The role of renewable energy supply and carbon tax in the improvement of energy security: a case study of Pakistan.
Anwar, Javed
ABSTRACT
In this paper, we examine the effects of renewable portfolio supply
(RPS), and carbon tax on diversification of energy resources, technology
mix in energy supply side and demand side, energy efficiency, energy
conservation and energy security during the planning horizon 2005-2050.
The analyses are based on a long term integrated energy system model of
Pakistan using the MARKAL framework to analyse the long term effects of
different policy options during 2005-2050. The effects related to energy
security are represented through a set of energy security indicators
such as energy import dependency, diversification of energy resources
through DoPED and SWI, and vulnerability. Renewable Portfolio Standards
(RPS) is a policy option to improve energy security. As renewable energy
sources are a very small portion of Pakistan primary energy mix,
therefore RPS may not be a suitable policy option for energy security
improvement in the short run, but may help in the improvement of energy
security in the long term. Carbon tax is an indirect policy option for
energy security enhancement working through emission reduction. As
carbon tax is not a direct policy option for enhancement of energy
security and it targets emission reduction, but still it affect the
energy import and the shares of other primary energy sources. Therefore,
policy of renewable portfolio supply and carbon tax may be policy
options for the enhancement of energy security.
JEL Classification: Q4, Q41, Q47, Q5, Q51, Q53
Keywords: Renewable Portfolio Supply (RPS), Carbon Tax, Energy
Supply and Technology Implications, MARKAL based Pakistan Energy System
Model
1. INTRODUCTION
As energy is a vital element for sustained economic growth and
development, therefore energy consumption is used as a basic indicator
of people's living standards. Due to technological and industrial
development, the demand of energy in Pakistan is increasing more than
the total primary energy supply; therefore, it is confronting the severe
energy deficit today. So there should be a serious concern for the
government about the energy security and should take actions for the
development of indigenous alternative and renewable energy resources.
Renewable portfolio supply (RPS), and carbon tax are the two
indirect policy options used for the improvement of energy security.
Renewable Energy Promotion is used to reduce greenhouse gas emission,
promote local energy sources and improve energy security through
reducing energy dependency and diversification of energy sources. Carbon
tax is an indirect policy option for energy security enhancement through
emission reduction. Imposing tax on carbon emission will alter the
primary energy supply mix, more efficient fuel and technologies will be
substituted for less efficient fuel and technologies. This will reduce
the primary energy demand and lead to improved energy security.
Energy security, particularly security of oil supply, has become a
key political, and economic issue in recent years. Energy security in
simple words means the security of energy supply. From economic point of
view, energy security refers to the provision of reliable and adequate
supply of energy at reasonable prices in order to sustain economic
growth.
Pakistan as an energy deficient country is facing the challenge of
energy security. A few papers analysed this issue highlighting just the
energy situation of the country, ignoring the analytical side of the
issue. Sahir and Qureshi (2007) gave an overview of the energy security
issues in the global and regional perspectives and presented the
specific implications and concerns for Pakistan. Moreover, the global
and regional energy security is not vulnerable to shortage of energy
resources but may be exposed to energy supply disruption,
non-availability of tradable resources and threatened by growing
terrorism and geopolitical conflicts.
Due to limited fossil fuel resources and poor economy, a huge
portion of the population in Pakistan still have no access to modern day
energy services such as electricity [see Mirza, et al. (2003); Mirza, et
al. (2007a); Mirza, et al. (2007b)]. To overcome energy shortage,
Pakistan should develop its indigenous fossil energy resources and
alternative renewable resources such as mini-hydro, solar and wind
resources [see Mirza, et al. (2007a); Mirza, et al. (2007b)]. Pakistan
has a vast potential of mini-hydro, solar and wind energy resources, the
exploitation of these resources could produce a enough electricity,
which could be provided to the northern hilly areas and the southern and
western deserts. This will help in reducing dependency on fossil fuels
imports and also improve energy security.
Pakistan recorded a shortfall of 40 percent between demand and
supply of electricity in 2008 [see Asif (2009)]. To overcome this
shortfall, Pakistan has many sustainable energy options including hydro,
biomass, solar, and wind resources. The total estimated hydropower
potential is more than 42 GW and so for only 6.5 GW has been utilised.
Although biomass is another conventional resource of energy in Pakistan
but still it is not commercialised. Solar and wind options are also
identified as potential energy resources but still these are not in
operation on a vast scale.
This paper is aimed at analysing the effects of policies of
renewable portfolio supply (RPS), and carbon tax on diversification of
energy resources, technology mix in energy supply side and demand side;
energy efficiency and energy conservation; and energy security during
the planning horizon 2005-2050. A MARKAL-based model for an integrated
energy system of Pakistan was developed to accomplish the research.
The paper is structured as follows. Section 2 gives an overview of
Pakistan energy outlook. Section 3 provides the methodology and model
formulation. Section 4 gives a brief description of the scenarios while
analysis of the base case, renewable portfolio supply case and carbon
tax case is given in Section 5. Finally, Section 6 presents the main
conclusions.
2. PAKISTAN ENERGY OUTLOOK
Pakistan energy sector consists of electricity, gas, petroleum and
coal. Oil and gas are major contributors to the Pakistan's primary
energy supply mix. (Fig. 1.) The primary energy supply mix of Pakistan
consists of 78 percent oil and gas, 13 percent hydro, 8 percent coal and
1 percent nuclear (see Pakistan Economic Survey, 2006-07). The most
interesting feature of Pakistan's primary energy supply mix is that
share of oil decreases from 32 percent in 2005-2006 to 29 percent in
2010-2011, and share of gas increases from 39 percent in 2005-2006 to 43
percent in 2010-2011, while the shares of other resources remained
almost constant over the same period. It shows that Pakistan energy
sector is switching from oil to gas and other resources.
Pakistan indigenous oil production meets only one-sixth of the
current oil demand while imports one-third of the total energy demand.
This implies that Pakistan is unable to meet energy demand from its
internal resources, and is a net importer of energy.
Historical data shows that Pakistan has been dependent on oil
imports from the Middle East since it came into being. The crude oil
imports for the year 2005-06 were about 8.56 mtoe as compared to local
production of crude oil of 3.24 mtoe and the imports of petroleum
products were about 5.85 mtoe. The cost of all these oil and petroleum
products was equivalent to US$ 4.6 billion, which is roughly equal to
25-30 percent of the total import bill. This huge import bill put
enormous pressure on the economy [Pakistan (2005)]. On the other hand,
the primary energy demand has increased significantly but the primary
energy supply remained at the same level, which created a huge gap
between demand and supply. As a result, the country is facing huge
energy shortage.
Pakistan imports about 29 percent of total primary commercial
energy. Although Pakistan has a variety of energy resources, but
approximately 80 percent of the energy supply is from oil and natural
gas. The dependence on imported fuels especially on imported oil is
likely to increase, which will affect Pakistan's economy adversely.
To avoid this negative impact, we should explore opportunities for
untapped large renewable energy resources in the form of mini-hydro,
solar and wind projects so that Pakistan can fulfil its energy needs and
keep up its economic growth.
Table 1 displays the annual trends of primary energy supplies and
their per capita availability from 1996-97 to 2005-06, which indicates
that the primary energy supply has increased by 50 percent and the per
capita availability by 26 percent in the last 10 years.
3. METHODOLOGY
3.1. Model Formulation
This study makes use of bottom up MARKAL-based least cost energy
system model (1) as an analytical framework for the analysis of energy
security in case of Pakistan [Loulou, et al. (2004)]. It models the
flows of energy in an economy from the source of primary energy supply,
conversion of primary energy into secondary energy, and finally the
delivery of various forms of energy to the end-use services. In the
model, these flows of energy are described through detailed
representation of technologies providing an end-use demand. Figure 2
shows the simplified structure of the MARKAL modelling framework through
reference energy system.
Basically, Pakistan energy system model consists of four modules;
primary energy supply, conversion technologies, end-use technologies and
demand for energy services. Primary energy supplies are hydro, crude
oil, natural gas, imports of oil, nuclear, solar wind etc., while
conversion technologies module consists of power generation and
transmission systems, oil refineries, natural gas processing and
transmission systems. Service energy demand is grouped into five
sectors: agriculture, residential, commercial, industrial, and transport
sector (see Figure 2).
End use demands are a measure of the useful energy output provided
by the demand technologies in each end use demand category. It is
assumed in MARKAL that the essential energy demand is for some service
(an amount of cooking or heating), while the basic service is fixed, it
can be provided by different mixes of devices and fuels. End-use demand
technologies and conversion technologies are described in detail in
Appendix A&B.
The objective function of the least cost energy system is to
minimise the total discounted cost during the planning horizon; the
total cost comprises of capital cost net of salvage value, fuel cost,
operation, and maintenance costs. The optimal solution given by the
model must satisfy energy demand, capacity and energy demand-supply
balance constraints.
[FIGURE 2 OMITTED]
3.2. Service Demand Projection
Service energy demand is projected through three different
techniques using econometric models as well as using identity relating
service energy demand in particular sector to GDP and Value Added of the
particular sector. In the econometric approach, the dependent variables
are number of energy devices, passenger kilometres, ton kilometres etc.
The independent variables are Gross Domestic Product (GDP) and
population. The other approaches consider the service demand of
particular sector in particular year as dependent on the service demand
of sector in base year multiplied by the ratio of the current year GDP
and base year GDP; the service demand of particular sector in particular
year depends on the service demand of sector in base year multiplied by
the ratio of the current year value added and base year value added.
The econometric approach was used to project the service energy
demand in transport and residential sectors, while the service energy
demand in industrial, commercial and agriculture sectors was projected
through economic value added and GDP approach.
Service demand projection for fans, air conditioners and cooking is
based on the GDP growth through the following formulation:
[SD.sub.i,k,t] = [SD.sub.i,k,0] x [GDP.sub.t]/[GDP.sub.0]
Where [SD.sub.i,k,t], [SD.sub.i,k,0] are service demand of sector i
sub-sector k, in year t and base year respectively, [GDP.sub.t] and
[GDP.sub.0] represent Gross Domestic Product in year t and Gross
Domestic Product in base year.
Service demand projection for agriculture, commercial and
industrial sectors is based on the following formulation:
[SD.sub.i,k,t] = [SD.sub.i,k,0] x [VA.sub.i,k,t]/[VA.sub.i,k,0]
Where [SD.sub.i,k,t] is service demand of sector i subsector k in
year t, [SD.sub.i,k,0] is service demand of sector i subsector k in base
year, [VA.sub.i,k,0] is the in, sector [k.sub.th] subsector value added
in the base year and [VA.sub.i,k,t] is the [i.sub.th] sector [k.sub.th]
subsector value added in the year t.
Electricity-related service demand and supply were considered in
six time slices along with two seasons (summer and winter) and two
periods (peak and off-peak) so that the variation of electricity loads
on the energy system can be reflected.
3.3. Energy Security Indices
The prime objective of this research is to classify policy options
for the improvement of energy security of Pakistan. The fundamental and
suitable criterion for the classification of policy options are the
calculation of energy security indices for the whole planning horizon
2005-2050. In this study, four energy security indicators are used, i.e.
Net Energy Import Ratio (NEIR), Shannon-Wiener Index (SWI),
Diversification of Primary Energy Demand (DoPED), Vulnerability Index
(VI) and Energy Intensity (EI). These indicators are estimated by using
the MARKAL model which is energy-system model depicting long-term
development of the energy-system. The indicators are explained as
follows:
NEIR = Net Imports/(Domestic Production + Net Imports)
The value of NEIR close to 1 indicates that the energy system of
that country is to a large extent dependent on energy imports.
SWI = - [[summation].sub.i] [x.sub.i] ln([x.sub.i])
where [x.sub.i] represents the share of energy supply from each
source. A higher value of SWI means well diversified energy sources
ultimately leading to improved energy security while a lower value
implies low diversification of energy sources and poorer energy security
[Grubb, el al. (2006)].
DoPED = [square root of ([Coal.sup.2] + [Oil.sup.2] + [Hydro.sup.2]
+ [Biomass.sup.2] + [Other.sup.2] /Total Primary Energy Demand)]
Where the value of DoPED close to 1 indicates that the economy is
reliant on one energy resource while a value close to zero (0) means
that the energy sources in the economy are uniformly spread among
several energy resources.
Vulnerability may be linked to strong energy import dependency i.e.
it may also be linked to the high level of energy import value in GDP.
It refers both to the quantity and cost of energy imports.
VI = EEI/GDP
where; EEI is expenditure on energy import and GDP is Gross
Domestic Product.
EI = TPES/GDP
Where EI is Energy Intensity, TPES is Total Primary Energy Supply
and GDP is Gross Domestic Product.
4. SCENARIOS DESCRIPTION
Three scenarios were studied: (i) Base case, (ii) renewable
portfolio supply (RPS) case, and (iii) carbon tax case. Details of the
scenarios are explained as follows.
4.1. Base Case
In this case, Pakistan GDP growth rate was assumed to grow at an
annual growth rate of 7.0 percent and the growth rate of population was
estimated at an annual growth rate of 1.9 percent based on the GDP and
population data for the period of 2000-2013 [Pakistan (2006-07), World
Economic Outlook Database (2008)].
Under the base case, the maximum available stock of fossil energy
resource (e.g., coal, oil and petroleum products, and natural gas) was
estimated as the sum of proven reserve of the resource, its probable
reserve and its possible reserve. In the power sector, renewable energy
options (hydro, wind, and solar), natural gas-based power plants as well
as nuclear power plants were included in the model (see Appendix B). The
options considered for the transportation sector include road, water and
air transports.
4.2. Renewable Portfolio Supply Scenario
Renewable Energy Promotion is used to reduce emissions, promote
local energy sources and improve energy security through reducing energy
dependency and diversification of energy sources. To assess the effects
of renewable portfolio supply (RPS), we implemented five different
constraints and calculated energy security indicators for the whole
planning horizon 2005-2050. The constraints are:
(a) RPS 10--Total renewable based electricity generation is set to
be 10 percent of total electricity generation (excluding large hydro)
during period of 2005 to 2050.
(b) RPS20--Total renewable based electricity generation is set to
be 20 percent of total electricity generation (excluding large hydro)
during period of 2005 to 2050.
(c) RPS30--Total renewable based electricity generation is set to
be 30 percent of total electricity generation (excluding large hydro)
during period of 2005 to 2050.
(d) RPS40--Total renewable based electricity generation is set to
be 40 percent of total electricity generation (excluding large hydro)
during period of 2005 to 2050.
(e) RPS50--Total renewable based electricity generation is set to
be 50 percent of total electricity generation (excluding large hydro)
during period of 2005 to 2050.
4.3. Carbon Tax Scenario
Carbon tax is an indirect policy option for energy security
enhancement through emission reduction. Imposing tax on carbon emissions
will alter the primary energy supply mix, more efficient fuel and
technologies will be substituted for less efficient fuel and
technologies. This will reduce the primary energy demand and lead to
improved energy security. To assess the effects of carbon tax on energy
security, we implemented different constraints in the model. The
constraints are:
(a) C[O.sub.2]-10-Impose a tax of 10US$/tC[O.sub.2] until 2050.
(b) C[O.sub.2]-15-Impose a tax of 15US$/tC[O.sub.2] until 2050.
(c) C[O.sub.2]-20-Impose a tax of 20US$/tC[O.sub.2] until 2050.
(d) C[O.sub.2]-25-Impose a tax of 25US$/tC[O.sub.2] until 2050.
(e) C[O.sub.2]-30-Impose a tax of 30US$/tC[O.sub.2] until 2050.
5. ANALYSIS OF THE BASE CASE
Energy system development of Pakistan during the planning horizon
of 2005-2050 under the base case is discussed as follows:
5.1. Primary Energy Supply in the Base Case
As Can be seen from Figure 3, the primary energy supply in the base
case under the renewable portfolio supply scenario shows an increasing
trend over the whole planning horizon 2005-2050 indicating the rising
energy supply and per capita energy availability. The primary energy
supply in Pakistan is found to increase from 2475 PJ in 2005 to 35,559
PJ in 2050. Results from model simulation show that oil and gas are the
major parts of primary energy supply in the base case, while coal and
renewables are also contributing to primary energy supply. Over the
time, primary energy supply mix is changed and the cheap resources
(renewables and coal) dominate the primary energy supply mix.
As can be seen from Figure 4, the primary energy supply in the base
case under the carbon tax scenario shows an increasing trend over the
whole planning horizon 2005-2050. The primary energy supply is estimated
to increase from 2475 PJ in 2005 to 22,684 PJ in 2050. Results from
model simulation show that oil and gas have major contribution to
primary energy supply in the base case, while coal and renewables are
also contributing to primary energy supply. Over the time, primary
energy supply mix is changed and the cheap resources (renewables) and
oil dominate the primary energy supply mix.
Sector wise fuel consumption in both scenarios is presented in
Figure 5 and Figure 6. In the renewable portfolio supply scenario,
industrial sector, residential sector and transport sector dominate the
sectoral fuel consumption in 2005, while the shares of industrial sector
and transport sector have increased considerably while the share of
residential sector has declined in 2050. Similarly under carbon tax
scenario, transport sector holds the largest share in the sector wise
fuel consumption followed by industrial sector and residential sector in
2005, while the share of residential sector has declined and shares of
transport sector and industrial sector have grown significantly in 2050.
[FIGURE 3 OMITTED]
[FIGURE 4 OMITTED]
[FIGURE 5 OMITTED]
[FIGURE 6 OMITTED]
5.2. Results and Discussion
5.2.1. Energy Security under Renewable Portfolio Supply and Carbon
Tax Scenarios
For the classification of policy options for the improvement of
energy security of Pakistan, we imposed five different types of
Renewable Portfolio Supply and Carbon Tax constraints (These constraints
are briefly explained in section-4.2 and 4.3) in the MARKAL model for
Pakistan. On the basis of these constraints, we analysed import
dependency, diversification of energy resources, vulnerability, and
energy intensity for the whole planning horizon.
5.2.1.1. Energy Import Dependency under Renewable Portfolio Supply
and Carbon Tax Scenario
Energy Import Dependency is one of the key aspects of energy
security that can be calculated as a percentage of net energy imports in
total primary energy supply. Energy security indicator based on net
energy import ratio (NEIR) is shown in Figure 7 and Figure 8. As can be
seen from Figure 7, the net energy imports from the rest of the world
indicated by NEIR would increase from 24 percent in 2005 to 41 percent
in 2050 under renewable portfolio supply scenario indicating higher
energy import dependency, but as more renewable energy resources are
exploited and enter in the energy system, the energy import dependency
decreases from 41 percent in base case to 38 percent in RPS50 scenario,
which is a considerable reduction in energy import dependency. The main
factor behind the reduction of energy import dependency is the share of
renewable resources based electricity generation in the total
electricity generation, which increases significantly as compared to the
base case and that is a signal towards energy security improvement in
Pakistan.
On the other hand, energy import dependency under carbon tax
scenario would increase from 24 percent in 2005 to 45 percent in 2050 as
shown in Figure 8. Energy import dependency in carbon tax scenario has a
mixed trend, but as more and more carbon tax is imposed, import
dependency increases. The main reason behind the increased energy import
dependency is the increased shares of imported oil in the primary energy
supply in 2050 under carbon tax scenario.
5.2.1.2. Diversification under Renewable Portfolio Supply and
Carbon Tax Scenario
Diversification of primary energy sources is another important
factor of energy security. DoPED and Shannon-Wiener Index (SWI)
illustrate the diversification of the primary energy supply mix of the
future energy system. As can be seen from Figure 9, the value of DoPED
drops from 61 percent in the 2005 to 56 percent in 2050 in the base case
implying better diversification among different energy resources under
the renewable portfolio supply scenario. Diversification decreases up to
2015 and then in the long run, it increases up to 2050 in all renewable
portfolio supply scenarios. On the other hand, diversification under
carbon tax scenario reflected somewhat mixed trend (Figure 10). First,
diversification of energy resources improves up to 2025 in the base case
and then it deteriorates up to 2050. While in case of all carbon tax
scenarios, diversification improves up to 2035 and then starts to
deteriorate up to 2050.
Diversification can also be examined through Shannon-Wiener Index
(SWI); higher value of SWI implies better diversification among
different energy resources. Figure 11 and Figure 12 depicts the model
simulated values for SWI under the renewable portfolio supply and carbon
tax scenarios. As can be seen from Figure 11, the value of SWI increases
from 51 percent in the 2005 to 55 percent in 2050 in the base case
implying better diversification among different energy resources under
the renewable portfolio supply scenario. Diversification index does not
perform well up to 2015 and then in the long run, it shows improved
performance up to 2050 in all renewable portfolio supply scenarios. On
the other hand, diversification under carbon tax scenario demonstrates a
mixed trend in different time periods (Figure 12). First,
diversification of energy resources improves up to 2025 in the base case
and then it drops up to 2050. While in case of all carbon tax scenarios,
diversification shows better performance up to 2035 and then starts to
worsen up to 2050.
Both the indices ultimately imply better diversification of energy
resources by 2035 as compared to 2005 that leads to energy security
improvement in Pakistan by 2035.
5.2.1.3. Vulnerability and Energy Intensity under Renewable
Portfolio Supply and Carbon Tax Scenario
The energy security indices NEIR, SWI, and DoPED quantify the
physical availability of primary energy supply to the economy ignoring
the monetary significance of energy imports. To capture the economic
significance of energy imports, we used vulnerability index.
As can be seen from Figure 13, vulnerability under renewable
portfolio supply scenario shows a declining trend up to 2020 and then
reflects rising trend up to 2050 in the base case as the amount of
imports in the total primary energy increase over the time. Under all
renewable supply portfolio scenarios, vulnerability index exhibits the
increasing trend, however, it declines as more and more renewable energy
enters into the system over time. The declining behaviour of
vulnerability index (Figure 13) implies that vulnerability will decrease
in the long run as compared to short run in all cases that will lead to
enhanced energy security of Pakistan under the renewable portfolio
supply scenarios.
Under carbon tax scenario, vulnerability decreases up to 2020 in
base case as well as in all carbon tax scenarios and then it increases
up to 2050 (Figure 14). The main reason for increasing vulnerability is
the rising shares of energy imports from the Middle East.
The other energy security indicator such as energy intensity
(Figure 15 and Figure 16) is a measure of the energy efficiency of an
economy. It is calculated as units of energy per unit of GDP. High
energy intensities indicate a high price or cost of converting energy
into GDP and low energy intensity indicates a lower price or cost of
converting energy into GDP. In case of renewable portfolio supply
scenario, energy intensity has a rising trend showing economic
inefficiency in the base case (Figure 15), while energy intensity
decreases with the inclusion of renewable energy in the system that
reflects economic efficiency of the energy system under all renewable
portfolio supply scenarios. This is an indication of energy security
enhancement in the renewable portfolio supply scenarios.
In case of carbon tax scenario (Figure 16), energy intensity
decreases up to 2020 in the base case, which is a sign of economic
efficiency as more efficient technologies are put in place under carbon
tax scenario and after 2020, energy intensity shows a mixed trend up to
2050 in the base case as well as in all carbon tax scenarios.
5.2.1.4. Green House Gases Emission under Renewable Portfolio
Supply and Carbon Tax Scenario
Environmental emissions are decomposed into green house gases
emissions e.g. C[O.sub.2], C[H.sub.4] CO, S[O.sub.2], N[O.sub.x], and
[PM.sub.10]. According to Figure 17, total cumulative green house gases
emissions decrease from 165 million tons in base case to 151 million ton
in RPS50 scenario i.e. there is 9 percent reduction in green house gases
emissions under renewable portfolio supply scenario, which is quite
significant. As can be seen from Figure 18, total cumulative greenhouse
gases emissions is reduced from 72 million tons in base case to 19
million ton in CT30 scenario, which is a significant reduction in
greenhouse gases emissions under carbon tax scenario.
All these facts imply that renewable portfolio supply and carbon
tax policies can be used as combined policy options for the enhancement
of energy security in case of Pakistan.
[FIGURE 17 OMITTED]
[FIGURE 18 OMITTED]
6. CONCLUSIONS
This paper investigates the effects of renewable supply portfolio
and carbon tax policies on diversification of energy resources,
technology mix in energy supply side and demand side; energy efficiency
and energy conservation; and energy security during the planning horizon
2005-2050. A MARKAL-based model for an integrated energy system of
Pakistan was developed for this cause.
Renewable Portfolio Supply (RPS) is an important policy option to
improve energy security. Renewable energy promotion is used to reduce
emission, promote local energy sources and improve energy security
through reducing energy dependency and diversification of energy
sources. As more renewable energy resources are exploited and entered
into the energy system, the energy import dependency decreases by 3
percent in RPS50 scenario, which is a considerable reduction in energy
import dependency. Diversification of primary energy sources measured
through DoPED and Shannon-Wiener Index (SWI) demonstrate 5 percent
increase in diversification of the primary energy supply mix of the
future energy system. Declining vulnerability and intensities in RPS
Scenarios reflect enhanced energy security in long run. All the energy
security indicators reflect better position under renewable portfolio
supply scenarios; therefore Renewable Portfolio Supply (RPS) is a
suitable policy option for energy security improvement in the long term
in case of Pakistan.
Carbon tax is an indirect policy option for energy security
enhancement through emission reduction. Imposing tax on carbon emission
will alter the primary energy supply mix, more efficient fuel and
technologies will be substituted for less efficient fuel and
technologies. This will reduce the primary energy demand and lead to
improved energy security. Under carbon tax, import dependency has
reflected an increasing trend, while diversification of energy
resources, vulnerability and energy intensity show better energy
security up to 2035. Therefore Carbon Tax Policy may be a suitable
policy option for energy security improvement in the long term.
Under Renewable Portfolio Supply (RPS) and Carbon Tax scenarios,
Green House Gases (GHG) emissions are reduced by 9 percent, which is a
significant reduction. This reduction in GHG emission is a sign of
environmental security. So these two policy options not only enhance
energy security, but also ensure environmental security.
Javed Anwar <
[email protected]> is Assistant Professor,
International Islamic University, Islamabad-Pakistan, and PhD Candidate
(Energy Economics and Planning) Asian Institute of Technology, Thailand.
Appendices
APPENDIX-A
End-use Demand Technologies
Sector End-use Demand Technologies
Agriculture Tractors and Electric Motors
Commercial AC, Lighting, Refrigerators, Thermal Use
and Other Electric Appliances
Industrial Cement, chemical, electricity,
equipment, food, paper, steel, sugar,
textile, others.
Residential Air-conditioning, cooking, fan, iron,
lighting, refrigerator, TV and other
electric appliances.
Transport Air Passenger Air plane
Air Freight Air Plane
Water Freight Ship
Rail Passenger Locomotive rail
Rail Freight Locomotive rail
Road Passenger Car, bus, van, pickup, taxi,
three-wheelers, two-wheelers
Road Freight Trucks, Tankers, Pickups
APPENDIX-B
Conversion Technologies
Technology Fuel Type
Power Generation
Hydro
a) Hydro Reservoir
b) Hydro Canal
Fossil Fuels
a) Fluidised bed combustion(FBC) Coal
b) Gas Turbine Gas and HSD
c) Combine Cycle Gas and HSD
d) Gas Turbine Gas
e) Steam Dual Fuel Combustion (Gas + FO)
f) Oil Fired Fuel Oil
g) Gas Turbine Combine Cycle Gas and FO fired
Gas and HSD oil Fired
Nuclear
a) Nuclear Power Plant Uranium
Renewable
Solar Photovoltaic, Solar Thermal,
Wind Turbine, Mini Hydro
Process Technologies
a) Oil refinery Crude Oil
b) Gas Processing Plant Natural Gas
APPENDIX-C
Model Formulation
Objective Function of the Integrated Energy System Cost Model
The objective function is the sum over all of the discounted
present value of the stream of annual costs incurred in each year of the
horizon (no reference for this?). Therefore:
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (1)
where, NPV is the net present value of the total cost for all
regions, ANNCOST(r, t) is the annual cost in region r for period t, d is
the general discount rate, NPER is the number of periods in the planning
horizon, NYRS is the number of years in each period t, R is the number
or regions.
In order to minimise total discounted cost, the MARKAL model must
satisfy a number of constraints. These constraints show the physical and
logical relationships to describe the associated energy system.
(a) Satisfaction of Energy Service Demands
For each time period t, region r, demand d, the total activity of
end-use energy technologies must be at least equal to the specified
demand. Hence:
[[summation].sup.all d.sub.k] CAP(r,t,k) [greater than or equal to]
D(r,t,d) ... (2)
where CAP(r, t, k) is the installed capacity of technology k, in
period t, in region r, D(r, t, d) is the energy demand for end-use d in
region r, in period t.
(b) Use of Capacity
In each time period, the model may use some or all of the installed
capacity according to the technology availability factor (AF) i.e. the
model may utilise less than the available capacity during certain
time-slices, or even throughout one whole period. Therefore, the
activity of the technology may not exceed its available capacity.
ACT(r,t,k,s) [less than or equal to] AF(r,t,k,s) CAP(r,t,k) ... (3)
where ACT(r, t, k, s) is the activity level of energy technology k,
in period t, in region r, for time slice s, AF(r, t, k, s) is the
availability parameters.
(c) Demand-Supply of Energy Balance
For each commodity c, time period t, region r, this constraint
requires that the disposition of each commodity may not exceed its
supply.
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (4)
where Output(r, t, k, c) is the amount of energy commodity c,
produced per unit of technology k in region r in period t, MINING(r, t,
c, l) is the quantity of energy commodity c extracted in region r at
price level l in period t, FR(s) is the fraction of the year covered by
time-slice s, IMPORI(r, t, c, l) is the quantity of energy commodity c,
price level l, exogenously imported or exported by region r in period t,
Input(r, t, k, c) is the amount of energy commodity c required to
operate one unit of technology k, in region r and period t, EXPORI(r, t,
c, l) is the quantity of energy commodity c, price level l, exogenously
imported or exported by region r in period t.
REFERENCES
Alhajji, A. F. (2007) What Is Energy Security? Definitions and
Concepts. Middle East Economic Survey 50:45.
Alhajji, A. F. and James L. Williams (2003) Measures of Petroleum
Dependence and Vulnerability in OECD Countries. Middle East Economic
Survey 46:16.
Anwar, J. (2010) Analysis of Energy Security using Partial
Equilibrium Model: A Case of Pakistan. The Pakistan Development Review
49:4, 925-940.
APEC Energy Demand and Supply Outlook (2006) Projections to 2030.
Economy Review.
Asia Pacific Energy Research Centre (2003) Energy Security
Initiative.
Asia Pacific Energy Research Centre (APERC) (2007) A Quest for
Energy Security in the 21st Century. Japan: Institute of Energy
Economics, Japan.
Asif, M. (2009) Sustainable Energy Options for Pakistan, Renewable
and Sustainable Energy Reviews 13, 903-909.
Bahgat, G. G. (2005) EU Seeks Energy Security in Stronger Suppliers
Ties. Oil and Gas Journal, Oct 10, 2005.
Bertel, E. (2005) Nuclear Energy and the Security of Energy Supply.
Facts and Opinions, NEA News 2005--No. 23.2.
Bielecki, J. (2002) Energy Security: Is the Wolf at the Door? The
Quarterly Review of Economics and Finance 42:2, 235-250.
Blyth and Leferve (2004) Energy Security and Climate Change Policy
Interactions: An Assessment Framework. (IEA Information Paper).
Bohi, D. R. and M. A. Tom (1996) The Economics of Energy Security.
Kluwer Academic Publishers: Boston/Dordrecht/London.
Bohi, Douglas R. (1989) Energy Price Shocks and Macroeconomic
Performance. Resources for the Future Washington (DC).
Bohi, Douglas R. and W. D. Montgomery (1982) Oil Prices, Energy
Security and Import Policy. Resources for the Future Washington (DC).
Christoph, W. Fre (2004) The Kyoto Protocol--A Victim of Supply
Security? Or: If Maslow were in Energy Politics. Energy Policy 32,
1253-1256.
Clingendael Institute/Clingendael International Energy Programme
(CIEP) (2004) EU Energy Supply Security and Geopolitics
(Tren/C1-06-2002) CIEP Study. The Hague, Netherlands.
Costantini, V., F. Gracceva, A. Markandya, and G. Vicini (2007)
Security of Energy Supply: Comparing Scenarios from a European
Perspective. Energy Policy 35:1, 210-226.
European Commission (2001) Green Paper: Towards a European Strategy
for the Security of Energy Supply.
Fishbone, L. G., G. Giesen, H. A. Hymmen, M. Stocks, H. Vos. D.
Wilde, D., R. Zoelcher, C. Balzer, and H. Abilock (1983) Users Guide for
MARKAL: A Multi-Period, Linear Programming Model for Energy Systems
Analysis. BNL Upton, NY, KFA, Julich, Germany, BNL 51701.
Gnansounou, E. and J. Dong (2004) Opportunity for Inter-regional
Integration of Electricity Markets: The Case of Shandong and Shanghai in
East China. Energy Policy 32:15, 1737-1751.
Grubb, M., L. Butler, and P. Twomey (2006) Diversity and Security
in UK Electricity Generation: The Influence of Low-carbon Objectives.
Energy Policy 34:18, 4050-4062.
Helm, D. (2006) Energy Policy: Security of Supply, Sustainability
and Competition. Energy Policy 30:3, 173-184.
Hillard, G. Huntington and P. A. Brown Stephen (2004) Energy
Security and Global Climate Change Mitigation. Energy Policy 32,
715-718.
Hogan, Lindsay, Lindsay Fairhead, and Andrew Gurney (2005) Energy
Security in APEC: Assessing the Costs of Energy Supply Disruptions and
the Impacts of Alternative Energy Security Strategies. (ABARE Research
Report 05.2).
IEA (2004) World Energy Outlook 2004. Paris: International Energy
Agency.
IEA (2007a) Energy Security and Climate Change; Assessing
Interactions. Paris: International Energy Agency.
IEA (2007b) Contribution of Renewable to Energy Security. Paris:
International Energy Agency.
IEA (2007c) World Energy Outlook 2007- China and India Insights.
Paris: International Energy Agency.
Kruyt, B., et al. (2009) Indicators for Energy Security. Energy
Policy.
LaCasse, C. and A. Plourde (1995) On the Renewal of Concern for the
Security of Oil Supply. The Energy Journal 16:2, 1-24.
Li, Xianguo (2005) Diversification and Localisation of Energy
Systems for Sustainable Development and Energy Security. Energy Policy
33, 2237-2243.
Loulou, R. and A. Kanudia (1998) The Kyoto Protocol,
Inter-Provincial Cooperation, and Energy Trading: A Systems Analysis
with Integrated MARKAL Models, GERAD Discussion PaperG-98-42, GERAD and
McGill University, Montreal, Canada.
Loulou, R., G. Goldstein, and K. Noble (2004) Energy Technology
Systems Analysis Programme: Documentation for the MARKAL Family of
Models.
Lucia, Pantelimon (1999) Technological Development and Strategies
for Achieving Energy Security and Constraints. Applied Energy 64,
307-310.
Mamdouh, G. Salameh (2003) The New Frontiers for the United States
Energy Security in the 21st Century. Applied Energy 76, 135-144.
Maw, U. K. M. (2005) Current Energy Situation and MARKAL Activities
in Myanmar, Annex IX Technical Conference, ETSAP Taipei, Taiwan, 4-7
April 2005.
Mirza, U. K., M. Mercedes Maroto-Valer, and Nasir Ahmad (2003)
Status and Outlook of Solar Energy Use in Pakistan. Renewable and
Sustainable Energy Reviews 7, 501-514.
Mirza, U. K., M. Nasir Ahmad, and Tariq Majeed (2007) An Overview
of Biomass Energy Utilisation in Pakistan. Renewable and Sustainable
Energy Reviews 7, 501-514.
Mirza, U. K., M. Nasir Ahmad, Tariq Majeed, and Khanji Harijan
(2007a) An Hydropower use in Pakistan: Past, Present and Future.
Renewable and Sustainable Energy Reviews 12, 1641-1651.
Mirza, U. K., M. Nasir Ahmad, Tariq Majeed, and Khanji Harijan
(2007b) Wind Energy Development in Pakistan. Renewable and Sustainable
Energy Reviews 11, 2179-2190.
Muneer, T. and M. Asif (2005) Prospects for Secure and Sustainable
Electricity Supply for Pakistan. Renewable and Sustainable Energy
Reviews 20, 1-19.
Neff, L. T. (1997) Improving Energy Security in Pacific Asia:
Diversification and Risk Reduction for Fossil and Nuclear Fuels. Center
for International Studies. Massachusetts Institute of Technology.
Owen, Anthony D. (2004) Oil Supply Insecurity: Control versus
Damage Costs. Energy Policy 32, 1879-1882.
Pakistan, Government of (2006-07) Pakistan Economic ? Survey
2006-07. Islamabad: Ministry of Finance, Government of Pakistan.
Pakistan, Government of (Various Years) Pakistan Energy Yearbook
1995 to 2005. Islamabad: Hydrocarbon Development Institute of Pakistan.
Saghir, Jamal (2006) World Bank Perspective on Global Energy
Security, G8 Energy Ministerial Meeting, March 16, 2006 Moscow.
Sahir, Mukhtar H. and Arshad H. Qureshi (2007) Specific Concerns of
Pakistan in the Context of Energy Security Issues and Geopolitics of the
Region. Energy Policy 35 2031-2037.
Siddiqi, Toufiq A. (1995) India-Pakistan Cooperation on Energy and
Environment to Enhance Security. Asian Survey 35, 280-290.
Tempest, P. (2004) Energy Security in an Insecure World. IAEE News
Letter 1st Quarter 2004, pp. 19-20.
Verma, Shiv Kumar (2007) Energy Geopolitics and Iran-Pakistan-India
Gas Pipeline. Energy Policy 35, 3280-3301.
World Economic Outlook Database (October 2008) International
Monetary Fund.
Wright, Philip (2005) Liberalisation and the Security of Gas Supply
in the UK. Energy Policy 33, 2272-2290.
(1) Model formulation is described in Appendix-C.
Table 1
Primary Energy Supply and Per Capita Availability
Primary Energy Per Capita
Supply Availability
(Tons of Oil (Tons of Oil
Year Equivalent) % Change Equivalent) % Change
1996-97 38.515 -0.6 0.295 -3.0
1997-98 40.403 4.9 0.305 3.3
1998-99 41.721 3.3 0.313 2.7
1999-00 43.185 3.5 0.317 1.2
2000-01 44.404 2.8 0.319 0.6
2001-02 45.068 1.5 0.315 -0.1
2002-03 47.056 4.4 0.324 2.7
2003-04 50.831 8.0 0.341 5.3
2004-05 55.533 9.3 0.363 6.7
2005-06 57.855 4.2 0.372 2.2
Source: Pakistan Economic Survey 2006-07.
Fig. 1. Primary Energy Supply Mix (2005-2010)
2005-06 2008-09 2010-11
Oil 32 29 29
Gas 39.3 43.7 43.2
LPG 1.8 1.5 1.3
Electricity 16.2 15.3 16.2
Coal 10.6 10.4 10.4
Source: Pakistan Economic Survey 2011-12.
Fig. 7. Import Dependency under Renewable Portfolio
Supply Scenario
2005 2010 2015 2020 2025
BASE 24 22 30 30 35
RPS10 24 22 31 31 36
RPS20 25 22 30 31 36
RPS30 24 22 29 30 36
RPS40 24 22 29 30 36
RPS50 24 22 30 30 36
2030 2035 2040 2045 2050
BASE 38 40 41 41 41
RPS10 39 40 41 41 41
RPS20 39 40 40 40 40
RPS30 38 40 40 40 40
RPS40 38 39 39 39 39
RPS50 38 39 39 39 38
Fig. 8. Import Dependency under Carbon Tax Scenario
2005 2010 2015 2020 2025
BASE 24 27 30 32 41
CT10 24 28 29 30 40
CT15 24 29 29 30 40
CT20 24 29 29 30 40
CT25 24 29 29 31 40
CT30 24 28 29 35 46
2030 2035 2040 2045 2050
BASE 43 44 44 45 45
CT10 42 44 44 44 50
CT15 42 43 44 50 50
CT20 42 43 50 50 50
CT25 45 50 50 50 50
CT30 49 50 50 50 50
Fig. 9. Diversification of Energy Resources under Renewable
Portfolio Supply Scenario
2005 2010 2015 2020 2025
BASE 0.61 0.65 0.69 0.66 0.64
RPS10 0.61 0.65 0.70 0.66 0.64
RPS20 0.61 0.65 0.70 0.66 0.64
RPS30 0.61 0.65 0.70 0.67 0.65
RPS40 0.61 0.65 0.70 0.67 0.65
RPS50 0.61 0.65 0.70 0.67 0.65
2030 2035 2040 2045 2050
BASE 0.62 0.59 0.58 0.57 0.56
RPS10 0.62 0.60 0.58 0.57 0.56
RPS20 0.62 0.60 0.58 0.57 0.57
RPS30 0.63 0.60 0.59 0.58 0.57
RPS40 0.63 0.61 0.60 0.58 0.58
RPS50 0.64 0.62 0.61 0.59 0.60
Fig. 10. Diversification of Energy Resources under Carbon Tax Scenario
2005 2010 2015 2020 2025
BASE 0.56 0.55 0.55 0.54 0.53
CT10 0.56 0.56 0.56 0.56 0.55
CT15 0.56 0.56 0.57 0.57 0.55
CT20 0.56 0.56 0.58 0.57 0.55
CT25 0.56 0.56 0.58 0.57 0.55
CT30 0.56 0.56 0.58 0.58 0.55
2030 2035 2040 2045 2050
BASE 0.54 0.54 0.54 0.55 0.55
CT10 0.54 0.54 0.55 0.55 0.59
CT15 0.54 0.54 0.55 0.55 0.59
CT20 0.54 0.56 0.58 0.58 0.59
CT25 0.54 0.54 0.55 0.58 0.59
CT30 0.54 0.54 0.58 0.58 0.59
Fig. 11. Diversification of Energy Resources under Renewable
Portfolio Supply Scenario
2005 2010 2015 2020 2025
BASE 0.51 0.46 0.44 0.48 0.50
RPS10 0.51 0.46 0.43 0.48 0.50
RPS20 0.51 0.46 0.43 0.48 0.50
RPS30 0.51 0.46 0.43 0.47 0.49
RPS40 0.51 0.46 0.43 0.47 0.49
RPS50 0.51 0.46 0.43 0.47 0.49
2030 2035 2040 2045 2050
BASE 0.52 0.53 0.54 0.55 0.55
RPS10 0.52 0.53 0.54 0.55 0.55
RPS20 0.51 0.53 0.54 0.54 0.55
RPS30 0.51 0.53 0.53 0.54 0.54
RPS40 0.50 0.52 0.52 0.53 0.53
RPS50 0.50 0.51 0.52 0.53 0.52
Fig. 12. Diversification of Energy Resources under Carbon Tax Scenario
2005 2010 2015 2020 2025
BASE 0.55 0.56 0.56 0.58 0.58
CT10 0.55 0.54 0.54 0.54 0.57
CT15 0.55 0.54 0.52 0.54 0.57
CT20 0.55 0.53 0.52 0.53 0.56
CT25 0.55 0.53 0.52 0.53 0.56
CT30 0.55 0.53 0.51 0.52 0.56
2030 2035 2040 2045 2050
BASE 0.57 0.57 0.57 0.56 0.56
CT10 0.57 0.57 0.56 0.56 0.50
CT15 0.57 0.57 0.56 0.56 0.50
CT20 0.57 0.54 0.52 0.51 0.50
CT25 0.57 0.57 0.56 0.51 0.50
CT30 0.57 0.57 0.52 0.51 0.50
Fig. 13. Vulnerability under Renewable Portfolio Supply Scenario
2005 2010 2015 2020 2025
BASE 0.13 0.10 0.10 0.10 0.13
RPS10 0.13 0.10 0.10 0.10 0.13
RPS20 0.13 0.10 0.09 0.10 0.13
RPS30 0.13 0.10 0.09 0.10 0.13
RPS40 0.13 0.10 0.09 0.10 0.13
RPS50 0.13 0.10 0.09 0.10 0.13
2030 2035 2040 2045 2050
BASE 0.17 0.19 0.21 0.22 0.24
RPS10 0.17 0.19 0.20 0.22 0.23
RPS20 0.16 0.19 0.20 0.22 0.23
RPS30 0.16 0.19 0.20 0.21 0.22
RPS40 0.16 0.18 0.19 0.21 0.22
RPS50 0.16 0.18 0.19 0.21 0.22
Fig. 14. Vulnerability under Carbon Tax Scenario
2005 2010 2015 2020 2025
BASE 0.12 0.11 0.09 0.10 0.15
CT10 0.12 0.10 0.08 0.09 0.14
CT15 0.12 0.10 0.08 0.09 0.14
CT20 0.12 0.10 0.08 0.09 0.14
CT25 0.12 0.10 0.08 0.09 0.14
CT30 0.12 0.10 0.08 0.11 0.16
2030 2035 2040 2045 2050
BASE 0.18 0.20 0.21 0.23 0.25
CT10 0.17 0.19 0.21 0.22 0.27
CT15 0.17 0.19 0.21 0.25 0.27
CT20 0.17 0.19 0.23 0.25 0.26
CT25 0.18 0.22 0.23 0.25 0.26
CT30 0.19 0.22 0.23 0.25 0.26
Fig. 15. Energy Intensity under Renewable Portfolio Supply Scenario
2005 2010 2015 2020 2025
BASE 0.049 0.089 0.086 0.092 0.093
RPS10 0.049 0.089 0.083 0.092 0.093
RPS20 0.049 0.089 0.082 0.092 0.093
RPS30 0.049 0.089 0.078 0.092 0.093
RPS40 0.049 0.089 0.078 0.092 0.093
RPS50 0.049 0.089 0.075 0.092 0.092
2030 2035 2040 2045 2050
BASE 0.090 0.090 0.088 0.087 0.085
RPS10 0.090 0.090 0.087 0.086 0.085
RPS20 0.090 0.089 0.088 0.087 0.087
RPS30 0.089 0.088 0.088 0.087 0.088
RPS40 0.089 0.089 0.088 0.088 0.089
RPS50 0.090 0.089 0.088 0.088 0.090
Fig. 16. Energy Intensity under Carbon Tax Scenario
2005 2010 2015 2020 2025
BASE 0.051 0.049 0.043 0.046 0.050
CT10 0.051 0.048 0.041 0.043 0.047
CT15 0.051 0.046 0.041 0.043 0.047
CT20 0.051 0.045 0.041 0.043 0.047
CT25 0.051 0.045 0.041 0.043 0.047
CT30 0.051 0.045 0.041 0.043 0.047
2030 2035 2040 2045 2050
BASE 0.049 0.051 0.052 0.053 0.054
CT10 0.049 0.050 0.052 0.053 0.052
CT15 0.049 0.051 0.052 0.053 0.052
CT20 0.049 0.048 0.050 0.051 0.052
CT25 0.049 0.051 0.052 0.051 0.052
CT30 0.049 0.051 0.050 0.051 0.052
