Governance and Institutional Indicators

1. Outcome

After working through this Circular, readers will be able to compile governance and institutional indicators from ocean governance accounts, measuring the quality, effectiveness, and coverage of institutional arrangements governing ocean use and conservation. Readers will understand how to derive indicators across five principal dimensions—legal framework coverage, MPA management effectiveness, tenure and access rights security, compliance and enforcement performance, and institutional capacity—and to interpret these indicators using a governance stocks-and-flows analytical framework, in which governance stocks (legal instruments, rights allocations, MPA designations) are distinguished from governance flows (enforcement actions, monitoring patrols, management expenditures). The guidance includes a worked example drawn from the Indonesia Gili Matra MPA pilot, illustrating how governance stocks and flows can be compiled and interpreted at MPA scale. It supports national monitoring of SDG 14.2, SDG 14.C, SDG 16.6, and SDG 16.7^[1], and draws on the governance accounts methodology in TG-3.7 Governance Accounts and the general indicator methodology in TG-2.1 Aggregate Biophysical Indicators of Environmental State.

2. Requirements

Essential prerequisites:

• TG-3.7 Governance Accounts—for the compiled governance account data that underpin all indicators in this Circular, including legal framework registers, MPA management records, tenure registry data, and enforcement statistics
• TG-0.1 General Introduction to Ocean Accounts—for the conceptual framework connecting governance arrangements to ecosystem and economic outcomes

Helpful background:

• TG-0.2 Overview of Relevant Statistical Standards—for the international statistical standards underpinning ocean accounting
• TG-2.1 Aggregate Biophysical Indicators of Environmental State—for the general indicator methodology, including normalisation and composite index construction, which applies directly to governance indicator compilation
• TG-6.6 Deep Sea and ABNJ Accounting—for ecosystem accounting methods in Areas Beyond National Jurisdiction, relevant to BBNJ governance indicator tracking

3. Guidance Material

Governance of ocean systems encompasses the legal, institutional, and procedural arrangements through which societies make and implement decisions about ocean use, protection, and distribution of benefits^[2]. The quality of these arrangements shapes whether ocean resources are used sustainably, whether conservation investments achieve their objectives, and whether benefits are equitably distributed. Ocean governance accounts (TG-3.7) systematise the data on these arrangements into a consistent statistical framework; this Circular translates that data into indicators suitable for policy monitoring and international reporting.

This section presents the governance indicator framework (Section 3.1), legal framework coverage indicators (Section 3.2), MPA management effectiveness indicators (Section 3.3), tenure and access rights indicators (Section 3.4), compliance and enforcement indicators (Section 3.5), and institutional capacity indicators (Section 3.6). Section 3.7 presents the compilation procedure, and Section 3.8 provides a worked example.

3.1 Governance Indicator Framework

3.1.1 Dimensions of ocean governance

Ocean governance indicators span five analytically distinct dimensions, each corresponding to a component of governance accounts compiled under TG-3.7^[3]. Table 3.1.1 below summarises these dimensions.

Table 3.1.1: Dimensions of ocean governance indicators

A sixth dimension—climate-adaptive governance—is increasingly recognised as relevant to ocean governance assessment. This dimension measures whether governance frameworks incorporate adaptive management provisions, climate risk assessment requirements, and flexible spatial management mechanisms that can accommodate climate-driven shifts in species distributions and ecosystem boundaries. Methods for assessing climate-adaptive governance quality are still developing; compilers interested in this dimension should consult the climate indicator framework in TG-2.8 Climate Change Indicators and document existing adaptive provisions as a narrative component of governance accounts.

3.1.2 Relationship to SDG and international frameworks

Table 1 maps governance indicator dimensions to relevant SDG and international monitoring frameworks, providing the reporting linkages that underpin compilation priorities.

Table 1: Governance Indicator Dimensions and International Framework Mapping

Participation indicators are documented in governance accounts per TG-3.7 §3.7 and can be compiled alongside the five primary indicator dimensions.

The Kunming-Montreal Global Biodiversity Framework (KM-GBF) Target 3 calls for 30% of marine areas to be under effective conservation and management (30×30)^[5]. "Effective" is the operative qualifier—designation without management does not satisfy Target 3, making MPA management effectiveness indicators directly relevant to national GBF reporting.

The FAO Small-Scale Fisheries Guidelines (SSF Guidelines) establish international recognition of the right of small-scale fishers to secure and equitable access to marine resources^[6], making tenure security indicators the accountability mechanism for SSF Guidelines implementation.

SDG 14.6.1 measures "Progress by countries in the degree of implementation of international instruments aiming to combat illegal, unreported and unregulated fishing"^[7], for which compliance and enforcement indicators provide the quantitative underpinning.

For indicators tracking national implementation of multilateral environmental agreements (UNCLOS, CBD, UNFCCC) more broadly, see TG-2.10 Multilateral Environmental Agreement Indicators, which provides complementary indicator guidance for MEA reporting obligations.

For the governance and indicator implications of the BBNJ Agreement (area-based management tools, ABNJ EIA requirements, marine genetic resources benefit-sharing), see TG-6.6 Deep Sea and ABNJ Accounting. Compilers should track national ratification progress and document operational BBNJ implementation indicators as a data gap pending further international methodological guidance^[8].

3.1.3 Relationship to biophysical and economic outcomes

Governance indicators may function as leading indicators: improvements in governance quality can precede measurable changes in ecosystem condition and economic performance, though the causal pathways and time lags are often long and context-dependent. This causal structure, where it can be established, has implications for indicator interpretation. A country may show declining biophysical condition despite strong governance scores if governance improvements are recent and ecosystems have not yet responded. Conversely, stable biophysical indicators may mask governance deterioration if legacy institutional arrangements are eroding.

Compilers should present governance indicators alongside the relevant biophysical and economic outcome indicators from TG-2.1 Aggregate Biophysical Indicators of Environmental State through TG-2.7 Pollution Flows and Pressures Indicators to support integrated assessment. The cross-indicator relationships—for example, the correlation between MPA management effectiveness scores and coral reef condition indicators—provide empirical material for exploring potential relationships between governance quality and ecosystem outcomes, though attribution remains subject to the causal and time-lag caveats noted above.

3.1.4 Governance Stocks and Flows

A useful analytical distinction within the governance accounts framework separates governance stocks from governance flows, following the accounting logic of the System of National Accounts as applied in the SEEA^[9].

Governance stocks are the institutional arrangements existing at a specific point in time—the architecture of the governance system. In the marine domain, governance stocks include: the spatial extent of marine jurisdictions (territorial sea, EEZ, ABNJ zones); the legal instruments in force (fisheries acts, MPA enabling legislation, coastal zone management acts); rights allocations (fishing licences, quota allocations, TURF designations); MPA zoning designations and associated management plans; and formal co-management agreements with IPLCs or community groups. Governance stocks map directly onto the legal framework coverage and tenure and access rights dimensions described in Section 3.1.1.

Governance flows are the activities, transactions, and interventions generated by the institutional architecture over a defined accounting period. Governance flows include: enforcement actions taken (boardings, inspections, prosecutions); licensing revenues collected and management expenditures disbursed; compliance monitoring patrols conducted; observer deployments; and measurable stakeholder participation in decision-making processes. Governance flows map onto the compliance and enforcement and institutional capacity dimensions described in Section 3.1.1.

This stocks-and-flows framing can clarify a common governance failure pattern: the governance stock may appear robust—a well-designed management plan exists, zones are legally designated, rights are formally allocated—while governance flows are inadequate, meaning that enforcement is absent, management budgets are unspent, and monitoring is not conducted. The Gili Matra pilot (Section 3.8) provides an empirical illustration of this pattern. Compilers should assess both the stock architecture and the flow performance of governance systems, and present them together, to enable integrated diagnosis. The management effectiveness indicators in Section 3.3 and compliance indicators in Section 3.5 operationalise governance flow performance; legal framework and tenure indicators in Sections 3.2 and 3.4 operationalise governance stock coverage.

3.2 Legal Framework Coverage Indicators

3.2.1 Ocean legislation registry

The governance accounts compiled under TG-3.7 include a legal framework register recording the existence, date of adoption, and implementation status of key ocean governance instruments. Table 3.2.1 below summarises the indicators derived from this register.

Table 3.2.1: Indicators derived from the ocean legislation registry

3.2.2 International obligations coverage

UNCLOS implementation score—SDG indicator 14.C.1 measures progress in implementing UNCLOS through legal, policy and institutional frameworks^[11]. Governance accounts provide the data for scoring national implementation across the seven dimensions of the SDG 14.C.1 methodology (see Section 3.7.4 for the DOALOS voluntary national reporting tool used in compilation).

Regional fisheries body participation—proportion of stocks within national jurisdiction that fall under the management framework of a Regional Fishery Management Organisation (RFMO) or Regional Fishery Body (RFB) to which the country is a member or cooperating non-contracting party (a status allowing non-member states to participate in conservation obligations without full membership rights).

Port State Measures Agreement implementation—scored assessment of national implementation of PSMA obligations: port designation, advance notification requirements, inspection procedures, and information sharing with flag states and RFMOs^[12].

3.2.3 Relationship to Macroeconomic Governance Indicators

The World Bank Worldwide Governance Indicators (WGI) provide annual composite scores for six governance dimensions across more than 200 economies^[13]. National WGI scores may provide useful macroeconomic context—compilers may note them as metadata when reporting governance indicator results.

However, the WGI framework is insufficiently granular for sector-specific ocean governance assessment. Its six dimensions characterise whole-of-government performance at national scale and cannot capture MPA management quality, fishing rights security, or compliance patrol intensity at the level needed for ocean accounts purposes. Where country-level WGI data are available, compilers may note in indicator metadata the degree of alignment or divergence between national governance scores and sector-level ocean governance indicator values, as this divergence can itself be analytically informative.

3.3 MPA Management Effectiveness Indicators

Area coverage alone (the "paper park" metric) is an insufficient measure of conservation impact; effective management requires functioning governance, resources, and adaptive management processes.

3.3.1 Coverage and representativeness

Marine area under formal protection (%)—proportion of the national marine jurisdiction (territorial sea + EEZ) designated as marine protected areas, marine reserves, or other effective area-based conservation measures (OECMs). This is the metric tracked by SDG 14.5.1, SDG 14.2.1 (proportion of national EEZ managed using ecosystem-based approaches), and KM-GBF Target 3.

Ecosystem representativeness—proportion of each major marine ecosystem type (coral reefs, seagrass meadows, mangroves, pelagic zones, seamounts) covered by protected areas, measured in hectares and as a proportion of total ecosystem extent from ecosystem extent accounts (TG-3.1 Asset Accounts for the Ocean). Reporting only total area coverage masks systematic gaps in protection of particular ecosystem types.

Protection level classification—distribution of protected areas across IUCN protection categories (I-VI), indicating the range of restrictions on extractive and other activities within the protected area network. Areas under strict protection (Categories I-II) are distinguished from those permitting sustainable use (Categories V-VI).

3.3.2 Management effectiveness scoring

METT-4 score—Management Effectiveness Tracking Tool (METT-4) score averaged across all MPAs in the national system, measuring six dimensions: context, planning, inputs, processes, outputs, and outcomes^[14]. METT-4 is the primary global tool for MPA management effectiveness assessment and is used for KM-GBF Target 3 "effective" reporting.

Proportion of MPAs with current management plans—MPAs with a management plan adopted within the past five years as a proportion of all designated MPAs. Management plans more than five years old without renewal indicate institutional stagnation.

Proportion of MPAs with operational monitoring programmes—MPAs with documented, regularly implemented biological and compliance monitoring as a proportion of all designated MPAs. Monitoring is the prerequisite for adaptive management.

Staff-to-area ratio—number of full-time equivalent management staff per 1,000 km² of protected area, providing a measure of management intensity. Consistent with the staffing data compiled in governance accounts (TG-3.7), this indicator is standardised by area to enable cross-site and cross-country comparison.

MPA management budget per km²—recurrent management expenditure (excluding capital) per km² of protected area, derived from the institutional expenditure accounts in TG-3.7. Budget-to-area ratios below empirically established minimum thresholds (typically USD 600--2,500 per km² depending on location, management intensity, and ecosystem type) may indicate severe resource constraints^[15].

3.3.3 Ecological effectiveness

Condition differential (MPA vs. non-MPA)—comparison of ecosystem condition indicators (coral cover, fish biomass, seagrass density) inside and outside MPAs, controlling for environmental gradients. A positive differential indicates that MPAs are generating ecological benefits; the absence of a differential suggests enforcement or management failure. This indicator integrates governance accounts data (MPA boundaries and management quality) with condition account data from TG-3.1 Asset Accounts for the Ocean.

Spillover index—estimated biomass of target fish species in the unprotected zone adjacent to MPAs as a proportion of MPA interior biomass, measuring whether MPAs are generating fisheries benefits for adjacent communities through adult spillover^[16].

3.4 Tenure and Access Rights Indicators

3.4.1 Fishing rights coverage and security

Proportion of fishing effort under formal rights instruments—fishing effort (days at sea or gross registered tonnes-days, GRT-days, where GRT-days are a measure of vessel capacity utilisation combining gross registered tonnage with fishing days) under formal fishing licences, individual transferable quotas, territorial use rights in fisheries (TURFs), or community rights agreements as a proportion of total estimated fishing effort. A low proportion indicates a predominantly open-access regime with weak incentives for conservation.

Fishing rights security index—composite indicator of rights security, drawing on governance accounts data on:

• Duration of rights (long-term rights score higher than seasonal or annual licences)
• Transferability (tradeable rights score higher)
• Exclusivity (exclusive rights score higher than non-exclusive)
• Legal basis (statutory rights score higher than administrative allocations)

Each criterion is scored 0--3 (0: absent; 1: limited; 2: moderate; 3: strong). The composite index is the unweighted mean of the four criterion scores, normalised to 0--100 by multiplying by 100/3. Compilers should document and justify any alternative weighting scheme using the composite index procedure in TG-2.1 Aggregate Biophysical Indicators of Environmental State, Section 3.4.

Small-scale fisheries (SSF) rights coverage—proportion of small-scale fishing households with documented access rights to their primary fishing grounds. This indicator operationalises SDG 14.b.1 (progress on implementation of the SSF Guidelines)^[17] and should be disaggregated by sex and IPLC status where data permit.

IPLC customary rights recognition—proportion of IPLC communities with ocean-based customary use areas that have received formal legal recognition of their rights, either through statutory instruments, co-management agreements, or customary fishing area designations. Formal recognition is distinguished from mere acknowledgement in policy documents.

3.4.2 Rights equity and distribution

Gender equity in fishing rights—proportion of formal fishing licence holders who are women, and proportion of fishing quota or community rights allocations held by women's organisations. Disaggregated by gear type and fishing area. Governance accounts should record rights holder identity disaggregated by sex where administrative records allow. Data for this indicator should be drawn from social accounts compiled per TG-3.5 Social Accounts, Section 3.2 on rights and access disaggregation. For broader social equity context, see TG-3.5 Social Accounts.

Gini coefficient for fishing quota distribution—inequality in the distribution of quota or fishing effort allocations across rights holders, where transferable quota systems exist. High concentration of quota in few holders signals equity and market power concerns relevant to small-scale fisheries policy. This indicator applies only in jurisdictions with tradeable quota systems (ITQs or similar); in licensing or effort-based fisheries management systems, alternative distribution metrics such as the proportion of licensed effort held by small-scale versus large-scale operators may be substituted.

Community tenure area—hectares of marine area subject to community tenure arrangements (TURFs, locally managed marine areas, community conserved areas), and this area as a proportion of total managed marine area. Community tenure arrangements can provide effective management at lower cost than state-managed systems where communities have strong incentives and local knowledge.

3.5 Compliance and Enforcement Indicators

3.5.1 IUU fishing and compliance

IUU fishing prevalence index—estimated proportion of total catch in national waters attributable to illegal, unreported, and unregulated (IUU) fishing, derived from catch reconstruction applied to governance accounts data on reported and estimated actual catches. For estimation methods (port sampling, observer programmes, VMS comparison, stock-assessment back-calculation, trade-based analysis, satellite surveillance) and the SEEA CF accounting treatment, see TG-1.5 OA and Fisheries Management Section 3.6.3. This indicator is the outcome measure against which enforcement performance indicators should be assessed.

Vessel monitoring system (VMS) coverage—proportion of commercial fishing vessels above the licensing threshold that are equipped with operational VMS transponders and transmitting to the national fisheries monitoring centre. VMS coverage is a prerequisite for effective at-sea compliance monitoring.

Observer coverage rate—proportion of commercial fishing trips with on-board fisheries observers (human or electronic), by gear type and fleet segment. Observer coverage significantly below 20% is generally considered insufficient for reliable catch verification in high-value fisheries in many contexts, though appropriate coverage rates depend on fishery type and risk level—some RFMOs require 100% coverage (human or electronic) for high-value tuna fisheries^[18].

Boarding and inspection rate—number of at-sea boardings per 1,000 commercial fishing vessel days, indicating the probability of inspection that a vessel faces. Low rates reduce deterrence.

Violation detection and prosecution rate—proportion of detected fishing violations that result in formal charges, sanctions, or permit revocation. A high detection-to-prosecution ratio indicates an effective enforcement chain; a low ratio suggests administrative or judicial bottlenecks.

Satellite-based monitoring is increasingly providing proxy enforcement indicators that can supplement administrative enforcement records where data coverage is weak. Technologies such as Automatic Identification System (AIS) vessel tracking, as operationalised through platforms such as Global Fishing Watch, can generate measurable proxies including vessel days in MPAs, AIS transponder activity rates, and dark vessel detection rates in designated zones. These emerging data sources may be especially useful in contexts where on-water patrol capacity is limited, though compilers should treat them as supplementary rather than primary compliance indicators pending broader methodological validation^[19].

3.5.2 Flag and port state control

Flag state control performance—proportion of vessels flagged to the country that have been inspected by port state control authorities in foreign ports and found to have fishing-related deficiencies, sourced from RFMO IUU vessel lists and port state inspection databases.

Port state inspection coverage—proportion of foreign fishing vessels calling at national ports that are subject to PSM Agreement inspection requirements and that receive documentary or physical inspection, measuring compliance with PSMA obligations^[20].

IUU vessel listing—number of vessels flagged to the country appearing on RFMO IUU vessel lists during the reference year, indicating flag state control failures.

3.6 Institutional Capacity Indicators

3.6.1 Human and financial resources

Fisheries management staff per 1,000 active fishers—full-time equivalent staff in the national fisheries authority per 1,000 licensed or registered commercial and small-scale fishers. This ratio measures the capacity of the authority relative to the scale of the sector it manages.

Ocean governance budget as share of ocean economy GDP—total recurrent expenditure on fisheries management, MPA management, coastal monitoring, and related governance functions as a proportion of ocean economy GDP (as compiled under TG-2.5 Ocean Economy Structure and Activity). This ratio contextualises governance investment relative to the economic value at stake.

Fisheries research expenditure intensity—expenditure on stock assessment, ecosystem monitoring, and fisheries science as a proportion of total fisheries management expenditure, indicating the share of capacity devoted to the evidence base for management decisions.

3.6.2 Coordination and integration

Inter-agency ocean coordination score—scored assessment of the effectiveness of formal coordination mechanisms between the fisheries authority, environment/conservation agency, coastguard/maritime enforcement, tourism authority, and other relevant bodies. Scoring criteria are applied as follows: (1) existence of a formal inter-agency coordination body; (2) meeting frequency; (3) joint planning processes; (4) shared data systems; (5) joint enforcement operations. Score each criterion 0 (absent), 1 (exists but not operational), 2 (operational but limited), 3 (fully operational and effective). Average across criteria: 0.0--1.0 = weak coordination; 1.1--2.0 = partial coordination; 2.1--3.0 = effective coordination. The coordination score methodology is consistent with TG-3.7 §3.6 coordination effectiveness scoring. Coordination failures are a primary cause of governance fragmentation in multi-sector ocean management.

Marine spatial plan adoption and implementation—binary indicator of whether a binding marine spatial plan covering at least 50% of national waters is in force, plus a scored assessment of plan implementation status. Draws on legal framework data (Section 3.2) and governance accounts process records (TG-3.7).

Integrated coastal monitoring system coverage—proportion of the coastline covered by an integrated monitoring system that combines biophysical data (condition accounts), economic activity data (economic accounts), and governance compliance data (enforcement records), enabling evidence-based adaptive management.

3.7 Compilation Procedure

3.7.1 Step 1: Assemble governance account data

Governance and institutional indicators are derived from the governance accounts compiled under TG-3.7. Before indicator compilation, confirm that the following governance account components are available and current:

• Legal framework register (Section 3.7.2 of TG-3.7)
• MPA register with METT-4 assessments and management plan status
• Rights registry: fishing licences, quotas, TURFs, community agreements
• Enforcement records: inspections, boardings, detections, prosecutions
• Institutional budget and staffing records

Where governance account data are incomplete, note data gaps in indicator metadata and apply conservative assumptions rather than extrapolating from incomplete records. Transparency about data quality is more valuable to users than spuriously precise estimates.

3.7.2 Step 2: Calculate indicator values

For each indicator, apply the formula or scoring procedure specified in Sections 3.2 through 3.6, drawing on the relevant governance account component. Record the numerator, denominator, and source data reference for each indicator to enable verification and updating.

Scoring indicators. Where indicators involve scored assessments (legal framework alignment, inter-agency coordination), apply the scoring rubric consistently across all spatial units or time periods being assessed. Document the scoring criteria and, where expert judgement is involved, identify the assessor and date of assessment.

Composite indicators. Where composite indicators aggregate multiple sub-indicators (fishing rights security index, composite governance indicator (Section 3.8.5)), apply the normalisation and weighting procedure from TG-2.1 Aggregate Biophysical Indicators of Environmental State, Section 3.4 on composite index construction. The weighting scheme and normalisation method must be documented.

Worked compilation: Fishing rights security index (§3.4.1)

Data source: Rights registry component of the TG-3.7 governance accounts. Each fishing right entry records: duration class (annual / multi-year / permanent), transferability flag, exclusivity class, and legal basis type.

Step 1—Score each criterion per TG-3.7 rights registry entry:

Criterion	0	1	2	3
Duration	No rights	Annual or seasonal	Multi-year (2--9 years)	Long-term (≥10 years) or permanent
Transferability	Non-transferable	Conditionally transferable	Freely transferable within class	Freely transferable, heritable
Exclusivity	Open access	Non-exclusive licence	Exclusive area or quota	Exclusive TURF with enforcement
Legal basis	No documentation	Administrative allocation	Regulatory instrument	Statutory/constitutional right

Step 2—Average across criteria for each rights class and compute the composite:

Unweighted mean of the four criterion scores. Normalise to 0--100: index = (mean score ÷ 3) × 100.

Example: A small-scale fishing community holds 5-year TURFs, transferable within the community, exclusive to licence holders, established by fisheries regulation: Duration = 2, Transferability = 1, Exclusivity = 2, Legal basis = 2. Mean = 7 ÷ 4 = 1.75. Index = (1.75 ÷ 3) × 100 = 58.3.

Step 3—Aggregate across rights classes: Where a fishery has multiple rights instruments (e.g., commercial licences and community TURFs), compute a weighted average using fishing effort (days at sea or GRT-days) as the weight, then report the overall index alongside the disaggregated class-level scores.

Step 4—Record metadata: Source register (TG-3.7 rights registry), reference year, number of rights entries scored, assessor, and any rights classes where data were incomplete.

3.7.3 Step 3: Temporal and spatial disaggregation

Governance indicators should be compiled at the same temporal and spatial resolution as the governance accounts from which they are derived. For most indicators, an annual compilation cycle aligned with the governance accounts reference year is appropriate.

Spatial disaggregation. Where governance accounts are compiled at sub-national level (provincial, district, or management unit), governance indicators should be reported at that level to support sub-national policy analysis. The sub-national governance accounts methodology is addressed in TG-3.11 Sub-National Ocean Accounts.

Temporal trends. Governance indicators are most informative as time series. A minimum of three reference years is needed to identify trend direction; five or more years is recommended for tracking reform impacts.

3.7.4 Step 4: SDG and international reporting

Map compiled indicator values to the relevant SDG metadata frameworks and international reporting obligations identified in Table 1. Where national governance indicators use different methodologies from the SDG global indicator standards, document the methodological alignment and note any adjustments applied to produce SDG-compatible values.

For SDG 14.C.1 reporting, the governance account data support the structured assessment across the seven implementation dimensions of the voluntary national reporting tool developed by the UN Division for Ocean Affairs and the Law of the Sea (DOALOS)^[21].

3.7.5 Step 5: Data quality flags and limitations

Governance data are subject to quality constraints that compilers should document systematically. Common data gaps include: incomplete administrative records for enforcement activities (inspection logs, prosecution outcomes); inconsistent licensing database formats across agencies; and delayed or partial reporting from subnational institutions. Where scoring on qualitative governance dimensions involves expert judgement, the assessor, date, and scoring rationale should be recorded as metadata attached to the indicator value.

Several governance indicators may be politically sensitive in some country contexts—enforcement failure rates, IUU vessel listings, and compliance rates may reflect negatively on agencies that are also primary data custodians. Compilers should apply standard statistical quality assurance principles and, where possible, triangulate sensitive indicators from multiple independent data sources (e.g., VMS records cross-checked against vessel logbooks and observer reports). Where data cannot be disclosed due to confidentiality or sensitivity, the indicator should be reported as "not available" with a note on the data gap, rather than suppressed without explanation.

Scored assessments (METT-4, coordination scores, legal alignment scores) reflect the judgement of assessors at a point in time. Governance accounts should include version tracking for scored assessments, recording assessment date, assessor identity, and any changes in scoring methodology between periods.

3.8 Worked Example: Gili Matra Marine Protected Area (Indonesia)

This section presents a worked example based on the Indonesia Gili Matra MPA pilot ocean accounts, demonstrating how governance stocks and flows indicators can be compiled and interpreted at MPA scale. Gili Matra—comprising the islands of Gili Meno, Gili Ayer, and Gili Trawangan in West Nusa Tenggara Province, Indonesia—was selected by GOAP as a primary pilot site for ocean accounting implementation^[22]. The pilot produced accounts for ecosystem extent, flows to the economy, flows to the environment, and ocean governance, providing one of the first empirically grounded illustrations of governance indicator compilation from ocean accounts at MPA scale. The ecosystem extent and condition accounts underlying this example were compiled following the methods described in TG-6.1 Coral Reef Ecosystem Accounting, TG-6.2 Mangrove and Coastal Wetland Accounting, and TG-6.3 Seagrass Ecosystem Accounting.

3.8.1 Context: governance stocks and biophysical account findings

The Gili Matra MPA operates under a 2014--2034 Management and Zoning Plan, which establishes the core governance stock: defined zone boundaries, legal designation as a protected area, and formally documented management responsibilities. The MPA is managed across four institutional tiers—the designated working unit (Satker), local Gili Indah Village officials, the North Lombok Regency, and the West Nusa Tenggara Provincial government—creating a complex institutional architecture that spans sub-village, district, and provincial jurisdictions simultaneously^[23].

Ecosystem extent accounts for the period 2015--2021 showed that despite this formal governance stock, biophysical condition deteriorated: coral reef extent decreased by 18.16 hectares and mangrove extent decreased by 7.38 hectares, while seagrass experienced a minor gain of 8.43 hectares. During the same period, the total measured economic value derived from the MPA's ecosystems increased substantially, from IDR 45.24 billion per year in 2015 to IDR 64.17 billion per year in 2021—driven primarily by rising tourism demand and inflation^[24]. Without integrated governance and biophysical accounts, a standard economic evaluation would suggest a growing, well-managed system, entirely masking the underlying depletion of the natural capital base. This divergence between economic and biophysical trends is precisely the analytical value that governance indicators are designed to expose.

3.8.2 Legal framework and governance stock indicators

Table 2: Gili Matra Governance Stock Indicators

The governance stock assessment reveals a pattern common in small island and coastal developing settings: a legally sound management plan exists (positive governance stock), but the institutional architecture for implementing it is fragmented across jurisdictions without a formal coordination mechanism. This creates conditions for regulatory gaps and enforcement ambiguity.

3.8.3 Compliance and enforcement governance flows

The "Flows to Environment" account documented the solid waste burden generated by the growing tourism economy as a proxy indicator of governance flow adequacy. In 2021, the MPA generated 4.45 thousand tonnes of solid waste, of which 56.38% was organic. Of the total waste stream, only 25.86% was successfully recycled or managed; the remaining 70.4%—approximately 3,133 tonnes—was accumulated in local landfills, posing an ongoing contamination threat to adjacent reef and seagrass ecosystems^[25].

Waste management performance can function as a proxy governance flow indicator—specifically, the proportion of solid waste managed to standard reflects both enforcement capacity and infrastructure investment by the responsible authorities. A managed waste rate of approximately 26% against a target of substantially higher coverage indicates a significant governance flow deficit relative to the governance stock (the zoning plan) that ostensibly requires environmental protection.

Table 3: Gili Matra Proxy Governance Flow Indicators

3.8.4 MPA coverage and biophysical governance indicators

Table 4: Gili Matra Integrated Governance and Biophysical Indicators (2015--2021)

The divergence between the economic value trend (strongly positive) and the biophysical extent trend (negative for coral and mangrove) is the diagnostic signal that governance indicators are designed to interpret. In the absence of governance account data, the economic trend would dominate the policy narrative. With governance accounts, the compiler can identify that the institutional architecture—four tiers without formal coordination, a waste management system operating well below required capacity—is generating governance flow deficits that are translating into biophysical loss even as economic indicators appear favourable.

3.8.5 Lessons for indicator compilation

The Gili Matra pilot illustrates several principles for MPA-scale governance indicator compilation:

1. Compile governance stocks and flows separately. A management plan in force is a positive stock indicator; waste management performance at 26% is a negative flow indicator. Combining them into a single composite score would obscure this diagnosis.

2. Use biophysical accounts as the outcome benchmark. Governance indicator values should be interpreted against biophysical trends—not economic trends alone—when assessing whether governance is performing its conservation function.

3. Institutional fragmentation is a measurable governance stock indicator. The number of institutional tiers, the existence (or absence) of a formal coordination instrument, and the clarity of jurisdictional boundaries are all quantifiable and should be included in governance accounts alongside the management plan and zoning data.

4. Proxy indicators can substitute where direct enforcement data are absent. Where patrol records and inspection logs are not available, solid waste management rates, wastewater treatment coverage, and similar environmental outcome proxies can indicate whether governance flows are adequate.

4. 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]

5. References