METHODOLOGY FOR THE ESTIMATION OF THE BLUE ECONOMY SECTORS’ DATA

DATA SOURCES

The main data source to estimate the extent of the EU Blue Economy is the Structural Business Statistics (SBS) compiled by Eurostat. The SBS were complemented by the EU Data Collection Framework (DCF)^[1] for the primary sectors (capture fisheries and aquaculture). 

For Coastal tourism, an ad hoc extraction of data was performed by Eurostat, which was complemented with information from the Eurostat’s nights spent at tourist accommodation establishments.

The 2025 EU Blue Economy Report incorporated estimations for previously missing sectors, such as desalination, blue biotechnology, retail of fish products in non-specialised shops (e.g. supermarkets). The specific of these new sectors are also detailed below.

For the calculation of the maritime proportions, some specific sources, such as PRODCOM, were used as explained in the specific below. 

Finally, overall GVA and emplpyment published by Eurostat were used for the comparisons of the EU Blue Economy with the overall EU economy.

The tables extracted from the Eurostat (and from the DCF) to make the estimations of economic indicators for the established sectors of the EU Blue Economy are summarised in next Table 1.

Table 1. Main data sources for the Established Sectors of the EU Blue Economy

Text version: Table 1: This table details the main data sources used to estimate the EU Blue Economy data. Most of the data comes from different annual data sets produced by Eurostat, and fisheries and aquaculture economic statistics coming from the JRC.

_{[1] Council Regulation (EC) No 199/2008 of 25 February 2008 concerning the establishment of a Community framework for the collection, management and use of data in the fisheries sector and support for scientific advice regarding the Common Fisheries Policy.}

TIMEFRAME

Given the time lag in the release of SBS and DCF data, the latest available data in the 2025 EU Blue Economy Report (and from the end of May 2025 in the EU Blue Economy Observatory) is from 2022. 

Data on persons employed and GVA in 2023 is an estimation based on Eurostat’s preliminary data, assuming that they follow the same trend as the preliminary turnover data.

SCOPE OF ACTIVITIES

SBS data are based on enterprise data grouped under the declared main activity of each enterprise, according to the statistical classification of economic activities in the European Community (NACE Rev.2). Out of the 615 classes of activities singled out through a four-digit NACE code, 46 classes have been identified that have a principal or significant maritime component. They have been classified into sectors and subsectors (Table 2).

Text version

While certain economic activities can be clearly identified as fully marine (e.g. Shipping and Maritime transport), for other sectors, the NACE classification includes both land and maritime activities (e.g. cargo handling, warehousing and extraction of oil and gas). In this latter case, alternatives sources are used for the estimation of the maritime proportion (see next Section).

MARITIME PROPORTIONS

As indicated above, several approaches have been followed to estimate the maritime proportions for those activities that encompass a maritime with a non-maritime component.

For industrial activities (i.e. Equipment and machinery and some activities in Processing of fish products), the Eurostat statistics on the production of manufactured goods (PRODCOM) were used to estimate the maritime proportion in two steps: 1) specific maritime products were identified within each NACE class; 2) the production value share over the total production of the class was calculated. (Table 3).

For Oil and gas, the production onshore and offshore according to Rystad Energy UCube^[2] was used to estimate the maritime proportions. For Marine renewable energy, the share of offshore wind energy was obtained from Wind Europe, compared to the total energy production from Eurostat.

For Other minerals, the following sources were used. For marine Aggregates (B 08.12), the statistics on aggregate production published by the European Aggregates Association. For Extraction of salt (B 08.93), the proportion of solar salt obtained from the European Salt Producers’ Association. Finally, for Support activities for other mining and quarrying the maritime proportion was calculated as the share of maritime B 08.12 (Aggregates) and maritime B 08.93 (Extraction of salt) over the total of mining activities (B05, B07 and B08). The estimations for Coastal tourism were calculated following a specific treatment (See specific Section below).

^{[2] Source: Rystad Energy UCube, version 2018-12-10.}

INDICATORS AND VARIABLES

SBS statistics provide a series of variables usually derived from the financial statements of the companies. For the analysis of the established sectors, the following selection of variables and indicators was used: employment (number of persons employed), wages and salaries, turnover, gross value added, gross operating surplus (profit or loss), gross investments and net investments. In addition, the following derived indicators were calculated: average annual wage per persons employed, GVA to turnover, profit margin (gross operating surplus to turnover – gross profit margin), labour productivity (GVA per person employed) and net investment ratio (net investment to GVA). This report focuses on the main variables, further details and breakdowns are available on the online Blue Economy Indicators tool. More details about the indicators and variables are explained in the glossary.

DATA IMPUTATION AND ASSUMPTIONS

While the SBS database is quite comprehensive, a few missing points were still detected. To obtain a balance panel, a series of assumptions were made by applying the following rules:

• Imputations are based on other data from the same Member State (i.e. no estimations based on data for other Member States).
• Interpolation or the closest value over the time series of a NACE class (4-digit code) was used to impute missing values.
• When no data were available for a NACE class (4-digit code), the data for the parent NACE group (3-digit code) were evenly distributed among the classes in the group.

RETAIL SALE OF SEAFOOD IN NON-SPECIALISED STORES

Retail sale of seafood (fish, crustaceans and molluscs) in non-specialised stores froms part of the Retail in non-specialised stores with food, beverages or tobacco predominating (NACE G 47.11).

The sum of the turnover from Retail sale of fish, crustaceans and molluscs in specialised stores (NACE G 47.23), and the turnover from the sales of seafood in Retail in non-specialised stores with food, beverages or tobacco predominating (part of NACE G 47.11) should be (at least) equal to the Expenditure for consumption of fish at home, which is published by Eurostat. Hence, the turnover from the sales of seafood in Retail in non-specialised is estimated by subtracting the turnover of the G 47.23 to the Eurostat’s Expenditure for consumption of fish (including also crustaceans and molluscs) at home.

The share of the Retail sale of seafood in non-specialised stores in the Retail in non-specialised stores with food, beverages or tobacco predominating (NACE G 47.11) by EU Member State is used as a maritime proportion of NACE G 47.11 to estimate the rest of the variables for Retail sale of seafood in non-specialised stores.

CONSUMPTION OF SEAFOOD OUTSIDE HOUSEHOLDS

Activities NACE G 47.11 and G 47.23 account for the retail of seafood (including fish, crustaceans and molluscs) to be consumed at home, while here we consider the consumption not taking place at home (e.g. at restaurants).

This consumption of seafood outside households would be part of the Food and beverage service activities (I 56.00), but the maritime proportion is uncertain.

According to Love et al. (2020), the US consumed 37% of seafood by weight and 65% in value away from home. While in the EU it is estimated that 25%of the seafood was consumed away from home (EUMOFA, Euromonitor, 2023). If we consider a similar proportion of the US expenditure compared to consumption, then EU citizens would spend about 44% of their seafood expenditure in restaurants and similar establishments, which in turn is about 5% of the total expenses in the HORECA sector. Thus, we consider 5% as the maritime proportion for the Food and beverage service activities (I 56.00), corresponding to the consumption of seafood outside households. 

BLUE BIOTECHNOLOGY

Market Research Future Report (MRFR, 2024) provides market values (i.e., turnover) for the blue biotechnology in different countries, including EU countries, from 2019.

Here, we assume that blue biotechnology follows a similar cost structure and employment per turnover than the average of Manufacture of basic pharmaceutical products (NACE C2110), Manufacture of pharmaceutical preparations (NACE C2120), and Research and experimental development on biotechnology (NACE M72.11). 

Hence, the share of the turnover of blue biotechnology in the total of the three activities (NACE C2110, C2120 and M72.11) by EU Member State is used as a maritime proportion to estimate the rest of the blue biotechnology variables from the sum of the three activities.

Considering a Compound annual growth rate (CAGR) of 6.8% for turnover (MRFR, 2024), we estimate backwards the values of the EU’s blue biotechnology back to 2009, allowing to estimate all the EU’s blue biotechnology variables for the entire time-series.

DESALINATION

The desalination turnover is estimated from DesalData based on the production capacity, the average operating expenses (OPEX) and capital (expenses) of Spain, Italy and Cyprus per cubic meter, and a 10% profit margin.

The desalination turnover is used to estimate the maritime proportion from Water collection, treatment and supply (NACE E 36.00). This maritime proportion is used to estimate the rest of the Desalination variables.

COASTAL TOURISM

Coastal tourism, is not a single economic activity but rather a set of activities undertaken by a specific type of consumer (the tourist). Coastal tourism happens when a visitor takes a trip to a coastal municipality.

Coastal municipalities are those Local Administrative Units (LAU) with a coastline or with 50 % of its territory within 10 km of the sea. The classification of regions has been established by the TERCET Regulation: Regulation (EU) 2017/2391 of the European Parliament and of the Council of 12 December 2017 amending Regulation (EC) No 1059/2003 as regards the territorial typologies. Some ad-hoc corrections on the request of the Member State exist, e.g. certain major cities are treated differently, e.g. Rome and Amsterdam.

 It is considered to be part of this category the expenditures in accommodation, transport and other expenditures by tourists (for instance, cultural and recreation good, goods in specialised stores and food and beverage services). To calculate the contribution of Coastal tourism to the Blue Economy, a specific methodology has been followed.

Expenditure by inbound tourism

The data for Coastal tourism are based on the tourism statistics^[3] compiled by Eurostat from the collection by national authorities and, in particular, on the data on the expenditure by visitors on trips. In principle, Member States compile data on outbound trips (e.g. the data for trips from Austria to France and Italy are compiled by the Austrian authorities). Once all Member States have transmitted the micro-data on their outbound trips, Eurostat can calculate the data for inbound trips (e.g. the data for trips to Greece from all Member States). The data refer to the expenditure of those trips with a breakdown for three categories: accommodation, transport and other expenditure as well as a breakdown for domestic trips and cross border trips (between EU Member States).

Variables: Turnover (expenditure), employment

The value of expenditure calculated as indicated above is assimilated to the turnover for the three subsectors in Coastal tourism (i.e. Accommodation, Transport, Other expenditure).

The rest of variables (employment, GVA, employee’s compensation) are estimated from the proportion of each one of them to turnover for the activities indicated in Table A.2.

Coastal vs. non-coastal

Finally, the indicator is further adjusted to take into account the maritime proportion. This is achieved by using data in tour_occ_ninatdc to calculate the fraction of coastal tourism (F_coast) as the number of nights spent in coastal areas (N_coast) over total nights (N_tot) ) by domestic and foreign tourists:

_{[3] For further details, see: https://ec.europa.eu/eurostat/web/tourism/methodology.}