Assessing the impact of energy efficiency on the EU energy consumption in 2010-2022 : an index decomposition analysis.
Date: 2025-03-17
Institutions: Joint Research Centre
Subjects: EU Member State, electrical energy, energy consumption, energy efficiency, energy research, household consumption, price of energy, report

ISSN 1831-9424 

Assessing the impact of Energy Efficiency 
on the EU Energy Consumption in 2010-
2022: an index decomposition analysis 

Clementi, E., Tsemekidi Tzeiranak, S., Paci, D. 

2025 

 EUR 40252 

This document is a publication by the Joint Research Centre (JRC), the European Commission’s science and knowledge 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 Commission nor any 
person acting on behalf of the Commission is responsible for the use that might be made of this publication. For information 
on the methodology and quality underlying the data used in this publication for which the source is neither Eurostat nor 
other Commission services, users should contact the referenced source. The designations 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. 

Contact information  
 Name: Enrico CLEMENTI 
Address: Via Enrico Fermi 2749, Ispra (VA), Italy 
Email: enrico.clementi@ec.europa.eu 

 EU Science Hub 
https://joint-research-centre.ec.europa.eu 

JRC139305 

EUR 40252  

PDF 

ISBN 978-92-68-25357-1 

ISSN 1831-9424  doi:10.2760/6952798 

KJ-01-25-161-EN-N 

Luxembourg: Publications Office of the European Union, 2025 

© European Union, 2025 

The reuse policy of the European Commission documents is implemented by the Commission Decision 2011/833/EU of 12 December 2011 
on the reuse of Commission documents (OJ L 330, 14.12.2011, p. 39). Unless otherwise noted, the reuse of this document is authorised 
under the Creative Commons Attribution 4.0 International (CC BY 4.0) licence (https://creativecommons.org/licenses/by/4.0/). This means that 
reuse is allowed provided appropriate credit is given and any changes are indicated.  

For any use or reproduction of photos or other material that is not owned by the European Union permission must be sought directly from 
the copyright holders. 

How to cite this report: European Commission: Joint Research Centre, Clementi, E., Tsemekidi Tzeiranak, S. and Paci, D., Assessing the impact 
of Energy Efficiency on the EU Energy Consumption in 2010-2022: an index decomposition analysis, Publications Office of the European Union, 
Luxembourg, 2025, https://data.europa.eu/doi/10.2760/6952798, JRC139305.

JRC136466

EUR 31971 EN

PDF 

ISBN 978-92-68-17991-8 

ISSN 1831-9424 doi:10.2760/96121

KJ-NA-31-971-EN-N

Luxembourg: Publications Office of the European Union, 2024

Contents 

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

Executive summary .............................................................................................................................................................................. 3 

1  Introduction ....................................................................................................................................................................................... 6 

2  Methodology ..................................................................................................................................................................................... 9 

3  Decomposition results .............................................................................................................................................................. 16 

3.1  Primary energy consumption ..................................................................................................................................... 16 

3.2  Final energy consumption of productive sectors ............................................................................................ 19 

3.2.1  Monetary-based activity measure for productive sectors (industry, services and 
agriculture) ............................................................................................................................................................................... 19 

3.2.2  Monetary-based activity measure (gross value added) combined with labour force . 22 

3.2.2.1 

Industry .......................................................................................................................................................... 22 

3.2.2.2  Services .......................................................................................................................................................... 25 

3.2.2.3  Agriculture .................................................................................................................................................... 28 

3.2.3  Activity revaluation approach (Industry) ................................................................................................ 30 

3.3  Transport ............................................................................................................................................................................... 32 

3.3.1  Air transport ........................................................................................................................................................... 37 

3.4  Residential ............................................................................................................................................................................ 40 

4  Russo-Ukrainian war impact on European prices and energy consumption ............................................. 43 

4.1 Gas consumption, inflation rates and prices ....................................................................................................... 44 

4.2 Natural and liquid gas imports and storage ....................................................................................................... 47 

5  Conclusions ..................................................................................................................................................................................... 50 

References ............................................................................................................................................................................................. 52 

List of abbreviations and definitions ...................................................................................................................................... 54 

List of boxes.......................................................................................................................................................................................... 55 

List of figures ....................................................................................................................................................................................... 56 

List of tables ......................................................................................................................................................................................... 58 

Annexes ................................................................................................................................................................................................... 59 

1 

 
 
 
 
 
 
 
Abstract  

This report examines the determinants of changes in primary and final energy consumption at EU27 
and Member State levels over the period 2010 to 2022 to track and understand the progress towards 
2030  energy  efficiency  targets.  Energy  consumption  trends  are  driven  by  several  factors  beyond 
energy  efficiency  improvements,  which  can  have  a  profound  effect  in  the  aggregate  energy  use, 
irrespective of the impact of energy efficiency policies and measures. To understand the latest energy 
consumption trends in the EU, the Logarithmic-Mean Divisia Index (LMDI) approach, a widely used 
Index  Decomposition  Analysis  (IDA)  method,  was  applied  to  study  both  aggregated  and  sectoral 
energy consumption changes at EU and Member State levels over the examined period and quantify 
the impact of factors such as economic activity, demographics, productivity, lifestyle and weather 
changes.  The  results  suggest  that  significant  energy  efficiency  gains  during  the  studied  period, 
without which the progress achieved towards 2030 EU energy efficiency targets would have been 
difficult to attain. However, the results attained in recent years should be considered with caution: as 
they  have  been  significantly  influenced  by  exceptional  external  factors,  such  as  the  COVID-19 
pandemic  which  led  to  a  drop  in  energy  consumption,  followed  by  a  rebound  effect  once  the 
restrictions  ceased,  and  the  invasion  of  Ukraine,  which  resulted  in  an  increase  in  energy  prices 
(especially  for  gas)  with  strong  interventions  by  the  EU  and  Member  States  to  limit  energy 
consumption.  With  the  aim  to  investigate  the  impact  of  the  Russo-Ukrainian  war  on  energy  and 
economic matters, an analysis at the European level has been carried out. 

2 

 
Executive summary 

Policy context 

Energy  efficiency  plays  an  integral  role  in  the  EU  energy  policy,  with  the  2030  target  on  energy 
efficiency expected to contribute towards a long-term vision for a climate-neutral Europe. This target 
aims to accelerate energy efficiency efforts across the EU, while contributing to the decoupling of 
economic  growth  from  future  energy  demand.  The  new  2030 targets  for  energy  efficiency  (to  be 
achieved at the EU level with the contribution of each of the 27 Member States) in the recast of the 
Energy Efficiency Directive (EED  recast) that entered into force on 10 October 2023, are equal to 
992.5 Mtoe for primary energy consumption and 763 Mtoe for final energy consumption.  Primary 
energy consumption measures the total energy demand of a country. It covers consumption of the 
energy sector itself, losses during transformation (for example, from oil or gas into electricity) and 
distribution of energy, and the final consumption by end users (Tsemekidi-Tzeiranaki et. al., 2022).  

This report contributes to the European Commission’s actions to monitor the progress towards the 
2030  EU  energy  targets  defined  by  Article  4  of  the  EED  recast.  Monitoring  progress  towards  the 
targets requires identifying the main influencing factors behind the latest energy consumption trends 
in  order  to  capture  real  energy  efficiency  gains.  Decomposition  analysis  enables  the  isolation  of 
factors such as changes in economic activity and structural shifts from general energy consumption 
trends. 

To examine the progress towards the 2030 targets on energy efficiency, this report identifies and 
measures the impact of the main determinants of changes in primary and final energy consumption 
over the period 2010 to 2022. The study covers all EU27 Member States. 

Main findings 

In 2010-2022, the combined primary energy consumption (PEC) of the EU fell by 13.7%. Considering 
the decomposition of primary energy consumption changes in this period, it was found that lower 
final energy demand was the most decisive explanatory factor behind the decrease in primary energy 
consumption (-8%), followed by efficiency improvements in transformation (-6%). On the contrary, 
distribution and own energy sector systems slightly increased in the same period (+0.2%). 

Accounting for over 41% of the total final energy consumption (FEC), the productive sectors of the 
economy  (industry,  services  and  agriculture)  followed  a  declining  trend  in  energy  consumption  in 
2010-2022,  with  a  total  drop  in  consumption  equivalent  to  a  10.6%.  Industry  experienced  the 
smallest  decline  (-8.3%),  followed  by  the  service  sector  (-19.7%),  while  agriculture  registered  an 
increase  of  +3.6%.  Considering  the  activity  indicators  separately  (employment1  and  labour 
productivity2), the opposite trends are revealed. In particular, labour productivity was the major driver 
in energy consumption in all productive sectors of the economy, with the highest relative impact in 
agriculture (+19%), followed by services (+14%) and then industry (+12%). On the other hand, the 
employment effect (labour factor) was negative in all the sectors (-12% in agriculture, -2% in industry 
and -0.1% in services). 

1 Calculated as the total number of hours worked. 
2 Gross Value Added (GVA)/total number of hours worked (L). 

3 

 
 
 
The energy efficiency gains, associated with the productive sectors of the European economy during 
the examined period based on the above analysis, were also attained if a production-based indicator 
was used as a proxy for energy efficiency. However, the comparison between decomposition analysis 
results using production-based units and monetary-based units for industry showed that the use of 
gross value added (GVA) generally overestimated efficiency improvements. Despite this, our analysis 
using the production-based units still indicates a strong inhibitor effect exerted by the production-
based energy efficiency proxy. 

Regarding the residential sector (corresponding to the 24.8% of the total final energy consumption), 
3)  and  total  effects  was  found,  indicating  the 
a  strong  correlation  between  weather  (HDD/HDDref
decisive impact of weather fluctuations on the overall energy consumption of the sector. The weather 
effect alone contributed to a reduction of -15% in 2010-2022. In addition to this, the major factor 
contributing to the residential final energy consumption decline of  -16% compared to 2010 levels 
was found to be the intensity effect4 (-17%), which counterbalanced the combined positive effects 
of population and wealth (+18% in total during the studied period). Among the residential drivers of 
final  consumption,  the  wealth  effect  had  the  greatest  impact  (+16%  compared  to  2010  levels), 
whereas  the  population  effect  accounted  for  only  +2%.  Both  of  these  effects  represented  a 
progressively increasing driving force of consumption over the years for the sector. 

Finally, the transport sector’s consumption (responsible for the remaining final energy consumption) 
increased in 2022. In 2020 it was the most affected sector by the worldwide pandemic among those 
considered in this report; however, in 2021 it has been observed a reverse trend (rebound effect) due 
to the progressive end of the circulation restrictions. This upward trend was also confirmed in 2022. 
The  energy  consumption  increased  by  +0.8%  in  2010-2022.  In  the  examined  period,  passenger 
transport experienced an increase of +2%, together with freight transport (+10%), while air transport 
showed a decreased of 15%. Considering the two main subsectors, passenger and freight transport 
(collectively covering the modes of rail, road and inland waterways) and air transport5 as a separate 
sector, the decomposition results reveal varying trends for some effects. In particular, for the first 
two  subsectors,  the  activity  effect  (passenger-kilometres  or  tonnes-kilometres)  drove  up  the 
consumption for both of them. The same results are showed by the modal shift effect, while intensity 
improvements are not sufficient to counterbalance the above-mentioned positive effects. With regard 
to  air  transport,  the  productivity  (expressed  as  tonnes/flights)  and  the  intensity  effects  played  as 
driving factors for the air energy consumption; however, their effects were not enough to offset the 
inhibitor role played by the activity effect. 

The availability of data for the time period studied in this analysis allowed the implementation of a 
preliminary analysis on the impact of the Russo-Ukrainian war on the European Union. In particular, 
the trends of gas consumption (natural and liquid) have been investigated in parallel with the Russian 
invasion effect on European inflation, gas prices, imports and storage. The results are presented and 
discussed in detail in the Section 4 of the report. 

3 Heating Degree Days/Heating Degree Days reference. 
4 FECi/GVAi. 
5 Decomposed separately due to current statistical data limitations. 

4 

 
 
 
Related and future JRC work 

The JRC reports regularly on the updated results of the decomposition analysis to inform policy DGs 
on the drivers of energy consumption trends towards 2030 targets and the plan is to continue to do 
so in future years.  

The  methodological  approach  has  been  revised  and  expanded  (e.g. through  the  application  of  the 
activity revaluation approach) and the data are updated to cover the period 2010-2022. The results 
of this report offer valuable insights into the factors behind recent consumption trends at both EU 
and Member States levels, but also highlight the need  for further investigation to provide a more 
comprehensive  analysis.  This  will  be  feasible  with  the  inclusion  of  additional  factors  and  the 
availability of more detailed and granular statistical data in the future. For a more complete picture, 
decomposition  analysis  should  be  used  alongside  other  methodological  tools  (e.g.  econometrics) 
focusing on the investigation of policy efforts on energy consumption trends. 

The  Logarithmic-Mean  Divisia  Index  method  used  in  this  study,  would  require  finer  levels  of 
disaggregation to conduct more detailed decomposition and to strengthen the analytical framework: 
as of today, disaggregated data are often accompanied with various data gaps and quality issues. 
Sectors with significant challenges include the transport sector as no full compatibility is currently 
offered between energy and activity data, and services as the breakdown of energy consumption by 
services subsector and end use is currently not available in the official statistics. This analysis has 
also  highlighted  the  need  for  the  collection  of  physical  index  data  at  compatible  disaggregation 
categories  with  energy  consumption  and  monetary  output  data  in  national  accounts,  in  order  to 
deepen the decomposition of the industrial energy changes using physical-based indicators, Future 
investigations  may  include  the  impact  of  appliances  on  residential  energy  consumption  and  the 
consideration  of  weather  effect  (including  the  distinction  between  heating  and  cooling)  for  more 
sectors. The JRC welcomes ongoing efforts made by Eurostat and national statistical offices that can 
help enhance its ability for more detailed and precise energy efficiency monitoring.  

Quick guide 

Index decomposition analysis (IDA) is a widely adopted analytical tool used by researchers. This is 
done by breaking down changes in an aggregate indicator and assigning the effects to a number of 
predefined factors. To identify the driving factors and their contributions behind the latest energy 
consumption  trends  in  the  EU27,  the  Logarithmic-Mean  Divisia  Index  method  (LMDI)  method,  a 
widely-used  IDA method, was  applied  to  study  both  aggregated  and  sectoral  energy  consumption 
changes at EU and Member State levels over the period 2010-2022 in this report. All applications 
were  run  using  the  latest  available  Eurostat  data6,  with  a  few  exceptions  where  data  from  other 
sources  were  considered  (namely,  Odyssee  database,  JRC,  DG  MOVE  pocketbook  data  and  OECD 
library data). Based on the analysis conducted, the primary energy consumption trends in 2010-2022 
were decomposed into final energy demand, transformation and distribution/energy sector effects. 
Changes in final energy consumption of end use sectors (industry, services, agriculture, transport and 
residential) were decomposed separately to best reflect the particularities of each sector and their 
specific underlying driving forces. 

6 Old methodology data from Energy Balances extracted in April 2022. 

5 

 
 
 
1 

Introduction  

As part of its 2050 decarbonisation objectives, the EU has set even more ambitious targets for 2030 
to reduce primary and final energy consumption. The amended Energy Efficiency Directive7 set an EU 
energy efficiency target for 2030 of at least 32.5% (compared to the 2030 baseline of the 2007 
Reference scenario), with a clause for a possible upwards revision by 2023. However, as part of the 
Green Deal and the ‘Fit for 55’ package, the Commission adopted in July 2021 a proposal for a recast 
of the Energy Efficiency Directive. 

The EED recast, which entered into force on 10 October 20238, sets more ambitious 2030 energy 
consumption targets, corresponding to a consumption reduction of  -11.7% compared to the 2030 
baseline of the 2020 Reference scenario at EU level. These are equal to a primary energy consumption 
of 992.5 Mtoe and a final energy consumption of 763 Mtoe by 2030. 

The  analysis  of  energy  consumption  trends  is  therefore  essential  in  assessing  policy  impact  and 
informing policymakers in the context of EU decarbonisation goals. Energy consumption trends are 
driven  by  several  factors  beyond  energy  efficiency,  including  economic  activity,  demographics, 
lifestyle changes and weather. These can all have a profound effect in the aggregate energy use, 
irrespective of the impact of energy efficiency policies and measures. As more and more countries 
rely on energy savings and efficiency measures as a means to address unprecedented challenges 
associated  with  increased  dependence  on  energy  imports,  scarce  energy  resources  and  climate 
change, robust methodologies that enable monitoring and measuring progress towards these targets 
are increasingly important.  

From 2010 to 2022, EU energy consumption followed a decreasing trend (Figure 1). The decrease in 
energy consumption was accompanied by an overall drop in energy intensity and energy consumption 
per capita, reflecting a possible increase in competitiveness in the same period. Comparing the last 
year available (2022) to the previous one (2021) (Tsemekidi-Tzeiranaki et. al., 2022), both primary 
and final energy consumption fell to 1,258.2 Mtoe (primary energy consumption) and  940.5 Mtoe 
(final energy consumption), returning close to the historical low 2020 levels (COVID-19 pandemic). 

After  showing  a  decreasing  trend  since  2010,  primary  energy  consumption  (PEC)  started  to  grow 
again in 2015 (+1.7% with respect to 2014) and peaked in 2017. As of 2018,  PEC started to fall 
again,  reaching  in  2020  the  lowest  values  in  the  time  period  considered,  1,236  Mtoe  (-15.2% 
compared to 2010). In 2021, it increased again by 6.1%. Final energy consumption (FEC) followed 
the  same pattern  of  primary  energy  consumption.  It  decreased till  2014  and  started  to  rise  from 
2015 to 2018. As of 2019, FEC started to fall again, reaching in 2020 the value of 906 Mtoe (-11.5% 
compared  to  2010).  In  2021,  it  increased  again  by  +6.9%.  The  rebound  effect  for  both  energy 
consumption indicators was widely expected in 2021. In fact, the year 2020, heavily affected by the 
COVID-19 pandemic,  saw all  sectors  (productive,  residential,  and  transport  sectors)  in  all Member 
States  showed  an  exceptional  fall  (in  some  cases  a  complete  stop)  of  the  energy  consumption. 
However,  the  rebound  effect  in  energy  consumption  lasted  for  only  one  year,  as  in  2022  a  new 
significant  decrease  in  both  primary  energy  consumption  and  final  energy  consumption  were 
observed at EU level. The reductions can be attributed to high energy prices, especially for gas, and 
these developments are mainly a result of the invasion of Ukraine and the EU’s reduction in Russian 

7 EU2018/2002. 
8 http://data.europa.eu/eli/dir/2023/1791/oj 

6 

 
 
 
fossil fuel imports. The EU and its Member States took active measures to save energy9 and, as a 
result,  this  led  to  significant  decreases  in  energy  consumption  by  industry  and  households.  More 
analytically,  in  the  context  of  the  Russian  aggression  against  Ukraine  in  2022,  the  European 
Commission  implemented  the  REPowerEU  plan10  to  save  energy  (including  a  voluntary  target  of 
reducing gas demand by 15%), to diversify gas suppliers and to ensure the production of clean energy. 

Figure 1. EU primary and final energy consumption from 2010 to 2022. 

Source: Eurostat 

A  complete  analysis  of  the  drivers  behind  the  latest  energy  consumption  trends  requires  the 
examination of a wider range of factors beyond policy efforts. The separation of energy efficiency 
impacts  from  structural  and  activity  changes  of  the  economy  as  well  as  other  factors  has  been 
examined extensively in the literature through the application of decomposition analysis techniques. 
Indeed,  decomposition  analysis  has  been  used  by  several  international  bodies  including  the 
International Energy Agency to quantify the impact of such factors in historical energy - or emission 
- related trends (IEA, 2018). Index decomposition analysis has been used to single out the impact of 
energy  efficiency  in  all  sectors  of  the  economy  using  data  from  national  statistics  and  energy 
balances. Many of these studies commonly relate energy efficiency with energy intensity, although 
more  recent  attempts  have  been  made  focusing  on  the  use  of  physical  indicators  (in  addition  to 
monetary indicators) to measure output, which, in turn, enables the consideration of more reliable 
energy efficiency indicators. 

9 For instance, the Council Regulation on coordinated demand reduction measures for gas (EU/2022/1369): Member States 

agreed to reduce their gas demand by 15% compared to their average consumption in the past five years. 

10  REPowerEU (europa.eu) 

7 

 
 
 
 
To  track  and  understand  the  progress  towards  the  2030  energy  efficiency  targets,  this  report 
examines the determinants of changes in primary and final energy consumption at EU and Member 
States levels over the period 2010-2022. The year 2022 represents the latest available year covered 
by energy balance statistics. This report builds up from the previous reports published by the EC Joint 
Research  Centre  (Economidou,  2017;  Economidou  and  Román-Collado,  2019;  Economidou,  2021; 
Clementi et al., 2024) informing policymakers on the latest progress towards the targets through the 
application of decomposition analysis. In addition to the previous versions, this report also includes 
an analysis of the effect of the Russo-Ukrainian war on both energy  and economic factors in  the 
European Union. The purpose of this analysis is to understand how the energy consumption – and in 
particular, natural and liquid gas consumption– and the economy (energy prices and inflation rates) 
have been affected by the sudden gas shortage observed after the war. To carry out this analysis, 
monthly data from either the year 2020 or from the period from 2021 to 2024 have been used. 

Index  decomposition  analysis,  and  in  particular  the  widely  used  Logarithmic  Mean  Divisia  Index 
methodology,  is  applied  in  this  report  to  study  the  aggregated  and  sectoral  energy  consumption 
changes at EU and Member State levels. The sectors considered include the productive sectors of the 
economy (that is, industry, services and agriculture) as well as transport and residential sectors. The 
application  of  the  activity  revaluation  approach,  proposed  by  Ang  and  Xu  (2013)  and  applied  by 
Román-Collado and Economidou (2021) and Clementi et al. (2024) in the EU, is used to decompose 
energy consumption changes in industry based on physical units. The hybrid model outlined by  Xu 
and Ang (2014) is applied for the residential sector. The decomposition of primary energy, is based 
on a simplified version of Reuter, Patel and Eichhammer (2017). 

The report is structured as follows. Section 2 describes the methodological approach and presents in 
detail  the  analytical  framework  of  the  decomposition  options  considered  in  the  work.  Section  3 
discusses the results of the decomposition. The Russo-Ukrainian war focus is reported in Section 4. 
Conclusions are drawn in Section 5. 

8 

 
2  Methodology 

The  Logarithmic  Mean  Divisia  Index  (LMDI-I)  is  used  to  decompose  changes  in  primary  and  final 
energy consumption in all EU Member States. In its simplest form, the following identity11 is used to 
decompose energy consumption changes in activity, structure and intensity effects (Ang, 2005; Ang, 
2015): 

𝐸 = ∑ 𝐸𝑖
𝑖

= ∑ 𝑌

𝑖

𝑌𝑖
𝑌

𝐸𝑖
𝑌𝑖

= ∑ 𝑌𝑆𝑖𝐼𝑖

𝑖

(1) 

where 𝑖 denotes the sector, 𝐸 is the total energy consumption, 𝑌 represents the economic activity 
such as Gross Domestic Product or Value added, 𝑆𝑖 is the proportion of the economic activity of sector 
𝑖 in relation to the whole economy (𝑌𝑖 𝑌⁄ ) and 𝐼𝑖 is energy intensity (𝐸𝑖 𝑌𝑖⁄ ) of sector 𝑖.  

The LMDI-I decomposition of additive change in energy consumption (∆𝐸) between time 0 and 𝑡 is 
expressed as: 

∆𝐸 = 𝐸𝑡 − 𝐸0 = 𝐷𝑎𝑐𝑡 + 𝐷𝑠𝑡𝑟 + 𝐷𝑖𝑛𝑡 

(2) 

where 𝐷𝑎𝑐𝑡, 𝐷𝑠𝑡𝑟 and 𝐷𝑖𝑛𝑡 denote the overall activity, structure, and intensity effects, respectively. 
When the effects are positive, they act as drivers and if they are negative, they will act as inhibitors 
of energy consumption changes. In its multiplicative form, the LMDI-I decomposition of the ratio of 
energy consumption between 0 and 𝑡 is defined as: 

𝑅 =

𝐸𝑡
𝐸0

= 𝑅𝑎𝑐𝑡 .  𝑅𝑠𝑡𝑟 . 𝑅𝑖𝑛𝑡 

(3) 

In the multiplicative decomposition, when the effects are above 100%, they act as drivers and if 
they are lower than 100%, they will act as inhibitors of energy consumption changes. 

Concretely, the LMDI-I decomposition is carried out using the following formulae: 

𝐷𝑎𝑐𝑡 = ∑ 𝑤𝑖𝑙𝑛 (

𝑌𝑡
𝑌0

) ,  𝐷𝑠𝑡𝑟 = ∑ 𝑤𝑖𝑙𝑛 (

𝑖

)

,  𝐷𝑖𝑛𝑡 = ∑ 𝑤𝑖𝑙𝑛 (
𝑖

𝐼𝑖,𝑡
𝐼𝑖,0

)

𝑅𝑎𝑐𝑡 = 𝑒

∑ 𝑤𝑖̃ 𝑙𝑛(

𝑖

𝑌𝑡
𝑌0

)

,  𝑅𝑠𝑡𝑟 = 𝑒

∑ 𝑤𝑖̃ 𝑙𝑛(

𝑖

∑ 𝑤𝑖̃ 𝑙𝑛(

𝑖

𝐼𝑖,𝑡
𝐼𝑖,0

)

 (4) 

𝑆𝑖,𝑡
𝑆𝑖,0
𝑆𝑖,𝑡
𝑆𝑖,0

)

where 𝑤𝑖 =

𝐸𝑖,𝑡−𝐸𝑖.0
𝐸𝑖,𝑡
𝐸𝑖,0

𝑙𝑛(

)

  and  𝑤𝑖̃ =

,  𝑅𝑖𝑛𝑡 = 𝑒
(𝐸𝑖,𝑡−𝐸𝑖,0)/𝑙𝑛(

(𝐸𝑡−𝐸0)/𝑙𝑛(

)

𝐸𝑖,𝑡
𝐸𝑖,𝑡
𝐸𝑡
𝐸0

)

While  applications of decompositions may  deviate from the above canonical  setup by considering 
various other factors, the logic underpinning the analysis remains the same. Based on the data review 
focusing on the availability and comparability of possible input data at sectoral and sub-sectoral level 
in European countries carried out by Economidou (2017), the LMDI-I was applied to decompose: 

11 Identity refers to the governing decomposition equation that describes the relationship between the decomposed 

indicator (e.g. energy consumption or GHG emissions) and the various factors. 

9 

 
 
 
 
 
 
 
 
 
 
 
1.  primary energy consumption into activity, transformation and distribution effects; 

2.  final energy consumption of productive end use sectors12 (namely, industry, services and ag-

riculture) into activity, productivity, intensity and wherever possible structural effects; 

3.  final energy consumption of passenger and freight transport into activity, modal shift and 

efficiency effects; 

4.  final energy consumption of residential sector into population, wealth, weather and intensity 

effects. 

Various  adaptations  to  the  formulae  (1)-(4)  were  made  to  best  reflect  the  particularities  of  each 
sector and their specific underlying driving forces. Whenever possible, physical indicators were used 
to define activity and intensity effects.  

The methodology used in this analysis is based on the approach adopted by Economidou and Román-
Collado (2019) and Román-Collado and Economidou (2021) and Clementi et al. (2024). A schematic 
diagram summarising this approach is shown in Table 1. 

Table 1. Decomposition identities and explanation of factors considered for various sectors of the economy 

Sectors 

Energy   Decomposition identity 

Factors 

All 

Primary 
(PEC) 

(1) 𝑃𝐸𝐶

= ∑ 𝐹𝐸𝐶

𝐸𝐴𝑇
𝐹𝐸𝐶

𝑃𝐸𝐶
𝐸𝐴𝑇

• 

Final demand of the energy 
consumption for end users (𝐹𝐸𝐶) 

It measures changes in primary energy 
consumption due to changes in the final 
demand of the energy consumption for end 
users and is negative in cases where the 
demand for final energy by end users drops. 

•  Global efficiency of the transformation 

sector (

𝑃𝐸𝐶

𝐸𝐴𝑇

)  

It accounts for the average efficiency of the 
whole energy transformation system, 
providing an indication of the quantity of 
energy lost in the conversion/transformation 
processes. Negative transformation effect 
corresponds to cases where the overall 
efficiency of the conversion/transformation 
distribution system increases, i.e. the 
difference between the total energy which 
enters the system and the total energy 
available after transformation drops. 

•  Global efficiency of the final part of the 

process (

𝐸𝐴𝑇

𝐹𝐸𝐶

) 

12 Freight transport was treated separately. 

10 

 
 
 
 
It captures efficiency gains in the 
distribution system and is negative if losses 
during distribution processes and/or 
reduction in energy sector consumption are 
realised. 

•  Activity (𝐺𝑉𝐴) 

It shows the change in 𝐹𝐸𝐶 due to a change 
in total 𝐺𝑉𝐴. The activity effect is positive if 
GVA grows due to additional energy demand 
of increased economic activity.  

• 

Structure (𝐺𝑉𝐴𝑖 𝐺𝑉𝐴⁄

) 

It accounts for changes in energy 
consumption due to change in the relative 
importance of sectors with different energy 
intensities (industry, services, agriculture). 
The structural effect is positive if sectors of 
high energy intensity grow more relative to 
less intensive sectors. 

• 

Intensity (𝐹𝐸𝐶𝑖 𝐺𝑉𝐴𝑖

⁄

) 

It accounts for changes in total energy 
consumption due to technology 
improvements, policy effects and other 
factors (represented by final energy 
consumption per value added in monetary 
terms). The intensity effect is negative if 
there is a drop in energy intensity. 

  • 

Labour (𝐿) – Employment effect 

It accounts for changes in energy 
consumption due to a change in the overall 
employment measured in total number of 
hours worked. The labour effect is positive if 
the increase in global hours worked L grows 
the final energy demand. 

• 

Labour productivity  (𝐺𝑉𝐴 𝐿⁄ ) 

It reflects changes in the productivity of 
each sector due to e.g. of advancements in 
equipment used in production processes or 
penetration of technologies that enhance 
the output per unit hour worked. The labour 
productivity effect is positive when an 
increase in labour productivity drives up 
final energy demand. 

• 

Structure (𝐺𝑉𝐴𝑖 𝐺𝑉𝐴⁄

) 

(See above – only available for services) 

Final 
(FEC) 

(2)𝐹𝐸𝐶 = ∑ 𝐺𝑉𝐴

𝑖

𝐺𝑉𝐴𝑖
𝐺𝑉𝐴

𝐹𝐸𝐶𝑖
𝐺𝑉𝐴𝑖

Productive 
sectors 
(industry, 
services, 
agriculture) 

Final 
(FEC) 

(3)𝐹𝐸𝐶 = ∑ 𝐿

𝑖

𝐺𝑉𝐴
𝐿

𝐺𝑉𝐴𝑖
𝐺𝑉𝐴

𝐹𝐸𝐶𝑖
𝐺𝑉𝐴𝑖

Productive 
sectors 
(industry, 
services, 
agriculture) 

11 

 
 
• 

Intensity (𝐹𝐸𝐶𝑖 𝐺𝑉𝐴𝑖

⁄

) 

(See above) 

See box 1 for details 

•  Activity 

Industry 
(manufacturing, 
construction) 

Final 
(FEC) 

It accounts for the change in final energy 
demand due to the change in physical units 
produced.  

• 

Structure 

It shows the change in final energy demand 
due to a change in the relative importance 
of physical production of sectors on total 
production. The structural effect is positive 
if sectors of high energy intensity grow 
more relative to less intensive sectors. 

• 

Intensity  

It accounts for the change in final energy 
demand to a change in energy intensity 
measured in terms of physical units.  

•  Activity (𝑃𝐾𝑀 or 𝑇𝐾𝑀) 

It shows the change in final energy 
consumption due to a change in the physical 
units (passenger-kilometres or tonnes-
kilometres). 

•  Modal shift (𝑃𝐾𝑀𝑖 𝑃𝐾𝑀⁄

 or 

𝑇𝐾𝑀𝑖 𝑇𝐾𝑀⁄

) 

It shows the change in final energy 
consumption due to the modal shift change.  

• 

Intensity (𝐹𝐸𝐶𝑖 𝑃𝐾𝑀𝑖

⁄

 or 𝐹𝐸𝐶𝑖 𝑇𝐾𝑀𝑖

⁄

) 

It shows the change in final energy 
consumption due to the change in final 
energy consumption by mode of transport 
per physical activity unit. 

•  Activity  (𝑁𝐹) 

It accounts for the changes in final energy 
consumption of air transport due to the 
number of flights. 

• 

Productivity  (𝐺𝑇 𝑁𝐹⁄

) 

It reflects the change in final energy 
consumption due to a change in the weight 
of tonnes of goods and passengers 
transported per flight.  

Transport 
(passenger*, 
freight*) 

*Excludes air 
transport  

Final 
(FEC) 

(4)𝐹𝐸𝐶 = ∑ 𝑃𝐾𝑀

𝑖

𝑃𝐾𝑀𝑖
𝑃𝐾𝑀

𝐹𝐸𝐶𝑖
𝑃𝐾𝑀𝑖

(5)𝐹𝐸𝐶 = ∑ 𝑇𝐾𝑀

𝑖

𝑇𝐾𝑀𝑖
𝑇𝐾𝑀

𝐹𝐸𝐶𝑖
𝑇𝐾𝑀𝑖

Transport (air, 
domestic) 

Final 
(FEC) 

(6)𝐹𝐸𝐶𝑎𝑖𝑟 = ∑ 𝑁𝐹

𝐺𝑇
𝑁𝐹

𝐹𝐸𝐶𝑎𝑖𝑟
𝐺𝑇

12 

 
 
 
 
 
 
Residential 

Final 
(FEC) 

= 𝑃𝑂𝑃.

(7)𝐹𝐸𝐶 = 𝐹𝐸𝐶ℎ𝑒𝑎𝑡 +   𝐹𝐸𝐶𝑜𝑡ℎ𝑒𝑟
′
𝐹𝐸𝐶ℎ𝑒𝑎𝑡
𝑇𝐹𝐴
𝐹𝐸𝐶𝑜𝑡ℎ𝑒𝑟
𝐺𝐷𝐼

𝑇𝐹𝐴
𝑃𝑂𝑃
𝐺𝐷𝐼
𝑃𝑂𝑃

𝐻𝐷𝐷
𝐻𝐷𝐷𝑟𝑒𝑓

+ 𝑃𝑂𝑃

• 

Intensity (𝐹𝐸𝐶𝑎𝑖𝑟 𝐺𝑇⁄

) 

It shows the change in final energy 
consumption due to a change in the energy 
consumption by tonnes transported. 

• 

Population (𝑃𝑂𝑃) 

It accounts for changes in energy 
consumption due to a change in the 
population size. 

•  Wealth (𝑇𝐹𝐴 𝑃𝑂𝑃

⁄

 or 𝐺𝐷𝐼 𝑃𝑂𝑃

⁄

) 

It reflects changes in energy consumption 
due to changes in the wealth represented by 
the total floor area of dwellings (𝑇𝐹𝐴) per 
capita for the heating end use and gross 
disposable income in purchasing power 
standard (𝐺𝐷𝐼) per capita for all other end 
uses. 

•  Weather (𝐻𝐷𝐷 𝐻𝐷𝐷𝑟𝑒𝑓

⁄

13

) 

It accounts for changes to energy 
consumption due to weather changes. If 
weather effect is negative, energy 
consumption has dropped due to warmer 
climate. The weather adjustment was 
considered only for the final energy 
consumption attributed to the heating use 
(𝐹𝐸𝐶ℎ𝑒𝑎𝑡). 

• 

Intensity effect  (𝐹𝐸𝐶ℎ𝑒𝑎𝑡
𝐹𝐸𝐶𝑜𝑡ℎ𝑒𝑟 𝐺𝐷𝐼

) 

⁄

′

𝑇𝐹𝐴⁄

 or 

It represents the final energy consumption 
per unit of physical activity; that is, TFA and 
GDI respectively for the space heating and 
other end-uses. 

Source: Authors’ elaboration 

The  principal  source  of  data  used  in  this  analysis  was  the  statistical  database  of  the  European 
Commission Eurostat (ESTAT, 2024), which inter alia collects economic and energy use data for all 
European countries. These originate from national accounts data and are harmonised by Eurostat to 

13 Heating degree day (HDD) index is a weather-based technical index designed to describe the need for the heating energy 
requirements of buildings. The calculation of HDD relies on the base temperature, defined as “the lowest daily mean air 
temperature not leading to indoor heating”. The value of the base temperature depends in principle on several factors 
associated  with  the  building  and  the  surrounding  environment.  By  using  a  general  climatological  approach,  the  base 
temperature is set to a constant value of 15°C in the HDD calculation. The formula of HDD index is: if Tm ≤ 15°C, then 
[HDD = ∑i(18°C - Tim)] Else [HDD = 0], where Tm is the mean air temperature of day i. In the case of this study, HDDref is 
equal to the average of HDD of the years 2000-2020. 

13 

 
 
 
 
ensure data quality, consistency and comparability across Member States. To complement current 
data shortcomings, the Odyssee-Mure database (Odyssee, 2024) and OECD data (OECD, 2024) were 
used to cover specific data needs. These are discussed in more detail below. 

The  final  energy  consumption  of  each  productive  sector  (industry,  services  and  agriculture)  is 
decomposed  following  the  factorisation  identity  provided  in  equation  1  in  three  factors:  activity, 
structural and intensity. As the breakdown of energy consumption of the services and agricultural 
sectors is not covered by official statistics, the structural effect within each of these sectors cannot 
be  examined  at  this  stage.  Industry  is  the  only  sector  of  the  economy  for  which  a  detailed 
decomposition examining the sector's structural effect is currently feasible. A detailed disaggregation 
of both energy and activity data of industry is available, allowing a fine level of decomposition for 
this  sector.  The  division  of  industrial  subsectors  was  done  on  the  basis  of  the  lowest  available 
disaggregation  level  of  energy  consumption.  This  resulted  in  the  consideration  of  11  industrial 
subsectors14.  Based  on  Román-Collado  and  Colinet  (2018),  the  final  energy  consumption  of  each 
productive sector of the economy (services, industry and agriculture) was also decomposed using the 
factorisation identity that takes into account employment (labour) and labour productivity effects. 

To identify differences in decomposition results between the use of monetary versus physical units, 
the refined activity revaluation (AR) approach, proposed by Ang and Xu (2013) is considered (see Box 
1 for more methodology details). This comparison has only been carried out for the industrial sector 
because  industry  is  the  only  sector  where  physical  production  data  are  available.  Concretely,  the 
industrial production index has been used as a proxy of industrial physical production. However, as 
the industrial production index data are not provided separately for all 11 subsectors, the industry 
has been broken down in 2 industrial subsectors: (1) manufacturing and (2) construction (see Box 1 
for more details on the methodology). 

The passenger and freight transport are decomposed as separate sectors. While various studies use 
turnover to define activity in the transport sector (Zhang et al., 2011; Liang et al., 2017; Achour and 
Belloumi, 2016), this study opts for a physical  unit based activity measure expressed in terms of 
transport volume (that is, passenger kilometres and freight kilometres). This raises the need to deal 
with each transport subsector ― passenger and freight transport — separately. Given that statistical 
data on transport energy consumption are scarce, various additional sources were considered in this 
analysis, e.g. Odyssee data were used to calculate the share of passenger versus freight transport 
for road and rail modes, while the Statistical Pocket book on EU Transport in Figures prepared by DG 
MOVE  were  used  for  passenger  and  freight  kilometres  by  transport mode  (European  Commission, 
2023). Furthermore, data from the OECD library has been used for the year 2022. The air transport 
was singled out from the other transport modes, as a distinction between passenger and freight in 
terms of energy consumption is not covered by any statistical source. For this reason, the total number 
of passengers, tonnes of goods transported (𝐺𝑇) and number of flights (𝑁𝐹) were used instead. 

In line with the decomposition analysis applied to the transport sector in which passenger and freight 
transport are treated separately, decomposition of the residential energy consumption can also be 
carried out at the subsector level. Xu and Ang (2014) proposed a hybrid model, which decomposes 
the energy consumption of various energy services in the residential sector according to their specific 

14 Industrial subsectors considered: 1. Food and Tobacco, 2. Textile and Leather, 3. Wood and Wood Products, 4. Paper, Pulp 
and Print, 5. Chemical and Petrochemical, 6. Metals, 7. Machinery, 8. Non-Metallic Minerals, 9. Other manufacturing, 10. 
Transport equipment, 11. Construction. 

14 

 
 
 
underlying driving forces. The approach used by these authors informs the methodology used in this 
study. Due to data restrictions, residential energy consumption was split into two main end uses: (1) 
space heating, and (2) all other end-uses. 

Box 1. Decomposition of industrial energy changes using physical units 

The activity indicator can be either given by a monetary measure, such as value added, or physical measure, such as physical units of production. Liu and 
Ang (2007) showed that monetary-based activity is adopted in over 90% of the empirical studies on industrial  energy use  in the index decomposition 
analysis literature. The popularity of the monetary measure can be associated with the fact that the aggregate activity level and activity structure can be 
easily computed and data are readily available through national accounts (Ang and Xu, 2013). However, the choice of activity in monetary terms typically 
implies the use of monetary-based intensity effect, which may not provide a reliable proxy to measure energy efficiency changes as there  are a number 
of limitations due to both price fluctuations and productivity changes (Norman, 2017). The challenges of using a physical measure are associated with the 
fact that actual production units cannot be added up and data requirements for obtaining aggregate physical output measures are high, limiting their use 
in practice (Nanduri, Nyboer and Jaccard, 2002; Farla and Blok, 2001). 

There are a number of techniques  available to overcome some of the issues associated with the use of physical units. One of these techniques is the 
refined activity revaluation (AR) approach, proposed by Ang and Xu (2013). This technique is appropriate in cases when monetary output data is available 
for all subsectors and physical output data  are available for some or all subsectors. The activity factorization (AR) approach utilising the Montgomery-
Vartia (M-V) index is preferred to the alternative Laspeyres index as the former offers perfect decomposition (with no residual) and maintains consistency 
in the index procedures used throughout the method (Ang and Xu, 2013). 

When physical output data are available for subsectors 𝑖 (𝑄𝑖), the following revaluation procedure is used to adjust the activity indicator: 

After adjusting the activity indicators of the relevant subsectors, the aggregate energy consumption is decomposed as follows: 

 (8) 

(9) 

This  factorisation  allows  us  to  decompose  the  additive  and  multiplicative  change  of  final  energy  consumption  in  three  effects:  activity,  structural  and 
intensity. The additive decomposition formulae proposed by Ang and Xu (2013) are expressed as: 

, 

,  

 is the energy consumption per physical output unit of subsector 𝑖 (

) and 

 is the Montgomery–Vartia (M–

where 

V) index: 

The multiplicative formulae are given by: 

(10) 

,

, 

 (11) 

The activity effect accounts for the change in final energy demand due to the change in physical units produced. The structure effect shows the 
change in final energy demand due to a change in the relative importance of physical production of sectors on total production. The intensity effect 
accounts for the change in final energy demand to a change in energy intensity measured in terms of physical units. 

15 

00TtiiiQAYQ=111nnniiiiiiiiiAEEEAASIAA======01(,)lnntMVactiiiDLEEQ−==001(,)lntntiistriiMViQQDLEEQ−==0int01(,)tntiiiiiUCDLEEUC==iUCiiiUCEQ=MVQ−0001(,)expln(,)ttnMViiitiiLYYQQLYYQ−==()()001,expln,tniiMVacttiLEERQLEE−==()()0001,expln,ttniiiistrMVtiLEEQQRQLEE−==()()0int001,expln,ttniiitiiLEEUCRUCLEE== 
  
  
 
  
3  Decomposition results 

3.1  Primary energy consumption15 

In 2010-2022, the EU primary energy consumption16 decreased by −13.7% from 1,458 to 1,259 Mtoe 
(Figure 2). Over 73% of the overall decrease was attributed to four countries, namely Germany (−55 
Mtoe), France (-49 Mtoe), Italy (−27 Mtoe) and the Netherlands (-15 Mtoe). On the contrary, some 
countries experienced an increase over the study period, namely  Poland (+2 Mtoe), Bulgaria (+1.5 
Mtoe) and Lithuania (+0.1 Mtoe). These results also have to be analysed considering the year 2020, 
where the energy consumption was heavily affected by the COVID-19 pandemic. 

According  to  the  decomposition  results,  the  main  explanation  behind  the  fall  in  primary  energy 
consumption in the period 2010-2022 is attributed to the lowering of final energy demand together 
with the transformation effect over the examined period (Figure 3). On the contrary, the distribution 
losses increased in the same period (+4 Mtoe). Notwithstanding the 2020 exceptional year due to the 
pandemic crisis, the total primary energy consumption continued decreasing in 2021 and 2022, only 
the distribution effect showed a reverse trend by increasing from 2020.  

Figure 2. Decomposition of changes in EU primary energy consumption (Mtoe) in 2010-2022 using the 
additive Logarithmic Mean Divisia Index approach (LMDI) 

Source: JRC 

The country results are shown in Table 2. In 2010-2022, the decline in final energy consumption17 
played a major role in limiting primary energy consumption in all Member States except for Bulgaria 
(+2 Mtoe or +12%), Ireland (+0.06 Mtoe or +1%), Lithuania (+0.7 Mtoe or +12%), Hungary (+1.1 Mtoe 
or +5%), Malta (+0.3 Mtoe or +29%), Poland (+8.6 Mtoe or +9%), and Romania (+2 Mtoe or +7%). In 
all other Member States, lower final energy consumption was attributed to a decline in primary energy 
consumption, with the largest impact being registered in the Netherlands, Denmark and Greece (a 

15 Decomposition formula of PEC: (1) 𝑃𝐸𝐶 = ∑ 𝐹𝐸𝐶

𝐸𝐴𝑇

𝑃𝐸𝐶

𝐸𝐴𝑇
16 Data source: ESTAT database “FINAL-ENERGY-CONSUMPTION-OLD-METHODOLOGY”, ESTAT code: PEC2020-2030. 
17 Data source: ESTAT database “FINAL-ENERGY-CONSUMPTION-OLD-METHODOLOGY”, ESTAT code: FEC2020-2030. 

𝐹𝐸𝐶

16 

 
 
 
 
 
 
drop equivalent to at least -14% compared to 2010 primary energy consumption levels was found in 
this group of countries). In terms of transformation efficiency of the energy sector, 18 Member States 
experienced (to a varying level) improvements in the overall efficiency, thereby contributing to a drop 
in their overall primary energy consumption. The strongest improvements are observed in Greece, the 
Netherlands,  and  Denmark  with  an  equivalent  drop  of  at  least  -30%  compared  to  2010  primary 
energy consumption levels. On the opposite side, transformation efficiency had a counteracting effect 
(i.e., drove up consumption) in 9 countries, among the worst performing, there were Czechia, France, 
Croatia, Latvia, Lithuania, Luxembourg, Hungary, Austria and Slovenia. Improvements in distribution 
and energy sector efficiency acted as an inhibitor factor in 12 Member States, with the largest drop 
identified in Hungary, France, Lithuania, Luxembourg, and Malta. In contrast, worsening of distribution 
and energy sector efficiency effect was the strongest in Greece, Netherlands, Sweden, and Denmark. 

Figure 3. Yearly multiplicative LMDI decomposition results at EU level in 2010-2022 

Source: JRC 

17 

 
 
 
 
Table 2. Additive and multiplicative decomposition results of primary energy consumption changes Member 
State level in 2010-2022 and 2020-2022 

Additive results [ktoe] 

Multiplicative results [%] 

Period 

Total 
effect 

Final energy 
demand effect 

Transformation 
efficiency effect 

Distribution 
and own 
conversion 
effect 

Total 
effect 

Final energy 
demand effect 

Transformation 
efficiency effect 

Distribution and 
own conversion 
effect 

BE 

BG 

CZ 

DK 

DE 

EE 

IE 

EL 

ES 

FR 

HR 

IT 

CY 

LV 

LT 

LU 

HU 

MT 

NL 

AT 

PL 

PT 

RO 

SI 

SK 

FI 

SE 

EU27 

2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 

-8134 
-3512 
1531 
364 
-3905 
-914 
-3941 
-364 
-54570 
-10906 
-1111 
272 
-370 
513 
-6312 
592 
-9779 
1773 
-48884 
-19254 
-557 
36 
-27638 
-5966 
-192 
171 
-250 
-160 
145 
-315 
-816 
-391 
-750 
-1061 
-43 
119 
-15621 
-4652 
-2704 
-1456 
2039 
-5357 
-1889 
1234 
-1942 
-2083 
-839 
-140 
-1973 
-1033 
-5247 
-1304 
-5876 
-854 
-199628 
-54650 

-6591 
-3304 
2065 
-519 
-630 
-1820 
-2691 
-629 
-27276 
-6611 
-221 
-89 
61 
650 
-4491 
1244 
-11675 
1250 
-24951 
-6876 
-408 
-94 
-21564 
-3919 
-150 
178 
-175 
-104 
695 
-312 
-681 
-398 
1106 
-1020 
273 
146 
-15889 
-4329 
-1934 
-1788 
8595 
-3748 
-1831 
1278 
2023 
-1756 
-492 
-5 
-2306 
-1352 
-3792 
-2029 
-4479 
-1060 
-117409 
-37014 

88% 
93% 
112% 
97% 
99% 
95% 
86% 
96% 
91% 
98% 
95% 
98% 
100% 
105% 
85% 
106% 
91% 
101% 
90% 
97% 
96% 
99% 
87% 
97% 
95% 
108% 
96% 
98% 
112% 
95% 
85% 
91% 
105% 
96% 
139% 
119% 
78% 
93% 
94% 
94% 
109% 
96% 
92% 
107% 
107% 
95% 
93% 
100% 
87% 
92% 
89% 
94% 
91% 
98% 
92% 
97% 

93% 
97% 
96% 
78% 
100% 
99% 
70% 
100% 
87% 
94% 
79% 
107% 
89% 
102% 
57% 
104% 
86% 
95% 
117% 
93% 
142% 
109% 
96% 
91% 
93% 
105% 
109% 
112% 
113% 
94% 
117% 
93% 
135% 
104% 
82% 
111% 
67% 
93% 
104% 
111% 
94% 
94% 
94% 
106% 
99% 
90% 
102% 
114% 
99% 
101% 
97% 
89% 
77% 
99% 
94% 
95% 

104% 
103% 
101% 
134% 
92% 
103% 
133% 
102% 
105% 
104% 
107% 
101% 
110% 
97% 
159% 
93% 
119% 
106% 
77% 
101% 
69% 
93% 
100% 
108% 
106% 
95% 
90% 
88% 
80% 
107% 
83% 
107% 
68% 
96% 
83% 
87% 
150% 
106% 
94% 
91% 
99% 
105% 
106% 
94% 
89% 
110% 
93% 
86% 
104% 
101% 
99% 
116% 
125% 
101% 
100% 
104% 

-3666 
-1406 
-951 
-4600 
-216 
-364 
-6210 
-50 
-40084 
-15986 
-1337 
316 
-1660 
314 
-13418 
849 
-18560 
-6059 
36360 
-14849 
2942 
747 
-5246 
-13113 
-163 
114 
406 
513 
797 
-433 
727 
-273 
7111 
894 
-167 
84 
-24432 
-3969 
1043 
3270 
-5496 
-6352 
-1149 
1110 
33 
-3356 
75 
828 
-247 
177 
-1277 
-3728 
-11419 
-239 
-86205 
-65559 

2122 
1198 
417 
5482 
-3060 
1270 
4961 
315 
12790 
11690 
446 
45 
1229 
-452 
11597 
-1501 
20457 
6581 
-60293 
2472 
-3091 
-617 
-827 
11065 
120 
-122 
-481 
-569 
-1346 
430 
-861 
281 
-8967 
-935 
-149 
-110 
24699 
3646 
-1813 
-2938 
-1060 
4742 
1092 
-1154 
-3997 
3029 
-422 
-963 
579 
142 
-178 
4452 
10022 
445 
3986 
47923 

85% 
93% 
109% 
102% 
91% 
98% 
80% 
98% 
83% 
96% 
81% 
106% 
97% 
104% 
77% 
103% 
92% 
102% 
81% 
91% 
94% 
100% 
83% 
96% 
93% 
107% 
95% 
96% 
102% 
95% 
82% 
91% 
97% 
96% 
95% 
116% 
78% 
92% 
92% 
95% 
102% 
95% 
92% 
106% 
94% 
94% 
88% 
98% 
89% 
94% 
85% 
96% 
88% 
98% 
86% 
96% 

Source: JRC 

18 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
3.2  Final energy consumption of productive sectors 

3.2.1  Monetary-based activity measure for productive sectors (industry, services 

and agriculture)18 

Figure 4 illustrates the decomposition of final energy consumption changes in 2010–2022 at EU level 
into activity (labour productivity + employment), structural and intensity effects. The EU27 energy 
consumption  fell  by  –41  Mtoe,  corresponding  to  a  reduction  of  −9.6%  compared  to  the  2010 
consumption level. Among the various productive sectors, the largest drop was experienced by service 
sector (-16%) followed by industry (-8%), while agriculture’s consumption increased by +5%. These 
are discussed in more detail in Section 3.2.2. As shown in Figure 5, the overall drop in final energy 
consumption of productive sectors was explained by negative structural and intensity effects, partially 
offset  by  the  positive  activity  effects  (employment  and  labour  productivity).  In  particular,  activity 
effect alone accounted for a +81 Mtoe increase (of which +66 Mtoe in 2010-2021, and +15 Mtoe in 
2021–2022)  and structural  effect  for a  –17 Mtoe drop  (-13 Mtoe in 2010–2021, and  -4 Mtoe in 
2021–2022). The intensity effect had the biggest impact, responsible for a fall in consumption by -
106 Mtoe (-66 Mtoe in 2010–2021 and -40 Mtoe in 2021–2022). 

The country results for the period 2010–2022 are shown in Figure 5. Based on these, Ireland, Poland, 
Hungary,  Bulgaria,  Latvia,  Malta,  and  Cyprus  experienced  an  overall  growth  in  final  energy 
consumption of their productive sectors over the period 2010–2022. In relative terms, this growth 
was the largest in Malta (49% compared to 2010 levels), Ireland (20%), Latvia (10%), Hungary (7%), 
Bulgaria  (7%),  Lithuania  (2%),  Cyprus  (2%)  and  Poland  (2%).  While  it  is  not  surprising  that  the 
contribution of each effect was different among countries, the intensity effect was negative for all 
countries. In absolute terms, the countries with the largest drop accounted by intensity gains were 
Estonia, Ireland, Luxembourg, Malta, and the Netherlands. In contrast, the EU tendency of negative 
structural effect was not universal at Member State level; 19 countries recorded an average decrease 
in energy consumption due to structural shifts towards less intensive sectors in the period 2010–
2022.  Increased  activity  in  the  period  2010–2022 acted  as  a driver  of  energy  consumption in  all 
countries except for Greece, Malta and Cyprus. In absolute terms, the activity effect was a dominant 
force of rising energy consumption in Germany, Poland, France, the Netherlands, and Sweden. In terms 
of  structural  effect,  the  largest  shift  towards  less  intensive  sectors  were  noted  in  Spain,  France, 
Poland, Finland and Romania, while the opposite is true for Germany, Austria and the Netherlands. 

18 Decomposition formula for productive sectors FEC: (2) 𝐹𝐸𝐶 = ∑ 𝐺𝑉𝐴

𝑖

𝐺𝑉𝐴𝑖
𝐺𝑉𝐴

𝐹𝐸𝐶𝑖
𝐺𝑉𝐴𝑖

19 

 
 
 
 
Figure 4. Decomposition of changes in final energy consumption (Mtoe) of the productive sectors of the 
economy in 2010-2022 in EU using the additive Logarithmic Mean Divisia Index approach (LMDI) 

Source: JRC 

20 

 
 
Figure 5. Decomposition of final energy consumption changes in the productive sectors in 2010-2022 at 
Member State level 

Source: JRC 

21 

 
 
3.2.2  Monetary-based activity measure combined with labour force 

3.2.2.1  Industry19 

In  the  period  2010-2022,  the  energy  consumption  of  the  EU  industry  decreased  by  −20  Mtoe, 
corresponding to a drop of −8% compared to 2010 consumption levels. Industry is the most energy 
intensive  sector  and  is  responsible  for  nearly  two  thirds  (63%)  of  the  combined  consumption  of 
industry, services, and agriculture. If structural and intensity effects would not have come into play, 
economic growth would have driven up industrial energy consumption by +37.7 Mtoe (+30.3 Mtoe in 
2010-2021 and +7.4 Mtoe in 2021-2022).  

The  main  driver  of  the  overall  industrial  energy  consumption  decline  was  associated  with  energy 
intensity improvements, which contributed to a drop in energy consumption by −47.8 Mtoe (-24.9 
Mtoe in 2010-2021 and -22.9 Mtoe in 2021-2022). The structural effect played a secondary role in 
limiting energy consumption as structural changes in the industrial sector (a shift from subsectors of 
higher energy intensity towards those of lower intensity20) led to a reduction in energy consumption 
equivalent to  -9.9 Mtoe (-6.4  Mtoe in  2010-2021  and  -3.5  Mtoe  in  2021-2022).  The  gross  value 
added  of  all  industry  sectors  increased  in  2022,  after  the  dramatic  fall  as  a  consequence  of  the 
pandemic  crisis  in  2020.  Looking  at  2022  results,  it  has  been  observed  a  trend  reversal  for 
employment effect (+12.2 Mtoe in 2021, compared to -16.8 Mtoe in 2020) and structural effect (-
2.9 Mtoe in 2021, +1.3 Mtoe in 2020), while for the intensity effect is confirmed the trend started in 
2020 (+5.8 Mtoe in 2021, +2.7 Mtoe in 2020). The intensity effect was the main responsible in driving 
down the energy consumption (together with a small contribution from the structural effect). In the 
last  year,  from  2021  to  2022,  both  labour  productivity  and  employment  effects  have  instead 
contributed positively to the industry final energy consumption, with the latter being the main driver. 
While industry GDP in 2022 continued its upward trend, the rebound effect in energy consumption 
observed in 2021, after the end of the COVID-19 restrictions, did not continue in 2022. 

19 Industry sector includes the subsectors: 1. Food & tobacco (ESTAT code: B_101830); 2.  Textile & leather (ESTAT code: 
B_101835);  3.  Wood,  paper,  etc.  (ESTAT  code:  B_1018851+B_101840);  4.  Chemical  &  petrochemical  (ESTAT  code: 
B_101815);  5.  Metals  and  machinery  (ESTAT  code:  B_101847+B_101805+B_101810);  6.  Non-metallic  minerals 
(ESTAT code: B_101820+B_101853); 7. Transport equipment (ESTAT code: B_101846); 8. Construction (ESTAT code: 
B_101852). Decomposition formula for industry FEC: (3) 𝐹𝐸𝐶 = ∑ 𝐿𝑖

𝐺𝑉𝐴

𝐺𝑉𝐴𝑖
𝐺𝑉𝐴

𝐹𝐸𝐶𝑖
𝐺𝑉𝐴𝑖

𝐿

20 Energy intensive subsectors include Food & tobacco, Pulp and paper, Chemicals, Metals and Non-metallic minerals. Less 

intensive subsectors include: Wood, Construction, Textile & leather, Machinery and Transport equipment. 

22 

 
 
 
 
Figure 6. Decomposition of changes in EU industrial energy consumption (Mtoe) in 2010-2022 using the 
additive LMDI approach 

Source: JRC 

Figure 7. Yearly multiplicative LMDI decomposition results of industrial energy consumption changes at EU 
level in 2010-2022 

Source: JRC 

At country level, the industry sector grew in 13 countries, among the fastest Malta (+66%), Hungary 
(+51%),  Ireland  (+16%),  and  Latvia  (17%)  in  2010-2022  were  observed,  while  20  countries 
experience  a  decline  in  industry  consumption  over  the  same  period  (Table  3).  The  decomposition 
results show that labour productivity effect in terms of number of hours worked per unit of gross 
value added in industrial activities was one of the main drivers of industrial energy consumption. In 
relative terms, the largest negative impact in terms of productivity can be seen in Estonia, Lithuania, 
Poland, and Latvia. 

Employment effect also acted as a major driver of energy consumption in industry in 2010-2022, as 
it was positive in 20 Member States. In particular, the number of hours worked had the most relatively 
profound impact as energy consumption driver in Ireland, Lithuania, Hungary, and Malta, while it acted 
as  inhibitor in  Bulgaria,  Greece,  Spain,  Italy,  Portugal,  Slovakia,  and  Finland.  In terms  of  structural 
shift, the industrial sector moved to less intensive sectors in 16 Member States. On the other end of 

23 

 
 
 
the  spectrum,  Belgium,  Bulgaria,  Ireland,  Greece,  Spain,  France,  Croatia,  Lithuania,  Luxembourg, 
Portugal,  and  Slovakia  were  the  countries  with  the  largest  increase  in  consumption  due  to  shift 
towards more intensive activities in relative terms (in comparison to 2010 consumption  levels). In 
contrast, countries with significant shift towards less intensive industrial subsectors included Czechia, 
Estonia, and Hungary. Intensity improvements drove down consumption in all Member States except 
for Czechia, Hungary, Finland, and Malta.  

Table 3. Additive and multiplicative decomposition results of industrial energy consumption changes at 
Member State level in 2010-2022 and 2021-2022 

Period 

2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 

Total 
effect 

-1132 
-832 
55 
-116 
-337 
-488 
-139 
-124 
-3574 
-3385 
-215 
-24 
286 
-94 
-923 
29 
-3326 
-2241 
-1258 
-1780 
-197 
-64 
-5617 
-4073 
3 
-1 
129 
14 
-5 
-157 
-210 
-64 
1500 
-466 
29 
4 
-2112 
-1129 
-97 
54 
1595 
-1267 
-911 
-32 
-1068 
-1121 
-95 
-113 
-433 
-549 
-1217 
-1153 
-787 
88 
-20058 
-19082 

BE 

BG 

CZ 

DK 

DE 

EE 

IE 

EL 

ES 

FR 

HR 

IT 

CY 

LV 

LT 

LU 

HU 

MT 

NL 

AT 

PL 

PT 

RO 

SI 

SK 

FI 

SE 

EU27 

Employment 
effect 

Additive results [ktoe] 
Labour 
productivity 
effect 

Structural 
effect 

Multiplicative results [%] 

Intensity 
effect 

Total 
effect 

Employment 
effect 

Labour 
productivity 
effect 

Structural 
effect 

Intensity 
effect 

104% 
102% 
89% 
99% 
101% 
104% 
114% 
104% 
103% 
99% 
111% 
103% 
149% 
110% 
84% 
104% 
85% 
102% 
101% 
102% 
104% 
102% 
94% 
104% 
104% 
102% 
106% 
102% 
120% 
103% 
112% 
101% 
122% 
103% 
123% 
102% 
107% 
103% 
108% 
100% 
112% 
102% 
91% 
102% 
101% 
101% 
107% 
103% 
97% 
102% 
99% 
102% 
105% 
101% 
98% 
98% 

110% 
101% 
130% 
123% 
126% 
102% 
128% 
98% 
118% 
102% 
159% 
99% 
126% 
101% 
94% 
101% 
107% 
103% 
100% 
99% 
126% 
103% 
115% 
101% 
106% 
98% 
138% 
98% 
153% 
99% 
111% 
100% 
119% 
104% 
126% 
98% 
126% 
101% 
117% 
103% 
133% 
103% 
126% 
104% 
122% 
104% 
126% 
96% 
121% 
93% 
94% 
97% 
116% 
101% 
112% 
99% 

105% 
102% 
114% 
117% 
71% 
95% 
94% 
100% 
94% 
101% 
87% 
95% 
109% 
101% 
106% 
99% 
103% 
101% 
110% 
101% 
105% 
99% 
97% 
97% 
92% 
104% 
93% 
103% 
100% 
100% 
104% 
101% 
77% 
92% 
82% 
100% 
96% 
96% 
98% 
101% 
86% 
100% 
103% 
101% 
88% 
85% 
88% 
90% 
107% 
94% 
86% 
87% 
87% 
100% 
99% 
101% 

76% 
89% 
77% 
67% 
106% 
93% 
69% 
92% 
83% 
93% 
41% 
98% 
57% 
85% 
88% 
97% 
90% 
84% 
86% 
93% 
62% 
91% 
78% 
84% 
99% 
95% 
86% 
99% 
54% 
84% 
56% 
88% 
136% 
92% 
130% 
106% 
67% 
92% 
80% 
96% 
87% 
87% 
70% 
93% 
78% 
94% 
79% 
102% 
71% 
97% 
110% 
106% 
88% 
99% 
99% 
109% 

452 
268 
-292 
-33 
57 
276 
286 
97 
1645 
-489 
52 
9 
821 
207 
-626 
89 
-3441 
414 
406 
610 
47 
26 
-1625 
1123 
25 
5 
42 
17 
190 
33 
75 
4 
881 
135 
13 
1 
947 
403 
663 
41 
1635 
326 
-522 
66 
74 
61 
87 
37 
-83 
101 
-55 
168 
512 
95 
2266 
4091 

1106 
73 
684 
529 
1700 
125 
524 
-48 
10328 
1128 
231 
-5 
474 
19 
-272 
36 
1381 
501 
139 
-355 
254 
32 
3678 
312 
11 
-5 
256 
-19 
440 
-14 
65 
-1 
686 
194 
13 
-2 
3454 
154 
1415 
295 
4403 
522 
1084 
172 
1216 
219 
286 
-48 
878 
-282 
-676 
-364 
1654 
102 
35409 
3272 

523 
216 
345 
404 
-2610 
-356 
-131 
7 
-3781 
308 
-66 
-19 
142 
29 
160 
-19 
662 
135 
3080 
244 
66 
-13 
-539 
-730 
-11 
10 
-52 
24 
0 
4 
22 
4 
-1034 
-403 
-11 
0 
-544 
-560 
-216 
66 
-2374 
24 
155 
46 
-712 
-999 
-164 
-127 
291 
-250 
-1581 
-1523 
-1562 
-31 
-9943 
-3510 

-3213 
-1390 
-682 
-1016 
517 
-533 
-818 
-180 
-11766 
-4333 
-431 
-8 
-1151 
-349 
-185 
-77 
-1929 
-3291 
-4883 
-2279 
-564 
-109 
-7130 
-4777 
-22 
-12 
-117 
-9 
-636 
-180 
-372 
-71 
968 
-392 
14 
4 
-5968 
-1126 
-1958 
-347 
-2069 
-2139 
-1628 
-316 
-1646 
-401 
-303 
26 
-1518 
-117 
1096 
566 
-1392 
-78 
-47789 
-22935 

91% 
93% 
102% 
96% 
96% 
94% 
94% 
95% 
94% 
95% 
62% 
94% 
116% 
96% 
73% 
101% 
85% 
89% 
96% 
94% 
85% 
95% 
82% 
86% 
101% 
99% 
117% 
102% 
99% 
86% 
72% 
89% 
151% 
90% 
166% 
106% 
86% 
92% 
99% 
101% 
112% 
92% 
83% 
99% 
84% 
84% 
92% 
91% 
90% 
88% 
89% 
90% 
93% 
101% 
107% 
108% 

Source: JRC 

24 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
3.2.2.2  Services21 

The energy consumption of the service sector decreased by  -23 Mtoe (-20%) from 2010 to 2022 
(Figure 8). The services sector became more relevant in terms of its energy consumption over the 
examined period, to the detriment of industry whose consumption, as discussed in section 3.2.2.1, 
decreased by −16% in 2010-2022. However, the services sector remains the second most intensive 
sector compared to industry and represents the 30% of the total consumption of industry, services 
and agriculture combined in 2022. Despite this, it remains the fastest growing productive sector in 
the EU both in terms of energy and economic output. 

The decomposition results (Figure 8) show that until 2019 the economic growth of the service sector 
(i.e., the combined effect of employment effect and labour productivity effect) was the main factor 
that led to the increase in total energy consumption. However, in 2020 the drop of both employment 
and intensity effects resulted in a decrease of the total energy consumption. Only labour productivity 
remained on a growth trend. In 2021, both employment and intensity effects started to grow again 
(rebound effect from 2020). In the last year available (2022), the intensity effect started decreasing 
again after the 2021 rebound and contributed, together with the labour productivity effect, in lowering 
the  services  consumption,  while  the  employment  effect  showed  an  opposite  trend.  Specifically, 
employment effect (measured in hours worked) resulted in an increase in consumption equivalent to 
+15.9  Mtoe  (+8.3  Mtoe  in  2010-2022;  +7.6  Mtoe  in  2021-2022).  Labour  productivity  effect  was 
associated with a consumption growth of +10.6 Mtoe compared to 2010 level (+13.3 Mtoe in 2010-
2021 and -2.7 Mtoe in 2021-2022). The intensity effect restricted this growth (−49.4 Mtoe in 2010-
2022), resulting to a drop in total effect of -22.9 Mtoe. As the industry sector, services have been 
affected by COVID-19 restrictions in 2020 and has experienced a rebound effect that partially offset 
the  results  of  2020.  Further  examination  behind  the  evolution  of  energy  intensity  effect  of  the 
services sector is required, specifically through the separation of intensity from structural changes 
and the inclusion of the weather effect. Studies suggest that space heating and cooling are a major 
end  use  in  this  sector,  which  highlights  the  need  to  take  into  account  the  impact  of  the  weather 
fluctuations in the decomposition analysis of this sector. As explained in the methodology section, the 
structural effect within the services sector cannot be currently examined as the breakdown of energy 
consumption  by  service  subsectors  is  not  yet  available.  On-going  efforts  made  by  Eurostat  and 
statistical offices to address some of these challenges – that is, breakdown of the services sector by 
type of activity and end use – are welcome and will certainly strengthen the analytical capability of 
tools such as the LMDI method in the future. 

21 ESTAT code: B_10235. Decomposition formula for services FEC : (3) 𝐹𝐸𝐶 = ∑ 𝐿𝑖

𝐺𝑉𝐴

𝐿

𝐺𝑉𝐴𝑖
𝐺𝑉𝐴

𝐹𝐸𝐶𝑖
𝐺𝑉𝐴𝑖

25 

 
 
 
 
Figure 8. Yearly multiplicative LMDI decomposition results of energy consumption changes in services at EU 
level in 2010-2022 

Source: JRC 

Figure 9. Decomposition of changes in EU services energy consumption (Mtoe) in 2010-2022 using the 
additive LMDI approach 

Source: JRC 

In  terms  of  energy  consumption  at  Member  State level,  the  services  sector  grew  fastest  in  Malta 
(+31%), Ireland (+28%), and Bulgaria (+20%). In total, the services consumption of 9 Member States 
increased in this period. On the opposite side, the consumption of services sector declined the most 
in Slovakia (−40%), Hungary (-38%), Germany (−27%), the Netherlands (-24%), Slovenia (-22%), and 
Belgium  (-21%).  The  decomposition  results  show  that  employment  in  terms  of  increased  hours 
worked  was  one  of  the  main  drivers  of  services  energy  consumption,  as  the  employment  effect 
(Labour) was positive in almost all Member States (from 2010 to 2022). Labour productivity effect 
also contributed towards growing consumption of services in 2010-2022, with the analysis showing 
that the above-mentioned effect was positive in 25 Member States. Labour productivity had the most 
profound impact as a driver in Bulgaria, Czechia, Croatia, Hungary, Romania, Latvia, Lithuania, Ireland, 
Estonia,  Poland,  Slovakia,  and  Malta.  As  inhibitor,  labour  productivity  in  Greece  and  Luxembourg 
resulted in  a  drop  of  consumption,  which  ranged  from  -13% (compared  to  2010  consumption)  in 

26 

 
 
 
Greece to -5% in Luxembourg. Improvements in energy intensity of services were noted in all Member 
States. 

Table 4. Additive and multiplicative decomposition results of services energy consumption changes at 
Member State level in 2010-2022 and 2021-2022 

BE 

BG 

CZ 

DK 

DE 

EE 

IE 

EL 

ES 

FR 

HR 

IT 

CY 

LV 

LT 

LU 

HU 

MT 

NL 

AT 

PL 

PT 

RO 

SI 

SK 

FI 

SE 

EU27 

Period 

2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 

Total 
effect 

-1037 
-557 
201 
21 
-389 
-84 
-215 
-136 
-9456 
-4572 
31 
-56 
400 
118 
-208 
45 
-422 
-103 
-4166 
-1756 
11 
-39 
-2449 
289 
2 
26 
-26 
-28 
20 
-23 
28 
-103 
-1164 
-186 
28 
15 
-1875 
-411 
-239 
-148 
-699 
-238 
-42 
138 
28 
54 
-118 
-6 
-840 
-127 
-207 
-118 
-112 
-42 
-22915 
-8028 

Additive results [ktoe] 
Labour 
productivity 
effect 

Employmen
t effect 

Intensity 
effect 

Total effect 

Multiplicative results [%] 
Labour 
productivity 
effect 

Employmen
t effect 

Intensity 
effect 

79% 
88% 
120% 
102% 
88% 
97% 
90% 
93% 
73% 
85% 
107% 
89% 
128% 
107% 
89% 
103% 
96% 
99% 
83% 
92% 
101% 
95% 
86% 
102% 
101% 
112% 
96% 
95% 
103% 
96% 
107% 
82% 
62% 
91% 
131% 
114% 
76% 
94% 
91% 
94% 
92% 
97% 
98% 
108% 
101% 
103% 
78% 
99% 
60% 
91% 
93% 
96% 
97% 
99% 
96% 
107% 

122% 
110% 
104% 
102% 
111% 
106% 
113% 
104% 
112% 
108% 
121% 
105% 
135% 
109% 
106% 
105% 
110% 
105% 
119% 
110% 
105% 
102% 
105% 
105% 
127% 
105% 
111% 
116% 
124% 
116% 
143% 
103% 
122% 
103% 
163% 
104% 
118% 
104% 
108% 
104% 
121% 
104% 
108% 
105% 
117% 
101% 
113% 
105% 
102% 
104% 
112% 
103% 
118% 
103% 
100% 
94% 

102% 
94% 
134% 
102% 
123% 
98% 
109% 
98% 
106% 
95% 
124% 
98% 
135% 
97% 
87% 
102% 
108% 
102% 
100% 
94% 
122% 
107% 
101% 
99% 
110% 
101% 
121% 
90% 
123% 
90% 
95% 
100% 
125% 
104% 
138% 
108% 
106% 
101% 
106% 
101% 
134% 
101% 
105% 
102% 
162% 
109% 
112% 
99% 
128% 
101% 
105% 
99% 
117% 
100% 
114% 
102% 

64% 
84% 
87% 
98% 
65% 
94% 
73% 
91% 
61% 
83% 
72% 
86% 
70% 
102% 
97% 
96% 
81% 
92% 
69% 
89% 
79% 
87% 
81% 
98% 
72% 
105% 
71% 
92% 
68% 
92% 
78% 
79% 
41% 
85% 
58% 
102% 
61% 
89% 
79% 
90% 
57% 
92% 
86% 
101% 
54% 
93% 
61% 
95% 
46% 
86% 
80% 
94% 
71% 
96% 
83% 
110% 

864 
405 
45 
21 
295 
167 
235 
82 
3492 
2053 
86 
25 
486 
146 
100 
84 
823 
468 
3636 
2015 
41 
18 
580 
761 
55 
12 
65 
86 
133 
94 
155 
17 
417 
54 
56 
4 
1076 
257 
192 
84 
1533 
330 
148 
90 
283 
18 
53 
21 
42 
56 
329 
88 
681 
108 
15900 
7564 

129 
-246 
313 
29 
638 
-68 
169 
-37 
1958 
-1331 
100 
-9 
499 
-59 
-277 
27 
773 
203 
125 
-1376 
164 
55 
247 
-180 
21 
2 
110 
-62 
122 
-65 
-19 
-2 
488 
85 
34 
8 
407 
38 
140 
34 
2379 
92 
90 
37 
886 
166 
56 
-5 
309 
10 
130 
-17 
619 
20 
10610 
-2652 

-2030 
-716 
-157 
-29 
-1322 
-183 
-619 
-181 
-14905 
-5294 
-155 
-72 
-585 
31 
-30 
-66 
-2017 
-774 
-7927 
-2396 
-194 
-113 
-3275 
-293 
-74 
12 
-200 
-52 
-236 
-52 
-108 
-117 
-2069 
-325 
-62 
2 
-3358 
-705 
-571 
-265 
-4612 
-659 
-280 
11 
-1141 
-130 
-227 
-23 
-1191 
-193 
-666 
-189 
-1413 
-171 
-49425 
-12940 

Source: JRC 

27 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
3.2.2.3  Agriculture22 

Agriculture is the smallest sector among the productive sectors of the EU economy (industry, services, 
and agriculture), accounting for just 7% of the final energy consumption of these combined sectors 
in  2022.  The  agricultural  energy  consumption  increased  by  +5%  over  the  period  2010-2022.  As 
shown in Figure 10, employment has been on a gradual declining trend from 2010 to 2020, with a 
small increase in 2021 and 2022, reflecting a negative employment effect throughout the examined 
period.  In  the  period  2010-2022,  this  has  been  the  largest  inhibitor  of  the  agricultural  energy 
consumption (−4 Mtoe). On the other hand, rapid improvements in labour productivity and intensity 
have been a major driver of energy consumption (+4 Mtoe and +1.5 Mtoe in 2022 compared to 2010, 
respectively). The opposite forces exerted by the employment and labour productivity effects may 
point out to the fact that production processes in the agricultural sector are most likely becoming less 
labour-intensive and more capital-intensive instead. Moreover, the agriculture sector has been less 
affected by the 2020 pandemic crisis compared to industry and services. This can be seen by looking 
at the effect trends that are relatively stable over the examined period. The reasons behind these 
results may be related to the nature of the agricultural sector; it provides goods of first necessity that 
have been mostly excluded from the Member States COVID-19 control measures. 

Figure 10. Yearly multiplicative LMDI decomposition results of agricultural energy consumption changes at 
EU level in 2010-2022 

Source: JRC 

22 ESTAT code: B_102020+B_102030. Decomposition formula for agriculture FEC : (3) 𝐹𝐸𝐶 = ∑ 𝐿𝑖

𝐺𝑉𝐴

𝐿

𝐺𝑉𝐴𝑖
𝐺𝑉𝐴

𝐹𝐸𝐶𝑖
𝐺𝑉𝐴𝑖

28 

 
 
 
 
 
Figure 11. Decomposition of changes in EU agricultural energy consumption (Mtoe) in 2010-2022 using the 
additive LMDI approach 

Source: JRC 

In terms of trends at country level trends, the agricultural sector grew fastest in Germany, Czechia, 
Spain,  Cyprus,  Latvia,  Hungary,  Malta,  and  Romania  (Table  5).  In  total,  the  agricultural  energy 
consumption  of  17  Member  States  increased  in  this  period.  The  decomposition  results  show  that 
employment in terms of fewer hours worked was one of the main inhibitors of energy consumption 
in all Member States except Czechia, the Netherlands and Belgium. All the other countries showed a 
drop in consumption due to a reduction in employment. As in the case with the EU-wide results, labour 
productivity was an important explanation behind consumption growth in agriculture across many EU 
countries.  This  was  indeed  the  case  for  all  Member  States  except  for  Belgium,  Denmark,  and 
Luxembourg.  Improvements  in  energy  intensity  were  noted  in  13  countries,  with  largest  inhibiting 
impact in Bulgaria, Ireland, Greece, the Netherlands, Austria, Finland, Sweden and Slovakia. On the 
other hand, energy intensity drove up the consumption of the agricultural sectors in Belgium, Czechia, 
Germany, Estonia, Spain, Croatia, Cyprus, Hungary, Malta, and Romania. 

29 

 
 
 
 
Table 5. Additive and multiplicative decomposition results of agriculture energy consumption changes at 
Member State level in 2010-2022 and 2021-2022 

Period 

Total effect 

Additive results [ktoe] 

Employment 
effect 

Labour 
productivity 
effect 

Intensity 
effect 

Total effect 

Multiplicative results [%] 
Labour 
productivity 
effect 

Employment 
effect 

Intensity 
effect 

BE 

BG 

CZ 

DK 

DE 

EE 

IE 

EL 

ES 

FR 

HR 

IT 

CY 

LV 

LT 

LU 

HU 

MT 

NL 

AT 

PL 

PT 

RO 

SI 

SK 

FI 

SE 

EU27 

2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 

-46 
-557 
9 
21 
68 
-84 
-194 
-136 
2380 
-4572 
2 
-56 
38 
118 
-514 
45 
653 
-103 
244 
-1756 
9 
-39 
168 
289 
5 
26 
44 
-28 
16 
-23 
-1 
-103 
113 
-186 
13 
15 
-1196 
-411 
-21 
-148 
-409 
-238 
23 
138 
162 
54 
5 
-6 
-10 
-127 
-90 
-118 
-26 
-42 
1442 
-8028 

31 
405 
-66 
21 
7 
167 
-52 
82 
-663 
2053 
-53 
25 
-44 
146 
0 
84 
-191 
468 
-972 
2015 
-132 
18 
-156 
761 
-5 
12 
-17 
86 
-38 
94 
-2 
17 
-254 
54 
-2 
4 
164 
257 
-111 
84 
-748 
330 
-205 
90 
-244 
18 
-7 
21 
-17 
56 
-165 
88 
-49 
108 
-3989 
7564 

-210 
-246 
123 
29 
28 
-68 
-68 
-37 
792 
-1331 
16 
-9 
201 
-59 
55 
27 
47 
203 
962 
-1376 
96 
55 
68 
-180 
3 
2 
85 
-62 
67 
-65 
-3 
-2 
154 
85 
8 
8 
156 
38 
217 
34 
198 
92 
231 
37 
254 
166 
14 
-5 
62 
10 
308 
-17 
119 
20 
3982 
-2652 

133 
-716 
-48 
-29 
33 
-183 
-74 
-181 
2251 
-5294 
39 
-72 
-120 
31 
-570 
-66 
797 
-774 
254 
-2396 
45 
-113 
256 
-293 
7 
12 
-24 
-52 
-13 
-52 
4 
-117 
213 
-325 
7 
2 
-1515 
-705 
-127 
-265 
141 
-659 
-4 
11 
152 
-130 
-3 
-23 
-54 
-193 
-234 
-189 
-95 
-171 
1449 
-12940 

95% 
91% 
105% 
100% 
112% 
95% 
78% 
99% 
285% 
99% 
102% 
110% 
113% 
133% 
36% 
92% 
129% 
94% 
105% 
101% 
104% 
101% 
106% 
103% 
112% 
93% 
128% 
101% 
114% 
104% 
95% 
103% 
123% 
90% 
257% 
133% 
72% 
76% 
96% 
91% 
89% 
87% 
105% 
91% 
141% 
98% 
107% 
101% 
93% 
95% 
89% 
95% 
96% 
112% 
112% 
100% 

104% 
98% 
71% 
92% 
102% 
98% 
94% 
98% 
76% 
99% 
63% 
94% 
85% 
94% 
96% 
100% 
93% 
96% 
81% 
100% 
57% 
103% 
95% 
99% 
88% 
101% 
92% 
95% 
70% 
105% 
93% 
100% 
68% 
89% 
85% 
108% 
104% 
101% 
81% 
98% 
82% 
98% 
64% 
95% 
59% 
96% 
91% 
100% 
88% 
101% 
81% 
100% 
91% 
103% 
88% 
100% 

79% 
88% 
192% 
104% 
104% 
93% 
91% 
95% 
127% 
95% 
111% 
81% 
224% 
113% 
116% 
113% 
103% 
84% 
124% 
107% 
151% 
93% 
102% 
103% 
105% 
100% 
158% 
113% 
184% 
96% 
89% 
101% 
129% 
82% 
169% 
86% 
104% 
96% 
149% 
109% 
106% 
103% 
166% 
99% 
182% 
80% 
121% 
97% 
154% 
86% 
147% 
97% 
120% 
99% 
119% 
100% 

116% 
106% 
77% 
104% 
106% 
104% 
91% 
107% 
294% 
105% 
147% 
144% 
59% 
125% 
32% 
82% 
134% 
117% 
106% 
94% 
120% 
105% 
109% 
101% 
121% 
92% 
88% 
94% 
88% 
103% 
114% 
101% 
140% 
123% 
179% 
144% 
66% 
79% 
79% 
85% 
103% 
86% 
98% 
97% 
132% 
128% 
96% 
103% 
68% 
109% 
74% 
97% 
87% 
111% 
107% 
100% 

3.2.3  Activity revaluation approach (Industry) 

Source: JRC 

The change in industrial energy consumption in 2010-2022, disaggregated into manufacturing and 
construction, is decomposed using two approaches, the monetary LMDI and the AR, as described in 
the  methodology  section.  As  discussed  in  Section  2,  the  activity  effect  in  the  monetary-based 
approach  accounts  for  changes  in  final  energy  consumption  due  to  changes  in  GVA,  while  in  the 
physical units (AR) approach, the activity effect shows changes in final energy consumption due to 
changes in the physical production index. 

30 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
The use of the industrial physical index confirms the general trends previously identified. That is, over 
the examined period the intensity effect has been the dominant driver in reducing the energy demand. 
A positive structural effect is found in this analysis. The structural effect is influenced by the specific 
grouping  considered  (the  11  subsectors),  as  the  results  are  usually  closely  related  to  the 
disaggregation level used in the analysis. 

A comparison of the EU27 decomposition results carried out using production-based activity measure 
(industrial  production  index  -  IPI)  versus  monetary-based  activity  measure  (gross  value  added)  is 
shown  in  Figure  12.  The  use  of  industrial  physical  index  confirms  the  general  trends  previously 
identified.  Over  the  examined  period,  in  both  methodologies,  the  intensity  effect  has  been  the 
dominant  driver  in  reducing  energy  demand.  A  positive  structural  effect  is  found  in  this  analysis. 
However, this result could be linked to the lack of fine disaggregation of the industry by subsector in 
the latter case due to data restrictions discussed in the methodology section; in contrast to the 11 
subsectors considered before, the analysis here is based on two subsectors only. The structural effect 
is thus influenced by the specific grouping of subsectors, and the accuracy of the results is usually 
closely related to the granularity level of the underlying input data. 

Considering the AR approach, the industrial energy consumption decreased by −0.9 Mtoe, which is 
mainly attributed to the intensity effect (−22 Mtoe) and partially compensated by structural effect 
(+6.3 Mtoe) and activity effect (+14.7 Mtoe). In general, differences can be observed between the 
production-based  and  monetary-based  results.  Structural  effects  seem  to  have  a  less  dominant 
impact  (if  positive)  on  the  energy  consumption  in  the  case  of  the  production-based  results.  The 
negative IPI-based intensity effect is of lower magnitude, which may indicate that the use of GVA 
generally overestimates intensity (efficiency) improvements. At Member States level, results vary and 
are sometimes counterintuitive which reflects the need of further, more detailed analysis including 
the study of potential differences in accounting of physical units by countries. This analysis highlights 
the need for the collection of more detailed physical index data, at compatible disaggregation level 
with energy consumption and monetary output data in national accounts to better track efficiency 
improvements and allow the use of physical-based indicators in future decomposition analyses. 

Figure 12. Differences in industrial decomposition results between the use of monetary-based (GVA) versus 
physical-based (IPI) units 

Source: JRC 

31 

 
 
 
3.3  Transport23 

Taking into account all transport modes, transport energy consumption24 increased by 0.5% in the 
period  2010-2022.  Air  transport  (domestic)  consumption  alone  rose  by  7.5%  over  this  period, 
followed by an almost zero increase in road transport (0.3%), while both rail and inland waterways 
transport decreased by -18.4% and 18.7%, respectively.  

Figure 13 shows year-on-year results for EU27 passenger transport. While the total effect had been 
declining  from  2010 to  2013,  this  trend  was  reversed  in  2014.  In  2021, the  passenger  transport 
consumption increased by +0.1% since the lowest recorded consumption in 2013 and decreased by 
-4% compared to 2010. The influence of the economic recession on the passenger transport is evident 
from  the  subtle  decline  in  activity  effect (measured  in  passenger  kilometres) in  the  period  2010-
2012.  Since  2013,  the  activity  effect  has  had  a  strong  positive  impact  on  the  overall  energy 
consumption  until  2019.  In  2020,  the  COVID-19  crisis  resulted  in  an  unprecedented  drop  of  the 
activity effect. The lockdowns and teleworking rules established by the majority of the Member States 
almost zeroed the movement of people  and goods for some time. As seen from the results, both 
intensity  and  modal  shift  effects  rose  in  2020.  The  intensity  effect  has  been  mostly  negative 
throughout the study period. In 2021, intensity and activity effects played a dominant role in driving 
up  energy  consumption,  weakly  compensated  by modal  shift  changes.  In  2022  the  activity  effect 
drove the sector’s energy consumption up, while modal shift and intensity contributions were lower 
and towards a reduction.    The impact of the modal  shift effect has been marginal  for the entire 
period  except  for  the  years  2020  and  in  2022.  The  latest  change  in  EU  passenger  transport 
consumption is captured in Figure 14. In 2022, activity effect was responsible for an increase of +6 
Mtoe of overall passenger transport consumption compared to 2021 levels. Modal shift and intensity 
accounted for a reduction of 0.9 and 1.6 Mtoe respectively. 

Figure 13. Yearly multiplicative LMDI decomposition results of passenger transport energy consumption 
changes at EU level in 2010-2022 

Source: JRC 

23 Decomposition formula for passenger FEC: (4) 𝐹𝐸𝐶 = ∑ 𝑃𝐾𝑀

𝑖

𝐹𝐸𝐶 =   ∑ 𝑇𝐾𝑀𝑖

𝑇𝐾𝑀𝑖
𝑇𝐾𝑀

𝐹𝐸𝐶𝑖
𝑇𝐾𝑀𝑖

𝑃𝐾𝑀𝑖
𝑃𝐾𝑀

𝐹𝐸𝐶𝑖
𝑃𝐾𝑀𝑖

 ; decomposition formula for freight FEC: (5) 

24 Transport sector includes Road transport (ESTAT code: B_101920); Rail transport (ESTAT code: B_101910); Air transport 

(ESTAT code: B_101932); Water transport (ESTAT code: B_101940). 

32 

 
 
 
 
 
Figure 14. Decomposition of changes in EU passenger transport energy consumption (Mtoe) in 2010-2022 
using the additive LMDI approach 

Source: JRC 

At  country  level,  12  countries  experienced  growth,  although  restrained  for  most  of  them,  in  their 
passenger energy consumption in the period 2010-2022. Among those countries that registered a 
passenger  energy  consumption  decrease,  Greece  (−35%),  Netherlands  (−29%),  Cyprus  (−24%), 
Sweden (-23%), Luxembourg (-19%), Finland (-17%), Belgium (−16%), Italy (-11%), Ireland (-6%), 
Austria (-13%), Portugal (-4%), Germany (-4%) France (-5%) Latvia (-4%), and Denmark (−2%) (Table 
6). Poland, Malta, Lithuania, and Hungary, on the other hand, had a considerable increase in passenger 
transport. A shift to cleaner modes was noted only in Cyprus (-9%), Italy (-1%), and the Netherlands 
(-2%).  Improvements  in  efficiency  were  registered  in  Belgium,  Czechia,  Denmark,  Estonia,  Ireland, 
Greece, France, Cyprus, Latvia, Luxembourg, the Netherlands, Austria, Portugal, Romania, Finland, and 
Sweden. 

With  regards  to  the  EU  freight  transport,  energy  consumption  (Figure  15)  was  on  a  recovering 
trajectory following a decline in 2010-2013 until 2019, then also in this transport sector in 2020 
there was a drop followed by a rebound effect of 3.9% in 2022 compared to 2010 levels. The activity 
and intensity effects showed an increase in 2022, while the modal shift effect declined in the same 
year. The intensity effect decreased by -4%, followed by the modal shift (-0.3%), while the activity 
effect (i.e. lower tonne kilometres) and the modal shift effect increased by 14% and 1% in the 2010-
2022 period, respectively. 

33 

 
 
Figure 15. Yearly multiplicative LMDI decomposition results of freight transport energy consumption changes 
at EU level in 2010-2022 

Source: JRC 

Figure 16. Decomposition of changes in EU freight transport energy consumption (Mtoe) in 2010-2022 using 
the additive LMDI approach 

Source: JRC 

34 

 
 
 
 
 
Table 6. Additive and multiplicative decomposition results of passenger transport energy consumption 
changes at Member State level in 2010-2022 and 2021-2022 

Period 

Total effect 

Additive results [ktoe] 
Modal 
shift ef-
fect 

Activity effect 

Intensity ef-
fect 

Total ef-
fect 

Activity ef-
fect 

Modal shift ef-
fect 

Intensity ef-
fect 

Multiplicative results [%] 

BE 

BG 

CZ 

DK 

DE 

EE 

IE 

EL 

ES 

FR 

HR 

IT 

CY 

LV 

LT 

LU 

HU 

MT 

NL 

AT 

PL 

PT 

RO 

SI 

SK 

FI 

SE 

EU27 

2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 

-850 
85 
372 
3 
742 
127 
-54 
-8 
-1319 
484 
35 
12 
-135 
160 
-1312 
105 
-42 
583 
-1293 
956 
71 
58 
-2283 
892 
-127 
-5 
-25 
-31 
295 
-8 
-272 
-160 
761 
233 
47 
30 
-2136 
-17 
-701 
-219 
3991 
335 
-144 
187 
310 
234 
60 
107 
79 
47 
-408 
-88 
-1088 
-177 
-5425 
3926 

-655 
193 
-19 
73 
503 
55 
193 
56 
-5677 
120 
76 
-1 
434 
208 
-114 
113 
-2857 
528 
1505 
1530 
-144 
87 
-4010 
1114 
90 
69 
-3 
16 
-20 
34 
202 
23 
341 
137 
23 
12 
-1056 
428 
-40 
119 
1518 
900 
418 
245 
716 
38 
-1 
41 
-27 
54 
-45 
57 
-623 
157 
-9271 
6406 

132 
-46 
94 
11 
714 
42 
119 
10 
1447 
389 
9 
-1 
33 
-2 
70 
6 
460 
195 
-106 
-742 
59 
8 
-250 
-288 
-41 
-48 
37 
1 
10 
-5 
16 
-5 
259 
-6 
1 
0 
-94 
-169 
270 
89 
844 
-178 
40 
-15 
111 
37 
21 
-12 
-4 
-32 
4 
-24 
-66 
-86 
4191 
-870 

-327 
-62 
297 
-81 
-475 
30 
-366 
-74 
2912 
-25 
-51 
14 
-603 
-46 
-1267 
-15 
2355 
-140 
-2692 
168 
155 
-36 
1977 
66 
-176 
-25 
-60 
-49 
305 
-37 
-490 
-178 
161 
102 
23 
18 
-986 
-276 
-932 
-428 
1629 
-387 
-602 
-43 
-517 
160 
40 
78 
110 
25 
-367 
-120 
-398 
-248 
-345 
-1610 

84% 
102% 
120% 
100% 
119% 
103% 
98% 
100% 
96% 
101% 
108% 
103% 
94% 
108% 
65% 
105% 
100% 
104% 
95% 
104% 
105% 
104% 
89% 
105% 
76% 
99% 
96% 
95% 
132% 
99% 
81% 
88% 
134% 
108% 
138% 
121% 
71% 
100% 
87% 
95% 
144% 
103% 
96% 
106% 
112% 
109% 
105% 
110% 
108% 
105% 
83% 
96% 
77% 
95% 
102% 
108% 

87% 
104% 
101% 
103% 
113% 
101% 
107% 
102% 
86% 
100% 
118% 
100% 
119% 
110% 
96% 
105% 
81% 
104% 
105% 
106% 
90% 
106% 
78% 
106% 
123% 
118% 
102% 
103% 
94% 
103% 
115% 
102% 
115% 
105% 
118% 
108% 
85% 
108% 
99% 
103% 
115% 
107% 
113% 
107% 
133% 
101% 
99% 
104% 
97% 
106% 
98% 
103% 
86% 
104% 
103% 
114% 

103% 
99% 
105% 
101% 
118% 
101% 
104% 
100% 
104% 
101% 
102% 
100% 
102% 
100% 
103% 
100% 
104% 
101% 
100% 
97% 
105% 
101% 
99% 
98% 
91% 
89% 
106% 
100% 
101% 
100% 
101% 
100% 
110% 
100% 
101% 
100% 
98% 
97% 
106% 
102% 
108% 
99% 
101% 
100% 
104% 
101% 
102% 
99% 
100% 
97% 
100% 
99% 
99% 
98% 
102% 
98% 

94% 
99% 
114% 
96% 
89% 
101% 
88% 
97% 
108% 
100% 
90% 
103% 
78% 
98% 
66% 
99% 
119% 
99% 
91% 
101% 
112% 
97% 
116% 
100% 
68% 
94% 
89% 
92% 
139% 
97% 
69% 
86% 
106% 
104% 
116% 
112% 
86% 
95% 
82% 
91% 
116% 
97% 
84% 
99% 
80% 
106% 
104% 
107% 
112% 
103% 
85% 
94% 
91% 
94% 
98% 
97% 

Source: JRC 

The results at country level are shown in Table 7. Freight transport energy consumption dropped in 
11 countries in 2010-2022. The largest drops were registered in Luxembourg (-48%), Sweden (-23%), 
Portugal (-21%), Denmark (-19%), the Netherlands (-15%), Spain (-13%), and Slovakia (-12%). On 
the opposite side, freight transport consumption increased considerably in Bulgaria (+70%), Romania 
(+59%), Lithuania (+41%), Poland (+36%), Malta (+33%), Croatia (+32%), and Slovenia (+30%). The 
activity effect was the main driver of freight transport consumption in Bulgaria, Denmark, Croatia, 
Lithuania,  Malta,  Poland,  Romania,  and  Slovenia.  On  the  opposite  side,  Belgium,  Estonia,  Greece, 
France, Italy, Cyprus, Latvia, the Netherlands, Austria, and Portugal experienced a negative activity 

35 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
effect. A shift to cleaner transport modes is noted in 10 Member States, while improvements in energy 
intensity are observed in 17 of them. 

Table 7. Additive and multiplicative decomposition results of freight transport energy consumption changes 
at Member State level in 2010-2022 and 2021-2022 

Period 

Total effect 

Activity ef-
fect 

Modal shift 
effect 

Intensity ef-
fect 

Total ef-
fect 

Activity ef-
fect 

Modal shift ef-
fect 

Intensity ef-
fect 

Additive results [ktoe] 

Multiplicative results [%] 

BE 

BG 

CZ 

DK 

DE 

EE 

IE 

EL 

ES 

FR 

HR 

IT 

CY 

LV 

LT 

LU 

HU 

MT 

NL 

AT 

PL 

PT 

RO 

SI 

SK 

FI 

SE 

EU27 

2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 

557 
38 
462 
0 
342 
41 
-299 
-29 
599 
-170 
-2 
3 
121 
70 
-189 
149 
-2240 
683 
1253 
516 
190 
28 
277 
530 
-7 
-3 
-38 
-24 
260 
-32 
-364 
-57 
426 
150 
20 
8 
-591 
-24 
190 
-147 
2822 
237 
-559 
109 
1297 
230 
173 
64 
-142 
44 
-4 
-93 
-590 
-77 
3966 
2241 

-238 
-209 
412 
20 
451 
58 
449 
423 
24 
145 
-206 
-68 
162 
-11 
-1104 
-62 
3009 
-430 
-747 
-499 
263 
19 
-1536 
492 
1 
83 
-100 
-9 
457 
-153 
-18 
152 
179 
18 
28 
28 
-210 
-75 
-31 
-32 
5186 
745 
-211 
22 
2297 
923 
242 
-37 
49 
9 
1 
-42 
376 
3 
9186 
1512 

73 
-70 
65 
-16 
7 
9 
-3 
-13 
-463 
-383 
116 
23 
-9 
0 
234 
36 
76 
190 
-444 
-59 
6 
-11 
-357 
82 
-2 
0 
181 
-5 
216 
64 
17 
5 
-23 
1 
-41 
-54 
-276 
-132 
-152 
-68 
494 
-17 
-30 
-45 
951 
261 
22 
14 
58 
29 
147 
84 
161 
9 
1022 
-67 

721 
317 
-14 
-5 
-116 
-26 
-745 
-440 
1039 
68 
89 
48 
-32 
80 
680 
175 
-5325 
923 
2443 
1074 
-78 
20 
2170 
-44 
-7 
-85 
-119 
-10 
-413 
58 
-363 
-214 
270 
131 
33 
34 
-104 
184 
373 
-47 
-2858 
-491 
-318 
132 
-1951 
-954 
-91 
87 
-249 
7 
-151 
-136 
-1127 
-89 
-6243 
796 

116% 
101% 
170% 
100% 
118% 
102% 
81% 
98% 
104% 
99% 
99% 
101% 
110% 
106% 
94% 
105% 
87% 
105% 
109% 
103% 
132% 
104% 
102% 
104% 
97% 
99% 
92% 
95% 
141% 
97% 
52% 
87% 
124% 
107% 
133% 
111% 
85% 
99% 
108% 
95% 
136% 
102% 
79% 
106% 
159% 
107% 
130% 
109% 
88% 
104% 
100% 
95% 
77% 
96% 
110% 
108% 

94% 
95% 
163% 
102% 
125% 
103% 
140% 
140% 
100% 
101% 
48% 
79% 
113% 
99% 
70% 
98% 
124% 
97% 
95% 
97% 
147% 
103% 
90% 
104% 
98% 
145% 
79% 
98% 
186% 
85% 
111% 
143% 
113% 
101% 
145% 
145% 
94% 
98% 
98% 
99% 
186% 
107% 
91% 
101% 
223% 
132% 
147% 
95% 
107% 
101% 
101% 
98% 
119% 
100% 
114% 
105% 

102% 
98% 
111% 
99% 
101% 
100% 
100% 
99% 
97% 
98% 
152% 
108% 
99% 
100% 
108% 
101% 
101% 
101% 
97% 
100% 
102% 
99% 
98% 
101% 
99% 
100% 
152% 
99% 
125% 
107% 
103% 
101% 
99% 
100% 
59% 
48% 
93% 
96% 
94% 
98% 
106% 
100% 
99% 
98% 
143% 
108% 
103% 
102% 
105% 
103% 
109% 
105% 
107% 
100% 
101% 
100% 

121% 
108% 
94% 
100% 
94% 
99% 
58% 
71% 
107% 
100% 
137% 
118% 
98% 
106% 
125% 
106% 
69% 
107% 
117% 
107% 
89% 
103% 
116% 
100% 
100% 
68% 
76% 
98% 
60% 
107% 
46% 
61% 
111% 
106% 
154% 
157% 
98% 
106% 
116% 
98% 
69% 
95% 
88% 
107% 
50% 
75% 
86% 
113% 
78% 
101% 
91% 
93% 
61% 
96% 
96% 
103% 

Source: JRC 

36 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
3.3.1  Air transport25 

In  the  period  2010-2022,  energy  consumption  of  the  EU  air  transport  increased  by  2.8  Mtoe, 
corresponding to a +7.5% increase compared to 2010 levels (Figure 17). Air transport represents the 
most  affected  sector  by  COVID-19  restrictions  in  2020.  Many  reasons  are  behind  the  fall  of  the 
activity (-18.6 Mtoe in 2010-2021 and +15.9 Mtoe in 2021-2022) and the productivity (expressed 
as  tonnes/flights)  (+1.1  Mtoe  in  2010-2021  and  +8.4  Mtoe  in  2021-2022)  effects:  generalised 
lockdowns among member states have resulted in people being unable to travel. In addition, most 
Member  States  have  closed  their  borders  in  order  to  contain  the  spread  of  the  virus.  Finally,  the 
uncertainty  arising  from  countries’  health  situations  has  caused  an  inevitable  slowdown  in  both 
people and goods flows. All the three effects have shown an inverse trend in both 2021 and 2022 
compared to 2020. Activity and productivity effects started increasing again in 2021, offsetting the 
drop of the intensity effect. Therefore, the energy consumption of the air transport sector experienced 
a rebound effect in 2021 (continuing in 2022), even if the consumption level is still quite far from 
the level reached in 2019, before the pandemic crisis. 

Figure 17. Yearly multiplicative LMDI decomposition results of air transport energy consumption changes at 
EU level in 2010-2022 

Source: JRC 

The reason for the decreasing trend in consumption is directly consequence of the pandemic crisis 
that occurred in 2020. Before this year, the energy consumption of the air transport was showing a 
constant increase since 2013. The productivity effect  (expressed as tonnes/flights) increased over 
time (with the exception of year 2020), which was another major driving force of the air transport 
consumption. These were mostly counterbalanced by improvements in energy intensity until 2019, 
then it registered a strong increase in 2020 held back by a new decrease dynamic in 2021 and 2022 
(-4.1 Mtoe over the 2010-2022 period). This effect has been consistently an inhibitor force of the air 
transport consumption since the beginning of the examined period. 

25 Decompositon formula of air transport FEC: (6) 𝐹𝐸𝐶𝑎𝑖𝑟 = ∑ 𝑁𝐹

𝐺𝑇

𝑁𝐹

𝐹𝐸𝐶𝑎𝑖𝑟
𝐺𝑇

37 

 
 
 
 
 
Figure 18. Decomposition of changes in EU air transport energy consumption (Mtoe) in 2010-2022 using the 
additive LMDI approach 

Source: JRC 

At Member States level, air transport consumption decreased in 10 countries, namely Czechia (-18%), 
Denmark (-13%), France (-1%), Italy (-12%), the Netherlands (-5%), Austria (-23%), Slovenia (-32%), 
Slovakia (-43%), Finland (-14%), and Sweden (-15%). The activity effect contributed to an increase 
in consumption in 13 countries, while for all the other countries the activity effect played a key role 
in  decreasing  the  energy  consumption.  Productivity  in  terms  of  goods/passengers  transferred  per 
flight  has  increased  in  all  Member  States.  This  effect  contributed  to  an  increase  of  the  energy 
consumption  of  at  least  20%  in  each  country,  with  a  peak  of  more  than  100%.  Finally,  intensity 
improvements caused a drop in energy consumption in 24 countries, with the most notable results in 
Italy, Cyprus, Luxembourg, Malta, Portugal, Slovakia, and Hungary. 

38 

 
 
 
 
Table 8. Additive and multiplicative decomposition results of air transport energy consumption changes at 
Member States level in 2010-2022 and 2021-2022 

BE 

BG 

CZ 

DK 

DE 

EE 

IE 

EL 

ES 

FR 

HR 

IT 

CY 

LV 

LT 

LU 

HU 

MT 

NL 

AT 

PL 

PT 

RO 

SI 

SK 

FI 

SE 

EU27 

Period 

2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 

Additive results [ktoe] 

Multiplicative results [%] 

Total  
effect 

Activity 
effect 

Productivity  
effect 

Intensity  
effect 

Total  
effect 

Activity  
effect 

Productivity  
effect 

Intensity  
effect 

148% 
132% 
152% 
124% 
180% 
136% 
134% 
130% 
125% 
129% 
172% 
127% 
121% 
138% 
130% 
129% 
122% 
128% 
133% 
125% 
148% 
129% 
125% 
129% 
126% 
123% 
151% 
145% 
167% 
133% 
172% 
106% 
181% 
132% 
125% 
143% 
121% 
132% 
155% 
131% 
167% 
126% 
153% 
152% 
288% 
132% 
168% 
141% 
123% 
200% 
143% 
151% 
138% 
142% 
114% 
112% 

99% 
61% 
84% 
77% 
81% 
71% 
82% 
70% 
118% 
73% 
92% 
62% 
103% 
65% 
83% 
91% 
87% 
81% 
85% 
80% 
79% 
87% 
72% 
79% 
73% 
93% 
115% 
80% 
91% 
76% 
68% 
64% 
69% 
80% 
67% 
71% 
73% 
64% 
73% 
64% 
96% 
82% 
66% 
77% 
58% 
65% 
86% 
89% 
57% 
51% 
89% 
64% 
86% 
74% 
105% 
88% 

413 
239 
55 
58 
-60 
112 
-112 
313 
1082 
3015 
28 
14 
320 
572 
450 
538 
913 
2659 
-86 
2446 
73 
89 
-545 
1554 
11 
108 
32 
65 
44 
40 
243 
24 
8 
136 
22 
49 
-159 
754 
-192 
213 
505 
415 
582 
750 
140 
29 
-12 
12 
-24 
11 
-107 
273 
-125 
305 
3501 
14791 

-112 
590 
9 
66 
-220 
119 
-209 
368 
-1950 
3475 
11 
26 
144 
649 
331 
358 
403 
2513 
-1199 
2459 
50 
72 
-439 
1497 
24 
79 
-23 
49 
13 
39 
168 
273 
-20 
126 
42 
47 
213 
1213 
-285 
302 
148 
393 
531 
576 
-1 
50 
-18 
8 
-13 
11 
-285 
284 
-261 
272 
-2948 
15914 

606 
454 
88 
43 
184 
64 
235 
152 
2040 
1951 
27 
12 
122 
224 
313 
282 
1116 
1152 
1856 
1117 
48 
37 
761 
716 
61 
44 
51 
40 
33 
23 
265 
36 
112 
51 
23 
36 
674 
779 
280 
140 
331 
179 
530 
464 
246 
40 
12 
6 
4 
19 
246 
176 
237 
201 
10504 
8437 

-81 
-805 
-41 
-51 
-24 
-72 
-138 
-207 
993 
-2411 
-10 
-24 
53 
-301 
-194 
-102 
-605 
-1005 
-744 
-1130 
-25 
-20 
-868 
-659 
-74 
-14 
4 
-24 
-2 
-22 
-190 
-285 
-84 
-41 
-42 
-34 
-1046 
-1238 
-187 
-229 
26 
-158 
-479 
-289 
-105 
-61 
-6 
-2 
-14 
-19 
-68 
-187 
-102 
-168 
-4055 
-9561 

131% 
116% 
131% 
134% 
82% 
170% 
87% 
170% 
112% 
148% 
190% 
131% 
144% 
228% 
150% 
163% 
117% 
176% 
99% 
162% 
171% 
184% 
88% 
174% 
104% 
168% 
129% 
182% 
196% 
163% 
159% 
104% 
106% 
209% 
124% 
163% 
95% 
131% 
77% 
151% 
206% 
170% 
159% 
196% 
138% 
123% 
68% 
204% 
57% 
150% 
86% 
190% 
85% 
171% 
85% 
122% 

89% 
144% 
102% 
140% 
57% 
176% 
80% 
187% 
76% 
157% 
119% 
166% 
115% 
255% 
139% 
139% 
111% 
170% 
88% 
163% 
146% 
164% 
98% 
170% 
112% 
146% 
75% 
157% 
128% 
162% 
135% 
154% 
84% 
198% 
148% 
160% 
108% 
154% 
69% 
179% 
129% 
165% 
157% 
168% 
82% 
142% 
47% 
162% 
81% 
148% 
68% 
195% 
72% 
161% 
71% 
124% 

Source: JRC 

39 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
3.4  Residential26 

In the period 2010-2022, energy consumption27of the EU27 residential sector decreased by -45 Mtoe, 
corresponding  to  a  drop  of  −16%  compared  to  2010  levels  (Figure  19).  Improvements  in  energy 
intensity contributed to a reduction of −44.6 Mtoe in this sector. Warmer winters over this period 
resulted in an energy consumption drop of −41.3 Mtoe in 2022 compared to 2010. Both effects more 
than  offset  the  driving  forces  behind  residential  consumption,  namely  the  population  and  wealth 
effects that together accounted for an increase of +41.4 Mtoe in the same period. At Member State 
level,  residential  consumption  dropped  in  all  but  two  countries.  Member  States  with  the  most 
pronounced  decline  in  residential  energy  consumption  included  Ireland  (-46%  compared  to  2010 
levels), Belgium (-45%), Denmark (-43%), France (-41%), Spain (-31%), Latvia (−36%), Italy (-32%), 
and Croatia (-32%). In contrast, residential consumption increased in Malta (+122%) and Malta (+8%).  

In  2021-2022,  a  consumption  decrease  (-18.6  Mtoe)  was  recorded  at  EU  level.  Based  on  the 
decomposition results, the effects that contributed to this trend are a decrease in weather effect (-
19.2 Mtoe), a drop in the intensity effect (-5.6 Mtoe), partially offset by increases in wealth (+6.1 
Mtoe)  and  population  effects  (+0.03  Mtoe).  Indeed,  residential  consumption  decreased  in  most 
countries in 2022 compared to 2021 except for Greece, Cyprus, and Malta. 

The  yearly  results  (Figure  20)  show  a  strong  correlation  between  the  weather  and  total  effects, 
indicating the strong impact the weather effect has on the total energy consumption of the residential 
sector. Both wealth and population effects have been on constant rise in the period 2010-2020, while 
in 2021 and 2022 the latter registered a small drop compared to 2020. The intensity effect shows a 
trend inversion in 2020, as it was declining after a breakdown since 2018 and it started to rise again 
in 2020 due to the COVID-19 crisis that had the effect of reversing the intensity trend. From 2021, 
the intensity effect started dropping again. 

Figure 19. Decomposition of changes in EU residential energy consumption (Mtoe) in 2010-2022 using the 
additive LMDI approach 

Source: JRC 

26 Decomposition formula for residential FEC: (7) 𝐹𝐸𝐶 = 𝐹𝐸𝐶ℎ𝑒𝑎𝑡 +   𝐹𝐸𝐶𝑜𝑡ℎ𝑒𝑟 = 𝑃𝑂𝑃. 𝑇𝐹𝐴

𝑃𝑂𝑃

′
𝐹𝐸𝐶ℎ𝑒𝑎𝑡
𝑇𝐹𝐴

𝐻𝐷𝐷
𝐻𝐷𝐷𝑟𝑒𝑓

+ 𝑃𝑂𝑃 𝐺𝐷𝐼
𝑃𝑂𝑃

𝐹𝐸𝐶𝑜𝑡ℎ𝑒𝑟
𝐺𝐷𝐼

27 ESTAT code: FC_OTH_HH_E 

40 

 
 
 
 
 
Figure 20. Yearly multiplicative LMDI decomposition results of residential energy consumption changes at EU 
level in 2010-2022 

Source: JRC 

At the country level, the decomposition results show that intensity and weather effects were the two 
main  inhibitory  factors  restricting  energy  consumption  growth  in  the  residential  sector  in  most 
countries. Intensity acted as a limiting factor in all Member States. The weather effect contributed to 
restrict energy consumption in all countries, except for Greece, Cyprus, and Malta. The largest drop in 
residential energy consumption due to warmer winters in 2010-2022 was noted in Belgium, Denmark, 
Germany,  Ireland,  Spain,  France,  Netherlands,  Portugal,  and  Sweden.  In  relative  terms  (%  change 
compared to 2010 consumption levels), the largest impact energy intensity improvements had on the 
residential sector can be seen in Belgium, Estonia, Ireland, Spain, Croatia, Cyprus, Latvia, Lithuania, 
Luxembourg, Hungary, Netherlands, Lithuania, and Portugal (greater than 25% compared to 2010 
consumption  levels).  On  the  opposite  side,  the  population  and  wealth  effects  were  the  main 
determinants of growth in residential energy consumption in many Member States in 2010-2022. 
The  population  effect  was  positive  in  all  countries  except  Bulgaria,  Estonia,  Greece,  Croatia,  Italy, 
Lithuania,  Latvia,  Hungary,  Poland,  Portugal,  and  Romania.  The  wealth  effect  was  a  driver  in  all 
countries. 

41 

 
 
 
 
 
 
 
 
 
 
 
 
 
Table 9. Additive and multiplicative decomposition results of residential energy consumption changes at 
Member State level in 2010-2022 and 2021-2022 

Additive results [ktoe] 

Wealth 
effect 

Intensity 
effect 

Weather 
effect 

Total 
effect 

Multiplicative results [%] 
Wealth 
effect 

Population 
effect 

Intensity 
effect 

BE 

BG 

CZ 

DK 

DE 

EE 

IE 

EL 

ES 

FR 

HR 

IT 

CY 

LV 

LT 

LU 

HU 

MT 

NL 

AT 

PL 

PT 

RO 

SI 

SK 

FI 

SE 

EU27 

Period 

2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 
2010-2022 
2021-2022 

Total 
effect 

-2509 
-1338 
-106 
-265 
-555 
-801 
-1198 
-539 
-7601 
-558 
-63 
-1 
-965 
-388 
-446 
41 
-2884 
-479 
-10403 
-5001 
-484 
-154 
-6124 
-1548 
13 
6 
-277 
-91 
-114 
-92 
-61 
-24 
-859 
-565 
34 
4 
-4164 
-1575 
-603 
-1141 
-1682 
-1518 
-307 
-91 
-207 
-870 
-369 
-115 
-414 
-300 
-857 
-377 
-1219 
-815 
-44423 
-18595 

Populatio
n effect 
571 
42 
-306 
-17 
29 
15 
255 
23 
897 
55 
-1 
1 
314 
30 
-260 
-87 
298 
10 
1968 
122 
-255 
-19 
-319 
-105 
35 
3 
-153 
-11 
-168 
-2 
124 
8 
-204 
-26 
19 
1 
578 
60 
480 
36 
-639 
-104 
-55 
14 
-501 
-69 
32 
-1 
22 
-13 
188 
13 
851 
52 
3803 
33 

Weather 
effect 

74% 
86% 
92% 
92% 
81% 
89% 
76% 
93% 
75% 
88% 
84% 
96% 
81% 
96% 
111% 
100% 
76% 
89% 
74% 
84% 
84% 
89% 
84% 
91% 
140% 
114% 
87% 
97% 
85% 
94% 
80% 
84% 
86% 
91% 
135% 
117% 
72% 
88% 
83% 
89% 
82% 
92% 
75% 
91% 
90% 
92% 
84% 
89% 
87% 
90% 
85% 
94% 
82% 
95% 
85% 
92% 

55% 
71% 
91% 
79% 
86% 
80% 
57% 
76% 
77% 
98% 
88% 
100% 
54% 
76% 
86% 
101% 
69% 
94% 
59% 
76% 
68% 
88% 
68% 
90% 
108% 
104% 
64% 
85% 
86% 
89% 
78% 
90% 
76% 
83% 
222% 
107% 
44% 
71% 
83% 
72% 
85% 
87% 
80% 
94% 
95% 
81% 
53% 
80% 
75% 
80% 
73% 
86% 
73% 
80% 
84% 
93% 

115% 
101% 
76% 
98% 
101% 
100% 
113% 
101% 
104% 
100% 
100% 
100% 
124% 
102% 
88% 
96% 
104% 
100% 
110% 
101% 
81% 
98% 
98% 
99% 
122% 
102% 
78% 
98% 
80% 
100% 
165% 
103% 
94% 
99% 
158% 
102% 
113% 
101% 
116% 
101% 
94% 
99% 
96% 
101% 
88% 
98% 
106% 
100% 
102% 
99% 
107% 
101% 
125% 
101% 
102% 
100% 

141% 
108% 
196% 
97% 
201% 
117% 
152% 
110% 
149% 
108% 
256% 
111% 
149% 
107% 
98% 
110% 
126% 
104% 
130% 
105% 
193% 
114% 
125% 
107% 
131% 
106% 
290% 
115% 
323% 
115% 
141% 
106% 
198% 
112% 
154% 
96% 
148% 
109% 
146% 
112% 
190% 
113% 
119% 
106% 
229% 
97% 
158% 
111% 
173% 
113% 
147% 
104% 
136% 
101% 
116% 
103% 

45% 
76% 
66% 
90% 
52% 
77% 
44% 
74% 
67% 
103% 
41% 
93% 
36% 
72% 
90% 
96% 
69% 
101% 
55% 
85% 
52% 
87% 
67% 
94% 
48% 
84% 
33% 
78% 
39% 
82% 
42% 
98% 
47% 
82% 
67% 
94% 
37% 
73% 
60% 
72% 
58% 
85% 
95% 
96% 
52% 
93% 
38% 
82% 
49% 
80% 
54% 
88% 
52% 
83% 
83% 
98% 

875 
180 
763 
-33 
2039 
419 
834 
171 
7214 
1218 
297 
33 
467 
80 
25 
175 
1685 
291 
3503 
593 
661 
87 
3065 
759 
49 
13 
521 
56 
659 
68 
41 
6 
1604 
241 
19 
-4 
1322 
247 
917 
240 
5490 
928 
384 
117 
2834 
-46 
165 
33 
470 
105 
860 
87 
873 
33 
37636 
6094 

-2139 
-697 
-469 
-120 
-1631 
-703 
-1596 
-561 
-3306 
3514 
-236 
-7 
-1289 
-430 
-579 
-50 
-2719 
82 
-6903 
-1088 
-591 
-45 
-5132 
-292 
-119 
-28 
-504 
-112 
-423 
-85 
-137 
28 
-1638 
-393 
-9 
4 
-3636 
-1082 
-1077 
-818 
-3806 
-1165 
-369 
-135 
-1968 
-258 
-415 
-56 
-634 
-187 
-1345 
-261 
-1907 
-615 
-44576 
-5561 

-1816 
-863 
-94 
-95 
-993 
-532 
-690 
-172 
-12407 
-5345 
-123 
-28 
-458 
-68 
367 
3 
-2149 
-862 
-8972 
-4629 
-298 
-177 
-3737 
-1910 
48 
18 
-142 
-24 
-181 
-72 
-89 
-66 
-621 
-386 
4 
3 
-2428 
-800 
-923 
-599 
-2727 
-1176 
-267 
-86 
-573 
-497 
-151 
-90 
-271 
-205 
-560 
-216 
-1036 
-285 
-41286 
-19160 

Source: JRC 

42 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
4  Russo-Ukrainian  war  impact  on  European  prices  and  energy 

consumption 

Over the 2021-2022 period, energy prices were significantly higher than in recent past years. Prices 
started rising sharply in summer of 2021 alongside the economic recovery following the easing of 
COVID-19 restrictions. In this scenario, Russia’s invasion of Ukraine in 2022 and its “weaponisation” 
of energy sources meant substantially lower levels of gas delivery and increased disruptions of gas 
supply. This contributed to a further increase of the gas prices, which in turn influenced the price of 
electricity from gas-fired power plants. 

As a response, already in October 2021 the EU implemented an “energy prices toolbox” (European 
Commission,  2021)28with  measures  to  address  and  mitigate  high  energy  prices.  This  included 
measures for income support, gas saving, and tax breaks.  

Following  the  Russian  invasion  of  Ukraine  in  February  2022  the  EU  adopted,  in  May  2022,  the 
REPowerEU (European Commission, 2022)29 plan. The objective of the plan is to accelerate the end 
of  EU  dependence  on  Russian  fossil  fuels  and  it  is  based  on  three  pillars:    energy  savings, 
diversification of energy supplies and accelerated roll-out of renewable energy to replace fossil fuels 
in  homes,  industry  and  power  generation.  The  plan  includes  an  increase  from  9%  to  13%  of  the 
binding Energy Efficiency Target under the ‘Fit for 55’ package of European Green Deal legislation, 
with important and immediate effects on energy consumption. 

In summer of 2022, the EU also introduced a gas demand reduction target (European Union, 2022b) 
30 and minimum filling obligations for gas storage (European Union, 2022a)31, in order to address 
issues on energy dependence and energy security.  

Furthermore, in October 2022, the Council adopted an emergency plan to address high energy prices 
in the Union. The Council Regulation introduced common measures to reduce electricity demand and 
to collect and redistribute the energy sector's surplus (European Union, 2022c)32. 

In  the  years  2020-2022,  a  series  of  external  factors  and  strong  policy  intervention  significantly 
influenced  the  dynamics  of  energy  consumption  in  the  EU.  All  these  deserve  to  be  considered  to 
complement the decomposition analysis and this chapter offers specific insights on Ukraine war and 
energy prices. 

28  Communication  from  the  Commission  to  the  European  Parliament,  the  European  Council,  the  Council,  the  European 
Economic and Social Committee and the Committee of the Regions Tackling rising energy prices: a toolbox for action 
and support - COM/2021/660 final. 

29  Communication  from  the  Commission  to  the  European  Parliament,  the  European  Council,  the  Council,  the  European 

Economic and Social Committee and the Committee of the Regions REPowerEU Plan - SWD(2022) 230 final. 

30 Council Regulation (EU) 2022/1369 of 5 August 2022 on coordinated demand-reduction measures for gas: https://eur-

lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A32022R1369. 

31 Regulation (EU) 2022/1032 of the European Parliament and of the Council of 29 June 2022 amending  Regulations (EU) 
storage  https://eur-lex.europa.eu/legal-

(EC)  No  715/2009  with 

regard 

gas 

to 

2017/1938  and 
content/EN/TXT/?uri=celex%3A32022R1032. 

32 Council Regulation (EU) 2022/1854 of 6 October 2022 on an emergency intervention to address high energy prices. 

43 

 
 
 
 
 
4.1 Gas consumption, inflation rates and prices 

From  the  beginning  of  2022,  until  the  last  data  available,  the  natural  gas  consumption  dropped 
significantly (Figure 21) compared to the same month of previous year (peaking in November 2022, 
-27%), while in both 2020 and 2021 years the decrease was not so marked. Moreover, in the last 
months of 2020 and up to the middle 2021, the natural gas consumption increased. This is probably 
a consequence of the restrictions still in place during those months to contain the COVID-19 virus 
spread.  

The drop in 2022 can be attributed to a combination of factors: 

•  Price-driven demand severe reduction: high gas prices forced industries to reduce production, 
especially in energy-intensive sectors. In addition to this, households responded to price in-
creases by reducing their energy consumption; 

•  Government interventions:  several Member States implemented policies aimed at reducing 
gas  consumption,  such  as  encouraging  energy  conservation  measures  and  temporarily 
switching to alternative energy sources (e.g. coal and renewables); 

•  Energy sources substitution: efforts to ramp up renewable energy production and substitute 
with coal or oil products helped ease the strain on natural gas demand, although with envi-
ronmental consequences. 

Figure 21. Monthly EU natural gas inland consumption % change with respect to the same month previous 
year, 2020-2024. 

Source: JRC elaboration on Eurostat data 

44 

 
 
Figure  22  shows  the  EU’s  inflation  rate  on  a  monthly  basis  and  the  biannual  natural  gas  prices 
distinguishing by consumption band33. Before the Russian invasion, EU was already facing inflationary 
pressures due to post-COVID-19 economic recovery; however, the invasion of the Ukrainian territory 
played a key role in accelerating the inflation increase, leading it to peaking in late 2022. Natural gas 
prices followed similar trajectories, with the steepest increases observed between June and December 
2022; this applies both to households and non-households prices for all the bands. Furthermore, it is 
possible  to  observe  that  among  the  two  consumers’  categories,  the  smallest  ones  in  terms  of 
consumption  band  (less  than  20  GJ  for  households  and  less  than  1,000  GJ  for  non-households) 
suffered the major price increases. This suggests that vulnerable groups, especially those closes the 
energy  poverty  threshold,  have  suffered  most  from  the  increases  in  prices,  potentially  worsening 
socio-economic disparities across the European Union. As energy costs surged, households with lower 
incomes faced difficulty in managing both energy costs and general inflation. 

Figure 22. EU inflation rate and natural gas prices by consumption band, households and non-households, 
2021-2024. 

Source: JRC elaboration on Eurostat data 

These  steep  price  increases  can  be  attributed  to  EU's  heavy  reliance  on  Russian  gas.  The  sudden 
supply interruption and the urgency to replace the Russian supply through more expensive natural 
and liquid gas imports led to significant price volatility.  

33 For household consumers: band D1 consumption less than 20 GJ; band D2 consumption from 20 GJ to 199 GJ; band D3 
consumption  from  200  GJ.  For  non-household  consumers:  band  I1  consumption  less  than  1,000  GJ;  band  I2 
consumption from 1,000 GJ to 9,999 GJ; band I3 consumption from 10,000 GJ to 99,999 GJ; band I4 consumption 
from 100,000 GJ to 999,999 GJ; band I5 consumption from 1,000,000 GJ to 3,999,999 GJ; band I6 consumption from 
4,000,000 GJ. 

45 

 
 
 
 
Figure  23  emphasizes  that  the  sharp  increase  in  energy  prices,  particularly  for  natural  gas  and 
petroleum,  was  a  key  driver  of  overall  inflation  increase  after  the  war’s  start.  This  is  crucial  to 
understanding the widespread economic impact of the Russian invasion, as energy prices affect a 
wide array of industries and services, further fostering price increases across the economy. The jump 
of energy sources inflation affected both direct energy costs (electricity, heating) and indirect costs 
(transport, manufacturing, agriculture), exacerbating inflation throughout the broader economy. Even 
as inflation in other sectors eased toward late 2023, energy inflation remained a significant driver 
due to its pervasive role in EU industries and the European dependence on imports. 

Figure 23. EU total and energy sources inflation rates, 2021-2024. 

Source: JRC elaboration on Eurostat data 

46 

 
 
 
 
 
 
 
 
 
 
 
 
 
4.2 Natural and liquid gas imports and storage 

After the Russian invasion of Ukraine, a crucial issue was the willingness of EU institutions to reduce 
energy dependence on Russia, especially natural and liquid gas (Figure 24). Thus, from March 2022, 
the imports from Russia have consistently decreased (first graph of Figure 24), reaching a minimum 
level of about 25,000 million cubic metres in October 2022.  

Figure 24. Monthly EU natural gas and liquid gas imports, total, from Russia, Africa, Middle-East and other, 
2021-2024. 

Source: JRC elaboration on Eurostat data 

47 

 
 
The total EU imports did not show a great diminishing trend. Indeed the second graph of Figure 24 
tells us that in the same period the EU had increased their gas imports from other countries. Although 
data for the first four months of 2022 are missing, it is possible to observe how African countries (i.e. 
Tunisia, Algeria, Nigeria, Morocco, and Egypt) became crucial for the European Union gas needs. To a 
lesser extent than African countries, Middle Eastern countries also contributed to EU energy needs. 

Strictly related to gas consumption and reduced dependency from imports, the data showed in Figure 
25 reports that EU natural gas storage levels reached their highest point in recent history in 2023, 
hitting  100%  capacity  in  November.  The  fact  that  seasonal  storage  trends  remained  relatively 
unchanged in previous years, despite crises like the COVID-19 pandemic, highlights how extraordinary 
the 2022/2023 storage levels are. The Ukrainian war forced the European Union to adopt a much 
more aggressive approach to natural gas storage as part of its strategy to counter potential shortages 
in the winter months. This shift was driven by concerns over Russian supply disruptions, which led to 
mandates for increased storage capacities ahead of the winter seasons. Higher storage levels also 
reflect EU’s success in securing alternative gas supplies, particularly from liquid natural gas sources, 
to  mitigate  the  supply  risks  posed  by  Russia.  Furthermore,  by  securing  high  levels  of  stored  gas, 
Europe managed to avoid a potential energy crisis in the winter of 2022/2023, despite significantly 
reduced Russian gas imports. However, this aggressive storage effort came with increased costs, as 
filling these reserves required purchasing gas at elevated market prices. 

Figure 25. EU monthly natural gas storage (% and TWh), 2020-2024. 

Source: JRC elaboration on Eurostat data 

The war in Ukraine has irremediably reshaped European energy priorities and policies. From a socio-
economic point of view, the immediate consequence was an upsurge in energy prices, leading to a 
sharp increase in prices and the exacerbation of the energy poverty. Industries such as manufacturing 
and agriculture, heavily reliant on natural gas and derivatives, suffered from high input costs, leading 
to reduced output or even shutdowns. 

48 

 
 
From  a  geopolitical  perspective,  EU’s  decoupling  from  Russian  energy  marked  a  significant 
geopolitical realignment. While Europe has successfully reduced its dependence on Russian gas, this 
has required the need to set-up new energy partnerships, particularly with North Africa and the Middle 
East countries. The shift has also accelerated Europe’s efforts to transition to renewable energy, as 
part of its long-term strategy to enhance energy security.  

In relation to this, the Ukrainian war has highlighted the importance of energy security in EU. Member 
States have accelerated the diversification of energy supplies, made strategic reserves and invested 
in  new  liquid  natural  gas  terminals  and  renewables.  While  these  efforts  have  reduced  immediate 
supply  risks,  Europe  remains  vulnerable  to  further  energy  shocks,  particularly  in  relation  to  liquid 
natural gas price volatility and global competition for gas. 

The  Russian  invasion  has  had  far-reaching  consequences  for  EU’s  energy  markets.  From  soaring 
energy prices – especially for the natural gas ones – and inflation to major changes in gas imports, 
consumption, and storage, the effects have been profound. The European Union has taken significant 
steps  to  mitigate  these  impacts  by  diversifying  gas  supplies,  increasing  storage,  and  reducing 
consumption, but challenges remain. The ongoing conflict in Ukraine continues to pose risks, especially 
as EU strives to balance energy security with 2030 and 2050 sustainability goals. 

49 

 
 
5  Conclusions 

This report applied the logarithmic-mean Divisia index method to study both aggregated and sectoral 
energy consumption changes at both EU and Member State levels over the period 2010-2022. The 
LMDI-I decomposition analysis enables the determination the influence of different effects on energy 
consumption changes registered in this timeframe. 

In 2010-2022, the EU primary energy consumption decreased by −13.7% from 1,458 to 1,259 Mtoe. 
The decomposition of primary energy consumption changes indicates that the fall in primary energy 
consumption in the period 2010-2022 is attributed to the lowering of final energy demand together 
with  the  transformation  effect  over  the  examined  period.  On  the  contrary,  the  distribution  losses 
increased in the same period (+4 Mtoe). Notwithstanding the exceptional year of 2020 due to the 
pandemic crisis, the total primary energy consumption kept decreasing in 2021 and 2022, with the 
exception of the distribution effect that showed a reverse trend of increase from 2020 onwards.  

Looking at the productive sectors of the economy (industry, services, and agriculture), intensity had 
the biggest inhibiting effect among the different factors followed by structural changes which were 
attributed to the increasing importance of services as a whole across the EU economies. Together 
with the negative intensity effect, these two effects offset the positive activity effect in 2010-2022, 
resulting in an overall drop in consumption. Focusing on the last years with available data (2021-
2022), a drop in consumption was observed after the 2021 rebound, as a consequence of the rising 
energy prices and the restrictive measures motivated by the Russo-Ukrainian war. While the activity 
effects were increased, the decrease in the intensity effect has been the main driver of the decrease 
in consumption, especially for industry and services. In terms of activity, employment was the main 
factor  in  service  and  industry  sectors,  with  the  highest  relative  impact  observed  on  services. 
Concerning industry, the comparison between production-based and monetary-based results showed 
that the use of gross value added generally overestimated efficiency improvements. 

The decomposition of energy consumption in the transport sector showed some interesting findings. 
Similar to the previous analyses, the activity effect was the main driver in driving up the consumption 
of all the transport subsectors (passenger, freight, and air). The intensity effect contributed to the 
increase of the consumption in both passenger and freight transport, while air transport confirmed a 
reverse trend since 2020. 

As regards residential sector, the results showed the decisive impact of the weather fluctuations34 
and the wealth effect on the overall energy consumption of this sector. Their positive contribution to 
the energy consumption trends were compensated by the weather effect but also by the important 
contribution of the intensity effect, which drove energy consumption down in the considered period. 
The population effect showed a negligible impact on the consumption.  

The analysis allowed drawing some conclusions that could be useful for the decision-makers. Even if 
the 2020 Energy Efficiency targets were achieved, the 2021 results showed a rebound, while 2022 
has been again a year of decreasing consumption. Important external factors contributed to this trend: 
the exceptionally high energy prices – especially the gas-related ones – and the consequences of the 
invasion to Ukraine leading to the EU’s reduction of Russian fossil fuel imports. During 2022, the EU 
and its Member States took active measures to save energy: by adopting the Council Regulation on 

34 These fluctuations are associated with changes in wintertime temperatures only. In the future, the analysis may consider 

the effect of summertime temperature variations on electricity and specifically cooling-related consumption. 

50 

 
 
 
coordinated  demand  reduction  measures  for  gas  (EU/2022/1369),  the  Member  States  agreed  to 
reduce their gas demand by 15% compared to their average consumption in the past five years.  

Alongside this, the recast of the European Energy Efficiency Directive of 2023, in the context of the 
European  Green  Deal,  has  set  more  ambitious  targets  for  primary  and  final  energy  consumption 
reduction for the year 2030. Consequently, the EU Member States should increase and strengthen 
their energy efficiency policy efforts in the forthcoming period. Some examples of sectoral policies 
contributing  to  this  could  be  the  following:  the  implementation  of  more  ambitious  legislation  (i.e. 
buildings standards and other measures under the 2024 recast EPBD)  as well  as the provision of 
financial and fiscal incentives for deep renovation of existing buildings. Furthermore, the development 
of energy efficiency technologies, the electrification of vehicles, and a modal shift to public transport 
could also be important in an attempt to ensure the achievement of the targets. 

The overall results of the decomposition analysis offered valuable insights into the factors behind 
the recent consumption trends at both EU and Member State levels. However, this study has also 
highlighted  the  need  for  further  investigation  to  provide  a  more  comprehensive  analysis.  To 
strengthen the analytical framework of tools such as the LMDI method, this research has shown that 
finer  levels  of  disaggregation  are  necessary  to  conduct  more  detailed  decomposition  as 
disaggregated  data  are  often  accompanied  with  various  data  gaps  and  data  quality  issues.  The 
sectors with significant challenges are: the transport sector - as the currently available energy and 
activity  data  are  not  fully  consistent,  and  the  services  sector  -  as  the  breakdown  of  energy 
consumption  by  subsector  and  by  end  use  is  currently  not  available  in  official  statistics.  The  JRC 
welcomes the on-going efforts made by Eurostat and national statistical offices that aim to increase 
the level of detail in their statistics. In the future, JRC also plans to consider alternative databases 
which are fully compliant with the Eurostat Energy Balances dataset but provide very detailed annual 
information on the energy system and its underlying drivers. An example could be the JRC Integrated 
Database of the European Energy System (JRC-IDEES). 

51 

 
 
References  

Achour, H. and Belloumi, M. (2016) ‘Decomposing the influencing factors of energy consumption in 
Tunisian  transportation  sector  using  the  LMDI  method’,  Transport  Policy,  52,  pp.  64–71.  doi: 
10.1016/j.tranpol.2016.07.008 

Ang, B. W. (2005) ‘The LMDI approach to decomposition analysis: a practical guide’,  Energy Policy, 
33(7), pp. 867–871. doi: 10.1016/J.ENPOL.2003.10.010 

Ang, B. W. (2015) ‘LMDI decomposition approach: A guide for implementation’, Energy Policy, 86, pp. 
233–238. doi: 10.1016/J.ENPOL.2015.07.007 

Ang, B. W. and Xu, X. Y. (2013) ‘Tracking industrial energy efficiency trends using index decomposition 
analysis’, Energy Economics, 40, pp. 1014–1021. doi: 10.1016/J.ENECO.2013.05.014 

Clementi, E., Tsemekidi-Tzeinaraki, S. and Bertoldi, P. (2024), Assessing the impact of Energy Efficiency 
on the EU Energy Consumption by using an index decomposition analysis in 2010-2021, Publications 
Office of the European Union, Luxembourg, 2024, doi:10.2760/96121, JRC136466. 

Economidou, M. (2017) Assessing the progress towards the EU energy efficiency targets using index 
decomposition analysis, Luxembourg: Publications Office of the European …. doi: 10.2760/675791 

Economidou, M. (2021) Assessing the progress towards the EU energy efficiency targets using index 
decomposition analysis in 2005-2019, Luxembourg: Publications Office of the European Union 

Economidou,  M.  and  Román-Collado,  R.  (2019)  Assessing  the  progress  towards  the  EU  energy 
efficiency  targets  using  index  decomposition  analysis.  Luxembourg:  Publications  Office  of  the 
European Union. doi: 10.2760/61167 

Communication from the Commission to the European Parliament, the European Council, the Council, 
the  European  Economic  and  Social  Committee  and  the  Committee  of  the  Regions  Tackling  rising 
energy prices: a toolbox for action and support - COM/2021/660 final. 

Communication from the Commission to the European Parliament, the European Council, the Council, 
the European Economic and Social Committee and the Committee of the Regions REPowerEU Plan - 
SWD(2022) 230 final. 

Regulation (EU) 2022/1032 of the European Parliament and of the Council of 29 June 2022 amending  
Regulations  (EU)  2017/1938  and  (EC)  No  715/2009  with  regard  to  gas  storage  https://eur-
lex.europa.eu/legal-content/EN/TXT/?uri=celex%3A32022R1032. 

Council Regulation (EU) 2022/1369 of 5 August 2022 on coordinated demand-reduction measures 
for gas: https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A32022R1369. 

Council Regulation (EU) 2022/1854 of 6 October 2022 on an emergency intervention to address high 
energy prices. 

European  Commission:  Directorate-General  for  Mobility  and  Transport,  EU  transport  in  figures  – 
European  Union,  2023, 
Statistical 
https://data.europa.eu/doi/10.2832/319371 

pocketbook  2023,  Publications  Office 

the 

of 

Eurostat  Database  [Online],  Available:  https://ec.europa.eu/eurostat/data/database  [Accessed  April 
2024] 

Farla,  J.  C.  M.  and  Blok,  K.  (2001)  ‘The  quality  of  energy  intensity  indicators  for  international 

52 

 
comparison in the iron and steel industry’, Energy Policy, 29(7), pp. 523–543. doi: 10.1016/S0301-
4215(00)00148-8 

IEA (2018) Energy Efficiency 2018: Analysis and outlooks to 2040. Paris. Available at: www.iea.org  

Liang,  Y.  et  al.  (2017)  ‘Factors  affecting  transportation  sector  CO<inf>2</inf>emissions  growth  in 
China: An LMDI decomposition analysis’, Sustainability (Switzerland), 9(10). doi: 10.3390/su9101730 

Liu, N. and Ang, B. W. (2007) ‘Factors shaping aggregate energy intensity trend for industry: Energy 
intensity 
doi: 
versus 
10.1016/J.ENECO.2006.12.004 

product  mix’, 

Economics, 

609–635. 

Energy 

29(4), 

pp. 

Nanduri, M., Nyboer, J. and Jaccard, M. (2002) ‘Aggregating physical intensity indicators: results of 
applying the composite indicator approach to the Canadian industrial sector’, Energy Policy, 30(2), pp. 
151–163. doi: 10.1016/S0301-4215(01)00083-0 

Norman,  J.  B.  (2017)  ‘Measuring  improvements  in  industrial  energy  efficiency:  A decomposition 
analysis applied to the UK’, Energy, 137, pp. 1144–1151. doi: 10.1016/J.ENERGY.2017.04.163 

Odyssee Database [Online], Available: http://www.odyssee-mure.eu/ [Accessed July 2024] 

OECD Data explorer [Online], Available: https://www.oecd.org/en.html [Accessed July 2024] 

Reuter, M., Patel, M. K. and Eichhammer, W. (2017) ‘Applying ex-post index decomposition analysis to 
primary  energy  consumption  for  evaluating  progress  towards  European  energy  efficiency  targets’, 
Energy Efficiency, 10(6), pp. 1381–1400. doi: 10.1007/s12053-017-9527-2 

Román-Collado, R. and Colinet, M. J. (2018) ‘Are labour productivity and residential living standards 
drivers  of  the  energy  consumption  changes?’,  Energy  Economics,  74,  pp.  746–756.  doi: 
10.1016/j.eneco.2018.07.030 

Román-Collado, R. and Economidou, M. (2021) ‘The role of energy efficiency in assessing the progress 
towards the EU energy efficiency targets of 2020: Evidence from the European productive sectors’, 
Energy Policy, 156(March). doi: 10.1016/j.enpol.2021.112441 

Tsemekidi Tzeiranak, S., Bertoldi, P., Castellazzi, L., Gonzalez Torres, M., Clementi, E. and Paci, D., Energy 
Consumption and Energy Efficiency trends in the EU, 2000-2020, EUR 31266 EN, Publications Office 
of  the  European  Union,  Luxembourg,  2022,  ISBN  978-92-76-58515-2,  doi:10.2760/727548, 
JRC130732 

Xu, X. Y. and Ang, B. W. (2014) ‘Analysing residential energy consumption using index decomposition 
analysis’, Applied Energy, 113, pp. 342–351. doi: 10.1016/J.APENERGY.2013.07.052 

Zhang,  M.  et  al.  (2011)  ‘Decomposition  analysis  of  energy  consumption  in  Chinese  transportation 
sector’, Applied Energy, 88(6), pp. 2279–2285. doi: 10.1016/j.apenergy.2010.12.077 

53 

 
List of abbreviations and definitions  

AR 

EC 

EED 

Activity revaluation 

European Commission 

Energy Efficiency Directive 

ESTAT   

European Statistical Office 

EU 

FEC 

GVA 

HDD 

IDA 

IEA 

IPI 

JRC 

Ktoe 

LMDI 

MS 

Mtoe 

PEC 

European Union 

Final energy consumption 

Gross value added 

Heating degree days 

Index Decomposition Analysis 

International Energy Agency 

Industrial production index 

Joint Research Centre 

Kilo-tonne of oil equivalent 

Logarithmic Mean Divisia 

Member State 

Megaton of oil equivalent 

Primary energy consumption 

54 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
List of boxes  

Box 1. Decomposition of industrial energy changes using physical units........................................................ 15 

55 

 
 
List of figures  

Figure 1. EU primary and final energy consumption from 2010 to 2022. ......................................................... 7 

Figure 2. Decomposition of changes in EU primary energy consumption (Mtoe) in 2010-2022 using 
the additive Logarithmic Mean Divisia Index approach (LMDI) ................................................................................ 16 

Figure 3. Yearly multiplicative LMDI decomposition results at EU level in 2010-2022 ........................... 17 

Figure 4. Decomposition of changes in final energy consumption (Mtoe) of the productive sectors 
of the economy in 2010-2022 in EU using the additive Logarithmic Mean Divisia Index approach 
(LMDI) ........................................................................................................................................................................................................ 20 

Figure 5. Decomposition of final energy consumption changes in the productive sectors in 2010-
2022 at Member State level ........................................................................................................................................................ 21 

Figure 6. Decomposition of changes in EU industrial energy consumption (Mtoe) in 2010-2022 
using the additive LMDI approach ............................................................................................................................................. 23 

Figure 7. Yearly multiplicative LMDI decomposition results of industrial energy consumption 
changes at EU level in 2010-2022 .......................................................................................................................................... 23 

Figure 8. Yearly multiplicative LMDI decomposition results of energy consumption changes in 
services at EU level in 2010-2022 ........................................................................................................................................... 26 

Figure 9. Decomposition of changes in EU services energy consumption (Mtoe) in 2010-2022 using 
the additive LMDI approach .......................................................................................................................................................... 26 

Figure 10. Yearly multiplicative LMDI decomposition results of agricultural energy consumption 
changes at EU level in 2010-2022 .......................................................................................................................................... 28 

Figure 11. Decomposition of changes in EU agricultural energy consumption (Mtoe) in 2010-2022 
using the additive LMDI approach ............................................................................................................................................. 29 

Figure 12. Differences in industrial decomposition results between the use of monetary-based 
(GVA) versus physical-based (IPI) units .................................................................................................................................. 31 

Figure 13. Yearly multiplicative LMDI decomposition results of passenger transport energy 
consumption changes at EU level in 2010-2022 ............................................................................................................. 32 

Figure 14. Decomposition of changes in EU passenger transport energy consumption (Mtoe) in 
2010-2022 using the additive LMDI approach ................................................................................................................. 33 

Figure 15. Yearly multiplicative LMDI decomposition results of freight transport energy 
consumption changes at EU level in 2010-2022 ............................................................................................................. 34 

Figure 16. Decomposition of changes in EU freight transport energy consumption (Mtoe) in 2010-
2022 using the additive LMDI approach ............................................................................................................................... 34 

Figure 17. Yearly multiplicative LMDI decomposition results of air transport energy consumption 
changes at EU level in 2010-2022 .......................................................................................................................................... 37 

Figure 18. Decomposition of changes in EU air transport energy consumption (Mtoe) in 2010-2022 
using the additive LMDI approach ............................................................................................................................................. 38 

56 

 
Figure 19. Decomposition of changes in EU residential energy consumption (Mtoe) in 2010-2022 
using the additive LMDI approach ............................................................................................................................................. 40 

Figure 20. Yearly multiplicative LMDI decomposition results of residential energy consumption 
changes at EU level in 2010-2022 .......................................................................................................................................... 41 

Figure 21. Monthly EU natural gas inland consumption % change with respect to the same month 
previous year, 2020-2024............................................................................................................................................................. 44 

Figure 22. EU inflation rate and natural gas prices by consumption band, households and non-
households, 2021-2024. ................................................................................................................................................................ 45 

Figure 23. EU total and energy sources inflation rates, 2021-2024. ................................................................ 46 

Figure 24. Monthly EU natural gas and liquid gas imports, total, from Russia, Africa, Middle-East 
and other, 2021-2024. .................................................................................................................................................................... 47 

Figure 25. EU monthly natural gas storage (% and TWh), 2020-2024. ........................................................... 48 

57 

 
 
List of tables  

Table 1. Decomposition identities and explanation of factors considered for various sectors of the 
economy ................................................................................................................................................................................................... 10 

Table 2. Additive and multiplicative decomposition results of primary energy consumption changes 
Member State level in 2010-2021 and 2020-2021 ...................................................................................................... 18 

Table 3. Additive and multiplicative decomposition results of industrial energy consumption 
changes at Member State level in 2010-2021 and 2020-2021 ............................................................................ 24 

Table 4. Additive and multiplicative decomposition results of services energy consumption changes 
at Member State level in 2010-2020 and 2020-2021 ................................................................................................ 27 

Table 5. Additive and multiplicative decomposition results of agriculture energy consumption 
changes at Member State level in 2010-2021 and 2020-2021 ............................................................................ 30 

Table 6. Additive and multiplicative decomposition results of passenger transport energy 
consumption changes at Member State level in 2010-2021 and 2020-2021 .............................................. 35 

Table 7. Additive and multiplicative decomposition results of freight transport energy consumption 
changes at Member State level in 2010-2021 and 2020-2021 ............................................................................ 36 

Table 8. Additive and multiplicative decomposition results of air transport energy consumption 
changes at Member States level in 2010-2021 and 2020-2021 .......................................................................... 39 

Table 9. Additive and multiplicative decomposition results of residential energy consumption 
changes at Member State level in 2010-2021 and 2020-2021 ............................................................................ 42 

Table 10. Input datasets ............................................................................................................................................................... 59 

58 

 
 
Annexes  

Table 10. Input datasets 

Indicator 

Source 

ESTAT dataset 

ESTAT code 

Available 
time period 

Unit 

Primary Energy 
Consumption (PEC) 

ESTAT 

FINAL-ENERGY-
CONSUMPTION-
OLD-
METHODOLOGY 

PEC2020-2030 

2010-2022 

Mtoe 

Final Energy 
Consumption (FEC) 

Total 

Food & Tobacco 

Textile & Leather 

Wood, paper, etc. 

Chemical & 
Petrochemical 

Metals/Machinery 

Non-metallic minerals 
etc. 

Transport equipment 

Construction 

Services 

Agriculture etc. 

Road transport 

Rail transport 

Air transport  

Water transport 

ESTAT 

FINAL-ENERGY-
CONSUMPTION-
OLD-
METHODOLOGY 

B_101700 

2010-2022 

B_101830 

2010-2022 

B_101835 

2010-2022 

B_101851+ 

B_101840 

2010-2022 

B_101815 

2010-2022 

B_101847+ 

B_101805+ 

2010-2022 

Mtoe 

B_101810 

B_101820+ 

B_101853 

2010-2022 

B_101846 

2010-2022 

B_101852 

2010-2022 

B_102035 

2010-2022 

B_102020+ 

B_102030 

2010-2022 

B_101920 

2010-2022 

ESTAT 

Odyssee
35 

FINAL-ENERGY-
CONSUMPTION-
OLD-
METHODOLOGY 

B_101910 

2010-2022 

B_101932 

2010-2022 

Mtoe 

B_101940 

2010-2022 

35 Odyssee database has been used to calculate car, bus, truck & light vehicles, motorcycles, rail passenger and rail freight 

shares. 

59 

 
 
 
 
 
 
 
 
Residential 

ESTAT 

nrg_d_hhq 

FC_OTH_HH_E 

2010-2022 

Mtoe 

Gross Value Added 
(GVA) 

Total 

Food & Tobacco 

Textile & Leather 

Wood, paper, etc. 

Chemical & 
Petrochemical 

ESTAT 

2010-2022 

2010-2022 

2010-2022 

2010-2022 

2010-2022 

Metals/Machinery 

nama_10_a64 

B1G 

2010-2022 

Non-metallic minerals 
etc. 

Transport equipment 

Construction 

Services 

Agriculture etc. 

Thousands hours 
worked 

Food & Tobacco 

Textile & Leather 

Wood, paper, etc. 

Chemical & 
Petrochemical 

2010-2022 

2010-2022 

2010-2022 

2010-2022 

2010-2022 

C10-C12 

2010-2022 

C13-C15 

2010-2022 

C16-C18 

2010-2022 

C19-C20 

2010-2022 

Metals/Machinery 

ESTAT 

C24_C25+C28 

2010-2022 

nama_10_a64e 

Non-metallic minerals 
etc. 

Transport equipment 

Construction 

Services 

Agriculture etc. 

Volume Index 
Production 

Manufacturing 

Construction 

ESTAT 

sts_inpr_a 

60 

C23 

C30 

F 

G-I+J+K+L+M_N+O-
Q+R-
U+C33+E+T+H49+H5
0+H51 

A 

C 

- 

2010-2022 

2010-2022 

2010-2022 

2010-2022 

2010-2022 

2010-2022 

2010-2022 

- 

- 

Chain 
linked 
volumes 
(2010), 
million EUR 

Thousands 
hours 
worked 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
Passenger kilometres 

Road 

Rail 

Air 

DG 
MOVE  
OECD 
Library36 

Tonne kilometres 

Road 

Rail 

Water 

Total floor area  Odyssee 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

2010-2022 

2010-2022 

Billion pkm 

2010-2022 

2010-2022 

2010-2022 

Billion tkm 

2010-2022 

2010-2022 

m2 

Gross Disposable 
Income per capita (GDI) 

ESTAT 

nasa_10_nf_tr 

B6G 

2010-2022  Million EUR 

Population 

ESTAT 

demo_pjan 

Climatic correction 
factor 

JRC 

- 

- 

- 

2010-2022 

Number 

2010-2022 

- 

36 OECD Library database has been used to calculate passengers and freight transport (road and rail) for year 2022. 

61 

 
 
 
 
 
 
 
 
 
 
 
 
Getting in touch with the EU 

In person 

All over the European Union there are hundreds of Europe Direct centres. You can find the address of the 
centre nearest you online (european-union.europa.eu/contact-eu/meet-us_en). 

On the phone or in writing 

Europe Direct is a service that answers your questions about the European Union. You can contact this 
service: 

— by freephone: 00 800 6 7 8 9 10 11 (certain operators may charge for these calls), 

— at the following standard number: +32 22999696, 

— via the following form: european-union.europa.eu/contact-eu/write-us_en. 

Finding information about the EU 

Online 

Information about the European Union in all the official languages of the EU is available on the Europa 
website (european-union.europa.eu). 

EU publications 

You  can  view  or  order  EU  publications  at  op.europa.eu/en/publications.  Multiple  copies  of  free 
publications can be obtained by contacting Europe Direct or your local documentation centre (european-
union.europa.eu/contact-eu/meet-us_en). 

EU law and related documents 

For access to legal information from the EU, including all EU law since 1951 in all the official language 
versions, go to EUR-Lex (eur-lex.europa.eu). 

EU open data 

The  portal  data.europa.eu  provides  access  to  open  datasets  from  the  EU  institutions,  bodies  and 
agencies.  These  can  be  downloaded  and  reused  for  free,  for  both  commercial  and  non-commercial 
purposes. The portal also provides access to a wealth of datasets from European countries. 

 
 
 
 
 
 
 
The Joint Research Centre (JRC) provides 
independent, evidence-based knowledge and 
science, supporting EU policies to positively 
impact society 

EU Science Hub 
Joint-research-centre.ec.europa.eu