POLES-JRC model documentation.
Date: 2017-10-24
Institutions: Joint Research Centre
Subjects: air quality, energy consumption, energy research, environmental monitoring, greenhouse gas, reduction of gas emissions, research report, simulation

POLES-JRC model documentation 

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

2017 

EUR 28728 EN 

i 

 
 
 
 
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: Expo Building - C/ Inca Garcilaso 3 - 41092, Seville, Spain 
Email: JRC-POLES@ec.europa.eu 

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

JRC107387 

EUR 28728 EN 

PDF 

ISBN 978-92-79-71801-4 

ISSN 1831-9424 

doi:10.2760/225347 

Luxembourg: Publications Office of the European Union, 2017 

© European Union, 2017 

Reuse  is  authorised  provided  the  source  is  acknowledged.  The  reuse  policy  of  European  Commission 
documents is regulated by Decision 2011/833/EU (OJ L 330, 14.12.2011, p. 39). 

For any use or reproduction of photos or other material that is not under the EU copyright, permission must be 
sought directly from the copyright holders. 

How to cite this report: Keramidas, K, Kitous, A., Després, J., Schmitz, A., POLES-JRC model documentation. 
EUR 28728 EN, Publications Office of the European Union, Luxembourg, 2017, ISBN 978-92-79-71801-4, 
doi:10.2760/225347, JRC107387 

All images © European Union 2017, except: Cover image, sofronn, #521369701, 2017. Source: 
Shutterstock.com. 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Contents 

Contents ............................................................................................................... 1 

Authors ................................................................................................................. 1 

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

Introduction ........................................................................................................... 3 

1  General description ............................................................................................ 4 

1.1  Scope and objective ..................................................................................... 4 

1.2  Modelling principles and methodology ............................................................. 6 

1.3  Geographical breakdown .............................................................................. 10 

1.4  Main activity drivers .................................................................................... 10 

2  Final energy demand ........................................................................................ 11 

2.1 

Industry .................................................................................................... 11 

2.2  Residential and services ............................................................................... 13 

2.3  Transport................................................................................................... 15 

2.4  Agriculture ................................................................................................. 19 

2.5  Energy prices ............................................................................................. 20 

3  Energy transformation ...................................................................................... 21 

3.1  Electricity sector ......................................................................................... 21 

3.2  Heat production .......................................................................................... 25 

3.3  Hydrogen .................................................................................................. 25 

3.4  Synthetic liquids ......................................................................................... 27 

3.5  Other transformation and losses ................................................................... 28 

4  Primary energy supply ...................................................................................... 29 

4.1  Oil ............................................................................................................ 29 

4.2  Gas ........................................................................................................... 33 

4.3  Coal .......................................................................................................... 36 

4.4  Biomass .................................................................................................... 38 

4.5  Uranium .................................................................................................... 39 

4.6  Hydro potential ........................................................................................... 40 

4.7  Wind potential ............................................................................................ 40 

4.8  Solar potential ............................................................................................ 41 

4.9  Geothermal electricity ................................................................................. 42 

5  Emissions ........................................................................................................ 43 

5.1  Greenhouse gases ....................................................................................... 43 

5.2  Pollutant emissions ..................................................................................... 48 

6  Energy and climate policy implementation............................................................ 51 

7  Data ............................................................................................................... 52 

i 

 
 
7.1  Economic activity ........................................................................................ 52 

7.2  Energy ...................................................................................................... 52 

7.3  Greenhouse gas emissions ........................................................................... 52 

7.4  Air pollutants ............................................................................................. 53 

7.5  Summary .................................................................................................. 53 

References ............................................................................................................ 55 

List of abbreviations and definitions .......................................................................... 60 

List of figures ........................................................................................................ 63 

List of tables ......................................................................................................... 65 

Annex 1: ISIC classification of sectors in POLES-JRC ................................................... 66 

Annex 2: Country mappings .................................................................................... 67 

ii 

 
Authors 

Main authors: Kimon Keramidas, Alban Kitous, Jacques Després, Andreas Schmitz 

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

All contributors are from the European Commission's Joint Research Centre (JRC), except 
(*): Silvana Mima (GAEL Laboratory, University of Grenoble, France). 

1 

 
 
 
Abstract 

This report is a public manual for the POLES-JRC model, the in-house tool of the European 
Commission 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) series. 

Complementary information can be found on the JRC Science Hub website: 

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

2 

 
 
 
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). 

3 

 
 
 
                                          
1  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: 

o  demand and supply linked through prices, 
o  detailed  representation  of  end-use  sectors,  power  generation  and  other 

transformation sectors and primary supply, 
o  disaggregation to all types of energy fuels, 
o  explicit technology dynamics, 
o  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 

1.1  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 
as  well  as  air  pollutants  are  covered,  be  they  associated  with  the  energy  sector  or  with 
other economic activities (4). 

(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 

4 

 
 
                                          
 
The model’s scope is global, with an explicit representation of 66 geographical entities (see 
Section 1.3). 

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 5.2). 

  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 interaction 

and  LULUCF  (land  use,  land  use  change  and  forestry)  are  derived  from  a  linkage  with  the  GLOBIOM  model 
(IIASA 2016a) (see Section 5.1.5). 

(5)  Through a linkage with the GAINS model (IIASA 2016b) 

5 

 
                                                                                                                                     
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. 

1.2  Modelling principles and methodology 

1.2.1  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 determines 
the  required  capacity  to  cover  these  needs,  accounting  for  the  decommissioning  or 
underutilisation of existing equipment. 

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. 

6 

 
 
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. 

1.2.2  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" 

3 
Inter-options 
competition 

New additional Option 1 

New additional Option 2 

New additional Option 3 

2 
Remaining equipment 
("un-scrapped") 

"Scrapping" 

1. The standard demand equation follows the general form: 

𝑫𝒆𝒎𝒂𝒏𝒅 = 𝑨𝒄𝒕𝒊𝒗𝒊𝒕𝒚𝒆𝒚 × 𝒇[(𝑨𝒗𝒈. 𝑷𝒓𝒊𝒄𝒆)−𝒆𝒔] × 𝒇[(𝑨𝒗𝒈. 𝑷𝒓𝒊𝒄𝒆)−𝒆𝒍] × 𝑻𝒓𝒆𝒏𝒅 

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. 

 

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 

7 

 
 
 
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. 

𝑴𝒂𝒓𝒌𝒆𝒕 𝒔𝒉𝒂𝒓𝒆𝒐𝒑𝒕𝒊𝒐𝒏 𝒊 =

−𝒆

𝒂𝒊 × 𝑪𝒊
∑ 𝒂𝒊 × 𝑪𝒊

𝒊

−𝒆

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). 

1.2.3  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. 

8 

 
 
                                          
𝑪𝒕 = 𝑸𝒕

𝒍𝒏(𝟏−𝑳𝑹𝒕)/𝒍𝒏(𝟐) 

with 

C  =  Costs of unit production (€/W) 

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. 

9 

 
 
 
 
 
 
1.3  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. 

1.4  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 7 on data): 

 
 

for the EU: the Ageing Report (European Commission 2015); 
for  non-EU  regions:  UN  for  population  (UN  2015),  IMF  and  OECD  for  economic 
growth (respectively IMF 2016 and OECD 2013). 

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. 

10 

 
 
 
 
2  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. 

2.1  Industry 

2.1.1  Disaggregation and general approach 

is  disaggregated 

Industry 
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), reference 
scenario 

e
o
t
G

4,5

4,0

3,5

3,0

2,5

2,0

1,5

1,0

0,5

0,0

1990 1995 2000 2005 2010 2015

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 

(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) 

11 

 
 
 
 
                                          
 
 
 
2.1.2  Steel sector 

2.1.2.1 

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: 

𝑪𝒐𝒏𝒔
𝑮𝑫𝑷

= 𝒇 (

) , 𝒇 (

𝑮𝑫𝑷
𝑷𝒐𝒑

𝟏
𝑮𝑫𝑷
𝑷𝒐𝒑

) , 𝑭𝒍𝒐𝒐𝒓 

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

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 

S Korea 

Japan 

Saudi Arabia 

Canada 

India 

Brazil 

EU28 

Australia 

USA 

5

10

15

20

25

30

35

40

45

50

GDP/cap (k USD/cap) 

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. 

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). 

12 

 
 
 
 
2.1.2.2 

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  5.1.2  Energy-related 
emissions). 

2.1.3  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. 

2.1.4  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. 

2.2  Residential and services 

The  residential  sector  and  the  services  sector  share  a  number  of  common  features,  since 
they are mostly related to buildings. The energy demand in each sector depends on specific 
activity variables (the number and surface of dwellings for the residential and the sectoral 
value  added  of  services,  respectively),  the  energy  prices  and  the  cost  of  technologies 
(heaters, boilers and insulation). 

The  modelling  in  the  residential  sector  is  described  below.  The  same  modelling  applies  to 
the services sector, with sectoral value added instead of surfaces. 

2.2.1  Surfaces in residential building 

Total residential dwellings surface is derived from: 

 

the total number of dwellings, which captures the evolution of population and of the 
number of people per household which tends to decrease with increasing income per 
capita; 

13 

 
 

the  surface  per  dwelling,  which  increases  with  income  per  capita:  larger  dwellings 
are  constructed  to  meet  the  new  demand  for  dwellings  and  to  replace  scrapped 
dwellings. 

Three types of surfaces are described, associated to a specific energy consumption pattern: 
standard, medium and low consumption. 

Diffusion of medium- and low-consumption buildings takes places on new (medium and low 
consumption) or renovated surfaces (medium consumption). It is calculated endogenously 
driven  by  the  return  on  investment  of  investing  in  insulation  compared  to  the  energy 
savings realised, and can therefore vary across different scenarios in response to changes 
in policy assumptions and prices. 

Figure 7: Example of the distribution of standard-, medium- and low-energy consumption surfaces in 
the residential sector, world, in a Reference and a 2 °C scenario 

2

m
G

300

250

200

150

100

50

0

Low

Medium

Standard

Ref

2°C

Ref

2°C

Ref

2°C

Ref

2°C

2020

2030

2040

2050

Source: GECO 2016. 

2.2.2  Space heating, water heating and cooking 

Energy  needs  for  space  heating,  water  heating  and  cooking  can  be  addressed  by  several 
fuel sources. Total energy needs link to surfaces, the evolution of the energy prices and the 
level of insulation. 

The  need  for  new  energy  installations  is  calculated  by  the  gap  created  by  the  increased 
consumption  and  the  installed  equipment  after  taking  account  of  the  replacement  of 
obsolete  equipment.  This  gap  is  met  by  investments  in  new  equipment;  the  share  of 
competing types of new equipment — which is defined through  their primary fuel, i.e. oil, 
gaseous  fuels  (natural  gas  and  hydrogen),  electricity,  modern  biomass  and  solar 
heating (11)  —  is  determined  through  a  competition  based  on  the  total  costs  of  fuel  use. 
This includes the fuel price, an infrastructure cost and the fuel utilisation efficiency. It also 
considers historically calibrated factors to cover for elements going beyond the purely cost-
based modelling, such as consumer preferences. 

The remaining fuels follow historically calibrated trends and fuel-specific constraints: 

coal, also sensitive to the carbon price; 
traditional biomass, sensitive to the biomass potential; 

 
 
  district heating, constrained through the availability of distribution networks. 

(11)  Solar heating is sensitive to the resource potential (see Section 3.2 Heat production), 

14 

 
 
                                          
 
2.2.3  Space cooling, appliances and lighting 

Specific  electricity  demand  (for  lighting,  space  cooling  and  appliances)  is  calculated  based 
on  needs  per  dwelling,  which  depends  on  the  electricity  price,  the  income  and  an 
autonomous technological trend. 

Consumption of specific electricity per capita (Figure 8) is still 5 to 10 times lower than for 
heat-related uses, but shows a strong growth since 1990, with a fairly linear relation  with 
income per capita. 

Figure 8: Specific uses of electricity per capita vs income per capita, residential, 1990-2010 

2000

1500

1000

500

p
a
c
/
h
W
k

0

0

5

2.3  Transport 

10
Income per capita (k USD/cap) 

20

15

25

30

35

World

OECD

Non-OECD

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. 

2.3.1  Mobility 

2.3.1.1 

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’. 

15 

 
 
 
Figure 9: Equipment rate vs income per capita for select G20 countries, 1990-2015 

800

700

600

500

400

300

200

100

p
a
c
.
k
/
h
e
V

India 

0

0

USA 

Australia 

Canada 

Russia 

Brazil 

EU28 

Japan 

S Korea 

Saudi Arabia 

China 

5

10

15

20

25

30

35

40

45

50

Income per capita  (k$05 PPP / cap) 

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. 

Figure 10 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 10: Passenger mobility, average by mode (left), as a function of income (right) 

1990-2015 

OECD 

World 

Non-OECD 

30
Income per capita (k USD/cap) 

10

20

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

16 

 
 
 
 
 
 
 
 
 
 
2.3.1.2 

Freight 

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

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

Maritime  trade  is  modelled  at  the  global  level  and  identifies  the  following  commodities 
(UNCTAD 2015), each characterised by a specific activity driver: 

-  oil and liquid biofuel imports/exports, 
coal and solid biofuel imports/exports, 
- 
LNG flows, 
- 
iron (driven by primary steel production), 
- 
chemical goods (driven by the value added of the chemicals industry), 
- 
-  other industrial products (driven by the value added of ‘other industry’), 
- 
-  grains (driven by the cereals trade (12)). 

containers (driven by the gross world product), 

Figure 11: Freight mobility, world 

160000

140000

120000

100000

80000

60000

40000

20000

m
k
t
G

0
1990

Maritime (other goods)

Maritime (oil/gas/coal)

Rail

Road

1995

2000

2005

2010

2015

2.3.2  Energy consumption in transport 

2.3.2.1 

Road 

In road transport, energy service needs (i.e. the activity variables of passenger mobility or 
freight  tonnage)  are  associated  with  each  of  the  five  vehicle  types  described  —  cars, 
motorcycles, buses, light trucks and heavy trucks — driving total energy needs. 

The model describes six different engine technologies: 

- 

‘conventional vehicles’ with an internal combustion engine (ICE), which can function 
with gasoline or diesel or a blend of either with liquid biofuels; 

-  plug-in  hybrid  vehicles  that  combine  an  ICE  engine  and  an  electric  battery  that 

consumes electricity; 
full-electric vehicles; 
compressed natural gas vehicles; 

- 
- 
-  hydrogen fuel cell vehicles; 
- 

thermal  hydrogen  vehicles,  which  use  hydrogen  through  a  reformer  to  propel  a 
classical ICE. 

(12)  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) 

17 

 
 
                                          
 
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. 

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 (13)  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 12: 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. 

2.3.2.2 

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). 

(13)  See footnote 6. 

18 

 
 
                                          
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. 

2.3.2.3 

Air transport 

Air transport energy demand is determined by  the evolution of domestic and international 
air passenger mobility and by fuel prices. 

A short-term price elasticity captures the changes in fuel consumption per passenger of the 
existing  fleet,  while  a  long-term  price  elasticity  captures  the  evolution  of  planes’  fuel 
efficiency and air transport organisation. 

A price-based competition takes place between kerosene and aviation biofuel. 

2.3.2.4 

Waterways 

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

2.3.2.5 

International maritime bunkers 

Energy  consumption  for  maritime  bunkers  is  driven  by  the  evolution  of  trade  volume 
(expressed in tonne-kilometres) and energy consumption per tonne-kilometre. 

The energy consumption per unit transported evolves with marginal abatement cost curves 
(IMO 2015) that interact with bunker fuel prices for: 

 
 

the existing fleet: incremental improvements, 
the newly commissioned fleet: new technological options. 

The distribution of consumption between oil and gas-fuel engines depends on the evolution 
of the oil price. 

Figure  13:  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

2.4  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. 

19 

 
 
 
2.5  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. 

20 

 
 
 
3  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 (14): refining of 
oil  and  gas;  and  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. 

3.1  Electricity sector 

Electricity  is  an  energy  carrier  that  has  been  experiencing  an  increasing  role  in  the  final 
energy  demand,  influenced  by  the  evolution  of  the  economy  towards  services, 
electrification  in  industry  and  the  widespread  uptake  of  electronic  consumer  devices 
(including ICT applications). Figure 14 shows that this applies to all regions and that non-
OECD countries in particular are catching up quickly. 

At the same time, the power production sector appears as a key sector for decarbonisation 
with various mitigation options that are often cheaper than the mitigation options available 
at the end-user side, which further supports electrification within the framework of carbon 
constraints. 

Figure 14: 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

3.1.1  Electricity demand 

The  total  electricity  demand  is  computed  by  adding  together  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  auto-
consumption of power plants and of the rest of the energy sector, 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 substitutable energy needs. 

Each sector has its own load shape for a summer and a winter representative day, with a 2-
hour  time-step  —  Figure  15  shows  an  example.  Therefore,  the  total  electricity  load  curve 
changes over time (year of the simulation) and place (country) with the sectoral evolution 
of electricity demand. 

(14)  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. 

21 

 
 
                                          
Demand-side  management  is  allowed  in  some  sectors,  most  notably  electric  vehicles  and 
space heating. 

Figure 15: Illustrative decomposition of electricity demand by sector in 2-hour blocks, for the winter 
and summer days. 

W
G

Exports
Electrolysis
Autoconsumption
Grid losses
Transport
Residential
Services
Industry
Agriculture

0
0
:
2
-
0
0
:
0

0
0
:
4
-
0
0
:
2

0
0
:
6
-
0
0
:
4

0
0
:
8
-
0
0
:
6

0
0
:
0
1
-
0
0
:
8

0
0
:
2
1
-
0
0
:
0
1

0
0
:
4
1
-
0
0
:
2
1

0
0
:
6
1
-
0
0
:
4
1

0
0
:
8
1
-
0
0
:
6
1

0
0
:
0
2
-
0
0
:
8
1

0
0
:
2
2
-
0
0
:
0
2

0
0
:
4
2
-
0
0
:
2
2

0
0
:
2
-
0
0
:
0

0
0
:
4
-
0
0
:
2

0
0
:
6
-
0
0
:
4

0
0
:
8
-
0
0
:
6

0
0
:
0
1
-
0
0
:
8

0
0
:
2
1
-
0
0
:
0
1

0
0
:
4
1
-
0
0
:
2
1

0
0
:
6
1
-
0
0
:
4
1

0
0
:
8
1
-
0
0
:
6
1

0
0
:
0
2
-
0
0
:
8
1

0
0
:
2
2
-
0
0
:
0
2

0
0
:
4
2
-
0
0
:
2
2

Summer

Winter

3.1.2  Power plant technologies 

The  electricity  generating  technologies  (Table  1)  include  existing  technologies  as  well  as 
emerging or future technologies. They are categorised as either centralised or decentralised 
technologies,  which  are  modelled  differently.  The  development  and  production  from 
centralised  technologies  are  based  on  a  competition  between  grid-level  plants,  while  the 
decentralised technologies compete with the retail prices perceived by consumers. 

The technologies are initialised for each country with the historical capacities and electricity 
produced: vintage of installed power mix (Platts 2015), market share in new capacities and 
observed load factor. 

Each  technology  has  the  following  technical  characteristics:  input  fuel,  transformation 
efficiency, lifetime, auto-consumption rate and CO2 capture rate when relevant. 

The economic characteristics are: 

  a fixed cost: 
o 

investment,  which  evolves  according  to  technology  learning  curves  (see 
Section 1.2.3 Energy technology dynamics), 

o  operation and maintenance (O&M), 
o  subsidies or taxes on investments, 
o 

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

  a variable cost: 
fuel cost, 

o 
o  variable O&M cost, 
o  subsidies or taxes on power output or fuel input (including a potential carbon 

value), 
for CCS technologies, CO2 transport and storage costs; 

o 

  a discount rate. 

In addition renewables have a maximum resource potential (see Chapter 4 Energy supply). 
Similarly the deployment of CCS technologies is linked to region-specific geological storage 
potential. 

In addition to these technical and economic characteristics, non-cost factors are calibrated 
to capture the historical relative attractiveness of each technology specific to each country, 

22 

 
 
 
in terms of investments and of operational dispatch.  The coefficient evolves depending on 
assumptions of future societal, political and market factors. 

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

Table 1: Electricity generating technologies 
Technologies 
Fuel 
Conventional nuclear design 
Nuclear 

New nuclear design (4th generation) 

Coal 

Pressurised coal supercritical 

Integrated coal gasification with combined cycle (IGCC) 

Lignite  

Coal conventional thermal 

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  

Wind 

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

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

Solar 

PV power plant (centralised) 

Decentralised PV (*) 

Solar thermal power plant 

Solar thermal power plant with thermal storage 

Hydrogen 

Hydrogen fuel cell (*) 

Option with CCS 

Yes 

Yes 

Yes 

Yes 

(*) 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 3.2. 
(***)  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. 

3.1.3  Electricity production 

The  power  sector  operation  assigns  the  generation  by  technology  to  each  of  the  2-hour 
blocks.  The  available  technologies  must  meet  the  overall  demand,  including  the  grid 
exports. The operation is decided following priorities and rules as shown below. 

The  decentralised  production  is  considered  first.  This  includes  decentralised  solar  PV, 
decentralised  CHP,  small  hydro  and  stationary  fuel  cells.  They  are  considered  to  be 
distributed  at  the  customer  site  and  thus  compete  with  the  retail  electricity  price.  Their 
production is deduced from the grid-level demand with set production profiles. 

23 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Non-dispatchable  centralised  technologies  (wind,  large  solar,  marine)  then  produce 
according to specific profiles and are deduced from the demand that has to be met with all 
dispatchable centralised technologies, unless curtailment takes place (see below). 

Figure  16  shows  some  production  profiles  of  the  main  non-dispatchable  renewables 
technologies. 

Figure 16: Typical production profiles for solar and wind technologies 

300%

250%

200%

150%

100%

50%

0%

r
o
t
c
a
f
d
a
o

l
y
l
r
a
e
y
f
o
%

Solar photovoltaic

Solar thermodynamic

Solar therm. w/ storage

Wind

0
0
:
2
-
0
0
:
0

0
0
:
4
-
0
0
:
2

0
0
:
6
-
0
0
:
4

0
0
:
8
-
0
0
:
6

0
0
:
0
1
-
0
0
:
8

0
0
:
2
1
-
0
0
:
0
1

0
0
:
4
1
-
0
0
:
2
1

0
0
:
6
1
-
0
0
:
4
1

0
0
:
8
1
-
0
0
:
6
1

0
0
:
0
2
-
0
0
:
8
1

0
0
:
2
2
-
0
0
:
0
2

0
0
:
0
-
0
0
:
2
2

0
0
:
2
-
0
0
:
0

0
0
:
4
-
0
0
:
2

0
0
:
6
-
0
0
:
4

0
0
:
8
-
0
0
:
6

0
0
:
0
1
-
0
0
:
8

0
0
:
2
1
-
0
0
:
0
1

0
0
:
4
1
-
0
0
:
2
1

0
0
:
6
1
-
0
0
:
4
1

0
0
:
8
1
-
0
0
:
6
1

0
0
:
0
2
-
0
0
:
8
1

0
0
:
2
2
-
0
0
:
0
2

0
0
:
0
-
0
0
:
2
2

Summer

Winter

NB: 100 % is the average load factor over the year. 

Nuclear and hydro are considered as ‘must-run technologies’ and are characterised by their 
yearly load factor and a default production profile. Their profiles can adapt to the total load 
profile  (e.g.  forced  outages  for  nuclear  maintenance)  and  also  include  some  flexibility  to 
accommodate for the high penetration of variable renewable energy sources (VREs). 

Hydro  pump  and  storage  adds  flexibility,  storing  when  VRE  production  is  high  and 
producing when VRE production is low. 

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

The  remaining  technologies,  constrained  by  their  available  capacity  on  each  2-hour  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. 

3.1.4  Electricity capacity planning 

Decentralised  technologies  are  planned  separately,  and  compete  directly  with  grid 
electricity  on  new  needs.  All  other  technologies  are  developed  to  cover  the  remaining 
electricity demand from the grid. 

The  planning  of  centralised  capacities  is  based  on  the  expected  10-year  trend  of  the 
demand evolution (net of the contribution of decentralised means). This trend is corrected 
every  year  (rolling  myopic  expectations).  The  expected  load  is  distributed  between  the 
expected peak and expected base load, to form expected load blocks with various capacity 
factors. 

24 

 
 
 
 
 
 
 
The  distribution  across  technologies  per  load  block  follows  a  portfolio  approach,  whereby 
total  production  cost  per  load  block  and  a  historically  calibrated  non-cost  factor  set  the 
market share. 

Most  renewable  technologies  are  capped  by  their  maximum  potential.  The  modelling 
ensures that the market shares are distributed in a balanced mix of technologies. 

Figure 17 shows an example of the resulting investment by technology and by block. 

Figure  17:  Illustrative  10-year  capacity  expectations  of  the  main  technologies,  split  into  duration 
blocks 

W
G

Oil
Gas turbines
Gas CC
Coal
Biomass
Wind
Solar
Nuclear
Hydro

730h

2190h

3650h

5110h

6570h

8030h 8760h

For  each  technology,  the  sum  of  the  expected  capacities  over  the  different  blocks  is 
compared  to  the  remaining  capacities  in  10 years  (the  decommissioning  of  old  plants  is 
captured  through  the  vintage  of  installed  capacities)  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. 

Finally, an assumption on technical availability for large-scale deployment is made for some 
new technologies, such as fossil technologies with CCS (from 2035, which can be fully new 
plants, or refurbished fossil fuel plants) or fourth-generation nuclear plants (from 2050). 

3.2  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. 

3.3  Hydrogen 

The  complete  processing  chain  for  hydrogen  use,  production,  transport,  delivery  and 
storage (15) is represented. 

3.3.1  Hydrogen demand 

Hydrogen demand comes from: 

25 

 
 
                                          
 
 
-  Stationary sources: 

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

o  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. 

Stationary 

Table 2: Hydrogen demand sectors 
Fuel cell 
Distributed power generation in 
demand sectors (industry, 
residential, services) 
Engine type in road transport 
vehicles (passenger, freight) 

Transport 

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). 

3.3.2  Hydrogen production 

Hydrogen can be produced through chemical, thermo-chemical or electrical routes. Table 3 
shows the different hydrogen production technologies represented in the model. 

Table 3: Hydrogen production technologies 
Process 
Energy input 
Gas steam reforming 
Gas 

Coal 

Oil 

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. 

3.3.3  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). 

26 

 
 
 
 
 
 
 
 
 
 
Table 4: 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. 

3.4  Synthetic liquids 

3.4.1  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. 

3.4.2  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  4.4  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. 

27 

 
 
Figure 18: Biofuel production for domestic consumption and for international trade, world, Reference 
scenario 

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

3.5  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 ( 16 )  is  captured  through  a 
coefficient based on historical statistics. 

(16)  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. 

28 

 
 
 
 
                                          
 
4  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  on-site  energy  inputs  (see  below)  and  CH4  from  fugitive  emissions  in  both 
production and transport (see Section 5.1.2.2). 

4.1  Oil 

4.1.1  Production and fuel types 

The  oil  market  is  considered  as  one  ‘great  pool’  with  no  clustering  into  regional  markets, 
and whereby only net imports/exports are calculated. 

Producers (88 countries or groups of countries) are defined as either large producers (41) 
that  can  export  to  the  world  market,  or  as  small  producers  (47)  that  produce  only  for 
domestic needs. 

Figure 19: POLES-JRC geographic breakdown for oil and gas production 

The  modelling  differentiates  between  different  types  of  fuel,  which  can  be  grouped  in  the 
following categories: 

 

crude oil in onshore and shallow offshore fields, including NGLs and tight and shale 
oil; 

  other  ‘non-conventional’  resources:  bitumen  (oil  sands),  extra-heavy  oil  and  oil 

shale (kerogen); 

  environmentally  sensitive  oil,  which  is  differentiated  into  deep-water  oil  (> 500 m 
depth) and Arctic oil (north of the Arctic Circle), with a limited number of countries 
having resources of either type. 

4.1.2  From oil resources to oil reserves 

The  ultimate  recoverable  oil  resources  are  inputs  to  the  model  (BGR  2015,  USGS  2013, 
Schenk 2012). 

29 

 
 
For conventional oil, the modelling of reserves increases in two ways, following Figure 20: 

  new fields discovery (arrow from (C) to (B)): each year, the level of drilling activity 
drives forward a curve of decreasing return of the cumulative drilling effort; drilling 
activity is dependent on the oil price (can decrease); 

  enhanced  oil  recovery  (EOR,  arrow  from  (D)  to  (B))  that  increases  the  size  of 
discovered reserves; the recovery ratio is dependent on the oil price. It is capped by 
a maximum ratio of oil in place (typically 70 %). 

The reserves are then the difference between discoveries and cumulated production. 

For  non-conventional  resources,  reserves  are  derived  from  the  evolution  of  the  recovery 
rate, which is a logistic function of the price of oil. 

Figure 20: Schematic representation of the oil sector from resources to reserves 

(A)  
Cumulative 
Production 

(B)  
Reserves 

Discoveries 

(C)  
Undiscovered 
Reserves 

EOR 

(D)  
Sub-economic 
Resources 

URR 

(E)  
Undiscovered 
Sub-economic 
Resources 

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

Increasing geological certainty 

Source: Derived from McKelvey (1972). 

4.1.3  Oil production costs 

Production costs follow cost curves with an energy return on investment logic (EROI curve). 
The  production  cost  curve  is  calculated  and  is  impacted  by  changes  in  energy  prices  and 
production levels via investment needs and energy inputs to production. 

The  EROI  curve  is  determined  by  the  cumulative  extraction  of  resources.  As  production 
increases,  the  energy  required  for  additional  resource  extraction  increases;  a  cost-based 
competition takes place to determine which fuels will supply the energy required. 

𝑬𝑹𝑶𝑰 = [𝒆𝒙𝒑 (𝜶 ×

𝑪𝒖𝒎. 𝑷𝒓𝒐𝒅
𝑼𝑹𝑹

) − 𝟏] + 𝑬𝑹𝑶𝑰𝟎 

With α  = parameter driving EROI evolution 

Cum.Prod  = cumulated production 

URR  = ultimately recoverable resources 

EROI0  = EROI at first output produced. 

30 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
The investment and energy inputs to production are calculated considering: 

  direct  inputs:  combustion  fuels  for  steam  and  heating,  electricity  for  pumping  and 
gas for upgrading from bitumen to syncrude; using the energy prices for industry; 
indirect  inputs  embedded  in  steel  production  for  machinery,  fuel  usage  in  various 
support  services,  etc.:  determined  with  a  resource-specific  direct/indirect  energy 
input ratio and an average indirect energy intensity. 

 

4.1.4  Oil production 

The production of conventional oil is summarised in Figure 21. 

OPEC  producers  are  distinguished  from  non-OPEC  producers.  Non-OPEC  producers  are 
considered  as  ‘fatal  producers’  while  OPEC  producers  can  behave  as  ‘swing  producers’  in 
order to match the level of global demand (‘call on OPEC’). 

For  non-OPEC  producers,  production 
the 
reserves/production ratio (R/P, expressed in years). 

is 

ratio  between 

reserves  and 

the 

  Reserves evolve as explained in Section 4.1.2. 
  The  R/P  ratio follows  a standard  equation  influenced  by  the  oil  price  and  is capped 

by a minimum value, reflecting resource management policies. 

  A comparison of the production cost with the oil price affects new production. 

For  non-conventional  resources,  the  production  depends  on  the  level  of  reserves  (with  a 
floor  on  the  reserves/production  ratio),  a  logistic  function  of  the  difference  between  the 
production cost and the oil price. 

The  total  call  on  OPEC  conventional  oil  production  is  calculated  as  the  difference  between 
total oil demand on the one hand (including international bunkers) and the contribution of 
other  sources  (non-OPEC  producers,  environmentally  sensitive  oil  production,  non-
conventional  oil  production,  liquids  from  coal  and  from  gas).  OPEC  production  is  then 
distinguished between: 

  OPEC non-Gulf producers: the production is a function of the total call on OPEC and 
of  the  producer’s  share  in  the  total  OPEC  reserves,  capped  by  a  floor  to  the 
reserves/production ratio; 

  OPEC  Gulf  producers:  once  non-Gulf  producers  have  been  taken  into  account,  the 
remaining  production  is  allocated  according  to  the  oil  production  capacities  of  Gulf 
producers that depend on the reserves/capacities ratio and the capacities utilisation 
rate. 

31 

 
Figure 21: Schematic representation of oil production for conventional oil 

World demand 

Oil price 

World demand, conventional 

Non-conventional production 

Non-OPEC countries 
Produce what they can 
Production 

Total non-OPEC 
production 

Reserves vs Production ratio 

OPEC non-Gulf countries 
Produce based on reserves-based production quota 

Total OPEC 
production 

Production 

Total non-Gulf 
production 

Discoveries 

Reserves 

OPEC Gulf countries 
Swing producers: production capacities utilisation influence price 

Total Gulf 
production 

Production 

Discoveries 

Reserves 

Reserves vs Production ratio 

Production capacity 

Capacity utilisation rate 

4.1.5  Oil price 

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

 

 

the  marginal  production  cost,  derived  from  the  cumulated  production  curve  ranked 
according to production costs across all resource types; 
the  variations  in  the  capacity  utilisation  rate  of  the  Gulf  countries;  the  elasticity  to 
the capacity utilisation rate of Gulf countries is variable, reaching high values when 
the call on OPEC Gulf is very demanding; 

  oil  stocks  variations  that  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  (10  years),  assuming  a  balance 
over the long run. 

32 

 
 
4.2  Gas 

The gas supply module shares some common features with the oil supply module: detailed 
producers,  different  fuel  types  and  a  process  to  transform  resources  into  reserves.  But  it 
also has distinct features, in particular with respect to the trade pattern that captures the 
important role of transport infrastructures and regionalisation of markets. 

4.2.1  Gas production and fuel types 

The  gas  consumption  is  split  into  14  regional  markets  that  are  supplied  by  88  producers 
(see Figure 19), differentiated into small  producers (47), which produce only for domestic 
needs, and large producers (41) that can export to importing markets. Unlike for oil, trade 
flows of gas are directional from producers to the different consuming markets. 

The  modelling  differentiates  between  various  types  of  natural  gas  sources,  which  can  be 
grouped into the following categories: 

  onshore and shallow offshore ‘conventional’ gas fields; 
  other ‘non-conventional’ resources: shale gas and coal-bed methane; 
  environmentally  sensitive  gas,  which  is  differentiated:  deep-water  gas  (> 500 m 
depth) and Arctic gas (north of the Arctic Circle), with a limited number of countries 
having resources of either type. 

Figure 22: 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 | S Korea | Japan | New Zealand | Rest Pacific 

NB: Large + small  producers  correspond  to  oil  producers  (Figure  19).  ‘Rest’  regions  are  adapted  to 
the  relevant  singled  out  producers;  they  are  different  from  the  energy  demand  and  coal  production 
regions. 

4.2.2  From gas resources to gas reserves 

Recoverable gas resources (BGR 2015) are transformed into reserves through discoveries, 
which  depend  on  the  drilling  effort  —  in  common  with  the  oil  sector  (reflecting  common 
E & P activity). 

33 

 
 
4.2.3  Gas production costs 

The production costs description is the same as for oil. 

4.2.4  Gas production and trade capacities 

The production is calculated to meet the demand, split into 14 regional markets. 

Large  producers  supply  the  various  consuming  markets,  net  of  the  contribution  of  small 
producers within each regional market. 

The  modelling  first  assesses  the  supply  capacities  of  large  producers  for  regional  markets 
and  then  the  actual  supply.  The  model  identifies  three  types  of  gas  transport  route: 
onshore  pipeline,  offshore  pipeline  and  LNG.  All  routes  from  producers  to  markets  are 
characterised by a distance and a cost (see Figure 23). 

Figure 23: Gas transport costs in the POLES-JRC model 

u
t
B
M
M
/
D
S
U

7

6

5

4

3

2

1

0

Pipeline - onshore

Pipeline - offshore

LNG

0

1000

2000

3000

4000

5000

6000

7000

km 

Source: GasNatural Fenosa (2012). 

The  modelling  of  supply  capacities  per  route  considers  the  evolution  of  the  consuming 
market,  a  depreciation  of  existing  capacities  and  a  minimum  utilisation  of  the  existing 
capacities. The allocation of new trade capacity by a producer to a given market is based on 
the  reserves  and  on  a  return  on  investment  calculation  comparing  the  gas  price  and  the 
transport cost to that market. 

Actual  gas  supply  is  then  calculated  based  on  the  use  of  these  capacities;  market  shares 
are  determined  by  the  exporter’s  reserves/production  ratio,  the  variable  costs  over  the 
route and coefficients reflecting the historically observed trade matrix. 

For small producers, gas production evolves with the reserves and the reserves/production 
ratio. 

  Reserves evolve as explained in Section 4.1.2. 
  The  R/P  ratio  follows  a  standard  equation  influenced  by  gas  market  prices  and  is 

capped by a minimum value, reflecting resource management policies. 

The functioning of the production and trade module is summarised in Figure 24. 

34 

 
 
 
Figure 24: Schematic representation of the gas production and trade module 

Demand 

Small 
producers 

Net demand 
market 

Export capacities 

Export capacities 

Total for producer 

Total for gas 
market 

Exporter-to-market 
route 

ROI calculation 

Production 
(=exports) 

Production costs 

Transport costs 

Production non-
conventional 

Production 
conventional 

Reserves vs 
Production ratio 

Total production 

Additional gas sources 

The modelling also identifies additional sources of natural gas: 

  urban waste methane,  
  underground coal methane,  
  gas production fugitive emissions.  

The  production  is  derived  from  CH4  emissions  from  the  respective  source  being 
recovered and used as an energy source (see Section 5.1 Greenhouse gases). 

4.2.5  Price 

Trade  is  grouped  into  three  large  continental  markets  for  international  gas  prices:  Asia, 
America and Europe-Africa. 

In each of the three markets, the gas price includes: 

the transport cost to that market (weighted average of flows and routes); 

 
  an indexation on the variation of the reserve/production ratio of the supplies inside 

that continental market to capture possible supply tension; 

  an  indexation  to  the  evolution  of  the  international  oil  price  to  reflect  long-term 

contracts; 

  an  indexation  to  the  gas  price  in  the  two  other  continental  markets,  capturing  the 

integration of prices due to the development of worldwide LNG supply. 

In addition, the gas import price for each of the 14 regional markets also takes into account 
a transport capacity utilisation factor. 

35 

 
 
 
 
 
4.3  Coal 

Figure 25 shows the different coal producers (81) considered in the modelling. 

Figure 25: 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: 

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

geological conditions and a decrease in the energy content; 

o  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; 

o 
o  energy: price of oil and electricity to industry; 
o  materials use: energy price of steelmaking; 
o  other components: considered as remaining constant. 

36 

 
 
 
 
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 Figure 24). 

Trade takes place between large producers (26 countries or regions) and demand markets 
(15  regional  markets)  (see  Figure  26).  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 26: 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  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. 

37 

 
 
 
 
4.4  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 (17).  An  international  price  of  cellulosic  biomass  is 
derived from the aggregation of regional cost curves. 

Figure 27: 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
production 

2

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. 

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. 

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

38 

 
 
                                          
 
Figure 28: Biomass-for-energy production and potential, world 

200

150

J
E

100

50

0
2000

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: 

forests, 

  agricultural land: food production and energy crops, 
 
  built areas (evolves with urban population), 
 
  other unused land, as the difference with total area. 

inland water and deserts (fixed), 

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

4.5  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) (18). 

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. 

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 

(18)  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). 

39 

 
 
 
                                          
 
magnitude.  The  interaction  of  nuclear  fuel  cycles  for  conventional  and  advanced  nuclear 
design is taken into account. 

4.6  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 2015a). 

The production profile is adjusted to account for storage needs (see Section 3.1.3 Electricity 
production). 

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 29: Hydroelectricity potential in 2015 (left) and % of potential used over 1990-2015 (right) 

h
W
T

12000

10000

8000

6000

4000

2000

0

50%

40%

30%

20%

10%

0%

OECD Non-OECD

1990

1995

World

2000

2005

OECD

2010
Non-OECD

2015

4.7  Wind potential 

Wind power production is determined from production profiles (see Section 3.1.3 Electricity 
production). 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 (19),  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 (20). 

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

(19)  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) 

(20)  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. 

40 

 
 
                                          
 
  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 30: Schematic representation of the wind potential treatment in POLES-JRC 

Wind energy resource 

Meteorological potential (km

2
) 

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. 

4.8  Solar potential 

4.8.1  Solar power plant potential 

Solar  power  plants’  production  is  determined  from  production  profiles  (see  Section  3.1.3 
Electricity production). 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. 

4.8.2  Solar distributed photovoltaic potential 

Distributed PV power production is determined from production profiles (see Section 3.1.3 
Electricity  production).  Its  deployment,  in  competition  with  grid  electricity  and  other 
decentralised technologies in buildings, is determined by costs and potential. 

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. 

41 

 
 
 
 
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. 

4.8.3  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 3.2 Heat production). 

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

4.9  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. 

42 

 
 
 
5  Emissions 

5.1  Greenhouse gases 

5.1.1  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 31: 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% 

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). 

For GHG emissions from AFOLU (21), baseline emissions and mitigation potential are derived 
from the specialised GLOBIOM model (IIASA 2016a). 

(21)  AFOLU: agriculture, forestry and land use. 

43 

 
 
                                          
Global warming potentials (GWPs (22)) are applied to non-CO2 greenhouse gases to convert 
emissions to CO2-equivalent. 

Figure 32 gives as an illustration the aggregated MACCs of the main sectors at world level, 
which sums the country and regional level MACCs. 

Figure  32:  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%

60%

Emission reductions 
(% emissions of no policy case) 
LULUCF

Agriculture

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). 

5.1.2  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. 

5.1.2.1 

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. 

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

(22)  GWP as defined in the IPCC assessment reports. 

44 

 
 
 
                                          
 
Table 5: CO2 emission factors 

Fuel/sector 
Oil 
Gas 
Coal 
Oil: domestic and international air transport 
Oil: international maritime bunkers 
Biomass (*) 

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

(*) Biomass and its products are considered as carbon-neutral in the emissions balances. 

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. 

5.1.2.2 

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: 

o  processes  in  the  oil  industry  (exploration,  production  and  refining,  venting 

and flaring); 

o  upstream processes of natural gas production; 
o 
o  underground mining and surface coal mining. 

transmission and distribution in the natural gas sector; 

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

in: 

o 
o 
o 

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. 

5.1.3  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. 

  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. 

45 

 
5.1.4  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. 

5.1.5  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). 

5.1.6  Greenhouse gas coverage summary 

Table 6 provides a summarised view of the GHG emissions flows in the model. 

46 

 
 
Table 6: Greenhouse gas emission sources 
Sector 
Energy 

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 

Industrial processes 

Waste 

Agriculture, forestry and 
other land use (AFOLU) 

Agriculture 

Forestry and land use 

GHG 
CO2 
CH4 

CH4 

N2O 
CH4 
SF6 
CO2 

CO2 

CO2 
N2O 
PFCs 

SF6 

HFCs 

CH4 
N2O 
CH4 
N2O 

CO2 

Emission activity 
Burning of fossil fuel 
Production, transmission and distribution 

Underground and surface mining 

Combustion by-products 

Transmission and distribution 
Iron ore reduction  

Carbonate decomposition  

Steam reforming 
Nitric and adipic acid 
Primary aluminium  
Semiconductor and PV 
Magnesium refining, 
Semiconductor and PV 
Air conditioning, refrigeration 
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  

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

Electricity production 
Tonnes of steel using thermal processes 

Non-metallic minerals industry value 
added 
Chemicals industry value added 

‘Other’ industry value added 

Industry value added 

Industry value added 

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 

47 

 
 
5.2  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). 

5.2.1  Pollutants covered 

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

(NM)VOCs 

: sulphur dioxide, 
: nitrogen oxides, 

: non-methane volatile organic compounds, 

  SO2 
  NOx 
 
: carbon monoxide, 
  CO 
: black carbon, 
  BC 
  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 
  NH3 

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

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

5.2.2  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. 

48 

 
 
 
Table 7: Pollutants considered with emission factors as direct inputs 
Sector 
Industry 

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 

Buildings 

Transport 

Agriculture 
Power generation 

Other energy 
transformation 

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

49 

 
 
 
Table 8: Pollutants considered with emission factors recalculated from historical data 
Sector 
Industrial 
production 

Source 
Activity indicator 
Cement 
Total energy in non-metallic minerals industry 
Chemicals 
Total energy in chemicals industry 
Fertilisers 
Total energy in chemical feedstocks industry 
Solvents 
Value added of chemicals industry 
Other combustion 
Oil in other industry 
Total energy in other industry 
Other processes 
Other/unattributed  Oil in residential and services 
Other/unattributed  Oil in road transport 

Buildings 
Surface 
transportation 
Agriculture 
Energy 
transformation 
Other energy 
transformation 

Fires 

Waste 
Other 

Non-energy 
Other/unattributed  Oil inputs in power generation 

N2O emissions from agriculture  

Oil 

Gas 

Coal 

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) 

Forest fires 
Savannah fires 
Peat fires 
Agricultural waste 
burning 
All 
Other/unattributed  Population 

Urban population 

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 ( 23 ).  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 (24). Further 
reductions can be achieved as co-benefits of a climate policy, caused by the reduction of 
fossil fuel consumption. 

(23)  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  

(24)  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). 

50 

 
 
 
                                          
6  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 

51 

 
 
 
 
7  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. 

7.1  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 2013). 

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. 

7.2  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). 

7.3  Greenhouse gas emissions 

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

52 

 
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 (25). 

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. 

7.4  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 (26) 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. 

7.5  Summary 

Table  9  provides  a  synthetic  view  of  the  data  sources  used  in  the  POLES-JRC  model.

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

objectives. 

(26)  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  

53 

 
                                          
Historical data 

UN, Eurostat 

World Bank 

GECO projections 

UN (medium fertility) 

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

Table 9: Data sources in POLES-JRC 
Series 

Population 

GDP, growth 

Other activity 
drivers 

Energy 
resources 

Value added 

World Bank 

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. 

54 

 
 
 
 
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Rafaj, P., Schöpp, W., Russ, P., Heyes, C., Amann, M., 2013. Cobenefits of post-2012 
global climate mitigation policies. Mitig. Adapt. Strateg. Glob. Change 18 (6), 801-824. 

Rao, S., et al. (2016). Future air pollution in the Shared Socio-economic Pathways. 
Global Environmental Change Vol.42, pp. 346–358. 
http://dx.doi.org/10.1016/j.gloenvcha.2016.05.012 

Sagar, A. and van der Zwaan, B.C.C. (2006). Technological Innovation in the Energy 
Sector: R & D, Deployment, and Learning-by-Doing. Energy Policy, 34, 17, pp.2601-
2608. 

Schenk, C.J., 2012, An estimate of undiscovered conventional oil and gas resources of 
the world, 2012: US Geological Survey Fact Sheet 2012–3042, 6 p. 
https://pubs.usgs.gov/fs/2012/3042/ Last accessed: 25/04/2017 

UIC (2014). RAILISA online database. International Union of Railways, 2014. 
http://www.uic.org/statistics Last accessed: 01/12/2015 

UN (2015). 2015 Revision of World Population Prospects. Department of Economic and 
Social Affairs, Population Division, United Nations. http://esa.un.org/wpp/Excel-
Data/population.htm Last access 14/04/2016 

57 

 
Unctad (2015). Review of Maritime Transport 2015. Unctad/RMT/2015. United Nations 
Conference on Trade and Development, Geneva, Switzerland. 

UNFCCC (2015). Flexible GHG data queries. United Nations Framework Convention on 
Climate Change, Bonn, 2014. http://unfccc.int/di/FlexibleQueries.do 

USGS (US Geological Survey) (2013). World Petroleum Assessment. 
http://energy.usgs.gov/OilGas/AssessmentsData/WorldPetroleumAssessment.aspx and 
http://pubs.usgs.gov/dds/dds-069/dds-069-ff/ Last accessed: 18/04/2016 

van Vuuren, P., Strengers, B. J., and de Vries, H. J. M. (1999). Long-term perspectives 
on world metal use — a model-based approach. RIVM — National Institute of Public 
Health and the Environment. RIVM report 461502 021, 1999. 

Van Wijk, A.J.M., Coelingh, J.P. (1993). Wind power potential in the OECD countries. 
ECN, The Netherlands. 

World Bank (2016). World development indicators. World Bank, 2016. 
http://data.worldbank.org/data-catalog/world-development-indicators Last accessed: 
30/09/2016 

WEC (2013a). World Energy Resources: 2013 Survey. ISBN: 978 0 946121 29 8. World 
Energy  Council,  2013.  http://www.worldenergy.org/data/resources/resource/coal/  Last 
accessed: 18/04/2016 

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ISBN:  978  0  94612  130  4.  http://www.worldenergy.org/publications/2013/world-
energy-perspective-cost-of-energy-technologies/ Last accessed: 18/04/2016 

WEC (2016a). World Energy Resources, Geothermal. World Energy Council, 2016. 
http://www.worldenergy.org/data/resources/resource/geothermal/ 

WEC (2016b). World Energy Resources, Hydropower. World Energy Council, 2016. 
http://www.worldenergy.org/data/resources/resource/hydropower/ 

Wiesenthal, T., Dowling, P., Morbee, J., Thiel, C., Schade, B., Russ, P., Simoes, S., 
Peteves, S., Schoots, K., Londo, M. (2012). Technology Learning Curves for Energy 
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The Netherlands. http://www.wise-uranium.org/index.html Last accessed: 07/11/2016 

Wright, T.P. (1936). Factors affecting the cost of airplanes. J. Aeronaut. Sci. 3(2), 122-
128. 

WSA (2015). Steel Statistical Yearbook. World Steel Association, 2015. 
https://www.worldsteel.org/publications/bookshop.html Last accessed: 01/12/2015 

58 

 
 
 
Selected research projects (27) 

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

  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 

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

59 

 
 
 
                                          
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 

NREL   

O & M   

Nuclear Energy Agency 

natural gas liquids 

non-metallic minerals 

National Renewable Energy Laboratory (United States) 

operation and maintenance 

60 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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 

61 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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 

thousand dollars 

1 000 USD 

trillion dollars  

1 000 000 000 000 USD 

K USD  

tn 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 

62 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
List of figures 

Figure 1: Schematic representation of the POLES-JRC model architecture .................... 4 

Figure 2: Schematic representation of the POLES-JRC sectors .................................... 6 

Figure  3:  Schematic  representation  of  the  energy  needs  and  depreciation  procedure  in 
POLES-JRC .......................................................................................................... 7 

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

Figure  5:  World  consumption  of  industry  per  sector  (left)  and  industry  value  added 
(right), reference scenario ................................................................................... 11 

Figure 6: Apparent steel consumption per unit of GDP versus income per capita for select 
G20 countries, 1990-2015 ................................................................................... 12 

Figure  7:  Example  of  the  distribution  of  standard-,  medium-  and  low-energy 
consumption surfaces in the residential sector, world, in a Reference and a 2 °C scenario
 ....................................................................................................................... 14 

Figure  8:  Specific  uses  of  electricity  per  capita  vs  income  per  capita,  residential,  1990-
2010 ................................................................................................................ 15 

Figure 9: Equipment rate vs income per capita for select G20 countries, 1990-2015 .... 16 

Figure 10: Passenger mobility, average by mode (left), as a function of income (right) 16 

Figure 11: Freight mobility, world ......................................................................... 17 

Figure 12: Schematic representation of road transport in POLES-JRC ......................... 18 

Figure 13: International bunkers’ energy consumption and their share in total transport 
energy consumption ........................................................................................... 19 

Figure 14: Share of electricity in total final energy consumption, 1990-2015 .............. 21 

Figure 15: Illustrative decomposition of electricity demand by sector in 2-hour blocks, for 
the winter and summer days. .............................................................................. 22 

Figure 16: Typical production profiles for solar and wind technologies ........................ 24 

Figure 17: Illustrative 10-year capacity expectations of the main technologies, split into 
duration blocks .................................................................................................. 25 

Figure  18:  Biofuel  production  for  domestic  consumption  and  for  international  trade, 
world, Reference scenario ................................................................................... 28 

Figure 19: POLES-JRC geographic breakdown for oil and gas production .................... 29 

Figure 20: Schematic representation of the oil sector from resources to reserves ........ 30 

Figure 21: Schematic representation of oil production for conventional oil .................. 32 

Figure 22: Mapping of gas producers and demand markets ...................................... 33 

Figure 23: Gas transport costs in the POLES-JRC model ........................................... 34 

Figure 24: Schematic representation of the gas production and trade module ............. 35 

Figure 25: POLES-JRC geographic breakdown for coal production .............................. 36 

Figure 26: Mapping of large coal producers and demand markets ............................. 37 

Figure 27: Schematic representation of biomass flows in the POLES-JRC model .......... 38 

Figure 28: Biomass-for-energy production and potential, world ................................ 39 

Figure  29:  Hydroelectricity  potential  in  2015  (left)  and %  of  potential  used  over  1990-
2015 (right) ...................................................................................................... 40 

Figure 30: Schematic representation of the wind potential treatment in POLES-JRC ..... 41 

63 

 
Figure 31: World greenhouse gas emissions per activity type, 2014 .......................... 43 

Figure  32:  Marginal  abatement  cost  curves  (all  gases,  all  sources,  world,  2030)  — 
volume (left) relative to baseline emissions (right) .................................................. 44 

64 

 
 
 
List of tables 

Table 1: Electricity generating technologies ........................................................... 23 

Table 2: Hydrogen demand sectors ...................................................................... 26 

Table 3: Hydrogen production technologies ............................................................ 26 

Table 4: Hydrogen transport chains ...................................................................... 27 

Table 5: CO2 emission factors .............................................................................. 45 

Table 6: Greenhouse gas emission sources ............................................................ 47 

Table 7: Pollutants considered with emission factors as direct inputs ......................... 49 

Table 8: Pollutants considered with emission factors recalculated from historical data .. 50 

Table 9: Data sources in POLES-JRC ..................................................................... 54 

Table 10: List of 54 individual countries represented in POLES-JRC ........................... 67 

Table 11: Country mapping for the 12 regions in POLES-JRC .................................... 68 

Table 12: List of 77 individual oil and gas producing countries represented in POLES-JRC
 ....................................................................................................................... 69 

Table 13: Country mapping for the 11 oil and gas producing regions in POLES-JRC ..... 70 

Table 14: List of 59 individual coal producing countries represented in POLES-JRC ...... 71 

Table 15: Country mapping for the 12 coal producing regions in POLES-JRC ............... 72 

65 

 
 
 
Annex 1: ISIC classification of sectors in POLES-JRC 

Using: International Standard Industrial Classification of All Economic Activities, 
Rev.4 (28) 

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 

o  Chemical feedstocks: part of Group 201 
o  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 

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

66 

 
 
 
                                          
Annex 2: Country mappings 
Energy and emissions balances 

54 individual countries + 12 regions 

Table 10: 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 

Czech Republic 

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. 

67 

 
 
 
 
Table 11: Country mapping for the 12 regions in POLES-JRC 
Rest Central America 
Bahamas 

Rest Balkans 
Albania 

Barbados 

Belize 

Bermuda 

Costa Rica 

Cuba 

Dominica 

Dominican Republic 

El Salvador 

Grenada 

Guatemala 

Haiti 

Honduras 

Jamaica 

Nicaragua 

NL Antilles and Aruba 

Panama 

São Tomé and Príncipe 

St Lucia 

St Vincent and Grenadines 

Trinidad and Tobago 

Rest South America 
Bolivia 

Colombia 

Ecuador 

Guyana 

Paraguay 

Peru 

Suriname 

Uruguay 

Venezuela 

Bosnia and Herzegovina 

Former Yugoslav Republic of Macedonia  

Kosovo  

Moldova 

Montenegro 

Serbia 

Rest CIS 
Armenia 

Azerbaijan 

Belarus 

Georgia 

Kazakhstan 
Kyrgyzstan 

Tajikistan 

Turkmenistan 

Uzbekistan 

Mediter. Middle East 
Israel 

Jordan 

Lebanon 

Syria 

Rest of Persian Gulf 
Bahrain 

Iraq 

Kuwait 

Oman 

Qatar 

United Arab Emirates 

Yemen 

Morocco and Tunisia 
Morocco 

Tunisia 

Algeria and Libya 
Algeria 

Libya 

Rest South Asia 
Afghanistan 

Bangladesh 

Bhutan 

Maldives 

Nepal 

Pakistan 

Seychelles 

Sri Lanka 

Rest South East Asia 
Brunei 

Cambodia 

Laos 

Mongolia 

Myanmar/Burma 

North Korea 

Philippines 

Singapore 

Taiwan 

Rest Pacific 
Fiji 

Kiribati 

Papua New Guinea 

Samoa (Western) 

Solomon Islands 

Tonga 

Vanuatu 

Rest Sub-Saharan Africa 
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 

Eritrea 

Ethiopia 

Gabon 

Gambia 

Ghana 

Guinea 

Guinea-Bissau 

Kenya 

Lesotho 

Liberia 

Madagascar 

Malawi 

Mali 

Mauritania 

Mauritius 

Mozambique 

Namibia 

Niger 

Nigeria 

Rwanda 

Senegal 

Sierra Leone 

Somalia 

Sudan 

Swaziland 

Tanzania 

Togo 

Uganda 

Zambia 

Zimbabwe 

68 

 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
 
  
  
 
  
  
 
  
  
 
  
  
 
  
  
 
  
  
 
  
  
 
  
  
  
  
Oil and gas production 

77 individual countries + 11 regions 

(*): 41 exporters 

Table 12: List of 77 individual oil and gas producing countries represented in POLES-JRC 
Non-EU individual countries 
Algeria  (*) 

EU-28 Member States 
Austria 

Mexico (*) 

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 

Czech Republic 

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 (*) 

69 

 
 
 
 
 
 
 
 
Rest South Asia 
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 

Table 13: Country mapping for the 11 oil and gas producing regions in POLES-JRC 
Rest Balkans 
Rest Central America 
Albania 
Bahamas 

Rest Sub-Saharan Africa 
Benin 

Barbados 

Belize 

Bermuda 

Costa Rica 

Cuba 

Dominica 

Dominican Republic 

El Salvador 

Grenada 

Guatemala 

Haiti 

Honduras 

Jamaica 

Nicaragua 

NL Antilles and Aruba 

Panama 

São Tomé and Príncipe 

St Lucia 

St Vincent and Grenadines 

Rest South America 
Guyana 

Paraguay 

Suriname 

Uruguay 

Bosnia and Herzegovina 

Former Yugoslav Republic of Macedonia 

Kosovo 

Moldova 

Montenegro 

Serbia 

Rest CIS (*) 
Armenia 

Belarus 

Georgia 

Kyrgyzstan 

Tajikistan 

Mediter. Middle East 
Israel 

Jordan 

Lebanon 

Syria 

Rest of Persian Gulf 
Bahrain 

Yemen 

Morocco and Tunisia 
Morocco 

Tunisia 

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 

Tanzania 

Togo 

Uganda 

Zambia 

Zimbabwe 

70 

 
  
 
  
  
 
  
  
 
  
  
 
  
  
 
  
  
 
  
  
 
  
  
 
  
  
 
  
  
 
  
  
 
  
  
 
  
  
 
  
  
 
  
  
 
  
  
 
  
  
 
  
  
  
  
Coal production 

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

(*): 16 exporters (4 of which with infra-national detail) 

Table 14: List of 59 individual coal producing countries represented in POLES-JRC 
Non-EU individual countries 
Argentina 

EU-28 Member States 
Austria 

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 (*) 

Belgium 

Bulgaria 

Croatia 

Cyprus 

Czech Republic 

Denmark 

Estonia 

Finland 

France 

Germany 

Greece 

Hungary 

Ireland 

Italy 

Latvia 

Lithuania 

Luxembourg 

Malta 

Netherlands 

Poland (*) 

Portugal 

Romania 

Slovakia 

Slovenia 

Spain 

Sweden 

United Kingdom 

71 

 
 
 
 
Rest South Asia 
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 

Table 15: Country mapping for the 12 coal producing regions in POLES-JRC 
Rest Central America 
Bahamas 

Rest Balkans 
Albania 

Rest Sub-Saharan Africa 
Angola 

Barbados 

Belize 

Bermuda 

Costa Rica 

Cuba 

Dominica 

Dominican Republic 

El Salvador 

Grenada 

Guatemala 

Haiti 

Honduras 

Jamaica 

Nicaragua 

NL Antilles and Aruba 

Panama 

São Tomé and Príncipe 

St Lucia 

St Vincent and Grenadines 

Trinidad and Tobago 

Rest South America 
Bolivia 

Ecuador 

Guyana 

Paraguay 

Peru 

Suriname 

Uruguay 

Bosnia and Herzegovina 

Former Yugoslav Republic of Macedonia 

Kosovo 

Moldova 

Montenegro 

Serbia 

Rest CIS 
Armenia 

Azerbaijan 

Belarus 

Georgia 

Kyrgyzstan 

Tajikistan 

Turkmenistan 

Uzbekistan 

Mediter. Middle East 
Israel 

Jordan 

Lebanon 

Syria 

Rest of Persian Gulf 
Bahrain 

Iraq 

Kuwait 

Oman 

Qatar 

United Arab Emirates 

Yemen 

Morocco and Tunisia 
Morocco 

Tunisia 

Algeria and Libya 
Algeria 

Libya 

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 

Gabon 

Gambia 

Ghana 

Guinea 

Guinea-Bissau 

Kenya 

Lesotho 

Liberia 

Madagascar 

Malawi 

Mali 

Mauritania 

Mauritius 

Namibia 

Niger 

Nigeria 

Rwanda 

Senegal 

Sierra Leone 

Somalia 

Sudan 

Swaziland 

Tanzania 

Togo 

Uganda 

Zambia 

Zimbabwe 

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GETTING IN TOUCH WITH THE EU 

In person 

All over the European Union there are hundreds of Europe Direct information centres. You can find the 
address of the centre nearest you at: http://europea.eu/contact 

On the phone or by email 

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

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

- at the following standard number: +32 22999696, or 

- by electronic mail via: http://europa.eu/contact 

FINDING INFORMATION ABOUT THE EU 

Online 

Information about the European Union in all the official languages of the EU is available on the Europa 
website at: http://europa.eu 

EU publications 
You can download or order free and priced EU publications from EU Bookshop at: 
http://bookshop.europa.eu. Multiple copies of free publications may be obtained by contacting Europe 
Direct or your local information centre (see http://europa.eu/contact). 

 
 
 
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doi:10.2760/225347 

ISBN 978-92-79-71801-4