harvests can be imputed using local market prices for equivalent products. The 2025 SNA provides guidance on valuing own-account production for inclusion in household income and consumption measures.
Food security encompasses four dimensions: availability, access, utilisation, and stability. Table 3.3.3 below summarises how ocean ecosystem dependencies affect each dimension.
Indicators of food security vulnerability related to ocean ecosystems include:
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. 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.
Which of the three applies here. Coastal protection is a sub-service of Flood control services, defined in Table 6.3 as “ecosystem contributions of linear elements in the seascape (e.g. coral reefs, sand banks, dunes or mangrove ecosystems along the shore) to protecting the shore and thus mitigating the impacts of tidal surges or storms on local communities”. Storm mitigation services are a different reference service, covering “the impacts of windstorms, sandstorms and other types of storms (other than water-related events)”. Wave attenuation and storm surge dissipation are water-related events, so this Circular’s subject is the coastal protection service under flood control. Compilers should record it there, not against storm mitigation, or national entries will not reconcile with Table 6.3 or with cross-country comparisons keyed to it. 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.
The coastal protection service entry in the Chapter 7 physical supply and use table — the service providing area, the service benefiting area, and the physical and monetary service flows — is specified in TG-3.2 Flows from Environment to Economy Section 3.3, and the data sources and hydrodynamic models that populate the counterfactual are in TG-4.1 Remote Sensing and Geospatial Data Section 3.2.6. Neither is restated here.
What this Circular adds is the dependency reading of those entries: how many people the service protects, how much of their exposure it removes, and what happens to that dependency when the SPA degrades. The indicators below take the compiled service flow as their input.
Three published national-scale applications are recommended as templates:
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 lowers the composite where substitutes are strong. o, p and q are proxies for the population’s capacity to replace lost protection or adapt to its loss. That is adaptive capacity, a component of vulnerability (see the livelihood vulnerability index, Section 4.2, Step 3), not of dependence: the service-flow approach in §3.4.1 records the protection a population receives whether or not it could replace it. The composite therefore mixes dependence (m) with exposure (n) and adaptive capacity (o, p, q). Compilers using it should report m, n and the substitutability term (o × p × q) alongside the composite, and should not combine the composite with the livelihood vulnerability index, which would count exposure and adaptive capacity twice. 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.
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”. Whilst 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.
Marine cultural services encompass:
Recreation-related services: The ecosystem contributions to recreational activities including swimming, diving, snorkelling, surfing, recreational fishing, and wildlife watching. These are considered final ecosystem services since they are directly enjoyed by people.
Indicators of recreational dependency include:
Visual amenity services: The contribution of marine seascapes to aesthetic enjoyment and property values.
Education and research services: The contribution of marine ecosystems to scientific research, environmental education, and the generation of knowledge.
Spiritual, artistic, and symbolic services: The contributions to cultural identity, spiritual practices, and artistic inspiration. For many coastal and island cultures, the ocean holds profound spiritual significance that cannot be adequately captured through quantitative indicators.
For Indigenous Peoples and traditional coastal communities, cultural dependencies on ocean ecosystems may be the deepest 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, 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 emphasise the need for community-led methods that respect Indigenous data sovereignty. Quantitative proxies may include:
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”.
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, enabling integration of cultural ecosystem service flows with tourism economic accounts.
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. Vulnerability encompasses sensitivity or susceptibility to harm and lack of capacity to cope and adapt.
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”. 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”.
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:
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:
Human capital:
Social capital:
Financial capital:
Institutional capital:
Understanding the specific ecosystem services on which livelihoods depend, and the current and projected condition of those services, enables targeted vulnerability assessment.
Table 3 provides a summary of livelihood vulnerability indicators organised 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.
Table 3: Livelihood vulnerability indicators by component
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. Because a composite index aggregates components whose relative importance is contestable, its credibility rests on a single condition: that every methodological choice is transparent and revisable. A defensible index therefore (1) uses a transparent and replicable methodology, (2) weights its components against local context and priorities, (3) documents the theoretical framework underpinning the choice of components and weights, (4) is validated through community consultation, and (5) is updated as conditions change.
For guidance on integrated indicator frameworks and quality assurance of composite indices, see TG-0.7 Quality Assurance.
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, and specialised 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.
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, 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.
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).
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). These cases — where ex ante dependency evidence would have changed the decision — are the standing reference for the indicator set.
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:
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:
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.
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:
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.
Objective: Measure the share of total employment attributable to ocean-dependent sectors.
Data requirements:
Procedure:
Extract employment counts for ocean-dependent industries from labour force survey:
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).
Adjust for informal and subsistence employment using fisheries registry:
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.
Sum employment across ocean industries to derive direct ocean employment total
Compute ocean employment dependency ratio:
Ocean employment ratio = Direct ocean employment / Total employment
Report both absolute employment count and percentage share
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 set out at the start of Section 4.2 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.
Objective: Measure the contribution of marine-sourced protein to total animal protein supply.
Data requirements:
Procedure:
Compile total fish supply from food balance sheet:
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
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.
Compile total animal protein supply from food balance sheet:
Compute fish protein dependency ratio:
Fish protein dependency = Fish protein supply / Total animal protein supply
Compute per capita fish protein:
Per capita fish protein (kg/year) = Fish protein supply / Population
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.
Objective: Produce composite vulnerability index for ocean-dependent coastal community using exposure, sensitivity, and adaptive capacity components.
Data requirements:
Procedure:
Define the spatial unit for vulnerability assessment:
Compile and normalise exposure indicators (scale 0—1, higher = greater exposure):
Compile and normalise sensitivity indicators (scale 0—1, higher = greater sensitivity):
Compile and normalise adaptive capacity indicators (scale 0—1, higher = greater capacity):
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.
Interpret result using threshold classification (all thresholds are illustrative — see note at start of Section 4.2):
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:
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.
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.
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:
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, high 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.
Table 2: Recommended compilation frequency and maximum data vintage by indicator
Two principles govern the practical use of Table 2:
Synthetic data for a hypothetical Small Island Developing State (SIDS) illustrate the three core compilation procedures. Compilers should substitute national data sources as described in Section 4.1.
Step 1 — Sum direct ocean employment by sector:
Maritime transport (8,000 persons) is secondary-tier employment (Section 3.2.1) and seafood processing (6,000 persons) is value-chain employment (Section 4.2 Step 1 sub-step 2a). Both are reported separately and are not part of the direct total.
Step 2 — Compute the ocean employment ratio:
Ocean employment ratio = Ocean employment / Total employment
= 40,500 / 420,000 = 9.6%
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 = 40,500 × 1.8 = 72,900
Total ocean-dependent employment share = 72,900 / 420,000 = 17.4%
Compilers should report both the direct ratio (9.6%) and the multiplier-adjusted ratio (17.4%), 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.
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.
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 in its earlier (AR4) form, which places exposure inside vulnerability; the IPCC Sixth Assessment Report treats exposure as a separate determinant of risk alongside hazard and vulnerability. Each component is normalised 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.
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 (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). Policy interventions targeting financial inclusion and livelihood diversification would improve adaptive capacity and reduce the composite vulnerability score.
For guidance on weighting, normalisation methods, and quality assurance of composite indices, see TG-2.1 Indicator Design Principles and TG-0.7 Quality Assurance.
This section describes the policy decision contexts where livelihood dependency indicators compiled following this Circular directly inform management choices.
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.
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.
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.
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 recognise and support the dependencies of coastal communities on marine ecosystems.
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.
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 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):
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) 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. The use case is therefore presented as the operational frame for the indicator set rather than as a substitute for the PDNA sector methodology.
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, ), whilst the developer requires an Environmental and Social Impact Assessment (ESIA) under IFC Performance Standard 5 and the Equator Principles IV 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.
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, with PNA Office minimum benchmark prices 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.
Note on SNA 2025 asymmetry. Under SNA 2025, output from a DWFN vessel is allocated to the residency of the vessel operator (the DWFN), whilst 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.
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:
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, whilst 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 — 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:
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.
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.
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 (40,500 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 9.6% to approximately 8.8% 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.
Livelihood dependencies are spatially concentrated in coastal communities, requiring attention to geographic disaggregation:
Dependencies may vary seasonally (monsoon fishing patterns, tourism seasons) and change over time (shifts in employment structure, declining fish stocks). Time series data enable:
Authors: [To be confirmed]
Reviewers: [To be confirmed]
Section 3.4 (illustrative composite) references:
Cite this report
Social and Livelihood Dependencies on Ocean Ecosystems (2026) (GOAP Technical Guidance on Ocean Accounting, TG-2.3, v0.9.2). GOAP Secretariat. https://guidance.oceanaccounts.org/circulars/section-2/2-3-livelihood-dependencies/
guidance.oceanaccounts.org/circulars/section-2/2-3-livelihood-dependencies
© 2026 Global Ocean Accounts Partnership Secretariat · CC BY-NC 4.0 · The report, its PDF and this deck are built from the same source.
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