MODELING OF THE INTERACTIONS BETWEEN
SECTORAL CO2 EMISSIONS AND ENERGY EFFICIENY
UNDER CO2 EMISSION RESTRICTION POLICIES
Kemal Sarica’, Nihan Karali’
! Department of Industrial Engineering, Bogazi¢i University, 34 342, Bebek, Istanbul,
Turkey
ABSTRACT
The purpose of this study is to understand the dynamics of carbon dioxide (CO2) emission
restrictions and energy price policies on two highly energy intensive sectors of industry;
iron&steel industry and cement industry over a 12 year period. Model is designed so that
critical decisions such as growth and efficiency investments are based upon profit and
extra costs incurred due to CO2 emissions. The profit margin, CO2 cost over revenue and
Energy Cost Share in Total Annual Production are important key variables in the model.
This study, giving attention to the economical and energy-emission policy aspects, aims to
analyze the causal relationships and the feedback structures among industry’s production
capacity, investments on new and energy efficient technologies, financial burden, and CO2
emissions.
Keywords: Sectoral Emission, Energy Efficiency, Emission Restriction, Energy Cost
1. INTRODUCTION
The purpose of this study is to understand the dynamics of carbon dioxide (CO2) emission
restrictions and energy price policies on two highly energy intensive sectors of industry;
iron&steel industry and cement industry over a 12 year period.
According to some experts, world energy consumption is expected to be 40% higher in 2020
than it is today. If no action is taken, these trends are likely to lead to supply interruptions,
security risks of fossil fuel dependence, and price shocks caused by limited fossil fuel
resources. Moreover, current level and profile of energy production mainly contributes to
enormous emissions of COz (and other greenhouse gases) into the atmosphere, leading to the
likelihood of serious climate changes in the not too distant future. Being strong proponent of
the Kyoto Protocol, the European Union (EU) calls its members to adjust for an EU-wide
' Corresponding author. Tel.: +90-212-3597072; fax: +90-212-2651800.
E-mail addresses: saricake@ boun.edu.tr ; nihan.karali@ boun.edu.tr
emission reduction obligation of 8 % for the first commitment period, 2008-2012. As an EU
candidate country, with which accession negotiations have started (in October 2005), Turkey
shall adopt the community acquis.
According to the “First National Communication of Turkey on Climate Change” report,
Turkey’s energy and electricity consumptions have grown at an annual rate of 3.7% and 7.2%,
respectively over the period 1990-2004. Moreover, total CO2 emissions will increase at an
average rate of 6.3% annually between 2003 and 2020 and will reach 604.63 million tons/year
by 2020. In 2004, oil had the largest share of total final energy consumption with 37% while
natural gas followed it with 23%. Hard coal, lignite and renewable (including
hydroelectricity) also contributes energy consumption with shares of 16%, 11%, and 13%
respectively. Transportation, industrial, and household/service sectors became the main
energy demanding figures of the country over the past 20 years. The share of industrial sector
in the total final energy consumption was 42% in 2004.
From this point of view, it is clear that it has vital importance to explore the impacts of
emission restriction payments on Turkish industrial sectors. Therefore, it is significant to
examine the dynamics of country’s main energy consumed sectors under certain emission
restriction and energy price policies.
The main purpose of this paper is therefore to focus on a model that serves as a basis for
establishing a reference projection of two main Turkish industries on energy use, economic
growth and emission reduction by giving concern on energy efficient technologies.
This paper, giving attention to the economical and energy-emission policy aspects, aims to
analyze the causal relationships and the feedback structures among industry’s production
capacity, investments on new and energy efficient technologies, financial burden, and CO
emissions. By using Stella software, a stock-flow diagram of the system is constructed and
dynamic behavior of the system variables is analyzed.
2. PROBLEM DEFINITION
Iron&steel and cement industry are chosen as the sectors that will be discussed in this paper
because of their high-energy demand and their strong impact on climate change.
The basic production processes of steel industry such as arc furnaces, open-hearth furnace, or
basic oxygen furnace are highly depended on energy usage which follows a wide fuel range.
Average energy consumption between 2000 and 2005 is estimated 3.80 Mwh/ton. In 2000,
14.3 Mt (million tons) of crude steel was produced in Turkey. About 47% of energy
consumed in the production of this amount came from electricity. Petroleum products
followed electricity by 32% while natural gas had an 18% share. In 2000, the total amount of
CO2 emissions, related to the direct use of energy in steel production, was estimated around
6.8 Mt.
In 2005, the total amount of CO2 emissions, related to the direct use of energy in steel
production, was estimated 8.4 Mt while total production amount was 20.9Mt. The share of
energy mix used in the production in the sector changed as 54% electricity, 15% petroleum
products, and 28% natural gas. This is basically related with the increases in availability of
natural gas.
Moreover, according to the projections of “First National Communication of Turkey on
Climate Change” report crude steel production is expected to increase to 28.37 Mt in 2010,
32.36Mt in 2015, and 33.86Mt in 2020.
The cement industry is the second highest energy intensive sector with a unit energy
consumption rate of 1.06Mwh/ton. In 2005, 67.8 Mt (million tons) cement was produced in
Turkey. About 42% of energy consumed in the production of this amount came from
petroleum coke, which faces the highest emission factor value among all fossil fuels. Lignite
also had a 42% share while coal amounted the 13% of remaining. In 2005, the total amount of
CO2 emissions, related to the direct use of energy in cement production, was estimated
around 11.45 Kt (Kilo tons).
The purpose of this study is to model the dynamic structure that reflects the transition from
old technologies to energy efficient technologies as emission restrictions put into board. The
model is constructed from the view point of financial accounts. Capacity is depreciated with a
fixed amount. Transition between old and new technologies, so the increase in sectoral
efficiency index, totally depends on the financial burden of emission taxes and also the energy
prices. Unit product price is used in the decision process of energy related payments.
The effect of most of the variables in both industrial sectors occurs in very short time periods.
The production increases immediately as growth rate goes up, efficiency index is
instantaneously formed by new technology investments due to added cost of both CO2
Emission deviation payment and high energy prices. Therefore, the time unit is chosen as year
for this study and time horizon is determined as 12 years.
3. MODEL FORMULATION
In this section the dynamic hypothesis behind the model is discussed via causal loop
diagrams. The first subsection gives the definitions of some key variables in the model, their
effects on other variables, and effects of other variables on these variables.
3.1. Definitions of Variables
Production Capacity: (tons)
e Itis the physical capacity limitation of the sector determined by physical limits such as
machinery, human resources, land area and etc.
e It is modeled as a stock and has inflow new capacity addition, and outflow
depreciation.
e It has initial value of year 2000 production capacity limits.
New Capacity Addition: (tones/year)
e Inflow of the Production Capacity stock. It occurs as a result of growth rate which is
mainly determined by profitability of sector.
e It’s value is dependent on Production Capacity and growth rate.
Depreciation: (tons/year)
e Itis the natural degradation of physical production capacity.
e It increases as the Production Capacity increases by a multiplicative effect
formulation.
Annual Production: (tons/year)
e It is the physical production of sector using its’ production capacity.
e Itis a function of Production Capacity. It is determined by Capacity Utilization factor.
e Capacity utilization factor is constant for steel sector while it is a function of Unit
Price for Cement sector.
Efficiency Index: (unitless)
e It is modeled as a stock that reflects the energy efficiency of new capacity additions
compared to base year 2000. It is the ratio of energy needed producing one unit
product in year 2000 and current year. Efficiency index more(less) than 100 reflects
the fact that new capacity additions will be less (more) energy efficient.
e It affects current average efficiency index value with a delay thus effecting overall
efficiency of the sector.
e It has two inflows: Investment on Efficiency due to Energy Prices and Investment on
Efficiency due to CO2 Cost.
Investment on Efficiency due to Energy Prices: (unitless)
e Itis one of inflow of Efficiency Index.
e Its’ value is determined by ratio of annual energy cost to Total Annual Cost of
Production.
e If the ratio is under a certain value it may lead to increase in efficiency index thus
reducing efficiency.
Investment on Efficiency due to CO2 Cost: (unitless)
e Itis one of inflow of Efficiency Index.
e Its’ value is determined by ratio of CO2 cost to Revenue.
e If the ratio is under a certain value it may lead to increase in efficiency index thus
reducing efficiency.
Total CO2 Emission: (tones)
e It is the sum of Annual CO2 emissions during simulation period. It determines the
average annual CO2 emission.
e Itis modeled as a stock. Inflow is Annual CO2 emission. It has no outflow.
Annual CO2 Emission: (tones)
e Itis the inflow of Total CO2 Emission. It is product of Annual Energy Consumption
and Sector Specific CO2 Emission factor.
e Can be reduced by only reducing Annual Production and / or increasing efficiency
(Current Energy Efficiency index)
Target Deviation: (tons)
e Itis the difference between Average Annual CO2 Emission and CO2 Cap Level.
e Used for determining Reduction Cost of CO2.
CO2 Cap Level: (tones/year)
e ItisCO2 cap level of the specific sector.
e Itis 8% below from the year 2000 thousand emission level of the sector.
Revenue: (YIL)
e Sum of financial transaction of sector via the sales of produced good. Measured in
terms of real prices based on year 2000.
e Itis the product of unit price and A nnual Production.
Profit: (YTL)
e Net financial gains of sector after costs are dropped down from the revenue.
e Accounted costs are Total Cost of Annual Production and CO2 Cost
Growth Rate: (%)
e Itis the ratio of capacity expansion to the current Physical Capacity levels.
e Itis calculated by the product of target growth rate and Profit effect on Growth.
3.2. Dynamic Hypothesis and Causal Loop Diagrams
Model design and construction is based upon following principles and assumptions:
Model is designed so that critical decisions such as growth and efficiency investments are
based upon profit and extra costs incurred due to CO2 emissions. The profit margin, CO2 cost
over revenue and Energy Cost Share in Total Annual Production are important key variables
in the model.
Model horizon is kept short since our main objective is to cover possible responses of energy
intensive sectors in Turkey in near 10-12 year horizon with possible policy implementations
such as energy prices, emission restriction levels and CER/VER Prices. Also short time
horizon includes some advantages such as possible model simplifications. This may become
important in the long run such as emission factor of energy used by the sector which is
currently determined by historical data and possible future trends.
4
New Capacity Production
Addition Capacity
RF
+
Annual Production
‘Revenue:
Figure 1 Main Growth Loop of the model
Figure 1 displays the self reinforcing growth loop of a general sector. As new capacity is
added to Production capacity, Annual Production increases. Increase in annual production
leads to more revenue and more profit. Increase in profit leads higher growth rate and more
New Capacity addition. This loop is the main driving power of a sector without CO2 and
energy price effects.
Figure 2 displays the self balancing loop of CO2 emission and efficiency improvement loop.
As the energy consumption per unit production increases Annual energy consumption
increases leading to higher Total CO2 Emission. This increase in Total CO2 emission levels
increases average annual CO2 emission that pushes the current deviation level from base year
emissions to higher levels (Target Deviation from Base year CO2 Level). Thus reduction cost
of CO2 emissions by purchasing CER/VER certificates becomes more costly. This leads to
investment on efficiency improvements due to CO2 emission which decreases efficiency
index values. This decrease, with a delay, will decrease the average efficiency index value
thus reducing Energy Consumption per unit production.
+
jeutPoiason
Current Average
ficiency Index Value
‘Anmal Energy
Consumption
Efficiency
ki Index
Total CO2
Emission
+
Average Annual Investment on Efficiency
C02 Emission due to CO2 Cost
4
& £02 Cost
‘Talyet Deviation from £
Base year CO2 Level
Reduction Cost of CO2
+ per unit Production
Figure 2 CO2 Emission - Efficiency Improvement Loop
Figure 3 shows two loops. First loop is Production - CO2 emission interaction balancing loop.
Increase in Production Capacity leads to higher Annual Production. This increases annual
energy consumption which directly increases average annual CO2 emission. This leads to
higher deviation from the base year CO2 level. Therefore CO2 Cost decreases profit of the
sector. Low profit level decreases growth rate, growth rate decreases Production capacities.
Lower Production capacity decreases A nnual Production.
Second loop is Production — Energy Price interaction self reinforcing loop. Increase in annual
production cost leads to higher energy cost in annual production cost, thus increases energy
6
cost share in total production cost. This trigger increases the investment on efficiency due to
energy prices. Energy efficiency index decreases due to investments. With a delay average
efficiency index value decreases leading to decrease in annual energy consumption. With
same interactions of the first loop after Annual energy consumption, Annual Production
increases.
Energy Price
+
+ Energy Cost in Annual
Production Cost.
. ee
7 Finan “hay
+
Production
Energy Cost Share in Total
+ Capacity Cost of Annual Production
Cost
Growth Rate f
i Investment on Efficiency
ddue to Energy Prices
Profit
Hificiency
Index
Investment on Efficiency
C02 Cost due to CO2 Cost
* +
Anmal Energy Soe
4 E Index Value
‘Taryet Deviation from Commmpen ay
Base year CO2 Level Ps Me
BSc | hvenmiviet’ Energy Consumption 4%
C02 Emission per unit Production
Figure 3 Production - CO2 emission and Production - Energy Price Interaction loops
Overall causal-loop diagram of model can be observed in Figure 4. This figure shows all
relationships between variables. In addition to increase in capacity due to new capacity
addition, there is a depreciation rate which is fixed at a certain amount. Target growth rate,
energy price, unit production cost, CER/VER prices, capacity utilization rate, profit level per
unit production, base year energy consumption per unit production, and emission factor are
the sector specific exogenous variables of the model.
Exogenous variables are obtained analyzing the past six-year data of each sector. Energy
prices and emission factors are the weighted average of sector specific primary energy
resource prices and emissions, respectively.
+
z ins pint
New Cacty ge Se
Aces eam Energy Cost Share in Total
os Canal Pirin
f ca
Groh Rate :
and Pion
% , Come Avene
| 3 talony index Vee + i
\ Energy Consumption Investment on Efficiency
| peat Pon ste Ey Pos
Pit Eton ia
Gow habe
2 commpten .
Production
cl
Total COZ Cap Level
Emission )
o Investment on Eiciency
Average Anmual due to CO2 Cost
C02 Emission i
SSA Taetdeiaion CERWVER Prices
Unit Production
—— Cost
Reduction Cost of CO2*
ert Production
¥ C02 Cost
Figure 4 Overall causal loop diagram of the model
4, FORMAL MODEL
4.1. Stock - Flow Diagrams
Capacity is the main stock variable of the model. It is one of two drivers determining annual
production levels. New capacity addition and deprecations are the flows interacting with this
stock. New capacity addition is the product of sector growth rate and stock value while
depreciation flow is the product of stock value and a constant rate. Efficiency Index is the
second stock of the model. It affects the efficiency levels of new capacity additions. Net
inflow is sum of efficiency index change due to CO2 Emissions and efficiency index change
due to energy prices. These flows are constructed as graphical functions which are depended
on Investment on efficiency Improvement and Energy Cost Share in Total Production,
respectively. Efficiency Index X Capacity stock is used as a memory tool for the calculation
of Current Average Efficiency Index Value. Total CO2 Emission Stock sums up the annual
CO2 emission values caused by the energy consumption of the sector. There is only inflow,
no outflow. Inflow of this stock is obtained by the multiplication of sector specific emission
factor with annual energy consumption of related sector. Profit effect on Growth and
Investment on Efficiency Improvement are also represented as graphical functions. All energy
units are represented as Mwh. Main assumptions and graphical functions of the model are
given in Table 1 & 2 and Figure 6 and 7, respectively.
Efficient index x Capacity
ficiency Index x
Capacity Change
Total CO2 Emission
DY Emission CAP Level
Figure 5 Stock Flow Diagram of the Model
Table 1 Base Y ear Data
BaseY ear Data (2000) Tron& Steel Sector Cement Sector
Capacity (Million tons) 443 64
Emission factor (ton CO2/Mwh) 0.38 0.33
Energy Consumption per unit production (Mwh/ton) 4.19 1.09
Energy Price (YTL) Bist 13.0
Unit Production Cost (YTL/ton) 143.0 28.9
Table 2 Base Y ear Assumptions
Base Y ear Assumptions: Iron& Steel Sector Cement Sector
Depreciation rate 1/30 1/30
Initial Efficiency Index 100 100
CER/VER Prices (YTL) 6.67 6.67
Emission Restriction Level (%) 8.0% below the base year 8.0% below the base year
Average Energy Price (YTL) 32.3 13.0
Average Target Growth Rate (%) 12.5% 1.7%
Average Capacity Utilization (%) 86% 58%
Profit Level per Unit Production 18% 100%
All prices of model are in YTL form and based on year 2000. They also are kept constant for
time window. When the past data of the iron&steel sector is analyzed, it is observed that
capacity utilization rate is nearly constant for the time horizon. Besides, unit production price
and profit have negligible impacts on capacity utilization. Growth rate of capacity expansion,
independent from price dynamics, is also found nearly constant for the time horizon.
Nevertheless, profit margin and growth rate interaction is found significant. Therefore, it is
constructed as average of past data changing with profit margin (see also Figure 6). Other
graphical functions of iron&steel model, representing certain relationships, are also illustrated
in Figure 6.
Dynamics of cement sector differs from the iron&steel sector in a few aspects. Capacity
utilization rate is not constant in this case. A causal relationship is found between unit product
price and utilization rate. Real prices, which are discounted to year 2000, of unit products are
nearly constant overtime. Graphical functions of cement model, representing certain
relationships, are also illustrated in Figure 7.
10
Profit Effect on Growth
Efficiency Index Change due to COZ
—?— Profit Effect on Growth
1,10
Profit Margin(%)
—- Efficiency Index Change due to CO2
25
2,0
15
10
05
0,0
a5 4
Investment on Efficiency Improvement
—#-Efficiency Index Change due to Energy Price
Efficiency Index Change due to
+
ri —
5 10 15
Energy Cost Share in Total Production Cost
—¢— Investment on Efficiency Improvement
12,0
o 5
of
a
o
2
°
Investment on Efficiency Improvement
2g Fad
° °
CO2 Cost over Revenue
Figure 6 Graphical Functions of Iron& Steel Industry
11
—¢— Profit Effect on Growth —¢— Efficiency Index Change due to Energy Price
2
16 7]
5 vo :
8 12 -eeee ee a By
o be ~
e ue
2 08 38
H Bg
04 = =
s en : Fy
* oo t €
° 20 40 60 80
Profit Margin(%|
" manos) Energy Cost Share in Total Production Cost
i+ Efficiency Index Change due to CO2 —e—Investment on Efficiency Improvement
2,5
2,0
12,0
10,0
15
8,0
10
6,0
0,5
0,0 4,0
4
2,0
-1,0
“15
0,0
Efficiency Index Change due to CO2
° 5 10
Investment on Efficiency Improvement
Investment on Efficiency Improvement
CO2 Cost over Revenue
Figure 7 Graphical Functions of Cement Industry
5. MODEL CALIBRATION AND VERIFICATION
The model is calibrated for a reference scenario with year 2000 being the base year (there
were no extra ordinary political, social or economic events that could affect energy
consumption habits or economic balances in year 2000) and solved in one year time steps.
Results for the first period are compared with actual realizations, verifying the dynamic
behavior pattern. Production, capacity, and unit price dynamics between 2000 and 2005 are
compared with the actual dynamics. Pattern similarities are recognized.
5.1. Base Case Scenario Results
Base Case scenario data and assumptions are listed in Table 1 & 2.
5.1.1. Iron& Steel Sector
12
As can be seen from Figure 8, capacity continuously grows over the time horizon. However,
there is a clear reduction in capacity change percentage due to decline in profit margin levels
caused by additional CO2 emission restriction costs.
Capacity Growth Dynamics Financial Dynamics
2000 2002 2004 «2006 «2008-2010. «2012
—P— Capacity (tonne) W-Capacity Change (5) Profit{yTl) Profit Margni%
Figure 8 Capacity Growth and Financial Dynamics of Base Scenario for Steel Sector
CO2 emission dynamics shows a different pattern than growth dynamics. It increases over
time horizon as capacity increases, but change in increase rate (CO2 Emission Change) is in
opposite direction. This is clearly because of increase in efficiency index (see also Figure 9).
The model prefers not to invest on efficiency improvement even though CO2 emissions
restriction costs are on board.
Average Annual CO2 Emission Efficiency Index
Dynamics
Figure 9 CO2 Emission and Efficiency Index Dynamics
5.1.2. Cement Sector
Capacity dynamics of cement sector (Figure 10), with a smaller growth rate, are similar to
the iron&steel sector dynamics. Capacity continuously grows over the time horizon and
capacity change percentage reduces due to decline in profit margin levels caused by
additional CO2 emission restriction costs.
13
Capacity Growth Dynamics Financial Dynamics
+—Ceprcity tonne) = Copecity Change (8)
Figure 10 Capacity Growth and Financial Dynamics of Base Scenario for Cement Sector
CO2 emission increases over time horizon as capacity increases, as it happens in steel case.
However, efficiency index dynamics shows a different pattern than steel case. Efficiency
index value improves slowly due to its high sensitivity to energy price which is 50% of unit
production cost in base case data (see also Figure 11).
Average Annual CO2 Emission Efficiency Index
Dynamics
Figure 11 CO2 Emission and Efficiency Index Dynamics
6. SCENARIO ANALYSIS
In addition to the base case scenario, two other scenarios including different CER/V ER prices,
different energy prices, and different emission restriction levels are defined as shown in Table
Table 3 Scenario Definitions
Scenario Description
Base Case Business-As-Usual
Scenario2 Base Case + Doubling in CER/VER Prices + Doubling in Energy Prices
Scenario3 Scenario2 +20% Decrease in CO2 Emission Restriction Level
6.1. Iron& Steel Sector
Scenario Analysis starts with the annual profit dynamics. As Figure 12 illustrates, Scenario2
and Scenario3 lead to serious decreases in profit values. Besides, between 2001 and 2008,
profit levels stay almost constant. Only after year 2008, increase with a lower slope compared
to Base Case in profit can be seen. Moreover, both scenarios have the same profit patterns
14
with a bias. It is clear that decrease in CO2 emission restriction level does not lead any pattem
change.
Annual Profit Dynamics
30.000
2
s
= 25.000
* L771
20.000
15.000
10.000
5.000
oO
2000 2002 2004 2006 2008 2010 2012 2014
—*Scenario2 —™-Base tr Scenario3
Figure 12 Profit Dynamics of Steel Sector for Three Scenarios
Also annual profit change (%), which is the change of profit in two successive years, shows
that Scenario2 and Scenario3 causes some decreases in profit values in the beginning of the
time horizon. The difference between the patterns of Scenaro2 and Scenario3 occurs because
of difference in the initial restriction levels. Scenario 3 has greater profit change values than
Base Case at the late periods of time horizon (see Figure 13).
Annual Profit Change (%)
21,0%
16,0%
11,0%
6,0%
10%
2000 2 2004 2006 2008 2010 2012 2014
-4,0%
—@-Scenario2 —@-Base —r— Scenario3
Figure 13 Change in Annual Profits of Steel Sector for Three Scenarios
Higher energy and emission related prices (CER/VER and energy prices) lead to more
investments on efficiency which improves (decreases) efficiency index value in Scenario2
compared to Base Case (see Figure 14). In Scenario3, additional burden of reduced restriction
level on Scenario2 causes more investment on efficiency.
15
Efficiency Index
110
100
90
80
70 AN
60 i al
50
2000 2002 2004 2006 2008 2010 2012 2014
—@-Scenario2 Base ak Scenario3
Figure 14 Efficiency Index of Steel Sector for Three Scenarios
Energy consumption per unit production decreases since efficiency improves in Scenario2 and
Scenario3. Figure 15 shows how efficiency index penetrates energy consumption per unit
production with a delay. The first four years it is almost constant because of the time delay
occurring in the investment on new physical capacity.
Energy Consumption/Unit Production
(Mwh/tonne)
4,30
4,20
4,10
4,00
3,90
3,80
3,70
3,60 ian
3,50 *
2000 2002 2004 2006 2008 2010 2012 2014
—@-Scenario2 -™-Base —a#—Scenario3
Figure 15 Energy consumption per unit production of Steel Sector for Three Scenarios
Percent change in CO2 emission levels decreases as efficiency improves in Scenario2 and
Scenario3. Figure 16 clearly illustrates the positive impact of CO2 emission restriction levels
on CO2 Emission dynamics when Scenario2 and Scenario3 are analyzed.
16
CO2 Emission Change (%)
75%
7,0%
65%
6,0%
55%
5,0%
45%
4,0%
2000 2002 2004 +«=2006 «= 2008 «S| 2010» 2012S 2014
—®-Scenario2 —l- Base tk Scenario3
Figure 16 CO2 Emission change of Steel Sector for Three Scenarios
6.2. Cement Sector
As can be seen in Figure 17, Scenario2 and Scenario3 lead to serious decreases in profit
values but not in patterns compared to Base Case. When Scenario2 and Scenario3 are
compared within themselves, the clear difference of profit values caused by reduced CO2
emission restriction level can be observed.
Annual Profit Dynamics
1.150
1.100
Millions
1.050
1.000
950
900
850
800
750
700
2000 2002 2004 2006 2008 2010 2012 2014
—@-Scenario2 —f-Base a Scenario3
Figure 17 Profit Dynamics of Cement Sector for Three Scenarios
Higher energy and emission related prices (CER/VER and energy prices) lead to more
investments on efficiency which improves (decreases) efficiency index value in Scenario2
compared to Base Case (see Figure 18) as it happens in steel case. In Scenario3, additional
burden of reduced restriction level on Scenario2 causes more investment on efficiency.
17
Efficiency Index
110
100
90
° |
70
60
50
2000 2002 2004 2006 2008 2010 2012 2014
—®Scenario2 -™ Base —*~Scenario3
Figure 18 Efficiency Index of Steel Sector for Three Scenarios
There is a negligible decrease (compared to steel case) in energy consumption per unit
production value because of Scenario2 and Scenario3 effects (see Figure 19).
Energy Consumption/Unit Production
(Mwh/tonne)
1,095
1,09 ee
1,085
1,08 Na
1,075
1,07 At
1,065
1,06 at
1,055
2000 2002 2004 2006 2008 + 2010 2012 2014
—®-Scenario2 —@- Base -—— Scenario3
Figure 19 Energy consumption per unit production of Steel Sector for Three Scenarios
Percent change in CO2 emission levels decreases in small steps as efficiency improves in
Scenario2 and Scenario3. Figure 20 clearly illustrates the positive impact of CO2 emission
restriction levels on CO2 Emission dynamics when Scenario2 and Scenario3 are analyzed like
in steel case.
18
CO2 Emission Change (%)
1,0%
0,9%
0,8%
0,7%
0,6%
0,5%
0,4%
0,3%
0,2%
0,1%
0,0%
2000 2002 2004 + «2006 ~=§ 2008 «S| 2010S 2012S 2014
—@Scenario2 —M-Base —w~Scenario3
Figure 20 CO2 Emission change of Cement Sector for Three Scenarios
7. MODEL SENSITIVITY TO KEY PARAMETERS
For model sensitivity analysis the model which is calibrated for steel sector is used. Model
sensitivity to energy & CER/VER prices and CO2 emission restriction levels, as well as to the
capacity utilization is explored. As expected, the lower the energy price, the higher value is
placed on the energy consumption/unit production and CO2 emission growth (Figure 21).
Moreover, increase in energy price has a positive influential impact on efficiency
improvement (lower efficiency index value).
2000-2002 ««2004 «= 2008-2008 201020122014
2018
—o—Energy Prices 646)T “Base ate Energy Price= 1.6277.
e-EnergyPrice=6.45"T. —aase | Energy Price=1.62ym
a. nergy Consumption'Unit Production (Mush'tonne)
b. CO2 Emission Change
2000 20022004 2006-2008 «= 2010 22am
—e-EnergyPrce=646"T “M-Base | —#— Energy Price= L Ezy
¢. Efficiency Index
Figure 21 Sensitivity to Energy Price Change
19
Increase in CER/VER price leads to a decrease in absolute value of profit at the beginning of
the period. Later on increase rate of profit accelerates continuously (see also Figure 22.a).
Furthermore, the higher the CER/V ER price, the lower the profit margin is. Similar to ‘energy
price’ case, decrease in CER/VER price value causes higher efficiency index values.
However, it is clearly observed in Figure 22.c, there is a delayed effect of CER/VER price on
efficiency index value compared to ‘energy price’ case. CER/VER price is also the only
single effect among the other discussed that can achieve important decline in annual increase
rate of CO2 Emission (see Figure 22.d).
ane
2000 2002-2004 2006. 2008-2010 20122014
TP CER/VER price=13.54YTL “A Base a CER/VER price= 3.337.
02 13.34YTL Base 2 CER/VER price 3.337
a. Annual Profit Change b. Profit Margin
130 ao%
108 | t can
100 :
cy att
Pa sx
8 sox
80
1090, 100; 004 1006 008 010 101; 14 ane
zoos 200220082008. 2008-« 20102012208
O—CER/VER price= 13.347. “MP Bace ae CER/VER price=3.33YTL
«. Efficiency Index 4, CO2 Emission Change
Figure 22 Sensitivity to CER/VER Price Change
CO2 emission restriction level is another factor that has direct impacts on model dynamics.
The higher the restriction level, the higher efficiency index (lower improvement in efficiency)
is (see Figure 23.a).
20
7.0%
192
aos 658
190
6.0%
%
ss ! 53%
2000 «200220042005 2008 201020122014 2000 7002-2008 «2006. 0B 2010's«samtE ONE
“P-20NRelmed —H-Base | a 10W Restricted + 20% Relaned Base a 20% Restricted
a, Efficiency Index b, CO2 Emission Change
cc
as |
6
3
a
n
10 -
2000-2002, 200k 2008S 08201220
“20% Relmed “Mase —2~10N Restricted
«. Profit Margin
Figure 23 Sensitivity to CO2 Emission Restriction Level Change
Moreover, the lower the capacity utilization rate, the higher value is placed on the efficiency
index value (see Figure 24.a). Among the three efficiency patterns, the one that has the
highest capacity utilization rate achieves to tum down the growing trend in 12 year period.
Further investigation also may show us decreasing trends of two other patterns that may
reveal the delaying effect of capacity utilization rate.
aos 74%
72%
102 pe
‘i sax
6.0%
190 san
62%
” r 23 = 3 es bated
2000 2002-2004 200520082010 20122014 2000 2002 2008 ©2008-2008 201020122014
capacity Unlzation=0 92 —M- Capacity Utiizstion=0 86 + Capacity Unitzation=3 52 —#- Capacity Ublizabon=0.86
4 capsesy Uuieanon=6 89 a Ceoscity Ubkeation=0.80
a. Efficiency Index b. CO2 Emission Change
16
6
2000 200220082008 «2008-2010 2012 ond
> Capacity Ubtization=0 92 “Capacity Ubsizaton=0.86
—#— Capacity Utlization=0 50
¢, Profit Margin
Figure 24 Sensitivity to Capacity Utilization Rate
21
8. CONCLUSION
This study analyzes the economical and energy-emission related policy aspects of the
industrial sector of Turkey; and aims to analyze the causal relationships and the feedback
structures among industry’s production capacity, investments on new and energy efficient
technologies, financial burden, and CO emissions. The model takes efficiency improvement
into consideration. The interactions between the CO2 emissions and efficient technology
investment depend on the price of energy and CO2 emission payment (CER/VER prices). In
addition to a Base Case Scenario, two energy-emission related scenarios are defined
restricting CO2 emissions under higher CER/V ER and energy price trajectories.
The model simulation shows that in any case it is not possible to achieve the target emission
levels (8% below the base year levels). For steel case, the most influential factor on CO2
emission reduction is found as CER/VER price. Energy price follows it as the second driver
factor. CO2 emission restriction level does not have a comparable impact on CO2 emissions
as much as energy price and CER/VER prices do. Profit margin decay rate is mainly
dependent on CER/VER price changes. Restriction level changes create significant biases but
the pattern stays unchanged.
Cement industry is less responsive to restriction and price scenarios relative to steel industry.
This is because of less energy intensive production and less emission factor composition of
primary energy resources.
As a further research, it is possible to apply the model to more sectors of industry. Moreover,
to get an insight about the interactions and linkage among three or more sectors a redesigned
version the existing model can be constructed and analyzed.
22
REFERENCES
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DCUD (2007). Turkish Iron and Steel Producers Association, In Internet
http://www.dcud.org.tr/bulten.asp.
EUROSTAT (2007). Energy Y early Statistics 2005.
Ministry of Environment and Forestry (2007). First National Communication of Turkey on
Climate Change Report.
ODYSSEE (2007). Energy Efficiency Indicators of Europe
http://www.odyssee-indicators.org/Indicators/Energy% 20saving.html.
Sterman, J. (2000). Business Dynamics: Systems Thinking and Modeling for a Complex
World. Irwin McGraw-Hill.
TCMB (2007). Turkish Cement Manufacturers' A ssociation, In Internet
http://www.tcma.org.tr/index.php?lang=EN.
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