Aligning historical international aviation and maritime transport data to the scope of EU climate policies : a methodology to estimate activity, energy use, and emissions occurring within the boundaries of the European Union and European Economic Area.
Date: 2024-10-03
Institutions: Joint Research Centre
Subjects: EU Member State, European Economic Area, air transport, environmental monitoring, environmental statistics, greenhouse gas, maritime transport, reduction of gas emissions, research report, transport statistics

ISSN 1831-9424 

Aligning historical international aviation 
and maritime transport data to the scope 
of EU climate policies  

A methodology to estimate activity, energy use, and emissions 
occurring within the boundaries of the European Union and 
European Economic Area 

Jaxa-Rozen, M., Rózsai, M., Neuwahl, F. 

 EUR 40059 

This document is a publication by the Joint Research Centre (JRC), 
service. It aims to provide evidence-based scientific support to the European policymaking process. The contents of this 
publication do not necessarily reflect the position or opinion of the European Commission. Neither the European 
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employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the 
part of the European Union concerning the legal status of any country, territory, city or area or of its authorities, or 
concerning the delimitation of its frontiers or boundaries. 

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JRC139028 
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EUR 40059 

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ISBN 978-92-68-20958-5   ISSN 1831-9424   doi:10.2760/6836357 

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© European Union, 2024 

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How to cite this report: European Commission, Joint Research Centre, Jaxa-Rozen, M., Rózsai, M. and Neuwahl, F., Aligning historical interna-
tional aviation and maritime transport data to the scope of EU climate policies, Publications Office of the European Union, Luxembourg, 
https://data.europa.eu/doi/10.2760/6836357, JRC139028. 

 
 
 
 
 
 
 
 
 
 
Contents 

Abstract ...................................................................................................................................................................................................................................................... 2 

Acknowledgements .............................................................................................................................................................................................................................. 3 

1  Motivation ......................................................................................................................................................................................................................................... 4 

2  Data sources ................................................................................................................................................................................................................................... 5 

2.1  Aviation.................................................................................................................................................................................................................................. 5 

2.2  Maritime transport .......................................................................................................................................................................................................... 6 

3  Decomposition and calibration methodology ............................................................................................................................................................... 8 

3.1  Aviation.................................................................................................................................................................................................................................. 8 

3.1.1 

Activity data ....................................................................................................................................................................................................... 9 

3.1.2 

Energy and emissions data .................................................................................................................................................................... 14 

3.2  Maritime transport ....................................................................................................................................................................................................... 18 

3.2.1 

Domestic navigation .................................................................................................................................................................................. 19 

3.2.2 

International maritime bunkers ........................................................................................................................................................... 22 

3.3  Robustness checks ....................................................................................................................................................................................................... 28 

4  Conclusions ................................................................................................................................................................................................................................... 30 

References ............................................................................................................................................................................................................................................. 31 

List of abbreviations and definitions ...................................................................................................................................................................................... 32 

List of figures ....................................................................................................................................................................................................................................... 33 

List of tables......................................................................................................................................................................................................................................... 34 

Annexes ................................................................................................................................................................................................................................................... 35 

Annex 1. Calibration results ................................................................................................................................................................................................. 35 

Annex 2. Detailed results for robustness checks ..................................................................................................................................................... 39 

Annex 3. Eurostat fuel code correspondence ............................................................................................................................................................. 46 

1 

 
 
 
Abstract 

The climate targets of the European Union (EU) are defined in relation to historical benchmarks: for 
instance, the European Climate Law requires net domestic greenhouse gas (GHG) emissions to be 
reduced by at least 55% by 2030 compared to 1990. However, for aviation and maritime transport, 
primary statistics on emissions are not available at the level of geographical detail needed to 
suitably track the contribution of international voyages to domestic emissions. To this end, this 
report describes a calibration methodology that harmonizes available statistics and yields an 
internally-consistent decomposition of 1990-2021 activity, energy use, and emissions for aviation 
and maritime transport in the EU and European Economic Area (EEA). The resulting dataset matches 
Eurostat energy balances and distinguishes intra-/extra-EU and/or intra-/extra-EEA departures for 
each EU Member State, each EEA country, and the United Kingdom. The dataset is therefore 
consistent with the scope of EU climate policies and can inform further research and decision-
making. The dataset is included in the latest release of the Joint Research Centre's Integrated 
Database of the European Energy System (JRC-IDEES-2021), which is freely accessible through the 
JRC Data Catalogue under an open-access license. 

2 

 
Acknowledgements 

This report and dataset have been developed in the context of the latest release of the Joint 

-IDEES-2021). As such, 

the final product draws upon the prior pivotal work of the initial developers of JRC-IDEES; the 
authors of this report wish to especially acknowledge the essential contributions of Leonidas 
Mantzos and Tobias Wiesenthal. We also thank Stefano Santacroce and Bernat Goni-Ros (DG 
CLIMA) for their technical inputs. 

Authors 

Marc Jaxa-Rozen 
Mate Rozsai 
Frederik Neuwahl 

3 

 
 
 
1  Motivation 

To assess progress towards climate targets, the European Climate Law and related legislative 
instruments use historical benchmarks for greenhouse gas (GHG) emissions in the European Union 
(EU): for instance, the EU is committed to reduce net domestic GHG emissions by at least 55% by 
2030 compared to 1990 (European Union, 2021). In the accounting of historical EU emissions 
falling under the legal scope of these instruments, the geographical assignment of reported 
emissions from stationary sources is straightforward, but it is less so when it comes to emissions 
from international transport activities. In fact, to find the emissions of international aviation and 
maritime transport that are attributable to different geographical scopes, emissions need to be 
accounted by the destination of voyages. For example, the EU Emissions Trading Scheme (ETS) 
(European Union, 2023a) covers flights between EU Member States (MS), and departures to Norway, 
Iceland, Switzerland, and the United Kingdom (UK).  

However, there is a mismatch in the scopes of available primary statistics: the national GHG 
inventories submitted to the United Nations Framework Convention on Climate Change (UNFCCC) 
report domestic and international transport emissions, but do not distinguish intra-/extra-EU 
contributions; Eurostat provides intra-EU and extra-EU activity statistics by MS, but aggregates 
energy use across all international destinations. In the case of international aviation, commercial 
flight databases may offer the required origin-destination granularity, but generally cover a limited 
period and may be inconsistent with official statistics. Despite efforts at reconciling these sources 
for specific MS and/or periods (Alonso et al., 2014; Aminzadeh, 2017), there is a lack of consistent 
EU-wide data to guide decision-making. Maritime transport faces similar issues to distinguish 
voyages within the EU   for which emissions 
EU ETS   from voyages that depart or arrive outside the EU, and for which emissions are only 
covered at 50%. Although the recent MRV Regulation (European Union, 2023b) has strengthened 
reporting in maritime transport, there remains a gap in the past data needed for historical 
benchmarks. 

 and by the 

To this end, this report describes a calibration methodology that harmonizes available primary 
statistics and provides a transparent, internally-consistent decomposition by geographical scope of 
1990-2021 activity, energy use, and emissions for international aviation and maritime transport. 
The resulting dataset is strictly consistent with Eurostat energy balances and covers each EU MS, 
each European Economic Area (EEA) country, and the UK. For EU MS, departures are split into 
domestic, intra-/extra-EU, and intra-/extra-EEA components. For EEA countries and the UK, domestic 
and intra-/extra-EEA components are accordingly reported. The dataset thus complies with the 
scope of EU climate policies and can facilitate the tracking of progress towards emission reduction 
targets. This dataset has been published and concisely documented within the Joint Research 

-IDEES), which covers the full 

European energy system and is available through the Data Catalogue of the Joint Research Centre 
(Rozsai et al., 2024). This report extends the documentation of the latest JRC-IDEES-2021 release 
(European Commission et al., 2024) by detailing the data sources and calibration methodology used 
for aviation and maritime transport.  

Section 2 follows this introduction by summarizing the historical data sources used to calibrate 
aviation and maritime transport in JRC-IDEES-2021. Section 3 then describes each step of the 
calibration methodology, along with selected results and robustness checks under alternative 
calibration assumptions. Section 4 outlines conclusions and directions for future work.  

4 

 
 
2  Data sources 

2.1  Aviation 

Table 1 summarizes the source, geographical level of detail, and time coverage of the main 
datasets used to decompose and calibrate international aviation. In cases where multiple data 
sources may be available for the same indicator, the calibration generally prioritizes official EU 
statistics from Eurostat or DG MOVE or, alternatively, data provided by EUROCONTROL (which acts 
as network manager for EU airspace under Commission Implementing Decision (EU) 2019/709). 

Table 1. Main historical datasets for aviation transport. 

Provider 

Description 

Geographical level 

Available time period 

By country for all 
EEA+UK countries, 
disaggregating domestic 
and intl. aviation 

1990-2021 

By origin-destination 
country pair for all 
EEA+UK countries 

1993-2021 with 
variable coverage 
(Figure 1) 

Eurostat (2023, 
2024) 

Complete energy balances (nrg_bal_c) 

Air passenger transport by type of 
schedule, transport coverage and country 
(avia_paoc) 

International intra-EU air passenger 
transport by reporting country and EU 
partner country (avia_paincc) 

International extra-EU air passenger 
transport by reporting country and partner 
world regions and countries (avia_paexcc) 

Aircraft traffic data by reporting country 
(avia_tf_acc) 

Freight and mail air transport by type of 
schedule, transport coverage and country 
(avia_gooc) 

International intra-EU freight and mail air 
transport by reporting country and EU 
partner country (avia_goincc) 

International extra-EU freight and mail air 
transport by reporting country and partner 
world regions and countries (avia_goexcc) 

DG MOVE Statistical 
Pocketbook 
(European 
Commission, 2023) 

Passenger and freight 
transport activity 

EUROCONTROL 
Environmental 
Management 
Information Service 
(Eurocontrol, 2023) 

Passenger and freight flights 
Flight distance 
Emissions 
Energy consumption 

Sabre Global 
Demand Data 
(Sabre, 2021) 

Passenger flights 
Passengers 
Mileage 

EU total a, aggregating 
domestic and intra-EU 
activity 

1995-2021 

Individual flights for all 
EEA+UK countries 

2004-2021 

By origin-destination 
airport pair for all 
EEA+UK countries 

2010-2021 

Notes: 
a  Including historical editions of the Pocketbook that reported EU27 and EU28 data.  

Source: JRC analysis. 

The availability of Eurostat data varies across years, reporting departure countries, and destinations; 
Figure 1 shows total availability across the 30 modelled countries (EU, UK, Norway, Iceland) and 
their destinations, where 100% means that all departure and destination countries are covered in a 

5 

 
given year. Availability is lower after 2019 due to missing UK data; data gaps are generally 
estimated from relative changes over time in sectoral final energy consumption, assuming constant 
shares of intra-/extra-EU (or intra-/extra-EEA) activity. Further processing assumptions are detailed 
where relevant in Chapter 3. 

Figure 1. Availability over time of Eurostat datasets for aviation transport. 

Source: JRC analysis of Eurostat data. 

The EUROCONTROL dataset for passenger flights is filtered to remove military, cargo, business, or 
unclassified flights, as well as flights on aircraft with less than 19 seats. This filtering yields a good 
match with Eurostat flight data for each destination category (Figure 2, left panel) without 
significantly affecting corresponding CO2 emissions (Figure 2, right panel). 

Figure 2. Left panel: Passenger flights in Eurostat and EUROCONTROL (after filtering). Right panel: Change in 
total CO2 emissions of EUROCONTROL flights after filtering. 

Passenger flights

2,000,000

1,000,000

0

2004
2008
Eurostat - Domestic
Eurostat - Intra-EU
Eurostat - Extra-EU

0%

-1%

-2%

-3%

-4%

-5%

2012

2016

2020

2004

2008

2012

2016

2020

EUROCONTROL - Domestic
EUROCONTROL - Intra-EU
EUROCONTROL - Extra-EU

Domestic

Intra-EU

Extra-EU

Source: JRC analysis using Eurostat and EUROCONTROL data. 

2.2  Maritime transport 

Table 2 summarizes the main datasets used for domestic and international maritime transport. As 
with aviation, Eurostat coverage is incomplete for early years (Figure 3) and missing data is 
generally estimated using the relative change over time in energy consumption of domestic or 
international navigation. These adjustments assume a constant share of intra-/extra-EU (and/or 
intra-/extra-EEA) activity where needed. Note that Eurostat energy balances report the quantities of 
fuels delivered within each country, to ships of any national registration that are engaged in 
navigation (including ships not engaged in commercial activity). 

6 

1990199219941996199820002002200420062008201020122014201620182020avia_paocavia_painccavia_paexccavia_tf_acc-agravia_goocavia_goinccavia_goexcc0%25%50%75%100% 
 
 
THETIS-MRV (European Maritime Safety Agency, 2024) reports ship-level CO2 emissions and 
efficiency indicators for voyages arriving in/departing ports under EU-MRV jurisdiction1, voyages 
between EU-MRV ports, and within ports at berth. Missing efficiency indicators in THETIS-MRV are 
estimated from CO2 emissions and average reported efficiencies. To address missing emissions 
data, the total reporting of CO2 emissions is prioritized (e.g. emissions of each voyage category are 
proportionally scaled so that their sum matches the total, in case of discrepancies). In parallel, the 
emissions data is processed to assign 50% of emissions of voyages arriving in/departing EU-MRV 
jurisdiction to the latter. The use of THETIS-MRV for calibration assumes the dataset covers two-
thirds of domestic coastal shipping emissions and mileage, and 90% of total emissions and mileage 
at the EEA level. These assumptions follow expert input and initial assessments of the THETIS-MRV 
system (European Commission, 2019), and their impact is tested in section 3.3. 

Table 2. Main historical datasets for maritime transport. 

Provider 

Description 

Geographical level 

Available time period 

Complete energy balances (nrg_bal_c) 

1990-2021 

Eurostat (2023, 2024) 

Inland waterways transport measurement   
Transport by type of good (iww_go_atygo) 

Inland waterways transport measurement   
Vessel traffic (iww_tf_vetf) 

Maritime transport   Vessels arriving in the main 
ports by type of vessels (mar_mt_am_csvi) 

Maritime transport   Detailed tables per each 
reporting country (mar_go_am_*) 

DG MOVE Statistical 
Pocketbook (European 
Commission, 2023) 

EMSA THETIS-MRV 
(European Maritime 
Safety Agency, 2024) b 

Freight transport activity 

CO2 emissions by voyage origin/destination 

Efficiency indicators (kgCO2 per km and per 
tonne-km) 

By country, for all 
EEA+UK countries. 
Energy balances 
disaggregate domestic 
and intl. navigation. 

By origin-destination 
port pair for all 
EEA+UK countries 

By country, for all EU 
countries a 

2007-2021 with 
variable coverage 
(Figure 3) 

1997-2021 with 
variable coverage 
(Figure 3) 

1995-2021 

By ship, in all EEA+UK 
countries 

2018-2021 

Notes: 
a  Including historical editions of the Pocketbook that reported EU27 and EU28 data. 
b  MRV versions: 2018 - v270 (2023.10.11), 2019 - v217 (2023.10.11), 2020 - v194 (2023.10.11), 2021 - v181 (2024.01.13). 

Source: JRC analysis. 

Figure 3. Availability over time of Eurostat datasets for maritime transport. 

Note: availability of datasets is computed based on the calibration of domestic navigation, which models 19 countries for 

inland waterways (iww_*) and 24 countries for domestic coastal shipping (mar_*)   see section 3.2.1. 

Source: JRC analysis of Eurostat data. 

1 Generally corresponding to EU/EEA ports of call: https://climate.ec.europa.eu/eu-action/transport/reducing-emissions-

shipping-sector/faq-monitoring-reporting-and-verification-maritime-transport-emissions_en 

7 

1990199219941996199820002002200420062008201020122014201620182020iww_go_atygoiww_tf_vetfmar_mt_am_csvimar_go_am0%25%50%75%100% 
 
                                                 
 
3  Decomposition and calibration methodology 

3.1  Aviation 

The calibration methodology for international aviation is summarized in Figure 4. The next subsections 
detail the calibration of aviation activity and energy/emissions indicators, following each step bolded 
in the figure (where steps A or B concern transport activity of passenger or freight modes; steps C 
cover energy/emissions for both modes). Under each step in the figure and in the corresponding 
subsections, line numbers (e.g. L73-81) point to data rows in the JRC-IDEES EU27/JRC-IDEES-
2021_x1990_Aviation_EU and JRC-IDEES-2021_x1990_Aviation_EEAwUK data files. These files 
report 1990-2021 results across sheets for each country and the EU or EEA+UK total, respectively. In 
each sheet, departures are accordingly grouped into domestic, international intra-EU (or intra-
EEA+UK), and extra-EU (or extra-EEA+UK) destinations. Key calibration results for the EU are 
summarized in Annex 1, Figure 13-14. 

Figure 4. Graphical overview of the calibration of international aviation. 

Source: JRC analysis. 

8 

For each country: Eurostat domesticandtotal international consumptionAt EU level: EUROCONTROLinternational intra-EU/extra-EU ratioAt EU and EU+UK level:Statistical Pocketbook total intra-EU and intra-EU+UK activitiesEurostatEUROCONTROLSABREConstraintsDirect calculationUsed indirectlyExternal data usedA2, B3:Total flights [number]A3, B2:Load factor [pass. / flight, tonne/ flight]C2: Effective energy efficiency [kgoe/ 100 km]A4, B4:Average flight distance[km / flight]C3: CO2emissions [ktCO2]A4, B4:Total mileage [million km]L43-51L53-61L145-153L23-31L83-91L123-133L135-143L200-210L73-81L3-11A5, B5:Total activity [million pass.-km, million tonne-km]C2: Energy consumption [ktoe]C1: Technical energy efficiency [kgoe/ 100 km]A1, B1:Volume carried[passengers, tonnes] 
 
The variables used in the decomposition and calibration are computed and reported for different 
combinations of modes, departure country, destinations, and years. The following steps accordingly 
use subscripts to distinguish for a variable 𝑉𝑚,𝑐→𝑑,𝑡 each combination of mode 𝑚2, departure 
country or region3 𝑐, destination4 𝑑,  and year 𝑡. Unless specified, variables are computed for each 
combination of 𝑚, 𝑐, 𝑑 and 𝑡 independently. Where needed, a superscript specifies the data source. 
For instance, the expression below denotes the number of passenger flights 𝐹 from EUROCONTROL, 
summing across departures from all EU MS and to all destinations within the EU and UK, in year t : 

3.1.1  Activity data 

𝐸𝑈𝑅𝑂𝐶 

𝐹𝑃,𝐸𝑈→∑{𝑑𝑜𝑚,𝑖𝑛𝑡𝑟𝑎𝐸𝑈,𝑈𝐾},𝑡

Activity data are estimated using a departure-based logic to harmonize data from Eurostat, 
EUROCONTROL, and the DG MOVE Statistical Pocketbook through the following steps. The overall 
goal for the calibration of activity is to derive technically representative characteristics for each 
flight category, while respecting available Eurostat statistics and the total intra-EU and intra-EU+UK 
activities reported by the Statistical Pocketbook. 

For passenger transport: 

Step A1   Passengers: The total number of passengers on flights departing country 𝑐 to the 
destination 𝑑 in year 𝑡, 𝑃𝑐→𝑑,𝑡 (L74-77 of the data files) is based on Eurostat datasets avia_paoc, 
avia_paincc, avia_paexcc. Missing data are estimated by iterating backwards, using total passenger 
flights 𝐹𝑃 (step A2) and the smoothed load factor 𝐿𝐹𝑃 (passengers/flight; step A3). The impact of 
this assumption on missing data is assessed in section 3.3 and Annex 2. 

𝑃𝑐→𝑑,𝑡 = 𝐹𝑃,𝑐→𝑑,𝑡 (𝐿𝐹𝑃,𝑐→𝑑,𝑡+1 ∙ (

𝐿𝐹𝑃,𝑐→𝑑,𝑡+1
𝐿𝐹𝑃,𝑐→𝑑,𝑡+10

)

0.05

)

(1) 

Step A2   Passenger flights: The total number of passenger flights 𝐹𝑃,𝑐→𝑑,𝑡 (L44-47) is directly 
taken when available from Eurostat datasets avia_paoc, avia_paincc, avia_paexcc. Missing data 
(generally before 2003) are estimated by iterating backwards, using total passengers 𝑃 (step A1) 
and the smoothed load factor 𝐿𝐹𝑃 (step A3); section 3.3 and Annex 2 present an alternative 
calibration assuming constant values for missing data. 

𝐹𝑃,𝑐→𝑑,𝑡 =

𝑃𝑐→𝑑,𝑡

𝐿𝐹𝑃,𝑐→𝑑,𝑡+1 ∙ (

𝐿𝐹𝑃,𝑐→𝑑,𝑡+1
𝐿𝐹𝑃,𝑐→𝑑,𝑡+10

0.05  
)

(2)

In cases where Eurostat flight data are missing but EUROCONTROL data may be available, flights 
are estimated using the relative annual change in passenger flights reported by EUROCONTROL for 
each departure-destination combination. If passenger data 𝑃 is also unavailable (generally before 
1995), flights are assumed to vary proportionally to final energy consumption in Eurostat energy 
balances (for domestic or international aviation, depending on the destination being calibrated).  

2 P, passenger; F, freight. 
3 EU: all Member States of the European Union (EU27, from 1.2.2020). 
4 Destinations are split into: dom, domestic; intraEU, international intra-EU27; UK: United Kingdom; NO+IS: Norway and 

Iceland; extraEEA: international extra-EEA and extra-UK. For final reporting in the JRC-IDEES-2021_x1990_Aviation 
files, destinations are aggregated into intra-/extra-EU27 or intra-/extra-EEA+UK. 

9 

 
 
 
                                                 
 
Step A3   Passenger load factor: The average load factor 𝐿𝐹𝑃,𝑐→𝑑,𝑡 (passengers/flight; L84-87) 
is directly calculated from passenger and flight data: 

𝐿𝐹𝑃,𝑐→𝑑,𝑡 =

𝑃𝑐→𝑑,𝑡
𝐹𝑃,𝑐→𝑑,𝑡

(3) 

Step A4   Passenger flight distance and mileage: The average passenger flight distance 
𝐸𝑈𝑅𝑂𝐶  
𝐷𝑃,𝑐→𝑑,𝑡 (km/flight; L54-57) is computed from EUROCONTROL passenger vehicle mileage 𝑀𝑃,𝑐→𝑑,𝑡
𝐸𝑈𝑅𝑂𝐶 . EUROCONTROL flights are here used for consistency with 
(million vehicle-km) and flights 𝐹𝑃,𝑐→𝑑,𝑡
the mileage statistics. 

𝐷𝑃,𝑐→𝑑,𝑡 =

𝐸𝑈𝑅𝑂𝐶  
𝑀𝑃,𝑐→𝑑,𝑡
𝐸𝑈𝑅𝑂𝐶  ∙ 1 000 000 
𝐹𝑃,𝑐→𝑑,𝑡

(4) 

Flight distance is assumed constant before 2004 by lack of EUROCONTROL data. This flight 
distance is combined with the Eurostat number of flights to obtain final passenger mileage 𝑀𝑃,𝑐→𝑑,𝑡 
(million vehicle-km; L24-27): 

𝑀𝑃,𝑐→𝑑,𝑡 =

𝐹𝑃,𝑐→𝑑,𝑡 ∙ 𝐷𝑃,𝑐→𝑑,𝑡
1 000 000

(5) 

Step A5   Passenger activity: An initial estimate of passenger transport activity 𝐴𝑐𝑡𝑃,𝑐→𝑑,𝑡
(million passenger-km) is derived from flight distance and passengers: 

𝑖𝑛𝑖𝑡 

𝑖𝑛𝑖𝑡 
𝐴𝑐𝑡𝑃,𝑐→𝑑,𝑡

  =

𝐷𝑃,𝑐→𝑑,𝑡 ∙ 𝑃𝑐→𝑑,𝑡
1 000 000

When aggregating flights across routes of varying distances, a discrepancy emerges between the 
average load factors that are based on activity (e.g. passenger-km / vehicle-km), or based on flights 
(passengers / flight). To account for this discrepancy (further discussed in Annex 2), the initial 
activity estimate is multiplied by a dimensionless factor 𝑅𝑐→𝑑,𝑡
estimate 𝐴𝑐𝑡𝑃,𝑐→𝑑,𝑡
activity 𝐴𝑐𝑡𝑃,𝑐→𝑑,𝑡
𝑆𝐴𝐵𝑅𝐸 , and flights 𝐹𝑃,𝑐→𝑑,𝑡
𝑃𝑐→𝑑,𝑡

𝑆𝐴𝐵𝑅𝐸   (million vehicle-km), passengers 
𝑆𝐴𝐵𝑅𝐸 . The factor is kept constant before 2010 by lack of SABRE data. 

 (million passenger-km). The factor is derived from SABRE data for passenger 

𝑆𝐴𝐵𝑅𝐸  (million passenger-km), mileage 𝑀𝑃,𝑐→𝑑,𝑡

  (Figure 5) to yield a revised 

𝑟𝑒𝑣

𝑅𝑐→𝑑,𝑡

=

𝑆𝐴𝐵𝑅𝐸  𝑀𝑃,𝑐→𝑑,𝑡
𝑆𝐴𝐵𝑅𝐸 
𝐴𝑐𝑡𝑃,𝑐→𝑑,𝑡
⁄
𝑆𝐴𝐵𝑅𝐸 
𝑆𝐴𝐵𝑅𝐸  𝐹𝑃,𝑐→𝑑,𝑡
⁄
𝑃𝑐→𝑑,𝑡

𝑟𝑒𝑣
𝐴𝑐𝑡𝑃,𝑐→𝑑,𝑡

  = 𝐴𝑐𝑡𝑃,𝑐→𝑑,𝑡

𝑖𝑛𝑖𝑡 

∙ 𝑅𝑐→𝑑,𝑡

𝑆𝐴𝐵𝑅𝐸 

(6) 

(7) 

Figure 5. Activity adjustment factor derived from flight-level data for each destination, EEA average. 

1.5

1.4

1.3

1.2

1.1

1.0

𝑅EEA→𝑑, 𝑡

Domestic

Intra-EU

UK

NO+IS

Extra-EEA

1990

1995

2000

2005

2010

2015

2020

Source: JRC analysis. 

10 

 
 
 
 
 
 
 
 
 
 
 
For flights departing Norway and Iceland, 𝐴𝑐𝑡𝑃,𝑁𝑂+𝐼𝑆→𝑑,𝑡
destinations. For flights departing each EU MS and the UK, 𝐴𝑐𝑡𝑃,𝑐→𝑑,𝑡
domestic flights, flights to Norway and Iceland, and extra-EEA flights.  

𝑟𝑒𝑣

𝑟𝑒𝑣

 is used as the final activity for all 

 is used as the final activity for 

For international intra-EU and UK destinations, a final adjustment is used to match total Statistical 
Pocketbook passenger activity for the EU and EU+UK, in each available year. A multiplication factor 
 of all EU MS, then iterated so the 
(uniform across MS) is first applied to activity 𝐴𝑐𝑡𝑃,𝑐→𝑖𝑛𝑡𝑟𝑎𝐸𝑈,𝑡
 , 
sum of activities for domestic and intra-EU destinations across all MS, 𝐴𝑐𝑡𝑃,𝐸𝑈→∑{𝑑𝑜𝑚,𝑖𝑛𝑡𝑟𝑎𝐸𝑈},𝑡
matches the Statistical Pocketbook EU total. 

𝑟𝑒𝑣

𝑟𝑒𝑣

A second uniform multiplication factor is applied to UK-destined activity of all EU MS, and to the 
total EU-destined activity of the UK. This factor is iterated so the sum of activities for domestic, 
intra-EU and UK destinations across all EU MS and the UK, 𝐴𝑐𝑡𝑃,∑{𝐸𝑈,𝑈𝐾}→∑{𝑑𝑜𝑚,𝑖𝑛𝑡𝑟𝑎𝐸𝑈,𝑈𝐾},𝑡
matches the Statistical Pocketbook EU+UK total. The two multiplication factors are kept constant 
before 1995 by lack of Statistical Pocketbook data.  L4-7 of the data files show the final activity. 

𝑟𝑒𝑣

, 

For freight transport, activity is estimated similarly. However, representative flights are used instead 
of actual Eurostat flights to better account for mixed passenger-freight activity, which is calibrated 
iteratively in step B3. 

Step B1   Initial freight weight: For each combination of departure country-destination, an 
initial estimate for total weight of freight and mail on departing flights, 𝑊𝑐→𝑑,𝑡
identified from weight data 𝑊𝑐→𝑑,𝑡
years in which 𝑊 
to the total passenger flights; 𝑊 
years. This initial estimate is later revised in step B3 after iterating the load factor. 

𝑖𝑛𝑖𝑡 is estimated to vary proportionally 
𝑖𝑛𝑖𝑡 also includes minor revisions to e.g. linearly interpolate missing 

𝐸𝑆𝑇𝐴𝑇 in Eurostat datasets avia_gooc, avia_goincc, avia_goexcc. For 

𝐸𝑆𝑇𝐴𝑇 is missing (generally before 2005), 𝑊 

𝑖𝑛𝑖𝑡  (tonnes), is 

Step B2   Initial freight load factor: An average load factor for freight 𝐿𝐹𝐹,𝑑,𝑡
estimated for each destination, and assumed to apply for departures from all countries. Using 
Eurostat data for weight 𝑊 
departures across all EU MS and the UK: 

𝐸𝑆𝑇𝐴𝑇 and for the number of freight flights 𝐹 𝐹,𝑐→𝑑,𝑡

𝐸𝑆𝑇𝐴𝑇  , summing 

𝑎𝑣𝑔 (tonnes/flight) is 

𝑎𝑣𝑔 =

𝐿𝐹𝐹,𝑑,𝑡

𝐸𝑆𝑇𝐴𝑇

𝑊∑{𝐸𝑈,𝑈𝐾}→𝑑,𝑡
𝐸𝑆𝑇𝐴𝑇
𝐹 𝐹,∑{𝐸𝑈,𝑈𝐾}→𝑑,𝑡

(8) 

For 2018-2021, Eurostat data are used directly. Before 2018, to smooth the impact of missing 
𝑎𝑣𝑔 ≤ 𝐿𝐹𝐹,𝑑,𝑡
𝑎𝑣𝑔 is constrained so that 0.9 𝐿𝐹𝐹,𝑑,𝑡+1
data, 𝐿𝐹𝐹
constant before 2003 by lack of Eurostat data. 

𝑎𝑣𝑔 ≤ 𝐿𝐹𝐹,𝑑,𝑡+1

. The load factor is assumed 

𝑎𝑣𝑔

 is derived from the total weight 𝑊 

Step B3   Freight flights, revised load factor, revised freight weight : For each combination 
of departure country - destination, an initial estimate for number of representative freight flights 
𝑖𝑛𝑖𝑡
𝑖𝑛𝑖𝑡 (step B1), and from an initial country-specific load 
𝐹𝐹,𝑐→𝑑,𝑡
factor that uses Eurostat data for weight carried and for freight flights. The average EU+UK load 
factor 𝐿𝐹𝐹,𝑑,𝑡
denominator if country-specific Eurostat data is missing: 

𝑎𝑣𝑔 (step B2) is used to constrain the country-specific load factor, or used directly as 

𝑖𝑛𝑖𝑡
𝐹𝐹,𝑐→𝑑,𝑡

  =

𝑖𝑛𝑖𝑡
𝑊𝑐→𝑑,𝑡
𝑎𝑣𝑔 ,
𝑎𝑣𝑔 , max (0.9 𝐿𝐹𝐹,𝑑,𝑡

min (1.1 𝐿𝐹𝐹,𝑑,𝑡

𝐸𝑆𝑇𝐴𝑇
𝑊𝑐→𝑑,𝑡
𝐸𝑆𝑇𝐴𝑇 ))
𝐹 𝐹,𝑐→𝑑,𝑡

(9)

11 

 
 
 
 
 
The resulting representative flights are summed across the EU+UK and used to divide the total 
EU+UK weight, to obtain a revised average load factor for each destination: 

𝐿𝐹𝐹,𝑑,𝑡

𝑟𝑒𝑣 =

𝑖𝑛𝑖𝑡

𝑊∑{𝐸𝑈,𝑈𝐾}→𝑑,𝑡
𝑖𝑛𝑖𝑡
𝐹𝐹,∑{𝐸𝑈,𝑈𝐾}→𝑑,𝑡

(10) 

Finally, this revised average load factor is rebalanced across geographical categories to yield a final 
load factor 𝐿𝐹𝐹,𝑑,𝑡 , which i) ensures the resulting total number of representative flights in the EU 
matches total Eurostat freight flights 𝐹 𝐹
better accounts for mixed passenger-freight flights within the EU by applying regional multipliers to 
shift load away from extra-EEA flights. The impact of these multipliers is assessed in section 3.3 
and Annex 2. The final load factor is assumed to apply for departures from all countries and 
defined as follows, where 𝛼𝑡 is a scaling factor to be iterated. 

𝐸𝑆𝑇𝐴𝑇 , in years with complete data (after 2011); and ii) 

For domestic flights, intra-EU flights (also used for departures to UK, Norway and Iceland), and 
extra-EEA flights: 

𝐿𝐹𝐹,𝑑𝑜𝑚,𝑡 = 𝛼𝑡 ∙

∑

𝑑𝜖{𝑑𝑜𝑚,𝑖𝑛𝑡𝑟𝑎𝐸𝑈,𝑈𝐾}

 (𝐿𝐹𝐹,𝑑,𝑡

∑

𝑑𝜖{𝑑𝑜𝑚,𝑖𝑛𝑡𝑟𝑎𝐸𝑈,𝑈𝐾}

𝑖𝑛𝑖𝑡
𝑟𝑒𝑣 ∙ 𝐹𝐹,∑{𝐸𝑈,𝑈𝐾}→𝑑,𝑡
𝐹𝐹,∑{𝐸𝑈,𝑈𝐾}→𝑑,𝑡

𝑖𝑛𝑖𝑡

)

𝐿𝐹𝐹,𝑖𝑛𝑡𝑟𝑎𝐸𝑈,𝑡 = 𝛼𝑡 ∙ 1.1 ∙

∑

𝑑𝜖{𝑖𝑛𝑡𝑟𝑎𝐸𝑈,𝑈𝐾,𝑁𝑂+𝐼𝑆}

 (𝐿𝐹𝐹,𝑑,𝑡

∑

𝑑𝜖{𝑖𝑛𝑡𝑟𝑎𝐸𝑈,𝑈𝐾,𝑁𝑂+𝐼𝑆}

𝑖𝑛𝑖𝑡
𝑟𝑒𝑣 ∙ 𝐹𝐹,∑{𝐸𝑈,𝑈𝐾}→𝑑,𝑡
𝐹𝐹,∑{𝐸𝑈,𝑈𝐾}→𝑑,𝑡

𝑖𝑛𝑖𝑡

𝐿𝐹𝐹,𝑒𝑥𝑡𝑟𝑎𝐸𝐸𝐴,𝑡 = 𝛼𝑡 ∙ 0.8 ∙ 𝐿𝐹𝐹,𝑒𝑥𝑡𝑟𝑎𝐸𝐸𝐴,𝑡

𝑟𝑒𝑣

)

(11) 

(12) 

(13) 

The number of representative freight flights 𝐹𝐹,𝑐→𝑑,𝑡 is then found from corresponding load factors 
for each destination: 

𝐹𝐹,𝑐→𝑑,𝑡 =

𝑖𝑛𝑖𝑡
𝑊𝑐→𝑑,𝑡
𝐿𝐹𝐹,𝑑,𝑡

(14) 

This calculation is iterated to find a value of 𝛼𝑡 for which total freight flights at EU level, 
𝐹𝐹,𝐸𝑈→∑{𝑑𝑜𝑚,𝑖𝑛𝑡𝑟𝑎𝐸𝑈,𝑈𝐾,𝑁𝑂+𝐼𝑆,𝑒𝑥𝑡𝑟𝑎𝐸𝐸𝐴},𝑡 , match the Eurostat total. The resulting final number of 
flights, load factor, and recalculated equivalent weight carried 𝑊𝑐→𝑑,𝑡
91, and L78-81 of the data files respectively. 

 are reported in L48-51, L88-

Step B4   Freight flight distance and mileage: The average freight flight distance 𝐷𝐹,𝑐→𝑑,𝑡 
(km/flight; L58-61) is computed from EUROCONTROL data for freight mileage and freight flights: 

𝐷𝐹,𝑐→𝑑,𝑡 =

𝐸𝑈𝑅𝑂𝐶 
𝑀𝐹,𝑐→𝑑,𝑡
𝐸𝑈𝑅𝑂𝐶 
𝐹𝐹,𝑐→𝑑,𝑡

(15) 

In case of missing data, the passenger flight distance 𝐷𝑃,𝑐→𝑑,𝑡 is used instead; before 2004, the 
flight distance is assumed constant by lack of EUROCONTROL data. The flight distance is then 
combined with the number of flights to find total freight mileage (million vehicle-km; L28-31): 

𝑀𝐹,𝑐→𝑑,𝑡 =

𝐹𝐹,𝑐→𝑑,𝑡 ∙ 𝐷𝐹,𝑐→𝑑,𝑡
1 000 000

(16) 

12 

 
 
 
 
 
 
 
 
 
 
Step B5   Freight activity: An initial estimate of freight transport activity 𝐴𝑐𝑡𝐹,𝑐→𝑑,𝑡
tonne-km) is derived using flight distance 𝐷𝐹 (step B4), carried freight weight 𝑊 (step B3), and the 
factor 𝑅𝑐→𝑑,𝑡
 (step A5) that harmonizes activity-based and flight-based load factors: 

 (million 

𝑖𝑛𝑖𝑡

𝑖𝑛𝑖𝑡
𝐴𝑐𝑡𝐹,𝑐→𝑑,𝑡

=

𝐷𝐹,𝑐→𝑑,𝑡 ∙ 𝑊𝑐→𝑑,𝑡

  ∙ 𝑅𝑐→𝑑,𝑡

1 000 000

(17) 

For flights departing Norway and Iceland, 𝐴𝑐𝑡𝐹,𝑁𝑂+𝐼𝑆→𝑑,𝑡
destinations. For flights departing each EU MS and the UK, 𝐴𝑐𝑡𝐹,𝑐→𝑑,𝑡
domestic flights and for flights to Norway and Iceland. 

𝑖𝑛𝑖𝑡

𝑖𝑛𝑖𝑡

  is used as the final activity for all 

 is used as the final activity for 

   matches total EU and EU+UK Statistical Pocketbook freight activity when 

As with passenger transport, international intra-EU and UK destinations are then scaled so the final 
activity estimate 𝐴𝑐𝑡𝐹
available. First, a dimensionless adjustment factor 𝑅𝐸𝑈→𝑖𝑛𝑡𝑟𝑎𝐸𝑈,𝑡
 for intra-EU international freight 
activity is computed from total EU Statistical Pocketbook freight activity, 𝐴𝑐𝑡𝐹,𝐸𝑈→∑{𝑑𝑜𝑚,𝑖𝑛𝑡𝑟𝑎𝐸𝑈},𝑡
. 
This factor is kept constant before 1995 by lack of Transport Pocketbook data, and applied to the 
initial intra-EU activity estimate for each MS: 

𝑃𝐵

𝑃𝐵

𝑃𝐵

𝑅𝐸𝑈→𝑖𝑛𝑡𝑟𝑎𝐸𝑈,𝑡

=

𝑃𝐵

𝐴𝑐𝑡𝐹,𝐸𝑈→∑{𝑑𝑜𝑚,𝑖𝑛𝑡𝑟𝑎𝐸𝑈},𝑡

𝑖𝑛𝑖𝑡
− 𝐴𝑐𝑡𝐹,𝐸𝑈→𝑑𝑜𝑚,𝑡

𝑖𝑛𝑖𝑡

𝐴𝑐𝑡𝐹,𝐸𝑈→𝑖𝑛𝑡𝑟𝑎𝐸𝑈,𝑡

𝐴𝑐𝑡𝐹,𝑐→𝑖𝑛𝑡𝑟𝑎𝐸𝑈,𝑡 = 𝑅𝐸𝑈→𝑖𝑛𝑡𝑟𝑎𝐸𝑈,𝑡

𝑃𝐵

𝑖𝑛𝑖𝑡

∙ 𝐴𝑐𝑡𝐹,𝑐→𝑖𝑛𝑡𝑟𝑎𝐸𝑈,𝑡

(18) 

(19) 

This factor is also used to adjust extra-EEA destinations for each MS, assuming it historically varied 
in a similar but weaker pattern. 

𝐴𝑐𝑡𝐹,𝑐→𝑒𝑥𝑡𝑟𝑎𝐸𝐸𝐴,𝑡 =

𝑃𝐵
) ∙ 𝐴𝑐𝑡𝐹,𝑐→𝑒𝑥𝑡𝑟𝑎𝐸𝐸𝐴,𝑡
(1 + 𝑅𝐸𝑈→𝑖𝑛𝑡𝑟𝑎𝐸𝑈,𝑡
2

𝑖𝑛𝑖𝑡

The 
Pocketbook activity. L8-11 of the data files show the final activity results. 

-EEA freight activities are adjusted similarly using total EU+UK Statistical 

𝑃𝐵

𝑅𝑈𝐾→∑{𝐸𝑈,𝑒𝑥𝑡𝑟𝑎𝐸𝐸𝐴},𝑡

=

𝑃𝐵

𝐴𝑐𝑡𝐹,∑{𝐸𝑈,𝑈𝐾}→∑{𝑑𝑜𝑚,𝑖𝑛𝑡𝑟𝑎𝐸𝑈,𝑈𝐾},𝑡

𝑃𝐵

− 𝐴𝑐𝑡𝐹,𝐸𝑈→∑{𝑑𝑜𝑚,𝑖𝑛𝑡𝑟𝑎𝐸𝑈},𝑡

𝑖𝑛𝑖𝑡
𝐴𝑐𝑡𝐹,𝑈𝐾→𝐸𝑈,𝑡

𝑖𝑛𝑖𝑡
+ 𝐴𝑐𝑡𝐹,𝐸𝑈→𝑈𝐾,𝑡

𝑃𝐵
𝐴𝑐𝑡𝐹,𝑈𝐾→𝐸𝑈,𝑡 = 𝑅𝑈𝐾→∑{𝐸𝑈,𝑒𝑥𝑡𝑟𝑎𝐸𝐸𝐴},𝑡

𝑖𝑛𝑖𝑡
∙ 𝐴𝑐𝑡𝐹,𝑈𝐾→𝐸𝑈,𝑡

𝐴𝑐𝑡𝐹,𝑈𝐾→𝑒𝑥𝑡𝑟𝑎𝐸𝐸𝐴,𝑡 =

𝑃𝐵

(1 + 𝑅𝑈𝐾→∑{𝐸𝑈,𝑒𝑥𝑡𝑟𝑎𝐸𝐸𝐴},𝑡
2

) ∙ 𝐴𝑐𝑡𝐹,𝑈𝐾→𝑒𝑥𝑡𝑟𝑎𝐸𝐸𝐴,𝑡

𝑖𝑛𝑖𝑡

(20) 

(21) 

(22) 

(23) 

13 

 
 
 
 
 
 
 
 
 
 
 
 
3.1.2  Energy and emissions data 

The calibration of energy data harmonizes Eurostat and EUROCONTROL data through the following 
steps. The overall calibration i) ensures that domestic and total international energy consumption 
matches Eurostat energy balances for each country5, ii) respects at EU level the international intra-
/extra-EU ratios of energy consumption and emissions reported by EUROCONTROL, and iii) aims to 
realistically represent technical characteristics and resulting consumption for each flight category, 
using EUROCONTROL flight-level data. 

Step C1   Technical energy consumption: To rescale EUROCONTROL flight-level fuel 
consumption and emissions data to the representative flight distances found in steps A4 and B4, 
the EUROCONTROL flight dataset is first used to estimate piecewise functions that approximate CO2 
emissions 𝑒̂ (kg) for flight distance 𝐷 (km) in a given year, 𝑒̂ = 𝑓𝑡(𝐷). These functions are estimated 
on the subset of all flights in a given year without any further distinction of modes, countries, 
destinations, or aircraft types. To reduce processing overhead, the functions are only estimated for 
five representative years (2005, 2010, 2012, 2018, 2019). Figure 6 compares actual 
EUROCONTROL emissions per flight in 2019 (using variable distance bands of 50-500 nautical 
miles (nm) to bin the flight data), the approximation function, and the EU-level representative flight 
distances for all mode-destination combinations in that year. As a measure of estimation bias and 
accuracy (Wen et al., 2022), the figure shows mean percentage error and mean absolute 
percentage error (wMPE, wMAPE) weighted by emissions over the binned range of 150-6500 nm.  

Section 3.3 and Annex 2 present an alternative calibration using only public ICAO data (International 
Civil Aviation Organization, 2024). 

Figure 6. CO2 emissions as a function of flight distance, EU average for 2019. 

400,000

40,000

4,000

0

Emissions per flight (kgCO2)

wMPE: -1.9%
wMAPE: 7.8%

EUROCONTROL (<2400 nm)
EUROCONTROL (>=2400 nm)
Approximation function (<2400 nm)
Approximation function (>=2400 nm)
Average flight distances (EU)

2,000

4,000

6,000

8,000

10,000

12,000
Flight distance (km)

Source: JRC analysis using EUROCONTROL data. 

5 Excepting countries where the calibration yields no domestic aviation activity, but energy balances report a small 

consumption in domestic aviation: Belgium, Cyprus, Estonia, Hungary, Lithuania, Luxembourg, Latvia, Malta, The 
Netherlands, Slovenia, Slovakia. In these cases, domestic aviation energy consumption is assumed to be zero and 
reassigned to other energy sectors in JRC-IDEES. 

14 

 
 
 
                                                 
 
For each representative year, the corresponding approximation function is applied to the individual 
EUROCONTROL flights that fall under each combination of modes, departure countries and 
destinations. The estimated emissions are converted to a fuel consumption using an emission factor 
for kerosene of 71.9 kgCO2/GJ (European Commission, 2018), and aggregated to an average 
technical energy efficiency for each combination, 𝑐̂𝑚,𝑐→𝑑,𝑡(kgoe/km) in each representative year. 

To later estimate 𝑐̂𝑚,𝑐→𝑑,𝑡 in other years, a dimensionless discrepancy factor  𝛿𝑚,𝑐→𝑑,𝑡 is first 
expressed as the ratio between the technical energy efficiency, and the energy efficiency found by 
applying the approximation function to the average flight distances 𝐷𝑚,𝑐→𝑑,𝑡 (steps A4, B4) for each 
combination of modes, countries and destinations:  

𝛿𝑚,𝑐→𝑑,𝑡 =

𝑐̂𝑚,𝑐→𝑑,𝑡

(
0.041868 𝐺𝐽 𝑘𝑔𝑜𝑒

𝑓𝑡(𝐷𝑚,𝑐→𝑑,𝑡)
⁄

∙ 71.9 𝑘𝑔𝐶𝑂2 𝐺𝐽⁄

   for 𝑡 in {2005,2010,2012,2018,2019}

(24) 
(24)

⁄

 𝐷𝑚,𝑐→𝑑,𝑡

)

This discrepancy factor is computed directly for the chosen representative years (Table 3). For other 
years, it is linearly interpolated and assumed constant before 2005 and after 2019.  

Table 3. Discrepancy factor based on flight distance approximation, EU average. 

Discrepancy factor a 

Passenger transport 

Domestic 

International - intra-EEA 

2005 

2010 

2012 

2018 

2019 

1.7% 

8.1% 

2.7% 

8.2% 

2.7% 

8.8% 

3.6% 

9.3% 

3.7% 

8.9% 

International - extra-EEA 

33.9% 

32.9% 

29.7% 

32.8% 

31.9% 

Freight transport 

Domestic 

International - intra-EEA 

International - extra-EEA 

3.8% 

5.8% 

2.3% 

-0.9% 

-1.0% 

-1.0% 

-1.0% 

3.9% 

6.0% 

2.7% 

6.1% 

2.2% 

5.8% 

2.1% 

3.9% 

Notes: 
a  Shown as 𝛿𝑚,𝐸𝑈→𝑑,𝑡 − 100%.  

Source: JRC analysis. 

To estimate 𝑐̂𝑚,𝑐→𝑑,𝑡 in each year 𝑡 other than the five representative years, the interpolated 
discrepancy factor is then combined with representative flight distances and with the approximation 
function for the nearest available year 𝑡′: 

𝑐̂𝑚,𝑐→𝑑,𝑡 = 𝛿𝑚,𝑐→𝑑,𝑡 (

𝑓𝑡′ (𝐷𝑚,𝑐→𝑑,𝑡)
⁄

0.041868 𝐺𝐽 𝑘𝑔𝑜𝑒

∙ 71.9 𝑘𝑔𝐶𝑂2 𝐺𝐽⁄

⁄

 𝐷𝑚,𝑐→𝑑,𝑡

) 

(25) 

L145-153 of the data files show the resulting technical energy efficiency. Combining this result 
with mileage (step A4, B4) yields a corresponding technical energy consumption (ktoe): 

𝐶̂𝑚,𝑐→𝑑,𝑡 = 𝑐̂𝑚,𝑐→𝑑,𝑡 ∙ 𝑀𝑚,𝑐→𝑑,𝑡 

(26) 

15 

 
 
 
 
  
  
  
  
  
  
  
  
  
  
 
 
 
 
 
 
 
 
 
 
Step C2   Effective energy consumption: An effective energy consumption 𝐶𝑚,𝑐→𝑑,𝑡
then introduced to harmonize the technical energy consumption 𝐶̂ derived from flight 
characteristics in each flight category, and the aggregated Eurostat energy consumption statistics 
reported for domestic and international flights. For years before 2004 without EUROCONTROL data, 
𝐶𝑒𝑓𝑓 is assumed to vary proportionally to technical energy consumption 𝐶̂. 

 (ktoe) is 

𝑒𝑓𝑓

For  passenger  flights,  effective  energy  consumption  is  based  on  the  real  EUROCONTROL  energy 
𝐸𝑈𝑅𝑂𝐶   of flights in each country-destination combination. In all countries, 𝐶𝐸𝑈𝑅𝑂𝐶 is 
consumption 𝐶𝑚,𝑐→𝑑,𝑡
used  directly  for  domestic,  UK,  Norway  and  Iceland,  and  extra-EEA  destinations.  For  international 
intra-EU  destinations,  a  dimensionless  scaling  factor  𝜀𝑃,𝑡   is  applied  uniformly  to  all  departure 
  intra-EU  share  of  total  international 
countries,  and 
consumption:  

𝑒𝑓𝑓 = 𝐶𝑃,𝑐→𝑑,𝑡
𝐶𝑃,𝑐→𝑑,𝑡

𝐸𝑈𝑅𝑂𝐶     for 𝑑 in {𝑑𝑜𝑚, 𝑈𝐾, 𝑁𝑂 + 𝐼𝑆, 𝑒𝑥𝑡𝑟𝑎𝐸𝐸𝐴}

𝑒𝑓𝑓

𝐶𝑃,𝑐→𝑖𝑛𝑡𝑟𝑎𝐸𝑈,𝑡

= 𝜀𝑃,𝑡 ∙ 𝐶𝑃,𝑐→𝑖𝑛𝑡𝑟𝑎𝐸𝑈,𝑡

𝐸𝑈𝑅𝑂𝐶

(27) 
(27) 

(28) 

For freight flights, effective energy consumption is assumed proportional to the technical energy 
consumption, using the passenger flight results: 

𝑒𝑓𝑓
𝐶𝐹,𝑐→𝑑,𝑡

= 𝐶̂𝐹,𝑐→𝑑,𝑡 ∙

𝑒𝑓𝑓
𝐶𝑃,𝑐→𝑑,𝑡
𝐶̂𝑃,𝑐→𝑑,𝑡

(29) 

To obtain a final Eurostat-compliant energy consumption 𝐶𝑚,𝑐→𝑑,𝑡 (ktoe; L123-133 of the data 
𝐸𝑆𝑇𝐴𝑇 (in which 
files), the effective energy consumption is scaled to Eurostat energy balances 𝐶𝑐→𝑑,𝑡
modes are aggregated, and destinations are aggregated into domestic flights or all international 
flights). Domestic freight flights are estimated by proportionally scaling to Eurostat their share of 
domestic effective energy consumption, summed across modes. The remaining Eurostat domestic 
flight energy consumption is assigned to domestic passenger flights:  

𝐶𝐹,𝑐→𝑑𝑜𝑚,𝑡 = 𝐶𝑐→𝑑𝑜𝑚,𝑡

𝐸𝑆𝑇𝐴𝑇

∙

𝑒𝑓𝑓

𝐶𝐹,𝑐→𝑑𝑜𝑚,𝑡
𝑒𝑓𝑓
𝐶∑{𝑃,𝐹},𝑐→𝑑𝑜𝑚,𝑡

𝐶𝑃,𝑐→𝑑𝑜𝑚,𝑡 = 𝐶𝑐→𝑑𝑜𝑚,𝑡

𝐸𝑆𝑇𝐴𝑇 − 𝐶𝐹,𝑐→𝑑𝑜𝑚,𝑡

For international flights, the effective energy consumption of each combination of modes and 
destinations is scaled to match total Eurostat international energy consumption, assuming all 
international modes and destinations are scaled uniformly: 

𝐶𝑚,𝑐→𝑑,𝑡 = 𝐶𝑚,𝑐→𝑑,𝑡

𝑒𝑓𝑓

∙

𝐸𝑆𝑇𝐴𝑇

𝐶𝑐→∑{𝑖𝑛𝑡𝑟𝑎𝐸𝑈,𝑈𝐾,𝑁𝑂+𝐼𝑆,𝑒𝑥𝑡𝑟𝑎𝐸𝐸𝐴},𝑡
𝑒𝑓𝑓
𝐶∑{𝑃,𝐹},𝑐→∑{𝑖𝑛𝑡𝑟𝑎𝐸𝑈,𝑈𝐾,𝑁𝑂+𝐼𝑆,𝑒𝑥𝑡𝑟𝑎𝐸𝐸𝐴},𝑡

(30) 

(31) 

(32) 

Finally, this step C2 is iterated to solve for a value of the intra-EU scaling factor 𝜀𝑃,𝑡 for which the 
intra-EU share of total international final energy consumption, summed across all EU MS, matches 
EUROCONTROL. 

𝐶∑{𝑃,𝐹},𝐸𝑈→𝑖𝑛𝑡𝑟𝑎𝐸𝑈,𝑡
𝐶∑{𝑃,𝐹},𝐸𝑈→∑{𝑖𝑛𝑡𝑟𝑎𝐸𝑈,𝑈𝐾,𝑁𝑂+𝐼𝑆,𝑒𝑥𝑡𝑟𝑎𝐸𝐸𝐴},𝑡

=

𝐸𝑈𝑅𝑂𝐶

𝐶∑{𝑃,𝐹},𝐸𝑈→𝑖𝑛𝑡𝑟𝑎𝐸𝑈,𝑡
𝐶∑{𝑃,𝐹},𝐸𝑈→∑{𝑖𝑛𝑡𝑟𝑎𝐸𝑈,𝑈𝐾,𝑁𝑂+𝐼𝑆,𝑒𝑥𝑡𝑟𝑎𝐸𝐸𝐴},𝑡

𝐸𝑈𝑅𝑂𝐶

(33) 

16 

 
 
 
 
 
 
Step C3   Emissions: In the last calibration step, emissions 𝐸𝑚,𝑐→𝑑,𝑡 (ktCO2; L200-210 of the data 
files) are computed from the final energy consumption using a uniform emission factor for 
kerosene (European Commission, 2018). Table 4 summarizes these emissions for the EU. 

𝐸𝑚,𝑐→𝑑,𝑡 = 𝐶𝑚,𝑐→𝑑,𝑡 ∙

71.9 𝑡𝐶𝑂2 𝑇𝐽⁄

∙ 41.868 𝑇𝐽 𝑘𝑡𝑜𝑒
1000

⁄

(34) 

The JRC-IDEES data files (L256-266) additionally report the discrepancy between this result and the 
UNFCCC national inventory submissions 𝐸𝑈𝑁𝐹𝐶𝐶𝐶 (ktCO2) for domestic and international aviation, 
expressed as (𝐸𝑈𝑁𝐹𝐶𝐶𝐶 − 𝐸). This assumes the total discrepancy by country is distributed 
proportionally to emissions 𝐸 across modes and, for international aviation, across international 
destinations. Figure 7 shows equivalent relative discrepancies as (𝐸𝑈𝑁𝐹𝐶𝐶𝐶 − 𝐸)/𝐸 , aggregated at EU 
level. Domestic aviation here shows larger differences, as the sectoral energy use (and consequently 
the emissions) reported in UNFCCC submissions can differ from Eurostat energy balances. 

Figure 7. Relative discrepancy of UNFCCC emissions vs. JRC-IDEES effective emissions for aviation, EU total. 

Passenger flights: Discrepancy of UNFCCC emissions

20%

10%

0%

-10%

-20%

-30%

Freight flights: Discrepancy of UNFCCC emissions

20%

10%

0%

-10%

-20%

-30%

1990

2000

1995
Domestic
Extra-EEAwUK

2005

2010

2020

2015
Intra-EEAwUK
Total

1990

1995

2000

2005

2010

2015

2020

Source: JRC-IDEES. 

Table 4. Summary of EU aviation emissions in JRC-IDEES. 

CO2 emissions (ktCO2) 
Total 
Domestic 
International 

International - intra-EEA+UK 
International - extra-EEA+UK 

International share of total 

Intra-EEA+UK share of international 
Extra-EEA+UK share of international 

Passenger transport 

Domestic 
International - intra-EEA+UK 
International - extra-EEA+UK 

Freight transport 
Domestic 
International - intra-EEA+UK 
International - extra-EEA+UK 

Notes 

      a : Shown as 2019 / 1990   100%.  

Source: JRC-IDEES. 

1990 
69 844 
14 930 
54 914 
23 124 
31 790 
78.6% 
42.1% 
57.9% 

64 929 
14 261 
22 549 
28 119 
4 915 
669 
575 
3 672 

2005 
111 229 
17 464 
93 765 
37 750 
56 015 
84.3% 
40.3% 
59.7% 

101 180 
16 944 
36 764 
47 472 
10 049 
520 
987 
8 543 

2019 
145 313 
19 506 
125 807 
55 017 
70 790 
86.6% 
43.7% 
56.3% 

135 096 
19 199 
53 735 
62 161 
10 216 
307 
1 281 
8 628 

1990-2019 change a 
108.1% 
30.7% 
129.1% 
137.9% 
122.7% 
8.0% 
1.6% 
-1.6% 

108.1% 
34.6% 
138.3% 
121.1% 
107.9% 
-54.2% 
122.9% 
135.0% 

17 

 
 
 
 
  
  
  
  
 
  
 
 
  
  
 
 
  
  
 
 
  
3.2  Maritime transport 

Figure 8 summarizes the calibration of domestic navigation (steps D) and international maritime 
bunkers (steps E); JRC-IDEES does not distinguish passenger transport in maritime modes, and 
assigns all energy use and activity to freight transport. Under the domestic navigation steps, line 
numbers (e.g. L3-5) point to the JRC-IDEES EU27/JRC-IDEES-2021_x1990_Navigation_Domestic file, 
which provides 1990-2021 data across sheets for each country, the EU, and EEA. For international 
bunkers, the line numbers refer to the EU27/JRC-IDEES-2021_x1990_Navigation_International_* 
files, in which departures are respectively aggregated into intra-/extra-EU or intra-/extra-EEA 
destinations. Key calibration results for the EU are summarized in Annex 1, Figure 15-16. 

Figure 8. Graphical overview of the calibration of maritime transport. 

Similarly to the aviation methodology, the following steps use subscripts to distinguish for a 
variable 𝑉𝑚,𝑐→𝑑,𝑡 each combination of means 𝑚 (IWW, inland waterways; DC, coastal shipping; B, 

Source: JRC analysis. 

18 

At EU and EU+UK level:Statistical Pocketbook total intra-EU andintra-EU+UK activitiesEurostatTHETIS-MRVConstraintsL3-5L11-13L19-21L3-5Statistical PocketbookExternal data usedL23-35L64-66At EEA level: THETIS-MRVtotal CO2emissions forinternational intra-EEA and extra-EEA destinations, 2018-2021 (under assumptions on partial coverage)E1: Activity[million tonne-km]L12-14L21-23L53-55L25-37L66-68L83-85L89-93E3: Technicalenergy efficiency[kgoe/ 100 km]International bunkersDomestic navigationL51-53L81-83L85-89D4: Energy consumption and emissions[ktoe, ktCO2]E5: THETIS-MRV / UNFCCC technical emissions [ktCO2]At EEA level: THETIS-MRVtotal mileage forinternational intra-EEA and extra-EEA destinations, 2018-2021 (under assumptions on partial coverage)E2: Load factor and mileage[tonne/ movement, million km]D1: Activity [million tonne-km]D2: Load factor and mileage[tonne/ movement, million km]E4: Energy consumption and emissions[ktoe, ktCO2]For each country: Eurostat consumption by fuelAt EEA level: THETIS-MRVtotal CO2emissions forinternational intra-EEA destinations, 2018-2021 (under assumptions on partial coverage)D3: Technical energy efficiency[kgoe/ 100 km] 
 
international bunkers), departure country or region6 𝑐, destination7 𝑑, and year 𝑡. Final energy 
consumption is additionally reported for individual fuels 𝑓 following the Eurostat correspondence in 
Annex 3. Where needed for clarity, a superscript denotes the data source.  

3.2.1  Domestic navigation 

The calibration of inland waterways excludes countries that do not report any activity for this 
means (Cyprus, Denmark, Estonia, Greece, Spain, Ireland, Latvia, Malta, as well as Iceland and 
Norway). Domestic coastal shipping is excluded for landlocked countries (Austria, Czechia, Hungary, 
Luxembourg, Slovakia) and Bulgaria, which reports negligible coastal activity so that all domestic 
navigation is assigned to inland waterways. Slovenia reports no energy use in domestic navigation 
and is excluded entirely; all remaining countries are assumed to have activity in both means. 

The results of this calibration are reported in the EU27/JRC-IDEES-2021_x1990_Navigation_Domestic 
data file for each country, the EU, and EEA, at the line numbers specified in each step. 

Step D1   Activity: For inland waterways, activity 𝐴𝑐𝑡𝐼𝑊𝑊,𝑐,𝑡 (million tonne-km; L5 of the data 
file) is taken from the DG MOVE Statistical Pocketbook for each EU MS and the UK. 
2021 activity is assumed to vary proportionally to the 
domestic navigation. 

 Eurostat energy consumption for 

 2020-

For domestic coastal shipping, the activity 𝐴𝑐𝑡𝐷𝐶,𝑐,𝑡 (million tonne-km; L4) is estimated by 
combining port-level data on gross weight of goods from Eurostat mar_go_am, with port-to-port 
route distances from Bertoli et al. (2016) and own spatial analysis. For missing years (generally 
before 2000), activity is assumed to vary proportionally to inland waterways activity. 

Step D2   Load factor and mileage: For inland waterways, the load factor 𝐿𝐹𝐼𝑊𝑊,𝑐,𝑡 (tonne / 
movement) is first estimated using 𝐴𝑐𝑡𝐼𝑊𝑊,𝑐,𝑡 and mileage statistics 𝑀𝐼𝑊𝑊,𝑐,𝑡
when available:  

𝐸𝑆𝑇𝐴𝑇  (million vehicle-km) 

𝐿𝐹𝐼𝑊𝑊,𝑐,𝑡 =

𝐴𝑐𝑡𝐼𝑊𝑊,𝑐,𝑡 
𝐸𝑆𝑇𝐴𝑇
𝑀𝐼𝑊𝑊,𝑐,𝑡

(35) 

These mileage statistics are taken from Eurostat dataset iww_tf_vetf or national statistics where 
available. Missing data for 2000-2015 are otherwise estimated from the transport calibration in 
JRC-IDEES-2015 (Mantzos et al., 2017). For this purpose, the JRC-IDEES-2015 load factor 
𝐿𝐹𝐼𝑊𝑊,𝑐,𝑡
where iww_tf_vetf is more complete: 

𝐼𝐷𝐸𝐸𝑆2015 is rescaled proportionally to the new activity estimate obtained for the Netherlands, 

𝐿𝐹𝐼𝑊𝑊,𝑐,𝑡 = 𝐿𝐹𝐼𝑊𝑊,𝑁𝐿,𝑡 ∙

𝐿𝐹𝐼𝑊𝑊,𝑐,𝑡
𝐿𝐹𝐼𝑊𝑊,𝑁𝐿,𝑡

𝐼𝐷𝐸𝐸𝑆2015 
𝐼𝐷𝐸𝐸𝑆2015 

(36) 

6 EU: all Member States of the European Union (EU27, from 1.2.2020). EEA: EU27, Norway, and Iceland; United Kingdom 

included for 1990-2020. 

7 Destinations are split into: dom, domestic; intraEU, international intra-EU27; UK: United Kingdom; NO+IS: Norway and 
Iceland; intraEEA: international intra-EEA (with UK for 1990-2020); extraEEA: international extra-EEA and extra-UK. 
For final reporting in the JRC-IDEES-2021_x1990_Navigation_International_* files, destinations are aggregated into 
intra-/extra-EU27 or intra-/extra-EEA. 

19 

 
 
 
 
 
                                                 
 
The load factor is assumed constant before the earliest year available in either iww_tf_vetf or JRC-
IDEES-2015. Remaining missing years (generally after 2015) are estimated from the annual change 
𝐸𝑆𝑇𝐴𝑇 , which splits 
in total activities and total Eurostat consumption for domestic navigation, 𝐶𝑓,𝑐,𝑡
consumption by fuel 𝑓 without distinguishing means. Using total consumption across all fuels to 
estimate the final load factor (L21):  

𝐿𝐹𝐼𝑊𝑊,𝑐,𝑡 = 𝐿𝐹𝐼𝑊𝑊,𝑐,𝑡−1 (

𝐸𝑆𝑇𝐴𝑇 /𝐴𝑐𝑡𝐼𝑊𝑊,𝑐,𝑡 

𝐶𝑡𝑜𝑡𝑎𝑙,𝑐,𝑡
𝐸𝑆𝑇𝐴𝑇
𝐶𝑡𝑜𝑡𝑎𝑙,𝑐,𝑡−1

/𝐴𝑐𝑡𝐼𝑊𝑊,𝑐,𝑡−1

− 

𝐴𝑐𝑡𝐼𝑊𝑊,𝑐,𝑡
2∙𝐴𝑐𝑡∑{𝐼𝑊𝑊,𝐷𝐶},𝑐,𝑡

)

(37) 

For domestic coastal shipping in countries that also have inland waterway activity, the load factor 
𝐿𝐹𝐷𝐶,𝑐,𝑡 (tonne/movement; L20) is estimated from the load factor for international maritime 
bunkers 𝐿𝐹𝐵,𝑐,𝑡 (step E2): 

𝐿𝐹𝐷𝐶,𝑐,𝑡 =

𝐿𝐹𝐵,𝑐,𝑡
2

(38) 

For countries without inland waterways, the load factor 𝐿𝐹𝐷𝐶,𝑐,𝑡 is instead taken from JRC-IDEES-
2015 for 2000-2015. This load factor is assumed constant before 2000, and adjusted after 2015 
using the annual change in activity and Eurostat consumption similarly to 𝐿𝐹𝐼𝑊𝑊,𝑐,𝑡. 

The resulting load factors yield total mileage 𝑀𝑚,𝑐,𝑡 (million vehicle-km; L11-13) for each of the 
two domestic means of navigation:  

𝑀𝑚,𝑐,𝑡 =

𝐴𝑐𝑡𝑚,𝑐,𝑡
𝐿𝐹𝑚,𝑐,𝑡

(39) 

Step D3   Technical energy consumption: To later estimate energy efficiency of vessels using 
country-specific characteristics, a reference EU-level energy efficiency 𝑐̂𝑚,𝐸𝑈,𝑡
km) is first estimated for each domestic means of navigation, and applied to all countries. For 
inland waterways, the value 𝑐̂𝐼𝑊𝑊,𝐸𝑈,2015
Other years are then computed from the change in load factor over time, using an efficiency scaling 
exponent of 0.85 (Notteboom & Cariou, 2009) to account for economies of scale: 

 is taken from the last year available in JRC-IDEES-2015. 

 (toe/million tonne-

𝑟𝑒𝑓

𝑟𝑒𝑓

𝑟𝑒𝑓
𝑐̂𝐼𝑊𝑊,𝐸𝑈,𝑡

𝑟𝑒𝑓

= 𝑐̂𝐼𝑊𝑊,𝐸𝑈,2015

(

−0.85

𝐿𝐹𝐼𝑊𝑊,𝐸𝑈,𝑡
𝐿𝐹𝐼𝑊𝑊,𝐸𝑈,2015

)

For domestic coastal shipping, the reference energy efficiency is estimated similarly from the 
average EU technical energy efficiency and load factor of international maritime bunkers: 

𝑟𝑒𝑓
𝑐̂𝐷𝐶,𝐸𝑈,𝑡

= 𝑐̂𝐵,𝐸𝑈,𝑡 (

−0.85

𝐿𝐹𝐷𝐶,𝐸𝑈,𝑡
𝐿𝐹𝐵,𝐸𝑈,𝑡

)

(40) 

(41) 

To meet total EU-level Eurostat consumption for domestic navigation across all fuels and means, 
𝐸𝑆𝑇𝐴𝑇
𝐶𝑡𝑜𝑡𝑎𝑙,𝐸𝑈,𝑡
efficiency 𝑐̂𝑚,𝐸𝑈,𝑡

 , the reference energy efficiency of each means is rescaled to a technical energy 
 (toe/million tonne-km) using the relative EU-level activity of each means: 

𝑐̂𝑚,𝐸𝑈,𝑡

𝑟𝑒𝑓
= 𝑐̂𝑚,𝐸𝑈,𝑡

∙

𝐸𝑆𝑇𝐴𝑇
𝐶𝑡𝑜𝑡𝑎𝑙,𝐸𝑈,𝑡

∑

𝑚𝜖{𝐼𝑊𝑊,𝐷𝐶}

(𝐴𝑐𝑡𝑚,𝐸𝑈,𝑡 ∙ 𝑐̂𝑚,𝐸𝑈,𝑡

𝑟𝑒𝑓

)/1000

(42) 

20 

 
 
 
 
 
 
This EU-level technical energy efficiency is then adjusted to each country (including non-EU 
countries), using local load factors and the same efficiency scaling exponent. The resulting load 
factor is shown in L51-53 of the data files. 

𝑐̂𝑚,𝑐,𝑡

= 𝑐̂𝑚,𝐸𝑈,𝑡

(

−0.85

𝐿𝐹𝑚,𝑐,𝑡
𝐿𝐹𝑚,𝐸𝑈,𝑡

)

This efficiency is combined with activity (step D1) to find the corresponding technical energy 
consumption 𝐶̂𝑚,𝑐,𝑡

 (ktoe) for each country and each domestic means: 

𝐶̂𝑚,𝑐,𝑡

= 𝑐̂𝑚,𝑐,𝑡

∙ 𝐴𝑐𝑡𝑚,𝑐,𝑡

(43) 

(44) 

Step D4   Effective energy consumption and CO2 emissions: To ensure each 
consumption for domestic navigation still meets Eurostat energy balances after adjusting for load 
factors, the technical energy consumption for each domestic means is rescaled to an effective 
energy consumption 𝐶𝑚,𝑓,𝑐,𝑡
domestic means, proportionally to their reported total consumption in energy balances: 

 (ktoe; L23-35). This variable now splits individual fuels 𝑓 across 

𝐶𝑚,𝑓,𝑐,𝑡

= 𝐶𝑓,𝑐,𝑡

𝐸𝑆𝑇𝐴𝑇 ∙

𝐶̂𝑚,𝑐,𝑡

𝐶̂
∑{𝐼𝑊𝑊,𝐷𝐶},𝑐,𝑡

Corresponding effective emissions 𝐸𝑚,𝑓,𝑐,𝑡
factors 𝑒𝑓

   (tCO2/toe) (European Commission, 2018): 

 (ktCO2) are computed using fuel-specific emissions 

𝐸𝑚,𝑓,𝑐,𝑡

= 𝐶𝑚,𝑓,𝑐,𝑡

∙   𝑒𝑓

(45) 

(46) 

The JRC-IDEES data files report the 𝐸𝑚,𝑡𝑜𝑡𝑎𝑙,𝑐,𝑡
(L64-66), and their discrepancy with UNFCCC country inventory submissions for domestic navigation 
(L85-89). This assumes the discrepancy is distributed proportionally to the emissions 𝐸𝑚,𝑡𝑜𝑡𝑎𝑙,𝑐,𝑡
each transport means. Figure 9 shows the relative discrepancy as (𝐸𝑈𝑁𝐹𝐶𝐶𝐶 − 𝐸)/𝐸 ; the larger 
discrepancy for 2020-2021 may be due to data quality issues for the last years of statistics. 

 emissions aggregating all fuels for each means 

 of 

L81-83 additionally report 2018-2021 emissions under assumptions for THETIS-MRV coverage (see 
step E5 of international maritime bunkers). 

Figure 9. Relative discrepancy of UNFCCC emissions vs. JRC-IDEES effective emissions (domestic navigation). 

Discrepancy of UNFCCC emissions, EU total

60%

50%

40%

30%

20%

10%

0%

1990

1995

2000

2005

Inland waterways

Domestic coastal shipping

2010

Total

2015

2020

Source: JRC-IDEES. 

21 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
3.2.2  International maritime bunkers 

The calibration of maritime bunkers excludes landlocked countries that accordingly do not report 
any consumption in energy balances: Austria8, Czechia, Hungary, Luxembourg and Slovakia.  
Results are reported for each country, the EU and EEA in the EU27/JRC-IDEES-2021_x1990_ 
Navigation_International files, at the line numbers specified in each step. 

The calibration of mileage, energy, and emissions uses THETIS-MRV data under the assumption that 
the dataset covers two-thirds of domestic coastal shipping voyages and 90% of total voyages, with 
a proportional coverage of mileage and emissions. These assumptions are based on expert input as 
well as preliminary analysis of the THETIS-MRV system (European Commission, 2019), and are 
revisited in the robustness checks discussed in section 3.3 and Annex 2. 

𝐸𝑆𝑇𝐴𝑇  
Step E1   Activity: A first estimate of maritime bunker activity using Eurostat data, 𝐴𝑐𝑡𝐵,𝑐→𝑑,𝑡
(million tonne-km), combines data on gross weight of goods from Eurostat mar_go_am9 with port-
to-port distances from Bertoli et al. (2016) and own spatial analysis. This estimate is harmonized in 
two steps with total EU-level Statistical Pocketbook activity and with country-level activity for 
domestic coastal shipping (step D1).  

𝑖𝑛𝑖𝑡

 (million tonne-km) scales the Eurostat data to match Statistical 

First, an estimate 𝐴𝑐𝑡𝐵,𝑐→𝑑,𝑡
Pocketbook intra-EU activity at total EU level, 𝐴𝑐𝑡𝐵,𝐸𝑈→∑{𝑑𝑜𝑚,𝑖𝑛𝑡𝑟𝑎𝐸𝑈},𝑡
  for available years (1995-
2021). For missing years, the estimate is assumed to vary proportionally to total maritime bunker 
consumption from Eurostat energy balances. For EU MS, this intra-EU activity is estimated by: 

𝑃𝐵

𝑖𝑛𝑖𝑡

𝐴𝑐𝑡𝐵,𝑐→𝑖𝑛𝑡𝑟𝑎𝐸𝑈,𝑡

𝐸𝑆𝑇𝐴𝑇
= 𝐴𝑐𝑡𝐵,𝑐→𝑖𝑛𝑡𝑟𝑎𝐸𝑈,𝑡

∙

𝑃𝐵

𝐴𝑐𝑡𝐵,𝐸𝑈→∑{𝑑𝑜𝑚,𝑖𝑛𝑡𝑟𝑎𝐸𝑈},𝑡

− 𝐴𝑐𝑡𝐷𝐶,𝐸𝑈,𝑡

𝐸𝑆𝑇𝐴𝑇

𝐴𝑐𝑡𝐵,𝐸𝑈→𝑖𝑛𝑡𝑟𝑎𝐸𝑈,𝑡

For Norway and Iceland, intra-EU activity is estimated similarly but without domestic coastal 
shipping: 

𝑖𝑛𝑖𝑡

𝐴𝑐𝑡𝐵,𝑁𝑂+𝐼𝑆→𝑖𝑛𝑡𝑟𝑎𝐸𝑈,𝑡

= 𝐴𝑐𝑡𝐵,𝑁𝑂+𝐼𝑆→𝑖𝑛𝑡𝑟𝑎𝐸𝑈,𝑡

𝐸𝑆𝑇𝐴𝑇

∙

𝑃𝐵

𝐴𝑐𝑡𝐵,𝐸𝑈→∑{𝑑𝑜𝑚,𝑖𝑛𝑡𝑟𝑎𝐸𝑈},𝑡
𝐴𝑐𝑡𝐵,𝐸𝑈→𝑖𝑛𝑡𝑟𝑎𝐸𝑈,𝑡

𝐸𝑆𝑇𝐴𝑇

(47) 

(48) 

Scaling to Pocketbook data for the total EU+UK, the initial estimate of UK-destined activity for EU 
MS and Norway and Iceland is accordingly: 

𝑖𝑛𝑖𝑡
𝐴𝑐𝑡𝐵,𝑐→𝑈𝐾,𝑡

= (𝐴𝑐𝑡𝐵,𝑐→∑{𝑖𝑛𝑡𝑟𝑎𝐸𝑈,𝑈𝐾},𝑡

𝐸𝑆𝑇𝐴𝑇

∙

𝑃𝐵

𝐴𝑐𝑡𝐵,∑{𝐸𝑈,𝑈𝐾}→∑{𝑑𝑜𝑚,𝑖𝑛𝑡𝑟𝑎𝐸𝑈,𝑈𝐾},𝑡
𝐴𝑐𝑡𝐵,∑{𝐸𝑈,𝑈𝐾}→𝑖𝑛𝑡𝑟𝑎𝐸𝑈,𝑡

𝐸𝑆𝑇𝐴𝑇

𝑖𝑛𝑖𝑡
) − 𝐴𝑐𝑡𝐵,𝑐→𝑖𝑛𝑡𝑟𝑎𝐸𝑈,𝑡

(49) 

The EU-destined activity 
destined for Norway, Iceland, and extra-EEA is carried over from Eurostat without scaling. 

. For all countries, activity 

8 Austria reports a small consumption (from voyages on the Danube), which is reassigned to domestic navigation. 
9 Reported at port level for intra-EU activity, or at country level for extra-EU activity. 

22 

 
 
 
 
                                                 
 
Secondly, to ensure activity remains consistent with the Statistical Pocketbook at total EU+UK level, 
this estimation is iterated to obtain a final activity 𝐴𝑐𝑡𝐵,𝑐→,𝑡
Norway and Iceland, this activity for intra-EU and UK destinations is given by: 

 (million tonne-km). For all EU MS, 

𝐴𝑐𝑡𝐵,𝑐→𝑖𝑛𝑡𝑟𝑎𝐸𝑈,𝑡

=

𝑖𝑛𝑖𝑡

𝐴𝑐𝑡𝐵,𝑐→𝑖𝑛𝑡𝑟𝑎𝐸𝑈,𝑡

𝑃𝐵

∙ (𝐴𝑐𝑡𝐵,𝐸𝑈→∑{𝑑𝑜𝑚,𝑖𝑛𝑡𝑟𝑎𝐸𝑈},𝑡
𝐴𝑐𝑡𝐵,𝐸𝑈→𝑖𝑛𝑡𝑟𝑎𝐸𝑈,𝑡

𝑖𝑛𝑖𝑡

− 𝐴𝑐𝑡𝐷𝐶,𝐸𝑈,𝑡)

𝐴𝑐𝑡𝐵,𝑐→𝑈𝐾,𝑡

=

𝑖𝑛𝑖𝑡
𝐴𝑐𝑡𝐵,𝑐→𝑈𝐾,𝑡

𝑃𝐵

∙ (𝐴𝑐𝑡𝐵,∑{𝐸𝑈,𝑈𝐾}→∑{𝑑𝑜𝑚,𝑖𝑛𝑡𝑟𝑎𝐸𝑈,𝑈𝐾},𝑡
𝑖𝑛𝑖𝑡
𝑖𝑛𝑖𝑡
+ 𝐴𝑐𝑡𝐵,𝑈𝐾→𝐸𝑈,𝑡
𝐴𝑐𝑡𝐵,𝐸𝑈→𝑈𝐾,𝑡

𝑃𝐵

− 𝐴𝑐𝑡𝐵,𝐸𝑈→∑{𝑑𝑜𝑚,𝑖𝑛𝑡𝑟𝑎𝐸𝑈},𝑡

(50) 

(51) 

)

The U

-destined activity is then: 

𝐴𝑐𝑡𝐵,𝑈𝐾→𝐸𝑈,𝑡

= (𝐴𝑐𝑡𝐵,∑{𝐸𝑈,𝑈𝐾}→∑{𝑑𝑜𝑚,𝑖𝑛𝑡𝑟𝑎𝐸𝑈,𝑈𝐾},𝑡

𝑃𝐵

𝑃𝐵

− 𝐴𝑐𝑡𝐵,𝐸𝑈→∑{𝑑𝑜𝑚,𝑖𝑛𝑡𝑟𝑎𝐸𝑈},𝑡

) − 𝐴𝑐𝑡𝐵,𝐸𝑈→𝑈𝐾,𝑡

− 𝐴𝑐𝑡𝐷𝐶,𝑈𝐾,𝑡 (52) 

For all countries, Eurostat activity for Norway, Iceland, and extra-EEA destinations is again carried 
over. L3-L5 of the data files show the final activity estimate. 

𝑇𝐻𝐸𝑇𝐼𝑆

Step E2   Mileage and load factors: The total EEA bunker mileage for intra-EEA and extra-EEA 
destinations in 2018-202110 , 𝑀𝐵,𝐸𝐸𝐴→𝑑,𝑡
dataset. Assuming this dataset covers two-thirds of domestic coastal shipping voyages, 
international intra-EEA mileage 𝑀𝐵,𝐸𝐸𝐴→𝑖𝑛𝑡𝑟𝑎𝐸𝐸𝐴,𝑡
 is the difference between total intra-EEA mileage 
from THETIS-MRV, and two-thirds of estimated EEA domestic coastal shipping mileage (step D2). A 
corresponding average load factor 𝐿𝐹𝐵,𝐸𝐸𝐴→𝑑,𝑡
extra-EEA destinations using EEA activity from the last step: 

 (million vehicle-km) is obtained from the THETIS-MRV 

 (tonne / movement) is defined for intra-EEA and 

𝑇𝐻𝐸𝑇𝐼𝑆

𝑟𝑒𝑓

𝑟𝑒𝑓
𝐿𝐹𝐵,𝐸𝐸𝐴→𝑑,𝑡

=

𝐴𝑐𝑡𝐵,𝐸𝐸𝐴→𝑑,𝑡 
𝑇𝐻𝐸𝑇𝐼𝑆
𝑀𝐵,𝐸𝐸𝐴→𝑑,𝑡

      for 𝑑 in {𝑖𝑛𝑡𝑟𝑎𝐸𝐸𝐴, 𝑒𝑥𝑡𝑟𝑎𝐸𝐸𝐴}

(53) 
(53) 

This load factor is adapted to country-specific vessel data from Eurostat dataset mar_mt_am_csvi, 
which reports by vessel type the number and gross tonnage of vessels arriving in main ports for 
each country. To this end, empirical regressions11 are first applied at the level of reported vessel 
types to convert reported gross tonnage to deadweight tonnage. To choose vessel types that are 
typical for intra- and extra-EEA destinations by country, two sets of vessel types are considered: 
𝑆𝑎𝑙𝑙 (liquid or dry bulk carriers, container carriers, specialised carriers, and general cargo); and 𝑆𝑙𝑎𝑟𝑔𝑒 
(liquid or dry bulk carriers, and container carriers). An average deadweight tonnage can then be 
computed for each vessel set by country, 𝐷𝑊𝑇  𝑆𝑎𝑙𝑙,𝑐,𝑡 and 𝐷𝑊𝑇 𝑆𝑙𝑎𝑟𝑔𝑒,𝑐,𝑡 (tonnes/vessel). 

Next, the initial country-specific load factor for intra-EEA and extra-EEA destinations, 𝐿𝐹𝐵,𝑐→𝑑,𝑡
(tonnes/movement), is estimated by scaling the average EEA load factors and deadweight tonnages. 
This uses a scaling exponent of 0.4 to model economies of scale and smooth discrepancies across 
countries (see robustness checks), and assumes 𝑆𝑎𝑙𝑙 is generally typical of intra-EEA voyages 
and 𝑆𝑙𝑎𝑟𝑔𝑒 of extra-EEA voyages: 

𝑖𝑛𝑖𝑡

10 Excluding the UK for 2021. 
11 Papanikolaou (2014), Figs. A.16, A.44, A.70, A.86. 

23 

 
 
 
 
 
 
 
 
 
  
                                                 
 
𝑖𝑛𝑖𝑡

𝐿𝐹𝐵,𝑐→𝑖𝑛𝑡𝑟𝑎𝐸𝐸𝐴,𝑡

𝑟𝑒𝑓

= 𝐿𝐹𝐵,𝐸𝐸𝐴→𝑖𝑛𝑡𝑟𝑎𝐸𝐸𝐴,𝑡

𝑖𝑛𝑖𝑡

𝐿𝐹𝐵,𝑐→𝑒𝑥𝑡𝑟𝑎𝐸𝐸𝐴,𝑡

𝑟𝑒𝑓

= 𝐿𝐹𝐵,𝐸𝐸𝐴→𝑒𝑥𝑡𝑟𝑎𝐸𝐸𝐴,𝑡

(

min (𝐷𝑊𝑇  𝑆𝑎𝑙𝑙,𝑐,𝑡 , 𝐷𝑊𝑇  𝑆𝑙𝑎𝑟𝑔𝑒,𝑐,𝑡)
𝐷𝑊𝑇  𝑆𝑎𝑙𝑙,𝐸𝐸𝐴,𝑡

)

0.4

(

max (𝐷𝑊𝑇  𝑆𝑎𝑙𝑙,𝑐,𝑡 , 𝐷𝑊𝑇  𝑆𝑙𝑎𝑟𝑔𝑒,𝑐,𝑡)
𝐷𝑊𝑇  𝑆𝑙𝑎𝑟𝑔𝑒,𝐸𝐸𝐴,𝑡

)

0.4

(54) 

(55) 

For each country-destination pair, these load factors are used to divide activity, and rescaled to 
yield a mileage 𝑀𝐵,𝑐→𝑑,𝑡
 (million vehicle-km; L12-14) that is consistent with total THETIS-MRV 
mileage for intra-EEA and extra-EEA destination groups. Earlier years without THETIS-MRV data use 
a constant scaling ratio based on 2018.  

𝑀𝐵,𝑐→𝑑,𝑡

=

𝐴𝑐𝑡𝐵,𝑐→𝑑,𝑡
𝑖𝑛𝑖𝑡
𝐿𝐹𝐵,𝑐→𝑖𝑛𝑡𝑟𝑎𝐸𝐸𝐴,𝑡

 ∙

𝑇𝐻𝐸𝑇𝐼𝑆

𝑀𝐵,𝐸𝐸𝐴→𝑖𝑛𝑡𝑟𝑎𝐸𝐸𝐴,𝑡
𝑀𝐵,𝐸𝐸𝐴→𝑖𝑛𝑡𝑟𝑎𝐸𝐸𝐴,𝑡

𝑖𝑛𝑖𝑡

  for 𝑑 in {𝑖𝑛𝑡𝑟𝑎𝐸𝑈, 𝑈𝐾, 𝑁𝑂 + 𝐼𝑆}

𝑀𝐵,𝑐→𝑒𝑥𝑡𝑟𝑎𝐸𝐸𝐴,𝑡

=

𝐴𝑐𝑡𝐵,𝑐→𝑒𝑥𝑡𝑟𝑎𝐸𝐸𝐴,𝑡
𝐿𝐹𝐵,𝑐→𝑒𝑥𝑡𝑟𝑎𝐸𝐸𝐴,𝑡

𝑖𝑛𝑖𝑡

∙

𝑇𝐻𝐸𝑇𝐼𝑆

𝑀𝐵,𝐸𝐸𝐴→𝑒𝑥𝑡𝑟𝑎𝐸𝐸𝐴,𝑡
𝑀𝐵,𝐸𝐸𝐴→𝑒𝑥𝑡𝑟𝑎𝐸𝐸𝐴,𝑡

𝑖𝑛𝑖𝑡

(56) 
(56) 

(57) 

Finally, load factors for each country-destination pair (L21-23) are recomputed for this mileage: 

𝐿𝐹𝐵,𝑐→𝑑,𝑡

=

𝐴𝑐𝑡𝐵,𝑐→𝑑,𝑡
𝑀𝐵,𝑐→𝑑,𝑡

(58) 

Step E3   Technical energy consumption and emissions: The calibration of technical energy 
consumption aims to i) translate EEA-level THETIS-MRV efficiency data to representative country-
specific efficiencies, given vessel characteristics identified in step E2; and ii) account for plausible 
gaps in THETIS-MRV coverage. 

𝑇𝐻𝐸𝑇𝐼𝑆

THETIS-MRV is first used to find a reference EEA-level technical energy efficiency for international 
intra- and extra-EEA destination groups, 𝑐̂𝐵,𝐸𝐸𝐴→𝑑,𝑡
 (ktCO2) , mileage 𝑀𝐵,𝐸𝐸𝐴→𝑑,𝑡
emissions 𝐸𝐵,𝐸𝐸𝐴→𝑑,𝑡
𝑡𝑜𝑡𝑎𝑙 (ktCO2/ktoe)12. As with mileage, this assumes international intra-EEA 
emissions factor,  𝑒𝑡
emissions are the difference between total THETIS-MRV intra-EEA emissions, and two-thirds of the 
total EEA domestic coastal shipping emissions found in step D4. 

 (kgoe/km), using corresponding reported 
 (million vehicle-km), and average sector-wide 

𝑇𝐻𝐸𝑇𝐼𝑆

𝑇𝐻𝐸𝑇𝐼𝑆

𝑇𝐻𝐸𝑇𝐼𝑆 =
𝑐̂𝐵,𝐸𝐸𝐴→𝑑,𝑡

𝑇𝐻𝐸𝑇𝐼𝑆
𝐸𝐵,𝐸𝐸𝐴→𝑑,𝑡
∙ 𝑒𝑡

𝑇𝐻𝐸𝑇𝐼𝑆
𝑀𝐵,𝐸𝐸𝐴→𝑑,𝑡

𝑡𝑜𝑡𝑎𝑙   for 𝑑 in {𝑖𝑛𝑡𝑟𝑎𝐸𝐸𝐴, 𝑒𝑥𝑡𝑟𝑎𝐸𝐸𝐴}

(59) 

This reference technical energy efficiency is scaled to find an initial country-specific estimate of 
technical energy consumption 𝐶̂
with the same scaling exponent as domestic coastal shipping (step D3; see robustness checks). 

 (ktoe), using the reference and country-specific load factors 

𝑖𝑛𝑖𝑡
𝐵,𝑐→𝑑,𝑡

12 Computed for the total maritime bunker sector, using total EEA consumption of each fuel in energy balances and 

standard emissions factors (European Commission, 2018). The average factor thus varies over time due to changes 
in sectoral fuel shares. 

24 

 
 
 
 
 
 
 
 
 
 
 
 
                                                 
 
𝐶̂

𝑖𝑛𝑖𝑡
𝐵,𝑐→𝑑,𝑡

𝑇𝐻𝐸𝑇𝐼𝑆

= 𝑐̂𝐵,𝐸𝐸𝐴→𝑖𝑛𝑡𝑟𝑎𝐸𝐸𝐴,𝑡

∙ 𝑀𝐵,𝑐→𝑑,𝑡

∙ (

0.85

𝐿𝐹𝐵,𝑐→𝑑,𝑡
𝐿𝐹𝐵,𝐸𝐸𝐴→𝑖𝑛𝑡𝑟𝑎𝐸𝐸𝐴,𝑡

𝑟𝑒𝑓

)

 for 𝑑 in {𝑖𝑛𝑡𝑟𝑎𝐸𝑈, 𝑈𝐾, 𝑁𝑂 + 𝐼𝑆}

(60) 
(60) 

𝐶̂𝐵,𝑐→𝑒𝑥𝑡𝑟𝑎𝐸𝐸𝐴,𝑡
𝑖𝑛𝑖𝑡

𝑇𝐻𝐸𝑇𝐼𝑆

= 𝑐̂𝐵,𝐸𝐸𝐴→𝑒𝑥𝑡𝑟𝑎𝐸𝐸𝐴,𝑡

∙ 𝑀𝐵,𝑐→𝑑,𝑡

∙ (

0.85

𝐿𝐹𝐵,𝑐→𝑑,𝑡
𝐿𝐹𝐵,𝐸𝐸𝐴→𝑒𝑥𝑡𝑟𝑎𝐸𝐸𝐴,𝑡

𝑟𝑒𝑓

)

Corresponding emissions 𝐸̂𝐵,𝑐→𝑑,𝑡

𝑖𝑛𝑖𝑡

(ktCO2) are found with the same average emissions factor: 

𝐸̂𝐵,𝑐→𝑑,𝑡
𝑖𝑛𝑖𝑡

= 𝐶̂

𝑖𝑛𝑖𝑡
𝐵,𝑐→𝑑,𝑡

𝑡𝑜𝑡𝑎𝑙

∙ 𝑒𝑡

(61) 

(62) 

To harmonize THETIS-MRV emissions across domestic and international destinations, a 
dimensionless THETIS coverage factor 𝐾 
1990-2021. This factor represents total coverage of international voyages in THETIS-MRV, 
assuming coverage of 90% of emissions across all destinations and two-thirds for domestic coastal 
shipping, consistently with earlier steps: 

𝑇𝐻𝐸𝑇𝐼𝑆 is derived for 2018 and assumed constant for 

𝑇𝐻𝐸𝑇𝐼𝑆   =

𝐾 

𝑇𝐻𝐸𝑇𝐼𝑆

𝐸𝐵,𝐸𝐸𝐴→∑{𝑖𝑛𝑡𝑟𝑎𝐸𝐸𝐴,𝑒𝑥𝑡𝑟𝑎𝐸𝐸𝐴},2018

𝑇𝐻𝐸𝑇𝐼𝑆

(𝐸𝐵,𝐸𝐸𝐴→∑{𝑑𝑜𝑚,𝑖𝑛𝑡𝑟𝑎𝐸𝐸𝐴,𝑒𝑥𝑡𝑟𝑎𝐸𝐸𝐴},2018
0.9

)

− 𝐸𝐷𝐶,𝑡𝑜𝑡𝑎𝑙,𝐸𝐸𝐴,𝑡

≈ 0.941

(63)

This factor is then used to rescale the initial technical energy consumption to a technical energy 
consumption 𝐶̂𝐵,𝑐→𝑑,𝑡
 (ktCO2), which match total EEA THETIS-MRV 
emissions for intra- and extra-EEA destination groups under the assumptions on coverage. L53-55 
of the data files divide this consumption by mileage to yield a technical energy efficiency.  

 (ktoe) and emissions 𝐸̂𝐵,𝑐→𝑑,𝑡

𝐶̂

𝐵,𝑐→𝑑,𝑡

=

𝑖𝑛𝑖𝑡
𝐶̂
𝐵,𝑐→𝑑,𝑡
𝑇𝐻𝐸𝑇𝐼𝑆 ∙ 𝐸̂

𝐾 

𝑇𝐻𝐸𝑇𝐼𝑆

∙ 𝐸𝐵,𝐸𝐸𝐴→𝑖𝑛𝑡𝑟𝑎𝐸𝐸𝐴,𝑡

𝑖𝑛𝑖𝑡
𝐵,𝐸𝐸𝐴→∑{𝑖𝑛𝑡𝑟𝑎𝐸𝑈,𝑈𝐾,𝑁𝑂+𝐼𝑆},𝑡

   for 𝑑 in {𝑖𝑛𝑡𝑟𝑎𝐸𝑈, 𝑈𝐾, 𝑁𝑂 + 𝐼𝑆}

𝐶̂𝐵,𝑐→𝑒𝑥𝑡𝑟𝑎𝐸𝐸𝐴,𝑡

=

𝐶̂𝐵,𝑐→𝑒𝑥𝑡𝑟𝑎𝐸𝐸𝐴,𝑡
𝑖𝑛𝑖𝑡
𝑇𝐻𝐸𝑇𝐼𝑆 ∙ 𝐸̂

𝐾 

𝑇𝐻𝐸𝑇𝐼𝑆

∙ 𝐸𝐵,𝐸𝐸𝐴→𝑒𝑥𝑡𝑟𝑎𝐸𝐸𝐴,𝑡
𝑖𝑛𝑖𝑡
𝐵,𝐸𝐸𝐴→𝑒𝑥𝑡𝑟𝑎𝐸𝐸𝐴,𝑡

𝐸̂𝐵,𝑐→𝑑,𝑡

= 𝐶̂

𝐵,𝑐→𝑑,𝑡

𝑡𝑜𝑡𝑎𝑙

∙ 𝑒𝑡

(64) 
(64) 

(65) 

(66) 

Step E4   Effective energy consumption and emissions: In the last calibration step, the 
technical energy consumption and emissions are adjusted to ensure i) consumption of each fuel by 
country meets Eurostat energy balances for bunkers 𝐶𝑓,𝑐,𝑡
intra-EEA destinations still match THETIS-MRV emissions when summed at EEA level, with 
discrepancies being allocated to extra-EEA destinations. 

𝐸𝑆𝑇𝐴𝑇, and ii) corresponding emissions for 

The final Eurostat-compliant effective energy consumption 𝐶𝐵,𝑓,𝑐→𝑑,𝑡
distinguishes fuels 𝑓 following the Eurostat codes in Annex 3. Consumption is computed by country, 
starting from the total of all fuels for extra-EEA destinations and introducing the intra-EEA 
correction factor 𝜖𝐵,𝑡. This factor is iterated later in this step and kept constant before 2018. 

 (ktoe; L25-37) accordingly 

𝐶𝐵,𝑡𝑜𝑡𝑎𝑙,𝑐→𝑒𝑥𝑡𝑟𝑎𝐸𝐸𝐴,𝑡 = max [0.75 (

𝐶̂𝐵,𝑐→𝑒𝑥𝑡𝑟𝑎𝐸𝐸𝐴,𝑡

𝐸𝑆𝑇𝐴𝑇
∙ 𝐶𝑡𝑜𝑡𝑎𝑙,𝑐,𝑡

𝐶̂
𝐵,𝑐→∑{𝑖𝑛𝑡𝑟𝑎𝐸𝑈,𝑈𝐾,𝑁𝑂+𝐼𝑆,𝑒𝑥𝑡𝑟𝑎𝐸𝐸𝐴},𝑡
𝐶̂
𝐸𝑆𝑇𝐴𝑇
∙ 𝐶𝑡𝑜𝑡𝑎𝑙,𝑐,𝑡
𝐵,𝑐→∑{𝑖𝑛𝑡𝑟𝑎𝐸𝑈,𝑈𝐾,𝑁𝑂+𝐼𝑆},𝑡
𝐶̂
𝐵,𝑐→∑{𝑖𝑛𝑡𝑟𝑎𝐸𝑈,𝑈𝐾,𝑁𝑂+𝐼𝑆,𝑒𝑥𝑡𝑟𝑎𝐸𝐸𝐴},𝑡

)  ,

)] 

𝐶𝑡𝑜𝑡𝑎𝑙,𝑐,𝑡

𝐸𝑆𝑇𝐴𝑇 − 𝜖𝐵,𝑡 (

(67) 

25 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
For other destinations: 

𝐶𝐵,𝑡𝑜𝑡𝑎𝑙,𝑐→𝑑,𝑡

= 𝐶̂

𝐵,𝑐→𝑑,𝑡

(

𝐸𝑆𝑇𝐴𝑇 − 𝐶𝐵,𝑡𝑜𝑡𝑎𝑙,𝑐→𝑒𝑥𝑡𝑟𝑎𝐸𝐸𝐴,𝑡
𝐶𝑡𝑜𝑡𝑎𝑙,𝑐,𝑡
𝐶̂
𝐵,𝑐→∑{𝑖𝑛𝑡𝑟𝑎𝐸𝑈,𝑈𝐾,𝑁𝑂+𝐼𝑆},𝑡

)   for 𝑑 in {𝑖𝑛𝑡𝑟𝑎𝐸𝑈, 𝑈𝐾, 𝑁𝑂 + 𝐼𝑆}

(68) 
(68) 

The total consumption is then split across fuels. Blended biofuels, natural gas, and biogas are 
distributed proportionally across all destinations: 

𝐶𝐵,𝑓,𝑐→𝑑,𝑡

=

𝐸𝑆𝑇𝐴𝑇 ∙ 𝐶𝐵,𝑡𝑜𝑡𝑎𝑙,𝑐→𝑑,𝑡
𝐶𝑓,𝑐,𝑡
𝐶𝐵,𝑡𝑜𝑡𝑎𝑙,𝑐→∑{𝑖𝑛𝑡𝑟𝑎𝐸𝑈.𝑈𝐾,𝑁𝑂+𝐼𝑆,𝑒𝑥𝑡𝑟𝑎𝐸𝐸𝐴},𝑡

  for 𝑓 in {𝐵𝐹, 𝑁𝐺, 𝐵𝐺}

(69) 
(69) 

Gas oil/diesel oil is assumed to be weighted more heavily in intra-EEA destinations, using a weight 
of 5 (see robustness checks) to allocate intra-EU then other intra-EEA use: 

𝐶𝐵,𝐺𝐷𝑂,𝑐→𝑖𝑛𝑡𝑟𝑎𝐸𝑈,𝑡

= min [𝐶𝐵,𝑡𝑜𝑡𝑎𝑙,𝑐→𝑖𝑛𝑡𝑟𝑎𝐸𝑈,𝑡

− 𝐶𝐵,∑{𝐵𝐹,𝑁𝐺,𝐵𝐺},𝑐→𝑖𝑛𝑡𝑟𝑎𝐸𝑈,𝑡

  ,

5 ∙ 𝐶𝐺𝐷𝑂,𝑐,𝑡

𝐸𝑆𝑇𝐴𝑇 ∙ 𝐶𝐵,𝑡𝑜𝑡𝑎𝑙,𝑐→𝑖𝑛𝑡𝑟𝑎𝐸𝑈,𝑡

5 ∙ 𝐶𝐵,𝑡𝑜𝑡𝑎𝑙,𝑐→𝑖𝑛𝑡𝑟𝑎𝐸𝑈,𝑡

+ 𝐶𝐵,𝑡𝑜𝑡𝑎𝑙,𝑐→∑{𝑈𝐾,𝑁𝑂+𝐼𝑆,𝑒𝑥𝑡𝑟𝑎𝐸𝐸𝐴},𝑡

] 

𝐶𝐵,𝐺𝐷𝑂,𝑐→𝑑,𝑡

= min [𝐶𝐵,𝑡𝑜𝑡𝑎𝑙,𝑐→𝑑,𝑡

− 𝐶𝐵,∑{𝐵𝐹,𝑁𝐺,𝐵𝐺},𝑐→𝑑,𝑡

 ,

𝐸𝑆𝑇𝐴𝑇 − 𝐶𝐵,𝐺𝐷𝑂,𝑐→𝑖𝑛𝑡𝑟𝑎𝐸𝑈,𝑡

5 ∙ (𝐶𝐺𝐷𝑂,𝑐,𝑡
5 ∙ 𝐶𝐵,𝑡𝑜𝑡𝑎𝑙,𝑐→∑{𝑈𝐾,𝑁𝑂+𝐼𝑆},𝑡

) ∙ 𝐶𝐵,𝑡𝑜𝑡𝑎𝑙,𝑐→𝑑,𝑡
+ 𝐶𝐵,𝑡𝑜𝑡𝑎𝑙,𝑐→𝑒𝑥𝑡𝑟𝑎𝐸𝐸𝐴,𝑡

 ]   for 𝑑 in {𝑈𝐾, 𝑁𝑂 + 𝐼𝑆} 

𝐶𝐵,𝐺𝐷𝑂,𝑐→𝑒𝑥𝑡𝑟𝑎𝐸𝐸𝐴,𝑡

= 𝐶𝐺𝐷𝑂,𝑐,𝑡

𝐸𝑆𝑇𝐴𝑇 − 𝐶𝐵,𝐺𝐷𝑂,𝑐→∑{𝑖𝑛𝑡𝑟𝑎𝐸𝑈,𝑈𝐾,𝑁𝑂+𝐼𝑆},𝑡

Remaining total consumption is allocated to fuel oil and other oil products: 

𝐶𝐵,𝐹𝑂,𝑐→𝑑,𝑡

= 𝐶𝐵,𝑡𝑜𝑡𝑎𝑙,𝑐→𝑑,𝑡

− 𝐶𝐵,∑{𝐺𝐷𝑂,𝐵𝐹,𝑁𝐺,𝐵𝐺},𝑐→𝑑,𝑡

Corresponding effective emissions 𝐸𝐵,𝑓,𝑐→𝑑,𝑡
emissions factors (European Commission, 2018) then aggregated across fuels for reporting. 

  (ktCO2; L66-68) are computed with fuel-specific 

𝐸𝐵,𝑓,𝑐→𝑑,𝑡

= 𝐶𝐵,𝑓,𝑐→𝑑,𝑡

∙ 𝑒𝑓

(70) 

(71) 

(72) 

(73) 

(74) 

Finally, this step E4 is iterated to find a value of the intra-EEA correction factor 𝜖𝐵,𝑡 for which 
effective emissions (Table 5), summed at EEA level across all intra-EEA destinations, match intra-
EEA THETIS-MRV emissions under the assumed coverage of international voyages: 

𝐾 

𝑇𝐻𝐸𝑇𝐼𝑆 ∙ 𝐸𝐵,𝑡𝑜𝑡𝑎𝑙,𝐸𝐸𝐴→∑{𝑖𝑛𝑡𝑟𝑎𝐸𝑈,𝑈𝐾,𝑁𝑂+𝐼𝑆},𝑡

𝑇𝐻𝐸𝑇𝐼𝑆

= 𝐸𝐵,𝐸𝐸𝐴→𝑖𝑛𝑡𝑟𝑎𝐸𝐸𝐴,𝑡

(75) 

Table 5. Summary of EU maritime bunkers emissions in JRC-IDEES. 

CO2 emissions (ktCO2) 

Total 

  Intra-EEA 

  Extra-EEA 

Source: JRC-IDEES. 

1990 

106 345 

30 525 

75 820 

2005 

152 051 

39 919 

112 131 

2019 

138 152 

39 089 

99 064 

1990-2019 change 

29.9% 

28.1% 

30.7% 

26 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
  
  
  
Step E5   Comparison of emissions with THETIS-MRV and UNFCCC: For 2018-2021, a final 
reporting step aligns the country-level emission reporting with THETIS-MRV. The JRC-IDEES-2021 
files accordingly report emissions 𝐸𝐵,𝑐→𝑑,𝑡
reporting. This accounts for the pre-processing that applied a 50% factor to extra-EEA emissions 
𝐸𝐵,𝐸𝐸𝐴→𝑒𝑥𝑡𝑟𝑎𝐸𝐸𝐴,𝑡

 (where the UK is shifted to the extra-EEA aggregation in 2021), so that: 

 (ktCO2; L83-85) that comply with EEA-level THETIS-MRV 

𝑇𝐻𝐸𝑇𝐼𝑆

′

′

𝐸𝐵,𝐸𝐸𝐴→∑ 𝑖𝑛𝑡𝑟𝑎𝐸𝑈,𝑈𝐾,𝑁𝑂+𝐼𝑆},𝑡

𝑇𝐻𝐸𝑇𝐼𝑆

= 𝐸𝐵,𝐸𝐸𝐴→𝑖𝑛𝑡𝑟𝑎𝐸𝐸𝐴,𝑡

′

𝐸𝐵,𝐸𝐸𝐴→𝑒𝑥𝑡𝑟𝑎𝐸𝐸𝐴,𝑡

= 2 𝐸𝐵,𝐸𝐸𝐴→𝑒𝑥𝑡𝑟𝑎𝐸𝐸𝐴,𝑡

𝑇𝐻𝐸𝑇𝐼𝑆

(76) 

(77) 

These emissions are computed by country for each destination as follows, where 𝛿𝑖𝑛𝑡𝑟𝑎𝐸𝐸𝐴 , 
𝛿𝑒𝑥𝑡𝑟𝑎𝐸𝐸𝐴 are adjustment factors iterated to meet EEA-level totals. Table 6 summarizes the results. 

′

𝐸𝐵,𝑐→𝑑,𝑡

= min (𝐸̂

𝐵,𝑐→𝑑,𝑡

∙ 𝐾 

𝑇𝐻𝐸𝑇𝐼𝑆 ∙ 𝛿𝑖𝑛𝑡𝑟𝑎𝐸𝐸𝐴 ,  𝐸𝐵,𝑡𝑜𝑡𝑎𝑙,𝑐→𝑑,𝑡

)   for 𝑑 in {𝑖𝑛𝑡𝑟𝑎𝐸𝑈, 𝑁𝑂 + 𝐼𝑆}

(78) 

′

𝐸𝐵,𝑐→𝑈𝐾,𝑡

= min (𝐸̂

𝐵,𝑐→𝑈𝐾,𝑡

∙ 𝐾 

𝑇𝐻𝐸𝑇𝐼𝑆 ∙ min(𝛿𝑖𝑛𝑡𝑟𝑎𝐸𝐸𝐴, 𝛿𝑒𝑥𝑡𝑟𝑎𝐸𝐸𝐴) ,  𝐸𝐵,𝑡𝑜𝑡𝑎𝑙,𝑐→𝑈𝐾,𝑡

)   for 𝑡 in {2018,2019,2020}

(79) 

′

𝐸𝐵,𝑐→𝑈𝐾,2021

= 2 min(𝐸̂𝐵,𝑐→𝑈𝐾,𝑡

∙ 𝐾 

𝑇𝐻𝐸𝑇𝐼𝑆 ∙ min(𝛿𝑖𝑛𝑡𝑟𝑎𝐸𝐸𝐴, 𝛿𝑒𝑥𝑡𝑟𝑎𝐸𝐸𝐴) , 0.5 𝐸𝐵,𝑡𝑜𝑡𝑎𝑙,𝑐→𝑈𝐾,𝑡

)

′

𝐸𝐵,𝑐→𝑒𝑥𝑡𝑟𝑎𝐸𝐸𝐴,𝑡

= 2 min(𝐸̂𝐵,𝑐→𝑒𝑥𝑡𝑟𝑎𝐸𝐸𝐴,𝑡

∙ 𝐾 

𝑇𝐻𝐸𝑇𝐼𝑆 ∙ 𝛿𝑒𝑥𝑡𝑟𝑎𝐸𝐸𝐴 ,  0.5 𝐸𝐵,𝑡𝑜𝑡𝑎𝑙,𝑐→𝑒𝑥𝑡𝑟𝑎𝐸𝐸𝐴,𝑡

)

(80) 

(81) 

Table 6. Summary of EU maritime bunkers emissions using THETIS-MRV compliant reporting. 

CO2 emissions, MRV-compliant (ktCO2) 

Total 
  Intra-EEA 
  Extra-EEA 

Source: JRC-IDEES. 

2018 

125 196 
36 780 
88 416 

2019 

127 361 
37 114 
90 247 

2020 

113 078 
30 985 
82 093 

2018-2020 change 

-9.7% 
-15.8% 
-7.2% 

L91-93 additionally report discrepancies between effective emissions and UNFCCC country 
inventory submissions for international navigation, assuming discrepancies are distributed 
proportionally to emissions. Figure 10 shows the relative discrepancy as (𝐸𝑈𝑁𝐹𝐶𝐶𝐶 − 𝐸)/𝐸 . 

Figure 10. Relative discrepancy of UNFCCC emissions vs. JRC-IDEES effective emissions (maritime bunkers). 

Discrepancy of UNFCCC emissions, EU total

1990

1995

2000

2005

2010

2015

2020

2%

0%

-2%

-4%

-6%

-8%

Intra-EEA

Extra-EEA

Total

Source: JRC-IDEES. 

27 

 
 
 
 
 
 
 
 
 
 
 
  
  
  
  
 
 
 
 
3.3  Robustness checks 

For selected steps, Figure 11 and Figure 12 summarize the impact of key alternative calibration 
assumptions on EU results for aviation and international bunkers. These assumptions are applied to 
intermediate steps before recalibrating to the same constraints for activity, energy, and emissions 
as in the reference calibration. Where applicable, the figures show the change in total effective CO2 
emissions for international intra-EEA destinations in 1990, 2005 and 2019 relative to the reference 
calibration. For steps A1 and A5, the change in international intra-EEA passenger activity is shown 
instead, as the alternative assumptions yield the same emissions after recalibrating to the 
constraints. Annex 2 provides further results and background for each step. 

Figure 11. Aviation transport: impact of alternative calibration assumptions on selected EU results, for 
international intra-EEA destinations. 

Change vs. ref. calibration, EU → intra-EEA+UK total

-1.0%

0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

6.0%

A1: Missing passenger data estimated 
with constant load factor

A2: Missing flight data estimated 
with constant load factor

A5: SABRE load factor 
adjustment set to 1

B3: Regional freight load 
factor adjustments set to 1

C1: Simplified estimation of 
flight-level fuel consumption

Passenger activity

Aviation CO2 emissions

Passenger activity

Aviation CO2 emissions

Aviation CO2 emissions

1990

2005

2019

Source: JRC analysis. 

For aviation transport, these alternative choices on missing data and calibration parameters 
generally have a minor impact on the geographical split of CO2 emissions. This is mainly due to the 
good quality and availability of aviation-specific data from Eurostat and EUROCONTROL, which can 
be effectively harmonized with Eurostat energy balances to yield a robust calibration. However, the 
greater sensitivity of step A5   which used flight-level data to adjust country activities for the 
reference calibration   shows the importance of accounting for granular route-level trends in 
passenger transport, which would not have been captured by available aggregate statistics. 

28 

 
 
Figure 12. International maritime bunkers: impact of alternative calibration assumptions on selected EU 
results, for international intra-EEA destinations. 

Change in maritime bunkers CO2 emissions vs. ref. calibration, EU → intra-EEA total

-1.0%

0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

6.0%

E2: Load factor scaling exponent 
set to 1

E3: Energy efficiency scaling 
exponent set to 1

E4: Intra-EEA diesel oil 
weighting set to 1

E5: Assumed THETIS-MRV 
coverage: 50% domestic, 97% intl.

1990

2005

2019

Source: JRC analysis. 

For the maritime bunkers sector, there is a lack of comparably detailed datasets that would 
consistently combine activity and energy consumption data throughout the calibration period. 
Similarly, the technical diversity of maritime transport (vessel types, cargo types, etc.) is more 
difficult to capture in this relatively aggregated analysis, and requires more extensive technical 
assumptions to translate EU-level statistics to country-specific outcomes. These challenges are 
reflected in the higher sensitivity of the chosen methodology for older years. Nonetheless, the 
sensitivity of geographical allocation remains well within typical uncertainty ranges that can be 
expected for primary statistics in the sector (e.g. International Maritime Organization (2009), 
Appendix 1). In parallel, further reporting under the THETIS-MRV system is expected to improve the 
robustness of available data in the future (Luo et al., 2023)   particularly as the sector stabilizes 
following the COVID-19 pandemic.  

29 

 
 
4  Conclusions 

Under the European Climate Law, the climate targets of the European Union are defined in relation 
to historical benchmarks   such as a reduction of net domestic emissions by 55% by 2030 
compared to 1990. Progress towards these benchmarks has generally been tracked through the 
emissions inventories reported to the UNFCCC. However, existing primary statistics do not match the 
geographical scopes required to attribute domestic emissions of international aviation and maritime 
transport, for instance by distinguishing intra- and extra-EU components of international transport. 
To address this gap, this report detailed a calibration methodology that disaggregates the activity, 
energy use, and CO2 emissions of international aviation and maritime transport across geographical 
categories that are consistent with EU climate policies. This approach harmonizes top-down primary 
statistics with bottom-up technical data sources that capture the evolution of each transport sector 
in finer detail. The resulting dataset complies with Eurostat energy balances and covers 1990-2021 
for each EU Member State, each EEA country, and the United Kingdom. This dataset was first 
published i
System (JRC-IDEES) (Rozsai et al., 2024) and is freely accessible through the JRC Data Catalogue. 

atabase of the Energy 

In the aviation sector, the calibration methodology extensively used flight-level data from 
EUROCONTROL and other sources. 
balances and aviation-specific datasets, so that missing data or the exact choice of calibration 
parameters generally had a minor impact on the geographical attribution of CO2 emissions. 
Similarly, the resulting estimated CO2 emissions were generally well-aligned with UNFCCC national 
inventory submissions. Domestic aviation here showed some greater discrepancies due to 
differences in the sectoral energy use reported by countries to the UNFCCC, relative to the Eurostat 
energy balance data used in JRC-IDEES.  

For the maritime transport sector, more extensive technical assumptions were required to make up 
for a lesser availability of detailed statistics in this sector. This led the methodology to be more 
sensitive to alternative calibration assumptions. Nonetheless, the geographical attribution of 
emissions was generally robust throughout the calibration period and can be expected to lay within 
typical uncertainty ranges for primary energy statistics. In parallel, the THETIS-MRV system has 
significantly improved the availability of data in the maritime transport sector (Luo et al., 2023). 
Assumptions on the completeness of this dataset were fairly influential on the calibration results; 
however, as the system has only recently been introduced, and as the post-COVID-19 disruption of 
transport activity and reporting has made it more difficult to draw robust trends, the use of THETIS-
MRV within JRC-IDEES has so far necessarily been limited. Further data collection within the system 
and expected updates to Eurostat statistics will enable a better understanding of the sector, and will 
inform periodic updates to the JRC-IDEES methodology and dataset.  

30 

 
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European Commission, Joint Research Centre, Rozsai, M., Jaxa-Rozen, M., Salvucci, R., Sikora, P., Tattini, J., & Neuwahl, F. 

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system models. Applied Energy, 325, 119906. 

31 

 
 
List of abbreviations and definitions 

Abbreviations 

Definitions 

EEA 

EU 

GHG 

UK 

UNFCCC 

MRV 

European Economic Area 

European Union 

Greenhouse gas 

United Kingdom 

United Nations Framework Convention on Climate Change 

Monitoring, Reporting, and Verification 

32 

 
 
 
 
List of figures 

Figure 1. Availability over time of Eurostat datasets for aviation transport. .................................................................................................... 6 

Figure 2. Left panel: Passenger flights in Eurostat and EUROCONTROL (after filtering). Right panel: Change in total CO2 
emissions of EUROCONTROL flights after filtering............................................................................................................................................................. 6 

Figure 3. Availability over time of Eurostat datasets for maritime transport. .................................................................................................. 7 

Figure 4. Graphical overview of the calibration of international aviation. .......................................................................................................... 8 

Figure 5. Activity adjustment factor derived from flight-level data for each destination, EEA average. ....................................... 10 

Figure 6. CO2 emissions as a function of flight distance, EU average for 2019. ......................................................................................... 14 

Figure 7. Relative discrepancy of UNFCCC emissions vs. JRC-IDEES effective emissions for aviation, EU total. ....................... 17 

Figure 8. Graphical overview of the calibration of maritime transport. ............................................................................................................. 18 

Figure 9. Relative discrepancy of UNFCCC emissions vs. JRC-IDEES effective emissions (domestic navigation). ..................... 21 

Figure 10. Relative discrepancy of UNFCCC emissions vs. JRC-IDEES effective emissions (maritime bunkers). ....................... 27 

Figure 11. Aviation transport: impact of alternative calibration assumptions on selected EU results, for international intra-
EEA destinations. ................................................................................................................................................................................................................................. 28 

Figure 12. International maritime bunkers: impact of alternative calibration assumptions on selected EU results, for 
international intra-EEA destinations. ....................................................................................................................................................................................... 29 

Figure 13. Selected calibration results for aviation (EU total) (1/2). ................................................................................................................... 35 

Figure 14. Selected calibration results for aviation (EU total) (2/2). ................................................................................................................... 36 

Figure 15. Selected calibration results for domestic navigation (EU total). .................................................................................................... 37 

Figure 16. Selected calibration results for international maritime bunkers (EU total). ............................................................................. 38 

Figure 17. Robustness check, step A1: Average EU passenger load factors using the oldest available load factor to impute 
missing data. ......................................................................................................................................................................................................................................... 39 

Figure 18. Robustness check, step C1: Simplified approximation of flight emissions. .............................................................................. 42 

33 

 
 
 
List of tables 

Table 1. Main historical datasets for aviation transport. ............................................................................................................................................... 5 

Table 2. Main historical datasets for maritime transport. ............................................................................................................................................ 7 

Table 3. Discrepancy factor based on flight distance approximation, EU average. ..................................................................................... 15 

Table 4. Summary of EU aviation emissions in JRC-IDEES. ...................................................................................................................................... 17 

Table 5. Summary of EU maritime bunkers emissions in JRC-IDEES. ................................................................................................................. 26 

Table 6. Summary of EU maritime bunkers emissions using THETIS-MRV compliant reporting. ......................................................... 27 

Table 7. Robustness check, step A1: Total EU activity, using the oldest available passenger load factor to impute missing 
data for passengers. ......................................................................................................................................................................................................................... 39 

Table 8. Robustness check, step A2: Total EU emissions, using the oldest available passenger load factor to impute 
missing data for passenger flights. .......................................................................................................................................................................................... 40 

Table 9. Stylized example for the harmonization of flight-based and activity-based load factors. .................................................. 40 

Table 10. Robustness check, step A5: Total EU activity, without flight-level adjustment of load factors...................................... 41 

Table 11. Robustness check, step B3: Total EU emissions without regional freight load factor adjustments. ............................ 41 

Table 12. Robustness check, step C1: Total EU emissions using simplified approximation of flight emissions. ........................ 42 

Table 13. Robustness check, step E2: Total EU emissions using linear scaling of country-specific load factors in maritime 
bunkers. .................................................................................................................................................................................................................................................... 43 

Table 14. Robustness check, step E3: Total EU emissions using linear scaling of energy efficiency in maritime bunkers. . 43 

Table 15. Robustness check, step E4: Total EU emissions without regional rebalancing of diesel oil consumption. ............... 44 

Table 16. Robustness check, step E5: Total EU emissions assuming 50% coverage of domestic voyages in THETIS-MRV 
and 97% coverage of international voyages. ..................................................................................................................................................................... 45 

Table 17. Correspondence between labels used in the JRC-IDEES data files, Eurostat energy balance sectors, and Eurostat 
energy balance fuel codes. ............................................................................................................................................................................................................ 46 

34 

 
 
 
Annexes 

Annex 1. Calibration results 

Figure 13. Selected calibration results for aviation (EU total) (1/2). 

Step A2: Total passenger flights 
(thousand)
L45-L47

Step A1: Passengers
(million)
L75-L77

500

400

300

200

100

0

3,500

3,000

2,500

2,000

1,500

1,000

500

0

Step A3: Load factor 
(passengers/flight)
L85-L87

200

150

100

50

0

1990

2000

2010

2020

 Domestic          

 Intra-EEAwUK          

2000
1990
 Extra-EEAwUK  

2010

2020

1990

2000

2010

2020

Step A4: Average flight distance 
(km/flight)
L55-L57

4,500

4,000

3,500

3,000

2,500

2,000

1,500

1,000

500

0

Step A4: Total mileage 
(million vehicle-km)
L25-L27

4,500

4,000

3,500

3,000

2,500

2,000

1,500

1,000

500

0

Step A5: Total activity
(million passenger-km)
L5-L7

800,000

700,000

600,000

500,000

400,000

300,000

200,000

100,000

0

1990

2000

2010

2020

1990

2000

2010

2020

1990

2000

2010

2020

 Domestic          

 Intra-EEAwUK          

 Extra-EEAwUK  

Step C1: Technical efficiency, passenger flights 
(kgoe/100km)
L147-L149

800

600

400

200

0

Step C1: Technical efficiency, freight flights
(kgoe/100km)
L151-L153

800

600

400

200

0

1990

1995

2000

2005

2010

2015

2020

1990

1995

2000

2005

2010

2015

2020

 Domestic          

 Intra-EEAwUK          

 Extra-EEAwUK  

Source: JRC-IDEES. Line numbers correspond to data file JRC-IDEES-2021_x1990_Aviation_EEAwUK, sheet EU27.  

35 

 
 
 
 
     
 
 
 
 
     
 
 
 
     
 
Figure 14. Selected calibration results for aviation (EU total) (2/2). 

Step C2: Ratio between effective and technical efficiency, 
passenger flights (-)
L188-L190

Step C2: Ratio between effective and technical efficiency, 
freight flights (-)
L192-L194

1.50

1.25

1.00

0.75

1990

1995

2000

2005

2010

2015

2020

1990

1995

2000

2005

2010

2015

2020

 Domestic          

 Intra-EEAwUK          

 Extra-EEAwUK  

Step C2: Effective energy consumption, passenger flights 
(ktoe)
L127-L129

Step C2: Effective energy consumption, freight flights 
(ktoe)
L131-L133

4,500

4,000

3,500

3,000

2,500

2,000

1,500

1,000

500

0

1.50

1.25

1.00

0.75

25,000

20,000

15,000

10,000

5,000

0

1990

1995

2000

2005

2010

2015

2020

1990

1995

2000

2005

2010

2015

2020

 Domestic          

 Intra-EEAwUK          

 Extra-EEAwUK  

Step C3: Emissions, passenger flights
(ktCO2)
L204-L206

70,000

60,000

50,000

40,000

30,000

20,000

10,000

0

Step C3: Emissions, freight flights
(ktCO2)
L208-L210

14,000

12,000

10,000

8,000

6,000

4,000

2,000

0

1990

1995

2000

2005

2010

2015

 Domestic          

 Intra-EEAwUK          

2020
 Extra-EEAwUK  

1990

1995

2000

2005

2010

2015

2020

Source: JRC-IDEES. Line numbers correspond to data file JRC-IDEES-2021_x1990_Aviation_EEAwUK, sheet EU27. 

36 

 
 
 
     
 
 
 
     
 
 
 
     
 
Figure 15. Selected calibration results for domestic navigation (EU total). 

Step D2: Total mileage (million
vehicle-km) L12, L13

Step D2: Load factor 
(tonne/movement) L20, L21

160,000

Step D1: Activity (million
tonne-km) L4, L5

120,000

80,000

40,000

0

2,500

2,000

1,500

1,000

500

0

250

200

150

100

50

0

1990

2000

2010

2020

1990

2000

2010

2020

1990

2000

2010

2020

 Inland waterways          

 Domestic coastal shipping  

Step D4: Ratio between effective and technical
efficiency (-) L57, L58

1.4

1.2

1.0

0.8

0.6

0.4

0.2

0.0

Step D4: Effective energy consumption (ktoe)
L24, L30

6,000

5,000

4,000

3,000

2,000

1,000

0

1990

1995

2000

2005

2010

2015

2020

1990

1995

2000

2005

2010

2015

2020

 Inland waterways          

 Domestic coastal shipping  

Step D4: Emissions (ktCO2)
L65, L66

16,000

14,000

12,000

10,000

8,000

6,000

4,000

2,000

0

1990

1995

2000

2005

2010

2015

2020

Inland waterways

Domestic coastal shipping

Source: JRC-IDEES. Line numbers correspond to data file JRC-IDEES-2021_x1990_Navigation_Domestic, sheet EU27. 

37 

 
 
 
 
     
  
 
 
     
  
 
 
 
Figure 16. Selected calibration results for international maritime bunkers (EU total). 

Step E2: Total mileage (million 
vehicle-km) L13, L14

Step E1: Activity (million 
tonne-km) L4, L5

8,000,000

7,000,000

6,000,000

5,000,000

4,000,000

3,000,000

2,000,000

1,000,000

0

300

250

200

150

100

50

0

Step E2: Load factor 
(tonnes/movement)
L22, L23

45,000

40,000

35,000

30,000

25,000

20,000

15,000

10,000

5,000

0

1990

2000

2010

2020

1990

2000

2010

2020

1990

2000

2010

2020

 Intra-EEA          

 Extra-EEA 

Step E4: Ratio between effective and technical energy 
efficiency (-) L59, L60

10,000

Step E3: Technical energy efficiency (kgoe/100km)
L54, L55

9,000

8,000

7,000

6,000

5,000

4,000

3,000

2,000

1,000

0

3.0

2.5

2.0

1.5

1.0

0.5

0.0

1990

1995

2000

2005

2010

2015

2020

1990

1995

2000

2005

2010

2015

2020

 Intra-EEA          

 Extra-EEA 

Step E4: Effective energy consumption (ktoe)
L26, L32

45,000

40,000

35,000

30,000

25,000

20,000

15,000

10,000

5,000

0

Step E4: Effective emissions (ktCO2)
L67, L68

140,000

120,000

100,000

80,000

60,000

40,000

20,000

0

1990

1995

2000

2005

2010

2015

2020

1990

1995

2000

2005

2010

2015

2020

 Intra-EEA          

 Extra-EEA 

Source: JRC-IDEES. Line numbers correspond to data file JRC-IDEES-2021_x1990_Navigation_International_EEA, sheet EU27. 

38 

 
 
 
 
     
 
  
 
 
     
 
 
 
     
Annex 2. Detailed results for robustness checks 

Step A1: Passengers 

Missing years for the number of passengers in Eurostat datasets avia_paoc, avia_paincc, 
avia_paexcc are estimated by keeping the oldest available load factor (passengers/flight) in each 
country, rather than by estimating a load factor trend from available years. This assumption does 
not change energy consumption and CO2 emissions, after recalibrating to the constraints on activity 
(1995-2021 Transport Pocketbook total domestic and intra-EU activity) and energy consumption 
(1990-2021 domestic and total international consumption by country). However, pre-2005 
domestic and extra-EEA activities in particular tend to increase (Table 7). As the load factors used in 
the reference calibration are more consistent with the generally increasing trend shown by available 
load factors (Figure 17, solid lines), this simplified assumption (dashed lines) would likely 
overestimate activity in early years.  

Figure 17. Robustness check, step A1: Average EU passenger load factors using the oldest available load 
factor to impute missing data. 

Passengers/flight

170

150

130

110

90

70

50

1990

1995

2000

2005

2010

2015

2020

Domestic
Domestic - const. LF

Intra-EEA+UK
Intra-EEA+UK - const. LF

Extra-EEA+UK
Extra-EEA+UK - const. LF

Source: JRC analysis. 

Table 7. Robustness check, step A1: Total EU activity, using the oldest available passenger load factor to 
impute missing data for passengers. 

Activity vs. ref. calibration 
Passenger transport 

Domestic 
International - intra-EEA 
International - extra-EEA 

Freight transport 
Domestic 
International - intra-EEA 
International - extra-EEA 

Source: JRC analysis. 

1990 
mpkm, mtkm 
+15 285 
+2 924 
+2 004 
+10 357 
- 
- 
- 
- 

% 
3.7% 
5.2% 
1.1% 
5.9% 
- 
- 
- 
- 

2005 
mpkm, mtkm 
+97 
+11 
+21 
+65 
- 
- 
- 
- 

% 
0.0% 
0.0% 
0.0% 
0.0% 
- 
- 
- 
- 

2019 
mpkm, mtkm  % 
- 
- 
- 
- 
- 
- 
- 
- 

- 
- 
- 
- 
- 
- 
- 
- 

Difference in 1990-
2019 trend (%) 

-12.8% 
-9.9% 
-3.8% 
-23.1% 
- 
- 
- 
- 

39 

 
 
 
 
 
 
Step A2: Passenger flights 

Similarly, data for the number of passenger flights missing in Eurostat datasets avia_paoc, 
avia_paincc, avia_paexcc are estimated using the oldest available load factor by country (generally 
before 2003) rather than using the trend of available data. In this case, after recalibrating to the 
activity and energy consumption constraints, the change in passenger flight data propagates to a 
proportional shift in freight outcomes (Table 8). However, the overall change in the intra-/extra-EEA 
share of effective emissions remains minor. 

Table 8. Robustness check, step A2: Total EU emissions, using the oldest available passenger load factor to 
impute missing data for passenger flights.  

CO2 emissions vs. ref. calibration 
Total 
Domestic 
International 

International - intra-EEA 
International - extra-EEA 

Passenger transport 

Domestic 
International - intra-EEA 
International - extra-EEA 

Freight transport 
Domestic 
International - intra-EEA 
International - extra-EEA 

Source: JRC analysis. 

1990 

2005 

2019 

ktCO2 
- 
- 
- 
-86 
+86 

-107 
-11 
-95 
-1 
+107 
+11 
+8 
+87 

% 
- 
- 
- 
-0.4% 
0.3% 

-0.2% 
-0.1% 
-0.4% 
-0.0% 
2.2% 
1.7% 
1.5% 
2.4% 

ktCO2 
- 
- 
- 
- 
- 

- 
- 
- 
- 
- 
- 
- 
- 

% 
- 
- 
- 
- 
- 

- 
- 
- 
- 
- 
- 
- 
- 

ktCO2 
- 
- 
- 
- 
- 

- 
- 
- 
- 
- 
- 
- 
- 

% 
- 
- 
- 
- 
- 

- 
- 
- 
- 
- 
- 
- 
- 

Difference in 1990-
2019 trend (%) 

- 
0.0% 
- 
0.9% 
-0.6% 

0.3% 
0.1% 
1.0% 
0.0% 
-4.4% 
-0.8% 
-3.2% 
-5.5% 

Step A5: Passenger activity 

As shown in the stylized example below (Table 9), the aggregation of flights across routes of 
different distances and capacities yields a discrepancy between the equivalent 
factor (total passengers / total flights), and 
total vehicle-km). Furthermore, these variables are not available from Eurostat at consistent 
geographical scopes. To harmonize activity-based and flight-based load factors in the reference 
calibration, an adjustment factor using SABRE data for 2010-2021 was therefore applied to the 
initial activity estimate and kept constant before 2010.  

-

-

-km / 

Table 9. Stylized example for the harmonization of flight-based and activity-based load factors. 

Passengers/flight 

Flights 

km/flight 

Total passengers 

Vehicle-km 

Passenger-km 

Route 1 

Route 2 

Total 

120 

100 

10 

10 
20 a 

1500 

500 

1200 

1000 
2200 a 

15 000 

1 800 000 

5 000 
20 000 a 

500 000 
2 300 000 b 

a : Known at country level 
b : Known at EU level 

Total flight-based load factor 

2200 / 20 000 = 110 passengers / flight 

Total activity-based load factor 

2 300 000 / 20 000 = 115 passenger-km / vehicle-km 

Equivalent adjustment factor 

115 / 100 = 1.045 

Source: JRC analysis. 

40 

 
 
 
 
 
 
 
 
 
 
  
  
  
 
 
 
 
 
 
  
  
  
  
 
 
 
 
 
 
 
 
 
This adjustment factor enables a more accurate estimation of activity (passenger-km) by MS: as 
activity statistics are only directly reported at EU level, the MS-level activity could otherwise only be 
estimated from the other flight variables, e.g. 

𝑝𝑎𝑠𝑠𝑒𝑛𝑔𝑒𝑟-𝑘𝑚𝑡𝑜𝑡𝑎𝑙

=   𝑣𝑒ℎ𝑖𝑐𝑙𝑒-𝑘𝑚𝑡𝑜𝑡𝑎𝑙 ∙ 𝑝𝑎𝑠𝑠𝑒𝑛𝑔𝑒𝑟𝑠 𝑓𝑙𝑖𝑔ℎ𝑡
= 2 200 000 𝑝𝑎𝑠𝑠𝑒𝑛𝑔𝑒𝑟-𝑘𝑚 

⁄

= 20 000 𝑣𝑒ℎ𝑖𝑐𝑙𝑒-𝑘𝑚 ∙

2200 𝑝𝑎𝑠𝑠𝑒𝑛𝑔𝑒𝑟𝑠
20 𝑓𝑙𝑖𝑔ℎ𝑡𝑠

In this example, without the adjustment factor, the estimated activity would have been 4.5% lower 
than the actual reported activity of 2 300 000 pkm.  

For the reference calibration, the country-specific adjustment factors were estimated using a flight-
level passenger dataset. Table 10 shows an alternative calibration in which the adjustment factors 
would be set to 1 assuming detailed flight-level data was not available. Although emissions do not 
change relative to the reference calibration (due to the recalibration to energy consumption 
constraints), domestic and extra-EEA activities are significantly reduced.  

Table 10. Robustness check, step A5: Total EU activity, without flight-level adjustment of load factors. 

Activity vs. ref. calibration 
Passenger transport 

Domestic 
International - intra-EEA 
International - extra-EEA 

Freight transport 
Domestic 
International - intra-EEA 
International - extra-EEA 

Source: JRC analysis. 

1990 

2005 

2019 

mpkm, mtkm 
-39 583 
-6 209 
+7 057 
-40 431 
-1 956 
-31 
+30 
-1 955 

% 
-9.7% 
-11.0% 
4.0% 
-22.9% 
-13.2% 
-8.9% 
3.1% 
-14.5% 

mpkm, mtkm 
-87 593 
-10 075 
+15 189 
-92 707 
-4 063 
-37 
+54 
-4 079 

% 
-10.3% 
-11.2% 
4.2% 
-22.9% 
-13.9% 
-10.4% 
3.1% 
-15.1% 

mpkm, mtkm 
-136 090 
-11 281 
+17 152 
-141 961 
-7 135 
-20 
+28 
-7 143 

% 
-9.4% 
-9.8% 
2.9% 
-19.2% 
-14.9% 
-7.8% 
1.2% 
-15.8% 

Difference in 1990-
2019 trend (%) 
1.2% 
2.5% 
-3.8% 
19.8% 
-6.4% 
0.9% 
-4.4% 
-5.2% 

Step B3: Freight load factor 

The regional multipliers applied to the freight load factor for intra-EU flights (1.1) and extra-EEA 
flights (0.8) are set to 1, then recalibrated to the same activity and energy consumption constraints. 
This significantly reduces extra-EEA freight emissions (Table 11): the higher load factor yields a lower 
number of flights for a given fixed activity, lowering energy consumption and emissions for this 
category. However, resulting load factors exceed plausible values given the share of narrow-body 
aircraft in freight transport (Ackert, 2018; Budd & Ison, 2017), justifying the regional adjustment.  

Table 11. Robustness check, step B3: Total EU emissions without regional freight load factor adjustments. 

1990 

2005 

2019 

CO2 emissions vs. ref. calibration 
Total 
Domestic 
International 

International - intra-EEA 
International - extra-EEA 

Passenger transport 

Domestic 
International - intra-EEA 
International - extra-EEA 

Freight transport 
Domestic 
International - intra-EEA 
International - extra-EEA 

Source: JRC analysis. 

ktCO2 
- 
- 
- 
+48 
-48 
+563 
- 
-13 
+575 
-563 
- 
+61 
-623 

% 
- 
- 
- 
0.2% 
-0.2% 
0.9% 
- 
-0.1% 
2.0% 
-11.4% 
- 
10.5% 
-17.0% 

% 
- 
- 
- 
0.5% 
-0.3% 
1.2% 
- 
0.2% 
2.5% 
-12.5% 
- 
11.0% 
-15.9% 

ktCO2 
- 
- 
- 
+227 
-227 
+1 174 
-7 
+55 
+1 126 
-1 174 
+7 
+172 
-1 353 

% 
- 
- 
- 
0.4% 
-0.3% 
0.9% 
-0.0% 
0.1% 
1.8% 
-11.5% 
2.3% 
13.4% 
-15.7% 

ktCO2 
- 
- 
- 
+188 
-188 
+1 252 
- 
+79 
+1 172 
-1 252 
- 
+108 
-1 360 

41 

  Difference in 1990-
2019 trend (%) 

- 
0.0% 
- 
0.5% 
-0.4% 
0.0% 
-0.1% 
0.4% 
-0.5% 
-0.1% 
1.1% 
5.8% 
3.7% 

 
 
 
  
  
  
  
 
  
  
 
 
  
  
 
 
  
Step C1: Technical energy consumption 

To assess the impact of the flight-level estimation of technical energy consumption and emissions, 
an alternative calibration uses public ICAO data (International Civil Aviation Organization, 2024) to 
approximate flight emissions. Based on EUROCONTROL flight frequencies, this assumes flights 
below 7500 km are served by a 50%-50% mix of Boeing 737-800 and 777-300 aircraft, and 
longer flights by 777-300 aircraft alone. Figure 18 and Table 12 compare the resulting emissions 
per flight and final emissions with the reference calibration using EUROCONTROL flight data. The 
calibration of passenger transport and the overall intra-/extra-EEA split appear robust, despite 
significant discrepancies for some categories (e.g. for extra-EEA freight flights, where emissions for 
the representative flight distances are more sensitive to the simplified approximation). 

Figure 18. Robustness check, step C1: Simplified approximation of flight emissions. 

400000

Emissions per flight (kgCO2)

40000

4000

0

2000

4000

6000

8000

EUROCONTROL (<2400 nm)
EUROCONTROL (>=2400 nm)
Approximation function (<2400 nm)
Approximation function (>=2400 nm)
ICAO approximation
Average flight distances (EU)

10000

12000
Flight distance (km)

Source: JRC analysis using EUROCONTROL and ICAO data. 

Table 12. Robustness check, step C1: Total EU emissions using simplified approximation of flight emissions. 

1990 

2005 

2019 

CO2 emissions vs. ref. calibration 
Total 
Domestic 
International 

International - intra-EEA 
International - extra-EEA 

Passenger transport 

Domestic 
International - intra-EEA 
International - extra-EEA 

Freight transport 
Domestic 
International - intra-EEA 
International - extra-EEA 

Source: JRC analysis. 

ktCO2 
- 
- 
- 
+15 
-15 

+272 
+11 
-33 
+294 
-272 
-11 
+48 
-309 

% 
- 
- 
- 
0.1% 
-0.0% 

0.4% 
0.1% 
-0.1% 
1.0% 
-5.5% 
-1.6% 
8.3% 
-8.4% 

% 
- 
- 
- 
0.3% 
-0.2% 

0.5% 
0.1% 
0.0% 
1.0% 
-5.2% 
-2.9% 
8.3% 
-6.9% 

ktCO2 
- 
- 
- 
+114 
-114 

+494 
-57 
-14 
+565 
-494 
+57 
+128 
-679 

% 
- 
- 
- 
0.2% 
-0.2% 

0.4% 
-0.3% 
-0.0% 
0.9% 
-4.8% 
18.7% 
9.9% 
-7.9% 

ktCO2 
- 
- 
- 
+95 
-95 

+525 
+15 
+13 
+497 
-525 
-15 
+82 
-592 

42 

  Difference in 1990-
2019 trend (%) 

- 
- 
- 
0.3% 
-0.3% 

-0.1% 
-0.5% 
0.3% 
-0.3% 
1.5% 
9.4% 
3.4% 
1.4% 

 
 
  
  
  
  
 
  
 
 
 
 
 
 
 
 
 
  
 
 
  
  
 
 
  
 
 
Step E2: Country-specific load factor scaling for maritime bunkers 

To adjust country-specific load factors in this step of the reference calibration, a scaling exponent 
of 0.4 was applied to the ratio between country-specific and EEA-level average deadweight 
tonnage. The exponent tends to smooth variations across countries while representing potential 
non-linearities of average capacity utilization as a function of ship size. As this exponent is 
uncertain and sensitive to technical vessel characteristics, an alternative calibration instead linearly 
scales the country-specific load factors proportionally to the country-specific deadweight tonnages. 
Table 13 shows the result after recalibrating to the same constraints. This linear scaling tends to 
reduce the load factor of the smaller ships used for intra-EEA voyages, hence reducing their energy 
efficiency. This is then reflected in a greater share of intra-EEA emissions.  

Table 13. Robustness check, step E2: Total EU emissions using linear scaling of country-specific load factors 
in maritime bunkers.  

CO2 emissions vs. ref. calibration 
Total 

International - intra-EEA 
International - extra-EEA 

CO2 emissions, MRV-compliant 
vs. ref. calibration 
Total 

International - intra-EEA 
International - extra-EEA 

Source: JRC analysis. 

1990 

2005 

2019 

ktCO2 
- 
+614 
-614 

% 
- 
2.0% 
-0.8% 

ktCO2 
- 
+583 
-583 

% 
- 
1.5% 
-0.5% 

ktCO2 
- 
+42 
-42 

% 
- 
0.1% 
-0.0% 

  Difference in 1990-
2019 trend (%) 

- 
-2.4% 
1.0% 

2018 

2019 

2020 

ktCO2 
+0 
+58 
-58 

% 
0.0% 
0.2% 
-0.1% 

ktCO2 
+0 
+68 
-68 

% 
0.0% 
0.2% 
-0.1% 

ktCO2 
+0 
+62 
-62 

% 
0.0% 
0.2% 
-0.1% 

  Difference in 2018-
2020 trend (%) 

-0.0% 
0.0% 
-0.0% 

Step E3: Technical energy efficiency scaling exponent 

The reference calibration scales country-specific technical energy consumption by applying an 
exponent of 0.85 to the ratio between country-specific and EEA-level load factors. This represents 
the non-linearity of energy consumption as a function of load factor (Notteboom & Cariou, 2009): 
larger load factors improve energy efficiency, but slightly less than proportionally. An alternative 
calibration assumes this scaling would be proportional, favouring the efficiency of larger ships. This 
assumption improves average technical energy efficiency, but for extra-EEA destinations in 
particular. As a result, after recalibrating to the original constraints, the relative share of intra-EEA 
emissions tends to increase (Table 14) following the smaller improvement in efficiency for these 
destinations. However, this factor is less influential than the load factor scaling in step E2. 

Table 14. Robustness check, step E3: Total EU emissions using linear scaling of energy efficiency in maritime 
bunkers. 

CO2 emissions vs. ref. calibration 
Total 

International - intra-EEA 
International - extra-EEA 

CO2 emissions, MRV-compliant 
vs. ref. calibration 
Total 

International - intra-EEA 
International - extra-EEA 

Source: JRC analysis. 

1990 

2005 

2019 

ktCO2 
- 
+243 
-243 

% 
- 
0.8% 
-0.3% 

ktCO2 
- 
+554 
-554 

% 
- 
1.4% 
-0.5% 

ktCO22 
- 
-23 
+23 

% 
- 
-0.1% 
0.0% 

2018 

2019 

2020 

ktCO2 
+0 
-33 
+33 

% 
0.0% 
-0.1% 
0.0% 

ktCO2 
+0 
-38 
+38 

% 
0.0% 
-0.1% 
0.0% 

ktCO2 
+0 
-32 
+32 

% 
0.0% 
-0.1% 
0.0% 

  Difference in 1990-
2019 trend (%) 

- 
-1.1% 
0.5% 

  Difference in 2018-
2020 trend (%) 

-0.0% 
-0.0% 
0.0% 

43 

 
  
  
 
  
 
 
 
 
 
 
 
 
 
  
  
 
  
 
  
  
 
  
 
 
 
 
 
 
 
 
 
  
  
 
  
Step E4: Intra-EEA weighting of gas oil/diesel oil consumption 

Heavy fuel oil is generally used for low-speed diesel engines and boilers in large ships, while gas 
oil/diesel oil is typically used for smaller, higher-speed engines (Papanikolaou, 2014), auxiliary 
boilers, or navigation in Emission Control Areas (European Commission, 2019). To account for these 
characteristics within maritime bunkers, the reference calibration weighted intra-EEA voyages more 
heavily by a factor of 5:1 vs. extra-EEA voyages when disaggregating sector-level gas oil and diesel 
oil consumption. This weight was estimated from average fuel shares in energy balances for 
domestic and international navigation. To assess its impact on emissions, an alternative calibration 
removes this weight so that fuel shares for each destination are equal to their sector-level shares. 
The result (Table 15) only shows a minor change in the geographical split of effective emissions: 
the higher emissions factor of fuel oil could be expected to increase intra-EEA emissions, but the 
recalibration to THETIS-MRV emissions slightly decreases the share of intra-EEA voyages in total 
energy consumption. The reference calibration is therefore robust to this assumption. 

Table 15. Robustness check, step E4: Total EU emissions without regional rebalancing of diesel oil 
consumption. 

CO2 emissions vs. ref. calibration 
Total 

International - intra-EEA 
International - extra-EEA 

CO2 emissions, MRV-compliant 
vs. ref. calibration 
Total 

International - intra-EEA 
International - extra-EEA 

Source: JRC analysis. 

1990 

2005 

2019 

ktCO2 
- 
+56 
-56 

% 
- 
0.2% 
-0.1% 

ktCO2 
- 
-130 
+130 

% 
- 
-0.3% 
0.1% 

ktCO2 
- 
+18 
-18 

% 
- 
0.0% 
-0.0% 

  Difference in 1990-
2019 trend (%) 

- 
-0.2% 
0.1% 

2018 

2019 

2020 

ktCO2 
+0 
-4 
+4 

% 
0.0% 
-0.0% 
0.0% 

ktCO2 
+0 
+2 
-2 

% 
0.0% 
0.0% 
-0.0% 

ktCO2 
+0 
+7 
-7 

% 
0.0% 
0.0% 
-0.0% 

  Difference in 2018-
2020 trend (%) 

-0.0% 
0.0% 
-0.0% 

Step E5: Assumed reduction in domestic coverage of THETIS-MRV 

The reference calibration assumes that THETIS-MRV data covers two-thirds of domestic voyages, 
and 90% of total voyages (equating to 94.1% of international voyages in 2018). This is a relatively 
conservative assumption based on expert input and initial analysis of the THETIS-MRV system 
(European Commission, 2019). As the MRV Regulation covers ships above 5000 gross tonnage, 
THETIS-MRV coverage can be expected to be less complete for relatively smaller vessels and 
shorter trips. An alternative calibration therefore keeps the assumption of 90% total coverage, but 
with a coverage of 50% for domestic voyages (equating to 97% of international voyages in 2018).  

As shown by Table 16, this assumption has a fairly significant impact on the geographical split of 
emissions: when recalibrating to the intra-EEA THETIS-MRV emissions constraint, the lower assumed 
domestic contribution is compensated by a greater share of international intra-EEA emissions. 
Future updates and analysis of the THETIS-MRV dataset will enable a better understanding of the 
reporting performance of the system, and will guide revisions to this calibration methodology if 
needed. 

44 

 
  
  
 
  
 
 
 
 
 
 
 
 
 
  
  
 
  
 
 
 
Table 16. Robustness check, step E5: Total EU emissions assuming 50% coverage of domestic voyages in 
THETIS-MRV and 97% coverage of international voyages. 

CO2 emissions vs. ref. calibration 
Total 

International - intra-EEA 
International - extra-EEA 

CO2 emissions, MRV-compliant 
vs. ref. calibration 
Total 

International - intra-EEA 
International - extra-EEA 

Source: JRC analysis. 

1990 

2005 

2019 

ktCO2 
- 
+874 
-874 

% 
- 
2.9% 
-1.2% 

ktCO2 
- 
+1 252 
-1 252 

% 
- 
3.1% 
-1.1% 

ktCO2 
- 
+1 201 
-1 201 

% 
- 
3.1% 
-1.2% 

  Difference in 1990-
2019 trend (%) 

- 
0.3% 
-0.1% 

2018 

2019 

2020 

ktCO2 
+2 768 
+2 465 
+303 

% 
2.2% 
6.7% 
0.3% 

ktCO2 
+2 814 
+2 483 
+331 

% 
2.2% 
6.7% 
0.4% 

ktCO2 
+2 349 
+2 088 
+262 

% 
2.1% 
6.7% 
0.3% 

  Difference in 2018-
2020 trend (%) 

-0.1% 
0.0% 
-0.0% 

45 

 
 
  
  
 
  
 
 
 
 
 
 
 
 
 
  
  
 
  
 
 
 
 
 
Annex 3. Eurostat fuel code correspondence 

Table 17. Correspondence between labels used in the JRC-IDEES data files, Eurostat energy balance sectors, 
and Eurostat energy balance fuel codes. 

JRC-IDEES sector 

JRC-IDEES fuel label 

Energy balance sector 

Energy balance fuel codes 

Aviation (domestic) 

Total 

FC_TRA_DAVI_E 

TOTAL 

Aviation (international) 

Total 

INTAVI 

TOTAL 

Gas oil and diesel oil 
(excluding biofuel portion) 

Fuel oil and other oil products 

O4671XR5220B 

O4000XBIO   
O4671XR5220B 

Domestic coastal shipping, 
inland waterways 

Blended biofuels 

FC_TRA_DNAVI_E 

RA000   R5300 

Natural gas 

Biogases 

Gas oil and diesel oil 
(excluding biofuel portion) 

Fuel oil and other oil products 

G3000 

R5300 

O4671XR5220B 

O4000XBIO 
O4671XR5220B 

International maritime 
bunkers 

Blended biofuels 

INTMARB 

RA000   R5300 

Natural gas 

Biogases 

G3000 

R5300 

Source: JRC analysis and Eurostat. 

46 

 
 
  
 
 
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