POLES-JRC model documentation : 2018 update.
Date: 2018-11-26
Institutions: Joint Research Centre
Subjects: air quality, energy consumption, energy research, environmental monitoring, farming sector, greenhouse gas, land use, reduction of gas emissions, research report, simulation

 POLES-JRC model documentation 

2018 update 

Després, J., Keramidas, K., Schmitz, A.,  
Kitous, A., Schade, B. 

2018  

EUR 29454 EN

 
 
 
This publication is a Technical report by the Joint Research Centre (JRC), the European Commission’s science 
and knowledge service. It aims to provide evidence-based scientific support to the European policymaking 
process. The scientific output expressed does not imply a policy position of the European Commission. Neither 
the European Commission nor any person acting on behalf of the Commission is responsible for the use that 
might be made of this publication. 

Contact information 
Address:  European Commission, Joint Research Centre, Expo Building - C/ Inca Garcilaso 3 - 41092, Seville, 

Email: 

Spain 
JRC-POLES@ec.europa.eu 

EU Science Hub 
https://ec.europa.eu/jrc 

JRC113757 

EUR 29454 EN 

PDF 

ISBN 978-92-79-97300-0 

ISSN 1831-9424 

doi:10.2760/814959 

Luxembourg: Publications Office of the European Union, 2018 

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How to cite this report: Després, J., Keramidas, K., Schmitz, A., Kitous, A., Schade, B., POLES-JRC model 
documentation – 2018 update, EUR 29454 EN, Publications Office of the European Union, Luxembourg, 2018, 
ISBN 978-92-79-97300-0, doi:10.2760/814959, JRC113757 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Contents 

Foreword .............................................................................................................. 1 

Acknowledgements ................................................................................................ 2 

Abstract ............................................................................................................... 3 

1  Introduction ...................................................................................................... 4 

2  General description ............................................................................................ 5 

2.1  Scope and objective ..................................................................................... 5 

2.2  Modelling principles and methodology ............................................................ 7 

2.2.1  Model structure ................................................................................... 7 

2.2.2  New equipment and competition across options ....................................... 8 

2.2.3  Energy technology dynamics ................................................................. 9 

2.3  Geographical breakdown ............................................................................ 10 

2.4  Main activity drivers ................................................................................... 10 

3  Final energy demand ........................................................................................ 12 

3.1  Industry ................................................................................................... 12 

3.1.1  Disaggregation and general approach ................................................... 12 

3.1.2  Steel sector ....................................................................................... 13 

3.1.2.1  Steel activity ............................................................................... 13 

3.1.2.2  Steel industry energy demand ....................................................... 14 

3.1.3  Other industrial sectors ...................................................................... 14 

3.1.4  Non-energy uses of fuels .................................................................... 14 

3.2  Residential and services ............................................................................. 14 

3.2.1  Space heating ................................................................................... 15 

3.2.2  Water heating and cooking.................................................................. 16 

3.2.3  Space cooling .................................................................................... 18 

3.2.4  Appliances and lighting ....................................................................... 19 

3.3  Transport .................................................................................................. 20 

3.3.1  Mobility ............................................................................................ 20 

3.3.1.1  Passengers .................................................................................. 20 

3.3.1.2  Freight ....................................................................................... 22 

3.3.2  Energy consumption in transport ......................................................... 23 

3.3.2.1  Road .......................................................................................... 23 

3.3.2.2  Rail ............................................................................................ 24 

3.3.2.3  Air transport ................................................................................ 25 

3.3.2.4  Waterways .................................................................................. 26 

3.3.2.5  International maritime bunkers ...................................................... 26 

3.4  Agriculture ................................................................................................ 27 

i 

3.5  Energy prices ............................................................................................ 27 

4  Energy transformation ...................................................................................... 28 

4.1  Electricity sector ........................................................................................ 28 

4.1.1  Technologies for power supply ............................................................. 28 

4.1.2  Electricity demand ............................................................................. 30 

4.1.2.1  Data used ................................................................................... 31 

4.1.2.2  Representative days ..................................................................... 31 

4.1.2.3  Description of the electricity load curve .......................................... 33 

4.1.3  Operation of the power system ............................................................ 33 

4.1.4  Planning of electricity capacities .......................................................... 34 

4.1.4.1  Production capacities .................................................................... 34 

4.1.4.2  Storage and demand response capacities ........................................ 36 

4.2  Heat production ......................................................................................... 37 

4.3  Hydrogen .................................................................................................. 37 

4.3.1  Hydrogen demand ............................................................................. 37 

4.3.2  Hydrogen production .......................................................................... 38 

4.3.3  Hydrogen transport ............................................................................ 38 

4.4  Synthetic liquids ........................................................................................ 39 

4.4.1  Liquids from coal and gas ................................................................... 39 

4.4.2  Liquids from biomass ......................................................................... 39 

4.5  Carbon Capture and Sequestration ............................................................... 40 

4.6  Other transformation and losses .................................................................. 40 

5  Primary energy supply ..................................................................................... 41 

5.1  Oil and gas ............................................................................................... 41 

5.1.1  Geographical disaggregation and fuel types........................................... 41 

5.1.2  Discoveries: from resources to reserves ............................................... 42 

5.1.3  Production costs ................................................................................ 43 

5.1.4  Production process and trade for oil ..................................................... 45 

5.1.5  Production process and trade for gas .................................................... 46 

5.1.6  Prices ............................................................................................... 48 

5.2  Coal ......................................................................................................... 49 

5.3  Biomass ................................................................................................... 51 

5.4  Uranium ................................................................................................... 52 

5.5  Hydro potential ......................................................................................... 53 

5.6  Wind potential ........................................................................................... 53 

5.7  Solar potential ........................................................................................... 54 

5.7.1  Solar power plant potential ................................................................. 54 

5.7.2  Solar distributed photovoltaic potential ................................................. 54 

ii 

5.7.3  Solar thermal potential ....................................................................... 55 

5.8  Geothermal electricity ................................................................................ 55 

6  Emissions ....................................................................................................... 56 

6.1  Greenhouse gases ..................................................................................... 56 

6.1.1  Modelling principles and marginal abatement cost curves ........................ 56 

6.1.2  Energy-related emissions .................................................................... 57 

6.1.2.1  Combustion-related emissions ....................................................... 57 

6.1.2.2  Non-CO2 energy-related emissions ................................................. 58 

6.1.3  Process emissions in industry .............................................................. 58 

6.1.4  Waste............................................................................................... 59 

6.1.5  Agriculture, forestry and other land use (AFOLU) ................................... 59 

6.1.6  Greenhouse gas coverage summary ..................................................... 59 

6.2  Pollutant emissions .................................................................................... 61 

6.2.1  Pollutants covered ............................................................................. 61 

6.2.2  Emission calculation ........................................................................... 61 

7  Energy and climate policy implementation .......................................................... 64 

8  Data .............................................................................................................. 65 

8.1  Economic activity ....................................................................................... 65 

8.2  Energy ..................................................................................................... 65 

8.3  Greenhouse gas emissions .......................................................................... 66 

8.4  Air pollutants ............................................................................................ 66 

8.5  Summary ................................................................................................. 66 

9  Conclusions .................................................................................................... 68 

References ......................................................................................................... 69 

List of abbreviations and definitions ....................................................................... 75 

List of figures ...................................................................................................... 78 

List of tables ....................................................................................................... 80 

Annexes ............................................................................................................. 81 

Annex 1. ISIC classification of sectors in POLES-JRC ............................................ 81 

Annex 2: Country mappings .............................................................................. 82 

Annex 3: Equation details of residential and services end-uses .............................. 91 

Annex 4: Sources for hourly electricity load data ................................................. 93 

iii 

Foreword 

This document presents the Prospective Outlook on Long-term Energy Systems (POLES) 
model of the Joint Research Centre, as used in the 2018 edition of the Global Energy and 
Climate Outlook (GECO).  

The  model  has  been  updated  compared  to  the  2017  edition  (Keramidas  2017),  in 
particular in the building demand, power sector and the oil and gas sectors. 

1 

Acknowledgements 

Authors 

Main  authors:  Jacques  Després,  Kimon  Keramidas,  Andreas  Schmitz,  Alban  Kitous  (*), 
Burkhard Schade 

Contributions by:  
Ana Diaz Vazquez, Silvana Mima (**), Peter Russ, Tobias Wiesenthal 

All  contributors  are  from  the  European  Commission's  Joint  Research  Centre  (JRC), 
except: 

(*):  Alban  Kitous  (Directorate-General  for  Energy,  European  Commission,  Brussels, 
Belgium). 

(**): Silvana Mima (GAEL Laboratory, University of Grenoble, Grenoble, France). 

2 

Abstract 

This  report  is  a  public  manual  for  the  POLES-JRC  model,  the  European  Commission  in-
house tool for global and long-term analysis of greenhouse gas (GHG) mitigation policies 
and evolution of energy markets. The model includes a comprehensive description of the 
energy  system  and  related  GHG  emissions  for  a  large  set  of  significant  economies  and 
residual regions, covering the world and including international bunkers. Through linkage 
with  specialised  tools  it  also  provides  a  full  coverage  of  GHG  emissions,  including  from 
land use and agriculture, as well as of air pollutant emissions. 

The POLES-JRC model builds on years of development of the POLES model while adding 
specific features developed internally within the JRC. 

The model version presented in this report is used in particular to produce the JRC Global 
Energy and Climate Outlook (GECO) edition of 2018. 

Complementary information can be found on the following JRC Science Hub websites: 

http://ec.europa.eu/jrc/poles 
http://ec.europa.eu/jrc/geco 

3 

1  Introduction 

The use of quantitative models of the energy sector in supporting policymaking has been 
increasing drastically in recent decades. The global partial equilibrium model POLES has a 
strong  track  record  in  providing  analyses  for  the  preparation  of  policy  proposals  in  the 
area  of climate change and energy. To this end, the model has continuously evolved so 
as to better match the needs of the policymakers. 

The POLES (Prospective Outlook on Long-term Energy System) model has been used for 
more  than  two  decades  as  an  analytical  tool  for  providing  energy  scenarios  that  inform 
the  energy  policy  trade-offs  for  sustainable  energy  development  at  both  world  and  EU 
levels.  It  was  initially  developed  in  the  1990s  at  the  University  of  Grenoble  (France)  in 
the  then  IEPE  laboratory (1)  and  was  first  funded  under  the  JOULE  II  and  JOULE  III 
programmes  of  Directorate-General  XII  of  the  European  Commission  and  under  the 
Ecotech programme of the French CNRS. The model then was transferred to a simulation 
software by the Joint Research Centre (JRC). 

Since then the model has been improved and  extended  on several occasions to capture 
the  most  recent  market  and  policy  developments.  Modelling  upgrades  include  final 
energy demand, electricity production, the role of hydrogen as an energy vector, the oil, 
gas and coal international markets and GHG emission projections. 

Its  features  and  the  extensive  range  of  results  produced  have  been  used  to  support  a 
number  of  studies  on  energy  prospects  and  on  GHG  emission  mitigation  policies  for 
various European, international and national institutions over the last 20 years. 

The  JRC  has  co-developed  the  model  for  some  time  and  recently  issued  the  POLES-JRC 
version. 

This report documents the latest version of the POLES-JRC model as of early 2017, which 
shares  elements  with  other  versions  of  the  POLES  model  used  by  other  institutions (2). 
Following  a  general  description  of  the  model  and  the  economic  activity,  it  details  the 
approach  implemented  in  the  various  end-use  and  supply  sectors.  Considering  the 
application  of  the  model  in  assessing  global  GHG  emission  scenarios,  specific  sections 
address the calculation of emissions and of scenario building. This version is used for the 
JRC Global Energy and Climate Outlook series (GECO) (3). 

(1)  Now part of the GAEL laboratory: https://gael.univ-grenoble-alpes.fr/?language=en 
(2)  GAEL (French research laboratory) https://gael.univ-grenoble-alpes.fr/research-areas/energy-

axis?language=en; Enerdata https://www.enerdata.net/  

(3)  www.ec.europa.eu/jrc/geco, see GECO 2016 (Kitous et al. 2016). 

4 

                                           
2  General description 

This section gives an overview of the model in terms of scope and objectives, modelling 
principles and geographical breakdown. 

The POLES-JRC is a simulation model designed for energy and climate policy analysis. Its 
main features are the following: 

● 

full description of the energy sector: 

(cid:1)  demand and supply linked through prices, 

(cid:1)  detailed representation of end-use sectors, power generation and other 

transformation sectors and primary supply, 

(cid:1)  disaggregation to all types of energy fuels, 

(cid:1)  explicit technology dynamics, 

(cid:1)  historically calibrated behaviour of economic agents; 

●  energy and non-energy related emissions of GHGs and air pollutants; 

●  a global coverage while keeping regional detail; 

●  updated information (historical data up to current year – 1); 

●  an annual time step and typical projection horizon until 2050. 

Figure 1. Schematic representation of the POLES-JRC model architecture 

Model inputs 

Modelling 

Macro assumptions 
(GDP, Pop, …) 

Carbon constraints 
(tax, cap on emissions…) 

Specific energy policies 
(subsidies, efficiency…) 

Resources 

Oil 
Gas 
Coal 
Uranium 
Biomass 
Wind 
Solar 
Hydro 

Technology 1 
(costs, efficiency…) 

66 energy demand regions 
Service needs (mobility, surfaces, 

heating …) 
Energy demand 
Energy transformation 
Energy supply 
Fuel/technology competition 

International prices 

Trade 

International markets 

Oil (88 producers – 1 mkt) 
Gas (88 producers – 14 import mkts) 
Coal (81 producers – 15 import mkts) 
Biomass (66 producers – 1 mkt) 

Technology learning 

Model outputs 

66 regional balances 

Regional Energy Balance 

Primary energy production 
Power generation and other 

transformations 
Final energy demand  
Energy-related land use 

End-user prices 
Energy supply investments 

GHG emissions 
Air pollutants emissions 

2.1  Scope and objective 

The  POLES-JRC  model  is  a  simulation  model  for  the  development  of  long-term  energy 
supply and demand scenarios, including related emissions, for the different regions of the 
world.  It  simulates  technology  dynamics  and  follows  the  discrete  choice  modelling 
paradigm  in  the  decision-making  process.  It  determines  market  shares  (portfolio 
approach)  of  competing  options  (technologies,  fuels)  based  on  their  relative  cost  and 
performance while also capturing non-cost elements like preferences or policy choices. 

POLES-JRC covers the entire energy sector, from production to trade, transformation and 
final use for a wide range of fuels and sectors. In addition, non-energy greenhouse gases 

5 

 
as  well  as  air  pollutants  are  covered,  be  they  associated  with  the  energy  sector  or  with 
other economic activities (4). 

The  model’s  scope  is  global,  with  an  explicit  representation  of  66  geographical  entities 
(see Section 2.3). 

The  POLES-JRC  model  runs  in  annual  time  steps,  with  the  model’s  outlooks  typically 
extending from 1990 to 2050, the time horizon for which the technological representation 
is  most  relevant;  for  very  long-term  climate  mitigation  assessments  the  model  can  be 
run to 2100. 

The model is conceived for the purpose of providing analytical support on the following: 

●  Assessment of policies related to the energy sector 

The model is used to quantify the impact of policies on the evolution of the energy sector 
compared  to  its  evolution  without  that  intervention  or  with  an  alternative  policy 
formulation.  This  is  achieved  through  the  comparison  of  scenarios  concerning  possible 
future  developments  of  world  energy  consumption  and  corresponding  GHG  emissions 
under different assumed policy frameworks. Policies that can be assessed include: energy 
efficiency,  support  to  renewables,  energy  taxation/subsidy,  technology  push  or 
prohibition, access to energy resources, etc. 

●  Greenhouse gas emissions abatement strategies 

The model can assist the formulation of GHG emissions reduction strategies in a national 
or international perspective. The high sectoral and technological detail of the model can 
help  in  identifying  and  prioritising  strategic  areas  of  action  for  mitigation  through  the 
comparative analysis of multiple reduction scenario pathways in terms of emissions and 
costs. Additionally, it can be used to assess the costs of compliance with global, national 
and sectoral emission targets. 

Finally, the model allows assessment of the impact of energy and climate policies on air 
pollutants (5) (see Section 6.2). 

●  Technology dynamics 

The  model  can  assess  the  market  uptake  and  development  of  various  new  and 
established  energy  technologies  as  a  function  of  changing  scenario  conditions.  The  key 
parameters characterising the costs and performances as well as the diffusion process of 
these  technologies  are  incorporated  in  the  model  for  power  generation,  hydrogen 
production, vehicles and buildings. 

The global coverage allows an adequate capture of the learning effects that usually occur 
in  global  markets.  In  particular  the  modelling  of  power  production  technologies  is 
associated with dynamic technology learning. 

● 

International fuel markets and price feedback 

The  model  can  provide  insights into  the  evolution  of  the  global  primary  energy  markets 
and  the  related  international  and  regional  fuel  prices  under  different  scenario 
assumptions.  To  this  end,  it  includes  a  detailed  representation  of  the  costs  in  primary 
energy  supply  (in  particular  oil,  gas  and  coal  supply),  for  both  conventional  and 
unconventional resources. At the same time, the (regional) demand for the various fuels 
is simulated and matched through price adjustments. 

The  model  can  therefore  be  used  to  analyse  the  impacts  of  energy  and  climate  policies 
and  energy  taxation/subsidy  phase-in/out  on  the  international  energy  markets.  The 

(4)  The  model  provides  a  full  coverage  of  GHG  emissions:  detailed  emissions  from  the  energy  sector  and 
industrial  processes  are  derived  directly  from  the  core  modelling,  while  emissions  from  agricultural 
activities  and  LULUCF  (land  use,  land  use  change  and  forestry)  are  derived  from  a  linkage  with  the 
GLOBIOM model (IIASA 2016a) (see Section 6.1.5). 
(5)  Through a linkage with the GAINS model (IIASA 2016b) 

6 

                                           
interaction  of  regions  and  energy  fuels  allows  for  the  study  of  the  effects  of  policies  on 
producers’ revenues, of the price feedback on consumers or of carbon leakage. 

2.2  Modelling principles and methodology 

2.2.1  Model structure 

POLES-JRC is a partial equilibrium model of the energy system (i.e. without feedback on 
the economic system) using recursive simulation. 

Figure 2. Schematic representation of the POLES-JRC sectors 

Primary Energy Supply 

- Oil (6 types) 
- Gas (5 types) 
- Coal (2 types) 
- Uranium 
- Biomass (3 types) 
- Primary energy carriers for electricity: wind, solar, 
hydro, nuclear, geothermal, ocean 

Energy transformation 

- Electricity production 
- Hydrogen production 
- Liquid fuels production: coal, gas and biomass conversion 
- Heat production, including solar heat 
- Other transformation and losses 

Final energy demand 
11 main sectors (10 sub-sectors in transport) 

- Buildings: Residential; Services 
- Transport: Road (passenger (private, public), freight (light, 

heavy)); Rail (passenger, freight); Air (domestic, international); 
Water (domestic waterways, international bunkers) 

- Industry: Steel; Non-metallic minerals; Chemicals (Energy uses; 

Chemical feedstocks; Rubber and plastics); Other 

- Agriculture 

Energy  demand  by  region  and  sector  is  derived  from  socioeconomic  developments 
(exogenous  assumptions),  policy  conditions  and  the  evolution  of  international  energy 
prices. It is met by the operation of the installed equipment, be it transformed or primary 
energy.  Simultaneously  the  model  identifies  expected  future  energy  needs  and 
the 
to  cover 
determines 
decommissioning or underutilisation of existing equipment. 

these  needs,  accounting 

the  required  capacity 

for 

Primary  energy  consumption  by  region  is  given  by  the  aggregated  sectors’  final  energy 
demand and energy used in transformation. It is supplied by domestic energy production 
via international markets. The comparison of demand dynamics and export capacities for 
each  market  establishes  the  market  equilibrium  and  the  determination  of  the  price  for 
the following period, which impacts future demand and supply with lagged variables. 

7 

 
For each region, the model represents three main modules, themselves with several sub-
modules,  as  described  in  the  Figure  2.  This  structure  allows  for  the  simulation  of  an 
energy balance for each region. 

2.2.2  New equipment and competition across options 

The  model  makes  use  of  a  common  modelling  approach  across  sectors  in  order  to 
represent the need for new energy equipment and the competition across options: 

1.  The  evolution  of  the  total  stock  (or  capacity)  is  set  by  activity  drivers,  energy 

prices and technological development; 

2.  The  installed  equipment  can  meet  part  of  the  total  demand,  once  depreciation 

(scrapping) has been taken into account; 

3.  The  remaining  needs  after  contribution  of  the  un-scrapped  equipment  is  covered 

by a competition between options (fuels or technologies). 

Figure 3. Schematic representation of the energy needs and depreciation procedure in POLES-JRC 

Sectoral demand 
at t 

Option 1 

Option 2 

Option 3 

1 
Sectoral demand 
at t+1 

"Gap" 

New additional Option 1 

New additional Option 2 

New additional Option 3 

3 
Inter-options 
competition 

2 
Remaining equipment 
("un-scrapped") 

"Scrapping" 

1. The standard demand equation follows the general form: 

(cid:1)(cid:2)(cid:3)(cid:4)(cid:5)(cid:6) = (cid:8)(cid:9)(cid:10)(cid:11)(cid:12)(cid:11)(cid:10)(cid:13)

(cid:2)(cid:13)

× (cid:15)(cid:16)(cid:17)(cid:8)(cid:12)(cid:18). (cid:20)(cid:21)(cid:11)(cid:9)(cid:2)(cid:22)

(cid:23)(cid:2)(cid:24)

(cid:25) × (cid:15)(cid:26)(cid:17)(cid:8)(cid:12)(cid:18). (cid:20)(cid:21)(cid:11)(cid:9)(cid:2)(cid:22)

(cid:23)(cid:2)(cid:27)

(cid:28) × (cid:29)(cid:21)(cid:2)(cid:5)(cid:6)

It combines: 

●  an  income  or  activity  effect,  through  an  activity  elasticity  (ey):  the  activity 
variables  are  sector-specific:  income  per  capita,  sectoral  value  added, 
household surfaces etc; 

●  price effects: the structure of the equations allows for taking into account both 
short-term  (es)  and  long-term  (el)  price  elasticities,  with  a  distributed  lag 
structure  over  time  and  possible  asymmetries  between  the  increasing  or 
decreasing price effect; 

●  an  autonomous  technological  trend  that  reflects  non-price-dependent  evolution 
of the equipment performance, due to purely technological advancements or to 
non-price policies (e.g. efficiency standards); 

● 

the  fact  that  the  activity  elasticity  and  the  trend  can  be  dynamic  so  as  to 
capture saturation effects. 

8 

 
 
 
2.  Installed  equipment  is  determined  by  a  survival  law  that  considers  the  general 
dynamics of total demand, the average lifetime of the equipment and the evolution of the 
relative cost of use of the option compared to others. 

3.  In  order  to  take  into  account  the  flexibilities  and  rigidities  introduced  by  existing 
capital  stocks,  the  competition  between  options  takes  place  only  in  the  space  created 
between  the  total  needs  and  the  un-scrapped  equipment:  a  ‘gap’  to  be  filled  by  new 
equipment (‘putty-clay’ demand function). 

Fuel  or  technology  market  shares  are  calculated  in  a  cost-based  competition  process 
using a discrete choice formulation. 

It takes into account: 

● 

the  user  cost  of  the  different  options  (Ci),  which  includes  the  investment  cost, 
the  lifetime,  a  time  discounting  factor (6),  the  fuel  utilisation  efficiency  and  the 
fuel price; 

●  a  weighting  factor  to  capture  the  observed  deviation  from  pure  cost-based 
competition  (ai),  calibrated  on  historical  market  shares  and  reflecting  non-
economic preferences; it can evolve exogenously over the simulation to capture 
infrastructure developments, technology choices, etc. 

(cid:30)(cid:4)(cid:21)(cid:31)(cid:2)(cid:10)	(cid:24)!(cid:4)(cid:21)(cid:2)"#(cid:10)(cid:11)"(cid:5)	(cid:11) =

(cid:23)(cid:2)

(cid:4)(cid:11) × $(cid:11)
(cid:11)
∑ (cid:4)(cid:11) × $(cid:11)

(cid:23)(cid:2)

The total demand by option is then the sum of remaining demand after depreciation and 
of the new demand. 

This formulation is found in the final demand sectors and in fossil fuel supply. 

The planning of power capacities follows a similar logic, except that the competition takes 
place  over  the  entire  expected  demand  looking  10  years  ahead.  This  expected  demand 
takes  into  account  investors’  expectations  on  the  evolution  of  the  policy  framework,  as 
well  as  fuel  prices;  expectations  are  based  on  extrapolations  of  historical  trends,  and 
therefore  do  not  constitute  a  perfect  foresight.  Since  the  capacity  planning  in  power 
generation is of a recursive nature, investment decisions taken in year t can be modified 
in year t + 1 except for those installations for which construction was assumed to have 
started already (a tenth of the total). 

2.2.3  Energy technology dynamics 

The  concept  of  technology  learning  (7)  links  the  improvements  in  performance, 
productivity  and/or  cost  of  a  technology  to  the  accumulation  of  experience.  Instead  of 
trying  to  disentangle  the  technology  cost  reductions  to  multiple  items,  the  model  uses 
the  ‘One-factor-learning-curve’ (8)  approach  that  links  the  unit  cost  development  of  a 
technology to the evolution of the accumulated production of that technology. 

Due  to  the  global  nature  of  the  power  equipment  market,  learning  is  assumed  to  take 
place  as  a  function  of  the  worldwide  installed  capacity  of  a  certain  technology  (in  W). 
Depending on the scenario settings, which affect the deployment of  a given technology, 
different trajectories of the technology costs can be derived. 

(6)  The  time  discounting  factor  used  for  investment  decisions  includes  a  discount  rate  and  a  sector-specific 

risk preference factor. 

(7)  See for example Wright (1936). 
(8)  The  literature  identifies  more  complex  formulations,  which  include  for  instance  learning  by  researching, 
learning by using, learning by scaling and learning by copying (i.e. knowledge spillovers) (Sagar and van 
der  Zwaan,  2006).  The  ‘learning  by  searching’  in  particular  (linked  to  R & D  expenditures)  has  been 
explored with the POLES model — see the SAPIENT, SAPIENTIA and CASCADE MINTS projects. However 
Wiesenthal et al. (2012) show that lack of historical data and robust projections of the associated drivers 
make them difficult to handle. 

9 

 
                                           
$(cid:10) = &(cid:10)
C  =  Costs of unit production (€/W) 

with 

(cid:27)(cid:5)(cid:17)'(cid:23)()(cid:10)(cid:22)/(cid:27)(cid:5)(cid:17)+(cid:22)

Q  =  Cumulative Production (W) 

LR  =  Learning rate 

t  =  Technology 

Learning  rates  (LR)  correspond  to  the  percentage  decrease  of  investment  cost  of  a 
technology when the installed cumulated capacity of the technology doubles. 

For  Carbon  Capture  and  Storage  (CCS)  plants,  different  learning  applies  to  the  power 
production  facility  on  the  one  hand  and  to  the  carbon  capture  component  on  the  other 
hand.  Similarly,  in  concentrated  solar  power  plants,  the  investment  cost  of  storage  is 
separated from the learning in the rest of the plant. 

2.3  Geographical breakdown 

The world energy consumption is decomposed into 66 geographical entities (see Figure 
4):  the  EU-28,  26  large  economies  (including  detailed  OECD  countries,  G20  and 
emerging  Asian  countries)  and  12  country  aggregates.  International  bunkers  (air  and 
maritime) are also taken into account. 

Figure 4. POLES-JRC geographic breakdown 

The geographical decompositions for oil, gas and coal production are different, in order to 
represent  resource-rich  countries  in  greater  detail,  with  more  than  80  individual 
producers. Mappings are provided in Annex 2: Country mappings. 

2.4  Main activity drivers 

Population and economic activity expressed as GDP (gross domestic product) — together 
with a sectoral decomposition of the value added — are direct inputs to the model driving 
the  evolution  of  sectoral  energy-consuming  activity  variables.  The  main  information 
sources used are (see also Section 8 on data): 

● 

for the EU: the Ageing Report (European Commission 2015); 

10 

 
 
 
 
 
● 

for non-EU regions: UN for population (UN 2015), IMF and  OECD for economic 
growth (respectively IMF 2016 and OECD 2014). 

The  evolution  of  other  socioeconomic  variables  like  housing  needs  (number,  size)  and 
mobility (both passengers and freight) is also derived from the inputs on population and 
growth of GDP per capita 

Economic variables are expressed in real monetary terms (constant US dollars). Data on 
GDP and sectoral value added are expressed in purchasing power parity. 

11 

3  Final energy demand 

Final  energy  demand  in  the  model  is  dealt  with  explicitly  for  four  sectors:  industry, 
residential  and  services,  transport  and  agriculture.  This  chapter  describes  for  each  of 
them  the  related  activity  drivers  and  resulting energy  needs.  In  addition,  one  section  is 
dedicated  to  the  formulation  of  energy  prices,  which  play  an  important  role  in  the 
dynamics of energy demand. 

3.1  Industry 

3.1.1  Disaggregation and general approach 

Industry  is  disaggregated  into  different  manufacturing  sectors  and  mining  and 
construction (9).  In  line  with  the  IEA/Eurostat  balances,  industrial  energy  consumption 
does  not  include  transport  used  by  industry  (which is  reported  under  transport);  it  also 
excludes the fuel input for auto-production but includes the auto-produced electricity. 

Final energy demand in industry is divided into four energy sectors: 

● 

iron and steel; 

●  chemicals; 

●  non-metallic minerals (NMM): cement, lime, glass, ceramics and other NMM; 

●  other industry: other manufacturing, mining and construction. 

The  industrial  sectors  that  are  modelled  individually  were  chosen  because  of  their 
energy-intensive  processes;  they  represented  approximately  50 %  of  the  energy 
consumption (10) of industry at world level in 2010. 

Additionally,  the  consumption  of  fuels  for  non-energy  uses  is  captured  for  two  types  of 
products: chemical fertilisers and plastics and rubber. 

Figure 5 shows the evolution of total input fuels (both for energy uses and non-energy-
use) and industry value added since 1990. The sector undertook a decoupling with total 
value  added  more  than  doubling  while  fuel  inputs  increased  only  by  60 %  over  the 
period. 

Figure 5. World consumption of industry per sector (left) and industry value added (right) 

e
o
t
G

4.5

4.0

3.5

3.0

2.5

2.0

1.5

1.0

0.5

0.0

35

30

25

20

15

10

5

0

D
S
U
n
t

Plastics & rubber
Chemical feedstocks
Other industry
Non-metallic Minerals
Chemicals
Steel
Industry value added

Non-energy 
use

Energy use

1990 1995 2000 2005 2010 2015

Source: IEA 2017a, Enerdata 2017a 

(9)  The energy transformation industry (transformation of energy fuels, including the power sector) is treated 

separately from the industry that is a final energy consumer. 

(10)  Energy uses only, see Enerdata (2015a) 

12 

 
                                           
 
3.1.2  Steel sector 

3.1.2.1  Steel activity 

The activity indicator for the steel industry is the tonnes of steel produced. 

Regional steel demand is modelled using the concept  of intensity of use (van Vuuren et 
al.  1999;  Hidalgo  et  al.  2003):  consumption  per  unit  of  GDP  first  increases  through  a 
rapid  equipment  phase  and  industrialisation,  then  peaks  and  goes  through  an  extended 
decreasing  phase  as  the  economy  shifts  to  services.  It  is  modelled  with  the  following 
equation: 

This behaviour is calibrated for each world region (see Figure 6). 

$"(cid:5)(cid:24)
,(cid:1)(cid:20)

= (cid:15) -

'
,(cid:1)(cid:20)
(cid:20)"#

. , (cid:15) 0

,(cid:1)(cid:20)
(cid:20)"#

1 , 2(cid:27)""(cid:21)

The model evaluates the stock of steel in the economy and the amount of scrap available 
each year, considering obsolescence factors in the different sectors consuming steel. 

Figure 6. Apparent steel consumption per unit of GDP versus income per capita for select G20 
countries, 1990-2015 

70

60

50

40

30

20

10

)

D
S
U
/
t
(
P
D
G
/
l
e
e
t
S

0

0

0.2 t/cap

0.1 t/cap

0.05 t/cap

0.5 t/cap

1 t/cap

China

Russia

India

Brazil

Saudi Arabia

EU28

S Korea

Japan

Canada

Australia

USA

5

10

15

20

25

30

35

40

45

50

GDP/cap (k USD/cap)

Source: WSA 2015 

NB: Russia’s high point in steel/GDP corresponds to 1992 figure. GDP is in USD 2005 PPP. 

The model differentiates between secondary steel (electric arc furnaces), which depends 
on the amount of scrap available (estimated from the stock of steel and average lifetime 
of  use  in  the  different  sectors),  and  primary  steel  (from  thermal  processes)  that  makes 
the difference from total steel production needs. 

13 

 
 
 
After  consideration  of  the  decommissioning  of  existing  capacities,  the  additional 
production  capacities  —  distinguished  between  electric  arc  and  integrated  steel 
production  —  are  allocated  across  all  regions  with  a  competition  based  on  pre-existing 
capacities,  evolution  of  local  steel  consumption  and  steel  production  cost  (energy  cost 
and a fixed infrastructure cost). 

3.1.2.2  Steel industry energy demand 

The  consumption  of  each  fuel  is  distributed  between  thermal  processes  and  electric 
processes  considering  tonnes  produced  by  process,  theoretical  energy  needs  per  tonne 
and  fuel  conversion  efficiency  in  the  case  of  auto-production  of  electricity.  Coking  coal 
and marketed heat are always assigned to thermal processes. 

In  each  of  the  two  processes,  total  demand  for  non-electric  fuels  in  competition  and 
electricity  follows  a  standard  demand  equation,  determined  by  the  evolution  of  the 
tonnes  of  steel  and  the  energy  price.  Within  non-electric  fuels  in  competition  (oil,  gas, 
coal and biomass), the fuel substitution processes and the equipment lifetime are similar 
to those in the other industrial sectors. 

Coking  coal  in  thermal  processes  follows  its  own  standard  demand  equation,  influenced 
by  the  price  of  coking  coal.  Blast  furnace  gas  produced  during  the  combustion  of  coke 
and  used  as  a  fuel  input  in  steel  processing  is  also  calculated  based  on  coke 
consumption.  This  is  taken  into  account  in  the  emissions  from  the  sector  (see  6.1.2 
Energy-related emissions). 

3.1.3  Other industrial sectors 

This  section  applies  to  the  three  non-steel  industrial  sectors:  chemistry,  non-metallic 
minerals  and  other  industry.  In  each  case  the  activity  indicator  is  the  sector’s  value 
added. 

For  each  sector,  total  demand  for  process  heat  is  calculated  with  a  standard  demand 
equation,  influenced  by  the  sector’s  value  added  and  the  average  price  of  the  fuels  in 
competition.  Within  that  total,  a  cost-based  competition  takes  place  between  oil,  gas, 
coal and biomass. For each fuel, costs include fixed infrastructure costs, a fuel utilisation 
efficiency and fuel-specific weighting factors reflecting the initial historical distribution of 
fuel demand and evolving towards cost-only competition. 

Demand for electricity is calculated separately, with a standard demand equation. 

3.1.4  Non-energy uses of fuels 

Two economic activities using fuels as raw material are differentiated: 

●  chemical fertilisers that can consume oil, gas and coal; 

●  plastics and rubber that can consume oil and biomass. 

The activity indicator is the chemical industry value added. 

Related process and end-use CO2 emissions are covered. 

3.2  Residential and services 

The residential sector and the services sector share a number of common features, since 
these activities are mostly related to buildings. Their energy consumption is modelled per 
end-use. The energy demand in each end-use is based on observed correlations between 
energy  consumption  data  and  specific  activity  variables  or  potential  driving  indicators 
(number  and  surface  of  residential  dwellings,  surface  of  service  buildings,  services 
sectoral value added), GDP, energy prices and technology costs (insulation, heaters and 
boilers). When relevant, climate indicators (degree days) are also used to account for the 
effects of climate change on the residential and service sector. 

14 

The calibration work is made difficult by the low consistency of data, due to differences in 
reporting,  methodologies  and  country  specificities  (11).  The  modelling  methodology  is 
briefly described below.  

3.2.1  Space heating 

The  space  heating  energy  consumption  is  based  on  building  surfaces,  impacted  by  the 
evolution  of  population,  number  of  people  per  household  and  surface  per  dwelling.  For 
services it is driven by the share of the population working in the service sector.  

Residential  space  heating  is  computed  based  on  the  total  heating  comfort,  which 
indicates what useful energy a house with no insulation at all will consume, or how much 
insulation comfort is needed for a house to be passive (not requiring any useful heating 
consumption).  It  is  either  achieved  through  insulation  comfort  or  useful  energy 
consumption: 

34567	8965:;<	=4>?4@5 = A;BC765:4;	=4>?4@5 D EB9?C7	9;9@<F	=4;BC>G5:4;
The  total  heating  comfort  needs  is  shown  to  depend  on  the  Heating  Degree  Days 
(HDD).  HDD is an indicator based on the  summation of the difference between  external 
temperatures and a reference temperature (12); they reflect the energy needed to heat a 
building. 

34567	8965:;<	=4>?4@5	G9@	BC@?6=9 = 0.005 ∗ KLL

The  insulation  comfort is calculated from the U-value (in W/m2/K), which defines the 
insulation  of  each  component  of  a  building  assuming  a  standard  building  envelope  and 
approximating  the  temperature  difference  between  inside  and  outside.  The  insulation 
comfort diffuses across the building stock depending on the new demand and renovation 
of  building  surfaces.  The  insulation  comfort  of  new  buildings  is  assumed  to  be  more 
ambitious than renovated buildings (easier works).  

																		(cid:17):;	OP8/>

(cid:22)

M.N

Q

Useful  energy  consumption  is  the  result  of  energy  consumed  multiplied  by  its 
efficiency.  

An illustration of the decomposition of the total heating comfort is shown in Figure 7. 

Figure 7. Decomposition of the final energy consumption for space heating, for France in the 
reference scenarios, decomposition of the total heating needs (left) and by fuel demand (right) 

Source: GECO 2018 (Keramidas 2018). 

(11)  The  calibration  in  the  residential  and  service  sectors  is  based on  available  data from  the IDEES  (Mantzos 

2018) and ODYSSEE database together with some estimates of end-use decomposition by Enerdata. 

(12) The reference temperature for computing HDD is 18°C. 

15 

 
 
 
                                           
Space  heating  in  service  buildings  follows  the  same  building  codes  and  trends  than  the 
residential  buildings  (based  on  the  residential  heating  comfort,  insulation  and  U-value), 
but are also impacted by their own evolution of surfaces. 

The  evolution  of  useful  energy  needs  (and  therefore,  final  energy  consumption)  takes 
into  account  the  retirement  of  obsolete  equipment  and  new  needs.  Coal,  traditional 
biomass  and  district  heat  are  represented  separately,  following  exogenous  trends 
calibrated on historical data and fuel-specific constraints: 

●  Coal  is  sensitive  to  the  carbon  price  and  its  trend  decreases  progressively 

between 2020 and 2050; 

● 

traditional biomass is sensitive to the biomass potential. 

Low  temperature  solar  heat  is  also  separated,  based  on  its  return  on  investment  and 
constrained by its potential. 

The  remaining  needs  for  new  heating  equipment  are  split  between  oil,  gaseous  fuels 
(natural  gas  and  hydrogen),  electricity  (resistive  heating  and  heat  pump)  and  modern 
biomass as follows: 

● 

If there is no need for further heating equipment, each energy source is shrunk 
proportionately to its importance.  

●  Otherwise, the choice of fuels is based on a competition between fuels based on 
fuel  efficiency  and  costs,  installation  costs  and  infrastructure  cost,  combined 
with  a  historically  calibrated  factor,  to  cover  for  elements  going  beyond  the 
purely cost-based modelling, such as consumer preferences. 

3.2.2  Water heating and cooking 

The  water  heating  and  cooking  energy  consumption  are  calibrated  and  expressed  as 
functions of population, HDD, GDP (for residential buildings) and service value added (for 
service  buildings),  as  shown  in  Figure  8  (the  corresponding  equations  are  detailed  in 
annex 3).  

16 

Figure 8. Water heating (top) and cooking (bottom) useful energy per capita, in residential (left) 
and service (right) buildings. 

Source: based on IDEES, ODYSSEE and some estimates by Enerdata, 2000 – 2010. 

The main outlying data are countries with a relatively low reliability, and usually show a 
trend of converging towards the identified formula. For both water heating and cooking in 
the  residential  sector,  the  countries  with  the  highest  difference  with  the  rest  of  data 
sources  are  corrected  and  brought  to  a  range  around  the  formula,  defined  by  the 
standard deviation of all available data. For the few countries without historical data, the 
formulas are used.  

The new useful energy needs is obtained by following the same absolute variation as the 
formula. There is a ceiling (level of the formula for 80 k$/cap of GDP/cap for residential 
or 80 k$/cap of service value added for services).  

The need for new energy installations per fuel is calculated as with space heating. Coal, 
traditional  biomass  and  heat  follow  their  dynamic,  while  oil,  gas  and  hydrogen,  modern 
biomass  and  electricity  are  in  competition  for  filling  the  gap  created  by  the  updated 
consumption and the remaining non-decommissioned equipment. 

17 

 
 
3.2.3  Space cooling 

The factors impacting space cooling are Cooling Degree Days (CDD) (13), electricity prices 
and  economic  development  (GDP  for  the  residential  sector,  service  value  added  for  the 
service sector). 

The consumption of space cooling in the residential sector is decomposed in two factors: 
the  Air  Conditioning  (AC)  equipment  rate  of  households  (itself  decomposed  into 
maximum  penetration  potential  and  actual  equipment  saturation)  and  the  consumption 
per equipped household.  

The maximum penetration is calibrated on existing data. 

The actual level of saturation is dependent on the starting point and on time. 

The energy consumption of a single equipped household is assumed to be dependent on 
the climate (CDD) but also on the proportion of the household expenditures dedicated to 
space cooling: the budgetary coefficient. 

R65C@65:4;ST,UVWX =

YZC:GG9[84CB9847[B
\6]:>C>	G9;95@65:4;ST

^C[<956@F	=49??:=:9;5	4?	BG6=9	=447:;<	CB9?C7	9;9@<F	4?	9ZC:GG9[	84CB9847[B

This  coefficient  follows  its  historical  trend  of  evolution  at  the  beginning  of  the  scenario, 
but then converges towards the formula. 

=

RG6=9	=447:;<	979=5@:=:5F	=4;BC>G5:4;
YZC:GG9[	[_977:;< ∗ 9??:=:9;=F

∗

979=5@:=:5F	G@:=9
`La

The space cooling energy used in services is linked to value added of services, CDD and 
to a price effect, as shown in Figure 9 (see also annex 3 for full equations). 

Figure 9. Service space cooling, with suggested formula.  

Source: based on IDEES, ODYSSEE and some estimates by Enerdata, 2000 – 2010. Countries with CDD under 
150 were excluded. 

The  useful  energy  used  in  services  for  space  cooling  evolves  with  the  same  absolute 
variations  as  the  formula  (therefore  reacting  to  changes  in  CDD,  population,  service 

(13) The reference temperature for computing CDD is 18°C. 

18 

 
 
 
 
                                           
value  added  and  electricity  prices).  There  is  also  a  ceiling  of  the  effect  of  service  value 
added (80k$/cap, like in service water heating and cooking). 

3.2.4  Appliances and lighting 

Based on IDEES and ODYSSEE databases, appliances and lighting can be linked to wealth 
(GDP  per  capita  for  residential  sector,  service  value  added  for  services).  The  suggested 
formulas  are  chosen  to  fit  better  the  IDEES  and  ODYSSEE  data  points  (see  Figure  10 
and annex 3 for the corresponding equations).  

Figure 10. Appliances (top) and lighting (bottom) energy consumption per capita in residential 
(left) and service (right) sectors 

Source: based on IDEES and ODYSSEE data and (Fleischmann, 2015), 2000 - 2014. 

The  evolution  in  time  of  the  per-capita  consumption  of  appliances  and  lighting  shows 
additional characteristics. Countries with low per-capita revenues are picking up on richer 
countries  in  terms  of  modern  appliances  (television,  mobile  phones,  refrigerators,  etc.). 
Therefore this additional component is added on top of the formula for appliances. 

19 

 
 
For lighting, the overall trend is negative but no specific driver was found, with a general 
improvement of efficiency across all countries. 

There is also a price effect (elasticities of -0.33 for appliances and -0.7 for lighting) and 
an additional impact of carbon policies for appliances, which represent improved  energy 
regulation  of  appliances.  The  trajectories  of  per-capita  consumption  of  all  countries  and 
regions are influenced by the historical starting point and historical trend. 

As  a  result  of  this  modelling,  a  developing  country  will  see  a  measurable  increase  in 
consumption as the GDP increases, but will then stabilise or decrease. 

3.3  Transport 

POLES-JRC  projects  vehicle  stocks  per  engine  type,  related  energy  use  and  GHG 
emissions. It allows a comparison of energy consumption and emissions across all modes 
and regions for different scenarios. 

Transport  energy  demand  satisfies  the  needs  of  passenger  mobility  and  freight.  In  road 
transport,  the  modelling  includes  a  detailed  representation  of  the  vehicle  stock  and 
propulsion technologies; in rail, air and water transport energy demand is directly related 
to the activity indicators. 

3.3.1  Mobility 

3.3.1.1  Passengers 

Passenger  mobility is  expressed  in passenger-kilometres  and  takes  place  on  land (road, 
rail)  and  by  air.  It  is  driven  by  income  and  energy  prices  in  the  different  modes,  with 
partial  substitution  taking  place  across  modes:  private  means  (cars,  motorcycles)  or 
public means (buses, rail, air). 

For  private  means,  the  vehicles  are  modelled  explicitly,  with  a  vehicle  stock  and  new 
annual  sales.  The  total  mobility  is  the  product  of  the  vehicle  stock  and  the  average 
mileage per vehicle. 

The  vehicle  stock  is  defined  by  a  per  capita  equipment  rate,  influenced  by  income  and 
capped  by  a  saturation  level;  for  motorcycles,  the  equipment  rate  decreases  with  the 
income per capita. 

The  average  mileage  per  vehicle  is  driven  by  the  equipment  rate  (more  vehicles 
translates  into  lower  usage  per  vehicle)  and  average  fuel  price  (decrease  of  use  with 
higher prices). Motorcycles are bundled with private cars by translating their mobility into 
a ‘car equivalent’. 

20 

Figure 11. Equipment rate vs. income per capita for select G20 countries, 1990-2015 

800

700

600

500

400

300

200

100

0

p
a
c
.
k
/
h
e
V

USA

Canada

Australia

Japan

S Korea

Saudi Arabia

EU28

Russia

Brazil

India

0

5

China
10

15

20

25

30

35

40

45

50

Income per capita  (k$05 PPP / cap)

Source: equipment rate (Eurostat, 2015), (OECD, 2014). 

The land-based mobility by public means (buses, rail) is driven by per capita income and 
average fuel prices through a positive elasticity which translates partial substitution with 
private means. 

Air  mobility  grows  with  GDP  per  capita  (positively)  and  by  the  average  fuel  price 
(negatively,  considering  both  fossil-based  kerosene  and  liquid  biofuel).  A  distinction  is 
made between domestic and international air transport. An additional distinction is made 
for intra-EU air transport. 

Figure  12  shows  the  average  evolution  at  world  level,  which  has  increased  from 
5 000 km  per  capita  in  1990  to  about  8 000 km  in  2015.  While  there  is  still  a  large 
potential for mobility increase in non-OECD countries with increasing income per capita, 
it seems to have stabilised over the last few years in OECD countries. 

Figure 12. Passenger mobility, average by mode (left), as a function of income (right) 

World

Air (international)

Air (domestic)

Rail

Road (public)

Road (private)

8000

7000

6000

5000

4000

3000

2000

1000

r
a
e
y
/
p
a
c
/
m
k

0
1990

1995

2000

2005

2010

2015

r
a
e
y
/
p
a
c
/
m
k

18000
16000
14000
12000
10000
8000
6000

4000
2000
0

0

1990-2015

OECD

World

Non-OECD

10

20

30

Income per capita (k USD/cap)

Source: Road (public & private) from (Eurostat, 2015), (OECD, 2014), Rail (UIC, 2014), Air (ICAO, 2017). 

21 

 
 
 
 
3.3.1.2  Freight 

The model describes freight transport in road, rail, air and maritime ships. 

Rail  and  road  freight  in  each  country  and  region  evolve  with  GDP,  with  saturation 
depending  on  population  size.  Road  transport  distinguishes  between  light  trucks  (up  to 
0.5 tonnes) and heavy trucks. 

Air  freight  transport  grows  with  GDP  (positively)  and  is  negatively  impacted  by  the 
average fuel price (which is a blend of fossil-based kerosene and liquid biofuel). 

Maritime  transport  is  modelled  at  the  global  level  and  differentiated  per  commodity 
(UNCTAD  2015).  The  development  of  maritime  transport  is  influenced  by  commodity-
specific drivers. 

Table 1. Commodities of maritime transport and their drivers 

Commodity 

Driver 

Oil and liquid biofuel 

Export and import of oil and biofuel 

Coal and solid biomass 

Export and import of coal and biomass 

LNG 

Iron 

LNG flows 

Primary steel production 

Chemical industry 

Value added of chemical industry 

Other industry 

Value added of other industry 

Containers 

Grain 

Value added of all industry 

Cereal trade14 

Figure 13. Freight mobility, world 

160000

140000

120000

100000

80000

60000

40000

20000

m
k
t
G

0
1990

Aviation

Maritime (other goods)

Maritime (oil/gas/coal)

Rail

Road

1995

2000

2005

2010

2015

Source: Road (public & private) from (Eurostat, 2015), (OECD, 2014), Rail (UIC, 2014), Maritime  (UNCTAD, 
2015), Air (ICAO, 2017). 

(14)  Based on look-up curves that take into account the reaction of cereals trade to the price of carbon and the 

price of biomass-for-energy, derived from the GLOBIOM model (IIASA 2016a) 

22 

 
 
                                           
3.3.2  Energy consumption in transport 

3.3.2.1  Road 

In  road  transport,  energy  service  needs  (for  passenger  mobility  or  freight  tonnage)  are 
separated in five vehicle types: cars, motorcycles, buses, light trucks and heavy trucks. 

The model describes six different engine technologies: 

Table 2. Vehicle types 

Vehicle types 

Description 

Conventional vehicles 

Internal Combustion Engine (ICE), which can function with 
gasoline or diesel or a blend of either with liquid biofuels 

Plug-in hybrid vehicles  

Combine  an  ICE  engine  and  an  electric  battery  that 
consumes electricity 

Full-electric vehicles 

Full electric battery vehicle 

Gas vehicles 

Compressed natural gas (CNG) driven vehicle 

Hydrogen fuel cell vehicles 

Fuel cell with a hydrogen tank and storage 

Other fuel cell vehicles 

Fuel  cell,  in  which  the  hydrogen  is  produced  on-board  by 
using  e.g.  natural  gas,  methane,  ethanol,  methanol  or 
other carbohydrates 

Liquid biofuels can penetrate as blends with oil-based liquid fuels, in the consumption of 
ICE and hybrid vehicles. Their penetration is driven by price considerations or standards 
and  is  capped  by  a  technical  maximum  blending  (differentiated  for  biodiesel  and 
bioethanol). 

In  each  time  step,  the  total  demand  for  vehicles  and  the  remaining  vehicles  per  engine 
type  after  scrapping  are  calculated,  determining  the  needs  for  new  sales  within  each 
vehicle type. 

Investment costs for vehicles with different engine technologies are determined based on 
separate cost developments of their components body, powertrain and engine. 

Table 3. Vehicle components 

Vehicle component 

Parts 

Body 

Body  (e.g.  cabin),  exterior  (e.g.  doors),  interior  (e.g.  seats), 
chassis  (e.g.  breaking  system),  electronics  (e.g.  convenience 
electronics) 

Powertrain 

Powertrain (e.g. transmission, electric motor) 

Electronics (e.g. drive electronics, board network) 

Engine 

Engine (e.g. base  engine,  engine cooling system, fuel system, 
battery) 

Electronics (e.g. engine management) 

23 

For  each  vehicle  type  a  cost  development  on  the  component  level  was  assumed.  The 
assumptions  consider  the  cost  development  of  e.g.  batteries,  electric  motor,  hydrogen 
tank and fuel cell. 

Technology  substitution  among  the  engine  technologies  occurs  in  the  new  sales,  based 
on the vehicles’ cost of use considering the annualised fixed cost (investment, a sector-
specific  time  discounting  factor (15)  and  lifetime)  as  well  the  variable  cost  (consumption 
and  fuel  price).  An  additional  maturity  factor  accelerates  or  decelerates  the  adoption  of 
new  technologies,  reflecting  the  development  of  new  infrastructure  and  consumer 
preference. 

Figure 14. Schematic representation of road transport in POLES-JRC 

Energy service 
needs: mobility 
passengers, freight 

Passengers 

retirement of old 
vehicles 

Technology choice 
on new sales 
vehicle cost, fuel cost 

price-adjusted 
mobility, 
penetration of 
biofuels 

Cars 

Motorcycles 

Buses 

Light trucks 

Heavy trucks 

Goods 

Light vehicles 

6 engine 
technologies 

Heavy vehicles 

6 engine 
technologies 

Vehicle use 
energy consumption, 
emissions 

Fuel efficiency evolves with a price effect. Fuel or emission standards on new vehicles can 
be imposed. 

Private and commercial vehicles use different prices for oil products as a consequence of 
distinct taxation regimes they are exposed to. 

Actual energy consumed and GHG emissions are the result of the use of the vehicle stock 
considering behavioural effects via short-term price elasticities. 

3.3.2.2  Rail 

Rail  satisfies  energy  services  for  passengers  (passenger-kilometres  grow  with  GDP  and 
with price of road transport) and for freight (tonne-kilometres grow with GDP). 

Total  rail  transport  energy  demand  then  follows  the  evolution  of  the  total  rail  mobility 
need. It can be satisfied by three different types of fuels: electricity, oil and coal, with the 
last two following historically calibrated trends. 

(15)  See footnote 6. 

24 

 
                                           
3.3.2.3  Air transport 

Energy consumption for air transport is driven  by the evolution of air transport demand 
(in passenger-kilometres and tonne-kilometres) and fuel efficiency of aircrafts.  

The aircraft fleets are determined by the existing fleet, new aircrafts and scrapping. Each 
year a certain share of  planes is scrapped (1/lifetime). New planes are brought into the 
market to fill the gap of scrapping and to meet the new demand.  

Efficiency  parameters  are  calculated  for  both  the  fleet  and  the  new  purchased  aircraft. 
Flights  with  low  ranges  (e.g.  560  km)  have  a  rather  low  efficiency  (around  4  liter/100 
seat-km). The specific consumption decreases with higher ranges and has its optimum at 
around 2000 km, increasing thereafter due to the increase of the weight of fuel on large 
distance flights.  

Figure 15. Fuel efficiency of new aircrafts 

4.5

4.0

3.5

3.0

2.5

2.0

1.5

1.0

0.5

0.0

n
o
i
t
p
m
u
s
n
o
c

c
i
f
i
c
e
p
s

)
t
a
e
s
/
m
k
0
0
1
/
r
e
t
i
l
(

80s

90s

2000s

2010s

0

2000

4000

6000
range category (km)

8000

10000

12000

Source: own calculation based on various sources from manufacturers. 

To  capture  the  effect  of  the  link  between  fuel  efficiency  and  range,  new  aircrafts  are 
distinguished  between  five  distance  classes.  For  each  country,  the  average  flight 
distances for domestic and international air transport are exogenously calculated (based 
on  statistics)  by  dividing  passenger  traffic  (in  passenger-kilometres)  by  the  number  of 
passengers carried. 

The efficiency can further improve, based on the fuel price of the region or country (16). 
This is  determined  using  Marginal  Abatement Cost  Curves  (MACC)  for  new  aircrafts  and 
for  the  existing  fleet  (IATA  2013;  Dahlmann  et  al.  2016).  Among  the  most  important 
measures  for  the  existing  fleet  are  a  change  of  flight  patterns  (lower  altitudes  for 
planes),  air  traffic  management  (including  ground  handling),  engine  retrofit  and  cabin 
weight  reductions.  For  new  aircrafts  the  focus  is  set  on  the  next  generation  aircrafts, 
redesigned  for  new  flight  patterns  and  more  efficient  engines.  The  implementation  time 
for  these  measures  depends  on  the  region  or  country  income  per  capita  (10  years  for 
highest income; 20 years for lowest income). In addition, best technologies and practices 
diffuse to third countries with a 10 year time lag. 

The  energy  consumption  is  derived  from  the  air  transport  activity  combined  with  the 
overall efficiency (existing fleet and new aircrafts). Calibration factors are used to adjust 
the  theoretical  fuel  efficiencies  with  the  real  fuel  consumption.  As  the  efficiency  is 
measured  in  energy  per  seat-kilometre,  air  transport  activity  has  to  be  converted  from 

(16)  New  aircrafts  are  not  designed  specifically  for  the  most  expensive  countries  or  regions,  therefore  in  the 

computation the effect of fuel price in these countries is capped. 

25 

 
                                           
 
 
passenger-kilometre to seat-km, using a country-specific occupancy rate, which remains 
constant over time. In a similar way, activity in ton-km is translated into pkm assuming a 
weight of each passenger of 100kg (including baggage). Furthermore it is considered that 
half  of  the  freight  transport  is  carried  by  passenger  aircrafts  and  the  other  half  by 
dedicated  freight  aircrafts.  These  are  around  10%  more  fuel  efficient  than  passenger 
aircrafts. 

Finally,  energy  needs  for  air  transport  are  split  between  kerosene  and  aviation  biofuel, 
based on a price competition. 

3.3.2.4  Waterways 

Oil consumption for domestic water transport (domestic sea lines, inland water transport) 
is determined by GDP and by fuel prices. 

3.3.2.5  International maritime bunkers 

Energy  consumption  for  maritime  bunkers  is  driven  by  the  evolution  of  maritime 
transport (in tkm) and fuel efficiency of ships (in toe/tkm).  

The  determination  of  ship  fleets  considers  the  existing  fleet,  new  ships  and  scrapping. 
Each  year  a  certain  share  of  ships  is  scrapped  (1/lifetime).  New  ships  are  brought  into 
the market to fill the gap of scrapping and to meet the new demand.  

Driven  by  bunker  fuel  prices,  the  efficiency  can  improve  along  two  MACC,  one  for  new 
ships  and  one  for  the  fleet  (IMO  2015).  Among  the  most  important  measures  for  the 
existing  fleet  are  speed  reduction,  retrofit  (e.g.  hull),  propeller  maintenance  and 
operational  measures.  For  new  ships  the  focus  is  set  on  propeller  and  propulsion 
measures, hull coating and air lubrication.  

Energy  consumption  for  maritime  bunkers  is  determined  by  the  demand  for  maritime 
transport  (expressed  in  tonne-kilometres)  and  by  fuel  efficiency.  The  global  maritime 
transport volume is split into countries and regions based on the drivers listed in Table 1 
of  the  freight  mobility  section  3.3.1.2  (e.g.  export  and  import  of  oil  of  country  A  is 
divided by global export and import of oil).  

The fuel consumption is distributed between oil, gas, biofuel and hydrogen depending on 
the  relative  prices  of  those  fuels  and  some  additional  limitations  (e.g.  oil  tankers  use 
mostly oil, LNG tankers mostly gas).  

Figure 16. International bunkers’ energy consumption and their share in total transport energy 
consumption 

e
o
t
M

3000

2500

2000

1500

1000

500

0

30%

25%

20%

15%

10%

5%

0%

Aviation bunkers

Maritime bunkers

Domestic transport

% Bunkers

1990

1995

2000

2005

2010

2015

Sources: IEA 2017a, Enerdata 2017a 

26 

 
3.4  Agriculture 

This sector actually encompasses energy consumed in the agriculture sector, fishing and 
forestry. It includes oil demand for running tractors and agricultural equipment. 

For each fuel, total energy demand is determined by the value added of agriculture and 
an  additional  trend  depending  on  income  per  capita,  which  reflects  the  potential 
intensification of agricultural production. 

Climate  policies  negatively  affect  oil  and  gas  consumption  that  are  substituted  by 
biomass and electricity. 

3.5  Energy prices 

Final  user  energy  prices  are  calculated  from  the  variation  of  import  prices  (themselves 
derived from the variation of international prices) to which is applied: 

● 

the value added tax (in percentage), 

●  scenario-specific energy fiscal policy evolution (taxes, subsidies), 

●  environmental policy elements (e.g. carbon pricing). 

By  default  the  volumes  of  price  components  not  explicitly  represented  in  the  model 
remain  the  same  as  historical  levels  (excise  taxes,  transport  and  distribution  duties, 
other taxes and duties). 

Subsidised  fuels  are  identified  at  the  start  of  the  simulation  by  comparing  final  user 
prices  with  a  fuel-specific  reference  price,  which  is  the  import  price  or  the  fuel  price  at 
the closest energy market (for fuel exporters), plus value added tax. The subsidies ratio 
can then be kept constant or can be progressively phased out. 

Domestic  final  user  prices  of  transformed  fuels  (electricity,  hydrogen,  synthetic  liquids) 
are  deduced  from  the  evolution  of  production  costs.  Transport  and  distribution  costs  as 
well as excise taxes are assumed to remain constant. For electricity, the production costs 
of base load production are assigned to the price for industry and those of peak load to 
residential services. 

27 

4  Energy transformation 

Energy  transformation  comprises  all  activities  of  energy  conversion  from  primary  forms 
of  energy  to  end-use  energy.  Energy  transformation  consists  of  several  sectors (17): 
refining of oil and gas, production of electricity, heat (and co-generation with electricity), 
hydrogen  and  synthetic  liquid  fuels  from  coal,  gas  and  biomass.  Most  prominent  within 
energy  transformation  is  the  electricity  sector,  in  which  a  broad  range  of  energy 
carriers/fuels are converted to electricity. 

4.1  Electricity sector 

Electricity is an energy carrier that has been experiencing an increasing role in the final 
energy demand, driven by the evolution of the economy towards services, electrification 
in  industry  and  the  widespread  uptake  of  electronic  consumer  devices  (including  ICT 
applications).  Figure  17  shows  that  this  applies  to  all  regions  and  that  non-OECD 
countries in particular are catching up quickly. 

At  the  same  time,  the  power  generation  sector  appears  as  a  key  sector  for 
decarbonisation  with  various  mitigation  options  that  are  relatively  easy  to  implement 
compared  to  the  end-user  side.  This  further  supports  electrification  as  a  way  to  meet 
carbon constraints. 

Figure 17. Share of electricity in total final energy consumption, 1990-2015 

25%

20%

15%

10%

5%

0%

1990

1995

2000

2005

2010

2015

World

OECD

Non-OECD

Sources: IEA 2017a, Enerdata 2017a 

4.1.1  Technologies for power supply 

The electricity generating technologies (Table 4) include existing technologies as well as 
emerging or future technologies. They are categorised as either centralised technologies, 
for  which  operation  and  investments  are  based  on  a  competition  between  grid-level 
plants,  or  decentralised  technologies,  which  compete  with  the  retail  prices  perceived  by 
consumers. 

The  technologies  are  initialised  for  each  country  with  the  installed  capacities  and  its 
vintage  (Platts  2015),  share  of  new  capacities,  electricity  generated  by  technology  and 
fuel as well as observed load factors. 

(17)  The  transformation  of  coking  coal  is  not  covered  in  this  sector  but  in  the  iron  and  steel  industry  sector. 
Final  energy  consumption  in that  sector  includes  coal consumption  for  its  conversion  into  coke  in  coking 
ovens. 

28 

 
                                           
Each  technology  has  the  following  technical  characteristics:  input  fuel,  transformation 
efficiency, lifetime, self-consumption rate and CO2 capture rate, if applicable. 

The economic characteristics are: 

●  Fixed cost: 

(cid:1) 

investment, which evolves according to technology learning curves (see 
Section 2.2.3 Energy technology dynamics), 

(cid:1)  operation and maintenance (O&M), 

(cid:1) 

(cid:1) 

subsidies or taxes on investments, 

for CCS technologies, CO2 capture costs and related loss of efficiency; 

●  Variable cost: 

(cid:1) 

fuel cost, 

(cid:1)  variable O&M cost, 

(cid:1) 

(cid:1) 

subsidies  or  taxes  on  power  output  or  fuel  input  (including  a  potential 
carbon value), 

for CCS technologies, CO2 transport and storage costs, 

●  Discount rate. 

In addition, renewables have a maximum resource potential (see Chapter 5 on renewable 
potential).  Similarly,  the  deployment  of  CCS  technologies  is  linked  to  region-specific 
geological storage potential (and how saturation is anticipated over time). 

In  addition  to  these  technical  and  economic  characteristics,  non-cost  factors  are 
calibrated  to  capture  the  historical  relative  attractiveness  of  each  technology  for  each 
country,  in  terms  of  investments  and  of  operational  dispatch.  In  the  scenarios,  these 
coefficients  evolve  depending  on  assumptions  of  future  societal,  political  and  market 
factors. Some future technologies become mature in the future (CCS, new nuclear). 

Considering a learning curve (cost component) and an increasing cost-based competition 
(decreased role of the non-cost component), the diffusion process follows a truly dynamic 
approach with path-dependency. 

29 

Option with CCS 

Yes 

Yes 

Yes 

Table 4. Electricity generating technologies 

Fuel 
Nuclear 

Technologies 
Conventional nuclear design 

New nuclear design (e.g. Generation IV) 

Coal 

Lignite conventional thermal 

Coal conventional thermal 

Pulverized coal supercritical 

Integrated coal gasification with combined cycle 

Gas 

Gas conventional thermal 

Gas turbine 

Gas combined cycle 

Gas fuel cell* 

Combined Heat and Power (CHP) (*) (**) 

Oil 

Oil conventional thermal 

Oil-fired gas turbine 

Water 

Large hydro 

Small hydro (< 10 MW) 

Tidal and wave  

Geothermal  Geothermal power 

Biomass 

Biomass conventional thermal 

Biomass gasification  

Yes 

Wind 

Wind onshore (3 different resource quality areas (***)) 

Wind offshore (3 different resource quality areas (***)) 

Solar 

Photovoltaic (PV) power plant (centralised) 

Decentralised PV (*) 

Solar thermal power plant 

Solar thermal power plant with thermal storage 

Hydrogen 

Hydrogen fuel cell (*) 

(*) These technologies are considered as decentralised; they compete with grid electricity. 
(**) Gas-fired CHP is considered as driven by electricity needs, heat co-generation is a by-product. 
More information on heat production can be found in Section 4.2. 

(***) The onshore and offshore wind technologies have each been divided into three types of wind 
resource potential, based on the average wind speed for onshore technologies and on the average 
wind speed and distance to the coast for offshore technologies. 

4.1.2  Electricity demand 

The  total  electricity  demand  is  computed  by  adding  the  electricity  demand  from  each 
sector  presented  in  the  previous  section:  residential,  services,  transport,  industry  and 
agriculture.  This  is  complemented  by  ‘other  consumptions’,  which  include  the  self-
consumption of power plants by technology (or of other energy transformation sectors), 
the grid losses, the water electrolysis consumption (for hydrogen production) and the net 
electricity exports. 

The evolution over time of the sectoral electricity demand is driven by the activity of each 
sector, as well as by the relative fuel prices for energy needs where electricity is in direct 
competition with other energy carriers. 

The  specific  challenges  of  the  power  system  require  a  detailed  representation  of  the 
electricity  demand.  In  particular,  with  increasing  shares  of  wind  and  solar  power  in  the 

30 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
system,  the  load  to  be  covered  by  dispatchable  sources  is  ever  more  impacted  by  the 
non-dispatchable,  intermittent  wind  and  solar  supply.  Therefore  when  building  the 
electricity load curve, we account for: 

● 

● 

the hourly variations (demand, wind, solar), 

the daily patterns (demand and solar),  

●  and the simultaneity between demand and generation from wind & solar.  

These characteristics are described by a set of representative days with an hourly time-
step from which the annual load curve is approximated. 

4.1.2.1  Data used 

The  electricity  load  data  was  collected  for  a  wide  range  of  countries  around  the  world, 
shown in Figure 18. 

Figure 18. Map of the electricity load data collected 

For some countries and regions, the electricity load is based on a neighbouring country or 
a representative country within the region. In this way we reconstitute reasonable hourly 
load profiles for all POLES countries and regions. 

The  meteorological  data  used  to  compute  the  hourly  production  of  wind  and  solar 
capacities (wind speeds, solar irradiation, temperature) is based on NASA data (MERRA2 
database, (NASA 2015a, 2015b)), which provided a global set of gridded data. The wind 
and solar production is then computed based on a typical production curve and a mix of 
current  and  future  expected  locations  of  plants  (impacted  by  today's  population  density 
and the areas with high resource). 

4.1.2.2  Representative days 

The  representative  days  are  chosen  separately  for  each  country  or  region.  They  are 
based on the simultaneous profiles of electricity load and of wind and solar generation. A 
hierarchical clustering algorithm is used to group the data into a pre-defined number  of 
days  (six  in  the  general  case).  The  objective  of  the  algorithm  is  to  keep  a  maximum 
diversity  of  days,  in  terms  of  demand,  wind  production  and  solar  production18,  while 

18 One cluster is composed of 72 components: 24 hours of demand, 24 hours of wind production and 24 hours 
of solar production, each being normalised to their annual maximum. At each aggregation step, each day 

31 

 
                                           
reducing  the  amount  of  data  treated  in  POLES.  Each  representative  day  represents  a 
different number of days of the year (see Figure 19). 

Figure 19. Illustration of the aggregation of days into six representative days 

An analysis of the days grouped in each "representative day" give insights on the share 
of business days, Saturday or Sunday, the share of summer, winter or swing season, the 
average temperature, or the daylight hours. 

The  description  by  representative  day  was  validated  by  evaluating  the  accuracy  of  net 
load (load minus generation form wind & solar). Net load is the relevant quantity as the 
non-dispatchable  energy  sources  have  a  zero-marginal  cost  and  under  normal  system 
operation  will  have  priority  before  dispatchable  plants  with  positive  production  costs19. 
The comparison of the representative days with the actual hourly data shows a relatively 
small loss of accuracy. The loss of accuracy induced by the representative days depends 
on  the  number  of  days;  a  study  of  the  total  error  induced  (see  Figure  20)  led  to  the 
choice of keeping six days per year. 

or cluster of days is compared to the others with an Euclidian distance; the number of clusters is reduced 
progressively by merging the clusters that least increase the Euclidian distance of the resulting clusters to 
the original dataset.  

19  A  possible  exception  is  the  thermal  plants  seeing  a  benefit  in  temporary  negative  bidding  due  to  lack  of 

flexibility. 

32 

 
                                                                                                                                    
Figure 20. Illustration of the accuracy loss due to the use of representative days 

The three zones where the load net of wind and solar shows a loss of accuracy are: 

1.  The  maximum  residual  load,  of  particular  relevance  for  the  dimensioning  of  the 
peaking  plants  investments  needs.  This  error  is  computed  for  each  region  and 
applied in the modelling of the capacity planning process (see section below). 

2.  The overall shape of the residual load duration curve, impacting the number of full 

load hours of dispatchable plants. 

3.  The  lowest  residual  load,  impacting  the  baseload  needs  and/or  the  curtailed 

energy due to over-production. 

The share of wind and solar in the system impact the three types of error, which is also 
reflected in the modelling of the capacity planning. 

4.1.2.3  Description of the electricity load curve 

The  annual  electricity  demand  and  its  decomposition  per  end-use  are  given  by  other 
sections of the model. 

The  end-uses  are  distributed  between  the  chosen  representative  days  based  on  the 
characteristics  of  the  day:  temperature,  day  of  the  week,  season.  Each  end-use  has  an 
associated  profile,  collected  from  the  literature  and  calibrated  to  match  the  actual  total 
electricity load profile.  

Therefore,  the  total  electricity load  curve  of  each  representative  day  is  impacted  by  the 
evolution of each electricity end-use. 

4.1.3  Operation of the power system 

The  power  sector  operation  assigns  the  generation  by  technology  to  each  hour  of  each 
representative  day.  The  supplying  technologies,  grid  imports  and  production  of  storage 
technologies must meet the overall demand, including the grid exports and consumption 
of storage technologies. In cases of over-supply, some production curtailment is possible. 
The  operation  of  the  power  system  in  POLES  is  simulated  following  the  priorities  and 
rules shown below. 

The  decentralised  production  is  considered  first.  This  includes  decentralised  PV, 
decentralised  CHP,  small  hydro  and  stationary  fuel  cells.  They  are  considered  to  be 

33 

 
distributed  at  the  customer  site  and  thus  compete  with  the  retail  electricity  price.  Their 
production is deduced from the total demand with set production profiles. 

The  resulting  grid-level  demand  is  covered  in  priority  by  non-dispatchable  centralised 
technologies  (wind,  large  solar  with  or  without  thermal  storage,  hydro  run-of-river, 
marine).  They  produce  according  to  specific  profiles,  which  for  wind  and  solar 
technologies  are  defined  by  the  clustering  algorithm  presented  above.  The  rest  of  the 
demand has to be met with dispatchable centralised technologies. 

Nuclear  and  hydro  lakes  are  calculated  first.  Hydro  lakes  offer  a  strong  flexibility  at  the 
yearly  and  daily  timescales  (from  full  rated  power  down  to  5%  of  average  production 
level), while nuclear offers some flexibility (only a few percentage points) on a seasonal 
scale in terms of planned outages for maintenance works. Their profiles can adapt to the 
total load profile but also to the production of non-dispatchable energy sources (including 
wind and solar). 

Some demand sectors also mitigate the variations of the remaining load across the year 
and the representative days: 

●  The  total  annual  amount  of  net  exports  is  fixed  in  time,  but  the  hourly  profile 

partly accommodates the remaining load; 

●  Hydrogen  production  from  electrolysis  can  absorb  electricity  across  the  year, 

except during peak net load hours; 

●  Electric  Vehicles  have  a  smart  charging  across  the  day  (lower  demand  when 
other  sectors'  load  is  high),  although  vehicles  are  topped  up  at  the  end  of  the 
night (not all the load can be placed at sunlight hours); 

●  Hydro  pumping  and  other  storage 

technologies  (stationary  batteries, 
Compressed  Air  Energy  Storage,  Vehicle-to-Grid  and  Demand  Side 
Management) add further flexibility, storing when the remaining demand is low 
and  producing  when  it  is  high.  Each  technology  has  its  characteristics  but  the 
modelling limits their operation within a day. 

Finally, production curtailment is allowed in the case of a combined oversupply of solar, 
wind, hydro, marine and nuclear power and once storage consumption is accounted for. 

The remaining technologies, constrained by their available capacity on each hourly block, 
compete  based  on  their  variable  production  costs  taking  into  account  a  non-cost  factor 
based on the historical tendencies of dispatching practices. 

The  electricity  prices  are  based  on  the  result  of  this  dispatch:  a  price  for  industrial 
consumers  is  derived  from  the  evolution  of  the  average  cost  of  supplying  the  industrial 
loads, while the electricity price for other consumers follows the evolution of the average 
cost of supplying the non-industrial loads. 

The operation of the power technology also gives the overall primary fuel consumption of 
the power sector. 

4.1.4  Planning of electricity capacities 

4.1.4.1  Production capacities 

Decentralised  technologies  are  planned  separately,  and  compete  directly  with  electricity 
retailing companies. All other technologies are developed in a balanced mix to cover the 
expected  electricity  demand  from  the  grid.  The  capacity  planning  modelling is  an  intent 
to account for the particular non-dispatchable nature of wind and solar. 

The  current  and  expected  load  is  described  by  the  days  chosen  with  the  clustering 
algorithm,  at hourly  time-step.  Using  six  days  gives  144  time-steps  of  different  weights 
(according to the weight of each of the representative days).  

34 

The  investment  decisions  are  taken  based  on  the  10-year  trend  of  demand,  net  of  the 
extrapolated contribution of decentralised and storage technologies and of the  expected 
remaining capacities of centralised wind and solar in 10 years.  

1.  The resulting expected net demand is ranked into a residual load duration curve, 
which is then distributed into seven blocks of different expected capacity factors, 
from peak to base load.  

2.  On  each  block,  wind  and  solar  are  confronted  with  dispatchable  plants  in  a  first 

competition ("fictitious" investments).  

3.  It  is  assumed  that  the  investment  block  that  leads to  most  wind  and  solar  gives 
the  total  amount  of  wind  and  solar  built  (because  of  their  particular  non-
dispatchable nature). The investment blocks are then updated by subtracting the 
actual  contributions  of  new  wind  and  solar  in  each  of  the  blocks,  the  rest  of 
expected  load  being  covered  by  dispatchable  plants.  Peak  load  is  also  scaled  to 
account  for  the  loss  of  accuracy  in  peaking  needs  due  to  the  choice  of 
representative days (correction factor calculated prior). 

4.  Dispatchable  plants  are  distributed  per  block  following  a  portfolio  approach, 
accounting  for  both  the  total  production  cost  per  load  block  and  a  historically 
calibrated  non-cost  factor.  This  factor  represents  technical  availability  (e.g.  CCS 
technologies starting in 2030, new nuclear technology starting in 2050), technical 
constraints  (e.g.  flexibility  across  load  blocks),  country  policies  (e.g.  statute  on 
building  new  nuclear)  and  other  non-cost  factors  (calibrated  on  recent  historical 
patterns of investment).  

For  some  technologies,  a  maximum  potential  caps  the  installable  capacities  (CCS, 
biomass, wind, solar, geothermal, ocean).   

Figure 21 shows graphically the capacity planning process in an example country. 

35 

 
 
Figure 21. Illustrative capacity expansion mechanism 

This  process  determines  what  new  wind  and  solar  capacities  are  to  be  built  and  what 
dispatchable  capacities  are  expected  in  total.  The  decommissioning  of  old  plants  is 
captured through the vintage of installed capacities. The expected need for dispatchable 
capacities in 10 years is compared to the capacity expected to remain in 10 years and an 
investment gap is computed. Finally, the actual investments carried out on the following 
year  are  a  tenth  of  that  identified  gap.  In  this  manner,  the  electricity  module  of  the 
model  recognises  the  importance  of  inertia,  caused  by  the  particularly  long  lifetime  of 
equipment.  The  computation  of  expected  needs  is  updated  every  year  (rolling  myopic 
expectations) and the investments adapt accordingly. 

4.1.4.2  Storage and demand response capacities 

Electricity  storage  capacities  and  demand  response  (DR)  capacities  are  not  net 
generating capacities and are therefore not directly competing with generating capacities. 
However, storage and DR can combine several economic values, represented in POLES as 
follows: 

●  Arbitrage:  buying  and  selling  in  the  gross  market;  here  we  focus  on  storage 
within  a  day;  this  value  is  the  sum  across  representative  days  of  all  potential 
profits  allowed  by  power  prices  for  a  storage  plant  operator  (including  storage 
efficiency losses); 

36 

 
 
 
 
●  Capacity:  equivalent  of  capacity  markets  and/or  discharging  at  times  of  very 
high power prices; this is approximated by the minimum of the fixed cost of the 
plants  built  in  a  given  country  or  region,  reflecting  the  cost  of  an  additional 
peaking plant prevented by additional storage; 

●  Ancillary  services:  frequency  and  balancing  reserves,  usually  contracted  with 
system operators; an indicative value is computed (based on hydro revenues in 
the  French  balancing  market  in  2008  and  2013)  and  indexed  on  the  share  of 
wind and solar in the system (because of their variability that the system has to 
cope with).  

The capacity and ancillary service values are shared with already existing storage and DR 
plants. The sum of all values of a potential new storage or DR capacity is compared with 
the annualised investment cost and investment decisions follow accordingly. Storage and 
DR are capped by a maximum potential. 

4.2  Heat production 

Sectoral district heat demand follows a trend. 

The  related  supply  comes  from  co-generation,  either  distributed  or  centralised  in  some 
regions  (represented  in  the  model  as  a  by-product  of  electricity)  or  from  heat  plants 
(which follow the heat demand). 

Heat  from  low-temperature  solar  develops  through  a  logistic  curve  that  compares  the 
cost and potential of solar heat to the average price of fuels for space and water heating. 
A higher return on investment triggers more investments. 

4.3  Hydrogen 

The  complete  processing  chain  for  hydrogen  use,  production,  transport,  delivery  and 
storage is represented. 

4.3.1  Hydrogen demand 

Hydrogen demand comes from: 

●  Stationary sources: 

(cid:1)  hydrogen  fuel  cells  in  industry,  residential  and  commercial  sectors; 
their  use  is  in  competition  with  grid  electricity  and  other  forms  of 
distributed power generation; 

(cid:1)  hydrogen  can  also  be  mixed  with  natural  gas  and  used  for  thermal 

applications; 

● 

transport  sources:  road  transport,  in  private  cars  and  freight  transport,  where 
two types of engine use this fuel: hydrogen fuel cell vehicles and direct thermal 
hydrogen engine vehicles. 

Table 5. Hydrogen demand sectors 

Fuel cell 

Stationary  Distributed power generation in 

Transport 

demand sectors (industry, 
residential, services) 
Engine type in road transport 
vehicles (passenger, freight) 

Direct combustion 
Mixed with natural gas in gas grid 

Engine type in road transport vehicles (passenger, freight) 

The hydrogen prices for each sector are derived from production costs and transport and 
delivery costs (see below). 

37 

 
4.3.2  Hydrogen production 

Hydrogen can be produced through chemical, thermo-chemical or electrical routes. Table 
6 shows the different hydrogen production technologies represented in the model. 

Table 6. Hydrogen production technologies 

Energy input 
Gas 

Coal 

Oil 

Process 
Gas steam reforming 

Coal gasification 

Oil partial oxidation 

Biomass 

Biomass pyrolysis 

Biomass gasification 

Solar 

Solar methane reforming  

Solar thermal high-temperature thermolysis  

Nuclear 

Nuclear thermal high-temperature thermolysis 

Wind 

Grid 

Water electrolysis with dedicated nuclear power plant 

Water electrolysis with dedicated wind power plant 

Water electrolysis from grid electricity 

Option with CCS 
Yes 

Yes 

Yes 

The  projected  hydrogen  production  capacities  are  calculated  on  the  basis  of  the  total 
costs:  investment  costs  and  fuel  costs  (and  storage  cost  for  CCS  options).  Each  year, 
production among the different technologies is distributed based on the variable costs of 
each technology and under the constraints of existing capacities. 

4.3.3  Hydrogen transport 

Due  to  its  relatively  low  volumetric  energy  density,  transportation  and  final  delivery  to 
the point of use is one significant cost component of the hydrogen supply. 

Five transport chains are identified in the model, being combinations of the type of plant 
that produces hydrogen (big, small), the transport means (pipeline, truck) and the type 
of  use  downstream  (direct  use  for  stationary  demand,  refuelling  stations  for  mobile 
demand). 

Table 7. Hydrogen transport chains 

Transport means 

Capacity of production 

Large 

Small 

Pipeline 
1. Direct use 
2. Refuelling station 
3. Direct use 
4. Refuelling station 

Truck 
n/a 

5. Refuelling station 

The calculation of the cost of transport and delivery in the model is realised as the sum 
of: 

● 

● 

● 

the cost of transport, which depends on the hydrogen flow in this chain, on the 
population  density  and  on  the  distance  of  transport  in  this  chain  (itself 
depending on the size of the installations and population density); 

the  delivery  cost,  which  depends  on  the  size  of  production  installations  and 
population density; 

the  variable  cost,  which  depends  on  the  type  of  consumption  for  every  chain 
(electricity or diesel oil for the transport by truck). 

For each demand sector, a loss factor on transport and distribution is added. 

38 

 
 
 
 
 
 
 
 
 
4.4  Synthetic liquids 

4.4.1  Liquids from coal and gas 

Liquids from coal and natural gas can contribute to the demand for liquid fuels. 

The  development  of  liquefaction  is  determined  by  the  comparison  of  the  process  cost 
with  the  difference  between  the  value  obtained  from  selling  liquid  products  on  the 
international  oil  market  and  the  value  of  the  coal  or  gas  directly  sold  on  the 
corresponding national or regional market. The diffusion follows a logistic curve. 

The liquefaction processes are described by investment costs and conversion efficiencies. 
Both routes exist with the option to do carbon sequestration. 

Coal liquefaction and gas liquefaction take place in a limited number of regions, identified 
as key coal or gas producers. 

4.4.2  Liquids from biomass 

Different  liquid  biofuel  types  are  distinguished:  first-generation  biofuels  (biodiesel  and 
bioethanol  from  dedicated  agricultural  crops)  and  second-generation  biofuels  (biodiesel 
and bioethanol from cellulosic materials) (see Section 5.3 Biomass for the supply of solid 
biomass). 

Demand for liquids from biomass is driven by competition with fossil-based liquids in the 
transport sector, subject to a technical cap on blending. 

The  model  identifies  four  production  technologies:  biodiesel  first  generation,  biodiesel 
second  generation,  bioethanol  first  generation  and  bioethanol  second  generation.  The 
production  technologies  are  described  with  fixed  investment  costs,  O & M  costs  and  a 
conversion efficiency. Additionally, second-generation technologies exist with and without 
CCS. For each biofuel a cost-based competition takes place to distribute new production 
capacity between the various options. 

International trade is allowed and competes with domestic production. The international 
price is set as the average of global production costs and an international transport cost. 
A cost-based competition takes place to allocate that production, based on each region’s 
production cost and each region’s remaining potential for biomass for liquefaction. 

Figure 22. Biofuel production for domestic consumption and for international trade, world 

e
o
t
M

80

70

60

50

40

30

20

10

0

20%

18%

16%

14%

12%

10%

8%

6%

4%

2%

0%

Bioethanol - Traded

Biodiesel - Traded

Bioethanol - Sourced
domestically

Biodiesel - Sourced
domestically

% Traded - total

1990

1995

2000

2005

2010

2015

Sources: IEA 2017a, Enerdata 2017a 

39 

 
4.5  Carbon Capture and Sequestration 

The modelling represents several CCS technologies. 

CO2 capture technologies for CCS are described by a set of techno-economic parameters, 
reflecting  extra  investment  cost  and  decreased  efficiency  compared  to  their  non-CCS 
counterpart  (Freund  and  Davison,  2002);  they  also  include  a  cost  component  for  CO2 
transport  and  storage  (Zero  Emissions  Platform,  2011),  which  consists  in  a  cost  curve 
that increases with the use of the storage potential. 

The  power  sector  allows  the  development  of  CCS  associated  with  biomass  (with 
gasification), gas (combined cycle) and coal (supercritical pulverized coal and integrated 
coal gasification with combined cycle). These technologies are not developed as peaking 
plants. 

Direct  Air  CCS  (DACCS)  is  also  represented  as  a  backstop  option  (Socolow  2011).  Its 
electricity  and  heat  consumption  are  accounted  for.  Its  development  is  dependent  on  a 
return  on  investment  given  its  production  costs  and  revenue  it  could  get  from  funds 
generated by carbon pricing policies. 

Industry can also implement CCS with a cost premium when their process uses biomass 
or  coal  (for  example  in  steel  production).  A  maximum  CCS  penetration  potential  is 
associated  with  each  industrial  branch,  reflecting  the  fact  that  CCS  is  more  likely  to  be 
used in large installations. 

Several  fuel  transformation  processes  can  be  coupled  with  CCS:  second  generation 
biofuels, hydrogen (with biomass gasification, coal gasification and gas steam reforming) 
and liquids from coal or gas. 

The  development  potential  of  CCS  is  constrained  by  technology  availability,  starting  in 
2030  with  a  progressive  adoption  and  diffusion.  The  development  of  CO2  transport  and 
sequestration infrastructure is assumed to stay in the range of the historically observed 
annual  growth  of  oil  and  gas  transport  infrastructure,  or  to  be  able  to  reuse  that 
infrastructure  if  oil  and  gas  demand  decreases.  CCS  is  also  constrained  by  a  geological 
storage  potential  by  region  (IEA  2009,  2010),  and  by  an  anticipation  of  its  saturation; 
ocean storage can then become an option at higher cost. 

4.6  Other transformation and losses 

Losses and self-consumption in oil refineries are determined with an efficiency factor and 
the  ratio  of  oil  products  needs  covered  by  domestic  refineries  (calculated  on  historical 
energy statistics. 

Transport and distribution losses for coal, gas and oil are calculated with factors based on 
historical energy statistics. 

Own-consumption for oil, gas and coal production is calculated per fuel type and adjusted 
to historical statistics. 

The  remaining  energy  consumption  in  the  energy  sector (20)  is  captured  through  a 
coefficient based on historical statistics. 

(20)  Other  treatment  of  fuels  (e.g.  uranium,  gas,  coal  refineries),  gas  infrastructure  (e.g.  operation  of  LNG 

storage facilities) and operation of water distribution system. 

40 

                                           
5  Primary energy supply 

All  existing  primary  energy  fuels  are  represented  in  the  model:  oil,  natural  gas,  coal, 
uranium,  biomass,  hydro,  wind,  solar  and  geothermal.  In  the  case  of  fossil  fuels  and 
biomass the representation further distinguishes fuel types by fuel quality and production 
technology. 

GHG  emissions  from  fossil  fuel  production  and  transport  are  represented  in  the 
modelling:  CO2  from  energy  used  in  resource  extraction  (see  below)  and  CH4  from 
fugitive emissions in both production and transport (see Section 6.1.2.2). 

5.1  Oil and gas  

5.1.1  Geographical disaggregation and fuel types 

The  model  describes  88  producers  of  oil  and  gas  (countries  or  groups  of  countries), 
shown in Figure 23. 

Figure 23. POLES-JRC geographic breakdown for oil and gas  

The following oil types are taken into account: 

crude oil of onshore fields; 
crude oil of offshore fields, including deep-water oil (> 500 m depth); 
tight and shale oil; 

• 
• 
• 
•  extra-heavy oil; 
•  other ‘non-conventional’ resources: bitumen (oil sands) and oil shale (kerogen); 
•  Arctic oil (north of the Arctic Circle). 

In addition, natural gas liquids (NGL) are accounted as oil supply; the amount of NGL is 
derived from gas supply based on the wetness index of each producer. 

For gas, the following fuel types are distinguished: 

•  natural gas of onshore fields; 
•  natural gas of in offshore fields, including deep-water gas (> 500 m depth); 
• 
•  Arctic gas (north of the Arctic Circle); 
• 

coal-bed methane. 

shale gas; 

41 

 
5.1.2  Discoveries: from resources to reserves 

Known  oil  and  gas  fields  which  are  economically  feasible  for  extraction  are  classified  as 
reserves; cumulated discoveries are the historical cumulative production and the current 
reserves.  By  comparison,  the  resources  include  reserves  and  additional  fields  which  are 
not feasible for extraction or undiscovered. Figure 24 represents the segmentation of oil 
resources and reserves and its possible evolution, with economic and technical feasibility 
along its y-axis and the certainty of reserves and resources along its x-axis. The ultimate 
recoverable  resources  (URR,  green  rectangle)  are  crucial inputs  to  the  model,  based  on 
geological surveys (BGR 2015, USGS 2013, Schenk 2012). 

Figure 24. Schematic representation of the oil sector from resources to reserves 

Production 

(A)  
Cumulative 
Production 

(B)  
Reserves 

New discoveries 

c
i
m
o
n
o
c
e
g
n
i
s
a
e
r
c
n

I

y
t
i
l
i

b
i
s
a
e
f

l

a
c
i
n
h
c
e
t
d
n
a

(D)  
Sub-economic 
Resources 

EOR 

Ultimate Recoverable Resources 

(C)  
Undiscovered 
Reserves 

(E)  
Undiscovered 
Sub-economic 
Resources 

Increasing geological certainty 

Source: Derived from McKelvey (1972). 

For each fuel type, the modelling of reserves increases in two ways: 

•  For oil and gas, new fields discovery (arrow from (C) to (B)): each year, the level 
of  new  discoveries  is  defined  by  a  curve  of  decreasing  returns  of  the  remaining 
undiscovered  resources.  For  any  given  year,  discoveries  are  dependent  on  the 
profitability  ratio  of  fuel  market  price  to  production  cost  (i.e.  no  discoveries  are 
made  if  the  production  cost  exceeds  fuel  market  price);  an  example  of  this 
correlation is provided in Figure 25. 

•  For oil, enhanced oil recovery (EOR, arrow from (D) to (B)) that increases the size 
of  discovered  reserves;  for  any  given  year,  the  increase  of  the  recovery  ratio  is 
dependent  on  the  differential  of  oil  price  and  operational  expenditure.  The 
recovery ratio is capped by a maximum ratio of oil in place (typically 70 %). 

The  reserves  are  computed  as  the  difference  between  cumulative  discoveries  (including 
EOR)  and  cumulated  production.  The  production  cost  of  each  producer  is  defined  by  a 
weighted average of production costs for new and existing reserves.  

42 

 
 
 
 
 
 
 
 
 
 
 
 
Figure 25. Correlation of new discoveries (expressed as a share of remaining resources to be 
discovered and scaled by the Reserves/Production ratio) and the Oil price / Production cost ratio, 
for conventional onshore oil  

i

i

P
v
R
*
)
g
n
n
a
m
e
R
/
s
e
i
r
e
v
o
c
s
i
D
%

(

3.0

2.5

2.0

1.5

1.0

0.5

0.0

Observed
Fitting curve (Gompertz form)

0%

200%

400%

600%

800%

1000%

1200%

1400%

1600%

Oil Price / Production Cost

Source: Derived from BP Statistical Review (BP 2015). Data for 2008-2015. 

Note: Discoveries are expressed as a share of the remaining resources to be discovered (resulting 
volume  of  new  discoveries  decreases  as  more  discoveries  are  made)  and  are  scaled  by  the 
Reserves/Production ratio, reflecting historical pattern of exploration activity. 

5.1.3  Production costs 

Production  costs  are  distinguished  between  investment  costs  (R&D,  exploration, 
infrastructure),  operational  costs  (notably  energy  inputs  in  production)  and  additional 
costs  (taxes,  royalties).  Production  costs  per  fuel  type  refer  to  a  range  of  sources  (21). 
Production  costs  are  further  disaggregated  in  direct  energy  and  indirect  energy 
components. Direct energy needs per fuel rely on literature (22). 

The decomposition is the following: 

Investment costs (capital expenditure, CAPEX): 

•  Direct  component:  reflects  the  energy  embedded  in  infrastructure  construction; 

• 

energy efficiency in materials production decreases costs. 
Indirect  component:  reflects  the  non-energy  costs  related  to  technological  R&D, 
exploration  activities  and  services  required  for  the  preparation  of  a  production 
facility. It is calibrated on the historical investments dedicated to exploration of oil 
producers.  Energy  intensity  decrease  indirectly  decreases  costs  (the  energy 
intensity  of  services  was  used  in  this  modelling).  This  indirect  component 
increases with the oil price. 

Operational costs (operational expenditure, OPEX): 

•  Direct  component:  refers  to  energy  inputs  in  production,  detailed  by  production 

• 

step, and linked to output and to Energy Return On energy Invested (EROI). 
Indirect component: reflects the inputs embedded in materials production for new 
machinery,  fuel  usage  in  various  support  services,  as  well  as  purely  non-energy 
components such as labour costs and other costs; these indirect energy needs are 
converted into costs using the energy intensity of the economy. 

(21)  Databases:  knoema;  Rystad  Energy  UCube.  Reports  by:  Arthur  D.  Little;  BREE  Australia;  Credit  Suisse; 
Deutsche  Bank;  EIA;  Energy  Studies  Institute,  NUS;  Exxon  Mobil;  EY;  IEA  –  ETSAP;  IEA  World  Energy 
Investment  2016;  IHS  CERA;  Morgan  Stanley;  Oxford  Institute  for  Energy  Studies;  Shell;  WGM  Nexant. 
Articles in: CNN; Financial Times; Natural Gas Europe; oilprice.com; Slate; Wall Street Journal. 

(22)  Aucott 2013, Brandt 2008, 2011, 2013, 2015, Ghandi 2015, Nguyen 2012 

43 

 
                                           
 
 
These components are allocated to the various production fuel types as shown in Table 8. 

Table 8. Oil and gas production cost components and its drivers 

Component: 

Representing: 

Initial value 
based on: 

Evolution driven by: 

CAPEX direct  

Production facility, 
other CAPEX 

Rest of CAPEX 

Energy intensity of steel 
production 

CAPEX 
indirect  

R&D,  
exploration 

Share of CAPEX 

Oil price, Energy intensity of 
services 

Drilling, 
pumping 

Literature, per 
fuel type 

Fixed % of output or EROI; 
cost of energy (per fuel type) 

OPEX direct 

Steam needs 

Literature, per 
fuel type 

EROI; cost of energy (per 
fuel type) 

Other energy 

Literature, per 
fuel type 

Fixed % of output; cost of 
energy (per fuel type) 

OPEX 
indirect 

Support services, 
labour costs,  
other OPEX 

Remaining 
OPEX 

Energy intensity of GDP 

Taxes, 
royalties 

Taxes,  
royalties 

Literature 

Scenario-dependent  

Note: For  producers  where  only  total  production  cost  was  found  in  the  literature,  a  split  into 
components was made based on neighbouring producers for the same fuel type. 

Three production steps are distinguished in oil and gas production: drilling and pumping 
(energy  for  drilling,  injection  and  lifting);  heat  (energy  for  upgrading  the  underground 
resource into synthetic crude); other (energy for transport, lighting, etc.). Table 9 details 
these steps by fuel type produced and how the energy requirements for each evolve over 
time in the modelling. 

For  each  step,  energy  is  used  in  different  forms,  either  directly  or  locally  transformed 
with conversion efficiencies (e.g. fuels to produce steam, fuels to generate electricity on-
site).  The  energy  fuels  can  come  from  energy  markets  (purchased)  or  from  the  own 
gross production (own-use). The opportunity cost is compared to the prices for industry 
and the competition determines how much fuel is bought. 

The  sum  of  fuel  inputs  across  production  steps  and  fuel  types,  net  of  energy  own-use, 
are identified in energy statistics as the energy inputs in the oil and gas sectors. 

44 

Table 9. Energy inputs per production step and per produced fuel 

Production 
step 

Production 
fuel type 

Onshore 

Drilling 
and 
pumping  

Offshore, 
Arctic 

Total 
energy 
need 

fixed % of 
output, 
EROI 

fixed % of 
output, 
EROI 

Energy 
used in the 
form of… 

… supplied 
by… 

… provided 
by: 

oil, 
electricity 

oil, grid 
electricity 

purchased 

oil, 
electricity 

oil, gas, grid 
electricity 

own-use, 
purchased 

Tight oil 

EROI 

oil, gas 

oil, gas 

own-use 

Shale gas 

EROI 

oil, gas 

oil, gas 

own-use 

Heat 

Other 

Onshore,  
Heavy oil 

EROI 

steam 

oil, gas, 
coal, 
biomass, 
nuclear 
electricity 

own-use, 
purchased 

Kerogen 

EROI 

electricity 

oil, grid 
electricity 

own-use, 
purchased 

All except 
heavy oil 

fixed % of 
output 

oil, 
electricity 

oil, grid 
electricity 

purchased 

Heavy oil 

fixed % of 
output 

oil, 
electricity 

oil, grid 
electricity 

own-use, 
purchased 

For  each  step,  energy  needs  can  evolve  either  proportionally  to  produced  output  (fixed 
share found in literature) or as the result of the EROI curve. 

The  EROI  curve  captures  rising  energy  requirements,  and  thus  costs,  for  additional 
resource extraction with increasing cumulative extraction (23): 

b)cd =

'

e D

$f(cid:3). (cid:20)(cid:21)"(cid:6)
g))

D h

With: α, β = parameters per fuel type, defined by historical production; 

Cum.Prod = cumulated production; 

URR = ultimately recoverable resources. 

For  each fuel type and  production step, the  EROI curve defines total energy needs. The 
EROI-dependent  components  in  Table  9  are  the  remaining  energy  needs  once  all  other 
components are calculated. 

5.1.4  Production process and trade for oil  

The  oil  market  is  considered  as  one  'great  pool'  with  no  regional  markets.  Only  net 
imports and exports are calculated for each country or region. 

World oil demand is met by existing production facilities (calculated based on last year's 
production with an average decline rate per oil type) and new production. 

(23)  The perimeter of the energy spent and the energy produced is the production facility. Energy to refine the 

fuel into final products and distribute it to final users is captured in other parts of the model. 

45 

 
                                           
The  new  production  is  distributed  across  producers  following  their  variable  production 
costs  and  some  constraints  on  production  capacity,  based  on  the  Reserves/Production 
ratio (a minimum value reflects resource management policies) and on annual growth of 
production capacity (historical expansion when available, exogenous assumption for new 
producers/resources). 

5.1.5  Production process and trade for gas 

The global gas consumption is split into 14 regional markets that are supplied by the 88 
producers (see Figure 23), differentiated into small producers (47), which produce only 
for domestic needs, and large producers (41) that can export to meet the demand of all 
14 markets, net of the contribution of the (small) local producers. These trade flows are 
directional (from producers to the different consuming markets). 

Figure 26. Mapping of gas producers and demand markets 

Large producers 

Canada | USA | Mexico | Trinidad & Tobago 
Venezuela | Colombia | Ecuador | Brazil | Argentina | Bolivia | Peru 
Netherlands | UK | Norway 
Algeria | Libya | Egypt | Nigeria | Gabon | Angola 
Russia | Azerbaijan | Kazakhstan | Turkmenistan | Uzbekistan | Rest CIS 
UAE | Kuwait | Oman | Qatar | Saudi Arabia | Iran | Iraq 
Pakistan | Myanmar | India | Brunei | China | Malaysia | Indonesia | Australia 

Demand Markets 

N America | Central America | S America 
Europe | N Africa | Sub-Saharan Africa | CIS | Middle East 
China | India | Rest S Asia | Rest SE Asia | Japan & S Korea | Pacific 

Small producers 

Chile | Rest Central America | Rest S America 
Other EU28 x26 | Iceland | Switzerland | Balkans | Turkey | Ukraine 
Morocco & Tunisia | S Africa | Rest Africa | Mediterranean Middle-East | Rest Persian Gulf 
Thailand | Vietnam | Rest S Asia | Rest SE Asia | South Korea | Japan | New Zealand | Rest Pacific 

Note: Large + small  producers  correspond  to  oil  producers  (Figure  23).  ‘Rest’  regions  exclude  the 
relevant  singled  out  producers;  they  are  different  from  the  energy  demand  and  coal  production 
regions. 

The modelling first assesses the export capacities of large producers towards each of the 
regional markets. In a second step, the actual supply by each producer is computed.  

The  model  identifies  three  types  of  transport  routes  for  gas:  onshore  pipeline,  offshore 
pipeline  and  LNG.  All  routes  from  producers  to  markets  are  characterised  by  a  distance 
and a cost (see Figure 27). 

46 

 
Figure 27. Gas transport costs in the POLES-JRC model 

Pipeline - onshore

Pipeline - offshore

LNG

u
t
B
M
M
/
D
S
U

7

6

5

4

3

2

1

0

0

1000

2000

3000

4000

5000

6000

7000

km

Source: GasNatural Fenosa (2012). 

The  development  of  existing  export  capacity  per  route  considers  the  evolution  of  the 
consuming market, a depreciation of existing capacities and the producer's capability to 
continue  supplying  that  route  given  its  reserves.  New  trade  routes  can  emerge  if  the 
producer  has  enough  reserves  and  if  the  return  on  investment  justifies  it  (gas  selling 
price in a new market vs. production cost and transport cost to that market). 

Actual gas supply (all gas types combined) is then calculated based on the use of these 
capacities.  Market  shares  are  determined  by  the  historical  trade  matrix,  the  exporter’s 
reserves/production ratio and the variable costs over the route. 

For each exporter, total gas production towards all routes is then distributed across each 
gas  type.  Production  is  met  by  existing  production  facilities  (calculated  on  last  year's 
production with an average decline rate per gas type) and new production facilities. 

Total needs for new production is distributed across gas types following their production 
costs and some capacity constraints, based on the Reserves/Production ratio (a minimum 
value  reflects  resource  management  policies)  and  on  annual  growth  of  production 
capacity  (historical  expansion  when  available,  exogenous  assumption 
for  new 
producers/resources). 

For small producers, gas production per gas type evolves with: 

•  Reserves, evolving as explained in Section 5.1.2. 
•  The  Reserves/Production  ratio,  following  a  standard  equation  influenced  by  gas 
market  prices  (and  capped  by  a  minimum  value  to  reflect  resource  management 
policies). 

A simplified diagram of the production and trade module is shown in Figure 28. 

47 

 
Figure 28. Schematic representation of the gas production and trade module 

Note: ROI is Return on Investment, R/P is the ratio of reserves over production. 

Additional gas sources 

The  modelling  also  identifies  additional  sources  of  natural  gas.  These  correspond  to 
recovered  methane  that  would  otherwise  be  emitted;  instead  they  are  used  as  energy 
sources (see Section 6.1 Greenhouse gases): 

—  urban waste methane; 

—  underground coal methane; 

—  fugitive emissions of the gas production.  

5.1.6  Prices 

The oil price converges towards a value that depends on the following factors: 

• 

the  marginal  production  cost,  derived  from  the  cumulated  production  curve 
ranked according to production costs across all oil types; 

•  an 

indicator  of 

resource  scarcity,  based  on 

the  evolution  of 

the 

Reserves/Production ratio across all producers and oil types; 

•  an indicator of production capacity saturation, based on the effective use of newly 

installed capacities over all producers and oil types; 

•  oil  stocks  variations,  which  either  add  a  mark-up  above  the  marginal  price  or 
deflate  the  price  towards  the  average  production  cost;  the  effect  of  historical 
stocks variation is progressively phased down over time, assuming a balance over 
the long run. 

For  gas,  in  each  of  the  14  importing  markets,  the  price  is  calculated  as  a  weighted 
average of LNG and pipeline suppliers' prices, with: 

• 

• 

• 

for LNG suppliers, the marginal supply cost of all suppliers, taking into account the 
production cost and the transport cost to that market; 
for  pipeline  suppliers,  the  weighted  average  of  supply  costs,  taking  into  account 
the  production  cost  and  the  transport  cost  to  that  market,  and  an  indexation  to 
the evolution of the international oil price to reflect long-term contracts; 
in  addition,  an  indicator  of  resource  scarcity,  based  on  the  evolution  of  the 
reserve/production ratio of all supplies to that market. 

Gas market prices are then grouped into three large continental markets for international 
gas prices – Asia, America and Europe-Africa – in order to reflect the inter-dependency of 
markets. These are the prices used as import prices for consuming regions. 

48 

 
5.2  Coal 

Figure 29 shows the different coal producers (81) considered in the modelling. 

Figure 29. POLES-JRC geographic breakdown for coal production 

NB: The  following  countries  are  broken  down  into  sub-national  production  regions:  Australia  (2 
regions), China (4 regions), India (4 regions), United States (4 regions). 

The coal supply module is based on three main sub-modules: 

●  key producers, 

●  demand, 

● 

trade. 

Coking  coal  and  steam  coal  are  differentiated  but  modelled  in  similar  ways.  A  link 
between  coking  and  steam  coal  modelling  is  implemented  at  the  resource  level,  since 
both types of coal share the same resources in the model. Mining costs are differentiated 
between  steam  coal  and  coking  coal  so  as  to  account  for  quality  differences,  while 
transport costs are common (all calculated in USD/t). 

The resources are based on ‘proved amount of coal in place’ (WEC 2013a). 

The price of coal includes both mining and transport costs. 

The  modelling  of  the  mining  cost  captures  both  the  evolving  need  and  use  of  the 
production  factors  and  the  cost  evolution  of  each  of  these  factors:  labour,  energy  use, 
materials  use  and  others  components  (repairs,  machinery,  mining  parts,  tyres, 
explosives;  including  processing  and  additional  administrative  costs  and  taxes).  It 
combines: 

●  an aggregated cost curve including: 

●  a  long-term  component  reflecting  changes  in  accessibility  of  the  resource, 

geological conditions and a decrease in the energy content; 

●  a short-term component reflecting the utilisation rate of existing capacities; 

● 

● 

the evolution of the cost of the different factors is as follows: 

labour: income per capita; 

49 

 
●  energy: price of oil and electricity to industry; 

●  materials use: energy price of steelmaking; 

●  other components: considered as remaining constant. 

Transport costs are the sum of: 

● 

● 

inland transport costs from mining site to export terminal (rail); 

inland transport costs from export terminal to importer (rail); 

●  maritime transport costs from export terminal to importer: sum of port charges 
(port  facilities,  loading,  unloading)  and  freight  charges  that  depend  on  the 
distance from exporter to importer and on the price of maritime bunker fuel. 

The functioning of the production and trade module is similar to that of gas (see Section 
5.1.5). 

Trade  takes  place  between  large  producers  (26  countries  or  regions)  and  demand 
markets  (15  regional  markets)  (see  Figure  30).  Small  producers  (55)  only  produce  for 
domestic  consumption,  based  on  domestic  demand  and  coal  prices  (positive  elasticity); 
their contribution decreases regional supply needs.  

Coal  trade  (of  steam  coal  and  of  coking  coal)  is  calculated  based  on  demand.  The 
competition between coal producers for market shares in each importing market is driven 
by the total costs (mining and transport) with an elasticity and weighting factors allowing 
the historical trade matrix to be recreated. 

Figure 30. Mapping of large coal producers and demand markets 

Large producers: 26 

Canada | USA x4 | Colombia | Venezuela 
Poland | Ukraine | Russia | Kazakhstan 
Mozambique | S Africa 
India x4 | Vietnam | Indonesia | Mongolia | China x4 | Australia x2 

Demand Markets: 15 

N America | Central America | S America 
Europe | N Africa | Sub-Saharan Africa | CIS | Middle East 
China | India | Rest S Asia | Rest SE Asia | Japan | S Korea | Pacific 

Small producers: 55 

Mexico | Argentina | Brazil | Chile | Rest Central America | Rest S America 
Other EU28 x27 | Iceland | Norway | Switzerland | Balkans | Turkey | Rest CIS 
Morocco & Tunisia | Algeria & Libya | Egypt | Rest Africa 
Mediterranean Middle-East | Saudi Arabia | Iran | Rest Persian Gulf 
Thailand | Malaysia | Rest S Asia | Rest SE Asia | S Korea | Japan | New Zealand | Rest Pacific 

NB: ‘Rest’  regions  are  adapted  to  the  relevant  singled-out  producers;  they  are  different  from  the 
energy demand and oil and gas production regions. 

Traded volumes are adjusted through export capacities: for each large producer both the 
expansion  of  new  capacities  and  the  total  supply  capacity  are  capped,  to  reflect 
respectively bottlenecks in industrial organisation capabilities and resource management 

50 

 
policy. The global need for new supply capacities is then allocated to each trade route in 
order to satisfy demand. 

The resulting coal price for a demand market is the weighted average of the total costs of 
supply to that market. 

Lignite remains a local resource, and its price is not affected by the coal market. 

5.3  Biomass 

The  model  distinguishes  three  primary  biomass  resource  types  for  energy  uses.  Each 
type is associated with an energy potential and a supply cost curve. They are: 

● 

forest residues (cellulosic), 

●  short rotation energy crops (cellulosic), 

●  dedicated  agriculture  energy  crops  (non-cellulosic)  for  first-generation  liquid 

biofuels. 

For  agriculture  crops  (used  in  first-generation  liquid  biofuels),  the  energy  potential  is 
derived from available area (assumed decreasing share of agricultural areas) and yield. 

For  cellulosic  biomass  (forest  residues  and  short  rotation  crops,  used  in  all  other  uses: 
heating,  electricity,  second-generation  biofuel)  the  energy  potential  and  the  production 
cost  curve  come  from  the  GLOBIOM  model (24).  An  international  price  of  cellulosic 
biomass is derived from the aggregation of regional cost curves. 

Figure 31. Schematic representation of biomass flows in the POLES-JRC model 

USD/GJ 

USD/GJ 

USD/GJ 

Forestry residues 

GJ 

Forest crops 

GJ 

Agriculture crops 

GJ 

Production cost = f (potential used) 

Solid biomass: combustion 

Electricity & District Heat & H
2
production 

Direct use, modern 
(Industry, Buildings) 

Direct use, traditional 
(Buildings; at no cost) 

Transformation into 
liquid biofuels for transport 

st
 Generation 

1

nd

 Generation 

2

Urban waste 

The  domestic  production  of  agriculture  crops  for  energy  purposes  is  determined  by 
domestic  needs  for  first-generation  liquid  biofuels  production,  considering  the  trade  in 
liquid biofuels. 

(24)  The cost curves integrate a carbon value dimension (see Havlik et al. 2014, IIASA 2016a). 

51 

 
                                           
 
The  domestic  consumption  of  cellulosic  biomass  is  determined  by  needs  for  combustion 
and  conversion  into  second-generation  liquids.  A  competition  takes  place  between 
domestic  production  and  imports,  comparing  the  local  production  cost  and  the 
international market price. 

200

150

J
E

100

50

0
2000

Figure 32. Biomass-for-energy production and potential, world 

Low agreement: 875 EJ

Medium agreement: 200 EJ

High agreement: 90 EJ

2010

2020

2030

2040

2050

Potential estimates

2°C

INDC

Reference

Sources: Biomass production: GECO 2016; potential estimate and qualification of agreement in literature: 
Creutzig et al. (2015). 

Land use in POLES-JRC 

The following land areas are identified: 

—  agricultural land: food production and energy crops, 

—  forests, 

—  built areas (evolves with urban population), 

—  inland water and deserts (fixed), 

—  other unused land, as the difference with total area. 

Historical data are from FAO (FAO 2015) and evolution of agriculture and forest areas is 
according to GLOBIOM (IIASA 2016a). 

5.4  Uranium 

The  conventional  nuclear  power  technology  in  POLES-JRC  corresponds  to  generic  light 
water  reactors  using  enriched  uranium  fuel  (3.5 %  U235  from  about  0.7 %  in  natural 
uranium). 

The  price  of  nuclear  fuel  takes  into  account  all  costs  within  the  nuclear  fuel  cycle  from 
mining via enrichment to fabricating fuel rods (WISE 2016). A global cost-resource curve 
for  mining  natural  uranium  includes  resources  of  up  to  14.5  Mt  of  natural  uranium 
(IAEA/OECD 2013) (25). 

Mass  flows  of  nuclear  material  and  its  interactions  are  implemented  on  a  global  level. 
This  allows  the  tracking  of  the  amount  of  high  radioactive  waste  and  depleted  uranium 
(0.3 % U235), which can be tapped as a resource for nuclear fuel by taking into account 
re-enrichment. The implementation of nuclear mass flows also allows further insights into 
the resource availability of uranium for nuclear power generation. 

(25)  This  comprises  uranium  resources  in  the  categories  identified,  inferred  and  undiscovered  resources 
according to the annual revised estimations of the International Atomic Energy Agency (IAEA) and OECD 
Nuclear Energy Agency (NEA). 

52 

 
                                           
Advanced nuclear design (fourth generation) using breeder technology is introduced from 
the middle of the century onwards. The advanced nuclear design breeds plutonium from 
fertile  U238,  thus  increasing  the  theoretical  availability  of  nuclear  fuel  by  two  orders  of 
magnitude. The interaction of nuclear fuel cycles for conventional and advanced nuclear 
design is taken into account. 

5.5  Hydro potential 

Three  types  of  hydro  power  plants  are  modelled:  large  hydro  (> 20  MW),  small  hydro 
(< 20 MW) and hydro pump and storage. 

Yearly  hydro  power  production is  determined  by  capacity  and  load  factor  and  is  capped 
by a production potential that is detailed for large and small hydro (Enerdata 2017a). 

The production profile is adjusted to account for storage needs (see section 4.1.3). 

The model allows for soft linkage towards hydrological models to capture possible future 
change  in  rain  patterns  and  consequent  seasonal  or  yearly  availability  of  water  for 
hydroelectricity. 

Figure 33. Hydroelectricity potential in 2015 (left) and % of potential used over 1990-2015 (right) 

12000

10000

8000

6000

4000

2000

0

h
W
T

50%

40%

30%

20%

10%

0%

OECD Non-OECD

1990

1995

World

2000

2005

OECD

2010
Non-OECD

2015

Source: IEA 2017a, Enerdata 2017a 

5.6  Wind potential 

Wind  power  production  is  determined  from  production  profiles  (see  section  4.1.3).  Its 
deployment is determined by costs and potential. 

Onshore  and  offshore  wind  potential  is  derived  from  a  detailed  technological 
representation.  The  wind  potential  is  derived  from  NREL  (2013)  using  the  following 
factors. 

●  The  meteorological  potential (26),  in  available  area  (km2)  where  the  mean 
wind  over  time  exceeds  a  certain  value  at  10 m  height,  is  aggregated  into  six 
classes according to wind speeds and distance from the shore (27). 

●  Exclusion  factors  are  applied,  due  to  land-use  (e.g.  marine  protected  areas, 
share  of  forest  area)  and  social  constraints  (dependent  on  population  density 

(26)  Wind atlases for onshore and offshore are elaborated using wind resource models like WAsP (Wind Atlas 
Analysis  and  Application  Program),  which  computes  the  annual  mean  wind  speed  for  thousands  of  grid 
points (van Wijk et al. 1993: Matthies et al. 1993) 

(27)  Classes  correspond  to  US  wind  energy  classes  (see  NREL  2013):  C1-C2  (not  suitable  for  wind  power 
generation), C3 (lower energy content, 5.35 m/s), C4 (intermediate, 5.8 m/s) and C5-C7 (most energetic 
winds, 6.7 m/s and above). Aggregation for onshore: C3; C4; C5-C7. Aggregation for offshore: C5-C7 at 
0-10km distance; C3-C4 at 0-10km distance; C5-C7 at 10-30km distance. 

53 

 
                                           
and on income per capita). In addition, to account for other types of constraints 
(minimum  distance  to  heritage  sites,  NIMBY-type  opposition  etc.),  the 
installation  potential  of  wind  onshore  is  capped  at  0.1  MW/km2  on  average  in 
the country/region. 

●  Wind machine and wind field characteristics provide the technical potential: it 
is  obtained  from  the  power  rating  per  turbine  and  a  wind  machine  density  in 
wind  farms,  which  depends  on  the  turbine  spacing  and  the  diameter  of  the 
turbine. 

Figure 34. Schematic representation of the wind potential treatment in POLES-JRC 

Wind energy resource 

2
Meteorological potential (km

) 

2
Site potential (km

) 

Technical potential (GW) 

Technical limitations 
(wind speeds, distance from shore) 

Exclusion factors 
(land use, social constraints) 

Technology characteristics 
(turbine power, mast height,  
 wind farm density) 

The  potential  (W)  is  then  implemented  as  a  limitation  (Wh)  to  wind  capacities  in  the 
model  using  load  factors  (site-specific  starting  point  with  historical  data).  The  average 
wind  capacity  load  factor  evolves  as  new  installations  join  the  total  capacities.  The  load 
factor  for  new  installations  is  derived  from  a  function  of  the  wind  power  density  at  hub 
height, which grows over time. 

5.7  Solar potential 

5.7.1  Solar power plant potential 

Solar power plants’ production is determined from production profiles (see section 4.1.3). 
Their deployment, in competition with centralised power production means, is determined 
by costs and potential. 

The  potential  of  solar  power  plants  (concentrated  solar,  with  and  without  storage,  and 
utility-scale PV) relate to available surface for constructing such facilities; they develop in 
desert  areas  and  a  share  of  grasslands.  The  potential  to  produce  per  surface  relates  to 
solar irradiation, taking into account geographical and environmental factors. 

The  surface  on  which  power  plants  can  be  deployed  is  related  to  a  solar  power  supply 
curve,  which  provides  the  load  factor  as  a  function  of  the  percentage  of  used  surface 
(Pietzcker et al. 2014). The potential surface is shared between technologies according to 
expected power needs. 

The resulting load factor is an input in the electricity sector. 

5.7.2  Solar distributed photovoltaic potential 

Distributed  PV  power  production  is  determined  from  production  profiles  (see  section 
4.1.3).  Its  deployment,  in  competition  with  grid  electricity  and  other  decentralised 
technologies in buildings, is determined by costs and potential. 

54 

 
Distributed PV is assumed to be installed on rooftops of dwellings and service buildings. 
The  potential  of  distributed  PV  is  estimated  as  share  of  total  rooftop  surface  and  an 
average  unit  production  (kWh/m²)  which  is  derived  from  average  solar  irradiation  and 
technical efficiency. 

The share available for distributed PV considers that: 

●  only  a  portion  of  the  actual  surface  is  available  due  to  characteristics  of  the 
buildings  (orientation,  type  of  rooftop  etc.),  construction  norms  or  social 
factors; 

●  PV competes with solar thermal installations for rooftop surface. 

5.7.3  Solar thermal potential 

Solar thermal heat production (i.e. solar collectors) follows a return on investment logic 
to address space and water heating needs (see Section 4.2 Heat production). 

The evolution of the potential is analogously modelled to distributed PV (with an average 
unit production given in toe/m2). 

5.8  Geothermal electricity 

World  geothermal  potential  is  set  at  50  GW,  in  the  lower  end  of  the  range  provided  by 
the  World  Energy  Council  (WEC  2016)  of  35-200  GW.  The  regional  distribution  depends 
on installed capacities and identified projects. 

55 

6  Emissions 

6.1  Greenhouse gases 

6.1.1  Modelling principles and marginal abatement cost curves 

The  GHGs  emitted  by  human  activities  that  are  covered  by  the  model  are  the  six  ones 
identified  in  the  UNFCCC  Kyoto  Protocol:  carbon  dioxide  (CO2),  methane  (CH4),  nitrous 
oxide  (N2O),  hydrofluorocarbons  (HFCs),  perfluorocarbons  (PFCs)  and  sulphur 
hexafluoride (SF6). 

GHGs are  emitted in a range of economic activities. Energy and industry (CO2 and non-
CO2 combined), which form the focus of the model, represent a very important share of 
total  emissions  (83 %  in  2014);  CO2  emissions  from  the  energy  sector  (i.e.  combustion 
of fuels) are the most important single contributor (69 % in 2014). 

Figure 35. World greenhouse gas emissions per activity type, 2014 

Agriculture 11%

LULUCF 2%

Waste 3%

Non-CO2 Indus. 
process 2%
CO2 Indus. process 
5%

Non-CO2 energy 
8%

48.2 GtCO2e
(2014)

CO2 energy 69%

Source: UNFCCC 2015 

NB: Waste is non-CO2; agriculture is non-CO2; LULUCF is CO2. 

For  CO2  emissions  from  fossil  fuel  combustion,  emission  volumes  are  obtained  directly 
from the use of individual fossil fuels with an emission factor. 

CCS technology can develop both in power generation and in industry sectors. 

For  other  GHG  emissions  from  energy  and  industrial  processes,  the  projection  is  based 
on: 

●  a  sector-specific  economic  driver  (sectoral  value  added,  energy  production  or 

energy consumption); 

●  a trend capturing technology changes; 

●  a  marginal  abatement  cost  curve  (MACC)  that  describes  the  interaction  with 

climate mitigation policies; MACCs comes from the EPA (2013). 

56 

 
For  GHG  emissions  from  AFOLU (28),  baseline  emissions  and  mitigation  potential  are 
derived from the specialised GLOBIOM model (IIASA 2016a). 

Global  warming  potentials  (GWPs (29))  are  applied  to  non-CO2  greenhouse  gases  to 
convert emissions to CO2-equivalent. 

Figure  36  gives  as  an  illustration  the  aggregated  MACCs  of  the  main  sectors  at  world 
level, which sums the country and regional level MACCs. 

Figure 36. Marginal abatement cost curves (all gases, all sources, world, 2030) — volume (left) 
relative to baseline emissions (right) 

200

150

100

50

0

2
O
C
t
/
D
S
U

0

10000

5000
Emission reductions (MtCO2e)

15000

20000

0%

Energy CO₂

Other Energy & Industry

20%

40%

Emission reductions
(% emissions of no policy case)
LULUCF

Agriculture

60%

Sources: Energy CO2 from POLES-JRC, other GHGs from energy and industry from EPA (2013), agriculture and 
LULUCF from GLOBIOM (IIASA 2016a). GWPs from SAR (IPCC 1996a). 

The model allows GHG  emissions trading markets to be represented via the comparison 
between emissions resulting from equalisation of marginal abatement costs and emission 
permits (or emission endowments). 

6.1.2  Energy-related emissions 

Energy-related  emissions  refer  to  GHG  emissions  where  the  primary  driver  is  energy 
production  or  consumption.  They  consist  in  CO2  emissions  from  fossil  fuel  combustion 
and non-CO2 emissions from energy-related activities. 

6.1.2.1  Combustion-related emissions 

CO2  emissions  from  the  combustion  of  fossil  fuels  are  the  most  important  GHG  source 
(about 64 % of the global total GHG emissions in 2014). 

Emissions are calculated from energy balances by applying a fuel-specific emission factor 
according to IPCC guidelines (IPCC 2006). 

CO2  emissions  from  the  combustion  of  solid  biomass  are  not  accounted  for,  with  the 
assumption that the chain of biomass-for-energy production is carbon neutral; however, 
an emission factor was included in order to account for carbon captured when biomass is 
used  in  CCS.  Similarly,  the  combustion  of  liquid  biofuels  is  considered  to  be  carbon 
neutral (CO2 is only emitted due to the energy use in their production process, which is 
captured endogenously); however, an emission factor can be used for  the calculation of 
vehicle emission standards. 

(28)  AFOLU: agriculture, forestry and land use. 
(29)  GWP as defined in the IPCC assessment reports. 

57 

 
 
                                           
A finer level of detail is given for emissions from oil products in transport, where specific 
carbon emission factors have been introduced. 

Table 10. CO2 emission factors 

Fuel/sector 
Oil 
Gas 
Coal 
Oil: domestic and international air transport 
Oil: international maritime bunkers 
Biomass (*) 
(*) Biomass and its products are considered as carbon-neutral in the emissions balances. 

Emission factor (tCO2/toe) 
3.17 
2.34 
3.98 
2.93 
3.19 
4.19 

Total  emissions  balances  take  into  account  carbon  that  is  captured  in  CCS  (in  power 
plants,  synthetic  fuel  production,  hydrogen  production  and  industry)  and  the  uptake  of 
carbon in steelmaking from coking coal. 

6.1.2.2  Non-CO2 energy-related emissions 

These  emissions  are  captured  by  an  emission  intensity  (with  a  MACC)  applied  to  the 
relevant  activity  (see  summary  below).  The  following  GHG  emissions  relate  directly  to 
energy production, transport or consumption. 

●  CH4 emitted by the fossil fuel sector evolves with the projected production and 

transport of fossil fuels: 

(cid:1)  processes  in  the  oil  industry  (exploration,  production  and  refining, 

venting and flaring); 

(cid:1)  upstream processes of natural gas production; 

(cid:1) 

transmission and distribution in the natural gas sector; 

(cid:1)  underground mining and surface coal mining. 

●  CH4 and N2O as by-products of incomplete combustion processes are accounted 

for in: 

(cid:1) 

(cid:1) 

(cid:1) 

the electricity and industrial sectors; 

the residential and service sectors; 

the transport sector. 

●  SF6  is  used,  and  emitted,  in  electricity  transmission  and  distribution  for 

insulation and current interruption. 

6.1.3  Process emissions in industry 

These  GHG  emissions  are  the  result  of  chemical  or  physical  reactions  other  than 
combustion  and  where  the  primary  purpose  of  the  industrial  process  is  not  energy 
production. 

● 

● 

● 

In the iron and steel sector: CO2 is emitted from the use of coal and coking coal 
in the iron ore reduction process. 

In  the  non-metallic  mineral  industry  (cement,  glass,  ceramics):  CO2  is  emitted 
when carbonates contained in the raw material are thermally decomposed in the 
process. 

In the chemical industry: CO2 process emissions occur in some processes (e.g. 
ammonia production), while N2O emissions take place in the production of nitric 
acid and adipic acid. 

58 

●  A  variety  of  HFCs  are  emitted  from  air  conditioning,  refrigeration,  foams, 

solvents and other processes. 

●  PFCs  are  emitted  in  the  production  of  primary  aluminium  and  other  industrial 

processes (semiconductors, solvents etc.). 

●  SF6 is emitted in magnesium refining and semiconductor processing. 

The projected emissions evolve with the sectoral value added, a technological trend and 
the abatement potential in case of GHG mitigation policy. 

6.1.4  Waste 

CH4 is emitted from solid waste disposal (municipal and industrial origin) and wastewater 
treatment.  N2O  is  generated  from  processing  wastewater  due  to  the  de/nitrification 
processes of the nitrogen present. 

The main drivers of emissions increases are urban population and industrial value added, 
a technological trend and the abatement potential in case of GHG mitigation policy. 

6.1.5  Agriculture, forestry and other land use (AFOLU) 

The  agriculture  sector  is  a  source  of  CH4  and  N2O  emissions.  CH4  is  emitted  by  various 
activities  such  as  enteric  fermentation,  manure  management,  soils  and  rice  cultivation. 
N2O is emitted from manure management and soils (fertilisers). 

LULUCF  emissions  include  CO2  emissions  from  net  forest  conversion  (CO2  emissions  by 
deforestation and CO2 sinks by afforestation) and CO2 emissions from other forestry and 
land use. 

Projections of AFOLU emissions evolve based on look-up data from the GLOBIOM model 
(Havlik et al. 2014, IIASA 2016a) (curves that take into account the price of carbon and 
the price of biomass in order to determine biomass use and AFOLU emissions). 

6.1.6  Greenhouse gas coverage summary 

Table 11 provides a summarised view of the GHG emissions flows in the model. 

59 

 
Table 11. Greenhouse gas emission sources 

Sector 
Energy 

Industrial processes 

Waste 

Category 
Fuel combustion 
Oil and gas sector  

Coal production 

Power and heat, 
transport, residential 
Power systems 
Steel  

Non-metallic minerals  

Chemistry  

Aluminium 

Magnesium, 
semiconductors 
Residential, services, 
transport  
Waste 

Agriculture, forestry and 
other land use (AFOLU) 

Agriculture 

Forestry and land use 

CH4 

N2O 
CH4 
SF6 
CO2 

CO2 

GHG  Emission activity 
CO2 
CH4 

Burning of fossil fuel 
Production, transmission and 
distribution 
Underground and surface mining 

Combustion by-products 

Modelling driver 
Fossil fuel combustion 
Oil and gas production 
Gas transport and use 
Coal production (underground and surface 
differentiated) 
Sectoral final energy consumption 

Transmission and distribution 
Iron ore reduction  

Electricity production 
Tonnes of steel using thermal processes 

Carbonate decomposition  

Non-metallic minerals industry value added 

Steam reforming 
Nitric and adipic acid 

CO2 
N2O 
PFCs  Primary aluminium  

Semiconductor and PV 

Chemicals industry value added 

‘Other’ industry value added 

SF6  Magnesium refining, 

Semiconductor and PV 
HFCs  Air conditioning, refrigeration 

Industry value added 

Industry value added 

CH4 
N2O 
CH4 
N2O 

CO2 

aerosols, foams, solvents 
Solid waste and wastewater 
Burning of waste 
Enteric fermentation, manure 
management, soils and rice 
cultivation  
Deforestation, afforestation, other 
forestry and land use  

Urban population (urban waste) 
Industry value added (industrial waste) 
Default emission profile from GLOBIOM, influenced 
by the biomass price as a proxy for land use 
activities. 
Biomass price (derived from GLOBIOM cost curves) 
as a proxy for forestry activity and other land use 

60 

 
6.2  Pollutant emissions 

The  coverage  of  this  dimension  in  the  POLES  model  is  done  through  a  linkage  towards 
the  specialist  GAINS  model  that  provides  emission  factors  per  pollutant  and  sector  fuel 
(see IIASA 2015 and IIASA 2016a) that are then mapped to POLES series. 

Symmetrically,  the  POLES-JRC  energy  balances  have  also  been  used  as  inputs  to  the 
GAINS model to derive the evolution of air pollutant emissions (see Rafaj et al. 2013). 

6.2.1  Pollutants covered 

The following air pollutants and short-lived climate forcers are represented in the model: 

●  SO2  : sulphur dioxide, 

●  NOx  : nitrogen oxides, 

● 

(NM)VOCs  : non-methane volatile organic compounds, 

●  CO  : carbon monoxide, 

●  BC  : black carbon, 

●  OC  : organic carbon, which can be converted into organic matter (OM), 

●  PM2.5 : particulate matter of 2.5 µm, the sum of BC, OM and other PM2.5, 

●  PM10 : particulate matter of 10 µm, the sum of PM2.5 and other PM10 

●  NH3  : ammonia. 

Pollutants  resulting  from  the  interaction  of  the  above  species  with  other  gases 
(precursors) such as ozone are not modelled 

6.2.2  Emission calculation 

The  pollutant  emissions  are  calculated  as  the  product  of  activity  and  the  emissions 
intensity factor (specific for each pollutant and sector). 

The  pollutant  emissions  flows  in  the  model  are  listed  below,  with  their  corresponding 
activity indicators, totalling 48 flows per pollutant. 

61 

 
 
Table 12. Pollutants considered with emission factors as direct inputs 

Sector 
Industry 

Buildings 

Transport 

Agriculture 
Power generation 

Other energy 
transformation 

Activity indicator 
Biomass in industry 
Coal in industry (excluding coking coal) 
Gas in industry (excluding non-energy uses) 
Oil in industry (excluding non-energy uses) 
Tonnes of steel 

Biomass in residential and services 
Coal in residential and services 
Oil in residential and services 
Gas in residential and services 
Coal in transport (rail) 
Gas use in transport 
Diesel used in road transport 

Source 
Biomass 
Coal 
Gas 
Oil 
Steelmak
ing 
Biomass 
Coal 
Gas 
Oil 
Coal 
Gas 
Diesel 
Gasoline  Gasoline used in road transport 
Oil 
Oil 
Oil 
Oil 
Oil 
Biomass 
Coal 

Oil products in non-road, non-air transport 
Oil products in domestic air transport 
Oil products in maritime bunkers 
Oil products in international air bunkers 
Oil products in agriculture 
Biomass inputs in power generation (*) 
Coal inputs in power system for capacity historically installed 
(conventional coal) 
Coal inputs in newly installed power capacity (conventional 
coal) 
Coal inputs in newly installed power capacity (advanced 
coal) (*) 
Gas inputs in power generation (*) 
Oil inputs in power generation 
Losses in refineries 
Oil and gas production (on-site own consumption) 

Coal 

Coal 

Gas 
Oil 
Oil 
Oil 

(*)  An  additional  flow  is  considered  when  associated  to  CCS,  where  a  multiplying  emission 
coefficient is applied to the coefficient without CCS. 

62 

 
 
Table 13. Pollutants considered with emission factors recalculated from historical data 

Sector 
Industrial 
production 

Buildings 

Surface 
transportation 
Agriculture 
Energy 
transformation 
Other energy 
transformation 

Fires 

Waste 
Other 

Activity indicator 
Total energy in non-metallic minerals industry 
Total energy in chemicals industry 
Total energy in chemical feedstocks industry 
Value added of chemicals industry 

Source 
Cement 
Chemicals 
Fertilisers 
Solvents 
Other combustion  Oil in other industry 
Other processes 
Other/unattribute
d 
Other/unattribute
d 
Non-energy 
Other/unattribute
d 
Oil 

Oil in road transport 

Total energy in other industry 
Oil in residential and services 

N2O emissions from agriculture  
Oil inputs in power generation 

Oil auto-consumption of the energy transformation 
sector and oil T & D losses 
Gas auto-consumption of the energy transformation 
sector and gas T & D losses 
Coal auto-consumption of the energy transformation 
sector and coal T & D losses 
None (trend) 
None (trend) 
None (trend) 
None (trend) 

Urban population 
Population 

Gas 

Coal 

Forest fires 
Savannah fires 
Peat fires 
Agricultural waste 
burning 
All 
Other/unattribute
d 

The future evolution of the emissions intensity factors are based on IIASA (2016a). Their 
future  evolution  moves  within  boundaries  defined  by  current  legislation  and  maximum 
technical feasible reductions (as defined by GAINS scenarios — see IIASA 2015). 

The  default  behaviour  in  the  model  reflects  current  legislation  adopted  by  countries 
around  the  world  in  the  medium  term (30).  In  the  longer  term,  it  is  assumed  that 
technologies  and  air  pollution  policies  diffuse  across  world  regions  at  different  speeds 
depending  on  per  capita  income.  This  results  in  a  ‘middle-of-the-road’  trajectory  of 
emission intensity factors, between factors frozen at their last historical point and factors 
corresponding to the best technology expected to be available in the future (31). Further 
reductions can be achieved as co-benefits of a climate policy, caused by the reduction of 
fossil fuel consumption. 

(30)  For  example,  the  2030  objectives  of  the  EU’s  ‘Clean  air  programme’  (Directive  2016/2284/EU),  see: 

http://ec.europa.eu/environment/air/pollutants/ceilings.htm  

(31)  These  assumptions  are  compatible  with  the  socio-political  definition  of  the  SSP2  scenario.  For  emission 
intensity factors going beyond those derived from current legislation, their evolution by country group and 
across time is similar to the method in Rao et al. (2016). 

63 

                                           
7  Energy and climate policy implementation 

The  model  allows  variant  scenarios  to  be  developed  and  policies  to  be  translated  into 
quantitative modelling inputs, by sector and region, by 2050 in a standard configuration 
and up to 2100 for long-term mitigation strategies. 

It can also be connected to specialised models to expand the assessment towards other 
policy areas (e.g. macroeconomics, land use, water, etc.). 

The following dimensions can be considered. 

Socioeconomic context 

●  Population, economic growth, income, urbanisation 

●  Discount rates on energy investments 

●  Lifestyle analysis (dwellings and mobility) 

Energy resources 

●  Assumptions on ultimately recoverable resources or accessible resources 

● 

Indigenous fossil fuel resources management 

Climate and energy policies 

Climate and environmental policies 

●  Cap on all or selected GHG emissions 

●  Pricing of all or selected GHG emissions 

Technology support policies 

●  Technology availability, costs and learning rate assumptions 

●  Technology purchase: subsidies and low interest rate loans 

●  Power-specific policies: feed-in tariffs or premiums 

●  Transport-specific policies: development of infrastructure for alternative vehicle 

technologies 

Energy consumption policies 

●  Fiscal  policy  on  energy  fuels  to  assess  the  impact on  energy  consumption  and 

energy independence 

●  Subsidy on energy fuel 

●  White certificate to spur energy efficiency 

●  Building-specific  policies:  renovation  rates  of  buildings,  development  of 

insulation 

●  Transport-specific policies: fuel and emission standards, modal shift. 

64 

8  Data 

The model uses annual historical data to initialise the projections, typically for the period 
1980 to the latest data available (for most series: up to the year preceding the current 
year). 

Due  to  the  recursive  simulation  nature  of  the  model,  projected  data  presents  a  high 
degree of continuity with historically observed data. 

The  historical  data  is  used  to  derive  parameters  that  enhance  the  model’s  capability  to 
take  into  account  country  and  sector  specificities  in  investment  and  consumption 
behaviour:  elasticity  to  price  or  activity,  autonomous  technological  trends  and  non-cost 
weighting parameter in the competition for new equipment or fuels. 

The following information is needed: 

●  socioeconomic  and  activity  variables:  population  (total,  urban  vs.  rural),  GDP, 

sectoral value added, mobility, number of dwellings, surfaces, etc., 

●  energy balances: final demand, transformation, supply, 

●  energy prices and taxes, 

●  energy reserves and resources, 

●  GHG emissions. 

8.1  Economic activity 

Data for EU population and activity comes from Eurostat for history (Eurostat 2015) and 
the EU Ageing Report for projections (European Commission 2015). 

Population  data  for  non-EU  countries  and  regions  from  the  UN  (UN  2015)  and  the  EU 
Ageing  Report.  Historical  GDP  and  value  added  come  from  the  World  Bank  (WB  2016), 
while projections of GDP growth come from the IMF (for the next 5 years, IMF 2016) and 
the OECD for the longer term (OECD 2014). 

Information  on  sectoral  activity  variables  (mobility,  surfaces,  dwellings,  industrial 
production, etc.)  come  from  the  IRF  (2014),  UIC  (2014),  ICAO  (2015),  Unctad  (2015), 
WSA  (2015),  World  Bank  (2016),  Enerdata  (2015a),  Enerdata  (2015b)  and  national 
sources. 

8.2  Energy 

Data for energy balances and prices comes from Enerdata (2015a), with additional 
information from: 

●  Eurostat: energy balance of EU countries (Eurostat 2015); 

● 

IEA:  energy  balance  for  non-EU  countries,  energy  prices  (IEA  2015a,  IEA 
2015b); 

●  Platts: power plant capacities (Platts 2015); 

●  BP: oil and gas reserves and production (BP 2015); 

●  Specialist  studies  for  energy  resources:  fossil  fuels  (BGR  2015,  USGS  2013, 
Schenk  2012),  hydro  (WEC  2016b),  wind  (NREL  2013),  solar  (Pietzcker  et  al. 
2014), bioenergy (IIASA 2016a), geothermal (WEC 2016a); 

●  Specialist studies for technology costs: the JRC (2014b), WEC (2013b), IRENA 

(2015), IEA (2014). 

65 

8.3  Greenhouse gas emissions 

Historic emissions for CO2 emissions from combustion processes are derived from energy 
balance data. 

For  UNFCCC  Annex  I  countries,  all  other  GHG  historical  emissions  are  from  UNFCCC 
inventories (UNFCCC 2015). 

For Non-Annex I countries, emissions from energy, industry and agriculture are from the 
EDGAR  database  (European  Commission  JRC  2011,  2014a),  while  CO2  emissions  from 
LULUCF  refer  in  principle  to  FAOSTAT  (FAO  2015)  and  for  some  countries  to  national 
inventories (Brazil, Mexico). For Indonesia the LULUCF CO2 emissions from FAOSTAT are 
complemented by emissions from peat fires from EDGAR data (32). 

Certain additional emissions time series are included in the model to determine specific 
aspects  of  energy/emissions  accounting:  total  CO2  emissions  of  the  road  transport 
sector,  total  CO2  emissions  of  the  steel  sector  and  process  CO2  emissions  of  the  steel 
sector. 

8.4  Air pollutants 

Sources for historical air pollutant emissions are: 

●  GAINS ECLIPSE v5a (IIASA 2015) for most sectors; 

●  estimates from emission factors using IPCC emissions guidelines (IPCC 1996b) 

and AERO2k (33) for air transport emissions; 

●  EDGAR v4.2 (European Commission JRC 2011) for fires; 

●  national sources to complement. 

Information on future emissions and future emissions factors is from: 

●  GAINS ECLIPSE v5a for most sectors; 

●  AERO2k for air transport; 

●  UNEP report for CCS technologies; 

●  national sources for policies. 

8.5  Summary 

Table  14  provides  a  synthetic  view  of  the  data  sources  used  in  the  POLES-JRC  model.

(32)  Fires introduced as an exogenous series to complete country emissions; can be modified to reflect policy 

objectives. 

(33)  FP6 

project; 

https://www.researchgate.net/publication/224796937_AERO2k_Global_Aviation_Emissions_Inventories_f
or_2002_and_2025; http://www.aerodays2006.org/sessions/A_Sessions/A1/A13.pdf  

66 

                                           
Table 14. Data sources in POLES-JRC 

Series 

Population 

GDP, growth 

Other activity 
drivers 

Energy 
resources 

Value added 

Historical data 

UN, Eurostat 

World Bank 

World Bank 

GECO projections 

UN (medium fertility) 

European Commission, IMF, OECD (see Dellink et al. 
2014) 

Mobility, vehicles, households, tonnes of steel 

Sectoral databases 

Oil, gas, coal 

Uranium 

Biomass 

Hydro 

Wind, solar 

BGR, USGS, WEC, sectoral information 

IAEA/OECD 

GLOBIOM model 

Enerdata 

NREL, Pietzcker et al. (2014) 

POLES-JRC model 

Energy 
balances 

Reserves, production 

Demand by sector and fuel, transformation 
(including. power), losses 

BP, Enerdata 

Enerdata, IEA 

Power plants 

Platts 

Energy prices 

International prices, prices to consumer 

Enerdata, IEA 

POLES-JRC model 

GHG emissions 

Energy CO2 

Derived from POLES-JRC energy balances 

POLES-JRC model 

Other GHG Annex 1 

Other GHG Non-Annex 1 (excl. LULUCF) 

UNFCCC 

EDGAR 

POLES-JRC model, GLOBIOM model 

POLES-JRC model, GLOBIOM model 

LULUCF Non-Annex 1 

National inventories, FAO 

POLES-JRC model, GLOBIOM model 

Air -pollutant emissions 

Technology costs 

GAINS model, EDGAR, IPCC, national 
sources 

GAINS model, national sources 

POLES-JRC learning curves based on literature, including but not limited to: JRC, WEC, IEA 
Technology Roadmaps, TECHPOL database(*) 

(*) Developed in several European research projects: SAPIENT, SAPIENTIA, CASCADE MINTS. 

67 

 
 
9  Conclusions 

This  model  documentation  informs  of  the  main  characteristics  of  the  POLES-JRC  model 
as  of  2018.  The  latest  modelling  upgrades  and  changes  are  therefore  included.  The 
objective is to document the model used for producing the GECO report of 2018.  

68 

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storage.html 

73 

 
Selected research projects (34) 

In the following projects, research versions of the POLES model were used: 

●  CD-links 

●  ADVANCE (Advanced Model Development and Validation for Improved Analysis 

of Costs and Impacts of Mitigation Policies), for DG Research (FP7), 2013-2016. 
http://cordis.europa.eu/project/rcn/104887_en.html, http://www.fp7-
advance.eu/ 

●  AMPERE (Assessment of Climate Change Mitigation Pathways and Evaluation of 
the Robustness of Mitigation Cost Estimates), for DG Research (FP7), 2011-
2013. http://cordis.europa.eu/project/rcn/98809_en.html, http://ampere-
project.eu/ 

●  EMF27 (Energy Modelling Forum 27: Global Model Comparison Exercise), 

coordinated by Stanford University, 2010-2013. 
http://emf.stanford.edu/projects/emf-27-global-model-comparison-exercise 

●  ADAM (Adaptation and Mitigation Strategies), for DG Research (FP6), 2006-
2009. http://cordis.europa.eu/project/rcn/78409_en.html. Final report: 
http://www.cambridge.org/es/academic/subjects/earth-and-environmental-
science/environmental-policy-economics-and-law/making-climate-change-
work-us-european-perspectives-adaptation-and-mitigation-strategies 

●  MENGTECH (Modelling of Energy Technologies Prospective in a General and 

Partial Equilibrium Framework), for DG Research (FP6), 2006-2008. 
http://cordis.europa.eu/project/rcn/75100_en.html 

●  WETO-H2 (World Energy Technology Outlook — 2050), for DG Research (FP6), 

2004-2005. http://cordis.europa.eu/project/rcn/73908_en.html. Final report: 
http://bookshop.europa.eu/en/world-energy-technology-outlook-pbKINA22038/ 

●  CASCADE MINTS (CAse Study Comparisons And Development of Energy Models 

for INtegrated Technology Systems), for DG Research (FP6), 2004-2006. 
http://cordis.europa.eu/project/rcn/73909_en.html 

●  SAPIENTIA (Systems analysis for progress and innovation in energy 

technologies for integrated assessment), for DG Research (FP5), 2002-2004. 
http://cordis.europa.eu/project/rcn/64924_en.html 

●  SAPIENT (System analysis for progress and innovation in energy technologies), 

for DG Research (FP5), 2000-2002. 
http://cordis.europa.eu/project/rcn/51184_en.html 

(34)  For a complete list of publications, see: http://ec.europa.eu/jrc/poles/publications  

74 

 
                                           
List of abbreviations and definitions 

Acronyms and definitions 

AFOLU  

agriculture, forestry and land use 

CCS 

CHP 

carbon capture and storage 

combined heat and power 

CNRS   

Centre national de la recherche scientifique 

DG 

EDGAR  

E & P   

EOR 

EPA 

EROI 

FAO 

GDP 

directorate-general (European Commission) 

Emission Database for Global Atmospheric Research 

exploration and production (fossil fuels) 

enhanced oil recovery 

Environmental Protection Agency (United States) 

energy return on investment 

UN Food and Agriculture Organisation 

gross domestic product 

GECO   

Global Energy and Climate Outlook (JRC report) 

GHG 

GWP 

IAEA 

ICE 

ICT 

IEA 

greenhouse gas 

global warming potential 

International Atomic Energy Agency 

internal combustion engine 

information and communication technology 

International Energy Agency 

IEPE 

Institut d’Economie et de Politique de l’Energie 

IIASA   

International Institute for Applied Statistical Analysis 

IMF 

IMO 

IPCC 

International Monetary Fund 

International Maritime Organisation 

Intergovernmental Panel on Climate Change 

IRENA  

International Renewable Energy Agency 

ISIC 

JRC 

LNG 

LR 

International Standard Industrial Classification 

Joint Research Centre (European Commission) 

liquefied natural gas 

learning rate 

LULUCF 

land use, land use change and forestry 

MACC   

marginal abatement cost curve 

NEA 

NGL 

NMM 

Nuclear Energy Agency 

natural gas liquids 

non-metallic minerals 

NREL   

National Renewable Energy Laboratory (United States) 

O & M   

operation and maintenance 

75 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
OECD   

OPEC   

PPP 

PV 

R/P 

SSP 

Organisation for Economic Cooperation and Development 

Organisation of the Petroleum Exporting Countries 

purchasing power parity 

solar photovoltaic 

ratio of reserves over production 

shared socioeconomic pathway 

T & D   

transmission and distribution 

UNFCCC 

United Nations Framework Convention on Climate Change 

VRE 

WEC 

variable renewable energy source 

World Energy Council 

Chemical species 

BC 

CH4 

CO 

CO2 

HFC 

NH3 

NOx 

N2O 

OC 

OM 

PFC 

PM 

SF6 

SO2 

black carbon 

methane 

carbon monoxide 

carbon dioxide 

hydrofluorocarbon 

ammonia 

nitrogen oxide 

nitrous oxide 

organic carbon 

organic matter 

perfluorocarbon 

Particulate matter 

sulphur hexafluoride 

sulphur dioxide 

(NM)VOC 

(non-methane) volatile organic compound 

Models 

GAINS  

Greenhouse Gas — Air Pollution Interactions and Synergies 

GLOBIOM 

Global Biosphere Management Model 

GEM-E3 

General Equilibrium Model for Economy — Energy — Environment 

POLES  

Prospective Outlook on Long-term Energy Systems 

Country and regional codes 

CIS 

EU 

Commonwealth of Independent States 

European Union 

EU-28   

European Union of 28 Member States 

G20 

Group of Twenty 

OECD   

Organisation for Economic Cooperation and Development 

76 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
OPEC   

Organisation of the Petroleum Exporting Countries 

Units 

Energy 

Bcm  billion cubic metres 

EJ 

exajoule 

1 000 000 000 000 000 000 J 

Gtoe  billion tonnes of oil equivalent 

1 000 000 000 toe 

Mtoe  million tonnes of oil equivalent 

1 000 000 toe 

Electricity 

GW 

gigawatts 

kW 

thousand watts 

kWh 

thousand watt-hours  

1 000 000 000 W 

1 000 W 

1 000 Wh 

TWh 

tera watt-hours 

1 000 000 000 000 Wh 

W 

watts 

Emissions 

GtCO2e giga-tonnes of CO2-equivalent 

1 000 000 000 tCO2 

tCO2e  tonnes of CO2-equivalent emissions 

Monetary units 

USD 

US dollars 

USD PPP 

USD at purchasing power parity 

K USD  

thousand dollars 

1 000 USD 

tn USD  

trillion dollars  

1 000 000 000 000 USD 

Other 

cap 

capita 

Gm2  billion square metres  

1 000 000 000 m2 

Gtkm  billion tonne-kilometres 

1 000 000 000 tkm 

kcap 

thousand capita 

1 000 cap 

km 

kilometres 

t 

metric tonnes 

tkm 

tonne-kilometres 

pkm  passenger-kilometres 

77 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
List of figures 

Figure 1. Schematic representation of the POLES-JRC model architecture ................... 5 

Figure 2. Schematic representation of the POLES-JRC sectors ................................... 7 

Figure 3. Schematic representation of the energy needs and depreciation procedure in 
POLES-JRC ........................................................................................................... 8 

Figure 4. POLES-JRC geographic breakdown .......................................................... 10 

Figure 5. World consumption of industry per sector (left) and industry value added 
(right) ............................................................................................................... 12 

Figure 6. Apparent steel consumption per unit of GDP versus income per capita for 
select G20 countries, 1990-2015 ........................................................................... 13 

Figure 7. Decomposition of the final energy consumption for space heating, for France 
in the reference scenarios, decomposition of the total heating needs (left) and by fuel 
demand (right) ................................................................................................... 15 

Figure 8. Water heating (top) and cooking (bottom) useful energy per capita, in 
residential (left) and service (right) buildings. ......................................................... 17 

Figure 9. Service space cooling, with suggested formula. ........................................ 18 

Figure 10. Appliances (top) and lighting (bottom) energy consumption per capita in 
residential (left) and service (right) sectors ............................................................ 19 

Figure 11. Equipment rate vs. income per capita for select G20 countries, 1990-2015 21 

Figure 12. Passenger mobility, average by mode (left), as a function of income (right)
......................................................................................................................... 21 

Figure 13. Freight mobility, world ........................................................................ 22 

Figure 14. Schematic representation of road transport in POLES-JRC ....................... 24 

Figure 15. Fuel efficiency of new aircrafts ............................................................. 25 

Figure 16. International bunkers’ energy consumption and their share in total transport 
energy consumption ............................................................................................ 26 

Figure 17. Share of electricity in total final energy consumption, 1990-2015 ............. 28 

Figure 18. Map of the electricity load data collected ............................................... 31 

Figure 19. Illustration of the aggregation of days into six representative days ........... 32 

Figure 20. Illustration of the accuracy loss due to the use of representative days ...... 33 

Figure 21. Illustrative capacity expansion mechanism ............................................ 36 

Figure 22. Biofuel production for domestic consumption and for international trade, 
world ................................................................................................................. 39 

Figure 23. POLES-JRC geographic breakdown for oil and gas ................................... 41 

Figure 24. Schematic representation of the oil sector from resources to reserves ....... 42 

Figure 25. Correlation of new discoveries (expressed as a share of remaining resources 
to be discovered and scaled by the Reserves/Production ratio) and the Oil price / 
Production cost ratio, for conventional onshore oil ................................................... 43 

Figure 26. Mapping of gas producers and demand markets ..................................... 46 

Figure 27. Gas transport costs in the POLES-JRC model .......................................... 47 

Figure 28. Schematic representation of the gas production and trade module............ 48 

Figure 29. POLES-JRC geographic breakdown for coal production ............................ 49 

78 

Figure 30. Mapping of large coal producers and demand markets ............................ 50 

Figure 31. Schematic representation of biomass flows in the POLES-JRC model ......... 51 

Figure 32. Biomass-for-energy production and potential, world ............................... 52 

Figure 33. Hydroelectricity potential in 2015 (left) and % of potential used over 1990-
2015 (right) ....................................................................................................... 53 

Figure 34. Schematic representation of the wind potential treatment in POLES-JRC ... 54 

Figure 35. World greenhouse gas emissions per activity type, 2014 ......................... 56 

Figure 36. Marginal abatement cost curves (all gases, all sources, world, 2030) — 
volume (left) relative to baseline emissions (right) .................................................. 57 

79 

 
List of tables 

Table 1. Commodities of maritime transport and their drivers ................................... 22 

Table 2. Vehicle types .......................................................................................... 23 

Table 3. Vehicle components ................................................................................ 23 

Table 4. Electricity generating technologies ............................................................ 30 

Table 5. Hydrogen demand sectors ........................................................................ 37 

Table 6. Hydrogen production technologies ............................................................. 38 

Table 7. Hydrogen transport chains ....................................................................... 38 

Table 8. Oil and gas production cost components and its drivers ............................... 44 

Table 9. Energy inputs per production step and per produced fuel ............................. 45 

Table 10. CO2 emission factors .............................................................................. 58 

Table 11. Greenhouse gas emission sources ........................................................... 60 

Table 12. Pollutants considered with emission factors as direct inputs ........................ 62 

Table 13. Pollutants considered with emission factors recalculated from historical data  63 

Table 14. Data sources in POLES-JRC .................................................................... 67 

Table 15. List of 54 individual countries represented in POLES-JRC ............................ 82 

Table 16. Country mapping for the 12 regions in POLES-JRC .................................... 83 

Table 17. List of 77 individual oil and gas producing countries represented in POLES-JRC
......................................................................................................................... 85 

Table 18. Country mapping for the 11 oil and gas producing regions in POLES-JRC ..... 86 

Table 19. List of 59 individual coal producing countries represented in POLES-JRC ...... 88 

Table 20. Country mapping for the 12 coal producing regions in POLES-JRC ............... 89 

Table 21. Sources for hourly electricity load data for countries and regions used. ........ 93 

80 

 
Annexes 

Annex 1. ISIC classification of sectors in POLES-JRC 

Using: International Standard Industrial Classification of All Economic Activities, 
Rev.4 (35) 

Building and services: G (45-47) I (55-56) J (58-63) K (64-66) L (68) M (69-75) 
N (77-82) O (84) P (85) Q (86-88) R (90-93) S (94-96) T (97-98) U (99) 

Transport: Groups 491-492 and Divisions 50-53 

Agriculture (covers fishing and forestry): Divisions 01-03 

Industry: 

● 

Iron and steel: Group 241 and Class 2431 

●  Chemicals: Divisions 20 and 21 

(cid:1)  Chemical feedstocks: part of Group 201 

(cid:1)  Plastics and rubber: part of Group 201 

●  Non-metallic minerals (cement, lime, glass, ceramics): Division 23 

●  Other  industry  (other  manufacturing,  mining  and  construction):  Divisions  07; 

08; 10-18; 22; 25-33; 41-43; Groups 099; Group 242 and Class 2432 

Energy transformation: 

●  Power generation: Division 35 

●  Other energy transformation: Divisions 05, 06, 19, 36-39; Groups 091, 493 

(35)  http://unstats.un.org/unsd/cr/registry/regcst.asp?Cl=27  

81 

 
 
                                           
Annex 2: Country mappings 

Energy and emissions balances 

54 individual countries + 12 regions 

Table 15. List of 54 individual countries represented in POLES-JRC 

Non-EU individual countries 
Argentina 

EU-28 Member States 
Austria 

Australia 

Brazil 

Canada 

Chile 

China 

Egypt 

Iceland 

India 

Indonesia 

Iran 

Japan 

Malaysia 

Mexico 

New Zealand 

Norway 

Russia 

Saudi Arabia 

South Africa 

South Korea 

Switzerland 

Thailand 

Turkey 

Ukraine 

United States 

Vietnam 

Belgium 

Bulgaria 

Croatia 

Cyprus 

Czechia 

Denmark 

Estonia 

Finland 

France 

Germany 

Greece 

Hungary 

Ireland 

Italy 

Latvia 

Lithuania 

Luxembourg 

Malta 

Netherlands 

Poland 

Portugal 

Romania 

Slovakia 

Slovenia 

Spain 

Sweden 

United Kingdom 

NB: Hong Kong and Macau are included in China. 

82 

 
 
 
Table 16. Country mapping for the 12 regions in POLES-JRC 

Rest Central 
America 
Bahamas 

Barbados 

Belize 

Bermuda 

Costa Rica 

Cuba 

Dominica 

Dominican Republic 

El Salvador 

Grenada 

Guatemala 

Haiti 

Honduras 

Jamaica 

Nicaragua 

Rest Balkans 

Rest Sub-Saharan Africa 

Rest South Asia 

Albania 

Bosnia and Herzegovina 

Former Yugoslav Republic of 
Macedonia  
Kosovo  

Moldova 

Montenegro 

Serbia 
Rest CIS 
Armenia 

Azerbaijan 

Belarus 

Georgia 

Kazakhstan 
Kyrgyzstan 

Tajikistan 

Angola 

Benin 

Botswana 

Burkina Faso 

Burundi 

Cameroon 

Cape Verde 

Central African Republic 

Chad 

Comoros 

Congo 

Democratic Republic of the 
Congo 
Côte d’Ivoire 

Afghanistan 

Bangladesh 

Bhutan 

Maldives 

Nepal 

Pakistan 

Seychelles 

Sri Lanka 
Rest South East 
Asia 
Brunei 

Cambodia 

Laos 

Mongolia 

Djibouti 

Myanmar/Burma 

Equatorial Guinea 

North Korea 

NL Antilles and Aruba 

Turkmenistan 

Panama 

São Tomé and 
Príncipe 
St Lucia 

Uzbekistan 

Mediter. Middle East 

Israel 

Jordan 

St Vincent and 
Grenadines 
Trinidad and Tobago 
Rest South America  Syria 

Lebanon 

Bolivia 

Colombia 

Ecuador 

Guyana 

Paraguay 

Peru 

Suriname 

Uruguay 

Venezuela 

Rest of Persian Gulf 
Bahrain 

Iraq 

Kuwait 

Oman 

Qatar 

United Arab Emirates 

Yemen 
Morocco and Tunisia 
Morocco 

Tunisia 

Algeria and Libya 
Algeria 

Libya 

Philippines 

Singapore 

Taiwan 

Rest Pacific 
Fiji 

Kiribati 

Papua New 
Guinea 
Samoa (Western) 

Solomon Islands 

Tonga 

Vanuatu 

Eritrea 

Ethiopia 

Gabon 

Gambia 

Ghana 

Guinea 

Guinea-Bissau 

Kenya 

Lesotho 

Liberia 

Madagascar 

Malawi 

Mali 

Mauritania 

Mauritius 

Mozambique 

Namibia 

Niger 

Nigeria 

Rwanda 

Senegal 

Sierra Leone 

83 

  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
 
  
Somalia 

Sudan 

Swaziland 

Tanzania 

Togo 

Uganda 

Zambia 

Zimbabwe 

84 

  
 
  
  
 
  
  
 
  
  
 
  
  
 
  
  
 
  
  
 
  
  
  
  
Oil and gas production 

77 individual countries + 11 regions 

(*): 41 exporters 

Table 17. List of 77 individual oil and gas producing countries represented in POLES-JRC 

Non-EU individual countries 
Algeria  (*) 

Mexico (*) 

EU-28 Member States 
Austria 

Angola (*) 

Argentina (*) 

Australia (*) 

Azerbaijan (*) 

Bolivia (*) 

Brazil (*) 

Brunei (*) 

Canada (*) 

Chile 

China (*) 

Colombia (*) 

Ecuador (*) 

Egypt (*) 

Gabon (*) 

Iceland 

India (*) 

Myanmar/Burma (*) 

New Zealand 

Nigeria (*) 

Norway (*) 

Oman (*) 

Pakistan (*) 

Peru (*) 

Qatar (*) 

Russia (*) 

Saudi Arabia (*) 

South Africa 

South Korea 

Switzerland 

Thailand 

Trinidad and Tobago (*) 

Turkey 

Indonesia (*) 

Turkmenistan (*) 

Ukraine 

Iran (*) 

Iraq (*) 

Japan 

Kazakhstan (*) 

Kuwait (*) 

Libya (*) 

Malaysia (*) 

Belgium 

Bulgaria 

Croatia 

Cyprus 

Czechia 

Denmark 

Estonia 

Finland 

France 

Germany 

Greece 

Hungary 

Ireland 

Italy 

Latvia 

Lithuania 

Luxembourg 

Malta 

United Arab Emirates (*) 

Netherlands (*) 

United States (*) 

Uzbekistan (*) 

Venezuela (*) 

Vietnam 

Poland 

Portugal 

Romania 

Slovakia 

Slovenia 

Spain 

Sweden 

United Kingdom (*) 

85 

 
 
 
 
 
 
 
Table 18. Country mapping for the 11 oil and gas producing regions in POLES-JRC 

Rest Balkans 

Rest Sub-Saharan Africa 

Rest South Asia 

Rest Central 
America 
Bahamas 

Barbados 

Belize 

Bermuda 

Costa Rica 

Cuba 

Dominica 

Dominican Republic 

El Salvador 

Grenada 

Guatemala 

Haiti 

Honduras 

Jamaica 

Nicaragua 

Albania 

Bosnia and Herzegovina 

Former Yugoslav Republic of 
Macedonia 
Kosovo 

Moldova 

Montenegro 

Serbia 
Rest CIS (*) 

Armenia 

Belarus 

Georgia 

Kyrgyzstan 

Tajikistan 
Mediter. Middle East 
Israel 

NL Antilles and Aruba 

Jordan 

Panama 

São Tomé and 
Príncipe 
St Lucia 

Lebanon 

Syria 

Rest of Persian Gulf 

Bahrain 

St Vincent and 
Grenadines 
Rest South America  Yemen 
Guyana 

Paraguay 

Suriname 

Uruguay 

Morocco and Tunisia 
Morocco 

Tunisia 

Afghanistan 

Bangladesh 

Bhutan 

Maldives 

Nepal 

Seychelles 

Sri Lanka 
Rest South East 
Asia 
Cambodia 

Laos 

Mongolia 

North Korea 

Philippines 

Singapore 

Taiwan 
Rest Pacific 
Fiji 

Kiribati 

Papua New 
Guinea 
Samoa (Western) 

Solomon Islands 

Tonga 

Vanuatu 

Benin 

Botswana 

Burkina Faso 

Burundi 

Cameroon 

Cape Verde 

Central African Republic 

Chad 

Comoros 

Congo 

Democratic Republic of the 
Congo 
Côte d’Ivoire 

Djibouti 

Equatorial Guinea 

Eritrea 

Ethiopia 

Gambia 

Ghana 

Guinea 

Guinea-Bissau 

Kenya 

Lesotho 

Liberia 

Madagascar 

Malawi 

Mali 

Mauritania 

Mauritius 

Mozambique 

Namibia 

Niger 

Rwanda 

Senegal 

Sierra Leone 

Somalia 

Sudan 

Swaziland 

86 

  
 
  
  
 
  
  
 
  
  
 
  
  
 
  
  
 
  
  
 
  
  
 
  
  
 
  
  
 
  
  
 
  
  
 
  
  
 
  
Tanzania 

Togo 

Uganda 

Zambia 

Zimbabwe 

87 

  
 
  
  
 
  
  
 
  
  
 
  
  
  
  
Coal production 

59 individual countries (4 of which with infra-national detail) + 12 regions 

(*): 16 exporters (4 of which with infra-national detail) 

Table 19. List of 59 individual coal producing countries represented in POLES-JRC 

Non-EU individual countries 
Argentina 

Australia (x 2 regions) (*) 

Brazil 

Canada (*) 

Chile 

China (x 4 regions) (*) 

Colombia (*) 

Egypt 

Iceland 

India (x 4 regions) (*) 

Indonesia (*) 

Iran 

Japan 

Kazakhstan (*) 

Malaysia 

Mexico 

Mongolia (*) 

Mozambique (*) 

New Zealand 

Norway 

Russia (*) 

Saudi Arabia 

South Africa (*) 

South Korea 

Switzerland 

Thailand 

Turkey 

Ukraine (*) 

United States (x 4 regions) (*) 

Venezuela (*) 

Vietnam (*) 

EU-28 Member States 
Austria 

Belgium 

Bulgaria 

Croatia 

Cyprus 

Czechia 

Denmark 

Estonia 

Finland 

France 

Germany 

Greece 

Hungary 

Ireland 

Italy 

Latvia 

Lithuania 

Luxembourg 

Malta 

Netherlands 

Poland (*) 

Portugal 

Romania 

Slovakia 

Slovenia 

Spain 

Sweden 

United Kingdom 

88 

 
 
 
Table 20. Country mapping for the 12 coal producing regions in POLES-JRC 

Rest Balkans 

Rest Sub-Saharan Africa 

Rest South Asia 

Rest Central 
America 
Bahamas 

Barbados 

Belize 

Bermuda 

Costa Rica 

Cuba 

Dominica 

Dominican Republic 

El Salvador 

Grenada 

Guatemala 

Haiti 

Honduras 

Jamaica 

Nicaragua 

Albania 

Bosnia and Herzegovina 

Former Yugoslav Republic of 
Macedonia 
Kosovo 

Moldova 

Montenegro 

Serbia 
Rest CIS 
Armenia 

Azerbaijan 

Belarus 

Georgia 

Kyrgyzstan 

Tajikistan 

Turkmenistan 

Angola 

Benin 

Botswana 

Burkina Faso 

Burundi 

Cameroon 

Cape Verde 

Central African Republic 

Chad 

Comoros 

Congo 

Democratic Republic of the 
Congo  
Côte d’Ivoire 

Djibouti 

Equatorial Guinea 

Afghanistan 

Bangladesh 

Bhutan 

Maldives 

Nepal 

Pakistan 

Seychelles 

Sri Lanka 
Rest South East 
Asia 
Brunei 

Cambodia 

Laos 

Myanmar/Burma 

North Korea 

Philippines 

Singapore 

Taiwan 

Rest Pacific 

Fiji 

Kiribati 

Papua New 
Guinea 
Samoa (Western) 

Solomon Islands 

Tonga 

Vanuatu 

NL Antilles and Aruba  Uzbekistan 

Panama 

São Tomé and 
Príncipe 
St Lucia 

St Vincent and 
Grenadines 
Trinidad and Tobago 

Mediter. Middle East 
Israel 

Jordan 

Lebanon 

Syria 

Rest South America  Rest of Persian Gulf 
Bolivia 

Bahrain 

Ecuador 

Guyana 

Paraguay 

Peru 

Suriname 

Uruguay 

Iraq 

Kuwait 

Oman 

Qatar 

United Arab Emirates 

Yemen 
Morocco and Tunisia 
Morocco 

Tunisia 
Algeria and Libya 
Algeria 

Libya 

Eritrea 

Ethiopia 

Gabon 

Gambia 

Ghana 

Guinea 

Guinea-Bissau 

Kenya 

Lesotho 

Liberia 

Madagascar 

Malawi 

Mali 

Mauritania 

Mauritius 

Namibia 

Niger 

Nigeria 

Rwanda 

Senegal 

Sierra Leone 

Somalia 

89 

  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
 
  
  
 
  
Sudan 

Swaziland 

Tanzania 

Togo 

Uganda 

Zambia 

Zimbabwe 

90 

  
 
  
  
 
  
  
 
  
  
 
  
  
 
  
  
 
  
  
  
  
Annex 3: Equation details of residential and services end-uses 

Water heating 

j9B:[9;5:67	_659@	8965:;<	CB9?C7	9;9@<FkXl	mnkVon = 0.105 ∗ 9]Gq−2.27 ∗ 9]G(cid:17)−0.000318 ∗ KLL ∗ (cid:17)`La =6G:56

⁄

(cid:22)

x.Qy

(cid:22)z							(cid:17):;	 549 =6G⁄

(cid:22)

Cooking 

R9@{:=9	_659@	8965:;<	CB9?C7	9;9@<FkXlmnkVon = 0.0291 ∗ exp 0−2.22 ∗ exp 0−0.0943 ∗

(cid:129)67C9‚[[9[
=6G:56

11							(cid:17):;	 549 =6G⁄

(cid:22)

Space cooling 

R9@{:=9	=44O:;<	CB9?C7	9;9@<FkXlmnkVon = 0.0164 ∗ exp 0−4.19 ∗ exp 0−0.294 ∗

(cid:129)67C9‚[[9[
=6G:56

11							(cid:17):;	 549 =6G⁄

(cid:22)

j9B:[9;5:67	=44O:;<	CB9?C7	9;9@<FkXl	mnkVon = 0.023							(cid:17):;	 549 =6G⁄

(cid:22)

\6]:>C>	G9;95@65:4;ST = >6] „2%, 100% ∗ q1 − 9

(cid:23)x.xxQ†∗(cid:17)T‡‡(cid:23)Mxx(cid:22)

zˆ

R65C@65:4;ST,UVWX =

YZC:GG9[84CB9847[B
\6]:>C>	G9;95@65:4;ST = R65C@65:4;ST,UVWX(cid:23)M D 18.9% ∗ qR65C@65:4;ST,UVWX(cid:23)M − R65C@65:4;ST,UVWX(cid:23)M

Q

z

^C[<956@F	=49??:=:9;5	4?	BG6=9	=447:;<	CB9?C7	9;9@<F	4?	9ZC:GG9[	84CB9847[B =

= 0.00173 D 0.0000356 ∗ ‰LL

x.Š†

RG6=9	=447:;<	979=5@:=:5F	=4;BC>G5:4;
YZC:GG9[	[_977:;< ∗ 9??:=:9;=F

∗

979=5@:=:5F	G@:=9
`La

R9@{:=9	BG6=9	=447:;<	CB9?C7	9;9@<FG9@	=6G:56 = ‰LL ∗ 0.00014 ∗ -1 − exp -−0.0611 ∗ exp -−0.823 ∗

Appliances  

(cid:129)67C9‚[[9[
=6G:56

Y79=5@:=:5F	G@:=9

(cid:22)
0.3 ...							(cid:17):;	 549 =6G⁄

j9B:[9;5:67	6GG7:6;=9B	979=5@:=:5F	=4;BC>G5:4;kXl	mnkVon = 0.009 ∗ 0

`La
=6G:56

x.†‹

1

							(cid:17):;	 549 =6G⁄

(cid:22)

R9@{:=9B	6GG7:6;=9B	979=5@:=:5F	=4;BC>G5:4;kXl	mnkVon = 0.2 ∗ 01 − exp 0−0.0564 ∗ exp 0−0.0956 ∗

(cid:129)67C9‚[[9[
=6G:56

111							(cid:17):;	 549 =6G⁄

(cid:22)

j9B:[9;5:67	6GG7:6;=9B	5@9;[	_:58	@9BG9=5	54	76_ = 0	:?	

`La
=6G:56

>

50O$
=6G

,

97B9	

0.0002471 ∗

y

`La
=6G:56

	 − 	0.022165 ∗

`La
=6G:56
100

Q

	 D 	0.5056 ∗

`La
=6G:56

	 − 	0.8397

91 

 
 
 
 
 
 
 
 
 
 
 
Lighting 

R9@{:=9	6GG7:6;=9B	5@9;[	_:58	@9BG9=5	54	76_ = max 0−0.001,0.05195 − 0.001631 ∗

(cid:129)67C9‚[[9[
=6G:56

1

j9B:[9;5:67	7:<85:;<	979=5@:=:5F	=4;BC>G5:4;kXl	mnkVon = max (cid:144)0,0.00632 D 0.00381 ∗ 7; 0

`La
=6G:56

1 −

0.0000382
x.Šx†∗

‘‡’
mnk

(cid:23)QM.“”								(cid:17):;	 549 =6G⁄

(cid:22)

R9@{:=9B	7:<85:;<	979=5@:=:5F	=4;BC>G5:4;kXl	mnkVon = 0.103 ∗ exp 0−3.03 ∗ exp 0−0.0702 ∗

11							(cid:17):;	 549 =6G⁄

(cid:22)

0.00536 D 9
(cid:129)67C9‚[[9[
=6G:56

•:<85:;<	5@9;[	_:58	@9BG9=5	54	76_ = −3%

92 

 
	
 
 
 
 
 
Annex 4: Sources for hourly electricity load data 

Table 21. Sources for hourly electricity load data for countries and regions used. 

Country / Region  Regional  
grid zones 

Period 

Comment 

Source 

Link 

Argentina 

Australia 

Austria 

Belgium 

Brazil 

Bulgaria 

Canada 

1 

6 

1 

1 

1 

1 

4 

2014 

Assembled from typical daily profiles 
(weekday, Saturday, Sunday) per month of 
total grid. 

CAMESA (Compañía 
Administradora del Mercado 
Mayorista Eléctrico), Argentina 

http://portalweb.cammesa.com/memn
et1/Pages/descargas.aspx 

1999-2015 

2006-2014 

2006-2014 

Australian Energy Market 
Operator (AEMO), Australia 

http://www.aemo.com.au 

ENTSOE 

ENTSOE 

https://transparency.entsoe.eu/ 

https://transparency.entsoe.eu/ 

http://www.ons.org.br/Paginas/resulta
dos-da-operacao/historico-da-
operacao/curva_carga_horaria.aspx 

2010-2017  Actually 4 zones, but downloaded as 
aggregate for total zone. 

Operador Nacional do Sistema 
Eléctrico, Brazil 

2006-2014 

2011-2017 

ENTSOE 

https://transparency.entsoe.eu/ 

4 zones of Canada:  
- British Columbia Hydro and 
Power Authority 
- Alberta Electric System  
   Operator (AESO) (6 subzones) 
- Independent Electricity 
System Operator (IESO)  
- New Brunswick  
   Power Corporation 

https://www.bchydro.com/energy-in-
bc/operations/transmission/transmissio
n-system/balancing-authority-load-
data/historical-transmission-data.html; 

https://www.aeso.ca/market/market-
and-system-reporting/data-
requests/hourly-load-by-area-and-
region-2011-to-2017/; 

http://www.ieso.ca/Pages/Power-
Data/default.aspx; 

93 

 
 
 
 
 
Chile 

SIC 

2004-2015 

SIC (Sistema Interconectado Central) 
covering > 70% of electricity production 

Coordinador Eléctrico Nacional, 
Chile 

https://sic.coordinador.cl/informes-y-
documentos/operacion-real/ 

http://tso.nbpower.com/Public/en/syst
em_information_archive.aspx 

China 

Croatia 

Cyprus 

Czechia 

Denmark 

Egypt 

Estonia 

Finland 

France 

Germany 

Greece 

Hungary 

Iceland 

India 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

Japan is used as proxy for this region. 

2006-2014 

2013-2014 

2006-2014 

2010-2014 

2010 

2009-2014 

2010-2014 

2006-2014 

ENTSOE 

ENTSOE 

ENTSOE 

ENTSOE 

Personal communication 

ENTSOE 

ENTSOE 

ENTSOE 

https://transparency.entsoe.eu/ 

https://transparency.entsoe.eu/ 

https://transparency.entsoe.eu/ 

https://transparency.entsoe.eu/ 

https://transparency.entsoe.eu/ 

https://transparency.entsoe.eu/ 

https://transparency.entsoe.eu/ 

2006-2014  All German zones aggregated by ENTSOE. 

ENTSOE 

https://transparency.entsoe.eu/ 

2006-2014 

2006-2014 

2010-2014 

ENTSOE 

ENTSOE 

ENTSOE 

https://transparency.entsoe.eu/ 

https://transparency.entsoe.eu/ 

https://transparency.entsoe.eu/ 

NRLDC 

2011-2016  Grid zones of WRLDC, ERLDC, SRLDC also 

available, but not used. 

Northern Regional Load 
Dispatch Centre (NRLDC), India 

http://www.nrldc.org 

94 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Indonesia 

Java & Bali 

2008-2016 

1 

1 

1 

2012-2016 

2008-2014 

2006-2014 

TEPCO 

2008-2015 

Iran 

Ireland 

Italia 

Japan 

Latvia 

Lithuania 

Luxembourg 

Malaysia 

Malta 

Mexico 

Netherlands 

New Zealand 

Norway 

1 

1 

1 

1 

7 

1 

1 

1 

2010-2014 

2006-2014 

2013-2016 

2015 

Cyprus is used as proxy for this region. 

Bidang Operasi System, 
Indonesia 

Iran Grid Management 
Company, Iran 

ENTSOE 

ENTSOE 

TEPCO, Japan 

ENTSOE 

ENTSOE 

ENTSOE 

http://isolator.pln-jawa-bali.co.id/app4 

https://www.igmc.ir/ 

https://transparency.entsoe.eu/ 

https://transparency.entsoe.eu/ 

http://www.tepco.co.jp/en/forecast/ht
ml/index-e.html 

https://transparency.entsoe.eu/ 

https://transparency.entsoe.eu/ 

https://transparency.entsoe.eu/ 

Tenaga Nasional Berhad, 
Malaysia 

https://www.tnb.com.my/suppliers-
investors-media-relations/daily-system-
generation-summary 

Personal communication with 
Centro Nacional de Control de 
Energía, Mexico 

https://www.gob.mx/cenace 

2006-2014 

ENTSOE 

https://transparency.entsoe.eu/ 

1998-2014 

Refers to generation 

Electricity Market Information, 
New Zealand 

https://www.emi.ea.govt.nz/Wholesale
/Datasets/Generation/Generation_MD/ 

2010-2014 

ENTSOE 

https://transparency.entsoe.eu/ 

95 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Poland 

Portugal 

Romania 

Russia 

Saudi Arabia 

Slovakia 

Slovenia 

South Africa 

South Korea 

Spain 

Sweden 

Switzerland 

Thailand 

Turkey 

1 

1 

1 

2 

4 

1 

1 

1 

1 

1 

1 

1 

1 

2006-2014 

2006-2014 

2006-2014 

2013-2015 

2013 

2006-2014 

2006-2014 

2010 

2016 

2006-2014 

2010-2014 

2006-2014 

2013-2016 

Philippines is used as proxy. 

96 

ENTSOE 

ENTSOE 

ENTSOE 

https://transparency.entsoe.eu/ 

https://transparency.entsoe.eu/ 

https://transparency.entsoe.eu/ 

Atsenergo, Russia 

http://www.atsenergo.ru 

Personal communication 

ENTSOE 

ENTSOE 

https://transparency.entsoe.eu/ 

https://transparency.entsoe.eu/ 

IRENA project: “Southern 
African Power Pool: Planning 
and Prospects for Renewable 
Energy” 

Korea Power Exchange (KPX) – 
Electric Power System 
Information System (EPSIS), 
Korea 

http://epems.kpx.or.kr/downpdf.kpx 

ENTSOE 

ENTSOE 

ENTSOE 

https://transparency.entsoe.eu/ 

https://transparency.entsoe.eu/ 

https://transparency.entsoe.eu/ 

Turkish Electricity Transmission 
Corporation, Turkey 

https://www.teias.gov.tr/ 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Ukraine 

United Kingdom 

1 

1 

2006-2014  Western grid of Ukraine used as proxy. 

ENTSOE 

https://transparency.entsoe.eu/ 

2010-2014 

ENTSOE 

https://transparency.entsoe.eu/ 

USA 

173 

2006-2016 

Form 714 of FERC. 

Federal Energy Regulatory 
Commission (FERC), USA 

https://www.ferc.gov/docs-
filing/forms/form-714/overview.asp 

2 

2 

3 

1 

Vietnam 

Algeria and Libya 

Morocco and 
Tunisia 

Rest Balkans 

Rest Central 
America 

Rest CIS 

Rest 
Mediterranean & 
Middle East 

Rest Pacific 

Rest of Persian Gulf 

Philippines is used as proxy for this region. 

2010 

Sum of Algeria & Libya. 

Personal communication 

2010 

Sum of Morocco & Tunisia. 

Personal communication 

2010-2014 

Sum of Bosnia and Herzegovina, FYR 
Macedonia and Serbia. 

ENTSOE 

https://transparency.entsoe.eu/ 

2013-2015 

Costa Rica as part of the region is  used as 
proxy; data refers to generation. 

Sistema Eléctrico Nacional, 
Costa Rica 

https://appcenter.grupoice.com/Cence
Web/CencePredespachoTecnicoNacion
al.jsf 

Russia is used as proxy for this region. 

Egypt is used as proxy for this region. 

Philippines are used as proxy for this 
region. 

Saudi Arabia is used as proxy for this 
region. 

Rest South America  1 

2014 

Colombia as part of the region is  used as 
proxy. 

Sistema Eléctrico Nacional, 
Columbia 

https://apps.grupoice.com/CenceWeb/
CencePredespachoTecnicoNacional.jsf 

97 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Rest South Asia 

1 

2011 

Pakistan as part of the region is  used as 
proxy. 

National Transmission and 
Dispatch Company (NTDC), 
Pakistan 

http://www.ntdc.com.pk/ 

Rest South East 
Asia 

Rest Sub-Saharan 
Africa 

1 

1 

2016-2017 

Philippines is used as proxy for this region.  Wholesale Electricity Spot 

2010 

Ivory Coast as part of the region is  used as 
proxy. 

Market (WESM), Philippines 

IRENA project: “Southern 
African Power Pool: Planning 
and Prospects for Renewable 
Energy” 

http://www.wesm.ph/inner.php/downl
oads/market_prices_&_schedules 

98 

 
 
 
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doi:10.2760/814959

ISBN 978-92-79-97300-0