Quality Assurance Principles

1. Outcome

After reading this Circular, you will understand the quality assurance principles—covering data quality dimensions, uncertainty categories, and documentation standards—that apply across all ocean accounting work. These consistent principles are referenced by all other circulars in the Technical Guidance.

2. Requirements

Essential prerequisites:

• TG-0.1 General Introduction to Ocean Accounts—for the conceptual framework and the three-domain structure of Ocean Accounts

Helpful background:

• TG-0.2 Overview of Relevant Statistical Standards—for the SEEA EA quality dimensions and statistical standards referenced throughout

3. Guidance Material

3.1 Purpose of Quality Assurance

Quality assurance in ocean accounting serves multiple purposes:

1. Credibility: Ensuring accounts meet standards expected of official statistics
2. Transparency: Enabling users to understand the basis for reported values
3. Comparability: Facilitating meaningful comparisons across time, space, and jurisdictions
4. Usability: Helping decision-makers understand the reliability of information
5. Improvement: Providing a basis for progressive enhancement of data and methods

Ocean accounts integrate data from diverse sources—environmental monitoring, economic statistics, social surveys, geospatial observations, and customary knowledge systems. Each source has different quality characteristics, and accounts compilation must address these differences systematically.

3.2 Six Dimensions of Data Quality

The SEEA EA identifies six dimensions of data quality relevant to environmental-economic accounting (para. 2.86). These apply directly to ocean accounts:

3.2.1 Relevance

Definition: The degree to which the data serve to address the purposes for which they are sought by users.

Ocean accounts considerations:

• Do the accounts address the policy questions being asked?
• Are the spatial and temporal resolutions appropriate for the intended use?
• Are the ecosystem types and economic sectors adequately disaggregated?

Example: An ocean account designed to inform marine spatial planning needs fine spatial resolution, while one supporting national budget processes may require less geographic detail but more robust monetary estimates.

3.2.2 Timeliness

Definition: The length of time between the data reference period and availability of the information.

Ocean accounts considerations:

• How current are the environmental monitoring data?
• Are there lags between economic activity and its recording in accounts?
• How frequently are accounts updated?

Example: Satellite-derived ecosystem extent data may be available within months, while comprehensive economic surveys may have 1-2 year lags.

3.2.3 Accuracy

Definition: The degree to which the data correctly describe the phenomena they were designed to measure.

Ocean accounts considerations:

• What are the measurement errors in environmental monitoring?
• How reliable are sampling-based estimates of fish stocks or ecosystem condition?
• What are the error margins for economic survey data?

Example: Fish stock assessments may have wide confidence intervals; accounts should report these rather than point estimates alone.

3.2.4 Coherence

Definition: The degree to which data from different sources or compiled using different methods can be reliably combined and compared.

Ocean accounts considerations:

• Are classifications consistently applied across data sources?
• Are spatial boundaries aligned between environmental and economic data?
• Are accounting periods synchronised?
• Are changes in accounts over time attributable to genuine change rather than methodological or classification revisions? Breaks-in-series should be documented and, where feasible, back-casted (compilers should consult the relevant Section 3 accounts compilation circular for revisions practice specific to each account type).

Example: Economic data by industry may not align perfectly with spatially-defined ecosystem service flows; bridging tables may be needed.

3.2.5 Interpretability

Definition: The availability of information to help users understand and properly use the data.

Ocean accounts considerations:

• Are methods and assumptions clearly documented?
• Are metadata standards applied?
• Are definitions consistent with international standards?

Example: Accounts should specify which ecosystem service classification is used—for example, CICES v5.1 or the SEEA EA Reference List of Ecosystem Services (see SEEA EA Annex 6.1)—and how services were measured. For SEEA EA accounts, the SEEA EA Reference List is the primary classification; CICES v5.1 may be used as a complementary mapping.^[1]

3.2.6 Accessibility

Definition: The ease with which users can access and use the data.

Ocean accounts considerations:

• Are accounts published in accessible formats?
• Are underlying data available for users who need more detail?
• Are there access restrictions (e.g., for confidential business data)?

Example: Accounts may be published in summary form with detailed underlying data available on request or through data portals.

3.3 Institutional Environment

Beyond the six dimensions of data quality, the SEEA EA emphasises the quality of the institutional environment in which data are compiled (para. 2.86). The five core elements are listed below. All five elements are consistent with the UN Fundamental Principles of Official Statistics (Resolution 2013/21):

• Independence: Statistical compilation free from political interference
• Mandate: Clear legal or institutional authority for accounts production
• Resources: Adequate funding and staffing
• Coordination: Effective arrangements between data-producing agencies
• Standards: Adherence to international statistical standards and principles

3.4 Four Categories of Uncertainty

The SEEA EA identifies four categories of uncertainty particularly pertinent to ecosystem accounting (paras. 2.90--2.94).^[2] All apply directly to ocean accounts:

3.4.1 Physical Measurement Uncertainty

Uncertainty related to the physical measurement of ecosystem services and ecosystem assets.

Sources in ocean accounts:

• Sampling variability in fish stock surveys
• Remote sensing classification errors
• Spatial interpolation between monitoring stations
• Temporal variability between observations

Approaches to address:

• Report confidence intervals where available
• Document sampling design and coverage
• Use ensemble methods or multiple data sources
• Distinguish between measurement precision and accuracy

3.4.2 Valuation Uncertainty

Uncertainty in the valuation of ecosystem services and ecosystem assets.

Sources in ocean accounts:

• Choice of valuation method (market prices, replacement cost, stated preference)
• Benefit transfer from other contexts
• Discount rate selection for asset values
• Treatment of non-marginal changes

Benefit transfer introduces additional uncertainty where the study site and policy context differ substantially from the transfer site; applicability conditions should be assessed and documented (see §3.5.4 Assumptions Made, and SEEA EA paras. 5.38--5.41).

Approaches to address:

• Report physical accounts alongside monetary values
• Use sensitivity analysis for key assumptions
• Document valuation method selection rationale
• Present ranges rather than point estimates for contested values

3.4.3 Ecosystem Dynamics Uncertainty

Uncertainty related to the dynamics of ecosystems and changes in flows.

Sources in ocean accounts:

• Non-linear ecosystem responses to pressures
• Threshold effects and regime shifts
• Cumulative and synergistic impacts
• Climate change effects on baselines

Approaches to address:

• Use scenario analysis where trends are uncertain
• Document known ecological thresholds
• Update reference conditions as understanding improves
• Link accounts to ecosystem monitoring programmes

3.4.4 Future Prices and Values Uncertainty

Uncertainty regarding future prices and values.

Sources in ocean accounts:

• Future market conditions for ocean products
• Changing social preferences for ecosystem services
• Policy changes affecting resource access or use
• Technological change affecting substitution possibilities

Approaches to address:

• Use current prices for flow accounts
• Apply sensitivity analysis for asset valuations
• Document assumptions about future conditions
• Update valuations periodically

3.5 Documentation Requirements

The SEEA EA requires that all accounting work document the scope of measurement, the definitions applied, the methods used, and the assumptions made (para. 2.95). Table 3.5.1 (below) maps these four documentation categories to the phases of the Generic Statistical Business Process Model (GSBPM v5.1).

3.5.1 Scope of Measurement

• Geographic coverage (EEZ, coastal zone, specific regions)
• Temporal coverage (accounting period, time series extent)
• Thematic coverage (ecosystem types, economic sectors, asset categories)
• What is included and excluded, with rationale

3.5.2 Definitions Applied

• Ecosystem type definitions and classification used
• Industry and product classifications
• Asset boundary definitions (e.g., treatment of aquaculture)
• Alignment with international standards (or departures with justification)

3.5.3 Methods Used

• Data sources and collection methods
• Compilation procedures and algorithms
• Valuation approaches (for monetary accounts)
• Spatial analysis methods
• Gap-filling and estimation procedures

3.5.4 Assumptions Made

• Key assumptions underlying estimates
• Sensitivity of results to assumption changes
• Known biases and their direction
• Expert judgement employed and its basis

3.6 Quality Flags for Accounts Tables

Ocean accounts tables should include quality indicators to help users understand reliability. Table 3.6.1 sets out a recommended flagging system.

Note: the "n/a" symbol replaces the double-hyphen form used in some earlier drafts; the double hyphen (--) is reserved for em-dashes in circular prose.

When account aggregates are derived from cells carrying different flags, the aggregate should carry the flag of the lowest-quality input (e.g., an aggregate of A and C cells carries flag C). Where the lowest-quality input contributes less than 5% of the aggregate value, compilers may exercise judgement and document the rationale.

3.7 Quality Improvement Strategies

Ocean accounts should be viewed as evolving systems that improve over time. Table 3.7.1 summarises four complementary strategies.

3.8 Tiered Implementation Frameworks

Throughout the GOAP Technical Guidance, tiered implementation frameworks are used to indicate graduated approaches calibrated to available data and institutional capacity. Tier designations are circular-specific—each circular defines the tiers appropriate to its subject matter—but in all cases Tier 1 represents the minimum viable approach and higher tiers represent more rigorous methods requiring greater capacity or data availability. Compilers should select the tier consistent with their data availability and intended account use, and may progress to higher tiers as capacity develops.

3.9 Circular-Specific QA Guidance

Each circular in Sections 3 and 4 of this Technical Guidance includes a section on quality assurance specific to that topic. These sections:

• Identify common data quality issues for the topic
• Recommend specific QA procedures
• Provide worked examples of uncertainty documentation
• Reference this circular for general principles

Table 3.9.1 maps each topic-specific circular to its QA focus.

4. Acknowledgements

This Circular has been approved for public circulation and comment by the GOAP Technical Experts Group. Authors and reviewers are listed below.

Authors: [To be confirmed]

Reviewers: [To be confirmed]

This circular draws on quality assurance frameworks from the SEEA EA (Chapter 2), the UN Fundamental Principles of Official Statistics, and the Generic Statistical Business Process Model (GSBPM).

5. References

1. United Nations. (2021). System of Environmental-Economic Accounting—Ecosystem Accounting, Chapter 2: Principles of ecosystem accounting. Paragraphs 2.86-2.95.

2. United Nations Economic and Social Council. (2013). Resolution 2013/21: Fundamental Principles of Official Statistics.

3. UNECE. (2019). Generic Statistical Business Process Model (GSBPM), Version 5.1.

4. SEEA. (2022). SEEA Ecosystem Accounting Glossary.