EU petroleum refining fitness check : OURSE modelling and results.
Date: 2015-12-16
Institutions: Joint Research Centre
Subjects: diesel fuel, energy research, motor fuel, oil refining, petrol, quality standard, research report, simulation

EU Petroleum Refining Fitness 
Check: OURSE Modelling and Results 

Umed Temurshoev, Marian Mraz,  
Luis Delgado Sancho and Peter Eder 

2 0 1 5  

EUR 27269 EN 

 
 
 
 
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The  scientific  output  expressed  does  not  imply  a  policy  position  of  the  European  Commission.  Neither  the 
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be made of this publication. 

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JRC96207 

EUR 27269 EN 

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ISSN 1831-9424 

doi:10.2791/037768 

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How to cite: Temurshoev U, Mraz M, Delgado Sancho L, Eder P.; An Analysis of the EU Refining Industry: OURSE 
Modelling and Results; EUR 27269 EN; doi:10.2791/037768 

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Abstract 

The  OURSE  (Oil  is  Used  in  Refineries  to  Supply  Energy)  model  is  used  to  assess  ex  post  the  likely  impact  on  the 
performance  and  international  competitiveness  of  the  EU  refineries  of  the  main  EU  legislation  included  in  the  EU 
Petroleum  Refining  Fitness  Check  (REFIT)  study.  Given  the  (dis)similar  nature  of  the  immediate  (i.e.  direct)  impact 
mechanisms of the legislation acts on refining industry, the considered directives were grouped into the following three 
(broader) categories for modelling purposes: 
1. 
2. 
Energy Taxation Directive (ETD); and 
3. 
Directive (LCPD), Integrated Pollution Prevention and Control Directive (IPPCD) and Air Quality Directive (AQD). 

Fuel quality specifications change due to the Fuels Quality Directive (FQD) and Marine Fuels Directive (MFD);  
Demand levels and composition change due to the requirements of the Renewable Energy Directive (RED) and 

Sulphur  dioxide  emissions  limits  change  as  implied  by  the  requirements  of  the  Large  Combustion  Plants 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
An Analysis of the EU Refining 
Industry: OURSE Modelling and Results  

Umed Temurshoeva,b, Marian Mrazb, Luis Delgado Sanchob and Peter Ederb 

Date: May 7, 2015 

a Department of Economics, Loyola University Andalusia, Campus Palmas Altas,  
41014 Seville, Spain 
b European Commission, Joint Research Centre, Institute for Prospective Technological 
Studies, 41092 Seville, Spain 

Table of Contents 
Acknowledgements ..................................................................................................................... 3 

Executive summary ..................................................................................................................... 4 

1  Demand for oil products ..................................................................................................... 7 

2  Refining capacities ............................................................................................................ 12 

3  CAPEX, OPEX and transportation costs .......................................................................... 14 

4  Model calibration .............................................................................................................. 15 

4.1  Collection and estimation of data that are endogenous in OURSE ........................... 16 

4.2  Calibration and the OURSE_QP model ..................................................................... 19 

5  Baselines, simulation scenarios and results ...................................................................... 31 

5.1  Benchmark (baseline) scenarios ................................................................................. 31 

5.2  Counterfactual scenarios and results .......................................................................... 36 

5.2.1 

5.2.2 

5.2.3 

Impact assessment of changes in fuels quality specifications ............................ 37 

Impact assessment of changes in products demand ............................................ 50 

Impact assessment of changes in pollution limits ............................................... 60 

References ................................................................................................................................. 68 

6  Appendix A: Tables .......................................................................................................... 71 

7  Appendix B: OURSE_LP equations ................................................................................. 76 

2 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
Acknowledgements 

We  are  grateful  to  the  members  of  IFP  Energies  nouvelles  (Paris,  France),  in  particular,  to 
Frederic Lantz, Jean-François Gruson and Valérie Saint-Antonin, for their useful comments, 

thorough and long discussions on various OURSE modelling issues, and sharing with us their 
refinery  expertise  (relevant  to  the  modelling  exercises  of  this  study)  throughout  the  entire 

period  of  this  modelling  project.  We  also  benefitted  from  comments  of  Wojciech  Suwała 
(AGH, Krakow, Poland). We are grateful to Pavel Ciaian (JRC IPTS) for introducing us to 

the  Positive  Mathematical  Programming  (PMP)  literature  and  Kamel  Louhichi  (JRC  IPTS) 
for further discussions on PMP and PMP-related calibration issues. We are also very thankful 

to  Jean  Mercenier  (U.  Panthéon-Assas  and  JRC  IPTS)  for  sharing  his  knowledge  on 
Computable General Equilibrium (CGE) modelling relevant for this study.   

3 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Executive summary 

We  use  OURSE  (Oil  is  Used  in  Refineries  to  Supply  Energy)  model  to  assess  ex  post  the 
likely  impact  on  the  performance  and  international  competitiveness  of  the  EU  refineries  of 

the  main  EU  legislation  included  in  the  EU  Petroleum  Refining  Fitness  Check  (REFIT) 
study. Given the (dis)similar nature of the immediate (i.e. direct) impact mechanisms of the 

legislation  acts  on  refining  industry,  the  considered  directives  were  grouped  into  the 
following three (broader) categories for modelling purposes: 

1.  Fuel  quality  specifications  change  due  to  the  Fuels  Quality  Directive  (FQD)  and 

Marine Fuels Directive (MFD);  

2.  Demand  levels  and  composition  change  due  to  the  requirements  of  the  Renewable 

Energy Directive (RED) and Energy Taxation Directive (ETD); and 

3.  Sulphur dioxide emissions limits change as implied by the requirements of the Large 
Combustion  Plants  Directive  (LCPD),  Integrated  Pollution  Prevention  and  Control 

Directive (IPPCD) and Air Quality Directive (AQD).  

The summary of the main results in terms of incurred costs by the EU refineries are presented 
in the following table, where all the cost figures are given in mln USD per year expressed in 

constant 2008 prices: 

Lower average 
estimate 

Upper average 
estimate 

Upper bound 
estimate 

-- Total costs 

-- Total costs 
---- CAPEX 
---- OPEX 

Overall costs of the considered directives 
753.1 

416.7 

Costs due to FQD and MFD 

154.3 
102.5 
51.8 

464.0 
408.3 
55.6 

204.6 

-- Net forgone earnings 

200.1 

Costs due to RED and ETD 

Costs due to SO2 regulations (LCPD, IPPCD and AQD) 

-- Total costs 
---- CAPEX 
---- Low-sulphur crude/feedstock  
       switching costs 

62.3 
33.0 

29.3 

84.5 
33.3 

51.1 

940.3 

550.4 
475.2 
75.1 

297.8 

92.1 
38.7 

53.4 

The details of these results are explained thoroughly in the text. The figure below shows the 
equivalent total costs estimates in terms of eurocents per barrel of processed crude oil (again 

in  2008  prices).  If  we  consider  the  upper  bound  estimates,  the  individual  contributions  of 
each group of directives to the estimated total costs of 18.3 eurocents per barrel of processed 

crude  have  the  following  distribution:  FQD/MFD  –  56%,  RED/ETD  –  34%,  and 
LCPD/IPPCD/AQD (SO2 only) – 10%. The average of these contributions over all the three 

4 

 
 
 
 
 
 
  
 
reported  estimates (i.e. lower average, upper average, and upper bound) is similar and gives 
FQD/MFD – 51%, RED/ETD  – 36%,  and  LCPD/IPPCD/AQD (SO2 only)  –  12%. Thus, it 

can be concluded that for the EU  refineries  the largest  costs implications  of the  considered 
directives are due to the FQD/MFD directives.  

20

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10

8

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6.2 

10.3 

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4.0 

3.1 

Lower Average

Upper Average

Upper Bound Estimate

FQD/MFD

RED/ETD

SO2 regulations

The  RED/ETD-related  costs,  which  quantify  the  forgone  profits  due  to  lower  fuel  demand 
caused  by  these  directives,  are  driven  mainly  by  the  RED  directive  that  accounts  for  about 
89% of these costs. In case of SO2 regulations related costs, however, it should be noted that 
our  estimates  of  the  CAPEX  costs  are  most  likely  underestimated  as  the  model  does  not 
capture all relevant SO2 emissions abatement measures adopted by refineries in practice.       

Using  the  so-called  relative  trade  balance  (RTB)  indicator,  it  is  found  that  the  European 
refining  industry  would  have  been  somewhat  more  internationally  competitive  in  a 

counterfactual  situation  where  tighter  fuels  quality  specifications  would  have  not  been 
imposed  in  Europe.  This  result,  however,  is  not  exclusively  about  the  external  competitive 

strength of Europe, but in addition also reflects the resulting trade structure of domestic and 
foreign  demand  for  refined  products  as  implied  by  the  optimal  reaction  of  all  refineries 

world-wide  to  the  new  counterfactual  European  circumstances  without  the  FQD  and  MFD 
requirements. 

The RED and ETD are assessed to cause a reduction of the EU refineries' crude distillation 

unit (CDU) utilisation rates, on average, by 0.9% to 1.9% over the entire 2000-2010 period.  
These  reductions  are  larger  in  North  Europe  (NE)  than  in  South  Europe  (SE)  by  average 

factor of 1.8 to 2.2, caused mainly due to higher penetration of biofuels in NE than in SE and 
also larger demand changes in NE as caused by the ETD directive. The maximum reduction 

5 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
of 3.1% CDU utilisation rate is observed in the second sub-period of 2005-2010 in NE, which 
is due to larger relevant changes in demand.  

Further, it is assessed that in the counterfactual situation without the RED and ETD in place, 

European  imports  of  diesel  oil  (from  Russia)  would  have  increased,  on  average  over  the 
2000-2010  period,  by  1%  to  6.3%,  with  an  upper  bound  of  8.9%  increase.  Thus,  if  one 

focuses  on  the  trade  dependency  issues,  reduction  in  diesel  imports  dependency  of  the  EU 
(from  Russia)  can  be  considered  as  the  most  noticeable  EU-wide  benefit  that  the  RED  and 

ETD directives brought about.      

Finally, the overall benefits of legislation acts on SO2 emissions regulation, notably  LCPD, 
IPPCD  and  AQD,  are  assessed  to  be  in  the  range  of  12.7%  to  32.5%  reductions  of  SO2 
emissions generated by the EU refineries in North Europe and South Europe over the covered 
period.  The overall European figures show SO2 emissions reduction of 18.4% to 28.2% over 
the entire 2000-2010 period. The incurred benefits in South Europe are  larger than those in 

North Europe by factor of 1.3 to 2.3.  

6 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Essentially, all models are wrong, but some are useful. 
(A quote from Box G.E.P. and Draper N.R. (1987), Empirical  
Model-Building and Response Surfaces, Wiley, p. 424) 

1  Demand for oil products 

Demand for petroleum products is exogenous in the OURSE model, and has two "nests" or 
structures.  The  first  nest  includes  data  obtained  from  available  datasets,  and  distinguishes 

between 12 types of products. These are liquefied petroleum gases (LPG), naphtha, gasoline, 
jet  fuel,  other  kerosene,  heating  oil,  diesel  oil,  residual  fuel  oil,  lubricants,  bitumen, 
petroleum  coke  and  marine  bunkers.    The  main  source  of  these  data  is  the  World  Energy 

Statistics  database  of  the  International  Energy  Agency  (IEA).  These  oil  products  demands 
include  IEA  data  on  final  consumption  of  oil  products  and  oil  demand  for  transformation 

processes  (where  mainly  demand  for  electricity  generation  dominates).  Refineries  self-
consumption is excluded because it is obtained from the model outcomes. It should be noted 

that the available data are not always consistent with the OURSE product nomenclature. Here 
we  briefly  discuss  concordances  of  the  two  nomenclatures  and  how  the  OURSE  demand 

products were derived from the IEA database: 

  LPG: LPG; 
  Naphtha: naphtha; 
  Gasoline:  motor  gasoline  and  aviation  gasoline  (including  aviation  gasoline  for 

international aviation bunkers); 

  Jet fuel: gasoline type jet fuel, kerosene type jet fuel (including kerosene type jet fuel for 

international aviation bunkers), other kerosene and white spirit; 

  Other kerosene (for cooking and heating): using other kerosene shares in corresponding 
heating  oil  from  IFPEN's  demand  data  for  2005,  and  applying  them  to  the  derived 
heating oil figures for all considered years; 

  Diesel oil: gas/diesel oil used for (road) transport activities; 
  Heating oil: gas/diesel oil, except for road transport; 
  Residual fuel oil: fuel oil; 
  Lubricants: lubricants and paraffin waxes;  
  Bitumen: bitumen; 
  Petroleum coke: petroleum coke; and 
  Marine bunkers: international marine bunkers.    

7 

 
 
 
 
 
 
 
 
The observed petroleum product demands for the nine OURSE regions, which are used in the 
baseline scenarios for 2000, 2005 and 2010, are reported in Table 6.1 of the Appendix. This 

table shows that global demand for all petroleum products increased over time, from roughly 
3.3 billion (metric) tonnes in 2000 to more than 3.6 billion tonnes in 2010.  The trend of this 

increase was, however, decreasing, i.e., while in the first considered period of 2000-2005 the 
world  oil  demand  increased  by  8.5%,  the  corresponding  growth  for  2005-2010  was  only 

1.8%.     

However, the changes in  refined products  demand are very heterogeneous  across  the world 
regions. Figure 1-1 presents region-specific growth rates of the total oil product demands for 

the  entire  period  of  2000-2010.  Relative  to  2000,  in  2010  we  observe  a  dramatic  92% 
increase of demand for petroleum products in China (CH), which is 8.8 times larger than the 

corresponding  global  growth  rate  of  10.4%.  Middle  East  (ME)  and  Africa  (AF)  -  regions 
taking,  respectively,  the  second  and  third  positions  in  the  list  of  top  oil  consumers  - 

experienced  an  increase  in  petroleum  products  demand  that  is  4%  larger  than  the  relevant 
world growth rate.     

Figure 1-1: Total oil products demand growth rates by region, 2000-2010 

100%

80%

60%

40%

20%

0%

-20%

CH ME

AF

SA

AS World RS

NE

NA

SE

 Note: For abbreviations, see the note to Table 6.1. 

Figure 1-1 also shows that from 2000 to 2010 in three world regions, namely, North Europe 
(NE),  North  America  (NA)  and  South  Europe  (SE),  total  refined  oil  demand  has  actually 

decreased  with  corresponding  growth  rates  of  -4.4%,  -5.8%  and  -10.4%.  However,  looking 
closer  to  the  data  in  Table  6.1  we  notice  that  this  decrease  took  place  in  the  second  sub-

period. That is, during 2000-2005 we observe an increase in total oil demand in NE, NA and 
SE  by  5.4%,  2.3%  and  2.3%,  respectively,  while  the  corresponding  rates  of  change  for  the 

2005-2010 period were -10.6%, -6.5% and -12.5%.  

8 

 
 
 
 
 
 
 
How come that a huge increase in total oil demand in CH, ME, AF and South America (SA) 

resulted  in  comparatively  "modest"  global  change  of  10.4%?  This  can  be  explained  by  the 
fact that the regions with decreasing oil demand make a rather large portion of the global oil 

consumption. Figure  1-2  shows the  regional  allocation of  world oil demand for the  year of 
2010, where the overall relevant proportion of NA, NE and SE is indeed quite significant and 

equals 44.5%. The region Other Asia and Oceania (AS) with the 2000-2010 rate of increase 
of oil demand of 11.0% also makes a considerable part of the world refined products demand. 

The  corresponding  portion  of  the  world  oil  demand  pie  was  23.0%  in  2010.  China  is  the 
fourth world largest consumer of petroleum products with the share of 10.6%.  

Figure 1-2: Regional oil products demand, 2010 

RS, 155, 4% 

AF, 148, 4% 

NA, 945, 26% 

SA, 208, 6% 

SE, 216, 6% 

ME, 286, 8% 

CH, 385, 11% 

NE, 456, 12% 

AS, 838, 23% 

All  in  all,  we  observe  that  in  absolute  values  more  than  half  of  the  world  demand  for 
petroleum  products,  namely  61.6%  in  2010,  comes  from  three  regions  of  NA,  AS  and  NE. 

European total oil demand in 2010 was equal to 672 million tonnes which makes 18.5% of 
the global oil consumption. It must be noted that NE and SE include also non-EU countries 

such as, for example, Iceland, Norway and Switzerland in case of NE, and Macedonia, Serbia 
and  Turkey  in  case  of  SE.  However,  the  overwhelming  majority  of  the  European  demand 

comes from the EU member states. For example, in 2010 the shares of total oil demand from 
the EU countries in NE and SE were 94.5% and 82.4%, respectively. All the details of these 

proportions distinguished by products and years are reported in Table 6.2.  

Within  the  OURSE  model  demand  figures  for  many  of  the  above-mentioned  12  types  of 
products are further disaggregated in order to represent finer level of different fuel qualities. 
This makes the second "nest" of (exogenous) demand modelling, which include splitting: 

  LPG into propane and butane, 

9 

 
 
 
 
 
 
 
 
  gasoline into five gasoline grades that differ in their specifications (such as research 

and motor octane number, vapour pressure, aromatic content), 

  diesel oil into four diesel oil qualities, 

  heating oil into low and high sulphur content heating oil (0.1% and 0.2%),  

 

residual fuel oil into low and high sulphur content heavy fuel oil (1% and 3.5%), and 

  marine bunkers into low and high sulphur marine bunkers, the specification of which 

changes over time (i.e., 1.5% vs. 4.5%, 1% vs. 3.5%). 

The  characteristics  of  the  above-mentioned  product  qualities  are  reported  in  Table  1.1  and 
Table  6.3.  Table  1.1  presents  the  (observed)  specifications  of  the  fuel  qualities  that  change 

over  time,  while  Table  6.3  includes  other  characteristics  of  fuels  that  remain  fixed  for  the 
entire period considered in the Fitness Check analysis. One of the main sources for obtaining 

figures,  particularly  on 

these 
regions,  was 
TransportPolicy.net  (http://transportpolicy.net/).  Given  that  the  non-EU  regions  are  very 

sulphur  specifications 

for  non-EU 

heterogeneous in  consumption of different  qualities  of fuels, mainly  due to  their legislation 
requirements with respect to fuels maximum sulphur content limits, approximate (or average) 

and most representative figures for each OURSE region were chosen. For example, in case of 
sulphur limits of diesel grade predominantly used in North America during the years close to 

2005 we chose the relevant requirements in the USA.  The US "diesel fuel regulation limited 
the sulphur content in on-highway diesel fuel to 15 ppm, down from the previous 500 ppm. 

Refiners were required to start producing the 15 ppm S [sulphur] fuel beginning June 1, 2006. 
… Refiners could also take advantage of a temporary compliance option that allowed them to 

continue  producing  500  ppm  fuel  in  20%  of  the  volume  of  diesel  fuel  they  produce  until 
December 31, 2009".1 Hence, the relevant sulphur content for 2005 is computed as 15*0.8 + 
500*0.2  =  112  ppm.  The  procedure  of  computing  the  relevant  figures  is  more  complicated 
(hence, more approximate) for other regions because, besides the diversity of countries fuel 

production/consumption structures included in one specific OURSE region, even within one 
country  sulphur  limits  may  be  quite  different.  For  example  in  China  there  are  nation-wide 

sulphur  limits  but  also  city-specific  limits,  e.g.,  imposed  in  Beijing,  Shanghai  and 
Guangdong, with the last being stricter. (Or in Brazil there are metropolitan and countryside 

diesel sulphur specifications.) In such cases, more focus was given to the nation-wide limits. 
The specifications, reported in Table 1.1 and Table 6.3, are all used in the simulations of the 
relevant baseline scenarios.  

As  an  example,  the  correspondence  figures  (or  elements  of  the  concordance  matrices)  of 

LPG,  gasoline,  diesel,  heating  oil,  residual  fuel  oil  and  marine  bunkers  that  allocate  these 

1This excerpt comes from  http://transportpolicy.net/index.php?title=US:_Heavy-duty:_Emissions (last accessed 
in May 30, 2014).  

10 

 
 
 
 
 
                                                 
fuels into their respective grades/qualities for 2005, are given in Table 6.4. It should be noted 
that these figures could change over time due to various factors, including regional legislation 

requirements  on  the  qualities  of  fuels  to  be  marketed  within  the  specific  region.  This  is 
exactly  the  way  how  we  model  the  changes  in  sulphur  requirements  set  on  the  production 

(and marketing) of heating oil, heavy fuel oil and marine bunkers per region.   

Table 1.1: Product specifications used in the baseline scenarios 

Specification 
Sulphur (ppm) 
Sulphur (ppm) 
Sulphur (ppm) 
Sulphur (ppm) 
Sulphur (ppm) 
Sulphur (ppm) 
Sulphur (ppm) 
Sulphur (ppm) 
Sulphur (ppm) 
Sulphur (ppm) 
Sulphur (% m/m) 
Sulphur (% m/m) 
Sulphur (% m/m) 
Sulphur (% m/m) 
Sulphur (% m/m) 
Sulphur (% m/m) 
PAH (% m/m) 
PAH (% m/m) 
PAH (% m/m) 
PAH (% m/m) 
Aromatics (% v/v) 
Aromatics (% v/v) 
Aromatics (% v/v) 
Aromatics (% v/v) 
Aromatics (% v/v) 
Benzene (% v/v) 
Benzene (% v/v) 
Benzene (% v/v) 
Benzene (% v/v) 
Benzene (% v/v) 

Product 
ReGasol92NAm 
ReGasol95NAm 
PremGasol1 
PremGasol2 
PremGasolEu 
JetFuel 
DieselNAm 
DieselLatAm 
DieselEu 
DieselChin 
HeatOil1 
HeatOil2 
HeavyFuelOil1 
HeavyFuelOil2 
MarineBunk1 
MarineBunk2 
DieselNAm 
DieselLatAm 
DieselEu 
DieselChin 
ReGasol92NAm 
ReGasol95NAm 
PremGasol1 
PremGasol2 
PremGasolEu 
ReGasol92NAm 
ReGasol95NAm 
PremGasol1 
PremGasol2 
PremGasolEu 

2000 
300 
300 
1000 
500 
150 
1000 
500 
3500 
350 
7000 
0.1 
0.2 
1 
3.5 
4.5 
5.0 
7 
11 
11 
20 
35 
35 
55 
45 
42 
1 
1 
5 
3 
1 

2005 
80 
80 
1000 
500 
50 
1000 
112 
2000 
50 
5000 
0.1 
0.2 
1 
3.5 
1.5 
4.5 
7 
11 
11 
20 
35 
35 
55 
45 
42 
1 
1 
5 
3 
1 

2010 
80 
80 
500 
150 
10 
1000 
15 
1000 
10 
2000 
0.1 
0.2 
1 
3.5 
1 
3.5 
7 
11 
8 
20 
35 
35 
45 
42 
35 
1 
1 
3 
1 
1 

Within  the  second  nest  of  demand  modelling,  besides  products  splitting  there  are  also  two 

cases where products from first nest are aggregated into one product. These are jet fuel and 
other kerosene on the one hand, and lubricants and high-sulphur heavy fuel on the other. As a 

consequence of above-mentioned oil products' splitting and aggregation, the total number of 
refined products modelled within OURSE is equal to 21.  

11 

 
 
 
  
 
 
2  Refining capacities 

The flexibility of processing a wide variety of crudes and capability of generating high-value 

products,  hence  performance  and  (domestic  and/or  international)  competitiveness,  of  any 
refinery depends on the extent to which the refinery possesses (or invests in) more complex 

processing  units  than  the  so-called  atmospheric  (or  crude/primary)  distillation  unit.  These 
include various processing units that are used, for example, for catalytic reforming, delayed 

coking,  catalytic  (hydro)cracking,  alkylation,  isomerization  and  other  treatments  of  the 
processed crude to increase further the quality and particular characteristics/specifications of 

the final  refined products.  In general,  OURSE models  47 processing units, and the data for 
their capacities comes from the IFP Energies nouvelles.  

In order to compare the overall processing capabilities of individual refineries usually the so-

called  Nelson  Complexity  Index  (NCI)  is  used.  This  concept  was  developed  by  Wilbur  L. 
Nelson  in  the 1960s  and appeared in  a series  of  his  papers published in  Oil  & Gas Journal 

(see  the  list  of  references).  There  are  other  complexity  indices  that  extend  the  NCI  by 
updating some of its unit-specific complexity factors, which are used in the calculation of the 

refinery  overall  complexity  measure.  In  general,  any  complexity  indicator  is  intended  to 
measure  not  only  the  investment  intensity  or  cost  index  of  a  refinery,  but  also  its  value 

addition  potential and flexibility.  In this study we make use of the NCI,  which  generally  is 
defined as follows: 

∑ (Complexity Factor of Unit 𝑘)×(Capacity of Unit 𝑘)

NCI = 

𝑘

Capacity of Distillation Unit

 ,                          (1) 

where  the  complexity  factors  quantify  the  cost  of  processing  unit  k  compared  to  those  of 

crude distillation unit (CDU). Hence, a factor of 1 is assigned to the distillation unit, while all 
other units are rated in terms of their costs relative to CDU. For example, the fluid catalytic 

cracking (FCC) unit has a Nelson  complexity factor of 6, which means  that to  build a new 
FCC  unit  it  would  cost  roughly  six  times  the  costs  of  constructing  new  CDU.  It  should  be 

noted that the numerator in (1) is called Equivalent Distillation Capacity (EDC) and is also 
often used as another indicator for comparisons of refinery costs and/or values.  

Using the relevant unit-specific Nelson factors and the processing units capacities used in our 
modelling, the values for EDC and NCI for the OURSE nine considered world regions have 
been  computed  and  are  reported  in  Table  2.1.    Without  going  into  the  details,  Table  2.1 

shows  that,  on  average,  the  North  American  representative  (average)  refinery  is  the  "most 
complex" refinery in the world. In 2005, its NCI was equal to 9.1. North and South European 
refineries take the second and third position, respectively, in this list with the 2005 NCIs of 

12 

 
 
 
 
 
 
 
 
6.6  and  6.3  that  make  70  -  73%  of  the  NCI  of  the  NA  refinery.  Hence,  NA  refinery,  on 
average,  has  roughly  1.4  times  more  flexibility  and/or  value  addition  potential  than  the 

European  refinery.  The  other  OURSE  regions  in  this  list  have  the  following  ranking  (with 
their  NCIs,  in  percentage  terms,  relative  to  the  NA  refinery's  NCI  is  reported  in  the 

parenthesis):  South  America  (65.2%),  Other  Asia  (59.4%),  Middle  East  (53.0%),  Russia 
(52.6%),  Africa  (45.7%)  and  China  (45%).  It  is  interesting  to  note  that  these  NCIs  are 

consistent with Wood Mackenzie average refinery complexities of 9.2 for North America, 6.6 
–  Europe,  6.2  –  Asia  Pacific,  4.3  –  Middle  East,  3.9  –  Former  Soviet  Union,  and  a  world 
average complexity of 6.4.2  

Table 2.1: Refining complexity comparison by region 

Year 

NA 

SA 

NE 

SE 

RS 

AF 

ME 

CH 

AS 

Total 

Equivalent Distillation Capacity (EDC, mln tonnes / year) 

2000 
2005 

2000 
2005 

9171 
9957 

1839 
1670 

3627 
3718 

1932 
1976 

1592 
1738 
Nelson Complexity Index (NCI) 

545 
681 

1599 
1732 

923 
1205 

3974 
4534 

25201 
27211 

8.6 
9.1 

5.3 
5.9 

6.4 
6.6 

5.8 
6.3 

4.2 
4.8 

3.3 
4.1 

4.5 
4.8 

4.5 
4.1 

4.9 
5.4 

5.9 
6.3 

Source: Own calculations based on Nelson factors and process units capacities (source: IFP Energies nouvelles). 

In terms of EDC, again North American refinery is, on average, the largest one (or the most 
expensive refinery) in the globe, which in 2005 had an equivalent distillation capacity of 9.96 

billion per year. For the same year, North American refinery is, in terms of EDC, 2.2 times 
larger  than  the  Asian  refinery  and  2.9  times  bigger  than  the  North  European  refinery.  The 

EDC  differences  of  the  NA  refinery  with  the  rest  of  the  regions'  refineries  are  more 
pronounced.  The  factors  by  which  the  EDC  of  the  NA  refinery  is  larger  than  that  of  the 

remaining regions are: Russia – 5.0, South Europe – 5.7, Middle East – 5.8, South America – 
6.0, China – 8.3 and Africa – 14.6.     

Over time the changes of both the EDC and NCI indicators are predominantly positive, and 

the relevant world average indicators increased by 8.0% and 7.1%, respectively. This clearly 
shows  the  trend  in  the  petroleum  refining  industry  world-wide  towards  building  (or 

upgrading existing refineries with the aim of having) more complex refinery configuration. In 
particular,  note  that  within  the  2000-2005  time  period  in  North  and  South  Europe  the  NCI 

indicator increases by 2.5% and 9.2%, respectively.    

2 These figures come from a presentation by Michael Hafner made at the Wood Mackenzie Global Refining 
Seminar (London, 22 February 2011) that is available from (last accessed in July 2014) 
https://www.energyinst.org/_uploads/documents/WoodMackenzieGlobalRefiningSeminar-IPWeek2011.pdf . 

13 

 
 
 
   
 
 
 
  
                                                 
3  CAPEX, OPEX and transportation costs 

The  model  cost  coefficients  of  capital  expenditures  (CAPEX)  of  processing  units  and  of 

operating  expenditures  (OPEX)  of  process-related  intermediate  products  are  assumed  to  be 
the same for all regions. Because OPEX costs are defined for 403 total intermediate products, 

we  do  not  report  these  numbers  here.  However,  for  transparency  purposes,  instead  the 
annualised  costs  of  capital  figures,  including  return  on  investment  (ROI)  of  15%  and 

annualised CAPEX on 15 years, are reported in Table 3.1. 

Table 3.1: Annual cost of capital (in 2008 USD) 

Refining unit 

Atmospheric distillation 

Vacuum distillation unit 

Desaslphalting unit C3 

Desaslphalting unit C5 

DAO HDT 

Residue hydroconversion (fixed bed) 

Residue hydroconversion (ebulated bed) 

Catalytic reformer 

Regenerative reformer 

Reformate splitter 

FCC feed HDT (vacuum GO) 

Mild hydrocracking 

Catalytic cracking (with feed pre-hydrotreatment) 

Catalytic cracking 

RCC feed HDT (long run residue) 

HDT long run residue catalytic cracking 

Long run residue catalytic cracking 

Hydrocracking full 

Hydrocracking jet 

Hydrocracking naphtha 

Hydrocracking 78 conv 

Deisopentanizer 

Isomerization once through 

Cost 

   Refining unit 

Cost 

Isomerization with recycling 

Alkylation (Hydrofluoric Acid - HF)  

Dimersol 

Tame unit on LG from FCC & RCC 

MTBE unit 

ETBE total unit 

Visbreaking (vacuum residue) 

Coking delayed 

Hydrodesulphurization (HDS) VGO CK 

HDS 90 20bar 

HDS 97-98 30bar 

Deep HDS 75 bar 

FCC gasoline desulphurization (Primeg20) 

FCC gasoline desulphurization (Primeg10) 

REF feed HDT 

Pressure swing absorber 

Steam reformer 

Partial oxydation (vacuum resid. & asph.) 

Natural gas cogeneration 

Natural gas combined cycle (NGCC) 

Integrated gas combined cycle (IGCC) 

MDEA+Claus+hydrosulpreen (SRI) 

15.54 

7.93 

30.34 

30.34 

43.26 

67.2 

73.92 

18.54 

31.52 

2.79 

28.19 

33.88 

45.45 

45.45 

56.14 

51.89 

51.89 

64.56 

53.84 

64.56 

64.56 

4.32 

26.19   

44.53 

144.82 

45.62 

92.05 

137.34 

137.34 

17.85 

59.46 

43.26 

17.4 

20.47 

29.53 

63.33 

63.33 

11.74 

630.23 

630.23 

223.93 

136.34 

421.39 

246.13 

165.51 

 Source: IFP Energies nouvelles. The reported capital costs include a ROI of 15% and annualised CAPEX on 15 years. 

The costs of investments in new processing units are thus based on the prices given in Table 
3.1,  which  also  enter  the  objective  function  of  the  OURSE  model.  These  are  taken  into 

account in the minimization of the overall costs of the world refining industry.  

Interregional  flows  of  crude  oil  and  petroleum  products  incur  transportation  costs.  In 
OURSE_LP model (to be explained in Section 4.2), estimated crude freight costs are applied 
to  all  types  of  crudes  trade,  and  similarly  all  refined  products  have  the  same  transportation 

costs per trade partner. The relevant values that have been used in by Lantz et al. (2005) and 
Lantz  et  al.  (2012)  are  reported  in  Table  3.2.  Note  that  the  crude  transportation  costs  are 

14 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
  
 
 
symmetric, i.e. costs of transporting crude from region r to regions s is assumed to be equal 
to the costs of transporting crude from region s to region r. However, products freight costs 

are asymmetric. In fact, a closer look at the figures reveals that all pair-wise trade costs are 
equal except for those between Russia (CIS) on the one hand, and North and South Europe, 

on the other hand. These costs are lower when a refined product is transported from Russia to 
North  and  South  Europe  by  2.78  USD  and  16.27  USD  relative  to  the  costs  of  the  reverse 

product  flows.  This  is  (was)  part  of  the  calibration  process  in  order  to  make  sure  that  the 
model generates European imports of middle distillates, in particular diesel oil, from Russia. 

Table 3.2: Transportation costs used in OURSE_LP (in 2008 USD, per tonne) 

NA 

SA 

NE 

SE 

RS 

AF 

ME 

CH 

AS 

NA 

SA 

NE 

SE 

RS 

AF 

ME 

CH 

AS 

NA 

SA 

NE 

SE 

RS 

AF 

ME 

CH 

AS 

11.31 
18.32 
26.30 
36.13 
25.59 
37.42 
15.00 
25.00 

18.21 
22.16 
23.95 
26.00 
42.53 
36.79 
13.00 
15.00 

11.31 

21.94 
25.79 
39.15 
27.86 
37.42 
15.00 
25.00 

18.21 

42.06 
30.39 
35.54 
23.27 
36.76 
43.11 
41.21 

18.32 
21.94 

16.96 
14.56 
22.93 
34.95 
34.00 
26.25 

22.16 
42.06 

18.81 
7.00 
36.94 
41.11 
57.52 
52.54 

Source: IFP Energies nouvelles. 

26.30 
25.79 
16.96 

Crude trade transport costs 
25.59 
36.13 
27.86 
39.15 
22.93 
14.56 
15.31 
20.79 
30.00 

20.79 
15.31 
29.71 
34.00 
26.25 

30.00 
17.37 
30.00 
23.00 
30.00 
23.00 
30.00 
Products trade transport costs 
42.53 
26.00 
23.27 
35.54 
36.94 
9.78 
23.39 
23.27 
23.27 

23.95 
30.39 
18.81 

7.00 
23.39 
38.72 
49.52 
44.54 

23.27 
41.11 
42.89 
42.89 

18.37 
42.45 
51.61 

37.42 
37.42 
34.95 
29.71 
30.00 
17.37 

11.09 
16.89 

36.79 
36.76 
41.11 
38.72 
41.11 
18.37 

23.74 
26.13 

15.00 
15.00 
34.00 
34.00 
30.00 
23.00 
11.09 

7.00 

13.00 
43.11 
57.52 
49.52 
42.89 
42.45 
23.74 

15.59 

25.00 
25.00 
26.25 
26.25 
30.00 
23.00 
16.89 
7.00 

15.00 
41.21 
52.54 
44.54 
42.89 
51.61 
26.13 
15.59 

Although for the crude trade we also use costs data as given in Table 3.2, the approach taken 
in this study is different in one important respect from earlier studies using OURSE: here we 

re-estimate trade costs such that  the  "observed"  trade  flows of refined  products  is  perfectly 
calibrated,  which  is  discussed  in  detail  in  Section  4.2.  This  will  result  in  asymmetric  trade 

costs estimates per product and per trade relation due to accounting for such factors as import 
shares, import-import substitution elasticities, and the share of transport costs in  a product's 

final consumer prices. 

4  Model calibration  

15 

 
 
 
 
  
 
  
 
 
 
 
 
 
 
  
 
  
 
 
 
 
 
 
 
  
 
 
Calibration of baseline scenarios to the relevant observed data is a very important and, at the 
same time, the most time-consuming step  in  any  policy-relevant and large-scale modelling. 

Depending  on  the  nature  of  a  model  under  consideration,  there  exist  different  calibration 
techniques. In case of OURSE, the following two crucial issues, which are discussed in some 

more detail in the next two subsections, needed to be addressed first in order to get a properly 
running model that calibrates the observed data in its baseline scenarios: 

  Collection,  adjustments  and/or  estimation  of  required  observed  data  covering  all 

countries of the world, and  

  Calibration of the baseline scenarios. 

4.1  Collection and estimation of data that are endogenous in OURSE 

The following are the main observed variables that the model has to calibrate properly (after 
the data is made  consistent with the OURSE regional dimensions) in the baseline scenarios 

for the  covered period of 2000-2012 that is represented by "individual" from  the modelling 
perspective years of 2000, 2005 and 2010: 

  Production of crude oil by country; 

  Production of refined products by country; 

  Trade flows of crude oil between countries;  

  Trade flows of refined products between countries;  

  Refinery  throughputs  (or  total  of  "conversion  in  refineries"),  which  mainly  include 

crude petroleum, feedstocks, natural gas and natural gas liquids. 

All  the  main  databases  were  carefully  analysed  and  the  relevant  data  were  compared  as  a 

consistency check (only in case when the same variable was available in different datasets). 
The most important sources used include  

  World Energy Statistics and Oil Information databases of the IEA (www.iea.org);  
  Energy  Statistics  Database  of  the  United  Nations  Statistics  Division  (UNSD), 

available on-line via the UNdata portal (http://data.un.org); 

  Commodity Trade Statistics Database (Comtrade) of the UNSD, available on-line via 

the UNdata portal (see above); 

  BP Statistical Review of World Energy3; 
  Market  Observatory 

and 

Statistics 

of 

the 

European  Commission 

(http://ec.europa.eu/energy/observatory);  
  JODI Oil World Database (www.jodidata.org); 
  US Energy Information Administration (http://www.eia.gov/petroleum/data.cfm). 

3 See http://www.bp.com/en/global/corporate/about-bp/energy-economics/statistical-review-of-world-
energy.html. 

16 

 
 
 
 
 
                                                 
Running  ahead  we  note  that  from  the  data  items  listed  in  the  beginning  of  this  section  the 

most  complete  information  include  data  on  production,  consumption  (also  referred  to  as 
domestic  supply)  and  international  trade  of  crude  oil,  while  complete  and  consistent  global 

trade data on petroleum products is largely missing. This leaves us with only one choice of 
estimating  trade  matrices  of  different  refined  products  in  our  first  stage  of  the  model 

calibration procedure (to be discussed in the next subsection) so that they are consistent with 
the  observed  data  of  total  exports,  total  imports  and  trade  structure.  The  last  include  facts 

and/or  knowledge  about  existence  or  non-existence  of  certain  interregional  trade  flows  of 
refined products.    

The procedure of obtaining total crude oil trade data consisted of the following steps: 

1.  The  IEA  World  Energy  Statistics  data  on  crude  total  exports  and  total  imports  (in 
physical units) at country level were aggregated to the regional level of OURSE. Given 

that  rarely  trade  data  are  consistent  in  the  sense  that  at  the  global  level  world  imports 
does not equal world exports whereas they should, we make adjustments to the exports 

data to match the world imports figures. The relevant differences before this adjustment 
were  4.2%,  3.4%  and  8.7%  for  the  2000,  2005  and  2010  data,  respectively.      We 

adjusted  exports  data  and  not  imports  because  we  think  that  imports  data  are  more 
reliable  in  general  than  exports  figures  (a  usual  assumption  taken  in  similar  cases,  in 

particular, in input-output studies).  

2.  The best  source for crude trade data was found to be the  UN Comtrade database. The 
total  exports  and  total  imports  derived  from  these  obtained  trade  data  showed  3%  to 
10%  discrepancies  when  compared  with  the  corresponding  IEA  data.  Thus,  in  the 

second step we have adjusted the trade matrices such that they became consistent with 
the IEA total exports and total imports figures from step 1. For this purpose we use the 

so-called  GRAS  method,  which  is  a  widely  used  method  for  balancing/estimation  of 
input-output tables or any other matrix (for details about GRAS, see e.g. Temurshoev et 

al., 2013).  

3.  Crude  trade  variables  in  OURSE  model  besides  exports  and  imports  also  include 
domestic  use  of  domestically  produced  crude  oil.  Hence,  these  must  be  added  to  the 
intra-regional  (i.e.  diagonal)  flows  of  the  trade  matrix  obtained  from  step  2  (which 

already  had  partial  information  on  the  intra-regional  flows  of  the  aggregate  regions 
coming from the country-level trade data). These are derived using crude oil production 

and consumption (also referred to as domestic supply) data from the IEA World Energy 
Statistics.  The missing intra-regional  flows were computed as  the relevant  averages  of 
(Production – Exports) and (Consumption – Imports) vectors per region.    

17 

 
 
 
 
 
4.  Finally, the intra-  and inter-regional  trade matrices from step 3 were  again  rebalanced 
(to get rid of insignificant differences after accounting for intra-regional flows) in order 

to  perfectly  match  the  IEA  crude  total  production  and  consumption  (or  throughputs) 
data using the GRAS method.   The ultimate  "observed" crude oil trade data for 2000, 

2005  and  2010,  after  ignoring  small  transactions  that  do  not  matter  for  modelling 
purposes anyhow, are reported in Table 4.1 below.  

As will be discussed and justified in Section 4.2, the derived total crude trade figures will not 

be calibrated per se. However, these data will be very useful in terms of having the right mix 
(structure)  of  various  types  of  crudes  supplied  to  each  region.  That  is,  for  each  importing 

refinery (region), we impose additional constraints in the model ensuring that the total crude 
imported  does  not  exceed  the  relevant  observed  import  shares  as  obtained  from  Table  4.1. 

Hence, the main characteristics (i.e. API degree and sulphur content) of total crude processed 
by each region will be largely consistent with those observed in reality.     

Table 4.1: Total crude oil supply/trade used in OURSE baseline scenarios 

From\To  

NA 

SA 

NE 

SE 

RS 

AF 

ME 

CH 

AS 

Total 

NA 
SA 
NE 
SE 
RS 
AF 
ME 
CH 
AS 
Total 

NA 
SA 
NE 
SE 
RS 
AF 
ME 
CH 
AS 
Total 

NA 
SA 
NE 
SE 
RS 
AF 
ME 
CH 
AS 

527.7 
126.7 
61.3 

87.9 
114.5 

7.6 
925.7 

537.2 
144.4 
40.1 

14.3 
123.5 
100.2 

187.3 

7.7 
6.1 

6.2 
229.4 

110.5 
51.9 
68.2 

9.0 

8.6 
16.3 
43.7 
69.9 
72.8 

2000 

219.1 

78.9 
26.9 

299.4 

201.0 

466.3 

220.3 

219.1 

105.8 

299.4 

2005 

187.2 

15.6 

8.6 
183.9 

179.0 
53.7 
49.3 

11.7 

6.1 
14.2 
73.3 
65.5 
65.2 

265.2 

96.6 
24.8 

321.6 

959.6 

202.8 

474.5 

236.0 

265.2 

121.4 

321.6 

2010 

506.3 
113.9 
17.8 

27.7 
117.8 
83.8 

192.5 

12.4 

7.2 
146.4 

180.2 
54.9 
27.1 

6.3 

13.0 
74.2 
48.5 
54.4 

291.6 

87.8 
23.3 

333.0 

6.0 

26.0 
457.0 
10.6 
175.6 
675.2 

23.8 
527.9 
7.3 
176.0 
735.0 

15.5 
30.6 
150.7 
8.8 
205.6 

5.7 

16.7 
38.3 
55.5 
174.3 
7.1 
297.6 

19.9 

19.8 

26.4 
61.8 
94.9 
200.5 
7.2 

33.3 
49.8 
466.0 

169.3 

542.7 
320.2 
299.2 
16.3 
373.4 
337.9 
1075.6 
161.3 
191.9 
3318.5 

548.9 
345.9 
230.1 
14.2 
548.6 
417.0 
1144.5 
181.6 
183.1 
3613.7 

512.6 
353.3 
164.3 
13.0 
633.4 
433.0 
1082.5 
200.5 
176.4 

18 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Total 

867.3 

204.9 

415.8 

196.4 

291.6 

111.1 

333.0 

410.7 

738.2 

3569.0 

Note: Unit is million metric tonnes.  

4.2  Calibration and the OURSE_QP model 

The original OURSE model as developed by Lantz et al. (2005) and Lantz et al. (2012) is a 
linear programming (LP) model. For the reasons soon to become clear, we refer to it as the 

OURSE_LP model.  For transparency purposes, the main equations of OURSE_LP are given 
in Appendix B, while further details can be obtained from the mentioned reports. However, 

sensible  attempts  to  use  the  OURSE_LP  for  the  EU  Petroleum  Refining  Fitness  Check 
(REFIT) showed two crucial problems that could not be simply overlooked. These were: 

 

the natural inability of OURSE_LP to calibrate (at least roughly) the base-year available 
or estimated observed data, and 

  model  results  of  jumpy  responses  in  simulation  exercises  (for  example,  constant 
switching between zero and non-zero values of significant size in interregional trade of 
refined products in response to smooth changes in exogenous variables, which e.g. does 

not allow solid analysis of international competitiveness). 

This is, of course, not surprising to anyone familiar with linear programming, since in such 
modelling framework the number of binding constraints determines the number of non-zero 
endogenous  variables  in  the  optimal  solution.  With  large-scale  modelling  such  as  that  for 

the  REFIT  purposes,  this  causes  a  big  problem  because  then  the  number  of  positive 
variables observed in  real  life significantly  exceed the number of binding constraints, and 

that immediately leads to unrealistic case of overspecialization.  

It  turns  out  that  these  problems  were  taken  seriously  in  by  now  a  huge  literature  in 
agricultural  economics  (e.g.  on  farm-level  production  modelling).  Until  the  late  80's 

agricultural economists in policy analyses with LP models introduced additional calibration 
constraints  as  a  solution  to  the  problem  of  overspecialization.  "However,  models  that  are 

tightly  constrained  can  only  produce  that  subset  of  normative  results  that  the  calibration 
constraints  dictate"  (Howitt,  1995a,  p.  330).  That  is,  any  kinds  of  policy  conclusions  are 

bounded  by  the  sets  of  constraints  that  were  additionally  imposed  in  order  to  make  the 
model's  outcomes  for  the  base  year  more  or  less  reasonable  compared  to  the  observed 

variables, but very often these constraints are inconsistent with the environment under the 
policy  changes.  Therefore,  a  more  formal  approach  called  Positive  Mathematical 

Programming (PMP) was developed that perfectly solved the  above-mentioned calibration 

19 

 
 
 
 
 
 
 
issues  in  agricultural  policy  analysis  modelling.4  Applications  of  PMP  date  back  to 
Kasnakoglu  and  Bauer  (1988),  but  a  formal  method  of  PMP  was  developed  by  Howitt 

(1995a)  that  made  this  approach  wide-spread  both  in  empirical  applications  and  further 
theoretical discussions. Review papers on the theory, applications, criticisms and extensions 

of  the  PMP  approach  include  Heckelei  and  Britz  (2005),  Henry  de  Frahan  et  al.  (2007), 
Heckelei et al. (2012), Langrell (2013), and Mérel and Howitt (2014).5       

In  this  study  we  will  borrow  ideas  from  the  PMP  literature  for  modelling  global  refining 

industry and, in  particular, adapt  a  PMP-like technique of calibration of  spatial  models  of 
trade proposed by Paris et al. (2011). To the best of our knowledge, PMP ideas have never 

been applied to the petroleum refining (economic) modelling, thus the current study makes 
first  such  attempt  as  a  consequence  of  paying  particular  attention  to  the  calibration  and 

jumpy-response issues of the standard LP refining models. 

Consider the following two mathematical programming problems: 

minimize  𝐜′𝐱 

minimize  𝐝′𝐱 + 0.5 ∙ 𝐱′𝐐𝐱 

    subject to: 

    subject to: 

𝐀𝐱 ≥ 𝐛
𝐱 ≥ 𝟎

𝐀𝐱 ≥ 𝐛
𝐱 ≥ 𝟎

(2) 

(a) OURSE_LP without calibration constraints 

(b) OURSE_QP problem 

In (2.a) we have the general LP problem formulation, in our case it would be OURSE_LP 

problem,  where    𝐜  is  the  vector  of  accounting  costs  per  unit  of  decision  (endogenous) 

variables 𝐱, which are restricted to be non-negative as  given by the last set of constraints. 

Any  linear  (i.e.  equality,  less-than-or-equal,  greater-than-or-equal)  constraints  can  be 

written in the form of the linear constraints 𝐀𝐱 ≥ 𝐛 which are the same for both problems in 
(2). It is important to note that these in (2.a) do not include artificial calibrating constraints, 

thus  the  OURSE_LP  presentation  in  (2.a),  while  content-wise  is  complete,  is  still 
incomplete for empirical simulations purposes. The only, but crucial, difference between the 

OURSE_LP and OURSE_QP problems given in (2) is that the second problem in (2.b) has 
a  quadratic  (i.e.  non-linear)  objective  function,  where  the  nonlinearity  is  captured  by  the 

quadratic term  0.5 ∙ 𝐱′𝐐𝐱 (hence, the term QP – quadratic programming). The significance 

4  Technically,  this  was  done  by  introducing  non-linear  terms  in  the  objective  function  of  the  model  used  for 
policy  analyses  such  that  its  optimality  conditions  are  satisfied  at  the  observed  levels  of  endogenous  (or 
decision) variables without introducing artificial constraints. 
5 Other recent related studies  worthwhile to  mention include  Heckelei and Wolff (2003), Mérel and Bucaram 
(2010),  Jansson  and  Heckelei  (2011),  Merél  et  al.  (2011),  Howitt  et  al.  (2012),  Louhichi  et  al.  (2013),  and 
Maneta and Howitt (2014). 

20 

 
 
 
    
 
 
 
                                                 
 
 
 
 
 
 
of introducing non-linear terms in the objective function lies in the fact that they: (i) allow 
for  perfect  calibration  without  introducing  artificial  constraints,  (ii)  allow  for  interior 

solutions and thus overcome the LP overspecialization problem, and (iii) results in smooth 
(hence, more realistic) reactions of the outcomes to exogenous shocks.  

In OURSE_QP, 𝐝 and 𝐐 are parameters of the "implicit cost function" (Howitt, 1995b) that 
need to be estimated such that the base year observations are exactly calibrated.6 It is called 
implicit cost function, because such quadratic function "is a behavioural function … that is 

intended  to  capture  the  aggregated  influence  of  economic  factors  that  are  not  explicitly 
included in the model" (Jansson and Heckelei, 2011, p. 140). Without having any additional 

information  on  cross-cost  (hence,  cross-price)  effects  and  following  the  logic  of  Occam's 
razor,  we  assume  that  𝐐 = 𝐪̂    is  a  diagonal  matrix.7  Factors  that  could  be  potentially 
captured  by  the  implicit  parameters  include,  for  example,  aggregation  bias,  data  errors, 
costs/price  expectations,  risk  behaviour,  or  any  type  of  model  misspecification.  The 

optimality  conditions  for  our  QP  problem  in  (2.b)  are  given  by  the  following  system  of 
equations (for details, see e.g. Murty, 1988): 

𝐮 = 𝐪̂𝐱 − 𝐀′𝐲 + 𝐝 ,                                                          (3)     

where  𝐲 and  𝐮 are the Lagrange multipliers associated with the linear and non-negativity 

constraints, respectively.  Let  𝐬 = 𝐀𝐱 − 𝐛  denote slack variables in the linear constraints, 

which in conjunction with (3) imply the optimal levels of the decision variables as  

𝐱 = −𝐪̂−1(𝐝 − 𝐮) + 𝐪̂−1𝐀′(𝐀𝐪̂−1𝐀′)−1{𝐀𝐪̂−1(𝐝 − 𝐮) + 𝐛 + 𝐬} .             (4)     

Take the derivative of x with respect to costs coefficients d, and neglecting the second term 
in (3), gives  𝜕𝑥𝑖/𝜕𝑑𝑖 = −1/𝑞𝑖𝑖, which yields the own-cost supply (or demand, depending 
on the modelling framework) elasticity of 𝜀̃𝑖𝑖 = (−1/𝑞𝑖𝑖)(𝑑𝑖/𝑥𝑖).8 Define this elasticity in 
absolute terms as  𝜀𝑖𝑖 = −𝜀̃𝑖𝑖. Then with exogenous values of the cost elasticities, observed 

6  This  procedure  is,  in  a  sense,  exactly  similar  to  CGE  modelling,  where  first  exogenous  parameters  are 
estimated in the base year calibration process.  
7 Obviously,  having a full positive (semi)definite  matrix  Q  results is a  more  flexible cost specification, but  it 
makes the problem too complex. In particular, for the refining model, if calibration is performed via trade flows 
as  in  this  paper,  Q  will  have  six  dimensions,  indicating  trade  flows'  sources  and  destinations  and  products 
traded, for example, its entry could show the direct cost impact of diesel trade between North Europe and Russia 
on gasoline trade between Africa and North America. However, "in the absence of information on cross-price 
effects, the benefit of a more flexible specification may be debatable" (Mérel and Howitt, 2014).  
8 In the agriculture policy analysis literature, these are supply elasticities with respect to (own) price, given that 
their model formulation is different from that of OURSE. They consider profit maximization with given prices, 
while in OURSE the objective is cost minimization without explicit consideration of price effects.  

21 

 
 
 
 
   
 
  
 
                                                 
base year values of endogenous variables 𝑥𝑖̅  and the estimated values of the direct implicit 
costs 𝑑𝑖 (to be explained below), one can readily obtain the estimate of  𝑞𝑖𝑖 from 

𝑞𝑖𝑖 =

1
𝜀𝑖𝑖

𝑑𝑖
𝑥𝑖̅

  .                                                                  (5) 

In the agriculture literature, calibrations using exogenous elasticities as in (5) are referred to 

as "myopic calibration methods", because in deriving the expression for elasticity the second 
complicated  expression  in  (4)  is  ignored,  which  is,  however,  a  widely  used  calibration 

method (see e.g. the survey of Heckelei et al., 2012). The ignorance of this term is equivalent 
to  not  accounting  for  the  effects  of  changes  in  shadow  values  of  the  linear  constraints  y. 
However, if the estimates are not ad hoc but come from rigorous estimation procedures, then 

using myopic calibration method is reasonable since then the estimates will already implicitly 
account for the impact of, for example, resource limitations.  

In  OURSE  we  calibrate  only  trade  flows  of  petroleum  products,  which  automatically  also 

imply calibration of production, total exports and total imports of refined products. This holds 
because the demand-supply balance of (production + imports = exports + consumption) has 

to  be satisfied per product, where consumption (or demand) is  exogenous. We also  tried to 
simultaneously exactly calibrate total crude trade flows as given in Table 4.1, but this did not 

work well, for example, one gets excess  supply. We think this is the direct  consequence of 
material  balances  of  inputs  and  outputs  at  each  stage  of  intermediate  production  within  a 

refinery,  because  the  model  is  an  aggregate  model  and  cannot  exactly  replicate  the 
appropriate  real  inputs-outputs  interrelations  (otherwise,  calibration  of  only  one  side  of  the 

model, either products side or crude side, would calibrate the explicitly non-calibrated part as 
well).  Also  in  view  of  the  material  balances,  there  are  inconsistencies  in  world  totals  of 

exogenous demand for products and of crude oil supply. In addition, given that crude data in 
Table 4.1 do not distinquish between its nine types as used in the model, it has been decided 

to  use  these  "observed"  crude  trade  data  for  the  purposes  of  allowing  or  not  allowing 
interregional  crude flows  and  for  getting the right  mix of crude oil  use  by  each purchasing 

region. 

However,  as  mentioned  earlier  there  is  a  problem  of  availability  of  global  trade  flows  of 
petroleum  products,  hence  these  had  to  be  estimated  first  using  the  total  exports  and  total 

imports data from the IEA World Energy Statistics. These trade totals are given in Table 4.2. 
Note that we restricted our focus  on four aggregate  refined products  because  the trade data 

22 

 
 
 
 
 
 
 
for the other products seemed to be unrealistic.9 The exports data are proportionally adjusted 
such  that  these  figures  sum  up  to  total  imports  per  product  (as  in  crude  case,  here  also  the 

global  balances  of  imports  and  exports  did  not  hold,  though  the  differences  were  generally 
small). Table 4.2 clearly shows the continuously strengthening position of Europe over time 

as net exporter of gasoline and net importer of jet fuel, diesel/gasoil and fuel oil (except the 
residual fuel oil trade position of South Europe). Exactly calibrating these figures in the three 

base-year scenarios thus would be very crucial for the entire analysis, because these data fully 
capture the main problem  facing the EU refining industry in  general,  which is  the  growing 

supply-demand  mismatch  for  gasoline  and  diesel.  Thus,  the  European  excess  supply  of 
gasoline in the EU market is reflected in the EU taking a net exporter position in the world 

gasoline market, while excess demand for middle distillates (in particular, diesel oil) at home 
resulted in the EU becoming a net importer of these fuels. Note, however, since we summed 

up exports and imports at country-level per region, these are not region-specific exports and 
imports as they include trade between countries included within the same region. The intra-

regional trade will be estimated by the model, given that these are not readily available data 
either for all regions.     

Table 4.2: Total exports and imports data calibrated in OURSE_QP baseline scenarios 

2000 

2005 

2010 

Imports  Exports  Net Exports 

Imports  Exports  Net Exports 

Imports  Exports  Net Exports 

NA 
SA 
NE 
SE 
RS 
AF 
ME 
CH 
AS 

NA 
SA 
NE 
SE 
RS 
AF 
ME 
CH 
AS 

NA 
SA 

33.3 
0.9 
34.4 
4.6 
3.9 
7.7 
4.0 
0.4 
15.3 

9.1 
1.0 
17.6 
2.0 
0.5 
3.8 
0.9 
5.7 
14.5 

21.0 
9.1 

14.5 
11.3 
41.1 
9.7 
5.8 
2.0 
3.2 
4.4 
12.5 

4.1 
3.9 
9.4 
2.0 
0.6 
3.9 
14.3 
2.0 
15.1 

14.8 
10.1 

-18.8 
10.4 
6.7 
5.1 
1.9 
-5.6 
-0.8 
4.0 
-2.8 

-5.0 
2.9 
-8.2 
0.1 
0.1 
0.0 
13.4 
-3.8 
0.6 

-6.2 
1.0 

Gasoline   

16.8 
13.2 
53.3 
18.1 
10.2 
2.7 
4.6 
5.5 
24.3 
Jet Fuel    
5.7 
3.9 
11.7 
1.9 
0.7 
3.6 
19.2 
2.6 
21.8 
Gas/Diesel Oil 
15.7 
9.2 

61.0 
1.8 
29.2 
5.2 
2.4 
10.6 
12.0 
0.4 
26.2 

12.1 
0.4 
27.9 
3.6 
0.6 
3.6 
1.1 
7.8 
13.8 

22.2 
8.3 

-44.2 
11.5 
24.0 
12.9 
7.8 
-7.9 
-7.3 
5.1 
-1.9 

-6.5 
3.6 
-16.2 
-1.7 
0.1 
-0.1 
18.1 
-5.2 
7.9 

-6.5 
0.9 

59.1 
3.4 
25.7 
3.3 
4.6 
17.3 
15.3 
0.4 
28.8 

7.8 
1.9 
31.4 
7.2 
1.0 
5.1 
1.5 
10.2 
10.1 

22.2 
23.3 

24.4 
4.2 
53.2 
20.6 
7.1 
1.5 
4.6 
5.3 
36.8 

6.8 
2.9 
12.6 
3.0 
1.8 
3.0 
17.7 
5.3 
23.2 

39.7 
4.6 

-34.7 
0.9 
27.6 
17.3 
2.5 
-15.8 
-10.7 
4.9 
8.0 

-1.0 
1.0 
-18.7 
-4.2 
0.8 
-2.2 
16.1 
-4.9 
13.1 

17.6 
-18.7 

9  For  example,  the  derived  total  exports  and  imports  data  for  naphtha  showed  that  North  Europe  was  largely 
dependent on imports from Other Asia & Oceania, while Russia had zero exports of naphtha. In case of LPG, 
the product balance for Middle East results in a huge value of negative consumption.  

23 

 
 
 
 
  
 
 
 
                                                 
NE 
SE 
RS 
AF 
ME 
CH 
AS 

NA 
SA 
NE 
SE 
RS 
AF 
ME 
CH 
AS 

63.7 
17.0 
3.9 
10.8 
3.2 
6.3 
30.5 

40.2 
0.6 
34.0 
20.7 
1.4 
1.8 
13.5 
18.4 
53.0 

47.1 
10.9 
25.2 
5.7 
22.2 
0.6 
29.0 

17.1 
17.9 
37.5 
7.4 
24.9 
14.1 
35.1 
1.6 
28.1 

-16.6 
-6.1 
21.3 
-5.2 
19.0 
-5.7 
-1.5 

-23.2 
17.3 
3.5 
-13.3 
23.5 
12.3 
21.7 
-16.8 
-24.9 

86.7 
28.8 
2.3 
10.4 
12.4 
4.1 
40.8 

48.0 
1.2 
49.3 
14.7 
1.4 
2.4 
15.7 
32.3 
50.9 

60.7 
15.1 
39.1 
4.4 
25.2 
1.4 
45.2 
Fuel Oil 
22.3 
19.3 
45.7 
10.0 
43.9 
12.2 
25.8 
3.5 
33.4 

-26.0 
-13.7 
36.8 
-6.0 
12.8 
-2.7 
4.4 

-25.7 
18.1 
-3.6 
-4.8 
42.5 
9.8 
10.0 
-28.8 
-17.5 

97.4 
35.0 
5.9 
21.0 
21.4 
7.0 
52.3 

36.0 
2.4 
62.8 
11.7 
2.8 
3.7 
19.4 
33.0 
76.2 

69.4 
17.0 
48.3 
1.5 
20.4 
4.5 
79.9 

33.0 
16.8 
51.8 
11.8 
60.4 
10.5 
23.1 
8.3 
32.4 

-27.9 
-18.0 
42.4 
-19.5 
-0.9 
-2.5 
27.5 

-3.0 
14.3 
-10.9 
0.1 
57.6 
6.8 
3.6 
-24.7 
-43.8 

Note: Unit is million metric tonnes; Source: IEA World Energy Statistics.  

Hence, within the model product trade flows (including intra-regional flows) of jet fuel and 
various qualities/grades of gasoline, diesel oil, heating oil and heavy fuels are estimates such 
that the margins of the obtained and relevant aggregate trade matrices are consistent with the 

data  given  in  Table  4.2,  while  trade  matrices  of  the  remaining  seven  (out  of  21)  products 
were  estimated  by  the  model  itself  in  the  view  of  the  absence  of  reliable/realistic  relevant 

total exports and total imports data.   

In what follows, we give the technical details of the calibration steps. The first step as already 
discussed above include estimating trade matrices of refined products whose margins exactly 

calibrate the observed data presented in Table 4.2. For this purpose we need the estimates of 
exogenous elasticities and direct costs in order to be able to compute the implicit costs of the 

non-linear terms using equation (4). Since, we are calibrating trade flows only and given the 
OURSE  model  formulation,  we  need  estimates  of  the  trade  elasticities  with  respect  to 
transport  costs  per  product  and  trade  partners.  Since  these  are  exogenous  parameters,  it  is 

important to use the best available relevant estimates. Going through numerous studies on the 
effect  of  transportation  costs  on  trade  flows,  we  ended  up  using  the  results  of  Hummels 
(1999)  and  Balistreri  et  al.  (2010).10    The  estimated  substitution  elasticities  from  these  two 
studies are reported in Table 4.3.  

Table 4.3: Estimates of substitution elasticities for refining sector 

LPG 

17 

--- 

2.615 

Balistreri et al. (2010) 

Hummels (1999) 

This study 

10 Some of other useful related studies, which however do not consider refining industry as such, include Limão 
and Venables (2001), Hummels (2007), Helliwell (1997), Martínez-Zarzoso et al. (2008), Bussiére et al. (2013), 
and Bensassi et al. (2014). 

24 

 
 
 
 
 
 
 
 
                                                 
Naphtha 
Gasoline 
Jet fuel, kerosene 
Diesel, fuel oil 
Residual fuel oil 
Other 
Total petroleum products 

25 
39 
15 
33 
21 
27 
---  

--- 
--- 
--- 
--- 
--- 
--- 
5.75 

3.846 
6.000 
2.308 
5.077 
3.231 
4.154 
--- 

Using  fixed-effects  gravity  regressions,  Balistreri  et  al.  (2010)  estimate  import-import 
substitution elasticities for seven refined products, which are reported in the second column 
of  Table  4.3.11  On  the  other  hand,  Hummels  (1999)  estimate  substitution elasticities  for  89 
sectors, where the estimation technique is motivated by his multi-sector model of trade. The 

author reports the values of elasticity of substitution for the entire petroleum refining industry 
of 5.61 and 5.75 in, respectively, his OLS and non-linear least squares estimates of imports 
demands, and the last value is presented in Table 4.3. We use the results of both these studies 

due to the following reasons. The product-specific estimates of Balistreri et al. (2010) provide 
(potentially)  very  useful  information  in  terms  of  heterogeneity  of  substitution  elasticities 

across different types of refined products. However, we think the values themselves are rather 
large, especially  in  view of their comparisons  to the usual values of substitution elasticities 

used in various CGE models and related results for other industries (e.g. results reported in 
the studies mentioned in footnote 10). From this perspective, we find the result of Hummels 

(1999)  more  reasonable.  Hence,  we  have  decided  to  choose  the  value  of  6,  essentially  that 
reported  in  Hummels  (1999),  as  an  estimate  of  the  elasticity  of  substitution  for  gasoline, 

which has the largest elasticity according to Balistreri et al. (2010) study, while keeping the 
heterogeneity  of  these  elasticities  across  different  products  as  estimated  by  the  last  study. 

Thus,  the  values  of  the  substitution  elasticities  used  in  this  study  were  computed  from 

(6/39)×(Balestreri  et  al.'s  estimate  of  substitution  elasticity)  and  are  reported  in  the  last 

column of Table 4.3.  

However, elasticity of substitution is not trade elasticity with respect to transportation costs. 
Given that the estimation of substitution elasticities are based on the use of CES expenditure 

system, it can be shown that the absolute value of the elasticity of trade flows of product  p 
𝑝   ,  is  equal  to  (for  the  proof,  see 
from  region  k  to  region  i  with  respect  to  transport  cost,  𝜀𝑘𝑖
Temurshoev and Lantz, 2015): 

𝑝 = 𝜎𝑝(1 − 𝑠𝑘𝑖
𝜀𝑘𝑖

𝑝 )𝜏𝑘𝑖

𝑝  ,                                                               (6) 

11 They also estimate substitution elasticities for six crude grades, which however are not used here as crude oil 
trade is not explicitly calibrated.  

25 

 
 
 
   
 
 
  
 
                                                 
𝑝   and  𝜏𝑘𝑖

𝑝   are,  respectively,  the  substitution  elasticity  of  refined  product  p,  the 
where  𝜎𝑝,  𝑠𝑘𝑖
share of imports from region k in total trade flows to region i, and the proportion of transport 
costs in the consumer (CIF) price of product  p of the considered trade flow. Thus, all other 

things being equal, equation (6) states that the trade elasticity with respect to transport costs 
is higher, the (a) higher the substitution elasticity of the product in question, (b) smaller the 

relevant import (or export) share, and (c) larger the contribution of transport costs to the final 
price of the product. Therefore, as expected, if a region is largely dependent on imports of, 

say,  gasoline  from  another  region,  then  changes  in  the  relevant  transport  costs  would  have 
rather  little  impact  on  the  affected  gasoline  trade  flow,  at  least,  in  the  short-run.  The 

qualitative  impacts  of  the  other  two  factors  captured  in  (6)  on  the  trade  elasticity  are  also 
consistent with a common-sense reasoning.  

𝑝 .  Given  that  the 
To  use  (6),  besides  the  estimates  of  𝜎𝑝,  we  need  the  values  of  𝑠𝑘𝑖
observed  global  trade  flows  of  refined  products  are  missing,  we  use  instead  the  2005  total 

𝑝   and  𝜏𝑘𝑖

exports  data  from  Table  4.2  as  these  would  give  us  the  trade  shares  which  vary  across  the 
𝑝  the exports shares are used, which are the 
source regions. Hence, as an approximation of 𝑠𝑘𝑖
same  for  each  purchasing  region  i,  but  different  across  products.  What  will  make  the 

estimates of trade elasticities also dependent on the importing region i, is the use of the values 
𝑝 ’s. The last are obtained by dividing direct products’ freight costs for 2008, as estimated 
of 𝜏𝑘𝑖
by  IFPEN  and  assumed  be  the  same  for  all  refined  products  (see  Table  3.2),  by  2008 
products’ CIF prices which are available for LPG, naphtha, gasoline (regular and super), jet 

fuel,  diesel  oil,  gasoil,  fuel  oil  with  1%  sulphur  content  and  fuel  oil  with  3.5%  sulphur 
content,  and  for  13  regions  of  the  world  (for  our  purposes,  the  product  and  region 

classifications  were  made  consistent  with  the  relevant  OURSE  classifications).  These  price 
data  come  from  the  IHS  database,  purchased  and  used  specifically  for  the  REFIT  study 

purposes. The final estimated according to (6) trade elasticities per product ant trade partners 
are reported in Table 4.4. 

Table 4.4: Trade elasticities with respect to transport costs used in OURSE_QP 

Product 

NA 

SA 

NE 

SE 

RS 

AF 

ME 

CH 

AS 

NA 
SA 
NE 
SE 
RS 
AF 
ME 
CH 
AS 
NA 
SA 
NE 

0.06 
0.12 

0.05 
0.02 
0.12 
0.13 
0.18 
0.13 
0.09 
0.17 

0.05 

0.08 
0.09 
0.11 
0.07 
0.12 
0.13 
0.11 
0.07 

0.11 

0.05 
0.04 
0.06 
0.07 
0.13 
0.11 
0.04 
0.04 

0.07 
0.05 

PropTot,  
ButanTot 

Naphtha 

0.07 
0.11 
0.02 
0.07 

0.07 
0.13 
0.13 
0.12 
0.11 
0.16 
0.03 

0.12 
0.07 
0.08 
0.07 
0.07 

0.06 
0.13 
0.14 
0.18 
0.10 
0.11 

0.11 
0.11 
0.08 
0.11 
0.12 
0.06 

0.07 
0.07 
0.15 
0.15 
0.12 

0.03 
0.12 
0.11 
0.13 
0.12 
0.12 
0.07 

0.04 
0.05 
0.16 
0.15 

0.04 
0.12 
0.10 
0.12 
0.13 
0.16 
0.08 
0.05 

0.06 
0.17 
0.13 

0.07 
0.09 
0.04 

0.02 
0.08 
0.12 
0.16 
0.12 
0.10 
0.13 
0.05 

26 

 
 
 
   
 
  
  
 
 
 
 
 
 
 
  
  
 
 
SE 
RS 
AF 
ME 
CH 
AS 
NA 
SA 
NE 
SE 
RS 
AF 
ME 
CH 
AS 
NA 
SA 
NE 
SE 
RS 
AF 
ME 
CH 
AS 
NA 
SA 
NE 
SE 
RS 
AF 
ME 
CH 
AS 
NA 
SA 
NE 
SE 
RS 
AF 
ME 
CH 
AS 
NA 
SA 
NE 
SE 
RS 
AF 
ME 
CH 
AS 
NA 
SA 
NE 
SE 
RS 
AF 
ME 

ReGasol92NAm, 
PremGasol1 

ReGasol95NAm, 
PremGasol2,  
PremGasolEu 

JetFuel 

DieselNAm, 
DieselLatAm, 
DieselEu, 
DieselChin,  
HeatOil, 
HeatOilHq 

HevFOilLowSulf 

HevFOilHiSulf 

0.10 

0.11 
0.19 
0.20 
0.17 
0.16 
0.23 
0.04 
0.14 

0.16 
0.28 
0.29 
0.25 
0.16 
0.23 
0.04 
0.15 

0.16 
0.29 
0.30 
0.26 
0.05 
0.07 
0.02 
0.05 

0.05 
0.07 
0.09 
0.06 
0.13 
0.18 
0.03 
0.11 

0.12 
0.19 
0.23 
0.17 
0.22 
0.31 
0.07 
0.21 

0.21 
0.34 
0.41 
0.34 
0.16 
0.22 
0.05 
0.15 

0.15 
0.24 

0.10 
0.10 

0.08 
0.19 
0.20 
0.26 
0.14 
0.16 
0.14 
0.15 

0.12 
0.28 
0.29 
0.27 
0.15 
0.17 
0.14 
0.15 

0.13 
0.29 
0.30 
0.08 
0.05 
0.07 
0.05 
0.05 

0.03 
0.09 
0.08 
0.21 
0.12 
0.14 
0.11 
0.10 

0.09 
0.22 
0.21 
0.35 
0.20 
0.27 
0.21 
0.17 

0.15 
0.39 
0.40 
0.24 
0.13 
0.18 
0.14 
0.12 

0.10 

0.16 
0.18 
0.08 

0.11 
0.10 
0.22 
0.23 
0.18 
0.23 
0.26 
0.12 

0.16 
0.15 
0.24 
0.24 
0.19 
0.25 
0.28 
0.13 

0.17 
0.16 
0.07 
0.08 
0.07 
0.08 
0.09 
0.04 

0.05 
0.03 
0.18 
0.19 
0.15 
0.19 
0.18 
0.10 

0.13 
0.11 
0.31 
0.31 
0.30 
0.35 
0.30 
0.16 

0.22 
0.20 
0.20 
0.20 
0.19 
0.23 
0.20 
0.11 

0.18 
0.16 
0.17 
0.09 

0.05 
0.07 
0.24 
0.22 
0.27 
0.25 
0.26 
0.14 

0.08 
0.07 
0.25 
0.24 
0.28 
0.26 
0.27 
0.15 

0.08 
0.02 
0.08 
0.10 
0.10 
0.09 
0.08 
0.04 

0.02 
0.06 
0.20 
0.20 
0.23 
0.17 
0.21 
0.10 

0.06 
0.10 
0.32 
0.37 
0.39 
0.28 
0.33 
0.17 

0.11 
0.06 
0.21 
0.24 
0.25 
0.18 
0.21 
0.11 

0.17 
0.18 
0.23 
0.11 
0.07 

0.08 
0.24 
0.19 
0.25 
0.26 
0.33 
0.16 
0.10 

0.09 
0.25 
0.21 
0.26 
0.27 
0.35 
0.17 
0.10 

0.03 
0.08 
0.09 
0.09 
0.09 
0.10 
0.03 
0.03 

0.07 
0.20 
0.18 
0.21 
0.17 
0.26 
0.12 
0.08 

0.12 
0.32 
0.35 
0.37 
0.29 
0.42 
0.20 
0.13 

0.08 
0.21 
0.23 
0.24 
0.19 
0.28 
0.13 

0.08 
0.10 
0.17 
0.14 
0.05 
0.05 

0.10 
0.08 
0.13 
0.15 
0.26 
0.22 
0.08 
0.08 

0.10 
0.08 
0.13 
0.15 
0.26 
0.23 
0.08 
0.08 

0.04 
0.04 
0.05 
0.05 
0.08 
0.05 
0.03 
0.02 

0.09 
0.08 
0.11 
0.11 
0.21 
0.17 
0.07 
0.06 

0.15 
0.16 
0.21 
0.18 
0.37 
0.29 
0.12 
0.11 

0.10 
0.10 
0.14 
0.12 
0.25 
0.20 

0.11 
0.14 
0.10 
0.15 
0.17 
0.14 
0.11 

0.17 
0.17 
0.22 
0.15 
0.23 
0.27 
0.22 
0.11 

0.17 
0.18 
0.22 
0.15 
0.24 
0.28 
0.23 
0.03 

0.07 
0.06 
0.07 
0.05 
0.05 
0.08 
0.06 
0.09 

0.16 
0.15 
0.15 
0.12 
0.17 
0.22 
0.17 
0.17 

0.34 
0.31 
0.29 
0.23 
0.34 
0.45 
0.36 
0.11 

0.22 
0.20 
0.19 
0.15 
0.22 

0.07 
0.03 
0.17 
0.18 
0.25 
0.18 
0.13 
0.25 

0.11 
0.04 
0.25 
0.27 
0.37 
0.27 
0.13 
0.26 

0.11 
0.05 
0.26 
0.28 
0.39 
0.28 
0.04 
0.09 

0.04 
0.02 
0.08 
0.06 
0.12 
0.07 
0.11 
0.21 

0.09 
0.03 
0.19 
0.18 
0.30 
0.20 
0.19 
0.37 

0.18 
0.05 
0.34 
0.35 
0.56 
0.42 
0.14 
0.27 

0.13 
0.04 
0.25 
0.25 

0.03 
0.11 
0.18 
0.23 
0.18 
0.14 
0.19 
0.08 

0.04 
0.16 
0.26 
0.33 
0.25 
0.15 
0.20 
0.08 

0.05 
0.17 
0.27 
0.34 
0.26 
0.05 
0.06 
0.03 

0.02 
0.05 
0.06 
0.10 
0.07 
0.12 
0.16 
0.07 

0.03 
0.12 
0.18 
0.26 
0.18 
0.21 
0.27 
0.14 

0.05 
0.22 
0.33 
0.48 
0.36 
0.15 
0.19 
0.10 

0.04 
0.15 
0.24 

27 

 
 
 
 
 
 
 
 
  
 
 
 
 
 
 
 
 
 
  
 
 
 
 
 
 
 
  
  
 
 
 
 
 
 
 
  
  
 
 
 
 
 
 
 
  
 
 
 
 
 
 
 
 
 
  
 
 
 
 
 
 
BituMed, 
PetCoke 

CH 
AS 

NA 
SA 
NE 
SE 
RS 
AF 
ME 
CH 
AS 

0.08 
0.08 

0.13 
0.13 
0.18 
0.16 
0.32 
0.25 
0.10 
0.10 

0.29 
0.23 

0.14 

0.28 
0.25 
0.24 
0.19 
0.28 
0.37 
0.30 

0.41 
0.30 

0.18 
0.35 

0.16 
0.05 
0.32 
0.32 
0.52 
0.39 

0.34 
0.26 

0.19 
0.25 
0.13 

0.05 
0.20 
0.30 
0.44 
0.34 

0.29 
0.24 

0.20 
0.28 
0.06 
0.20 

0.19 
0.31 
0.38 
0.31 

0.26 
0.27 

0.31 
0.17 
0.23 
0.18 
0.15 

0.13 
0.34 
0.35 

0.14 
0.13 

0.26 
0.26 
0.25 
0.29 
0.25 
0.14 

0.19 
0.17 

0.07 

0.08 
0.27 
0.31 
0.32 
0.23 
0.27 
0.14 

0.09 

0.09 

0.10 
0.27 
0.29 
0.31 
0.24 
0.35 
0.16 
0.11 

Note: All the figures are in fact negative, but instead their absolute values are given. This is consistent with the notation 
introduced in the text. Trade flows from row region to column region.  

Note  that  all  the  trade  elasticities'  estimates  are  less  than  unity,  as  expected.  Just  for 

illustration purposes, consider for example the elasticity for diesel oil that is supplied by CIS 
(notably  by  Russia)  to  Europe.  From  Table  4.4  it  follows  that  this  figure  is  equal  to  0.03, 

implying that a 1% increase in the costs of transporting diesel oil from CIS region to Europe 
would  decrease  the  corresponding  trade  flow  (in  physical  term)  only  by  0.03%.  This  is  a 

reasonable estimate,  given that Europe is  largely dependent  on imports of diesel  from  CIS, 
particularly from Russia.  

Next,  in  order  to  estimate  the  implicit  cost  terms    𝑞𝑖𝑗
flows between regions  i and j from an equation similar to (5), we need to define the initial 
𝑝   will  be  estimated  in  the  last  step  of  our 

estimate  of  the  direct  cost  term  𝑑𝑖𝑗
calibration process  so that the  model's  endogenous trade flows will be exactly  calibrated to 

𝑝   associated  with  product  p  and  trade 

0,𝑝,  while  𝑑𝑖𝑗

their  "observed"  values.  In order to take into account  model uncertainty,  the following  two 
rules (or calibration approaches) are used12: 

0,𝑝 = 𝑐𝑖𝑗

𝑝 ,  i.e.  the  uncalibrated  direct  implicit  trade  costs  are  equal  to  the 

Standard  rule:  𝑑𝑖𝑗
direct accounting costs. That is, in the OURSE_QP framework these are set to direct freight 
costs, which are mostly symmetric in terms of bilateral flows and assumed to be the same for 

all products.  

Average  cost  rule:  𝑑𝑖𝑗
uncalibrated  direct  cost  coefficients)  are  equal  to  their  respective  direct  accounting  costs. 

𝑝   ,  i.e.  it  is  assumed  that  average  costs  (using  the 

0,𝑝 + 0.5𝑞𝑖𝑗

𝑝 = 𝑐𝑖𝑗

𝑝 𝑥̅𝑖𝑗

Using (5), the average cost rule can be written as 

12 The expressions standard rule and average cost rule are borrowed from the PMP literature, which represent 
additional  assumptions  used  to  estimate  the  unknown  parameters  of  the  non-linear  terms  in  the  cost  (profit) 
function. However, the two approaches in the PMP literature and here are not equivalent.  

28 

 
 
 
 
  
  
 
 
 
 
 
 
 
  
 
 
 
 
 
                                                 
0,𝑝 =
𝑑𝑖𝑗

𝑝
2𝜀𝑖𝑗
𝑝 + 1

2𝜀𝑖𝑗

𝑝  .                                                                     (7) 

× 𝑐𝑖𝑗

From (7) it follows that average cost rule results in direct costs which, unlike in standard rule 

option,  are  asymmetric  in  bilateral  flows  and  are  product-specific.    Now,  we  are  ready  to 
explicitly give the mathematical formulation of the calibration procedure, which consists of 
the following three steps.  

Step  1:  Estimate  interregional  trade  flows  such  that  they  are  consistent  with  the  observed 

total exports and imports data presented in Table 4.2. This is implemented with the following 
LP program: 

minimize  𝐜′𝐱 + ∑ (𝑑𝑖𝑗

0,𝑝 +

𝑝,𝑖,𝑗

0,𝑝
𝑑𝑖𝑗
𝑝
𝑝 ) × 𝑇𝑖𝑗
𝜀𝑖𝑗

                                         subject to: 

∑

∑

𝑖,𝑝∈𝑎𝑝

𝑗,𝑝∈𝑎𝑝

𝐀𝐱 ≥ 𝐛 , 
𝑝
≥ 𝑖𝑚𝑗
𝑇𝑖𝑗
𝑝
≤ 𝑒𝑥𝑖
𝑇𝑖𝑗
𝐱 ≥ 𝟎, and  𝑇𝑖𝑗

𝑎𝑝 for all j, 
𝑎𝑝 for all i, 
𝑝 ≥ 0, 

𝑎𝑝  and  𝑒𝑥𝑖

where  ap  denotes  aggregate  product,  𝑖𝑚𝑗
imports and exports data from Table 4.2, and 𝑇𝑖𝑗
and  j.  𝐱  includes  all  other  variables  in  the  OURSE  refining  model,  each  of  them  having 
different dimensions, which for simplicity of presentation are all suppressed. The first set of 
constraints in the LP program above includes all constrains that are presented in Appendix B, 

𝑎𝑝  are,  respectively,  the  exogenous  total 
𝑝 is trade flow of product p between regions i 

while  the  second  and  third  constraints  make  our  calibration  constraints.    To  clarify  the 
difference  between  the  sets  ap  and  p,  as  an  example  consider  ap  being  gasoline,  then  the 

earlier  mentioned  five  grades  of  gasoline  constitute  products  p  making  up  this  aggregate 
product. Ideally, of course, one would like to have the total exports and imports data at each 

disaggregate  product  level  p  (in  the  absence  of  trade  matrices),  but  the  data  availability 
problem  forces  us  to  use  calibration  constraints  at  the  more  aggregate  product  level.  The 

motivation  for the choice of the specific form of the objective function used in  Step 1  will 
become clear shortly. The trade flows obtained from Step 1 optimization are then denoted as  
𝑝 and considered to be "observed" interregional trade flows. 
𝑇̂
𝑖𝑗

Step 2: Run the following auxiliary non-linear (NLP) program: 

29 

 
 
 
 
 
 
 
 
 
                                minimize  𝐜′𝐱 + ∑

𝑝,𝑖,𝑗

                               subject to: 

(𝑑𝑖𝑗

0,𝑝 × 𝑇𝑖𝑗

𝑝 + 0.5 × 𝑞𝑖𝑗

𝑝}
𝑝 × {𝑇𝑖𝑗

2

)

𝐀𝐱 ≥ 𝐛 , 
𝑝   for all i and  j, 
𝑝 = 𝑇̂
𝑖𝑗

𝑇𝑖𝑗

𝐱 ≥ 𝟎, and  𝑇𝑖𝑗

𝑝 ≥ 0, 

with  the  purpose  of  obtaining  dual  values  𝜆𝑖𝑗
that unlike the PMP procedure (see e.g. Howitt, 1995a, 1995b), here calibrating equations are 

𝑝   of  the  calibrating  constraints  𝑇𝑖𝑗

𝑝.  Note 
𝑝 = 𝑇̂
𝑖𝑗

defined as a set of equations, rather than inequalities. This approach is adopted from Paris et 
al. (2011), who also focuses on calibrating spatial models of trade. Given that the calibration 
𝑝  will be a free variable. This "specification is 
constraints are stated as a set of equations,  𝜆𝑖𝑗
based on the consideration that, if accounting transaction costs are measured incorrectly, then 
they may  be either over  or under  estimated. Thus, the magnitude  and sign of the estimated 
𝑝 ] will determine the effective unit transaction costs that will produce a calibrated solution 
[𝜆𝑖𝑗
of the quantities produced and consumed in each country" (Paris et al., 2011, p. 2511). Using 
the solution of Step 1 LP model as initial values for all the variables, Step 2 NLP program 

gives an immediate solution that is exactly the same as Step 1 solution. This is the reason for 
using the specific  form  of the objective  function used in  Step 1, whose formal proof is  not 

presented here further due to space consideration. Thus, the extra information obtained from 
Step 2 includes quantification of the shadow values of trade flows calibrating constraints.  

Step 3: The final calibrating model has the form given in (2.b), specifically 

                          minimize  𝐜′𝐱 + ∑

𝑝,𝑖,𝑗

                          subject to: 

({𝑑𝑖𝑗

0,𝑝 − 𝜆𝑖𝑗

𝑝 } × 𝑇𝑖𝑗

𝑝 + 0.5 × 𝑞𝑖𝑗

𝑝}
𝑝 × {𝑇𝑖𝑗

2

)

𝐀𝐱 ≥ 𝐛 , 
𝐱 ≥ 𝟎, and  𝑇𝑖𝑗

𝑝 ≥ 0. 

Note  that  the  effective  unit  transaction  cost  in  the  linear  cost  term  𝑑𝑖𝑗

uncalibrated  direct  costs  𝑑𝑖𝑗
𝑝 = 𝑑𝑖𝑗

𝑝 ,  i.e.,  𝑑𝑖𝑗

𝑝   adjusts  the  earlier 
0,𝑝  with  the  shadow  values  of  the  trade  flows  calibrating 
𝑝 .  Running  this  final  OURSE_QP  model,  using  the 
0,𝑝 − 𝜆𝑖𝑗

constraints  𝜆𝑖𝑗
solution  of  Step  2  NLP  program  as  initial  values  for  all  the  variables,  will  immediately 
produce exactly the same solution. Thus, it endogenously calibrate perfectly observed trade 
𝑝's,  production  of  products,  production  and  consumption  of  crudes  and  all  other 
flows  𝑇̂
𝑖𝑗

30 

 
 
 
 
  
 
 
 
  
 
variables used in the refining model that are consistent with the relevant base-year "observed" 
trade  flows  implied  by  the  products'  supply-demand  balances  and  all  material  balances 

included  in  the  model.  As  a  consequence,  the  final  OURSE_QP  model  can  now  be 
reasonably used, without including any artificial calibrating constraints, to evaluate the likely 

effects of and world refineries' reactions to various policy instruments captured by the model.    

Finally,  note  that  in  case  of  availability  of  trade  flows  data,  Step  1  in  our  calibrating 
procedure  would  be  redundant.  Hence,  if  one  has  a  luxury  of  having  access  to  detailed 

interregional trade data of refined products that are observed in reality, the above-presented 
calibrating procedure reduces to two steps in order to arrive at the final QP (or NLP) model 

that perfectly calibrates the observed data in its baseline scenario(s).     

5  Baselines, simulation scenarios and results  

Given that it is impractical to present all the details of the OURSE_QP modelling outcome, in 
this section we discuss and report the main inputs and/or outputs related to the construction 

and/or  implementing  the  necessary  baselines,  simulation  (counterfactual)  scenarios  and  the 
underlying main results.  

5.1  Benchmark (baseline) scenarios 

We  start  with  the  crude  prices,  which  are  exogenous,  but  quite  important,  variables  in  the 

OURSE model. These were derived using the relevant data from IEA and IHS. IEA data are 
crude oil spot prices in current dollars (USD) per barrel, while IHS crude price data are FOB 

prices in constant 2013 USD per barrel. Hence, the two data were used together in order to 
derive Brent (which is a reference crude in OURSE) prices in constant 2008 USD per tonne 

of processed crude, for the three sub-periods covered in the modelling exercises. We translate 
the prices into constant 2008 year prices, because all the costs coefficients, including those of 
operating  and  capital  expenditures,  are  expressed  in  2008  USD.  These  crude  oil  prices  are 

reported in Table 5.1.      

Table 5.1: Crude oil and feedstock prices (in 2008 USD per tonne)  

Period 

Brent 

2000 
2005 
2010 

194.78 
381.53 
691.95 

Arabian 
Light 
171.76 
347.46 
639.52 

Arabian 
Heavy 
116.32 
292.53 
585.45 

Forcados 

Condensate 

Feedstock 

186.56 
374.74 
687.53 

222.79 
436.09 
790.66 

82.40 
210.65 
423.85 

31 

 
 
 
 
 
 
 
 
The reference crude price averages over 2000-2002, 2003-2007 and 2008-2012 are taken as 
Brent prices for the three modelling time periods of 2000, 2005 and 2010, respectively.  The 

prices of the other four crude types and feedstock (mainly representing atmospheric residue) 
are  derived  using  the  long-term  equilibrium  (i.e.  cointegration)  relationships  between  these 

crudes and Brent, as estimated and reported in Lantz et al. (2012).    

Table 5.2: Crude oil supply to regional refineries in the OURSE_QP baselines 

Quantity 

API 

S (%) 

Quantity 

API 

S (%) 

Quantity 

API 

S (%) 

2000, standard rule 

2005, standard rule 

2010, standard rule 

793.9 
174.2 
432.2 
208.0 
205.1 
106.3 
298.5 
156.1 
579.7 
2953.9 

34.9 
31.5 
34.3 
32.7 
31.8 
32.0 
27.0 
34.8 
34.1 
33.2 

2000, average cost rule 

774.8 
171.5 
419.0 
202.4 
213.0 
104.4 
329.9 
153.1 
580.8 
2948.8 

33.2 
32.3 
32.7 
31.0 
34.6 
33.7 
27.0 
34.8 
33.3 
32.5 

1.30 
1.28 
1.15 
1.40 
1.81 
0.83 
2.69 
1.70 
1.57 
1.52 

1.35 
1.31 
1.22 
1.43 
1.70 
0.82 
2.69 
1.71 
1.78 
1.59 

817.7 
181.5 
448.7 
226.7 
243.7 
118.5 
347.3 
230.5 
628.4 
3243.0 

33.2 
32.1 
33.2 
31.6 
32.6 
32.7 
27.0 
33.6 
33.6 
32.4 

2005, average cost rule 

805.1 
171.7 
431.9 
221.4 
248.5 
120.0 
361.1 
237.2 
627.7 
3224.6 

32.9 
32.5 
32.7 
31.2 
33.5 
36.0 
27.0 
33.8 
33.0 
32.3 

1.30 
1.27 
1.27 
1.41 
1.64 
0.70 
2.69 
1.68 
1.73 
1.56 

1.29 
1.29 
1.28 
1.43 
1.68 
0.69 
2.69 
1.58 
1.88 
1.60 

787.6 
163.8 
422.0 
208.8 
266.7 
123.6 
372.5 
314.7 
641.4 
3301.1 

33.3 
32.5 
33.5 
31.6 
32.3 
35.9 
27.0 
32.8 
33.2 
32.4 

2010, average cost rule 

765.1 
177.3 
406.4 
203.9 
268.7 
116.6 
377.5 
332.3 
640.1 
3287.8 

33.4 
32.0 
33.1 
31.3 
32.2 
35.9 
27.0 
32.8 
33.2 
32.3 

1.21 
1.37 
1.23 
1.56 
1.57 
0.70 
2.69 
1.61 
1.69 
1.55 

1.30 
1.25 
1.12 
1.49 
1.71 
0.70 
2.69 
1.61 
1.69 
1.57 

NA 
SA 
NE 
SE 
RS 
AF 
ME 
CH 
AS 
World Total 

NA 
SA 
NE 
SE 
RS 
AF 
ME 
CH 
AS 
World Total 

Note: Quantities are in million tonnes, API is the API degree, and S denotes the sulphur content. For 2005 and 2010 baselines, respectively, 
the capacities in processing units of 2000 and 2005 are used in the estimation procedure. Standard rule and average cost rule indicate the 
specific calibration approach used (for details, see Section 4.2).  

The  total  quantities  and  characteristics  of  crude  oil  (excluding  feedstock),  supplied  to 

regional refineries as produced by the model for the baseline scenarios, are presented in Table 
5.2. As an example, however, in Table 6.5 in Appendix A we present the detailed trade flows 

of the six crude types and feedstock for the 2005 baseline scenario.  

The  characteristics  of  the  crudes  reported  in  Table  5.2  in  terms  of  API  degree  and  sulphur 
content  are  close  to  the  relevant  observed  average  figures  by  consuming  regions.  If  we 

compare the crude input quantities with those presented in Table 4.1, we find, for example, 
that  at  the  global  level  the  "observed"  crude  inputs  in  Table  4.1  are  larger  than  those 

endogenously  derived  by  the  model  and  ranges  from  8.1%  to  12.5%  depending  on  year 
and/or  calibration  approach  used.  At  the  regional  level  these  discrepancies  are  higher,  and 
range  from  -11.8% to  34.3%. Total  throughputs (i.e. inputs of crude  and feedstock)  for the 
three  baselines  and  the  two  calibration  approaches  used,  which  we  do  not  report  here  for 

32 

 
 
 
 
 
  
  
 
 
space consideration, show lower discrepancies. Thus, at the global level, the "observed" total 
throughputs are again larger compared to the model outcomes, ranging from 7.0% to 8.9%, 

while  the  corresponding  range  for  the  regional  level  differences  are  -1.7%  and  17.8%. 
However, these discrepancies should not be taken (too) seriously, because: (a) the "observed" 

crude  and  feedstock  trade  matrices  are  (best)  estimates  themselves  as  explained  in  Section 
4.1,  and  (b)  calibration  was  implemented  on  the  output-side  of  the  refinery,  hence  from  an 

input-output  material  balance  perspective  the  lower  amount  of  global  refined  products  vs. 
world crude (which seem to be in place, given that at least our demand data do not include 

the  unspecified  products  category  from  the  IEA  dataset)  would  result  in  the  model 
throughputs that are somewhat lower at the global level than the "observed" ones. The last is 

exactly  what  we  find,  and  this  may  also  partly  explain the  larger  deviations  at  the  regional 
levels  of  crude  and  total  throughput  supply.  Nonetheless,  the  obtained  crude  and  feedstock 

outputs  are  considered  sufficiently  reasonable,  at  least,  for  modelling  purposes.  We  remind 
that  the  data  from  Table  4.1  were  used  as  constraints  at  total  crude  level  in  terms  of 

maximum allowed import shares from various regions for each consuming refinery (region), 
which ensures that the API degree and sulphur content qualities of the model's crude supply 

per region, as reported in Table 5.2, match well with those characteristics observed in reality.  

Next  we  turn  to  reporting  some  of  the  OURSE_QP  outputs  for  arbitrary  selected  baseline 
scenarios. In Table 5.3 the products’ balances at a somewhat more aggregated levels for all 

regions  are  presented.  The  results  show  that  North  and  South  Europe  are  net  exporters  of 
gasoline,  and  net  importers  of  the  majority  of  other  reported  products,  in  particular,  diesel, 

heating oil, jet fuel and residual fuel oil. These results, of course, reflect the calibration step 
as  discussed  in  detail  in  the  previous  section.    The  bottom  right  part  of  Table  5.3  shows 

products balances at the world level. From these figures we can infer that in 2005 out of the 
estimated 387.2 mln tonnes of oil products traded world-wide, residual fuel oil trade accounts 

for  

Table 5.3: Product balances for all regions, 2005 baseline (average cost rule) 

Production 

Imports 

Exports 

Consumption 

Production 

Imports 

Exports 

Consumption 

LPG 
Naphta 
Gasoline 
JetFuel 
HeatingOil 
DieselOil 
ResFuelOil 
Bitumen 
PetCoke 
MarinBunk 

LPG 
Naphta 
Gasoline 

67.4 
0.7 
403.2 
92.6 
93.6 
148.0 
56.7 
38.3 
13.4 
33.4 

9.4 
12.3 
47.4 

North America 
4.8 

19.3 
44.2 
6.5 
6.5 

43.3 

17.5 

8.7 

2.9 

South America 
4.8 

2.0 
11.5 

4.5 
5.3 
19.6 
15.4 
10.3 
26.8 
29.1 

0.3 
6.1 

16.9 
36.8 
41.8 

Africa 

4.5 

7.9 
0.1 
4.8 
1.3 

2.6 
1.2 

4.5 

9.8 

Middle East 
4.1 

9.6 
33.9 

7.3 

9.0 
0.7 
27.5 
15.5 
15.1 
28.1 
19.3 
2.6 
1.5 
6.1 

11.4 
2.9 
49.1 

62.6 
20.0 
447.4 
99.1 
100.1 
148.0 
82.5 
38.3 
19.2 
33.4 

14.2 
10.3 
35.9 

33 

 
 
 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
 
 
  
  
  
  
 
 
 
 
 
 
JetFuel 
HeatingOil 
DieselOil 
ResFuelOil 
Bitumen 
PetCoke 
MarinBunk 

LPG 
Naphta 
Gasoline 
JetFuel 
HeatingOil 
DieselOil 
ResFuelOil 
Bitumen 
PetCoke 
MarinBunk 

LPG 
Naphta 
Gasoline 
JetFuel 
HeatingOil 
DieselOil 
ResFuelOil 
Bitumen 
PetCoke 
MarinBunk 

LPG 
Naphta 
Gasoline 
JetFuel 
HeatingOil 
DieselOil 

ResFuelOil 
Bitumen 
PetCoke 
MarinBunk 

12.6 
22.9 
45.5 
33.9 
3.6 
6.8 
7.3 

28.5 
28.8 
109.0 
34.9 
72.9 
104.5 
24.6 
14.2 
4.7 
39.3 

11.3 
5.2 
48.4 
13.7 
30.2 
55.5 
32.3 
9.2 
2.3 
16.2 

10.9 
24.6 
46.2 
12.9 
33.9 
41.5 

71.7 
5.3 
3.3 

3.6 
0.9 

18.1 

0.2 

North Europe 

10.1 

24.0 

2.3 

13.1 

16.2 
10.2 
15.8 
5.9 

2.1 

South Europe 
5.6 
4.5 

4.1 

12.9 

1.7 
2.9 
10.8 
4.8 

8.9 

CIS (Russia) 

2.4 

1.4 

0.3 
13.1 
10.2 
0.1 
10.2 
26.6 

42.5 

2.1 

9.0 
22.0 
45.5 
15.8 
3.6 
7.0 
7.3 

18.4 
41.9 
85.0 
51.2 
83.1 
120.3 
28.2 
14.2 
6.8 
39.3 

12.8 
9.7 
35.5 
15.4 
33.1 
66.3 
37.1 
9.2 
11.2 
16.2 

10.6 
11.5 
38.4 
12.8 
23.7 
14.9 

29.1 
6.7 
1.2 

18.1 
11.6 
1.3 
10.0 
19.1 
7.4 

4.3 
9.4 
5.1 

China 

5.2 
2.7 

28.8 
0.6 
1.0 

Other Asia & Oceania 

14.3 
26.0 
1.9 

1.2 

17.5 
14.5 

7.9 
5.6 

9.7 

World Total 

33.3 
63.0 
63.7 
29.7 
28.3 
27.8 

100.2 
19.1 
22.0 

33.3 
63.0 
63.7 
29.7 
28.3 
27.8 

100.2 
19.1 
22.0 

46.5 
48.5 
42.6 
61.9 
27.3 
8.0 
17.8 

22.6 
38.5 
54.2 
12.9 
62.1 
42.9 
13.9 
6.6 
7.6 
10.7 

32.6 
79.8 
119.5 
150.1 
102.5 
115.6 
74.2 

22.0 
47.9 

204.2 
231.9 
889.4 
391.5 
476.9 
622.9 

398.3 
104.6 
68.4 
178.7 

28.4 
36.9 
41.3 
51.8 
8.1 
0.6 
17.8 

18.2 
29.0 
49.1 
18.0 
64.8 
42.9 
42.7 
7.2 
8.6 
10.7 

46.9 
105.8 
121.4 
142.1 
98.1 
115.6 
91.8 
14.5 
12.3 
47.9 

204.2 
231.9 
889.4 
391.5 
476.9 
622.9 

398.3 
104.6 
68.4 
178.7 

Note: Quantities are in million tonnes. Source: OURSE_QP results. 2000 capacities are used in the model estimation.  

25.9%, gasoline – 16.5%, naphtha – 16.3%, jet fuel – 7.7%, heating oil – 7.3% and diesel oil 
– 7.2%.  

Given that the regions of North Europe and South Europe also include non-EU countries as 

discussed  in  Section  1,  the  product  balances  of  the  EU  regions  are  reported  separately  in 
Table 5.4. These figures were estimated using the relevant data from  

Table 5.3 and the demand shares reported in Table 6.2. Given that these shares are found to 
be quite high, ranging from 74.4% to 100%, except for the LPG demand proportion of the EU 

countries in South Europe being 65.1%, the qualitative and, to a large extent, the quantitative 
outcomes remain similar to those presented in  

Table 5.3 and discussed above.  

34 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
 
 
 
  
  
  
  
 
 
 
 
 
 
 
 
 
  
  
  
  
  
  
 
 
Table 5.4: Products balances for the EU regions, 2005 baseline (average cost rule) 

Production 

Imports 

Exports 

Consumption 

Production 

Imports 

Exports 

Consumption 

North Europe: EU countries 

South Europe: EU countries 

LPG 
Naphta 
Gasoline 
JetFuel 
HeatingOil 
DieselOil 
ResFuelOil 
Bitumen 
PetCoke 
MarinBunk 

26.7 
28.8 
102.2 
32.6 
66.4 
101.6 
24.0 
13.6 
4.3 
36.9 

9.5 

22.5 

2.2 

13.1 

15.1 
9.3 
15.3 
5.7 

1.9 

2.7 

11.3 

17.2 
41.9 
79.6 
47.7 
75.6 
116.9 
27.5 
13.6 
6.2 
36.9 

7.4 
4.6 
42.4 
11.6 
26.3 
48.1 
24.1 
7.2 
2.3 
15.1 

3.7 
4.0 

1.4 
2.5 
9.3 
3.5 

8.7 

8.4 
8.6 
31.1 
13.1 
28.8 
57.4 
27.6 
7.2 
11.1 
15.1 

Note: These  figures  were  obtained using the  observed demand shares reported in  Table 6.2 and the corresponding figures 
presented in Table 5.3.  

From this point onwards, most of the results of refined products will be presented in terms of 
three widely-used broad oil product groupings. These are light products (LPG, naphtha and 

gasoline), medium products (jet fuel, heating oil and diesel oil), and heavy products (residual 
fuel oil, marine bunkers and bitumen).  

The product trade flows for 2005 baseline (average cost calibration approach) are presented 

in Table 5.5. Focusing on the products trade of Europe with the other regions of the world, 
one can observe from Table 5.5 large dependence of Europe on imports of medium products 

from  CIS/Russia  and  Middle  East.  On  the  other  hand,  as  exporter  Europe's  largest  market 
destinations  for  its  gasoline  are  North  America  and  Africa.  In  terms  of  imports  of  heavy 

products, Europe again is largely dependent on CIS/Russia region. It should be noted that the 
model  captures  the  most  important  trade  flows,  and  not  all  small  transactions  that  maybe 

observed in reality. On the other hand, not each and every flow reported in Table 5.5 should 
be taken for granted as absolutely accurately representing the real flows, as these are only the 

model's  outcomes  and  thus  may  not  give  the  exact  size  of  the  real  trade  flows.  This 
possibility is unavoidable in the current study given that the observed global refined products 

trade  data  are  unavailable,  otherwise  the  calibration  step  of  the  OURSE_QP  would  have 
ensured  exactly  identical  observed  and  model's  trade  flows  per  product.  Nonetheless,  the 

results  are  encouraging  as  they  capture  the  main  features  of 
interregional  trade 
interdependencies  and  structure  in  global  petroleum  products  market,  which  is  more  than 

sufficient for modelling purposes. In any case, OURSE_QP improves significantly upon the 
OURSE_LP results in this respect.   

Table 5.5: Products trade flows, 2005 baseline (average cost calibration approach) 

NA 

SA 

NE 

SE 

RS 

AF 

ME 

CH 

AS 

35 

 
 
 
 
  
 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
  
  
  
 
 
 
 
 
NA 
SA 
NE 
SE 
RS 
AF 
ME 
CH 
AS 

NA 
SA 
NE 
SE 
RS 
AF 
ME 
CH 
AS 

NA 
SA 
NE 
SE 
RS 
AF 
ME 
CH 
AS 

4.82 
1.78 

16.79 
13.46 
21.68 
0.91 
10.17 

17.32 

21.33 
4.48 

8.61 

8.52 

4.77 
18.11 
2.28 

13.12 
9.76 

Light products 

2.37 

29.22 

13.11 

5.62 
5.24 

4.54 

Medium products 

72.43 

25.97 

17.03 
10.90 

2.93 

16.24 

4.51 

Heavy products 

4.49 
7.89 

2.68 

7.93 
6.04 

0.07 

8.22 

4.64 
3.23 

0.35 

26.15 
15.67 
24.28 

13.51 

2.88 

1.22 

4.96 

53.42 

17.53 

1.21 

43.40 

5.87 

9.97 
4.76 

1.36 

1.43 

2.42 
2.64 

18.79 

15.75 

10.59 

14.50 

33.37 

Note: Unit is in million tonnes. Source: OURSE_QP results. Observed 2000 capacities in processing units are used in the 
model estimation procedure. Intra-regional flows estimate the flows between countries of the same region.    

5.2  Counterfactual scenarios and results 

To  assess  the  likely  impacts  of  the  legislation  acts  included  in  the  EU  Petroleum  Refining 
Fitness  Check  (REFIT)  on  the  performance  and  international  competitiveness  of  the  EU 

refining industry, the following three types of scenarios are defined: 

4.  Fuel quality specifications change scenario: this includes changing fuel specifications 
as dictated by the Fuels Quality Directive (FQD) and Marine Fuels Directive (MFD).  
5.  Demand  level  and  composition  change  scenario:  this  assesses  the  impact  of  the 

Renewable Energy Directive (RED) and Energy Taxation Directive (ETD). 

6.  Pollution limits change scenario: this deals mainly with the assessment of the Large 
Combustion  Plants  Directive  (LCPD),  Integrated  Pollution  Prevention  and  Control 
Directive (IPPCD) and Air Quality Directive (AQD).  

The likely impacts of the remaining directives included within the REFIT scope will not be 

assessed  with  the  OURSE  model  because  of:  (a)  the  model's  unsuitability  of  evaluation,  at 
least at the current stage of the OURSE development, of a specific directive's impact (such as 

36 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
  
  
  
  
  
  
  
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
  
  
  
  
  
  
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
  
  
  
  
  
  
  
 
 
  
 
Strategic Oil Stocks Directive), (b) the prior knowledge of the irrelevance of a directive (such 
as the Directive on Clean and Energy Efficient Vehicles), or (c) the availability of the exact 

costs  and/or  benefits  incurred  by  the  individual  EU  refineries  due  to  the  requirements  of  a 
directive (such as EU ETS).  

5.2.1  Impact assessment of changes in fuels quality specifications 

Several  sets  of  scenarios  have  been  considered  in  order  to  take  into  account  modelling 

uncertainties and economic environment uncertainties. This is an ex post impact assessment 
study,  thus  the  main  point  of  departure  should  be  building  counterfactual  scenarios  for  the 

periods  considered  such  that  they  exclude  the  changes  in  fuels  quality  specifications 
exogenously  and  then  the  model  outcomes  need  to  be  compared  with  those  of  the 

corresponding  baselines.  But  given  that  refining  processing  units  capacities  and  products 
demands  as  model  inputs  play  crucial  role  in  the  OURSE  model  (or  any  other  refining 
mathematical programming model), the general structure of the three sets of the baseline and 
counterfactual  scenarios  are  "visualized"  in  Table  5.6.  It  can  be  easily  seen  from  this  table 

that the differences in  outcomes between the  relevant  baseline and  counterfactual scenarios 
quantify the impact of fuels' qualities changes.  

Table 5.6: Baseline and counterfactual scenarios main structure 

Scenario 

Refining capacities 

Product demands 

Fuel qualities 

2000-2010 baseline 
2000-2010 counterfactual 

2000-2005 baseline 
2000-2005 counterfactual 

2005-2010 baseline 
2005-2010 counterfactual 

2000 
2000 

2000 
2000 

2005 
2005 

2010 
2010 

2005 
2005 

2010 
2010 

2010 
2000 

2005 
2000 

2010 
2005 

Since the REFIT covers 2000-2012 time period, which in OURSE language is translated into 
three periods 2000-2005-2010, there are two possibilities of assessing the cumulative impact 

over  the  entire  period.  First,  consider  the  entire  2000-2010  period,  without  using  the 
information  on  the  intermediate  period  of  2005:  this  case  is  represented  as  the  first  set  of 

baseline and counterfactual scenarios in Table 5.6. However, ignoring the intermediate year 
data could give biased results due to likely significant changes in economic environment. In 

particular,  as  already  discussed  in  Section  1,  in  Europe  total  fuel  demand  increased  during 
2000-2005  period  (by  5.4%  and  2.3%  in  NE  and  SE  regions,  respectively),  but  then 

decreased largely in the second sub-period of 2005-2010 (the respective figures were -10.6% 

37 

 
 
 
 
 
 
 
 
and -12.5%). To take this change in demand into account, it is important to consider the two 
sub-periods separately. The main inputs structures of the relevant scenarios are given in the 

last two sets of scenarios presented in Table 5.6. 

To  account  for  different  economic  environments  (as  sensitivity  analysis),  two  cases  in  the 
modelling  exercises  will  be  considered.  In  Case  1  counterfactual  scenarios  (time-wise  as 

depicted in Table 5.6) were constructed, such that: 

  only for North Europe and South Europe the relevant fuels quality specifications were 
set  to  their  initial-year  (2000  or  2005)  levels  in  the  counterfactual  envirionment, 

which include maximum limits of sulphur for gasoline (PremGasolEu) and diesel oil 
(DieselEu),  of  polyaromatic  hydrocarbons  (PAH  for  DieselEu)  and  of  aromatic 

fraction (for PremGasolEu); the exact figures of these limits are given in Table 1.1; 

  only  for  European  regions  the  respective  initial-year  shares  that  split  exogenous 
aggregate demand of heating oil, residual fuel oil and marine bunkers into higher- and 
lower-sulphur content fuels are used instead of their final-year levels (2005 or 2010, 

depending on the scenario); the relevant splitting shares for 2005 are given in  Table 
6.4, and in general for European regions the consumption shares of low-sulphur fuels 

increase over time; and     

 

for  the  remaining  OURSE  regions,  the  relevant  fuels  specifications  and  the  above-

mentioned  three  fuels  disaggregation  consumption  shares  were  not  changed,  where 
the last were set at the end-year values of the counterfactual scenarios.  

A different economic environment for the counterfactual scenarios is assumed under Case 2 

simulations,  where  non-EU  regions  are  assumed  not  to  tighten  their  fuels  quality 
specifications that are instead made more stringent in the European regions, and vice versa. 

That is, under Case 2 is the mirror image of Case 1 environment and mimics the hypothetical 
environment where non-EU regions tighten all their fuel quality specifications, while North 

Europe and South Europe do not.   

In what follows we present some of our main findings related to the assessments of implied 
costs,  benefits,  and  impact  on  trade  and  international  competitiveness.  At  this  point  it  is 

important  to  note  that  in  order  to  assess  the  costs  and/or  benefits  implications  of  the 
mentioned  directives,  it  is  crucial  to  keep  trade  flows  of  petroleum  products  fixed  at  their 

baseline level. The reason is that we need the refineries to have the same production as in the 
baseline  scenario,  but  under  a  different  environment  related  to  the  assessment  goal,  for 

example,  under  less  stringent  regulations  on  fuels  quality  limits.  Given  that  demand  is 
exogenously  fixed,  keeping  trade  flows  unchanged  also  automatically  imply  unchanged 

production  levels  (due  to  products'  balances).  However,  when  the  focus  shifts  towards  the 

38 

 
 
 
 
 
 
assessment  of  likely  impacts  on  international  competitiveness,  trade  flows  should  be  kept 
endogenous  during  the  simulation  runs.  On  the  practical  note,  this  might  increase  the 

simulation-time considerably.  

5.2.1.1  Costs 

We  first  present  somewhat  detailed  results  for  an  arbitrary  selected  scenario  of  2005-2000 

(using the average cost calibration approach and Case 1 environment) to shed some light on 
the outcomes, and then  provide the costs estimates  from  all the simulations without further 
detailed explanations. The costs of investments in new processing units - capital expenditures 

(CAPEX) - and the operating costs (OPEX) from the 2005-2000 baseline and counterfactual 
scenarios are reported in Table 5.7.  

Table 5.7: CAPEX and OPEX costs, 2005-2000 scenarios, Case 1 (in 2008 mln USD) 

NA 

SA 

NE 

SE 

RS 

AF 

ME 

CH 

AS 

NE&SE 

Total 

Baseline 
Counterfactual 
Difference 

10709.7 

10988.7 

279.1 

778.0 

744.4 

-33.6 

4573.0 

4328.4 

-244.6 

373.6 

57.6 

-316.0 

1702.3 

1715.4 

13.1 

Baseline 
Counterfactual 
Difference 

11307.1 

11251.5 

1725.1 

1738.5 

5068.0 

4822.3 

1959.2 

1876.0 

1787.2 

1751.5 

-55.6 

13.4 

-245.7 

-83.2 

-35.6 

CAPEX 

2067.8 

17791.2 

27902.5 

106766.9 

4946.6 

172664.9 

2067.8 

17791.2 

27902.5 

106751.4 

4386.0 

172347.3 

0.0 
OPEX 

655.9 

655.9 

0.0 

0.0 

0.0 

-15.5 

-560.7 

-317.6 

3751.2 

3751.2 

0.0 

3319.1 

10027.5 

7027.2 

39600.2 

3319.1 

10002.2 

6698.2 

39168.1 

0.0 

-25.4 

-328.9 

-432.0 

Note: Difference = Counterfactual – Baseline. Calibration is based on the average cost specification rule. 

As  expected  with  weaker  fuels  quality  specifications  in  place,  Europe  would  have  had  less 

need  for  capital  investments  in  new  processing  units.  Also  note  that  some  other  non-EU 
regions (refineries), closely linked with the European petroleum product market, are affected 

by  the  European  legislation  as  well,  though  the  size  of  this  impact  (in  absolute  value)  is 
generally  much  less  than  that  on  the  EU  refineries.  This  is  a  crucial  advantage  of  global 

refinery  modelling,  as  in  open  economies  there  are  always  inter-regional  spillover  and 
feedback effects present. The extent of these inter-regional effects depend, of course, on the 
size of the relevant trade flows of crude oil and refined products, among other factors.   

According  to  the  considered  scenario,  the  total  estimated  CAPEX  costs  for  all  European 

refineries due to the FQD and MFD directives over the 2005-2000 period amounted to 560.7 
mln  USD,  or  112.13  mln  USD  (=560.7/5  years)  in  annualised  term.  Some  details  of  these 

results  in  terms  of  differences  in  new  investments  (in  mln  tonnes  per  year)  of  the 
counterfactual  and  baseline  scenarios  at  somewhat  aggregated  unit  group  levels  are  also 

presented  in  Table  5.8.  The  list  of  these  aggregate  units  grouping  and  the  associated 
individual processing units is given in Table 6.6. Thus, from Table 5.8 one observes that with 

tighter  fuels  specifications  for  gasoline,  diesel,  heating  oil,  residual  fuel  oil  and  marine 

39 

 
 
 
 
 
  
 
 
 
 
bunkers,  Europe  had  to  invest  (more)  in  gasoil  hydrodesulphurisation  units  (HDS  gasoil), 
naphtha  processing  units,  residue  catalytic  cracking,  hydrotreatment  (HDT)  of  vacuum  gas 

oil, and hydrogen units.   

Table 5.8: Differences in new investments, 2005-2000 scenarios (mln tonnes/year) 

Topping unit & VDU 
Naphtha processing units 
FCC 
RCC 
HDS  gas oil 
FCC gasoline desulphur. 
HDT vacuum gas oil 
HDT naphtha 
Hydrocraking units 
Residue 
hydroconversion 
Etherification units 
Visbreaking unit 
Coking unit 
Hydrogen units 

NA 

SA 

NE 

SE 

RS 

AF 

ME 

CH 

AS 

-5.5 

-1.7 

0.6 

0.0 

-0.1 

-0.6 
-43.8 

-0.2 

-29.5 

-4.3 

0.0 

0.3 
0.0 

-0.1 

0.0 

0.0 

0.0 
0.0 
0.0 
0.0 
0.0 

0.0 

0.4 

0.0 

-0.1 

0.0 

0.0 

0.0 

-0.1 

0.0 

0.0 
0.0 

0.0 

-0.2 
0.1 
0.0 

0.0 

0.0 

-3.7 

0.0 

0.0 

0.0 

0.0 

NE& 
SE 

Total 

0.3 
-6.6 
0.0 
-0.9 
-77.6 

-7.2 

-0.6 
-73.4 

-0.2 

-0.2 

-0.1 

0.4 
-0.2 
0.1 
-0.1 

-0.1 

-81.4 

-112.3 

Total difference 

0.4 

-0.1 

-50.1 

-31.3 

Note: CAPEX difference = Counterfactual CAPEX – Baseline CAPEX. 

Further impact on processing units use can be traced by looking at the capacities endowments 
as of 2000 and their use afterwards. These can be seen from capacities utilization rates in the 

2005-2000 baseline and counterfactual scenarios, which are reported in Table 5.9. These rates 
per each processing unit are computed as: 

Capacity utilisation rate =

Capacity used
Old capacity + New investments

× 100.                       (8) 

Thus, given that new investments in gasoil HDS units decrease without FQD/MFD, it should 

not  be  surprising  to  observe  an  increase  in  their  utilisation  rates  in  the  counterfactual  with 
weaker European fuel  quality specification  as  reported in  Table 5.9. On the other hand, for 

underutilized  units  with  zero  new  investments,  like  FCC  gasoline  desulphurization  units,  a 
decrease  of  utilisation  rate  in  the  counterfactual  scenario  implies  lower  need  for 

desulphurisation. This trend is observed for North Europe, where the average utilisation rate 
of the FCC desulphurisation units goes down from 61.1% to 17.1%. Finally, part of the costs 

of  more  desulphurisation  need  will  be  reflected  in  higher  operating  costs,  for  example, 
through the increase in operating variable costs, such as costs of catalysts use.       

Table 5.9: Capacities average utilisation rates, 2005-2000 scenarios, Case 1 

40 

 
 
 
 
  
  
  
  
  
  
  
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
  
  
 
  
 
 
100.0 

100.0 

35.9 

100.0 

88.8 

82.2 

73.9 

76.8 

62.4 

3.6 

100.0 

100.0 

100.0 

88.8 

85.5 

74.1 

80.1 

62.8 

3.6 

100.0 

100.0 

31.3 

93.0 

100.0 

100.0 

100.0 

100.0 

100.0 

100.0 

100.0 

95.8 

100.0 

31.3 

93.0 

100.0 

100.0 

100.0 

73.5 

79.9 

98.0 

98.8 

64.6 

61.1 

97.2 

1.1 

60.7 

50.6 

36.1 

100.0 

91.2 

73.8 

80.0 

99.6 

98.8 

83.6 

17.4 

100.0 

1.4 

56.7 

71.8 

30.7 

100.0 

76.9 

NA 

SA 

NE 

SE 

RS 

AF 

ME 

CH 

AS 

NE&SE 

Topping unit & VDU 

Naphtha processing units 

FCC 

RCC 

HDS  gas oil 

FCC gasoline desulph. 

76.4 

68.3 

84.8 

57.3 

65.2 

74.7 

100.0 

100.0 

44.5 

68.5 

17.0 

70.6 

75.1 

100.0 

98.8 

73.3 

61.1 

Baseline 

76.4 

84.7 

95.9 

44.2 

73.0 

98.3 

55.9 

65.2 

47.4 

91.7 

100.0 

100.0 

100.0 

100.0 

74.4 

100.0 

100.0 

100.0 

100.0 

100.0 

100.0 

95.8 

100.0 

HDT vacuum gas oil 

100.0 

100.0 

100.0 

94.4 

100.0 

100.0 

100.0 

HDT naphtha 

Hydrocraking units 

Residue hydroconversion 

3.8 

1.7 

87.1 

Etherification units 

100.0 

100.0 

Visbreaking unit 

Coking unit 

Hydrogen units 

Topping unit & VDU 

Naphtha processing units 

FCC 

RCC 

HDS  gas oil 

FCC gasoline desulph. 

90.4 

25.5 

83.0 

76.3 

68.3 

86.3 

69.3 

71.5 

57.2 

47.3 

74.3 

100.0 

100.0 

41.5 

71.3 

48.4 

1.3 

60.7 

100.0 

50.6 

53.6 

100.0 

82.5 

71.0 

75.3 

100.0 

98.8 

82.9 

17.4 

1.0 

1.0 

0.5 

100.0 

18.7 

100.0 

100.0 

76.6 

84.6 

99.2 

20.3 

60.7 

69.0 

100.0 
100.0 
Counterfactual  

44.0 

49.6 

98.3 

47.4 

91.7 

100.0 

100.0 

100.0 

84.3 

68.0 

5.0 

70.6 

36.7 

100.0 

100.0 

100.0 

100.0 

74.4 

100.0 

100.0 

100.0 

HDT vacuum gas oil 

100.0 

100.0 

100.0 

100.0 

100.0 

100.0 

100.0 

HDT naphtha 

Hydrocraking units 

Residue hydroconversion 

Etherification units 

Visbreaking unit 

Coking unit 

Hydrogen units 

1.6 

81.6 

100.0 

100.0 

25.1 

62.0 

4.2 

100.0 

76.4 

69.9 

Source: OURSE_QP results. 

1.3 

56.7 

100.0 

71.8 

43.9 

1.5 

1.0 

0.5 

100.0 

5.0 

70.6 

36.7 

100.0 

100.0 

37.4 

100.0 

17.5 

100.0 

100.0 

13.3 

61.6 

100.0 

100.0 

69.0 

65.5 

88.2 

100.0 

100.0 

100.0 

100.0 

100.0 

In Table 5.7 above also the operating costs (OPEX) are reported, which in particular include 

operating  variable  costs  (such  as  costs  of  catalysts,  solvents  and  chemicals)  and  operating 
(but  not  capital)  maintenance  costs.  The  annualised  OPEX  of  the  impact  of  the  FQD  and 

MFD,  according  to  the  scenario  discussed,  are  assessed  to  be  65.8  mln  USD  per  year.  A 
closer  examination  of  the  OPEX  numbers  would  give  the  details  of  potentially  each  and 

every  intermediate production contributions, the details  of which we do not  provide further 
here.  

More simulations were carried out. In particular, separate simulations for the 2000-2005 and 

2005-2010  sub-periods  using  both  calibration  approaches  were  implemented.  Besides,  also 
simulations  taking  the  entire  2000-2010  period  at  once  were  run  as  further  sensitivity 

analysis.  However,  as  discussed  earlier  it  is  likely  that  running  the  entire  2000-2010 
simulations  might  not  account  for  larger  demands  in  2005  and  bigger  change  in  fuels 

qualities  over  the  2000-2005  period  than  in  the  2005-2010  period.  Further,  all  the  two 

41 

 
 
 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
hypothetical  economic  environments  discussed  earlier  were  imposed/considered  in  all  the 
simulations in order to single out the likely impact of the FQD and MFD directives. The final 

annualised  total  CAPEX  and  OPEX  figures  of  all  these  counterfactual  simulations  as 
compared to the results of the relevant baseline scenarios are summarized in Table 5.10. 

Table 5.10: Summary of the total annualised CAPEX and OPEX costs (mln 2008 USD) 

2000-2005 
(1) 

2005-2010 
(2) 

Average of  
(1) and (2) 

2000-2010 

Annualised CAPEX 
Case 1: Old specs in Europe, new specs elsewhere 

341.47 
92.82 

408.35 
102.48 

Case 2: New specs in Europe , old specs elsewhere 

41.63 
24.48 

191.72 
31.44 

Annualised OPEX 
Case 1: Old specs in Europe, new specs elsewhere 

36.11 
37.81 

55.62 
51.80 

Case 2: New specs in Europe , old specs elsewhere 

4.86 
3.83 

15.82 
8.17 

Total annualised CAPEX and OPEX 
Case 1: Old specs in Europe, new specs elsewhere 

377.58 
130.63 

463.97 
154.27 

Case 2: New specs in Europe , old specs elsewhere 

46.49 
28.31 

207.54 
39.61 

475.23 
112.13 

341.81 
38.40 

75.13 
65.78 

26.79 
12.51 

550.36 
177.92 

368.60 
50.91 

143.99 
72.62 

31.13 
32.16 

45.70 
46.55 

5.66 
2.58 

189.68 
119.17 

36.78 
34.74 

Standard rule 
Average cost rule  

Standard rule 
Average cost rule  

Standard rule 
Average cost rule  

Standard rule 
Average cost rule  

Standard rule 
Average cost rule  

Standard rule 
Average cost rule  

          Source: OURSE_QP results; "specs" stands for "fuels quality specifications". 

The results of the two reverse environments are similar in terms of trends of the costs to the 

EU refineries.  Case 2  environment is  in  itself interesting to  consider as  this  gives the costs 
estimates  for  European  refineries  without  fuel  quality  improvement  in  all  non-EU  regions. 

However, given that Case 1 is most relevant for our quantification of the likely costs due to 
the FQD/MFD legislation, we  focus on the output  of  Case 1 only.  Using the average  costs 

figures for the 2000-2005 and 2005-2010 sub-periods (reported in column 4 of Table 5.10) as 
our final estimates, we can conclude that tighter fuels qualities specifications over the 2000-

2012 period for all European refineries cost, on average, from 154.27 to 463.97 mln USD per 
year  of  both  CAPEX  and  OPEX  expenditures.  The  corresponding  upper  bound  could  be 

considered the largest  observed  figure of 550.36 mln USD per  year, obtained for the 2000-
2005  sub-period  using  the  standard  rule  calibration  method.  Average  CAPEX  costs  are 

assessed to range from 102.48 to 408.35 mln USD per year, with an upper bound of 475.23 

42 

 
 
 
 
  
 
 
 
 
 
 
 
 
 
 
mln USD. Finally, the average OPEX are estimated to be in the range of 51.80 to 55.62 mln 
USD per year, and the corresponding upper bound is 75.13 mln USD.      

The  question  is  now  how  realistic  are  these  numbers,  given  that  this  study  is  an  ex  post 

impact assessment study.  In particular, we possess the relevant  refinery data from Solomon 
Associates (2014) and CONCAWE, and thus it is interesting to compare our estimates with 

the observed data. Of course, given that any model is a simplification of the reality, i.e. there 
are  way  too  many  factors  that  influence  business  decisions,  and  these  for  obvious  reasons 

cannot  be  taken  into  account  in  any  type  of  modelling,  ideal  match  cannot  be  obtained. 
Nevertheless,  it  is  instructive  to  compare  figures  referring  to  more  or  less  the  same  costs 

items and explain the differences.  

The observed total costs of investments in new processing units and of their modifications for 
clean  fuels  of  the  EU-28  countries  are  reported  in  the  first  part  of  Table  5.11.  In  Solomon 

Associates' surveys taking place every even year, refineries are also asked about their capital 
expenditures for the study year, one-, two- and three-years prior to the survey date. One item 

of the refineries' capital investments include "investments in new process unit/modifications 
for clean fuels", distinguished by its three components related to investments needed to meet 

gasoline,  diesel/gasoil  and  other  specifications  (we  do  not  consider  here  these  components 
separately). In each study year (bolded in Table 5.11) refineries report investment figures for 

each  of  the  three  previous  years,  and  some  discrepancies  in  reported  figures  occur  in 
overlapping years due to variation in the participating refineries in any given study year, as 

can  be  seen  in  Table  5.11.  We  take  here  a  pragmatic  approach  of  considering  both  the 
maximum and minimum values of the two matching figures reported, which are presented in 

the second block of Table 5.11. If we take the average over 1999-2012 period, the observed 
costs  of  investments  become  656  mln  EUR  per  year  when  using  minimum  values  of  the 

matching  figures,  and  689  mln  EUR  per  year  if  the  maximum  values  are  considered.  The 
upper bound estimate of CAPEX in our modelling assessment was 475.23 mln USD per year, 

which would be roughly  399.3 mln EUR per  year (using the  average of  2000-2012 official 
exchange  rates  from  Eurostat,  which  equals  0.8402  EUR  per  USD).  Hence,  Solomon 

Associates (2014) data report 1999-2012 average figures on European refineries' investments 
costs that are larger than our CAPEX  upper bound estimate by factors of  1.6 to  1.7. These 

differences can be explained as follows. 

Table 5.11: Investments in new process unit/modifications for clean fuels in the EU-28 
(mln EUR) 

2012 

2011 

2010 

2009 

2008 

2007 

2006 

2005 

2004 

2003 

2002 

2001 

2000 

1999 

Total investments  
according to 

134 

385 

549 
537 

673 
699 

883 

668 

43 

 
 
 
 
 
 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
average 
values (actual 
investments) 

942 

790 

699 
683 

779 
846 

1481 
1428 

Investments (min) 

Investments (max) 

134 

134 

385 

385 

537 

549 

673 

699 

883 

942 

668 

790 

683 

699 

779 

1428 

846 

1481 

874 
888 

874 

888 

686 
648 
648 

686 

652 
605 
605 

652 

377 
377 

377 

514 
514 

514 

Notes: Figures on EU-28 actual investments are  obtained from Solomon Associates (2014), by multiplying the number of 
refineries participating in Solomon Associates' surveys and the reported average investments values. All figures are rounded 
to the nearest integer. 

First,  we  should  note  that  our  modelling  exercises  assessed  the  impact  of  fuels  qualities 

changes that took place within the 2000-2010 period. In particular, the most important ones 
were  related  to  the  changes  in  gasoline  and  diesel  sulphur  maximum  limits  that  were 

implemented in 2005 and 2009. For heating oil the relevant limits changes occurred in 2008, 
while  sulphur  limit  change  of  heavy  fuel  oil  happened  much  earlier  in  2003.  Finally,  for 
marine fuels the sulphur limits changes, taken into account by our modelling evaluation, were 

implemented in 2006 (4.5% and 1% sulphur limits) and in 2010 (the change to 3.5% and 1%, 
respectively).  Taking  into  account  this  latest  date  captured  in  our  modelling  assessment,  it 

makes sense for fair comparison purposes to consider the reported investments costs only for 
the period of 1999-2009 and annualise it over the entire 1999-2012 period. This results in the 

observed annual investments costs comparable to OURSE outcomes of 581 mln EUR/year if 
minimum values of the matching cost numbers are chosen and 612 mln EUR/year if instead 

maximum  values  of  the  matching  figures  are  used.    Comparing  these  to  our  upper  bound 
CAPEX estimate of 399.3 mln EUR/year reduces the factor differences by  11% which now 

become 1.45 – 1.53.  

Second, there are items and/or real refinery characteristics that are captured in the observed 
data but not in the OURSE model. These may include the following. 

  There  are  items  included  in  the  observed  investment  costs  figures  that  are  not 
captured  by  the  CAPEX  coefficients  of  the  OURSE  model.  For  example,  our 

personal  communication  with  Solomon  Associates'  team  revealed  that  "investments 
in  the  new  units/modifications    for  clean  fuels  may  well  include  OSBL  [Outside 
Battery Limits] investments if they were needed as part of the project, such as a new 
tankage  and  any  pipework,  but  will  not  include  items  such  as  control  rooms, 

laboratories, or administrative facilities". Costs of new tankage and pipework is not 
captured  by  OURSE  CAPEX  figures,  and  there  could  be  more  factors  that  are  not 

accounted  for  in  our  modelling  framework,  but  still  are  important  in  the  real-life 
refinery functioning and decision making.13  

13 A colleague from IFP Energies nouvelles (Paris, France) with an extensive expertise in both refining industry 
and  OURSE  modelling  indicated  that  these  extra  real  costs  not  accounted  for  by  the  model,  if  indeed  taken 
place,  could amount up to but not more than 20% of the obtained CAPEX estimates.  

44 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
  
  
  
  
  
  
  
  
  
 
 
                                                 
  Similar to the previous observation, yearly capital maintenance costs are not included 
in the model's CAPEX figures.14 On the other hand, the observed investments costs 
include costs of replacement or modification of  a unit that are not  accounted for in 
OURSE.15 

  Finally,  OURSE  has  one  representative  refinery  per  region,  implying  that  all  the 
heterogeneities of individual  refineries  (in terms of complexity, location  advantage, 
size, etc.) that we observe in  real  life  within  one region  are not  fully  accounted for 

(which is an unfeasible task anyways). Thus, the model mainly accounts for common 
characteristics of refineries in each covered region. 

Hence,  a  very  conservative  way  to  account  for  the  discussed  items  missing  in  OURSE 

modelling assessment (see footnotes 13 and 14) could be to multiply the estimated CAPEX 
figure of 399.3 mln EUR/year by 1.35.  This would have yielded a CAPEX estimate of 539 

mln EUR/year that would have decreased the difference factor gap by additional 26% and the 
difference  factor  would  have  been  only  1.1.  We,  however,  do  not  do  such  upward 

adjustments in our final estimates mainly because of the lack of the relevant information but 
at the same time caution the reader about the mentioned underestimation possibility. Another 

explanation  of  this  remaining  gap  is  that  OURSE  costs  estimates  are  expressed  in  constant 
2008  prices,  while  those  based  on  Solomon  Associates  (2014)  data  are  given  in  current 

prices.  All  in  all,  given  our  discussions  above  and  the  fact  that  a  modelling  world  cannot 
account for all factors, we may conclude that our OURSE_QP outcomes reasonably evaluate 

the likely impact of the FQD/MFD directives on the EU refineries in terms of incurred costs.    

Finally, we present the costs estimates in terms of total crude processed. This normalisation is 
useful also for comparison of the modelling outcome and the observed figures, given that the 

processed crude figures will also vary in these cases. More importantly, the normalised costs 
estimates  are  easier  to  understand  in  many  contexts.  As  a  normalising  factor  we  chose 

processed crude oil, but not total throughputs given that the last does not include exactly the 
same components in the model and the observed figures (for example, refined products used 
as intermediates are not modelled in OURSE). The figures of Case 1  in Table 5.10 are first 

normalised  by  processed  crude  oil.  Given  that  the  NE  and  SE  regions  include  non-EU 
countries as well, the obtained processed crude had to be slightly adjusted downwards. Using 

the  IEA  data  we  found  that  the  shares  of  the  EU  countries  in  the  NE  and  SE  crude 
consumption were 0.93 and 0.87, respectively, in both years of 2005 and 2010. Consequently, 

14  Related  to  the  previous  footnote,  the  rough  bias  from  ignoring  capital  maintenance  costs  could  be  around 
10%-15%  of  the  estimated  CAPEX  figures  (source:  personal  communication  with  a  colleague  from  IFP 
Energies nouvelles). We note that these are extra possible differences in addition to the bias just indicated.  
15 In Solomon Associate's (2014)  Input Form and Instructions describing the data it is written that the item of 
investments costs in new units also "includes any applicable replacement or  modification of a  unit if the cost 
exceeds 50% of the total replacement costs or the project increases capacity greater than 40% of that unit".  

45 

 
 
 
 
    
                                                 
these numbers were used to obtain crude oil processed in NE and SE solely by EU countries. 
The figures then were converted to EUR using the average of EUROSTAT official exchange 

rates over the covered period of 2000 to 2012 (which is equal to 0.8402 EUR per USD), and 
then divided by 7.33 (a conversion factor that is based on worldwide average gravity) to get 

costs per barrel of processed crude oil. These results are reported in Table 5.12.  

Table 5.12: CAPEX and OPEX in eurocents per barrel of processed crude oil (at 
constant 2008 prices) 

2000-2005 
(1) 

2005-2010 
(2) 

Average of  
(1) and (2) 

2000-2010 

Standard rule 
Average cost rule  

Standard rule 
Average cost rule  

Standard rule 
Average cost rule  

8.9 
2.2 

1.4 
1.3 

10.3 
3.5 

          Source: OURSE_QP results. 

Annualised CAPEX 

6.9 
1.9 

7.9 
2.1 

Annualised OPEX 

0.7 
0.8 

1.1 
1.0 

Total annualised CAPEX and OPEX 

7.6 
2.7 

9.0 
3.1 

2.9 
1.5 

0.9 
1.0 

3.8 
2.5 

It turns out that all the discussions on comparing OURSE costs results in absolute terms with 

the  observed  CAPEX  figures  that  we  made  extensively  above,  hold  exactly  also  for  the 
normalised costs. Thus we do not repeat this discussion here again. We conclude that tighter 

fuels  qualities  specifications  over  the  2000-2012  period  for  all  European  refineries, 
according to the OURSE estimates, cost on average from 3 to 9 eurocents (at constant 2008 

prices) per barrel of processed crude per year of both CAPEX and OPEX expenditures. The 
corresponding  upper  bound  is  10.3  eurocents  per  barrel  of  processed  crude  per  year. 

Average  annual  CAPEX  costs  are  assessed  to  range  from  2  to  8  eurocents  per  barrel  of 
processed crude, with an upper bound of 9 eurocents. Finally, the average annual OPEX are 

estimated  to  be  1  eurocent  per  barrel  of  processed  crude  oil,  with  an  upper  bound  of  1.4 
eurocents.  Of  course,  the  possibility  of  CAPEX  costs  underestimation  due  to  the  reasons 

discussed  earlier  in  detail  is  still  valid,  but  generally  as  mentioned  earlier  the  modelling 
results are considered to be reasonable.      

5.2.1.2  Benefits 

The  benefits  of  the  FQD  and  MFD  directives  in  terms  of  minimizing  human  influence  on 
environment  are  straightforward  and  are  studied  extensively  elsewhere  (see  e.g.  the  impact 
assessment  documents  of  the  FQD,  or  Le  Tertre  et  al.,  2014).  These  are  also  discussed  in 

46 

 
 
 
 
  
 
 
 
  
 
 
more  detail  in  the  relevant  overview  chapters  of  the  REFIT  study.  Quantification  of  such 
environmental benefits (e.g. reduction in GHG emissions due to the use of cleaner fuels by 

consumers  and/or  producers)  is,  however,  outside  the  scope  of  the  OURSE  modelling 
framework  and  rather  needs  a  different  type  of  modelling  framework  (e.g.  models  used  in 

transport economics, economy-wide simulation models, econometric input-output models, or 
computable general equilibrium framework). Therefore, the issue of incurred benefits due to 

the implementation of the FQD and MFD requirements will not be further considered in this 
study.   

5.2.1.3  Trade and competitiveness  

The  simulations  results  discussed  in  this  section  do  not  fix  the  baselines'  trade  flows  of 

refined products as was required before. Instead now we focus on the likely impact of fuels 
international 
trade 
specifications  change  on 
interregional 
competitiveness  of 
that  considering 
the  EU  refineries.  From 
competitiveness within a trade setting only is a rather narrow definition of competitiveness in 

flows,  hence  also  on 
the  outset  we  note 

general, as other factors may well influence a refinery's competitive position both in domestic 
and global markets.  

Table 5.13: Production of the refineries (standard calibration rule) 

NA 

SA 

NE 

SE 

RS 

AF 

ME 

CH 

AS 

Light 
Medium 
Heavy 

Light 
Medium 
Heavy 

Light 
Medium 
Heavy 

Light 
Medium 
Heavy 

486.8 
333.9 
126.2 

1.0 
-0.3 
-2.3 

469.8 
326.6 
107.0 

-0.4 
-8.4 
4.1 

72.4 
81.1 
47.4 

0.5 
0.1 
2.6 

70.2 
79.9 
45.7 

-0.1 
0.9 
-0.2 

  Source: OURSE_QP results 

Counterfactual production  (mln tonnes, 2005) 

174.8 
211.8 
79.8 

70.2 
101.0 
57.3 

69.4 
89.3 
78.4 

25.0 
52.5 
36.7 

Counterfactual - Baseline (mln tonnes, 2005) 

0.7 
-0.5 
1.7 
Counterfactual production (mln tonnes, 2010) 

-0.7 
1.6 
-0.5 

-0.8 
0.9 
0.0 

0.5 
-0.1 
-1.2 

161.8 
213.3 
61.5 

67.3 
91.9 
44.8 

75.2 
99.9 
76.2 

37.8 
51.2 
27.0 

Counterfactual - Baseline (mln tonnes, 2010) 

0.6 
8.0 
2.7 

0.6 
0.0 
2.0 

-0.1 
0.8 
-6.2 

0.6 
-0.2 
-3.7 

82.2 
135.7 
102.5 

-2.0 
-1.8 
0.2 

105.8 
147.8 
98.7 

1.6 
2.3 
-3.8 

104.5 
117.9 
31.8 

0.3 
0.0 
0.0 

147.4 
171.0 
34.1 

-0.1 
-0.5 
-1.0 

240.2 
368.3 
121.5 

0.6 
0.1 
-0.7 

263.2 
394.7 
96.4 

-2.7 
-2.8 
5.6 

In Table 5.13 production figures and the difference between those of the counterfactual and 
baseline  scenarios  are  reported.  The  counterfactual  represents  Case  1  hypothetical 
environment (trade calibration is based on standard rule). What we observe is that Europe as 
a whole in both periods increases its production of light, medium and heavy products, except 

47 

 
 
 
 
  
 
  
 
 
 
 
 
for no change in light products production in the first period. This is primarily caused by the 
fact  that  in  response to  the relaxed sulphur specifications on  all fuels, in  the counterfactual 

scenario  EU  refineries  are  able  to  process  more  throughputs,  including  heavier  crude.  For 
example,  in  the  2010  scenario  the  total  throughputs  in  North  Europe  and  South  Europe 

increase by 2.6% and 1.3%, respectively.    

Table 5.14: Changes in trade (counterfactual - baseline, mln tonnes), 2010 

NA 

SA 

NE 

SE 

RS 

AF 

ME 

CH 

AS 

Total 

Light products 

0.00 
0.09 
0.77 
0.45 
0.06 

-0.79 
0.58 

0.00 
-1.02 

-1.34 
-2.36 

-2.63 
0.86 

NA 
SA 
NE 
SE 
RS 
AF 
ME 
CH 
AS 
Total 

NA 
SA 
NE 
SE 
RS 
AF 
ME 
CH 
AS 
Total 

NA 
SA 
NE 
SE 
RS 
AF 
ME 
CH 
AS 

0.16 
0.00 

0.16 

-1.94 

-9.25 

0.22 

0.00 
0.22 

-0.28 
0.19 

0.00 

-0.14 
0.00 

0.00 

-0.09 

-0.14 

Medium products 

0.02 
0.47 
1.44 
-0.17 

1.76 

-0.10 
-0.10 

0.44 

0.91 

-0.16 

0.36 

0.06 

-1.94 

-7.98 

-0.10 

Heavy 

-1.22 

-2.44 

-1.85 

-5.25 

-0.12 

0.00 
0.26 

0.08 
0.22 

0.00 
-0.48 

-0.48 

0.00 

-1.61 
-1.61 

0.02 
0.46 

1.12 

0.13 
0.95 

-3.36 
-0.04 

-0.09 

-3.27 

0.16 
0.09 
0.58 
0.64 
0.08 
0.47 
1.44 
-0.17 
-0.89 
2.39 

-10.76 
-1.02 

-0.12 
0.75 
0.00 
0.68 

-2.85 
-13.32 

1.12 
-2.63 
0.86 
-3.24 
-6.19 
-3.66 
-3.84 

Total 

-3.00 

-2.44 

-1.85 

-5.25 

0.99 

-5.56 

-17.58 

  Source: OURSE_QP results. Trade calibration is based on the standard specification rule.  

An  increase  in  European  fuel  production  has  straightforward  implication  in  terms  of  EU 

import  dependency.  The  product  balances  tell  us  that  per  product  we  have  the  following 
accounting identity: production + imports = exports + consumption. Given that consumption 

figures  are  fixed  in  our  counterfactual  scenarios,  in  terms  of  changes  the  last  identity  boils 
down  to  (change  in  production)  =  (change  in  net  exports).  Thus,  our  finding  of  higher 

production  implies  also  that  the  EU  net  exports  position  for  light,  medium  and  heavy 
products would have improved with the relaxed fuel quality specifications in place.  This also 

48 

 
 
 
 
 
 
 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
  
 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
  
 
  
  
  
  
  
  
  
  
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
  
 
largely  explains  the  resulting  trade  flows.  As  an  example,  the  differences  between  the 
counterfactual  and  baseline  trade  flows  for  2010  are  given  in  Table  5.14.  One  can  observe 

from  Table  5.14  that  indeed  with  relaxed  sulphur  regulation  Europe  increases  its  gasoline 
exports to the US. This is possible because even if Europe would have had its 2005 gasoline 

maximum sulphur limit of 50 ppm  (instead of 10 ppm), that  would be still stricter than the 
corresponding 2010 US limit of 80 ppm.  Similarly, in general, the EU decreases its overall 

imports dependency of middle and heavy products. The decrease in middle products imports 
comes mainly by reducing EU imports from the US, because of higher transportation costs, if 

compared  to  imports  from  Russia.  A  relatively  small  increase  of  middle  products  imports 
from Middle East reflects EU imports of jet fuel.  

One  of  the  indicators  of  industry  performance  or  industry  competitiveness  is  the  so-called 

relative  trade  balance  (RTB),  which  measures  the  trade  balance  relative  to  total  trade  in  a 
sector and for product i  is defined as (see e.g. European Commission, 2009): 

𝑅𝑇𝐵𝑖 =

𝑋𝑖 − 𝑀𝑖
𝑋𝑖 + 𝑀𝑖

 ,                                                                     (9) 

where 𝑋𝑖 and 𝑀𝑖 are, respectively, exports and imports of product i. Of course, it should be 
noted that negative RTB reflecting negative trade balance is not necessarily a bad thing, since 

imports also contribute to the domestic economy and stimulate production in (usually) more 
than one sector. In addition, trade balances reflect domestic and foreign demand. Thus, "this 

indicator  does  not  exclusively  reflect  external  competitive  strength;  it  also  indicates  a 
difference between domestic and international demand" (EC, 2009, p. 136).  

Table 5.15: Europe's relative trade balance (RTB) for all refined products 

2000 

2005 

2010 

2000 

2010 

Baseline 
Counterfactual 
Counterfactual – Baseline 

-0.391 
-0.391 
0.000 

Standard rule 
-0.208 
-0.194 
0.014 

-0.190 
-0.125 
0.065 

2005 
Average cost rule 
-0.314 
-0.300 
0.014 

-0.436 
-0.436 
 0.000 

-0.242 
-0.228 
0.015 

             Source: OURSE_QP results. Counterfactual scenario corresponds to Case 1 environment. 

For Europe as a whole the RTB index for all petroleum products were computed both for the 

baseline and counterfactual scenarios. The corresponding results are reported in  Table 5.15. 
In both scenarios for all years the European refinery RTB's are negative, indicating that more 

petroleum  products  were  imported  to  Europe  than  exported  by  Europe  to  the  rest  of  the 
world.  However,  in  both  scenarios  the  negative  values  of  the  RTB's  are  decreasing  (in 

absolute value), indicating improvement of the refining industry performance over time. This 

49 

 
 
 
 
 
 
 
  
 
 
period-to-period improvement is somewhat better in the counterfactual scenario.  Next if we 
compare the counterfactual with the baseline scenario, we find that, in general, the external 

competitive strength of Europe is higher in the counterfactual scenario, and this difference is 
increasing over time. Hence, according to the RTB indicator, the European refining industry 

would  have  been  somewhat  more  internationally  competitive  in  a  counterfactual  situation 
where tighter fuels quality specifications would have not been imposed in Europe. Again we 

remind  that  this  result  is  not  exclusively  about  the  external  competitive  strength  of  Europe 
only, but also reflects the optimal reaction of all (representative) refineries world-wide to the 

assumed counterfactual changes in Europe.     

5.2.2  Impact assessment of changes in products demand 

The  most  relevant  consequence  of  the  Renewable  Energy  Directive  (RED)  and  Energy 

Taxation Directive (ETD) for the EU refineries is lower demand for refined products due to, 
respectively, (a) substitution of the traditional fuels by biofuels, and (b) reduced fuels demand 

because of higher  fuel  prices  caused by  an increase in  minimum  energy  tax levels in  those 
Member States where such a change in minimum taxation was binding in comparison to their 

actual national taxation levels. Therefore, in both cases one may argue that without the RED 
and ETD in place, one would expect higher demand for refined products. Since both of the 

mentioned directives have an immediate impact of demand, their likely impact will be jointly 
assessed  in  this  modelling  study.  In  what  follows  we  first  quantify  the  changes  in  fuels 

demand  caused  by  the  RED  and  ETD,  and  then  evaluate  the  impact  of  the  counterfactual 
increased demand on the EU refining business.      

5.2.2.1  Demand change due to the RED and ETD 

Quantifying  fuels  demand  without  biofuels  is  rather  straightforward:  rather  than  using  the 

various biofuels targets indicated in the so-called National Renewable Action Plans, which in 
the majority of cases were not met by Member States in time, we collect real data on biofuels 

consumption  and  assume  it  to  be  instead  extra  demand  for  traditional  relevant  fuel  in  a 
counterfactual scenario without biofuels. Hence, using the IEA World Renewable and Waste 

Energy  Statistics  database  we  obtained  the  consumption  (i.e.  domestic  supply)  figures  of 
biogasoline and biodiesels for all OURSE regions, including EU countries separately.16 The 

16  The  IEA  definitions  of  the  two  biofuels  are  as  follows.  Biogasoline  includes  bioethanol  (ethanol  produced 
from  biomass  and/or  the  biodegradable  fraction  of  waste),  biomethanol  (methanol  produced  from  biomass 
and/or  the  biodegradable  fraction  of  waste),  bioETBE  (ethyl-tertio-butyl-ether  produced  on  the  basis  of 
bioethanol: the percentage by volume of bioETBE that is calculated as biofuel is 47%) and bioMTBE (methyl-
tertio-butyl-ether  produced  on  the  basis  of  biomethanol:  the  percentage  by  volume  of  bioMTBE  that  is 
calculated as biofuel is 36%). Biodiesels consist of biodiesel (a methyl-ester produced from vegetable or animal 
oil,  of  diesel  quality),  biodimethylether  (dimethylether  produced  from  biomass),  Fischer  Tropsch  (Fischer 

50 

 
 
 
 
 
 
                                                 
relevant data that was first made consistent with the OURSE classification is shown in Table 
5.16. For our three modelling periods the obtained 1998-2012 IEA data was transformed as 

follows: for 2000 we chose the average of 1998-2002 IEA data, for 2005 – average of 2003-
2007  IEA  data,  and  for  2010  –  average  of  2008-2012  IEA  data.    Note  that  we  have  first 
converted the relevant IEA data expressed in kt to energy units (i.e. in ktoe). 17  

In  Table  5.16  we  also  give  the  percentages  of  biogasoline  and  biodiesels  consumption  in, 
respectively,  the  relevant  baseline  demand  for  gasoline  and  diesel  oil.  For  this  purpose  the 

demand data were also  expressed in  energy terms using the relevant  data reported in  Table 
6.1. What we observe is that the "share" of biogasoline in gasoline demand for North Europe 

was  0.6%  in  2005  and  increased  to  3.1%  in  2010,  while  the  respective  figures  for  South 
Europe were 0.3% and 1.0%. The "penetration rates" of biodiesels were higher than those of 

biogasoline.  That  is,  in  NE  in  2005  the  size  of  biodiesels  consumption  relative  to  diesel 
demand was 2.2% which further increased to 5.4% by 2010. The respective figures for SE are 

0.6% and 4.1%.  

Table 5.16: Biogasoline and biodiesels consumption (ktoe) 

Biogasoline (ktoe) 

Biodiesels (ktoe) 

2000 
3939 
3070 
67 
14 

48 

2005 
9585 
3433 
518 
103 

335 
176 

2010 
23463 
3856 
2193 
300 

3 
1060 
652 

2000 
16 

552 
40 

0 

2005 
434 
70 
2616 
375 

52 
66 

2010 
1619 
2004 
7133 
2814 
23 

130 
1119 

Biogasoline/(Baseline gasoline demand) 

Biodiesels/(Baseline diesel demand) 

0.9% 
8.3% 
0.1% 
0.0% 

0.0% 

2.0% 
9.1% 
0.6% 
0.3% 

0.7% 
0.1% 

5.2% 
8.3% 
3.1% 
1.0% 

0.0% 
1.4% 
0.5% 

0.0% 

0.5% 
0.1% 

0.0% 

0.3% 
0.2% 
2.2% 
0.6% 

0.1% 
0.1% 

1.1% 
3.6% 
5.4% 
4.1% 
0.1% 

0.2% 
0.9% 

NA 
SA 
NE 
SE 
RS 
AF 
CH 
AS 

NA 
SA 
NE 
SE 
RS 
AF 
CH 
AS 

           Source: IEA World Renewable and Waste Energy Statistics. 

Table 5.16 also shows that, if compared to other regions, NE and SE were top consumers of 

biodiesels (relative to the corresponding demand for diesel oil), while in terms of biogasoline 

Tropsch  produced  from  biomass),  cold  pressed  biooil  (oil  produced  from  oil  seed  through  mechanical 
processing only) used straight as road diesel or for electricity and heat generation. 
17 The relevant conversion factors were obtained from European Commission (2006). 

51 

 
 
 
 
 
 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
                                                                                                                                                        
South America is the top consumer with a very large (compared to other regions) and stable 
"share" of 8% to 9% in SA's gasoline demand. This is, of course, not surprising as Brazil is 

known  to  be  one  of the largest  world  producers  of  biogasoline  and  in  fact  is  the  sole  main 
contributor to biogasoline consumption in the SA region.     

Quantification of reductions in fuels demands due to the ETD is more complicated. There are 

potentially  many  options  on  the  table,  including  using  various  large-scale  complicated 
models.  Given that there will never be  real fuel  consumption figures on the impact of the 

ETD available and there will always be uncertainties in their estimates, we adopted a rather 
straightforward and conservative (in the sense of giving maximum possible demand impact) 

approach, which is discussed in detail and implemented in the ETD overview chapter. Thus, 
these  discussions  are  not  repeated  here.  It  is  just  worth  mentioning  that  in  translating  the 

assessed  price  changes  of  gasoline  and  diesel  into  the  corresponding  quantity  demand 
changes,  the  estimates  of  gasoline  and  diesel  price  elasticities  of  transport  fuel  demand 

reported in an extensive study of Dahl (2012) were used. Dahl (2012) presents two estimates 
of  gasoline  demand  price  elasticities,  one  set  of  which  takes  into  account  the  impact  of 

introduction of turbo-charged fuel injection diesel engines in Europe causing a switch from 
gasoline use to diesel consumption. In particular, the author "increased gasoline demand price 

elasticities by 50% for all those countries that have seen a strong switch towards diesel fuel 
with  gasoline  consumption  decreasing"  (Dahl,  2012,  p.  7).  We  used  the  higher  reported 

gasoline  demand  price  elasticities  that  tries  to  explicitly  account  for  European  fuel 
consumption switch from gasoline to diesel. 

Of course, not all Member States were affected by the ETD requirements, as one of the main 

objectives of changing minimum taxation levels was harmonizing taxation of energy products 
across  Member  States.  The  ETD  overview  chapter  analyses  the  relevant  price  and  demand 

impacts  on  Member-States  level,  but  here  we  investigate  the  impact  at  the  more  aggregate 
level  of  North  Europe  and  South  Europe.  The  OURSE-relevant  final  estimates  of  demand 

reductions obtained in the ETD analysis are reported in Table 5.17.  

Table 5.17: Estimated demand reductions in NE and SE due to ETD (in kt) 

Gasoline   
DieselOil  

Gasoline   
DieselOil  

Demand change (kt) 

Share in baseline demand 

2005 

2010 

2005 

2010 

North European EU countries (EU1) 

102.15 
72.51 

114.61 
175.65 

0.12% 
0.06% 

South European EU countries (EU2) 

84.14 
99.08 

122.24 
408.93 

0.24% 
0.15% 

0.17% 
0.13% 

0.45% 
0.60% 

D/G ratios with ETD 

D/G ratios without ETD 

52 

 
 
 
  
 
 
 
  
 
 
 
EU1 
EU2 
EU1 & EU2 

1.468 
1.858 
1.576 

1.994 
2.384 
2.099 

1.467 
1.856 
1.575 

1.993 
2.389 
2.100 

Source: Own calculations. D/G stands for the ratio of diesel demand to gasoline demand. 

The first observation to be made from demand reduction figures in Table 5.17 and Table 5.16 

is that the ETD had much lower impact on demands compared to the RED, in particular, at 
the entire European level. This is to be expected since the Member States directly affected by 

the ETD newly set minimum taxation levels  were mostly small economies compared to  the 
other  EU  national  economies  (see  the  ETD  overview  chapter).  The  demand  reductions 

relative to the corresponding baseline demands shows a maximum percentage change of only 
0.17% in North Europe for gasoline in the modelling period of 2010. For South Europe the 

maximum change of 0.60% was estimated for diesel oil demand during the same modelling 
period of 2010. Note that the demand impacts (relative to baseline observed demand levels) 

in SE were always larger than those is NE by factors ranging roughly from 2.0 to 4.5.     

Figure 5-1: The trends of European demands for gasoline and diesel (baselines data)  

220

200

180

160

140

120

100

80

1.10 

1.55 

2.10 

2.2

2.0

1.8

1.6

1.4

1.2

1.0

Gasoline

Diesel

D/G ratio

2000

2005

2010

Note: "D/G ratio" refers to the ratio of European diesel demand to European gasoline demand, whose scale is 
given in the right-hand side vertical axis. The unit for the left-hand side vertical axis is mln tonnes. 

In the bottom of Table 5.17 also the ratios of diesel demand to gasoline demand (D/G ratio) 

as observed with the ETD in place, and D/G ratios in the counterfactual environment without 
the ETD are given. The same figures for the observed demand are illustrated in Figure 5-1. In 

particular,  it  shows  that  the  ratio  of  observed  (i.e.  baseline)  European  demand  for  diesel  to 
that for gasoline was 1.10 in 2000, 1.55 in 2005, and further increased to 2.10 by 2010. The 
respective ratios for all EU countries only are very similar and were equal to 1.12, 1.57 and 
2.10  for  the  mentioned  periods,  respectively.  This  mismatch  of  the  EU  diesel-gasoline 

demand with  the relevant  supply of the  gasoline-oriented EU refineries  is by now a widely 

53 

 
 
 
 
 
 
 
known issue that has implication for European refining industry (e.g. overcapacity problem). 
However,  comparing  the  observed  and  counterfactual  D/G  ratios  in  Table  5.17  we  observe 

very  negligible  changes  in  these  ratios  from  -0.07%  to  0.21%.  This  implies  that  we  do  not 
find any discernable impact of the ETD in terms of the European consumption switch from 

gasoline  to  diesel  at  this  level  of  aggregation.  Further  details  of  this  impact  at  the  Member 
State level is discussed in detail the ETD overview chapter.   

Similar  to  the  RED  modelling,  in  the  counterfactual  scenario  without  the  EDT  the  fuels 

demand  would  be  higher  by  the  amounts  shown  in  Table  5.17.  To  conclude,  the  European 
demand  change  figures  due  to  both  the  ETD  and  RED  are  for  easier  comparison  purposes 

presented  together  in  Table  5.18.  The  last  two  rows  in  this  table  show  that  the  overall 
European gasoline and diesel demand changes are mainly driven by biofuels substitution of 

these products, implying that any aggregated European results related to gasoline and diesel 
demand  changes  are  to  the  large  extent  caused  by  biofuels  consumption  impact  on  the 

refining  industry  rather  than  that  of  the  EU  minimum  energy  taxation  changes.  The  only 
exception  is  for  gasoline  demand  change  in  the  first  period  in  SE,  where  the  impact  of  the 

ETD  and  RED  is  roughly  the  same  (i.e.  the  respective  contributions  to  the  overall  2005 
demand  change  are  46%  and  54%).  While  in  NE  biofuels  policy  has  dominating  effect 

responsible for at least 82.9% of the demand changes, in SE this impact is somewhat smaller, 
where  the  ETD  accounts  for  12.1%  to  46.1%  of  the  demand  changes  for  gasoline  and/or 

diesel.  

Table 5.18: Summary of demand changes due to ETD and RED (mln tonnes) 

Demand change due 
to ETD 

Demand change due 
to RED 

Demand change due to 
both ETD and RED 

2005 
0.10 
0.07 
0.08 
0.10 

2010 
0.11 
0.18 
0.12 
0.41 

2005 
0.49 
2.59 
0.10 
0.37 

2010 
2.09 
7.06 
0.29 
2.79 

2005 
0.60 
2.66 
0.18 
0.47 

Contributions of the ETD and RED to total demand change 

17.1% 
2.7% 
46.1% 
21.1% 
23.9% 
5.5% 

5.2% 
2.4% 
29.9% 
12.8% 
9.1% 
5.6% 

82.9% 
97.3% 
53.9% 
78.9% 
76.1% 
94.5% 

94.8% 
97.6% 
70.1% 
87.2% 
90.9% 
94.4% 

100% 
100% 
100% 
100% 
100% 
100% 

2010 
2.20 
7.24 
0.41 
3.20 

100% 
100% 
100% 
100% 
100% 
100% 

NE 
NE 
SE 
SE 

Gasoline   
DieselOil  
Gasoline   
DieselOil  

Gasoline   
NE 
DieselOil  
NE 
Gasoline   
SE 
SE 
DieselOil  
NE&SE  Gasoline   
NE&SE  DieselOil  

            Source: Own calculations. 

The  proportions  of  total  demand  changes  due  to  both  the  ETD  and  RED  relative  to  the 

relevant baseline demand levels for gasoline and diesel oil are also given in Table 5.19. These 
figures are used in increasing (giving shock to) the exogenous gasoline and diesel demands in 

54 

 
 
 
 
 
 
 
  
  
  
  
 
OURSE assessment exercises. One can readily see from the table that larger demand impacts 
are  estimated  for  the  second  period  for  both  fuels,  with  corresponding  factor  differences 

ranging  from  2.5  to  6.6.  On  the  other  hand,  larger  changes  are  obtained  for  diesel  demand 
compared to gasoline demand changes for both European regions and both periods, where the 

relevant factor differences range between 1.4 and 3.2.       

Table 5.19: Total demand increase relative to the baseline demand (%) 

2005 
0.70% 
0.51% 
2.21% 
0.71% 

2010 
3.23% 
1.49% 
5.49% 
4.66% 

Gasoline   
NE 
Gasoline   
SE 
DieselOil  
NE 
SE 
DieselOil  
 Source: Own calculations. 

5.2.2.2  Costs 

Given that we have two sub-period demand changes due to the relevant fuel price increase, 

we  run  two  sets  of  counterfactual  scenarios  of  2000-2005  and  2005-2010.  Here  we  do  not 
consider the counterfactual covering the entire period 2000-2010, as it makes more sense to 

implement  demand changes for the two of its sub-periods separately  due to  higher demand 
levels in 2005. Not accounting for this trend in demand would be particularly inappropriate in 

analysing demand shocks scenarios.  

It  is  clear  that  higher  exogenous  demand  within  the  model  will  always  result  in  higher 
CAPEX and OPEX expenditures. By simply running the model with higher demands, it was 

found  that  capital  investments  similar  to  those  in  our  earlier  assessment  of  the  FQD/MFD 
directives are required. This in essence means that even though demand for fuels are higher 

without the RED and ETD, refineries still have to obey by the European fuel specifications 
which require additional investments. As a result this would imply that refineries "benefitted" 

from  lower  demand,  otherwise  they  would  have  had  to  make  additional  investments. 
However, one can argue that this makes little sense since those costs are already taken into 

account  in  the  FQD/MFD  assessment.  We  solve  this  problem  by  changing  the  original 
capacities in the counterfactual scenario in such a way that the new solution does not require 
any  new  (endogenous)  investments  in  processing  units.  Technically,  such  evaluation 

procedure consists of two steps. In the first step the new counterfactual scenarios are run with 
corresponding  original  capacities  that  results  in  additional  investments.  Then  in  the  second 

step the same counterfactual are implemented but with adjusted capacities, where we add the 
new  investments  from  the  first  step  outcome  to  the  original  capacities.  This  results  in  zero 

investments in processing units, and also lower OPEX compared to the first-step results.  

55 

 
 
 
 
  
  
 
 
 
The question now is  what  are refineries' costs  related to  the RED and ETD directives?  We 

consider forgone earning due to lower demand as "costs" incurred by the EU refineries. That 
is,  in  the  counterfactual  scenario  without  the  RED  and  ETD,  the  EU  refineries  would  have 

made  more  (gross  and  net)  revenues  due  to  higher  demand  for  gasoline  and  diesel.  To 
estimate  these  net  forgone  revenues  (i.e.  profits),  the  baseline  and  counterfactual  (from  the 

mentioned  second  step  estimation)  results  for  all  refined  products  are  valued  at  their 
corresponding  shadow  prices  as  provided  endogenously  by  the  model.  Then,  per  region 

(refinery), by subtracting from these revenues the costs of:  

  crude and feedstock (atmospheric residue) purchases, including transportation costs, 

  other inputs, such as natural gas, ethanol, methanol, etc., and 

  OPEX costs, 

we  obtain  the  estimates  of  the  forgone  net  earnings.  Thus,  the  (opportunity)  costs  of  the 

refineries due to the RED/ETD is calculated as 

             Net forgone earnings due to RED/ETD 

= (Total revenues without RED/ETD  − Total costs without RED/ETD)

− (Total revenues with RED/ETD  − Total costs with RED/ETD).  

To  give  a  full  picture  of  our  derivations,  we  provide  the  estimation  details  on  an  arbitrary 
chosen  scenario  of  the  second  period  (average  2010)  demand  change  when  the  standard 

calibration approach is used. These are reported in Table 5.20.    

Table 5.20: Details of forgone net earnings derivations (2010, standard calibration rule) 

Production (mln tonnes) 
without 
with 
RED/ETD 
RED/ETD 

Shadow prices (USD per tonne) 

Revenue (mln USD) 

with 
RED/ETD 

without 
RED/ETD 

with  
RED/ETD 

without 
RED/ETD 

NE 

SE 

NE 

SE 

NE 

SE 

NE 

SE 

NE 

SE 

LPG 
Naphta 
Gasoline 
JetFuel 
HeatingOil 
DieselOil 

ResFuelOil 
Bitumen 

PetCoke 

NE 

22.9 
42.4 
95.9 
31.7 
60.6 
113.1 

9.5 
12.8 

5.2 

SE 

11.8 
10.2 
44.6 
12.2 
29.1 
50.5 

16.2 
9.4 

5.3 

22.9 
42.4 
98.1 
31.7 
60.6 
120.3 

9.5 
12.8 

5.2 

11.8 
10.2 
45.0 
12.2 
29.1 
53.7 

16.2 
9.4 

5.3 

MarinBunk 

36.4 

17.2 

36.4 

17.2 

814 
763 
787 
763 
754 
764 

636 
544 

619 

636 

1197 
712 
717 
711 
707 
717 

682 
610 

609 

682 

817 
763 
788 
763 
755 
765 

633 
540 

609 

633 

1119 
720 
732 
720 
715 
726 

673 
596 

589 

673 

18635 
32373 
75455 
24166 
45656 
86364 

6065 
6963 

3237 

14065 
7286 
32015 
8695 
20589 
36221 

11049 
5723 

3239 

18717 
32381 
77328 
24172 
45707 
92018 

6040 
6908 

3185 

13151 
7374 
32964 
8801 
20827 
38983 

10903 
5598 

3131 

23157 

11745 

23059 

11590 

Total revenues 

322071 

150627 

329516 

153321 

Costs (mln USD) 

Crude & feedstock  
Crude transportation 

with  
RED/ETD 

without 
RED/ETD 

285445 
5093 

135319 
4336 

292298 
5208 

137728 
4410 

Other inputs costs 

9335 

3674 

9546 

3748 

56 

 
 
 
 
      
 
 
 
 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
CAPEX costs 
OPEX costs 

6379 
4904 

800 
2012 

6379 
4985 

800 
2048 

Total costs 

311156 

146141 

318415 

148734 

Net revenues 

10915 

4487 

11101 

4587 

Forgone net earnings due to RED/ETD (mln USD) 

186 

101 

Source: OURSE_QP results. 

Baseline  (with  RED/ETD)  and  counterfactual  (without  RED/ETD)  production  levels  for 
North and South Europe are given in the second to fifth columns in Table 5.20. Then shadow 

prices  of  the  corresponding  fuels  are  reported.  We  do  not  use  exogenous  prices  (i.e.  prices 
excluding duties and taxes) because this would be inconsistent with the model outcomes (e.g. 

the  shadow  prices  of  the  same  fuel  are  different  between  the  baseline  and  counterfactual 
scenarios  reflecting  different  economic  environments  of  the  two  cases).  These  two  sets  of 

outcomes are used to compute the gross revenues of the two regions, which are presented in 
the last four columns of the top part of Table 5.20.  The bottom part of the table provides the 

details  of  the  associated  costs.  Subtracting  the  counterfactual  net  revenue  from  that  of  the 
baseline scenarios gives us finally the estimate of net forgone earnings of 287 mln USD per 

year for all European refineries. These are estimates per year, because the estimated demand 
changes due to both RED and ETD are average changes over the two periods of 2003-2008 

and 2009-2012/13. Note that the net forgone earnings in NE are larger than thos in SE mainly 
because the positive changes in gasoline and diesel demands are larger in NE than in SE.    

The aggregate European outcomes of the net forgone earnings (or costs) due to the RED/ETD 

are summarized in  Table  5.21. Taking the averages of the two periods,  the estimates of  the 
RED  and  ETD-related  costs,  incurred  by  the  EU  refineries  in  the  form  of  net  forgone 

earnings, are assessed to range from 200 to 205 mln USD per year. The corresponding upper 
bound  estimate  is  298  mln  USD  per  year,  which  corresponds  to  the  EU  refineries'  forgone 

earnings in the second period. The first period estimates are lower than their corresponding 
second-period costs because in the second period the size of demand reductions is higher (see 

Table  5.19).  Recall  also  our  earlier  discussions  that  these  changes  are  to  the  large  extent 
driven by the RED rather than ETD impact. 

Table 5.21: Forgone earnings and net margins of the EU refineries due to RED and 
ETD 

2000-2005 
(1) 

2005-2010 
(2) 

Average of  
(1) and (2) 

Standard calibration rule 
Average cost rule 

Standard calibration rule 

Net forgone earnings due to RED/ETD (mln USD per year) 
200 
287 
113 
205 
298 
111 
Forgone net margins due to RED/ETD (USD/bbl) 
3.0 

3.8 

3.4 

57 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
 
 
 
 
 
 
  
 
 
 
 
 
 
  
  
  
  
  
  
 
 
 
  
 
 
Average cost rule 

3.9 

3.1 

3.5 

Source: OURSE_QP results. Net margins are computed as extra net earnings per extra crude and feedstock processed by 
European refineries (i.e. extra = counterfactual – baseline). 

The  question  is  now  how  realistic  are  the  derived  costs  estimates  due  to  the  RED/ETD 

directives.  To  answer  this  question  we  compute  the  underlying  net  margins  of  these 
estimates,  that  is  the  obtained  net  earnings  are  divided  by  the  extra  European  throughput 

needed to satisfy the changes in demand. These are reported in the bottom part of Table 5.21. 
The implied net margins thus range on average from 3.4 to 3.5 USD per barrel of throughput, 

with  a  maximum  net  margin  of  3.9  USD/barrel  of  throughput.  These  are  entirely  realistic 
figures, if not  somewhat overestimated, and are in general  consistent with the HIS margins 

data. This means that our results do make sense, given that one could simply calculate the EU 
refineries' forgone net  earnings (costs) by multiplying the average observed net  margins  by 

the  extra  required  throughput  for  the  relevant  period,  and  the  results  would  be  have  been 
close to what is reported in Table 5.21.  

As in case with the FDQ/MFD directives, we provide the final results presented in Table 5.21 

in  EUR  and  per  processed  crude.  These  are  reported  in  Table  5.22.  Thus,  the  net  forgone 
earnings  of  the  EU  refineries  due  to  the  RED  and  ETD  directives  are  assessed  to  be,  on 

average, roughly 4 eurocents per barrel of processed crude oil per year, with an upper bound 
of 6.2 eurocents per barrel of processed crude.    

Table 5.22: Net forgone earnings due to the RED and ETD directives, in eurocents per 
processed crude oil (constant 2008 prices) 

2000-2005 
(1) 

2005-2010 
(2) 

Average of  
(1) and (2) 

Standard calibration rule 
Average cost rule 

2.1 
2.2 

5.8 
6.2 

4.0 
4.2 

                  Source: OURSE_QP results. 

If one is interested in the separate contribution of the ETD and RED directives, then from the 

bottom part of Table 5.18 one can find that the overall average contributions of the ETD and 
RED  impact  on  the  entire  EU  demand  changes  over  2000-2012  period  are  11%  and  89%, 

respectively.  These  numbers  can  be  used  to  correspondingly  allocate  the  total  EU-wide 
estimated  costs  to  ETD  and  RED.  Hence,  the  annual  net  forgone  earnings  of  the  EU 

refineries due to the RED directive are estimated to be, on average, 3.65 eurocents per barrel 
of  processed  crude  oil.  The  remaining  0.45  eurocents  per  barrel  of  crude  are  thus  the 

average annual net forgone earnings of the EU refineries due to the ETD directive. 

58 

 
 
 
 
 
 
  
 
 
5.2.2.3  Capacity utilisation rates and the EU trade dependency 

Higher demand with fixed exports and imports automatically implies higher production levels 
in the EU regions. This also means higher capacity utilisation rates of the EU refineries. The 

percentage changes in utilisation rates of crude distillation unit (CDU) in North Europe and 
South Europe due to lower demands related to the RED and ETD directives relative to those 

in  the  counterfactual  scenarios  are  reported  in  Table  5.23.  It  shows  that  compared  to  the 
counterfactual  CDU  utilisation  rates  without  the  RED  and  ETD  in  place,  on  average  the 

baselines CDU utilisation rates decreased from 0.9% and 1.9%. Thus, the RED and ETD are 
found  to  cause  a  reduction  of  the  EU  refineries'  utilisation  rates,  on  average,  by  0.9%  to 

1.9% over the entire 2000-2010 period.  These figures are higher (in absolute value) in NE 
than in SE by average factor of 1.8 to 2.2, which as discussed earlier is caused mainly due to 

higher  penetration  of  biofuels  in  NE  than  in  SE  (see  Table  5.18)  and  also  due  to  larger 
demand  changes  in  NE  as  caused  by  the  ETD  directive.  The  maximum  reduction  of  3.1% 

utilisation  rate  is  observed  in  the  second  sub-period  of  2005-2010  in  NE,  which  is  due  to 
larger relevant changes in demand. On average, the reduction in utilisation rates in the second 

sub-period is assessed to be larger than those in the first sub-period by factor of 2.2 to 4.2.  

Table 5.23: Reduction in utilisation rates of CDU due to RED and ETD (%) 

2000-2005 
(1) 

2005-2010 
(2) 

Average of 
(1) and (2) 

-1.4 
-0.3 
-0.8 

-0.6 
-0.4 
-0.5 

Standard calibration rule 
-2.1 
-1.7 
-1.9 
Average cost calibration rule 
-3.1 
-1.3 
-2.2 

-1.7 
-1.0 
-1.4 

-1.9 
-0.9 
-1.4 

NE 
SE 
Average 

NE 
SE 
Average 

                     Source: OURSE_QP results. CDU stands for crude distillation unit. 

In terms of trade the RED and ETD theoretically have two opposite effects. On the one hand, 
larger  European  conventional  diesel  demand  has  to  be  satisfied,  and  given  that  European 

refineries are mainly gasoline-oriented, Europe dependence on diesel imports (notably, from 
Russia)  is  expected  to  increase.  On  the  other  hand,  more  European  conventional  gasoline 

demand  can  reduce  Europe  dependence  on  gasoline  exports  markets  (notably,  to  the  US). 
However,  it  should  be  noted  that  these  are  not  two  separate  effects.  If  European  refineries 

increase  their  production  of  gasoline  in  response  to  higher  gasoline  demand,  this  will  also 
allow them to increase their diesel  production. Thus, there is  no straightforward one-to-one 

59 

 
 
 
 
 
  
 
 
 
 
link  of  the  mentioned  imports  and  exports  effects  to  the  exogenous  changes  in  diesel  and 
gasoline demands, respectively. This calls for modelling assessment.  

The relevant OURSE results with free trade are reported in Table 5.24. In terms of gasoline 

exports there seems to be a rather small impact. For example, in the environment without the 
RED  and  ETD,  on  average  over  the  2000-2012  period,  European  gasoline  exports  are 

estimated  to  decrease  only  by  1%  (according  to  the  average  cost  calibration  method;  the 
standard  rule  essentially  does  not  show  any  discernable  average  impact).  However,  the 

impact is somewhat larger in terms of the EU diesel imports dependency. It is assessed that in 
the counterfactual situation without the RED and ETD in place, European imports of diesel 

oil  would  have  increased,  on  average  over  the  2000-2010  period,  by  1%  to  6.3%,  with  an 
upper bound of 8.9% increase in imports corresponding the 2005-2010 period (average cost 

calibration  rule).  These  flows  are  all  related  to  Russian  exports  of  diesel  oil.  Thus,  if  one 
focuses on trade dependency issues, reduction in diesel imports dependency of the EU (from 

Russia)  can  be  considered  as  the  most  noticeable  (obvious)  EU-wide  benefit  that  the  RED 
and ETD directives brought about.      

Table 5.24: Changes in European gasoline exports and diesel imports without the ETD 
and RED 

2000-2005 
(1) 

2005-2010 
(2) 

Average of  
(1) and (2) 

Change in European gasoline exports (%) 

Standard calibration rule 
Average cost calibration rule 

-0.3% 
-0.6% 

0.5% 
-1.5% 

Change in European diesel imports (%) 

Standard calibration rule 
Average cost calibration rule 

0.4% 
3.7% 

1.6% 
8.9% 

0.1% 
-1.0% 

1.0% 
6.3% 

              Source: OURSE_QP results.        

5.2.3  Impact assessment of changes in pollution limits 

The  detailed  discussion  of  the  Large  Combustion  Plants  Directive  (LCPD),  Integrated 
Pollution  Prevention  and  Control  Directive  (IPPCD),  and  Air  Quality  Directive  (AQD)  are 

given in the relevant overview chapters of the REFIT study. The main message taken from 
these discussions relevant for OURSE modelling is that during the time period analysed here 
there were no strict and definitive legal obligations imposed on industrial activities (including 

refining of mineral oil and gas) with regard to their emissions reduction. In addition, the long-
term  objectives  of  the  directives  were  implemented  very  differently  across  Member  States 

during  the  time  period  analysed  here.  Hence,  there  do  not  exist  objective  and  well-defined 

60 

 
 
 
 
 
  
 
 
 
   
policy  instruments  (measures)  that  could  be  used  in  our  OURSE  evaluation  of  the  likely 
impact  of the  LCDP,  IPPCD and AQD.  Therefore, instead we use a pragmatic approach of 

applying the observed emission intensities (i.e. emissions generation per processed crude) in 
the modelling exercises, the details of which is given below. From the list of emissions that 
are targeted by the mentioned directives, in OURSE only sulphur dioxide (SO2) emissions are 
modelled, hence only the impact of SO2 emissions regulations will be considered here.  

In all the baseline scenarios, used also earlier for the evaluation of the FQD/MFD impact, we 
imposed exogenously on the EU refineries SO2 emissions intensities per processed crude oil. 
These  are  derived  using  Solomon  Associates  (2014)  data  on  SOx  emissions  per  net  input 
figures.  The  last  were  multiplied  by  the  appropriate  ratio  (Net  raw  material  input)/(Total 
crude processed) in order to obtain SO2 emissions intensities per processed crude. These are 
reported in Table 5.25 below.  

Table 5.25: Sulphur emissions intensities per processed crude (gram per tonne) 

Year 

2000 
2004 
2005 
2006 
2008 
2010 
2012 

SOx emissions intensities using 
Solomon Associates (2014) data 

Chosen SO2 emissions intensity limits 
for the European refineries 

784.3 

855.0 
721.3 
558.7 
450.1 

1490.0 

784.3 

450.1 

Note:  The  second  column  gives  SOx  in  gram  per  tonne  processed  crude,  computed  on  the  base  of  Solomon  Associates 
(2014). The third column reports SO2 intensity limits for the NE and SE refineries used in OURSE_QP modelling. No other   
SO2 intensity limits were applied to other regions, except for North America, where in the absence of further information it 
was assumed to be 1490 g per tonne of processed crude in all periods of 2000, 2005 and 2010. 

The choice of the values of SO2 intensities for the three modelling period were based on the 
corresponding  intensities  of  SOx  emissions.  We  adopt  a  conservative  approach  here  in  the 
sense of choosing the smallest (relevant in terms of time) intensity value. For 2005 we choose 
the intensity value of 784.3 g SO2 per tonne processed crude, which is the SOx intensity of 
2004  as  obtained  from  Solomon  Associates  (2014).  Note  that  we  do  not  use  the  higher 
intensity  of 855.0  reported for 2006 (hence, our  approach is  conservative as we choose the 
stricter intensity value).   Similarly, for 2010 period's SO2 intensity we choose the obtained 
SOx  intensity  of  450.1  reported  for  2012.  For  the  modelling  years  close  to  2000  for  which 
Solomon  Associates  (2014)  data  were  missing,  we  used  the  relevant  information  from 

CONCAWE  (2010)  survey  for  1998.  In  particular,  CONCAWE's  survey  of  77  refineries 
shows  that  in  1998  the  total  crude  intake  was  502  mln  tonnes  and  total  sulphur  emitted  at 
refinery (from all sources) was 374 kt. These result in SO2 emission intensity of 1490 g per 

61 

 
 
 
 
 
 
 
 
 
 
  
 
tonne of processed crude. This value was also imposed on the North American refinery for all 
three periods in the absence of further relevant information, while no such restrictions were 

imposed on the refineries of the remaining regions.   

5.2.3.1  Costs 

In  constructing  the  counterfactual  scenarios,  we  relax  SO2  emission  intensity  limits.  These 
are set to the initial year values, and the choice of year depends on the scenario considered. 
The  structure  of  such  choices  is  exactly  similar  to  that  reported  in  Table  5.6  for  the 
FQD/MFD  impact  assessment  case.  That  is,  substituting  "Fuels  qualities"  in  this  table  to 
"SO2  emissions  intensities"  will  provide  the  full  overview  of  the  way  the  intensities  have 
been relaxed in each of the six considered counterfactual scenarios.  

As  before  in  all  simulation  runs  we  keep  the  trade  flows  of  refined  products  fixed  at  their 

appropriate  baseline  levels.  However,  if  we  run  the  counterfactuals  without  any  other 
constraints, OURSE results  in  more investments costs than  in  the environment  with  stricter 
observed  SO2  emissions  intensities  imposed.  This  result  might  be  at  first  glance 
counterintuitive, but makes sense if we  recall that in OURSE world-wide refining costs are 

minimized  and  not  only  those  of  the  European  regions.  Thus  what  happens  then  is  that  it 
becomes  optimal  for  NE  and  SE  with  relaxed  SO2  emissions  limits  to  switch  to  cheaper 
crudes with higher sulphur content, which however require additional capital investments to 
be  refined  to  petroleum  products.  For  example,  in  various  simulations  scenarios  European 

regions are found to replace 10% to 25% of Brent crude with higher sulphur content crudes 
such  as  Arabian  Light  and  Arabian  Heavy.  We,  however,  find    such  extent  of  crude 

substitution effect unrealistic (in particular, due to the short-run nature of the analysis), hence 
impose constraints on residue catalytic cracking (RCC) and naphtha-processing capacities of 

the  NE  and  SE  regions  only  that  do  not  allow  new  investments  in  these  units  above  their 
corresponding baseline levels. Another set of scenarios are run fixing additionally crude mix 

supply to the NE and SE regions, while similar restriction is not imposed on other regions.     

Before providing the costs estimates, it is interesting to look in which scenarios the sulphur 
emissions limits will be binding. To give a more policy-relevant flavour to the relevant issue, 

it is interesting to consider the appropriate emission limit values as advocated in the LCPD 
and  IPPCD  (currently,  Industrial  Emissions  Directive).  According  to  the  LCPD  that  came 

into force in November 2002, for example, Member States had to take appropriate measures 
in order to ensure that operation permits given to the EU refineries (with thermal  input size 

between 50 to 300 MW) contain certain conditions that are (more or less) consistent with the 
adopted sulphur emission limit value (ELV) of 1700 mg SO2 per Nm3 (O2 content of 3%) of 
liquid fuels use/burnt. In OURSE model there is SMOKE variable (measured in Nm3) which 

62 

 
 
 
 
 
 
is  generated  from  the  use  of  liquid  and  refinery  fuels,  gas  and  coke  along  all  the  relevant 
intermediate inputs processes within the refinery.18 Using this variable output together with 
the appropriate SO2 emissions estimates of the model, for all baseline scenarios the observed 
counterpart of the sulphur ELVs referred to as refinery combustion bubble concentrations for 
SO2 as produced by the OURSE_QP model were computed, which are reported in Table 5.26.  

All the "observed" SO2 concentrations show the expected tendency of decreasing over time, 
with the exception of the intermediate period (for both calibration options) for NE, where in 
2000 the SO2 bubble concentrations are lower than those in 2005. As can be seen from Table 
5.26, this will also imply that from 2000 to 2005 sulphur emissions in NE increase. Given our 

conservative  approach,  we  will  ignore  the  results  related  to  2000-2005  counterfactual. 
Otherwise,  one  could  state  that  relaxing  SO2  emissions  intensities  is  equivalent  to  relaxing 
SO2 ELVs to the values which, depending on the scenarios chosen, will be rather close to the 
relevant SO2 concentration values reported in Table 5.26.          

Table 5.26: Baselines' refinery combustion bubble concentrations for SO2 (mg/Nm3) and 
SO2 emissions (kt) 

2000 

1007 
1696 
1200 

247.4 
162.4 
409.8 

NE 
SE 
NE&SE 

NE 
SE 
NE&SE 

2005 

Standard rule 
2010 
SO2 mg per Nm3 

2000-2010 

2000 

2005 

Average cost rule 
2010 
SO2 mg per Nm3 

2000-2010 

796 
938 
838 

1010 
1663 
1200 
SO2 emissions (kt) 
261.5 
106.9 
368.4 

189.9 
94.0 
283.9 

797 
918 
833 

190.2 
92.7 
282.9 

1037 
1694 
1217 

265.7 
162.9 
428.6 

729 
913 
781 

1301 
1652 
1398 
SO2 emissions (kt) 
338.7 
163.3 
502.0 

182.9 
91.8 
274.8 

738 
954 
798 

183.7 
92.3 
276.0 

    Source: OURSE_QP results. "NE&SE" gives the overall results for both North Europe and South Europe.  

It  is  interesting  to  compare  OURSE-based  SO2  concentrations  estimates  reported  in  Table 
5.26 with the observed ones estimated by CONCAWE (2010) based on their sulphur surveys 
of the European refineries for the years of 1998, 2002 and 2006. In particular, CONCAWE 
(2010, p. 11) reports the weighted average (using refinery production as weights) combustion 
bubble  SO2  concentrations  of  1116  mg/Nm3,  791-816  mg/Nm3  and  594  mg/Nm3, 
respectively,  for  the  years  of  1998,  2002  and  2006.19  Further,  the  presented  cumulative 
distributions  of  the  SO2  concentrations  (Figure  5)  show  that  35%,  25%  and  22%  of  the 

18 In its calculation it is assumed, for example, that the flue gas volume of FCC coke is equal to 11000 Nm3 per 
tonne,  or  of  liquid  fuel  is  11500  Nm3  per  tonne,  etc.  Somewhat  detailed  explanation  of  the  calculation 
methodology of the refinery combustion bubble SO2 concentration is discussed in CONCAWE (2010, p.10 and 
Appendix 1).    
19  CONCAWE  (2010),  however,  caution  about  citing  the  2006  value  as  "this  was  based  on  only  80%  of  the 
overall refinery oxidised sulphur emission due to missing stack and fuel information in some responses" (p.11). 

63 

 
 
 
 
 
 
  
 
 
 
                                                 
surveyed refineries in 1998, 2002 and 2006 had the bubble concentration values, respectively, 
in the ranges of 1000-5200 mg/Nm3, 1000-4200 mg/Nm3 and 1000-3180 mg/Nm3. Thus, our 
estimates  of  the  European  refineries'  SO2  concentrations  are  not  unrealistic  and  are  largely 
consistent with those reported in CONCAWE (2010).  

We start with the quantitative estimates of the crude mix substitution effects lying behind the 
SO2  restrictions  relaxation  policies  and  the  relevant  costs  estimates  to  be  presented  soon. 
These are percentage changes of crude type (and feedstock) purchases in the counterfactual 

scenarios relative to the relevant baselines in NE and SE, and are presented in Table 5.27. As 
an  example,  consider  the  2005-2010  simulation  results  from  the  average  cost  calibration 

approach,  where  we  observe  a  decrease  in  Brent  purchase  by  -2.5%  and  a  simultaneous 
increase in purchase of Arabian Light by 2.9% in North Europe. This is essentially crude mix 
substitution effect taking place because of the relaxed SO2 emissions regulations, i.e. it now 
becomes  optimal  for  the  EU  refineries  to  purchase  somewhat  larger  quantities  of  cheaper 

sourer crude than continue using more expensive sweeter crude. In general, all the reported 
estimates of crude mix substitution effects are reasonable (which range from -2.5% to 2.9%), 

since  large  changes  are  likely  to  be  unrealistic,  at  least,  for  relatively  short-run  analysis 
covered in this study.    

Table 5.27: Crude mix substitution effect in NE and SE (percentage changes) 

Brent 
(0.32% S) 

ArLght 
(1.86% S) 

ArHeav 
(2.69% S) 

Forcad 
(0.18% S) 

Conden 
(0.01% S) 

FeedStock 

Total  
throughput 

NorthEurop
e 
SouthEurop
e 

NorthEurop
e 
SouthEurop
e 

NorthEurop
e 
SouthEurop
e 

NorthEurop
e 
SouthEurop
e 

2005-2010, Standard calibration rule (%) 

0.7 

-1.1 

-0.1 

1.2 

0.1 

-0.6 

0.1 

-0.9 

2005-2010, Average cost calibration rule (%) 

2.9 

-1.6 

0.6 

0.1 

-0.2 

0.0 

0.0 

-0.1 

-0.3 

0.1 

2000-2010, Standard calibration rule (%) 

-0.1 

-0.1 

0.1 

0.0 

0.0 

-1.9 

2000-2010, Average cost calibration rule (%) 

1.4 

-0.1 

-0.3 

0.0 

0.2 

0.0 

-1.0 

-0.1 

0.6 

-0.4 

-0.2 

0.1 

0.7 

-0.6 

0.9 

0.0 

0.01 

-0.07 

-0.03 

-0.06 

0.03 

-0.07 

-0.04 

-0.02 

-1.2 

0.0 

-2.5 

2.8 

-1.1 

0.0 

-1.7 

0.0 

Source: OURSE_QP results.  

64 

 
 
 
 
 
  
 
 
 
 
 
The summary of the incurred CAPEX costs, and crude and feedstock switching costs due to 
stricter SO2 emissions regulations, as assessed by the OURSE_QP model, is reported in Table 
5.28. It shows that the impact of the EU and Member State-level SO2-related regulations is 
assessed  to  cost  EU  refineries  in  terms  capital  investments,  as  captured  by  the  OURSE 

model,  on average over  the entire 2000-2012 period, 33 mln USD annually, with an upper 
bound estimate of  38.7  mln USD.  The  crude and switching costs  account  for the burden of 

switching to low-sulphur crude and fuels (as feedstock) to be taken by EU refineries in order 
to limit their sulphur emissions. These essentially quantify the costs of crude mix substitution 

effects discussed above. Low-sulphur switching costs are assessed to be, on average over the 
entire 2000-2012 period, 29.3 to  51.1  mln USD  per year, with an upper  bound estimate of 

53.4 mln USD.  Hence, even though the degree of crude mix substitution effects reported in 
Table 5.27 might have seemed small at the first glance, their underlying costs are not small at 
all.  All  in  all,  the  annual  total  costs  associated  with  the  EU  and  Member  State-level  SO2-
related  regulations  are  assessed,  on  average  over  the  2000-2010  period,  to  range  between 

62.3 and 84.5 mln USD, with an upper bound estimate of 92.1 mln USD. 

Table 5.28: CAPEX and crude/feedstock switching costs due to SO2 emissions 
regulations (mln 2008 USD per year) 

2005-2010 
(1) 

2000-2010 
(2) 

Average of  
(1) and (2) 

Standard calibration rule 
Average cost calibration rule 

Standard calibration rule 
Average cost calibration rule 

Standard calibration rule 
Average cost calibration rule 

           Source: OURSE_QP results.  

32.99 
33.34 

38.67 
37.43 

CAPEX costs(mln USD per year) 
27.31 
29.26 
Crude and feedstock switching costs (mln USD per year) 
19.07 
53.44 
Total costs (mln USD per year) 
46.38 
82.70 

78.23 
86.27 

62.31 
84.48 

39.56 
48.84 

29.32 
51.14 

Typical  investments  that  make  up  the  above  mentioned  CAPEX  include  investments  in 
vacuum  gasoil  hydrotreatment  (HDT)  and  naphtha  processing  units.  As  an  example, 
additional savings in new units expressed in mln tonnes per year due to relaxed SO2 emission 
limits for 2010-2000 simulation are reported in Table 5.29.  

Table 5.29: Differences in new investments, 2010-2000 scenarios (mln tonnes/year) 

NA 

SA 

NE 

SE 

RS 

AF 

ME 

CH 

AS 

Topping unit & VDU 
Naphtha processing units 
FCC 
RCC 

-0.7 

-1.7 

0.0 

0.0 

0.0 
0.0 
0.0 

0.0 
0.0 
0.0 
0.0 

0.0 
0.0 
0.0 
0.0 

0.0 
0.0 

0.0 

NE& 
SE 

Total 

-2.4 

0.0 
-2.4 
0.0 
0.0 

65 

 
 
 
 
  
 
 
 
 
 
  
  
  
  
  
  
  
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
HDS  gas oil 
FCC gasoline desulphur. 
HDT vacuum gas oil 
HDT naphtha 
Hydrocraking units 
Residue 
hydroconversion 
Etherification units 
Visbreaking unit 
Coking unit 
Claus 
Hydrogen units 

0.0 

0.0 

0.0 

-1.0 

0.0 

0.0 
0.0 
0.0 

0.0 
0.0 

0.0 

0.0 

0.0 

0.0 

0.0 

0.0 

0.0 
0.0 
0.0 

0.0 

0.0 

0.0 
0.0 

0.0 

0.0 

0.0 

-1.7 

0.0 

0.0 

0.0 

0.0 

0.0 

0.0 

0.0 
0.0 
-1.0 

0.0 

0.0 
0.0 
0.0 

-0.1 

-3.5 

-1.0 

0.0 

0.0 

-3.4 

Total difference 

0.0 

0.0 

-1.7 

Note: CAPEX difference = Counterfactual CAPEX – Baseline CAPEX. Trade calibration is based on the average cost rule. 

Now  we  return  to  the  discussion  of  to  what  extent  our  CAPEX  estimates  are  realistic.  We 

have  Solomon  Associates  (2014)  data  on  capital  investments  on  "Refinery  emissions  and 
effluent"  which  include  "costs  related  to  fuels  refinery-based  environmental  items,  such  as 

wastewater treating and atmospheric emissions, and related to the handling and treatment of 
solid and hazardous wastes". Similarly, we have data from recent CONCAWE survey related 

to  the  REFIT  study  on  "Total  air  emissions  abatement  capital  investments",  which  include 
costs related to diverse abatement measures adopted/installed by the EU refineries in order to 
reduce emissions of SOx, NOx, carbon monoxide (CO), dust and metal emissions, and volatile 
and  organic  compound  (VOC)  emissions.  The  last  data  includes  also  costs  on  waste  water 

treatment, which if compared to costs on air emissions abatement shows that the share of air 
emissions abatement capital investments in total air emissions and wastewater treatment for 

EU refineries is, on average, 83%. However, Solomon Associates (2014) include also costs 
related  to  handling  and  treatment  of  solid  and  hazardous  wastes,  hence  the  share  of  air 

emissions  related  costs  in  their  data  must  be  lower  than  83%.  However,  we  do  not  have 
information  on  the  exact  costs  contribution  related  to  SO2  emissions  reduction  only.  A 
personal communication with a CONCAWE member suggested to use a figure of 50% "as a 
rough estimate of the share of CAPEX for SOx emissions abatement measures in  ''Total air 

emissions  abatement  capital  investments",  basing  this  estimate  on  the  relevant  data  for 
Repsol  Cartagena  emissions  abatement  capital  investments  figures.  Using  this  figure 
CONCAWE data shows an estimate of annual SOx emissions abatement CAPEX of 124 mln 

EUR/year obtained as an average of 1998-2012 investments. Applying 30% and 40% shares 
(these share are less than 50% figure used above, because of extra cost due to "related to the 

handling  and  treatment  of  solid  and  hazardous  wastes")  to  1998-2012  Solomon  Associates 
(2014)  figures  on  "Refinery  emissions  and  effluent"  investments  costs  netted  of  wastewater 
treatment costs, gives SO2 emissions abatement CAPEX estimates ranging between 97 to 129 
mln  EUR/year  according  to  the  observed  data.  Therefore,  our  upper  estimate  of  SO2 
emissions abatement CAPEX of roughly 35 mln EUR/year is lower than the relevant values 
discussed above by factors ranging from 2.8 to  3.7. Hence, it is likely that our estimates of 

66 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
  
  
 
SO2 emissions abatement costs are underestimated. The reason for such underestimation lies 
in  the  fact  that  the  OURSE  model  does  not  capture  all  the  relevant  measures 
adopted/installed by refineries in real life in order to reduce SO2 emissions.  

Finally, for easier readability/comparability purposes as with other directives, the total costs 
figures  from  Table  5.28  were  translated  to  normalized  figures  expressed  in  eurocents  per 

processed crude. These are reported in Table 5.30, and show that the annual total costs of EU 
refineries  due  to  SO2  emissions  regulations,  as  captured  by  OURSE,  are  assessed  to  be 
roughly 2 eurocents per processed crude.  

Table 5.30: Total costs due to SO2 emissions regulations in eurocents per processed 
crude oil 

2005-2010 
(1) 

2000-2010 
(2) 

Average of  
(1) and (2) 

1.6 
1.8 

0.9 
1.7 

1.3 
1.8 

Standard calibration rule 
Average cost calibration rule 
Source: OURSE_QP results. 

5.2.3.2  Benefits 

The benefits of abiding by various air emissions regulations are obvious: air emissions should 
be  reduced  by  the  industrial  activities  and/or  final  consumers.  Since  SO2  emissions  are 
modelled in OURSE, such benefits in terms of SO2 emissions reduction by refineries can be 
assessed.  For  all  the  above  considered  scenarios  the  relevant  results  are  presented  in  Table 
5.31.  It  reports  the  total  percentage  decrease  in  SO2  emissions  in  the  baseline  environment 
compared to those in the counterfactual scenarios with weaker environmental regulations.  

Table 5.31: Decrease in SO2 emissions with regulations (% change per period) 

Simulation  Calibration approach 

2005-2010 

2000-2010 

Standard calibration rule 

Average cost calibration rule 

Standard calibration rule 

Average cost calibration rule 

Source: OURSE_QP results. 

NE 
-13.8 
-25.8 
-12.7 
-25.7 

SE 
-31.3 
-32.4 
-28.0 
-32.5 

NE&SE 
-20.5 
-28.2 
-18.4 
-28.1 

From Table 5.31 the following conclusions can be made: 

  The overall  benefits  of legislation acts  on SO2 emissions  regulation, notably  LCPD, 
IPPCD  and  AQD,  are  assessed  to  be  in  the  range  of  12.7%  to  32.5%  reductions  of 

67 

 
 
 
 
 
  
 
 
 
 
SO2  emissions  generated  by  the  EU  refineries  in  North  and  South  Europe  over  the 
covered  period.    The  overall  European  figures  show  SO2  emissions  reduction  of 
18.4% to 28.2% over the entire 2000-2010 period.  

  The incurred benefits in South Europe are larger than those in North Europe by factor 
of 1.3 to 2.3. This finding most probably has to do with the fact that there was more 
room in reduction of sulphur dioxide emissions for South European refineries than for 

refineries located in North Europe.  

Finally,  the  estimates  of  the  size  of  SO2  emissions  reduction  can  be  computed  easily  by 
combining the relevant OURSE_QP results reported in Table 5.26 and Table 5.31, which we 

will not discuss further here.  

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6  Appendix A: Tables 

Table 6.1: Observed demand for refined products used in OURSE baseline scenarios 

Product 

NA 

SA 

NE 

SE 

LPG        
Naphta     
Gasoline   
JetFuel    
Kerosene   
HeatingOil 
DieselOil  
ResFuelOil 
Lubricant  
Bitumen    
PetCoke    
MarinBunk  
Total 

LPG        
Naphta     
Gasoline   
JetFuel    
Kerosene   

67.1 
18.6 
418.4 
96.7 
5.0 
98.1 
127.0 
74.1 
10.9 
36.5 
13.8 
36.5 
1002.5 

62.6 
20.0 
447.4 
94.0 
5.1 

14.0 
11.9 
35.4 
8.9 
0.6 
16.9 
40.3 
18.5 
1.3 
3.7 
5.9 
5.6 
163.2 

14.2 
10.3 
35.9 
8.2 
0.8 

15.7 
41.0 
97.9 
42.0 
5.7 
87.5 
102.0 
28.3 
5.6 
13.9 
5.4 
32.5 
477.5 

18.4 
41.9 
85.0 
45.8 
5.4 

13.5 
12.6 
39.6 
13.0 
0.4 
32.2 
49.0 
48.9 
2.7 
7.3 
8.3 
13.6 
241.0 

12.8 
9.7 
35.5 
14.9 
0.4 

AF 

ME 

CH 

AS 

Total 

6.9 
1.5 
23.0 
11.7 
2.9 
11.9 
22.7 
14.0 
1.1 
1.8 
0.2 
7.0 
104.8 

9.0 
0.7 
27.5 
11.8 
3.7 

9.2 
2.6 
36.6 
19.8 
7.5 
29.0 
31.6 
43.3 
1.4 
5.2 
0.3 
14.6 
201.1 

11.4 
2.9 
49.1 
18.9 
9.5 

12.0 
22.0 
35.6 
11.6 
1.3 
43.2 
25.3 
30.7 
4.3 
3.9 
4.6 
6.1 
200.5 

18.2 
29.0 
49.1 
16.1 
2.0 

43.9 
82.3 
108.8 
91.8 
49.2 
92.6 
110.5 
108.1 
6.5 
14.5 
6.9 
39.8 
755.1 

46.9 
105.8 
121.4 
90.0 
52.2 

189.9 
201.6 
829.1 
306.1 
73.0 
434.5 
522.5 
404.1 
37.3 
92.7 
46.9 
155.7 
3293.5 

204.2 
231.9 
889.4 
312.0 
79.5 

RS 

2000 

7.7 
9.1 
33.9 
10.7 
0.4 
23.1 
14.1 
38.2 
3.5 
5.9 
1.4 
0.0 
147.9 
2005 

10.6 
11.5 
38.4 
12.4 
0.4 

71 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
  
  
  
HeatingOil 
DieselOil  
ResFuelOil 
Lubricant  
Bitumen    
PetCoke    
MarinBunk  
Total 

LPG        
Naphta     
Gasoline   
JetFuel    
Kerosene   
HeatingOil 
DieselOil  
ResFuelOil 
Lubricant  
Bitumen    
PetCoke    
MarinBunk  
Total 

100.1 
148.0 
73.2 
9.3 
38.3 
25.1 
33.4 
1056.5 

56.6 
15.6 
431.0 
77.0 
4.6 
90.4 
146.5 
34.9 
8.6 
28.1 
17.2 
34.3 
944.8 

22.0 
45.5 
14.4 
1.4 
3.6 
7.0 
7.3 
170.6 

16.0 
10.8 
44.3 
10.2 
1.2 
30.7 
54.7 
16.7 
1.6 
4.6 
9.0 
8.7 
208.4 

83.1 
120.3 
23.2 
5.0 
14.2 
6.8 
39.3 
488.3 

18.8 
42.4 
68.3 
45.9 
4.5 
69.7 
131.9 
16.1 
4.4 
12.8 
5.2 
36.4 
456.5 

33.1 
66.3 
34.4 
2.7 
9.2 
11.2 
16.2 
246.6 

11.7 
10.2 
27.3 
16.1 
0.4 
29.1 
68.5 
13.9 
2.2 
9.4 
9.8 
17.2 
215.9 

23.7 
14.9 
25.2 
3.9 
6.7 
1.2 
0.0 
149.0 
2010 

12.2 
12.7 
47.5 
14.0 
0.4 
22.3 
19.3 
14.6 
2.3 
6.2 
1.7 
1.6 
154.7 

15.1 
28.1 
17.6 
1.7 
2.6 
1.5 
6.1 
125.5 

11.2 
1.0 
34.6 
12.4 
4.6 
18.8 
37.3 
15.8 
1.8 
2.2 
2.2 
5.7 
147.6 

36.9 
41.3 
50.3 
1.6 
8.1 
0.6 
17.8 
248.5 

11.6 
5.0 
58.6 
19.9 
12.3 
47.6 
50.5 
51.3 
0.7 
6.6 
0.4 
21.7 
286.2 

64.8 
42.9 
34.7 
8.0 
7.2 
8.6 
10.7 
291.2 

20.6 
41.2 
72.2 
23.5 
2.4 
80.4 
72.5 
16.9 
11.5 
10.4 
12.3 
21.1 
384.9 

98.1 
115.6 
85.2 
6.5 
14.5 
12.3 
47.9 
796.5 

58.4 
124.1 
134.5 
79.8 
52.5 
98.6 
126.0 
68.1 
6.9 
16.5 
13.1 
59.8 
838.1 

476.9 
622.9 
358.2 
40.1 
104.6 
74.4 
178.7 
3572.7 

217.1 
263.1 
918.3 
298.7 
82.8 
487.7 
707.2 
248.2 
40.1 
96.7 
70.9 
206.5 
3637.1 

Note: Unit is expressed in million metric tonnes. NA – North and Central America, SA – South/Latin America, 
NE – North Europe, SE – South Europe, RS – Russia and other CIS countries, AF – Africa, ME – Middle East, 
CH – China, and AS – other Asia and Oceania.   

Table 6.2: Observed demand shares of the EU countries in OURSE regions of Europe 

Product 
LPG        
Naphta     
Gasoline   
JetFuel    
Kerosene   
HeatingOil 
DieselOil  
ResFuelOil 
Lubricant  
Bitumen    
PetCoke    
MarinBunk  
Total 

2010 

2000 

2005 
EU in NE  EU in SE  EU in NE  EU in SE  EU in NE  EU in SE 
63.0% 
78.6% 
88.4% 
77.6% 
76.4% 
76.4% 
83.4% 
87.0% 
60.1% 
69.4% 
95.8% 
97.8% 
82.4% 

93.5% 
100.0% 
93.7% 
95.4% 
91.1% 
91.1% 
97.2% 
98.0% 
97.2% 
95.6% 
91.1% 
94.0% 
95.0% 

93.8% 
100.0% 
93.4% 
94.7% 
90.2% 
90.2% 
96.4% 
97.8% 
97.7% 
94.8% 
81.4% 
91.8% 
94.5% 

65.1% 
88.4% 
87.7% 
83.2% 
87.0% 
87.0% 
86.6% 
80.7% 
68.1% 
78.3% 
98.6% 
93.4% 
85.2% 

65.4% 
87.7% 
88.4% 
88.1% 
87.1% 
87.1% 
86.7% 
83.7% 
79.7% 
81.2% 
97.5% 
97.0% 
86.1% 

93.0% 
99.9% 
94.1% 
94.1% 
91.9% 
91.9% 
97.4% 
98.4% 
96.7% 
95.3% 
88.6% 
94.0% 
95.1% 

Table 6.3: Other (constant) characteristics of gasoline and diesel oil used in OURSE 

Specification 

Gasoline grades 

72 

 
 
 
  
  
 
 
 
 
 
 
  
 
 
 
 
MON 
RON 
Density min 
Density max 
Oxygenate max 
Olefine max 
Vapour pressure 

Specification  

Density min 
Density max 
Cetane  
Cloud point 

ReGasol92NAm  ReGasol95NAm 
85 
95 
0.72 
0.775 
2.7 
18 
0.75 
Diesel oil grades 

82 
92 
0.72 
0.775 
2.7 
18 
0.75 

PremGasol1  PremGasol2  PremGasolEu 
85 
95 
0.72 
0.775 
2.7 
18 
0.6 

84 
94 
0.72 
0.775 
2.7 
18 
0.8 

80 
90 
0.72 
0.775 
2.7 
20 
0.8 

DieselNAm 

DieselLatAm 

DieselEu 

DieselChin 

0.82 
0.86 
46 
-31.8 

0.82 
0.88 
46 
-31.8 

0.82 
0.845 
49 
-31.8 

0.82 
0.88 
46 
-31.8 

Table 6.4: Further grades/quality split of fuels within OURSE, 2005 

NA 

SA 

NE 

SE 

RS 

AF 

ME 

CH 

AS 

Propane 
Butane 

40% 
60% 

40% 
60% 

40% 
60% 

40% 
60% 

LPG split 
40% 
60% 

40% 
60% 

40% 
60% 

40% 
60% 

40% 
60% 

Gasoline split 

70% 
30% 

100% 

ReGasol92NAm 
ReGasol95NAm 
PremGasol1 
PremGasol2 
PremGasolEu 

DieselNAm 
DieselLatAm 
DieselEu 
DieselChin 

80% 
20% 

50% 

50% 

HeatingOil1 
HeatingOil2 

80% 
20% 

100% 

85% 
15% 

80% 
20% 

90% 
10% 

100% 

70% 
30% 

100% 

100% 

Diesel oil split 

50% 

30% 

60% 

100% 

100% 

50% 
Heating oil split 

100% 

85% 
100% 
15% 
Residual fuel oil split 

100% 

70% 

40% 

100% 

100% 

30% 
60% 
10% 

30% 
30% 
20% 
20% 

5% 
95% 

HeavyFuelOil1 

80% 

10% 

90% 

90% 

10% 

10% 

10% 

10% 

40% 

73 

 
 
 
 
 
 
 
 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
  
  
 
 
 
 
 
 
 
HeavyFuelOil2 

20% 

90% 

10% 

MarineBunk1 
MarineBunk2 

60% 
40% 

100% 

70% 
30% 

90% 

90% 
10% 
Marine bunker split 
70% 
30% 

100% 

100% 

90% 

90% 

60% 

100% 

100% 

100% 

Note: For the specifications of the above-listed fuels, see Table 1.1 in the main document. 

Table 6.5: Crude oil types and feedstock trade, 2005 baseline (standard rule) 

NA 

SA 

NE 

SE 

RS 

AF 

ME 

CH 

AS 

NA 
SA 
NE 
SE 
RS 
AF 
ME 
CH 
AS 
NA 
SA 
NE 
SE 
RS 
AF 
ME 
CH 
AS 
NA 
SA 

Brent 

ArLght 

ArHeav 

51.20 
7.75 
20.38 

9.66 

16.43 

60.46 

4.96 

29.18 

20.77 

2.23 

8.13 

10.07 

15.62 

4.74 

1.49 
22.61 

22.33 

0.55 

82.11 

9.44 

1.86 

4.09 

80.01 

1.02 

136.14 

8.23 

20.07 

100.18 

1.10 
47.36 

133.99 

130.99 
42.81 

78.57 

3.44 

74 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
  
  
  
  
  
  
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
  
  
  
  
  
  
  
 
 
 
 
 
 
 
 
 
 
 
 
 
NE 
SE 
RS 
AF 
ME 
CH 
AS 
NA 
SA 
NE 
SE 
RS 
AF 
ME 
CH 
AS 
NA 
SA 
NE 
SE 
RS 
AF 
ME 
CH 
AS 

NA 
SA 
NE 
SE 
RS 
AF 
ME 
CH 
AS 

Forcad 

Conden 

FeedStock 

4.64 

2.08 

31.59 

31.57 

2.43 
12.93 

51.14 

85.39 

46.59 

62.61 

24.23 

347.30 

2.07 

42.99 
81.87 

326.10 
5.27 

101.76 
64.42 
9.13 

1.24 
64.42 

59.96 

4.62 

61.42 

15.10 

10.88 

39.65 

37.65 
8.03 

12.61 

0.10 
0.33 

1.35 

5.90 
5.12 
6.92 
49.09 

0.22 

24.27 

71.06 

2.50 

1.42 
23.12 

4.60 

0.05 

0.64 
20.06 

22.43 

0.84 

3.05 

3.25 
3.78 

5.11 

7.55 

1.78 

25.71 
75.54 
9.33 

0.10 

29.62 

1.67 

27.26 

3.56 

5.90 
2.85 
0.52 

4.77 

22.11 

4.97 

0.90 

18.32 

17.59 

0.96 
5.99 

0.56 
5.02 

61.32 

10.15 
41.46 

10.77 
65.56 

43.53 

Note: Unit is million tonnes. Source: OURSE_QP results. 

Table 6.6: Unit groupings for reporting purposes 

Unit group 

Topping unit & VDU 
Topping unit & VDU 
HDT vacuum gas oil 

Residue hydroconversion 
Residue hydroconversion 
Naphtha processing units 
Naphtha processing units 
Naphtha processing units 
HDT vacuum gas oil 
HDT vacuum gas oil 
FCC 
FCC 
Residue hydroconversion 
RCC 
RCC 
Hydrocraking units 
Hydrocraking units 

Unit 
Atmospheric distillation 
Vacuum distillation unit 
DAO HDT 
Residue hydroconversion (fixed bed) 
Residue hydroconversion (ebulated bed) 
Catalytic reformer 
Regenerative reformer 
Reformate splitter 
FCC feed HDT (vacuum GO) 
Mild hydrocracking 
Catalytic cracking (EC) 
Catalytic cracking (CC) 
RCC feed HDT (long run residue) 
HDT long run residue catalytic cracking 
Long run residue catalytic cracking 
Hydrocracking full 
Hydrocracking jet 

75 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
  
  
  
  
  
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
  
  
  
  
  
  
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
  
  
  
  
  
  
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
  
  
  
  
  
  
  
 
Hydrocraking units 
Hydrocraking units 

Naphtha processing units 
Naphtha processing units 
Naphtha processing units 
Naphtha processing units 
Etherification units 
Etherification units 
Etherification units 
Visbreaking unit 
Coking unit 
HDT vacuum gas oil 
HDS  gas oil 
HDS  gas oil 
HDS  gas oil 
HDS  gas oil 
FCC gasoline desulphurization 
FCC gasoline desulphurization 
HDT naphtha 
Hydrogen Units 
Hydrogen Units 

Claus 

Hydrocracking naphtha 
Hydrocracking 78 conv 
Deisopentanizer 
Isomerization once through 
Isomerization with recycling 
Alkylation (Hydrofluoric Acid - HF)  
Tame unit on LG from FCC & RCC 
MTBE unit 
ETBE total unit 
Visbreaking (vacuum residue) 
Coking delayed 
Hydrodesulphurization (HDS) VGO CK 
HDS 90 20bar 
HDS 97-98 30bar 
REVAMP HX 50bar  
Deep HDS 75 bar 
FCC gasoline desulphurization (Primeg20) 
FCC gasoline desulphurization (Primeg10) 
REF feed HDT 
Pressure swing absorber 
Steam reformer 
MDEA+Claus+hydrosulpreen (SRI) 

Note: In eight units not listed in this table new investments were not allowed due to the short-term nature of our assessment. 

7  Appendix B: OURSE_LP equations 

Here  we  present  the  mathematical  formulation  and  relevant  interpretations  of  the  main  (but  not  all 

the)  equations  of  the  OURSE_LP  model.  For  easy  readability  purposes,  all  the  exogenous  (resp. 

endogenous) variables are written in red and with lower-case letters (resp. in black and with upper-

case  letters),  while  the  description  excerpt  of  each  individual  set  of  equations  is  separated  by  lines 

from its top and bottom parts.   

(1) Balance of raw crude:  

sum{reg_to, RAW_CRUDE_TRADE(raw_crude, reg, reg_to)} 

 crude_supply_limit(raw_crude, reg); 

where  RAW_CRUDE_TRADE(raw_crude,  reg_from,  reg_to)  =  interregional  trade  (supply)  of  crude  oil, 
crude_supply_limit(raw_crude, reg) =  crude supply limits by region (reg). 

76 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Interpretation: Total crude oil supplied from the source region to all destination regions is limited (constrained) 
by the source region's crude oil availability at the point in time considered.  

(2) Balance of crude trade:  

sum{raw_crude, RAW_CRUDE_TRADE(raw_crude, reg_from, reg_to)* trans_crude(raw_crude, reg_from, 
crude)} = CRUDE_TRADE(crude, reg_from, reg_to) ; 

where  trans_crude(raw_crude,  reg,  crude)  =  crude  yields  from  (raw)  crude  types  (a  matrix  transforming 
raw_crude  to  crude)    with  the  property  that  sum{crude,  trans_crude(raw_crude,  reg,  crude)}  =  1  for  each 
raw_crude and reg. 

Interpretation: Translating the endogenous interregional supply of the more disaggregated crude oil types to the 
corresponding interregional trade (supply) flows of the more aggregated crude types used in the model.   

(3) Balance of crude consumption in regions:  

sum{reg_from, CRUDE_TRADE(crude, reg_from, reg)} -  INTER_PROD(crude, reg) = 0; 

where CRUDE_TRADE(crude, reg_from, reg_to) =  interregional  supply of crude oil (positive  variable in the 
model),  INTER_PROD(crude,  reg)  =  intermediate  production/use  of  crude  by  the  representative  refinery  of  a 
region. 

Interpretation: Total crude coming to a destination region from all possible source regions is entirely used by 
the destination region's refinery as intermediate product (which is equivalent to the intermediate production of 
that refinery).    

(4) Balance of crudes and their cuts: 

INTER_PROD(crude, reg) – sum{cut$(crude_cut(crude, cut)), INTER_PROD(cut, reg)} = 0; 

where  crude_cut  is  a  set  showing  a  crude  and  its  corresponding  cuts,  e.g.  crude_cut(Brent,  cut)  = 
{Brent.BrentAcut,  Brent.BrentBcut}.  

Interpretation:  This  equation  guarantees  that  the  total  quantity  of  the  processed  crude  in  each  region  (or 
representative refinery) is equal to the sum of the different relevant processed cuts of the crude.  

(5) Balances of intermediate products:  

sum{intp, INTER_PROD(intp, reg)*INTER_PROD_FACTORS(intp_eq, intp)} = 0; 

where INTER_PROD_FACTORS(intp_eq, intp) =  factors for intermediate products, intp_eq = interim products 
names  for  production  equations,  INTER_PROD(intp,  reg)  =  production  of  intermediate  product  intp  in  regin 
reg. 

77 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Interpretation: This is the material balance of intermediate products for each refinery that states that production 
is equal to the internal use. That is, the sum of intermediate use by a refinery equals its intermediate production. 
Depending on whether the intermediate products at the production "stage", indicated by intp_eq, are inputs or 
outputs, the corresponding interim-production factors INTER_PROD_FACTORS(intp_eq, intp) are positive or 
negative, respectively.  

(6) Balances of final products:  

FIN_PROD(fp, reg) = sum{intp,  INTER_PROD(intp, reg)*PROD_FACTORS(fp, intp)} ; 

Where  PROD_FACTORS(fp, intp) =  production factors/coefficients of  final products per unit of intermediate 
product (whenever non-zero, for all "market" final petroleum products these are set to unity; for such products 
as  sulphur,  refining  gas,  isobutene,  propane,  etc.  these  are  all  in  the  (0,  1)  interval,  being  much  closer  to  the 
lower zero bound). 

Interpretation:  Whenever  an  intermediate  production  contributes  to  the  production  of  a  certain  type  of  final 
product, the corresponding production factor reflects this. Hence, multiplying production factors by the volume 
of  intermediate  production  and  summed  over  all  possible  intermediate  products  gives  the  total  of  a  particular 
final product.  

(7) Balances of regional production, demand and trade of final products (excluding marine bunker):  

FIN_PROD(fp_not_marine_bunker, reg) 

** Use for other products 

           + sum{intp, INTER_PROD(intp, reg)*USE_FACTOR(fp_not_marine_bunker, intp)} 

** Import 

           + sum{reg_from, EXIMPORT(fp_not_marine_bunker, reg_from, reg)} 

** Export 
- sum{reg_to, EXIMPORT(fp_not_marine_bunker, reg, reg_to)} 
** Supplies of final products 

            = FIN_SUPPLY(fp_not_marine_bunker, reg); 

where USE_FACTOR(fp, intp) = factors for blending  components  (if nonzero, these are all  negative) and are 
defined  for  the  following  final  products  (fp):  LiquidFuel,  Feedstock,  Natural  Gas,  Sulphur,  RefinGas, 
PropaneC3,  ButaneC4,  ButaneN4,  IsoBUTAn,  IsoBUTEn,  NormBUTEn,  MTBEbio,  METHANOLbio, 
ETBEbio, EMHvBio, HydrogPure, Hydrogen, ChemHPsteam, and  SulfGas; 
FIN_SUPPLY(fp, reg) = Supplies of final products by region. 

Interpretation: For each region the balance of product which is not a marine bunker is given. That is, the total 
supply  of  final  product  of  region  is  equal  to  the  production  of  final  product  in  the  region  (refinery),  plus  the 
internal use of the product by the refinery, plus imports from and minus exports to other regions. In other words, 
this  balancing  equation  guarantees  that  supply  (i.e.,  domestic  production  and  imports)  equals  demand  (i.e., 
domestic consumption and exports).  

(8) Balance of global production, demand and trade of marine bunkers:  

78 

 
 
 
 
  
 
 
 
 
 
 
          
          
 
 
 
 
 
sum[reg, FIN_PROD(marine_bunker, reg) 
** Use for other products 
+ sum{intp,  INTER_PROD(intp, reg)*USE_FACTOR(marine_bunker, intp)}] 
** Supplies of final products 
= sum{reg, FIN_SUPPLY(marine_bunker, reg)}; 

Interpretation: This is similar to (7) an equality of demand and supply balancing equation for marine bunkers. 
In contrast to (7), however, now there is no separate regional dimension and no exports and imports. This is due 
to the fact that the demand and supply corresponding for marine bunkers are satisfied only at the world level; 
hence, for marine bunkers OURSE has a separate, global balance.  

(9) Demand of market products balance:  

FIN_SUPPLY(fp, reg) 
              sum{prodem, DEMPROD(prodem, reg)*COM_PROD(prodem, reg, fp) 

** From original GRANDOURSE modification of demand for Petroleum Coke 

              - (0.1*sum[reg_from, RAW_CRUDE_TRADE("BXHC", reg_from, reg)])$(prodem ="PetCoke"))}; 

where  DEMPROD(prodem,  reg)  =  demand  for  petroleum  product  prodem  (12  types)  by  consumers  in  region 
reg, COM_PROD(prodem, reg, fp) =  composition of demanded products from final products: these are shares 
that transform the 12 types of demanded products into the 24 final products of the OURSE model, thus sum{fp, 
COM_PROD(prodem, reg, fp)} = 1 for each demanded product prodem and region reg.  

Interpretation: The exogenous demand has to be  satisfied; hence, the endogenous supply of a final product is 
equal to the corresponding exogenously given demand. [Not sure why modification to the demand for petroleum 
coke is necessary, ask IFPNE. It seems that 10% of the total use of Bitumen and Extra-Heavy Oil of a region [in 
the data it is only North America] also satisfies the demand for petroleum coke.] 

(10) Balance of final products in terms of volume:  

VOLUME(fp, reg) = sum{intp, INTER_PROD(intp, reg)*VOLUME_FACTORS(fp, intp)}; 

where  VOLUME_FACTORS(fp,  intp)  =  volume  factors  of    final  products  (if  nonzero,  they  are  positive  and 
often larger than unity).  

Interpretation: It is similar to the balance of final product given in (6), with the only difference that this balance 
is  given  in  terms  of  volume  (while  in  equation  (6)  above  it  is  expressed  in  terms  of  weight).  The  volume 
equations  are  defined  only  for  the  following  final  products:  different  grades  of  gasoline  (ReGasol92Nam, 
ReGasol95NAm,  PremGasol1,  PremGasol2,  PremGasolEu),  jet  fuel,  diesel  (DieselNAm,  DieselLatAm, 
DieselEu,  DieselChin),  heating  oil  (HeatOil,  HeatOilHq),  heavy  fuel  oil  (HevFOilLowSulf,  HevFOilHiSulf, 
HevFOilULowSulf), marine bunkers, bitumen, and liquid fuel. 

(11) Product quality specification equations for MIN conditions:  

sum{fp1, FIN_PROD(fp, reg)*Spec_Min_Prod_Factor(spec, fp, fp1)} 

+ sum{intp, INTER_PROD(intp, reg)*SPEC_MIN_FACTORS(spec, fp, intp) } 

79 

 
 
 
      
        
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
+ sum{intp, VOLUME(fp, reg)*Spec_Min_Vol_Factor(spec, fp, intp)} 

 0; 

where Spec_Min_Prod_Factor = final products' quality minimum specification coefficient/factor for production 
variables  and  are  defined  only  for  density  (=1)  and  viscosity  (<0)  specifications;  SPEC_MIN_FACTORS  = 
minimum  specifications  for  final  products  in  tons  per  unit  of  intermediate  product  (>0)  and  are  defined  for 
RON,  MON,  vescosity,  and  cetan  index);  Spec_Min_Vol_Factor  =  final  products'  minimum  specification 
factors for volume variables (<0) and are defined for density, RON, MON and cetan index.  

However, given that in the data Spec_Min_Prod_Factor and Spec_Min_Vol_Factor have only one dimension of 
final product, the above specification equation can simply be re-written as follows: 

FIN_PROD(fp, reg)*Spec_Min_Prod_Factor(spec, fp) + VOLUME(fp, reg)*Spec_Min_Vol_Factor(spec, fp) 

+ sum{intp, INTER_PROD(intp, reg)*SPEC_MIN_FACTORS(spec, fp, intp)} 

 0; 

Interpretation:  This  equation  specifies  that  final  products  must  meet  a  number  of  legal  and  technical  quality 
minimum  specifications.  Each  intermediate  product  quantity  (in  volume  or  in  weight  term)  generates  certain 
specification  quality  as  captured  by  the  SPEC_MIN_FACTORS  coefficients.  The  final  products'  quality 
minimum  specifications  are  given  and  controlled  by  Spec_Min_Prod_Factor  and  Spec_Min_Vol_Factor 
coefficients. The last should reflect, for example, existing (or earlier) legal requirements on final products.   

(12) Product quality specification equations for MAX conditions:  

sum{fp1,  FIN_PROD(fp, reg)*Spec_Max_Prod_Factor(spec, fp, fp1)} 

+ sum{intp, INTER_PROD(intp, reg)*SPEC_MAX_FACTORS(spec, fp, intp)} 
+ sum{intp, VOLUME(fp, reg)*Spec_MAX_Vol_Factor(spec, fp, intp)} 

 0; 

where  Spec_Max_Prod_Factor    =  final  products'  quality  maximum  specification  coefficients/factors  for 
production variables and are defined for density (=1), CO2 emissions, oxygen content,  sulphur content,  cloud 
point,  viscosity  and  poly-aromatics  fraction  (all  <0);  SPEC_MAX_FACTORS  =  maximum  specifications  for 
final products in tons per unit of intermediate product (>) and are defined for CO2, AromaticFrac, BenzenFrac, 
CloudPoint,  FracOlefin,  PolyArmo,  SulphurFrac,  VaPress,  and  viscosity;  Spec_Max_Vol_Factor  =  final 
products'  maximum  specification  factors  for  volume  variables  (<0)  and  are  defined  for  AromaticFrac, 
BenzeneFrac, Density, FracOlefin, and VaPress. 

Similar  to  (11),  in  the  data  Spec_Max_Prod_Factor  and  Spec_Max_Vol_Factor  have  only  one  dimension  of 
final product, hence the above specification equation can be simply re-written as follows: 

FIN_PROD(fp, reg)*Spec_Max_Prod_Factor(spec, fp) + VOLUME(fp, reg)*Spec_Max_Vol_Factor(spec, fp) 

+ sum{intp, INTER_PROD(intp, reg)*SPEC_MAX_FACTORS(spec, fp, intp)} 

 0; 

Interpretation:  This  equation  specifies  that  final  products  must  meet  certain  number  of  legal  and  technical 
quality  maximum  specifications.  Each  intermediate  product  quantity  (in  volume  or  in  weight  term)  generates 
certain specification quality as captured by the SPEC_MAX_FACTORS coefficients. The final products' quality 
maximum  specification  are  given  and  controlled  by  the  Spec_Max_Prod_Factor  and  Spec_Max_Vol_Factor 
coefficients. The last should reflect, for example, current (or earlier) legal maximum specification requirements 
on final products. 

80 

 
 
 
 
 
 
 
  
 
 
 
 
 
 
 
 
(13) Capacity equation:  

CAPA_OLD(unit, reg) + CAPA_NEW(unit, reg) 

sum{intp, INTER_PROD(intp, reg)*CAPACITY_FACTORS(unit, intp)}; 

where  CAPACITY_FACTORS(unit,  intp)  =    indicate  the  use  of  certain  unit  in  the  production/processing  of 
intermediate  products  (if  non-zero,  all  are  set  to  unity),  CAPA_OLD(unit,  reg)  =  installed  capacity  at  the 
beginning of the year; CAPA_NEW(unit, reg) = new investments in  capacities (positive variable in the model). 

Interpretation:  This  capacity  constraint  states  that  the  input  flows  of  a  processing  unit  are  limited  by  the 
capacity  installed  in  the  past  and  new  capacity  (investments).  Capacity  is  the  only  "dynamic"  variable  in  the 
model. The usual stock-flow formula is used to derive capacity of a processing unit at the end of time  t that is 
used as the beginning capacities of year t+1as follows:  

CAPA_END(unit, reg) = CAPA_OLD(unit, reg)*{1-1/lifetime(unit)} + CAPA_NEW(unit, reg), 

where lifetime(unit) = 30 years. 

(14) Equation for combusted fuels:  

TOT_COMBUS(reg) = sum{intp, INTER_PROD(intp, reg)*Ref_Fuel_Weight_Factor(intp)}; 

where TOT_COMBUS(reg) =  total fuel combusted, Ref_Fuel_Weight_Factor(intp) =  factors for refinery fuel 
balance in weight terms (if nonzero, all are unity). 

Interpretation: Total combusted fuels, on the demand side of the refinery fuel requirements, is proportional to 
the  inputs  of  the  processing  units.  The  refinery  fuel  demand  is  satisfied  by  either  the  intermediate  or  final 
products, which have different calorific values.  

(15) Net consumption of utilities in energy terms:  

UTIL_NET_USE(util, reg) + sum{intp, INTER_PROD(intp, reg)*UTIL_NET_USE_FACTOR(util, intp)} 

+ UTIL_NET_USE(util, reg)*UTIL_USE_UTIL(util, util) = 0; 

where  util  denotes  'utility'  that  include  electricity,  low  pressure  steam,  high  pressure  steam  and  refinery  fuel, 
UTIL_NET_USE_FACTOR(util, reg) =  factors of net use/production of utility in utilities (energy) terms (can 
be negative or positive), and UTIL_USE_UTIL(util, util) = factors of utilities use for the refinery fuel. The last 
coefficients  are  as  follows:  UTIL_USE_UTIL('RefFuel',  'Electr')  =  -0.00022,  UTIL_USE_UTIL  ('RefFuel', 
'HighPreSteam') = -0.071, and UTIL_USE_UTIL('RefFuel', 'LowPreSteam') = -0.059. 

Interpretation: This equation  states  that the net use of utilities (in utilities terms)  should equal the production 
and consumption of utilities during all stages of the intermediate production within the refinery.  
Note:  There  are  two  other  options  of  this  equation,  'greater  than'  and  'less  than'  equations,  denoted  as   
EQ_UTIL_NET_USE_G(util,  reg)  and  EQ_UTIL_NET_USE_L(util,  reg),  respectively.  The  last  are  exactly 
similar to the above equations, except the equality sign, which becomes 

, respectively.  

 or 

81 

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(16) Emissions balance equation:  

TOTAL_EMISSIONS(reg, polutant) =  
** Thermal emissions 
sum{intp, INTER_PROD(intp, reg)*EMISSION_FACTOR(polutant, intp)} 
** CO2 emissions from upgraders 
+[sum{reg_from, RAW_CRUDE_TRADE("BXHC", reg_from, reg)}*0.2588]$(if pollutant="CO2") 
$ontext 
**  CO2 emissions from hydrogen production 
+[1.01*INTER_PROD("TOTinfeedHydrogUnit", reg) + 
sum{reg_from,(EXIMPORT("HYDROGEN",reg_from, reg))}/0.30]$(if pollutant="CO2") 
$offtext 
**  CO2 emissions from chemical  processing 
+[sum{intp, EMIS_FACT_CHEM(intp, reg)*INTER_PROD(intp, reg)}]$(if pollutant="CO2"); 

where  EMISSION_FACTOR(polutant,  intp)  =    emissions  factors  for  SO2  and  CO2  per  unit  of  intermediate 
product, EMIS_FACT_CHEM(intp, reg) = CO2 emission factors from chemical processing.  

Interpretation:  Atmospheric  pollution  of  SO2  and  CO2  emissions  include  total  thermal  emissions,  which  are 
computed assuming that pollution content of the refinery fuels are proportional to the quantities of fuels burnt 
(as captured by the values of emission coefficients), and, in case of CO2 emissions, also direct emissions from 
the  processing  units.  The  last  include  CO2  emissions  from  upgraders,  hydrogen  production  and  chemical 
processing.   

(17) Equations for calculation of emissions:  

TOTAL_EMISSIONS(reg, polutant) 

 EMISSIONS_LIMIT(reg, polutant); 

where EMISSIONS_LIMIT(reg, polutant)  = limit on emissions (in kt). 

Interpretation: The pollutant emission in the stack emission can be restricted using this constraint.  

(18) Objective function - costs minimization:  

TOTAL_COSTS = 

** Crude oil  costs 
+ sum{(crude, reg_from, reg_to), CRUDE_TRADE(crude, reg_from, 

reg_to)*CRUDE_PRICE(crude)} 

** Crude oil freight costs 
 + sum{(crude, reg_from, reg_to),  
              CRUDE_TRADE(crude, reg_from, reg_to)*FREIGHT_CRUDE(crude, reg_from, reg_to)} 

** Products freight costs 
+ sum{(fp, reg_from, reg_to),  

EXIMPORT(fp, reg_from, reg_to)*FREIGHT_PROD(reg_from, reg_to)} 

** Investement costs 

82 

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+ sum{(unit, reg), CAPA_NEW(unit, reg)*CAPEX(unit)} 

** Operational costs 
+ sum{(intp, reg), OPEX(intp)*INTER_PRODUCTION(intp, reg)} 

**  Carbon cost 
+ sum{reg, TOTAL_EMISSIONS(reg, "CO2")*CARBON_PRICE(reg)}; 

Interpretation:  All  the  above-mentioned  components  of  total  costs  are  self-explanatory.  Note  that  pollution 
permits can be introduced in the objective function via positive carbon pricing.  

83 

 
 
 
       
 
      
       
 
      
       
 
 
 
 
 
 
 
 
 
 
 
 
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doi:10.2791/037768 
ISBN 978-92-79-48398-1 

2 | P a g e