EU reference scenario 2020 : energy, transport and GHG emissions : trends to 2050.
Date: 2021-07-16
Institutions: Directorate-General for Climate Action, Directorate-General for Energy, Directorate-General for Mobility and Transport
Subjects: emission trading, energy efficiency, energy policy, energy use, environmental indicator, reduction of gas emissions, renewable energy, statistics, transport statistics

EU Reference Scenario 2020 

Energy, transport and GHG emissions - Trends to 2050 

July – 2021 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
This  publication  was  prepared  for  the  Directorate-General  for  Energy,  the 
Directorate-General for Climate Action and the Directorate-General for Mobility and 
Transport by E3-Modelling, in cooperation with the International Institute for Applied 
Systems  Analysis  (IIASA)  and  EuroCARE  and  represents  those  organisations’ 
views  on  energy,  transport  and  GHG  emissions  facts,  figures  and  projections.  It 
reflects the views only of the authors, and the European Commission is not liable 
for any consequence stemming from the reuse of this publication. 

Authors 

E3-Modelling:  Prof.  P.  Capros  (scientific  coordinator),  A.  De  Vita  (project 
coordinator), A. Florou (report editor) 

PRIMES  model:  M.  Kannavou  (PRIMES  coordinator),  T.  Fotiou  (PRIMES 
Buildings  and 
Industry  sectors  coordinator),  P.  Siskos  (Transport  sector 
coordinator),  I.  Tsiropoulos,  N.  Katoufa,  I.  Mitsios,  S.  Evangelopoulou,  G. 
Asimakopoulou, T. Kalokyris 

GEM-E3  model:  L.  Paroussos  (GEM-E3  coordinator),  K.  Fragkiadakis,  P. 

Karkatsoulis 

IIASA -GAINS model: Lena Höglund-Isaksson, Wilfried Winiwarter, Pallav Purohit, 
Adriana Gómez-Sanabria, Peter Rafaj, Laura Warnecke  

IIASA –GLOBIOM/G4M models: Andre Deppermann, Mykola Gusti, Stefan Frank, 
Pekka Lauri, Fulvio di Fulvio, Nicklas Forsell, Petr Havlík 

EuroCARE-CAPRI model: Peter Witzke, Monika Kesting 

EUROPEAN COMMISSION 

Directorate-General for Energy, Directorate-General for Climate Action, Directorate-General for Mobility and Transport 

Contact: EC-REFERENCE-SCENARIO@ec.europa.eu  

European Commission 
B-1049 Brussels 

 
 
 
 
 
 
 
 
EUROPEAN COMMISSION 

EU Reference Scenario 2020 
Energy, transport and GHG emissions - Trends to 2050  

Directorate-General for Energy, Directorate-General for Climate Action, Directorate-General for Mobility and 
Transport   

2021 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Manuscript completed in July 2021 

First edition 

LEGAL NOTICE 

This document has been prepared for the European Commission however it reflects the views only of the authors, and the 
European Commission is not liable for any consequence stemming from the reuse of this publication. More information on the 
European Union is available on the Internet (http://www.europa.eu). 

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ISBN 978-92-76-39356-6  

doi:10.2833/35750 

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Table of Contents 

Table of Contents ................................................................................................. 5 

1. 

Introduction ................................................................................................... 8 

1.1. The EU Reference Scenario 2020: approach and process ........................... 8 

1.1.1. The Reference Scenario approach: projection, not forecast ............................ 8 

1.1.2. The Reference Scenario process .................................................................... 9 

1.2. The Reference Scenario modelling suite .................................................... 10 

1.2.1. Description and role of each model ................................................................11 

1.2.2. Main methodological improvements since 2016 .............................................16 

2.  Framework conditions for the Reference Scenario.................................. 27 

2.1. Macro-economic and demographic assumptions ....................................... 27 

2.1.1. EU population projections ...............................................................................27 

2.1.2. EU economic projections ................................................................................29 

2.1.3. EU sectorial projections ..................................................................................30 

2.2. World fossil fuel prices ............................................................................... 32 

2.3. The COVID-19 impact on economic activity .............................................. 33 

2.3.1. Impact on transport ........................................................................................33 

2.3.2. Impact on industry ..........................................................................................34 

2.3.3. Impact on services and residential .................................................................34 

2.3.4. Impact on agriculture ......................................................................................34 

2.4. Technological options ................................................................................ 34 

2.4.1. Classification of technologies .........................................................................34 

2.4.2. Power generation technologies ......................................................................36 

2.4.3. Energy demand-side technologies .................................................................36 

2.4.4. Learning curves for energy technologies ........................................................38 

2.4.5. Non-CO2 mitigation options ............................................................................39 

2.4.6. LULUCF mitigation options .............................................................................39 

2.5. Key policies modelled in the Reference Scenario ...................................... 40 

2.5.1. Overview of the EU ETS and projections on carbon prices .............................40 

2.5.2. Energy efficiency ............................................................................................42 

2.5.3. Renewable energy policies .............................................................................44 

2.5.4. Transport policies ...........................................................................................46 

2.5.5. Other policies impacting sectors covered by the ESR ....................................47 

2.5.6. LULUCF Regulation .......................................................................................49 

2.5.7. Assumptions on implementation of internal energy market policies ................50 

2.5.8. Updates in infrastructure developments .........................................................50 

2.6. Other important assumptions ..................................................................... 51 

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EU REFERENCE SCENARIO 2020 

2.6.1. Discount rates ................................................................................................51 

3.  Results of the Reference Scenario ............................................................ 54 

3.1. Energy consumption ................................................................................... 54 

3.1.1. Industrial sector ..............................................................................................57 

3.1.2. Residential sector ...........................................................................................64 

3.1.3. Tertiary sector ................................................................................................68 

3.1.4. Transport sector .............................................................................................71 

3.2. Energy supply ............................................................................................ 80 

3.2.1. Power and heat production .............................................................................80 

3.2.2. Steam and heat supply ...................................................................................89 

3.2.3. Primary energy supply ....................................................................................90 

3.2.4. Import dependence ........................................................................................92 

3.3. Energy policy indicators ............................................................................. 95 

3.3.1. RES target......................................................................................................95 

3.3.2. Energy efficiency target ..................................................................................98 

3.4. Electricity prices and costs ......................................................................... 98 

3.5. Greenhouse gas emissions and removals ............................................... 101 

3.5.1. CO2 emissions (excluding LULUCF) ............................................................. 101 

3.5.2. Non-CO2 emissions and their drivers ............................................................ 107 

3.5.3. Total GHG, ETS, ESR emissions (excl. LULUCF) ........................................ 119 

3.5.4. LULUCF emissions and removals and their drivers ...................................... 119 

3.6. Total energy system and other mitigation costs ....................................... 124 

3.6.1. Investment expenditures .............................................................................. 124 

3.6.2. Energy system costs .................................................................................... 125 

3.6.3. Household expenditures ............................................................................... 127 

4.  Conclusion ................................................................................................. 129 

Annex I: Detailed EU policies included in the EU Reference Scenario 2020133 

Energy efficiency policies ....................................................................................... 133 

Power generation and energy markets ................................................................... 135 

Climate policies ...................................................................................................... 136 

Transport policies ................................................................................................... 136 

Infrastructure, innovation, and RTD funding ........................................................... 139 

Environmental policies ........................................................................................... 140 

International policies .............................................................................................. 140 

Implementation of non CO2 policies ....................................................................... 141 

Annex II: Background on macro-economic assumptions ............................ 144 

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EU REFERENCE SCENARIO 2020 

Annex III: Background on techno-economic assumptions .......................... 148 

Annex IV: Assumptions on fossil fuel prices projections ............................ 154 

Annex V: Note on discount rates used in PRIMES ........................................ 159 

Glossary ............................................................................................................ 175 

List of Figures ................................................................................................... 179 

List of Tables .................................................................................................... 181 

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EU REFERENCE SCENARIO 2020 

1. Introduction 

1.1. The  EU  Reference  Scenario  2020:  approach  and 

process 

The  purpose  of this  publication  is  to  present  the "EU  Reference Scenario 2020"  (named 
thereafter "Reference Scenario"), which updates the previous version published in 20161. 

The Reference Scenario projects the impact of macro-economic, fuel price and technology 
trends and policies on the evolution of the EU energy system, on transport, and on their 
greenhouse  gas  (GHG)  emissions.  The  projections  concern  the  27  EU  Member  States 
individually and altogether. The Reference Scenario also includes GHG emission trends not 
related  to  energy.  This  publication  presents  and  discusses  the  projection  results  and 
analyses various interactions among energy system sectors and impact of different policies.  

In essence, the Reference Scenario is an informed, internally consistent, and policy relevant 
projection  on  the  future  developments  of  the  EU  energy  system,  transport  system  and 
greenhouse  gas  GHG  emissions  that  acts  as  a  benchmark  for  new  policy  initiatives.  It 
reflects  policies  and  market  trends  used  by  policymakers  as  baseline  for  the  design  of 
policies that can bridge the gap between where EU energy and climate policy stands today 
and where it aims to be in the medium- and long-term, notably in 2030 and 2050. 

1.1.1. The Reference Scenario approach: projection, not forecast 

The Reference Scenario presents a projection, not a forecast, of the evolution of the EU 
energy  system, transport  system  and GHG  emissions. It  does  not  predict  how  these  will 
look in the future, but provides a model-based simulation of a possible future outlook, given 
the  current  policy  context,  based  on  certain  framework  conditions,  assumptions,  and 
historical trends, notably in the light of the most recent statistical data on energy system 
(Energy Balances), transport and GHG emissions (GHG inventories)2. 

The Reference Scenario builds on EU and Member State policies. The former concerns the 
EU energy, transport, and climate acquis as of end of 20193. Energy policies have recently 
been updated with the “Clean Energy for All Europeans” package (2019)4, to facilitate the 
clean energy transition and to deliver on the EU’s commitments under the UNFCCC. This 
package consists of eight legislative acts setting the EU energy targets for 2030 and paving 
the  way  for  their  achievement.  The  recast  Renewable  Energy  Directive  (RED  II)5  and 
amending  Directive  on  the  Energy  Efficiency6  and  the  Energy  Performance  of  Buildings 
Directive7, as well as the new Electricity Regulation8 and the amending Electricity Directive9 
are central pillars of the package. Furthermore, EU revised policies like the EU ETS10, the 
CO2  standards  for  vehicles11,  the  Regulation  on  F-gas12,  the  legislation  on  waste13,  the 
Directive  on  alternative  fuels  infrastructure  (AFID)14,  the  TEN-T  Regulation15,  the  Fourth 

1 EU Reference Scenario 2016: https://ec.europa.eu/energy/data-analysis/energy-modelling/eu-reference-scenario-2016_en  
2 The Reference Scenario uses the Eurostat Energy Balances of February 2021 and the UNFCCC-CRF submissions of April 
2020, as reported by the EEA. 
3 Including some EU policies considered adopted at the end of 2019 although only published in 2020. 
4 https://ec.europa.eu/energy/topics/energy-strategy/clean-energy-all-europeans_en   
5 Directive (EU) 2018/2001   
6 Directive (EU) 2018/2002  
7 Directive (EU) 2018/844 
8 Regulation (EU) 2019/943 
9 Directive (EU) 2019/944 
10 Directive (EU) 2018/410 
11 Regulation (EU) 2019/631 
12 Regulation (EU) No 517/2014 
13 Directive (EU) 2018/851 
14 Directive 2014/94/EU 
15 Regulation (EU) No 1315/2013 

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EU REFERENCE SCENARIO 2020 

Railway  Package16,  the  Clean  Vehicles  Directive17,  etc.  are  also  considered  in  the 
Reference  Scenario.  Some  of  the  transport  policies  considered  were  part  of  the  three 
Mobility Packages (2017-2018)18. Annex I provides the detail of EU policies considered. 

National policies accounted for in the Reference Scenario include the main ones laid out in 
the  National  Energy  and Climate  Plans  (NECPs)19  as  well  as in  other  national  plans  put 
forward  as  of  end  of  2019,  i.e.,  the  Long-Term  Renovation  Strategies20,  National  Policy 
Frameworks and National Implementation Reports under the Directive of alternative fuels 
infrastructure. In particular, the Reference Scenario assumes achievement of the national 
contributions  towards  the  current21  EU  2030  energy  targets  on  energy  efficiency  and 
renewables (respectively 32.5% and 32%). It thus projects slight overshooting of the current 
EU  2030  renewables  target  and  an  ambition  gap  towards  the  current  EU  2030  energy 
efficiency target, which is consistent with the assessment of the NECPs22. The Reference 
Scenario  is  guided  by  principal  indicators23  of  the  WAM  scenarios  (With  Additional 
Measures – projection) submitted by Member States, or the WEM scenarios (With Existing 
Measures – projection) in the cases where the WAM scenarios were not submitted. Overall, 
the  NECPs  were  accommodated  in  terms  of  projections  and  announced  polices  to  the 
extent possible, while striving to provide a consistent Reference Scenario approach based 
on harmonised assumptions across Member States. 

Regarding GDP and demographics, the statistical data from Eurostat available at the time 
of the modelling (early 2020) has been used; preliminary data from May 2020 was used to 
establish the 2020 projections, with selected updates carried out in November 2020. The 
2021 Ageing Report24  provides the basis for this exercise, depicting long-term population25 
and  GDP  growth  trends,  while  the  short-  and  medium-term  GDP  growth  projections  are 
taken  from  the  Spring  2020  DG  ECFIN  forecast,  which  includes  assumptions  about  the 
impact  of  the  COVID-19  pandemic26.  The  fuel  price  projections  have  been  updated  to 
account  for  recent  developments.  Techno-economic  assumptions  have  been  revised 
following  updated 
large-scale  stakeholder  consultation. 
Assumptions are described in greater detail in Section 2 and Annex II and III. Projections 
are presented from 2020 onwards in 5-year-steps until 2050. 

research  and 

literature 

1.1.2. The Reference Scenario process 

For the preparation of the Reference Scenario a dedicated experts’ group was set up by the 
European Commission, composed of national experts from EU Member States. In addition, 
and  specifically  for  the  work  on  technology  assumptions,  a  workshop  with  over  100 
stakeholders from the industry and relevant non-governmental organisations was organised 
in November 2019 to inform the techno-economic assumptions. Technology assumptions 
were also consulted with national experts from the Member States.  

The first plenary meeting with Member States took place in November 2019 to present the 
Reference  Scenario  process,  the  modelling  set-up  and  the  key  demographic  and  GDP 
assumptions,  as  well  as  key  elements  of  techno-economic  assumptions.  Following  the 
dissemination of a detailed questionnaire on national policies and the collection of written 

16 https://ec.europa.eu/transport/modes/rail/packages/2013_en    
17 Directive (EU) 2019/1161 
18 https://ec.europa.eu/transport/modes/road/news/2018-05-17-europe-on-the-move-3_en;  
https://ec.europa.eu/transport/modes/road/news/2017-11-08-driving-clean-mobility_en; 
https://ec.europa.eu/transport/modes/road/news/2017-05-31-europe-on-the-move_en  
19 https://ec.europa.eu/info/energy-climate-change-environment/implementation-eu-countries/energy-and-climate-
governance-and-reporting/national-energy-and-climate-plans_en  
20 https://ec.europa.eu/energy/topics/energy-efficiency/energy-efficient-buildings/long-term-renovation-strategies_en   
21 “current” as of the EU policy framework in place in May 2021 
22 COM/2020/564 final 
23 e.g., final energy consumption and RES shares, deployment of alternative fuel vehicles 
24 “2021 Ageing Report: Underlying Assumptions and Projection Methodologies. European Economy 11/2020”, Directorate-
General for Economic and Financial Affairs (DG ECFIN)  
25 Aligned on the Eurostat EUROPOP 2019 projection 
26 “European Economic Forecast. Spring 2020. European Economy 5/2020”, Directorate-General for Economic and 
Financial Affairs (DG ECFIN) 

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EU REFERENCE SCENARIO 2020 

replies from the majority of Member States, bilateral meetings were held in the first half of 
2020 to further clarify and substantiate Member State input. As the severity of the COVID-
19 pandemic became evident, an update of GDP assumptions and resulting activity levels 
was  deemed  necessary:  revised  assumptions  were  thus  presented  in  a  second  plenary 
meeting in July 2020. The draft outcomes of the modelling and sectorial activity projections 
were presented in the third plenary meeting in October 2020 and consultation with Member 
Sates begun in order to refine the results. Member States were also consulted on the draft 
transport activity, energy and CO2 emission projections, non-CO2 emissions and LULUCF 
projections.  

The comments received from the Member State experts have been accommodated to the 
extent possible, while striving to provide a consistent Reference Scenario approach based 
on harmonised assumptions. 

1.2. The Reference Scenario modelling suite 

The projections for the Reference Scenario are performed with the help of computational 
models  for  energy  and  GHG  system  analysis.  The  models  use  detailed  and  up-to-date 
databases to produce projections per sector and per country. Calibration ensures continuity 
between historical data and projections. 

The  Reference  Scenario  modelling  suite  is  owned  by  a  consortium  consisting  of  IIASA, 
EuroCare GmbH and E3-Modelling S.A., who leads the consortium.  

The modelling capacity consists of a series of interlinked models, combining technical and 
economic methodologies. Since the last Reference Scenario of 2016, it has been updated 
to support the  preparation  of the  EU’s  2050  long-term  strategy  “A  Clean Planet for  All”27 
(2018)  and  the  “Clean  Energy  for  All  Europeans  Package”  (2019),  as  well  as  the  2030 
Climate Target Plan and the Sustainable and Smart Mobility Strategy tabled in 2020. The 
different  models  have  been  peer-reviewed  and  model  developments,  as  well  as 
assumptions and projection results have been published in scientific journals.  

The models follow an approach which is based on micro-economics, solve a price-driven 
market equilibrium, and integrate engineering and economic representations for all sectors.  

The  energy  system  model  PRIMES,  central  to  the  modelling  suite,  allows  for  mixed-
complementarity and in this way enables the handling of multiple targets via dual variables 
(shadow prices) associated with target constraints. This is useful for analysing emissions 
reduction,  energy  efficiency  and  renewables’  targets  all  at  once.  This  approach  also 
assumes the incorporation of technology dynamics (vintages), which allows to represent in 
detail the technology progress relevant for emission formation and reduction. 

A general characteristic of all models is that the mathematical design is a purposeful and 
simplified representation of aspects of reality. The complexity and degree of sophistication 
is purposeful since a model is designed to answer specific policy analysis purposes. The 
modelling suite used for the Reference Scenario 2020 has sufficient complexity to assess 
both  business-as-usual  and  substantial  transformation  outlooks,  such  as  transition  to 
climate neutrality. It covers in detail several sectors and sub-sectors of energy demand and 
supply, the Europe-wide markets, and the national systems. The numerical projections are 
subject  to  uncertainties  related  to  data  and  assumptions  about  future  evolution  of 
technologies. Although relying largely on official data, as those from Eurostat, the degree 
of segmentation is high and requires additional detailed information with sometimes varying 
degrees of completeness. Despite large consultation about future evolution of technologies, 
the projections are conditional on technology-related development uncertainties. 

27 COM/2018/773 final 

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EU REFERENCE SCENARIO 2020 

1.2.1. Description and role of each model  

The full projection of GHG emissions, as described in the Reference Scenario, is a complex 
interaction  of  models  covering  all  sectors  of  the  economy,  all  emission  sources  and 
abatement options. Projections are from 2020 onwards, while past years (2010 and 2015) 
are calibrated to relevant historical statistics. 

E3-Modelling operates the PRIMES energy system model and its modules, which represent 
the  core  pillar  of  the  modelling  capacity,  delivering  energy,  transport,  and  CO2  emission 
projections. Years of enhancing and further expanding the modelling capacity have turned 
PRIMES into a modelling suite that encompasses a range of (sectoral) models, which can 
operate together or independently of one another. 

The PRIMES modelling suite has been considerably enhanced with PRIMES- BuiMo, a new 
module for buildings’ renovation, and PRIMES-Maritime module, which covers international 
maritime 
transportation.  The  GEM-E3  macroeconomic  model,  also  operated  by 
E3-Modelling,  is  an  advanced  general  equilibrium  model  for  the  entire  economy  used to 
deliver  value-added  projections  by  branch  of  activity  in  the  context  of  the  Reference 
Scenario.  

IIASA  operates  the  GAINS  and  GLOBIOM/G4M  models;  GAINS  provides  non-CO2 
emission projections and air pollution impact assessments, while GLOBIOM/G4M delivers 
projections of LULUCF emissions and removals.  

EuroCARE GmbH Bonn operates the CAPRI model, which is used for agricultural activity 
projections.  

Finally,  the  Joint  Research  Centre  (JRC)  of  the  European  Commission  operates  the 
POLES-JRC model, which is used for international fossil fuel price trajectories. 

All  models  can  be  used as stand-alone sectoral models  or coupled  together  to  deliver  a 
complete description of the GHG emissions from the EU economy and energy system. The 
interactions between the various models in the preparation of the Reference Scenario are 
summarised in Figure 1.  

Figure 1: Modelling suite for the EU Reference Scenario 2020  

The linking of the models started in 2007  with an FP7 project28 and has been enhanced 
since, through years of collaboration between the modelling teams. The formal linking of 
the models was established with the creation of standardised data exchanging routines; this 
allows  the  modelling  framework  to  use  common  underlying  framework  conditions  (e.g. 
evolution of population, GDP, Value Added per sector, transport activity, fossil fuel prices) 
and exchange critical scenario-specific information, such as energy balances, power mix, 

28 http://www.ec4macs.eu/   

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EU REFERENCE SCENARIO 2020 

investment,  prices,  and  technology  costs,  in  order  to  maximise  consistency  across  the 
modelling tools and their results.  

A  brief  description  of  the  role  and  functioning  of  each  model  is  provided  in the  following 
sections. 

POLES-JRC 

The POLES-JRC model is used to provide the global energy and climate policy context29. 

POLES-JRC  is  a  global  energy  model  that  covers  the  entire  energy  balance,  from  final 
energy demand, transformation and power production to primary supply and trade of energy 
commodities  across  countries  and  regions.  It  allows  assessing  the  contribution  to  future 
energy  needs  of  the  various  energy  types  (fossil fuels,  nuclear,  renewables)  and energy 
vectors. 

In addition, it calculates the evolution of all GHG emissions: endogenously for the energy-
industry sectors and through linkage with specialist models for GHG emissions from land-
use and agriculture (global version of GLOBIOM-G4M), and air pollution (global version of 
GAINS). 

The  model  includes  a  detailed  geographical  representation,  with  a  total  of  39  non-EU 
regions and countries covering the world; it includes all G20 countries, detailed OECD, and 
the  main  non-OECD  economies.  It  operates  on  a  yearly  time  step,  allowing  integrating 
recent developments. 

The POLES-JRC model is well suited to evaluate the evolution of energy demand in the 
main world economies and international markets as well as to assess international climate 
and  energy  policies.  The  POLES-JRC  model  has  participated  in  numerous  research 
projects and has contributed to peer-reviewed analyses published widely30. 

For the Reference Scenario POLES-JRC provides fossil fuel price trajectories used as EU 
import price assumptions in PRIMES and GEM-E3. 

GEM-E3 

GEM-E3, operated by E3-Modelling, is a global (46 countries/regions), multi-sectorial (67 
economic  activities)  Computable  General  Equilibrium  (CGE)  model,  calibrated  to  a  wide 
range  of  datasets  (incl.  GTAP,  EUORSTAT,  IEA).  The  model  provides  the  country-level 
macro-economic projections to the entire suite of models used in the Reference Scenario 
and uses the energy system-related results from the other models (energy, transportation, 
agriculture, biomass, air quality, climate effects, etc.) to perform macro-economic and social 
impact analysis. The model closes the loop between sectorial and economy-wide analysis 
for emissions, emission reductions and costs, and can calculate the associated economic, 
employment  and  social  implications.  The  model  is  a  state-of-the-art,  large-scale  applied 
CGE model, featuring a number of innovations such as the explicit representation of the 
financial  sector,  the  semi-endogenous  dynamics  of  technical  progress  induced  by  R&D, 
knowledge  spill-overs,  the  representation  of  multiple  households,  unemployment  in  the 
labour market and endogenous formation of labour skills. Details on the model can also be 
found in Annex II. 

The  purpose  of  the  GEM-E3  within  the  Reference  Scenario  is  to  provide  to  the  energy 
system models consistent sectorial value-added and consumption projections. The sectorial 
projections match the aggregate GDP and population projections by country, provided by 
the  2021  DG  ECFIN’s  “2021  Ageing  Report”  and  the  Eurostat  Europop  demographic 
assumptions. 

29 https://ec.europa.eu/jrc/en/poles 
30 https://ec.europa.eu/jrc/en/poles/publications 

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PRIMES Energy system model  

PRIMES Energy system model, is a large-scale applied energy system model that provides 
detailed projections of energy demand, supply, prices, and investment, covering the entire 
energy system, including emissions from energy combustion and industrial processes. The 
distinctive  feature  of  PRIMES  is  the  combination  of  behavioural  modelling,  following  a 
micro-economic  foundation,  with  engineering  aspects,  and  the  coverage  of  all  energy 
sectors and markets. The model represents in detail policy instruments related to energy 
markets and climate, including market drivers, standards, and targets by sector and for the 
whole energy system. PRIMES handles multiple policy objectives, such as GHG emissions 
reductions, energy efficiency, and renewables targets, provides pan-European simulation 
of internal markets for electricity and gaseous fuels (including hydrogen) and simulates fully 
the  EU  Emission  Trading  System  (ETS)  in  its  current  form.  PRIMES  uses  as  inputs 
macroeconomic and multi-sectorial projections from GEM-E3, projections of world energy 
prices  from  POLES-JRC  and  conveys  projections  to  GAINS,  GEM-E3,  CAPRI  and 
GLOBIOM models. 

Within the Reference Scenario PRIMES provides the energy system projection for demand 
and supply side sectors including full energy balance, investment costs, prices, and related 
CO2  emissions  (energy  and  process)  per  country.  Moreover,  it  calculates  total  GHG 
emissions using inputs of other models on non-CO2 GHG emissions (GAINS). 

PRIMES-Industry 

PRIMES  Industry  is  very  detailed,  covering  10  industrial  sectors,  i.e.,  iron  and  steel 
(integrated  steelworks,  electric  arc);  non-ferrous  metals  (primary  aluminium,  secondary 
aluminium,  copper,  zinc,  lead,  other  nonferrous);  chemicals  (fertilizers,  petrochemical, 
inorganic  chemicals,  low  energy  chemicals);  building  materials  (cement,  ceramics,  etc.); 
paper,  pulp  and  publishing;  food,  drink  and  tobacco;  engineering  goods;  textiles;  other 
industrial sectors; and non-energy sectors. These 10 industrial sectors are further split in 
31  sub-sectors  and  a  total  of  234  energy  uses.  22  different  fuels,  including  “new”  fuel 
carriers,  i.e.,  hydrogen,  biofuels,  and  process  emissions  are  modelled;  hydrogen  and 
synthetic fuels can be used directly as an energy source and as a feedstock. The model 
also provides extended possibilities of electrification and includes the possibility to use non-
fossil hydrocarbon feedstock in all relevant sectors (e.g., iron and steel, chemicals, etc.). 
The  penetration  of  new  technologies,  energy  savings,  electrification  and  the  use  of 
alternative fuels are endogenous and dynamic depending on technology progress, prices, 
standards, and policy targets. Perceived costs, uncertainty and risk factors influence costing 
and decisions, and can vary by scenario. 

The model solves for each sector. First, it models demand for useful energy forms with a 
split  into  various industrial  processes. The  demand  model  links  processes  to  exogenous 
macroeconomic  activity  by  sector,  organises  the  processes  into  flows  and  formulates 
substitutions between alternative processes (e.g., electrical vs thermal processing) where 
applicable.  Second,  it  models  energy  production  from  various  types  of  equipment  and 
technologies in the industry, which purchase fuels from the markets in order to operate and 
may, in turn, sell excess fuels to the markets. The model determines the energy production 
system  intertemporally  with  simultaneous  consideration  of  heat  recovery  and  horizontal 
energy  efficiency  investment.  Substitution  possibilities,  perfect  or  imperfect,  as  well  as 
complementarities play an important role in the modelling of the correspondence between 
technologies and processing types. A nested logit model is used for this purpose. 

PRIMES Power and steam/heat 

The  PRIMES  Electricity  and  heat/steam  supply  and  market  model  is  a  fully  new  model 
version, which includes: (i) the hourly unit commitment model, with a pan-European market 
simulation  over  the  grid  constraints  and  detailed  technical  operation  restrictions;  (ii)  the 
long-term power system expansion model; (iii) the costing and pricing electricity and grid 
model;  (iv)  the  integration  of  heat  supply  and  industrial  steam  supply  with  synchronised 

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hourly operation. The PRIMES power and steam/heat supply model is based on a database 
of over 13,000 power plants in Europe; it simulates the simultaneous production of electricity 
and steam/heat production to meet the demand coming from the stationary demand and 
mobility sectors.  

The  PRIMES  power  and  steam/heat  module  also  calculates  the  flexibility  needs  of  the 
power system and the storage requirements (battery or chemical, as well as hydro); it is 
linked to the production of hydrogen and other new fuels in order to fulfil the demand also 
of  the  emerging  needs  from  the  energy  system  (still  limited  in  a  Reference  Scenario 
context).  

The model also includes simulation of the steam/heat requirements from industry and from 
other  stationary  demand.  The  demand  can  be  met  through  boilers  (industrial,  refinery, 
district heating) or through Combined Heat- and Power (CHP) plants: these are either utility 
or industrial plants. Industrial plants are further split into onsite plants and grid connected 
plants.  

PRIMES BuiMo 

PRIMES  BuiMo  model  is  a  new  module  of  the  PRIMES  model  which  projects  energy 
consumption, rates and depths of buildings’ envelope renovation, choice and replacement 
of  equipment,  fuel  mix  and  CO2  emissions  for  residential  and  service  buildings  in  each 
Member State. The model calculates costs, investment, and operating expenses, as well 
as  implications  on  affordability  of  energy  and  access  to  efficient  technologies.  The  key 
innovative  aspect  of  PRIMES  BuiMo  is  that  it  brings  together  engineering  aspects  and 
technical  constraints  with  a  very  detailed  representation  of  subjective  and  behavioural 
factors. In this way, the model captures the non-market barriers, which influence to a large 
extent the effectiveness of energy efficiency policies. Moreover, the model disaggregates 
the building stock in many categories (270 building classes), representing different building 
types,  geographic  locations,  ages  of  construction,  income  classes,  and  services  sub-
sectors.  While  doing  so,  the  model  represents  heterogeneous  decision-makers  with 
different  characteristics  regarding  their  preferences  for  each  category  of  buildings,  thus 
addressing the drawbacks of the representative consumer hypothesis. 

PRIMES-TREMOVE  

PRIMES-TREMOVE Transport model projects the evolution of demand for passengers and 
freight  transport  by  transport  mode,  and  transport  vehicle/technology,  following  a 
formulation  that  is  based  on  microeconomic  foundation  of  decisions  of  multiple  actors. 
Operation,  investment  and  emission  costs,  various  policy  measures,  utility  factors  and 
congestion are among the assumptions the model uses to project transport activity, fleet 
development, new technologies and alternative fuels, energy consumption and emissions 
(and other externalities). PRIMES-TREMOVE is based on and extends the features of the 
open source TREMOVE model, developed by the TREMOVE31 modelling community.  

PRIMES-TREMOVE depicts the links with the recharging and refuelling infrastructure and 
the heterogeneity of stylised trips. The model links trip distance heterogeneity with vehicle 
choice behaviour, which determines the choice of vehicles and fuels with range limitations 
and  to  some  extent  of  alternative  fuel  vehicles  such  as  FCEVs  (hydrogen  vehicles)  and 
LNG. 

PRIMES-TREMOVE also includes an econometric module for transport activity projections. 
It takes GEM-E3 projections (GDP, activity by sector, demographics, and bilateral trade by 
product and country) as drivers to produce transport activity projections, which are then fed 
into the model as input values for the initial activity projections in the Reference Scenario 

31 http://www.tmleuven.be/methode/tremove/home.htm  

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exercise. The econometric exercise also includes fuel prices coming from POLES-JRC, as 
well as transport network infrastructure (length of motorways and railways), as drivers. 

PRIMES-Maritime 

The PRIMES-Maritime module is a new development within the PRIMES modelling suite, 
which aims at better representing the maritime sector in the energy-economy-environment 
modelling nexus. The module addresses the maritime sector and can run in stand-alone 
and linked mode with PRIMES and PRIMES-TREMOVE.  

The model covers the European intra-EU and extra-EU maritime shipping, both freight and 
passenger  sectors.  Trade  activity  between  non-EU  countries  is  outside  the  scope  of  the 
model. Instead, the model considers the transactions (bilateral trade by product type) of the 
EU-Member  States  with  non-EU  countries  and  aggregates  these  countries  in  regions. 
Several types and sizes of vessels are considered. 

PRIMES Maritime solves a virtual market equilibrium problem, where demand and supply 
interact dynamically in each consecutive time period, influenced by a variety of exogenous 
policy  variables,  notably  fuel  standards,  pricing  signals  (e.g.,  EU  ETS),  efficiency 
regulations  and  more.  The  model  projects  the  volume  of  trade  (in  tons)  and  maritime 
transport activity (in tkm) disaggregated by Member State, by cargo type and by geographic 
region. Also, the model projects energy consumption by fuel type and cargo type, CO2 and 
other pollutant emissions, costs, such as fuel costs, and investment expenditures in new 
vessels.  The  model  covers  a  variety  of  fuels  such  as  fossil  fuels,  biofuels  (bioheavy32, 
biodiesel, bio-LNG), synthetic fuels (synthetic diesel, fuel oil and gas, e-ammonia, and e-
methanol)  produced  from  renewable  electricity,  hydrogen  produced  from  renewable 
electricity (for direct use and for use in fuel cell vessels) and electricity for electric vessels. 

Well-To-Wake  emissions  are  calculated  thanks  to  the  linkage  with  the  PRIMES  energy 
systems model, which derives ways of producing such fuels. Environmental regulation, fuel 
blending mandates, GHG emission reduction targets, pricing signals and policies increasing 
the availability of fuel supply and supporting the alternative fuel infrastructure are identified 
as drivers, along fuel costs, for the penetration of new fuels. As the model is dynamic and 
handles vessel vintages, capital turnover is explicit in the model influencing the pace of fuel 
and vessel substitution. 

CAPRI 

CAPRI is a multi-country agricultural sector model that supports decision-making regarding 
the  Common  Agricultural  Policy  and  environmental  policy.  For  this  reason,  it  offers 
considerable  technical  and  policy  detail  on  all  EU  countries  and  most  neighbouring 
countries with a more aggregate representation of other world regions and countries. The 
model  takes  inputs  from  GEM-E3,  PRIMES  and  PRIMES-Biomass  models,  provides 
outputs  to  GAINS,  and  exchanges  information  with  GLOBIOM  on  livestock,  crops,  and 
forestry  as  well  as  LULUCF  effects.  For  the  Reference  Scenario  CAPRI  provides  the 
agricultural  outlook,  in  particular  on  livestock  and  fertilisers  use.  Further,  it  projects  how 
changes in biofuel demand may affect the agricultural sector. Cross-checks are undertaken 
ex-ante and ex-post to ensure consistency with GLOBIOM on overlapping variables, for the 
crop sector in particular. 

GAINS 

The  GAINS  model  delivers  projections  of  air  pollution  and  non-CO2  GHG  emissions 
abatement  strategies  and  associated  costs.  GAINS  explores  trade-offs  and  synergies 
between GHG emission reductions and air pollution. Moreover, the model evaluates and 
projects  atmospheric  dispersion,  air  quality  impacts,  health  impacts  and  impacts  on 
ecosystems.  The  model  takes  energy  sector  activity  projections  as  input  from  PRIMES, 

32 Bioheavy refers to bio heavy fuel oil.  

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PRIMES-TREMOVE, and agricultural sector activity projections as input from CAPRI, and 
produces  own  activity  projections  for  the  waste  and  F-gas  sectors  in  consistency  with 
population  and  macroeconomic  drivers  from  GEM-E3.  To  further  ensure  internal 
consistency among the models, GAINS exchanges data with the PRIMES model on supply 
limits for organic waste available to the energy sector and aligns the uptake of technology 
for biogas generation from manure co-digestion with PRIMES biofuel projections.  

GLOBIOM / G4M 

The global economic land-use simulation model GLOBIOM and the detailed forest sector 
model G4M  are  operated  by  IIASA  and commonly  applied  in an  iterative manner for  the 
estimation of LULUCF emission pathways. For the EU, GLOBIOM-G4M receives input data 
from  the  GEM-E3,  PRIMES-biomass  and  CAPRI  models,  while  the  POLES-JRC  model 
provides  bioenergy  demand  projections  for  regions  outside  of  the  EU.  For  the  EU 
agricultural  sector  GLOBIOM  is  aligned  with  the  CAPRI  model  to  ensure  consistency  in 
Reference Scenario projections.  

Within  the  Reference  Scenario  process  GLOBIOM-G4M  provides  the  outlook  for  the 
LULUCF sector which includes changes in land use, forest management and related GHG 
emissions.  GLOBIOM  mainly  models  CO2  emissions  or  sequestrations  from  soil  and 
biomass on cropland and grassland. G4M estimates the emissions from the forest (forest 
management,  afforestation,  deforestation).  The  model  receives  important  inputs  from 
GEM-E3, PRIMES and CAPRI models, as well as POLES-JRC, which provides bioenergy 
demand projections for the global analysis. 

1.2.2. Main methodological improvements since 2016 

The PRIMES model version used for the Reference Scenario 2020 builds on a number of 
developments  to  improve  the  representation  of  sectors  and  fuels  (and  their  interaction), 
particularly in light of the transition towards carbon neutrality, as analysed in the in-depth 
analysis in support of the “EU Clean Planet for All” Communication33.  

PRIMES-BuiMo 

As previously explained in the sections above, an entirely new buildings model has been 
developed and used for the first time in 2018 for the preparation of the EU’s 2050 Long-
Term  Strategy.  This  was  a  milestone  in  the  enhancement  of  the  PRIMES  modelling 
capacity34. 

In order to project the energy efficiency and fuel mix in the residential and services sectors, 
the  PRIMES  BuiMo  model  assesses  the  cost-effectiveness  and  impacts  of  policy  and 
regulatory  measures.  It  covers  market  and  non-market  barriers,  hidden  costs  and 
perceptions  affecting  consumer  behaviour,  and  models  a  variety  of  policy  instruments  in 
order  to  influence  decisions  and  possibly  remove  barriers.  All  this,  while  respecting 
engineering constraints and specificities, and tapping possibilities for transformation. The 
model calculates costs, investment, and operating expenses and reveals the implications 
for energy affordability and access to efficient technologies.  

PRIMES BuiMo runs for residential and service buildings independently and covers every 
Member State individually. The model splits the stock of buildings in many categories aside 
from  house/building  types,  namely:  geographic  locations,  age  of  construction,  income 
classes and service sector sub-sectors. Income classes help simulate the heterogeneity of 
actors  and  their  idiosyncratic  behaviour.  Instead  of  a  single  actor,  the  model  includes  a 
variety  of  actors  with  distinct  behavioural  patterns.  On  top,  distinct  discount  rates  apply, 
where every income class has their specific discount rate; the highest income class has the 

33 COM (2018) 773 
34 Fotiou, T.; De Vita, A.; Capros, P. (2019): Economic-Engineering Modelling of the BUildings Sector to Study the Transition 
towards Deep Decarbonisation in the EU, Energies, Issue 12. https://doi.org/10.3390/en12142745 

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lowest  discount  rate,  and  the  lowest  income  class  has  the  highest  discount  rate, 
representing  the  difficulty  for  such  users  to  apply  for  financing.  This  allows  to  produce 
decisions that capture the heterogeneity of consumers in each class, and so to address the 
drawbacks of the representative consumer assumption. 

The model is also based on the concept of dynamic discrete choice, where, out of a finite 
set  of  dynamic  strategies,  heterogeneous  agents  choose  the  most  cost-efficient  ones.  A 
dynamic strategy may involve renovation of the envelope, equipment selection, premature 
replacement of equipment, and fuel switching. 

The model uses a very large database for buildings, equipment, and electric appliances. 
The database is constructed by compiling information from many sources. This is necessary 
as it is often the case that sources do not include all data required. The data is also fully 
checked for their compatibility with the overall energy system data to avoid over- or under-
estimations. If there is lack of data for certain elements these data have been approximated 
based on expert knowledge and calibration/modelling results. To fill the entire matrix in all 
its elements, the entropy method is used to split the data based on the available entries. 

Residential sector 

For  the  residential  sector  PRIMES  BuiMo  includes  a  buildings  database,  heating,  and 
cooling  equipment,  as  well  as  electric  appliances.  The  buildings  database  includes  54 
building types for each Member State, which are split into single or multi-storey buildings; 
by  age  of  construction  (9  age  bands  covering  the  period  1920-2015);  and  by  spatial 
allocation, i.e., urban, semi-urban and rural. For the 54 building types, energy consumption 
is  calculated  based  on  country  characteristics,  which  include  size  of  dwellings,  heating 
degree days and thermostat settings, differentiated by country and income class. 

The model includes 28 different types of heating and cooling equipment for space heating, 
water heating and food preparation. Each of these is further split into four categories ranging 
from currently available technology to Best Not Available Technology (BNAT). On top, the 
model  includes  11  categories  of  electric  appliances:  refrigeration,  freezing,  dishwashers, 
washing machines, dryers, lighting, information and communication, entertainment, vacuum 
cleaners, ironing, and small appliances. 

Each technology – heating and cooling equipment and electric appliances for each maturity 
category – is characterised by efficiency, technical lifetime, investment cost, operation and 
maintenance cost and economic lifetime, mimicking their techno-economic characteristics. 
Modelling  a high  number  of technologies makes  it  possible to  reflect  in a more accurate 
manner  the  Ecodesign  Directive  (2009/125/EC)  and  its  implementing  regulations.  The 
techno-economic characteristics are derived from various literature sources including the 
preparatory studies for the Ecodesign Directive. 

Services sector 

The services sector has been divided into the following sub-sectors: 

  Trade   

  Commercial Buildings 

  Warehouses 

  Cold Storages 

  Market Services 

  Private offices and other buildings in market services 

  Hotels and Restaurants 

  Non-Market Services   

  Public Offices 

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EU REFERENCE SCENARIO 2020 

  Hospitals and Health Institutions 

  Schools an Educational Buildings 

Different characteristics apply for each sub-sector with regard to the condition of the building 
stock  (age,  U-values),  internal  temperature  set-points,  ventilation  demands;  further  the 
entire building stock has been divided by ventilation type into mechanically- and naturally- 
ventilated.  For each sub-sector, the following energy uses are defined: space heating and 
cooling, water heating, cooking lighting, IT services, and steam uses. The different sectors 
are characterised by different energy usage patterns: for instance, office buildings have high 
cooling loads for air conditioning and electricity consumption for lighting and IT appliances; 
hotels have high hot water consumption while restaurants consume energy for cooking. As 
in the residential sector, each energy use can be satisfied by different equipment or electric 
appliances.  The  latter  are  split  into  technology  categories  based  on  the  maturity  of  the 
technology  ranging  from  current  technology  to  BNAT.  Again,  the  techno-economic 
characteristics  of  the  technologies  have  been  derived  from  the  Ecodesign  preparatory 
studies, as well as the HealthVent Project.  

PRIMES-Industry 

In light of projecting the functioning of a decarbonised energy system, the PRIMES Industry 
model  has  been  enhanced  to include  a  high-resolution split  of  industrial consumption  by 
sector (31 subsectors) and type of industrial process (234 different energy uses). The split 
of  the  energy  uses  allows  to  estimate  at  a  high  level  of  detail  the  possible  fuel  and 
technology  substitutions  linked  with  varying  mitigation  potentials.  Further,  the  split  of  the 
sectors allows to assess which processes can be electrified and which require other fuels 
or technological solutions. The projections for process emissions are now fully linked to the 
fuel  consumption  of the relevant  sectors,  which allows  to  assess  in  an  accurate  way  the 
interplay between changes in fuel consumption and the evolution of emissions (e.g., switch 
to hydrogen in iron production).  

The industrial energy demand module has been extended to include direct uses of hydrogen 
in  high-temperature  applications  (e.g.,  iron  and  steel  for  direct  reduction  of  iron  ore),  in 
furnaces,  and  in  the  chemical  industry  as  a  fuel  and  as  a  feedstock  to  synthesise 
petrochemicals  together  with  captured  CO2,  high  temperature  heat  pumps,  steam  and 
vapour recompression and other advanced electrification technologies.  

In  addition,  circular  economy  elements  can  be  mimicked  in  the  modelling  through 
assumptions on recycling opportunities and/or reduction of primary material requirement. In 
that regard, the model captures waste heat that can be used for improving the efficiency of 
horizontal industrial processes. 

PRIMES Power and Steam/Heat  

An entirely new model version has been developed and used for this Reference Scenario, 
which  extends  the  existing  long-term  power  system  capacity  expansion  model  to 
encompass the hourly unit commitment model, with a pan-European market simulation over 
the  grid  constraints  and  detailed  technical  operation  restrictions.  The  model  solves  the 
interconnected system of all European countries simultaneously and captures the sharing 
of balancing resources. The inclusion of the detailed unit commitment model, alongside the 
existing  capacity  expansion,  allows  to  assess  the  flexibility  and  storage  requirements  of 
systems  with  high  shares  of  renewable  energy  sources  (RES)  at  all-time  scales,  while 
factoring in the inherent flexibility and characteristics of existing power plants and the use 

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EU REFERENCE SCENARIO 2020 

of  interconnectors  across  Europe.  The  unit  commitment  model  rests  on  PRIMES  IEM 
module, which models the Internal Electricity Market in the EU and beyond35,36. 

Another feature of the new model version is the representation of the chemical storage of 
electricity as well as the synchronous operation of power generation, load, RES, and energy 
storage, including chemical storage inputs and the charging and discharging of the various 
storage systems.  

Investment in storage is endogenous and depends on the costs of storage technologies, 
the  prices  of  the  storage  inputs  and  the  marginal  costs  of  the  power  systems;  the  latter 
further  depends  on the RES  availability  and the  demand  by  end-users for  hydrogen  and 
synthetic  fuels.  This  way,  the  production  of  hydrogen  and  synthetic  fuels  takes  place  at 
times when renewables are abundant. In fact, the production of e-fuels offers high reliability 
and flexibility to the electrical system, because it allows for the indirect chemical storage of 
electricity.  In  this  way,  e-fuels  maximise  the  use  of  RES  in  times  of  RES  excess  when 
produced,  and  make  up  for  the  electricity  needed  when  RES  are  not  available.  This 
synergistic  interaction  helps  reduce  power  and  load  fluctuations  and  improve  system 
reliability and flexibility. 

Representation of renewable energy categories and renewable energy potential 

The representation of RES in the power sector has also been updated and improved in the 
current Reference Scenario. In order to better capture the diversity and advantages, as well 
as  disadvantages,  of  some  RES 
the 
categorisation of solar PV, wind onshore and wind offshore has been modified.  

improve  the  projection, 

technologies,  and 

The categorisation is used for the representation of the technologies as well as for the split 
of  the  potential;  the  potential  is  categorised  by  e.g.,  shallow  or  deep  for  offshore  or 
placement  area  for  solar  (residential,  commercial,  etc.)  and  the  technology  type  is 
categorised based on e.g., hub height. The new technological split is shown in Table 1. 

Table 1: New categorisation of RES technologies in PRIMES 

Wind onshore 

Wind onshore - low 
resource area, high 
hub height 

Wind onshore - 
medium resource 
area, medium height 

Wind onshore - high 
resource area, 
medium height 

Wind onshore - very 
high resource area, 
low hub height 

Wind offshore 

Wind offshore Power, 
Shallow, Near-shore, 
Low 
Wind offshore Power, 
Deep, Near-shore, 
Low 

Wind offshore Power, 
Shallow, Near-shore, 
High 
Wind offshore Power, 
Deep, Near-shore, 
High 

Wind offshore Power, 
Shallow, Far-shore, 
Low 

Wind offshore Power, 
Shallow, Far-shore, 
High 

Wind offshore Power, 
Deep, Far-shore, Low 

Wind offshore Power, 
Deep, Far-shore, High 

Solar PV 

Solar PV Residential 
Low 
Solar PV Commercial 
Low 

Solar PV Utility Low 

Solar PV Residential 
Medium 
Solar PV Commercial 
Medium 
Solar PV Utility 
Medium 

Solar PV Residential 
High 
Solar PV Commercial 
High 

Solar PV Utility High 

Solar PV Residential 
Very High 
Solar PV Commercial 
Very High 
Solar PV Utility Very 
High 

35 Kannavou, M., Capros, P.(2019): Model-based assessment of removing distortions in the European electricity market in 
view of the EU 2030 targets, International Conference on the European Energy Market (EEM16), 
10.1109/EEM.2019.8916518
36 Capros, P.; Tasios, N.; Kannavou, M.; et al.  (2017): Modelling study contributing to the Impact Assessment of the 
European Commission of the Electricity Market Design Initiative, E3Mlab

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Moreover, lifetimes of equipment have been raised to 30 years in most cases and capacity 
factors  are  fully  Member-State  specific,  following  information  gathered  from  the  ENergy 
Systems Potential Renewable Energy Sources (ENSPRESO) database of the JRC37 and 
other databases. 

The potential for RES development has been updated in the newest version of PRIMES 
using the information available in the ENSPRESO database. It is now adapted on a country 
by country level. The load and operation hours of RES power plants have been also adapted 
at country level based on historical data. 

In  the  PRIMES  unit  commitment  model,  load  curves  are  sector  specific.  This  makes  it 
possible to simulate the specificities of the load curves and improve the representation of 
the  aggregate  needs  that  the  electricity  and  steam/heat  systems  have  to  cover.  This  is 
particularly relevant for simulating the changes in the load curves deriving from the higher 
penetration of heat pumps in stationary demand or battery electric vehicles in the transport 
sector. In essence, the model is now better equipped to simulate the transformation towards 
carbon-neutral energy system.38  

PRIMES-TREMOVE 

The  PRIMES-TREMOVE  model  has  undergone  important  enhancements  since  the 
previous Reference Scenario. The representation of CO2 standards has been introduced 
also  for  the  heavy-duty  vehicles39,  while  preparing  the  Impact  Assessment40  that 
accompanied  the  European  Commission’s  proposal  for  the  Regulation  on  CO2  emission 
standards for heavy-duty vehicles41.  

Furthermore, the list of available fuels has been expanded. Gasoline and diesel are now 
split in fossil, biofuels, and synthetic fuels; in aviation the split includes fossil, biofuels, and 
synthetic fuels. Several technological pathways are represented for biofuels, in particular 
when  linked  with  PRIMES  biomass.  The  choice  of  fuels  is  endogenous  in  the  model, 
determined by the increase in fuel costs that is derived from the PRIMES and the PRIMES 
Biomass model.  

Moreover, the representation of policy instruments has been refined, particularly for CO2 
standards  for  cars,  vans  and  trucks  as  well  as  alternative  standards,  namely  efficiency 
performance standards. The model has been updated to incorporate the switch from the 
new  European  driving  cycle  (NEDC)  to  the  worldwide  harmonised  light  vehicles  test 
procedure (WLTP) regulatory testing cycles for passenger cars and vans. 

A  higher  resolution  for  new  fuels  (e.g.,  synthetic  fuels  in  road  freight,  hydrogen  in  rail 
transport) has been represented in the model. 

The representation of the aviation sector has been also improved significantly. The aviation 
sub-model has been designed to simulate changes in travel demand between an Origin and 
a  Destination  (O-D),  which  are  induced  by  changes  in  the  cost  of  fuels.  Taxation  (e.g., 
excise duty or ticket tax) or blending mandates for sustainable aviation fuels are factors that 
influence  the  cost  of  fuels.  Changes  in  travel  demand  would  also  affect  the  use  of  the 
existing  fleet  and  consequently  the  consumption  of  energy  and  emissions.  It  would  also 
affect investment trends for purchasing new aircrafts. According to the model, a change in 
fuel costs would drive an adjustment of airfares. Airfares comprise of fuel costs, capital costs 
(related to purchasing of the aircraft), and other operation costs. Fuel costs and taxes are 
assumed to be passed through to the consumers. The changes in airfares induce changes 

37 https://publications.jrc.ec.europa.eu/repository/handle/JRC116900     
38 These features while moderately important in the Reference Scenario, become important in scenarios featuring deeper 
emission reductions and therefore greater system transformation.
39 Siskos, P.; Moysoglou, Y. (2019): Assessing the impacts of setting CO2 emission targets on truck manufacturers: A model 
implementation and application for the EU, Transportation Research Part A: Policy and Practice vol. 125, pg. 123-138, 
https://doi.org/10.1016/j.tra.2019.05.010  
40 SWD (2018) 185 final 
41 Regulation (EU) 2019/1242  

20 

 
                                                 
 
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in  travel  demand  via  two  channels:  (i)  the  income  effect,  i.e.,  reduced  travel  in  case  of 
increased  airfares;  and  (ii)  the  substitution  effect,  i.e.,  the  traveller  opts  for  some  other 
transport mode, where available. Non-air transport modes that could compete with aviation 
are  high-speed  railways  and,  to  a  lesser  extent,  cars  (for  short  distances).  The  model 
captures  a  variety  of  choices of  aircraft  technologies through  a non-linear  cost-efficiency 
curve, denoting the frontier of technology possibilities. The fuel consumption of an aircraft 
depends on the length of the trip; the length determines different ratios between the landing 
and take-off (LTO) and cruise phases of the aircraft trip. Typically, as the length of the trip 
decreases, the specific fuel consumption (measured in kg/flight-km) of an aircraft increases 
due to the high fuel consumption associated with the LTO phase. 

PRIMES-Maritime  

PRIMES-Maritime  features  a  modular  approach  based  on  the  demand  and  the  supply 
modules. The demand module projects maritime activity for each EU MS by type of cargo 
and corresponding partner. Econometric functions correlate demand for maritime transport 
services with economic indicators considered as demand drivers, including GDP, trade of 
energy  commodities (oil,  coal,  LNG), trade  of  non-energy  commodities,  international fuel 
prices, etc. The supply module simulates a representative operator controlling the EU fleet, 
who offers the requested maritime transport services. The operator of the fleet decides the 
allocation of the vessels’ activity to the various markets (representing the different EU MS) 
where  different  regulatory  regimes  may  apply  (e.g.,  environmental  zones).  The  fleet  of 
vessels disaggregated into several categories is specific to cargo types. PRIMES-Maritime 
utilises a stock-flow relationship to simulate the evolution of the fleet of vessels throughout 
the projection period and the purchasing of new vessels. 

PRIMES-Sectoral integration 

With the need to assess carbon neutrality scenarios alongside other policy scenarios, it was 
necessary to extend the PRIMES model to include more storage options and new synthetic 
fuels such as hydrogen and its derivatives42. In this context, PRIMES has been expanded 
to model the production of synthetic fuels and storage technologies43. The following new or 
synthetic fuels are now addressed by the model: 

-  Hydrogen:  whereas  previously  applied  only  in  transport,  the  new  model  version 
considers the use of hydrogen in all supply and demand sectors and for blending in 
the  natural  gas  grid.  Green  hydrogen  is  produced  via  electrolysis  that  uses 
renewable electricity;  it  can  serve  as  energy carrier  (either  combusted  or  used in 
fuel  cells  in  stationary  or  mobile  applications),  as  feedstock  to  produce  synthetic 
fuels and/or as storage to balance the generation of variable renewables. Hydrogen 
can be transferred via dedicated pipelines or blended in the natural gas stream up 
to a certain share (15%). 

-  E-gas:  also  referred  to  as  synthetic  methane  or  clean  gas,  is  an  output  of 
methanation  process,  which  uses  hydrogen  and  carbon  dioxide  as  inputs  and 
requires  significant  amounts  of  electricity.  E-gas  has  a  lower  net  carbon  intensity 
compared  to  natural  gas  or  can  be  even  considered  carbon  free  if  the  carbon 
molecule  is  assumed  to  be  sourced  from  biogenic  sources  or  from  Direct  Air 
Capture. 

-  E-liquids: usually referred to as Power-2-Liquids (P2L, PtL), these are liquid fuels 
for use in the transport sector, mainly maritime and aviation, and less so in long-
distance  road  freight  transportation.  They  can  fully  substitute  petroleum-based 

42 Evangelopoulou, S.; De Vita, A.; Zazias, G.; Capros, P. (2019): Energy System Modelling of Carbon-Neutral Hydrogen as 
an Enabler of Sectoral Integration within a Decarbonization Pathway, Energies, Issue 12, 
https://doi.org/10.3390/en12132551 
43 Evangelopoulou, S.; Kannavou, M.; G., Zazias; P., Capros (2019): Model-Based Assessment of Electricity Storage in a 
European System Producing Hydrogen and Hydrocarbons from Renewable Energy, 16th International Conference on the 
European Energy Market (EEM16), vol. 2019, 10.1109/EEM.2019.8916518 

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products in mobile applications with no radical changes in ICE powertrains. Their 
competition  with  electrification  is  assessed  by  the  enhanced  PRIMES  modelling 
suite.  Still,  e-liquids  would  probably  find  more  room  for  development  in  transport 
modes  where  decarbonisation  options  are  limited  and  where  they  must  compete 
only  with  advanced  biofuels  and/or  technologies  with  low  TRL  (Technology 
Readiness Levels), i.e., electric aircrafts. In the model, e-liquids are produced via 
two main pathways, whose intermediate products are either syngas (blend of CO 
and H2) or alcohols (methanol). 

The new model version that reflects possibilities for sectoral integration therefore includes 
the additional interactions between the different modules of PRIMES: the enhancements 
imply additional fuel options in the demand side sectors and additional production capability 
requirements in the supply side, as well as a novel approach for trading hydrogen44. 

The module also  takes care  to  balance  the capturing  of  carbon dioxide  in several  ways, 
competing  against  each  other  (DAC,  biomass,  combustion,  industrial  processes).  The 
module allocates carbon dioxide to various ways of storage, including its use as a feedstock 
for synthetic fuels, sequestration in materials (e.g., feedstock for chemical substances) and 
storage in underground caverns. 

Figure 2: Sectoral integration chains covered in the updated PRIMES model 

PRIMES-Biomass 

The PRIMES-Biomass supply model links the demand for bio-energy products to the supply 
of  biomass  feedstock  and  its  conversion  to  bioenergy.  Bio-energy/biofuel  conversion 
technologies are evolving at different pace. The model is used to derive the availability of 
supply for a given demand for bioenergy and verify the compliance with the sustainability 
criteria of RED II. Since the Reference Scenario 2016, a number of chains have become 
increasingly  important  for  the  bioenergy  in  particular  for  biofuel  production.  Techno-
economic  advancements  of  advanced  biofuel  technologies  have  been  revisited  and 
improved  over  the  past  years  including  their  interaction  with  agricultural  production45. 
Compared to Reference Scenario 2016, the PRIMES-Biomass supply model, includes new 

44 While the Reference scenario by assumption only includes limited use of hydrogen (in transport), policy scenarios which 
(will) use the Reference as a basis have the option of trading hydrogen between Member States.  
45 Borzęcka, Magdalena; Oberč, Barabara Pia; Haffner, Robert; Fragkiadakis, Kostas; Moiseyev, Alexander; Zazias, Georgios; 
Fragkos,  Panagiotis;  Dzene,  Ilze;  Verkerk,  Hans;  Ball,  Ingo;  Hussen,  Karel  van;  Witzke,  Peter;  Pantelis,  Capros  (2018): 
Research and Innovation perspective of the mid-and long-term Potential for Advanced Biofuels in Europe, Report for European 
Commission, https://op.europa.eu/en/publication-detail/-/publication/448fdae2-00bc-11e8-b8f5-01aa75ed71a1 

22 

 
 
                                                 
EU REFERENCE SCENARIO 2020 

chains such as the alcohol-to-jet and the Hydro processed Esters and Fatty Acids (HEFA) 
bio-kerosene production pathways. 

GAINS non-CO2 emissions  

GAINS non-CO2 emissions: agriculture 

The GAINS model covers CH4 and N2O emissions from livestock systems, agricultural soils 
and burning of agricultural waste residuals. Historical data on livestock numbers have been 
updated to reflect national statistics as reported to EUROSTAT (2019). Projections of future 
livestock numbers and milk yield have been adapted to the trend estimated by the CAPRI 
model. Information on manure management structures in the EU (i.e., fractions of animals 
in liquid or solid systems) was updated in GAINS in 2019 as part of a review consultation 
process under the preparation of EU’s Clean Air Outlook46.  

CH4 livestock implied emission factors (emissions per head) were calibrated to emissions 
reported by countries to the UNFCCC (2019) for the year 2015. For a given technology and 
manure  management  system,  the  implied  2015  emission  factors  stay  constant  in  future 
years. Hence, a decline in implied emission factors for future years can be fully referred to 
changes  in  technology,  in  this  case  installation  of  farm  biogas  plants  for  co-digestion  of 
manure (farm AD). Historical uptake of this technology was adapted to information provided 
by  EurObserv’ER  (2020)  with  the  development  in  future  capacity  following  the  trend  in 
biogas  production  from  anaerobic  digestion  as  projected  by  the  PRIMES  model.  The 
assumed order of the uptake of farm AD on different types of livestock farms, is based on 
GAINS estimates of marginal abatement costs. This results in the following order of uptake 
(consistent across all countries): First the potential on the largest pig farms (>500LSU) is 
exhausted, then on the second largest (100-500 LSU) pig farms, then on dairy and cattle 
farms with > 500 LSU, and finally dairy and cattle farms with 100-500 LSU. Uptake on farms 
with  less  than  100  LSU  is  considered  economically  infeasible  in  GAINS.  There  were  no 
recent updates to the model structure or methodology for estimation of N2O emissions from 
livestock. 

The activity data for mineral fertilizer use was updated for historical years to be aligned with 
information reported to EUROSTAT (2019) and UNFCCC (2019), as well as checked for 
consistency against industry sales information from Fertilizers Europe. There were no other 
recent updates in GAINS for the estimation of N2O emissions from agricultural soils.        

The  GAINS  model  accounts  for  CH4  emissions  from  the  burning  of  agricultural  field 
residuals. The EU regulation (EC 1259/1999) prohibits open burning of field residuals and 
consequently most EU countries do not report emissions from this activity to the UNFCCC. 
However,  remote  sensing  data (e.g., from  MODIS)  consistently  shows the  occurrence  of 
hundreds  of  fires  every  year.  Interpretation  of  remote  sensing  data  is  associated  with 
multiple uncertainties and depending on the instrument used and models and data (e.g., 
land  use  cover)  applied,  the  resulting  estimate  of  mass  burned  vary  significantly.  Given 
these uncertainties, the GAINS model aims to reflect the multi-year trend rather than the 
annual variability. Using country-specific information retrieved from the MODIS instrument 
and further processed by the Global Fires Emissions Database (GFED) and cross-checked 
against  other  published  information,  GAINS  reflects  major  trends  and  changes  in  the 
amount of biomass burned on fields. 

GAINS non-CO2 emissions: waste 

Estimations  of  CH4  from  municipal  solid  waste  (MSW)  disposal  and  treatment  in  the  EU 
were  revised  in  GAINS  to  take  account  of  updated  EUROSTAT  statistics  on  MSW 
generation and composition and extended information on MSW treatment streams provided 
in the national emission inventories submitted by countries to the UNFCCC (version 2019). 

46 COM (2021)3 

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Annual data on historical MSW generation and composition 1995-2017 has been taken from 
EUROSTAT  (2019).  MSW  waste  composition  categories  considered  were  extended  to 
Food  and  garden,  Paper,  Textile,  Wood,  Plastics,  Glass,  Metal,  and  Other.  Total  MSW 
generation per capita is assumed affected by changes in economic development (average 
GDP  per  capita)  and  average urbanization rate. Elasticities were  estimated  to  determine 
the relative impact of these two variables on the per capita MSW generation. As the average 
national  income  level  is  expected  to  affect  the  elasticity  of  MSW  generation,  separate 
income  elasticity  estimates  were  obtained  for  three  levels  of  average  GDP  per  capita;  < 
20,000 Euro (ɛincome=0.21), 20,000 to 40,000 Euro (ɛincome=0.33), and above 40,000 Euro 
(ɛincome=0.67). Hence, according to these estimates, a higher per capita income tends to 
accentuate the generation of MSW. Projections of future generation of total MSW, as well 
as  generation  of  waste  in  the  period  prior  to  1995,  are  derived  using  the  estimated 
elasticities. 

Information  on  historical  uptake  of  waste  treatment  measures  was  collected  from  the 
national  reporting to the UNFCCC  (version 2019)  and  EUROSTAT (2019). This  includes 
information on overall recycling rates and specific recycling rates for paper waste, amounts 
of  waste  allocated  to  various  types  of  landfills,  composting  and  anaerobic  digestion  in 
biogas  facilities,  waste  incineration  and  open  burning  of  waste,  as  well  as  the  Methane 
Correction  Factor  (MCF)  for  different  types  of  landfills.  In  consistency  with  the  reported 
treatment information and emission factors derived for each waste category and treatment 
stream using default IPCC (2006, 2019) methodology, the emission generation potential of 
waste is determined for each country in five-year steps from 1970 to 2015. Using a simplified 
version of the IPCC First-Order-Decay method, waste with organic content disposed off to 
landfills is divided into fast-decaying (food and garden waste) and medium-to-slow decaying 
(paper, wood, textile, other) waste, assuming on average a ten year delay between disposal 
and emission release for fast-decaying waste and a twenty year delay for medium-to-slow 
decaying waste. Composting and anaerobic digestion are assumed to give rise to limited 
CH4  emissions,  while  no  CH4  emissions  are  assumed  from  recycling  of  paper,  wood,  or 
textile  waste.  N2O  emissions  are  accounted  for  from  composting  using  IPCC  default 
emission  factors.  To  account  for  the  impact  of  decay  times  on  emissions  from  1990 
onwards, we must estimate the generation and treatment attribution of MSW for the entire 
period 1970 to 1990. This extrapolation was made assuming that in year 1970 all MSW was 
landfilled  except  a  small  fraction  that  was  openly  burned  (same  fraction  as  reported  for 
1990).  Between 1970  and  1990,  a  linear  transition  to the  1990 treatment  attribution  was 
assumed. Simulations of future waste treatment pathways and associated emissions take 
as starting point the current treatment structure identified from national information reported 
to  the  UNFCCC  for  the  year  2017  and  assumes  that  countries  meet  the  targets  of  the 
amended EU Waste Directive from 2018.   

GAINS non-CO2 emissions: HFCs in cooling, refrigeration, and other uses 

The general approach in GAINS for estimation of historical HFC emissions is to combine 
activity  data  (consumption  of  HFC-by-HFC  species)  from  the  national  reporting  to  the 
UNFCCC (Common Reporting Format tables, version 2019) with sector- and technology-
specific emission factors (i.e., leakage rates) in a consistent manner across countries. The 
use of constant emission factors across countries means that GAINS emission estimates 
do not always correspond to national inventory emission levels despite using the same HFC 
consumption. An exception to the use of reported HFC consumption data is made for the 
commercial  and  residential  air  conditioning  (AC),  as  only  total  use  in  stationary  AC  is 
reported to the UNFCCC. In GAINS, HFC consumption in these sectors is derived bottom-
up  from  information  on  macroeconomic  drivers,  commercial  floor  space,  number  of 
households and average AC ownership in households.  

Depending on the sector, drivers for future HFC use are e.g., growth in GDP per capita, 
population,  expected  changes  in  average  cooling-degree  days  (CDDs),  and  uptake  of 
alternative  technologies  replacing  HFC  use  in  response  to  measures  to  comply  with 

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EU REFERENCE SCENARIO 2020 

regulations. A number of recent updates were made to the model structure to better reflect 
the expected implications of the EU F-gas regulation. The commercial air conditioning (AC) 
and refrigeration  sectors  were  split  into  small  and large  units to  reflect  differences  in the 
feasibility  of  available  alternatives  to  HFC  use.  For  commercial  AC,  feasible  alternatives 
include propane and HFC-32 for small units and water chillers and HFO-1234yf for large 
units. For residential AC, the options considered for HFCs are propane or HFC-32. Note 
that  HFC-32  has  a GWP100  of  677  but  is  still  tolerated  in  the  F-gas regulation  when  it  is 
compensated  by  a  high  energy-efficiency  of  the  installed  devices.  For  commercial 
refrigeration, feasible alternatives considered for small units are hydrocarbons (HC-600a, 
HC-290, HC-1270) and HFC-152a, while pressurized CO2 and HFC-152a are considered 
for  large  units.  For  domestic  refrigerators,  all  use  of  HFCs  is  assumed  replaced  by 
hydrocarbons (isobutane) by 2030. For industrial refrigeration, the model structure was split 
by small and large units, with feasible HFC alternatives being NH3, pressurized CO2, HFOs 
(HFO-1233ze,  HFO-1233zd and  HFO-1336mzz),  and HFC/HFO  blends (e.g.,  R-446A/R-
447A). The modelling of the heat pump (HP) sector was updated to account for HFC use 
not only in ground-source HPs but in all heat pump types. Country-specific information on 
installed HP capacity was taken from published sources.  

At the EU level, the HFC release reported for 2015 to UNFCCC attributes 34% of source 
sector use to Medical Dose Inhalers (MDIs). The 2015 use of HFCs in MDIs is assumed to 
remain into the future due  to the  difficulty  of replacing  HFCs  in medical uses.  No recent 
model revisions were made for the sectors fire extinguishers, foams, HCFC-22 production, 
mobile  AC,  solvents,  and  transport  refrigeration,  beyond  updates  of  activity  data  and 
technology uptake to comply with the expected impacts of the F-gas regulation. 

GAINS non-CO2 emissions: industrial processes and other sources 

The GAINS model structure covers N2O emissions from production of adipic acid, nitric acid 
glyoxylic acid/glyoxal and caprolactam, HFC-23 emissions from HCFC-22 production, PFC 
emissions  from  primary  aluminium  production,  PFC  and  NF3  emissions  from  the 
semiconductor  industry,  and  SF6  emissions  from  magnesium  production  and  casting.  In 
addition, GAINS covers non-CO2 emissions from a number of other minor sources, including 
direct use of N2O in hospitals and food industry, SF6 from high- and mid- voltage switches, 
and other use of F-gases to the extent that such have been reported by countries to the 
UNFCCC. Apart from updates of historical activity data and revisions of control strategies 
to comply with adopted F-gas regulations, no recent updates to the model structure were 
made for these emission sources.  

CAPRI 

The  CAPRI  model  is  used  and  developed  for  various  purposes  such  that  several 
improvements have been implemented since the Reference Scenario 2016, in particular:  

-  The  initiation  of  a  rolling  process  of  “stable  releases”  with  defined  and  tested 
properties’ facilitates to keep track of which innovations to incorporate in a specific 
application like the reference run.  

- 

In  this  context  a  thorough  revision  of  the  feed  allocation  module  has  been 
undertaken to reduce the role of hard bounds as opposed to target values for the 
feed  allocation.  This  improved  plausibility  of  feed  balancing  in  the  animal  sector 
which is directly linked to nutrient balances in the crop sector via excretions. 

-  The  fertiliser  allocation  mechanism  has  been  likewise  revised  both  to  be  able  to 
reduce  arbitrariness  in  differences  among  crops  and  to  explicitly  include  policy 
restrictions by crop where such information could be collected.  

-  The recently developed manure trade module has been used for the first time in the 

context of reference run projections. 

Other  innovations  (for  example  on  GHG  mitigation,  LULUCF  representation,  handling  of 
short run projections) are useful for comparisons of scenario results within in the modelling 

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EU REFERENCE SCENARIO 2020 

suite  but  they  do  not  directly  affect  the  information  flow  which  follows  the  established 
channels. 

By contrast it has been crucial to update the database and some “external” modelling inputs 
to CAPRI, most of all the incorporation of the latest Commission Agricultural Outlook as well 
as recent fertiliser projections from the European Fertiliser Manufacturers’ Association.  

Finally,  the  role  of  critical  feedback  from  Member  State  national  experts  for  the  CAPRI 
projections  has  to  be  acknowledged.  Early  draft  projections  have  been  checked  and 
reconsidered, leading to revisions in various areas. A common theme in the CAPRI related 
consultations has been a certain moderation of expectations for future increases in average 
milk yields and decline of dairy herds. 

GLOBIOM/G4M updates 

An improved version of GLOBIOM now covers the entire forest sector in much greater detail 
than before; from production and harvesting, including logging residues, up to the demand 
for  final  products,  covering  industrial  processes  and  also  accounting  for  industrial  by-
products  and  their  demand/use  in  other  parts  of  the  industry  or  for  energy  production 
purposes.  In  relation  to  the  forest  sector  update  in  GLOBIOM,  the  residues  extraction 
algorithm in G4M has been updated. Furthermore, G4M parameters reflecting the intensity 
of  forest  management  have  been  updated,  using  the  information  on  forest  management 
intensities47.  The  input  data  used  in  the  GLOBIOM-G4M  models  were  updated  for  the 
Reference  Scenario.  Forest  harvest  removals  were  calibrated  to  FAOSTAT  data 
(downloaded in March 2020) or in exceptional cases to individual data contributions from 
Member States. The afforestation and deforestation rates in G4M have been calibrated to 
data extracted from the 2020 UNFCCC submissions. Historical harvest removals from 1960 
onwards are based on FAOSTAT data and have been considered in the calculation of the 
harvested wood sink. Agricultural market balances, areas and prices have been calibrated 
to EUROSTAT statistics based on the CAPRI database and are aligned with most recent 
FAOSTAT data.  

UNFCCC  2020  data  was  used  for  the  ex-post  calibration  of  model  results  to  ensure 
consistency  with  UNFCCC  submissions.  A  trend  on  the  expansion  of  settlements  was 
included  in  the  projections  based  on  historical  UNFCCC  2020  time  series  (2008-2018). 
GLOBIOM-G4M area balances were consolidated with the reported UNFCCC 2020 data to 
improve  consistency  (i.e.,  natural  grasslands  were  split  out  from  the  “other  natural 
vegetation” aggregate and included under grassland management together with pastures). 
The  ex-post  calibration  of  the  model  has  been  extended  to  non-CO2  emissions  from 
LULUCF. 

Changes in the energy balances 

The  PRIMES  model  has  been  adapted  to  reflect  the  new  definitions  of  the  EUROSTAT 
balances. 

The new reporting of PRIMES mimics the new Eurostat energy balances. The key changes 
in this regard are:  

  Blast furnaces are now included in the Energy Branch sector rather than in the final 
energy demand of the Iron and Steel sector, implying that this consumption is no 
longer part of final energy demand.  

  Energy demand from international aviation is now treated as bunker fuel and also 

excluded from final energy demand.  

47 Nabuurs, GJ.; Verweij, P.; Van Eupen, M.; et al. (2018) : Next-generation information to support a sustainable course for 
European Forests, Nat. Sustain., vol. 2 p. 815-818, https://doi.org/10.1038/s41893-019-0374-3 

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For the energy efficiency indicators to comply with the Energy Efficiency Directive and for 
renewable  energy  shares  to  comply  with  the  Renewable  Energy  Directive,  the  previous 
definition of final energy demand is used to allow consistency with past time series. 

2. Framework conditions for the Reference Scenario  

2.1. Macro-economic and demographic assumptions 

The  macroeconomic  outlook  used  in  the  Reference  Scenario  provides  the  framework 
projections on how the European economy will evolve in the coming decades. The outlook 
is important as it offers a view of the future structure of sectors and activities of the European 
economy. 

The macroeconomic scenario builds on recent demographic and economic projections for 
the EU countries provided by Eurostat and the joint work of the Economic Policy Committee 
and the European Commission.  

The  GEM-E3  model  is  used  for  simulating  developments  of  each  GDP  component 
(investment, consumption, and trade) and of the sectorial production in each Member State. 
As a GCGE model, GEM-E3 ensures that the macroeconomic and sectorial projections of 
the EU economy are consistent with a global economy context. Details on the methodology, 
data and assumptions can be found in Annex II. 

The impacts of the COVID-19 pandemic have been reflected in the macro-economic and 
demographic projections as well as in the sectoral composition of GDP. 

2.1.1. EU population projections 

According  to EUROSTAT,  the  EU  population is projected to decline  over  the  (very)  long 
term.  However,  there  are  wide  differences  in  national  population  trends,  with  population 
growing  in  11  Member  States  and  dropping  in  the  others.  On  1  January  2020,  the  EU 
population was estimated at 447.7 million. The total EU population change was positive with 
0.9 million more inhabitants during 2019 attributed to net migration.  

Figure 3 shows the projection of population up to 2050 from EUROPOP 2019 as used in 
the Reference Scenario. 

Figure 3: EU population trajectory 

Source: EUROPOP 2019 

27 

-0.30-0.20-0.100.000.100.200.300.400.500.600100200300400500600200020102020203020402050Change in %Population (million)Population% change pa5 years avg. % change 
 
EU REFERENCE SCENARIO 2020 

Fertility,  life  expectancy  and  migration  dynamics  shape  the  EU’s  demographic  old-age 
dependency ratio, i.e., the ratio between people aged 65 years and over and those aged 
20-64, which is projected to continue to rise sharply over the coming decades. 

Fertility  rates  in  the  EU  rise  from  1.52  in  2019  to  1.61  in  2050.  Life  expectancy  also 
increases by 4.8 years for males and 4 years for females until 2050. Annual net migration 
inflows  fall  gradually  overtime  from  approximately  1.3  million  people  in  2019  to  about  1 
million in 2050 (0.2% of the EU population).   

From about 29% in 2010 the old-age dependency ratio rose to 34% in 2019 and is projected 
to rise further to almost 57% by 2050. This implies a shift from less than four working-age 
people  for  every  person  aged  65  years  and  over  in  2010  to  below  two  in  2050.  Table  2 
provides the population projection by Member State. 

Table 2. Projected population per MS (million inhabitants) 

EU 

Austria 

Belgium 

Bulgaria 

Croatia 

Cyprus 

Czechia 

Denmark 

Estonia 

Finland 

France 

Germany 

Greece 

Hungary 

Ireland 

Italy 

Latvia 

Lithuania 

Luxembourg 

Malta 

2020 

447.7 

8.90 

11.51 

6.95 

4.06 

0.89 

10.69 

5.81 

1.33 

5.53 

67.20 

83.14 

10.70 

9.77 

4.97 

60.29 

1.91 

2.79 

0.63 

0.51 

2025 

449.3 

9.03 

11.66 

6.69 

3.94 

0.93 

10.79 

5.88 

1.32 

5.54 

68.04 

83.48 

10.51 

9.70 

5.27 

60.09 

1.82 

2.71 

0.66 

0.56 

2030 

449.1 

9.15 

11.76 

6.45 

3.83 

0.96 

10.76 

5.96 

1.31 

5.52 

68.75 

83.45 

10.30 

9.62 

5.50 

59.94 

1.71 

2.58 

0.69 

0.59 

Netherlands 

17.40 

17.75 

17.97 

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2020 

37.94 

10.29 

19.28 

5.46 

2.10 

47.32 

10.32 

2025 

37.57 

10.22 

18.51 

5.47 

2.11 

48.31 

10.75 

2030 

37.02 

10.09 

17.81 

5.44 

2.11 

48.75 

11.10 

Poland 

Portugal 

Romania 

Slovakia 

Slovenia 

Spain 

Sweden 

Source: EUROPOP 2019 

2.1.2. EU economic projections 

The aggregate GDP projections used in the Reference Scenario (Figure 4) are retrieved 
from  the  European  Commission’s  2021  Ageing  Report,  itself  based  on  the  Spring  2020 
Economic Forecast for short-term projections.  

The 2021 Ageing Report is based on a growth accounting methodology that uses Eurostat’s 
EUROPOP 2019 projections and builds upon assumptions regarding trends in the labour 
force  and  the  growth  of  total  factor  productivity,  which  is  assumed  to  converge  across 
Member  States  in  the  long  run.  Subsequently,  projections  are  made  regarding  sectoral 
trends to define a macro-economic baseline down to the level of sectoral value added.  

The projection for 2030 is 2.3% lower compared to pre-COVID-19 estimates of the 2018 
Ageing Report.  

Figure 4: EU GDP in aggregate terms 

Source: DG ECFIN (Spring 2020 Economic Forecast & Ageing Report 2021) 

Projections on GDP growth are characterised by very large uncertainty in the wake of the 
COVID-19 pandemic since the impacts are highly complex and widely varied. The pandemic 
struck the European economy at the moment it had started to grow at a moderate pace, 
recovering  from  the  2008  financial  crisis.  Now  much  will  depend  on  the  duration  of  the 

29 

-10-8-6-4-20246802000400060008000100001200014000160001800020000200020102020203020402050Change in %GDP (billion € 2015)EU GDP% change pa5 years avg. % change 
 
 
EU REFERENCE SCENARIO 2020 

pandemic but also on new trends emerging in the post-COVID-19 era regarding work, global 
trade, production, and supply chains.  

The  interplay  of  pre-existing  vulnerabilities,  namely  fading  demographic  dividends  and 
structural  bottlenecks,  with  the  effects  of  the  pandemic,  i.e., firm  bankruptcy  due  to debt 
accumulation,  investor  risk  perception  shaped  by  debt  sustainability  concerns  and  non-
performing  loans  along  with  permanent  changes  regarding  consumption  patterns  and 
human capital formation, can have an even larger impact on long-term growth prospects.  

Figure 5: Components of GDP in the EU 

The macroeconomic components of EU GDP are projected to record only marginal changes 
by 2050 in their shares. The composition of the EU GDP continues current trends with high 
and  increasing  shares  of  private  consumption  followed  by  investments  and  government 
consumption (Figure 5). Private consumption continues to account for the largest part of 
GDP in the EU in 2050.  

Government  consumption  is  projected  to  marginally  lower  its  share  of  GDP,  reflecting 
adjustments  in  the  aftermath  of  the  crisis  induced  by  the  pandemic  and  contraction  of 
government spending. Investments are projected to account for 22% of GDP in 2050. Trade 
surplus  with  non-EU  regions  continues  to  account  for  a  small  share  of  EU  GDP,  which 
remains close to present levels. 

2.1.3. EU sectorial projections 

Figure 6 and Figure 7 show the evolution of the sectoral gross value added over time. The 
services sector dominates, generating slightly over 76% of gross value added in the EU by 
2050. Industry and construction are projected to decline slightly by 2030 and more so by 
2050 due to structural shifts in the economy and reduced investments resulting from lower 
economic  growth.  Industries  linked  to  construction,  such  as  cement,  also  record 
improvements in sectorial activity to 2050. Energy-intensive industries maintain their shares 
in gross value added over time. 

30 

56%20%22%2%2050Source: GEM-E354%22%21%3%2015HouseholdConsumptionGovernmentConsumptionInvestmentNet exports 
 
EU REFERENCE SCENARIO 2020 

Figure 6: Sectoral Gross Value Added in the EU 

Note: “Other sectors” refers to agriculture and energy. 

Iron,  steel,  and  non-ferrous  metals  sectors  maintain  production  in  the  EU  thanks  to  the 
existence of tight links with the EU equipment goods industry. The value of the  chemical 
sector has increased through specialisation and a focus on digitalisation even as growth in 
chemical production volumes has stabilised, due the strong competition from China, India, 
and  the  USA.  Within  the  chemical  sector,  the  EU  production  of  fertilizers  and  inorganic 
chemicals  is  projected  to  stabilize  and  slightly  decline  in  the  long  term  as  a  result  of 
increasing international competition and low EU level demand.  

The equipment goods industry (engineering) is projected to remain a dynamic sector in the 
EU  industry,  growing  at  steady  pace,  but  faced  with  higher  competition  from  emerging 
markets. Affected by international competition, the textile industry is projected to decline. 

Growth  is  projected  to  be  slow  in  agriculture  and  the  energy  sector  (in  terms  of  activity 
volume). This would be however compensated by a moderate increase in the share of less 
energy-intensive market and non-market services. The share of the energy sector in total 
gross  value  added  is  expected  to  remain  broadly  unchanged  as  the  substitution  from 
imported fossil fuels to higher value added domestic electricity production is expected to 
continue. 

31 

020004000600080001000012000140001600018000201020152020202520302035204020452050GVA (billion € 2015)Energy IntensiveIndustriesNon - EnergyIntensive IndustriesOther SectorsServicesSource: GEM-E3 
 
EU REFERENCE SCENARIO 2020 

Figure 7: Sectoral shares in Gross Value Added in the EU  

Note: Other sectors refer to agriculture and energy. 

2.2. World fossil fuel prices 

Alongside  socio-economic  projections,  EU  energy  modelling  requires  projections  of 
international fuel prices. The 2020 values are estimated from information available by mid-
2020. The projections of the POLES-JRC model – elaborated by the JRC and derived from 
the Global Energy and Climate Outlook (GECO48) – are used to obtain long-term estimates 
of the international fuel prices.  

The COVID-19 pandemic has had a major impact on international fuel prices49. The severe 
disruption  in  economic  activity  in  2020  created  a  historic  shock  on  energy  demand,  in 
particular fossil fuels. The lost demand caused an oversupply, leading to decreasing prices. 
The effect on prices compared to pre-COVID-19 estimates is expected to still be felt up to 
2030. Energy prices are expected to bounce back progressively  with the recovery of the 
global economic activity. Yet, actual development of oil prices in particular will depend on 
the recovery of global oil demand as well as the evolution of supply side policies50. The table 
below shows the international fuel prices assumptions of the Reference Scenario.  

Table 3: International fuel prices assumptions  

In $ per 
boe

2000 

2005 

2010 

2015 

2020 

2025 

2030 

2035 

2040 

2045 

2050 

Oil 

38.4 

65.4 

86.7 

52.3 

39.8 

59.9 

80.1 

90.4 

97.4 

105.6 

117.9 

Gas (NCV) 

26.5 

35.8 

45.8 

43.7 

20.1 

30.5 

40.9 

44.9 

52.6 

57.0 

57.8 

Coal 

11.2 

16.9 

23.2 

13.1 

9.5 

13.6 

17.6 

19.1 

20.3 

21.3 

22.3 

48 https://ec.europa.eu/jrc/en/geco    
49 IEA, Global Energy Review 2020, June 2020 
50 IEA, Oil Market Report, June 2020 and US EIA, July 2020 

32 

54%54%54%55%56%26%27%28%28%28%10%10%10%9%9%10%9%8%7%7%0%20%40%60%80%100%20152020203020402050Share in total GVA (%)ServicesNon energy intensive industriesEnergy intensive industriesOther sectorsSource: GEM-E3 
 
 
 
                                                 
EU REFERENCE SCENARIO 2020 

In € per 
boe

2000 

2005 

2010 

2015 

2020 

2025 

2030 

2035 

2040 

2045 

2050 

Oil 

34.6 

58.9 

78.2 

47.2 

35.8 

54.0 

72.2 

81.5 

87.8 

95.2 

106.3 

Gas (NCV) 

23.4 

31.7 

40.6 

38.7 

17.8 

27.0 

36.2 

39.7 

46.6 

50.5 

51.2 

Coal 

9.9 

15.0 

20.6 

11.6 

8.4 

12.0 

15.6 

16.9 

18.0 

18.9 

19.7 

Source: Estimates, derived from JRC, POLES-JRC model, Global Energy and Climate Outlook (GECO) 

Details on the global energy context and the approach for the projection of the international 
fuel prices can be found in Annex V of this report.  

2.3. The COVID-19 impact on economic activity 

More than a year after the COVID-19 outbreak the pandemic continues to cast a shadow of 
uncertainty51 over the pace and strength of economic recovery. The disruptions in economic 
value chains caused by the lockdowns have set off a sharp decline in economic activity, 
which  affects  the  possible  evolution  of  productive  systems  along  with  expectations 
surrounding  consumption,  savings,  production,  and  investment  plans  in  the  medium-  to 
long-term. Notably, the slump in economic activity has triggered a strong decline in sectoral 
activity  and  in  energy  demand.  Transport  has  been  significantly  affected,  especially  the 
passengers transport segment, while freight transport activity has been affected to a lesser 
extent. International fuel prices have been also hit and the effect is projected to persist to 
some extent to 2030. 

The DG ECFIN Spring 2020 short-term economic forecast used for the Reference Scenario 
points to a sharp drop in output in 2020 followed by fast recovery, while the projection to 
2030 reveals a permanent loss of output of around 2.3% compared to the pre-COVID-19 
projections. Modelling the year 2020 has proven to be particularly challenging, mainly due 
to the implications of the unfolding COVID-19 crisis. The modelling is based on extrapolation 
from historical data (until 2019) and preliminary data from 2020 (e.g., monthly statistics of 
electricity  consumption).  Moreover,  the  effects  of  COVID-19  on  the  different  sectors, 
presented below, are also factored in. 

2.3.1. Impact on transport 

Since the COVID-19 outbreak, several governments took drastic measures to contain its 
spread. As a result, the activity in several economic sectors slowed down. The transport 
sector  has  been greatly  affected  due  to  international  travel  bans,  confinement  measures 
and stay-at-home requirements. Global trade between countries decreased to some extent 
due  to  lower  economic  activity  and  reduction  in  manufacturing  output52.  The  impact  was 
particularly visible in the international maritime sector and to a lesser extent to road freight 
transport, as far as the EU freight transport sector is concerned and consequently freight 
transport. 

The calibration process of the model took place for the most part in 2020, when data was 
not  yet  available  (also  preliminary  data  was  scarce).  The  projections  show  intra-EU 
passenger transport activity (expressed in pkm) to be 24% lower in 2020 compared to 2019, 
with public road transport, passenger cars and two-wheelers accounting for about 2/3 of the 
reduction. Passenger transport by rail, inland navigation and intra-EU aviation have been 
projected to drop by half.  

51 "Uncertainty and the pandemic shocks”, November 2020, Monetary Dialogue Papers, European Parliament  
52  https://unstats.un.org/unsd/ccsa/documents/covid19-report-ccsa.pdf  

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Across modes,  extra-EU  aviation has  been  projected to  be  hit the  hardest  by  COVID-19 
pandemic, with activity (expressed in pkm) projected to drop by more than 50% in 2020. In 
the EU, freight transport activity (expressed in tkm) has been projected to decrease by about 
7%  but  the  reduction  in  international  maritime  freight  activity  has  been  envisaged  to  be 
significantly higher, declining by almost 30% in 2020 compared to 2019 levels. 

The  approach  for  estimating  the  effects  of  the  pandemic  has  sought  to  capture  possible 
changes  in mobility  trends  mainly  influencing  aviation (e.g., reduced business  travel  and 
commuting,  domestic  tourism),  especially  until  2025.  For  urban  mobility,  the  projections 
show a preference towards private transport modes compared to road public transport and 
tram and metro. Moreover, the projections do not account for significant structural changes 
and shifts towards other modes of transport, such as e-scooters, (e-) bicycles, or walking 
and  other  kinds  of  deep  transformations,  i.e.,  substantial  reductions  in  long-distance 
business trips and substantial increases in remote working.  

2.3.2. Impact on industry 

The  COVID-19  pandemic  affected  industrial  output  and  led  to  a  sharp  drop  in  capacity 
utilisation,  reducing  the  need  for  investment  linked  to  capacity  expansion  and  lowering 
incentives for upgrading the capital stock in 2020. After strong declines in March and April 
2020  industrial  growth  picked  up  between  May  and  July  and  remained  rather  stable 
afterwards. The overall growth since then has been generally sufficient to recoup the losses 
of the most acute phase of the pandemic. The activity projections used in PRIMES therefore 
have seen a slight decline in 2020 followed by almost full recovery by 2025 for almost all 
industrial sectors, compared to pre-COVID-19 levels. 

2.3.3. Impact on services and residential  

Services sectors, including retail, hospitality, tourism, and leisure have been particularly hit 
by the COVID-19 pandemic. The turnover of accommodation and food services (hotels and 
restaurants) at the end of 2020 was only about half of what it had been one year earlier. 
Administrative and support services (e.g., employment services, security, cleaning), usually 
bought by businesses, fell to a level of 78.8 % between the last quarter of 2019 and the last 
quarter  of  202053.  Construction  has  also  suffered  from  a  double-digit  fall  in  gross  value 
added in 2020, negatively affecting providers of inputs to the sector, including cement and 
other non-metallic minerals54 - the effects of the latter are included in the relevant industrial 
sector of PRIMES. The abrupt shift to working from home has caused energy consumption 
to increase slightly in the residential sector. 

2.3.4. Impact on agriculture 

The  CAPRI  projection  for  the  Reference  Scenario  builds  on  the  2020  EU  Agricultural 
Outlook55, which incorporates the impact of the COVID-19.  

2.4. Technological options  

2.4.1. Classification of technologies 

An  essential  input  to  the  modelling  exercise,  and  one  which  has  a  high  influence  on 
modelling  results,  relates  to  the  assumptions  about  technological  developments;  both  in 
terms of performance and costs. Mapping the new and emerging technologies and, more 

53 https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Impact_of_Covid-19_crisis_on_services  
54 SWD (2020) 176 final  
55 EC (2020), EU agricultural outlook for markets, income, and environment, 2020-2030. European Commission, DG 
Agriculture and Rural Development, Brussels 

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EU REFERENCE SCENARIO 2020 

importantly, the knowledge that exists about their current and future cost and performance 
is crucial for envisaging the transformation of the energy system.  

While one cannot have complete knowledge of all technologies that will be deployed, some 
indication already exists of the technologies that are being currently developed, their costs, 
performance, and possible evolution. Private companies and public authorities have already 
made investments in research and demonstration but also full-scale industrial application 
of  these  technologies.  Key  technological  developments  underpinning  the  Reference 
Scenario are outlined below.  

The  impressive  decline  in  renewable  energy  costs  that  has  been  recorded  since  2010 
continue and so “renewable power generation technologies are not just competing head-to-
head with fossil fuel options without financial support, but increasingly undercutting them”. 
Rapidly falling costs of solar PV drive a notable reduction in the cost of storage technologies, 
i.e., battery installations. Meanwhile, novel fuel technologies are progressively brought to 
the  market.  These  include  synthetic  fuels/e-fuels  such  as  e-methane  and  more  complex 
hydrocarbons;  hydrogen  produced  from  increasingly  carbon-free  electricity;  and  the 
accompanying infrastructure, namely networks and refuelling stations  for the distribution, 
storage,  and  conversion  of  the  new  fuels  (e.g.,  CO2  capturing,  hydrogen  production, 
methanation or production of more complex hydrocarbons for the transport sector). 

The  revision  of  the  assumptions  used  in  the  Reference  Scenario  2016  and  of  the 
assumptions used in the in-depth analysis underpinning the “EU Clean Planet for All”56  rests 
on a rigorous literature review and an extensive consultation organised by the European 
Commission  with the  participation  of  industry  stakeholders  and Member State  experts  in 
November  2019.  The  stakeholder  consultation  addressed  the  PRIMES  technologies, 
including transport, and the technologies used within the GAINS and GLOBIOM models. 

The modelling of technologies in PRIMES is characterised by the following features: 

  Technology vintages are tracked in the entire model. 

  Cost-supply-potential  curves  (non-linear)  for  RES,  power  plant  sites,  energy 
savings,  etc.  are  used  in  demand  and  supply  models  to  mimic  the  increasing 
difficulty of exploiting a resource close to potential, the increasing marginal costs of 
energy efficiency, the increasing cost of RES development in remote areas, etc. 

  Progress reducing the cost gap between different scales, influencing the emergence 

of decentralized power plants. 

  Risk premium and perceived costs used to influence the uptake of not yet mature 
technologies obstructed by low access to financing or the reluctance of customers 
to  buy  technologies  which  are  not  yet  well  known  and  for  which,  for  example, 
maintenance services are uncertain.  

The technologies considered and reviewed for the purpose of the Reference Scenario are 
divided in the following categories: 

  Power and heat  

  Domestic appliances and equipment 

  Renovation costs 

 

Industry  

  Transport 

  New fuels 

  Non-CO2 mitigation technologies 

56 COM (2018) 773 

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EU REFERENCE SCENARIO 2020 

  LULUCF mitigation technologies 

The  following  section  presents  an  overview  of  the  main  assumptions  about  key 
technologies.  More  information  on  techno-economic  assumptions  for  the  technologies  in 
the modelling is provided in Annex III. 

2.4.2. Power generation technologies 

In recent years, the development of RES technologies for power generation have improved 
significantly and at a faster pace than what was predicted only a few years ago (including 
in the Reference Scenario 2016). In order to better capture the diversity and advantages 
(as  well  as  disadvantages)  of  some  technologies,  and  so  improve  the  projection,  the 
categorisation of solar PV, wind onshore and wind offshore has been modified (see above 
and Annex C). Moreover, the lifetimes of equipment have been increased, in most cases to 
30  years  and  capacity  factors  are  fully  Member-State  specific,  following  information 
gathered from ENSPRESO and other databases. 

Solar photovoltaic (PVs) 

Techno-economic  improvements  in  the  solar  PV  industry,  having  surpassed  previous 
expectations  of  costs,  have  been  re-estimated  using  updated  data.  The  development  of 
PVs  therefore  starts  from  lower  costs  than  previously  expected  and  continues  to  exploit 
learning  potential  in  the  future.  However,  costs  hit  a  floor  which  is  justified  by  the 
incompressible  costs  of  the  modules  and  components  such  as  inverters,  frames,  and 
installation costs.  

Wind  

Wind onshore: a steadily decreasing trend is visible. The remaining potential for learning is 
estimated to be small, but costs can decrease due to the size of turbines and their height. 

Wind offshore: large uncertainty surrounds the costs for offshore wind. Cost increases have 
been recorded due to previously unforeseen challenges. Surveys have identified significant 
potential of cost decrease due to economies of scale and possibilities of improvement in 
logistics; these cost decreases are likely to occur towards 2030. 

Nuclear  

The Reference Scenario builds on the approach of the Reference scenario 2016, assuming 
high capital costs for third generation nuclear. The latest Power Purchase Agreement (PPA) 
contracts which have been awarded for nuclear power plants have prices in the order of 
100€/MWh or higher. 

CCS 

The cost of CCS power plants construction has been revised upwards, as a result of lack 
of project developments. 

2.4.3. Energy demand-side technologies 

For  stationary  energy  uses,  technologies  are  distinguished  by  technology  vintages 
(ordinary, improved, advanced and best technologies), which have increasing capital costs 
and efficiency. The features of the ordinary technology change over time in accordance with 
Ecodesign Regulations, where these are available. Perceived costs and technology-specific 
risk premium decrease over time for the advanced and best technologies closing the cost 
differences to the ordinary category.  

Efficiency policies and ecodesign allow advanced and best technologies to reach maturity 
earlier, since barriers are removed, and manufacturers get higher market certainty.  

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EU REFERENCE SCENARIO 2020 

This includes for example the most up to date studies for the preparation of the Ecodesign 
Regulations. 

Transport  

All the technologies for transport have been reviewed, consulted, and updated. They include 
the capital cost for the vehicles and other transport equipment but do not include any other 
fixed costs that may occur during the lifetime of the vehicles (e.g. replacement of battery 
costs). 

The  PRIMES-TREMOVE  model  includes  the  following  modes  and  technology  types  for 
passenger transport: 

-  Private road: small, medium, large cars, powered 2-wheelers57; for each type of car, 
different power trains are available including conventional ICEs (with different fuel 
options)  and  alternative  power  trains  (Battery  Electric  Vehicles  and  Fuel  Cell 
Vehicles), as well as hybrid technologies, distinguished between conventional and 
plug-in. 

-  Public  road:  buses  and inter-urban coaches;  like  with the  private  road,  the  public 

road includes ICEs as well as alternative power trains. 

-  Rail: conventional trains and high-speed trains, tram, and metro. 

- 

Inland navigation (including national maritime). 

-  Aviation: based on aircraft size and motor type. Also, electric power trains for small 

aircrafts are included. 

For freight transport the split of technologies includes: 

-  Road freight: light commercial vehicles, 3.5-7.5t, 7.5-16t, 16-32t, >32 t heavy goods 
vehicles;  for  each  size  different  power  trains  are  available  including  conventional 
ICEs  (with  different  fuel  options)  and  alternative  power  trains  (Battery  Electric 
Vehicles and Fuel Cell Vehicles), as well as hybrid technologies. 

-  Rail: conventional rail. 

- 

- 

Inland navigation, including national maritime. 

International  maritime  (short-sea  and  deep-sea)  shipping:  incl.  oil  tankers, 
containers, dry bulk carriers and general cargo vessels. Different fuels are available 
(with different technologies): conventional liquid fuels (diesel, residual fuel oil, etc.), 
biofuel and synthetic counterparts, LNG, hydrogen.  

In  addition,  the  costs  related  to  the  recharging  and  refuelling  infrastructure  have  been 
reviewed, consulted, and updated. 

Industry 

Substitution  possibilities,  perfect  or  imperfect,  as  well  as  complementarities,  play  an 
important role in modelling the link between technologies and processing types. The nested 
substitution  possibilities  combined  with  the  structure  of  processes  is  a  good  basis  to 
estimate the realistic possibilities of electrification and penetration of cleaner fuels, such as 
hydrogen,  gas,  and  biomass  in  industry.  Furthermore,  the  segmentation  of  processes 
combined with the representation of technology vintages is a basis to estimate the potential 
of heat recovery and energy savings.  

The PRIMES Industry model represents heat and steam uses in connection to processes, 
and  links  steam  supply  to  the  model  handling  of  industrial  boilers,  industrial  CHP,  and 
distributed steam endogenously. For this purpose, steam demand and supply data in the 

57 This includes motorcycles and mopeds. E-bikes and pedelecs are not considered. 

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EU REFERENCE SCENARIO 2020 

PRIMES database are estimated differently than in Eurostat, which shows only distributed 
steam. Also, the database includes a complete allocation of fuels to the various processes 
and uses, which also goes beyond Eurostat information. 

The  inventory  of  possible  industrial  technologies  includes  the  classification  of  BAT 
regulation.  Thus,  PRIMES  Industry  can  handle  technology  and  emission  performance 
standards. Primary versus secondary production (steel, aluminium, copper, glass, paper, 
and  clinker)  is  handled  explicitly,  but  depends  mostly  on  activity-related  projections, 
regarding  recycling  and  circular  economy.  Renewables  (biomass,  biogas,  waste,  black 
liquor, solar and geothermal) are part of the possibilities, but cost-potential curves limit their 
expansion. They can be used in specific processes. 

Domestic appliances and equipment 

The  assumptions  for  domestic  appliances  and  equipment  refer  to  purchasing  costs,  i.e., 
total  acquisition  costs,  and  efficiency  by  vintage  for  several  space  and  water  heating 
technologies  and  appliances  used  in  the  buildings  sector  (residential  and  services).  The 
technical and economic characteristics of each technology category change over time as a 
result of learning-by-doing and economies of scale in industrial production.  

Renovation costs 

Renovation  cost  assumptions  concern  the  costs  reflected  in  the  PRIMES  BuiMo  for 
renovation  per  climate  zone  and  depth.  Investment  costs  are  the  energy  related 
expenditures needed to implement the indicated level of energy renovation of a building, 
excluding  usual  renovation  expenditures  needed  for  other  purposes  (structure,  finishing 
materials,  decoration  etc.).  The  energy  savings  rate  refers  to  a  typical  building  as  in the 
current stock of existing buildings, not savings in new constructions. 

New fuels 

Technologies  for  the  production,  transmission,  and  distribution  of  the  so-called  synthetic 
fuels (e-fuels) as well as storage technologies are presented. The following items are listed: 
“Investment costs”, “Fixed O&M costs”, “Heat rate” (ratio of energy input requirements over 
output),  “Feedstock  input  requirements”  (feedstock  input  required  for  the  production  of  1 
unit of output from each technology). 

2.4.4. Learning curves for energy technologies 

The techno-economic characteristics of existing and new energy technologies used in the 
demand  and  the  supply  sectors  of  the  energy  system  evolve  over  time  and  improve 
according to exogenously specified trends including learning rates. Learning curves apply 
for specific technologies, thus reflecting decreasing costs and increasing performances as 
a  function  of  cumulative  production.  The  steepness  of  the  learning  curve  differs  by 
technology, depending also on their current stage of maturity.  

For power generation technologies the Reference Scenario takes the view that all power 
technologies  known  today  are  projected  to  improve  in  terms  of  unit  cost  and  efficiency, 
without however assuming breakthroughs in technology development.  

At any given time, several technologies are competing with different performance and costs. 
Following  the  logic  developed  in  the  previous  Reference  Scenarios,  consumers  and 
suppliers are generally hesitant to adopt new technologies before they become sufficiently 
mature.  They  behave  as  if  they  perceive  a  higher  cost  (compared  to  engineering  cost 
evaluations  for  the  operation  of  such  equipment)  when  deciding  upon  adoption  of  new 
technologies. 

Public policies at EU and national level, through information campaigns, industrial policy, 
R&D  support,  taxation,  and  other  means,  aim  at  pushing  more  rapid  adoption  of  new 
technologies by removing or compensating uncertainties associated with their use. In this 

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way, the technologies themselves reach maturity more rapidly as a result of “learning-by-
doing”  effects  and  economies  of  scale.  Supportive  policies  for  the  adoption  of  new 
technologies thus lead to modifications of their overall perception. 

Considering the technology portfolio available, energy efficiency gains in the scenarios are 
driven by microeconomic decisions, reflecting the market agents' aim of minimizing costs 
and maximizing economic benefits operating in the context of public policies that promote 
energy  efficiency.  Similarly,  renewables  and  CHP  development  are  driven  by  private 
economic considerations also taking into account supportive policies which are assumed to 
continue in the Reference Scenario and gradually decrease in the longer term (see policy 
assumptions).  

On the macro-economic level, GDP growth is associated with continuous improvement of 
the technological basis leading to improved energy intensity. This is also supported by the 
effects from structural change in the economy. 

Last  but  not  least,  the  deployment  of  some  of  the  new  technologies  depends  on  the 
development of regulations and new infrastructure, which are partly driven by policies and 
require investments from the relevant actors, i.e., the TSOs and DSOs. Examples are the 
building of interconnectors and expansion of the grid, the development of CCS to transport 
and store captured CO2, the electrification of transport etc. This is also valid for the uptake 
of “new” fuels including hydrogen.  

2.4.5. Non-CO2 mitigation options 

For non-CO2 greenhouse gas-emitting technologies, abatement costs are differentiated per 
sector, technology, and pollutant to reflect the wide variety of sectors and activities covered. 
The following main sectors are distinguished: agriculture, waste and wastewater, energy, 
cooling  and  refrigeration,  and  industry.  These  sectors  are  split  into  sub-activities  where 
needed  (e.g.,  non-dairy  and  dairy  farms).  Given  the  uncertainty  in  terms  of  future 
developments, costs as well as efficiencies of options for reducing methane, nitrous oxides 
and F-gas emissions are assumed to remain constant over time.  

2.4.6. LULUCF mitigation options 

The land-use simulation model GLOBIOM and the forest sector model G4M are commonly 
applied in an iterative manner for the estimation of LULUCF emission pathways for each 
EU  Member  State.  GLOBIOM  and  G4M  models  cover  together  all  UNFCCC  land  use 
categories  of  relevance  for  CO2  emissions,  only  wetlands  and  settlements  are  added 
exogenously based on the 2020 GHG inventory. Also, non-CO2 emissions from LULUCF 
are added by an offset-calibration procedure.  

G4M covers the forestry sector and delivers emissions from biomass, dead organic matter 
and soil from afforestation and deforestation activities and biomass emissions from forest 
management. GLOBIOM provides emissions from cropland and grassland management. 

GLOBIOM58 computes a market equilibrium for agricultural and forest products by allocating 
land  use  among  production  activities  to  maximize  the  sum  of  producer  and  consumer 
surplus,  subject  to  resource,  technological,  demand  and  policy  constraints.  G4M59  then 
estimates the impact of forestry activities (afforestation, deforestation, residue harvest and 
forest  management)  on  biomass  and  other  carbon  pools.  By  comparing  the  net  present 
value of managed forest (difference of wood price and harvesting costs, income from storing 
carbon in forests) with the potential income from alternative land use on the same place, a 
decision on afforestation or deforestation is made.  

58 See also: https://iiasa.github.io/GLOBIOM/.  
59 See also: www.iiasa.ac.at/G4M 

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2.5. Key policies modelled in the Reference Scenario  

The  Reference  Scenario  builds  on  policies  at  EU  and  Member  State  level,  whose 
implementation  intensifies  until  2030  and  continues  afterwards,  assuming  no  additional 
measures apply between 2030 and 2050.  

EU level policies cover those adopted in the fields of energy, transport, and climate until 
December 2019 (cut-off date). These include the directives and regulations included in the 
“Clean Energy for All Europeans” package, the revised EU ETS Directive, and key transport 
policies  such  as  the  CO2  standards  for  vehicles,  the  Directive  on  alternative  fuels 
infrastructure, the Clean Vehicles Directive, etc. 

National policies considered in the Reference Scenario are the ones adopted as part of the 
NECPs and other national plans, as well as those planned to be adopted. This includes in 
particular coal phase-out and nuclear related policies.  

Many EU countries have plans to phase-out coal-fired power generation, driven by political 
decisions  but  also  by  the  rising  carbon  price  following  the  strengthening  of  the  Market 
Stability  Reserve.  Denmark,  Finland,  Greece,  Hungary,  Ireland,  Italy,  the  Netherlands, 
Portugal, Slovakia, and Spain have committed to stop using coal until 2030. France is set 
to phase out coal before 2025 and keep some coal plants to meet the need for reserves, 
while Germany has opted for a gradual phase-out which should be completed by 2038. In 
Sweden coal is used only at peak times and so market forces are expected to drive coal 
phase-out whereas in Slovenia coal-fired generation is set to end by 2050. Bulgaria, the 
Czech  Republic,  Croatia,  Poland,  and  Romania  have  not  made  any  commitment  to  exit 
coal, while eight Member States have no operational coal power plants in place60.  

As per nuclear energy, while some Member States maintain nuclear energy as part of the 
mix,  others  including  Germany  and  Belgium  but  also  France  have  made  political 
commitments to either ban or reduce nuclear from their power mix by 2035 at the latest. 
Already, the EU’s Long-Term Strategy61 projected the share of nuclear in Europe’s power 
mix to drop to 15% in 2050, while  according to the IEA without lifetime extension and/or 
long-term operation, the share of nuclear in the power mix could fall from 25% in 2017 to 
5% in 204062. 

Other  important  national  policies  considered  in  the  Reference  Scenario  are  policies  that 
help Member States reach national targets, e.g., contributions to the EU energy efficiency 
(EE)  and  renewable  energy  (RES)  targets,  national  transport  mandates  and  domestic 
targets for ESR emission reductions. Such policies include support schemes for RES and 
buildings’ deep renovation, programmes for the large-scale electrification of the public fleet 
and  Public-Private  Partnerships  (PPPs)  for  the  uptake  of  EVs  and  infrastructure  rollout. 
Also,  measures  for  fuel  blending,  incentives  to  boost  demand  response  and  self-
consumption, and energy and transport taxation schemes. 

2.5.1. Overview of the EU ETS and projections on carbon prices 

The EU ETS is modelled following the revision of the EU ETS Directive in 2018, which paves 
the  way  for  phase  IV  of  the  EU  ETS  (2021-2030)  and  allows  the  EU  to  meet  the  2030 
emission reduction target and deliver on its commitment to the Paris Agreement. The ETS 
sector  includes  energy  related  combustion,  process  emissions  of  the  relevant  industrial 
sectors  and  certain  industrial  non-CO2  GHGs  i.e.,  N2O  from  adipic  acid,  nitric  acid  and 
glyoxylic  acid/glyoxal  production  and  PFC  from  primary  aluminium  production. While  the 
former are directly part of the PRIMES model, the non-CO2 GHGs are integrated based on 
results of GAINS non-CO2 modelling (see section on non-CO2 emission results). 

60 Austria, Belgium, Cyprus, Estonia, Latvia, Lithuania, Luxembourg, and Malta 
61 See the "In-depth analysis in support of the Commission Communication  
COM (2018) 773”: com_2018_733_analysis_in_support_en_0.pdf (europa.eu) 
62 “European Union 2020 – Energy Policy Review”, June 2020, International Energy Agency  

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In phase IV (2021-2030), the Market Stability Reserve (MSR) is reinforced. The cap on EU 
ETS allowances (hereinafter allowances) is subject to an annual linear reduction factor of 
2.2%. The modelling accounts for the different allowance allocation rules (auctioning, free 
allowances  based  on  benchmarks)  foreseen  in  the  legislation  for  the  different  sectors, 
including the provisions for sectors at risk of carbon leakage.  

The  latter  are  however  assumed  to  meet  the  benchmarks  required  by  legislation  and  to 
have an incentive to shift towards cleaner fuels and processes, despite being eligible for 
free allocation of allowances. The EU ETS legislation is assumed to continue in its current 
scope (phase IV) throughout the projection period to 2050; also,  the rules relating to the 
MSR, and carbon leakage are assumed to remain unchanged in the character of “current 
policies” of the Reference scenario. The Reference Scenario assumes that from 2045 the 
ETS  keeps  reducing  emissions,  albeit  at  a  lower  rate  than  the  current  Linear  Reduction 
Factor. 

Aviation emissions are partly covered by the EU ETS, yet the geographic scope is limited 
to  intra-EEA  flights  from  2017  until  the  end  of  2023.  This  is  in  support  of  the  resolution 
adopted in 2016 by the International Civil Aviation Organization (ICAO) on a global market-
based  measure,  i.e.  the  'Carbon  Offsetting  and  Reduction  Scheme  for  International 
Aviation’  (CORSIA).  The  EU  ETS  for  aviation  is  subject  to  a  review  in  light  of  the 
international  developments  related  to  the  operationalization  of  CORSIA.  The  review 
considers  how  to  implement  the  global  measure  in  Union  law.  In  the  absence  of  the 
amendment, the EU ETS would revert back to its original full scope from 2024. Aviation is 
modelled in the scope covered by Eurostat (and therefore PRIMES), namely it is based on 
fuels sold in the EU, which corresponds to domestic and outgoing international flights.  

PRIMES simulates emission reductions in ETS sectors as a response to current and future 
ETS  prices  as  well  as  other  price  assumptions  and  policy  drivers,  considering  the  risk-
averse behaviour of market agents which leads to banking of allowances, perfect foresight 
of the carbon price progression in the period 2025-2050 and the fact that no borrowing from 
the future is permitted, however banking is possible. ETS prices are endogenously derived 
with model iterations until the cumulative ETS cap is met and the provisions of the MSR are 
respected.  

The ETS emissions target for 2030 is overachieved. Alongside the ETS price there are also 
a  wide  variety  of  additional  policies  being  implemented,  particularly  Member  State  coal 
phase-out  policies,  RES  support  policies  but  also  Ecodesign  and  the  Energy  Efficiency 
Directive (EED), which influence the demand for ETS sector allowances.  

The assumptions at the time of the modelling63 points to EUR 30/tCO2 in 2030 (Figure 8). 
From 2030 onwards, the ETS price increases to meet continued reductions in the cap, while 
being confronted with the reduction or phasing out of other policy drivers and a combination 
of energy supply factors. These include delayed technology developments of CCS, public 
acceptance  of  nuclear  energy  and  CO2  storage,  offshore  wind  cost  assumptions  and 
phasing out of RES support as well as trends in world fuel prices.  

63 The modelling applies to the ETS policy framework for 2030 as of end of 2019, and was realised before the political 
agreement on the new climate target of reducing EU’s net GHG emissions by at least 55% in 2030, which has very likely 
affected the dynamics of the ETS price since the end of 2020. 

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Figure 8: ETS emissions and carbon prices 

2.5.2. Energy efficiency 

With  regards  to  energy  efficiency,  the  Reference  Scenario  reflects  policies  at  EU  and 
Member  State  level,  including  the  Ecodesign  Directive64  and  the  Energy  Labelling 
Regulation65  as  well  as  the  implementing  measures,  the  revised  Energy  Efficiency 
Directive66 (EED) and the revised Energy Performance of Buildings Directive67 (EPBD). It 
also reflects the level of ambition of the national contributions set in the NECPs, meaning 
that  the  32.5%  energy  efficiency  target  for  the  EU  will  not  be  met  in  2030,  due  to  the 
collective ambition gap and insufficient efforts proposed by Member States. The following 
sections briefly discuss these policies and their impact on the energy system.  

PRIMES  can  simulate  different  energy  efficiency  policies  with  different  modelling 
techniques.  The  model-specific  instruments  used  affect  the  context  and  the  conditions 
under which individuals, represented by stylized agents per sector, take decisions regarding 
energy consumption and the related equipment.  

Modelling such policies and instruments can be done in different ways. One is to modify 
model  parameters  in  order  to  mirror  technology  performance  or  the  effects  of  building 
codes.  These  parameters  are  determined  jointly  in  the  process  of  calibrating  the 
interdependent model output to the observations of the most recent statistical year. Another 
way is to assume that, within the model projection and under certain scenario conditions, 
improved  equipment  and  appliances  become  available  to  consumers  as  future  choices. 
Furthermore, there are specific modelling instruments for capturing the effects of efficiency 
performance standards ranging from ordinary technologies i.e., the currently available and 
common technologies, to advanced and best available technologies. Eco-design standards 
have been taken into account for the entire spectrum of technologies, particularly to define 
the standard or ordinary technologies. 

64 Directive (EU) 2009/125/EC  
65 Regulation (EU) 2017/1369 
66 Directive (EU) 2018/2002 
67 Directive (EU) 2018/844 

42 

11.57.525.026.530.050.080.0120.0150.0201020152020202520302035204020452050Carbon price in ETS sectors (€'15/tCO2eq.)Source: PRIMES 
 
                                                 
EU REFERENCE SCENARIO 2020 

Modelling  instruments  can  be  individual  technologies  or  groups  of  technologies,  which 
either  modify  the  perception  of  modelled  agents  for  associated  costs  or  influence  the 
portfolio  of  technologies  that  will  be  available  for  consumer  choice  or  even  eliminate 
equipment  that  will  no  longer  be  available on the market  (for  example  due to mandatory 
minimum energy performance standards - MEPS).   

Measures  that  improve  consumer  information  through  education,  labelling,  energy 
performance certificates, correct metering and billing, energy audits and technology support 
schemes, inciting consumers to select more efficient technologies, are either addressed by 
the  modelling  instruments  discussed  in  this  section  or  directly  reflected  in  the  modelling 
mechanisms, where economic agents are informed correctly about the prevailing and - to 
some extent - future prices. This depends on the sector, since final demand sectors with 
shorter  equipment  lifetimes  offer  limited  foresight  compared  to  e.g.,  power  generation  or 
certain industrial sectors. 

The penetration of ESCOs as explicitly incited by the EED creates a low-risk environment 
for  consumers  to  engage  in  energy  efficiency  investments  in  the  building  structure  and 
energy  equipment.  As  is  the  case  with  labelling  policies,  the  potential  benefits  of  the 
penetration  of  ESCOs  are  modelled  through  reduced  discount  rates  for  certain  sectors, 
mirroring the changes in the decision-making conditions and constraints of e.g., households 
and services. In addition, these measures induce lower technical and financial risk, reducing 
the perceived costs of new technologies and saving investments (see also point above on 
perception of costs).  

Another key modelling tool relates to energy savings or efficiency value, acting as a virtual 
subsidy  (or  penalty)  that  makes  energy  saving  investment  more  profitable  for  decision-
makers.  The  energy  efficiency  value  may  represent  the  market  clearing  price  of  white 
certificates,  namely  the  marginal  cost  of  policies  that  oblige  utilities  to  perform  energy 
savings at their customer’s premises - otherwise they are subject to a penalty.  

In the Reference Scenario these values represent the implementation of the EED energy 
savings  obligations  in  domestic  and  service  sectors,  specific  building  renovation  policy 
efforts  or  a  large  range  of  other  pertinent  measures,  such  as  energy  audits,  energy 
management systems, good energy advice to consumers on the various benefits of energy 
efficiency investment and better practices, targeted energy efficiency education, significant 
voluntary agreements, etc. 

In  the  new  PRIMES  BuiMo  model  building  codes  are  explicitly  introduced.  The  country 
relevant regulations for new buildings as well as for major renovation of existing buildings 
are  directly  included  in  the  modelling.  The  Reference  Scenario  does  not  assume  full 
compliance with the minimum performance requirements; compliance rates concern only 
the building codes of new buildings. For the years until 2015 the model makes use of the 
compliance  rates recorded  in  the  relevant  study  led  by  ICF  International68  and from  that 
year onwards it assumes a steady increase of compliance rates with the EPBD. Moreover, 
the provisions of the EPBD for new buildings are considered, as well as country-specific 
regulations laid down in the NECPs, such as the Long-Term Renovation Strategies (where 
available). 

The EED includes specific public procurement provisions and induces multiplier effects, as 
the public sector assumes an exemplary role, i.e., private consumers are imitating the public 
sector energy efficiency actions. Energy efficiency improvements also occur on the energy 
supply side, through the promotion of investments in CHP and in distributed steam and heat 
networks. These investments are combined with incentives on the consumer side to shift 
towards  heating  through  district  heating,  both  in  the  residential  and  the  tertiary  sectors, 
where applicable based on country plans and regulations.  

68 https://ec.europa.eu/energy/sites/ener/files/documents/MJ-04-15-968-EN-N.pdf 

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Improvements  in  the  network  tariff  system  and  the  regulations  regarding  the  design  and 
operation of gas and electricity infrastructure are also required in the context of the EED; 
moreover,  the  EED  requires  Member  States  and  regulators  to  encourage  and  promote 
participation of demand side response in wholesale and retail markets. In this context, the 
Reference  Scenario  assumes  that  intelligent  metering  is  gradually  introduced  in  the 
electricity  system. This enables consumers to manage their  energy  use  more  actively.  It 
allows for demand response so as to decrease peak and over-charging situations, which 
generally  imply  higher  losses  in  the  power  grids.  Thus,  efficiency  is  also  improved  as  a 
result of the intelligent operation of systems.  

Finally,  some  policies  and  measures  that  do  not  target  energy  efficiency  per  se  lead  to 
significant additional energy efficiency benefits. Among these policies are the ETS Directive, 
the  Effort  Sharing  Decision  (ESR),  and  the  CO2  standards  for  light  duty  and  heavy-duty 
vehicles.  

Policies on promoting RES also indirectly lead to energy efficiency gains; in statistical terms 
many  RES,  such  as  hydro,  wind  and  solar  PV,  have  an  efficiency  factor  of  1;  thus,  the 
penetration of RES in all sectors, in particular in power generation, induces energy savings 
in primary energy terms.  

Other measures that foster energy efficiency relate to taxation, in particular excise duties 
and national carbon taxes. Excise duties are directly modelled in PRIMES by Member State 
and type of fuel, allowing for the full reflection of the effects of energy taxation and other 
financial instruments on end user prices and energy consumption. By assumption, current 
tax rates per Member State are kept constant in real terms throughout the projection period. 

2.5.3. Renewable energy policies 

The Reference Scenario starts from the assumption that the EU energy system would need 
to evolve based on the EU legally binding target for RES in the revised RES Directive (at 
least  32%  share  of gross final  energy consumption from  RES  by  2030), which is  slightly 
overachieved (33.1%) based on the aggregation of national contributions laid down in the 
NECPs.  

The  Reference  Scenario  considers  the  most  recent  available  data  on  RES  potentials  by 
Member State and the projections on RES share trajectories by sector (overall, RES-H&C 
for heating and cooling, RES-T for transport and RES-E for electricity) as expressed in the 
NECPs.  Furthermore,  RES  potentials,  which  previously  relied  on  different  sources,  have 
been updated and refined on the basis of the ENSPRESO database of JRC. 

According  to  the  projection,  the  enabling  conditions  for  the  penetration  of  RES  improve 
significantly, since the Reference Scenario incorporates known direct RES aids (e.g., feed-
in  tariffs,  feed  in  premium  schemes)  and  other  RES  supporting  policies,  such  as  priority 
access, grid development and streamlining of authorisation procedures. 

PRIMES provides a detailed modelling of Member State policies representing a variety of 
economic  support  schemes.  RES  investments  and  incentives  resulting  from  the  overall 
policy and economic context have been projected, assuming that investors evaluate project-
specific Internal Rates of Return including the financial incentives and decide upon investing 
accordingly. The projected RES investments implied directly for the financial incentives are 
considered  as  given  by  the  market  model,  which  decides  upon  the  remaining  potentially 
necessary investments (among all power generation technologies) based on pure economic 
considerations with a view to meeting the RES obligations.  

Moreover, special fuel and electricity price elements (fees) are accounted for in the model 
to recover fully all the costs associated with RES deployment, which are calculated through 
the incentives and the contracting obligations over time. The model keeps track of the RES 
technology vintages as projected too. 

The RED II requires fuel suppliers to supply a minimum of 14% of the energy consumed in 
road and rail transport by 2030 as renewable energy. Renewable energy used in aviation 

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EU REFERENCE SCENARIO 2020 

and maritime transport sectors can also contribute towards this target. For this target to be 
met, RED II defines a series of sustainability and GHG emission criteria that bioliquids used 
in transport must comply with. Some of these criteria are the same as in the original RED, 
while others are new or reformulated and are thus captured in the modelling. In particular, 
the RED II introduces sustainability for forestry feedstocks as well as GHG criteria for solid 
and gaseous biomass fuels. 

To address the issue of Indirect Land Use Change (ILUC) RED II sets limits on high ILUC-
risk  biofuels,  bioliquids  and  biomass  fuels  with  a  significant  expansion  in  land  with  high 
carbon stock. Member States will still be able to use (and import) fuels covered by these 
limits, but not include these volumes when calculating the extent to which they have fulfilled 
their RES targets (overall and in transport). These limits freeze at 2019 levels for the period 
2021-2023 and start to decrease gradually from the end of 2023 reaching zero in 2030. The 
Directive also introduces an exemption from these limits for biofuels, bioliquids and biomass 
fuels certified as low ILUC-risk. 

Within  the  14%  transport  sub-target,  there  is  a  dedicated  target  for  advanced  biofuels 
produced from feedstocks69. Their contribution as a share of final consumption of energy in 
the transport sector shall be at least 0.2 % in 2022, at least 1 % in 2025 and at least 3.5 % 
in 2030. Multipliers as foreseen in the RES-T calculation of the RED II are fully accounted 
for in the modelling for advanced biofuels as well as for electricity consumption in transport. 
The multipliers foreseen for aviation and maritime are also accounted in the modelling.  

For  Member  States  which  are  not  projected  to  achieve  their  RES  target  through  direct 
incentive policies in the first place, an additional instrument is included in the modelling, the 
so-called RES-value. In modelling terms, the RES-value is the shadow value of the RES 
target:  it  can  be  applied  to  all  types  of  RES  individually  e.g.,  to  RES-E,  RES  H&C  and 
RES-T. It represents yet unknown policies, which would be implemented by 2030 to provide 
the  necessary  incentives  to  reach  the  national  RES  shares.  These  could  include  further 
legislative  facilitations,  easier  site  availability  or  grid  access,  or  even  direct  financial 
incentives.  The  costs  related  to  investments  induced  through  the  RES-value  are  fully 
reflected in the model and recovered through electricity prices, the steam/heat prices and 
are accounted for in the investment costs of buildings for direct RES use. A separate RES-
value  for  transport  is  also  applied,  where  necessary,  to  achieve  the  14%  obligation  for 
RES-T in 2030.  

Beyond 2030, no additional RES targets are set and therefore no additional specific RES 
policy support is modelled, as a general rule.  

Although direct incentives are phased out in power generation, investments in RES continue 
beyond 2030 due to three main factors: (i) the learning-by-doing assumed in the techno-
economic  assumptions  (see  Annex  III),  which  makes  several  RES  technologies 
economically competitive; (ii) the increasing ETS carbon price; and (iii) extensions  in the 
grid  and  improvement  in  market-based  balancing  of  RES  as  well  as  maintaining  priority 
dispatch, although the possibility for RES curtailment is also modelled. The latter implies 
that RES curtailment is possible if the system requires it, however the continuation of RES 
priority dispatch in the Reference Scenario implies that this option is barely used under such 
conditions.  In  addition,  some  incentives  for  innovative  technologies  such  as  tidal, 
geothermal, solar thermal, and remote off-shore wind are phased out more gradually than 
for mature technologies.  

In transport, national blending obligations are assumed to be maintained post-2030, where 
these exist. 

69 Part A, Annex IX, Directive (EU) 2018/2001 

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2.5.4. Transport policies  

For the transport sector, the Reference Scenario reflects a wealth of policy measures at EU 
level, which drives: (i) the uptake of zero- and low-emission vehicles and the roll-out of the 
associated recharging/refuelling infrastructure70; (ii) the uptake of renewable and low carbon 
fuels71;  (iii)  improvements  in  transport  system  efficiency,  by  making  the  most  of  digital 
technologies and smart pricing and further encouraging multi-modal integration and higher 
use  of  sustainable  transport  modes72.  The  Reference  Scenario  also  includes  initiatives 
addressing  road  safety73,  contributing  in  this  way  to  the  reduction  of  external  costs  on 
transport. 

For the CO2 standards for light duty vehicles (LDVs), the Reference Scenario considers the 
Regulation that sets CO2 emission performance standards for new passenger cars and for 
new light commercial vehicles, adopted in 2019. The modelling assumes that average CO2 
emissions of new cars registered in the EU will have to be 15% lower in 2025 and 37.5% 
lower in 2030, compared to 2021. The CO2 emissions of new light commercial vehicles will 
need to be 15% lower in 2025 and 31% lower in 2030. These are represented as EU-wide 
fleet  targets  in  the  model,  in  line  with  the  Regulation.  The  CO2  reduction  effort  will  be 
distributed among manufacturers on the basis of the average mass of their vehicle fleet. 
Incentives provided for in the regulation are included in the modelling. 

For heavy duty vehicles (HDVs), the Reference Scenario takes into consideration the 2019 
Regulation74 that sets first-ever EU-wide CO2 emission performance standards to achieve 
a  15%  average  emissions  reduction  from  new  lorries  from  2025  onwards,  and  a  30% 
reduction from  2030. The targets  are  expressed  as  a  percentage  reduction  of  emissions 
compared to EU average in the reference period (1 July 2019 - 30 June 2020). Incentives 
provided for in the Regulation are included in the modelling. 

The  PRIMES-TREMOVE  model  has  been  shifted  entirely  to  WLTP  test  cycles  for  the 
achievement of the regulations and the true energy consumption and emissions are based 
on the real-world performance of cars.  

The  energy  consumption  calculated  in  the  model  accounts  for  the  gap  between  the 
laboratory  tests  and  the  real-world  performance  of  cars.  The  model  uses  the  COPERT 
methodology to calculate energy consumption by vehicle type, type of trip and time, as a 
function of the average speed. The model does not assume one single value for specific 
fuel  consumption  of  vehicles.  The  model  considers  discrete  specific  fuel  consumption 
formulas for all trip types (i.e., more than 30) and for all vehicle technologies. The congestion 
effect, which is partly responsible for the discrepancy, is also captured through changes in 
the  average  speed  of  vehicles.  Assuming,  for  example,  that  a  vehicle  is  mostly  used  in 
urban  areas,  this  results  in  lower  average  speed,  which  increases  its  specific  fuel 
consumption.  Furthermore,  different  types  of  technologies  (battery  electric,  internal 
combustion,  plug-in  hybrid)  have  different  characteristics  which  can  influence  their 
performance  depending  on  the  trip  type.  This  implies  that  the  model  calculates  different 
divergence factors taking into consideration vehicle type and trip type. The market uptake 
of new vehicles complying with these standards continues to drive emissions down post-
2030. 

70 It includes the post-2020 CO2 standards for new light duty and heavy-duty vehicles, the Clean Vehicles Directive, and the 
Directive on the deployment of alternative fuels infrastructure.
71 It covers the Renewables Energy Directive, the Fuel Quality Directive, and the Directive on the deployment of alternative 
fuels infrastructure. 
72 It includes the TEN-T Regulation supported by CEF funding, the fourth Railway Package, the Rail Freight Corridors 
Regulation, the Directive on Intelligent Transport Systems, the European Rail Traffic Management System European 
deployment plan, the Regulation establishing a framework for the provision of port services, the EU urban mobility package, 
etc. 
73 Based on the fatality and serious injury savings projected by the changes to the General Safety Regulation (Regulation 
(EU) 2019/2144) and the Road Infrastructure Safety Management Directive (Directive (EU) 2019/1936). 
74 Regulation (EU) 2019/1242 

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Moreover, the Reference Scenario considers the AFID Directive75, which promotes electro-
mobility  and  the  use  of  alternative  fuels  (e.g.,  liquefied  natural  gas  in  road  freight  and 
shipping), accounting for incentives for the uptake of alternative fuels infrastructure in place 
at Member State level. 

Improvements in transport system efficiency, by tapping into digital technologies and smart 
pricing and further encouraging multi-modal integration and shifts towards more sustainable 
transport modes, are facilitated by the TEN-T Regulation, the Fourth Railway Package, the 
Directive on Intelligent Transport Systems, the European Rail Traffic Management System 
European  deployment  plan,  the  Regulation  establishing  a  framework  for  the  provision  of 
port services, and others. 

For aviation, the Reference Scenario considers the implementation of the EU ETS (section 
2.5.1),  the  Single  European  Sky,  the  deployment  of  SESAR  solutions,  the  research  and 
development  of  cleaner  aircraft  technologies  lead  by  the  Clean  Sky  public-private 
partnership  and  aircraft  CO2  emissions  standards,  as  part  of  the  so-called  “basket  of 
measures” that aims to reduce emissions from the sector. 

For maritime, the Reference Scenario reflects the Energy Efficiency Design Index (EEDI) 
and the Ship Energy Efficiency Management Plan (SEEMP) adopted by the International 
Maritime  Organisation  (IMO),  as  well  as  IMO  MARPOL  Annex  VI  rules  as  regards  the 
reduction  of  nitrogen  and  sulphur  oxides  emissions,  the  latter  being  transposed  into  EU 
legislation  by  the  Sulphur  Directive76.  It  also  accounts  for  other  initiatives  addressing  air 
pollution from inland waterways vessels77. 

2.5.5. Other policies impacting sectors covered by the ESR 

For sectors not covered by the EU ETS, excluding LULUCF, the Effort Sharing Regulation 
(ESR)  sets  binding  annual  reduction  targets  for  Member  States  aiming  at  an  EU-wide 
emission reduction of 30% by 2030 compared to 2005. The ESR targets are set according 
to national wealth and cost-effectiveness and flexibilities such as transfers between Member 
States are foreseen. The national ESR 2030 targets range from 0% to -40%. To achieve 
the  targets,  the  ESR  also  defines  for  each  country  a  linear  reduction  trajectory  defining 
annual emission allocations between 2021 and 2030.  

The ESR maintains flexibilities of the Effort Sharing Decision (e.g., banking, borrowing, and 
buying and selling between Member States) and provides two additional flexibilities to allow 
for a fair and cost-efficient achievement of the targets: access to allowances from the EU 
ETS and access to credits from the land use sector. 

Overall  existing  policies  as  reflected  in  the  Reference  Scenario  result  in  an  emission 
reduction in the ESR by 2030 that is more ambitious than the set target.  

Accessing allowances from the EU ETS 

The  ESR  allows  certain  Member  States  that  have  national  reduction  targets  significantly 
above the Union average and their own cost-effective reduction potential, or that did not 
allocate any EU ETS allowances for free to industrial installations back in 2013, to use a 
limited amount of ETS allowances in the period 2021-2030 in order to offset emissions in 
the effort sharing sectors. Member States may as well request downward revisions of their 
percentages for later years in the compliance period in 2024 and 2027, respectively. Some 
Member States intend to use their full amount of flexibility, others have decided not to. The 

75 Directive 2014/94/EU 
76 Directive (EU) 2016/802 of the European Parliament and of the Council of 11 May 2016 relating to a reduction in the 
sulphur content of certain liquid fuels, OJ L 132, 21.5.2016, p. 58–78.
77 Regulation (EU) 2016/1628 of the European Parliament and of the Council of 14 September 2016 on requirements 
relating to gaseous and particulate pollutant emission limits and type-approval for internal combustion engines for non-road 
mobile machinery. 

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EU REFERENCE SCENARIO 2020 

allowances  used  under  the  ESR  are  deducted  as  of  2021  from  the  amounts  that  would 
normally be auctioned under the EU ETS and thus reduce the overall ETS cap.  

Accessing credits from the land-use sector 

Member States can use up to 262 million credits from the land-use sector over the entire 
period 2021-2030 to comply with their national targets deriving from the ESR. All Member 
States are eligible to make use of this flexibility if needed, while access is higher for Member 
States  with  a  larger  share  of  emissions  from  agriculture,  which  acknowledges  the  lower 
mitigation potential for emissions from the agriculture sector. The use of such credits is not 
explicitly modelled, given that the ESR as a whole is projected to be in compliance 

Agriculture  

Much  of  the  legislation  affecting  agriculture  has  impacts  on  projected  activity.  The  new 
Common Agricultural Policy (CAP) reform78  for the period 2021-2027 was agreed on 29 
June 2021 between the European Parliament and Council of the EU, and so the provisional 
start date has been pushed to 2023. The new CAP includes various changes to the system 
of direct payments which are included in CAPRI. Agricultural policies that aim at improved 
use  of  fertilizers  or  that  have  impacts  on  livestock  productivity  and  stock  numbers  are 
reflected  in  the  CAPRI  parameters  reflecting  nitrogen  use  efficiency.  However,  while  a 
general improvement in nitrogen use efficiency is included, the CAPRI projections do not 
include the specific targets set out in the Farm to Fork Communication79, for example a 25% 
share  for  organic  agriculture,  a  20%  reduction  in  fertiliser  use  and  a  50%  reduction  in 
nutrient losses. It neither includes the impact of CAP strategic plans under development. 
Requirements  to  avoid  further  conversion  of  permanent  grassland  into  other  uses  were 
directly implemented. The resulting changes in projected activity levels (herds and mineral 
fertiliser consumption) had direct impact on non-CO2 greenhouse gas emission estimates 
in GAINS via the use of CAPRI activity data as drivers. 

F-gases  

The  Reference  Scenario  considers  the  EU  F-gas  Regulation,  in  force  since  2015,  which 
aims to phase out the amount of hydrofluorocarbons (HFCs) that can be sold in the EU to 
1/5 of today's sales. In the Reference Scenario this is expected to cut EU F-gas emissions 
by 65% between 2015 and 2030, and by around 81% until 2050. The corresponding cuts in 
expected HFC emissions are 70% by 2030 and 88% by 2050. To assess the impact of the 
new Regulation, the modelling work has accounted for the useful lifetime of the refrigeration 
and air-conditioning units, market penetration of low-GWP HFC alternatives, etc. In GAINS, 
a  number  of  low  GWP  alternatives  to  HFCs  are  considered  (with  varying  applicability  to 
different sectors), i.e., hydrocarbons (i.e., HC-600a, HC-290, HC-1270), ammonia (NH3), 
pressurized carbon dioxide (CO2), hydrofluoroolefins (i.e., HFO-1234yf, HFO-1234ze, HFO-
1233zd,  HFO-1336mzz),  low-GWP  HFCs  and  blends  (HFC-32,  HFC-152a,  HFC-446A, 
HFC-447A),  water  chillers  and fluorinated ketone  (FK). Though  a  range  of  hydrocarbons 
have refrigerant applications, iso-butane (HC-600a) is the most frequently used in domestic 
fridges  and  freezers,  while  for  commercial  fridges  and  freezers  propane  (HC-290)  is 
substituting HFCs in smaller units and pressurized CO2 in larger units. Propane (HC-290) 
and  HFC-32  are  common  substitutes  for  HFCs  in  stationary  air-conditioning.  Due  to  its 
relatively high GWP100 of 677, the use of HFC-32 in residential ACs makes up most of the 
remaining warming potential from HFCs in 2050. Furthermore, impacts on PFC emissions 

78 https://ec.europa.eu/info/food-farming-fisheries/key-policies/common-agricultural-policy/future-cap_en    
79 COM/2020/381 final 

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from  a  voluntary  agreement  in  the  semiconductor  industry  have  been  considered  in  the 
GAINS Reference Scenario. 

Waste  

The Circular Economy Package was adopted in 2015 with the aim to stimulate Europe's 
transition  towards  a  circular  economy  that  boosts  competitiveness,  fosters  sustainable 
economic  growth,  and  generates  new  jobs.  The  Package  includes  revised  legislative 
proposals  on  waste,  which  have  been  adopted  and  included  in  the  Reference  Scenario. 
These  are:  Directive  on  Waste80  and  Packaging  Waste81,  Landfill82  and  Electrical  and 
Electronic  Waste,  End-of-life  Vehicles,  Batteries  and  Accumulators  and  Waste  Batteries 
and Accumulators83. 

By  2030  a  number  of  targets  have  been  set:  a  common  EU  target  for  recycling  65%  of 
municipal waste and a common EU target for recycling 75% of packaging waste; a binding 
landfill target to reduce landfill to maximum of 10% of municipal solid waste (MSW) by 2035 
and  a  ban  on  landfilling  of  separately  collected  waste.  The  Reference  Scenario  also 
includes a number of national waste policies that may go beyond the Directives. In GAINS, 
the respective future treatment paths are simulated, and associated emissions estimated 
by taking the current treatment structures as starting points and assuming that countries 
meet the targets of the amended EU Waste Directive from 2018. A linear phase-in of the 
future  targets  is  assumed  for  the  period  2020  to  2035.  The  targets  are  met  by  moving 
increasing amounts of MSW away from landfill disposal and towards other treatment options 
(recycling, anaerobic digestion, incineration with energy recovery), considering the specific 
options available for different types of waste and following the treatment priority order of the 
EU waste hierarchy. 

2.5.6. LULUCF Regulation 

The Land Use, Land Use Change and Forestry (LULUCF) Regulation84 adopted in 2018 
enshrines for the first time in EU law the requirement accounted GHG emissions from land 
use, land use change or forestry are balanced by at least an equivalent accounted removal 
of CO2 from the atmosphere in the period 2021 to 2030. In practice, the LULUCF Regulation 
sets a binding commitment for each Member State to ensure that accounted emissions from 
land use are entirely compensated by an equivalent accounted removal of CO2 from the 
atmosphere through action in the sector (“no debit” rule). Moreover, the scope is extended 
to all land uses. 

The Regulation points to the critical role of the land use sector in reaching long-term climate 
mitigation objectives and includes incentives to improve land use practices. It also foresees 
flexibility and trading clauses, and flexibility towards the ESR; namely if a Member  State 
has net accounted emissions from land use and forestry, they can use allocations from the 
ESR to satisfy the "no debit" rule. Moreover, Member States can buy and sell net accounted 
removals  from  and  to  other  Member  States  or  choose  to  enhance  removals  or  reduce 
emissions in the LULUCF sector, thereby helping compliance of the agriculture sector in the 
ESR where emissions from fertilizer and livestock are accounted. 

Regarding  emissions  from  biomass  used  in  energy,  these  are  recorded  and  accounted 
towards each Member State's 2030 climate commitments for the first time, through a novel 
accounting application introduced by the LULUCF Regulation.  

In October 2020, the Commission amended Annex IV of the Regulation with a delegated 
act that lays down forest reference levels (FRLs) which each country must apply between 
2021 and 2025. Forest reference levels are forward-looking benchmarks for accounting net 

80 Directive (EU) 2018/851 
81 Directive (EU) 2018/852 
82 Directive (EU) 2018/850 
83 Directive (EU) 2018/849 
84 Regulation (EU) 2018/841   

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emissions from the existing forests in each EU country. They are based on a continuation 
of sustainable forest management practices from the period 2000-2009. They draw on the 
best available data and dynamic age-related forest characteristics. 

2.5.7. Assumptions  on  implementation  of  internal  energy  market 

policies 

The  Reference  Scenario  modelling  includes  a  shift  towards  flow-based  allocation  of 
interconnection capacities, assuming a market model purely relating trade to market forces 
throughout the EU internal energy market with perfectly operating market coupling across 
all participating countries. The EU target model is assumed to be successfully implemented 
from 2025. This implies that the Net Transport Capacity (NTC) levels  will be higher than 
currently (closer to their physical capabilities) and that there is higher coordination between 
TSOs reducing the balancing costs.  

Consequently, the balancing of RES is assumed to occur in a very cooperative and cost-
efficient manner, allowing to avoid excessive investments in peak devices. Assuming that 
improvements  in  grid  infrastructure  take  place  and  that  the  Ten  Years  Network 
Development Plan (TYNDP) of ENTSO-E (see next section) is completed, the integration 
of  RES  is  significantly  enhanced.  Moreover,  market  improvements  and  market  coupling 
across  the  EU  keep  balancing  costs  for  RES  low,  which  further  eases  their  market 
penetration. 

2.5.8. Updates in infrastructure developments 

The  PRIMES  model  and  its  sub-models  consider  the  official  infrastructure  development 
plans from ENTSOE, ENTSOG and the TEN-T networks for transport. 

Electric grid 

All  interconnectors  between  Member  States  with  their  technical  characteristics  and 
capacities are represented in PRIMES; the import-export module further includes also non-
EU countries such as Switzerland and Norway, as well as the Southeast European area, 
due to their strong connection with the EU electricity market. Interconnections to and from 
these countries are fully included.  

Regarding grid development and the interconnectors between countries the developments 
of  the  ENTSOE  Ten  Year  Development  Plan  (TYNDP)  are  accounted  for  in  the  import-
export module of PRIMES. The timeline of the TYNDP is also followed. After the end of the 
TYNDP,  expansions  are  based  on  the known capacity  expansion  developments  and the 
developments of RES. Within countries the grid expansions are assumed to be a function 
of capacity expansion particularly for RES. 

The ENTSOE development plan regarding grid reinforcements within each country is also 
being  considered.  Such  reinforcements  aim  at  relaxing  some  of  the  tight  Net  Transfer 
Capacity constraints prevailing today and causing congestions, and so help maximise the 
integration  of  RES  into  the  grid.  The  combination  of  these  elements  implies  that  the 
ENTSOE development plan not only reinforces interconnection of countries, but also allows 
for  wide  market  coupling  in  parallel  with  inter-TSO  coordinated  dispatching.  This 
interconnector list is largely based on the ENTSOE Ten-Year Network Development Plan, 
answers to questionnaires distributed during the consultation procedure, other studies and 
further review undertaken by E3-Modelling. 

Gas networks 

The PRIMES-Gas module represents in detail the present and future gas infrastructure of 
each Member State and of gas producing and consuming countries of the Eurasian area, 
including Russia, Ukraine, Belarus, the Caspian countries, Middle East (including Israel), 
Persian  Gulf  (including  Qatar  which  is  the  largest  LNG  supplier  worldwide)  and  North 

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African  countries  (Algeria,  Libya  and  Egypt).  The  model  also  represents  the  supply 
possibilities of LNG worldwide and the demand for LNG. The infrastructure types include 
gas  production,  pipelines  (represented  as  a  network),  gas  storage  facilities,  LNG 
regasification  terminals  and  gas  liquefaction.  Operation  of  infrastructure  and  related  gas 
flows are constrained by a physical system involving pipelines, LNG terminals, gas storage 
facilities, liquefaction plants and gas producing wells. 

The PRIMES-Gas module considers a comprehensive list of PCI gas infrastructure projects, 
including major gas infrastructure projects with neighbourhood countries, interconnections 
between  EU  Member  States,  expansion  of  existing  pipeline  capacities,  new  bidirectional 
pipelines, LNG import terminals and storage facilities in each of the EU 27 Member States. 
This list is largely based on the ENTSOG Ten-Year Network Development Plan, answers 
to questionnaires distributed during the consultation procedure, other studies and further 
review undertaken by E3-Modelling. 

Transport infrastructure 

The developments in transport infrastructure mainly affect transport activity projections. In 
the EU Reference Scenario, the core TEN-T network is assumed to be completed by 2030 
and  the  comprehensive  TEN-T  network  by  2050.  Foreseen  developments  for  rail  and 
motorways are included, also reflecting information received through the replies to the MS 
policy questionnaires. 

Regarding high-speed rail, the plans foreseen in the TEN-T guidelines have been included, 
complemented by information received through the replies to the MS policy questionnaires. 
In addition, the replies to the MS policy questionnaires (including existing plans) have also 
been used for assumptions on rail electrification. 

2.6. Other important assumptions 

2.6.1. Discount rates 

The  PRIMES  model  is  based  on  individual  decision  making  of  agents  demanding  or 
supplying energy and on price-driven interactions in markets. The modelling approach is 
not  taking  the  perspective  of  a  social  planner  and  does  not  follow  an  overall  least  cost 
optimization of the entire energy system in the long-term. Therefore, social discount rates 
play no role in determining model solutions.  

On the other hand, private discount rates pertaining to individual agents play an important 
role in their decision-making. Agents’ economic decisions are usually based on the concept 
of cost of capital, which is, depending on the sector, either the weighted average cost of 
capital  (for  larger firms) or  a  subjective discount rate  (for  individuals  or  smaller firms).  In 
both cases, the rate used to discount future costs and revenues involves a risk premium 
which reflects business practices, various risk factors or even the perceived cost of lending. 
The discount rate for individuals also reflects an element of risk averseness.  

The discount rates vary across sectors. In the Reference Scenario modelling, the discount 
rates range from 7.5% (in real terms) applicable to public transport companies or regulated 
investments as for example grid development investments (in the form of weighted average 
cost of capital) up to 12% applicable to individuals (households). Additional risk premium 
rates are applied for some new technologies at their early stages of development impacting 
on perceived costs of technologies.  

The decision-making discount rates used by sectors are summarised in the following tables. 

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EU REFERENCE SCENARIO 2020 

Table 4: Discount rates in energy supply sectors 

Assumptions for EU Reference Scenario 2020  

Regulated monopolies and grids 

Companies in competitive energy supply markets 

RES investment under feed-in-tariff 

Investment under contract for differences 

RES  investment  under  feed-in  premium,  RES  obligation,  quota  systems  with 
certificates 

RES investment in competitive markets 

Discount 
rates 

7.5% 

8.5% 

7.5% 

7.5% 

8.5% 

8.5% 

Risk premium specific to immature or less accepted technologies 

1%-3% 

Risk  premium  specific  to  investment  surrounded  by  high  regulatory  or  political 
uncertainty 

Country-specific risk premiums 

Table 5: Discount rates of firms in energy demand sectors 

None 

None 

Assumptions for EU Reference Scenario 2020  

Discount rates 

Energy intensive industries 

Non energy intensive industries 

Services sectors 

Public transport (conventional rail, public road) 

Public transport (advanced technologies, e.g., high speed rail) 

Business transport sectors (aviation, heavy goods vehicles, LCVs, maritime) 

Country risks 

7.5% 

9% 

11% 

7.5% 

8.5% 

9.5% 

None 

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Table 6: Discount rates of individuals in energy demand sectors 

Assumptions for EU Reference 
Scenario 2020  

Discount 
rates 

Modified discount rates due to 
EE policies85 

Passenger cars and powered two wheelers 

11% 

Households  for  renovation  of  houses  and  for 
heating equipment 

14.75% 

Households for choice of appliances 

13.5% 

12% 

9.5% 

By income class (for the decision on renovation and the choice of equipment) 

Low 

Low-Mid   

Mid   

Mid-High 

14.1% 

13.6% 

13.2% 

12.8% 

Table 7: Discount rates of refuelling/recharging infrastructure  

Assumptions for EU Reference Scenario 2020  

Discount rates 

Refuelling/recharging infrastructure 

8.5% 

The  use  of  discount  rates  is  also  necessary  for  annualising  capital  or  investment 
expenditures (CAPEX) for cost reporting. The yearly energy system cost in the Reference 
Scenario  modelling  is  calculated  using,  over  the  entire  period  of  the  projection,  a  flat 
discount rate of 10% for annualising CAPEX of end-consumers.  

Details on the methodology related to the discount rates can be found in Annex V of this 
report.  

The  GAINS  Reference  Scenario  modelling  also  uses  private  discount  rates,  using  a  flat 
discount rate of 10% for decision-making and cost reporting. 

85 It is assumed that standard discount rate values are pushed downwards by policies addressing the barriers which caused 
the high discount rate values in the first place. 

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3. Results of the Reference Scenario  
The Reference Scenario reflects the outcomes of adopted EU level policies by the end of 
2019 and takes into account national contributions and planned policies as well as Member 
State  projections  as  provided  in  the  respective  NECPs  in  relation  to  transport  activity, 
energy demand, power generation and GHG emissions in the EU until 2050. Projections 
are available on a five-year basis for each Member State and for the EU as a whole86.   

Considering the timeline of the policies included in the Reference Scenario, the modelling 
results  are  presented  for  two  periods:  2015-2030  and  2030-2050.  For  the  decade  2021-
2030 the Reference Scenario captures the impacts of policies adopted at EU level by the 
end  of  2019  and  mirrors  the  targets  from  the  NECP  With  Additional  Measures  (WAM) 
scenarios  where  available,  while  maintaining  a  homogenous  and  consistent  set  of 
assumptions across all EU Member States. 

National  policies  and  measures  may  be  accompanied  by  voluntary  initiatives  at  sectoral 
and/or regional level, provided these include monitoring and some form of enforcement and 
sanctioning mechanisms. The aforementioned policies, measures and initiatives form part 
of the NECPs.  

The  aim  of  the  Reference  Scenario  is  not  to  reach  climate  neutrality  but  to  reveal  the 
distance to be covered to that end, having the current policy framework as starting point. 
For  this  reason,  as  described  in  section  2.5,  the  level  of  intensity  of  policies  does  not 
increase beyond 2030, but technology and market dynamics along with the EU ETS  are 
those factors shaping the projections in the period 2030-2050. 

3.1. Energy consumption 

After peaking in 2006, energy consumption had started to decline already before the global 
financial crisis of 2007-2009 hit. The decoupling of energy demand from economic growth 
has  been  the  trend  since  then.  The  fall  in  consumption  is  projected  to  be  halted  only 
between  2020  and  2025,  due  to  the  rebound  of  energy  demand  in  the  aftermath  of  the 
COVID-19 pandemic. Energy efficiency and RES policies accelerate the decoupling in the 
decade 2021-2030. Long-lasting effects of these policies and technology trends sustain the 
decoupling also after 2030. 

86 Summary results for EU27 and for each country are presented in the Annex. 

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Figure 9: GIC in relation to GDP 

Energy intensity of GDP varies by country (see Figure 10), depending on the structure of 
primary  energy  production,  industrial  structure  and  renovation  and  the  fuel  mix  used  for 
electricity generation. Overall, the energy intensity of the economy is improving throughout 
the projection period and over time in all countries and a slow convergence can be observed 
as energy intensity declines and GDP increases faster in countries with initially high energy 
intensity. 

Figure 10: Gross Inland Consumption over GDP (toe/m euro’2015) by Member State in 2015 and 2030 

The  decreasing  trend  of  total  primary  energy  requirements  is  associated  with  the 
developments in final energy demand, as well as a shift towards renewable energy sources 
in  power  generation.  Energy  efficiency  promoted  through  the  EED,  EPBD  and  the 
Ecodesign  Directives  and  a  host  of  regulations  for  specific  products,  CO2  emissions 
standards for vehicles, etc. is a key driver of the drop in energy demand. It is clear that the 
period 2021-2030 will set the ground for an economy with lower energy intensity, while the 
assumed  continuation  of  these  policies  is  expected  to  deliver  energy  efficiency 
improvements after 2030 too. 

Figure 11 shows the projection of final energy consumption (including international aviation) 
by fuel and by sector. The effects of COVID-19 pandemic in 2020 can be observed in all 
sectors,  and  especially  in  transport.  After  a  short-term rebound following  the  exceptional 

55 

050100150200250199520002005201020152020202520302035204020452050Index 1995=100Gross Inl. Cons./GDPGross Inland ConsumptionGDPTrend 1995-200720152030 
 
EU REFERENCE SCENARIO 2020 

decline recorded in 2020, final energy demand of transport is projected to decline gradually 
from 2025 onwards driven by energy efficiency improvements. The structure of final energy 
demand  by  sector  remains  rather  stable  over  time,  with  transport  occupying  the  largest 
share until the end of the projection period, followed by industry and residential.  

In industry,  the  uptake of  energy  efficiency  measures  and the  shift towards  higher  value 
added and less  energy  intensive  products  causes  energy  demand  to  drop. The  share of 
energy  consumption  in  houses  and  buildings  decreases  in  2030  thanks  to  the  energy 
efficiency  policies.  In  the  meantime,  the  EU  ETS  continues  to  indirectly  support  energy 
efficiency and higher RES penetration in the ETS sectors. 

Final  consumption  of  oil  decreases  over  time.  However,  oil  retains  a  significant  share  of 
total demand, due to its persistence in transport and the limited uptake of alternative fuels 
under  Reference  Scenario  assumptions.  Meanwhile,  natural  gas  is  projected  to  remain 
relatively  stable  over  time,  slightly  declining  both  in  share  and  absolute  terms.  The 
impressive decline in solid fuels is the result of fuel switching in industry triggered by the EU 
ETS and the phase-out of coal boilers, linked with market trends and domestic policies. The 
share of direct RES use in final energy increases steadily over time but direct use of RES 
remains small in volume. District heating remains almost stable until 2050 with a tendency 
to increase slightly. 

Figure 11: Final energy consumption by fuel and by sector 

Electrification of final energy demand is a persisting trend. The share of electricity in total 
final demand reaches 26% and 33% in 2030 and 2050, respectively (compared to only 22% 
in 2015, see Figure 11). This is attributed to two main trends: (i) the electrification of heating 
in buildings, with the uptake of heat pumps and the continuous increase in the use of electric 
appliances in the residential and the tertiary sectors (mainly IT, leisure, and communication 
appliances); (ii) the electrification of transport, due to the penetration of electric vehicles. 
Electricity shares increase in industrial processing also, yet at a slow pace, in accordance 
with the Reference Scenario context.  

At the end of the projection period, electricity consumption increases in the energy branch 
too,  driven  by  hydrogen  production  that  is  used  in  road  transport.  Overall,  the  absolute 
growth in gross  electricity  demand by  2050  is  driven  mainly  by  transport and  to  a  lesser 
extent by the energy branch sector at the end of the projection period.  

56 

262828282528242416161618322932302015202020302050Shares in %020040060080010001200199520002005201020152020202520302035204020452050MtoeFinal energy consumption by sectorTransport (incl.intern. aviation)TertiaryResidentialIndustrySource: PRIMES655393532252021191922232633556691113142015202020302050Shares in %020040060080010001200199520002005201020152020202520302035204020452050MtoeFinal energy consumption by fuel RES and H2Distr. HeatElectricityGasOilSolids 
 
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Figure 12: Trends in electricity demand by sector 

3.1.1. Industrial sector 

The macro-economic projection underlying the Reference Scenario implies that significant 
part  of  the  energy-intensive  industrial  productions  will  remain  in  the  EU  territory.  Still,  it 
assumes  a  gradual  shift  towards  high  value-added  industrial  products  over  time  in  an 
international competition context. This implies that production volume increases in the future 
at a pace slightly slower than the economic value added of the sectors and their contribution 
to GDP growth.  

Moreover,  the  Reference  Scenario  takes  into  consideration  strategies,  initiatives  and 
measures aimed at closing the loop in industry in the context of the Circular Economy Action 
Plan (2015). While the analysis does not consider the new Circular Economy Action Plan87 
and  the  dedicated  New  Industrial  Strategy  for  Europe88,  introduced  in  2020,  which  put 
circular  economy  at  the  heart  of  industrial  processes  and  so  help  deliver  the  European 
Green Deal, recycling of materials is assumed to progress over time, which implies a further 
improvement in the overall energy intensity of the sector, since recycled products are less-

87 COM/2020/98 final  
88 COM/2020/102 final 

57 

05001000150020002500300035004000450020002005201020152020202520302035204020452050Gross electricity demand by sector (TWh)excl. self consumption of power plants TransmissiondistributionlossesEnergy branchTransportTertiaryHouseholdsIndustry(incl.electricityfor processCCS)3434343426262826292926282223333277762015202020302050Shares in %-2.00-1.000.001.002.003.004.005.006.007.00IndustryHouseholdsTertiaryTransportIndustryHouseholdsTertiaryTransport30-'500.420.440.264.2415-'300.560.33-0.095.9505-'15-1.020.341.29-0.45Average annual growth rates of electricity demand (%)30-'5015-'3005-'15Source:PRIMES 
 
                                                 
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energy  intensive  than  non-recycled  ones.  Finally,  improvement  of  energy  efficiency 
supports  the  overall  improvement  of  energy  productivity  of  the  sector.  These  trends  are 
even more pronounced in non-energy intensive sectors.  

These structural shifts explain the growing gap between value-added and final energy in 
industry. The pace of energy intensity improvement is similar in all sectors. In 2020, industry 
felt the effects of the COVID-19 crisis but is assumed to be back to almost pre-COVID-19 
levels  by  2025.  In  the  medium  and  long-term,  energy  intensity  drops  along  the  cycle  of 
capital turnover (and investments in more efficient technologies and processes), driven also 
by carbon pricing. 

Figure 13: Industrial energy demand vs. activity 

The projection keeps track of vintages of productive equipment in industry. The recovery of 
activity  growth  in  the  short  term  implies  that  industries  use  mainly  existing  equipment, 
including  the  less  efficient  ones,  as  low  activity  growth  in  recent  years  has  discouraged 
investment and has left part of capacities unused. The post-pandemic economic rebound 
is  expected  to  lead  to  investment  in  new  productive  equipment.  Energy  efficiency 
technologies and practices are projected to be embedded in new industrial capital vintages 
in 
industrial 
competitiveness. 

the  period  2021-30,  allowing 

to  mitigate  costs  and 

thus  support 

58 

050100150200250199520002005201020152020202520302035204020452050Index 1995=100Energy IntensityFinal EnergyValue AddedTrend 1995-2008 
 
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Figure 14: Average annual change of energy consumption in the industry sector 

Overall,  the  structure  of  final  energy  consumption  in  industry  remains  rather  stable  over 
time. The variations are explained by the capital turnover, business cycles and the impact 
of policy divers such as the EU ETS (Figure 15). 

Figure 15: Final energy consumption in industry  

To reflect the fuel mix in industry, PRIMES fully captures how industries produce steam, the 
role of co-generation and the evolution of the use of industrial boilers. The model covers 10 
industrial  sectors,  on  top  of  non-energy  sectors,  further  split  into  31  subsectors.  Each 
subsector has its own processes, fuels, and equipment, as well as boilers and CHP units, 
where  applicable.  The  fuel  switching  possibilities  are  specific  to  each  subsector,  which 
allows capturing the options available at great detail. 

The  projections  point  to  a  lengthy  change  in  the  fuel  mix  under  Reference  Scenario 
conditions. The phase-out of high CO2 emitting fuels cannot certainly happen overnight and 

59 

-6.00-5.00-4.00-3.00-2.00-1.000.00Iron and steelNon ferrous metalsChemicalsNon metallic mineralsPaper and pulpFood, drink and tobaccoEngineeringTextilesOther industries%2005-152015-302030-50Source: PRIMES050100150200250300350199520002005201020152020202520302035204020452050MtoeOther industriesTextilesEngineeringFood, drink andtobaccoPaper and pulpNon metallicmineralsChemicalsNon ferrous metalsIron and steelSource: PRIMES 
 
 
EU REFERENCE SCENARIO 2020 

thus a structural shift away from oil and coal is not imminent. Still, restructuring is envisaged 
to take place in the long run, as a result of market forces. In the short- and medium-term, 
obligations arising from the Industrial Emissions Directive89 (IED), along with national coal 
phase-out, RES support policies and the EU ETS are gradually affecting the fuel mix.  

Electrification is manifested in the projection to 2030 and in the longer-term, with electricity 
shares rising by 3% every 10 years, on average. This is an important increase, considering 
the Reference Scenario context. Meanwhile, RES remain small in magnitude. The share of 
gas remains relatively constant over time. 

Figure 16: Final energy consumption in industry by energy form 

The upward trajectory of fossil fuel prices and EU ETS carbon prices prompt the switch to 
biomass, mainly biomass solids, in line with the quest for resource-efficiency. The use of 
waste, both industrial and waste gas for some industries such as chemicals is limited, since 
the carbon price that applies on emissions makes it less attractive. 

Finally, the provisions on cogeneration in the EED promote the penetration of highly efficient 
cogeneration  and  the  use  of  waste  heat  for  steam  generation  in  industrial  sites.  Also, 
industrial boilers and CHP follow similar trends regarding fuel split.  

89 Directive 2010/75/EU 

60 

05010015020025030035020002005201020152020202520302035204020452050MtoeRES and H2SteamElectricityGasOilSolids1716157109722728242731313542667891012132015202020302050Shares in %Source: PRIMES 
 
                                                 
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Figure 17: Fuels for industrial CHP and boilers 

Developments in Iron and Steel 

Production  from  integrated  steelworks  is  projected  to  be  almost  stationary  until  2050, 
whereas steel production from electric arc increases: overall value added increases. The 
use of solids drops, switching to natural gas takes place and biomass use increases. Overall 
process efficiency improves, however blast furnaces continue to use solid fuels, since the 
EU ETS prices assumed in the Reference Scenario are not high enough to force the switch 
to alternative processes such as direct reduction and hydrogen use.  

Figure 18: Fuel mix in iron and steel 

Developments in Non-Ferrous 

For  non-ferrous  metals,  physical  production  and  value  added  follow  a  similar  trajectory. 
However, as energy consumption decreases, a moderate decoupling from GDP growth is 
projected in the long run. The trends in energy consumption are characterised by increasing 
electrification and incremental improvement of process efficiency including heat recovery. 
Biomass consumption increases slowly in the long term.  

61 

02040608010012014016018020152020202520302035204020452050MtoeBiomass &wasteGaseousOilSolids2415393648354750202020302050Shares (%)Source: PRIMES05000100001500020000250003000020152020202520302035204020452050KtoeHeatElectricitySolidsOther (biomass, waste, H2)LiquidsGasSource: PRIMES 
 
 
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Figure 19: Fuel mix in non-ferrous metals 

Developments in Chemicals 

Chemicals include both energy intensive chemicals (such as fertilisers and petrochemical) 
as  well  as  low  enthalpy  chemicals  –  mainly  pharmaceuticals  and  cosmetics.  Energy 
intensity improvements occur in both categories. The overall savings in terms of emissions 
are clearly driven by the big improvements in energy intensive chemicals, where – like in all 
sectors  –  a  shift  away  from  oil  and  solids  can  be  observed  in  the  Reference  Scenario 
context. 

Figure 20: Fuel mix in chemicals 

Developments in Non-Metallic Minerals 

The  value  added  of  non-metallic  minerals  increases  throughout  the  projection  period, 
however  energy  consumption  decreases,  which  reveals  significant  progress  in  terms  of 
curbing energy intensity in all subsectors. Fuel switching is the key driver: liquids and solids 
reduce  significantly  and  are  replaced  by  gas  and  electricity.  Biomass  also  increases  its 
share. 

62 

02000400060008000100001200020152020202520302035204020452050KtoeElectricityLiquidsOther (biomass, waste, H2)SolidsGasHeatSource: PRIMES010000200003000040000500006000020152020202520302035204020452050KtoeElectricityHeatSolidsLiquidsGasOther (biomass, waste, H2)Source: PRIMES 
 
 
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Figure 21: Fuel mix in non-metallic minerals 

Developments in paper and pulp 

Output and value added are both projected to grow while energy intensity is on a downward 
path. The use of solids and liquids, being already very low, almost disappears. Likewise, 
gas reduces almost by half while biomass maintains its share while decreasing in absolute 
terms thanks to energy efficiency improvements. The use of electricity is on a rise. 

Figure 22: Fuel mix in paper and pulp 

Developments in non-energy intensive industries 

As shown in Figure 23, non-energy intensive sectors, namely Food, Drinks and Tobacco 
(FDT), Engineering (ENG), Textiles (TEXT) and Other Industries (OI) produce more value 
added than energy intensive industries. 

63 

0500010000150002000025000300003500020152020202520302035204020452050KtoeElectricitySolidsLiquidsGasHeatOther (biomass, waste, H2)Source: PRIMES0500010000150002000025000300003500020152020202520302035204020452050KtoeElectricityHeatSolidsLiquidsGasOther (biomass, waste, H2)Source: PRIMES 
 
 
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Figure 23: Value added in energy-intensive vs. non-energy-intensive sectors 

The main trend across sectors is the decrease in the use of fossil fuels (solids and liquids). 
A switch to gas is projected for FDT, ENG and TEXT while electrification and steam use 
increase in FDT, ENG and OI. 

Figure 24: Fuel mix trends in non-energy-intensive industries 

3.1.2. Residential sector 

A  model  that  combines  economics  and  engineering,  PRIMES  BuiMo  disaggregates  the 
building stock in 270 building categories, representing different building types, geographic 
locations, ages of construction, income classes, and service sub-sectors. This is critical for 
capturing  the  different  challenges  facing  the  building  sector  in  the  transition  to 
decarbonisation.  

64 

0500100015002000250020152020202520302035204020452050Value added (bllion €) Value Added (bnEUR) Energy Intensive IndustriesValue Added (bnEUR) Non Energy Intensive IndustriesSource: PRIMES05000100001500020000250003000020152020202520302035204020452050KtoeFood, Drinks & Tobacco GasLiquidsSolidsHeatElectricityOther(biomass,waste, H2)0500010000150002000025000300003500020152020202520302035204020452050KtoeEngineering01000200030004000500020152020202520302035204020452050KtoeTextiles0500010000150002000025000300003500020152020202520302035204020452050KtoeOther IndustriesGasLiquidsSolidsHeatElectricityOther(biomass,waste, H2)Source: PRIMES 
 
 
EU REFERENCE SCENARIO 2020 

Projections show a remarkable decoupling of energy demand from income growth, much 
above  historical  trends,  which  intensifies  in  the  period  2021-2030  as  a result  of  national 
renovation strategies and the policies included in the NECPs. These policies have an impact 
also  after  2030,  causing  demand  for  energy  to  decline.  However,  the  decline  is  not  as 
substantial in the absence of additional policies.  

Space heating continues to represent the largest share of residential energy demand, which 
decreases  nonetheless  due  to  efficiency  improvements  (Figure  25).  Economic  growth 
drives an increase in the stock of appliances (black and white). Between 2015 and 2030 the 
stock of white appliances increases on average by 2% per annum. For black appliances the 
growth is also of around 2% per annum with information and communication technologies 
increasing by approximately 4.5% per annum in that time period. Advancements in lighting 
technologies are projected to continue at a moderate pace.  

Cooking shares remain rather stable and so does demand for water heating. The increase 
in useful energy for cooling is associated with the increasing cooling degree days (CDD) 
and the increase in household income, which together lead to higher penetration of cooling 
equipment. The drop in the shares of heating comes as a result of renovation and more 
efficient  space  heating  equipment  becoming  available.  Again,  these  developments  are 
significant until 2030 and continue at a slower pace afterwards.  

Figure 25: Residential energy demand by use  

The fuel mix in the residential building stock is characterised by lower use of solids, due to 
policies for air quality, and lower use of oil due to the growing electrification, connection to 
gas networks and some extension of district heating infrastructure. Gas roughly maintains 
its market shares, which decline slightly in the long run.  

Electricity  shares  are  on  the  rise,  driven  by  the  increased  use  of  appliances  and  the 
penetration of heat pumps, albeit at a slow pace under Reference Scenario assumptions. 
The choice of heat pumps is facilitated by technological progress and measures taken by 
few Member States. The model also prioritises heat pumps in cases where deep renovation 

65 

0501001502002503002005201020152020202520302035204020452050MtoeElectricAppliancesLightingCookingWater heatingCoolingHeating050100150200250199520002005201020152020202520302035204020452050Residential Energy Demand versus Private Income Index 1995=100Energy IntensityFinal EnergyIncomeTrend 1995-2008 
 
EU REFERENCE SCENARIO 2020 

is pursued, or the building is highly insulated. Renewables shares are projected to increase 
until 2030, mainly thanks to support measures for solar thermal and biomass pellets, a trend 
that continues after 2030 also, yet at a slower pace. 

Figure 26 : Residential energy demand by fuel 

Historically,  demolitions  and  new  constructions  have  been  limited  in  the  EU.  Thus, 
renovation  has  come  under  the  spotlight  for  achieving  energy  savings.  While  today  the 
majority  of  renovations  are  described  as  “light”  offering  limited  energy  savings,  the 
implementation of the EED and EPBD at national level is expected to increase the depth 
and rate of renovation. The lack of additional policies after 2030, implies a slowdown of the 
trend.  

For  the  most  part,  Europe’s  renovation  rates,  i.e.,  the  number  of  houses  undergoing 
renovation over the total stock of houses, have been on average below 0.8% per annum. 
The first stream of energy efficiency policies pushed that rate upwards, and current policies 
are expected to boost it further towards 2030. Despite the slowdown, renovation rates are 
projected to remain above 0.8% per year also after 2030, which is attributed to the inertia 
of the currently implemented policies.  

66 

0501001502002503002005201020152020202520302035204020452050MtoeRESDistrictHeatingElectricityGasOilSolids4321312753233292525252935991011181822232015202020302050Shares in %Source: PRIMES 
 
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Figure 27: Renovation rates of residential buildings 

Analysing  renovation rates from  the  perspective of  income  class and  age,  the  projection 
reveals that the majority of renovations is carried out by medium and high-income classes 
(Figure 28). Limited access to capital forces lower income classes to undertake only lighter 
renovations,  which  are  less  capital  intensive.  In  the  absence  of  additional  policies  after 
2030, the depth of renovations decreases for all income classes. 

Furthermore,  according  to  the  simulation,  mid-aged  buildings  have  higher  average 
renovation rates compared to older buildings. Old and very old houses are more difficult to 
renovate for reasons related to unknown structures that may require additional works and 
owners belonging to low- and medium-income classes.  

At the same time however, the energy savings achieved in mid-aged buildings are lower 
than what would be the case in older houses, since mid-aged buildings already have some 
insulation; still, it is more cost-efficient to perform medium depth renovations in buildings of 
over 20 years. 

Member  States  have  already  put  in  place  and  are  projected  to  implement  policies  for 
overcoming  market  and  non-market  barriers  to  the  renovation  of  buildings.  While  these 
policies and measures are expected to lead to substantial fuel switching and energy savings 
in the residential sector, still the projected savings are found not to be in line with the EED 
target.  The  model  results  project  that  low-income  classes  in  particular  face  significant 
economic obstacles, which cannot be overcome in most Member States with the policies 
assumed in the Reference Scenario. 

67 

0,0%0,2%0,4%0,6%0,8%1,0%1,2%1,4%2010-152016-202021-252026-302031-352036-402041-452046-50Renovation rates0,0%0,1%0,2%0,3%0,4%0,5%0,6%0,7%0,8%2010-152016-202021-252026-302031-352036-402041-452046-50Renovation rates per type of energy renovation LightMediumDeepSource: PRIMES 
 
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Figure 28: Renovation rates by age/income class of residential buildings 

3.1.3. Tertiary sector 

As in the residential sector, projections of final energy demand in the tertiary sector point to 
a decoupling of demand from economic activity (Figure 29). In the services sector, energy 
efficiency improvements are significant, while in agriculture energy efficiency gains are less 
prominent. Policies for energy efficiency, ecodesign and energy performance of buildings 
bring about remarkable energy efficiency gains. These gains offset the effects of growing 
sectorial  activity  towards  2030  and  drive  final  energy  demand  below  2010  peak  levels 
throughout the projection period. 

Distinct efficiency progress is observed in heating and in electric non-heating uses, which 
brings  energy  consumption  down  between  2020  and  2030.  Space  heating  in  particular 
continues to be the single largest energy use. Final energy used for space heating drops 
however over time due to energy efficiency improvements. Specific electric uses exhibit the 
highest increase reflecting technology trends, including the development of data centres, 
increased  stock  and  use  of  electric  appliances.  In  agriculture,  energy  efficiency 
improvements,  as  well  as  potential  for  renewables,  are  projected  for  space  heating 
purposes  (greenhouses  mainly).  Beyond  2030,  where  no  additional  energy  efficiency 
policies are implemented, total final energy consumption decreases at a slow pace. 

68 

0,0%0,4%0,8%1,2%1,6%Low IncomeMediumIncomeHigh IncomeRenovation RateRenovation per income class0,0%0,5%1,0%1,5%2,0%>19751975-20002000-2015Renovation RateRenovation per year of construction0%10%20%30%40%50%Low IncomeMediumIncomeHigh IncomeEnergy savings 2021-252026-302031-500%10%20%30%40%50%60%>19751975-20002000-2015Energy savings2021-252026-302031-50Source: PRIMES 
 
EU REFERENCE SCENARIO 2020 

Figure 29: Final energy demand by use in the tertiary sector 

The  fuel  mix  used  in  buildings  of  the  tertiary  sector  is  marked  by  two  main  trends:  the 
significant  decline  of  solids  and  oil  due  to  substitutions  by  gas  in  the  medium  term  and 
electricity  in  the medium  and  long  term;  and  electrification,  which increases from  43%  in 
2015 to 50% in 2050, driven by the rise in specific electricity uses and in heat pumps. RES 
shares grow steadily in the course of the projection period, driven primarily by the increased 
penetration  of  geothermal  energy,  already  widely  used  in  commercial  buildings  in  many 
Member States and for which significant untapped potential exists. 

69 

050100150200250199520002005201020152020202520302035204020452050Energy Demand versus Value Added in Tertiary SectorsIndex 1995=100Energy IntensityFinal EnergyValue AddedTrend 1995-20080204060801001201401601802002005201020152020202520302035204020452050MtoeAgriculture: Motorsand equimentAgriculture: PumpingAgriculture: Heat usesServices: ElectricAppliancesServices: LightingServices: Other heatusesServices: CoolingServices: HeatingSource: PRIMES 
 
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Figure 30: Final energy demand by fuel in the tertiary sector 

In general, renovation rates are lower in the buildings of the services sector, but the share 
of demolitions and new buildings is higher. All new buildings are assumed to comply with 
the country-specific building codes and be more efficient than existing buildings. The role 
of energy efficiency policies is noteworthy, causing renovation rates to double in the period 
2026-2030, a trend that discontinues afterwards.  

Figure 31: Renovation rates of services buildings 

Policies for the energy upgrading of public buildings trigger medium and to a lesser extent 
deep renovation until 2030. The obligations under Art.5 of the Energy Efficiency Directive 
on the exemplary role of public buildings push renovation rates in the non-market sector 

70 

202013926252320434347506687459132015202020302050Shares (%)020406080100120140160180200199520052015202520352045MtoeRESDistrictHeatingElectricityGasOilSolidsSource: PRIMES0,0%0,2%0,4%0,6%0,8%2010-152016-202021-252026-302031-352036-402041-452046-50Renovation rates0,0%0,1%0,2%0,3%0,4%0,5%0,6%2010-152016-202021-252026-302031-352036-402041-452046-50Renovation rates per type of energy renovation LightMediumDeepSource: PRIMES 
 
 
EU REFERENCE SCENARIO 2020 

upwards. Commercial buildings, though  not getting renovated as often since energy bills 
take up a small share of company costs, when they do, they reach higher energy savings. 
Therefore,  although  more  rarely  renovated,  commercial  buildings  provide  a  stronger 
economic interest for deeper renovation, due to higher total energy bills and a possibly more 
rational investment appraisal. 

3.1.4. Transport sector 

Transport activity 

Transport activity for passenger and freight is projected to grow throughout the projection 
period, with freight growing faster than passenger transport activity. Following the significant 
decline in activity in 2020 due to the COVID-19 pandemic, a rebound is envisaged for 2025-
2030.  

In  passenger  transport,  cars  are  expected  to  maintain  their  dominant  role  (Figure  32). 
However, the share of cars activity is expected to slightly decline over time, while the share 
of intra-EU aviation is projected to increase considerably.  

Figure 32: Passenger transport activity by mode 

Note: Aviation includes only intra-EU aviation 

Passenger rail activity90, mainly high-speed rail where investments are foreseen, increases 
its modal share and competes with road transport and air transport in some cases. High 
congestion levels and rising fossil fuel prices, supported by the completion of the core and 
comprehensive TEN-T network by 2030 and 2050, respectively, and by other policies like 
the Fourth Railways Package, improve the competitiveness of railways and shift part of the 
passenger road traffic to rail in the long term. Inland navigation transport, which refers here 
to  inland  waterways  and  national  maritime,  holds  a  marginal  share  of  total  passenger 
transport activity.  

Intra-EU aviation, which takes place within the boundaries of one Member State or between 
Member States, is projected to be the second highest growing of all passenger transport 
modes (i.e., after high-speed rail), and increase its share from 8% in 2015 to 12% in 2050.  

90 Passenger rail activity covers here conventional and high-speed rail, plus light rail, and tram/metro in urban areas. 

71 

010002000300040005000600070008000199520002005201020152020202520302035204020452050GpkmAviationInland waterways& nationalmaritimeRailPublic RoadPowered 2-WheelersPassenger Cars7279716922229787869108510122015202020302050Shares (%)Source: PRIMES 
 
                                                 
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Extra-EU aviation activity, which covers flights between Member States and third countries, 
is projected to increase by 90% between 2015 and 2050 – only slightly less than intra-EU 
air transport activity.  

Table 8: Extra-EU aviation activity  

Activity (Gpkm) 

Extra-EU aviation 

Source: PRIMES-TREMOVE 

Reference Scenario 

2015 

1,150 

2020 

2030 

2050 

612 

1,645 

2,182 

Freight transport activity is expected to grow significantly in the period 2015-2050 due to 
the increased economic activity and demand for transportation of goods (Figure 33). Road 
remains  the  dominant  mode, representing 69% of total freight  transport activity  by  2050. 
Rail freight sees an increase in its shares from 17% in 2015 to 20% in 2050, which is driven 
by the completion of the TEN-T core and comprehensive network, supported by CEF. Inland 
waterways  and  national  maritime  traffic  also  benefits  from  the  TEN-T  core  and 
comprehensive network completion, which includes further support for logistic functions and 
multi-modal  integration,  and  from  other  policies  promoting  inland  waterways  and  port 
services. However, the relatively stronger growth in road and rail traffic leads to a decrease 
in the modal share of inland waterways and national maritime, from about 13% in 2015 to 
11% in 2050. 

Figure 33: Freight transport activity by mode 

International maritime freight transport activity grows at a 1.2% rate annually between 2015 
and 2030, and 1.1% annually between 2030 and 2050, which translates into 50% increase 
in  maritime  freight  transport  activity  in  2050  compared  to  2015  (Table  9).  International 
maritime  passenger  transport  activity  is  also  projected  to  increase  steadily  by  2050, 
following the reduction in activity in 2020 due to the COVID-19 pandemic.  

72 

0500100015002000250030003500400019952000200520102015202020302035204020452050GtkmRail freightInland waterways & national maritimeRoad freight7072716917161820131211112015202020302050Shares (%)Source: PRIMES 
 
 
 
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Table 9: International passenger and freight maritime transport activity 

Activity 

Reference Scenario 

2015 

2020 

2030 

2050 

Passenger, Gpkm 

1,962 

1,186 

2,426 

3,052 

Freight, Gtkm 

14,269 

11,327 

17,075 

21,444 

Source: PRIMES-Maritime 

Energy demand: Analysis by transport mode 

The growth of energy demand in transport has shown strong correlation with the evolution 
of transport activity over time. However, the projection indicates a significant decoupling of 
energy consumption from activity growth. In fact, the decoupling is far stronger compared 
to past trends for both passenger and freight transport.  

This  is  attributed  to  a  number  of  policies  and  technological  trends,  including:  the  CO2 
standards for LDVs and HDVs, policies favouring higher use of more sustainable transport 
modes and the electrification of rail, policies promoting the roll-out of recharging/refuelling 
infrastructure and other policies supporting progressive electrification of the road transport 
sector,  remarkable  technology  progress  that  helps  bring  down  the  unit  consumption  of 
conventional technologies, improvement in the techno-economic performance of alternative 
power trains (in particular battery electric vehicles), etc. 

Passenger  transport  activity  is  envisaged  to  recover  post-2020  and  to  strongly  decouple 
from the energy consumption by 2050 (Figure 34). 

Figure 34: Trends in passenger transport activity and energy consumption 

Note: Transport activity and energy use include intra-EU and extra-EU aviation but exclude international maritime. 

Having suffered less than passenger transport from the pandemic, freight transport activity 
continues to grow in the future at a pace that roughly follows that of GDP. The completion 
of the TEN-T core network by 2030 and of the comprehensive network by 2050 is expected 
to provide support for logistic functions and improve multi-modal integration (road, rail, and 
waterborne transport) through innovative information management systems that are part of 
the network. Freight transport activity is also projected to strongly decouple from the energy 
consumption by 2050 (Figure 35). 

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Figure 35: Trends in freight transport activity and energy consumption 

Note: Transport activity and energy use do not include international maritime transport.  

A rebound in energy use in transport is projected during 2020-2025, driven by the recovery 
in activity following the COVID-19 pandemic. Post-2025, EU level and national policies as 
well  as  techno-economic  developments  lead  to  a  gradual  drop  in  consumption.  Energy 
demand from cars, responsible for more than half of total final energy demand in transport 
in 2015, is projected to decrease considerably by 2050 (Figure 36), due to notable energy 
efficiency gains brought by the introduction of CO2 emission performance standards, the 
gradual renewal of the vehicle fleet, the emergence of advanced vehicle technologies and 
the increase in fuel prices in the long term. 

Figure 36: Total energy demand in transport  

The picture is different in aviation where efficiency gains do not suffice to compensate for 
the increased activity levels resulting in growing energy demand. Other transport sectors 
such as rail, inland waterways and national maritime are projected to maintain their share 
in  total  energy  demand  throughout  the  years.  Demand  for  energy  also  increases  in 
international maritime transport (freight and passenger) between 2015 and 2030, a trend 
which persists also post-2030.  

As said, the catalyst for the decoupling between activity and energy demand is the uptake 
of  more  fuel-efficient  technologies  and  fuel  substitution  (Figure  37).  In  road  passenger 

74 

52534536182320201211141912814172015202020302050Shares (%)050100150200250300350400199520002005201020152020202520302035204020452050MtoeAviationRailPowered 2-WheelersInternationalShippingFreight RoadPublic RoadInland waterways& nationalmaritimePassenger CarsSource: PRIMES 
 
 
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transport, energy efficiency of vehicles improves by 27% in 2030 and 52% in 2050 relative 
to 2015. Such development is the outcome of the implementation of the regulation on CO2 
emission standards for Light Duty Vehicles (LDVs), which covers new passenger cars and 
light  commercial  vehicles  and  is  projected  to  lead  to  more  fuel  efficient  LDVs  being 
introduced into the market. The deployment of low- and zero-emissions vehicles is further 
supported by the roll-out of recharging/refuelling infrastructure, driven by the Directive on 
alternative fuels infrastructure (AFID).  

Figure 37: Efficiency improvements by mode 

Note:  For  aviation  and  total  passenger  transport,  the  figure  reports  the  improvements  in  energy  efficiency  taking  into 
consideration domestic, international intra-EU flights and extra-EU flights. Regarding the efficiency improvements in overall 
freight transport, the figure considers the improvements in international shipping. 

In aviation, the improvement in specific fuel consumption is projected at 19% in 2030 and 
35% in 2050 relative to 2015. Such developments are driven by high efficiency gains due 
to the introduction of more energy efficient aircrafts and the renewal of the fleet, as well as 
due to operation efficiency gains. Hence, even though aviation experiences strong growth 
in its activity, efficiency gains limit the increase in the energy consumption. 

Efficiency  improvements  are  less  pronounced  in  passenger  rail  compared  to  road  and 
aviation.  This  is  because  significant  improvements  have  already  taken  place  in  the  rail 
sector in the past and the remaining potential is more limited relative to other modes. Still, 
compared to past projections, switching from diesel to electricity in rail is becoming more 
prevalent, driven by policies. 

Freight transport is also becoming increasingly efficient. CO2 emission standards for HDVs 
and  technological  progress  drive  significant  efficiency  improvements  in  road  transport 
throughout the projection period allowing manufacturers and fleet operators to reduce fuel 
costs, which make up a considerable amount of the operational costs of HDVs.  

Similarly, freight rail sees moderate improvements in the average specific fuel consumption 
up to 2030, pushed by policies that support the growing electrification of railways. Freight 
inland  waterways  and  national  maritime  achieve  energy  efficiency  improvements  in  the 
order of 6% by 2030 and 11% by 2050 relative to 2015. International maritime is projected 

75 

-26-27-14-19-50-52-28-35Passenger transportRoad transportRailAviation% change of consumed toe/Mpkm from 2015-12-24-15-6-3-26-37-29-11-6FreighttransportRoad transportRailInlandwaterways &nationalmaritimeInternationalMaritime% change of consumed toe/Mtkm from 201520302050 
 
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to see smaller improvements in fuel efficiency post-2015 in the Reference Scenario context. 
However, significant gains in the fuel efficiency (31%) have been already achieved during 
2005-2015.   

Energy  efficiency  improvements  for  passenger  cars  are  expected to  reduce  the  share  of 
cars in energy demand for passenger transportation from 75% in 2015 to 68% in 2030 and 
59% in 2050. On the contrary, the share of air transport rises to 29% in 2050 from 17% in 
2015 due to the growing demand for jet fuels (Figure 38). 

Figure 38: Shares of passenger transport modes in energy demand 

HDVs account for approximately 61% of the total energy demand from freight transport in 
2015. Their share would decrease over time to 57% in 2030 and 51% by 2050, due to the 
introduction of CO2 emission standards and increasing fossil fuel prices (Figure 39). Fuel 
for international freight shipping bunkers was reported to be roughly 35 Mtoe in 2015. This 
represents  34%  of  the  energy  demand  from  freight  transport.  Model  estimates  show  a 
growth in the freight bunker fuels consumption, up to 50 Mtoe in 2050 (42% of the energy 
demand in freight transport), driven by the high growth in the international maritime transport 
activity.  Rail  freight,  inland  waterways  and  national  maritime  are  projected  to  maintain  a 
limited share of the energy demand from freight transport by 2050.  

Figure 39: Shares of freight transport modes in energy demand  

76 

758168594345171222292222242015202020302050International maritimePowered 2-WheelersAviationPublic RoadRailInland waterways &national maritimePassenger CarsSource: PRIMES6166575133333333342937422015202020302050InternationalmaritimefreightInlandwaterways &nationalmaritimeRail FreightRoad FreightSource:PRIMES 
 
 
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Energy demand: Analysis by fuel 

The efficiency improvements induced in the segment of passenger cars have been evident 
since 2010-2015, when manufacturers started to market low-emission vehicles. This trend 
is expected to intensify. The CO2 standards for light and heavy duty vehicles, the 2030 RES 
target  for  transport,  the  deployment  of  alternative  fuel  infrastructure  and  particularly  the 
recharging infrastructure and other national policies combined with significant reductions in 
the  cost  of  batteries  are  changing the mix  of technologies in the  car fleet  towards  2030, 
giving considerable impetus to the emergence of battery electric power trains (Figure 40). 

Oil products, namely gasoline, diesel, kerosene, LPG and fuel oil, continue to represent the 
largest share in total energy consumption in transport, including international aviation and 
international maritime. From 95% in 2015, their share drops down to 87% in 2030 and 75% 
in 2050. The volumes of diesel and gasoline decrease over time, but the volume of kerosene 
increases, because of the increased air transport activity. Indeed, kerosene is projected to 
increase its share in the fuel mix over time. Only after 2035 bio-kerosene slowly starts to 
penetrate the aviation fuel mix (Figure 41)91. Natural gas increases its market penetration, 
but remains low in total volume terms. In particular, LNG develops in road freight, national 
and international maritime.  

Figure 40: Total energy consumption in transport by fuel  

Note: Including international aviation and maritime. 

The evolution of biofuel and biogas penetration in the EU energy mix is mainly driven by the 
legally binding target of 14% RES in the transport sector, including the dedicated sub-target 
of  advanced  biofuels  covering  3.5%  of  energy  consumption  by  2030  (including  the 
multipliers). Specific national mandatory blending regulations and incentives are considered 
in the Reference Scenario. The growth in biofuel and biogas consumption is projected to 
decelerate  after  2030  due  to  absence  of  dedicated  policies  and  the  uptake  of  electric 
vehicles. 

91 Fuel mandates for the aviation sector are not included in the Reference scenario projection.  

77 

191914853564739128141798910222238467613922015202020302050Shares (%)Source: PRIMES 050100150200250300350400199520052015202520352045MtoeFuel Mix in TransportHydrogenElectricityBiofuels andbiogasNatural GasLPGOther fossilKeroseneDieselGasoline 
 
                                                 
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The total share of electricity increases over time and is projected to reach 9% by 2050. This 
development is mostly the result of electric vehicles penetration in road transport and partly 
driven by the substitution of diesel-powered with electric rolling stock in rail transport. 

International  maritime  activity  is  projected  to  experience  significant  growth  by  2050.  Oil 
products continue to be the dominant energy source used for powering vessels. LNG and 
diesel oil, both fuels with low sulphur content thus in compliance with the Sulphur Directive 
and the SECA zones of EU waters, gradually penetrate the bunker fuels market and replace 
part of the fuel oil. The rollout of LNG infrastructure facilitates this process. Biofuels do not 
play a significant role under the Reference Scenario, in the absence of additional policies.92 

Figure 41: Energy demand in aviation and maritime by fuel 

Outlook on Light Duty Vehicles and Heavy Duty Vehicles 

The market segment of LDVs, which consists of private cars and light commercial vehicles, 
is  projected  to change  significantly  compared  to historical  trends  driven  by  policies.  The 
CO2 standards for LDVs brings down the shares of ICE Gasoline to 16% of the total LDVs 
stock in 2050 from 46% in 2020 and those of ICE Diesel down to 27% from 50% in 2020. 
Car manufacturers are assumed to comply with the standards and promote vehicles that 
have a hybrid system on their powertrain, which become more appealing to consumers due 
to their lower additional costs. 

Electric  vehicles  (EVs)  represent  a  growing  share  of  the  market  as  a  result  of  EU  and 
national policies, incentive schemes, roll-out of recharging infrastructure and lower battery 
costs. Strong incentives introduced by specific Member States in the form of tax exemptions 
or  subsidies  make  the  purchase  of  EVs  by  urban  commuters  and  early  adopters  easier. 
Battery Electric Vehicles (BEVs) present higher levels of maturity, particularly beyond 2030. 
Dropping  battery  costs  assumed  in  the  Reference  Scenario  bring  capital  costs  of  BEVs 
down, thus enabling their uptake. As a result, BEVs represent 32% and PHEVs 18% of total 
LDVs stock in 2050, respectively, in the Reference Scenario (Figure 42). Fuel cell vehicles 
still represent a niche market, reaching 3% at the end of the projection period due to their 
higher costs.  

92 Fuel obligations for maritime fuels are not included in the Reference scenario projection. 

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0102030405060199520002005201020152020202520302035204020452050MtoeInternational MaritimeBiofuelsLNGOther oilFuel oilDiesel0102030405060199520002005201020152020202520302035204020452050MtoeAviationBioKeroseneKeroseneSource:PRIMES 
 
                                                 
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The  modelling  takes  into  consideration  national  support  plans  for  advanced  technology 
vehicles  such  as  EVs  and  the  roll-out  of  recharging/refuelling  infrastructure.  Incentives 
including  subsidies,  lower  taxation,  premiums  etc.  are  treated  as  explicit  drivers  in  the 
model. Countries that have such plans in place and support electrification of private road 
transport are expected to show higher penetration of electric vehicles (i.e., higher than the 
EU average). Other energy forms such as LPG and natural gas occupy a small share out 
of the total LDVs stock by 2050.  

Figure 42: Outlook of LDVs stock by type and fuel 

The  market  segment  of  HDVs,  consisting  of  buses,  trucks  and  coaches,  undergoes 
moderate changes compared to historical trends. The introduction of CO2 standards and 
the Clean Vehicles Directive, together with rising fossil fuel prices, cause ICE Diesel to drop 
to 78% in 2050 (from 99% in 2020) and ICE Gaseous to reach 9% in 2030 and 18% in 2050 
from less than 1% in 2020 (Figure 43). The share of electric and fuel cell HDVs is projected 
to  be  more  limited  at  around  4%  of  the  total  HDV  stock  by  2050.  Electric  buses  would 
however represent around 20% of the bus stock by 2050, driven in particular by the Clean 
Vehicles Directive.  

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0501001502002503003502005201020152020202520302035204020452050Million vehiclesOutlook of LDVs stock(passenger cars and light commercial vehicles) Fuel CellBatteryElectricPlug-inhybridICEGaseousICE DieselICEGasoline48463616495044273344518113232015202020302050Shares in LDVs stock (%)Source: PRIMES  
 
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Figure 43: Outlook of HDVs by type and fuel 

3.2. Energy supply 

3.2.1. Power and heat production 

The projections for power and heat production are derived from the relevant PRIMES sub-
model, which models the electricity sector and market and their interaction with steam/heat 
demand, provided through combined heat and power (CHP) plants, district heating as well 
as industrial CHP and boilers. It is a very detailed model that has evolved significantly since 
the Reference Scenario of 2016. 

One  part  of  the  model  handles  the  optimisation  of  capacity  expansion  inter-temporally, 
looking at all existing power plants, one by one, in all countries. The model reflects different 
possibilities for refurbishment, investment (greenfield or brownfield) and non-linear potential 
cost  curves  for  all  resources.  The  database93  of  PRIMES  further  includes  all  planned 
investments 
lifetime  extensions  as  well  as 
decommissioning.  

that  are  currently  known, 

including 

Another part of the model performs unit commitment optimisation, by considering, in hourly 
resolution, the cyclical possibilities of power plant operation, the balancing of RES along 
with the role of storage and cross-border flows. These concern the EU as well as the UK, 
Norway,  Switzerland,  and  the  interactions  with  the  Balkan  region.  The  interconnections 
between countries of the ENTSOE 10-Year Development Plan are considered exogenously. 
Simultaneously  with  power  sector  optimisation,  the  model  performs  optimisation  of  co-
generation, respecting the variation of loads of electricity, heat, and steam in a synchronised 
manner. 

The  key  policy  drivers  underpinning  the  projections  of  power  and  heat  production  are 
summarised below.  

An important first driver is the  EU ETS carbon price, which is projected to reach, in the 
Reference  Scenario  policy  context,  30  €/tn  CO2 in  2030.  After  2030,  the  carbon  price  is 
projected to increase at a faster pace (see section 2.5.1). 

93 Data is retrieved from commercial databases (e.g., Platts) and plans of large companies in all Member States. 

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0123456789102005201020152020202520302035204020452050Million vehiclesOutlook of HDVs stock(buses, trucks and coaches) Fuel CellBatteryElectricICEGaseousICE DieselSource: PRIMES100999178191832015202020302050Shares in HDVs stock (%) 
 
                                                 
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Another crucial driver are technology trends, which confirm the cost-competitiveness of 
solar and wind power. Variable RES are assumed to experience a sustained investment 
pace, backed by support schemes in the short term and enabling conditions in the medium 
and long term. Such enabling conditions are reflected in the model in different ways: in the 
form of grid extensions that allow tapping RES potential in remote locations, enhanced rules 
for shortening licencing procedures, the formation of local energy communities etc.  

Enabling  conditions  resolve  the  challenges  associated  with  investing  in  RES,  which  is 
represented  in  the  model  by  non-linear  ascending  cost  curves  country  by  country  and 
technology by technology. In essence, it is about facilitating the investment without reducing 
the costs. These enabling conditions are assumed to intensify until 2030, helping Member 
States  reach  the  2030  RES  target  but  phase  down  after  2030  in  accordance  with  the 
Reference Scenario assumptions. However, the reduction in unit technology costs of wind 
and solar continues until the end of the projection period and, in combination with the rising 
EU ETS price, allow for the share of RES in the power mix to grow.  

For  variable  RES  to  develop  and  integrate  into  the  system,  balancing  and  reserves  are 
needed.  Gas  prices,  which  are  assumed  to  remain  rather  stable  especially  until  2030, 
contribute in the development of CCGTs. CCGTs are important for they replace coal and 
offer  balancing  services  to  variable  RES.  Balancing  services  are  also  provided  through 
storage technologies, which develop in the model projections after 2030. This combination 
of gas and storage helps to deliver a secure and reliable power system with high penetration 
of variable RES. 

Furthermore, the model considers provisions for excluding high-emitting plants from support 
schemes,  as  laid  down  in  the  Industrial  Emissions  Directive94,  the  latest  Electricity 
Directive95  and Electricity Market Regulation96. It also accounts for the prospect of rising 
EU ETS prices and the ambitious coal phase-out plans, some of which were announced 
after the submission of final NECPs. Altogether, these elements are assumed to discourage 
the refurbishment of old coal power plants. 

In the majority of cases lifetime extension of nuclear power plants takes place following 
refurbishment, while new constructions are in the pipeline in few Member-States. Lifetime 
extension is analytically assessed, based on a plant-by-plant survey of the age, construction 
type (generation) and national legislation. Building new power plants in new sites (i.e., in 
locations  where  there  are  currently  no  power  plants)  is  considered  very  difficult  due  to 
challenges  associated  with  public  acceptance;  information  on  the  cost  of  recent  nuclear 
projects97 has been used to inform the modelling in this regard. 

Electricity demand is projected to increase in the medium and long term after being stagnant 
in  recent  years.  The  main  reason  is  the  growing  electrification  in  demand  sectors,  a 
persisting  trend  in  the  projection  period.  While  higher  efficiency  partly  balances  out  the 
share  of  electricity  demand  in  buildings,  additional  electricity  demand  comes  from  the 
transport sector and to a lesser extent also from industrial processes. 

Electricity generation 

EU  and  national  supporting  policies  as  well  as  technology  trends  prompt  a  significant 
penetration  of  RES  in  power  generation.  By  2030,  more  than  half  of  power  generation 
comes from RES (59%) which is projected to reach 75% by 2050 under Reference Scenario 
conditions.  In  2030,  42%  of  RES  is  projected  to  come  from  variable  sources  (wind  and 
solar).  Technology  and  investment  trends  confirm  the  cost-competitiveness  of  solar  and 
wind power. However, in the absence of additional policies post-2030, further investments 

94 Directive (EU) 2010/75 
95 Directive (EU) 2019/944 
96 Regulation (EU) 2019/943  
97 Information was based on http://www.world-nuclear.org/ and related background links 

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in  RES  are  driven  only  by  market  forces,  the  ETS  and  the  improved  techno-economic 
characteristics of the technologies. 

The biggest increase in the EU power generation mix comes from wind, which more than 
triples  compared  to  2015  reaching  30%  of  total  net  electricity  generation  in  2030.  Wind 
installed capacity increases from 127 GW in 2015 to 349 GW in 2030 and 508 GW in 2050. 
Offshore wind capacity grows exponentially; from just 5.9 GW of installed capacity in 2015 
to 95 GW in 2050. Most of wind offshore investments takes place until 2030  – a fourfold 
increase compared to 2020 – in line with Member State commitments in the NECPs and 
related projections.  

The enabling policy framework between 2021 and 2030 and national commitments boost 
wind  development.  New  sites  are  being  exploited  and  wind  turbines  are  progressively 
replaced by new, taller ones, with higher installed capacity. Without supporting policies in 
place after 2030, wind onshore growth is still considerable with 40% capacity being added 
to the grid until 2050. 

Figure 44: Electricity generation by fuel type 

Figure 45: Electricity generation by plant type 

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05001000150020002500300035004000TWhOther RESSolarWindHydroBiomassGaseous fuelsOilSolidsNuclear2725171123148215191513665612131211101530404612182015202020302050Shares (%)Source: PRIMES05001000150020002500300035004000TWhOther RESSolarWindOffshoreWindOnshoreHydroBiomassOther gas/oilGas PlantsSolid PlantsNuclearSource: PRIMES272517112414814171314432565412131211914232927114612182015202020302050shares (%) 
 
 
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Solar power also expands from 87.8 GW in 2015 to 307 GW in 2030 and 513 GW in 2050. 
Investment in new solar capacity is driven mostly by support schemes in the short term, the 
decreasing  costs  of  solar  panels  and  high  sector  competitiveness.  Under  Reference 
Scenario assumptions solar PV covers 18% of electricity generation in 2050.  

Biomass use increases slightly but the share of power generation from biomass remains 
between  5-6%  throughout  the  projection  period.98.  Pure  biomass/waste  plant  capacities 
(excluding co-firing) remain between 30 and 39 GW throughout the projection period.  

Hydro generation remains rather stable. Net installed capacity increases by 8GW between 
2015 and 2050 with 3.5 GW of investments in hydro-reservoirs planned until 2030. Beyond 
this period the majority of investments are in small run-of-river plants. Geothermal, tidal and 
wave energy are not expected to develop before 2050. 

The ambitious policies on coal phase-out is the main trigger for the rapid drop in solid fuels 
until 2030 along with  their  limited competitiveness vis-a-vis RES and natural gas, the lack 
of refurbishments and the EU ETS prices. Oil consumption almost vanishes, except in the 
non-interconnected islands. 

Natural gas continues to play a role in power generation throughout the projection period 
acting as bridge fuel. It has low carbon intensity relative to oil and solids and contributes to 
emission reduction. At the same time, gas units are flexible enough to deliver necessary 
balancing services that allow variable RES to boost their share in power generation. For 
this  reason,  gas-firing  generation  is  projected  to  drop  modestly  until  2030  and  increase 
marginally in 2050. Total net investment in gas-fired plants in the period 2020-2050 amounts 
to  290  GW.  Capacities  thus  remain,  mainly  CCGT  plants,  as  in  a  Reference  Scenario 
context these represent the main flexibility options. 

The share of co-generation in steam production, as well as in electricity production, remains 
at similar levels throughout the projection period.  

Last, the decision for nuclear retirement in Germany before the end of 2022, the extended 
downtime of a number of units in countries like France and Sweden, and the delays in the 
commissioning  of  new-build  nuclear  plants,  e.g.,  in  Finland,  coupled  with  acceptance 
issues,  drive  electricity  generation  from  nuclear  downwards  throughout  the  projection 
period: starting with a capacity of approximately 107 GW in 2020, this declines to 94 GW in 
2030 and 55 GW in 2050. 

The projected investments in nuclear capacity mainly occur on existing sites or are lifetime 
extensions; there  are  very few  projected  investments  in  nuclear  capacities on  new  sites. 
More  specifically,  the  vast majority  of  investments  in nuclear  are planned  until  2030  and 
concern the retrofitting of existing plants. Beyond 2030 there are some investments in new 
nuclear power plants. However, most of these are brownfield investments on existing sites; 
cumulatively in the period 2035 to 2050 37% of investments are retrofits and 68% of new 
investments  are  on  existing  sites  and  only  in  Member  States  which  allow  for  nuclear 
investments. 

98 Calculated following Eurostat definitions, i.e., excluding energy consumed by industrial sectors and refineries for on-site 
CHP steam generation. 

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Figure 46: Operating power capacities 

Figure 47: RES-E shares in Member States in 2015 and 2030  

Investment patterns 

Incremental system capacities come almost exclusively from RES, notably solar PV, and 
wind. As the share of non-dispatchable generation increases, the rate of use of capacities 
for CCGT and for the remaining coal plants drops. In the case of CCGT, it is because gas 
plants  are  increasingly  used for flexibility  and  reserves purposes.  Coal  capacities on  the 
other hand decrease due to lower competitiveness and phase-out plans.  

84 

1312841814517161271074234431414111014182530101426372015202020302050Shares (%)02004006008001000120014001600GW netOther RESSolarWind OffshoreWind OnshoreHydroBiomassOther gas/oilGas PlantsCCSSolidsNuclearSource: PRIMES20152030 
 
  
 
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Figure 48: Investment in new capacities  

The  decade  2021-2030  is  projected  to  be  far  more  demanding  in  terms  of  new  capacity 
investment  compared  to  other  periods  (Figure  48).  This  correlates  with  the  Reference 
Scenario  assumption  that  Member  States  implement  the  NECPs  and  that  no  additional 
policies are introduced after 2030. Under these terms, solar PV and wind make up for the 
largest share of investment until 2030. Wind onshore develops strongly while wind offshore 
benefits from dedicated policies and attracts significant investment.  

The absence of such policies after 2030 implies a slowdown of investment. Between 2040 
and 2050 total investment in RES increases again, driven by the need to repower obsolete 
plants  (Figure  49),  high  ETS  prices  and  declining  cost  of  RES  technologies.  The  model 
assumes full possibility of repowering on existing sites. 

Figure 49: Investment in plant refurbishment  

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822131437158813572821233261797177110900501001502002503003504002011-202021-302031-402041-50GW (net)SolarWindOffshoreWindOnshoreHydroBiomassOthergas/oilGas PlantsSolidsNuclearSource:PRIMES62108977726783850501001502002502011-202021-302031-402041-50GW (net)SolarWindOffshoreWindOnshoreHydroBiomassOthergas/oilGas PlantsSolidsNuclearSource:PRIMES 
 
 
 
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That  said,  the  carbon  intensity  of  power  generation  (Figure  50)  is  set  to  decrease 
significantly.  Key  driver  is  the  development  of  RES, given  that  nuclear  is  not  expanding, 
complemented by the increase of thermal plants’ average efficiency due to the new CCGTs 
and the coal phase-out.  

Figure 50: Power sector indicators 

CHP also leads to greater energy efficiency since it optimises the combined generation of 
electricity  and  heat  from  the  same  input  fuel.  However,  CHP  is  projected  to  be  roughly 
stable, a result of the almost stable demand in heat and steam. 

Storage 

As expected, the increasing penetration of variable RES implies a significant increase in 
flexibility services in the power system. Gas plants, cross-border flows and storage plants 
are  the  main  balancing  resources.  Still,  storage  facilities  undergo  an  increase  by  2030, 
which accelerates afterwards  (Figure 51): pumped storage, which develops further but has 
potential limitations; batteries that also develop considerably and benefit from decreasing 
costs over time; and power-to-X, which emerges in the longer-term. As hydrogen makes 
little inroad under Reference Scenario assumptions, power-to-X has a small contribution to 
total storage, even in the long-term. 

86 

 - 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90201020152020202520302035204020452050Carbon intensity in power generation (tCO2/MWh)Combustion PlantsTotal Net Generation0102030405060708090100201020152020202520302035204020452050Non-fossil fuels and CCSin electricity generation (%)RESNuclear0102030405060708020152020202520302035204020452050System Indicators (%)Demand Load FactorRate of use of power capacitySource:PRIMES05101520253035404550201020152020202520302035204020452050Efficiency IndicatorsEfficiency of thermalelectricity production (%)CHP indicator(% of electricity from CHP) 
 
EU REFERENCE SCENARIO 2020 

Figure 51: Storage plants and energy 

Electricity trade patterns 

Over time the volume of trade in electricity is influenced by a number of factors. On the one 
hand, the full development of the internal market leads to higher NTCs, which, all else equal, 
increases trade flows; on the other hand, the higher penetration of decentralised RES leads 
to the construction of flexible capacities close to the demand centres. All else equal, this 
leads to a reduction of the trade volume. Finally, the harmonisation of electricity prices also 
tends to a reduction of trade volumes. 

These effects can be observed inTable 10 which shows the evolution of volume of trade. 
EU  countries  are  grouped  in  regions,  and  each  region  includes  countries  that  are  well- 
interconnected and form a relatively “closed” system. Looking at the trade flows, it can be 
seen that in the 2020-30 period, there is a decrease in total trade flows, while post-2030, 
the factors that increase trade volume overweigh, and total trade flows end up increasing 
until  the  end  of  the  projection  period.  At  the  same  time,Table  10  reveals  a  very  slight 
“opening” of the regional systems, as they increase trade with other regions relative to trade 
within the regions. In particular, in 2020 trade flows between different regions represents 
45% of total trade flows; this figure increases to 48% in 2030 and then stays almost stable 
for the remainder of the projection period reaching 49% in 2050. 

Looking more closely at the results for each region: the Iberian region appear to increase 
its trade with the Central West region; on the other hand, trade of the Central West with 
other  regions  (and  in  particular  the  Central  East  region)  decrease.  Southeast  Europe 
remains  a  closed  system  due  to  the  relatively  limited  developments  in  interconnection 
capacity assumed. 

Table 10: Volume of trade flows by region over time (MWh) 

Central West 

Iberian 

North 

Central South 

Central East 

Baltic 

South East 

2020 

(C/W) 

(IB) 

(N) 

(C/S) 

(C/E) 

(BA) 

(S/E) 

C/W 

73 

15 

7 

33 

24 

0 

0 

87 

05010015020025030020152020202520302035204020452050Stored Energy in TWhHydro pumpingBatteriesSynthetic FuelsSource:PRIMES012345678020406080Share of stored energy over net total power generationShare of variable RES over total power generationCorrelation of power stored in storage plants and variable RES penetration 
 
EU REFERENCE SCENARIO 2020 

7 

0 

0 

0 

0 

0 

0 

31 

0 

0 

2 

0 

0 

0 

4 

0 

0 

0 

0 

3 

0 

27 

1 

4 

0 

8 

0 

7 

10 

0 

1 

7 

0 

4 

0 

0 

IB 

N 

C/S 

C/E 

BA 

S/E 

Total 

Interregional trade as % of total  

0 

0 

9 

1 

0 

1 

279 

45% 

Central West 

Iberian 

North 

Central South 

Central East 

Baltic 

South East 

(C/W) 

(IB) 

(N) 

(C/S) 

(C/E) 

(BA) 

(S/E) 

78 

15 

13 

3 

5 

0 

0 

8 

6 

0 

0 

0 

0 

0 

4 

0 

17 

0 

1 

1 

0 

26 

18 

0 

0 

4 

0 

0 

1 

0 

7 

0 

16 

4 

3 

0 

0 

5 

0 

3 

11 

0 

0 

0 

0 

3 

3 

0 

2 

257 

48% 

Interregional trade as % of total  

Central West 

Iberian 

North 

Central South 

Central East 

Baltic 

South East 

2050 

(C/W) 

(IB) 

(N) 

(C/S) 

(C/E) 

(BA) 

(S/E) 

C/W 

10 

IB 

N 

C/S 

7 

0 

0 

6 

0 

16 

0 

19 

21 

0 

0 

4 

0 

4 

1 

88 

0 

0 

3 

0 

0 

0 

0 

1 

82 

15 

12 

8 

2030 

C/W 

IB 

N 

C/S 

C/E 

BA 

S/E 

Total  

 
EU REFERENCE SCENARIO 2020 

0 

0 

0 

3 

4 

0 

0 

0 

3 

19 

3 

4 

4 

12 

0 

2 

0 

3 

C/E 

BA 

S/E 

Total  

Interregional trade as % of total  

3.2.2. Steam and heat supply 

13 

0 

0 

280 

49% 

The demand for heat/steam remains relatively stable throughout the projection period.  

The  share  of  CHPs  in  electricity  remains  similar  in  the  mid-projection  period  before 
increasing  in  the  longer  term.  For  steam  generation  the  share  between  district  heating 
boilers and CHPs remains stable over time. In district heating boilers there is a gradual shift 
away from solids, oil and (in the long term) gas towards biomass, waste and other emerging 
renewables and electricity technologies, e.g., heat pumps, solar thermal, and geothermal. 

Electricity boilers, heat pumps, geothermal energy and solar thermal penetrate the district 
heating market and get a significant share in the long term. This is attributed to the high 
ETS  prices,  since  district  heating  is  covered  by  the  EU  ETS,  which  acts  as  market  and 
technology driver.  

Figure 52: Fuel input to district heating  

89 

910721191513131717171024712515257642015202020302050Shares (%)0510152025303540455020002005201020152020202520302035204020452050MtoeHeat fromCHPElectricity,Geothermal,SolarBiomass &wasteGaseousOilSolidsDistrictHeatingOutputSource: PRIMES 
 
EU REFERENCE SCENARIO 2020 

Figure 53: Fuel input to CHP plants (all plants) 

3.2.3. Primary energy supply 

Total primary energy supply declines after 2030 due to increased levels of energy efficiency, 
which brings primary energy demand down (Gross Inland Consumption). RES share more 
than  doubles  between  2015  and  2050  owing  to  the  growing  electrification  of  demand 
sectors. Oil maintains a large share due to limited substitution in the transport sector. Solid 
fuels decline as a result of coal phase-out in the power sector, while gas maintains its share 
and is widely used in all stationary and energy supply sectors (Figure 54). 

Figure 54: Gross inland consumption 

90 

02040608010012014016018020152020202520302035204020452050MtoeBiomass &wasteGaseousOilSolids24152393648354750202020302050Shares (%)Source: PRIMES020040060080010001200140016001800199520002005201020152020202520302035204020452050MtoeRenewableenergyformsElectricityNuclearNatural gasOilSolids171293343232272123222414141110141826362015202020302050Shares (%)Source: PRIMES 
 
 
  
EU REFERENCE SCENARIO 2020 

Primary energy requirements are increasingly covered by RES. RES achieve the largest 
share  in  the  long  run,  driven  by  an  impressive  development  in  the  power  sector.  RES 
increase by a factor of 1.8 in 2015 from 2005 levels and by a factor of 3.3 in 2030. The pace 
of growth slows down after 2030 and in 2050 RES are 4.6 times above their levels in 2005.  

Biomass, mainly traditional solid biomass, represents more than half of total RES followed 
by wind; wind onshore is projected to develop rapidly in the medium term, whereas wind 
offshore  grows  faster  in  the  long  term.  Solar  energy  exhibits  an  impressive  growth  until 
2030, which decelerates afterwards.  

The shift towards RES contributes significantly to lower primary energy intensity since, in 
statistical  terms,  RES  are  accounted  using  an  efficiency  factor  of  1,  as  opposed  to 
alternative fossil fuel or nuclear technologies, which are accounted using energy conversion 
factors below 1. 

Figure 55: Breakdown of RES shares in Gross Inland Consumption 

Primary energy production follows the declining trend of primary energy demand for solid 
fuels and the exhaustion of reserves for oil and gas. Moreover, the mix in primary energy 
production  changes  considerably  over  time,  with  RES,  including  biomass,  becoming 
dominant by 2050 (Figure 56) and almost compensating the reduction of indigenous fossil 
fuel production. 

91 

171293343232272124222414141110911131522332361113522015202020302050Shares (%)020040060080010001200140016001800199520002005201020152020202520302035204020452050Mtoeother RESsolarwindhydrobiomass-wasteNuclearNatural gasOilSolidsSource: PRIMES 
 
EU REFERENCE SCENARIO 2020 

Figure 56: Primary energy supply 

3.2.4. Import dependence 

Import dependence remains roughly stable over the projection period. Despite the dropping 
shares  of  fossil  fuels  in  final  energy  demand  and  the  decrease  of  overall  net  imports 
including crude oil, the limited domestic resources lead to an increase in imports of natural 
gas and oil products. A potentially stronger increase in imports is however mitigated by RES 
deployment,  energy  efficiency  improvements  and  stable  nuclear  production.  As  a  result, 
import dependence peaks in 2025 at 58% before dropping to 50% in 2050 (Figure 59). 

Nonetheless, the absolute level of imports is on decline, even as domestic resources are 
diminishing.  Imports  of  solids  and  crude  oil  and  feedstock  decrease,  while  oil  slightly 
increases. Natural gas net imports increase slightly in the long term, reaching approximately 
223 Mtoe in 2050. 

92 

21161245442119783231231921252828111526402015202020302050Shares (%)0100200300400500600700800199520002005201020152020202520302035204020452050MtoeRES (otherthanbiomass)Biomass &WasteNuclearNatural gasOilSolidsSource: PRIMES 
 
EU REFERENCE SCENARIO 2020 

Figure 57: Net imports by fuel 

In  2030  import  dependence  in  most  Member  States  is  projected  to  be  lower  than  2015, 
while Cyprus becomes a net exporter (of gas). In all other Member States energy import 
dependence  decreases  or  remains  constant.  The  external  fossil  fuel  bill  of  the  EU  is 
projected to rise in constant prices by 14.5% between 2015 and 2030 and by 36.3% until 
2050, reaching around 279 bn €’15 and 332 bn €’15 in 2030 and 2050, respectively. 

Figure 58: Energy import dependence by Member State in 2015 and 2030 (%) 

978-92-76-39356-6  

93 

-20002004006008001000120020052010202020302050Net imports (Mtoe)Renewable energyformsElectricityNatural gasOil productsCrude oil andFeedstocksSolids-39-14523-54-14910-82-2283SolidsOilGasIncremental net imports from 2015 (Mtoe)202020302050565656585453535150201020152020202520302035204020452050Import dependence of GIC (%)128645556553263433402015202020302050Shares of total imports  (%)Source: PRIMES 
 
 
 
EU REFERENCE SCENARIO 2020 

RES deployment, combined with energy efficiency improvements and nuclear production 
(which  remains  stable),  help  mitigate  the  potentially  stronger  increases  in  import 
dependence.  Incremental  net  imports  relative  to  2005  are  negative  for  all  fossil  fuels, 
including natural gas, while oil continues to represent the largest share in total imports of 
energy. Therefore, the import dependence indicator remains roughly stable over the entire 
projection period. 

Figure 59: Primary energy imports (% / Mtoe) 

Figure  60  shows  gas  import  dependence  projections  in  EU  Member  States  grouped  by 
region on the basis of geographical proximity and energy/gas system characteristics. The 
highest  increase  in  import  dependence  between 2020  and  2030  occurs  in  North  Europe 
(Denmark, Finland, Sweden) followed by Central West (Austria, Belgium, France, Germany, 
Ireland, Luxembourg, and the Netherlands). In regions with no gas resources and hence no 
production prospects (Iberian Peninsula and Baltics) net import dependence remains 100% 
over the period 2020-50. 

Figure 60: Natural gas import dependence (%) 

94 

464850525456586001002003004005006007008009001000201020152020202520302035204020452050%Mtoe Import dependence of GIC (%)Net imports (Mtoe)Source: PRIMES-0.200.20.40.60.811.2Central WestIberianNorthCentral SouthCentral-EastBalticSouth East202020302050Source:PRIMES 
 
 
EU REFERENCE SCENARIO 2020 

3.3. Energy policy indicators 

3.3.1. RES target 

The  overall  EU  RES  target  for  2030  is  slightly  over-achieved  (Figure  61).  According  to 
projections, the growth of RES share is significantly higher in the second half of the decade 
(2025-2030) compared to the first half. A slow-down is foreseen after 2030 in the absence 
of additional policies. This is particularly relevant for RES share in heating and cooling.  

The shares of RES in electricity continue to increase after 2030 – albeit slower than before 
– owing to the strong policies in place, the planned investments for 2030 and the market 
forces.  

RES-T is projected to increase significantly after 2030, due to the increase in electrification 
brought by the CO2 standards, the recharging infrastructure development, and the decrease 
in battery costs. The multipliers included in the current RES-T formula lead to the strong 
increase of the RES-T share post-2030, as calculated according to the current legislation.   

Figure 61: RES policy indicators 

RES-E 

In the RES-E share, the numerator represents total gross electricity generation from RES 
and the denominator represents electricity demand99.  

Between 2021 and 2030 energy efficiency policies curb demand for electricity, which brings 
about an additional (to what results from the increase in absolute amounts of renewable 
electricity) increase in RES-E share. In the long term, electrification of final demand sectors 
combined with a slower penetration of RES in power generation make it so that electricity 

99 Whenever the denominator decreases and the numerator does not change, then RES shares increase, without however 
RES changing as an absolute amount. 

95 

18.122.626.433.237.641.144.245.620152020202520302035204020452050Overall RES share 30.037.849.058.564.368.773.674.520152020202520302035204020452050RES-E20.623.226.332.836.539.240.140.820152020202520302035204020452050RES-H&C5.79.011.521.230.240.151.056.520152020202520302035204020452050RES-TSource:PRIMES 
 
                                                 
EU REFERENCE SCENARIO 2020 

demand  (denominator)  grows  slightly  faster  than  total  RES  (numerator),  resulting  in  a 
slower increase of the RES-E share (after 2030). Wind followed by solar power drive the 
increase  in  RES-E  share  over  time;  hydropower  increases  modestly  and  has  a  small 
contribution. 

Figure 62: Decomposition of RES-E change relative to 2010 (in %) 

RES-H&C 

Heating and cooling includes space heating, and all heat uses in buildings, industry, as well 
as the production of heat, i.e., the fuels used to produce heat in co-generation and in district 
heating/heat-only plants. 

According  to  the  projections,  energy  efficiency  of  heat  uses,  through  e.g.,  insulation  of 
buildings  and  efficiency  in  industry,  improves  substantially.  Cutting  back  the  demand for 
heat  (denominator)  in  all  final  demand  sectors  pushes  the  RES-H&C  share  strongly 
upwards until 2035. In the absence of further strengthening of policies for energy efficiency 
this trend slows down. 

Equally important in the growth of the RES-H&C share is the increased absolute use of RES 
in heating – both in final demand sectors and in supply (cogeneration and district heating). 
The numerator sums together the direct use of RES in final heat demand, RES share in co-
generation, and the penetration of heat pumps. Evidently, all three play a positive role in 
boosting in the RES-H&C share. 

Supported by strong policies RES extend their share in final heat demand in 2021-2030 and 
remain  stable for  the  rest  of  the  projection  period.  In  contrast,  RES  in  cogeneration  and 
heat-only  plants  continue  to  increase  also  post-2030  as  a  large  part  of  the  production 
coming  from  these  plants  is  subject  to  the  EU  ETS,  which  is  the  main  driver  of  RES 
development in 2030-2050 period. The electrification of heating via the penetration of heat 
pumps supported both by energy efficiency and RES policies has also a positive impact on 
the RES-H&C share through the ambient heat calculation. However, the projection for the 
use of heat pumps under Reference Scenario assumptions is rather conservative compared 
to climate neutrality scenarios, which tap fully on their vast potential. 

Put together, the absolute use of RES in final demand and in supply and the deployment of 
heat pumps have a combined positive effect on RES-H&C share, which  is much greater 
than the effect of energy efficiency improvement. 

96 

-1001020304050607020152020202520302035204020452050Electricity DemandSolar and otherWindHydroSource:PRIMES 
 
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Figure 63: Decomposition of RES-H&C change relative to 2010 (in %) 

RES-T 

The  evolution  of the  RES-T share  depends  on three factors:  energy  demand, the  use  of 
biofuels and RES electricity combined with transport electrification. As the electrification of 
transport advances and the share of RES in power generation increases, the two combined, 
together with the multiplier used in the RES-T formula, exert the strongest influence on the 
RES-T share.  

Figure 64: Decomposition of RES-T change relative to 2010 (in %) 

Under  Reference  Scenario  assumptions the contribution  of  biofuels  peaks  in 2030.  After 
2030 the demand for biofuels in transport remains relatively stable. Demand decreases in 
the passenger car segment, where electrification proceeds over time, and remains relatively 
stable  in  freight  road  transport.  Biokerosene  starts  making  inroads  into  aviation  in  2030-
2035 but it maintains a limited share by 2050. The share of biofuels in the maritime sector 
remains very limited by 2050. In relative terms, the contribution of biofuels to the increase 
in the RES-T share decreases over the projection period, due also to the way the formula 
is calculated.  

97 

05101520253020152020202520302035204020452050Heat PumpsRES in CHP/Heat plantsRES in Final HeatHeat demandSource: PRIMES0102030405060708020152020202520302035204020452050Energy DemandBiofuelsRES in ElectricitySource: PRIMES 
 
 
EU REFERENCE SCENARIO 2020 

3.3.2. Energy efficiency target 

Energy consumption measurement follows the 2020-2030 scope as defined by the current 
legislation (Table 11), under which international EU aviation and blast furnaces are included 
in final energy demand. The targets are expressed accordingly.  

The Reference Scenario projects that the sum of NECPs fails to meet the EU 2030 target 
of  32.5%  savings  in  final  energy  demand  with  regards  to  the  respective  year  value  of 
PRIMES 2007 baseline projection. In contrast, the primary energy savings EU 2030 target 
is achieved due to the combined effects of the energy efficiency policies and the plans of 
Member  States  to  phase  out  coal  and  nuclear  in  several  countries  and  replace  them  by 
RES100 and more efficient gas in the power sector.  

Table 11: Key policy indicators for the energy efficiency target 

Key policy indicators (Scope Europe 2020-
2030) 

2020 

2025 

2030 

Gross inland consumption (Mtoe) 

1260.6 

1300.6 

1229.6 

Non Energy consumption (Mtoe) 

84.5 

91.3 

98.7 

Primary Energy Consumption (Mtoe)101 

1176.1 

1209.3 

1130.9 

Final Energy Consumption (Mtoe) 

868.8 

930.5 

882.6 

Primary Energy Savings with regards to the respective 
year value of PRIMES 2007 baseline projection 

-28.3 

-27.4 

-32.4 

Final Energy Savings with regards to the respective 
year value of PRIMES 2007 baseline projection 

-27.5 

-24.5 

-29.6 

3.4. Electricity prices and costs 

In PRIMES electricity prices are calculated in such way that allows recuperating all costs, 
including  those  related  to  RES  policies  (e.g.,  feed-in-tariffs),  grid  costs,  charging 
infrastructure for EVs and investment costs including stranded investments, back-up, and 
reserve costs as well as profit margin. The PRIMES model differentiates electricity prices 
by sector reflecting load profiles, generation, and grid costs.  

Weighted average electricity prices, decomposed by cost items, tend to modestly increase 
until 2030. This is explained mainly by two factors. First, the application of carbon pricing 
and  taxes,  i.e.,  ETS  allowance  payments.  And  second,  the  higher  grid  costs  due  to 
infrastructure development to support grid expansion and new interconnections. Grid costs 
increase over  time due to  the growing  share of RES  and particularly  variable  distributed 
RES. Although not geographically defined, PRIMES uses functions to determine grid costs 
based on the share of distributed generation, mainly wind and solar. The function has been 
econometrically estimated based on the requirement for high, medium, and low voltage grid 
requirements.  In  the  period  to  2030,  grid  costs  increase  both  due  to  the  increase  of 
distributed RES as well as to the grid development of the TYNDP of ENTSOE.   

100 Wind and solar are accounted by a factor of 1 in primary energy, whereas thermal combustion uses the efficiency of the 
power plants and nuclear uses a statistical factor of 0.3. This explains the significant primary energy savings when coal or 
nuclear is substituted by RES in power generation.  
101 Primary Energy Consumption = Gross inland consumption – Non-energy consumption

98 

 
                                                 
 
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A small increase in capital costs by 2030 is linked with RES development. After 2030, capital 
costs of RES decrease thanks to learning by doing and falling technology costs. Also, after 
2030, the fuel cost component remains stable despite the increase in fuel prices, due to the 
decreasing share of combustion plants. 

Calculation of electricity prices in PRIMES 

The  electricity  prices  in  PRIMES  are  calculated  in  order  to  recuperate  all  costs  including  those 
related  to  renewables  (such  as  feed-in-tariffs),  grid  costs,  recharging  infrastructure  for  EVs  and 
investment costs including stranded investments, back-up and reserve costs, profit margin etc. 
The process to determine the electricity prices in PRIMES can be divided into four steps: 

  Determination  of  total  system  costs  under  least  cost  unit  commitment  and  least  cost 

expansion conditions mimicking well-functioning markets.  

  Simulation  of  wholesale  markets  by  country  and  estimation  of  marginal  system  prices 

reflecting long run marginal costs. 

  Matching of load profiles of customer-types with the duration curve of long term marginal 
prices with customers sorted in descending order of their load factor mimicking bilateral 
contracting. 

  Calculation of prices by sector based on price levels by customer type  are calculated to 
recover  the  total  power  system  budget  including  variable  generation  costs  and  annuity 
payments  for  capital  costs,  recovery  of  additional  costs  for  RES  and  cost  of  grid 
differentiated by voltage type. 

Grid  cost  recovery  is  based  exclusively  on  load  payments  at  average  grid  tariffs  determined  as 
levelised costs of regulated asset basis.  

The  pricing  approach  corresponds  to  the  Ramsey-Boiteux  methodology  and  allows  for  the 
differentiation of electricity prices by sector. 

99 

 
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Figure 65: Cost components of average electricity price 

Overall, prices of electricity across the EU Member States tend to converge towards the EU 
average in  the  projection  period. This  convergence  is  driven  by  a combination  of factors 
including the elimination of subsidies where these are still present, an increased penetration 
of RES in all countries, as well as deeper market coupling. 

Prices for services and households increase moderately in the medium term and remain 
stable  in  the  long  term.  Industrial  prices  remain  stable  or  reduce  over  time,  as  industry 
maintains base-load profile and is charged for a fraction of grid costs. As far as taxes are 
concerned, these apply mainly on prices for households and services. 

Figure 66: Electricity prices by sector 

100 

20152020203020402050Annual capital cost2329332923Fixed costs2523232527Variable costs11111Fuel costs1817161514Tax on fuels and ETS payments767810Grid costs2729323534Excise tax and VAT on electricity2019202120Average price of electricity(after tax)146151158159161020406080100120140160180€'15/MWhDecomposition of average electricity priceSource: PRIMES6080100120140160180200220240201020152020202520302035204020452050Avg. Electricity Prices after tax in €'15/MWh6080100120140160180200220240201020152020202520302035204020452050Avg. Electricity Prices before taxes in €'15/MWhHouseholdsServicesAveragepriceIndustrySource: PRIMES 
 
 
 
EU REFERENCE SCENARIO 2020 

3.5. Greenhouse gas emissions and removals 

3.5.1. CO2 emissions (excluding LULUCF) 

This  section  presents  the  projected  evolution  of  CO2  emissions,  emitted  from  the 
combustion  of  fossil  fuels  in  the  various  sectors  of  the  economy  and  from  industrial 
processes. 

Total CO2 emissions  

The energy projections presented in the previous sections point to a progressive decoupling 
of  energy  demand from GDP  growth and  a decoupling  of  CO2  emissions102 from  energy 
demand by 2030, strengthening the trend observed since 2005 (see Figure 67).  

It must be noticed that 2020 is an exceptional year, with a drop in GDP growth and in CO2 
emissions  due  to  the  pandemic.  Both  GDP  growth  and  CO2  emissions  are  projected  to 
bounce back by 2025, close to pre-COVID-19 levels.  

The implementation of energy efficiency policies is an important driver of the projected trend 
of reduction of the energy intensity of the GDP until 2030, which is complemented by carbon 
intensity savings of the energy mix as the result of coal phase-out policies and the further 
deployment of renewables in power generation and the other sectors.  

The improvement of the energy intensity slows down after 2030, in particular due to the lack 
of additional energy efficiency policies. 

Figure 67: Evolution of the energy intensity of GDP and of the carbon intensity of energy for the EU 

Note:  The  intensities  are  calculated  using  the  Gross  Inland  Consumption  of  energy,  the  GDP  is  expressed  in  EUR2015. 
Source: PRIMES model 

Figure  68  shows  the  comparison  of  the  carbon  intensity  of  GDP  in  2015  and  2030  by 
country, which reflects a general improvement, although for some countries, the persistence 
of coal use maintains a fairly high level of carbon intensity still in 2030. 

102 The PRIMES model computes endogenously energy-related CO2 emissions from the combustion of fossil fuels applying 
the emission factors of the Regulation on the monitoring and reporting of greenhouse gas emissions (601/2012 and updates 
thereof). This also concerns emissions for historical years, where these emission factors apply to energy balances calibrated 
on historical Eurostat energy balances. Matching of energy-related CO2 emissions with emission inventories is done through 
the calculation of residuals. In the particular case of the iron and steel sector, the process-related CO2 emissions are calibrated 
so that the sum of emissions of the different steps of the industrial process do match the sum of the following categories of 
the EEA GHG data viewer: 1.A.2.a - Iron and Steel, 1.A.1.c - Manufacture of Solid Fuels and Other Energy Industries, 2.C.1 
- Iron and Steel Production.   

101 

 
 
                                                 
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Figure 68: Carbon intensity of GDP in 2015 and 2030 

Falling energy and carbon intensity imply a steady decline in CO2 emissions throughout the 
projection period (Figure 69).  

Figure 69: Evolution of CO2 emissions (excl. LULUCF) 

Emissions reduction continues from 2030 to 2040, since in that decade coal phase-out is 
completed,  high  RES  shares  in  power  generation  are  established  and  energy  efficiency 
policies continue to have an impact. Post 2030, emissions reductions are relatively stronger 
in the ETS sectors compared to the non-ETS sectors, since the ETS is a strong policy driver 
that continues to apply over the period and affects emission reductions in the longer term 
(Figure 70). 

102 

20152030 
 
 
 
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Figure 70: Decomposition of energy-related CO2 Emission reduction relative to projection with CO2 intensity of 
GDP frozen to 2010 levels 

CO2 emissions in the power sector 

In particular, and as shown in Figure 71, emissions in the power sector decrease by 50% in 
2030 and 75% in 2050 compared to 2015, without however reaching carbon neutrality in 
the  long-term  in  the  Reference  Scenario  context.  This  is  primarily  attributed  to  the  ETS 
carbon price signal, the technology improvements  (contribution of renewables, increased 
efficiency  of  CCGT  plants)  and national  coal  phase-out  policies.  Emissions  in  the  power 
sector  decrease  by  50%  in  2030  and  75%  in  2050  compared  to  2015,  without  however 
reaching  carbon  neutrality  in  the  long-term  in  the  Reference  Scenario  context.  This  is 
primarily  attributed  to  the  ETS  carbon  price  signal,  the  technology  improvements 
(contribution of renewables, increased efficiency of CCGT plants) and national coal phase-
out policies.  

District-heating  units  also  see  a  reduction  in  emissions,  whereas  emission  reductions  in 
refinery and industrial boilers are relatively small. 

103 

-4.0-1.1-3.4-1.2-2.1-1.4-2.2-1.2-3.1-1.3-2.8-1.22011-20202021-20302031-20402041-2050Annual rates of change (%)Total CO2 emissionsTotal non-ETS CO2 emissionsTotal ETS CO2 emissions38173516320825642582226019591705156515142005201020152020202520302035204020452050Mt CO2CO2Emissions related to Energy and Industrial ProcessesTotal ETS CO2 emissionsTotal non-ETS CO2 emissionsTotal CO2 emissions49504747515053532020203020402050Shares (%)Total non-ETS CO2 emissionsTotal ETS CO2 emissionsSource:PRIMES 
 
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Figure 71: CO2 emissions in power and steam supply 

Until 2030, the carbon intensity of power generation (Figure 72) is projected to decline in all 
countries  driven  by  the decisions  to  phase  out coal,  the  development  of RES  and/or  the 
reliance on nuclear. Few countries which continue to use coal and lignite have higher carbon 
intensity in power generation.  

Figure 72: Carbon intensity of power generation by Member State in 2015 and 2030 

CO2 emissions in final energy consumption sectors 

In  final  energy  consumption  sectors  the  reduction  of  direct  CO2  emissions  is  in  general 
driven by the reduction of energy demand, complemented by fuel switching.  

This  is  for  instance  the  case  in  transport,  where  CO2  emissions  (including  international 
aviation, but excluding international maritime) are lower by 15% in 2030 and 36% in 2050 
compared  to  2015.  Unlike  the  other  transport  modes,  aviation  undergoes  an  increase  in 
emissions by 2030 driven by the growth in transport activity which leads to higher energy 
consumption met primarily by fossil fuels. CO2 emissions from international maritime also 
increase in the Reference Scenario, by 14% by 2030 and 32% by 2050 relative to 2015103. 

Emissions in the transport sector are the most difficult to abate. CO2 standards for light and 
heavy duty vehicles contribute significantly to lower the carbon intensity for the total fleet of 
LDVs and HDVs and reduced emissions by 2050. This is complemented by other policies 
leading  to  improvements  in  transport  system  efficiency,  by  making  the  most  of  digital 

103 By 2050, the CO2 emissions from international maritime would stabilise to the levels of 2008.  

104 

02004006008001000120014001600200020102020203020402050CO2emissions of power generation (MtCO2)RefineryboilersIndustrialboilersDistrictheating unitsThermalPower Plants0.560.500.390.320.200.180.000.100.200.300.400.500.60Carbon intensity of power generation from thermal plants (tCO2/MWhe+MWhth)Source: PRIMES20152030 
 
 
                                                 
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technologies and smart pricing and further encouraging multi-modal integration and higher 
use of sustainable transport modes.  

Still, while electrification develops, fuel switching by 2030 remains limited in the transport 
sector in the Reference Scenario (see section 3.1.4). A shift to alternative fuels is mainly 
projected in the longer run for the passenger cars segment (mainly electricity), and for road 
freight and maritime transport (e.g. LNG). Also, the role of biofuels is limited post-2030 in 
the  context  of  the  Reference  Scenario.  Under  these  conditions,  fossil  fuels  in  transport 
persist.  

Figure 73: Evolution of CO2 emissions in transport (excluding international maritime) 

Emissions from passenger transport go down by 17% in 2030 and 41% in 2050 compared 
to 2015 levels whereas emissions from freight transport reduce by 7% in 2030 and 18% in 
2050, respectively. 

Figure 74: Energy-related CO2 emissions in transport 

In  both  freight  and  passenger  transport,  the  biggest  drop  in  carbon  intensity  among 
transport modes is projected to come from railways. This is mainly due to the electrification 
of  the  rolling  stock  and  the  related  infrastructure,  a  trend  that  will  unfold  until  2030  and 
beyond, enabled also by the TEN-T Regulation. Cars follow suite, representing the second 
highest reduction in carbon intensity. This is mainly due to the growing market share of low- 
and  zero-emission  vehicles  by  2030.  Road  freight  is  also  expected  to  reduce  its  carbon 
intensity, attributed to the market penetration of LNG trucks and efficiency improvements in 
the fleet brought about by the introduction of CO2 standards for manufacturers (Figure 75). 

105 

020040060080010002005201020152020202520302035204020452050MtCO2Inland waterways& nationalmaritimeAviationRailRoadSource: PRIMES050100150200250300350400201020152020202520302035204020452050Freight transport  01002003004005006007008009001000201020152020202520302035204020452050MtCO2Passenger transportCO2 emissionreduction dueto energyintensity gainsCO2 emissionsreduction dueto lowercarbonintensity ofenergyActualEmissionsSource: PRIMES 
 
 
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Figure 75: Carbon intensity by transport mode 

In  the  industry,  process  emissions  in  2030  are  projected  to  be  at  their  2015  level,  with 
technologies for emission abatement difficult to adopt in the Reference Scenario context. 
On the other hand, emissions from energy uses decrease compared to 2015, with the EU 
ETS  driving  a  shift  towards  less  carbon  intensive  fuels  (see  section  3.1.1)  and 
accompanying the movement from energy-intensive products towards higher value-added 
and  less  energy-intensive  products.  Compared  to  2015,  energy  intensity  and  carbon 
intensity  gains  are  projected  to  bring  energy-related  CO2  emissions  in  industry  down  by 
17% in 2030 and 27% in 2050.  

Finally,  in  buildings  (including  both  residential  and  services),  fuel  switching  and 
electrification along with energy efficiency policies bring emissions down by 33% in 2030 
and  52%  in  2050  compared  to  2015.  Energy  intensity  drops,  in  particular  due  to  energy 
efficiency policies, with a shift in favour of less carbon intensive energy vectors in the long 
term (see section 3.1.2 and section 3.1.3).  

106 

-34-16-43-19-14-66-25-79-37-22CarsPublic roadRailAviationInlandwaterways &nationalmaritime% change of passenger transport carbon intensity in tCO2/Mpkm from 2015-29-47-14-47-81-20Road freightRailInland waterways &national maritime% change of freight transport carbon intensity in tCO2/Mtkm from 201520302050Source: PRIMES 
 
 
 
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Figure 76: Energy-related CO2 emissions in demand sectors (excl. transport) 

3.5.2. Non-CO2 emissions and their drivers 

In 2015, the EU emitted non-CO2 greenhouse gases are equivalent to 778 MtCO2-eq when 
converted  using  global  warming  potentials  (GWPs)  over  100  years  from  IPCC’s  Fifth 
Assessment  report  (2014)104.  Just  over  half  of  these  emissions  can  be  attributed  to  the 
agricultural sector, about a fifth to the waste and wastewater sectors, and the rest primarily 
to fossil fuel production and use and to cooling. Figure 77 illustrates how emissions in non-
agricultural  sectors  have  declined  by  30%  since  2005  and  are  projected  to  continue  to 
decline  reaching  53%  by  2030  and  67%  by  2050.  The  reasons  for  the  decline  can  be 
referred to existing policies to address non-CO2 emissions e.g., the EU directives regulating 
the  waste  sector  and  the  F-gas  regulations.  In  contrast,  the  agricultural  sector  has  seen 
emission  reductions  of  only  5%  since  2005,  and  with  modest  continued  reductions 
expected,  amounting  to  8%  by  2030  and  11%  by  2050.  Details  on  the  drivers  for  and 
estimation of sector-level non-CO2 emissions in the Reference Scenario are presented in 
this section. 

104 See Commission Delegated Regulation (EU) 2020/1044 supplementing Regulation (EU) 2018/1999 with regard to values 
for global warming potentials. 

107 

050100150200250300350400450201020152020202520302035204020452050MtCO2TertiaryCO2 emission reductiondue to energy intensitygainsCO2 emissions reductiondue to lower carbonintensity of energyActual EmissionsSource: PRIMES0100200300400500600700201020152020202520302035204020452050MtCO2Industrial0100200300400500600700201020152020202520302035204020452050MtCO2Residential 
 
                                                 
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Figure 77: Evolution of EU non-CO2 greenhouse gas emissions 

Agricultural sector 

Livestock is the primary source of agricultural non-CO2 emissions. Over three quarters of 
livestock emissions come from dairy cows and non-dairy cattle systems (see Figure 78), 
with  CH4  formation  during  enteric  fermentation  processes  in  the  digestive  system  of 
ruminants  contributing  the  most,  with  smaller  contributions  coming  from  manure 
management.  Just  over  a  third  of  agricultural  emissions  are  releases  of  N2O  from 
agricultural soils, which is closely linked to the amount of nitrogen (N) added from fertilizers. 
Finally, there is a small contribution of CH4 emissions from the burning of crop residues left 
on fields. 

Figure 78: Evolution of non-CO2 GHG emissions from the EU’s agricultural sector (excl. LULUCF) 

Drivers  used  in  GAINS  for  the  future  development  of  livestock  emissions  are  animal 
numbers and milk production in the case of dairy cows, with emissions further affected by 
the prevalence of technologies and practices that limit non-CO2 emissions. Figure 79 (panel 
a) shows how the EU stock of dairy cows is expected to decline by 10% between 2015 and 
2030, but due to simultaneous increases in milk yield, CH4 emissions drop by only 1.6% 
over the same period. Both CH4 and N2O emissions from non-dairy cattle are expected to 
follow closely a decline in the animal stock by about 10% between 2015 and 2030 (panel 

108 

01002003004005006007008009002005201020152020202520302035204020452050Mt CO2eqOtherAC&refrigerationWastewaterWasteEnergyIndustryAgricultureNon-agriculturalsectors:-30%          -53%-67%Reduction below 2005:    -18%          -31%-41%Agriculture:                           -5%           -8%-11%Source: IIASA-GAINS0501001502002503003504004502005201020152020202520302035204020452050Mt CO2eqAgriculturalwaste burningSoilsOther livestockSheep & GoatsPigsNon-dairy cattleDairy cowsSource: IIASA-GAINS 
 
 
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(b). There is no foreseen drop in pig numbers between 2015 and 2030 and only a limited 
decline in N2O emissions. CH4 emissions from pigs are estimated to have declined by 12% 
between 2005 and 2015 due to installations of farm capacity to treat manure in anaerobic 
digesters. These serve both as a source of biogas and as a way to reduce manure volumes 
and mitigate  bad  odour. With  increased  demand for  RES,  as  estimated by  PRIMES,  the 
installation of anaerobic digesters is expected to continue, resulting in additional reductions 
in  pig  CH4  emissions  by  14%  between  2015  and  2030.  At  the  EU  level,  CH4  and  N2O 
emissions from sheep and goats follow closely a declining trend in animal numbers. 

Figure 79: Evolution of EU livestock numbers and associated non-CO2 emissions 

Figure  80  shows  the  expected  evolution  in  nitrogen  input  on  agricultural  soils  in  the  EU. 
Policies implemented to control nitrogen emissions, e.g., the EU nitrate directive, provide 
continued incentives to reduce the use of mineral fertilizers and are expected to translate 
into an 8% reduction in N2O emissions from soils between 2015 and 2030. 

109 

0204060801001201402005201020152020202520302035204020452050Index 2005=100(a) Dairy cowsNo. of dairy cowsMilk productionDairy cows -CH4 emissionsDairy cows -N2O emissions0204060801001201402005201020152020202520302035204020452050Index 2005=100(b) Non-dairy cattleNo. of non-dairy cattleNon-dairy cattle -CH4 emissionsNon-dairy cattle -N2O emissions0204060801001201402005201020152020202520302035204020452050Index 2005=100(c) PigsNo. of pigsPigs -CH4 emissionsPigs -N2O emissions0204060801001201402005201020152020202520302035204020452050Index 2005=100(d) Sheep & goatsNo. of sheep and goatsSheep & goats -CH4 emissionsSheep & goats -N2O emissionsSource: IIASA-GAINS / CAPRI 
 
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Figure 80: Evolution in nitrogen input on EU agricultural soils 

The GAINS  model  attributes non-CO2  emissions from  livestock  by farm size  ranges and 
from  soils  by  farm  area  ranges.  This  reveals  that  55%  of  EU  livestock  emissions  are 
currently emitted from large farms with more than 100 LSU (see Figure 81), while one third 
of emissions from soils are released from the largest farms with more than 150 ha (Figure 
82).  These  findings  are  of  interest  as  large  farms  can  utilize  economies-of-scale  in 
abatement and therefore often have lower unit abatement costs than smaller farms.  

Figure 81: Livestock non-CO2 emissions by farm size for the EU (LSU=livestock units) 

110 

05000100001500020000250002005201020152020202520302035204020452050kt NManure fromstables to land(N input)Grazing animals(N input)Crop residues(N input)Mineralfertilizer (Ninput)Source: IIASA-GAINS0501001502002503002005201020152020202520302035204020452050Mt CO2eq> 500 LSU100 to 500 LSU50 to 100 LSU15 to 50 LSU< 15 LSUUnspecifiedSource: IIASA-GAINS 
 
 
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Figure 82: N2O emissions from EU agricultural soils (excl. histosols) by farm area 

Waste and wastewater sectors 

Drivers used in GAINS for generation of municipal solid waste (MSW) are GDP per capita, 
population, and changes in urbanization rate. The driver for industrial solid waste generation 
is  growth  in  value-added  of  the  respective  industry.  Figure  83  shows  how  the  expected 
increase  in  GDP  coupled  with  a  relatively  constant  population  size,  results  in  a  modest 
increase in the gross (pre-treatment) generation of MSW by 9% between 2015 and 2030, 
with  the  assumed  composition  of  the  waste  shown  in  Figure  83.  Over  the  same  period, 
industrial  solid  waste  generation  is  expected  to  increase  by  19%.  Due  to  policies 
implemented over the last two decades to address waste sector emissions, e.g., the EU 
Landfill and Packaging directives, emissions have declined by 20% between 2005 and 2015 
and are expected to be halved in the period 2015 to 2030. Due to the continued diversion 
of MSW away from landfills foreseen by the 2018 amendment of the Landfill directive (see 
Figure 83), emissions from MSW continue to decline also after 2035.  

Driver  in  GAINS  for  CH4  emissions  from  industrial  wastewater  is  the  chemical  oxygen 
demand  (COD)  content  of  the  wastewater.  This  is  in  turn  assumed  to  be  driven  by  the 
development  in  the  value-added  of  the  relevant  industries  and  results  in  an  expected 
increase in CH4 emissions from this source by 12% between 2015 and 2030. CH4 and N2O 
emissions from domestic wastewater are driven by population growth but are also affected 
by on-going extensions of centralized sewage systems and upgrades of such systems to 
secondary/tertiary  treatment.  This  results  in  a  steady  but  modest  decline  in  emissions, 
amounting to 4% between 2015 and 2030. 

111 

2005201020152020202520302035204020452050Mt CO2eq> 150 ha30 to 150ha< 30 haSource: IIASA-GAINS 
 
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Figure 83: Development in major drivers for EU non-CO2 emissions in the waste and wastewater sectors 

Figure 84: EU non-CO2 GHG emissions from waste 

Source: Data for 1990-2015 from EUROSTAT; extrapolations to other years performed in GAINS 

112 

0204060801001201401601802002005201020152020202520302035204020452050Index 2005=100Gross DomesticProduct (GDP)Value added -industryValue added -foodindustryPopulationMunicipal solid waste-massIndustrial solid waste -massMunicipal solid waste-CO2eq emissionsIndustrial solid waste -CO2eq emissionsIndustrial wastewater-CO2eq emissionsDomestic wastewater-CO2eq emissionsSource: IIASA-GAINS0204060801001201401601802002005201020152020202520302035204020452050Mt CO2eqDomesticwastewaterIndustrialwastewaterIndustrial solidwasteMSW -otherMSW -woodMSW -textileMSW -paperMSW -foodSource: IIASA-GAINS 
 
 
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Figure 85: EU waste treatment pathway to meet the 2018 Landfill Directive targets 

Overall, non-CO2 emissions from the waste and wastewater sectors have seen reductions 
of 18% between 2005 and 2015 (see Figure 86). Significant additional reductions of 41% 
are expected between 2015 and 2030 due to already implemented policies. 

Figure 86: Evolution of non-CO2 emissions from the EU waste and wastewater sectors 

Energy sector 

Energy  sector  activity  drivers  are  imported  in  GAINS  from  the  PRIMES  model.  The 
phasedown  in  EU  production  of  coal,  oil  and  natural  gas  projected  by  the  PRIMES 
Reference  Scenario  is  the  primary  reason  for  reductions  in  energy  sector  non-CO2 
emissions,  estimated  at  18%  between  2005  and  2015  and  with  expected  additional 
reductions by 30% between 2015 and 2030 (see Figure 87). Reduced use of fossil fuels in 
stationary combustion adds to reductions in emissions. Continued combustion of biomass 
in stationary sources and increased use of gas of non-fossil origin in the PRIMES energy 
projections, translate in GAINS into maintained levels of non-CO2 emissions from biomass 
combustion  and  modest  reductions  in  overall  leakage  from  the  gas  storage  and 
transportation systems.            

113 

05010015020025030019701975198019851990199520002005201020152020202520302035204020452050Mt MSWMSW -RecyclingMSW -IncinerationMSW -Compost&ADMSW -SWD(Landfill)Source: IIASA-GAINS05010015020025030019701975198019851990199520002005201020152020202520302035204020452050Mt  MSWMSW -otherMSW -glassMSW -metalMSW -plasticsMSW -woodMSW -textileMSW -paperMSW -food andgardenSource: IIASA-GAINS 
 
 
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Figure 87: Evolution of non-CO2 GHG emissions from the EU energy sector 

HFC source sectors 

The activity driver used for estimations of hydrofluorocarbons (HFCs) emissions in GAINS 
is  the  amount  of  HFCs used  in  different  source sectors.  Starting from the current  use  of 
HFCs,  the  evolution  of  a  future  fictive  demand  for  HFCs,  driven  by  economic,  climate 
change and other factors, is first derived under the assumption that no replacement of HFCs 
with alternative substances takes place. Thereafter, an alternative pathway is developed in 
which  the  fictive  demand  for  HFCs  in  different  applications  is  replaced  by  the  various 
alternatives  to  HFCs  that  are  expected  to  be  taken  up  in  response  to  existing  F-gas 
regulations. 

Figure  88  shows  how  EU  demand  for  coolants  in  stationary  air  conditioners  (ACs), 
expressed  in  HFC-equivalent  units,  is  expected  to  increase  by  34%  between  2015  and 
2030. For residential ACs, the increased demand for cooling is driven by a growing fraction 
of  households  owning  ACs,  which  in  turn  is  a  function  of  changes  in  income  (GDP  per 
capita) and the average number of annual cooling degree days (CDDs). The latter reflect 
expected  temperature  increases  in  response  to  climate  change  impacts.  Similarly,  the 
demand  for  coolants  in  commercial  ACs  is  driven  by  changes  in  GDP  per  capita  and 
commercial floor space area. Existing F-gas regulations are expected to replace HFCs with 
propane,  CO2-based  technology,  HFOs  and  HFC-32  in  small  commercial  ACs,  water 
chillers  primarily  in  large  commercial  ACs,  and  HFC-32  and  propane  in  residential  ACs. 
Between 2015 and 2030 the use of HFCs (excluding HFC-32) in stationary ACs is expected 
to drop by 70%.  

114 

0204060801001202005201020152020202520302035204020452050Mt CO2eqTransport -CH4 & N2OStationary combustionfossil fuels -CH4 & N2OStationary combustionbiofuels -CH4 & N2OAbandoned coal mines -CH4Coal mining -CH4Oil production &refinery -CH4Gas production -CH4Long-distance gastransmission -CH4Gas networks -CH4Source: IIASA-GAINS 
 
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Figure 88: Phase-in of alternatives to HFC use in stationary air conditioners (residential & commercial) in EU  

Figure  89  shows  how  EU  demand  for  refrigerants  in  mobile  air  conditioners  (MACs), 
expressed  in  HFC-equivalent  units,  is  expected  to  increase  by  16%  between  2015  and 
2030.  The  increased  demand  is  driven  by  expected  changes  in  the  numbers  and 
composition of the vehicle fleet in terms of transport modes, e.g., buses, cars, trucks, vans 
etc. The EU MAC directive is expected to effectively replace all current use of HFC-134a in 
MACs with HFO-1234yf by 2040, with a close to complete replacement achieved already 
by 2030.    

Figure 89: Phase-in of alternative to HFC use in mobile air conditioners in the EU 

Figure 90 shows how EU demand for refrigerants, expressed in HFC-equivalent units, for 
use in domestic, industrial, commercial and transport refrigerators is expected to increase 
by  25%  between  2015  and  2030.  The  increased  demand  is  for  commercial  refrigeration 
driven by the development in commercial sector value added and for industrial refrigeration 
by  the  development  in  industry  value  added.  For  domestic  refrigeration,  demand  for 
refrigerant  is  driven  by  changes  in  GDP  per  capita  and  saturation  rates  for  household 
refrigerator ownership, while for transport refrigeration it is driven by the growth in GDP per 
capita.  Existing  F-gas  regulations  are  expected  to  phase-down  current  use  of  HFCs  in 
refrigeration by 73% between 2015 and 2030. The use of HFCs in domestic refrigeration is 
expected to be completely replaced by hydrocarbons (isobutane) by 2030. In commercial 
refrigerators,  the  use  of  HFCs  is  expected  to  drop  by  three  quarters  between  2015  and 
2030,  replacing  HFCs  in  large  refrigerators  primarily  with  CO2-based  technology  and  in 

115 

0204060801001201402005201020152020202520302035204020452050kt HFC-equivalent unitshydrofluoroolefins(HFOs)CO2-basedtechnologyhydrocarbons(propane)Water chillersHFC-32HFCs -with goodpractice measuresSource: IIASA-GAINS0501001502002502005201020152020202520302035204020452050kt HFC-equivalent unitshydrofluoroolefins(HFOs)HFCs -withgoodpracticemeasuresHFCs -uncontrolledSource: IIASA-GAINS 
 
 
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small refrigerators primarily with propane. In industrial refrigeration, the current use of HFCs 
is  expected  to  drop  by  64%  between  2015  and  2030,  replacing  HFCs  primarily  with 
ammonia and some HFOs, but also some use of CO2-based technology in smaller units. 
The use of HFCs in refrigerated transport is expected to drop by 73% between 2015 and 
2030, replacing HFCs with CO2-based technology or propane.    

Figure  90:  Phase-in  of  alternatives  to  HFC  use  in  commercial  and  industrial  refrigeration  and  refrigerated 
transport in the EU 

Figure 90 shows how EU demand for the services currently offered by HFCs in other types 
of  applications,  i.e.,  foams,  aerosols,  heat  pumps  and  fire  extinguishers,  is  expected  to 
increase by 19% between 2015 and 2030. The demand for these services in foams, heat 
pumps and fire extinguishers is in GAINS driven by the development in GDP. The driver for 
future use of HFCs in aerosols for medical dose inhalers (MDIs) and purposes other than 
decorative  and  entertainment  (which  has  been  prohibited  since  2008),  is  the  expected 
growth in population. The F-gas regulation is expected to phase-down the current use of 
HFCs in these applications by 89% between 2015 and 2030. The use of HFCs as blowing 
agent for foams is expected by 2030 to be fully replaced by other alternatives, i.e., CO2, 
hydrocarbons (propane or butane), HFOs or HFCs with low global warming potential (e.g., 
HFC-152a). The current use of HFCs in fire extinguishers and heat pumps is expected to 
decrease by three quarters in 2030 and be completely replaced by 2050. The use in fire 
extinguishers is expected to be replaced by fluoro-ketone (FK-5-1-12), while the use in heat 
pumps is expected to be replaced by HFO-1234yf, propane or HFC-152a. For 2015, 34% 
of the reported use of HFCs in aerosols was attributed to MDIs, while 66% was reported 
used for other purposes. In GAINS is assumed that by 2025 the use of HFCs in aerosols 
for other purposes than MDIs is phased out and replaced by propane.   

116 

0501001502002502005201020152020202520302035204020452050kt HFC-equivalent unitsHFCs -with goodpracticemeasureshydrofluoroolefins (HFOs)CO2-basedtechnologyAmmoniahydrocarbons(propane,isobutane)HFC-32 or HFC-152aSource: IIASA-GAINS 
 
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Figure 91: Phase-in of alternatives to HFC use in aerosols, foams, heat pumps and fire extinguishers in the EU 

To summarize, GAINS projection for the Reference Scenario indicates that HFC emissions 
in the EU drop by 70% between 2015 and 2030 on a CO2-equivalent basis (see Figure 92). 
This  is  a  result  of  measures  implemented  to  comply  with  existing  F-gas  regulations. 
Emissions remaining in 2030 can primarily be referred to the use of HFC-32 in commercial 
refrigeration and residential air conditioning.  

Figure 92: Evolution of HFC emissions in the EU 

Industry and other non-CO2 source sectors 

Non-CO2 GHGs are emitted from a number of industrial processes and are partly included 
under the EU-ETS. Figure 93 illustrates how drastically N2O emissions from nitric and adipic 
acid  production  and  PFCs  from  primary  aluminium  production  fell  in  response  to  these 
sectors becoming subject to requirements to hold emission permits.  

117 

01020304050607080901002005201020152020202520302035204020452050kt HFC-equivalent unitshydrofluoroolefins(HFOs)CO2-basedtechnologyhydrocarbons(propane,isobutane)fluoroketonHFC-152aHFCs -goodpractice measuresHFCs -uncontrolledSource: IIASA-GAINS01020304050607080901002005201020152020202520302035204020452050Mt CO2eqOtherHCFC-22 productionSolventsFire extinguishersHeat pumpsFoamsAerosolsRefrigerated transportDomestic refrigeratorsIndustry refrigerationCommercialrefrigerationSource: IIASA-GAINS 
 
 
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Figure 93: Evolution of non-CO2 GHG emissions from ETS sectors in the EU 

Figure 94 shows the projected evolution of non-CO2 emissions in industry applications not 
covered  under  the  EU  ETS  and  other  smaller  source  sectors.  These  refer  to  N2O  from 
caprolactam production and direct use of N2O in hospitals, food industry and combustion 
applications. They also refer to the use of SF6 and PFCs in various industrial processes, as 
well  as  in  high-  and  mid-voltage  switches  and  soundproof  windows.  Driver  for  the 
development  in  future  use  of  these  gases  in  industrial  applications  is  value  added  in 
industry. Since 2006, the F-gas regulation bans the use of SF6 in soundproof windows. The 
amount of SF6 reported by countries to the UNFCCC as still used in soundproof windows 
in 2015, is in GAINS assumed to remain until 2025, but then be quickly phased-out by 2030 
as windows come to the end of an assumed lifetime of 25 years. Reported PFC emissions 
from the semiconductor industry have declined by 70% between 2005 and 2015, mostly as 
a result of the voluntary agreement introduced by the industry. No suitable alternative to the 
use of SF6 in high- and mid-voltage exists. Emissions from this source therefore remain and 
are expected to increase with a future increased demand for electricity.  

Figure 94: Evolution of non-CO2 GHG emissions from industry and other (non-ETS) applications in the EU 

118 

010203040502005201020152020202520302035204020452050Mt CO2eqNitric acidproduction -N2OAdipic acidproduction -N2OPrimary aluminumproduction -PFCSource: IIASA-GAINS024681012141618202005201020152020202520302035204020452050Mt CO2eqSoundproof windows -SF6Other SF6 and PFC useDirect N2O useHigh- and mid voltageswitches -SF6Other F-gas use inindustrySemiconductor industry -PFC and NF3Magnesium productionand casting -SF6Caprolactam production-N2OSource: IIASA-GAINS 
 
 
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3.5.3. Total GHG, ETS, ESR emissions (excl. LULUCF) 

Figure 95 shows the evolution of GHG emissions over the projection period. In 2030, the 
total reduction in GHG emissions is 42.8% compared to 1990.  

Figure 95: Evolution of total GHG emissions 

ETS sector emissions reduce faster than overall emissions: coal phase-out policies are the 
main driver in the time period to 2030 whereas in the longer term it is the rising ETS prices. 
GHG emissions in the ETS drop by 48.2% in 2030 relative to 2005. 

ESR sectors also see a decrease in emissions but not as strong, i.e., by 30.7% compared 
to  2005105,  and  the  national  existing  2030  ESR  targets  are  projected  to  be  achieved 
domestically in the majority of countries. This is the result of stronger reduction trends in 
sectors  like  waste  and  HFCs  and  lower  reduction  trends  in  other  sectors,  notably 
agriculture, transport, and wastewater. The decreasing trend in emissions also beyond 2030 
is  well  pronounced,  especially  for the  power generation  sector,  driven  by  the  continuous 
decrease of the ETS cap in line with current legislation.  

For 2050, in lack of additional policies post-2030, emissions reduce to 60.4% compared to 
1990. Compared to 2005 levels, ETS emissions are projected to reduce by 68.5%, and the 
non-ETS sectors by 48.1%.  

Therefore,  while  showing  considerable  improvements  compared  to  historical  trends,  the 
Reference Scenario projects a significant gap to achieve the new 2030 target of net 55% 
emission reduction in 2030 compared to 1990 and the climate neutrality objective by 2050. 

3.5.4. LULUCF emissions and removals and their drivers 

The LULUCF sector in the EU is at present a net carbon sink as it sequesters more carbon 
than it emits. Since the year 2000, the EU LULUCF sink was estimated to fluctuate at around 
-309 MtCO2-eq per year (including harvested wood products and non-CO2 emissions from 
LULUCF); in recent years, however, it shows a trend towards a slightly declining sink in the 
UNFCCC inventory106. While it is not possible to match all individual years with GLOBIOM-
G4M,  the  average  and  the  trends  have  been  replicated  closely.  Differences  in  individual 
years are related to uncertainties in the models and datasets, as well as different modelling 
and reporting approaches applied.  

In the Reference Scenario, the LULUCF sink is expected to be maintained until 2050 even 
though it is projected to decline a bit stronger in the short-run from about -292 MtCO2-eq in 

105 Compared to 2005 GHG emissions as per PRIMES-GAINS models. 
106 http://unfccc.int 

119 

0100020003000400050002005201020152020202520302035204020452050Mt CO2eq.ESR non-CO2ESR CO2ETS non-CO2ETS CO2Source: PRIMES/IIASA-GAINS 
 
                                                 
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2015, to -258 MtCO2-eq in 2030, while in 2050 the sink is projected to be -271 MtCO2-eq, 
which corresponds to a decrease  of  -12%  by  2030  and  -7%  by  2050  compared to 2015 
levels. This decline is the result of changes in different land use activities, of which changes 
in the forest sector are the most important. Figure 96 shows the projection of the total EU 
LULUCF sink in the Reference Scenario until 2050 and the contribution from different land 
use activities.  

The carbon sink in managed forests is the main contributor to the LULUCF sink. The forest 
management  sink  is  driven  by  the  balance  of  forest  harvest  and  forest  increment  rates 
(accumulation of carbon in forest biomass as a result of growth of the trees with the age). 
As forest harvest is projected to increase over time due to growing demand for wood for 
material  uses  (such  as  furniture  or  paper)  or  for  energy  production,  the  carbon  sink 
(biomass, soil, and dead organic matter) in managed forests declines until 2050. Growing 
demand for woody biomass for material use as projected by GLOBIOM is mainly driven by 
population  and  income  growth.  Increasing  demand  for  woody  biomass  for  energy 
production is directly taken from PRIMES Biomass projections. The significant decline in 
the managed forests carbon sink is partially compensated by decreasing emissions from 
deforestation. Furthermore, carbon removals from newly planted forest are transferred from 
the afforestation category to the forest management category after 20 years (in consistency 
with most countries UNFCCC reporting), which also balances the decline in the long run. 
The afforestation sink stabilizes after 2025 and remains at around -45±2 MtCO2-eq. 

Increasing demand for biomass drives wood prices up which results in increased income of 
forest owners and reduces deforestation to maintain forest area. Consequently, emissions 
from deforestation continue to decline in line with historical trends.  

Figure 96: EU LULUCF emissions/removals in MtCO2-eq until 2050 

Activities  in  the  agricultural  sector  have  a  smaller  impact  on  the  total  LULUCF  sink 
compared to the forest sector. Still, net carbon emissions from cropland are projected to 
decline by 6% by 2030 compared to 2015 as soils converge towards soil carbon equilibrium 
over time. In addition, perennial crops (miscanthus, switchgrass and short rotation coppice) 
that  typically  sequester  additional  carbon  in  soil  and  biomass  contribute  to  decreasing 
cropland emissions. By 2050, 4.5 million ha (Mha) of perennial crops are being cultivated. 
Total grassland emissions (incl. non-CO2) are expected to increase by 12% in 2030 and 
stabilizes at around 6-7 MtCO2-eq thereafter. 

120 

-500.0-400.0-300.0-200.0-100.00.0100.0200.020002005201020152020202520302035204020452050Mt CO2e y-1CroplandOther landSettlementsGrasslandWetlandsForest LandHWPTotal LULUCFSource: IIASA -GLOBIOM/G4M 
 
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Figure  97  shows  the  EU  LULUCF  sector  land  balance  until  2050.  Forest  area  includes 
afforestation as well as forest management area because the afforested area is reported as 
forest  management  area  after  20  years  and  thus,  keeping  the  two  categories  separated 
might be misleading. Over time, the total forest area expands by 1.3% in 2030 and 3.8% in 
2050 compared to 2015 at the expense of cropland and grassland taken out of production. 
Cropland  (-4.5%)  and  grassland  (-4.7%)  areas  decrease  slightly  until  2050  due  to 
afforestation  and  expansion  of  settlements.  The  area  for  perennial  crops  for  renewable 
energy production is growing slowly until 2030 and only thereafter at a higher pace. 

Figure 97: EU LULUCF sector land balance (in million ha) until 2050 

The following sections provide a more detailed overview of the drivers, emission projections 
and overall trends in the different LULUCF sub-sectors. 

Emissions from forest land 

The current net forest sink (the sum of forest management, afforestation, deforestation, and 
harvested wood products) is projected to decrease from  -382 MtCO2-eq in 2015, to -340 
MtCO2-eq in 2030 and -329 MtCO2-eq in 2050 which corresponds to a decline by 11% and 
14% in 2030 and 2050, respectively. This is the result of different, partly, opposing trends. 
Increasing wood demand is an important driver which increases forest harvest and drives 
biomass prices up, but also a projected decline in the increment of standing forest due to 
forest ageing on the one hand, and the establishment of newly planted forest on the other 
hand impact the forest sink.  

The carbon sink in managed forests declines from -343 MtCO2-eq in 2015 to -286 MtCO2-
eq in 2030 and -278 MtCO2-eq in 2050 as forest harvest removals increase steadily over 
time until 2040 and stabilize thereafter. At the same time, removals from afforestation are 
accounted for in the managed forest category after 20 years, which counterbalances the 
decline of the sink to a certain extent.  

Total forest harvest in the EU is projected to rise from 512 million m3 in 2015, to 575 million 
m3 in 2030 and 583 million m3 in 2050. Until 2030, additional forest harvest is mainly driven 
by increasing biomass demand for energy production while in the long run until 2050, the 
demand for biomass for energy production slightly decreases. The share of wood removed 
for energy production in the total forest harvest increases from 31% in 2015, to 35% in 2030 
and declines again to 32% in 2050.  

The area-specific increment of forests available for wood supply is slowly decreasing from 
5.6 m3/ha  in 2015  to  5.4  m3/ha  in  2050,  while the  total  increment  of forests  available  for 

121 

050100150200250300350400200020102020203020402050Million haTotal Forest LandCroplandmanagementGrasslandmanagementOther landWetlandsSettlementsSource: IIASA -GLOBIOM/G4M-15-10-505101520302050differences in Mha compared to 2015 
 
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wood supply increases from 743 million m3 in 2015 to 760 million m3 in 2050. The reason 
for the declining area-specific forest increment is a change in age class structure towards a 
higher share of older forest stands that grow at lower rates. Despite this, the total increment 
is increasing due to the area expansion effect from afforestation activities. 

By 2030, short rotation coppice provides 1.8 million m3 of biomass for energy production, 
until 2050 it rises to 10 million m3.  

The carbon sink in harvested wood products (biomass for material use is processed to final 
products which store carbon and have a lifespan of several years) is increasing over time 
with a peak in 2030, as the growth of the material sector is overcompensating the historical 
harvested wood pool decay over time. After 2030 it is stabilizing at a slightly lower level. 
Consequently, the carbon sink of harvested wood products increases from -38 MtCO2-eq in 
2015 to -54 MtCO2-eq in 2030 and is 51 MtCO2-eq in 2050.  

Figure 98: EU biomass harvest from forest (removals) and short rotation coppice (in million m3) until 2050 

The carbon sink from afforested areas is declining until 2030 and stabilizing thereafter (after 
20 years, afforested area is accounted for in the forest management category). The trend 
is also reflected in the area development, with 8.4 Mha afforested land reported in 2015, 
decreasing to 5.7 Mha in 2030 and stabilizing a bit above 5 Mha thereafter. 

The total forest area is projected to increase from 154.6 Mha in 2015, to 157 Mha in 2030 
and 160 Mha in 2050. With increasing age, the newly planted forests get more and more 
into  a  phase  of  high  production  and  become  gradually  available  for  biomass  supply. 
Towards 2050 these forests are therefore also taking harvest pressure from older forests 
and thus help to keep the sink up in managed existing forests.  

122 

010020030040050060070020002005201020152020202520302035204020452050Million m3Shortrotationcoppice usedfor energyForest woodused forenergyForest woodfor non-energy usesSource: IIASA -GLOBIOM/G4M 
 
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Figure 99: Development of the EU emissions/removals in the forest sector in MtCO2-eq until 2050 

Emissions from deforestation continue to decrease from 36 MtCO2-eq in 2015, to 21 MtCO2-
eq in 2030 and 13 MtCO2-eq in 2050 as deforestation drops from 106.000 ha in 2015 to 
46.000 ha in 2050. This development is consistent with historical trends and is driven by 
increasing  biomass  prices  that  incentivize  forest  owners  to  reduce  deforestation  and 
maintain forests. Figure 99 shows the development of the carbon sink in the forest sector 
for the different activities until 2050.  

Emissions from cropland and grassland 

Cropland  is  a  net  source  of  emissions  in  the  EU  at  present.  Over  time,  emissions  are 
projected to decrease from 42 MtCO2-eq in 2015, to 40 MtCO2-eq in 2030 (6% decrease in 
comparison to 2015) and 21 MtCO2-eq in 2050 (49% decrease). One of the main drivers for 
this  decline  is  a  saturation  effect  as  soils  emit  less  and  less  carbon  when  converging 
towards 
their  equilibrium  carbon  stocks  under  a  constant  management  regime. 
Disturbances of the equilibrium due to a change in management lead to a new equilibrium. 
The  emissions  or  removals  towards  the  equilibrium  get  smaller  over  time  as  the  new 
management continues. This is especially true for more intense management changes such 
as the conversion of annual crops to perennial crop cultivation. 

Another  important  driver  is  the  projected  establishment  of  perennial  crops  for  renewable 
energy production which has a positive effect on the amount of carbon stored in the soil 
compared to conventional crops. The PRIMES biomass demand indicates that with growing 
demand the supply of these crops will grow and substitute partially forest biomass as they 
are relatively cost-efficient. The area covered by perennial crops sums up to 0.8 Mha by 
2030 and 4.5 Mha by 2050. Emissions from cropland decline by 3 MtCO2-eq from 2015 until 
2030 and by 18 MtCO2-eq from 2030 until 2050.  Total cropland area is projected to remain 
constant until 2030 and decrease thereafter from 122 to 116 Mha in 2050.  

Grasslands  are  a  small  net  emission  source  at  present  in  the  EU  (including  non-CO2 
emissions  from  land  use).  Over  time,  emissions  increase  slightly  from  5  MtCO2-eq  to  6 
MtCO2-eq in 2030, stabilizing thereafter. Total grassland area declines from 77 Mha in 2015 
to 73 Mha in 2030 and stabilizes thereafter. 

123 

-500-400-300-200-100010020002005201020152020202520302035204020452050Mt CO2e y-1ForestmanagementAfforestation/reforestationHarvestedWood ProductsDeforestationTotal forestsectorSource: IIASA -GLOBIOM/G4M 
 
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Emissions from wetlands, settlements, and other land 

Emissions from wetlands are not modelled and kept constant at 2018 levels as reported in 
UNFCCC 2020 data107. Emissions from wetlands amount to 17 MtCO2-eq. Settlement area 
is assumed to increase at a smaller pace over time following a logarithmic expansion trend 
based on historical UNFCCC data. Consequently, settlements emissions are projected to 
decrease from 25 MtCO2-eq in 2015 to 20 MtCO2-eq by 2030 and 13 MtCO2-eq by 2050. 
Emissions  from  other  land  remain  stable  at  around  0.3  MtCO2-eq  over  time.  In  the  EU, 
around  24  Mha  are  covered  by  wetlands,  12  Mha  by  other  land  and  settlements  are 
projected to increase from 28 Mha in 2015 to 31 Mha by 2030 and 33 Mha by 2050. 

3.6. Total energy system and other mitigation costs 

3.6.1. Investment expenditures 

Investment  expenditures  reflect  the  amounts  paid  (and  not  annualised)  for  purchasing 
equipment  or  investing  in  energy  efficiency  improvements.  In  transport,  investment 
expenditures  concern  the  purchase  of  vehicles,  rolling  stock,  vessels,  aircraft,  and 
recharging/refuelling  infrastructure108.  Investment  in  stationary  energy  uses  concerns the 
purchase of equipment and investment in energy efficiency, e.g. heat recovery in industry 
or building renovation. Finally, investment also takes place in the supply side, to produce 
electricity or heat, and in networks.   

Figure 100 and Figure 101 show the evolution over time of investment expenditures in the 
various components of the energy system. 

While  the  investments  in the transport  sector  expand  steadily  over  the  projection  period, 
investment  expenditures  in  the  other  sectors  increase  until  2030  and  stabilise  or  even 
decline afterwards, in a context of no additional policies.  

When it comes to buildings the projection shows that investment in equipment will be higher 
than  investment  in  renovation,  particularly  until  2030.  Yet  the  amounts  for  building 
renovation are also projected to be high in relation to past trends.  

Investments in the power system peak in 2026-2030, which is followed by on the one hand 
a decline in the investments for production capacities and on the other hand a stabilisation 
of  the  investment  needs  in  the  electricity  grid  over  the  long  term,  which  drives  a 
progressively higher share of grid-related costs in total electricity price over time.   

107 http://unfccc.int 
108 Investment expenditures do not include expenditures for new road and rail network infrastructure.  

124 

 
 
                                                 
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Figure 100: Investment expenditures by energy sector (excluding transport) 

Source: PRIMES model 

Figure 101: Total investment expenditures (including transport) 

Source: PRIMES model 

3.6.2. Energy system costs 

The PRIMES model calculates energy system costs from an end-user perspective. Final 
consumers pay to purchase energy commodities – may that be electricity, district heating 
or  distributed  fuels.  End-users  also  pay  to  purchase  and  maintain  equipment  used  for 
energy and other purposes and also to improve energy efficiency conditions. In that sense, 
energy system costs are annual costs incurred for energy services of end-users including 
annualised  capital  costs,  variable  costs,  and  fuel  costs.  To  annualise  investment 
expenditures  of  end-users  for  reporting  purposes  only,  PRIMES  applies  a  10%  discount 
rate across all sectors and years.  

125 

050100150200250300350400450500bn€Investments in energy supplyPower plantsElectricity gridSteam boilers020040060080010001200bn€Investments in energy demand sectors (excl. transport)ResidentialTertiaryIndustry4.8%6.0%6.7%6.8%5.8%5.8%5.5%5.4%0%1%2%3%4%5%6%7%8%% GDPInvestment (incl. transport) as % of GDP0100020003000400050006000bn€Total investmentsOther sectorsTransportTotal 
 
 
 
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Figure 102: Share of CAPEX by final consumers in total energy costs (%) 

Energy system costs as a percentage of GDP reflect the amount that the entire economy 
has  to  pay  to  purchase  energy  services  (including  for  transport).  This  excludes  auction 
payments, which are very small compared to total energy system costs and do not represent 
an actual economic cost but rather revenues recycled in the economy. 

In 2015 energy system costs were 10.5% of GDP and are projected to reach 11.6% in 2030 
and drop to 9.4% in 2050, which is associated with the absence of additional policies and 
falling  technology  costs.  Looking  at  energy  costs  as  a  percentage  of  income  in  demand 
sectors,  these  grow  towards  2030,  more  for  expenditure  related  to  transports  than  for 
expenditure related to buildings. 

Figure 103: Evolution of energy system costs 

Reflecting the increasing capital intensiveness of the energy system, the share of CAPEX 
(reflecting  capital  costs  and  direct  efficiency  investments  incurred  by  individuals)  in  total 
system costs increases over time, reaching 33.6 % in 2030 and 36.9% in 2050 from 21% in 
2015  (excluding  ETS  auction  payments).  CAPEX  costs  increase  the  most  in  the  current 
decade  due  to  the  deployment  of  more  efficient  appliances  and  equipment,  which  have 
higher capital costs and lower fuel expenditures.  

126 

21.027.329.933.634.136.336.436.90.05.010.015.020.025.030.035.040.020152020202520302035204020452050Source: PRIMES10.611.710.59.710.911.611.010.59.89.40.02.04.06.08.010.012.014.02005201020152020202520302035204020452050Energy system cost, excl. auction payments as a % of GDP0.060.140.120.110.160.180.220.260.000.050.100.150.200.250.3020152020202520302035204020452050Auction payments as a % of GDPSource: PRIMES 
 
 
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The remaining expenditures incurred by end-users can be grouped together as operational 
expenditures (OPEX). These include the payment for energy supply. The electrification of 
the  residential  and  tertiary  sectors  over  time  makes  electricity  costs  the  main  OPEX 
component for these sectors, as well as distributed heat steam costs. Conversely, the share 
of other fuel costs declines over time, despite increasing fuel prices.  

For the industrial sector, fuel expenditures, including electricity, increase slightly throughout 
the projection period. Decreasing long term electricity prices to some extent compensate 
the  increase  in  fossil  fuel  expenditures.  Also,  for  this  sector  the  share  of  CAPEX  costs 
increases over time as more efficient investments in equipment occur. 

Figure 104: Breakdown of energy costs (%) 

Note:  OPEX  and  CAPEX  are  calculated  at  the  level  of  final  energy  consumers.  For  example,  payment  for  electricity 
consumption is OPEX from the perspective of the final consumer. 

In the transport sector capital costs play an increasing role; investment in electric vehicles 
lead to higher investment expenditures until 2030. The projected uptake of electric vehicles 
in the Reference Scenario is not sufficient to bring down battery costs, which would lead to 
lower costs and possibly lower fuel expenditures.  

3.6.3. Household expenditures 

The  share  of  annual  expenditures  for  transport  and  energy  services  in  total  private 
consumption tends to remain stable or even decrease in the longer run (Figure 105).  

127 

024681012142005201020152020202520302035204020452050%Energy costs as a % ofvalue added of industryEnergy costs as a % ofhouseholds' incomeEnergy costs fortransportation as a % ofGDPEnergy costs as a % ofvalue added of tertiarysectorSource: PRIMES 
 
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Figure 105: Annual energy-related expenditures for households 

For  households  (Figure  106),  CAPEX  increases  because  of  purchasing  more  expensive 
and  efficient  appliances  and  equipment  i.e.,  a  heat  pump  instead  of  a  gas  boiler,  and 
because of investing directly in energy efficiency, i.e., in renovation. The purchase of fuels 
remain  roughly  stable  compared  to  historical  levels,  as  the  product  of  reduced  overall 
consumption  (notably  of  fossil  fuels)  substituted  by  electricity,  which  displays  relatively 
higher prices. 

Figure 106: Energy expenditures in houses 

In transport, expenditures to purchase transport services increase over time, due mainly to 
rising  rail  and  aviation  ticket  prices,  and  the  increase  in  the  share  of  aviation  activity. 
Furthermore, despite dropping battery costs for BEVs and some market uptake of PHEVs, 
the cost of purchasing electric vehicles remains high, driving up the capital and fixed costs 
of vehicles. However, expenditures to purchase fuels progressively decrease over time, due 
to the gradual shift towards low and zero-emission vehicles (Figure 107). 

128 

051015202530201020152020202520302035204020452050Expenditures incurred by households forenergy (%)Annual energy expenses as % of total expendituresof which in housesof which for transportSource: PRIMES050000100000150000200000250000300000350000201020152020202520302035204020452050Energy expenditures in houses(million €)Expenditures to purchase energy productsAnnual capital cost of equipment and appliancesAnnual cost of direct efficiency investmentSource: PRIMES 
 
 
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Figure 107: Energy expenditures for households in transport 

4. Conclusion 
The Reference Scenario 2020 is a projection of the evolution of the energy and transport 
systems  and  the  associated GHG  emissions  in every  Member  State  and in the  EU  as a 
whole until 2050 subject to the policy framework in place as of December 2019, including 
the  NECPs.  The  projection  is  also  subject  to  assumptions  regarding  the  evolution  of 
population,  economic  and  industrial  activity,  world  fuel  prices,  technology  and  market 
trends.  

The assumed policy framework, including the NECPs, by design, have an implementation 
horizon until 2030. While there are no specific policy targets for the period after 2030, the 
assumed policy framework shows long-lasting impacts thanks to investment decisions and 
technological  developments  across the  sectors. The  EU  ETS  in particular  is  assumed  to 
include  provisions  to  the  horizon  of  2050  and  so  the  ensuing  carbon  prices  constitute  a 
driver that influences investment and choices until 2050. Nonetheless, the long-term effects 
get progressively weaker over time and the transformation that happens until 2030 slows 
down afterwards, up to 2050. 

The Reference Scenario is not a forecast and thus not a prediction of the most plausible 
evolution  of  the  energy  and  transport  systems.  It  is  a  projection  or,  in  other  words,  a 
quantification of causal effects of policy and other assumptions on the energy and transport 
systems.  The  usefulness  of  the  Reference  Scenario  projection  is  essentially  in  enabling 
comparisons to projections that mirror alternative policy frameworks and other assumptions.  

Key findings:  

The  Reference  Scenario  projects  significant  changes  in  the  EU  energy  and  transport 
systems resulting from the EU and national policies adopted to the horizon of 2030. The 
projection confirms that the climate, energy and transport policy framework as of December 
2019, if properly implemented, would lead to the achievement of most of the existing 2030 
climate and energy targets.  

The decoupling of GDP growth from energy demand growth is projected to intensify until 
2030 driven by renewables and energy efficiency policies, and continue post-2030, owing 
to the lasting effects of these policies and certain technology trends.  

129 

0100000200000300000400000500000600000700000800000201020152020202520302035204020452050Energy expenditures for transport(million €)Annual capital and fixed cost of vehiclesExpenditures to purchase transport servicesExpenditures to purchase energy productsNon-fuel variable costs of private travellingSource: PRIMES 
 
 
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GHG emissions are projected to decrease in the large majority of sectors, and particularly 
in power generation, despite the increase in gross electricity demand. This is due to rising 
EU  ETS  prices  and  renewable  technologies  reaching  maturity.  The  Reference  Scenario 
projections feature a large-scale integration of renewables in the power sector consistently 
with system reliability and affordability aims. Therefore, the projection also includes strong 
elements of sectoral coupling by growing electrification of heating and mobility combined 
with reducing the carbon footprint of electricity. This trend combined with energy efficiency 
gains and increasing direct uses of renewables allow to further reduce emissions also in 
final energy demand sectors.  

Non-CO2 emissions follow different trends between sectors, with substantial decreases in 
waste  and  HFCs  and  small  decreases  in  agriculture.  LULUCF  is  currently  a  sink  and  is 
projected to remain at current levels in the Reference Scenario projection.  

In the short to medium term the transformation of the energy system is substantial. GHG 
emissions  reduce  by  43.8%  in  2030  compared  to  1990  and  overall  renewables  share 
reaches 33.2% in 2030. The overall renewable energy share thus slightly over-achieves the 
existing  2030  target,  driven  mainly  by  developments  in  the  power  sector,  followed  by 
transport, and heating and cooling. 

Energy efficiency progresses, driven by the EU-level and national policies along the various 
energy  and  transport  sectors,  leads  to  a  reduction  in  the  respective  final  energy 
consumption by 2030. The Reference Scenario projection shows the achievement of the 
existing primary energy consumption efficiency target by 2030 but not of the final energy 
consumption target (29.6% reduction compared to 2007 Baseline is achieved, compared to 
a target of 32.5%). 

The  effects  of  COVID-19  pandemic,  while  striking  in  terms  of  energy  consumption  and 
emissions in 2020, are not projected to leave long-lasting impacts on the various energy 
and  transport  sectors  and  the  behaviours  of  the  related  market  actors/decision  makers. 
Projections show a recovery of the activity of the sectors hit in 2020 by the pandemic, by 
2025  or  at  the  latest  by  2030.  The  projection  of  the  Reference  Scenario  2020  does  not 
consider very significant structural changes on the behaviours of the individual energy and 
transport  decision-makers,  as  more  empirical  evidence  would  be  required  to  do  so.  It 
indicates a rather stable structure of final energy use by sector over time.  

The policies to achieve the EU level target for effort sharing sectors in 2030 are sufficient 
and a reduction of 30.7% in 2030 compared to 2005 is achieved in the ESR sectors.  

Renewable energy deployment, energy efficiency improvements and nuclear production – 
envisaged  to  remain  stable  –  prevent  import  dependency  from  increasing,  which  would 
otherwise occur due to the growth of the economy, the drop in lignite and coal use in the 
power and heat sectors, and the depletion of domestic oil and gas production. Incremental 
net  imports  relative  to  2005  are  negative for  all  fossils,  including  natural  gas,  despite  its 
importance in the balancing of the increasing amount of renewables in the power sector. 
Nonetheless, oil continues to represent the largest share in total imports of energy.  

The  transformation  of  the  energy  system  is  projected  to  be  capital-intensive.  The  power 
sector’s  shift  away  from  fossil  fuels  and  towards  renewables  does  not  entail  significant 
increases in levelized costs of electricity but requires significant investment in transmission 
and  distribution  systems  for  connections,  the  completion  of  the  internal  market  and  the 
ensuing broadening of flexibility and balancing resources. The capital-related cost increases 
will have an upward effect on electricity prices and on energy system costs until 2030.  

The market uptake of low and zero-emission vehicles that is already being recorded in the 
EU is projected to amplify by 2025 and, in particular, approaching 2030 and beyond. EU-
wide policies such as CO2 emission targets on vehicle manufacturers and provisions on the 
alternative fuel refuelling  and  recharging  infrastructure  support their  uptake.  At  the  same 
time,  national  policies  such  as  beneficial  fiscal  regimes  and  subsidies  are  projected  to 
significantly  trigger  the  demand  for  low  and  zero-emission  vehicles.  Further  renewables 
uptake  in  transport  and  CO2  emission  reduction  is  driven  by  the  uptake  of  biofuels  and 

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biogas following explicit national policies in this regard. National policies on the uptake of 
biofuels and biogas are driven by EU legislation such as Renewable Energy Directive and 
Fuel  Quality  Directive.  The  transport  system  is  also  projected  to  increase  its  overall 
efficiency  thanks to  the higher  use  of rail  and  short  sea shipping  by  2030,  driven  by  the 
TEN-T Regulation, supported by the Connecting Europe Facility and other EU level funding, 
and national policies. 

In the buildings sector, the renovation of the buildings envelope along with the replacement 
of  the  heating  and  cooling  equipment  are  driving  energy  consumption  and  emissions 
downwards.  The  energy  efficiency  measures  presented  in  the  NECPs,  as  well  as  in  the 
national  Long-Term  Renovation  Strategies,  include  financial  support  schemes,  tax 
incentives and regulatory measures and focus mainly on the increase of the renovation rate 
of  existing  buildings  and  notably  deep  renovation  measures  that  achieve  high  energy 
savings. The building renovation rates are projected to increase by 2030 with comparison 
to the recent past. Policies promoting the replacement of old fossils-fuelled equipment for 
space heating with new technologies, particularly with high-efficiency heat pumps are also 
included in many national plans, resulting in both energy efficiency and renewable energy 
uptake. Information campaigns to incentivise the uptake of new technologies as well as to 
influence  consumer  choice  are  also  included  in  the  policy  framework  and  enable  the 
transition  of  the  buildings  sector.  The  policy  setup  of  the  Reference  Scenario  regarding 
energy efficiency in the buildings sector is very ambitious and is projected to deliver high 
energy savings by 2030 but an up-scaling of the energy efficiency effort, across sectors, 
would still be required to meet the EU’s 2030 energy efficiency final energy consumption 
target.  

In  the  medium  to  long  term,  in  the  absence  of  additional  policies  after  2030,  GHG 
emissions are projected to reduce by 59.4% in 2050 compared to 1990, which falls below 
the  target  laid  down  in  the  European  Climate  Law.  Likewise,  renewables  continue  to 
develop  especially  in  the  power  sector,  albeit  at  a  slower  pace  than  before,  thanks  to 
technology  progress,  improved  market  conditions, the  lasting  effect  of  dedicated  policies 
implemented in the decade 2021-2030 and the ETS price signal that increases after 2030.  

Low-carbon  electricity  continues  to  enable  energy  efficiency  and  carbon  emission 
reductions  in  heating  and  transport,  albeit  at  a  pace  significantly  lower  than  required  for 
decarbonising these sectors. Electrification in selected transport market segments (e.g. cars 
and  light  commercial  vehicles)  is  projected  to  continue  developing  in the  post-2030  time 
horizon,  as  battery  costs  continue  to  progressively  decrease  and  the  vehicle  fleet  is 
renewed. The market share of biofuels and biogas is projected to remain relatively stable, 
as policies are not assumed to intensify compared to their 2030 ambition. 

Industry is projected to shift towards high value-added and less energy-intensive products 
over  time  in  an  international  competition  context,  with  a  significant  part  of  the  energy-
intensive production to remain in the EU. Growing electrification and fuel switching are the 
main  trends.  Iron  and  steel,  the  energy-intensive  branch  of  chemicals  and  non-metallic 
minerals  move  away  from  solids  and  oil;  in  non-ferrous  metals  and  paper  and  pulp 
electrification is on the rise. Non-energy-intensive industries, which produce far more value-
added compared to energy-intensive industries, undergo similar changes. 

As a result, by 2050 net imports of fossil fuels (in absolute terms) decrease to almost a third 
of the projected peak in 2025. 

System costs as percentage of GDP decrease after 2030, since technology improvement 
drives down the  cost reductions.  Electricity  prices  increase  in the  period  to  2030, mainly 
due to the increase in grid-related costs. Beyond 2030 electricity prices stabilise: the fuel 
cost  component  remains  stable,  despite  the  increase  in  fuel  prices,  due  to  the  dropping 
shares of combustion plants.  

Annual  energy-related  household  expenditures  in  residential  (as  percentage  of  total 
household  expenditures)  remain fairly  stable  by  2040,  before  declining  afterwards.  Even 
though  the  capital-related  part  of  final  consumer  expenditures  increases  significantly 

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throughout  the  projection  period,  fuel  expenditures  (including  electricity)  decrease  as  a 
share of total expenditures due to increased energy efficiency and fuel switching. 

The European Climate Law and the need for a new policy framework  

Overall the Reference Scenario projection shows a considerable development compared to 
past trends towards decarbonisation, in power generation as well as, to a lesser extent, in 
the demand side sectors. 

Still, the EU-wide and national policies, assumed in the Reference Scenario and reflected 
in the projection as fully implemented until 2030, are not sufficient to achieve the new EU 
climate objectives enshrined in the European Climate Law. It falls short of the new 2030 
emissions reduction objectives of 55%, notably by not spurring the necessary contributions 
stemming from the renewables and energy efficiency uptake. Even more so, over the longer 
run,  the  developments  are  insufficient  to  achieve  the  2050  climate  neutrality  goal.  The 
required new policy framework to bring the EU economy on this path are at the heart of the 
“Delivering the European Green Deal” (“Fit for 55”) policy package. 

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Annex  I:  Detailed  EU  policies  included  in  the  EU 
Reference Scenario 2020 

Energy efficiency policies  

Energy Efficiency 

Ecodesign Framework Directive  

Directive 2009/125/EC 

Stand-by Regulation 

Commission Regulation (EC) No 1275/2008 as 
amended by Commission Regulation (EU) No 
801/2013 

Office/street lighting Regulation 

Commission Regulation (EC) No 347/2010  

Lighting  Products  in  the  domestic  and 
Tertiary Sectors Regulations 

Commission Regulation (EU) 2019/2020 

Commission Regulation (EC) No 244/2009 

Commission Regulation (EC) No 245/2009 

Commission Regulation (EU) No 1194/2012 

External power supplies Regulation 

Commission Regulation (EU) 2019/1782 

TVs Regulation (+labelling) Regulation 

Commission Regulation (EU) 2019/2021 

Electric motors Regulation 

Commission Regulation (EC)No 640/2009  

Commission Regulation (EU) 2015/1095 

Freezers/refrigerators Regulation  

Commission Regulation (EU) 2019/2019 

Commission Regulation (EU) 2019/2024 

Household washing machines Regulation  Commission Regulation (EU) 2019/2023 

1 

Household dishwashers Regulations 

Commission Regulation (EU) 2019/2022 

Air conditioners 

Circulators Regulation 

Water pumps 

Tumble driers 

Computers and servers 

Vacuum cleaners 

Cooking appliances 

Power transformers 

Commission Regulation (EU) No 206/2012 

Commission  Regulation  (EU)  Regulation  No 
327/2011 

Commission Regulation (EU) No 1253/2014 

Commission Regulation (EU) 2016/2281 

Commission Regulation (EC) No 641/2009 as 
amended by Commission Regulation (EU) No 
622/2012  and  Commission  Regulation  (EU) 
2019/1781 

Commission Regulation (EU) No 547/2012 

Commission Regulation (EU) No 932/2012 

Commission Regulation (EU) No 617/2013 

Commission Regulation (EU) 2019/424 

Commission Regulation (EU) No 666/2013 

Commission Regulation (EU) No 66/2014 

Commission Regulation (EU) No 548/2014 

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Energy Efficiency 

Commission Regulation (EU) 2019/1783 

Council Directive 92/42/EEC  

Commission Regulation (EU) No 813/2013  

Commission Regulation (EU) No 814/2013  

Commission Regulation (EU) 2015/1185  

Commission  Regulation 
Commission Regulation (EU) 2016/2281 

(EU)  2015/1189 

Heaters Regulation 

Welding equipment 

Commission Regulation (EU) 2019/1784 

Omnibus 

Commission Regulation (EU) 2021/341 

Imaging equipment 

Game consoles 

Energy Labelling Directive  

and delegated Regulations covering: 

 

lamps and luminaires,



  air conditioners

  Electronic displays

2 

  household washing machines

  household refrigerating appliances

  household dishwashers

  household electric tumble-driers

  Labelling of tyres Regulations 

Voluntary  agreement  –  Report  from  the 
Commission to the European Parliament and 
the  voluntary  ecodesign 
the  Council  on 
scheme for imaging equipment   

COM/2013/023 final 

the 
Voluntary  agreement  -  Report 
Commission to the European Parliament and 
the  Council  on 
the  voluntary  ecodesign 
scheme for games consoles 

from 

COM/2015/0178 final 

Regulation (EU) 2017/1369  

supplemented  by  Delegated  Regulations  and 
Commission Directives 

Commission  Delegated  Regulation  (EU)  No 
874/2012 

Commission  Delegated  Regulation  (EU)  No 
626/2011 

Commission  Delegated  Regulation  (EU)  No 
1254/2014 

Commission  Delegated  Regulation 
2019/2013 

(EU) 

Commission Delegated Regulation (EU) 
2019/2014 

Commission Delegated Regulation (EU) 
2015/1094 

Commission Delegated Regulation (EU) 
2019/2016 

Commission Delegated Regulation (EU) 
2019/2018 

Commission Delegated Regulation (EU) 
2019/2017 

Commission Delegated Regulation (EU) No 
392/2012 

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  Cooking appliances 

Regulation (EU) 2020/740  

Energy Efficiency 

Omnibus 

Commission  Delegated  Regulation 
2015/1186 

(EU) 

Commission  Delegated  Regulation  (EU)  No 
811/2013 

Commission  Delegated  Regulation  (EU)  No 
812/2013 

Commission  Delegated  Regulation 
2015/1187 

(EU) 

Commission  Delegated  Regulation  (EU)  No 
65/2014 

Commission  Delegated  Regulation 
(EU) 
2021/340  of  17  December  2020  amending 
Delegated  Regulations  (EU)  2019/2013,  (EU) 
2019/2014, (EU) 2019/2015, (EU) 2019/2016, 
(EU) 2019/2017 and (EU) 2019/2018 

3 

Energy Performance of Buildings Directive 

4 

Energy Efficiency Directive 

 Directive  2010/31/EU  as  amended  by 
Directive (EU) 2018/844 

Directive 2012/27/EU as amended by Directive 
(EU) 2018/2002 

Power generation and energy markets 

Power generation and energy markets 

1 

Completion  of  the  internal  energy 
market (including provisions of the 3rd 
package). 

the Gas  and 
Since March  2011, 
the  3rd 
Electricity  Directives  of 
package  for  an  internal  EU  gas  and 
electricity market are transposed into 
national  law  by  Members  States  and 
the three Regulations: 

-  on  conditions  for  access  to  the 
natural gas transmission networks  

-  on  conditions  for  access  to  the 
network for cross-border exchange of 
electricity  

- on the establishment of the Agency 
for 
the  Cooperation  of  Energy 
Regulators (ACER) 

Directive 2009/73/EC 

Directive (EU) 2019/944 

Regulation (EC) No 715/2009 

 Regulation (EU) 2019/943 

 Regulation (EU) 2019/942 

2  Energy Taxation Directive 

Directive 2003/96/EC 

3  Regulation on security of gas supply  Regulation (EU) 2017/1938 

4 

Regulation  on  market  integrity  and 
transparency (REMIT) 

Regulation (EU) 1227/2011  

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Power generation and energy markets 

5  Nuclear Safety Directive 

Council Directive 2009/71/Euratom 

6  Nuclear Waste Management Directive  Council Directive 2011/70/Euratom 

7  Basic safety standards Directive 

Council Directive 2013/59/Euratom  

8 

Directive on the promotion of the use 
of energy from renewable sources  

Directive 2009/28 EC as amended by Directive (EU) 
2015/1513 

Recast Directive (EU) 2018/2001 will only take place 
formally on 1 July 2021, when Directive 2009/28 EC 
is repealed. 

9 

Guidelines 
for 
on  State 
environmental  protection  and  energy 
2014-20  

aid 

2014/C 200/01 

Climate policies  

(Cross-sectorial) Climate policies 

1 

EU ETS Directive  

Directive 2003/87/EC as amended notably by 
Directive 2008/101/EC (aviation), Decision (EU) 
2015/1814 (Market Stability Reserve), 
Regulation (EU) 2017/2392 (aviation “stop the 
clock” derogation) and Directive 2018/410 
(revision for 2030 climate and energy 
framework)  

2  Directive on the geological storage of CO2  Directive 2009/31/EC  

3  GHG Effort Sharing Regulation 

Regulation (EU) 2018/842 

4 

F-gas Regulation  

Regulation (EU) No 517/2014 

5 

EU framework for LULUCF 

(EU) 

Regulation 
amending 
Regulation (EU) No 525/2013 and Decision No 
529/2013/EU  

2018/841, 

6 

Communication from the Commission 
Guidelines on certain State aid 
measures in the context of the system 
for greenhouse gas emission allowance 
trading post-2021. 

2020/C 317/04 

Transport policies  

Transport related policies 

1 

CO2  emission  performance  standards  for 
new  passenger  cars  and  for  new  light 
commercial vehicles  

Regulation (EC) 2019/631  

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Transport related policies 

Improving testing procedures - real driving 
conditions  ('Real  Driving  Emissions'  – 
RDE) and improved laboratory test ('World 
Harmonised Light Vehicle Test Procedure' 
– WLTP) 

Commission Regulation (EU) 
2018/1832 Commission Regulation 
(EU) 2017/1151  

Commission Regulation (EU) 
2017/1154 

Commission Regulation (EU) 
2016/646 

Commission Regulation (EU) 
2016/427 

CO2  emission  performance  standards  for 
new heavy-duty vehicles 

Regulation (EU) 2019/1242 

Regulation determining CO2 emissions and 
fuel consumption of heavy-duty vehicles 

Regulation (EU) 2017/2400 

Regulation  on 
emissions from heavy-duty vehicles 

the  monitoring  of  CO2 

Regulation (EU) 2018/956 

2 

3 

4 

5 

6  Regulation EURO 5 and 6 

7 

Directive  on  the  promotion  of  the  use  of 
energy from renewable sources  

8 

Fuel Quality Directive 

9  Regulation Euro VI for heavy duty vehicles 

Regulation (EC) No 715/2007, 
implemented by Commission 
Regulation (EU) 2017/1151 

Recast  Directive  (EU)  2018/2001  will 
only  take  place  formally  on  1  July 
2021,  when  Directive  2009/28/EC  is 
repealed. 

Directive  98/70/EC,  as  amended  by 
Directive (EU) 2015/1513 

Regulation (EC) No 595/2009, 
implemented by Commission 
Regulation (EU) 582/2011  

10 

Eurovignette 
infrastructure charging 

Directive 

on 

road 

Directive 1999/62/EC, as amended by 
Directive 2011/76/EU 

11 

Directive  on  the  Promotion  of  Clean  and 
Energy  Efficient  Road  Transport  Vehicles 
(in public procurement) 

Directive 2009/33/EC, as amended by 
Directive (EU) 2019/1161 

12 

Directive on the  deployment  of alternative 
fuels infrastructure 

Directive 2014/94/EU 

13  Directive on weights & dimensions 

14  End of Life Vehicles Directive 

Directive  96/53/EC,  as  amended  by 
Directive 2015/719/EU  

Directive 2000/53/EC, as amended by 
Directive (EU) 2018/849 

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Transport related policies 

15 

Mobile  Air  Conditioning  in  motor  vehicles 
Directive  

Directive 2006/40/EC 

16 

Directive  on  the  sound  level  of  motor 
vehicles 

17  Roadworthiness Package 

Regulation (EU) No 540/2014 as 
amended by Regulation (EU) 
2019/839 

Directive 2014/45/EU, Directive 
2014/46/EU, Directive 2014/47/EU 

18  Road infrastructure safety management 

Directive (EU) 2019/1936 

19  General safety regulation 

Regulation (EU) 2019/2144 

20 

Intelligent Transport Systems Directive  

Directive 2010/40/EU 

21 

concerning 

Regulation 
type-approval 
requirements  for  the  deployment  of  the 
eCall in-vehicle system 

Regulation (EU) 2015/758 

22  Fourth railway package 

Directives (EU) 2016/798 on railway 
safety, Directive (EU) 2016/797 on 
railway interoperability and the 
Directive 2016/2370/EU regarding the 
opening of the market for domestic 
passenger transport services by rail 
and the governance of the railway 
infrastructure 

23 

Directive  establishing  a  single  European 
railway area (Recast) 

Directive 2012/34/EU 

24 

European Rail Traffic Management System 
European deployment plan 

Commission Implementing 
Regulation (EU) 2017/6 

25 

Regulation  on  electronic  freight  transport 
information 

Regulation (EU) 2020/1056 

26 

Regulation  on  noise-related  operating 
restrictions at Union airports  

Regulation (EU) No 598/2014 

27 

Regulations  governing  the  performance 
and  charging  schemes  as  well  as  the 
network  functions  of  the  Single  European 
Sky  

Commission Implementing 
Regulations (EU) No 390/2013, 
391/2013 and 677/2011; later 
replaced by Regulations (EU) 
2019/317 and 2019/123 

28 

Inland waterways and port services  

Directive 2016/1629/EU on technical 
requirements for inland waterway 
vessels and the Regulation on non-
road mobile machinery (NRMM) 

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Transport related policies 

Regulation (EU) 2017/352 
establishing a framework for the 
provision of port services 

29  Provision of port services 

Regulation (EU) 2017/352 

30  European Maritime Single Window 

Regulation (EU) 2019/1239 

31 

Directive on the sulphur content of marine 
fuels 

Directive 2012/33/EU  

32 

Monitoring,  reporting  and  verification  of 
carbon  dioxide  emissions  from  maritime 
transport 

Regulation (EU) 2015/757 

Infrastructure, innovation, and RTD funding 

Infrastructure, innovation and RTD and funding 

1   TEN-E guidelines 

Regulation (EU) 347/2013 

2  

Regulation establishing the Connecting 
Europe Facility 

Regulation (EU) 1316/2013 

3  

EEPR (European Energy Programme for 
Recovery) and NER 300 (New entrants 
reserve) CCS and innovative renewables 
funding programme 

4  

Horizon 2020 support to energy research 
and innovation 

Regulation  (EC)  No  663/2009,  ETS 
Directive  2009/29/EC  Article  10a  (8), 
further  developed  through  Commission 
Decision 2010/670/EU and implementing 
decisions,  e.g.  EC(2014)  4493  and 
C(2015) 6882 

Energy research under H2020: info 
available here: 
http://ec.europa.eu/programmes/horizon
2020/en/area/energy  

European Structural and Investment Funds109:  

European Regional Development Fund 
(ERDF) 

Regulation (EU) No 1303/2013  

European Social Fund (ESF) 

Regulation (EU) No 1301/2013  

5  

Cohesion Fund (CF) 

Regulation (EU) No 1304/2013  

European Agricultural Fund for Rural 
Development (EAFRD) 

European Maritime & Fisheries Fund 
(EMFF)  

Regulation (EU) No 1305/2013 

 Regulation (EU) No 508/2014 

109 As of May 2021, a revision of the regulations of the European Structural and Investment Funds has been agreed and is 
planned for publication.   

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Infrastructure, innovation and RTD and funding 

6  TEN-T guidelines 

Regulation (EU) No 1315/2013 supported 
by 
the  Connecting  Europe  Facility 
(Regulation (EU) No 1316/2013) 

Environmental policies 

Environment and other related policies  

General block exemption 
Regulation 

Commission Regulation (EU) 2014/651, 
Commission Regulation (EU) 2017/1084 

Landfill Directive  

Directive 99/31/EC 

EU Urban Wastewater Treatment 
Directive  

Directive 91/271/EEC, Directive 98/15/EEC, 
Implementing Decision 2014/431/EU 

Waste Management Framework 
Directive  

Directive 2008/98/EC 

1 

2 

3 

4 

5  Nitrate Directive  

Directive 91/676/EEC 

6  Common Agricultural Policy (CAP)  

e.g. Council Regulations (EC) No 1290/2005, 
No 1698/2005, No 1234/2007, No. 73/2009,  
Regulations (EU) No 1305-1308/2013, 
Regulation (EU) 2020/2220 

Industrial emissions (Recast of 
Integrated Pollution and 
Prevention Control Directive 
2008/1/EC and Large Combustion 
Plant Directive 2001/80/EC) 

Directive 2010/75/EU  

Directive on national emissions' 
ceilings for certain pollutants 

Directive 2001/81/EC, Directive (EU) 
2016/2284 

7 

8 

9  Water Framework Directive  

Directive 2000/60/EC 

10 

Substances that deplete the ozone 
layer 

Relevant EU legislation implementing the 
Montreal protocol, e.g. Regulation (EC) No 
1005/2009 as amended by Commission 
Regulation (EU) 744/2010, Regulation (EU) 
No 517/2014, Council Decision (EU) 
2017/1541 

International policies 

Other policies at international level 

1 

International Maritime Organisation (IMO) 
International convention for the prevention 
of pollution from ships (MARPOL), Annex VI  

2008 amendments - revised Annex 
VI (Prevention of Air Pollution from 
ships) 

Energy Efficiency Design Index 
(EEDI) and the Ship Energy 

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Other policies at international level 

Efficiency Management Plan 
(SEEMP), IMO Resolution 
MEPC.203(62) 

2 

3 

Voluntary agreement to reduce PFC 
(perfluorocarbons, potent GHG) emissions 
in the semiconductor industry  

International Civil Aviation Organisation 
(ICAO), Convention on International Civil 
Aviation, Annex 16, Volume II (Aircraft 
engine emissions) and Volume III (CO2 
emissions standard for aircraft) 

Implementation of non CO2 policies  

Sector 

Gas 

Policy 

Agriculture  CH4 

Feed-in tariffs 
or other 
subsidies to 
stimulate co-
digestion of 
manure on 
farms 

CH4 & 
N2O 

EU Common 
Agricultural 
Policy (CAP) 
and EU Nitrate 
Directive 
(EEC/676/1991) 
with revisions 

Regional 
coverage 

Italy, 
Netherlands, 
Latvia, 
Sweden, 
Cyprus, 
Austria, 
Croatia, 
Germany  

EU-wide 

Policy description and 
implementation in GAINS 
Reflected via assumptions on 
uptake of farm-scale biogas 
technology consistent with 
information from 
EurObserv'ER (2020) on 
installed capacity. Future 
uptake follows trend in biogas 
production from anaerobic 
digestion as projected in the 
PRIMES model Reference 
scenario. 
Reflected in GAINS through 
input of CAPRI model data on 
trends in livestock numbers, 
milk yield and fertilizer use.  

CH4 

Ban on burning 
of crop residues 

EU-wide 

Waste & 
wastewater 

CH4 

EU-wide 

EU Landfill 
Directive 
(EC/31/1999) 
with 
amendment 
(EC/850/2018) 
and EU Waste 
and Packaging 
Directives 

141 

Assumed not fully enforced. 
GAINS uses information 
derived from satellite images 
(e.g., MODIS) as approximate 
estimates of the mass of crop 
burned on fields.   
Biodegradable waste diverted 
away from landfills (relative 
1990 by -25% in 2006, -50% 
in 2009 and -65% in 2016). All 
landfill sites equipped with gas 
recovery by 2009. By 2035, 
countries must not landfill 
more than 10% of MSW 
generated. Member states 
that landfill more than 60% of 

 
  
  
 
 
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Sector 

Gas 

Policy 

Regional 
coverage 

Policy description and 
implementation in GAINS 

(EC/851/2018, 
EC/852/2018) 

EU-wide 

CH4 

EU Waste 
Management 
Framework 
Directive 
(EC/98/2008)  

CH4 

Decree on 
waste landfill 

Slovenia 

Germany 

Austria, 
Belgium, 
Denmark, 
Germany, 
Netherlands, 
Sweden 

EU-wide 

CH4 & 
N2O 

CH4 

CH4 

Legislation to 
replace current 
composting with 
anaerobic 
digestion of 
food waste  

Ban on landfill 
of 
biodegradable 
waste.  

EU urban 
wastewater 
treatment 
directive 
(EEC/271/1991) 

Industry 

N2O, 
PFCs 

EU ETS 
Directive 
(EC/29/2009): 
Primary 
aluminum 

EU-wide 

142 

MSW in 2013 are given a 5 
years grace period but must 
not landfill more than 25% in 
2035. GAINS Reference 
scenario assumes future 
targets will be met. 

The following hierarchy is to 
be respected in waste 
treatment: recycling and 
composting preferred to 
incineration/energy recovery, 
which in turn is preferred to 
landfill disposal. Considered in 
GAINS when simulating 
pathway for compliance with 
the Landfill Directive target. 
Decree on landfill of waste 
beyond EU Landfill Directive. 
Includes partial ban on landfill 
of biodegradable waste. 
In GAINS, the current 
composting of organic waste 
is phased-out linearly and 
replaced with anaerobic 
digestion between 2020 and 
2050. 

Complete ban on landfill of 
untreated biodegradable 
waste. Reflected in GAINS. 

GAINS reflects an 
"appropriate treatment" of 
wastewater from urban 
households (all 
agglomerations > 2000 
people) and food industry 
must be in place latest by end 
of 2005. This means 
discharge must ensure 
receiving waters meet 
relevant quality objectives.   
Industry needs to aquire 
tradable emission permits 
under the EU emission trading 
system (EU-ETS).  

 
 
 
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Sector 

Gas 

Policy 

Regional 
coverage 

Policy description and 
implementation in GAINS 

production and 
production of 
nitric acid, 
adipic acid, 
glyoxal and 
glyoxylic acid.  

PFCs  Voluntary 

EU-wide 

agreement in 
semiconductor 
industry 

F-gases 

HFCs, 
PFCs, 
SF6 

EU F-gas 
regulation (EC 
517/2014) 

EU-wide 

HFCs  EU MAC 

EU-wide 

Directive (EC 
40/2006) 

HFCs  EU Directive on 

EU-wide 

end-of-life 
vehicles (EC 
53/2000) 

HFCs, 
PFCs, 
SF6 

National F-gas 
regulations 
more stringent 
than EU 
regulation 

Semiconductor producers to 
reduce PFC emissions by 
2010 to a level at 10 percent 
of 1995 emissions. Accounted 
for in GAINS to the extent it is 
reflected in national emission 
inventories to the UNFCCC. 
Phase-down of F-gas sold on 
the market, banning of use in 
applications where 
alternatives to F-gases are 
readily available, and 
preventing emissions from 
existing use of F-gases 
through leakage control and 
end-of-life recovery.  
Mobile air conditioners: 
replacing the use of high 
GWP HFCs with cooling 
agents GWP100 < 150 in all 
new vehicle models placed on 
the market. 
Scrapped mobile air 
conditioners: recovery and 
proper handling 

Austria ("HFKW-FKW-SF6-Verordnung"), 
Belgium (end-of-life regulation from 2005 for 
large-scale refrigeration), Denmark (deposit-
refund scheme since 1992, tax since 2001 
and ban on import, sale and use since 2002), 
Germany ("Chemikalien-
Klimaschutzverordnung" specify maximum 
leakage rates), Netherlands ("STEK" since 
1992), Sweden (envirionmental fees since 
1998, specific regulation since 2007) 

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Annex II: Background on macro-economic assumptions 

Methodology  

GEM-E3  is  a  large  scale  multi-sectoral  CGE  model  that  since  the  1990s  has  been  
extensively  used  to  assess  the  socio-economic  implications  of  policies,  mostly  in  the 
domains of energy and the environment. The development of GEM-E3 involved a series of 
modelling  innovations  that  enabled  its  departure  from  the  constraining  framework  of 
standard / textbook CGE models (where all resources are assumed to be fully used) to a 
modelling  system  that  features  a  more  realistic  representation  of  the  complex  economic 
system. The key innovations of the model relate to the explicit representation of the financial 
sector,  semi-endogenous  dynamics  based  on  R&D  induced  technical  progress  and 
knowledge spillovers, the representation of multiple households (the model represents 460 
households  distinguished  by  income  group),  unemployment  in  the  labour  market  and 
endogenous formation of labour skills. The model has detailed sectoral and geographical 
coverage, with 67 products and 46 countries/regions (global coverage) and it is calibrated 
to  a  wide  range  of  datasets  comprising  of  IO  tables,  financial  accounting  matrices, 
institutional  transactions,  energy  balances,  GHG  inventories,  bilateral  trade  matrices, 
investment matrices and household budget surveys. All countries in the model are linked 
through endogenous bilateral trade transactions identifying origin and destination. Particular 
focus is placed on the representation of the energy system where specialized bottom-up 
modules of the  power generation,  buildings  and  transport  sectors  have been  developed. 
The model is recursive dynamic coupled with a forward-looking expectations mechanism 
and produces projections of the economic and energy systems until 2050 in increasing time 
steps:  annual  from  2015  to  2030  and  then  five-year  period  until  2050.  The  substitution 
elasticities  of  the  model  are  not  derived  from  the  general  literature  but  are  estimated 
according to its dimensions and functional forms using the latest available datasets. The 
model  is founded  on rigorous  and  sound  micro-economic theory  allowing it to study  in a 
consistent  framework  the  inter-linkages  of  the  economic  sectors  and  to  decompose  the 
impacts of policies to their key driving factors. It is acknowledged that the model simulations 
are sensitive to a number of input parameters and modelling assumptions including capital 
costs of power producing technologies and associated learning rates, cost of capital and 
financing availability, easiness to substitute production factors, preferences over domestic 
and imported goods etc.  

The Reference Scenario simulated with the GEM-E3 model provides numerical projections 
for the period 2020-2050 in 5-year time steps for each EU Member State and for the rest of 
the world, grouped in 18 countries/regions. The most important results, provided by GEM-
E3  are:  full  Input-Output  tables  for  each  country/region  identified  in  the  model,  dynamic 
projections in constant values and deflators of national accounts by country, employment 
by  economic  activity  and  by  skill  and  unemployment  rates,  capital,  interest  rates  and 
investment  by  country  and  sector,  private  and  public  consumption,  bilateral  trade  flows, 
consumption matrices by product and investment matrix by ownership branch. 

The projection for world energy prices is provided by the POLES-JRC model. GEM-E3 and 
POLES-JRC make use of exactly the same GDP and population assumptions. 

The  dynamic  calibration  of  the  GEM-E3  macroeconomic  projections  is  based  on  the 
assumption  that  countries  follow  a  sustainable  growth  path,  and  so  excessive  current 
account deficits or surpluses are gradually reduced. This assumption is compatible with a 
zero-output  gap,  i.e.  the  efficient  operation  of  the  economy.  Considering  the  differences 
between potential and actual GDP, the macroeconomic projection simulated with the GEM-
E3 model assumes that the output gap closes in 2024 so actual and potential GDP growth 
rates are the same from 2024 onwards. This assumption is compatible with the 2021 Ageing 
Report prepared by the European Commission.  

The model accounts for labour market imperfections since GEM-E3 computes involuntary 
unemployment  through an  empirical  wage curve.  In the  long term  it  is  assumed that the 

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economy  converges  to  full  potential  having  no  idle  resources.  The  Reference  Scenario 
design rests on the assumption that unemployment rate will decrease and converge in the 
long run with the natural rate of unemployment. This assumption is consistent with the 2021 
Ageing Report labour market projections to which the GEM-E3 model is calibrated. 

Public expenditures are dynamically adjusted in the model so that the public budget of each 
country is balanced in the long term and excess deficits or surpluses are reduced. Sectorial 
investment  is  derived  partly  endogenously,  by  comparing  the  sectorial  rate  of  return  on 
capital with the cost of replacing capital, and partly exogenously,  with the introduction of 
sectorial growth expectations.  

Data 

The macroeconomic scenario makes use of several well-established datasets for EU and 
non-EU countries. The compiled database has been updated with the latest available data, 
i.e.,  first  quarter  of  2020.  For  EU  countries  the  latest  Eurostat  statistics  have  been  used 
including historical data covering the period from 1995 to 2020.  

Data on the impact of COVID-19 pandemic on sectoral production are collected up to the 
2nd  quarter  of  2020  and  the  impact  on  public  financing,  investments  and  lockdown  in 
industry,  tourism,  transport,  retail  trade  has  been  evaluated  to  have  an  estimate  of  the 
sectoral structure by each region for the end of 2020. 

Depending on data availability the NACE 64, NACE 38 and NACE 10 datasets have been 
used. All past data are expressed in chain linked volumes of 2015. The methodology follows 
ESA2010 and NACE r2 (chained with NACE r1). In few cases normalization to the NACE 
10 figures has been performed. This approach has been employed in cases where the total 
gross  value  added  in  current  prices  was  not  equal  to the  sectoral  sum  in  NACE  64  and 
NACE  38.  Structural  Business  Statistics  (SBS)  have  also  been  used  in  order  to 
disaggregate some sectors into subsectors. For instance, the Chemicals sector has been 
disaggregated into Fertilisers, Petrochemicals, Other Chemicals and Pharmaceuticals. 

Sources of main exogenous projections 

Projections for the aggregate GDP of EU countries in 2020 and until 2025/2030 have been 
based on the Spring 2020 Economic Forecast (DG ECFIN, European Commission). For the 
period after 2030, projections on Member State GDP growth are based on the 2021 Ageing 
Report.  

Population projections for the EU make use of the European Population Projections, base 
year 2019 (EUROPOP 2019), for the period 2020 to 2050. The population projections used 
are  compatible  with  GDP  projections  as  the  starting  point  of  the  2021  Ageing  Report 
projections is also the EUROPOP 2019 population projections for the period 2020-2050.  

For  non-EU  countries, GDP growth for the  period 2015-19 have  been  based on the  IMF 
World Economic Outlook110. For the period 2020-2050, the GDP growth projections of the 
OECD Economic Outlook111 have been used. Population projections for non-EU countries 
have been based on the United Nations 2019 revision of World Population Prospects112. 

Global population projections 

Population projections show world population to grow from 7.4 billion in 2015 to 9.7 billion 
in 2050. Population growth is driven mainly by developing countries and is projected to slow 

110 “World Economic Outlook: A Long and Difficult Ascent”, October 2020, International Monetary Fund 
111 "Long-term baseline projections, No. 103, OECD Economic Outlook: Statistics and Projections (database)”, 2021, OECD  
112 “World Population Prospects 2019, Online Edition. Rev. 1”, 2019, United Nations, Department of Economic and Social 
Affairs, Population Division 

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down over time. Also, projections show a shift in the ageing structure of the world population 
with a fall in the population aged 15-64. 

Figure 108: Annual growth rate of population 

Source: UN, Population Division  

Global economic projections  

Population ageing is the dominant demographic phenomenon of the 21st century with major 
implications for the global and EU economy. Demographic change comes in addition to a 
phasedown in global trade and productivity growth coupled with structural changes and a 
potentially lasting impact of the COVID-19 pandemic. In fact, the COVID-19 pandemic hit 
at  a time  when global  GDP  growth  was  recovering from the global financial  crisis  and  is 
projected to cast a long shadow over the world’s economies.  

Following the deep recession of 2020, the global economy is expected to gain momentum 
gradually, as vaccines are deployed. After falling sharply, world GDP is projected to rise by 
4.2% in 2021 and by around 4% in 2022. Recovery is expected to be uneven but significant 
still (OECD 2020113). The contribution of Europe and North America to global growth will 
remain  smaller  than  their  weight  in  the  world  economy.  Growth  in  advanced  economies 
recovers,  still  at  a  slow  pace  reflecting  ageing  population  effects  and  the  slowdown  in 
investment, leading to low capital growth. The COVID-19 pandemic will continue to have a 
major  shock  on  investment  and  human  capital  in  emerging  market  and  developing 
economies, setting back key development goals. 

World GDP growth drops close to 2.5% in 2050 from close to 4% in 2025, mainly due to the 
deceleration  of  emerging  economies  (Brazil,  Russia,  India,  Indonesia,  China,  and  South 
Africa), which continue to account for the bulk of world economic growth. India and China 
take up a rising share of world output as the world’s economic centre of gravity shifts toward 
Asia (OECD 2018114). 

The  transition  of  the  Chinese  economy  from  infrastructure  and  manufacturing  to 
consumption and services is projected to continue to put pressure on commodity markets 
and impact GDP prospects in commodity exporters (i.e., Brazil, Canada, Australia, Russia) 
but also on economies with strong trade links to China (like Japan, Korea and the South-
East Asian economies).  

Despite the growth and financial weaknesses, world GDP is projected to rise to 2050 as a 
response  to  supportive  macroeconomic  policy  actions  like  stimulus  measures  in  China, 
regional trade agreements, structural reforms,  etc.  

113 “OECD Economic Outlook”, OECD Publishing, 2020, Paris. 
114 "The Long View: Scenarios for the World Economy to 2060", OECD Economic Policy Papers, No. 22, OECD Publishing, 
2018, Paris 

146 

-0.2%0.0%0.2%0.4%0.6%0.8%1.0%1.2%1.4%15-2020-2525-3030-3535-4040-4545-50Developed countriesDeveloping countriesWorld 
 
                                                 
EU REFERENCE SCENARIO 2020 

Figure 109: World GDP (excluding EU Member States) 

Source: IMF and OECD  

Global sectorial projections  

The macroeconomic projections show that at global level services account for 56% of total 
gross value added in 2050 from 54% in 2015. Services continue to play an important role 
in  the  economic  activity  of  developed  countries.  Services  also  increase  significantly  in 
developing countries; these are assumed to converge in terms of economic structure with 
developed economies, where services account for a large share of the economic activity. 

Figure 110: Structure of the world economic activity (excl. EU) 

Note: “Other sectors” refers to agriculture and construction 

Energy intensive industries and other sectors, including  agriculture and construction, are 
projected to reduce their share in total economic activity at world level by 2050. 

147 

0,0%0,5%1,0%1,5%2,0%2,5%3,0%3,5%4,0%02000040000600008000010000012000014000020152020202520302035204020452050billion€2015Growth ratesGDP54%54%54%54%55%55%56%56%26%27%28%28%28%28%28%28%10%10%10%10%10%9%9%9%10%9%8%8%8%7%7%7%20152020202520302035204020452050Share in total gross value addedServicesNon energy intensive industriesEnergy intensive industriesOther sectorsSource:GEM-E3 
 
 
 
 
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Annex III: Background on techno-economic 
assumptions 
For the preparation of the Reference Scenario, techno-economic assumptions have been 
adjusted accordingly since 2016. The technologies subject to review have been divided in 
several categories. To ensure the assumptions are robust, a dedicated workshop took place 
in November 2019 with industry representatives, well placed to provide insights on techno-
economic developments and future trends in each sector. Stakeholder input complemented 
the extensive literature review undertaken for the preparation of the assumption datasets 
used in the Reference Scenario. 

All  cost figures  presented  in the  PRIMES  and GAINS  techno-economic assumptions  are 
expressed  in  constant  €2015,  whereas  GLOBIOM  assumptions  in  constant  €2010.  This 
section  presents  background  information  on  the  techno-economic  assumption  data  sets 
which are included in the EXCEL file “REF2020_Technology Assumptions” published along 
with this report.  

Power and heat 

Data  on  technologies  and  costs  for  power  and  steam/heat  generation  include,  namely: 
"Overnight investment costs in a greenfield site”, “Fixed Operation and Maintenance costs”, 
“Variable  non-fuel  cost”,  “Electrical  efficiency  (net)  in  optimal  load  operation”,  “Self-
consumption of electricity”, “Technical lifetime” and average typical “Capacity Factors” for 
RES technologies. 

A new representation of variable RES technologies and resources intensity classes (that 
differ per country) are used to capture the possible differentiation in the technologies used 
across  different  sites  and  better  reflect  the  heterogeneity  of  resources  in  a  region. 
Therefore,  the  values  presented  for  capacity  factors  are  average,  shown  for  illustration 
purposes. The model further adjusts capacity factors per Member State based on observed 
performance of installed capacities. 

Regarding  fossil  power  generation,  adjustments  have  been  made  to  CCS  power  plants. 
Their  current  costs  (for  some  technologies)  have  increased  due  to  lack  of  project 
developments.  Efficiency  has  also  been  revised  downwards  due  to  lack  of  technological 
improvements.  For  IGCC,  costs  have  been  reduced  slightly  based  on  comments  from 
stakeholders, confirmed by literature.  

On new technologies, the latest information for large-scale batteries available from the EIA 
NEMS reports115 published in early 2020 have been included. CAES and flywheels have 
been amended using the HydroWIRES report116. Small scale batteries are amended based 
on the IRENA report. 

For fuel cells, Solid Oxide Electrolysis Cells (SOEC) have been amended in accordance 
with the EIA NEMS reports. 

115https://www.eia.gov/outlooks/aeo/assumptions/pdf/electricity.pdf;  https://www.eia.gov/analysis/studies/powerplants/capita
lcost/pdf/capital_cost_AEO2020.pdf  
116 https://www.energy.gov/sites/prod/files/2019/07/f65/Storage%20Cost%20and%20Performance%20Characterization%20
Report_Final.pdf  

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Transport 

The  techno-economic  assumptions  are  split  by  transport  equipment for  various transport 
modes, by size (for cars, trucks, vessels) and by powertrain/fuel categories (i.e., thermal 
engine  running  on  different  fuels,  plug-in  hybrid,  battery  electric,  hydrogen  fuel  cell, 
pantograph for trucks).  

The categories are: 

  Small cars (referring to A and B market segments) 

  Medium cars (referring to C and D market segments) 

  Large cars (referring to the rest of market segments) 

  Light Commercial vehicles (LCV- referring to a medium-sized LCV) 

  Heavy Goods vehicles (HGV) of below 16 tons gross-vehicle weight (also split 

into 3.5-7.5t and 7.5-16 t sizes) 

  Heavy Goods vehicles (HGV) of above 16 tons gross-vehicle weight (also split 
into  16-32t  and  above  32  t  sizes).  The  file  presents  assumptions  for  a  4x2  rigid 
regional 16-32t and 4x2 tractor long-haul >32t truck. 

  Buses (for urban mobility) and coaches (for inter-urban mobility) 

  Aviation (includes assumptions for a narrow-body aircraft)  

  Rail (includes the cost of the entire train for passenger and freight) 

 

 

 

Inland navigation (includes inland waterways and national maritime) 

International maritime (includes selected sized of oil tankers, containers, dry bulk 
carriers and general cargo vessels) 

Infrastructure  equipment  related  to  the  refuelling/recharging  of  alternative  fuel 
vehicles.  These costs  are  the  costs  for  refuelling  stations  at  distribution  level:  for 
hydrogen  also  the  costs  for  compression  and  liquefaction  are  included  for 
completeness. The investments are shown per kW-output. Fixed costs of refuelling 
technologies (operation and maintenance) are also presented per kW output. 

The techno-economic assumptions are related to the evolution of the capital costs (vehicle 
prices excluding taxes) of the various transport technologies until 2050 in 10-year time steps 
(i.e.,  2020,  2030,  2040,  2050).  The  assumptions  are  presented  consistently  across  the 
different technologies: 

Multiple efficiency improvement levels are available at different cost for transport vehicle 
technologies  equipped  with  thermal  engines,  for  each  time  period.  The  efficiency 
improvements  are  compared  against  a  2015  reference  vehicle.  For  example,  a  small 
gasoline car in 2030 offering a 25% improvement in its specific energy consumption relative 
to  2015  is  assumed  to  cost  EUR  14,592  ,  while  a  small  gasoline  car  offering  40% 
improvement  in  its  specific  energy  consumption  is  assumed  to  cost  EUR  16,114.  The 
reference vehicle is a small gasoline car which is assumed to cost EUR 14,228 in 2015 with 
an average specific energy consumption of 5.5 lt/100 km. The same logic applies through 
the  rest  of  the  transport  equipment  categories  (also  for  plug-in  vehicle  powertrains).  In 
particular, for hydrogen fuel cell vehicles the presentation of the cost assumptions is also 
linked  with  the  efficiency  (expressed  as  kgH2  per  100km);  however,  the  costs  of  the 
hydrogen fuel cell vehicles are strongly dependent on the assumptions on the evolution of 
the hydrogen fuel cell costs. 

For battery electric vehicle technologies (road, aviation, rail and waterborne) the capital 
cost of the equipment is linked with the available electric range offered by the equipment. 
Higher  range  requires  higher  battery  capacity  and  leads  to  higher  capital  costs.  For 
example, a medium size battery electric car with a range of 250 and 300 km is assumed to 
cost EUR 29,508 and EUR 31,847 in 2020, respectively. The costs of the electric vehicle 

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technologies are assumed to decrease in the future as a result of the assumed reduction in 
the battery costs. For example, the medium size battery electric car with a range of 250 km 
is assumed to cost EUR 23,138 in 2030.  

The assumptions on the technology costs for cars and LCVs draw on the work carried out 
by Ricardo and JRC117. The assumptions on the technology costs for trucks of above 16 
tons are based on the work of JRC118. The assumptions on the evolution of the battery costs 
follow  the  LOW  trajectory  of  the  assumptions  presented  in  the  report  of  Ricardo119.  The 
assumptions on the evolution of battery and hydrogen fuel cell costs have also been cross-
checked to be in line with the most recent reports from IEA120 121, US DOE and JRC. The 
assumptions on the maritime sector are based on the work of the IMO GLOMEEP122. 

Industry 

The assumptions include capital costs and efficiency for technologies used in the industrial 
sector.  Efficiency  is  expressed  as  an  index  compared  to  2015.  Learning  by  doing  is 
included.  Increase  in  the  efficiency  rate  implies  a  more  efficient  technology.  As  in  the 
“Domestic”  category,  for  every  technology,  the  model  considers  seven  (7)  technology 
categories, ranging from an ordinary to an advanced and a future category.  

Domestic appliances and equipment 

Includes technologies for the buildings sector (residential and services). Data shown include 
ranges of purchasing costs (that refer to total acquisition costs) and efficiency by vintage 
(reference  year  of  purchase),  for  several  space  and  water  heating  technologies  and 
appliances. For every item, the model considers a range of seven technology categories, 
ordered  from  an  ordinary  up  to  an  advanced  and  a  future  category.  The  technical  and 
economic  characteristics  of  each  technology  category  change  over  time  as  a  result  of 
learning  by  doing  and  economies  of  scale  in  industrial  production.  Not  all  technology 
categories are considered as fully mature from a user's perspective, but in general the users' 
acceptance  of  advanced  technology  categories  increases  over  time.  Policy  assumptions 
may  drive  acceleration  of  learning-by-doing  and  users'  acceptance  in  the  context  of  a 
scenario. An advanced technology category is more efficient than an ordinary one and in 
general more expensive to purchase at a given point in time. However, depending on the 
learning  potential  of  a  technology,  it  is  possible  that  an  advanced  technology  becomes 
cheaper  than  ordinary  technology  in  the  long-term  and  still  more  efficient.  For  currently 
mature technologies this is generally unlikely to happen.   

More specifically: 

  The  CHP  efficiencies  reported  refer  only  to  thermal  efficiencies.  The  model 

considers the electrical production separately.  

  Costs  of  space  and  water  heating  boilers  have  been  modified  to  align  with  the 
studies for space heaters123 and water heaters124 published on October 2, 2019 for 
the revision of the Ecodesign Directive.125 Costs are therefore in line with the latest 

117 https://ec.europa.eu/clima/sites/clima/files/transport/vehicles/docs/ldv_co2_technologies_and_costs_to_2030_en.pdf 
118 https://ec.europa.eu/jrc/en/publication/eur-scientific-and-technical-research-reports/heavy-duty-vehicle-co2-emission-
reduction-cost-curves-and-cost-assessment-enhancement-dione
119 https://ec.europa.eu/clima/sites/clima/files/transport/vehicles/docs/ldv_post_2020_co2_en.pdf (Table 2.4) 
120 https://www.iea.org/publications/reports/globalevoutlook2019/ 
121 https://www.g20karuizawa.go.jp/assets/pdf/The%20future%20of%20Hydrogen.pdf 
122http://www.imo.org/en/OurWork/Environment/PollutionPrevention/AirPollution/Documents/Air%20pollution/EE%20Apprais
al%20Tool_Advanced.xlsm 
123 https://www.ecoboiler-review.eu/study.htm   
124 https://www.ecohotwater-review.eu/documents.htm   
125 Following Article 7 of Commission Delegated Regulation (EU) No 811/2013, Commission Delegated Regulation (EU) No 
812/2013, Commission Regulation (EU) No 813/2013 and Commission Regulation (EU) No 814/2013 the regulations 
require review after 5 years of entering into force.

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available studies and aligned with the minimum ecodesign requirements (today 75% 
GCV – future 86% GCV).  

  The costs of the required chimney modifications for condensing boilers are included 

in the modelling, yet not under direct boiler costs.  

  Costs  and  learning  curves  for  solar  thermal  are  included  in  the  model  and  are 
comparable to those in literature (IEA-SHC “Solar Heat Worldwide” edition 2017 and 
2018).  Ultimate  costs  have  been  reduced  by  approximately  10%  to  reflect  higher 
learning and lower bounds of possible prices.  

 

Information  related  to  heat  pumps  is  taken  from  the  ETSAP  Technology  Brief  on 
Heat  Pumps  published  in  January  2013.  This  source  has  not  yet  been  updated.  
Hybrid heat pumps are included in the model as heat pumps with an additional small 
boiler  (electric  in  most  cases),  where  average  temperatures  require  such  an 
element. For geothermal heat pumps, values were double-checked with available 
information and the model is in line with estimates. The model makes the distinction 
between small-scale and large-scale geothermal heat pumps. 

All  relevant  equipment  has  the  option  of  using  hydrogen.  Since  for  the  most  part  the 
consumer has no influence on the delivered fuel mix (and therefore the share of hydrogen 
in the fuel mix), the conversion of the equipment to hydrogen is considered as a mark-up in 
the fuel price. Social aspects for the adoption of hydrogen are considered in the model as 
perceived  or  hidden  costs.  Technologies  that  are  not  yet  mature  face  more  significant 
penetration challenges, which reduce over time as technology maturity increases.  

Renovation  

Renovation refers to average renovation costs by climate type and renovation deepness, 
as  used  in  the  PRIMES  buildings  module.  Investment  costs  are  the  energy  related 
expenditures needed to implement the indicated level of renovation of a building, excluding 
usual  renovation  expenditures  needed  for  other  purposes  (structure,  finishing  materials, 
decoration etc.). The energy savings rate refers to a typical building as in the current stock 
of  existing  buildings,  not  savings  in  new  constructions,  which follow  the  buildings  codes' 
insulation standards. 

The  data  on  renovation  costs  included  in  the  EXCEL  file  “REF2020_Technology 
Assumptions“ are a summary of the data in the model which are more detailed and include 
several house types, house ages and geographical categories. 

New fuels  

Technologies  for  the  production,  transmission,  and  distribution  of  the  so-called  synthetic 
fuels (e-fuels) as well as storage technologies are presented. The following items are listed: 
“Investment costs”, “Fixed O&M costs”, “Heat rate” (ratio of energy input requirements over 
output),  “Feedstock  input  requirements”  (feedstock  input  required  for  the  production  of  1 
unit of output from each technology). 

Non-CO2 greenhouse gas emissions abatement options 

The  GAINS  model  projects  emissions  of  the  non-CO2  greenhouse  gases  by  combining 
activity drivers with the emission intensity specific for a sector and technology set-up. This 
section  lists,  for  each  sector,  key  mitigation  options  to  decrease  emissions  of  non-CO2 
greenhouse gases for  any  given  activity  level  through changes  in the technology  set-up. 
The  implementation  of  different mitigation  options  as  a result  of  current  legislation  in  the 
different member states has been taken into account in the construction of the projected 
non-CO2 greenhouse gas emissions in the EU Reference Scenario 2020. 

151 

 
EU REFERENCE SCENARIO 2020 

Agriculture: 

  Agricultural soils: Nitrification inhibitors (N2O) 

  Livestock manure: Farm-scale anaerobic digestion with biogas recovery (CH4) 

  Agricultural soils: Abandoning the use of organic soils (N2O) 

  Livestock  enteric  fermentation:  Breeding  through  selection  to  enhance  yield 
productivity and animal health, fertility and longevity, thereby minimizing kg CH4/kg 
milk (CH4) 

  Livestock  enteric fermentation:  Feed  additives and/or  changed feed management 

practices (CH4) 

  Agricultural soils: Variable rate technology (N2O) 

  Agricultural soils: Precision farming (N2O) 

  Rice cultivation: Intermittent aeration, alternative hybrids and sulphate amendments 

(CH4) 

  Enforcement of existing ban on open burning of field residuals and other agriculture 

waste (CH4) 

Energy: 

  Oil and gas production: Extended recovery of associated gas (CH4) 

  Gas  distribution  networks:  Improved  control  through  leak  detection  and  repair 

programs (CH4) 

  Gas distribution networks: Replacement of grey cast iron networks (CH4) 

  Gas  transmission  pipelines:  Set  of  measures  including  new  controllers  and 

replacement of wet with dry seals (CH4) 

  Abandoned coal mines: flooding (CH4) 

  Gas transmission pipelines: Pipeline upgrade through replacement (CH4) 

  Coal mining: Ventilation air methane oxidation (VAMOX) technology (CH4) 

  Oil and gas production: addressing unintended leakage through leak detection and 

repair programs (CH4) 

  Coal mining: pre-mining degasification (CH4) 

  Coal mining: combining VAMOX technology with improved mining ventilation (CH4) 

 

Industry and power plant boilers: modification of fluidized bed combustion (N2O) 

  Oil refinery: reduced leakage (CH4) 

  Oil and gas production: monitoring and control of residual venting after max recovery 

of associated gas (CH4) 

Industry: 

  Caprolactam production: Best available technology (N2O) 

  High and mid-voltage switches: leakage control, maintenance and recollection (SF6) 

  Semiconductor industry: Switching from PFC to NF3 with post-destruction (PFC) 

  Primary aluminium production: Move to advanced point-feed pre-bake technology 

(PFC) 

  Primary aluminium production: New technology e.g., inert anode technology (PFC) 

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EU REFERENCE SCENARIO 2020 

Air conditioning (AC) and refrigeration: 

  Stationary AC: Alternative agent -hydrocarbons e.g., butane or propane (HFC) 

  Commercial  sector  refrigeration:  Alternative  agent  -HFC  blends  with  GWP  <  150 

(HFC) 

  Commercial sector refrigeration: CO2-based technology (HFC) 

 

Industry sector refrigeration: Alternative agent -ammonia (HFC) 

  Mobile AC: Alternative agent -HFO-1234yf (HFC) 

  Stationary AC: Water chillers (HFC) 

  Commercial  sector  refrigeration:  Alternative  agent  -hydrocarbons  e.g.,  butane  or 

propane (HFC) 

  Stationary AC: CO2-based technology (HFC) 

  Refrigerated transport: CO2-based technology (HFC) 

  Stationary AC: Alternative agent -HFO-1234yf  (HFC) 

Wastewater: 

  Domestic wastewater: optimize process for low N2O (N2O) 

  Food  and  other  organic  manufacturing  industry:  2-stage  treatment,  anaerobic 

treatment with biogas recovery followed by aerobic (CH4) 

  Domestic wastewater: secondary/tertiary anaerobic treatment with biogas recovery 

(CH4) 

Solid waste:   

  Municipal  solid  waste:  source separation  with maximum  treatment,  i.e., recycling, 
anaerobic digestion with biogas recovery and/or incineration with energy recovery 
(CH4) 

  Food manufacturing industry: Anaerobic digestion with biogas recovery (CH4) 

  Textile industry: incineration with energy recovery (CH4) 

  Wood  industry:  Maximum  recycling  for  chip  board  production  with  the  rest 

incinerated with energy recovery (CH4) 

153 

 
 
 
 
Annex IV: Assumptions on fossil fuel prices projections  

Approach 

The EU Reference Scenario 2020 uses projections of international fossil fuel prices produced 
by  the  global  model  POLES-JRC126  which  are  derived  from  work  conducted for  the  Global 
Energy and Climate Outlook (GECO) JRC report127. The model’s country-level energy system 
parameters  are  driven  by  income  growth,  cost-based  competition  between  fuels  and 
technologies with expected technological development and adjustment of market equilibrium 
and  prices  with  lagged  variables;  energy  and  climate  policies  impact  user  costs  directly  or 
modify  agent  preference 
(see  POLES  model 
documentation128). Oil Brent price, EU natural gas import, and EU coal import prices were all 
obtained from a REF2020 “Global Context” energy scenario where energy and climate policies 
adopted  as  of  June  2019,  as  well  as  estimated  impacts  of  the  COVID-19  pandemic  on 
macroeconomic parameters and sectoral activities, were included. The list of national energy 
and  climate  policies  considered  at  global  level  can  be  found  in  the  GECO  2019  report129 
(Reference scenario); the representation of the effects of the Covid-19 pandemic can be found 
in the GECO 2020 report130 (Base_C19 scenario). In particular, EU energy and climate policies 
include  the  2030  objective  of  reducing  GHG  emissions  by  at  least  40%  compared  to  1990 
levels131; no specific objective is considered for the EU beyond 2030132. 

technology  options 

for  different 

In the modelling, fossil fuel prices are primarily the result of supply and demand equilibrium 
on  global  (oil)  or  regional  (gas,  coal)  markets.  They  constitute  an  important  marker  at  the 
macroeconomic level, as well as for the future of the energy system: any transition process 
away  from  fossil  fuels  will  have  to  compare  the  economics  of  alternative  pathways.  In  the 
modelling of the supply side, the production costs of different types of resources across the 
globe,  including  new  capacity  additions  and  constraints,  are  added to transportation  costs, 
defining the cost of supplying primary energy to the various regional markets. In this respect, 
unconventional  oil  and  gas  resources,  the  development  of  new  supply  routes,  as  well  as 
geopolitical parameters such as the OPEC’s swing supplier strategy, will play a crucial role in 
equilibrating markets.  

On the demand side, the consumption of fossil fuels depends on a variety of factors, including 
technological  and  behavioural,  but  also  policy-related,  notably  in  a  context  of  enhanced 
climate  policies.  End-user  prices  and  equipment  costs  are  key  drivers  of  energy  demand 
patterns, and the rapid evolution of costs for key alternatives to fossil fuels (wind and solar 
power generation, electric vehicles) will impact the future evolution of the energy mix and of 
the energy prices. 

The  COVID-19  pandemic  brought  significant  changes  to  the  global  economy  and  mobility 
patterns. First, the historic decline in energy demand due to sanitary measures caused a major 
drop in prices. The extent to which the ongoing rebound would return to (or even exceed) pre-

126 https://ec.europa.eu/jrc/en/poles  
127 https://ec.europa.eu/jrc/en/geco 
128 2018 documentation: Després, J., Keramidas, K., Schmitz, A., Kitous, A., Schade, B., Diaz Vazquez, A., Mima, S., Russ, H. 
and Wiesenthal, T., POLES-JRC model documentation, EUR 29454 EN, Publications Office of the European Union, Luxembourg, 
2018, ISBN 978-92-79-97300-0 (online), doi:10.2760/814959 (online), JRC113757 
129 Global Energy and Climate Outlook 2019: Electrification for the low-carbon transition, EUR 30053 EN, Publications Office of 
the  European  Union,  Luxembourg,  2020,  ISBN  978-92-76-15065-7  (online),978-92-76-15066-4  (print),  doi:10.2760/350805 
(online),10.2760/58255 (print), JRC119619 
130  Global  Energy  and  Climate  Outlook  2020:  A  New  Normal  Beyond  Covid-19,  EUR  30558  EN,  Publications  Office  of  the 
European Union, Luxembourg, 2021, ISBN 978-92-76-28417-8 (online), doi:10.2760/608429 (online), JRC123203 
131 The modelling of the EU with the POLES-JRC model in the “Global context” scenario is calibrated on the Baseline scenario of 
the EU LTS (see “In-depth analysis in support of the Commission Communication COM(2018) 773”), including the EU policy 
framework as of end of 2018 and notably the 2030 targets on climate (at least 40% reductions), renewable energy (32%) and 
energy efficiency (32.5%). 
132 With the exception of the EU ETS, which is maintained beyond 2030 with a linear reduction factor of the cap of 2.2%/year. 

154 

 
                                                 
crisis levels within a couple of years is still an open question. These short-term dynamics will 
depend on the degree to which some changes that took place during the pandemic will be 
perpetuated133.  For  the  REF2020  “Global  Context”  scenario,  assumptions  were  made  on 
economic recovery134, and in particular on a gradual recovery of sectoral transport activity to 
pre-pandemic levels. Finally, an important driver of future price is the behaviour of key global 
fossil fuel suppliers (notably from OPEC) in this context. 

Price projections 

Figure  111  provides  the  international  fuel  prices  trajectories  considered  for  the  Reference 
Scenario 2020. 

Figure 111: International fossil fuel prices in the EU Reference Scenario 2020 

Note: oil prices refer to Brent, gas and coal prices refer to the average imports to the European market. 
Source: historical (bold): Eurostat, PLATTS; 2020: estimates with data as of August 2020 

Oil 

For  decades,  oil  demand  has  known  a  sustained  growth,  in  line  with  macroeconomic 
indicators. The future, however, might go along a different path as new technologies contribute 
to cutting oil demand in key sectors develop. The recent economic context has brought even 
more  uncertainty  into  this  picture.  After  a  2019  year  around  60  $/bbl,  the  Brent  marker 
plummeted to around 20 $/bbl while demand significantly shrank to less than 75 Mb/d in April 
2020, compared to an average of ~100 Mb/d before the crisis stroke. Prices returned to pre-
crisis levels early in 2021. The recovery in prices was eased by a series of factors, including 
important supply cuts by the OPEC, and a gradual recovery of demand due to a progressive 
ease  of  lockdown  measures.  The  short-term  price  evolution  therefore  depends  a  lot  on 
assumptions made to define a “new normal” for the oil market and beyond. The transitory price 
decrease during the pandemic is also resulting in decreased investment in exploration and in 
the development of new reserves, and in the reorganization of the upstream sector in the form 
of a series of mergers and acquisitions. In the projection of international oil price for the EU 
Reference Scenario 2020, these factors combine into a pronounced increase of the price by 
the mid-2020s.  

In  the  longer  run,  supply  and  demand  equilibrium  in  a  context  of  increasingly  expensive 
resources will be the key driver setting the oil price.  

133 IEA Oil report 2021: https://www.iea.org/reports/oil-2021  
134 Macroeconomic assumptions were derived from World Bank (2000-2019), IMF WEO (2020-2024), OECD long-term baseline 
projections  (2027  and  beyond)  with  an  interpolation  of  GDP/capita  for  the  2024-2027  period, for  non-EU  countries;  the  2021 
Ageing Report was used for EU countries. Population was derived from JRC-IIASA 2018 for non-EU countries and Europop 2020 
for EU countries. 

155 

02040608010012020002005201020152020202520302035204020452050USD2015/boeOilGasCoal 
 
                                                 
Figure 112: World liquids demand by sector (left) and supply by type (right) in the REF2020 “Global Context” 
scenario  

Source: 2018:  BP Statistical Review, Enerdata, IEA, national statistics; projections: POLES-JRC model 

After total consumption of liquids recovers to pre-pandemic levels by 2025, it is maintained 
until 2030, with a similar structure. Beyond 2030, an important shift occurs as the main driver 
for  liquids demand, road  transport,  erodes at  an average growth rate  of  -2.0%  p.a.,  due  to 
increasing efficiency standards in road vehicles and the emergence of electrification in certain 
key market segments. This is the main cause for the cut of more than 20 Mbl/d of crude oil 
supply between 2030 and 2050, 15 of which are due to road transport. Therefore, both industry 
uses  (energy  and  non-energy)  and  international  bunkers  see  their  relative  contributions  in 
liquids demand rising. Alternative liquids supply (liquids from coal, gas and biomass) remain 
limited contributing 4% of total liquids demand globally in 2030 (up from 3% in 2019). Sales of 
electricity-powered  vehicles  (battery-electric,  plug-in  hybrids  as  well  as  hydrogen  fuel  cell 
vehicles) grow to make up one fifth of global annual sales in cars and light duty vehicles in 
2030. 

In parallel, the supply-side experiences important reductions in conventional oil supply from 
non-OPEC regions and non-conventional resources135.  

Figure 113: Comparison of oil price projections in 2030 

Gas 

Natural gas uses are dominated by different sectors than oil, namely power generation and 
heat production (including for industry and space heating); its economics and pricing principles 
depend essentially on the price of competitors (e.g. oil), or of the output it is used for (e.g. 
electricity). If the standard for gas import pricing has long been the oil-indexation of long-term 
contracts, the more recent emergence of gas hubs made prices more reactive, hence capable 
of reflecting current market conditions. Over the past few years, the global gas market growth 
was sustained by an increase in demand for power generation, driven by low gas prices, with 

135 Deepwater, arctic, tight, extra-heavy and tar sands. 

156 

010203040506070809010011020182020202520302035204020452050Mbl/dCtL, GtLBiofuelsNGLsTight, Extra-heavy, TarDeepwater, ArcticConventional - non-OPECConventional - OPEC010203040506070809010011020182020202520302035204020452050Mbl/dTransport: maritimeTransport: airTransport: rail & waterwaysTransport: roadBuildings & AgricultureIndustry: non-E usesIndustry: combustionOther transformation & lossesPower & Heat020406080100120140160IEO2019 LowWEO2020 SDWEO2019 SDWEO2020 SPIEO2019 RefEU Reference2020WEO2019 SPEU Reference2016IEO2019 HighEIAIEAIEAIEAEIACOMIEACOMEIAUSD2015/bbl 
 
 
                                                 
the US providing an important part of the incremental supply. A dynamic LNG market took in 
most of the additional trade needs136. 

The  COVID-19  pandemic  affected the  European  gas market  in different ways.  At  the  most 
critical moment of the crisis, European gas demand fell by 10% (Q2 2020), compared to the 
same period in the previous year. Industry but also power generation, due to the abundance 
of  renewables  compared  to  electricity  needs  in  the  same  period,  were  the  most  affected 
sectors. At the same time, prices dropped by more than 50% on a year-over-year basis. 

The  gas  price  projection  of  the  EU  Reference  Scenario  2020  is  in  line  with  the  recent 
fundamentals.  By  the  mid-2020s,  the  average  EU  import  price  progressively  recovers  its 
historical level. 

World natural gas production is projected to increase, with conventional gas still making up 
the  bulk  of  supply.  Alternative  natural  gas  supply  types,  such  as  shale,  deepwater  and 
coalbed, are projected to contribute a limited share of global supply (21% combined in 2030, 
up from 19% in 2019). Natural gas supplied by LNG grows to 18% of the global market in 2030 
(up from 10% in 2019). 

Figure 114: World natural gas demand by sector (left) and supply by type (right) in the REF2020 “Global Context” 
scenario 

Source: 2018: BP Statistical Review, Enerdata, IEA, national statistics; projections: POLES-JRC model 

The  size  of  the  EU  gas  market  is  small  compared  to  the  global  market.  In  2018,  EU  gas 
demand amounted to 12% of the global demand; it shrinks to less than 5% throughout the 
projection  period.  Other  OECD  regions  would  also  represent  a  lower  share  of  the  market, 
which would be balanced by Asian countries, especially China and India. 

Figure 115: Natural gas demand shares by region in the REF2020 “Global Context” scenario 

Source: 2018: BP Statistical Review, Enerdata, IEA, national statistics; projections: POLES-JRC model 

Coal 

While coal demand had grown in 2017 and 2018, the market faced a shrinkage for 2 years in 
a row, due to a fierce competition with gas for power generation in 2019 and, in addition, due 

136 IEA Gas report 2020: https://www.iea.org/reports/gas-2020 

157 

0100020003000400050006000700020182020202520302035204020452050bcmRecoveredCoalbedDeepwater, ArcticShaleConventional0100020003000400050006000700020182020202520302035204020452050bcmTransport: maritimeTransport: roadBuildings & AgricultureIndustry: non-E usesIndustry: combustionOther transf & lossesPower & Heat0%10%20%30%40%50%60%70%80%90%100%20182020202520302035204020452050CISME & AfricaC&S AmericaOther AsiaIndiaChinaOther OECDEU 
 
 
                                                 
to the COVID-19 outbreak in 2020. As a result, international trade took a serious hit, with a 
reduction of global coal exports of 11% in 2020 compared to 2019, mostly from thermal coal. 
Supply  adjusted  consequently  from  the  different  basins,  mainly  from  the  US,  Colombia, 
Indonesia and Australia. After an upsurge in 2016-2018, international coal prices withstood at 
70-100 USD/t in 2019, compared to 100-300 at the end of 2016. EU import prices for thermal 
coal have followed a downward path since 2018, and fell below 60 USD/t FOB in the second 
half of 2020. 

The main uncertainties surrounding the global coal market in the forthcoming years relate to 
the strategies of China and India – two countries whose economy heavily rely on coal, and 
their ability to secure access to cheap coal through national industrial and commercial plans137. 

At the sectoral level, global coal demand growth is sustained by coal use for power generation, 
increasing  by  80%  between  2020  (3.1  Gtce)  and  2050  (5.6  Gtce)  in  the  “Global  Context” 
scenario of REF2020. Industrial demand, the second largest consuming sector, decreases by 
28% from 1.5 to 1.1 Gtce, in the same period. Nearly all the growth in global coal demand is 
indeed driven by Asian economies, China and India in particular, while demand in the EU and 
OECD  economies  is  projected  to  decrease.  This  results  in  slightly  growing  prices  for 
internationally traded coal, with prices slowly recovering to their 2018 (high) level in the late 
2030s. 

Figure 116: World coal demand by sector (left) and region (right) in the REF2020 “Global Context” scenario 

Source: 2018: Enerdata, IEA, national statistics; projections:  POLES-JRC model 

Price indexes 

Overall, the price projections present a gradual evolution of current market dynamics. On an 
energy-equivalence  basis,  gas  progressively  gains  an  advantage  compared  to  oil  and 
progressively closes its competitiveness gap to coal. The gas/oil price ratio is stable around 
0.50 from 2020 onwards. The gas/coal price ratio reaches a value of 2.3 in 2030, back to the 
2010-2019 average, after a drop at 1.5 in 2018. 

Figure 117: Price indexes for average EU import prices for gas/coal (left axis) and gas/oil (right axis) 

137 IEA Coal report 2020: https://www.iea.org/reports/coal-2020 

158 

01234567820182020202520302035204020452050GtceCISME & AfricaC&S AmericaOther AsiaIndiaChinaOther OECDEU01234567820182020202520302035204020452050GtceOthersBuildingsIndustryPower & Heat0.00.20.40.60.81.001234201020152020202520302035204020452050Gas / OilGas / CoalRatio: Gas / CoalRatio: Gas / Oil 
 
 
                                                 
 
Annex V: Note on discount rates used in PRIMES 

Discount rates from a modelling approach  

The PRIMES model explicitly considers the time dimension and performs dynamic projections. 
Actors are simulated to take decisions in which they consider the time dimension of money 
flows. Following microeconomic theory, they are also assumed to have preferences138 about 
the time dimension of revenues and costs, in the sense that they have to discount an amount 
defined  at  future  time  to  make  it  equivalent  to  an  amount  available  at  present  time.  For 
example, the costs of energy efficiency or a renewable energy generation investment incur in 
the  first  year,  while  monetary  savings  or  revenues  accumulate  over  the  lifetime  of  the 
investment. To do cost-effectiveness comparisons, one has to aggregate the stream of money 
over time as a present value, which inevitably uses a discount rate. 

The PRIMES model mimics decentralised decisions of the actors so that each actor can apply 
its individual discount factor, in contrast with other models which formulate central planning 
optimisation and assume that the central planner applies a uniform discount factor on behalf 
of all actors.  

The central planning approach can be characterised as normative, whereas the descriptive 
approaches, as PRIMES follows, use market-based discount factors differing by agent.  

PRIMES follows a descriptive approach because it aims at assessing policy impacts as close 
as possible to reality in order to avoid under- or over- estimation of the costs and difficulties of 
transformation towards meeting targets and transition objectives (i.e. transition towards a low 
carbon economy). As it is known, the transitions are capital intensive (e.g. energy efficiency 
investment,  renewables  and  other  clean  energy  technologies,  electric  vehicles,  and 
infrastructure).  The  model  simulates  individual  decision  making  as  appropriate  by  type  of 
investment. The  decision  reflects  a private  perspective,  subject  to  uncertainties, risk  taking 
behaviours and limited access to funding. Some of the investments (e.g. infrastructure, public 
transport) are taken by entities which are state-owned or subject to regulation by the state. 
Also  for  these  cases,  PRIMES  uses  discounted  present  values  mimicking  the  practices 
followed by these entities. 

Other  models  may  have  different  aims,  as  for  example  to  evaluate  what  should  be  the 
“optimum” system from a social perspective. To do this they use a social discount rate, which 
is much lower than private discount rates, for all present value calculations. Obviously a social 
discount  rate  renders  capital  intensive  decisions  more  attractive  than  a  calculation  using 
private discount rates. Therefore, the approach based on social discount rates finds transitions 
less  costly  and  easier  than  approaches  using  private  discount  rates.  Even  in  a  no  policy 
scenario  the  social  discount  rate  approach  would  project  a  lot  of  energy  efficiency  and 
renewable energy investments that a private discount rate approach would find uneconomic 
without incentives. The social discount rate approach suggests that if the investments were 
undertaken they would entail negative costs for the society. If not undertaken in reality, then 
the only explanation would point to barriers and imperfections which influence the assessment 
of the decision-maker. Generally, the social discount rate approach tends to underestimate 
the intensity of policies which may enable the transition. For the same reasons, this approach 
can be misleading for policy making aiming at promoting clean technology diffusion. 

The approach followed by PRIMES (and other models, e.g. NEMS in the US DOE/EIA) using 
private discount rates postulates that fundamentally private discount rates differ from social 
ones,  and  only  the  former  can  realistically  mimic  individual  decision  making.  The  discount 

138 In economics, time preference is the relative valuation placed on a good at an earlier date compared with its valuation at a 
later date. In mathematical terms, the decision maker uses a discount factor, say 𝑑 (a rate measured as a percentage), so as to 
be indifferent when to choose between a present amount 𝐹 and a future amount 𝐹 ∙ (1 + 𝑑)−𝑡 available with certainty time t. The 
time preference has nothing to do with inflation and is subjective. In addition to pure time preference, a discount factor also 
reflects risk and opportunity costs. Future earnings are obviously more risky compared to those available at present with 
certainty. The amounts that are presently equivalent to uncertain future earnings depend on risk aversion or risk prone 
behaviour, which is also subjective.

159 

 
                                                 
 
rates reflect opportunity costs of funding capital intensive investment and these costs differ 
fundamentally  between  private  entities/persons  and  the  state.  Access  to  capital,  risk 
behaviours, finite horizon for individuals versus overlapping generation prospect for the state 
and  others  are  among  the  causes  of  this  difference.  In  addition,  risk  premium  factors 
expressing barriers, imperfections and other failures are part of the private discount rates and 
push them upwards.  

State-owned  entities  also  include  risk  premiums  in  real-world.  Hence  a  model  such  as 
PRIMES uses higher discount rates than social ones also for these entities. 

Modelling behaviours should not be confused with cost-benefit assessments of public policy. 
For example, consider a cost-benefit analysis of a policy which uses public money to subsidise 
energy efficiency investments of individuals. If the policy maker wants to assess whether it is 
worth funding energy efficiency compared to other destinations of subsidies, the cost-benefit 
analyses correctly has to use a social discount rate. This is because public funds are at stake 
and the beneficiary is the society as a whole. The same logic applies to cost-benefit analysis139 
of a public infrastructure investment, a regional development plan, etc.  

But if the policy maker wants to assess whether the amount of subsidies is sufficient to incite 
the targeted amount of energy savings, then the analysis has to use private discount rates to 
estimate the individual behaviours in the undertaking of energy efficiency investment. Using a 
social discount rate for this purpose would obviously be misleading.  

The  same  holds  for  assessing  costs  of  regulatory  policies  via  scenario  analysis.  The 
investment  decision  and  cost  figures  of  each  scenario  projection  must  be  generated  using 
simulation  of  individual  behaviours,  which  as  explained  has  to  use  private  discount  rates. 
Assessing transition scenarios which have different distributions over time of investments and 
benefits requires in addition calculating present values, in which it is appropriate to use a social 
discount rate for discounting costs and benefits occurring in the future.  

The  approach  of  PRIMES  never  leads  to  negative  costs  of  clean  energy  investments  just 
because the private discount rates account for the imperfections. Hence, to enable transitions 
which do not happen in a business-as-usual scenario, policies have to apply to offset the effect 
of  the  imperfections  or  to  remove  the  imperfections,  when  possible,  as  a  minimum  step 
towards enabling transitions. 

Capital-budgeting  decisions  are  simulated  by  the  PRIMES  model  in  all  sectors,  both  in 
demand and supply of energy. The simulation mimics the appraisal undertaken by a decision-
maker of whether purchasing of equipment or investing in energy savings or infrastructure is 
worth the funding.  

The decision involves comparison among alternative options, e.g. technologies, which have 
different  proportions  of  upfront  costs  and  variable  operating  expenditures  (including  fuel 
costs). As the cost structure, in terms of CAPEX and OPEX, differ across the various options, 
the  decision  maker  has  to  do  arbitration  over  time.  Therefore,  the  decision  maker’s  time 
preferences (their discount factor) influences their choices. The time preference is inherently 
subjective  and  the  decision  maker  appraises  whether  the  upfront  spending  is  worth  the 
funding, compared to other options of using the funds, while taking into account uncertainty 
surrounding the investment options and the scarcity of funding.  

Therefore the value of the discount factor is influenced by many factors, such as the interest 
rates prevailing in capital markets, the degree of access to such markets for fund raising, and 
mostly by the value that the actor associates to own funding resources, such as equity capital 
or savings of individuals.  

139  Sartori,  Davide;  Catalano,  Gelsomina;  Genco,  Mario;  Pancotti,  Chiara;  Sirtori,  Emanuela;  Vignetti,  Silvia;  Del  Bo,  Chiara 
(2015).  “Guide  to  Cost-Benefit  Analysis  of  Investment  Projects.  Economic  appraisal  tool  for  Cohesion  Policy  2014-2020.” 
European Commission (EC), DG REGIO. Luxembourg Publication Office. 

160 

 
                                                 
Therefore  private  discount  factors  can  be  defined  as  reflecting  opportunity  costs  of  raising 
funds by the actor on a private basis. Obviously, the opportunity costs of raising funds differ 
by sector and by type of actor, being very different by income class. They also vary with the 
degree  of  risk  associated  to  the  decision  options.  In  contrast,  social  discount  rates140  are 
defined as opportunity costs of raising funds by the state or the society; in this sense social 
discount rates are defined following a different perspective than private ones. 

In  addition,  the  value  of  discount  factors  may  be  influenced  by  policies  when  for  example 
actors  use  high  discount  rates  due  to  market  distortions  and  non-market  barriers.  Many 
examples of policies influencing discount rates can be conceived in sectors such as energy 
efficiency, renewables and even nuclear or CCS investment.  

The  state  may  apply  support  schemes  to  mitigate  risks  and  reduce  the  individual  discount 
rates,  such  as  feed-in-tariffs  (FIT),  contracts  for  differences  (CfD),  power  purchase 
agreements  (PPA),  sovereign  guarantees  on  investment,  reduced  taxation,  subsidies  on 
interest rates, and generally innovative financing mechanisms. Policies may also transfer risk 
hedging from individuals to institutions, the latter being able to manage risk collectively and 
thus  more  efficiently;  examples  are  the  energy  service  companies  (ESCO),  the  policies 
obliging utilities to save energy at the premises of their customers, the loans by development 
banks,  etc.  All  these  policies  are  modelled  in  PRIMES  as  reductions  of  individual  discount 
factors. 

Summary of the modelling of capital budgeting decisions in PRIMES 

An  investment  choice  always  involve  upfront  costs  and  variable-operating  expenditures  or 
revenues which take place over time (e.g. annually). The decision is based on a comparison 
of different investment options.  

The  PRIMES  model  uses  different  capital  budgeting  methods  in  the  various  sub-models. 
Examples are presented below. 

In the standard version of the power sector model, the choice of power capacity expansion 
investment options is based on comparison of equivalent annuity costs (EAC). This is included 
in  an  inter-temporal  minimization  of  costs  which  guide  investment  choices  within  stylised 
generator portfolios. In the model version which represents market imperfections version (not 
used in the Reference scenario context), expected Net Present Value of investment (NPV), 
which  include  risk  aversion  factors,  is  calculated  for  each  capacity  expansion  option  so  as 
either to invest by selecting among the options or to decide not to invest at all. 

In  the  sub-model  which  calculates  investment  based  on  feed-in  tariffs  or  on  contracts  for 
differences  (CfDs)  the  model  uses  a  method  based  on  Internal  Rate  of  Return  (IRR) 
calculation by type of investment project from which it derives the probability of investment 
implementation. Instead of assuming a single threshold value for acceptable IRR, the model 
uses a frequency distribution of threshold values depending on the IRRs in order to capture 
heterogeneity of actors and different investment circumstances. 

In  the  sub-models  which  calculate  tariffs  for  using  infrastructure  subject  to  regulation  as  a 
natural  monopoly  (power  grids,  gas  network,  recharging  infrastructure  for  vehicles,  etc.), 
PRIMES follows the NPV method and uses the regulated rate of return as discount factor. 

In  the  sub-models  which  include  investment  options  for  energy  savings  (e.g.  insulation  of 
buildings, control systems in industry, etc.) PRIMES calculates equivalent annuity costs of the 
energy  saving  investment  and  compares  annual  capital  costs  to  economised  annual 
expenditures due to lower energy consumption. The model calculates a payback period which 
is considered in relation to a frequency distribution of threshold values reflecting heterogeneity 
of consumers and installations.  

140 If social discount rates are used in simulations of private investment decisions, the modeller implicitly assumes that the 
economy has no funding scarcity and perfect capital markets allow unlimited liquidity. 

161 

 
                                                 
In the demand sub-models which include technology choice by type of equipment or vehicle, 
the  formulations  calculate  equivalent  annuity  costs  for  each  option  and  also  formulate  a 
frequency  distribution  of  technology  choices  based  on  relative  EACs  so  as  to  reflect 
heterogeneity of consumers. 

Methodology for defining values of discount rates 

The model follows different approaches by sector: 

A.  Decisions by firms generally follow the approach of the weighted average cost 

of capital (WACC) to define discount rates.  

The WACC expresses the unit cost of capital for a firm depending on the source of funding, 
with  each  type  of  source  using  a  different  interest/discount  rate.  The  main  distinction  is 
between  equity  capital  (𝐸)  and  borrowed  capital  (𝐷).  The  former  is  valued  at  a  subjective 
discount rate 𝑟𝑒 and the latter at a market-based lending rate 𝑟𝑑. A simple WACC formula is 
as follows: 

𝑊𝐴𝐶𝐶 =

𝐸
𝐸 + 𝐷

𝑟𝑒 +

𝐷
𝐸 + 𝐷

𝑟𝑑 

To  determine  the  discount  rate  on  equity  the  model  follows  the  methodology  of  the  capital 
asset pricing method (CAPM) which is: 

𝑅𝑒 = 𝑅𝑓 + 𝛽 ∙ (𝑅𝑚 − 𝑅𝑓)  ⟺  𝛽 =

𝑅𝑒 − 𝑅𝑓
𝑅𝑚 − 𝑅𝑓

In the above formula, 𝑅𝑓 is the risk-free interest rate, 𝑅𝑚 is the benchmark or specific market 
rate of return on capital (expressing the usual practice of the sector) and 𝛽 is a subjective ratio 
expressing risk premium of equity relative to risk free options over the usual risk premium of 
the  sector  expressed  by  the  difference  of  the  market  specific  rate  and  the  risk-free  rate. 
Obviously 𝛽 > 1 indicates a risk averse behaviour which implies high WACC values compared 
to risk prone behaviours using 𝛽 < 1. Technology- or project-specific risk premium values can 
also be reflected by using a value of 𝛽 higher than one.  

An alternative formulation for estimating the unit capital cost of equity (COE) is to decompose 
𝑅𝑒 as follows: 

𝐶𝑂𝐸  = 𝑅𝑒 =   𝑅𝑓   +  𝐸𝑅𝑃  +  𝑆𝑃  +  𝐼𝑅𝑃 +  𝐶𝑆𝑅𝑃 

In the above 𝑅𝑓 is the risk-free rate, ERP the equity risk premium, SP the size risk premium, 
IRP the industry risk premium and CSRP the company-specific risk premium.  

Surveys  of  equity  costs  for  various  firms  indicate  that  the  values  used  in  practice  differ  by 
country and over time reflecting country-specific and risks specific to economic context. The 
equity  costs  depend  on  the  sectorial  and  general  economic  context  rather  than  on  the 
conditions  of  drawing funds from the  banking  system. The  lending  conditions  influence  the 
capitalization ratio. The surveys of WACC (cost of capital) over firms generally confirm that 
capital intensive sectors generally use lower capital cost rates than labour-intensive sectors. 
The capital cost rates are higher in small scale businesses compared to large scale ones and 
they are higher in technologically emerging sectors or applications. The capital cost rates are 
lower  for  firms  holding  dominant  positions  in  markets  or  when  they  are  state-owned  or 
supported by the state (e.g. utilities, public transport), compared to firms operating in market 
competition conditions. Based on these considerations, the PRIMES model applies different 
WACC rates by business sector, by type of technology (mature versus emerging), by scale 
level (e.g. industrial or decentralised versus utility scale) and for regulated companies. In the 
PRIMES model, the cost of capital rates apply for energy-related investment in the industrial 
and services sectors. Therefore, additional considerations specific to energy consumption are 
necessary to determine cost of capital rates for these sectors in the PRIMES model. For other 
sectors represented in PRIMES, such as energy supply, power generation, grids, transport 
sectors, the cost of capital rates refer to the entire investment of the sector. 

162 

 
 
B.  Decisions  by  individuals  using  a  subjective  discount  rate  to  annualize 

investment (upfront) costs following the equivalent annuity cost method.  

Literature collected  as  part  of  PRIMES  modelling research has  shown numerous statistical 
surveys which estimate the subjective discount rate that individuals implicitly use when making 
a  choice  between  equipment  varieties  having  different  upfront  costs  and  different  variable 
operating costs.  

A pioneering research141, back in the ‘70s, has used a large sample of data based on surveys 
of purchasing of air-conditioning systems by individuals; the sample included a variety of air 
conditioning types with different purchasing costs and different energy efficiency rates. Using 
the sample, the author econometrically estimated the median value of the discount rate that 
implicitly individuals use to make their choices. He founds a median value between 24 and 
26% for the discount rate and points out to the fact that this value substantially exceeds values 
used in engineering calculations to determine the so called life-cycle costs for evaluating the 
trade-off between energy efficiency and higher initial capital costs.  

Figure 118: Illustration of dependence of individual discount rates on income 

The low rates used in engineering calculations suffer from two shortcomings: from a positive 
standpoint  they  are  too  low  to  forecast  accurately  consumer  behaviour  and  thus  can  be 
misleading for policy making purposes, while from a normative standpoint they are too low to 
suggest  how  individuals  should  make  their  choice  of  equipment.  The  lower  bound  of  the 
individual discount rate (within the confidence interval based on the sample population) was 
found equal to 15%, which is also much higher than values used in engineering calculations. 
The author compares the estimated values to the interest rate of 18% applied on credit cards 
at that time and finds logical that individuals value cash scarcity (opportunity costs of raising 
funding from a private perspective) at a rate above the rate prevailing in the credit market.  

From a public policy perspective, one may see the difference between the individual and the 
social discount rates as a non-price market barrier, a sort of market imperfection. Therefore, 
in circumstances with strong barriers, policies based on efficiency standards and labelling are 
better  placed  to  incite  energy-efficient  choice  of  appliances  than  pure  price-based  policies, 
precisely because of offsetting factors causing high individual discount rates.  

The  results  of  econometric  estimations  published  in  the  literature  suggest  that  the  implicit 
discount rate is inversely strongly correlated with income and can be as low as 3.6% (i.e. close 
to market interest rates) for high income classes. But it can well be a two digit number (i.e. 
much above market interest rates) for low and medium-to-low income classes. 

141 Jerry A. Hausman, “Individual discount rates and the purchase and utilization of energy-using durables”, The Bell Journal of 
Economics (Vol. 10, No 1, spring issue), 1979. 

163 

y = 107.33x-1.515R² = 0.9422020406080100120<6000 $6000 -10000$10000 -15000$15000 -25000$25000 -35000$>35000$Discount rates (%)Individual discount rates as function of income class0%5%10%15%20%25%30%FrequenciesIndividual discount rates 
 
                                                 
Economic theory  suggests  that  discount  rates  should decrease as income rises,  even  with 
perfect capital markets, since the marginal income tax rate rises with income and the gains 
from using efficient appliances are untaxed.  

A histogram of individual discount rates depending on income level is shown in Figure 118. 
The median value of the discount rates is 24% and the income elasticity is -1.5, which indicate 
a remarkably high increase of the discount rate for low income percentiles. 

The differentiation of discount rates has been confirmed by numerous studies and publications 
surveying  purchasing  behaviours  for  a  large variety  of  equipment types. To illustrate these 
findings, many authors proposed terms such as “energy efficiency gap” or “energy efficiency 
paradox”  to  describe  the  implications  of  using  high  individual  discount  rates  rather  than 
engineering-oriented or social ones.  

Kenneth Train142, as well as Sanstad, Blumstein and Stoft143 summarised the findings of many 
surveys  of  the  ‘80s  and  ‘90s  of  consumer  behaviour  for  a  large  number  of  equipment.  All 
surveys confirmed the strong inverse correlation of individual discount rates and income. The 
estimations confirmed the large variation of individual discount rates mainly as inverse function 
of income per household:  

  14% - 56% for heating equipment 

  5%-90% for cooling equipment 

  5%-30% for automobiles 

  4%-88% for insulation of houses 

  15%-45% for double glazing and other similar measures in buildings  

  15%-62% for cooking and water heating equipment 

  4%-51% for boilers (difference with heating equipment, see first bullet)  

  35%-100% for refrigerators and  

  20%-40% for small black appliances.  

A statistical estimation144 for the implicit discount rates used in vehicle choices, specifically for 
energy savings, shows a median value, estimated for a US sample,  of 21% (with standard 
deviation  6.5  percentage  points).  The  median  value  differentiates  by  income  class,  the 
maximum difference being 4 percentage points. There is significant uncertainty regarding the 
discount factor for car choices. The same author proposes discount factors between 16% and 
18% for car choices when using a different econometric estimation methodology. 

Various surveys145 also revealed that beside income, which is the main explanatory factor of 
variance  of  discount  rates,  the  range  is  also  influenced  by  the  age  of  the  persons  and  the 
ownership of the property.  

142  Kenneth Train, “Discount rates in consumers’ energy-related decisions: a review of the literature”, Energy, Vol. 10, No 12, 
pp. 1243-1253, 1985
143  Sanstad, Blumstein and Stoft, “How high are option values in energy-efficiency investment?” Energy Policy, Vol. 23, Mo 9, 
pp. 739-743, 1995 
144  Daziano  Ricardo  (2015)  “Inference  on  mode  preferences,  vehicle  purchases,  and  the  energy  paradox  using  a  Bayesian 
structural choice model”, Transportation Research Part B Methodological. June, Vol. 76, pp. 1-26 
145 The following references include data from surveys and econometric estimations of individual discount rates:  

- 

- 

- 

Dale,  Larry  and  K.  Sydny  Fujita  (2008)  “An  Analysis  of  the  Price  Elasticity  of  Demand  for  Household  Appliances”, 
Energy Analysis Department, Environmental Energy Technologies Division, Lawrence Berkeley National Laboratory, 
University of California ,Berkeley, CA 94720, February 2008. 
Dubin, Jeffrey A. (1992) “Market Barriers to Conservation: Are Implicit Discount Rates Too High?”, No 802, Working 
Papers from California Institute of Technology, Division of the Humanities and Social Science. 
Ekins Paul, Fabian Kesicki and Andrew Z.P. Smith (2011). “Marginal Abatement Cost Curves: A call for caution”. A 
report from the UCL energy Institute to Greenpeace, UK 

164 

 
                                                 
 
A similar approach is based on the concept of hurdle rates which express the minimum rate 
of return on  a  project  or  investment  required  by the  decision maker to  compensate for  risk 
associated to future gains. Several econometric studies based on surveys provided evidence 
that hurdle rates effectively used by individuals and small firms to make investment decisions 
on energy efficiency are set at levels much above interest rates considered by large firms for 
equity capital in the context of capital asset pricing methods.  

A  survey  carried  out  by  Ameli  and  Brandt  (2014)146  for  the  OECD  followed  by  a  literature 
survey147  confirms  that  “behavioural”  discount  rates  explain  the  underinvestment  in  clean 
energy  technologies  and  that  the  probability  of  investing  in  an  energy  efficiency  project 
significantly decreases for low income classes (estimated from a large sample146). This finding 
supports the idea that one of the main factors explaining the high behavioural discount factors 
is the perception of opportunity costs of raising funding, which obviously differ by income class. 

Figure 119: Probability of investing in energy efficiency as a function of income (x axis) - illustration 

This  is  further  confirmed  by  a  more  general  purpose  statistical  analysis  (Harrison  et  al., 
2002)148, which finds a strong negative correlation of individual discount rates and personal 
income. The income dimension is found to provide the highest correlation with discount rates 

- 

- 

- 

- 

- 

- 

- 

Gately,  Dermot  (1980)  “Individual  Discount  Rates  and  the  Purchase  and  Utilization  of  Energy-Using  Durables: 
Comment” The Bell Journal of Economics 11(1), spring: 499-523 
Harrison, Glenn W., Morten I. Lau, Melonie B. Williams (2002) “Estimating Individual Discount Rates in Denmark: A 
Field Experiment”, The American Economic Review, Vol. 92, No. 5 (Dec., 2002), pp. 1606-1617. 
Hausman, J.A. and Joskow, P.L. (1982) “Evaluating the costs and benefits of appliance efficiency standards”, American 
Economic Review 72: 220-225. 
Hausman, Jerry A. (1979) “Individual Discount Rates and the Purchase and Utilization of Energy-Using Durables”, The 
Bell Journal of Economics, Vol. 10, No. 1 (Spring, 1979), pp. 33-54. 
Houston Douglas A. (1983),”Implicit discount rates and the purchase of untried, energy-saving durable goods”, Journal 
of Consumer Research, Vol. 10, September 1983. 
Hughes, P. J., and J. A. Shonder (1998) “The Evaluation of a 4000-Home Geothermal Heat Pump Retrofit at Fort Polk”, 
Louisiana: Final Report. Oak Ridge, TN: Oak Ridge National Laboratory. ORNL/CON-460. March. 
Khawaja, Muhannad, Thomas Potiowsky, and H. Gil Peach (1990) “Cost-Effectiveness of Conservation Programs: The 
Hood River Experiment”, Contemporary Policy Issues, Vol. VIII, July 1990. 

-  Meier, Alan, and J. Whittier (1983) “Consumer Discount rates Implied by Purchases of Energy-Efficient Refrigerators” 

- 

- 

Energy. Vol. 8, no. 12, pp. 957-962. 
Pollitt, Hector; Barker, Anthony; Barton, Jennifer; Pirgmaier, Elke; Polzin, Christine; Lutter, Stephan et al. (2010): “A 
Scoping  Study  on  the  Macroeconomic  View  of  Sustainability”.  Final  report  for  the  European  Commission,  DG 
Environment. Edited by Sustainable Europe Research Institute (SERI), Cambridge Econometrics (CE). Cambridge. 
Train, Kenneth (1985) "Discount Rates in Consumers' Energy-Related Decisions: A Review of the Literature", Energy. 
vol. 10, no. 12. pp. 1243-12. 

146 Ameli, Nadia; Brandt, Nicola (2014) “Determinants of Households’ Investment in Energy Efficiency and Renewable: Evidence 
from the OECD Survey on Household Environmental Behavior and Attitudes”, OECD, Working Paper 1165 
147 Ameli, Nadia; Brandt, Nicola (2015) “What Impedes Households’ Investment in Energy Efficiency and Renewable Energy?”, 
OECD, Working Paper 1222 
148  Harrison,  Glenn W.,  Morten I.  Lau,  Melonie  B. Williams  (2002)  “Estimating Individual  Discount  Rates in  Denmark: A  Field 
Experiment”, The American Economic Review, Vol. 92, No. 5 (Dec., 2002), pp. 1606-1617 

165 

 
 
 
                                                 
than any other explanatory factor, such gender, age, education, etc. Another statistical survey 
(Newell, 2015)149 also finds strong inverse correlation of individual discount rates and income. 

An extensive literature surveyed (Mundaca et al., 2010)150 shows that households use high 
implicit  discount  rates  (50  or  even  200%)  also  because  of  imperfections,  such  as  lack  of 
information,  uncertainties  lack  of  sufficient  funding,  agency  costs,  transaction  and  hidden 
costs. The literature (Ameli et al., 2015)147 proposes to associate the imperfections or barriers 
with  specificities  of  energy-efficient  investments.  Longer  payback  periods  and  greater  risks 
and uncertainties imply higher subjective discount rates. According to the reviewed literature, 
the typology of possible causes can be summarised as follows:  

 

 

lack of information about cost and benefits of efficiency improvements 

lack of knowledge about how to use available information 

  uncertainties about the technical performance of investments 

 

 

lack of sufficient capital to purchase more expensive but efficient products (or capital 
market imperfections) 

income level and consequently savings resources; high transaction costs for obtaining 
reliable information 

  hidden costs, for example related to comfort, side payments and possibly temporary 

relocation,  

 

risk averse attitudes associated with possible financial failure of the investment 

  ownership status versus user status. 

This  justifies  the  practice of several  economic models,  including  PRIMES,  which mimic the 
effects  of  policy  instruments,  mainly  campaigns  and  labelling  programs,  by  using  lowered 
discount rates when these policies are implemented.  

Modern behavioural economics propose models which deviate from classical microeconomics 
(e.g. bounded rationality model151, loss aversion model152) which are asserted to explain the 
persistence of high hurdle rates (equivalently discount rates) in choices for energy-efficiency 
investments,  with  initial  investments  being  given  asymmetrically  greater  weight  than  future 
savings. 

But, despite the different explanatory approaches there is no doubt in the literature about the 
persistence of high hurdle and discount rates at levels much above engineering and social 
rates. Until today, there has been no statistical survey finding low hurdle or discount rates for 
individuals making selection of energy efficient investment or equipment.  

It is useful to clarify that several surveys of public policies funding energy efficiency find that 
in  practice  regulators  and  authorities  use  much  lower  discount  rates,  than  the  subjective 

149 Newell, Richard G. and Juha Siikamaki (2015). “Individual Time Preferences and Energy Efficiency”. NBER Working Paper 
No 20969 
150 Mundaca Luis, Lena Neiz, Ernst Worell and Michael McNeil (2010) “Evaluating energy efficiency policies with energy-economy 
models”, Ernest Orlando Lawrence Berkeley National Laboratory 
151 Bounded rationality is the idea in decision-making, rationality of individuals is limited by the information they have, the 
cognitive limitations of their minds and the finite time they have to make a decision. According to this theory, the decision maker 
is a satisfier, seeking a satisfactory solution rather than the optimal one. Nested decision making models, in which the first level 
nests refer to seemingly non-economic choices (e.g. colour, convenience, and modernity) imply biased selection of lower level 
nests, which involve economic considerations and thus the selection can deviate from economic optimality. 
152 In economics and decision theory, loss aversion refers to people's tendency to strongly prefer avoiding losses to acquiring 
gains. Most studies suggest that losses are twice as powerful, psychologically, as gains. This point of view can be represented 
also by classical microeconomic theory by assuming strong risk aversion. 

166 

 
                                                 
ones153,154,155. The difference is that in these cases the discount rates are used to calculate 
whether or not is it worth to allocate public money as a support to an energy efficiency project 
(example  house  refurbishment).  This  is  reasonable  from  a  public  perspective,  because  as 
appropriate discount rates close to social rates must be used for spending public money, to 
reflect  opportunity  costs  of  drawing  funds  by  the  public.  This  is  a  different  aim  than  in  the 
modelling which has the objective of simulating individual behaviours, in order to identify the 
size of incentives (such as prices or taxes) for increasing energy efficiency. To  perform this 
simulation accurately, the model has to reflect the opportunity costs of drawing funds from a 
private  perspective,  which  implies  using  subjective  discount  rates  higher  than  social  ones. 
This is also the conclusion of Harrison (2010)156 which on behalf of the Australian government 
suggests a method for cost benefit analyses. 

All these arguments advocate in favour of maintaining high values of discount and hurdle rates 
for individuals in the PRIMES modelling. The use of low discount rates, based on lending rates 
or social discount rates, has been criticized in the surveyed literature, which points out that 
transaction  and  hidden  costs  exist  in  reality,  as  for  example  for  retrofit  investments  being 
illiquid  and  risky  in  most  cases.  An  ECOFYS  survey  report157  mentions  that  “The  default 
subjective discount rates used in PRIMES for mimicking decision behaviour lie within the huge 
range of what literature provides”.  

A quite similar approach is followed by the NEMS model in the US DOE/EIA as recommended 
by Sanstad and McMahon (2008)158. The approach of NEMS is also evaluated by Mundaca 
and Neij (2010)159 confirming the relevance of using high implicit discount rates for modelling 
households’ decisions. 

C.  Discount factors used to evaluate tariffs of using infrastructure regulated as a 

natural monopoly. 

The  model160  uses  discount  rates  based  on  surveys  of  actually  applied  regulated  rates  of 
return  by  state  and  regulatory  agencies  in  various  countries  and  for  different  types  of 
infrastructure.  The  surveys  indicate  that  the  regulated  rates  of  return  on  assets  of  natural 
monopolies  are  set  significantly  above  social  discount  rates  and  are  based  on  the  WACC 
method. The main difference from private practices is that the state agencies or regulators do 
not accept high risk premium factors on equity capital, in contrast to private practices. This is 
justified  on  the  basis  that  the  natural  monopoly  business  has  by  definition  lower  risks 
compared to business subject to competition. 

153  Geller,  Howard;  Attali,  Sophie  (2005).  “The  Experience  with  Energy  Efficiency  Policies  and  Programs  in  IEA  Countries. 
Learning from the Critics.” IEA Information Paper. IEA. 
154 Lazar, Jim; Colburn, Ken (2013) “Recognizing the Full Value of Energy Efficiency”. Regulatory Assistance Project Publications 
155  Woolf,  T.,  Steinhurst,  W.,  Malone,  E.,  and  Takahashi,  K.  (2012).  “Energy  Efficiency  Cost-Effectiveness  Screening”.  The 
Regulatory Assistance Project publications. 
156 Harrison Mark (2010), “Valuing the Future: the social discount rate in cost-benefit analysis”, Commonwealth of Australia, The 
Productivity Commission, ISBN 978-1-74037-313-5 
157  Hermelink  Andreas  and  David  de  Jager  (2015)  “Evaluating  our  future:  the  crucial  role  of  discount  rates  in  European 
Commission energy system modelling”, ECOFYS, final report for Project no BUIDE16021 commissioned by ECEEE. 
158 “Aspects of Consumers’ and Firms’ Energy Decision-Making: A Review and Recommendations for the National Energy 
Modelling System (NEMS)”, Lawrence Berkeley National Laboratory, April, 2008
159  Mundaca,  L.,  &  Neij,  L.  (2010).  A  Meta-Analysis  of  Bottom-Up  Ex-Ante  Energy  Efficiency  Policy  Evaluation  Studies.  In 
International Energy Program Evaluation Conferences International Energy Program Evaluation. 
160 The tariffs of using infrastructure are calculated using the following formula: 

𝑇
𝑅𝐴𝐵 + ∑
𝑡=1

𝑃 =

𝑇
∑
𝑡=1

𝐶𝑡
(1 + 𝑑 ± 𝑟)𝑡
𝐷𝑡
(1 + 𝑑 ± 𝑟)𝑡

RAB is the regulated asset basis (roughly the cumulative cost of investment), 𝐶𝑡 are the annual operating variable and fixed costs, 
𝐷𝑡 denotes the expected future use of the infrastructure (measured as a volume indicator), 𝑇 is the time horizon, 𝑑 is the regulated 
discount  rate  expressing  the  allowed  rate  of  return  on  capital  and  𝑟  expresses  either  a  discount  on  return  on  capital  (if  it  is 
deduced) targeted by the regulator or a bonus (when it is added) used as an incentive for technology or coverage improvement. 

167 

 
                                                 
 
 
Values of discount rates used in the model 

Discount rates for investment decisions in power generation 

To determine discount rate values reflecting reality one has to start from a risk-free (or low 
risk) discount rate. According to business surveys, the common practice in industry is to take 
a value of 4%-5%.  

Business surveys indicate that equity risk premium (which is added on top of risk free discount 
rate) is usually defined at 6-9% plus a country- or project-specific risk which can vary between 
-1% up to 6%. Assuming a capitalization structure consisting of 65% borrowed funds at 5.5% 
interest rate and 35% equity capital valued at 9% cost of equity rate (large, capital intensive 
business), the minimum level of WACC would be: 

𝑊𝐴𝐶𝐶 
=  65% ∙ 5.5% (𝑑𝑒𝑏𝑡)
+ 35% ∙ (4% + 2.5% + 2.5% + 2%) (𝑒𝑞𝑢𝑖𝑡𝑦)
=  7.5% 

Where 4% is the risk-free rate, 2.5% the equity risk premium, 2.5% the industry risk premium 
and 2% the company-specific risk premium. 

The minimum WACC is used in the model as a proxy of the rate of return a regulator would 
agree  to  award  to  regulated  natural  monopoly  infrastructures.  This  value  corresponds  to 
common practice of regulators in Europe and in the USA (it is verified that in practice regulated 
rates of return on capital vary between 7% and 8%). In the model it applies to infrastructure 
for calculating tariffs of service. 

Large energy utilities operating in competitive markets would add 1-2 percentage points as a 
company-specific  risk  premium  and  small  or  medium  size  companies  would  add  1-3 
percentage points as a size-related risk premium. Therefore, the WACC ranges between 8% 
and 12% for power sector generation and trade companies operating in competitive markets. 
Adding country- or project- specific risk premiums would make the WACC vary between 8 and 
at least 18%. Relevant surveys can be seen in AFP (2011)161 and Fernandez et al. (2011)162, 
among others. 

The basic discount rate in competitive power, gas, coal and gas markets used in the model is 
8.5% based on the WACC calculation shown below: 

𝑊𝐴𝐶𝐶 
=  65% ∙ 5.5% (𝑑𝑒𝑏𝑡)
+ 35% ∙ (4% + 3.5% + 3.5% + 3%) (𝑒𝑞𝑢𝑖𝑡𝑦)
=  8.5% 

Where 4% is the risk-free rate, 3.5% the equity risk premium, 3.5% the industry risk premium 
and  3%  the  company-specific  risk  premium.  The  cost  of  equity  rate  is  assumed  14%  for 
companies  exposed  to  competition  and  11%  for  companies  protected  as  regulated 
monopolies. 

161  AFP  (2011),  “Current  Trends  in  Estimating  and  Applying  the  Cost  of  Capital”,  Report  of  Survey  Results,  Association  for 
Financial Professionals, www.AFPonline.org 
162 Fernandez Pablo, Javier Aguirreamalloa and Luis Corres (2011), “Market risk premium used in 56 countries in 2011 : A survey 
with 6,014 answers», IESE Business School, University of Navarra 

168 

 
 
 
                                                 
Table 12: Discount rates in energy supply sectors 

Assumptions for EU Reference Scenario 2020  

Regulated monopolies and grids 

Companies in competitive energy supply markets 

RES investment under feed-in-tariff 

Investment under contract for differences 

RES  investment  under  feed-in  premium,  RES  obligation,  quota  systems  with 
certificates 

RES investment in competitive markets 

Discount 
rates 

7.5% 

8.5% 

7.5% 

7.5% 

8.5% 

8.5% 

Risk premium specific to immature or less accepted technologies 

1%-3% 

Risk  premium  specific  to  investment  surrounded  by  high  regulatory  or  political 
uncertainty 

Country-specific risk premiums 

None 

None 

Power purchase agreements (PPA) has been applied since many years as a way of supporting 
generation investment. Other forms of PPA are the feed-in-tariff systems applied to support 
investment in renewables and the Contracts for Differences which can be concluded between 
private entities or with the state. The feature of these support schemes guaranteeing stream 
of revenues for the investor implies lowering risk premium factors. They also ease collecting 
funding  and  thus  borrowing  interest  rates  are  also  lower  than  without  revenue  guarantee. 
Therefore power projects supported by feed-in tariffs or CfD are considered in the model less 
risky than investment in competitive markets and the starting level of the WACC is 7.5%.  

A WACC applied to an investment project where upfront investment expenditures is recovered 
by a stream of annual revenues (as in the case of RES support schemes) can be also seen 
as the hurdle rate, i.e. the minimum IRR rendering investment financially feasible. The hurdle 
rate reflects the perspective of the investor and obviously includes risk premium factors as the 
WACC does. 

Country-specific  risk  premium  are  considered  in  business  practices  to  reflect  regulatory 
uncertainty,  revenue  risks  or  monetary  uncertainties,  which  are  specific  by  country.  It  is 
reported that for countries experiencing deficits in renewables accounts and having practiced 
retrospective changes in FIT contracts, the country risk premium can be 5-6% (as add-on) and 
so minimum IRR becomes in these cases close to 15%. By nature country-specific risks are 
short-term views of uncertainties and are less practised for long-term planning of investment. 

Other  renewable  support  schemes  may  involve  higher  uncertainty  about  future  stream  of 
revenues.  Feed-in-premium  schemes  depend  on  price  volatility  in  wholesale  markets  and 
therefore  1-3  percentage  points  of  risk  premium  are  added  following  common  practice. 
Similarly, renewable policies applying RES obligations on load serving entities or the quota 
systems with certificates imply higher risk premium, than feed-in-tariffs, as investors’ revenues 
will depend on procurement conditions depending on private entities (the load serving entities) 
or on volatile certificate prices. We consider adding 1-3 percentage points as risk premium. 

169 

 
Compared to an IRR of 7.5% assumed for RES investment covered by guaranteed stream 
revenues,  the  model  assumes  an  IRR  of  8.5%  for  RES  investment  supported  by  feed-in-
premium, RES obligations or quota systems with certificates. Similarly, the model applies an 
IRR of 8.5% for RES investment without financial support. 

Investments  in  power  projects  covered  by  contracts  for  differences  (e.g.  Hinckley  nuclear 
project in the UK and investment in renewables also based on CfD) theoretically enjoy similar 
certainty as RES projects under feed-in tariffs. Auctioning to determine the level of feed-in-
tariffs or of CfD do not alter the guarantee of revenues that enjoy feed-in-tariffs and CfD in 
which the price level is defined administratively. 

Project-specific risk premium is a common practice for immature renewables and for projects 
subject  to  uncertain  social  acceptance  (or  surrounded  by  high  political  or  regulatory 
uncertainty).  The  hurdle  rate  of  investment  in  yet  immature  RES  is  increased  by  1-3 
percentage points above the rates used for mature RES. Of course, the addition applies as 
long the immaturity persists.  

Although practiced in reality, the model does not assume additional risk premium for project 
surrounded by high regulatory or political uncertainty, such as nuclear or CCS.  

The model does not apply country-specific risk premiums. This is justified on the basis that 
the aim of the modelling is to project long term market trends and thus it ignores short term 
financial instabilities that would suggest country risk premiums in the EU different from zero. 

170 

 
 
 
Table 12 summarises the discount rate values used in business in the energy supply sectors of 
PRIMES for the Reference Scenario. 

Discount rates for energy-related investment decisions by non-energy firms 

The  WACC  for  industry  and  services  is  used  only  for  energy-related  investment  in  these 
sectors, and not for general productive investment, which is out of the scope of the PRIMES 
model.  

For energy-related investment of energy-intensive industries the model applies the minimum 
level of WACC, equal to 7.5%.  

The reason is that energy costs are a very significant component in energy intensive industries 
and therefore these industries pay attention to select the most cost-efficient investments. For 
this reason the model does not apply risk premium factors related to market competition. 

For other industries, which are not energy-intensive, the model applies a WACC of 9%, which 
is  equal  to  the  rate  assumed  for  all  purpose  investment  in  these  sectors.  The  non-
differentiation of WACC rates by type of investment in these sectors is justified by the fact that 
energy costs represent a small share in total costs. 

In  the  services  sector  energy  costs  are  also  a  small  fraction  of  total  costs  and  therefore  a 
WACC  for  all  purposes  investment  applies.  Energy-related  investment  decisions  compare 
advanced efficient solutions, which have high upfront costs, to conventional ones. The former 
however are usually less known to the decision maker, who applies a risk premium because 
they perceives uncertainty concerning technical performance. To capture this, the model uses 
a default value of WACC equal to 11% for energy-related investment. 

Table 13: Discount rates of firms in energy demand sectors 

Assumptions for EU Reference Scenario 2020  

Discount rates 

Energy intensive industries 

Non energy intensive industries 

Services sectors 

Public transport (conventional rail, public road) 

Public transport (advanced technologies, e.g., high speed rail) 

Business transport sectors (aviation, heavy goods vehicles, LCVs, maritime) 

Country risks 

7.5% 

9% 

11% 

7.5% 

8.5% 

9.5% 

None 

For the business activities of the transport sector, the model applies the minimum WACC rate 
of 7.5% to the cases of regulated business, such as public road transport and conventional 
rail,  which  is  dominated  at  large  extent  by  state-owned  enterprises.  For  more  advanced 
transport technologies in public transport, such as high speed rail, the models uses a higher 
value of WACC, namely 8.5%, to reflect risk premium of investment in such technologies. The 
WACC  values  are  used  to  calculate  ticket  prices  in  the  public  transport  sectors  and  for 
investment decisions in vehicles or rolling stock. 

For the private business activities in transport, such as trucks, LCVs, aviation and maritime, 
the  model  uses  a  WACC  value  of  9.5%  which  is  within  the  range  uses  for  industrial  and 
services  sectors.  These  WACC  values  are  used  in  investment  decisions  for  new  vehicles, 

171 

 
aircrafts and vessels. For the choice of private cars and motorcycles, the model applies the 
discount rates of decisions by individuals, which are discusses in the next section. 

Table 13 summarises the discount rate values used in business sectors of energy demand sectors 
of PRIMES for the Reference Scenario. 

Discount rates for investment decisions by households 

The choice of discount rate values employed for investment decisions by households is based 
on  the  literature  reporting  empirical  statistical  findings  of  surveys  which  calculate  implicit 
discount rates used for energy efficient equipment choice and investment. When the implicit 
discount rates are specified by income class or other classification of consumers, a weighted 
average discount rate has been calculated.  

Table 14: Definition of discount rates of individuals in energy demand sectors  

Assumptions for EU Reference 
Scenario 2020  

Discount 
rates 

Modified discount rates due to 
EE policies163 

Passenger cars and powered two wheelers 

11% 

Households  for  renovation  of  houses  and  for 
heating equipment 

14.75% 

Households for choice of appliances 

13.5% 

12% 

9.5% 

By income class (for the decision on renovation and the choice of equipment) 

Low 

Low-Mid   

Mid   

Mid-High 

14.1% 

13.6% 

13.2% 

12.8% 

Based  on  the  literature,  the  discount  rate  values  differ  by  type  of  decision  and  type  of 
equipment. For instance, surveys have found lower implicit discount rate values for choice of 
cars than for housing equipment. Surveys have also identified that for heating systems and 
for  thermal  integrity  expenditures  specifically  for  new-built  houses  (i.e.  choices  undertaken 
when building the house) the individual discount rates are much lower than in similar choices 
when  renovating  existing  houses.  The  reason  is  that  it  is  more  uncertain  to  undertake 
refurbishment investment than incorporating efficient technologies in new houses taking also 
into account that the efficiency choices for new houses will last longer than for existing houses. 
For this reason the model applies lower discount rates (than the default values shown in the 
first column of) for new buildings concerning thermal integrity and heating systems. 

It  is  assumed  that  the  default  discount  rates  values  are  influenced  downwards  by  policies, 
which focus on barriers and imperfections considered among the causes explaining the initially 
high discount rate values. Such policies are included in the Reference Scenario; examples are 
the  energy  labelling  and  certain  measures  included  in  Energy  Efficiency  Directive  and  the 
promotion  of  energy  service  companies. They  increase awareness of  individuals  about  the 

163 It is assumed that standard discount rate values are pushed downwards by policies addressing the barriers which caused 
the high discount rate values in the first place. 

172 

 
                                                 
benefits of advanced efficient solutions. They also support involvement of large companies 
such  as  utilities  or  energy  service  companies to  leveraging  individual  choices,  thus  helping 
individuals  perceiving  lower  financial  and  technical  risks  in  the  undertaking  of  efficiency 
investment. Table 14 indicates in separate columns the discount rates used as default values 
and  the  discount  rates  used  when  representing  the  effects  of  policies  targeting  removal  of 
barriers obstructing rational energy efficiency choices. 

“Discount rate” for costs reporting 

Once having ran the model for a scenario, which means after simulating behaviours 
and market clearing which are using the discount rates shown in the previous section, 
the  PRIMES  model  calculates  total  energy  system  costs  for  reporting  purposes.  In 
other  words,  the  modelling  framework  includes  two  distinct  stages:  a)  a  first  stage 
models  decision-making  behaviour  of  agents,  hence  investment  and  technology 
choices; b) a second stage calculates total costs for the entire energy system in order 
to support comparisons across scenarios. 

This section discusses how the calculations are defined in the second stage and what 
discount rates to use in this context. In an energy system there are demanders and 
suppliers  of  energy.  For  energy  system  analysis  and  in  order  to  assess  the  cost 
impacts from  a macroeconomic  perspective,  the  crucial element  is  the  amount  that 
end  use  sectors  (households  and  firms,  in  services  and  industry,  transport  and 
agriculture) are required to pay in order to get the energy services they need. Energy 
services  are  defined  by  how  energy  is  used,  for  example,  if  the  energy  supports 
heating, cooling, entertainment, mobility and transportation, industrial production, i.e., 
uses that enable utility and activity for final energy consumers. Energy services are 
delivered by using energy commodities purchased by end-consumers, which depend 
on energy efficiency at the consumption level. The end-users undertake investment 
for purchasing equipment (e.g. boilers, vehicles, etc.), for insulating buildings and for 
installing energy saving systems.  

The  accounting  of  capital  costs  for  end-users  (CAPEX)  is  based  on  the  part  of 
investment  expenditure  for  equipment  purchasing  that  corresponds  to  energy 
purposes; for example, the additional cost of a highly efficient vehicle (on top of cost 
of a conventional vehicle) incurred for energy purposes. In addition, the final energy 
consumers  incur  annual  variable  and  fixed  costs  which  include  the  purchasing  of 
energy  commodities  from  energy  supplying  and  trading  sectors,  the  maintenance 
costs of equipment and other annual costs (e.g. assurance costs, vehicle taxes, etc.). 
These annual costs are operating expenditures (OPEX). Energy supply and trading 
sectors fully recover their total costs (CAPEX and OPEX) from revenues paid by end-
consumers.  Therefore  the  total  energy  system  cost  only  includes  the  CAPEX  and 
OPEX incurred by end-consumers, with their OPEX already incorporating the CAPEX 
and OPEX costs incurred by the supply and trading sectors.  

The PRIMES model determines the prices of supply and trading sectors in a manner 
that fully recovers total supply costs using the WACC that represents the real unit cost 
of  capital  experienced  by  a  firm  operating  in  energy  supply  sectors.  The  PRIMES 
report aggregates CAPEX and OPEX of end-consumers to show a single total cost 
figure  with  annual  periodicity.  To  do  this,  also  the  CAPEX  figures  related  to 
investments by final energy demand consumers need to be annualised following the 
equivalent  annuity  cost  method  which  involves  use  of  a  discount  factor  over  the 
lifespan of the investment. The annualised equivalent cost expresses the cost incurred 
for  the  end-consumer  for  owning  an  asset  until  the  end  of  its  lifetime.  As  such  it 
expresses the gradual accumulation of resources to be able to replace the asset as 

173 

 
the  present  value  of  the  annuity  payments  for  capital  is  by  definition  equal  to  the 
investment (upfront) expenditure.  

The  choice  of  discount  rate  for  the  CAPEX  cost  reporting  by  final  energy  demand 
consumers should reflect the perspective of the private investor faced with real world 
investment constraints.  

One  approach  could  be  to  base  the  cost  reporting  of  the  CAPEX  by  final  energy 
demand consumers on true payments for capital costs. This implies that the CAPEX 
has to be annualised using lending rates for the part of capital borrowed from banks 
and equity rates for the rest. It has the drawback that it does not reflect the fact that 
there are also opportunity costs associated with higher debt rates (i.e. risk averseness 
as  well  as  reduced  incentives  to  make  other  investments).  In  addition,  detailed 
information would need to be collected to identify the borrowing rates faced by different 
end-users. Furthermore, equity rates are subjective and therefore assumptions must 
be made about their values. Finally, policies may enable reduction of equity discount 
rates and if this differs by scenario, comparability of costs is lost across scenarios. In 
conclusion, comparability across the scenarios is of key importance and implies that 
the discount rates used in the cost accounting must not vary between scenarios.  

It is important also to keep in mind that borrowing costs have experienced important 
variations in the past. While they are currently historically low, their evolution over the 
time  horizon  of  the  Reference  Scenario  (up  to  2050)  is  impossible  to  anticipate.  In 
addition, comparison of system costs across projected periods would be very difficult 
if using a rate that would be evolving over time.  

Considering the various elements listed above, the Reference scenario accounts the 
costs associated with CAPEX for all investments, including for final energy demand 
consumers, over the projected period (2020-2050), using a  rate that is more in line 
with  the  WACC  traditionally  used  for  the  supply  and  industry  sector.  Hence,  as 
simplification  and  in  a  long  time  horizon  perspective,  a  flat  and  constant  over  time 
discount  rate  of  10%  is  used  for  annualising  CAPEX  of  end-consumers  in  the  cost 
reporting of the Reference Scenario. 

174 

 
 
 
Glossary  

Aviation: The EU Reference Scenario 2020 distinguishes aviation into intra-EU and extra-
EU aviation. Intra-EU aviation covers flights within and between Member States and extra-
EU covers flights between Member States and countries outside the EU.  

Biofuels: Biofuels include bioethanol, biodiesel, biokerosene and bio-heavy. 

Blast furnace: A tall, cylindrical smelting furnace for reducing iron ore to pig iron; the blast 
of  air  blown  through  solid  fuel  increases  the  combustion  rate.  In  the  new  reporting  of 
Eurostat energy balances, blast furnaces are included in the Energy Branch sector and not 
in the final energy demand of the Iron and Steel sector, implying that this consumption is no 
longer part of final energy demand. 

Carbon capture and storage (CCS): Carbon capture and geological storage is a technique 
for  trapping  carbon  dioxide  emitted  from  large  point  sources,  compressing  it,  and 
transporting it to a suitable storage site where it is injected into the ground. 

Carbon intensity: The amount of CO2 emitted per unit of energy consumed or produced (t 
of CO2/tons of oil equivalent (toe) or MWh).  

CO2 Emissions to GDP: The amount of CO2 emitted per unit of GDP (carbon intensity of 
GDP – tons of CO2/million Euro). 

Cogeneration thermal plant: A system using a common energy source to produce both 
electricity and steam for other uses, resulting in increased fuel efficiency (see also: CHP). 

Combined Cycle Gas Turbine plant (CCGT): A technology which combines gas turbines 
and steam turbines, connected to one or more electrical generators at the same plant. The 
gas turbine (usually fuelled by natural gas or oil) produces mechanical power, which drives 
the generator, and heat in the form of hot exhaust gases. These gases are fed to a boiler, 
where  steam  is  raised  at  pressure  to  drive  a  conventional  steam  turbine,  which  is  also 
connected to an electrical generator. This has the effect of producing additional electricity 
from the same fuel com-pared to an open cycle turbine. 

Combined  Heat  and Power (CHP): This  means  cogeneration  of  useful  heat  and  power 
(electricity) in a single process. In contrast to conventional power plants that convert only a 
limited part of the primary energy into electricity with the remaining energy being discharged 
as  waste  heat,  CHP  makes  use  of  a  greater  proportion  of  this  energy  for  e.g.,  industrial 
processes, district heating, and space heating. CHP therefore improves energy efficiency 
(see also: cogeneration thermal plant). 

Efficiency  for  thermal  electricity  production:  A  measure  of  the  efficiency  of  fuel 
conversion  into  electricity  and  useful  heat.  It  is  calculated  as  heat  and  electricity  output 
divided by the calorific value of input fuel.  

Efficiency  indicator  in  freight  transport  (activity  related):  Energy  efficiency  in  freight 
transport is calculated based on the energy use per ton-km. Given some methodological 
inconsistencies between transport and energy statistics, in some cases absolute numbers 
(especially at the level of individual Member States) might be misleading. For that reason, 
the numbers given are only illustrative of the trends in certain cases.  

Efficiency  indicator  in  passenger  transport  (activity  related):  Energy  efficiency  in 
passenger transport is calculated based the energy used per passenger-km travelled. The 
inconsistencies between transport and energy statistics also apply to passenger transport 
(see also: Efficiency indicator in freight transport).  

Effort Sharing Regulation (ESR): The Effort Sharing Regulation specifies that sectors of 
the  economy  not  covered  by  the  EU  ETS  (buildings,  transport  except  aviation  and 
international  shipping,    agriculture,  non-ETS  industry,  agriculture  except  LULUCF,  and 
waste) must reduce emissions by 30% by 2030 compared to 2005 and establishes binding 
annual greenhouse gas emission targets for each Member State for the period 2021–2030 
based on the principles of fairness, cost-effectiveness and environmental integrity.  

175 

 
Electric arc furnace: An electric arc furnace is a furnace that heats charged material by 
means of an electric arc. 

Energy  branch  consumption:  Energy  consumed  in  refineries,  electricity,  and  steam 
generation and in other transformation processes. 

Energy intensity: energy consumption/GDP or another indicator for economic activity.  

Energy intensive industries: Iron and steel, non-ferrous metals, chemicals, non-metallic 
minerals, and paper and pulp industries. 

Energy Service Company (ESCO): A company that implements a broad range of energy 
efficiency projects. 

EU  Emissions  Trading  System  (EU  ETS):  The  EU  ETS  works  on  a  “cap  and  trade” 
principle  and  covers:  carbon  dioxide  (CO2)  from  electricity  and  heat  generation,  energy-
intensive  industrial  sectors  including  oil  refineries,  steel  works,  and  production  of  iron, 
aluminium, metals, cement, lime, glass, ceramics, pulp, paper, cardboard, acids and bulk 
organic chemicals, commercial aviation within the European Economic Area; nitrous oxide 
(N2O)  from  production  of  nitric,  adipic  and  glyoxylic  acids  and  glyoxal;  perfluorocarbons 
(PFCs) from production of aluminium.  

Feed-in  tariff:  The  price  per  unit  (of  electricity)  that  an  eligible  renewable  electricity 
produced receives according to cost-based calculations for the specific resource used.  

Feed-in-premium: A pre-established premium on the market price of energy given to an 
eligible renewable electricity  producer. The  payment  of this  premium  is guaranteed for  a 
certain time and is linked to the economic life of the relevant RES project. 

Final energy demand: Energy consumed in transport (excluding international shipping and 
aviation), industrial, household, services, and agriculture sectors; the latter two sectors are 
sometimes  aggregated  and  named  "tertiary".  It  excludes  deliveries  to  the  energy 
transformation sector (e.g., power plants) and to the energy branch. It includes electricity 
consumption  in  the  above-mentioned  final  demand  sectors.  In  some  cases,  final  energy 
consumption is also reported including international aviation. This is specifically mentioned 
in the report, where the case.  

Freight  transport  activity:  Covers  goods  transport  by  road,  rail,  inland  waterways  and 
national maritime, unless specified otherwise in the report. Road transport activity is defined 
according to the territoriality principle, in line with the available statistics from Eurostat.  

Fuel  cells:  A  fuel  cell  is  an  electrochemical  energy  conversion  device  that  converts 
hydrogen  and  oxygen  into  electricity  and  heat  with  the  help  of  catalysts.  The  fuel  cell 
provides  a  direct  current  voltage  that  can  be  used  to  power  various  electrical  devices 
including motors. 

Fuel input to power generation: Fuel use in power plants and CHP plants.  

Gas: Includes natural gas, blast furnace gas, coke-oven gas, and gasworks gas. 

Generation  capacity:  The  maximum  rated  output  of  a  generator,  prime  mover,  or  other 
electric  power  production  equipment  under  specific  conditions  designated  by  the 
manufacturer.  

Geothermal plant: A plant in which the prime mover is a steam turbine, which is driven 
either by steam produced from naturally hot water or by natural steam that derives its energy 
from heat in rocks or fluids beneath the surface of the earth. The energy is extracted by 
drilling and/or pumping. 

Greenhouse  Gas  (GHG):  a  group  of  gases  contributing  to  global  warming  and  climate 
change. The Kyoto Protocol, an environmental agreement adopted by many of the parties 
to the  United  Nations  Framework  Convention  on  Climate  Change  (UNFCCC)  in  1997  to 
curb global warming, nowadays covers seven greenhouse gases: the non-fluorinated gases 
which include: carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O); the fluorinated 
gases:  hydrofluorocarbons  (HFCs),  perfluorocarbons  (PFCs),  sulphur  hexafluoride  (SF6) 
and nitrogen trifluoride (NF3). 

176 

 
Gross Inland Consumption: Quantity of energy consumed within the borders of a country. 
It is calculated as primary production (plus) recovered products (plus) imports (plus)/(minus) 
stock  changes  (minus)  exports  (minus)  bunkers  (i.e.,  quantities  supplied  to  international 
sea-shipping). 

Gross Inland Consumption/GDP: Energy intensity indicator calculated as the ratio of total 
energy consumption to GDP (toe/million Euro). 

Hydro power plant: A plant that produces energy from moving water. In this report, hydro 
excludes pumped storage plants that generate electricity during peak load periods by using 
water previously pumped into an elevated storage reservoir during off-peak periods when 
excess  generating  capacity  is  available.  Energy  losses  in  pumping  are  accounted  for 
separately.  

Indirect  land  use  change  (ILUC):  Where  land  destined  for  food  and  feed  markets  is 
diverted  to  biofuel  production,  the  need  to  produce  food  and  feedstock  will  have  to  be 
satisfied  either  through  the  intensification  of  current  production  or  by  transforming  non-
agricultural to agricultural land elsewhere. The latter constitutes indirect land-use change 
and can lead to significant greenhouse gas emissions when the converted land is of high 
carbon stock.  

International maritime: International maritime refers to the transport activity between EU 
Member States and between the EU and the rest of the world.  

Import dependency: Demonstrates the extent to which a country relies on imports to meet 
its energy needs.  

Land Use, Land Use Change and Forestry (LULUCF): The LULUCF sector covers GHG 
emissions into the atmosphere and removal of carbon from the atmosphere resulting from 
the use of soils, trees, plants, biomass, and timber. 

New  fuels:  synthetic  fuels/e-fuels  such  as  e-methane  and  more  complex  hydrocarbons; 
hydrogen  produced  from  increasingly  carbon-free  electricity;  and  the  accompanying 
infrastructure,  namely  networks  and  refuelling  stations  for  the  distribution,  storage,  and 
conversion of the new fuels. 

Non-fossil fuels: Nuclear and renewable energy sources. 

Non-energy uses: The use of petrochemicals and other energy carriers for purposes other 
than  energy  production,  such  as  chemical  feedstocks,  lubricants,  and  asphalt  for  road 
construction. 

Nuclear  power  plant:  A  plant  in  which  a  nuclear  fission  chain  reaction  can  be  initiated, 
controlled, and sustained at a specific rate for production of energy. 

Oil:  Includes  crude  oil,  feedstocks,  refinery  gas,  liquefied  petroleum  gas,  kerosene, 
gasoline, diesel oil, fuel oil, naphtha, and other petroleum products. 

Peak devices:  Gas turbines,  internal  combustion engines  and other  small-scale thermal 
power plants which are usually used to supply electricity in peak hours. 

Passenger transport activity: Passenger transport activity, unless specified otherwise in 
the report, covers road transport (buses and coaches, passenger cars, powered 2-wheelers 
(excluding  e-bikes  and pedelecs),  rail  transport,  intra-EU  aviation,  inland  waterways  and 
national maritime. Tram and metro activity is provided together with rail.  

Primary production: Total indigenous production.  

Renewable energy sources (RES): Energy resources which are naturally replenishing but 
flow  limited.  These  are virtually  inexhaustible  but  limited  in  the  amount  of  energy  that  is 
available  per  unit  of  time.  Renewable  energy  resources  include  biomass,  waste  energy, 
hydro, wind, geothermal, solar, wave and tidal energy. 

Solar power plant: A plant producing energy with the use of radiant energy from the sun; 
includes solar thermal and photovoltaic (direct conversion of solar energy into electricity) 
plants. 

177 

 
Solids: Include both primary products (hard coal and lignite) and derived fuels (patent fuels, 
coke, tar, pitch and benzol). 

Thermal power plants: Type of electricity-generating plant in which the source of energy 
for the prime mover is heat (nuclear power plants are excluded). 

Wind  power  plant:  Typically,  a  group  of  wind  turbines  supplying  electricity  directly  to  a 
consumer  or  interconnected  to  a  common  transmission  or  distribution  system.  Offshore 
wind includes wind turbines located at sea (coastal wind turbines are usually included in 
onshore wind). 

Useful  energy  services:  Useful  energy  services  refer  to  the  provision  of  the  desirable 
amounts of energy to cover sufficiently the need for heating, cooling, and electricity. 

178 

 
 
 
List of Figures 

Figure 1: Modelling suite for the EU Reference Scenario 2020 ...................................................... 11 
Figure 2: Sectoral integration chains covered in the updated PRIMES model ............................. 22 
Figure 3: EU population trajectory .................................................................................................... 27 
Figure 4: EU GDP in aggregate terms ............................................................................................... 29 
Figure 5: Components of GDP in the EU .......................................................................................... 30 
Figure 6: Sectoral Gross Value Added in the EU ............................................................................. 31 
Figure 7: Sectoral shares in Gross Value Added in the EU ............................................................ 32 
Figure 8: ETS emissions and carbon prices..................................................................................... 42 
Figure 9: GIC in relation to GDP ........................................................................................................ 55 
Figure 10: Gross Inland Consumption over GDP (toe/m euro’2015) by Member State in 2015 and 
2030 .............................................................................................................................................. 55 
Figure 11: Final energy consumption by fuel and by sector .......................................................... 56 
Figure 12: Trends in electricity demand by sector .......................................................................... 57 
Figure 13: Industrial energy demand vs. activity ............................................................................. 58 
Figure 14: Average annual change of energy consumption in the industry sector ..................... 59 
Figure 15: Final energy consumption in industry ............................................................................ 59 
Figure 16: Final energy consumption in industry by energy form ................................................. 60 
Figure 17: Fuels for industrial CHP and boilers ............................................................................... 61 
Figure 18: Fuel mix in iron and steel ................................................................................................. 61 
Figure 19: Fuel mix in non-ferrous metals ........................................................................................ 62 
Figure 20: Fuel mix in chemicals ....................................................................................................... 62 
Figure 21: Fuel mix in non-metallic minerals ................................................................................... 63 
Figure 22: Fuel mix in paper and pulp ............................................................................................... 63 
Figure 23: Value added in energy-intensive vs. non-energy-intensive sectors............................ 64 
Figure 24: Fuel mix trends in non-energy-intensive industries...................................................... 64 
Figure 25: Residential energy demand by use ................................................................................. 65 
Figure 26 : Residential energy demand by fuel ................................................................................ 66 
Figure 27: Renovation rates of residential buildings ...................................................................... 67 
Figure 28: Renovation rates by age/income class of residential buildings .................................. 68 
Figure 29: Final energy demand by use in the tertiary sector ........................................................ 69 
Figure 30: Final energy demand by fuel in the tertiary sector ........................................................ 70 
Figure 31: Renovation rates of services buildings .......................................................................... 70 
Figure 32: Passenger transport activity by mode ............................................................................ 71 
Figure 33: Freight transport activity by mode .................................................................................. 72 
Figure 34: Trends in passenger transport activity and energy consumption............................... 73 
Figure 35: Trends in freight transport activity and energy consumption ..................................... 74 
Figure 36: Total energy demand in transport ................................................................................... 74 
Figure 37: Efficiency improvements by mode .................................................................................. 75 
Figure 38: Shares of passenger transport modes in energy demand ........................................... 76 
Figure 39: Shares of freight transport modes in energy demand .................................................. 76 
Figure 40: Total energy consumption in transport by fuel ............................................................. 77 
Figure 41: Energy demand in aviation and maritime by fuel .......................................................... 78 
Figure 42: Outlook of LDVs stock by type and fuel ......................................................................... 79 
Figure 43: Outlook of HDVs by type and fuel ................................................................................... 80 
Figure 44: Electricity generation by fuel type................................................................................... 82 
Figure 45: Electricity generation by plant type ................................................................................ 82 
Figure 46: Operating power capacities ............................................................................................. 84 
Figure 47: RES-E shares in Member States in 2015 and 2030 ........................................................ 84 
Figure 48: Investment in new capacities ........................................................................................... 85 
Figure 49: Investment in plant refurbishment .................................................................................. 85 
Figure 50: Power sector indicators ................................................................................................... 86 
Figure 51: Storage plants and energy ............................................................................................... 87 
Figure 52: Fuel input to district heating ............................................................................................ 89 
Figure 53: Fuel input to CHP plants (all plants) ............................................................................... 90 
Figure 54: Gross inland consumption ............................................................................................... 90 
Figure 55: Breakdown of RES shares in Gross Inland Consumption ............................................ 91 

179 

 
Figure 56: Primary energy supply ..................................................................................................... 92 
Figure 57: Net imports by fuel ............................................................................................................ 93 
Figure 58: Energy import dependence by Member State in 2015 and 2030 (%) ........................... 93 
Figure 59: Primary energy imports (% / Mtoe) .................................................................................. 94 
Figure 60: Natural gas import dependence (%) ................................................................................ 94 
Figure 61: RES policy indicators ....................................................................................................... 95 
Figure 62: Decomposition of RES-E change relative to 2010 (in %) .............................................. 96 
Figure 63: Decomposition of RES-H&C change relative to 2010 (in %) ......................................... 97 
Figure 64: Decomposition of RES-T change relative to 2010 (in %) .............................................. 97 
Figure 65: Cost components of average electricity price ............................................................. 100 
Figure 66: Electricity prices by sector ............................................................................................ 100 
Figure 67: Evolution of the energy intensity of GDP and of the carbon intensity of energy for 

the EU ......................................................................................................................................... 101 
Figure 68: Carbon intensity of GDP in 2015 and 2030 ................................................................... 102 
Figure 69: Evolution of CO2 emissions (excl. LULUCF) ................................................................ 102 
Figure 70: Decomposition of energy-related CO2 Emission reduction relative to projection with 

CO2 intensity of GDP frozen to 2010 levels ............................................................................ 103 
Figure 71: CO2 emissions in power and steam supply.................................................................. 104 
Figure 72: Carbon intensity of power generation by Member State in 2015 and 2030 .............. 104 
Figure 73: Evolution of CO2 emissions in transport (excluding international maritime) ........... 105 
Figure 74: Energy-related CO2 emissions in transport.................................................................. 105 
Figure 75: Carbon intensity by transport mode ............................................................................. 106 
Figure 76: Energy-related CO2 emissions in demand sectors (excl. transport)......................... 107 
Figure 77: Evolution of EU non-CO2 greenhouse gas emissions ................................................ 108 
Figure 78: Evolution of non-CO2 GHG emissions from the EU’s agricultural sector (excl. 

LULUCF) .................................................................................................................................... 108 
Figure 79: Evolution of EU livestock numbers and associated non-CO2 emissions ................. 109 
Figure 80: Evolution in nitrogen input on EU agricultural soils ................................................... 110 
Figure 81: Livestock non-CO2 emissions by farm size for the EU (LSU=livestock units) ......... 110 
Figure 82: N2O emissions from EU agricultural soils (excl. histosols) by farm area ................. 111 
Figure 83: Development in major drivers for EU non-CO2 emissions in the waste and 

wastewater sectors ................................................................................................................... 112 
Figure 84: EU non-CO2 GHG emissions from waste ...................................................................... 112 
Figure 85: EU waste treatment pathway to meet the 2018 Landfill Directive targets ................. 113 
Figure 86: Evolution of non-CO2 emissions from the EU waste and wastewater sectors ......... 113 
Figure 87: Evolution of non-CO2 GHG emissions from the EU energy sector ............................ 114 
Figure 88: Phase-in of alternatives to HFC use in stationary air conditioners (residential & 

commercial) in EU..................................................................................................................... 115 
Figure 89: Phase-in of alternative to HFC use in mobile air conditioners in the EU .................. 115 
Figure 90: Phase-in of alternatives to HFC use in commercial and industrial refrigeration and 

refrigerated transport in the EU .............................................................................................. 116 

Figure 91: Phase-in of alternatives to HFC use in aerosols, foams, heat pumps and fire 

extinguishers in the EU ............................................................................................................ 117 
Figure 92: Evolution of HFC emissions in the EU .......................................................................... 117 
Figure 93: Evolution of non-CO2 GHG emissions from ETS sectors in the EU .......................... 118 
Figure 94: Evolution of non-CO2 GHG emissions from industry and other (non-ETS) 

applications in the EU .............................................................................................................. 118 
Figure 95: Evolution of total GHG emissions ................................................................................. 119 
Figure 96: EU LULUCF emissions/removals in MtCO2-eq until 2050 ........................................... 120 
Figure 97: EU LULUCF sector land balance (in million ha) until 2050 ......................................... 121 
Figure 98: EU biomass harvest from forest (removals) and short rotation coppice (in million 

m3) until 2050 ............................................................................................................................ 122 

Figure 99: Development of the EU emissions/removals in the forest sector in MtCO2-eq until 

2050 ............................................................................................................................................ 123 
Figure 100: Investment expenditures by energy sector (excluding transport) ........................... 125 
Figure 101: Total investment expenditures (including transport) ................................................ 125 
Figure 102: Share of CAPEX by final consumers in total energy costs (%) ................................ 126 
Figure 103: Evolution of energy system costs ............................................................................... 126 
Figure 104: Breakdown of energy costs (%)................................................................................... 127 

180 

 
Figure 105: Annual energy-related expenditures for households................................................ 128 
Figure 106: Energy expenditures in houses ................................................................................... 128 
Figure 107: Energy expenditures for households in transport .................................................... 129 
Figure 108: Annual growth rate of population................................................................................ 146 
Figure 109: World GDP (excluding EU Member States) ................................................................ 147 
Figure 110: Structure of the world economic activity (excl. EU) .................................................. 147 
Figure 111: International fossil fuel prices in the EU Reference Scenario 2020......................... 155 
Figure 112: World liquids demand by sector (left) and supply by type (right) in the REF2020 

“Global Context” scenario ....................................................................................................... 156 
Figure 113: Comparison of oil price projections in 2030 .............................................................. 156 
Figure 114: World natural gas demand by sector (left) and supply by type (right) in the REF2020 
“Global Context” scenario ....................................................................................................... 157 
Figure 115: Natural gas demand shares by region in the REF2020 “Global Context” scenario157 
Figure 116: World coal demand by sector (left) and region (right) in the REF2020 “Global 

Context” scenario ..................................................................................................................... 158 
Figure 117: Price indexes for average EU import prices for gas/coal (left axis) and gas/oil (right 
axis) ............................................................................................................................................ 158 
Figure 118: Illustration of dependence of individual discount rates on income ........................ 163 
Figure 119: Probability of investing in energy efficiency as a function of income (x axis) - 

illustration .................................................................................................................................. 165 

List of Tables 

Table 1: New categorisation of RES technologies in PRIMES........................................................ 19 
Table 2. Projected population per MS (million inhabitants) ............................................................ 28 
Table 3: International fuel prices assumptions ................................................................................ 32 
Table 4: Discount rates in energy supply sectors ........................................................................... 52 
Table 5: Discount rates of firms in energy demand sectors ........................................................... 52 
Table 6: Discount rates of individuals in energy demand sectors ................................................. 53 
Table 7: Discount rates of refuelling/recharging infrastructure ..................................................... 53 
Table 8: Extra-EU aviation activity ..................................................................................................... 72 
Table 9: International passenger and freight maritime transport activity ..................................... 73 
Table 10: Volume of trade flows by region over time (MWh) .......................................................... 87 
Table 11: Key policy indicators for the energy efficiency target .................................................... 98 
Table 12: Discount rates in energy supply sectors ....................................................................... 169 
Table 13: Discount rates of firms in energy demand sectors ....................................................... 171 
Table 14: Definition of discount rates of individuals in energy demand sectors ....................... 172 

181 

 
 
 
 
In person 

GETTING IN TOUCH WITH THE EU 

All over the European Union there are hundreds of Europe Direct information centres. You can find the 
address of the centre nearest you at: https://europa.eu/european-union/contact_en 

On the phone or by email 

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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, or  

– by email via: https://europa.eu/european-union/contact_en 

Online 

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The EU Open Data Portal (http://data.europa.eu/euodp/en) provides access to datasets from the EU. 
Data can be downloaded and reused for free, for both commercial and non-commercial purposes.

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