Global Ocean Accounts Partnership Secretariat · GOAP Technical Guidance on Ocean Accounting · circular
TG-2.11 sits at the intersection of the monetary accounts (Section 3) and indicator synthesis (Section 2). It draws on physical flow data from TG-3.2 and economic measures from TG-3.3 to produce efficiency ratios that are consumed by TG-1.1 for budget policy reporting. Compilers who have not yet completed TG-3.3 should do so before applying this Circular.
This Circular provides guidance on compiling resource efficiency indicators from ocean accounts: ratios that relate economic outputs to physical resource inputs and indicate how effectively ocean industries transform natural capital into economic value. Indicators compiled using this guidance feed directly into budget and planning processes described in TG-1.1 Budget Processes. Prerequisites and cross-references are listed in Section 2.
Upon completing this Circular, readers will understand (1) the conceptual framework for resource efficiency indicators, (2) compilation procedures for deriving material efficiency indicators for fish, water, and energy, (3) methods for computing decoupling indicators that track the relationship between economic growth and resource use, (4) approaches to circular economy indicators for waste, recycling, and resource recovery, and (5) practical applications to sector-specific efficiency metrics for fisheries, aquaculture, maritime transport, and offshore energy.
This Circular requires familiarity with:
For additional context on physical flow accounting, see TG-3.2 Flows from Environment to Economy. For guidance on linking efficiency indicators to environmental asset stocks, see TG-3.1 Asset Accounts. For guidance on residual flows that inform circular economy indicators, see TG-3.4 Flows from Economy to Environment.
The SEEA Central Framework identifies resource efficiency and productivity indicators as a key application of environmental-economic accounting, noting that topics covered include “resource efficiency and productivity indicators, decomposition analysis, analysis of net wealth and depletion, sustainable production and consumption, input-output analysis and general equilibrium modelling”. The SEEA Applications and Extensions publication provides additional detailed guidance on constructing and interpreting resource efficiency indicators within the environmental-economic accounting framework. Compilers should consult that publication alongside this Circular for extended methodological guidance.
This circular covers four resource efficiency indicator types (material efficiency, energy intensity, water intensity, and decoupling). Material, energy and water efficiency are covered in Section 3.2 and decoupling in Section 3.3; Sections 3.4 and 3.5 add circular economy and sector-specific indicators.
Resource efficiency indicators express the relationship between economic outputs and natural resource inputs. The SEEA Central Framework establishes that combined presentations of physical and monetary data “structure information in a manner that supports the derivation of combined indicators, for example, decoupling indicators which track the relationship between the use of resources and growth in production and consumption”.
Resource efficiency indicators for the ocean economy serve four primary decision contexts:
Circular ocean economy tracking — governments committed to circular economy transitions require indicators that demonstrate whether ocean industries are reducing material throughput per unit of output, increasing recycling and reuse rates, and closing material loops. The European Commission’s Circular Economy Action Plan and similar national strategies call for monitoring progress through material flow indicators, waste generation rates, and recycling performance metrics.
Decoupling analysis — SDG Target 8.4 explicitly requires countries to “improve progressively, through 2030, global resource efficiency in consumption and production and endeavour to decouple economic growth from environmental degradation”. Resource efficiency indicators provide the empirical evidence to demonstrate whether ocean economy growth is accompanied by declining resource intensity (relative decoupling) or absolute reductions in resource extraction (absolute decoupling).
SDG 12 reporting — SDG Target 12.2 calls for achieving “sustainable management and efficient use of natural resources” by 2030. The SDG indicator framework includes material footprint per capita and per GDP (indicators 8.4.1 and 12.2.1) as headline measures of progress. Ocean economy resource efficiency indicators contribute directly to national reporting under these frameworks.
Green growth measurement — ministries of finance and planning agencies require evidence that ocean economy growth is compatible with environmental sustainability objectives. Resource efficiency indicators provide quantified metrics for budget presentations, medium-term expenditure frameworks, and national development plans.
Resource efficiency indicators follow a general ratio structure:
Resource Efficiency = Economic Output / Resource Input
Where:
The reciprocal formulation—resource intensity—expresses resource input per unit of economic output:
Resource Intensity = Resource Input / Economic Output
Both formulations provide valid analytical perspectives. Resource efficiency emphasises productivity gains (higher values indicate improvement), whilst resource intensity emphasises resource requirements (lower values indicate improvement). The SEEA Energy notes that “indicators of energy efficiency, energy expenditures by different industries and households, and energy intensity” can be derived through combined presentations.
The choice of economic measure in the numerator (or denominator for intensity indicators) affects interpretation and should be made deliberately. Three principal measures are available from the ocean economy thematic and extended accounts described in TG-3.3 Economic Activity Section 3.4:
Compilers should document which economic measure is used and ensure consistency across time series.
Resource efficiency can be analysed across the dimensions summarised in Table 3.1.1 below.
For ocean accounting, material efficiency is particularly relevant for fisheries and mineral extraction, energy efficiency for maritime transport and offshore energy, and water efficiency for aquaculture and coastal industries.
This circular covers four resource efficiency indicator types (material efficiency, energy intensity, water intensity, and decoupling). Material, energy and water efficiency are covered in Section 3.2 and decoupling in Section 3.3; Sections 3.4 and 3.5 add circular economy and sector-specific indicators.
Resource efficiency indicators can be compiled at the analytical levels summarised in Table 3.1.2 below.
The industry-level analysis is typically most useful for policy, as it reveals differential efficiency across ocean sectors and identifies opportunities for improvement. The SEEA Energy provides example analyses at the industry level, showing “energy intensities for selected industries” that enable comparison across sectors.
When moving between analytical levels, compilers must observe the following aggregation rules. Efficiency ratios must never be averaged across industries. Averaging ratio values produces misleading results because the importance of each industry (its share of total resource use and total output) is not preserved. Economy-wide indicators must instead be computed by summing the numerator and denominator separately across all relevant industries, then computing the aggregate ratio from those sums. For example, the correct economy-wide fish productivity indicator is total fishing-sector GVA divided by total gross catch — not the average of individual fleet or sub-sector fish productivity ratios. Cross-resource aggregation combines fish productivity, energy intensity, and water productivity into a single composite efficiency score.
This is not supported by SEEA methodology and should not be attempted without an explicit weighting framework such as environmental cost weights derived from SEEA Ecosystem Accounts. To illustrate the correct approach: if Industry A has GVA of 100 and resource input of 50, and Industry B has GVA of 400 and resource input of 300, the correct aggregate efficiency is (100 + 400) / (50 + 300) = 1.43, not the average of the two industry ratios (2.0 and 1.33), which yields 1.67 — an overestimate that misrepresents the aggregate productivity of the sector.
Material efficiency indicators measure the relationship between physical resource extraction and economic output. For ocean resources, the primary materials of interest are fish and other aquatic biomass, seawater (for desalination and industrial use), and seabed minerals.
Fish productivity indicators relate the value of fishing industry output to the physical quantity of fish extracted. Following SEEA CF guidance on aquatic resources, the key physical measure is gross catch—the total live weight of fish caught, including discarded catch but excluding pre-catch losses. This measure aligns with the physical flow accounts described in TG-3.2 Flows from Environment to Economy.
Key fish productivity indicators include:
Value added per tonne of catch — the gross value added of the fishing industry divided by total catch weight:
Fish Productivity ($/tonne) = Fishing Industry GVA / Gross Catch (tonnes)
This indicator reveals how much economic value is generated per unit of fish extracted. Higher values may indicate higher-value species composition, more value-added processing, or improved market access. However, compilers should be aware that high economic efficiency is compatible with overfishing if prices rise as stocks decline—a phenomenon that can occur when scarcity drives up market prices for depleted species. For this reason, fish productivity indicators should always be interpreted alongside sustainability indicators. SDG indicator 14.4.1 measures “proportion of fish stocks within biologically sustainable levels”, and the fisheries stock assessment methods described in TG-6.7 Fisheries Stock Assessment provide complementary measures of biological sustainability.
Fallback hierarchy for discard data unavailability. In many developing economies and some developed ones, official fisheries statistics record only landed weight. Discard estimates are produced by fisheries science bodies rather than national statistics offices and may not be available for all fisheries. Compilers should apply the following fallback hierarchy:
Where discard data are systematically unavailable, compilers should consult TG-6.7 Fisheries Stock Assessment for stock assessment-based discard estimates that can supplement national statistics office data.
Catch per unit of effort (CPUE) — whilst not strictly an economic efficiency indicator, CPUE relates physical catch to fishing effort and provides insight into the productivity of fishing operations and the abundance of fish stocks. CPUE is addressed in detail in TG-6.7 Fisheries Stock Assessment.
Sustainable yield ratio — comparing actual extraction to estimated maximum sustainable yield (MSY) for managed fish stocks, providing a complementary perspective on resource sustainability.
Water productivity indicators relate economic output to water abstraction and consumption. The SEEA-Water framework provides detailed guidance on water intensity and productivity measurement.
For ocean contexts, relevant water productivity indicators include:
Aquaculture water productivity — the value added of aquaculture production divided by water use. The appropriate water use measure depends on the aquaculture system type. Compilers must not make cross-system comparisons without normalising to a consistent consumption basis:
The distinction between abstraction and consumption follows the SEEA-Water framework. Compilers should consult SEEA-Water Annex 3 Table A3.2 for the precise consumptive-use calculation methodology. Using an intake-volume denominator for RAS systems, which recycle 90—99% of water internally, would produce artificially high water productivity figures that are not comparable with flow-through systems. Compilers working with mixed technology portfolios should present system-specific productivity figures separately and note that aggregate comparisons require normalisation to consumption basis.
This indicator is relevant for aquaculture operations where water efficiency is a management priority, as described in TG-3.9 Aquaculture Accounts.
Desalination efficiency — the ratio of freshwater output to energy input in desalination operations, relevant for coastal communities dependent on desalination.
Industrial water intensity — for coastal industries using seawater for cooling or processing, the volume of water abstracted per unit of output.
Energy productivity indicators relate economic output to energy consumption. The SEEA Energy framework provides extensive guidance on energy intensity indicators.
Key ocean-related energy productivity indicators include:
Maritime transport energy intensity — energy consumption per tonne-kilometre of freight or per passenger-kilometre:
Shipping Energy Intensity = Energy Use (joules) / Transport Output (tonne-km)
This indicator is central to monitoring decarbonisation progress in the maritime sector and relates to climate indicators described in TG-2.8 Climate Indicators. Energy intensity indicators for maritime transport must be disaggregated by fuel type to support decarbonisation tracking. Aggregating all fuel types into a single joules figure masks fuel substitution trends: a vessel switching from heavy fuel oil to liquefied natural gas (LNG) may show declining energy intensity under a single-fuel metric even if actual greenhouse gas emissions do not decline proportionally. Compilers should therefore report three sub-indicators for maritime transport energy intensity:
The fuel-type classification follows SEEA Energy Chapter 4 on energy supply and use tables. Total energy intensity alone is insufficient for decarbonisation tracking and must be supplemented by the fuel-type decomposition above and the GHG emission indicators described in TG-2.8 Climate Indicators.
Offshore energy extraction efficiency — for offshore oil and gas, the ratio of energy extracted to energy consumed in extraction operations (energy return on energy invested, or EROEI). See TG-3.10 Offshore Energy Accounts for detailed guidance.
Fishing fleet fuel efficiency — fuel consumption per tonne of catch, relevant for both cost efficiency and greenhouse gas emission reduction.
The SEEA Energy notes that energy use combined with economic data enables derivation of “energy use per unit of GDP”, which can be adapted to the ocean economy by relating ocean sector energy use to ocean sector GVA.
Decoupling indicators track the relationship between economic growth and resource use over time. They indicate whether an economy is achieving more output with less environmental pressure. SDG Target 8.4 explicitly calls upon countries to “improve progressively, through 2030, global resource efficiency in consumption and production and endeavour to decouple economic growth from environmental degradation”.
Decoupling occurs when the growth rate of an environmental variable (resource use, emissions, waste generation) is lower than the growth rate of the relevant economic variable (GDP, value added, output). Two forms are distinguished:
Relative decoupling — the environmental variable continues to grow, but at a slower rate than economic output. Resource intensity declines, but total resource use increases.
Absolute decoupling — the environmental variable declines in absolute terms whilst economic output grows. This represents genuine reduction in environmental pressure alongside economic growth.
A decoupling factor can be computed as:
Decoupling Factor = 1 - (Resource Use Growth Rate / Economic Output Growth Rate)
Where:
These interpretation rules apply when economic output growth is positive. When economic output growth is zero or negative, the decoupling factor must not be computed. Dividing by zero produces an undefined result. Dividing by a negative denominator produces counter-intuitive values that can spuriously indicate absolute decoupling during a recession merely because resource use declined faster than output. In such periods, compilers should instead report:
Figure 2.11.1 summarises the recommended approach, keying the method choice to the sign of economic-output growth: (1) positive growth throughout routes to the decoupling factor, (2) a mixed period is split at the structural break, and (3) zero or negative growth follows the fallback, in which the factor must not be computed and intensity ratios and index-number series are reported instead.
· Figure 2.11.1
Source TG-2.11 Resource Efficiency Indicators, §3.3 (decoupling decision tree and zero/negative-growth fallback).
Alternatively, an elasticity formulation expresses the responsiveness of resource use to economic growth:
Resource Elasticity = % Change in Resource Use / % Change in Economic Output
An elasticity less than 1 indicates relative decoupling. A negative elasticity with positive economic growth indicates absolute decoupling. Note that the same zero-denominator constraint applies: the elasticity formulation is undefined when economic output growth is zero.
Decoupling indicators are sensitive to the choice of base year and comparison period. Compilers must observe the following requirements:
Do not use structural break years as decoupling analysis endpoints. Common causes of structural breaks in ocean economy time series include pandemic-related economic contractions (e.g., 2020 COVID-19), major fisheries collapses or stock recovery events, policy changes that alter reporting practices, and natural disasters affecting coastal infrastructure. When a structural break year falls within the analysis period, compilers must: (a) flag the year in indicator metadata with a description of the cause, (b) report pre-break and post-break series separately, with decoupling factors computed within each sub-period, and (c) avoid computing a single decoupling factor that spans the break year as an endpoint. Including a crisis year as an endpoint can produce spurious decoupling results in either direction.
For ocean accounting, key decoupling indicators include:
Fish extraction decoupling — comparing growth in fishing industry GVA to growth in fish catch tonnage:
Fish Decoupling Factor = 1 - (Fish Catch Growth Rate / Fishing GVA Growth Rate)
Positive values indicate that the fishing sector is generating more value from each tonne of fish extracted—potentially through higher prices, better species mix, or more value-added processing. This indicator should be interpreted alongside stock sustainability indicators from TG-6.7 Fisheries Stock Assessment.
Maritime emissions decoupling — comparing growth in shipping sector GVA to growth in greenhouse gas emissions:
Shipping Emissions Decoupling = 1 - (Emissions Growth Rate / Shipping GVA Growth Rate)
This indicator tracks progress toward the International Maritime Organization’s decarbonisation goals and relates to the climate indicators described in TG-2.8 Climate Indicators.
Material footprint decoupling — SDG indicators 8.4.1 and 12.2.1 measure material footprint (per capita and per GDP), which can be computed for ocean-related materials to track decoupling at the economy-wide level.
Circular economy indicators measure the extent to which economic systems reduce waste, reuse materials, and recycle resources, moving from linear “take-make-dispose” models toward circular flows. SDG Target 12.5 aims to “substantially reduce waste generation through prevention, reduction, recycling and reuse”.
For ocean sectors, circular economy principles apply as summarised in Table 3.4.1 below.
These applications relate to the flows from economy to environment described in TG-3.4 Flows from Economy to Environment.
Key circular economy indicators for ocean sectors include:
Discard ratio in fisheries — the proportion of catch that is discarded rather than landed:
Discard Ratio = Discarded Catch / Gross Catch
The SEEA CF treats discarded catch as “natural resource residuals”—flows that are extracted but immediately returned to the environment. SDG indicator 14.4.1 and related fisheries management frameworks track bycatch and discard reduction.
Caveat — landing obligation jurisdictions. In jurisdictions with mandatory landing obligations, such as EU member states operating under the Landing Obligation (EU Regulation 1380/2013, Article 15), reported discard rates may approach zero through legal mandate rather than through genuine circular economy improvement. A near-zero discard ratio in such a jurisdiction does not indicate that all extracted biomass is being utilised effectively. A large share of the legally mandated landed catch may consist of fish below minimum conservation reference size (MCRS) with low or negative commercial value. Compilers in landing-obligation jurisdictions should therefore report alongside the discard ratio:
Economic Utilisation Ratio = Commercially Utilised Landed Catch / Gross Catch
This complementary indicator captures the economic dimension of catch utilisation that the discard ratio obscures when discard bans are in force.
Fish waste utilisation rate — the proportion of fish processing residues (heads, bones, offal) that are converted to useful products (fishmeal, fish oil, fertiliser):
Waste Utilisation Rate = Residues Utilised / Total Processing Residues
National recycling rate — SDG indicator 12.5.1 measures the national recycling rate, which can be disaggregated for materials relevant to ocean sectors (plastics, metals from shipbuilding).
Marine litter generation — flows of waste to the marine environment, representing a failure of circular economy systems. Addressed in TG-2.7 Pollution Flows and TG-6.12 Marine Litter.
For extractive ocean industries, resource recovery indicators measure the proportion of in-situ resources that are successfully captured:
Extraction efficiency for minerals — the proportion of identified reserves that are extracted over the lifetime of an operation, relevant for seabed mining and offshore hydrocarbons.
Energy recovery in offshore operations — the proportion of potential energy content captured in oil and gas extraction, accounting for losses from flaring, venting, and fugitive emissions. See TG-3.10 Offshore Energy Accounts for detailed guidance.
Different ocean sectors require tailored efficiency indicators that reflect their specific resource use patterns and economic characteristics.
For the fishing industry, key efficiency indicators include:
Table 2: Key fisheries efficiency indicators. For the fallback data hierarchy (preferred gross catch vs. landings vs. bounded estimate), see Section 3.2.
The relationship between these indicators and sustainable yield is addressed in TG-6.7 Fisheries Stock Assessment.
For aquaculture, efficiency indicators focus on feed conversion, water use, and energy intensity:
Table 3: Key aquaculture efficiency indicators. The feed conversion ratio applies to fed species (finfish, shrimp); it is inapplicable to filter-feeding bivalves (oysters, mussels, clams) that require no manufactured feed input. For bivalve aquaculture, use the three filter-feeder indicators listed above. For integrated multi-trophic aquaculture (IMTA) systems, efficiency should be assessed at the whole-system level: sum GVA across all trophic levels and divide by external inputs only, excluding internal nutrient cycling within the system. Detailed guidance on aquaculture system-boundary definition is provided in TG-3.9 Aquaculture Accounts.
For shipping and maritime transport, efficiency indicators serve two distinct purposes that require different data sources and compilation agencies. Compilers should not attempt to combine or aggregate indicators from the two sub-tables below into a single score.
Sub-table 4a: IMO-aligned physical efficiency indicators
These indicators are defined by the International Maritime Organization and compiled primarily by port authorities, ship registries, and flag state administrations rather than national statistics offices. NSOs should coordinate with the relevant maritime administration to obtain these data.
Table 4a: IMO-aligned physical efficiency indicators. Units are IMO standard (tonne-nautical-miles). These are regulatory compliance metrics; they do not involve monetary values and cannot be combined with the economic efficiency indicators in Table 4b.
Sub-table 4b: NSO-compiled economic efficiency indicators
These indicators are compiled by national statistics offices using monetary and physical data from the national accounts and transport statistics.
Table 4b: NSO-compiled economic efficiency indicators. The three energy sub-indicators (total intensity, fossil intensity, renewable share) are required for decarbonisation monitoring; total energy intensity alone is insufficient. See Section 3.2 for the rationale.
The International Maritime Organization’s greenhouse gas strategy establishes targets for reducing carbon intensity of international shipping.
For offshore energy extraction, efficiency indicators address both conventional and renewable energy:
Table 5: Key offshore energy efficiency indicators
The procedure described below follows the SEEA Central Framework guidance on combined presentations and indicator derivation.
Steps 1—3 are compilation, not indicator derivation. The efficiency ratios below divide an economic measure by a physical one, and both come from accounts compiled elsewhere:
The one condition that governs whether a ratio is meaningful: the physical and monetary accounts must use the same industry classification and the same reference period. Where they do not, reconcile through the TG-3.3 Table 2 concordance before computing anything — an efficiency ratio built across mismatched classifications is not an efficiency ratio, and the mismatch is invisible in the result. Common cases are disaggregating offshore energy from total energy extraction, and isolating the coastal tourism share of accommodation water use.
Step 4: Compute resource efficiency ratios. For each industry and resource type, compute the efficiency ratio:
Resource Efficiency = GVA (constant-price currency units) / Physical Input (physical units)
Or the intensity ratio:
Resource Intensity = Physical Input (physical units) / GVA (constant-price currency units)
For time-series resource efficiency indicators, GVA must be expressed in constant prices (volume terms) using the industry-specific output deflator, or the GDP deflator as a fallback. Expressing GVA in current prices will conflate price inflation with genuine efficiency improvement. If fish prices double whilst catch and processing methods are unchanged, current-price fish productivity doubles even though no real efficiency gain has occurred. Where only current-price GVA is available, compilers must present both the nominal (current-price) and real (constant-price) efficiency series side by side and prominently note the price basis of each. Deflation methodology follows TG-3.3 Economic Activity on price and volume measures. See also SEEA CF paras 6.107—6.110 and SNA 2025 Chapter 15 on constant-price GDP denominators.
Document the units clearly (e.g., “constant-2020-USD per tonne of fish caught” or “joules per constant-2020-USD of shipping GVA”) and the price base year.
Step 5: Compile time series and compute growth rates. For decoupling analysis, compile resource efficiency indicators for multiple years (minimum five years recommended). Compute annual growth rates for both economic output and physical input variables. Apply the decoupling formulas and decision tree from Section 3.3 to derive decoupling factors or elasticities, noting any years where zero or negative output growth requires the fallback procedure.
Step 6: Present results with context. Resource efficiency indicators should be presented alongside:
Resource efficiency indicators are ratio estimates derived from two independently compiled datasets — monetary accounts and physical flow accounts — each with its own measurement error. Compilers should assess and communicate data quality using the following framework.
Uncertainty propagation. The relative standard error (RSE) of a ratio indicator is approximately the root-sum-of-squares of the relative standard errors of the numerator and denominator:
RSE(ratio) ≈ √(RSE_numerator² + RSE_denominator²)
For example, if the GVA estimate has a relative standard error of 5% and the physical catch estimate has a relative standard error of 8%, the fish productivity ratio has an approximate relative standard error of √(25 + 64) = √89 ≈ 9.4%.
Three-tier quality classification. Compilers should assign each published resource efficiency indicator to one of three quality tiers:
Methodological note requirement. Any official publication of resource efficiency indicators must include a methodological note describing: (1) the data sources used for numerator and denominator, (2) the price basis and deflator applied, (3) the industry classification concordance method, (4) the revision policy, and (5) known limitations (e.g., substitution of landings for gross catch, or use of current-price GVA for recent years pending deflator availability). This requirement aligns with the UN Fundamental Principles of Official Statistics and the PARIS21 data quality assessment framework.
This section presents worked examples illustrating the compilation and interpretation of resource efficiency indicators for a hypothetical medium-income coastal state (“Country B”). All monetary values are expressed in constant 2020 USD unless otherwise noted. Physical quantities use standard SI units.
Context: Country B’s marine fishing industry reported the following data for 2023 and 2024 (GVA in constant 2020 USD):
Computation:
Fish productivity (2023):
USD 180,000,000 / 120,000 tonnes = USD 1,500/tonne
Fish productivity (2024):
USD 190,000,000 / 115,000 tonnes = USD 1,652/tonne
Productivity change:
(1,652 - 1,500) / 1,500 × 100 = 10.1% increase
Discard ratio (2023):
24,000 / 120,000 = 20%
Discard ratio (2024):
20,000 / 115,000 = 17.4%
Interpretation: Fish productivity increased by 10.1 per cent between 2023 and 2024, whilst total catch declined by 4.2 per cent. This suggests improved economic efficiency: the industry generated more value per tonne of fish extracted. The declining discard ratio (from 20 per cent to 17.4 per cent) indicates progress toward circular economy principles, with a larger proportion of catch being landed rather than discarded. However, compilers should verify whether the productivity increase reflects sustainable management (higher-value species, better processing) or scarcity effects (rising prices for depleted stocks). Cross-referencing with stock assessment data from TG-6.7 would provide this context. A practical diagnostic: if fish productivity (USD per tonne) is rising whilst catch per unit effort (CPUE) is declining and stock biomass is below B_MSY, the productivity increase likely reflects scarcity-driven price inflation rather than genuine efficiency improvement.
Context: Country B’s marine aquaculture industry operates primarily extensive flow-through pond systems for shrimp and finfish production. GVA is expressed in constant 2020 USD.
Note: water abstraction for flow-through pond systems (9 million m³ = 9,000,000 m³ in 2023) is used as the denominator in accordance with the system-type formula table in Section 3.2. Water use per tonne of production (2023) = 9,000,000 m³ / 45,000 tonnes = 200 m³/tonne, consistent with FAO benchmark ranges of 100—500 m³/tonne for semi-intensive tropical shrimp pond systems.
Computation:
Water productivity (2023):
USD 135,000,000 / 9,000,000 m³ = USD 15.00/m³
Water productivity (2024):
USD 145,000,000 / 9,200,000 m³ = USD 15.76/m³
Productivity change:
(15.76 - 15.00) / 15.00 × 100 = 5.1% increase
Output per water unit (2023):
45,000 tonnes / 9,000,000 m³ = 5.0 kg/m³
Output per water unit (2024):
48,000 tonnes / 9,200,000 m³ = 5.2 kg/m³
Plausibility check: FAO benchmark water use intensities for semi-intensive tropical pond aquaculture range from approximately 100 to 500 m³/tonne depending on exchange rate and climate. Country B’s 200 m³/tonne falls within this range. If a compiler’s calculated value falls outside 100—2,000 m³/tonne for any pond system, the input data should be reviewed before publication.
Interpretation: Aquaculture water productivity improved by 5.1 per cent, indicating that the industry generated more economic value per cubic metre of water abstracted. Physical output per water unit also increased slightly (from 5.0 to 5.2 kg/m³), suggesting genuine improvements in water use efficiency rather than price effects alone. This progress aligns with SDG Target 12.2 on efficient use of natural resources. The modest increase in total water abstraction (2.2 per cent) coupled with a 6.7 per cent increase in production demonstrates relative decoupling of aquaculture growth from water abstraction.
Context: Country B’s maritime transport sector (sea and coastal freight and passenger transport) reported (GVA in constant 2020 USD):
Note: 2020 is flagged as a structural break year due to the COVID-19 pandemic (see Section 3.3). The 2020 structural break triggers the sub-period pathway in the Section 3.3 decision tree. Apply that pathway as specified.
Computation — full period (2019—2024):
The indicators computed below are NSO-compiled economic efficiency indicators (Sub-table 4b). The IMO-defined physical indicators in Sub-table 4a require separate data and compilation procedures not illustrated here.
Energy intensity (2024):
840,000 toe / USD 490,000,000 = 1,714 toe per million USD GVA
Or per physical output:
840,000 toe / 162,000,000,000 tonne-km = 5.19 toe per million tonne-km
Energy intensity (2019):
850,000 toe / 145,000,000,000 tonne-km = 5.86 toe per million tonne-km
Five-year GVA growth (2019-2024):
(490 - 420) / 420 × 100 = 16.7%
Five-year energy consumption growth:
(840 - 850) / 850 × 100 = -1.2%
No single decoupling factor is computed for 2019—2024, because the period spans the 2020 break (Section 3.3). The factor is computed within the post-break sub-period; the pre-break side holds only 2019.
Post-break sub-period (2021—2024):
GVA growth (2021-2024):
(490 - 410) / 410 × 100 = 19.5%
Energy consumption growth (2021-2024):
(840 - 800) / 800 × 100 = 5.0%
Decoupling factor:
1 - (5.0% / 19.5%) = 0.74
Structural break sub-period (2019—2020): Index numbers (base 2019 = 100): GVA = 91.7, energy = 92.9.
Interpretation: Country B’s maritime transport sector achieved relative decoupling in the post-break period 2021—2024: GVA grew by 19.5 per cent whilst energy consumption grew by 5.0 per cent, a decoupling factor of 0.74 (between 0 and 1). Over 2019—2024 GVA grew by 16.7 per cent and energy consumption fell by 1.2 per cent, but no single factor is reported across the 2020 break. The energy intensity per tonne-km declined from 5.86 toe/million tonne-km in 2019 to 5.19 in 2024 (11.4 per cent improvement), demonstrating genuine efficiency gains. The temporary dip in 2020 (COVID-19 pandemic) has been flagged as a structural break. The sub-period analysis confirms that the 2019—2020 contraction does not by itself indicate efficiency improvement. This performance contributes to national progress under SDG Target 8.4 and aligns with the IMO’s greenhouse gas reduction strategy.
This example previously reported a single 2019—2024 decoupling factor of 1.07 (absolute decoupling), which spans the 2020 structural break that Section 3.3 says must split the series. Computed within the post-break sub-period, the factor is 0.74: relative, not absolute, decoupling.
Context: Country B’s total ocean economy reported the following aggregates over a ten-year period (GVA in constant 2020 USD). Per the base-year selection guidance in Section 3.3, the base year (2014) was selected as the year prior to Country B’s adoption of its first national ocean economy development plan. The comparison points use evenly spaced five-year intervals.
Note: 2019 and 2024 are the two five-year endpoints. The year 2020 has been excluded as a structural break year (COVID-19). It must not be used as a base year or comparison endpoint. Base-year sensitivity testing follows the procedure in Section 3.3. Alternative base years (2013 and 2015) yielded decoupling factors for fish extraction ranging from 1.18 to 1.24.
Computation — period 1 (2014—2019):
GVA growth: (1,720 - 1,380) / 1,380 × 100 = 24.6%
Computation — period 2 (2019—2024):
GVA growth: (1,950 - 1,720) / 1,720 × 100 = 13.4%
Interpretation: Across both sub-periods, Country B’s ocean economy decoupled absolutely from fish extraction and relatively from energy and water use. Absolute decoupling of fish extraction was stronger in the first period (factor 1.29) than the second (1.17), which may indicate that the easiest efficiency gains have been captured and further progress requires additional policy effort. Relative decoupling improved for energy use between the two periods (factor rising from 0.60 to 0.84), suggesting that energy productivity gains are slowing and the sector is approaching the limits of operational efficiency improvements without technology transition. Water abstraction exhibits consistent relative decoupling across both periods (0.54 and 0.62), with room for further improvement. These results can be reported under SDG indicators 8.4.1 and 12.2.1.
Authors: [To be confirmed]
Reviewers: [To be confirmed]
Cite this report
Resource Efficiency Indicators (2026) (GOAP Technical Guidance on Ocean Accounting, TG-2.11, v0.7.2). GOAP Secretariat. https://guidance.oceanaccounts.org/circulars/section-2/2-11-resource-efficiency/
guidance.oceanaccounts.org/circulars/section-2/2-11-resource-efficiency
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